An index data processing method for property examination

Abstract

The present application relates to the technical field of multi-dimensional time series data processing, and discloses a kind of index data processing method for property examination, comprising: obtaining the real-time sampling data stream of multiple data acquisition channels and calculating dynamic covariance to construct index state correlation graph;The variance value of feature component is monitored in 72h sliding sampling window, and when the variance value crosses the preset disturbance threshold, the local topological subgraph is locked and the data stream information entropy value is calculated to determine the local state entropy value;When the local state entropy value crosses the preset critical threshold, the weight proportion of feature component is adjusted, the dynamic correction weight vector is calculated and linear weighted summation is completed, and the examination score data is output.The present application eliminates the dilution of stationary data on transient mutation signals through adaptive weight scheduling, effectively improves the capture sensitivity and time series convergence accuracy of the system on sudden risk.

Description

Technical Field

[0001] This invention relates to a method for processing indicator data for property assessment, belonging to the field of multidimensional time-series data processing technology. Background Technology

[0002] Currently, performing convergence calculations on multidimensional time-series feature data streams and outputting evaluation data is a common technique. It typically uses a fixed weight matrix to sum the feature components of the distribution, and relies on a large number of sampled samples to perform statistical smoothing and filtering of high-frequency random noise, thereby maintaining the continuity and stability of the information system output results under global stable conditions. Alternatively, it can improve the sensing accuracy by adjusting the hardware at the acquisition level, such as the deployment of monitoring sensors or network communication routing.

[0003] Due to hardware limitations, the system struggles to counteract high-dimensional temporal disturbances at the source, and the control methods are inadequate. For example, Chinese invention patent application CN114021873A discloses a data index quantification method and a smart park enterprise value evaluation system. This system relies on a pairwise relative importance judgment matrix under a defined form. Faced with complex conditions such as discrete interruptions of distributed heterogeneous data sources, random network packet loss, or nonlinear phase deviations, such fixed-weight or global regression architectures lack local topological correlation adaptive tuning and noise correction mechanisms. Since the distribution density of nonlinear transient mutation information in the time series is much lower than that of conventional stationary data streams, when the system completes matrix superposition calculations within a continuous sliding window, the local disordered feature components are easily diluted and smoothed by high-frequency stationary data, leading to distorted calculation results and information loss of multi-dimensional temporal feature data streams during the convergence process. Traditional weighted architectures pay the price of significantly degraded high-risk mutation tracking sensitivity in order to maintain global noise resistance and stability.

[0004] Therefore, the technical problem to be solved by this invention is how to construct an adaptive adjustment closed loop based on the nonlinear causal constraints of node state evolution within the continuous sampling window of the time-series feature vector, so as to accurately capture transient abrupt changes under the disturbance of discrete interruption and phase deviation of multi-source heterogeneous data, and improve the system data processing efficiency and model convergence accuracy. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for processing indicator data for property assessment, comprising the following steps:

[0006] Step S1: Obtain real-time sampled data streams of multiple community property supervision dimensions input from multiple data acquisition channels, and calculate dynamic covariance based on historical time series data of real-time sampled data streams to construct an indicator status correlation diagram, wherein multiple community property supervision dimensions are mapped to multiple indicator nodes;

[0007] Step S2: When a missing node is detected in the real-time sampled data stream, retrieve the historical association coupling matrix between the missing node and the adjacent active node in the indicator status association graph.

[0008] Step S3: Use the real-time sampled data stream of adjacent active nodes and the historical correlation coupling matrix to complete the topological weighted interpolation calculation, reconstruct the complete estimate and fill it into the feature vector in situ;

[0009] Step S4: Calculate the mutual information value between the complaint component and the problem rectification feature component in the feature vector and the corresponding time series data. When the variance of any one of the following in the feature vector—the number of complaints against the community, the number of major problems in the community not reported within the prescribed time, and the number of non-compliance inspections of equipment rooms—exceeds the preset disturbance threshold and the mutual information value is lower than the preset gate threshold, the first-order attenuation factor is output through the credibility dynamic attenuation gate control unit to reduce the score of the problem rectification feature component and increase the weight gain value of the complaint component in the current sliding window in a first-order nonlinear manner to obtain a dynamically corrected weight vector.

[0010] Step S5: Retrieve the current scores of each feature component in the dynamically corrected weight vector and feature vector, perform linear weighted summation calculation, and output the assessment score data.

[0011] Preferably, the real-time sampling data streams of multiple community property supervision dimensions include: project information and status reporting, hidden danger investigation, community publicity, complaints, reporting of major community issues, cooperation with property supervision tasks, information release and feedback, problem rectification status, and community patrol reporting. Obtaining real-time sampling data streams of multiple community property supervision dimensions includes: splitting and aggregating the periodic time-series data streams corresponding to each dimension, and combining the periodic time-series data streams as inputs into a feature vector.

[0012] Preferably, step S2 includes the following sub-steps: step S21, identifying the missing node in the current sampling window within the index status association graph; step S22, determining the adjacent active nodes that have a direct topological connection with the missing node, retrieving the historical data sequence between the missing node and the adjacent active nodes for association analysis, and constructing a historical association coupling matrix.

