Real estate data analysis method based on multi-dimensional data fusion

Abstract

The application relates to the technical field of data processing, and discloses a real estate data analysis method based on multi-dimensional data fusion, which comprises the following steps: acquiring multi-source real estate data, extracting basic features and encoding a probability tensor, and analyzing multi-dimensional correlation between nodes; constructing a multi-layer graph network and initializing node features of the nodes; configuring independent calculation clocks for the nodes to execute asynchronous iteration calculation, and controlling the clocks to sleep or activate based on comparison of feature change rates and wake-up thresholds; monitoring cumulative disturbance amounts received by the nodes, and dynamically updating correlation edges of target nodes when the cumulative disturbance amounts exceed a reconstruction threshold; when matching to external deterministic events, converting feature inputs and waking up all sleeping clocks to perform global synchronous calculation until a shutdown threshold, and outputting a prediction result. Through independent calculation clocks, micro-asynchronous computing power scheduling is realized, invalid computing power loss is stripped and data congestion is dredged through dynamic correlation edge reconstruction, and event transmission time difference is eliminated.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a real estate data analysis method based on multidimensional data fusion. Background Technology

[0002] In real estate data analysis, it is often necessary to integrate multi-dimensional data such as transportation networks, capital flows, and urban planning to assess regional characteristics and predict development trends. With the increasing volume of data, graph neural networks are frequently used to build real estate analysis models to handle spatial and logical relationships.

[0003] Existing graph neural network methods generally employ a globally synchronized update mechanism. In each iteration, the feature matrices of all nodes in the network need to be updated simultaneously. In real-world real estate networks, the state changes of different nodes exhibit significant non-uniformity; data in some areas fluctuates frequently, while many other areas remain stable over extended periods. This globally synchronized update mechanism forces even stable nodes to continuously participate in inefficient matrix multiplication operations, which not only consumes significant computing resources but also reduces the overall computational efficiency of the analysis model.

[0004] Furthermore, existing analytical models typically use static relationships between nodes when constructing graph networks. Once the network topology is determined during initialization, it remains unchanged during subsequent feature extrapolation. When sudden local changes occur in the external environment, causing specific nodes to receive dense disturbance data within a short period, the fixed local connectivity limits the data propagation range. Densely input data features cannot be quickly dispersed through existing local edges, reducing the model's ability to handle concentrated external disturbances.

[0005] Current analytical methods for handling external events typically employ a single inference model, making it difficult to simultaneously address both uncertain predictions and rapid responses to deterministic events. The evolution of the real estate market is heavily influenced by macroeconomic external events. When uncertain events, initially probabilistic inputs, transform into definite events, the system cannot promptly adjust its underlying computational state. Conventional iterative mechanisms struggle to synchronously align defined feature parameters across the entire network within a very short timeframe, resulting in a time lag in the model's predictions when dealing with external deterministic events. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a real estate data analysis method based on multi-dimensional data fusion, which solves the technical problems of existing real estate data analysis models based on graph neural networks, such as invalid computing power loss caused by global synchronous updates, local data blockage caused by static network topology, and feature transmission lag in response to external deterministic events.

[0007] To achieve the above objectives, the present invention provides a real estate data analysis method based on multi-dimensional data fusion, comprising: Acquire multi-source real estate data, extract the basic features of the nodes to be constructed in the graph network, encode the probability tensor of uncertain events, and parse the multidimensional relationships between the nodes; A multi-layer graph network is constructed based on the multi-dimensional association relationship. The node features of the nodes are initialized using the basic features, and the nodes are connected by association edges. Each node is configured with an independent computing clock, and the node features are asynchronously iteratively calculated using a graph neural network. During the calculation, the feature change rate of each node is calculated. When the feature change rate of a node is less than a preset wake-up threshold, the independent computing clock of the corresponding node is controlled to sleep; otherwise, it is activated to achieve asynchronous computing power scheduling. During the monitoring and calculation process, the cumulative disturbance received by each node is used as the target node, and the associated edges of the target node in the multi-layer graph network are updated. When an external deterministic event matching the uncertain event is received, the corresponding probability tensor is converted into deterministic feature input, all dormant independent computing clocks are awakened to perform global synchronous calculations until the feature change rate of all nodes is less than a preset shutdown threshold, the feature synchronization and alignment of the entire network's underlying data structure is completed, and the target prediction data is output, that is, the final node feature tensor of the multi-layer graph network is mapped to the target prediction data.

[0008] Furthermore, the process of encoding the probability tensor of the uncertain event includes: extracting multiple potential evolution scenarios of the uncertain event; obtaining the prior probability and the corresponding impact feature vector of each potential evolution scenario; and summing the product of the prior probability and the corresponding impact feature vector to obtain the probability tensor.

[0009] Furthermore, the process of parsing the multidimensional relationships between the nodes and constructing a multi-layer graph network based on these relationships includes: extracting traffic network data, cross-regional migration and capital flow trajectory data, and school district and planning circle data from the multi-source real estate data; generating a physical space matrix based on the traffic network data, a directed economic flow matrix based on the cross-regional migration and capital flow trajectory data, and a policy coordination matrix based on the school district and planning circle data; assigning weights to the physical space matrix, the directed economic flow matrix, and the policy coordination matrix through an attention mechanism and weighting and aggregating them to generate an aggregated adjacency matrix of the multi-layer graph network, thereby representing the associated edges between nodes.

[0010] In one specific implementation, the process of initializing the node features of the node using the basic features further includes: extracting the price value or popularity value from the basic features as the initial node features; and simultaneously mapping the basic features to the capacity features of the node using a multilayer perceptron, wherein the capacity features are used to characterize the node's ability to resist external data shocks.

