City update obligee relationship network analysis system and method

Abstract

The invention discloses a city update obligee relationship network analysis system and method, and the system comprises a data collection module which is used for collecting various types of data related to city update, a relationship construction module which is used for constructing a city update obligee relationship network according to the collected data, and a data analysis module which is used for analyzing the city update obligee relationship network. The analysis module is used for carrying out deep analysis on the constructed obligee relation network, outputting an analysis result to a user by adopting a graph analysis algorithm and a data mining technology and a result output module, and the dynamic updating module is used for continuously monitoring the change condition of city updating related data and evaluating potential risks in a city updating project according to the analysis result of the obligee relation network. According to the method, data safety and reliability are ensured through encryption transmission and verification, a relation network is constructed through multiple factors, city updating is assisted through accurate analysis, practicability is improved through dynamic updating and risk assessment, and powerful technical support is provided for city updating projects.

Description

Technical Field

[0001] The present invention relates to the technical field of rights holder relationship network analysis systems, and in particular to a system and method for analyzing urban renewal rights holder relationship network. Background Art

[0002] With the continuous advancement of urbanization, urban renewal has become an important measure to improve urban quality and optimize urban functions. In the process of urban renewal, many rights holders are involved, including land owners, house owners, developers, government departments, etc. The relationship between these rights holders is intricate and has a significant impact on the smooth progress of urban renewal projects.

[0003] Traditional urban renewal models often focus on the transformation of physical space, but pay insufficient attention to the relationship between rights holders. In actual operations, the lack of in-depth understanding of the relationship between rights holders often leads to problems such as difficulty in project promotion, frequent conflicts of interest, and inefficient decision-making. For example, in the process of land acquisition and house demolition, if the demands and interest relations of rights holders cannot be accurately grasped, fierce conflicts and disputes may arise, affecting social stability.

[0004] At the same time, with the rapid development of information technology, big data and artificial intelligence technologies have provided new ideas and methods for urban renewal. By collecting and analyzing a large amount of urban renewal-related data, we can build a network of rights holders, gain an in-depth understanding of the connections and interactions between rights holders, and provide a scientific basis for urban renewal decision-making.

[0005] At present, although some data analysis methods have been applied in the fields of urban planning and land management, the analysis of the urban renewal rights holder relationship network is still in its infancy. The existing methods have the following main problems: first, the data source is single, and often only part of the rights holder’s information can be obtained, which makes it difficult to fully reflect the overall picture of the rights holder relationship network; second, the analysis method is simple, mostly using traditional statistical analysis methods, which cannot deeply explore the complex structure and dynamic changes of the rights holder relationship network; third, there is a lack of visualization means, which makes it difficult to intuitively display the analysis results to users, which is not conducive to decision-making and implementation.

[0006] In addition, urban renewal is a dynamic process, and the relationship between rights holders will change over time. Therefore, it is necessary to establish an analysis system that can monitor and update the network of rights holder relationships in real time, so as to timely grasp the dynamic changes in the relationship between rights holders and provide strong support for the smooth implementation of urban renewal projects.

[0007] In summary, developing a network analysis system and method for urban renewal rights holders solves the deficiencies in existing technologies and has important practical significance and application value. It can help urban managers better understand the rights holder relationship, coordinate the interests of all parties, improve the decision-making efficiency and implementation effect of urban renewal projects, and promote the sustainable development of cities. Summary of the invention

[0008] The urban renewal rights holder relationship network analysis system and method proposed in the present invention are intended to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a network analysis system for urban renewal rights holders, comprising:

[0010] The data collection module is used to collect various data related to urban renewal, including but not limited to land ownership information, real estate registration information, and right holder identity information. Suppose the data set is D = {d1, d2, ..., d n}, where d n Represents different types of data elements. This module ensures the accuracy and completeness of data by connecting with government databases and real estate registration agencies.

[0011] The relationship building module builds a relationship network of urban renewal rights holders based on the collected data. Suppose the rights holder set is P = {p1, p2, ..., p m}, the relationship matrix is ​​R = (r ij ) m×m , where r ij Represents the strength of the relationship between the rights holders. The relationship construction algorithm is f(D, P) = {r ij}, the specific formula is as follows:

[0012] If the land is adjacent, where d ij is the distance between lands, d0 is the reference distance, and α1 is the weight coefficient of land adjacent relationship.

