A method for evaluating and optimizing the resilience of territorial space

Abstract

The present invention discloses a method for evaluating and optimizing the resilience of territorial space, which relates to the technical field of spatial evaluation and optimization. The method includes collecting multi-modal data of territorial space and performing preprocessing; constructing a spatio-temporal coupling model based on the multi-modal data to calculate global coupling characteristics, and constructing a resilience evaluation model to analyze the resilience of territorial space; optimizing the territorial space based on the GAN model and genetic algorithm, and evaluating the optimized territorial space plan; forming optimization suggestions for the optimized territorial space plan for display and storing them in a database. By collecting territorial space data to generate a topological network and constructing a spatio-temporal coupling matrix to extract features of the topological network, and obtaining the resilience of territorial space through constructing a resilience evaluation model, the present invention effectively improves the accuracy and flexibility of territorial space evaluation, realizes efficient and rapid territorial space evaluation. At the same time, an optimized territorial space plan is generated and evaluated according to the GAN model and genetic algorithm, improving the effectiveness of the optimized territorial space plan.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial evaluation and optimization, and particularly to a method for evaluating and optimizing the resilience of territorial space. Background Art

[0002] With the continuous advancement of the processes of globalization and urbanization, the optimization and utilization of territorial space have gradually become a core issue affecting social and economic development and the sustainable management of natural resources. As the basis for resource allocation and human activities, the rationality of territorial space planning is directly related to the operating efficiency and risk resistance ability of the social and economic system. In recent years, as a key technology for measuring the stability and recovery ability of the spatial system in emergencies (such as natural disasters, social conflicts, etc.), the evaluation of territorial space resilience has received extensive attention. Although the existing territorial space resilience evaluation technologies have been preliminarily applied in fields such as urban planning, ecological protection, and infrastructure construction, there are still deficiencies. In the topological network analysis of the existing technology, the spatio-temporal coupling characteristics are ignored, and a coupling matrix reflecting the dynamic evolution of the spatial layout fails to be established, resulting in the evaluation results being difficult to accurately reveal the true resilience level of complex territorial space, and it is difficult to quickly generate a multi-objective optimization plan that takes into account both robustness and efficiency, leading to insufficient practicality and effectiveness of the optimization results. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method for evaluating and optimizing the resilience of territorial space, which solves the problems that in the topological network analysis of the existing technology, the spatio-temporal coupling characteristics are ignored, a coupling matrix reflecting the dynamic evolution of the spatial layout fails to be established, and it is difficult to quickly generate a multi-objective optimization plan that takes into account both robustness and efficiency.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a method for evaluating and optimizing the resilience of territorial space, which includes:

[0007] Collecting multi-modal data of territorial space and performing preprocessing;

[0008] Constructing a spatio-temporal coupling model based on the multi-modal data to calculate the global coupling characteristics, and constructing a resilience evaluation model to analyze the resilience of territorial space;

[0009] Optimizing the territorial space based on the GAN model and genetic algorithm, and evaluating the territorial space optimization plan;

[0010] Forming optimization suggestions for the territorial space optimization plan for display and storing them in the database.

[0011] As a preferred solution of the national territorial space resilience assessment and optimization method of the present invention, wherein: the collection and preprocessing of multi-modal data of the national territorial space refers to obtaining national territorial space data through network query and platform access, including natural ecological data, infrastructure data, social and economic data, and meteorological data, denoising the national territorial space data through the median filtering method, and performing standardization processing, and projecting all the standardized national territorial space data onto the WGS1984 coordinate system for coordinate unification.

[0012] As a preferred solution of the national territorial space resilience assessment and optimization method of the present invention, wherein: the construction of a spatio-temporal coupling model based on multi-modal data to calculate the global coupling characteristics refers to defining network nodes based on each category of national territorial space data to construct a topological sub-network , and forming a network set by integrating the topological sub-networks of n categories of national territorial space data ;

[0013] For each topological sub-network , analyze the functional state of the k-th node of the i-th sub-network through binarization , if the sub-network node is operating normally, the node functional state is 1, otherwise 0, and calculate the functional retention probability of the k-th node of the sub-network :

[0014]

[0015] where is the number of historical disturbances, is the number of times of function retention, and M < N;

[0016] Take the product of the functional retention probability of node k and the functional state as the self-coupling characteristic of node k;