[0011] In one specific implementation, the asynchronous iterative computation of the node features using a graph neural network is based on discrete partial differential equations. For each node corresponding to an active, independently computed clock, the system calculates a diffusion term based on the diffusion coefficient and the difference in features between adjacent nodes. Convection terms based on convection coefficients and the external perturbation term generated by the probability tensor transformation. The diffusion term, the convection term, and the external disturbance term are summed and the sum is divided by the capacity characteristic of the node to obtain the characteristic change rate of the node in the current calculation step. Its core calculation formula is expressed as:

[0012] in, This refers to the capacity characteristic of the node. The system is based on the derived rate of change of this characteristic. Update the node characteristics of this node.

[0013] Furthermore, the process of accumulating the disturbance received by each node during the monitoring calculation includes: based on the system's global time accumulator, within a preset time window, performing discrete step size accumulation on the absolute values ​​of the external disturbance terms continuously received by the node to obtain the accumulated disturbance.

[0014] Furthermore, the process of updating the associated edges of the target node in the multi-layer graph network includes: pausing the independent computation clocks of the target node and its neighboring nodes; updating the matrix elements in the aggregated adjacency matrix corresponding to the target node, specifically: updating the matrix elements representing disconnected nodes to non-zero connectivity weights to generate cross-region long-range edges, and setting the matrix elements representing the original local connectivity to zero to cut off the local connectivity edges; after completing the matrix element update, resuming the computation of the independent computation clocks of the target node and its neighboring nodes.

[0015] Furthermore, the process of waking up all the dormant independent computing clocks to perform global synchronization calculations includes: stopping the dormant judgment logic that compares the feature change rate with the wake-up threshold; forcibly activating all the dormant independent computing clocks and performing a global feature synchronization alignment operation.

[0016] Furthermore, the process of outputting target prediction data includes: extracting the final node feature distribution data of all nodes when the shutdown threshold is reached; inputting the final node feature distribution data into the decoder network for index mapping and outputting the target prediction index set; and generating and outputting the evolution path map of data transmission based on the historical records of the sleep and activation of each independent computing clock during the calculation process.

[0017] This invention provides a real estate data analysis method based on multidimensional data fusion. It has the following beneficial effects: 1. Significantly reduces the ineffective computing power and memory consumption of the underlying computer system. Addressing the issue of ineffective computing power loss in steady-state nodes caused by global synchronous iteration in graph networks, this invention configures each node with an independent computing clock. The system calculates the change rate of node features in real time and compares it with a preset wake-up threshold, directly blocking the clock feed of nodes with slight changes and putting them into sleep mode. This scheme eliminates redundant feature matrix multiplication at the underlying level, achieving precise asynchronous computing power scheduling at the micro-granularity, effectively breaking through computing bottlenecks and releasing the concurrent potential of hardware.

[0018] 2. Improved throughput and transmission efficiency of complex data structures in network topologies. Addressing the issue of local data transmission congestion caused by sudden variables in static topologies, this invention introduces a perturbation-driven underlying self-healing mechanism. By continuously monitoring the accumulated perturbation received by nodes, the system proactively updates the associated edges of the target node in the network once the impact potential energy exceeds the reconstruction threshold. This dynamic topology reconstruction breaks the limitations of the original static graph structure, giving the network an active "flood discharge" capability, ensuring the smooth transmission of high-dimensional complex information.

[0019] 3. To address the time lag issue in the model's transformation from fuzzy probabilities to deterministic events, this invention constructs a global state takeover logic. When an external deterministic event is matched, the system directly converts the feature input and forcibly wakes up all dormant clocks to perform global synchronous calculations. This mechanism instantly flattens the network from a scattered asynchronous state to a high-density synchronous aligned state, forcing deterministic features to quickly permeate the entire network, ensuring that the final prediction result closely matches the actual macroscopic oscillations. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a diagram of the multi-layer graph network construction and attention fusion architecture of the present invention; Figure 3 This is a logic diagram of node micro-calculation and asynchronous computing power scheduling in this invention; Figure 4 This is a schematic diagram illustrating the dynamic topology reconstruction and mask matrix operation of the present invention; Figure 5This is a diagram illustrating the external event triggering and global synchronization mechanism of the present invention; Figure 6 This is a diagram showing the heterogeneous computing architecture and thread mapping of computer devices according to the present invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see the appendix Figure 1 -Appendix Figure 6 This invention provides a real estate data analysis method based on multi-dimensional data fusion, comprising the following steps: Acquire multi-source real estate data, extract the basic features of the nodes to be constructed in the graph network, encode the probability tensors of uncertain events, and parse the multidimensional relationships between the nodes.

[0023] A multi-layer graph network is constructed based on the multi-dimensional association relationship. The node features of the nodes are initialized using the basic features, and the nodes are connected by association edges.

[0024] Each node is configured with an independent computing clock, and the node features are asynchronously iteratively calculated using a graph neural network. During the calculation, the feature change rate of each node is calculated. When the feature change rate of a node is less than a preset wake-up threshold, the independent computing clock of the corresponding node is controlled to go into sleep mode; otherwise, it is activated to achieve asynchronous computing power scheduling.

[0025] During the monitoring and calculation process, the cumulative disturbance received by each node is monitored. When the cumulative disturbance of the target node exceeds the preset reconstruction threshold, the associated edges of the target node in the multi-layer graph network are updated.

[0026] When an external deterministic event matching the uncertain event is received, the corresponding probability tensor is converted into deterministic feature input, all dormant independent computing clocks are awakened to perform global synchronous calculations until the feature change rate of all nodes is less than a preset shutdown threshold, the feature synchronization and alignment of the entire network's underlying data structure is completed, and the target prediction data is output, that is, the final node feature tensor of the multi-layer graph network is mapped to the target prediction data.

[0027] In the overall process described above, this invention transforms traditional static data analysis into a dynamic computer graph structure calculation process. The system first constructs the underlying spatial and logical topology based on multi-source real estate data. The node set of the multi-layer graph network is defined as follows: The set of associated edges is Each node This represents an analysis unit whose initial state is derived from the node features of the basic features. Representation. Simultaneously, the system extracts uncertainties from the external macroscopic environment and encodes them as probability tensors. , which acts as an external disturbance source that continuously affects the entire graph network.

