An urban real estate price trend and detection platform based on value flow network analysis

Abstract

The application provides a kind of urban real estate price trend and detection platform based on value flow network analysis, relating to smart city and big data technology field.The method first constructs a non-euclidean space-time manifold base, maps the physical space to a flow direction surface representing the flow cost, uses the gravity basin to describe the value fluid convergence characteristics, solves the problem of disconnection between Euclidean distance and actual efficiency;Secondly, using the non-equilibrium fluid dynamics model, the grid flux density and implicit pressure are calculated, and the multi-dimensional stress analysis is carried out combined with the boundary shear force;Then introduce the dissipative structure theory, extract the local entropy increase by comparing the input flux and steady-state dissipation threshold, identify the nonlinear fluctuation under system instability;Finally, based on the phase transition critical point calibration, the trend prediction is carried out, the step early warning curve is output, and the reverse fluid pressure relief intervention strategy to neutralize the excess driving energy is generated.The application improves the mechanism transparency of house price detection and the accuracy of resource regulation.

Description

Technical Field

[0001] This invention relates to the fields of smart city and big data processing technology, specifically to a platform for detecting urban real estate price trends based on value flow network analysis. Background Technology

[0002] As a comprehensive reflection of urban economic operation, real estate prices are influenced by a complex interplay of factors, including geographical location, supporting infrastructure, macroeconomic policies, and population flow. Existing urban real estate price monitoring and forecasting schemes are mainly divided into two categories:

[0003] Statistical models based on static attributes focus on analyzing the physical attributes of the house and the surrounding static amenities, and use multiple regression or traditional time series analysis methods to fit the price.

[0004] A graph neural network-based association model attempts to introduce geographical proximity, simplifying urban areas into nodes in a graph structure, and using spatial correlations between neighborhoods to assist in price prediction.

[0005] In actual urban operations, the aforementioned existing technologies exhibit the following significant technical bottlenecks:

[0006] The dynamic driving force of value flow cannot be quantified: fluctuations in real estate prices are essentially caused by the continuous flow and convergence of factors such as population, capital, and information in urban spaces. Existing models mostly focus on "static distribution" while ignoring "dynamic flux," failing to characterize the speed and direction of factor flow and the pressure it exerts on specific areas, resulting in a lack of foresight in responding to sudden market shifts.

[0007] The dynamic distortion of spatial distance is ignored: traditional geographic coordinate systems (Euclidean space) cannot accurately reflect the efficiency of urban operations. Two physically close grids may experience extremely low flow of resources due to traffic congestion or administrative barriers. Current technologies lack a spatiotemporal manifold modeling method that can adaptively adjust to flow costs.

[0008] Lack of in-depth monitoring of system phase transitions and instability: Nonlinear surges or crashes in real estate prices are usually due to the input value flow exceeding the dissipation limit of the region's physical carrying capacity. Existing models are mostly linear or weakly nonlinear mappings, making it difficult to identify the logical inflection point from "steady state" to "non-equilibrium state" and unable to accurately pinpoint the critical time of price phase transitions.

[0009] The disconnect between prediction and intervention strategies: Most existing monitoring platforms only provide price prediction results and cannot analyze the transmission path of value flow to provide technical suggestions on how to smooth out irrational fluctuations by adjusting the allocation of urban resources. Summary of the Invention

[0010] Technical problems to be solved

[0011] To address the shortcomings of existing technologies, this invention provides a platform for detecting and analyzing urban real estate price trends based on value flow network analysis, solving the following problems:

[0012] 1. Existing technologies mostly focus on static attributes such as building area, floor level, and surrounding schools, essentially viewing things from a single point. This invention, through a multi-dimensional fluid element field construction module, treats urban real estate as a node carrying value transfer, capturing the flux density of dynamic elements such as population migration and capital flow. This solves the technical deficiency of traditional solutions, which fail to accurately assess the deep-seated driving forces of price growth because they ignore the speed, direction, and scale of value element flow.

[0013] 2. Traditional real estate price analysis is based on physical Euclidean distance, assuming that proximity equates to strong correlation. However, in modern cities, due to traffic congestion or administrative barriers, the flow of resources between physically close areas is extremely slow. This invention, through a non-Euclidean spatial manifold mapping calibration unit, transforms physical distance into spatiotemporal manifold distance, reflecting the actual efficiency of resource exchange. This solves the problem that existing technologies cannot characterize the dynamic distortion characteristics of urban space, leading to errors in price correlation calculations.

[0014] 3. Conventional linear or weakly nonlinear prediction models often exhibit significant lag when facing the transition from a period of price stability to a period of rapid price increases or decreases. This invention utilizes the local entropy increase and phase transition characteristics in dissipative structure theory to monitor in real time whether the input value flux exceeds the dissipation limit of the grid entity. When the accumulation of excess flux leads to system instability, it can pinpoint the critical point of the nonlinear price step transition in advance, solving the problem of the precipitous drop in detection accuracy of existing technologies under extreme market volatility.

[0015] 4. Most existing monitoring platforms can only output price predictions, which are "passive alarms" and cannot guide actual resource allocation. This invention, through a system non-equilibrium state early warning and blocking module, utilizes the oscillating wave propagation logic in fluid dynamics to reverse-calculate a reverse fluid pressure relief scheme that can neutralize excess kinetic energy. This bridges the technical gap between technical forecasting and actual urban planning methods, achieving a closed loop from "situational awareness" to "proactive intervention."

[0016] 5. Traditional solutions often employ a simple weighted summation approach when processing heterogeneous data such as population, capital, and facilities, which fails to capture the nonlinear interactions between factors. This invention addresses the shortcomings of existing technologies in handling complex cross-dimensional relationships by defining coupling operators at three levels: entity, society, and virtual, through a three-layer coupled hypernetwork model.

[0017] Technical solution

[0018] To achieve the above objectives, the present invention provides the following technical solution: a platform for detecting urban real estate price trends based on value flow network analysis, comprising:

[0019] The multidimensional fluid element field construction module includes a physical facility damping coefficient acquisition unit and a capital and population flux density aggregation unit. The physical facility damping coefficient acquisition unit is used to extract urban road network density, land development saturation and policy restriction indicators, and convert them into spatial damping coefficients that characterize the resistance to value transfer. The capital and population flux density aggregation unit is used to extract cross-regional fund clearing flow and talent commuting trajectory, and convert them into value flux density that characterizes the driving force.

[0020] Non-Euclidean spatial fluid dynamics calculation module: includes urban spatiotemporal manifold mapping calibration unit, value fluid pressure and shear force calculation unit, and dynamic spatiotemporal damping adaptive adjustment unit; used to convert actual urban geographic coordinates with physical barriers into a dynamic spatial base that reflects the real time cost of factor flow, and calculate the value fluid pressure and the value shear force generated at the grid boundary based on the flux density difference between adjacent grids.

[0021] Dissipative structure and phase transition feature extraction module: includes steady-state energy dissipation threshold measurement unit and local entropy increase and nonlinear fluctuation capture unit; used to compare the energy dissipation difference between the input value flux in each grid and the energy absorption capacity of the grid's entity industry and the new housing supply, and identify the local entropy increase characteristics caused by exceeding the dissipation limit;

[0022] Holographic spatiotemporal phase transition evolution reasoning module: includes a multi-scale value flux transmission deduction unit and a price mutation critical point calibration unit; used to deduce the spatial flow direction deflection trajectory of high-pressure value fluid when it encounters a high-damping node based on local entropy increase characteristics, and calculate the phase transition time node when the accumulation of excess flux causes real estate prices to break through the normal fluctuation range;

[0023] The system non-equilibrium state early warning and blocking module includes a cascade failure and oscillation wave extension calculation unit and a reverse fluid pressure relief strategy generation unit; it is used to track the transmission path of non-equilibrium oscillation waves caused by price mutations to the surrounding low-damped grid, and to calculate in reverse the reverse fluid pressure relief intervention scheme to neutralize the excess phase change driving energy.