[0013] Preferably, step S3 includes the following sub-steps: step S31, extracting the real-time sampling data stream of each adjacent active node within the current sampling window; step S32, mapping the real-time sampling data stream into a state vector, using the historical correlation coupling matrix to perform a topological weighting transformation on the state vector to reconstruct the completion estimate of the missing node, and filling the completion estimate in place into the corresponding dimension of the feature vector.

[0014] Preferably, step S4 includes the following sub-steps: step S41, comparing the mutual information value with the gating threshold; step S42, when the mutual information value is lower than the gating threshold, determining that the complaint frequency time series data corresponding to the complaint component and the work order rectification feedback time series data corresponding to the problem rectification feature component have a nonlinear phase deviation on the time axis that exceeds the preset time delay tolerance, triggering the credibility dynamic decay gating unit to output a first-order decay factor.

[0015] Preferably, step S6 is included, which involves performing hierarchical multi-dimensional visualization rendering of the assessment score data and outputting a property service quality assessment report based on the dynamic trend of the assessment score data.

[0016] Preferably, the calculation of the mutual information value between the complaint component and the problem rectification feature component in the feature vector in step S4 includes: using a sliding window to extract historical data within a set time period, and normalizing the complaint frequency time series data corresponding to the complaint component in the feature vector and the work order rectification feedback time series data corresponding to the problem rectification feature component.

[0017] Preferably, step S4, which involves lowering the score of the problem rectification feature component and increasing the weight gain of the complaint component within the current sliding window using a first-order nonlinear method, includes multiplying the current score of the problem rectification feature component by a first-order decay factor.

[0018] Preferably, step S7 involves automatically outputting a property rectification instruction when the assessment score data is lower than the set assessment warning threshold for three consecutive sampling periods, and adjusting the supervision warning level of the corresponding community in the indicator status association diagram.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. In the data processing of property assessment indicators, the collected data is converted into a multi-dimensional time-series feature vector. Combined with the dynamic covariance of historical data, an indicator state correlation graph is constructed. Within a continuous 72-hour sampling window, variance mutations of specific components are captured. Local topological subgraphs are targeted and the data flow information entropy value is calculated. When the local state entropy value crosses the stability critical threshold, weight rearrangement is triggered, so that the initial weight of the risk component is amplified in a step-like manner, and the weight of the conventional stable component is compressed proportionally. Thus, a dynamic correction weight vector strongly coupled with transient risk is output, so that the final convergent comprehensive score produces a downward transition response to high-risk mutations, eliminating the engineering defect that mutation signals in static weighted systems are easily diluted by stable data.

[0021] 2. The system identifies missing nodes within the current sampling window in the indicator status association graph, extracts the historical association coupling matrix between the missing node and adjacent active nodes, and performs topological weighted interpolation calculation using the real-time sampling data stream of the remaining active nodes. It then reconstructs the complete estimate of the missing indicator in situ and inputs it into the feature vector. This adaptively ensures the continuous convergence output of the overall feature data chain even under non-ideal conditions such as sudden discrete interruptions or data omissions in the physical data channel. It overcomes the strong dependence of traditional information systems on complete and continuous data streams and avoids failure modes such as the overall paralysis of the scoring system or calculation divergence caused by abnormal interruptions at local measurement points.

[0022] 3. When the complaint frequency time-series data and feedback time-series data generated by the inconsistent data entry channels produce a non-linear phase deviation across 24 hours on the time axis, the system calls the credibility dynamic decay gating unit to periodically calculate the mutual information value between the complaint time-series and the feedback time-series. When the mutual information value is lower than the calibrated gating threshold, the gating unit adaptively outputs a first-order decay factor, punitively lowers the score of the problem rectification feature component, and non-linearly increases the weight gain value of the complaint component in the current sliding window. This achieves dynamic association arbitration of heterogeneous data sources and effectively suppresses the risk of random disturbance to the overall calculation accuracy of the system caused by the inconsistency of multi-party data collaboration. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of a method for processing indicator data for property assessment according to the present invention;

[0024] Figure 2 This is a topology diagram of an indicator data processing system for property assessment according to the present invention.

[0025] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0027] A method for processing indicator data for property performance evaluation includes the following steps:

[0028] Step S1: Obtain real-time sampled data streams of multiple community property supervision dimensions input from multiple data acquisition channels, and calculate dynamic covariance based on historical time series data of real-time sampled data streams to construct an indicator status correlation diagram, wherein multiple community property supervision dimensions are mapped to multiple indicator nodes;

[0029] Step S2: When a missing node is detected in the real-time sampled data stream, retrieve the historical association coupling matrix between the missing node and the adjacent active node in the indicator status association graph.

[0030] Step S3: Use the real-time sampled data stream of adjacent active nodes and the historical correlation coupling matrix to complete the topological weighted interpolation calculation, reconstruct the complete estimate and fill it into the feature vector in situ;

[0031] Step S4: Calculate the mutual information value between the complaint component and the problem rectification feature component in the feature vector and the corresponding time series data. When the variance of any one of the following in the feature vector—the number of complaints against the community, the number of major problems in the community not reported within the prescribed time, and the number of non-compliance inspections of equipment rooms—exceeds the preset disturbance threshold and the mutual information value is lower than the preset gate threshold, the first-order attenuation factor is output through the credibility dynamic attenuation gate control unit to reduce the score of the problem rectification feature component and increase the weight gain value of the complaint component in the current sliding window in a first-order nonlinear manner to obtain a dynamically corrected weight vector.