[0028] Traditional graph neural networks typically employ a globally synchronized message-passing mechanism, updating all nodes indiscriminately in each iteration. This leads to a large number of nodes with minimal feature changes consuming inefficient computational resources. To overcome this synchronization bottleneck inherent in the von Neumann computing architecture, this system uses a node set... Each node in the system is assigned an independent computing clock.

[0029] In discrete computation time step In this system, the iteration of the graph neural network is driven by the state changes of node features. The system monitors and calculates node features in real time. The characteristic change rate in the current calculation step is set to... The system introduces a preset wake-up threshold. To perform precise computing power allocation: direct comparison and The size. If This indicates that the information surge to the node is insufficient to trigger a significant state change. The system controls its independent computing clock to enter a sleep state, suspending the node's proactive feature updates and outward broadcasting operations, but maintaining its passive listening cache; conversely, if... An independent computing clock maintains the active state, and the node performs asynchronous iterative computation of the graph neural network normally.

[0030] Beyond calculating the internal features of nodes, this method further achieves adaptive adjustment of the underlying structure of the graph network. The system configures an independent local sliding window accumulator for each node to integrate the external perturbations continuously received by the node during the calculation process. The cumulative perturbation received by the node is defined as... With the injection of external disturbances, when the cumulative disturbance amount of a specific target node is detected... Exceeding the preset reconstruction threshold When this occurs, it indicates that the local information of the node is overloaded, and the original associated edges... The system could no longer support effective data transmission. It then triggered a local topology update operation, forcibly reconstructing the associated edges of the target node in the multi-layer graph network to alleviate the accumulated data flow.

[0031] The system possesses strict response and alignment logic to external events. During regular calculus, the probability tensor... The system is provided with an evolutionary boundary in the form of a fuzzy distribution. When the external system captures a deterministic event input that precisely matches the originally defined uncertain event, the system triggers a global takeover command. At this point, the system uses a high-dimensional probability tensor... Dimensionality reduction and collapse into deterministic feature input vectors .

[0032] The injection of deterministic features disrupts the original asynchronous scheduling steady state. To ensure that macroscopic events penetrate the entire microscopic network without omission, the system terminates the aforementioned wake-up threshold-based approach. The system's sleep detection logic sends a global interrupt signal to forcibly wake up all independent computing clocks in sleep mode. The entire graph network switches from asynchronous iteration to a globally synchronous computation mode, accelerating the propagation and alignment of deterministic features among nodes. This globally synchronous computation process continues until the system detects a node set... The rate of change of features of all nodes decays to less than the preset shutdown threshold. At this point, the system determines that the data transmission within the network has completed rebalancing, terminates the calculation iteration, and outputs the final target prediction result through the decoding network.

[0033] This embodiment details the process of deep analysis of multi-source real estate data and the construction of the underlying graph network architecture. The system's primary task is to perform mathematical quantification of external uncertainties and generate a multi-layer graph network topology.

[0034] In this invention, for undefined policies or economic changes in the macro environment, the system extracts multiple potential evolution scenarios of uncertain events based on preset macroeconomic rule templates or natural language processing models. For example, when the uncertain event is "central bank mortgage rate adjustment," the system classifies it into... There are three potential evolutionary scenarios: an increase of 50bp, no change, and a decrease of 50bp. The system obtains the prior probability of each potential evolutionary scenario based on historical frequency statistics of similar events or externally defined expert experience models. (For example, based on statistical or expert scoring, the probabilities of the three scenarios are 0.2, 0.5, and 0.3, respectively).

[0035] Simultaneously, the system acquires the impact feature vector corresponding to each scenario. The impact feature vector This vector has the same physical meaning as the underlying graph network node's basic feature vector (e.g., both include dimensions such as price, transaction volume, and population inflow). The values ​​of each element in this vector are used to precisely quantify the expected percentage change in the corresponding feature dimension after the scenario occurs. For example, if historical data clustering analysis indicates that the scenario of "a 50bp reduction" would lead to a 2% increase in housing prices and a 5% increase in transaction volume, then the impact feature vector under this scenario... The values ​​in the corresponding dimensions are quantized to 0.02 and 0.05. For the... The system analyzes the prior probabilities of the occurrence of various potential evolutionary scenarios. And extract the impact feature vector that quantifies the impact effect of this scenario on the system. .

[0036] The system sums the products of the prior probabilities under each scenario and the corresponding impact feature vectors to generate a probability tensor for encoding uncertain events. Its calculation expression is:

[0037] in, As a scalar, representing the first The statistical probability of a scenario occurring, and satisfying that the sum of the prior probabilities of all scenarios is 1; It is a multidimensional real vector; It aggregates the expected impacts under multiple scenarios and serves as a source of high-dimensional uncertainty disturbances, continuously inputting them into the subsequent graph neural network calculation process.

[0038] While completing event tensor encoding, the system performs in-depth analysis of the multidimensional relationships between nodes to construct a multi-layer graph network. The system extracts three types of core trajectory information from the acquired multi-source real estate data: transportation network data, inter-regional migration and capital flow trajectory data, and school district and planning area data.

[0039] Based on the three types of data mentioned above, the system generates three-dimensional correlation matrices in parallel. To ensure data compliance and privacy protection, the cross-regional migration and capital flow trajectory data are all sourced from anonymized signaling data from telecommunications operators and anonymized clearing data from macro-financial statistics platforms. All raw data have been stripped of personal identification tags and aggregated at the city grid (i.e., node) level.

[0040] The specific quantization rules for the correlation matrix are as follows: For physical space matrix System extracts nodes With nodes The actual commuting distance between The aforementioned This is the shortest path distance (in kilometers) between the center coordinates of two nodes calculated based on the actual urban traffic network topology, rather than a simple straight-line Euclidean distance. The system uses an inverse proportional smoothing function to generate matrix elements: This is to ensure that the closer the distance, the higher the connectivity.