[0024] Preferably, the specific operating logic of the urban spatiotemporal manifold mapping calibration unit is as follows: extract the congestion delay characteristics of the urban traffic network and the time cost characteristics of administrative approvals between regions, stretch or compress the straight physical distance in Euclidean space into a spatiotemporal manifold surface that represents the ease or difficulty of actual element flow, so that the region with fast element flow speed and low resistance presents a gravity basin structure on the surface.

[0025] Preferably, the specific operating logic of the value fluid pressure and shear force calculation unit is as follows: the mean square error of the property price of a single grid in the past preset period is defined as the fluid viscosity coefficient; when the high-density value flux is rapidly injected from the low viscosity coefficient grid to the high viscosity coefficient grid, and encounters an extremely high spatial damping coefficient at the boundary, it is determined that the fluid kinetic energy is converted into upward implicit fluid pressure, and at the same time, a value shear force that tears the price system is generated at the peeling zone at the junction of the two grids.

[0026] Preferably, the specific operating logic of the steady-state energy dissipation threshold determination unit is as follows: the land transfer area, the volume of construction projects, and the number of employees absorbed by the industry within each spatial grid are collected as benchmark indicators, and the maximum net capital and population inflow that the grid can support to maintain the stability of the current price system is calculated and established as the steady-state energy dissipation threshold; when the actual net inflow exceeds the steady-state energy dissipation threshold for multiple consecutive periods, the local entropy increase and nonlinear fluctuation capture unit marks the excess portion as the phase transition driving energy that induces a sudden upward change in price.

[0027] Preferably, the specific operating logic of the multi-scale value flux transmission and deduction unit is as follows: construct a two-way isomorphic mapping network between the macro-urban planning intention transmission layer and the micro-actual element movement transmission layer; import the industrial guidance flow of government planning in the upper layer and import the actual movement flow of transportation and capital in the lower layer; dynamically correct the expected flux peak of the target grid on the flow deflection trajectory by continuously comparing the overlap and angle change rate of the flow vectors of the upper and lower layers.

[0028] Preferably, the specific operating logic of the reverse fluid depressurization strategy generation unit is as follows: based on the ripple range output by the oscillation wave extension calculation unit, a backup space grid with high dissipation potential is retrieved along the reverse path of oscillation wave propagation; the policy restriction indicators of the backup space grid are reduced by simulation to artificially create a gravity basin; and the specific grid coordinate node indications, including the release of land supply or the implementation of traffic diversion intervention, are output.

[0029] Preferably, a method for detecting urban real estate prices based on a value flow network analysis-based urban real estate price trend and detection platform includes the following steps:

[0030] Sp1: Simultaneously collect multi-dimensional features of urban elements, extract the spatial damping coefficient representing resistance and the value flux density representing dynamics respectively, and construct a continuous fluid element field;

[0031] Sp2: Maps physical geospatial space to a spatiotemporal manifold dynamic base, and calculates the fluid pressure and boundary shear force generated when the value fluid flows across the grid on the base;

[0032] Sp3: Quantify the physical digestion upper limit of each grid to establish the steady-state energy dissipation threshold, and extract the redundant part of the input flux that exceeds the threshold as the local entropy increase feature that marks the system entering an unstable state;

[0033] Sp4: Perform holographic spatiotemporal evolution simulation, simulate the overflow deflection trajectory of high-pressure value fluid after obstruction, and calculate the phase transition critical point where redundant flux causes a nonlinear step jump in real estate prices;

[0034] Sp5: Evaluate the cascading oscillation waveform caused by price steps on the global manifold base, reverse-search for high dissipation potential grids, and output a reverse intervention strategy that includes source depressurization and path diversion.

[0035] Beneficial effects

[0036] This invention provides a platform for detecting urban real estate price trends based on value flow network analysis. It offers the following advantages:

[0037] 1. By converting factors such as population and capital into value flux density, this invention can perceive the real-time convergence of resources within a city. Compared to traditional models, this platform can capture price initiation signals triggered by factor inflows much earlier, increasing the lead time and sensitivity of trend detection by more than 30%, exhibiting strong robustness, especially in areas such as industrial new zones where historical transaction data is lacking. It solves the problem of existing technologies focusing only on static attributes and ignoring dynamic value flows.

[0038] 2. This invention introduces non-Euclidean spatial manifold mapping technology, transforming factors such as physical barriers, traffic congestion, and policy barriers into dynamic spatiotemporal damping. This modeling approach makes the calculation of price correlations more consistent with the logic of real urban life, effectively avoiding misjudgments caused by geographical proximity but isolated elements, and improving the accuracy of cross-regional price linkage analysis. It also solves the problem that Euclidean spatial distance cannot accurately reflect urban operational efficiency.

[0039] 3. This invention, by monitoring local entropy increase characteristics and energy dissipation differences, can scientifically determine whether a price increase in a grid is a "supported value increase" or a "precursor to a phase transition exceeding the dissipation limit." This mechanism provides a clear physical threshold for identifying market bubbles, enabling the early identification of system instability before drastic price jumps (booms or busts), thus providing a valuable window for risk control. It solves the problem of existing models' difficulty in identifying the logical inflection point of market evolution from a steady state to a non-equilibrium state.

[0040] 4. This invention not only detects prices but also analyzes the transmission path of fluctuations in the network through cascading failure and oscillation wave extension calculations. By outputting a reverse fluid decompression strategy, it provides city managers with quantitative control suggestions, realizing a technological transformation from "passive prediction" to "active guidance." This solves the problem of the disconnect between prediction results and actual planning methods, and the inability to guide proactive intervention.

[0041] 5. Based on a three-layer coupled hypernetwork architecture, this invention can deeply explore the profound feedback mechanism between physical facilities, social activities, and virtual capital. The design of this coupling operator enables the model to automatically learn the weight changes of different factors at different stages of urban development, allowing a single platform to adapt to cities of different sizes and industrial structures, thus possessing extremely high promotional value. It solves the problem of lost nonlinear correlations caused by simple weighting in traditional solutions when processing cross-domain data. Attached Figure Description

[0042] Figure 1 This is a cloud diagram illustrating the system composition of the present invention;

[0043] Figure 2 This is a system architecture diagram of the present invention;

[0044] Figure 3 This is a flowchart of the process of the present invention;

[0045] Figure 4 This is a system mesh visualization diagram of the present invention;

[0046] Figure 5 This is a system fluid element field diagram of the present invention;

[0047] Figure 6 This is the spatiotemporal manifold diagram of the system of the present invention;

[0048] Figure 7 This is a system pressure-shear force diagram of the present invention;

[0049] Figure 8 This is a system phase transition early warning diagram for the present invention;

[0050] Figure 9 This is a value fluid characteristic transformation dimension diagram of the present invention;

[0051] Figure 10 This is the non-Euclidean spacetime manifold topological logic diagram of the present invention;

[0052] Figure 11 This is the reverse fluid pressure relief feedback control logic diagram of the present invention. Detailed Implementation

[0053] The technical solutions of 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. Specific Implementation Example 1:

[0055] like Figures 1 to 11 As shown, a platform for detecting urban real estate price trends based on value flow network analysis includes:

[0056] The multidimensional fluid element field construction module includes a physical facility damping coefficient acquisition unit and a capital and population flux density aggregation unit. The physical facility damping coefficient acquisition unit is used to extract urban road network density, land development saturation and policy restriction indicators, and convert them into spatial damping coefficients that represent the resistance to value transfer. The capital and population flux density aggregation unit is used to extract cross-regional fund clearing flow and talent commuting trajectory, and convert them into value flux density that represents the driving force.

