Intelligent community property management system

Abstract

The application relates to the field of intelligent community operation and maintenance scheduling, and discloses an intelligent community property management system, which comprises a structured data access and mapping unit, a dynamic confidence calculation unit, a space-time consensus fuse check unit and a space gravity field scheduling engine.The structured data access and mapping unit is used for converting the structured event label of a service request into a basic score; the dynamic confidence calculation unit is used for calculating a confidence coefficient according to the historical operation and maintenance interaction record of an initiating user; the space-time consensus fuse check unit is used for triggering logical fuse and setting the priority to the highest when detecting that the number of space-time associated requests exceeds a threshold value; and the space gravity field scheduling engine is used for locking the highest priority request as an anchor point and weighting and correcting the priority of secondary requests in a physical neighborhood by using a space gravity coefficient when the fuse is not triggered, and generating an atomized continuous execution task package when the condition is met.The application introduces a space gravity field model, solves the dimensional conflict of logical sequencing and physical execution path, and avoids zigzag return scheduling loss.

Description

Technical Field

[0001] This invention relates to a smart community property management system, belonging to the field of smart community operation and maintenance scheduling technology. Background Technology

[0002] The existing smart community property management system uses a timestamp-based linear queuing mechanism as the core scheduling logic when processing repair requests, complaints and emergency response service requests. In typical scenarios with low request density and independent events, the system maintains basic processing order and fairness through a first-in-first-out rule.

[0003] As the community expands and service demands become more complex, the data input to the system exhibits high concurrency and heterogeneity. Requests include multiple attributes such as urgency, scope of impact, and geospatial dimensions. The linear queuing mechanism based on a single time dimension forcibly reduces the dimensionality of multidimensional vectors. The system cannot distinguish between high-value risk data and low-priority trivial matters in a resource-constrained dynamic environment. When regional emergencies occur, a large amount of duplicate, false alarm, or noise data based on superficial symptoms crowds out the channel, causing critical operation and maintenance resources to be occupied. Furthermore, the purely logical priority sorting ignores the physical spatial movement path cost of operation and maintenance entities, resulting in inefficient round-trip scheduling at the execution end based on geographical location.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a data processing scheme that can introduce multi-dimensional evaluation indicators from the data logic level, use spatiotemporal correlation features to clean and dynamically sort heterogeneous requests in real time, and achieve a balance between logical superiority and physical execution efficiency. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A smart community property management system, the system comprising:

[0006] The structured data access and mapping unit is used to receive service request data packets containing structured event tags, geographic grid identifiers and request initiation time, and convert structured event tags into basic scores according to a preset event urgency mapping table.

[0007] The dynamic confidence calculation unit is used to retrieve the historical operation and maintenance interaction records of the request initiating user, and calculate the confidence coefficient to characterize the reliability of the data source based on the effective repair rate and malicious cancellation rate in the records.

[0008] The spatiotemporal consensus circuit breaker verification unit is used to execute data cleaning logic. When the number of independent requests with associated event tags in the same geographic grid identification area and within a preset time window before the current request is initiated exceeds the preset consensus threshold, the logic circuit breaker state is triggered. In the logic circuit breaker state, the confidence coefficient is ignored and the priority of all service requests involving associated event tags is set to the highest level.

[0009] The spatial gravity field scheduling engine is used to execute priority reconstruction logic based on physical space constraints when the logical circuit breaker state is not triggered. The engine calculates the product of the base score and the confidence coefficient to generate the original priority, and locks the service request with the highest original priority value in the pending work order queue as the anchor request. The engine then traverses the pending work order queue, identifies secondary requests whose geographic grid identifiers are located within the preset physical neighborhood of the anchor request, and utilizes preset spatial gravity coefficients. Original priority of secondary requests Perform weighted correction operations to generate cluster priorities : ,in, To correct the clustering priority, the space gravity field scheduling engine executes comparison logic. When the clustering priority value of a secondary request is higher than the highest original priority in the pending work order queue except for the anchor request, a physical adsorption operation is performed to extract the secondary request from its original sorting position and bind it after the anchor request, generating an atomic continuous execution task package.

[0010] The task package distribution interface is used to send atomic, continuously executed task packages to the operation and maintenance terminal.

[0011] Preferably, the system also includes a dependency topology shielding unit, which has a pre-configured facility dependency topology library that defines the parent-child cascading relationships between community infrastructure nodes. The dependency topology shielding unit is used to activate the association shielding mode for the parent node facility in response to the logical circuit breaker state of the spatiotemporal consensus circuit breaker verification unit triggered by the service request for the parent node facility. In the association shielding mode, the dependency topology shielding unit intercepts new service requests for the child node facilities of the parent node facility, prohibits the generation of independent work orders for new service requests, and merges the identification information of the new service requests into the service requests of the parent node facility as an impact scope counting indicator.

[0012] Preferably, the spatiotemporal consensus circuit breaker verification unit is configured with dynamic time window adjustment logic, which is used to monitor the inflow rate of service requests within the current geographic grid identification area; when the inflow rate exceeds the preset burst gradient threshold, the spatiotemporal consensus circuit breaker verification unit automatically shortens the length of the preset time window and lowers the preset consensus threshold; when the inflow rate is lower than the preset steady-state gradient threshold, the spatiotemporal consensus circuit breaker verification unit extends the preset time window and raises the consensus threshold.