[0041] For the directed economic flow matrix System extracts nodes Flow to Node Average monthly capital and population turnover frequency This frequency is determined by analyzing data from nodes within a month. Mesh move to node The result is obtained by weighting and summing the number of active mobile devices in the grid and the corresponding number of enterprise macro-level fund settlement transactions. The system is categorized by node. Normalize the total outflow: .

[0042] For policy coordination matrix The system constructs binary isomorphic features based on school district divisions and urban planning boundaries. The system imports GIS spatial vector data (shapefile boundary polygons) containing the boundaries of school districts and planning boundaries from the government information disclosure platform. The system uses a "point within polygon" algorithm to determine whether the center coordinates of a node fall within the same boundary polygon. When a node... With nodes When located within the same school district polygon or core planning concentric circle polygon Otherwise, it is 0.

[0043] The three fundamental matrices mentioned above, which have specific physical and spatial meanings, constitute the bottom-level topology input of the multilayer graph network.

[0044] The multilayer graph network described in this invention refers to a network with the same set of physical nodes. The system consists of multiple parallel graph topology layers constructed based on different business semantics (physical connectivity, economic flow, policy isomorphism). In subsequent asynchronous iterative computations, each layer does not independently perform feature updates but relies on the fused single network base. Therefore, to facilitate unified computation of the graph neural network, the system needs to reduce the dimensionality of these multiple topology layers and fuse them.

[0045] In this embodiment, the system employs a learnable self-attention mechanism from graph attention networks to adaptively assign weights to the three matrices. The system is configured with a shallow feedforward neural network. To ensure that the matrix fusion weights reflect the evolution of the overall macro market, the system performs calculations at each current discrete step. Next, the node features of all activated nodes in the multi-layer graph network are extracted. The node feature matrix is ​​compressed using global average pooling to generate a global pooling vector representing the current state of the entire network. This global pooling vector is then input into the feedforward neural network. After being normalized by an internal single-layer linear connection mapping and the Softmax function, the weights of the physical space matrix at the current iteration step are dynamically output. Weights of the directed economic flow matrix and the weights of the policy coordination matrix (and satisfy) ).

[0046] In the initial state (i.e., the calculation step) Under the default settings In subsequent asynchronous iterative calculations, as the node features change, the global pooling vector changes accordingly. Based on this, the feedforward neural network recalculates and overwrites the weight values ​​of the previous calculation step in each calculation step, achieving dynamic adaptive adjustment.

[0047] Regarding the training of this shallow feedforward neural network model, it is not pre-trained in isolation, but rather constructed as an end-to-end joint training architecture with the entire graph neural network framework. During the training phase, the system does not set a separate loss function for this feedforward neural network; instead, it directly uses the mean squared error between the final output target prediction index set and the true historical evolution labels as the unified loss function for downstream prediction tasks. The system calculates the gradient of this downstream loss with respect to the attention weights through the backpropagation algorithm, thereby synchronously updating the internal connection parameters of the feedforward neural network.

[0048] After the above network computation, the system performs weighted aggregation of the dynamically acquired attention weights and the corresponding matrices to generate the aggregated adjacency matrix of the multilayer graph network in the current computation step. The calculation formula is:

[0049] The generated aggregate adjacency matrix It comprehensively reflects the combined effects of physical space, economic flow, and policy orientation. The non-zero elements in the matrix accurately represent the associated edges between nodes in the graph network and their initial connectivity strength.

[0050] In specific implementation scenarios, the "multi-source real estate data" includes, but is not limited to: publicly available land transfer records from government agencies, historical online transaction prices and listing volumes from real estate transaction platforms, regional population heat maps provided by mobile operators or internet maps, and urban information point distribution data. The extracted basic features constitute a high-dimensional basic feature vector. For example, the vector is set to 64 dimensions, specifically including: the average house price per unit area within the local space, the month-on-month growth rate of transaction volume over the past 30 days, the average daily inflow-outflow ratio of the regional population, and the density of transportation and education POIs per square kilometer. Those skilled in the art will understand that the selection of the above feature dimensions can be conventionally increased or decreased based on the richness of the actual data source.

[0051] After the multi-layer graph network topology is constructed, the system utilizes the basic features of each node extracted from multi-source real estate data to initialize the node features. The system configures a multi-layer perceptron network with a preset number of layers (e.g., 3 layers). This multi-layer perceptron includes: an input layer with the same dimension as the basic feature vector (e.g., 64 dimensions), a hidden layer for feature dimensionality reduction (e.g., 32 dimensions, ReLU activation function), and an output layer with a scalar output (1 dimension, Sigmoid activation function to ensure the output capacity feature is always a positive real number within the (0,1) interval). The system then initializes the node's basic feature vector... The data is input into the multilayer perceptron for nonlinear mapping. The output of the multilayer perceptron generates the capacity features of each node. The calculation process is expressed as follows:

[0052] in, This characterizes the nonlinear transformation process of a multilayer perceptron. This capacity feature... It is defined as an independent scalar, specifically used to characterize the inherent ability of the corresponding node to impede and buffer fluctuations when facing external data shocks and the transmission of characteristics from adjacent nodes.

[0053] For the initial state setting of node features, the system starts from the basic feature vector. The system analyzes core business metrics. When both price and popularity metrics exist in the data source, the system performs a weighted fusion of the two using preset hyperparameter weights (e.g., price weight is 0.7, popularity weight is 0.3). If only a single metric exists, it is extracted directly. To eliminate dimensional differences, the system uses Z-score normalization to globally normalize the extracted values, and the standardized feature vector is used as the initial node features for the graph network nodes. At this point, the system has completed the construction of the multi-layer graph network topology and the calibration of the initial state of the nodes, providing a rigorous underlying data environment for the scheduling of independent computing clocks and asynchronous iterative calculations.