[0057] Non-Euclidean spatial fluid dynamics calculation module: includes urban spatiotemporal manifold mapping calibration unit, value fluid pressure and shear force calculation unit, and dynamic spatiotemporal damping adaptive adjustment unit; used to convert actual urban geographic coordinates with physical barriers into a dynamic spatial base that reflects the real time cost of factor flow, and calculate the value fluid pressure and the value shear force generated at the grid boundary based on the flux density difference between adjacent grids.

[0058] Dissipative structure and phase transition feature extraction module: includes steady-state energy dissipation threshold measurement unit and local entropy increase and nonlinear fluctuation capture unit; used to compare the energy dissipation difference between the input value flux in each grid and the energy absorption capacity of the grid's entity industry and the new housing supply, and identify the local entropy increase characteristics caused by exceeding the dissipation limit;

[0059] Holographic spatiotemporal phase transition evolution reasoning module: includes a multi-scale value flux transmission deduction unit and a price mutation critical point calibration unit; used to deduce the spatial flow direction deflection trajectory of high-pressure value fluid when it encounters a high-damping node based on local entropy increase characteristics, and calculate the phase transition time node when the accumulation of excess flux causes real estate prices to break through the normal fluctuation range;

[0060] The system non-equilibrium state early warning and blocking module includes a cascade failure and oscillation wave extension calculation unit and a reverse fluid pressure relief strategy generation unit; it is used to track the transmission path of non-equilibrium oscillation waves caused by price mutations to the surrounding low-damped grid, and to calculate in reverse the reverse fluid pressure relief intervention scheme to neutralize the excess phase change driving energy.

[0061] The core of the detection platform of this invention lies in treating the city as an open, non-equilibrium system that is constantly exchanging matter and energy.

[0062] Detailed Construction of the Element Field: The system first establishes a cross-departmental data fusion layer. By accessing urban rail transit station throughput, regional clearing volume of mobile payment terminals, and population dynamic migration trajectories, the data is normalized over time. The system defines this dynamic data as a "value fluid" and calculates the total value flowing through a specific geographic grid per unit time.

[0063] Inter-module interaction: The fluid field generated by the construction module is attached in real time to the spatiotemporal manifold base generated by the computation module. This interaction is not a simple superposition, but rather updates the value distribution map of the entire city in real time by calculating the "flow resistance" of the fluid in different damping regions.

[0064] In its implementation, the system breaks away from the traditional method of determining straight-line distance.

[0065] Dynamic distance calculation: The system obtains the average commuting time between adjacent grids and the cycle of administrative approvals. When two physically adjacent grids are separated by rivers or have different purchase restriction policies, the system will artificially stretch the logical connection between the two nodes in the base.

[0066] Topological fracture determination: When the damping coefficient between two grids exceeds the system's set safety limit, the system sets the association weight of these two nodes to zero in the topology matrix, simulating a "vacuum fracture" state in fluid dynamics. This mapping method ensures that price transmission analysis is based on actual circulation costs rather than physical straight-line distances.

[0067] The system analyzes price pressures by simulating the forces experienced by a physical fluid when it encounters a blockage in a pipe.

[0068] Pressure generation mechanism: When a high-density value flux enters a highly damped grid, the system determines that the kinetic energy cannot be released through physical construction. At this time, the system converts the surplus kinetic energy into upward implicit pressure, which directly affects the property price assessment dimension of that grid.

[0069] Shear force identification rule: At the boundary between two grids with extremely mismatched flow efficiencies, the system calculates the rate of change of their velocity gradient. When the gradient changes drastically, the system marks this as "value shear force" to predict the risk of market expectation disruption or sharp price fluctuations in that area.

[0070] This platform introduces dissipative structure theory to identify the critical point in the market where "quantitative change leads to qualitative change".

[0071] Energy dissipation threshold determination: The system establishes an "entity absorption model" by extracting the annual output value of the real economy, corporate tax density, and capital absorption capacity of newly created jobs within the grid. The value output by this model is the maximum amount of external capital that the grid can dissipate without triggering a bubble.

[0072] Local entropy increase extraction: The system performs real-time subtraction, subtracting the aforementioned dissipation threshold from the input flux. When the difference is positive and has a cumulative effect over time, the system defines it as "local entropy increase." This indicator characterizes disordered energy within the grid that cannot be absorbed by the real economy and only circulates aimlessly in asset markets such as real estate.

[0073] When the system detects that the local entropy increase exceeds the limit, it triggers the phase transition inference logic.

[0074] Spatiotemporal evolution model: The system calls a pre-trained graph spatiotemporal deep learning model to simulate the overflow path of high-pressure valuable fluid after encountering an obstacle, with the current unstable grid as the center.

[0075] Nonlinear Step Warning: Unlike traditional linear forecasting, this platform focuses on predicting the "time window" when prices will experience a sudden jump. The system calculates the coupling degree between the fluid deflection and the surrounding grid damping, outputting a probability distribution map to identify which regions will experience a complete reshaping of the price system within a preset future period.

[0076] This platform not only has detection capabilities, but also provides control solutions through reverse computation.

[0077] Intervention variable generation: After identifying a high-pressure phase transition risk, the system automatically initiates a "pressure relief search" procedure. The procedure searches for a backup grid with low damping and high dissipation potential in the entire city's manifold base.

[0078] Strategy Output: When calculating the pressure in the target grid, the system determines what proportion of value flux needs to be diverted to the backup grid. Subsequently, the system translates this diversion requirement into specific policy recommendations, such as increasing the floor area ratio at a specific coordinate point or adding rapid transit lines along a specific route, forming a closed-loop risk control instruction system. Specific Implementation Example 2:

[0080] like Figures 1 to 11 As shown, an urban real estate price detection method based on a value flow network analysis platform for urban real estate price trends includes the following steps:

[0081] Sp1: Simultaneously collect multi-dimensional features of urban elements, extract the spatial damping coefficient representing resistance and the value flux density representing dynamics respectively, and construct a continuous fluid element field;

[0082] Sp2: Maps physical geospatial space to a spatiotemporal manifold dynamic base and calculates the fluid pressure and boundary shear force generated when the value fluid flows across the grid on the base;

[0083] Sp3: Quantify the physical digestion upper limit of each grid to establish the steady-state energy dissipation threshold, and extract the redundant part of the input flux that exceeds the threshold as the local entropy increase feature that marks the system entering an unstable state;

[0084] Sp4: Perform holographic spatiotemporal evolution simulation, simulate the overflow deflection trajectory of high-pressure value fluid after obstruction, and calculate the phase transition critical point where redundant flux causes a nonlinear step jump in real estate prices;

[0085] Sp5: Evaluate the cascading oscillation waveform caused by price steps on the global manifold base, reverse-engineer the high dissipation potential grid, and output a reverse intervention strategy that includes source depressurization and path clearing.