[0013] If there is a kinship relationship, then r ij =α2, where α2 is the kinship weight coefficient.

[0014] If there is a cooperative relationship, then r ij =α3×c ij , where c ij is the cooperation degree index, and α3 is the cooperation relationship weight coefficient.

[0015] final Where K is the total number of relationship types, w k is the weight coefficient of the Kth relationship type, is the relationship strength between the rights holders under the Kth relationship type.

[0016] The data analysis module conducts in-depth analysis of the constructed rights holder relationship network. It uses graph analysis algorithms and data mining techniques to analyze the structural characteristics, key nodes, and community structure of the network. Suppose the analysis result is A = (a1, a2, ..., a k ), where a k Indicates different types of analysis results. For example, by calculating the degree centrality and closeness centrality indicators of nodes, key rights holders in the network can be identified.

[0017] The result output module outputs the analysis results to the user in an intuitive way, including visual charts and reports. At the same time, this module can also provide personalized analysis result display and interpretation according to user needs.

[0018] Furthermore, the data acquisition module adopts encryption transmission and data verification technology to ensure the security and reliability of data.

[0019] Furthermore, the relationship building algorithm considers multiple factors, such as land adjacency, kinship, and cooperation.

[0020] Furthermore, a method for analyzing the urban renewal rights holder relationship network includes the following steps:

[0021] Collect urban renewal related data through the data collection module.

[0022] Use the relationship building module to build a rights holder relationship network based on the collected data.

[0023] The data analysis module analyzes the relationship network.

[0024] The analysis results are output to the user through the result output module.

[0025] Furthermore, a storage medium stores a computer program, and when the computer program is executed by a processor, a method for analyzing the relationship network of urban renewal rights holders is implemented.

[0026] Furthermore, a device is provided, characterized in that it comprises a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements a method for analyzing a relationship network of urban renewal rights holders.

[0027] Furthermore, a network analysis system for urban renewal rights holders also includes a dynamic update module:

[0028] This module continuously monitors the changes in urban renewal-related data and updates the rights holder relationship network in a timely manner when new data appears. Let the time variable be t and the new data set be Nt ={n1(t), n2(t), ..., n k (t)}, the update function is g(t, N t ), the specific formula is as follows:

[0029] Assume that the change index is If C ≥ θ, where θ is the update threshold, then g(t, N t )=update the relationship network; otherwise g(t, N t ) = Do not update the relationship network. N t represents the new data set at time n k (t) represents the kth data element in the new data set at time t. The update condition can be set according to the importance and quantity change factors of the data.

[0030] Furthermore, a network analysis system for urban renewal rights holders includes a risk assessment module:

[0031] This module evaluates the potential risks in urban renewal projects based on the analysis results of the rights holder relationship network. Suppose the risk assessment index set is F = {f1, f2, ..., f l}, the risk assessment function is h(A, F) = {r1, r2, ..., r p}, the specific formula is as follows:

[0032] For each risk assessment indicator f j ∈F, let its corresponding weight be ω j ,and For the analysis results a i ∈A, let its risk assessment index f j The influence coefficient is γ ij .but where r k Represents the k-th risk assessment result.

[0033] For example, by analyzing the community structure and key nodes in the relationship network, the risk of conflicts of interest that may arise during the project advancement can be assessed.

[0034] Furthermore, a network analysis system for urban renewal rights holders has a visualization customization module:

[0035] This module allows users to customize the style and content of visualization charts according to their own needs. Let the user's needs be U, and the visualization customization function be k(U) = {V1(U), V2(U), ..., V q (U)}, the specific formula is as follows:

[0036] Assume that user requirements include chart type requirements T U 、Color scheme requirements CU , labeling information requirements L U For the i-th visualization chart V i (U), according to the chart type requirements T U Select the corresponding graph generation algorithm g i (T U ), according to the color scheme requirements C U Determine the color mapping function c i (C U ), according to the labeling information requirement L U Determine the label generation function l i (L U ). Then V i (U) = g i (T U )×c i (C U )×l i (L U ).