[0054] This embodiment provides a detailed description of the asynchronous iterative computation and computing power scheduling mechanism for node features in a multi-layer graph network. In this invention, the system completely abandons the traditional fully connected synchronous message passing mode in graph neural networks. Instead, it configures an independent computing clock for each node in the network and implements asynchronous computing power scheduling based on the local feature evolution state.

[0055] In the computation process of graph neural networks, for each node corresponding to an independent computation clock that is in an active state, the specific computation process is strictly based on discrete partial differential equations. The system decomposes the evolution dynamics of node features into three independent computational variables: diffusion, convection, and external disturbance.

[0056] The system first calculates the diffusion term based on the diffusion coefficient and the differences in node characteristics between the node and its neighboring nodes. Let the current calculation step be... Target activation node The node features are Its set of neighboring nodes in the aggregated adjacency matrix is The system extracts the pre-configured diffusion coefficient. The undirected smooth diffusion effect of data between nodes is calculated using the discrete Laplace operator, and the diffusion term is... The calculation formula is:

[0057] in, Aggregate the adjacency matrix in the preceding steps Internal corresponding node With nodes The connection weights between them Adjacent nodes The node characteristics in the current computation step.

[0058] Subsequently, the system calculates a convection term based on the convection coefficient and the node characteristics of the node and its neighbors. Unlike the diffusion term, the convection term characterizes the directional flow of data within the network architecture driven by specific guidance. The system extracts pre-configured convection coefficients. Convection term The calculation formula is:

[0059] When calculating external influences, the system extracts the probability tensor of uncertain events generated in the previous encoding. This is transformed into an external perturbation term acting on the current node through a linear mapping. The system is configured with a learnable transformation weight matrix. Let the probability tensor be... The dimension is ( (Total number of scenarios), the dimension of node features is Then the transformation weight matrix The dimensions are strictly configured as The generated external disturbance term (dimension is) The formula for calculating ) is:

[0060] After obtaining the three computational variables mentioned above, the system performs superposition summation and feature update operations. The system then extracts the diffusion term... Convection terms External disturbance terms Perform linear superposition and summation. The capacity feature is generated by a multilayer perceptron during system call node initialization. Divide the sum of the sums by the capacity feature of the node. This accurately yields the characteristic change rate of the node in the current computation step. Its mathematical expression is:

[0061] This computational logic fully reflects the buffering effect of the node's inherent resistance to external shocks. Capacity characteristics. Larger nodes exhibit smaller rate of change in their characteristics when absorbing data disturbances of the same size. The system updates the node's characteristics based on this rate of change. .

[0062] For key parameters in the calculation process, in the exemplary implementation scenario, the pre-configured diffusion coefficient The value range of the convection coefficient is preferably set between [0.1, 0.5]. The value range is preferably set between [0.05, 0.2], and the specific value can be determined through grid search using historical validation sets. The preset wake-up threshold... It is generally set to a small positive real number (such as...) (orders of magnitude) to filter out weak numerical noise.

[0063] During the calculation process, the system will use the absolute value of the aforementioned characteristic rate of change. This serves as the core criterion for computing power scheduling. The system compares it with a preset wake-up threshold. Real-time comparison is performed. When the rate of change of the target node's characteristics is less than the preset wake-up threshold, it indicates that the current external shock and the transmission of neighborhood data are insufficient to cause a substantial change in the node's state.

[0064] At this point, the system issues a hardware blocking command, directly controlling the independent computing clock of the corresponding node to enter a sleep state. During the sleep period, the system suspends the node's active feature matrix multiplication, feature state overwriting, and feature broadcasting operations to neighboring nodes, and latches the node features actively output by the current node. Meanwhile, the system maintains a low-power passive listening buffer for this sleeping node. During the sleep period, this buffer does not perform full matrix multiplication; it only continuously receives and accumulates the feature changes broadcast by active neighbor nodes. and external disturbance terms The system calculates an approximate rate of change of latent features based on cached data. Its simplified calculation formula is:

[0065] The hardware processor compares the absolute values ​​of the rates of change of the aforementioned potential features cycle by cycle with extremely low computational overhead. With preset wake-up threshold When detected At this point, the system reactivates its independent computing clock, allowing it to retrieve cached data and continue participating in the discrete partial differential equation calculations of the next iteration. This mechanism leverages the non-uniformity of node state changes to significantly reduce the ineffective computing power consumption on steady-state nodes, achieving true asynchronous computing power scheduling.

[0066] This embodiment provides a detailed explanation of the disturbance monitoring mechanism during asynchronous iterative computation and the dynamic reconstruction process of associated edges in a multi-layer graph network. In this invention, the system not only updates node features at the microscopic level but also adaptively adjusts the underlying topology of the graph network at the macroscopic level based on local information overload.

[0067] During the continuous computation and feature propagation process, the system assigns a monitoring thread to each node in the node set to continuously track the scale of external shocks it experiences. The system configures an independent local sliding window accumulator for each node, setting the sliding time window length to [value missing]. (For example, The value ranges from 10 to 20 discrete computation steps. Within this time window, the system accumulates the discrete step lengths of the absolute values ​​of the external disturbance terms continuously received by the nodes. For any node... Its cumulative disturbance within the time window The calculation expression is:

[0068] in, For discrete computation steps within the time window, For nodes In the The step is based on the external perturbation term values ​​analyzed from the probability tensor. This accumulation operation is continuously performed, and at the end of each step, the system updates the accumulated perturbation. Compared with the preset reconstruction threshold Real-time comparison is performed. In an exemplary embodiment, this reconstruction threshold... The value range is preferably set between [5.0, 10.0], which represents the upper limit of the cumulative feature offset that a node can withstand within the time window.

[0069] When the cumulative disturbance of the target node is detected Strictly greater than or equal to the preset reconstruction threshold When the system determines that the local network structure of the target node is no longer able to effectively disperse the continuously accumulating external information disturbances, there is a risk of data blockage. At this point, the system immediately triggers a topology update command for the target node, forcibly reconstructing its associated edges in the multi-layer graph network.