[0032] Step S5: Retrieve the current scores of each feature component in the dynamically corrected weight vector and feature vector, perform linear weighted summation calculation, and output the assessment score data.

[0033] Preferably, the real-time sampling data streams of multiple community property supervision dimensions include: project information and status reporting, hidden danger investigation, community publicity, complaints, reporting of major community issues, cooperation with property supervision tasks, information release and feedback, problem rectification status, and community patrol reporting. Obtaining real-time sampling data streams of multiple community property supervision dimensions includes: splitting and aggregating the periodic time-series data streams corresponding to each dimension, and combining the periodic time-series data streams as inputs into a feature vector.

[0034] Preferably, step S2 includes the following sub-steps: step S21, identifying the missing node in the current sampling window within the index status association graph; step S22, determining the adjacent active nodes that have a direct topological connection with the missing node, retrieving the historical data sequence between the missing node and the adjacent active nodes for association analysis, and constructing a historical association coupling matrix.

[0035] Preferably, step S3 includes the following sub-steps: step S31, extracting the real-time sampling data stream of each adjacent active node within the current sampling window; step S32, mapping the real-time sampling data stream into a state vector, using the historical correlation coupling matrix to perform a topological weighting transformation on the state vector to reconstruct the completion estimate of the missing node, and filling the completion estimate in place into the corresponding dimension of the feature vector.

[0036] Preferably, step S4 includes the following sub-steps: step S41, comparing the mutual information value with the gating threshold; step S42, when the mutual information value is lower than the gating threshold, determining that the complaint frequency time series data corresponding to the complaint component and the work order rectification feedback time series data corresponding to the problem rectification feature component have a nonlinear phase deviation on the time axis that exceeds the preset time delay tolerance, triggering the credibility dynamic decay gating unit to output a first-order decay factor.

[0037] Preferably, step S6 is included, which involves performing hierarchical multi-dimensional visualization rendering of the assessment score data and outputting a property service quality assessment report based on the dynamic trend of the assessment score data.

[0038] Preferably, the calculation of the mutual information value between the complaint component and the problem rectification feature component in the feature vector in step S4 includes: using a sliding window to extract historical data within a set time period, and normalizing the complaint frequency time series data corresponding to the complaint component in the feature vector and the work order rectification feedback time series data corresponding to the problem rectification feature component.

[0039] Preferably, step S4, which involves lowering the score of the problem rectification feature component and increasing the weight gain of the complaint component within the current sliding window using a first-order nonlinear method, includes multiplying the current score of the problem rectification feature component by a first-order decay factor.

[0040] Preferably, step S7 involves automatically outputting a property rectification instruction when the assessment score data is lower than the set assessment warning threshold for three consecutive sampling periods, and adjusting the supervision warning level of the corresponding community in the indicator status association diagram.

[0041] Example 1: Under the operational conditions of the residential community intelligent monitoring platform, the project information and status reporting channel, hidden danger investigation channel, community publicity channel, complaint channel, community major issue reporting channel, property supervision task cooperation channel, information release feedback channel, problem rectification status channel, and community patrol reporting channel converge into a multi-dimensional time-series characteristic data stream. When the number of non-standard equipment room patrols surges instantaneously, or when the complaint channel generates high-frequency abnormal records, the static weight setting method dilutes the stable daily management indicator scores and smooths out the transient deterioration of specific indicators, resulting in non-linear missed alarms in the output property comprehensive score, which cannot be monitored. During the early warning phase, risk prediction is provided. The central processor inside the intelligent supervision platform reads the assessment indicator data of each residential community within the assessment cycle from the memory. The current scores of 12 feature components, including the percentage of mandatory field information filled, annual inspection hazard investigation indicators, monthly inspection hazard investigation indicators, completion status of publicity community, complaint frequency, number of times major issues were not reported within the prescribed time, number of times tasks were not responded to within the prescribed time, number of times information was not fed back within the prescribed time, number of times rectification was not completed within the prescribed time, number of times equipment room inspections were non-compliant, number of times cleaning inspections were non-compliant, and number of times security inspections were non-compliant, are combined into a multi-dimensional time-series feature vector.

[0042] Based on preset topology construction rules and the temporal evolution trend of the scores of each feature component within the historical evaluation period, the central processing unit (CPU) calculates the dynamic covariance between any two indicator nodes, constructs a state correlation diagram of indicator items with topological relationships, and outputs the dynamic coupling degree coefficient between nodes, representing the implicit causal constraints between indicator nodes. In the actual flow of data input and feature aggregation, to eliminate multidimensional feature scale conflicts caused by differences in original physical dimensions, statistical units, or value boundaries in different property supervision dimensions, the CPU combines various data inputs... Before the feature vector is generated, a built-in linear normalization subroutine is called to uniformly perform min-max normalization registration on the periodic time-series data streams of each channel. This makes the original values ​​of each regulatory dimension dimensionless and linearly mapped to the standard score range of 0 to 100. Through this dimension alignment interface, when subsequent score variance calculation and cross-channel mutual information calculation are performed within the same feature vector, all parameters involved in the logical correlation operation have been normalized to completely equivalent data dimensions and numerical scales. This reduces the impact of high absolute value indicators on volatility variance and eliminates dimension conflicts in the underlying control architecture.