[0017] Calculate the Euclidean distance between nodes in each sub-network , and construct the spatial matrix of each sub-network, and merge the spatial matrices of all sub-networks to form a comprehensive spatial matrix D;

[0018] Define the connection weight between nodes through the distance between sub-network nodes :

[0019]

[0020] where is the connection weight between node j and node k in the i-th sub-network, is the Euclidean distance between node j and node k in the i-th subnetwork;

[0021] Analyze and calculate the time coupling weight of the weights between nodes in the subnetwork through the time decay function :

[0022]

[0023] where t is the time, is the time coupling weight between node j and node k in the i-th subnetwork at time t, is the time decay factor;

[0024] Through the time coupling weight Obtain the time matrix of each subnetwork , and merge the time matrices of each subnetwork to form the comprehensive time matrix T;

[0025] Fuse the comprehensive spatial matrix D and the comprehensive time matrix T to construct the spatio-temporal coupling matrix A:

[0026]

[0027] where is the value at the (u, v) position in the spatio-temporal coupling matrix at time t, is the value at the (u, v) position in the comprehensive spatial matrix, is the value at the (u, v) position in the comprehensive time matrix at time t;

[0028] Calculate the global coupling characteristic P of the territorial spatial data according to the spatio-temporal coupling matrix:

[0029]

[0030] where is the number of columns of the spatio-temporal coupling matrix, x is the row number sequence in the spatio-temporal coupling matrix, is the column number sequence in the spatio-temporal coupling matrix, is the value at the (x, y) position in the spatio-temporal coupling matrix;

[0031] Calculate the node influence of each subnetwork :

[0032]

[0033] where is the node influence of node k in the i-th subnetwork, is the set of nodes contained in a circle centered at node k with a radius of a. is the set of neighbor nodes of node j. and are the degrees of node j and node o, respectively.

[0034] By calculating the node influence of each sub-network, the node with the greatest node influence in each sub-network is used as the key node of the sub-network. The key nodes of each sub-network are extracted, and the average value of the node influences of all sub-network key nodes is calculated as the comprehensive influence C.

[0035] The global coupling characteristic P and the comprehensive influence C are spliced and combined as the global coupling feature, and the self-coupling characteristic of the node and the node influence are spliced to form the node coupling feature.

[0036] As a preferred embodiment of the method for assessing and optimizing the resilience of territorial space according to the present invention, wherein: constructing the resilience assessment model to analyze the resilience of territorial space means respectively constructing a global resilience assessment model and a node resilience assessment model through a convolutional neural network, and respectively training the global resilience assessment model and the node resilience assessment model through a training set and a loss function.

[0037] The global coupling feature is input into the trained global resilience assessment model to obtain the resilience of territorial space, and the node coupling feature is input into the trained node resilience assessment model to obtain the node resilience.

[0038] As a preferred embodiment of the method for assessing and optimizing the resilience of territorial space according to the present invention, wherein: optimizing the territorial space based on the GAN model and the genetic algorithm means randomly sampling noise from the standard normal distribution, using the generator in the GAN model to generate an initial territorial space optimization scheme, and using the discriminator to judge the initial territorial space optimization scheme. The initial territorial space optimization scheme that passes the judgment is used as a genetic individual to form an initial population. Calculate the territorial space resilience of each genetic individual as the fitness, and select the two genetic individuals with the highest fitness for crossover and mutation to generate new genetic individuals for iteration. Stop the iteration after reaching the set number of iterations, output the genetic individual with the highest fitness after iteration as the optimal individual, and extract the territorial space optimization scheme in the optimal individual as the optimal territorial space optimization scheme.

[0039] As a preferred solution of the national territorial space resilience assessment and optimization method of the present invention, wherein: the assessment of the national territorial space optimization plan refers to extracting sub-network nodes in the optimal national territorial space optimization plan, calculating the resilience of the optimization nodes, calculating the average resilience of all optimization nodes and the average resilience of the current nodes, calculating the connectivity coefficient of each sub-network, setting an assessment threshold, and determining that the optimal national territorial space optimization plan passes the assessment when the connectivity coefficient of each sub-network is greater than the assessment threshold and the average resilience of the optimization nodes is greater than the average resilience of the current nodes; otherwise, regenerate the optimal national territorial space optimization plan.