[0013] Preferably, the preset physical neighborhood defined by the spatial gravity field scheduling engine adopts a dynamic hierarchical definition; when the structured event label of the anchor point request corresponds to a vertical pipeline facility, the preset physical neighborhood is limited to all floor grids that are in the same vertical projection coordinate as the anchor point request; when the structured event label of the anchor point request corresponds to a horizontal public facility, the preset physical neighborhood is limited to a set of horizontal grids centered on the geographic grid identifier of the anchor point request and within a preset radius in a straight line.

[0014] Preferably, the dynamic confidence calculation unit is configured with a nonlinear decay update mechanism; the nonlinear decay update mechanism is used to update the user's historical valid repair rate after each work order is completed and the on-site verification result is received from the operation and maintenance terminal; for feedback that the verification result is a false repair, the dynamic confidence calculation unit performs exponential penalty decay on the confidence coefficient; for feedback that the verification result is a genuine repair, the dynamic confidence calculation unit performs linear gain recovery on the confidence coefficient; the base of the exponential penalty decay is positively correlated with the user's cumulative number of false repairs in the current calendar year.

[0015] Preferably, the topology shielding unit is also configured with reverse causal inference logic; when the system receives independent service requests for multiple different child node facilities under the same parent node facility within a preset inference period, and the proportion of the number of independent service requests covers a preset percentage of the total number of child nodes under the parent node facility, the reverse causal inference logic forcibly generates a virtual root source work order for the parent node facility, and sets the priority of the virtual root source work order to be higher than the level of the service requests of the child node facilities.

[0016] Preferably, the structured data access and mapping unit is configured with an anti-ambiguity secondary index table; the anti-ambiguity secondary index table contains primary categories and secondary sub-items, and the structured event tags are generated by the user through the cascading menu in the client; the system prohibits receiving unstructured natural language text descriptions as input for priority calculation, and only stores natural language text descriptions as additional notes; the mapping process of the basic score is only indexed based on the unique encoded key value of the structured event tag.

[0017] Preferably, when the space gravity field scheduling engine performs physical adsorption operations, if multiple secondary requests simultaneously meet the condition that their clustering priority is higher than the highest original priority of non-anchor requests, the space gravity field scheduling engine performs secondary sorting based on the three-dimensional Manhattan distance between the secondary requests and the anchor requests, and prioritizes adsorbing the secondary request with the smallest three-dimensional Manhattan distance.

[0018] Preferably, the task package distribution interface is configured with a state locking protocol; when the atomic continuous execution task package is sent to the operation and maintenance terminal, the system locks the scheduling status of all service requests in the package, and prohibits subsequent new high-priority requests from interrupting or inserting into the distributed atomic continuous execution task package, until the operation and maintenance terminal returns the arrival confirmation signal of the first task in the atomic continuous execution task package.

[0019] Preferably, the system runs on a distributed cloud server cluster. The structured data access and mapping unit, the dynamic confidence calculation unit, and the spatiotemporal consensus circuit breaker verification unit are deployed as stateless microservice instances, while the spatial gravity field scheduling engine is deployed as a stateful singleton service to maintain the atomicity of the global pending work order queue. The preset event urgency mapping table and spatial gravity coefficients are stored in an in-memory database.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In smart community property management, a structured event mapping, historical performance confidence calculation, and spatiotemporal overlap verification mechanism are integrated to construct a data priority determination logic with self-calibration capabilities. Structured labels are used to ensure the machine readability of input data, and the request value is initially weighted based on the confidence coefficient of objective performance records. A spatiotemporal consensus mechanism is used as a logical circuit breaker. When the data density in a specific physical grid and time window exceeds a preset threshold, the physical event aggregation characteristics are used to forcibly cover the individual credit weight and lock the highest priority. The multi-mechanism collaborative processing enables the system to correct the logical deviation of single-point data by using the physical facts of group data when handling high-concurrency requests. This ensures that systemic risk data automatically floats to the top of the queue when computing power and execution resources are tight, suppressing discrete and invalid disturbances, and realizing dynamic optimization of channel resources at the data processing level.

[0022] 2. Based on the basic priority sorting, spatial gravity clustering logic is introduced. By calculating the dynamic gain of task weights on geographical proximity, the dimensional conflict between logical linear sorting and physical execution path is resolved. The system uses the current highest priority task as the anchor point and applies a gravity coefficient to secondary tasks located in the same geographical grid. Tasks that are originally discrete in the logical queue but are physically adjacent are reorganized into continuous execution units. Algebraic operations are used to eliminate the sawtooth back-and-forth movement path caused by strictly following linear priority at the data structure level, and invalid travel time is converted into job time, so as to achieve the unity of optimal logical scheduling and physical execution efficiency.