[0043] Within a continuous 72-hour sliding sampling window, the central processing unit (CPU) collects real-time input feature vectors. When a sudden change in the dispersion of any of the following indicators is detected: complaint frequency, number of times major issues are not reported within the prescribed time, or number of times equipment room inspections fail to meet standards, and the variance of the score of that indicator within the current 72-hour sliding sampling window exceeds a preset disturbance threshold, the CPU locks the local topological subgraph containing the node of the mutated indicator within the indicator state association graph, and calculates the data flow information entropy value of all associated nodes within the local topological subgraph within the current sampling window, which serves as the local state entropy value characterizing the disorder of the residential property operation in the community.

[0044] When the local state entropy value exceeds the preset stability critical threshold, the central processing unit triggers a weight rearrangement rule, increasing the initial weight ratio of the hidden danger investigation dimension indicators and the community major problem reporting dimension indicators, while proportionally compressing the weights of other daily management indicators, and outputting a dynamically corrected weight vector; Dynamic adjustment weights for each indicator The calculation formula is as follows: ,in, For the first Dynamically adjusted weights for each indicator, subscript For index variables; As the initial weights, This is the weight correction increment determined based on the local state entropy value. Its value is greater than 0 when an abnormal mutation occurs, and equal to 0 when no abnormal mutation occurs. The sum of all adjusted weights for all indicators, and the sum of weights The calculation formula is as follows: ,in, For the first The initial weights of each indicator, For the first The weight adjustment increment for each indicator. The index variable is a cyclic index variable whose value ranges from 1 to 12, and its weight is the sum of the values. The dynamic adjustment weight vector is normalized to 1, and the scoring calculation module retrieves the dynamic adjustment weight vector from the central processing unit. The scores of each feature component, calculated according to a preset benchmark score and a step-by-step rule for addition and subtraction, are linearly weighted and summed to output a comprehensive score representing the overall operational status of the residential community within the assessment period. The specific quantitative value of the aforementioned weight correction increment, which serves as the core variable, is determined by a segmented step mapping rule running within the central processing unit (CPU), used to transform the abstract local state entropy value into a deterministic weight change scalar. In specific operation, the CPU continuously monitors the calculated dimensionless local state entropy value. When the local state entropy value is greater than 0.60 and less than or equal to 0.70, the control program determines that the community is in a primary disorder state and automatically assigns a uniform weight correction increment of 0.1 to the indicator nodes belonging to the hidden danger investigation dimension and the community major problem reporting dimension in the feature vector. The weight adjustment increments for the remaining daily management indicator dimensions remain at 0. When the local state entropy value further surges to greater than 0.70 and less than or equal to 0.85, the community is determined to enter a high-risk operating state. The control program triggers a second step mapping, which forcibly increases the weight adjustment increments for the two high-risk dimensions and assigns them values ​​of 0.18 and 0.22, respectively. The weight adjustment increments for the remaining daily stable dimensions remain locked at 0. Thus, the weight adjustment increments are determined through a defined segmented mapping relationship. Due to the segmented increase in the weights of the risk characteristic components, the comprehensive scoring data produces a downward jump response to local high-risk mutations, enabling the generated property service quality assessment data to reflect the transient risks of the residential community. This eliminates the problem of the static weighted system diluting mutation signals and achieves the data processing objective of proactive prevention and early warning.

[0045] Example 2: In the continuously operating management environment of a residential community intelligent monitoring platform, a property assessment system built with a dual-core floating-point processor and high-speed random access memory processes raw channel sampling streams from a residential community with 1500 households over 180 consecutive days. When adjusting the sliding sampling window length parameter, the processor analyzes the temporal fluctuation characteristics of the property management response. A shorter sampling window causes frequent false alarms in the scoring system due to occasional random noise, while a longer window smoothly masks the characteristics of transient and abrupt signal changes. This parameter setting balances the real-time performance of data perception with the stability of multi-dimensional computational load. Therefore, the processor, combined with the technical characteristic of daily inspections having a fixed 24-hour cycle, sets the sliding sampling window... To avoid fundamental frequency aliasing due to regular diurnal fluctuations, the sampling window length was determined to be 72 hours. In a non-ideal data environment with random transmission communication noise of 5.2% packet loss rate and data reporting disturbance term of 1.5 amplitude superimposed on the time-series characteristic data stream, the multi-dimensional time-series characteristic data stream introduced a sudden signal at the 12th sampling point of the evaluation period, where the number of non-compliance inspections of equipment rooms jumped from the normal value of 0.2 times / day to the peak value of 8.4 times / day. The control test system included the existing static weighted control group without missing data reconstruction and local topology weight tuning, the local feature missing control group without local topology subgraph information entropy calculation, and the sample group of this invention containing a complete set of index processing steps.

[0046] During the continuous transmission of time-series feature data streams, when a momentary interruption of the communication link causes a missing data node in the hidden danger investigation channel of the data stream, the existing static weighted control group experiences a deadlock and shutdown due to the truncation of input parameters, resulting in the output of the subsequent comprehensive scoring calculation module. In the sample group of this invention, the central processing unit identifies the missing node in the indicator status association diagram, retrieves the historical association coupling matrix between the missing node and adjacent active nodes, where the historical dynamic covariance coefficient eigenvalue between the missing node and the normally reported complaint frequency node is 0.78. Based on this matrix, the central processing unit calls the real-time sampled data streams of the other active nodes to calculate the topology weighted interpolation result, outputs a complete estimate of the hidden danger investigation feature component with a value of 1.25, and fills it in place into the feature vector, maintaining the continuous convergence of the overall system data chain even with transmission disturbances.