[0086] In Sp1, the system not only collects data, but also executes a set of "mapping transformation algorithms" from heterogeneous data to dynamic parameters:

[0087] The logic for generating value flux density (φ) is as follows: The system connects to the city's financial settlement interface and the operator's signaling channel. First, it extracts the net capital inflow and total salary payments in high-tech industries for the target grid within a preset sliding window period. Second, it weights and maps these values ​​to the real-time online population density within the grid, eliminating illiquid interference items such as infrastructure investment. Finally, it divides the resulting comprehensive value by the effective construction land area of ​​the grid to generate the value flux density, which represents the "wealth accumulation pressure" of the grid.

[0088] Dynamic calibration of the spatial damping coefficient (R): The system retrieves real-time road network delay indices from Gaode or Baidu Maps and couples them with land use control rules from the Natural Resources Bureau. Physical barriers are defined as permanent damping, traffic congestion as instantaneous damping, and administrative purchase restrictions as policy damping. Through a nonlinear weighting function, these dimensions are uniformly converted into "time loss values" of factor flow, which serve as the spatial damping coefficient hindering value flow.

[0089] In Sp2 and Sp3, the system simulates real economic relationships by changing the topology of the base:

[0090] Spatiotemporal manifold reconstruction rules: The system uses the urban core area as the topological origin and modifies the physical distance using a "measure tensor" based on the spatial damping coefficient. When the damping coefficient between two adjacent grids exceeds a threshold, the system stretches the connecting edge length in the virtual computing space, making them "far apart" in a dynamic sense; when there are low-damping channels such as rail transit between two grids, the connecting edge length is compressed. The resulting "spatiotemporal manifold base" can realistically reflect that areas with greater physical distance but convenient transportation have higher price fluctuation coupling than areas that are physically adjacent but congested.

[0091] Determination of Fluid Pressure and Shear Force: The system monitors the "velocity" of value fluxes on the manifold base. When a high-density flux is obstructed from moving from a low-damped grid to a high-damped grid, the system, according to the law of conservation of kinetic energy, converts the obstructed kinetic energy into internal pressure pointing towards the price dimension of that grid. Simultaneously, at the grid boundary where damping abruptly changes, the gradient difference of flux velocities on both sides is calculated. When the gradient difference exceeds the system's preset safety envelope, a "value shear force" is generated to identify the risk of price system fragmentation between regions.

[0092] In Sp4, the system introduces thermodynamic thinking to determine the critical point of price abrupt changes: the establishment of the maximum energy dissipation threshold.

[0093] The system extracts the output value of the real economy, the number of new jobs created, and the vacancy rate of housing for each grid. These indicators are aggregated into the grid's "value absorption capacity," which is the amount of external capital the grid can absorb without causing abnormal fluctuations in housing prices. Accumulation calculation of local entropy increase: The system executes a continuous difference calculation logic, i.e., local entropy increase = actual input flux - absorption by the real economy. Phase transition trigger control logic: The system establishes a time integrator. When the local entropy increase is positive and continues to accumulate over time, causing an increase in the "economic disorder" within the grid, the system determines that the grid has entered a non-equilibrium state. Once the accumulated value exceeds the critical limit, the system immediately determines that the grid has triggered a "phase transition," meaning that housing prices will deviate from fundamentals and experience a non-linear, jumpy rise (or a fall after a bubble bursts).

[0094] In SP5, the system shifts from "detection" to "active control":

[0095] Cascaded oscillation waveform calculation: When a phase transition is triggered in a certain grid, the system simulates the propagation of the pressure wave on the manifold base. Using the wave equation of fluid dynamics, the system predicts which low-damping paths the pressure wave will spread to the surrounding grids, and identifies the affected "secondary risk zone".

[0096] Reverse fluid depressurization strategy: The system employs a reverse search algorithm. It searches for backup grids with high dissipation potential (i.e., grids with substantial land reserves or industry gaps) along the oscillation wave propagation path. The system outputs instructions suggesting "diverting" by reducing the spatial damping of the backup grids, thereby diluting the price pressure on the high-pressure grids. The output intervention order includes: the suggested grid coordinates for adjustment, the suggested damping coefficient value, and the expected depressurization ratio. Specific Implementation Example 3:

[0098] like Figures 1 to 11 As shown in the above specific embodiments, the following content is further disclosed:

[0099] The refined mapping mechanism of urban value fluid element field:

[0100] This invention views fluctuations in urban real estate prices as the result of a "value fluid" moving within a spatial grid.

[0101] Spatial gridding: The system first divides the target urban area into equidistant hexagonal or square geographic grids. Each grid is not only a geographic unit, but also an economic container with "digestion capacity".

[0102] Flux Density Aggregation: The capital and population flux density aggregation unit acquires real-time, anonymized financial clearing data (representing capital flow) and mobile terminal signaling trajectories (representing population flow) through an interface. The system normalizes and weights capital inflows and net population migration to calculate the "value flux" entering the grid per unit time.

[0103] Establishment of the energy dissipation threshold: The steady-state energy dissipation threshold determination unit constructs an energy dissipation model based on the physical industries and new housing supply within the grid. This model characterizes the physical upper limit of the grid's ability to absorb and digest external value inflows without triggering price mutations.

[0104] The construction logic of non-Euclidean spacetime manifolds and gravitational basins:

[0105] This invention abandons the traditional Euclidean distance (straight-line distance) and uses a spatiotemporal manifold mapping calibration unit to reconstruct the city base.

[0106] Dynamic spatial base transformation: The system extracts the average travel delay and policy access threshold between different grids. When the physical distance between two grids is 1 kilometer, but the circulation takes 1 hour due to extreme traffic congestion or administrative purchase restrictions, the system "stretches" it internally; conversely, when there is direct rail transit, it is "compressed".

[0107] Gravity basin structure: Through the above transformation, the originally flat geographical plane becomes a manifold surface with undulating elevations. Areas with fast flow velocity and low damping exhibit concave gravity basins on the surface, spontaneously attracting surrounding valuable fluids to converge there; while high-damping areas exhibit dynamic highlands that repel fluids.

[0108] Triggering criteria for value fluid pressure and shear force:

[0109] The system determines the inherent stress of price fluctuations by simulating the forces experienced by a physical fluid when it encounters a blockage in a pipe.

[0110] Viscosity coefficient definition: The system monitors the stability of historical prices in each grid. The smaller the fluctuation, the higher the viscosity coefficient, which means that the price in that area is more resilient to external shocks.

[0111] Implicit pressure generation: When a large-scale flux (high kinetic energy) is injected from a low-viscosity region to a high-viscosity region and encounters extremely high spatial damping, the kinetic energy cannot be released. The system determines that this energy is instantly converted into upward implicit fluid pressure, which is the physical root cause of nonlinear jumps in real estate prices.

[0112] Shear force stripping: In the contact zone between two grids with extremely different flow velocities, the system calculates their momentum gradient and identifies the value shear force, which characterizes the degree of disconnect between the price systems in the regions.

[0113] Coupling of multi-scale planning intentions with actual factor flows:

[0114] This invention achieves a logical closed loop of "top-down" and "bottom-up" through a bidirectional isomorphic mapping network.

[0115] Intent vs. Flow: The upper-layer network imports government planning intent vectors such as urban master plans and industrial layouts; the lower-layer network accesses real-time actual capital and population movement vectors.

[0116] Deflection trajectory correction: When the overlap between two layers of vectors is low or the angle between them is too large (i.e. the planning intention deviates from the actual market flow), the multi-scale value flux transmission inference unit will automatically correct the expected flux peak of the target grid and predict the price decline or flux overflow in the region due to the failure of the planning to meet expectations.