[0070] To ensure data consistency in network state during topology updates and prevent dirty data reads and writes, the system first sends a control interrupt signal to suspend the independent computation clocks of the target node and its first-order neighbor nodes. In this clock-suspended state, the relevant nodes cease performing any feature updates and information broadcasting operations based on discrete partial differential equations, and node features are frozen in the current video memory or memory address.

[0071] The system performs a bidirectional matrix element mutation operation: On one hand, the system traverses candidate nodes in the network that are not connected to the target node, calculating the Euclidean distance between the current node feature vectors of the target node and the candidate nodes. The system uses far-end nodes whose Euclidean distance is greater than a preset distance threshold (e.g., a dynamic threshold set to the top 1% to 10% of all nodes in the network) as long-range edge connection objects, and updates the corresponding matrix elements (originally zero) in the aggregated adjacency matrix with preset non-zero connectivity weights (e.g., initialized to a mean weight of 0.5), establishing new data flooding channels; on the other hand, the system scans all existing first-order neighbor nodes of the target node, identifying those in the aggregated adjacency matrix... The matrix elements corresponding to the locally connected edges with the smallest absolute value of the associated weight (i.e. the weakest connectivity) are set to zero. This cuts off some of the original locally connected edges and prevents the continued accumulation of overloaded information.

[0072] To prevent the aforementioned topology reconstruction operation from being dynamically weighted and overridden by the attention mechanism in subsequent graph network iterations, the system synchronously generates a topology mask matrix in memory. For newly generated long-range edges and severed locally connected edges, the system marks the corresponding positions in their mask matrix with an absolute lock flag, preventing them from participating in subsequent attention weight allocation; only the weights of unreconstructed associated edges continue to be updated according to the aforementioned attention mechanism. This ensures that the reconstructed associated edges can continuously and stably participate in subsequent calculations.

[0073] After confirming that the underlying data structure of the aggregated adjacency matrix has been updated and the new topological connectivity has been rewritten to the system cache, the system releases the previous control interruption. The system resumes the computation process of the target node and its neighboring nodes' independent computing clocks. The restored node will continue to use the graph neural network for asynchronous feature iteration based on the new network connection edges. Through the above-mentioned dynamic edge reconstruction mechanism driven by cumulative perturbation, the system endows the graph network with extremely strong self-healing and evolutionary capabilities.

[0074] This embodiment provides a detailed explanation of the global state switching mechanism and the generation process of the final prediction result in a multilayer graph network driven by deterministic events. In this invention, while the system performs local asynchronous iterative calculations, a background thread continuously monitors the data input from the macroscopic environment through an external data interface.

[0075] When a background thread captures an external deterministic event input, the system calls a pre-trained text feature extraction model (such as the BERT model). During computation and training, the underlying parameters of this model remain frozen and do not participate in gradient updates. The system inputs the text description of the external deterministic event into the model and directly extracts the fixed-dimensional feature output (usually 768 dimensions) corresponding to the special flag bit ([CLS] flag bit) in its last hidden layer. Subsequently, the system linearly projects the dimension of this feature output to the exact same dimension as the impact feature vector through a fully connected mapping layer, thereby transforming the input into a strictly aligned high-dimensional event vector and calculating its cosine similarity with the previously extracted impact feature vector of the uncertain event. The system determines that a matching external deterministic event has been received only if and only if the cosine similarity is greater than a preset matching threshold (e.g., 0.85), indicating that the system environment has transitioned from the fuzzy evolution stage to the concrete implementation stage. At this time, the system immediately performs dimensionality reduction and reconstruction operations on the external disturbance source, converting the corresponding probability tensor into a deterministic feature input. Specifically, the system extracts the absolute impact feature vector of the external deterministic event. This completely replaces the original probability tensor generated based on multi-scenario summation. External perturbation terms in subsequent node calculations will be directly calculated based on this deterministic feature input, ensuring absolute accuracy of the impact boundary.

[0076] Because deterministic events typically trigger system-wide chain reactions, the existing local asynchronous scheduling mechanism cannot meet the needs of rapid data propagation. The system then triggers a global takeover command, waking up all dormant independent computing clocks to perform global synchronous calculations. The system sends a blocking signal to the underlying scheduling controller, forcibly stopping the dormancy judgment logic that compares the characteristic change rate with the wake-up threshold, and revoking the nodes' autonomous dormancy privileges.

[0077] Following this, the system sends a global interrupt pulse, forcibly activating all dormant independent computing clocks in the multilayer graph network. In this globally activated state, all nodes in the network are forced to synchronize to a uniform clockbeat, utilizing the graph neural network to perform global feature synchronization and alignment operations in parallel. This mechanism forces deterministic features to penetrate all locally connected edges and long-range cross-region edges within an extremely short physical time, eliminating the time lag in information transmission.

[0078] During the global synchronous computation process, the system still calculates the characteristic change rate of each node frame by frame. The system introduces a very small constant as a preset shutdown threshold. This value is used to calibrate the dynamic equilibrium state reached by the network after fully absorbing data surges. The system uses the absolute value of the characteristic rate of change of each node. The shutdown threshold is compared in real time. A shutdown occurs if and only if the characteristic change rate of all nodes in the graph network strictly satisfies... When, or when the cumulative number of steps in the global synchronous computation reaches the global maximum number of iteration steps. When the number of steps (preferably between 500 and 2000) is reached, the system determines that the transmission and feedback of all network data has converged. The system then issues a termination command, locks all clock steps, and ends the iterative process of the graph neural network, thereby eliminating the risk of the system falling into an infinite loop from the underlying logic.