[0037] Furthermore, a network analysis system for urban renewal rights holders includes a data storage optimization module:

[0038] This module is responsible for optimizing the data storage method, improving storage efficiency and data retrieval speed. Suppose the amount of stored data is S, and the optimization function is m(S) = {S1, S2, ..., S r}, the specific formula is as follows:

[0039] If compression storage technology is used, and the compression ratio is β, the amount of compressed data is

[0040] If an index is established, let the index structure be I, the data retrieval time be t(I), and the optimization goal be to minimize the retrieval time, then the optimal index structure I is found through the optimization algorithm. * , so that t(I * ) is minimum, at this time S r =S×I * Indicates the storage method after index optimization.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention collects various data related to urban renewal through the data collection module, ensuring the comprehensiveness and accuracy of the data, from land ownership information to real estate registration information, and then to the identity information of the right holder, laying a solid foundation for building an accurate right holder relationship network.

[0043] The relationship building module uses scientific and reasonable algorithms to build a relationship network of urban renewal rights holders based on the collected data. It takes into account multiple factors such as land adjacency, kinship, and cooperative relationships to accurately quantify the relationship strength between rights holders. For example, for rights holders with adjacent land, a reasonable relationship strength is calculated through a formula so that the relationship network can better reflect the actual situation. This helps to fully understand the potential connections between rights holders and provide a strong basis for subsequent analysis and decision-making.

[0044] The data analysis module uses advanced graph analysis algorithms and data mining technology to deeply analyze the constructed rights holder relationship network. It can accurately identify the structural characteristics, key nodes and community structure of the network. By calculating indicators such as the degree centrality of the node, the key rights holders in the network can be quickly identified, providing clear goals for the focus and communication and coordination of urban renewal projects.

[0045] The result output module outputs the analysis results to users in an intuitive way, and visual charts and reports allow users to quickly understand and grasp the situation of the rights holder relationship network. At the same time, it can provide personalized display and interpretation according to user needs to meet the specific needs of different users.

[0046] The dynamic update module continuously monitors changes in urban renewal-related data, and promptly updates the rights holder relationship network to ensure that the analysis results always remain up-to-date. The risk assessment module evaluates potential risks based on the analysis results, and provides early warnings and response strategies for project advancement. The visualization customization module allows users to customize visualization charts to better meet users' personalized analysis needs. The data storage optimization module improves storage efficiency and data retrieval speed, providing guarantees for the efficient operation of the system. In short, the urban renewal rights holder relationship network analysis system and method of the present invention provide strong technical support for the smooth advancement of urban renewal projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic block diagram of a network analysis system for urban renewal rights holders proposed by the present invention;

[0048] Figure 2 This is a schematic diagram of a method for analyzing the urban renewal rights holder relationship network proposed in the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise" and "counterclockwise" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0051] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0052] Reference Figure 1-2 :A network analysis system for urban renewal rights holders, including:

[0053] The data collection module is used to collect various data related to urban renewal, including but not limited to land ownership information, real estate registration information, and right holder identity information. Suppose the data set is D = {d1, d2, ..., d n}, where d n Represents different types of data elements. This module ensures the accuracy and completeness of data by connecting with government databases and real estate registration agencies.

[0054] The relationship building module builds a relationship network of urban renewal rights holders based on the collected data. Suppose the rights holder set is P = {p1, p2, ..., p m}, the relationship matrix is ​​R = (r ij ) m×m , where r ij Represents the strength of the relationship between the rights holders. The relationship construction algorithm is f(D, P) = {r ij}, the specific formula is as follows:

[0055] If the land is adjacent, where d ij is the distance between lands, d0 is the reference distance, and α1 is the weight coefficient of land adjacent relationship.

[0056] If there is a kinship relationship, then r ij =α2, where α2 is the kinship weight coefficient.

[0057] If there is a cooperative relationship, then r ij =α3×c ij , where c ij is the cooperation degree index, and α3 is the cooperation relationship weight coefficient.

[0058] final Where K is the total number of relationship types, w k is the weight coefficient of the Kth relationship type, is the relationship strength between the rights holders under the Kth relationship type.