[0047] When the abnormal mutation occurred, the frequency of complaints entered through the complaint channel surged from the normal value of 1.1 times / week to 14.0 times / week. Furthermore, a 24.0-hour non-linear phase deviation occurred between the time-series data of complaints submitted by homeowners and the time-series data of work order rectification feedback entered by property management staff. Existing static weighted control groups smoothed out local score variations, causing the output comprehensive score to drop from 95.3 to 93.1. In contrast, the central processor in this invention calculates the mutual information value between the complaint time-series and the feedback time-series to obtain a stable... The calculated result of lowering the state value from 0.65 to 0.12 means that when the mutual information value is below the threshold of 0.35, the credibility dynamic decay gating unit outputs a first-order decay factor to penalize and lower the score of the problem rectification status dimension index. Furthermore, when the variance of the complaint frequency feature component exceeds the disturbance threshold, the processor locks the local topological subgraph and calculates the data flow information entropy value, obtaining a local state entropy value that jumps from a steady-state value of 0.24 to 0.85. Based on this value exceeding the stability critical threshold of 0.60, the system adjusts the weight increment according to the weight rearrangement rules. The values ​​were adjusted to 0.18 and 0.22 respectively, to dynamically adjust the weights of the overall indicators. The score was redistributed, and the linearly weighted summed output score dropped from 94.2 to 71.6.

[0048] In experiments involving numerical boundary determination to verify feature-bounded boundaries targeting the stability critical threshold, when the stability critical threshold was set to the lower limit of 0.15, environmental random noise caused the weight rearrangement rule to be frequently triggered, leading to dynamic correction of the weight vector. This generates high-frequency oscillations, causing the output property service quality assessment data to diverge. When the stability critical threshold is set to the upper limit value of 0.85, the weight adjustment mechanism enters a saturated region where it becomes unresponsive. Under the condition that the local state entropy value reaches 0.82, the weight correction increment... Maintaining a value of 0 prevents the amplification of early warnings for abnormal risks; when the stability critical threshold is set to the median value of 0.60, the weight adjustment increment... Numerical jumps only occur when the local state entropy exceeds the threshold, determining the response interval corresponding to the numerical range defined by the feature. In assessment scenarios facing abnormal risks and heterogeneous noise, the control group with missing local features outputs scalar judgment data that deviates from the preset range under composite perturbation. The sample group of this invention relies on the dynamic weight allocation of the indicator state correlation graph to shorten the system's response time for capturing abnormal risks from 24.0h to 1.5h in an environment where the data loss rate at the data input end is 5.2%. Through the conversion of the underlying time-series feature data stream between memory and processor, the output generates property service quality assessment score data that jumps downward, realizing a steady-state data processing state that eliminates daily data dilution and mutation signals.

[0049] Example 3: This example combines Figures 1 to 2 This paper describes a data processing method for property assessment indicators, such as... Figure 1 As shown, step S1 acquires real-time sampled data streams from multiple community property supervision dimensions and calculates dynamic covariance based on historical time-series data to construct an indicator state association graph, mapping the supervision dimensions to indicator nodes. Then, in step S2, when a missing node is detected in the real-time sampled data stream, the historical association coupling matrix between the missing node and adjacent active nodes in the indicator state association graph is retrieved. In step S3, topological weighted interpolation calculation is performed using the real-time sampled data streams of adjacent active nodes and the historical association coupling matrix to reconstruct the complete estimate and fill it in place into the feature vector. Then, in step S4, the mutual information value between the complaint component and the problem rectification feature component is calculated. When the variance of a specific score crosses a preset disturbance threshold and the mutual information value is lower than a preset gate threshold, a first-order attenuation factor is output through the confidence dynamic attenuation gate unit, lowering the rectification score and non-linearly increasing the complaint weight gain, combining to obtain a dynamically corrected weight vector. Finally, in step S5, the current score of each feature component in the feature vector is retrieved to perform a linear weighted summation calculation, thereby outputting the assessment score data.

[0050] like Figure 2 As shown, in order to support the automated operation of the above data processing flow, in the hardware and processing architecture topology, the residential community includes inspection sensor hardware and data entry module. The periodic time-series data streams generated by these sensors are transmitted through a physical channel network and input to the data processing architecture centrally deployed by the residential community smart monitoring platform via an external authorized data bus. This architecture includes a dual-core floating-point processor and a high-speed random access memory connected by a bidirectional communication line. The dual-core floating-point processor integrates a scoring calculation module and a confidence dynamic decay gating unit, while the high-speed random access memory is divided into an address register and a display module.