[0023] 3. Utilize a pre-built infrastructure-dependent topology library to establish a proactive data interception mechanism based on fault causal chains. When the system confirms a high-confidence fault in the parent node facility through spatiotemporal consensus logic, it immediately activates a shielding mode for downstream child node facilities. Subsequent input requests for symptoms derived from child or grandchild nodes are directly mapped to the parent node's task count indicators, prohibiting the generation of independent pending work orders. Based on a collaborative mechanism of static topology and dynamic state triggering, the data processing mode is transformed from passively receiving surface data to actively merging root cause data. This prevents the massive redundant data generated by cascading facility faults from impacting the processing queue at the source, ensuring that core computing and execution resources are always focused on resolving the root cause fault. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the logic of multi-dimensional data cleaning and spatial gravity scheduling in smart communities according to the present invention.

[0025] Figure 2 This is a comparison diagram of the dynamic and fixed window circuit breaker delay responses under burst flow conditions according to the present invention;

[0026] Figure 3 This is a schematic diagram of the distributed architecture of the present invention based on microservice clusters and a stateful core engine. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] This invention discloses a smart community property management system. Based on a distributed cloud server cluster architecture, it achieves real-time cleaning, dynamic sorting, and physical execution path optimization of heterogeneous service requests through the collaborative work of a structured data access and mapping unit, a dynamic confidence calculation unit, a spatiotemporal consensus circuit breaker verification unit, a spatial gravity field scheduling engine, and a task package distribution interface. The system utilizes message queues to transmit data streams between stateless microservices or stateful singleton services, converting natural language requests into structured numerical values. It dynamically adjusts request weights by combining historical credit data and geospatial density features, and finally reassembles discrete tasks into atomic continuous task packages using a spatial gravity algorithm. The unstructured nature of service request data makes it difficult to accurately quantify the urgency of events. This system addresses this by employing a structured data access and mapping unit to standardize data entry. This unit has a pre-built anti-ambiguity secondary index table, defining various events in community operations and maintenance scenarios as structured event tags with unique coded key values. The system client restricts users to selecting specific structured event tags via a cascading menu, submitting only plain text descriptions. The structured data access and mapping unit receives service request data packets containing structured event tags, geographic grid identifiers, and request initiation times. Based on an event urgency mapping table pre-stored in an in-memory database, it extracts the corresponding base score for each event using a key-value index. This process ensures that the data entering the core computing flow has definite numerical attributes. Addressing the issue of varying data source reliability in open community environments, this system employs a dynamic confidence calculation unit to construct a data value assessment mechanism based on objective performance records. This unit is configured with non-linear decay update logic, periodically retrieving historical maintenance interaction records from the database within a preset rolling time window, extracting valid repair rate and malicious cancellation rate indicators, and substituting them into the confidence calculation model to generate normalized confidence coefficients. After the work order is completed and the on-site verification results are received, if the result is a genuine repair request, the unit will set a confidence coefficient. Perform linear gain recovery; if the result is a false repair request, perform exponential penalty decay based on the user's cumulative number of false repair requests in the current calendar year.

[0029] To address the issue that a single linear priority ranking system cannot handle the high concurrency risks of regional public emergencies, this system introduces a spatiotemporal consensus circuit breaker verification unit to perform data cleaning based on spatiotemporal density. Before calculating the final priority, the system uses the geographic grid identifier of the current request as the center and retrieves the number of independent requests with associated event tags within a preset time window before the current request was initiated. When this number exceeds a preset consensus threshold, the system determines that there is a systemic failure with a high degree of certainty in the region and triggers a logical circuit breaker state. In the logical circuit breaker state, the system ignores the personal confidence coefficient of the request initiator involved in the event. Furthermore, the system prioritizes all service requests involving the associated event tag to the highest system level. To address the inefficient round-trip issue in physical execution paths caused by logical priority queues, this system integrates a space gravity field scheduling engine to perform priority refactoring based on physical space constraints. This engine calculates the base score. With confidence coefficient The product of these is used to generate the original priority. And lock the original priority in the queue of pending work orders. The highest-level request serves as the anchor request. The engine iterates through the remaining secondary requests in the queue, identifies targets whose geographic grid markers are located within the preset physical neighborhood of the anchor request, and utilizes a preset spatial gravity coefficient. Perform a weighted correction on the original priorities of secondary requests to generate cluster priorities. The calculation formula is: ,in, Original priority, For spatial gravity coefficients, if the clustering priority of the secondary request... The secondary request, which has a higher initial priority than the anchor request in the queue, performs a physical adsorption operation, binding it to the anchor request and generating an atomic, continuously executed task package. To address data redundancy issues caused by causal relationships in infrastructure failures, the system is configured with a dependency topology shielding unit. This unit defines a facility dependency topology library that defines the parent-child cascading relationships between community infrastructure nodes. When a parent node facility is confirmed as having a high degree of certainty due to spatiotemporal consensus logic and triggers a circuit breaker, this unit activates the association shielding mode for that parent node facility. In this mode, the system intercepts new service requests for child node facilities under that parent node, merges the identification information of these requests into the impact range count index of the parent node facility's service requests, returns the parent node's emergency repair status information to the initiating user, and prohibits the generation of independent child node pending work orders.