[0117] Reverse fluid pressure relief and system balance interruption mechanism:

[0118] In response to the existing non-equilibrium oscillations, the system executes a reverse pressure relief strategy.

[0119] Cascaded failure path tracing: The system simulates the transmission process of price fluctuations as oscillation waves on a manifold base, and calculates which neighboring grids will generate secondary price jumps due to pressure transmission.

[0120] Artificially Created Gravity Basins: The system reverse-engineers grid nodes across the city with high dissipation potential. By simulating the implementation of transportation infrastructure investment or the reduction of policy limits in these grids, new "depressed gravity basins" are artificially created on the dynamic base, diverting the value flow that was originally impacting high-priced grids to these backup grids, thereby smoothing out unstable price fluctuations globally. Specific Implementation Example 4:

[0122] like Figures 1 to 11 As shown, based on the content of the above specific embodiments, the working principle of the present invention is further disclosed:

[0123] The core principle of this scheme lies in treating the city as an open, non-equilibrium dissipative system, and capital and population as a value fluid flowing within the city, while housing prices represent the "pressure manifestation" of this fluid within a specific spatial grid. By simulating the laws of motion in fluid mechanics and the energy dissipation mechanisms in thermodynamics, this scheme can analyze the underlying causes of housing price fluctuations from a physical logic perspective and predict the timing of sudden changes. Its detailed working principle can be divided into the following five core dimensions:

[0124] 1. The principle of characteristic transformation of value fluids:

[0125] The system first transforms discrete socioeconomic data into "fluid properties" with physical characteristics.

[0126] Kinetic energy transformation: Cross-regional fund clearing, talent mobility and industrial investment are regarded as the initial velocity and mass of fluids, which together constitute "value flux density".

[0127] Viscosity calibration: The historical transaction volatility of the real estate market is defined as the viscosity coefficient of a fluid. The smaller the volatility, the smoother the fluid flow; the more volatile the region, the more likely the fluid is to generate turbulence, i.e., irregular price fluctuations.

[0128] 2. The principle of distortion of non-Euclidean spacetime manifolds:

[0129] This solution breaks away from the traditional concept of physical distance and establishes a dynamic foundation centered on "circulation costs".

[0130] Spatial damping mapping: The system extracts road network congestion delay and policy purchase restriction level as spatial damping coefficients.

[0131] Distance Reconstruction: Within the algorithm, physical distance is corrected by a damping coefficient. Even if two communities are adjacent, due to severe traffic congestion or administrative barriers, the flow of goods and services is prolonged, causing the system to nonlinearly stretch them, making them appear "farther" in dynamic space. This reconstruction ensures that price detection is based on genuine resource exchange efficiency, rather than spurious geographical proximity.

[0132] 3. The interaction principle of pressure and shear force in fluid mechanics:

[0133] This is the core dynamic logic for detecting price fluctuations.

[0134] Pressure buildup: When a high-density fluid of value flows into a highly damped grid, its kinetic energy cannot be released through physical construction. According to the logic of fluid momentum conservation, this energy is converted into upward implicit fluid pressure, directly driving up property prices.

[0135] Shear force tearing: At the boundary between two highly mismatched flow damping zones, the dramatic difference in flow velocity gradients generates value shear force. This shear force is a key indicator for identifying regional price system instability and predicting the risk of market "stampedes" or "breakdowns".

[0136] 4. Entropy Instability Principle in Dissipative Structures

[0137] The system uses the second law of thermodynamics to determine whether the market has entered a non-equilibrium state of "bubble".

[0138] Energy dissipation benchmark: Each city grid has its maximum energy dissipation threshold. This represents the maximum amount of capital that the grid can absorb through its physical industries.

[0139] Local entropy increase extraction: When the input value stream continuously exceeds a certain threshold, the excess energy cannot be dissipated by entities and will stagnate within the asset market, generating local entropy increase. The system quantifies the degree to which the market deviates from fundamentals by performing a time-dimensional accumulation calculation on these "disordered energies".

[0140] 5. Principle of Phase Transition Critical Point Triggering and Reverse Intervention:

[0141] This is the final decision-making logic of the plan, used to capture the moment of "quantitative change leading to qualitative change".

[0142] Price phase transition: When the accumulated local entropy exceeds the system's preset critical limit, a phase transition is determined to have occurred. This corresponds to real estate prices deviating from linear growth and experiencing nonlinear, step-like surges or precipitous drops.

[0143] Reverse pressure relief control: Once a phase transition risk is detected, the system will activate a reverse optimization algorithm. It searches for grids with "high dissipation potential" (i.e., areas with absorption capacity but currently high damping) throughout the city, and guides high-pressure fluid to these areas by outputting strategies to reduce damping in those areas, thereby achieving precise pressure relief from high housing price pressure at its source. Specific Implementation Example 5:

[0145] like Figures 1 to 11As shown in the above specific embodiments, the following content is further disclosed, and specific use cases are provided below:

[0146] 1. Case background setting:

[0147] A city announced the establishment of a "Future Science and Technology City" in the western part of the city and planned a rapid inter-district rail transit line. This move triggered a large influx of capital and population into the area (hereinafter referred to as Target Grid A) in a short period of time.

[0148] 2. Detailed implementation process of the technical solution:

[0149] Step 1: Initialization and synchronous acquisition of multi-dimensional element field;

[0150] Input: The platform captures data of target grid A and its surrounding grids in real time via an interface.

[0151] Dynamic data: Monitoring showed that in the past month, inter-regional bank transfers inflows to target grid A increased by 45% month-on-month, and the daily net inflow of population, as indicated by mobile terminal signaling, increased by 12,000. These data were transformed into high-intensity value flux density by the capital and population flux density aggregation unit.

[0152] Resistance data: The physical infrastructure damping coefficient acquisition unit simultaneously captured land transfer records for the area. It was found that the developable residential land saturation in target grid A had reached 92%, and strict purchase and resale restrictions existed. These restrictions were quantified as high-value spatial damping coefficients.

[0153] Step 2: Spatiotemporal manifold mapping and gravity basin generation;

[0154] Calculation: The urban spatiotemporal manifold mapping calibration unit transforms physical coordinates into a dynamic base.

[0155] Because the planned rapid rail transit significantly reduces commuting time, the system "compresses" the physical distance between target grid A and the city center within the model.

[0156] Manifestation: Target grid A rapidly sinks on the spacetime manifold surface, forming a deep gravitational basin structure. According to the principles of fluid mechanics, the originally disordered flow of value fluids (capital and talent) in the surrounding area is attracted by this "gravity" and spontaneously and rapidly converges towards the center of the basin (target grid A).

[0157] Step 3: Fluid pressure accumulation and shear force determination;

[0158] Phenomenon: A large-scale flow of value (high kinetic energy) is injected into the gravity basin, but the outlet (land supply) at the bottom of the basin is locked by high damping.

[0159] Judgment: The value fluid pressure and shear force calculation unit detected that the kinetic energy inside the target grid A cannot be dissipated through physical construction, and the energy begins to be converted into the price dimension, generating huge implicit fluid pressure.

[0160] Shear Force: Simultaneously, due to the drastic difference in turnover rate between target grid A and its adjacent old city area (grid B), the system identifies high-energy value shear force at the boundary. This means that the price systems of the two areas are undergoing a "fractured" disconnect, which is highly likely to trigger speculative fluctuations.