[0079] Subsequently, the system performs the parsing and output operations of the target prediction results. The system extracts the final node feature distribution data of all nodes at the time the shutdown threshold is reached from the cache space and constructs it into a high-dimensional feature matrix. The system inputs the final node feature distribution data into a preset decoder network for index mapping. The weight matrix of the fully connected layer of the two-layer decoder network is defined as follows: (its dimensions are) ,in (where the dimension is the target predictor), and the bias vector is... (dimension is) The nonlinear activation function is (e.g., Tanh activation function), the system outputs a set of target prediction indicators. The calculation formula is:

[0080] Regarding the transformation weight matrix The weight matrix of the fully connected layer of the decoder network and bias vector These parameters are generated using the Xavier uniform distribution initialization method in the initial stage of the system. During the pre-training phase, the system uses historical real estate uncertain event data and evolution results as label data. The deviation between the output predicted value and the true label is calculated using the mean squared error loss function. An adaptive moment estimation optimizer is used to execute the backpropagation algorithm, iterating and updating these parameter matrices multiple times until the loss function converges, thereby determining the parameter values ​​for the final runtime. In the exemplary training configuration, the pre-training set contains no fewer than 10,000 historical atlas evolution samples, the batch size for model training is set to 64, and the initial learning rate is set to... The maximum number of training epochs is set to 200, and an early stopping mechanism is configured—if the mean squared error loss on the validation set does not decrease significantly for 15 consecutive epochs, backpropagation training is terminated early. Furthermore, regarding the aforementioned preset stopping threshold… To ensure timely system convergence, it is usually set to A very small constant of magnitude.

[0081] This target predictive indicator set The system precisely quantifies the final evolution values ​​of each node (corresponding to a specific urban grid or real estate micro-area) in a multi-layer graph network after the impact of deterministic events (such as sudden interest rate cuts, changes in school district policies, or the completion of new transportation hubs). In specific business implementation, these final evolution values ​​are specifically mapped to: the predicted average transaction price index of second-hand homes in the target area for the next 3 to 6 months, the market transaction listing supply and demand turnover cycle (days), and the expected score of net inflow of population and capital. Through the mapping of the above indicators, the system effectively transforms abstract macroeconomic uncertainty shocks into quantitative decision-making basis that can guide actual real estate resource allocation and policy regulation. In addition to outputting numerical results, the system also visualizes and reconstructs the data transmission process based on the time dimension. The system accesses the scheduling logs of the underlying hardware to extract the precise historical records of the sleep and activation of each independent computing clock during the calculation process. The system compares the timing difference of the clock activation state flips between adjacent nodes with the associated topological order to track the main propagation channels of feature mutations. Based on the dual records of time series and topology, the system generates and outputs an evolution path map of data transmission, thereby clearly revealing the real impact chain of macroscopic external variables penetrating complex network structures.

[0082] In this embodiment, based on the aforementioned real estate data analysis method based on multi-dimensional data fusion, the present invention also provides a corresponding computer device for supporting the physical implementation of the aforementioned asynchronous graph network computation and dynamic topology reconstruction algorithms at the underlying hardware architecture level. This computer device, as a physical platform for performing node network computation, integrates a processor, memory, communication interfaces, and a high-speed system bus connecting the various components.

[0083] In the system architecture of this invention, the memory is composed of high-speed random access memory and non-volatile storage medium. The memory is internally divided into a separate data buffer area and an instruction execution area. The data buffer area is used to latch the original input of multi-source real estate data in real time, the encoded probability tensor, the aggregated adjacency matrix of the multi-layer graph network, and the updated node features and cumulative perturbations under various discrete step sizes. The instruction execution area contains pre-compiled computer-executable instructions, which completely encapsulate all logical judgments and operator operations from data parsing and graph network initialization to asynchronous computing power scheduling and global synchronization triggering.

[0084] The processor, serving as the core of control and computing power, employs a heterogeneous computing architecture combining a multi-core central processing unit (CPU) and a graphics processing unit (GPU) that supports high-concurrency operations. In terms of hardware task allocation, the CPU's core threads are responsible for executing the topology construction, attention weight calculation, independent computing clock scheduling and control, and event matching logic of the multilayer graph network. Meanwhile, high-dimensional tensor multiplication based on discrete partial differential equations, the superposition calculation of diffusion / convection terms, and the nonlinear mapping of the multilayer perceptron are offloaded to the GPU's CUDA core cluster via a high-speed PCIe bus for high-concurrency acceleration.

[0085] During the actual iterative computation cycle, the processor reads the feature change rate of each node in memory in real time. The processor's internal logic gates directly compare this feature change rate with the wake-up threshold preloaded in the register. When a node's feature change rate is less than the wake-up threshold, triggering "sleep," the processor performs resource reclamation: moving the node's task handle from the active computation queue to the event waiting queue, and actively releasing the CPU time slice or GPU stream resources currently occupied by the node; at this time, the relevant physical computation threads are released back to the thread pool for scheduling by other nodes. When the feature change rate reaches the threshold or an external deterministic event is detected, the processor issues a high-priority non-maskable interrupt request, reallocates the physical threads through event triggers to wake up the computation clock of the sleeping node, clears the asynchronous latency barriers of each node, and then schedules all computation cores to perform high-density global feature synchronization alignment and prediction index decoding output.

[0086] In addition to the hardware device itself, this embodiment of the invention also provides a computer-readable storage medium containing executable instructions. This computer-readable storage medium, as the physical carrier for statically embedding the aforementioned algorithm, employs a non-volatile solid-state or mechanical storage form such as a read-only memory, flash memory, magnetic disk, or optical disk. The computer program burned into this medium completely records the execution logic code corresponding to the independent claim and all its dependent claims.

[0087] When the computer-readable storage medium is mounted on a terminal or server cluster with computing capabilities, and the computer program stored within it is loaded and executed by the terminal processor, the processor is configured to forcibly reproduce the complete technical solution described above, which involves acquiring multi-source data, constructing a multi-dimensional dynamic graph network, performing asynchronous iteration and threshold control, reconstructing associated edges, and driving global deterministic synchronous computation. Through the mediation of this storage medium, the asynchronous clock scheduling framework and dynamic graph computation mechanism designed in this invention can be accurately deployed and reproduced across hardware platforms, ultimately outputting target prediction results and evolution path maps reflecting the impact of macroscopic environmental shocks.