[0030] Example 1: In a scenario where a sudden physical rupture of the water supply riser in Unit 2 of Building 3 causes a water supply interruption across multiple floors of the unit during peak water usage, the system faces the challenge of managing spatiotemporal constraints, including a rapid surge of heterogeneous service requests with varying descriptions and credit levels, and the scheduling of operational resources. The structured data access and mapping unit receives structured event tags, such as public water supply interruption and abnormal water pressure, submitted by users on different floors of the unit via a cascading menu. These tags are then mapped to predetermined base scores based on a pre-set event urgency mapping table. Meanwhile, the dynamic confidence calculation unit retrieves the historical operation and maintenance interaction records of the user who initiated the above request, calculates and outputs a confidence coefficient that characterizes the reliability of the individual data source. At this point, the initial queue generated by the system is based solely on a single point of logical priority. Right now and The products are sorted, but no systematic correlation has yet been identified behind the discrete requests.

[0031] When the time-space consensus circuit breaker verification unit detects that the number of independent requests with associated event tags in the geographic grid identification area of ​​Unit 2, Building 3 exceeds the preset consensus threshold within a preset time window, the unit determines that there is a regional fault with a high degree of certainty and immediately triggers the logical circuit breaker state, forcibly ignoring the personal confidence coefficients of all request initiators involved in the event. The maintenance task priority of the corresponding parent node facility's water supply riser is set to the highest level in the system. Simultaneously, the associated shielding mode is activated by relying on the topology shielding unit. Based on the facility dependency topology library, all new service requests for the child node facilities under this riser are intercepted and merged. The originally exponentially growing redundant repair work orders are converged into a single root cause fault count index, thereby completing the task redefinition from handling massive discrete symptoms to focusing on the unique fault entity at the logical level. The spatial gravity field scheduling engine locks the highest priority riser maintenance task as the anchor point request, traverses the work order queue to be processed and identifies the low-priority corridor light replacement secondary requests located in the preset physical neighborhood of the anchor point request, and uses the preset spatial gravity coefficient. The original priority of this secondary request Perform weighted correction operations to generate cluster priorities ,Right now This causes the secondary request, which was originally at the end of the queue logically, to gain priority due to the strong coupling of its physical location and be absorbed into the anchor request. Finally, the task package distribution interface generates and distributes an atomic continuous execution task package containing riser repair and roadside light replacement.

[0032] Example 2: This experiment aims to verify the actual operational efficiency and rationality of the core parameter settings of the smart community property management system of the present invention under complex operating conditions of high concurrency, strong noise, and limited resources. The experiment relies on a digital twin community simulation platform based on discrete event simulation technology. This platform is equipped with high-performance computing nodes to simulate the operating environment of a large community containing 50 buildings, 3000 households, and 20 maintenance personnel. The experimental data source consists of two parts: the basic background traffic is a Poisson-distributed random request sequence generated based on the historical maintenance logs of a real community over the past three years; the sudden event traffic is generated by injecting high-density fault signals at specific spatiotemporal coordinates. To ensure the experiment closely reflects engineering reality, Gaussian white noise with a signal-to-noise ratio of 15dB is actively superimposed on the data stream to simulate user geographical location drift, misoperation, and measurement errors of equipment sensors. Regarding the core parameter, the spatial gravity coefficient... The setting and decision-making logic are based on an engineering trade-off between sensitivity to movement costs and tolerance for task waiting time. The main influencing factors for parameter settings include the average movement speed within the community. and the basic urgency distribution of tasks, when At lower speeds, the proportion of movement costs in the total time increases, requiring a greater effort to improve physical execution efficiency. To enhance local adsorption capacity; if Excessive weighting leads to low-priority tasks excessively attracting high-priority anchors, causing response delays for high-priority tasks. This experiment, based on the performance inflection point measured in preliminary experiments, adjusts the experimental group... Set it to 0.5, and set... Low adsorption control group and An over-adsorption control group was used to verify the superiority of the parameter range.

[0033] The experiment simulated a scenario where a summer thunderstorm lasting 4 hours caused multiple facility failures. After the simulation clock started, the system background injected service requests at a rate of 50 to 200 requests per minute. The experiment set up four independently running test groups: the experimental group using the complete technical solution of this invention; control group A, which removed the space gravitational field scheduling engine and retained only the spatiotemporal consensus circuit breaker mechanism; control group B, which removed the spatiotemporal consensus circuit breaker mechanism and retained only the basic priority sorting mechanism; and the existing technology control group using the traditional first-in-first-out strategy. The system recorded the entire link data in real time from request entry, logical processing, queue reconstruction to final completion. To intuitively demonstrate the system's internal data processing logic and state transitions, a typical data segment of the experimental group at the simulation time T+30 minutes, i.e., the sudden peak moment, was selected, as shown in Table 1 below. This table follows the temporal logic of data processing and shows the process of the original input data being processed by the intermediate core algorithm and transformed into a deterministic scheduling output.