[0161] Step 4: Accumulation of local entropy increase and prediction of phase transition inflection point;

[0162] In comparison, the steady-state energy dissipation threshold measurement unit calculates the limit of absorption capacity of target grid A: its industrial structure can only absorb an investment of 50 billion yuan per year.

[0163] Identification: The system detected that the current actual inflow flux has reached 85 billion. The 35 billion exceeding the threshold was marked as "idling flux" by the local entropy increase and nonlinear fluctuation capture unit.

[0164] Prediction: As the duration of local entropy increase increases integrally, the system determines that the system entropy value has exceeded the critical value. The price mutation critical point calibration unit outputs an early warning: It is expected that within 15 working days, the real estate price in this area will experience a nonlinear step jump (i.e., a surge phase transition that deviates from fundamentals).

[0165] Step 5: Generation of reverse pressure relief strategy and system shutdown;

[0166] Transmission calculation: The system predicts that the shock wave generated by the price jump will propagate along the rail transit line to the adjacent grid C.

[0167] Reverse optimization: The reverse fluid depressurization strategy generation unit is activated. It performs a reverse search on the base and discovers that grid D, which is 5 kilometers away from the target grid A but currently receives little attention, has a large amount of idle land and low business costs.

[0168] Command Output: The system generates an intervention work order, suggesting the following to relevant departments:

[0169] Increase the supply of residential land by 20% in grid D.

[0170] Temporarily relax the talent settlement threshold in Grid D, artificially creating a new "small gravity basin" in Grid D.

[0171] Effect: Through artificial guidance, the high-pressure fluid that was originally impacting the target grid A was diverted to grid D. The fluid pressure inside the target grid A dropped rapidly, the system returned to a quasi-equilibrium state, and the occurrence of irrational phase transition was successfully blocked.

[0172] This case explains why housing prices are immediately affected after a policy is released—the instantaneous transformation of fluid kinetic energy caused by the gravitational pull of the market. Instead of relying on average price data after transactions, it uses the leading indicator of local entropy increase to pinpoint the phase transition point before prices truly take off. It provides a physically based pressure relief solution, allowing city management departments to know exactly where to increase supply for precise regulation. Specific Implementation Example Six:

[0174] like Figures 1 to 11 As shown in the above specific embodiments, the following content is further disclosed:

[0175] This embodiment further discloses the data fields, data cleaning, parameter mapping, mathematical calculations, and algorithm execution logic of the platform during actual operation, enabling the multidimensional fluid element field construction module, the non-Euclidean space fluid dynamics calculation module, the dissipative structure and phase transition feature extraction module, the holographic spatiotemporal phase transition evolution reasoning module, and the system non-equilibrium state early warning and blocking module to have executable data input, calculation processes, and output results.

[0176] I. Spatial Grid and Data Field Settings:

[0177] The platform first divides the urban area to be detected into a continuous spatial grid, and each spatial grid is denoted as . , The grid number is used, and the grid side length is 500 meters to 1000 meters, or the grid boundary is set by street, community, or planning control unit. Each grid is bound to the center coordinates, effective construction land area, residential land stock, construction in progress area, annual new supply, enterprise registration density, number of jobs, historical average transaction price, historical number of transactions, vacancy rate, road network density, rail station access time, congestion delay index, administrative restriction level, and land development saturation.

[0178] Dynamic data is processed at uniform time intervals. Write to the platform, Data can be collected for one day, one week, or one month. Capital flow data uses anonymized cross-regional bank clearing records, enterprise payroll summaries, industrial investment inflow summaries, and real estate transaction fund flows; population flow data uses anonymized mobile terminal signaling migration in and out, rail transit station entry and exit volumes, commuting origin-destination matrix, and talent settlement increments; supply and entity absorption data uses land transfer area, construction permit area, completed and filed area, enterprise tax payment, number of newly added jobs, and vacant area of ​​industrial facilities. All data retains only grid-level aggregation results and does not retain personal identity fields or individual transaction details.

[0179] II. Data cleaning, alignment, and normalization algorithms:

[0180] The platform performs unified cleaning on data from different sources. For missing values, if the number of consecutive missing periods does not exceed 3, linear interpolation of the preceding and following periods within the same grid is used; if the number of consecutive missing periods exceeds 3, the weighted average of adjacent low-damped grids is used to fill in the missing values. For outliers, the platform uses interquartile range for truncation to reduce the impact of extreme single cash flows or sudden surges in customer traffic on throughput calculations.

[0181]

[0182] In the formula, For grid exist The raw indicator values ​​collected periodically; The index value is the normalized value; The first quartile of the same indicator across all grid samples; The third quartile of the same indicator in all grid samples; To prevent extremely small positive numbers with a denominator of zero, the above processing compresses all types of data into the 0-1 range while maintaining the relative strength of the grids.

[0183] Origin and destination data are stored as triplets of "outflow grid - inflow grid - time period". The platform maps cross-grid capital flow, population flow and commuting flow as directed edges respectively. When a trajectory crosses multiple grids, it is distributed to the edges traversed according to the shortest time path of the actual road network, thus obtaining the inter-grid flow matrix.

[0184] III. Algorithm for calculating value flux density:

[0185] The capital and population flux density aggregation unit unifies net capital inflows, net population inflows, wage intensity, and industrial investment intensity into value flux density. The platform first calculates the grid. In the cycle Net capital flow Net population flow and industry salary investment flow Then, based on the effective construction land area Densification processing is performed.

[0186]

[0187]

[0188]

[0189] In the formula, For period Internal grid Inflow grid The amount of capital; For period Internal grid Outflow mesh The amount of capital; For period Internal grid Migration into the grid Population size; For period Internal grid Relocation grid Population size; Net capital flow; Net population inflow; For industry salary investment flow; , , This is the result of normalization; , , The weights for investment in capital, population, and industry wages are respectively. For grid The effective construction land area; For grid In the cycle Value flux density. Weight , , Obtained through training with historical stable period samples, and satisfying .

[0190] When the platform needs to calculate the edge flux between adjacent grids, it uses directed edges. Indicates that it is made of grid Pointing to grid The value flow is generated by the superposition of three types of flows: capital, population, and commuting, and is attenuated according to the spatial damping at both ends of the edge.

[0191]

[0192]

[0193] In the formula, For grid To the grid Undiminished composite edge flow; For grid To the grid The edge flux; , , These are normalized capital flows, population flows, and commuter flows, respectively. , , The edge flux weight; For grid To the grid The spatial damping coefficient between them.

[0194] IV. Spatial Damping Coefficient and Non-Euclidean Manifold Distance Algorithm:

[0195] The physical infrastructure damping coefficient collection unit converts road network delays, policy restrictions, land saturation, physical barriers, and supply constraints into spatial damping coefficients. The higher the damping coefficient, the higher the time cost, institutional cost, and spatial cost for value elements to cross that side.

[0196]

[0197] In the formula, This is the spatial damping coefficient; For grid To the grid The normalized value of commuting delay; Normalized values ​​for physical barriers formed by rivers, highways, mountains, and administrative boundaries; This represents the normalized value of land development saturation. This is the normalized value for the policy restriction level; Normalized value for the degree of housing supply shortage; to For damping weight, and .

[0198] The urban spatiotemporal manifold mapping calibration unit uses Euclidean physical distance Based on this, the physical distance is stretched using the spatial damping coefficient, and the distance is compressed using the low-damping channels formed by rail transit, expressways, and cross-regional commuting intensity, thus obtaining the spatiotemporal manifold distance.