[0051] Example 4: In the data transmission environment of a residential community intelligent monitoring platform with continuous flow control, when the multi-dimensional time-series feature data stream converged from nine acquisition channels (expanded into 12 subdivided feature components through internal feature decoupling) encounters 5.2% random communication transmission distortion due to signal jitter, and when the community inspection reporting channel corresponding to the number of non-compliant inspections of equipment rooms generates discrete indicator missing nodes due to occasional hardware failures of inspection sensors, the conventional weighted summation module will be interrupted due to the missing input vector dimension. Furthermore, when the time-series data of complaint frequency submitted by owners through the platform's complaint channel and the time-series data of work order rectification feedback entered by property staff produce a non-linear phase deviation spanning 24.0 hours on the time axis, due to the non-real business data input from the two heterogeneous channels, the conventional evaluation architecture is ineffective. The incomplete data flow consistency verification ultimately leads to distortion of the output comprehensive scoring index data, resulting in the omission of potential hidden risks. To eliminate the computational interruption caused by incomplete input vectors, the central processing unit (CPU) inside the intelligent monitoring platform retrieves the temporal evolution trend of the scores of each dimension index within the historical assessment period from high-speed random access memory. It automatically identifies missing nodes within the current sampling window and retrieves the historical correlation coupling matrix between the missing nodes and adjacent active nodes. The CPU extracts the real-time scores of complaint frequency nodes and major community problem reporting nodes within the current assessment period. It multiplies the current scores of each of the above active nodes with the corresponding historical dynamic covariance coefficient eigenvalue 0.78 in the matrix and performs weighted interpolation completion in the topology adaptive interpolation subroutine at the method level. The completed estimated value of the missing node is... The calculation formula is as follows: ,in, The estimated value for the completion of missing nodes. For missing nodes in the historical association coupling matrix and the first Historical feature weighting coefficients among active nodes For the first The current score of each active node. Circular index variable for active nodes; This represents the total number of active nodes that have a topological association with the missing node.

[0052] The central processing unit (CPU) calculates a complete estimate of the hazard investigation feature component value of 1.25 and fills it into the feature vector in situ to restore the complete structure of the multidimensional time-series feature vector. The score sequences corresponding to the complaint component and the problem rectification feature component are used as input variables to the joint probability distribution function. The joint probability distribution and the marginal probability distributions of the two sets of sequences are determined by statistically analyzing the frequency of each score point within the current window. The quotient of the product of the joint probability distribution and the marginal probability distribution is calculated using discrete logarithms and accumulation. The output is a mutual information value representing the temporal correlation between the two channels. When encountering the aforementioned 24.0-hour phase deviation condition, the processor calculates a value that decreases from the steady-state value of 0.65 to 0.12. Since the mutual information value is lower than the preset gating threshold of 0.35, the credibility dynamic decay gating unit outputs a first-order decay factor to penalize and lower the score of the problem rectification status dimension index. Furthermore, when the variance of the complaint frequency feature component exceeds the preset disturbance threshold, the processor locks the local topology subgraph containing the node of the mutation index in the index state association graph, and accumulates the logarithm of the data flow probability quotient of all associated nodes in the local topology subgraph within the current sampling window to obtain a local state entropy value that jumps from a steady-state value of 0.24 to 0.85. In the specific business time-series data flow processing, the nonlinear phase deviation spanning 24.0 hours is explicitly confirmed by the processor executing the time-delay correlation estimation procedure.

[0053] In the actual transformation process, the central processing unit actively introduces a time lag displacement with an increment of 1 hour into the complaint frequency score sequence, and iteratively calculates the time lag mutual information between the complaint sequence and the work order rectification feedback score sequence at each lag order. When the time lag displacement is detected to be exactly 24.0 hours, the corresponding time lag mutual information value shows a predicted local extreme value rebound. However, the mutual information value in the normal zero-delay state drops sharply from a steady-state 0.65 to 0.12 due to time domain misalignment. Based on this, the control program logically establishes a deterministic mapping path between the absolute time axis phase deviation and the sharp drop in the normal mutual information scalar. Since the local state entropy value exceeds the preset stability critical threshold of 0.60, the control system triggers the weight rearrangement rule, adjusting the weight increments of the indicators for hidden danger investigation with significant safety impact and the indicators for reporting major issues in the community. The values ​​were increased from 0 to 0.18 and 0.22 respectively, while the weighting of other stable daily indicators was reduced proportionally. This caused the final linear weighted summation output to produce a downward step-like response, with the score dropping from the normal 94.2 points to 71.6 points. This jump directly reduced the sensitivity time of the intelligent monitoring platform in detecting potential risks from 24.0 hours to 1.5 hours. As a result, when calling on processor and memory resources to carry out data transformation, the dilution of stable data on sudden signals was eliminated and the data flow convergence was ensured.

[0054] Example 5: When the system faces the deployment of a new project, the property assessment system receives standardized historical assessment records of the new residential community through an external authorized data bus and converts sensitive data items containing individual characteristics into de-identified coded data. The central processing unit reads the time-series scores of the 12 feature components from the previous 30 days in the coded data in memory, uses the offline covariance solver to calculate the initial dynamic covariance value of any two feature components in the state without abrupt changes, fills the initial dynamic covariance value into the indicator state association graph with topological correlation, establishes the initial steady-state topology structure of each indicator node, and writes the dimensionless reference ratio value into the address register of memory as the initial weight of each feature component. Benchmark.