[0034] Table 1: Example Table of Data in Core Algorithm Processing Procedure

[0035]

[0036] As shown in Table 1, the user confidence level of request R-2024-X05 was 0.65 and accompanied by coordinate noise. However, since the spatiotemporal consensus count reached 5, which is greater than the threshold of 3, the system identified a common risk and triggered a circuit breaker, establishing it as an anchor point. The low-priority secondary request R-X09 was attracted after being weighted by the gravity coefficient because it was within the neighborhood of the anchor point, verifying the logical necessity of the algorithm under complex interference. To verify the gradient change law and synergistic effect of the technical effect, the key performance indicators of each group were statistically analyzed throughout the time period. The data showed that the average response time of high-priority events in the experimental group was 12.4 minutes, which was 74.3% shorter than the 48.2 minutes of the control group of the existing technology. Compared with the 25.6 minutes of the control group B, it showed the contribution of the spatiotemporal consensus circuit breaker mechanism to the response speed. In terms of physical execution efficiency, the number of tasks completed by the operation and maintenance personnel in the experimental group was 2.1 per hour, while that in the control group A was 1.4 per hour.

[0037] Validation data for the parameter boundaries show that when the spatial gravity coefficient When set to 0.1, the physical adsorption rate is only 5%, the system degenerates into approximately linear scheduling, and the total mileage does not decrease; when When set to 0.9, although the physical adsorption rate surges to 85%, the average waiting time for high-priority tasks increases by 40%, indicating a performance inflection point. Beyond the preferred range of 0.3 to 0.6, excessive clustering leads to an imbalance in logical priorities, impairing the system's emergency response capability, thus proving the scientific validity and necessity of parameter range selection.

[0038] Example 3: This example combines Figures 1 to 3 A description of a smart community property management system, such as... Figure 1 As shown, the process begins with the generation of a service request data packet containing structured tags, geographic grids, and timestamps. This data packet is input to the structured data access and mapping unit to convert event tags into basic scores, and is also transmitted to the dynamic confidence calculation unit to calculate confidence coefficients based on historical records. The two data streams converge to the spatiotemporal consensus circuit breaker verification unit for spatiotemporal density detection and logical circuit breaker determination. During this process, the topology shielding unit intercepts redundant requests to child node facilities when association shielding is triggered and feeds them back to the structured data access and mapping unit. The priority and anchor point status signals output after verification enter the space gravity field scheduling engine to perform anchor point locking and physical adsorption clustering. Finally, the task packet distribution interface generates atomically executed continuous task packets and sends them to the operation and maintenance terminal for execution.

[0039] like Figure 2As shown in the figure, the horizontal axis represents the time axis, the left vertical axis indicates the request inflow rate, and the right vertical axis indicates the circuit breaker delay time. The figure shows that when the request inflow rate reaches its peak at time T+15, the data using the fixed window circuit breaker delay spikes to nearly 25 seconds, while the data using the dynamic window circuit breaker delay remains at a lower level. The dynamic adjustment mechanism effectively suppresses processing lag in high-concurrency scenarios. Figure 3 As shown, at the underlying deployment architecture level of the system, requests initiated by the user end enter a stateless microservice group consisting of structured data access, dynamic confidence calculation, and spatiotemporal consensus circuit breaker verification for initial processing. The cleaned data stream is transmitted via message queue (MQ) to the spatial gravity field scheduling engine, which serves as the core of the stateful singleton, for centralized scheduling. This engine calls down to the in-memory database storing mapping tables and coefficients, the facility dependency topology library storing BIM and drawings, and the business database storing historical records. After generating atomic task packages, the data is distributed to the operation and maintenance terminal, thereby constructing a layered, decoupled distributed cloud server cluster architecture that supports high-concurrency data throughput.

[0040] Example 4: This example aims to study the core parameters in the space gravitational field scheduling engine. The calibration procedure and the logic for defining the range of spatial gravity are described in detail in an engineering manner. Regarding the initial state definition procedure for the preset physical neighborhood, this system divides the physical space into a three-dimensional grid coordinate system, in which... The axis corresponds to the horizontal geographic coordinates. The axis corresponds to the vertical floor height. During the initialization phase, the system needs to import the community's building vector map data and define two neighborhood determination criteria: For vertical pipeline facilities, the preset physical neighborhood is limited to those that are in the same vertical projection coordinate as the anchor point request. All Axial floor grid set; for horizontal public facilities, the preset physical neighborhood is limited to the straight-line distance from the geographic grid identifier requested by the anchor point as the center. For example, it can be set as a three-dimensional space within 100 meters.

[0041] Regarding the spatial gravitational coefficient The calibration and calculation procedure for this coefficient is not a fixed constant, but a dynamic function based on the movement cost of maintenance personnel. The calculation follows the following quantitative procedure: the system measures the average movement speed of maintenance personnel within the community. Such as 1.5 m / s and average vertical speed. For example, 0.5 m / s includes the time spent waiting for the elevator; when the system locks the anchor point request. And scan for secondary requests within the neighborhood. At that time, calculate and The three-dimensional Manhattan distance between them, i.e., the horizontal distance and vertical distance The system calculates from Move to Estimated time cost required Finally, according to the formula Calculate the gravitational coefficient, where, The reference gravitational gain constant is 0.4 (preferably 0.4 in this embodiment). The time cost sensitivity coefficient is used (preferably 0.02 in this embodiment); this formula ensures that the secondary request with the closer the physical distance and the lower the movement cost receives the greater the gravitational gain. In actual operation, if the calculated secondary request... Clustering priority Greater than the highest original priority of non-anchor tasks in the queue ,Right now This triggers physical adsorption, which will insert after.