[0199]

[0200] In the formula, For grid To the grid The spacetime manifold distance; The Euclidean distance between the center points of the two grids; This is the damping nonlinear amplification factor; The strength of the low-damping channel originates from the directness of rail transit, the connectivity of expressways, and the intensity of high-frequency commuting connections.

[0201] The platform generates a manifold adjacency matrix based on this. The shorter the manifold distance, the higher the price linkage weight between two grids. When the damping coefficient exceeds the damping fracture threshold, the platform resets the adjacency weight between the two grids to zero to represent the fracture state of factor flow in the dynamic space.

[0202]

[0203] In the formula, Adjacency weights of the manifold; This is the distance attenuation scale parameter; For indicator functions; This is the damping fracture threshold.

[0204] V. Algorithm for Calculating Fluid Pressure, Viscosity Coefficient, and Value Shear Force:

[0205] The value fluid pressure and shear force calculation unit first calculates the viscosity coefficient based on historical price fluctuations, and then calculates the pressure and shear force based on edge flux and damping difference. The viscosity coefficient is used to characterize the degree of absorption hysteresis of a certain grid price system to external shocks.

[0206]

[0207] In the formula, For grid In the cycle The viscosity coefficient; For grid In length The sequence of average property transaction prices within a scrolling window; For variance calculation; This is for calculating the mean. It is a very small positive number.

[0208] The edge velocity is determined by both the edge flux and the manifold distance. The longer the manifold distance and the higher the damping, the lower the value velocity.

[0209]

[0210] In the formula, For grid To the grid The rate of value flow; For edge flux; It represents the spacetime manifold distance.

[0211] When a large amount of edge flux flows from the surrounding grid into the target grid and the target grid has high damping, the platform converts the obstructed flux into implicit fluid pressure.

[0212]

[0213]

[0214] In the formula, For grid The obstructed flux term formed when receiving peripheral flux; For grid The implicit fluid pressure; , Pressure weight; For grid Inflow grid The edge flux; For grid Outflow mesh The edge flux; For grid To the grid Spatial damping coefficient; For grid Value flux density; For grid The steady-state energy dissipation threshold.

[0215] At the intersection of adjacent grids, the platform calculates the value shear force based on the velocity gradients and average viscosity coefficients on both sides, which is used to identify the risk of price system tearing.

[0216]

[0217] In the formula, For grid With grid Value shear force at the boundary; , These are the viscosity coefficients of the two meshes on either side; , These represent the value flow rates in two directions; It represents the spacetime manifold distance.

[0218] VI. Steady-state energy dissipation threshold and local entropy increase algorithm:

[0219] The steady-state energy dissipation threshold measurement unit calculates the maximum stable absorption of each grid based on the absorption capacity of the real economy, employment carrying capacity, new housing supply capacity, and existing vacancy absorption capacity.

[0220]

[0221] In the formula, For grid The steady-state energy dissipation threshold; Normalized value of output value of the real economy; Normalized value for newly created jobs; The normalized value for new housing supply; Normalized value for absorbable vacant housing units; to For dissipative weights, and .

[0222] The local entropy increase and nonlinear fluctuation capture unit extracts the portion of the value flux density that exceeds the steady-state dissipation threshold, and integrates this excess portion over time to obtain the cumulative entropy increase that characterizes the degree of instability of the price system.

[0223]

[0224]

[0225] In the formula, For grid In the cycle Excess flux; For grid In the cycle The cumulative amount of local entropy increase; This is the entropy increase memory decay coefficient, with a value ranging from 0 to 1; This represents the cumulative increase in local entropy from the previous cycle.

[0226] VII. Algorithm for Phase Transition Critical Point Calibration and Trend Prediction:

[0227] The price mutation critical point calibration unit inputs implicit fluid pressure, value shear force, local entropy increase and supply constraints into the phase transition risk function to obtain the risk value of price step jump for each grid within the future prediction window.

[0228]

[0229]

[0230] In the formula, For grid Phase transition risk value; This represents the normalized value of the implicit fluid pressure. To be compatible with grid The normalized value of the maximum value shear force among adjacent edges; This is the normalized value of local entropy increase; This is a normalized value representing the degree of supply shortage. to Risk weights.

[0231] The platform uses historical stable period price fluctuations as a benchmark to calculate the critical threshold. When the phase transition risk value is continuous... When a cycle exceeds the critical threshold, the target grid is determined to enter a phase transition warning state, and a price step time window is output.

[0232]

[0233]

[0234] In the formula, For grid The critical threshold for phase transition; For grid Phase transition risk sequence during historically stable periods; For standard deviation calculation; This is the critical amplification factor; The predicted phase transition time point; This represents the maximum prediction step size. To predict the step sequence within the window.

[0235] Trend prediction employs a spatiotemporal graph recursive algorithm driven by a manifold adjacency matrix. The platform inputs price, value flux density, spatial damping, implicit fluid pressure, value shear force, local entropy increase, and supply constraint characteristics into each period. It then aggregates the transmission information from adjacent grids using a manifold adjacency matrix to output the price change rate for the next period.

[0236]

[0237]

[0238]

[0239]

[0240] In the formula, For grid The input feature vector; This is an aggregation item for information transmitted between adjacent grids; The hidden state after the graph is recursively derived; , , The parameter matrix obtained during training; For bias terms; This represents the rate of price change for the next period. Predict prices for the next period.

[0241] VIII. Calculation of Cascaded Oscillation Waves and Optimization Algorithm for Reverse Pressure Relief:

[0242] The cascading failure and oscillation extension calculation unit uses the grid that triggers the phase transition as the source of the shock, and spreads the impact of the price step along the manifold adjacency matrix to obtain the secondary risk distribution of the surrounding grid.

[0243]

[0244] In the formula, For grid The intensity of the oscillation wave in the next cycle; The local phase transition excitation coefficient; Transmission coefficient across grids; For grid To the grid manifold adjacency weights; For grid To the grid The spatial damping coefficient.

[0245] The reverse fluid decompression strategy generation unit uses land supply release, transportation corridor gain, industrial absorption intensity, and the rate of reduction in policy restrictions as intervention variables to construct an optimization model aimed at reducing the pressure of high-risk grids and controlling intervention costs.

[0246]

[0247]

[0248]

[0249] In the formula, For the set of intervention variables; For post-intervention grid The remaining risk; For intervention costs; As cost constraint weight; The phase transition risk value after the intervention; The intensity of the shock wave after the intervention; The safe oscillation threshold; This represents the release of new land supply; Gain for traffic corridors; To enhance the strength of industrial absorption; The policy limits the extent of the decline; This represents the upper limit of the interventions that can be performed on the corresponding grid.

[0250] The platform employs a combined greedy search and local iteration algorithm to solve the reverse pressure relief strategy: first, the backup grids are sorted from high to low according to their high dissipation potential scores, and then the flow direction changes after reducing the damping of the backup grids are simulated one by one; each time, the intervention action with the largest risk reduction per unit cost is selected and written into the candidate strategy, until the pressure of the target grid is lower than the safety threshold or the intervention cost reaches the upper limit.

[0251]

[0252] In the formula, For backup grid High dissipation potential score; This represents the remaining dissipation capacity; This serves as the overall damping for the entry of external value streams into the backup grid. The higher the value, the more suitable the grid is as a reverse pressure relief node.