[0040] As a preferred solution of the national territorial space resilience assessment and optimization method of the present invention, wherein: the formation of optimization suggestions from the national territorial space optimization plan for display refers to analyzing the differences between the sub-networks in the national territorial space optimization plan that has passed the assessment and the current sub-network, marking the difference points in the current sub-network, and generating optimization suggestions for the current sub-network based on the sub-networks in the national territorial space optimization plan for display to the staff.

[0041] As a preferred solution of the national territorial space resilience assessment and optimization method of the present invention, wherein: the storage in the database refers to storing the topological network set of national territorial space data and the optimization suggestions in the database. The database regularly updates and performs integrity checks on the stored data, and uploads the stored data to the cloud for backup.

[0042] In a second aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the national territorial space resilience assessment and optimization method as described in the first aspect of the present invention is implemented.

[0043] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, any step of the national territorial space resilience assessment and optimization method as described in the first aspect of the present invention is implemented.

[0044] The beneficial effects of the present invention are as follows: The present invention effectively improves the accuracy and flexibility of national territorial space assessment by collecting national territorial space data to generate a topological network and constructing a spatio-temporal coupling matrix to extract features of the topological network, realizes efficient and rapid national territorial space evaluation, and at the same time generates and evaluates a national territorial space optimization plan according to the GAN model and the genetic algorithm, improving the effectiveness of the national territorial space optimization plan. Description of the Drawings

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0046] Figure 1 It is a flowchart of the method for evaluating and optimizing the resilience of territorial space in Embodiment 1. Specific embodiments

[0047] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0049] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or selectively exclusive embodiment that is mutually exclusive with other embodiments.

[0050] Embodiment 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for evaluating and optimizing the resilience of territorial space, including the following steps:

[0051] S1. Collect multi-modal data of territorial space and perform preprocessing;

[0052] Specifically, collecting multi-modal data of territorial space and performing preprocessing means obtaining territorial space data through network queries and platform access, including natural ecological data, infrastructure data, socio-economic data, and meteorological data. Denoise the territorial space data through the median filtering method, and perform standardization processing. Project all standardized territorial space data onto the WGS1984 coordinate system for coordinate unification.

[0053] The collection of multi-modal data has greatly expanded the dimension and depth of territorial spatial information, capable of simultaneously capturing the characteristics of multiple aspects such as ecology, society, economy, and climate, forming a global description of the territorial space. This all-round data integration breaks through the traditional territorial spatial research method dominated by single data, laying a foundation for constructing a more comprehensive model. For example, natural ecological data can reflect the environmental carrying capacity of the territorial space, while socio-economic data can reveal the economic benefits of land use, and meteorological data provides support for analyzing the dynamic changes of the environment in terms of time dimension. Through the integration of these data, dynamic change monitoring, comprehensive risk analysis, and multi-dimensional optimization design of the territorial space can be realized. The median filtering method preserves the spatial characteristics of the data during the denoising process, especially performing well in dealing with territorial spatial data containing outliers. Standardization processing eliminates the scale differences between multi-modal data, enabling data of different dimensions to be modeled within the same framework. After uniformly projecting the data onto the WGS1984 coordinate system, the spatial reference systems of all data are consistent, avoiding data offset or distortion caused by coordinate differences. Through unified projection, the superposability and analysis consistency of multi-modal data are ensured, providing an accurate geospatial basis for subsequent resilience assessment and optimization.

[0054] S2. Based on multi-modal data, construct a spatio-temporal coupling model to calculate the global coupling characteristics, and construct a resilience assessment model to analyze the resilience of the territorial space;

[0055] Specifically, constructing a spatio-temporal coupling model based on multi-modal data to calculate the global coupling characteristics means defining network nodes based on each category of territorial spatial data to construct a topological sub-network , with the same number of nodes in each sub-network and different node positions, and forming a network set by integrating the topological sub-networks of n categories of territorial spatial data ;

[0056] For each topological sub-network , analyze the functional state of node k in the i-th sub-network through binarization . If the sub-network node is operating normally, the node functional state is 1, otherwise 0. The evaluation threshold of the sub-network node can be set according to the specific network application scenario and node characteristics to judge the operating state of the sub-network node, and calculate the functional retention probability of sub-network node k :

[0057]

[0058] where is the number of historical disturbances, is the number of times of function retention, and M < N. The number of historical disturbances and the number of times of function retention can be obtained from historical territorial spatial data;

[0059] The product of the function retention probability of node k and the function status is used as the self-coupling characteristic of node k ;