[0042] Example 5: This example aims to provide a transparent explanation of the core algorithm model, data privacy protection mechanism, and adaptive adjustment logic of key parameters in the disclosed smart community property management system. Addressing the potential black box of the algorithmic path in the associated event label determination logic of the spatiotemporal consensus circuit breaker verification unit, this example discloses specific knowledge graph construction and reasoning procedures. The system has a pre-built community event ontology knowledge base based on the OWL standard, which defines core facility classes such as water supply, power supply, elevators, and access control, as well as their subclasses, and fault attributes such as rupture, interruption, and anomaly. The knowledge base clarifies the causal relationship weights between events through a semantic network. For example, the correlation weight between a burst water supply riser and low water pressure in lower-floor residents is set to 0.9; when performing spatiotemporal consensus verification, the system does not simply perform label text matching, but performs graph-based semantic distance calculation: input the event label of the current request Event labels that already exist within the time window Calculate the shortest path length between the two in the ontology graph. And generate a correlation score based on this. ,when Only when the semantic association threshold is greater than the preset threshold, such as 0.6, are the two requests considered to have associated event labels and included in the consensus count. This ensures that the system can identify implicit associations such as water pipe leaks and ceiling seepage that are different in appearance but have the same root cause, thus avoiding the uncertainty of fuzzy matching.

[0043] Regarding the confidence coefficient in the dynamic confidence calculation unit This embodiment discloses a nonlinear decay update mechanism, including a specific mathematical model and parameter calibration basis, aiming to solve the asymmetric problem of rapid decline and slow rise in credit evaluation. Let the user's current confidence level be... When a confirmed false repair request is received, the system performs an exponentially decaying update: ,in, This represents the user's cumulative number of false repair requests within the current calendar year. To penalize the sensitivity coefficient, The setting is not based on empirical values, but rather on statistical analysis of historical malicious attack data. It aims to ensure that after a user submits three consecutive false reports, the confidence level should drop below the system's minimum threshold of 0.5. This is achieved by solving the equation... Calibration When a genuine repair request is received, a linear recovery update is executed: ,in The single-step gain step size is set to 0.05. This asymmetric update logic mathematically guarantees the system's high sensitivity to malicious behavior and fault tolerance to normal errors. Addressing the black box of facility dependency topology library construction and dynamic maintenance in the dependent topology shielding unit, this embodiment supplements specific structure-function mapping procedures. During the initialization phase, the system imports BIM data or CAD pipe network drawings from the community and automatically parses the physical connection relationships between facilities using graph theory algorithms, generating a directed acyclic graph (DAG). Each node in the graph represents a facility entity, and edges represent the direction of fluid or current transmission. For example, a water supply network is parsed as: municipal interface. Main water pump Building riser Unit branch pipe When the water meter is installed in a household, the system relies on a topology shielding unit to monitor the state changes of the parent node. Once the parent node, such as the building riser, triggers a circuit breaker, the system traverses all downstream child nodes of that node in the DAG and loads the IDs of these child nodes into a Bloom filter. For subsequent new requests, the system queries whether the facility ID involved in the request matches the Bloom filter, thus enabling the meter to be used for water metering. The interception and judgment are completed within a time complexity, thereby greatly reducing computational latency under high concurrency while ensuring interception accuracy. Finally, regarding the engineering implementation of the user privacy protection mechanism, this embodiment discloses a data anonymization and computation procedure based on differential privacy. When calculating the spatiotemporal heat or fault distribution of a user group, the system does not directly use the user's precise geographic coordinates. Instead, add Laplace noise: ,in For privacy budget parameters, the system uses noisy coordinates. Statistical aggregation of grid density is performed. Since the spatiotemporal consensus circuit breaker mechanism is based on grid rather than single-point coordinates, moderate noise interference will not affect the determination of macro-fault areas, but mathematically it is guaranteed that the exact location of a specific user cannot be deduced from the aggregated data.

[0044] Example 6: This example illustrates the offline calibration and data filling procedures that a smart community property management system must perform before formal deployment, as well as the pre-deployment calibration process. For the basic parameters upon which the spatiotemporal consensus circuit breaker verification unit and the dynamic confidence calculation unit rely, the system executes a standardized offline calibration procedure. During the system initialization phase, historical operation and maintenance log data from the target community for three consecutive months is collected as a training set. Using this dataset, a machine learning algorithm is used to train the system on a preset consensus threshold to determine the optimal integer solution that best distinguishes between regular fluctuations and sudden events. Simultaneously, based on the statistical distribution of historical cancellation and repair records, the maximum likelihood estimation method is used to adjust the penalty sensitivity coefficient in the confidence calculation model. and linear recovery step size Fitting is performed to ensure that the credit scoring system accurately reflects the behavioral characteristics of users in the community. For facilities that depend on the topology library, the approved building BIM model or CAD drawings are imported, and an initial directed acyclic graph is automatically generated through topology analysis algorithms. The connection logic of key nodes is then manually reviewed to ensure that physical dependencies are accurately mapped.

