Intelligent management system for apartments

Abstract

The invention provides an intelligent apartment management system. The intelligent apartment management system comprises an edge intelligent layer, an intelligent central layer, a block chain evidence storage layer and a distributed cloud. The edge intelligent layer collects equipment and tenant data, and fault early warning and behavior analysis are output through federal learning preprocessing; and the intelligent central layer constructs a knowledge graph event bus, semantically associates and triggers the self-optimization service chain control equipment, transmits key data to the block chain evidence storage layer for encryption and storage, and transmits summarized data to a distributed cloud. And performing distributed cloud cross-regional collaborative analysis, performing virtual simulation to generate an optimization strategy, dynamically scheduling edge computing resources and updating model parameters. According to the method, the efficiency is improved through four-layer cooperation, and hidden fault pre-judgment and behavior recognition are realized; the knowledge graph event bus enhances the service flexibility; the block chain mixed chain architecture is combined with zero-knowledge proof to ensure that data cannot be tampered and privacy is safe, federated learning improves cross-regional collaboration, and virtual simulation supports dynamic decision making; and a dynamic pricing mechanism optimizes rent and cost management.

Description

Technical Field

[0001] This invention relates to the field of smart building and Internet of Things (IoT) application technology, specifically an apartment smart management system. Background Technology

[0002] Traditional apartment management relies heavily on manual operation and independent management systems. In equipment management, manual inspections are essential for troubleshooting, resulting in untimely data collection, delayed fault warnings, and data from different devices operating independently, hindering collaborative analysis. For tenant management, check-in and check-out procedures must be completed offline, while rent payments and fee inquiries depend on manual records or simple online systems, leading to ineffective utilization of tenant behavior data. Regarding data storage and sharing, lease and maintenance data are often centrally stored, making them susceptible to tampering and offering insufficient privacy protection, while cross-departmental data sharing is difficult. In decision support, rent pricing and resource allocation rely on experience-based judgment, lacking data-driven dynamic optimization mechanisms and struggling to cope with complex operational scenarios.

[0003] In summary, the existing apartment management has the following problems:

[0004] 1. Severe data silos: Equipment data, tenant data, financial data, etc. are scattered in different systems, making it impossible to achieve linked analysis, resulting in low management efficiency;

[0005] Second, slow response speed: Equipment fault detection and safety incident handling rely on manual intervention and lack a real-time response mechanism, which can easily lead to increased losses;

[0006] Third, low decision-making intelligence: operational strategies lag behind actual needs, resource allocation is unreasonable, and it is difficult to maximize profits and minimize costs. Summary of the Invention

[0007] The purpose of this invention is to provide an apartment intelligent management system to solve the above problems. It improves efficiency through the collaborative operation of four layers: edge intelligence layer, intelligent central layer, blockchain evidence storage layer and distributed cloud, and realizes the prediction of hidden faults and behavior recognition.

[0008] An apartment intelligent management system includes an edge intelligence layer, an intelligent central layer, a blockchain evidence storage layer, and a distributed cloud;

[0009] The edge intelligence layer is used to collect apartment equipment operation data and tenant behavior data. After preprocessing by the federated learning edge model, it outputs equipment fault warnings and tenant behavior analysis results to the intelligent central layer.

[0010] The intelligent central layer is used to construct an event bus of knowledge graph based on the equipment fault warning and tenant behavior analysis results, trigger self-optimizing business chain through semantic association, and feed back the business chain optimization instructions to the edge intelligent layer to control the operation of apartment equipment;

[0011] The intelligent central layer is used to transmit key data generated during the business chain processing to the blockchain evidence storage layer for encrypted storage. The blockchain evidence storage layer returns the evidence storage result to the intelligent central layer for the intelligent central layer to call in the business chain verification process. The intelligent central layer also transmits the aggregated data and business chain operation status to the distributed cloud.

[0012] The distributed cloud is used to perform cross-regional collaborative analysis and virtual simulation based on the aggregated data and business chain operation status transmitted by the intelligent central layer, and to generate optimization strategies to be fed back to the intelligent central layer to adjust the business chain.

[0013] The distributed cloud is also used to dynamically schedule the computing resources of the edge intelligence layer according to the apartment density, and update the trained model parameters to the edge intelligence layer to update the federated learning edge model of the edge intelligence layer, thereby improving the data analysis and processing capabilities of the edge intelligence layer.

[0014] Furthermore, the edge intelligence layer includes an AIoT fusion terminal, which integrates an infrared thermal imaging sensor, an acoustic sensing module, a vibration sensor, and an adaptive decision chip, and accesses heterogeneous devices in the apartment through a quantum encryption protocol;

[0015] The infrared thermal imaging sensor detects temperature anomalies and room temperature distribution, the acoustic sensing module collects device acoustic signatures and door sounds, and the vibration sensor collects device vibration data.