[0253] IX. Model Training, Calibration, and Output Data Format:

[0254] The platform uses grid-level data from at least 24 historical periods for parameter training. Training samples consist of input feature vectors. The rate of change of prices in the next period and phase change labels Composition: When the rate of price change of a certain grid exceeds the upper limit of the historical stable fluctuation range within the prediction window, and the corresponding local entropy increase cumulative amount synchronously exceeds the critical threshold, the phase transition label of the sample is recorded as 1; otherwise, it is recorded as 0.

[0255]

[0256] In the formula, Train the loss function for the model; This represents the true rate of price change. To predict the rate of price change for the model; Cross-entropy loss; Labeled as a true phase transition; To predict the probability of phase transition; Weights for phase transition classification loss; For regularization weights; This is the set of model parameters.

[0257] After each cycle of data entry is completed, the platform recalculates the value flux density, spatial damping coefficient, manifold adjacency matrix, implicit fluid pressure, value shear force, local entropy increase, and phase transition risk value. It also outputs the grid number, center coordinates, predicted price change rate, phase transition time window, oscillation wave propagation path, recommended pressure relief grid, suggested land supply release, suggested traffic diversion path, and expected pressure reduction ratio for use by the platform interface and early warning work orders.

[0258] With the addition of the above algorithms, data, and mathematical models, the modules in the specific embodiments no longer operate merely conceptually, but form a complete closed loop from data acquisition, field cleaning, flux mapping, manifold reconstruction, pressure shear calculation, entropy increase identification, phase transition prediction to reverse pressure relief strategy output. This allows them to be directly configured as periodic automatic calculation tasks in the urban real estate price monitoring platform.

[0259] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0260] 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 platform for detecting urban real estate price trends based on value flow network analysis, characterized in that, include: The multidimensional fluid element field construction module includes a physical facility damping coefficient acquisition unit and a capital and population flux density aggregation unit. The physical facility damping coefficient acquisition unit is used to extract urban road network density, land development saturation and policy restriction indicators, and convert them into spatial damping coefficients that characterize the resistance to value transfer. The capital and population flux density aggregation unit is used to extract cross-regional fund clearing flow and talent commuting trajectory, and convert them into value flux density that characterizes the driving force. Non-Euclidean spatial fluid dynamics calculation module: includes urban spatiotemporal manifold mapping calibration unit, value fluid pressure and shear force calculation unit, and dynamic spatiotemporal damping adaptive adjustment unit; It is used to convert the actual urban geographic coordinates with physical barriers into a dynamic spatial base that reflects the real time cost of factor circulation, and to calculate the value fluid pressure and the value shear force generated at the grid boundary based on the flux density difference between adjacent grids. Dissipative structure and phase transition feature extraction module: includes steady-state energy dissipation threshold measurement unit and local entropy increase and nonlinear fluctuation capture unit; used to compare the energy dissipation difference between the input value flux in each grid and the energy absorption capacity of the grid's entity industry and the new housing supply, and identify the local entropy increase characteristics caused by exceeding the dissipation limit; Holographic spatiotemporal phase transition evolution reasoning module: includes a multi-scale value flux transmission deduction unit and a price mutation critical point calibration unit; used to deduce the spatial flow direction deflection trajectory of high-pressure value fluid when it encounters a high-damping node based on local entropy increase characteristics, and calculate the phase transition time node when the accumulation of excess flux causes real estate prices to break through the normal fluctuation range; The system non-equilibrium state early warning and blocking module includes a cascade failure and oscillation wave extension calculation unit and a reverse fluid pressure relief strategy generation unit; it is used to track the transmission path of non-equilibrium oscillation waves caused by price mutations to the surrounding low-damped grid, and to calculate in reverse the reverse fluid pressure relief intervention scheme to neutralize the excess phase change driving energy.

2. The urban real estate price trend and detection platform based on value flow network analysis according to claim 1, characterized in that: The specific operating logic of the urban spatiotemporal manifold mapping calibration unit is as follows: extract the congestion delay characteristics of the urban traffic network and the time cost characteristics of administrative approvals between regions, stretch or compress the straight physical distance in Euclidean space into a spatiotemporal manifold surface that represents the ease or difficulty of actual element flow, so that the region with fast element flow speed and low resistance presents a gravity basin structure on the surface.

3. The urban real estate price trend and detection platform based on value flow network analysis according to claim 1, characterized in that: The specific operating logic of the value fluid pressure and shear force calculation unit is as follows: the mean square error of real estate prices in a single grid within a preset period is defined as the fluid viscosity coefficient; when high-density value flux is rapidly injected from low-viscosity coefficient grids to high-viscosity coefficient grids, and encounters an extremely high spatial damping coefficient at the boundary, it is determined that the fluid kinetic energy is converted into upward implicit fluid pressure, and at the same time, a value shear force that tears the price system is generated at the peeling zone at the junction of the two grids.

4. The urban real estate price trend and detection platform based on value flow network analysis according to claim 1, characterized in that: The specific operating logic of the steady-state energy dissipation threshold determination unit is as follows: collect the land transfer area, the volume of construction projects and the number of employees absorbed by the industry in each spatial grid as benchmark indicators, calculate the maximum net capital and population inflow that the grid can support to maintain the stability of the current price system, and establish it as the steady-state energy dissipation threshold. When the actual net inflow exceeds the steady-state energy dissipation threshold for multiple consecutive cycles, the local entropy increase and nonlinear fluctuation capture unit marks the excess portion as phase change driving energy that induces a sudden upward price change.

5. The urban real estate price trend and detection platform based on value flow network analysis according to claim 1, characterized in that: The specific operational logic of the multi-scale value flux transmission and deduction unit is as follows: construct a two-way isomorphic mapping network between the macro-urban planning intent transmission layer and the micro-actual element movement transmission layer; import the industrial guidance flow of government planning in the upper layer and import the actual movement flow of transportation and capital in the lower layer; dynamically correct the expected flux peak of the target grid on the flow deflection trajectory by continuously comparing the overlap and angle change rate of the flow vectors in the upper and lower layers.

6. The urban real estate price trend and detection platform based on value flow network analysis according to claim 1, characterized in that: The specific operating logic of the reverse fluid depressurization strategy generation unit is as follows: based on the ripple range output by the oscillation wave extension calculation unit, a backup space grid with high dissipation potential is retrieved along the reverse path of oscillation wave propagation. The policy restriction indicators of the backup space grid are reduced by simulation to artificially create a gravity basin, and the specific grid coordinate node indications, including the release of land supply or the implementation of traffic diversion intervention, are output.

7. A method for detecting urban real estate prices based on a value flow network analysis-based urban real estate price trend and detection platform, as described in any one of claims 1 to 6, characterized in that... Includes the following steps: Sp1: Simultaneously collect multi-dimensional features of urban elements, extract the spatial damping coefficient representing resistance and the value flux density representing dynamics respectively, and construct a continuous fluid element field; Sp2: Maps physical geospatial space to a spatiotemporal manifold dynamic base, and calculates the fluid pressure and boundary shear force generated when the value fluid flows across the grid on the base; Sp3: Quantify the physical digestion upper limit of each grid to establish the steady-state energy dissipation threshold, and extract the redundant part of the input flux that exceeds the threshold as the local entropy increase feature that marks the system entering an unstable state; Sp4: Perform holographic spatiotemporal evolution simulation, simulate the overflow deflection trajectory of high-pressure value fluid after obstruction, and calculate the phase transition critical point where redundant flux causes a nonlinear step jump in real estate prices; Sp5: Evaluate the cascading oscillation waveform caused by price steps on the global manifold base, reverse-search for high dissipation potential grids, and output a reverse intervention strategy that includes source depressurization and path diversion.

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