[0088] It is important to emphasize that the real estate data analysis mentioned in this invention is essentially a process of dimensionality reduction and graph topology calculation for high-dimensional, heterogeneous, and non-stationary spatiotemporal correlated data. This invention configures independent computing clocks for multi-layer graph networks, dynamically reconstructs related edges, and manages global states. Its core purpose is to reduce the amount of redundant matrix calculations performed by the underlying processor when executing graph neural network iterations, optimize memory read / write strategies, and thus improve the computational efficiency of the computer system in handling large-scale concurrent events. These improvements constitute a substantial technical transformation of the internal architecture and data flow processing rules of the computer system, rather than simply commercial operational rules.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real estate data analysis method based on multidimensional data fusion, characterized in that, include: Acquire multi-source real estate data, extract the basic features of the nodes to be constructed in the graph network, encode the probability tensor of uncertain events, and parse the multidimensional relationships between the nodes; A multi-layer graph network is constructed based on the multi-dimensional association relationship. The node features of the nodes are initialized using the basic features, and the nodes are connected by association edges. Each node is configured with an independent computing clock, and the node features are asynchronously iteratively calculated using a graph neural network. During the calculation, the feature change rate of each node is calculated. When the feature change rate of a node is less than a preset wake-up threshold, the independent computing clock of the corresponding node is controlled to sleep; otherwise, it is activated to achieve asynchronous computing power scheduling. During the monitoring and calculation process, the cumulative disturbance received by each node is used as the target node, and the associated edges of the target node in the multi-layer graph network are updated. When an external deterministic event matching the uncertain event is received, the corresponding probability tensor is converted into deterministic feature input, all dormant independent computing clocks are awakened to perform global synchronous calculations until the feature change rate of all nodes is less than a preset shutdown threshold, the feature synchronization and alignment of the entire network's underlying data structure is completed, and the target prediction data is output, that is, the final node feature tensor of the multi-layer graph network is mapped to the target prediction data.

2. The real estate data analysis method based on multidimensional data fusion according to claim 1, characterized in that, The process of encoding the probability tensor of uncertain events includes: Extract multiple potential evolution scenarios of the uncertain events; Obtain the prior probability and corresponding shock feature vector for each potential evolutionary scenario; The probability tensor is obtained by summing the product of the prior probability and the corresponding impact feature vector.

3. The real estate data analysis method based on multidimensional data fusion according to claim 1, characterized in that, The process of parsing the multidimensional relationships between the nodes and constructing a multi-layer graph network based on the multidimensional relationships includes: Data on transportation networks, cross-regional migration and capital flow trajectories, and school districts and planning boundaries are extracted from the multi-source real estate data. A physical space matrix is ​​generated based on the traffic network data; a directed economic flow matrix is ​​generated based on the cross-regional migration and capital flow trajectory data; and a policy coordination matrix is ​​generated based on the school district and planning circle data. Weights are assigned to the physical space matrix, the directed economic flow matrix, and the policy coordination matrix using an attention mechanism, and then weighted and aggregated to generate an aggregated adjacency matrix for a multi-layer graph network, thereby representing the associated edges between nodes.

4. The real estate data analysis method based on multidimensional data fusion according to claim 1, characterized in that, The process of initializing the node features of the node using the basic features further includes: The basic features are mapped to the capacity features of the nodes using a multilayer perceptron, and the capacity features are used to characterize the ability of the nodes to withstand external data impacts. Extract the price or popularity value from the basic features and use it as the initial node features.

5. The real estate data analysis method based on multidimensional data fusion according to claim 4, characterized in that, The specific calculation process of asynchronous iterative calculation of the node features using graph neural networks is based on discrete partial differential equations. For a node corresponding to an independent computing clock that is in an active state, the calculation variables include: The diffusion term is based on the diffusion coefficient and the difference in node characteristics between the node and its neighboring nodes. The convection term is based on the convection coefficient and the node characteristics of the node and its neighboring nodes; The external perturbation term generated by the probability tensor transformation.

6. The real estate data analysis method based on multidimensional data fusion according to claim 5, characterized in that, The process of asynchronous iterative calculation of the node features includes: The diffusion term, the convection term, and the external disturbance term are summed by superposition. Divide the summation result by the capacity feature of the node to obtain the feature change rate of the node in the current calculation step, and update the node feature of the node accordingly.

7. The real estate data analysis method based on multidimensional data fusion according to claim 5, characterized in that, The process of receiving the cumulative disturbance amount at each node during the monitoring and calculation process includes: Based on the system's global time accumulator, within a preset time window, the absolute values ​​of the external disturbance terms continuously received by the node are accumulated discretely to obtain the cumulative disturbance amount.

8. The real estate data analysis method based on multidimensional data fusion according to claim 3, characterized in that, The process of updating the associated edges of the target node in the multi-layer graph network includes: Pause the independent computing clocks of the target node and its neighboring nodes; Update the matrix elements in the aggregated adjacency matrix corresponding to the target node: update the matrix elements representing unconnected nodes to non-zero connectivity weights to generate cross-region long-range edges, and set the matrix elements representing the original local connectivity to zero to cut off local connectivity edges. After updating the matrix elements, the calculation of the independent computing clocks of the target node and its neighboring nodes is restored.

9. The real estate data analysis method based on multidimensional data fusion according to claim 1, characterized in that, The process of waking up all the dormant independent computing clocks to perform global synchronization calculations includes: Stop the sleep judgment logic that compares the feature change rate with the wake-up threshold; Force activation of all dormant independent computing clocks and perform global feature synchronization alignment.

10. The real estate data analysis method based on multidimensional data fusion according to claim 1, characterized in that, The process of outputting target prediction data includes: Extract the final node feature distribution data of all nodes when the shutdown threshold is reached; The final node feature distribution data is input into the decoder network for index mapping, and the target prediction index set is output. Based on the historical records of the sleep and activation of each independent computing clock during the calculation process, an evolution path map of data transmission is generated and output.

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