[0059] The data analysis module conducts in-depth analysis of the constructed rights holder relationship network. It uses graph analysis algorithms and data mining techniques to analyze the structural characteristics, key nodes, and community structure of the network. Suppose the analysis result is A = (a1, a2, ..., a k ), where a k Indicates different types of analysis results. For example, by calculating the degree centrality and closeness centrality indicators of nodes, key rights holders in the network can be identified.

[0060] The result output module outputs the analysis results to the user in an intuitive way, including visual charts and reports. At the same time, this module can also provide personalized analysis result display and interpretation according to user needs.

[0061] The data acquisition module uses encryption transmission technology, such as using the Advanced Encryption Standard (AES) algorithm to encrypt the transmitted data. At the data sending end, the collected urban renewal related data is grouped, and each data group undergoes a series of operations such as key expansion, round key addition, byte replacement, row shift and column confusion, making it difficult to crack the data even if it is illegally intercepted during transmission. At the data receiving end, the encrypted data is decrypted by the corresponding decryption algorithm and key to restore the original data. At the same time, in order to prevent the data from being tampered with during transmission, the data acquisition module also uses message authentication code (MAC) technology. While sending data, the sending end calculates the MAC value of the data and sends it together. After receiving the data, the receiving end recalculates the MAC value of the data and compares it with the received MAC value. If the two are consistent, it means that the data has not been tampered with during transmission; otherwise, it means that the data may have been tampered with, and the receiving end will refuse to receive the data.

[0062] In addition, the data collection module also uses data verification technology. When collecting data, the source of the data is verified to ensure that the data comes from reliable government databases, real estate registration agencies, etc. For example, the identity of the data source is verified through digital certificates and public key infrastructure (PKI) technology. The data provider has its own digital certificate, which contains public keys and identity information. The data collection module ensures the reliability of the data source by verifying the validity and authenticity of the digital certificate. At the same time, the integrity of the collected data is verified, such as using a hash function to calculate the data and generate a hash value of the data. In the subsequent data processing process, the integrity of the data can be verified by recalculating the hash value and comparing it with the original hash value.

[0063] In the present invention, the relationship construction algorithm fully considers multiple factors to construct a more accurate and comprehensive urban renewal rights holder relationship network.

[0064] For the adjacent relationship of land, Geographic Information System (GIS) technology is used for analysis. First, the geographic coordinate information of the land is obtained, and the adjacent relationship is determined by calculating the distance between the land. If the distance between two pieces of land is less than a certain threshold, they are considered to be adjacent. At the same time, the shape, area and other factors of the land are considered to further refine the adjacent relationship. For example, for irregularly shaped land, the polygonal approximation method can be used to calculate the adjacent relationship. For a large area of ​​land, it can be divided into multiple small areas, and the adjacent relationship is calculated separately, and then the adjacent relationship of the entire land is determined by comprehensively considering the adjacent relationship of each small area.

[0065] In terms of kinship, the kinship information of the right holders can be obtained by connecting data with the household registration management department. Using graph database technology, kinship is represented as nodes and edges in the graph. For example, the right holder is represented as a node, and the kinship is represented as an edge. The weight of the edge can be set according to the closeness of the kinship. Through the query and analysis functions of the graph database, the kinship between the right holders can be quickly determined and incorporated into the construction of the relationship network.

[0066] For cooperative relationships, we determine them by analyzing historical project cooperation records, corporate association information, etc. We use data mining technology to extract cooperative relationship information from a large amount of text data. For example, we use natural language processing technology to analyze contract texts, news reports, etc. to identify keywords and phrases related to cooperation between rights holders. Then, we build a cooperative relationship network based on this information, and take it into consideration with land neighboring relationships and kinship relationships, so as to build a more comprehensive and accurate urban renewal rights holder relationship network.

[0067] The present invention also discloses a method for analyzing the urban renewal rights holder relationship network, comprising the following steps:

[0068] Collect urban renewal related data through the data collection module.

[0069] Use the relationship building module to build a rights holder relationship network based on the collected data.

[0070] The data analysis module analyzes the relationship network.

[0071] The analysis results are output to the user through the result output module.

[0072] The present invention also discloses a storage medium storing a computer program, which implements a method for analyzing a relationship network of urban renewal rights holders when executed by a processor.