[0060] Calculate the Euclidean distance between nodes in each sub-network , and construct the spatial matrix of each sub-network , and combine the spatial matrices of all sub-networks to form the comprehensive spatial matrix D:

[0061]

[0062] where n is the number of sub-networks;

[0063] Define the connection weight between nodes through the distance between sub-network nodes :

[0064]

[0065] where is the connection weight between node j and node k in the i-th sub-network, is the Euclidean distance between node j and node k in the i-th sub-network;

[0066] Analyze and calculate the time coupling weight of the weights between nodes in the sub-network through the time decay function :

[0067]

[0068] where t is the time, is the time coupling weight between node j and node k in the i-th sub-network at time t, is the time decay factor;

[0069] Obtain the time matrix of each sub-network through the time coupling weight , and combine the time matrices of each sub-network to form the comprehensive time matrix T;

[0070] Fuse the comprehensive spatial matrix D and the comprehensive time matrix T to construct the spatio-temporal coupling matrix A:

[0071]

[0072] where is the value at the (u, v) position in the spatio-temporal coupling matrix at time t,​ is the value at position (u, v) in the comprehensive spatial matrix, is the value at position (u, v) in the comprehensive time matrix at time t;

[0073] Calculate the global coupling characteristic P of the territorial spatial data according to the spatio-temporal coupling matrix:

[0074]

[0075] where is the number of columns of the spatio-temporal coupling matrix, x is the row number sequence in the spatio-temporal coupling matrix, is the column number sequence in the spatio-temporal coupling matrix, is the value at position (x, y) in the spatio-temporal coupling matrix;

[0076] Calculate the node influence of each sub-network :

[0077]

[0078] where is the node influence of node k in the i-th sub-network, is the set of nodes contained in a circle with node k as the center and radius a, is the set of neighbor nodes of node j, and are the degrees of node j and node o respectively, obtained through the number of node connections;

[0079] By calculating the node influence of each sub-network, the node with the largest node influence in each sub-network is used as the key node of the sub-network, extract the key nodes of each sub-network, and calculate the average value of the key node influences of all sub-networks as the comprehensive influence C;

[0080] Concatenate and combine the global coupling characteristic P and the comprehensive influence C as the global coupling feature, and concatenate the node's own coupling characteristic and the node influence to form the node coupling feature.

[0081] The process of constructing topological sub - networks from different categories of territorial space data and forming a network set can refine the hierarchical structure of network analysis. For example, the sub - networks of the transportation network can reveal the accessibility between nodes, while the ecological network can reflect the quality of ecological corridors between nodes. This way of hierarchical modeling breaks through the limitations of traditional single - network models, enabling the characteristics of each type of data to be fully expressed in specific sub - networks. Finally, through the integration of the comprehensive network set, it provides more comprehensive and multi - dimensional support for global coupling characteristics. The function retention probability quantifies the node robustness through historical perturbation data and is the core index for measuring node reliability. For example, in the event of a disaster, nodes with a higher function retention probability can maintain normal operation for a longer time, thus ensuring the overall function of the network. Compared with the reliability analysis based on static states in existing methods, the function retention probability introduces dynamic historical data and can more realistically reflect node reliability. Generating a spatio - temporal coupling matrix by integrating the spatial matrix and the time matrix can simultaneously reflect the spatial location relationship and time - varying characteristics between nodes. For example, in the practical application of territorial space, the spatial distance between nodes may be short, but if the weight decreases significantly due to time decay, its influence in the network will also decrease. Matrix integration enables the unified expression of spatial and time characteristics and lays the foundation for the calculation of global coupling characteristics. Global coupling characteristics describe the coupling strength of the entire territorial space system, while node influence is used to measure the importance of each node. The combination of the two can provide effective inputs for optimizing the model, helping to identify key nodes and optimize the overall network layout.

[0082] Furthermore, constructing a resilience assessment model to analyze the resilience of territorial space means respectively constructing a global resilience assessment model and a node resilience assessment model through convolutional neural networks, and training the global resilience assessment model and the node resilience assessment model through the training set and the loss function.

[0083] Inputting the global coupling features into the trained global resilience assessment model to obtain the territorial space resilience, and inputting the node coupling features into the trained node resilience assessment model to obtain the node resilience.