[0055] Before a newly deployed intelligent monitoring platform is put into operation, the central processing unit runs a 168-hour simulation test subroutine to calibrate control boundary indicators. The simulation test subroutine reads the indicator state correlation diagram and collects the time-series scores of each feature component, calculating the discrete volatility of each indicator node within the sliding sampling window under risk-free conditions. Preset disturbance threshold for each indicator The calculation formula is as follows: ,in, For the first The preset perturbation threshold for each indicator, subscript For index variables; For the first time within the benchmark assessment period The historical standard deviation of the scores for each indicator The calibration coefficient is set to a dimensionless number of 1.50. The central processing unit calculates the data flow entropy distribution within the local topological subgraph based on the discrete volatility. It determines the stability critical threshold as 1.50 times the statistical variance of the obtained data flow entropy values ​​and writes it into high-speed random access memory. When the stability critical threshold exceeds the upper limit of 0.85, the activation damping of the weight rearrangement rule increases. When the stability critical threshold is below the lower limit of 0.15, the system generates a high-frequency weight correction increment. .

[0056] The offline simulation calibration described above ensures that the system's criterion boundaries converge within the physical characteristic response space of the property operation status assessment. In the actual control system calibration, the settings of the aforementioned core quantitative parameters and threshold boundaries are all supported by clear engineering convergence simulations and physical effects, and are not arbitrarily customized. Specifically, the sliding sampling window length is locked at 72 hours to fully cover three complete daily inspection and information flow cycles, thereby filtering out daytime regular aliasing interference fluctuations with a 24-hour fundamental frequency on the time axis. The calibration coefficient is set to 1.50 based on the statistical confidence interval boundaries of the control chart. System simulations show that if this coefficient is lower than 1.10, daily environmental noise will cause the weights to automatically adjust. The adaptive program frequently generates false alarms, with the false alarm rate soaring to 45%. If the value exceeds 2.00, it will increase the system's response delay to real transient risks by 38%. Similarly, the median value of the stability critical threshold under normal conditions is determined to be 0.60, which corresponds to the 60% boundary point of the maximum theoretical information entropy value of the local system topology. If it is below 0.15, it will cause the dynamic correction weight vector to oscillate at high frequency under slight packet loss noise, resulting in non-convergence. If it is above 0.85, it will cause the system to enter the saturation dead zone where it cannot respond to risks. The gating threshold is set at 0.35, which is determined based on the offline statistical minimum value of the joint probability distribution mutual information of multi-source heterogeneous channels under normal business collaboration. If it is below this boundary value, it is determined that there is a multi-party input conflict.

[0057] Example 6: When the system faces concurrent data access from multiple cells accompanied by packet loss in the physical channel network, the processor runs a full-link fault tolerance calibration and boundary convergence verification procedure. It uses a centrally deployed data processing architecture to verify the distributed property assessment time-series feature vector. To calibrate the stable operating range of the assessment scoring system under multi-level disturbances, the verification procedure selects parameter endpoints and measures the full-link steady state under three preset parameter value combinations. In the first parameter configuration scenario, the system sets the stability critical threshold. The absolute lower limit was set to 0.15. However, this low setting caused a decrease in the system's noise immunity. A sudden 5.2% packet loss noise at the input triggered a high-frequency adjustment in the adaptive weight scheduling program, leading to a dynamic correction of the weight vector. High-frequency numerical oscillations occurred, with the assessment score data output by the calculation module unexpectedly fluctuating between 78.5 and 92.4. Furthermore, the data stream convergence process exhibited non-convergence characteristics, extending the end-to-end response convergence time to 18.5 hours. Dynamic adjustments to the weight vector were implemented. The calculation formula is: ,in, For the first Dynamically adjusted weights for each indicator, subscript For index variables, subscript Indicates a dynamic correction status; To initially configure weights, the subscript Indicates the initial state; The nonlinear weight correction increment is determined based on the local state entropy value, and the total weight is... The calculation expression is: ,in, The sum of all adjusted weights for all indicators, with subscripts... This represents a simplified form of summation. For the first The initial weights and subscripts of each indicator Indicates the initial state. For indicator cyclic index variables; For the first The weight adjustment increment for each indicator.

[0058] In the second parameter configuration condition, the system will set the stability critical threshold. The value was adjusted to a normal median of 0.60 to match the damping of the adaptive weighted scheduling program with the discrete fluctuations of the local operating state. Within a continuous 72-hour sliding sampling window, when a node with missing discrete indicator data occurs in the hazard investigation channel, the central processing unit (CPU) identifies the missing node in the indicator status correlation graph and retrieves the historical correlation coupling matrix between the missing node and adjacent active nodes. The historical dynamic covariance coefficient eigenvalue between the missing node and the normally reported complaint frequency node is stable at 0.78. The CPU utilizes the real-time sampled data stream from the active nodes... The topology adaptive interpolation program calculates a complete estimate of 1.25 and fills in the data in situ to restore the complete data structure. At the same time, when the time series data of the frequency of complaints submitted by the owners and the time series data of the rectification feedback of the work orders entered by the property staff show a 24-hour non-linear phase deviation on the time axis, the central processing unit calculates a mutual information value of 0.12. When this value is lower than the preset gate threshold of 0.35, the credibility dynamic decay gate control unit outputs a first-order decay factor, and multiplies the current score of the problem rectification feature component by the first-order decay factor to apply a penalty and reduce the score.