[0073] The present invention also discloses a device, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, a method for analyzing the relationship network of urban renewal rights holders is implemented.

[0074] The present invention also discloses a network analysis system for urban renewal rights holders, which also includes a dynamic update module:

[0075] This module continuously monitors the changes in urban renewal-related data and updates the rights holder relationship network in a timely manner when new data appears. Let the time variable be t and the new data set be N t ={n1(t), n2(t), ..., n k (t)}, the update function is g(t, N t ), the specific formula is as follows:

[0076] Assume that the change index is If C ≥ θ, where θ is the update threshold, then g(t, N t )=update the relationship network; otherwise g(t, N t ) = Do not update the relationship network. N t represents the new data set at time n k (t) represents the kth data element in the new data set at time t. The update condition can be set according to the importance and quantity change factors of the data.

[0077] The present invention also discloses a network analysis system for urban renewal rights holders, including a risk assessment module:

[0078] This module evaluates the potential risks in urban renewal projects based on the analysis results of the rights holder relationship network. Suppose the risk assessment index set is F = {f1, f2, ..., f l}, the risk assessment function is h(A, F) = {r1, r2, ..., r p}, the specific formula is as follows:

[0079] For each risk assessment indicator f j ∈F, let its corresponding weight be w j ,and For the analysis results a i ∈A, let its risk assessment index f j The influence coefficient is γ ij .but where r k Represents the k-th risk assessment result.

[0080] For example, by analyzing the community structure and key nodes in the relationship network, the risk of conflicts of interest that may arise during the project advancement can be assessed.

[0081] The present invention also discloses a network analysis system for urban renewal rights holders, which has a visualization customization module:

[0082] This module allows users to customize the style and content of visualization charts according to their own needs. Let the user's needs be U, and the visualization customization function be k(U) = {V1(U), V2(U), ..., V q (U)}, the specific formula is as follows:

[0083] Assume that user requirements include chart type requirements T U 、Color scheme requirements C U , labeling information requirements L U For the i-th visualization chart V i (U), according to the chart type requirements T U Select the corresponding graph generation algorithm g i (T U ), according to the color scheme requirements C U Determine the color mapping function c i (C U ), according to the labeling information requirement L U Determine the label generation function L i (L U ). Then V i (U) = g i (T U )×c i (C U )×l i (L U ).

[0084] The present invention also discloses a network analysis system for urban renewal rights holders, which includes a data storage optimization module:

[0085] This module is responsible for optimizing the data storage method, improving storage efficiency and data retrieval speed. Suppose the amount of stored data is S, and the optimization function is m(S) = {S1, S2, ..., S r}, the specific formula is as follows:

[0086] If compression storage technology is used, and the compression ratio is β, the amount of compressed data is

[0087] If an index is established, let the index structure be I, the data retrieval time be t(I), and the optimization goal be to minimize the retrieval time, then the optimal index structure I is found through the optimization algorithm. * , so that t(I * ) is the smallest, at this time S r =S×I * Indicates the storage method after index optimization.

[0088] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A network analysis system for urban renewal rights holders, characterized in that: include: The data collection module is used to collect various data related to urban renewal, including land ownership information, real estate registration information, and right holder identity information. Suppose the data set is D = {d1, d2, ..., d n }, where d n Represents different types of data elements, and connects data with government databases and real estate registration agencies; The relationship building module builds a relationship network of urban renewal rights holders based on the collected data. The rights holder set is P = {p1, p2, ..., p m }, the relationship matrix is ​​R = (r ij ) m×m , where r ij represents the strength of the relationship between the rights holders. The relationship construction algorithm is f(D, P) = {r ij }, the specific formula is as follows: If the land is adjacent, where d ij is the distance between lands, d0 is the reference distance, and α1 is the weight coefficient of land adjacency; If there is a kinship relationship, then r ij =α2, where α2 is the kinship weight coefficient; If there is a cooperative relationship, then r uj =α3×c ij , where c ij is the cooperation degree index, α3 is the cooperation relationship weight coefficient; Where K is the total number of relationship types, w k is the weight coefficient of the Kth relationship type, is the strength of the relationship between the rights holders under the Kth relationship type; The data analysis module conducts in-depth analysis on the constructed rights holder relationship network, using graph analysis algorithms and data mining technology to analyze the structural characteristics, key nodes, and community structure of the network. The analysis result is A = (a1, a2, ..., a k ), where a k Represents different types of analysis results, and determines the key rights holders in the network by calculating the degree centrality and closeness centrality indicators of the nodes; The result output module outputs the analysis results to the user, including visual charts and reports, and can also provide personalized analysis result display and interpretation according to user needs.