[0045] To address the adaptability issue of the space gravitational field scheduling engine in real physical environments, the system implemented a pre-deployment calibration procedure. Before system launch, maintenance personnel conducted at least three days of on-site path testing. During the testing, maintenance personnel carried terminals equipped with positioning modules and traversed all major passages and floors within the community at different time periods, i.e., peak and off-peak hours. The system recorded and analyzed the movement trajectory data, calculated the average travel time matrix between each area, and adjusted the baseline gravitational gain constant in the space gravitational coefficient calculation formula. With time cost sensitivity coefficient Fine-tuning is performed to eliminate discrepancies between the theoretical model and the actual physical environment, such as elevator waiting time and access control efficiency. In addition, the granularity of the geographic grid is calibrated to ensure that the number of residents and facility density in each grid are balanced, avoiding distortion of consensus judgments due to grids that are too large or too small. Through the strict implementation of the above procedures, it is ensured that the system has parameter configurations and logical benchmarks that are highly compatible with the specific community environment from the initial stage of operation.

[0046] Example 7: This example addresses the engineering implementation and parameter tuning of the dynamic time window adjustment logic in the spatiotemporal consensus circuit breaker verification unit under non-steady-state traffic conditions. It constructs an adaptive control procedure based on traffic gradient awareness to solve the lag problem of fixed windows in sudden peak scenarios. The system deploys a sliding time window counter at the bottom-level data flow inlet and sets the sampling period. The inflow rate of service requests within the current geographic grid is calculated in real time every 30 seconds. Furthermore, the first derivative of this rate with respect to time, i.e., the flow gradient, is calculated. This is used to quantify the acceleration characteristics of the arrival request, when the calculated flow gradient is obtained. Exceeding the system's preset burst gradient threshold At that time, the system determines that it is currently in a high-voltage sudden state and immediately starts the window compression algorithm, using the formula For the preset time window length Nonlinear reduction is performed, where, As the baseline window length, To address the attenuation index, which is determined through regression analysis of historical response delay data for sudden events, a value typically between 0.5 and 0.8 is used to ensure a positive correlation between the rate of contraction of the time window and the intensity of the event, thereby quickly identifying high-density spatiotemporal correlations before data backlog. To prevent time window jitter caused by instantaneous fluctuations in traffic, the system uses a traffic gradient... Falling back below the preset steady-state gradient threshold When the window is not immediately reset, the system enters a hysteresis recovery cycle. During this cycle, the system uses a linear stepping method, and each complete reference time window without fuse triggering is completed. Increase the current time window length by a fixed recovery step. until restored to .

[0047] Example 8: The facility dependency topology library uses a directed acyclic graph data structure to digitally map community infrastructure. During the initialization phase, the structured data access unit parses the building information model to export an IFC format file, extracts the unique identifiers and connection relationships of entity objects, and constructs an adjacency matrix with the main water supply pipe and main cable as root nodes and the inlet branch pipes and terminal switches as leaf nodes. The adjacency matrix is ​​stored in the graph database, and the matrix element values ​​represent the physical dependency strength between nodes. When executing the dependency topology shielding logic, a breadth-first search algorithm is directly applied to the adjacency matrix, traversing and locking the set of affected child nodes within a millisecond-level time window, establishing the boundary of the association shielding mode. The spatiotemporal consensus circuit breaker verification unit presets a consensus threshold. The quantitative calibration is performed based on the benchmark test procedure using the statistical characteristics of the Poisson distribution. During offline calibration, historical request data from the past 12 months of fault-free periods in the target grid area are retrieved, and the expected service request arrival rate within a unit time window is calculated. According to the Poisson distribution probability formula Calculate the number of events when the cumulative probability reaches 99.9%. ,Will Set as This calibration procedure ensures that the logic circuit breaker is triggered when the request density deviates from the normal background noise distribution or when a low-probability event with a probability of less than 0.1% occurs, thus eliminating the interference of normal fluctuations on priority.

[0048] 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.