[0084] The global resilience assessment model uses CNN to extract the spatial structure information in the global coupling features, and can capture the key patterns in the overall network of national land space. The node resilience assessment model learns the local patterns in the node coupling features through CNN, and can identify the key role of a single node in the network. Through the optimization of the training set and the loss function, the model can gradually improve its prediction ability of the resilience of national land space. For example, the training set constructed by historical perturbation data can enable the model to learn various perturbation patterns and recovery mechanisms in real scenarios; through the mean square error loss function, the model can more accurately fit the resilience law of national land space. Inputting the global coupling features into the global resilience assessment model, the output resilience value of national land space can provide a basis for overall planning for policymakers. For example, by predicting the overall resilience of a specific area under different disaster scenarios, resource allocation can be optimized or the defense capabilities of key areas can be enhanced. Similarly, inputting the node coupling features into the node resilience assessment model can identify and evaluate the resilience level of key nodes, and then guide the reinforcement measures at the node level.

[0085] S3. Optimize the national land space based on the GAN model and the genetic algorithm, and evaluate the optimized national land space plan;

[0086] Specifically, optimizing the national land space based on the GAN model and the genetic algorithm means randomly sampling noise from the standard normal distribution, using the generator in the GAN model to generate an initial national land space optimization plan, where the national land space optimization plan refers to each optimized topological sub-network, and using the discriminator to judge the initial national land space optimization plan. The initial national land space optimization plan that passes the judgment is used as a genetic individual to form an initial population, calculate the resilience of the national land space of each genetic individual as the fitness, and select the two genetic individuals with the highest fitness for crossover and mutation to generate new genetic individuals for iteration. Stop the iteration after reaching the set number of iterations, output the genetic individual with the highest fitness after iteration as the optimal individual, and extract the national land space optimization plan in the optimal individual as the optimal national land space optimization plan.

[0087] Through the adversarial training of the generator and discriminator, GAN can generate diverse initial national territorial space optimization schemes from random noise. Based on the input standard normal distribution noise, the generator generates optimization schemes with high robustness and applicability by learning the distribution characteristics of historical national territorial space data. The discriminator judges the generated schemes to further optimize the capabilities of the generator. By introducing GAN, the generation of initial schemes breaks through the limitations of traditional random generation or rule design, with a higher level of intelligence and adaptability. The discriminator judges the authenticity and rationality of the generated schemes and only retains the optimized schemes that pass the judgment as the initial population of the genetic algorithm. This process effectively filters out the initial solutions with poor quality, provides a high-quality basic population for the subsequent evolution of the genetic algorithm, reduces the waste of computing resources by low-quality individuals, and the introduction of the genetic algorithm enables the iterative process of the optimization scheme to have global search capabilities and self-adaptability, avoiding the risk of falling into local optima. The finally output optimal individual represents the national territorial space optimization scheme with the highest fitness, which includes the comprehensive results of being generated by GAN, screened by the discriminator, and iteratively optimized by the genetic algorithm. The optimal scheme can effectively improve the overall resilience of the national territorial space while taking into account the optimization needs of local nodes, with high practicality and operability.

[0088] Furthermore, evaluating the national territorial space optimization scheme means extracting the sub-network nodes in the optimal national territorial space optimization scheme, calculating the resilience of the optimization nodes, calculating the mean resilience of all optimization nodes and the mean resilience of the current nodes, calculating the connectivity coefficient of each sub-network, setting an evaluation threshold, and judging that the optimal national territorial space optimization scheme passes the evaluation when the connectivity coefficient of each sub-network is greater than the evaluation threshold and the mean resilience of the optimization nodes is greater than the mean resilience of the current nodes; otherwise, regenerating the optimal national territorial space optimization scheme.

[0089] By calculating and optimizing the node resilience and mean value, the contribution of the optimization scheme to the overall resilience improvement of the territorial space can be intuitively evaluated. The increase in the mean value of the optimized node resilience indicates that the optimization scheme effectively enhances the network's anti-interference ability. For example, in the simulated scenarios of natural disasters or emergencies, nodes with higher resilience can maintain stable functions and reduce the loss of the overall network functions. The sub-network connectivity coefficient is used to quantify the connection tightness of nodes within the sub-network and is an important indicator for evaluating the structural rationality of the optimization scheme. By setting the evaluation threshold, it can be ensured that while the optimization scheme improves the resilience, the basic connectivity of the sub-network is maintained. By comparing the mean value of the optimized node resilience and the current node resilience, and judging whether the sub-network connectivity coefficient exceeds the evaluation threshold, the present invention realizes the multi-dimensional comprehensive evaluation of the optimal territorial space optimization scheme. The optimization scheme can only pass the evaluation when both the resilience and connectivity meet the standards, otherwise, it is regenerated. This evaluation method ensures that the optimization scheme not only improves the resilience but also has rationality and operability in structure. When the optimization scheme fails to pass the evaluation, the mechanism of regenerating the optimization scheme enables the optimization process to have self-adaptive ability and can continuously improve the optimization results through multiple iterations. This mechanism avoids the failure of the scheme caused by the deficiencies of a single optimization scheme and improves the quality and adaptability of the final optimization scheme.