2. According to claim 1, a network analysis system for urban renewal rights holders is characterized by: The data acquisition module adopts encryption transmission and data verification technology to ensure the security and reliability of data.

3. According to claim 1, a network analysis system for urban renewal rights holders is characterized in that: The relationship building algorithm considers multiple factors, including land adjacency, kinship, and cooperation.

4. The urban renewal rights holder relationship network analysis system according to claim 1 is characterized in that: It also includes a dynamic update module: continuously monitor the changes in urban renewal-related data, and update the rights holder relationship network when new data appears. Let the time variable be t and the new data set be N. t ={n1(t), n2(t), ..., n k (t)}, the update function is g(t, N t ), the specific formula is as follows: Assume the change index is If C ≥ θ, where θ is the update threshold, then g(t, N t )=update the relationship network; otherwise g(t, N t )=Do not update the relationship network, N t represents the new data set at time n k (t) represents the kth data element in the new data set at time t, and the update condition is set according to the importance and quantity change factors of the data.

5. The urban renewal rights holder relationship network analysis system according to claim 1 is characterized in that: Also includes risk assessment modules: According to the analysis results of the rights holder relationship network, the potential risks in urban renewal projects are evaluated. The risk assessment index set is F = {f1, f2, ..., f l }, the risk assessment function is h(A, F) = {r1, r2, ..., r p }, the specific formula is as follows: For each risk assessment indicator f j ∈F, let its corresponding weight be ω j ,and For the analysis results a i ∈A, let its risk assessment index f j The influence coefficient is γ ij ,but where r k Represents the k-th risk assessment result.

6. The urban renewal rights holder relationship network analysis system according to claim 1 is characterized in that: Also includes visual customization modules: This module allows users to customize the style and content of visualization charts according to their own needs. Let the user's needs be U, and the visualization customization function be k(U) = {V1(U), V2(U), ..., V q (U)}, the specific formula is as follows: Assume that user requirements include chart type requirements T U 、Color scheme requirements C U , labeling information requirements L U , for the i-th visualization chart V i (U), according to the chart type requirements T U Select the corresponding graph generation algorithm g i (T U ), according to the color scheme requirements C U Determine the color mapping function c i (C U ), according to the labeling information requirement L U Determine the label generation function l i (L U ), then V i (U) = g i (T U )×c i (C U )×l i (L U ).

7. The urban renewal rights holder relationship network analysis system according to claim 1 is characterized in that: Also data storage optimization module: This module is responsible for optimizing the data storage method, improving storage efficiency and data retrieval speed. Suppose the amount of stored data is S, and the optimization function is m(S) = {S1, S2, ..., S r }, the specific formula is as follows: If compression storage technology is used, and the compression ratio is β, the amount of compressed data is If an index is established, let the index structure be I, the data retrieval time be t(I), and the optimization goal be to minimize the retrieval time, then the optimal index structure I is found through the optimization algorithm. * , so that t(I * ) is the smallest, at this time S r =S×I * Indicates the storage method after index optimization.

8. A method for applying the urban renewal rights holder relationship network analysis system as described in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Collect urban renewal related data through data collection module; S2. Use the relationship building module to build a rights holder relationship network based on the collected data; S3, data analysis module analyzes the relationship network; S4. Monitor changes in urban renewal-related data and update the rights holder relationship network when new data emerges; S5. Evaluate the potential risks in urban renewal projects based on the analysis results of the rights holder relationship network; S6, storage optimization module optimizes the data storage method to improve storage efficiency and data retrieval speed; S7. Output the analysis results to the user through the result output module.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the urban renewal rights holder relationship network analysis method as described in claim 8.

10. A device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for analyzing the urban renewal right holder relationship network as claimed in claim 8 is implemented.

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