[0049] 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 smart community property management system, characterized in that, The system comprises: a structured data access and mapping unit, configured to receive a service request data packet comprising a structured event label, a geographical grid identifier and a request initiation time, and convert the structured event label into a basic score according to a preset event emergency degree mapping table; a dynamic confidence calculation unit, configured to call historical operation and maintenance interaction records of a request initiation user, and calculate a confidence coefficient for representing data source reliability according to an effective repair rate and a malicious order cancellation rate in the records; a time-space consensus fuse check unit, configured to perform data cleaning logic, and trigger a logic fuse state when detecting that the number of independent requests with an associated event label in a same geographical grid identifier region and located in a preset time window before a current request initiation time exceeds a preset consensus threshold value, ignore the confidence coefficient in the logic fuse state, and set the priority of all service requests involving the associated event label to the highest level; a spatial attractive field scheduling engine, configured to perform priority reconstruction logic based on physical space constraints when the logic fuse state is not triggered; The spatial gravity field scheduling engine calculates the product of the base score and the confidence coefficient to generate an original priority, and locks the service request with the highest original priority value in the pending work order queue as an anchor request; the spatial gravity field scheduling engine traverses the pending work order queue, identifies secondary requests whose geographical grid identifiers are located within the preset physical neighborhood of the anchor request, and uses the preset spatial gravity coefficient the original priority of the secondary request perform a weighted correction operation to generate a cluster priority : wherein, is the corrected cluster priority, the spatial gravity field scheduling engine performs comparison logic, when the cluster priority value of the secondary request is higher than the highest original priority in the pending work order queue except for the anchor request, performs a physical adsorption operation, extracts the secondary request from the original sorting position and binds it after the anchor request, and generates an atomized continuous execution task package; a task package distribution interface, configured to send an atomized continuous execution task package to an operation and maintenance terminal; and the system further comprises a dependency topology shielding unit, which is further configured with a reverse causal inference logic; when the system receives independent service requests for multiple different child node facilities under a same parent node facility within a preset inference period, and the number ratio of the independent service requests covers a preset percentage of the total number of child node facilities under the parent node facility, the reverse causal inference logic forcibly generates a virtual root source work order for the parent node facility, and sets the priority of the virtual root source work order to be higher than that of the service requests of the child node facilities; when the spatial attractive field scheduling engine performs a physical adsorption operation, if there are multiple secondary requests that simultaneously meet the condition that the cluster priority is higher than the highest original priority of non-anchor point requests, the spatial attractive field scheduling engine performs secondary sorting according to the three-dimensional Manhattan distance between the secondary requests and the anchor point requests, and preferentially adsorbs the secondary request with the smallest three-dimensional Manhattan distance. 2.The smart community property management system of claim 1, wherein, The dependency topology shielding unit is preset with a facility dependency topology library defining the parent-child cascading relationship between community infrastructure nodes; The dependency topology shielding unit is configured to activate an associated shielding mode for the parent node facility in response to triggering of the logic fuse state of the time-space consensus fuse check unit by a service request for the parent node facility; in the associated shielding mode, the dependency topology shielding unit intercepts new service requests for child node facilities of the parent node facility, prohibits generation of independent work orders for the new service requests, and merges the identification information of the new service requests into the service request of the parent node facility as an impact range count index. 3.The smart community property management system of claim 1, wherein, The time-space consensus fuse check unit is configured with dynamic time window adjustment logic, which is used to monitor the service request inflow rate in the current geographical grid identifier region; when the inflow rate exceeds a preset burst gradient threshold value, the time-space consensus fuse check unit automatically shortens the length of the preset time window and reduces the preset consensus threshold value; when the inflow rate is lower than a preset steady state gradient threshold value, the time-space consensus fuse check unit prolongs the preset time window and increases the consensus threshold value.

4. The intelligent community property management system according to claim 1, characterized in that, The preset physical neighborhood defined by the spatial gravity field scheduling engine adopts a dynamic hierarchical definition; when the structured event label corresponding to the anchor point request corresponds to a vertical pipe network type facility, the preset physical neighborhood is defined as all floor grids in the same vertical projection coordinate as the anchor point request; when the structured event label corresponding to the anchor point request corresponds to a horizontal public facility, the preset physical neighborhood is defined as a horizontal grid set with the geographical grid identifier of the anchor point request as the center and a straight-line distance within a preset radius.

5. The intelligent community property management system according to claim 1, characterized in that, The dynamic confidence calculation unit is configured with a nonlinear decay update mechanism; the nonlinear decay update mechanism is used to update the historical effective repair rate of the user after each work order is completed and the on-site verification result feedback by the operation and maintenance terminal; for the feedback that the verification result is a false repair, the dynamic confidence calculation unit performs exponential penalty decay on the confidence coefficient; for the feedback that the verification result is a true repair, the dynamic confidence calculation unit performs linear gain recovery on the confidence coefficient; the base of the exponential penalty decay is positively correlated with the cumulative number of false repairs of the user in the current natural year.

6. The intelligent community property management system according to claim 1, characterized in that, The structured data access and mapping unit is configured with an anti-ambiguity secondary index table; The anti-ambiguity secondary index table includes a primary category and a secondary item, and the structured event label is generated by the user through the cascading menu of the client; the system prohibits receiving unstructured natural language text description as input basis for priority calculation, and only stores the natural language text description as additional note information; the mapping process of the basic score is only based on the unique coding key value of the structured event label for indexing.

7. The intelligent community property management system according to claim 1, wherein, The task package distribution interface is configured with a state locking protocol; after the atomized continuous execution task package is sent to the operation and maintenance terminal, the system locks the scheduling state of all service requests in the package, prohibits subsequent new high-priority requests entering the system from interrupting or inserting the distributed atomized continuous execution task package, and returns the on-site confirmation signal of the first task in the atomized continuous execution task package to the operation and maintenance terminal. 8.The intelligent community property management system of claim 1, wherein, The system runs on a distributed cloud server cluster, the structured data access and mapping unit, the dynamic confidence calculation unit and the space-time consensus fuse checking unit are deployed as stateless micro-service instances, the spatial gravity field scheduling engine is deployed as a stateful singleton service to maintain the atomicity of the global pending work order queue, and the preset event urgency mapping table and the spatial gravity coefficient are stored in the in-memory database.

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