[0090] S4. Form the territorial space optimization scheme into optimization suggestions for display and store them in the database;

[0091] Specifically, forming the territorial space optimization scheme into optimization suggestions for display means analyzing the differences between the sub-networks in the territorial space optimization scheme that has passed the evaluation and the current sub-network, marking the difference points in the current sub-network, and generating optimization suggestions for the current sub-network based on the sub-networks in the territorial space optimization scheme for display to the staff.

[0092] Furthermore, storing in the database means storing the topological network set of the territorial space data and the optimization suggestions in the database. The database regularly updates and performs integrity checks on the stored data and uploads the stored data to the cloud for backup.

[0093] This embodiment also provides a computer device applicable to the territorial space resilience evaluation and optimization method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the territorial space resilience evaluation and optimization method as proposed in the above embodiment.

[0094] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0095] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing the resilience assessment and optimization of territorial space as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0096] In summary, the present invention effectively improves the accuracy and flexibility of territorial space assessment by collecting territorial space data to generate a topological network and constructing a spatio-temporal coupling matrix to extract features of the topological network, realizes efficient and rapid territorial space evaluation by constructing a resilience assessment model to obtain the resilience of territorial space, and at the same time generates and evaluates a territorial space optimization plan according to the GAN model and genetic algorithm, improving the effectiveness of the territorial space optimization plan.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for assessing and optimizing the resilience of territorial space, characterized in that: Including, Collecting multi-modal data of the national territorial space and preprocessing it; Constructing a spatio-temporal coupling model based on the multi-modal data to calculate the global coupling characteristics, and constructing a resilience assessment model to analyze the resilience of the national territorial space; The calculation of the global coupling features by constructing a spatio-temporal coupling model based on multi-modal data refers to defining network nodes based on the national land space data of each category to construct a topological sub-network G i , and forming a network set G = {G1, G2,..., G n} by integrating the topological sub-networks of n categories of national land space data; For each topological sub-network G i , through binarization, analyze the functional state S of the k-th node in the i-th sub-network i，k . If the sub-network node operates normally, the node functional state S i，k is 1, otherwise it is 0, and calculate the functional retention probability φ of the k-th node in the sub-network i，k : Where N is the number of historical disturbances, M is the number of times of function maintenance, and M < N; Take the product of the function retention probability φ of node k i，k and the function state S i，k as the self-coupling characteristic P of node k i，k ; Calculate the Euclidean distance D between nodes in each sub-network i，kj and construct the spatial matrix D of each sub-network i and combine the spatial matrices D of all sub-networks i to form a comprehensive spatial matrix D; Define the connection weight W between nodes by the distance between sub-network nodes i，kj : Among which W i，kj is the inter-node connection weight between node j and node k in the i-th sub-network, and D i，kj is the Euclidean distance between node j and node k in the i-th sub-network; Analyze and calculate the time-coupling weight T of the weights between nodes in the sub-network through a time decay function i，kj : T i，kj (t) = e -λt * W i，kj ; where t is time, and T i，kj (t) is the time coupling weight between node j and node k in the i-th subnetwork at time t, and λ is the time decay factor; Through the time coupling weight T i，kj (t) to obtain the time matrix T of each sub-network i , and merge the time matrix T of each sub-network i to form a comprehensive time matrix T; Fusing the comprehensive spatial matrix D and the comprehensive time matrix T to construct a spatio-temporal coupling matrix A: where M uv (t) is the value at the (u, v) position in the spatio-temporal coupling matrix at time t, and D uv is the value at the (u, v) position in the comprehensive space matrix, and T uv (t) is the value at the (u, v) position in the comprehensive time matrix at time t; Calculating the global coupling characteristics P of the national territorial space data according to the spatio-temporal coupling matrix; where K is the number of columns of the spatio-temporal coupling matrix, x is the row number sequence in the spatio-temporal coupling matrix, y is the column number sequence in the spatio-temporal coupling matrix, and M xy (t) is the value at the position (x, y) of the spatio-temporal coupling matrix; Calculate the node influence C of each sub-network i，k : Among which C i，k is the node influence of node k in the i-th sub-network, B(k, a) is the set of nodes included in the circle centered at node k with a radius of a, Q(j) is the set of neighbor nodes of node j, l j and l o are the degrees of node j and node o respectively; By calculating the node influence of each sub-network, taking the node with the largest node influence in each sub-network as the key node of the sub-network, extracting the key nodes of each sub-network, and calculating the average value of the node influence of all sub-network key nodes as the comprehensive influence C; The global coupling characteristic P and the comprehensive influence C are spliced and combined as the global coupling feature, and the self-coupling characteristic P of the node i，k and the node influence C i，k are spliced to form the node coupling feature; Optimizing the national territorial space based on the GAN model and the genetic algorithm, and evaluating the optimized scheme of the national territorial space; Forming optimization suggestions from the optimized scheme of the national territorial space for display and storing them in the database.