[0059] Under this condition, the local state entropy value jumps from a steady-state value of 0.24 to 0.85, exceeding the set stability threshold of 0.60. The system then adjusts the weight increments of the hidden danger investigation dimension indicators and the community major problem reporting dimension indicators. When the values ​​were increased from 0 to 0.18 and 0.22 respectively, the comprehensive score output by the linear weighted summation showed a downward step-like response, with the score dropping from the normal 94.2 to 71.6. This shortened the capture time for abnormal risks from 24.0 hours to 1.5 hours, and the output data stream exhibited a smooth convergence characteristic. In the third parameter configuration condition, the system set the stability critical threshold. Raising the absolute upper limit to 0.85 causes the weight adjustment mechanism to enter a saturated, unresponsive region. Furthermore, when the set value is too high, even if the local state entropy reaches 0.82 under extremely deteriorating conditions, the weight adjustment increment for all feature components will still fail to cross the stability critical threshold of 0.85. Maintaining a value of 0 disables the adaptive weight scheduler, causing the assessment score to remain at 93.1, preventing further dives and resulting in a missed detection of systemic security risks. The data bus then reaches the stability critical threshold during the adaptive weight scheduler process. Under the condition of a fixed value of 0.60, a property service quality assessment report with stable convergence is continuously output.

[0060] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for processing indicator data for property performance evaluation, characterized in that, Includes the following steps: Step S1: Obtain real-time sampled data streams of multiple community property supervision dimensions input from multiple data acquisition channels, and calculate dynamic covariance based on historical time series data of real-time sampled data streams to construct an indicator status correlation diagram, wherein multiple community property supervision dimensions are mapped to multiple indicator nodes; Step S2: When a missing node is detected in the real-time sampled data stream, retrieve the historical association coupling matrix between the missing node and the adjacent active node in the indicator status association graph. Step S3: Use the real-time sampled data stream of adjacent active nodes and the historical correlation coupling matrix to complete the topological weighted interpolation calculation, reconstruct the complete estimate and fill it into the feature vector in situ; Step S4: Calculate the mutual information value between the complaint component and the problem rectification feature component in the feature vector and the corresponding time series data. When the variance of any one of the following in the feature vector—the number of complaints against the community, the number of major problems in the community not reported within the prescribed time, and the number of non-compliance inspections of equipment rooms—exceeds the preset disturbance threshold and the mutual information value is lower than the preset gate threshold, the first-order attenuation factor is output through the credibility dynamic attenuation gate control unit to reduce the score of the problem rectification feature component and increase the weight gain value of the complaint component in the current sliding window in a first-order nonlinear manner to obtain a dynamically corrected weight vector. Step S5: Retrieve the current scores of each feature component in the dynamically corrected weight vector and feature vector, perform linear weighted summation calculation, and output the assessment score data.

2. The method for processing indicator data for property assessment according to claim 1, characterized in that, The real-time sampling data streams for multiple community property supervision dimensions include: project information and status reporting, hidden danger investigation, community publicity, complaints, reporting of major community issues, cooperation with property supervision tasks, information release and feedback, problem rectification status, and community patrol reporting. Obtaining the real-time sampling data streams for multiple community property supervision dimensions involves: splitting and aggregating the periodic time-series data streams corresponding to each dimension, and combining the periodic time-series data streams as inputs into a feature vector.

3. The method for processing indicator data for property assessment according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S21, identify the missing node in the current sampling window within the indicator status association graph; Step S22, determine the adjacent active nodes that have a direct topological connection with the missing node, retrieve the historical data sequence between the missing node and the adjacent active nodes for association analysis, and construct the historical association coupling matrix.

4. The method for processing indicator data for property assessment according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S31, extract the real-time sampling data stream of each adjacent active node within the current sampling window; Step S32, map the real-time sampling data stream into a state vector, use the historical association coupling matrix to perform a topological weighting transformation on the state vector to reconstruct the completion estimate of the missing node, and fill the completion estimate in place into the corresponding dimension of the feature vector.

5. The method for processing indicator data for property assessment according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S41, compare the mutual information value with the gating threshold; Step S42, when the mutual information value is lower than the gating threshold, determine that the complaint frequency time series data corresponding to the complaint component and the work order rectification feedback time series data corresponding to the problem rectification feature component have a nonlinear phase deviation on the time axis that exceeds the preset time delay tolerance, and trigger the credibility dynamic decay gating unit to output a first-order decay factor.

6. The method for processing indicator data for property assessment according to claim 1, characterized in that, This includes step S6, which involves performing hierarchical multi-dimensional visualization rendering of the assessment scoring data and outputting a property service quality assessment report based on the dynamic trend of the assessment scoring data.

7. The method for processing indicator data for property assessment according to claim 1, characterized in that, Step S4 involves calculating the mutual information value between the complaint component and the problem rectification feature component in the feature vector and the corresponding time series data. This includes using a sliding window to extract historical data within a set time period and normalizing the complaint frequency time series data corresponding to the complaint component in the feature vector and the work order rectification feedback time series data corresponding to the problem rectification feature component.

8. The method for processing indicator data for property assessment according to claim 1, characterized in that, Step S4 involves lowering the score of the problem rectification feature component and increasing the weight gain of the complaint component within the current sliding window using a first-order nonlinear method, which includes multiplying the current score of the problem rectification feature component by a first-order decay factor.

9. The method for processing indicator data for property assessment according to claim 1, characterized in that, Including step S7, when the assessment score data is lower than the set assessment warning threshold for three consecutive sampling periods, the property rectification instruction is automatically output, and the supervision warning level of the corresponding community is adjusted in the indicator status association diagram.

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