2. The method for evaluating and optimizing the resilience of territorial space according to claim 1, wherein: The collection and preprocessing of the multi-modal data of the national territorial space refers to obtaining the national territorial space data through network query and platform access, including natural ecological data, infrastructure data, socio-economic data, and meteorological data, denoising the national territorial space data by the median filtering method, and performing standardization processing, and projecting all the standardized national territorial space data into the WGS1984 coordinate system for coordinate unification.

3. The method for evaluating and optimizing the resilience of territorial space according to claim 2, characterized in that: The construction of the resilience assessment model to analyze the resilience of the national territorial space refers to constructing a global resilience assessment model and a node resilience assessment model respectively through a convolutional neural network, and training the global resilience assessment model and the node resilience assessment model respectively through a training set and a loss function; Inputting the global coupling characteristics into the trained global resilience assessment model to obtain the resilience of the national territorial space, and inputting the node coupling characteristics into the trained node resilience assessment model to obtain the node resilience.

4. The method for evaluating and optimizing the resilience of territorial space according to claim 3, wherein: The optimization of the national territorial space based on the GAN model and the genetic algorithm refers to randomly sampling noise from the standard normal distribution, using the generator in the GAN model to generate an initial optimized scheme of the national territorial space, and using the discriminator to judge the initial optimized scheme of the national territorial space, taking the initial optimized scheme that passes the judgment as a genetic individual to form an initial population, calculating the resilience of the national territorial space of each genetic individual as the fitness, and selecting the two genetic individuals with the highest fitness for crossover and mutation to generate new genetic individuals for iteration, stopping the iteration after reaching the set number of iterations, outputting the genetic individual with the highest fitness after iteration as the optimal individual, and extracting the optimized scheme of the national territorial space in the optimal individual as the optimal optimized scheme of the national territorial space.

5. The method for evaluating and optimizing the resilience of territorial space according to claim 4, wherein: The evaluation of the optimized scheme of the national territorial space refers to extracting the sub-network nodes in the optimal optimized scheme of the national territorial space to calculate the optimized node resilience, calculating the average value of all optimized node resilience and the average value of the current node resilience, calculating the connectivity coefficient of each sub-network, setting an evaluation threshold, and judging that the optimal optimized scheme of the national territorial space passes the evaluation when the connectivity coefficient of each sub-network is greater than the evaluation threshold and the average value of the optimized node resilience is greater than the average value of the current node resilience, otherwise regenerating the optimal optimized scheme of the national territorial space.

6. The method for assessing and optimizing the resilience of territorial space according to claim 5, wherein: The formation and display of optimization suggestions from the territorial space optimization plan refer to analyzing the differences between the sub-networks in the territorial space optimization plan that have passed the evaluation and the current sub-network, marking the difference points in the current sub-network, and generating optimization suggestions for the current sub-network based on the sub-networks in the territorial space optimization plan for display to the staff.

7. The method for evaluating and optimizing the resilience of territorial space according to claim 6, wherein: The storage in the database refers to storing the topological network set of territorial space data and the optimization suggestions in the database. The database regularly updates and performs integrity checks on the stored data, and uploads the stored data to the cloud for backup.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the territorial space resilience assessment and optimization method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the territorial space resilience assessment and optimization method according to any one of claims 1 to 7.

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