[0016] Based on the aforementioned sensor data, the adaptive decision chip calculates the probability of device failure and identifies tenant behavior patterns through a federated learning edge model, and outputs the calculation results as preprocessed data to the intelligent central layer.

[0017] Furthermore, the preprocessing process of the federated learning edge model includes:

[0018] The formula for calculating the equipment failure probability based on the acoustic signature anomaly coefficient A and the vibration anomaly coefficient B is as follows:

[0019] ;

[0020] In the formula, The probability of equipment failure. The voiceprint anomaly coefficient is obtained by subtracting 1 from the ratio of the actual voiceprint frequency to the normal voiceprint frequency. The weight of the voiceprint anomaly coefficient, The vibration anomaly coefficient is obtained by subtracting 1 from the ratio of the actual vibration amplitude to the normal vibration amplitude. The weight of the vibration anomaly coefficient;

[0021] Based on tenant entry and exit records and electricity consumption data, behavioral patterns are identified. When there are no entries or exits for 7 consecutive days and the electricity consumption is below the threshold, a "long-term vacancy" behavior analysis result is generated, triggering the edge intelligent layer to shut down unnecessary appliances.

[0022] Furthermore, the intelligent central layer and the edge intelligent layer adopt an adaptive transmission protocol: high-frequency small data such as device fault warnings are transmitted via LoRaWAN, while low-frequency big data such as tenant monthly electricity consumption records are transmitted via 5G slicing.

[0023] The process of constructing and applying the event bus of the knowledge graph is as follows:

[0024] Based on the preprocessed data transmitted from the edge intelligence layer, a triple association of "device ID-tenant identity-environmental parameters-event" is established through a knowledge graph engine (e.g., "air conditioner KFR-35GW-tenant A-temperature anomaly-fault warning").

[0025] Natural language processing technology is used to semantically transform the raw event data (e.g., interpreting "door lock vibrates at 80Hz for 10 seconds" as "suspicious unlocking attempt") and associate it with the "security risk" category of the knowledge graph;

[0026] The system automatically matches and processes nodes based on the associated results to form an initial business chain, and dynamically adjusts the node order according to node execution efficiency data (such as work order response time). For example, when the efficiency of "manual review" is lower than the threshold for three consecutive times, the "AI pre-review" node is moved forward.

[0027] Furthermore, the blockchain evidence storage layer adopts a hybrid chain architecture: the consortium chain stores the core lease data and connects to the government leasing filing system, while the private chain stores the micro data of equipment maintenance.

[0028] When signing a lease, the intelligent central layer uploads rental data generated based on the dynamic pricing results of the distributed cloud to the consortium blockchain; after the equipment is repaired, it uploads an electronic certificate containing the fingerprint and timestamp of the repair personnel to the private blockchain; when it is necessary to verify the authenticity of the lease or the repair record, the intelligent central layer retrieves the encrypted evidence storage results from the blockchain evidence storage layer for decryption and verification.

[0029] Furthermore, the specific collaborative logic of the distributed cloud-integrated federated learning framework and digital twin engine is as follows:

[0030] The federated learning framework trains a cross-regional collaborative model based on the aggregated data transmitted from the intelligent central layer without sharing the original data, and transmits the model parameters to the edge intelligent layer to update the federated learning edge model.

[0031] The digital twin engine uses energy consumption formulas to perform virtual simulations based on equipment operation data and tenant behavior analysis results.

[0032] ;

[0033] In the formula, Total energy consumption of the apartment For the total number of devices, For the first The rated power of each device For the first The actual running time of each device For the first Energy efficiency coefficient of each device For the first The usage scenario coefficient of each device;

[0034] The simulation results are fed back to the intelligent central layer to optimize the equipment control strategy.

[0035] Furthermore, the AIoT fusion terminal of the edge intelligence layer also integrates a metaverse data interface. This interface, based on real-time data from an infrared thermal imaging sensor and an acoustic sensing module, drives the metaverse display module to achieve the following:

[0036] Immersive property viewing experience, with virtual water and electricity meter readings differing from physical meter readings by ≤0.1%;

[0037] The virtual renovation preview calculates the space utilization rate based on the tenant's customized furniture layout and transmits the utilization rate results to the intelligent central layer for the dynamic pricing module to calculate additional rent.

[0038] Furthermore, the intelligent central layer also includes a dynamic pricing module, which calculates the rent based on the following logic:

[0039] Obtain the competitor price coefficient C and vacancy rate coefficient V output from the distributed cloud;

[0040] Calculate the final rent using the formula:

[0041] ;

[0042] In the formula, For the final rent, Basic rent, This is the vacancy rate coefficient, with values ​​ranging from [0, 1]. The higher the vacancy rate, the higher the vacancy rate. The larger, This is the weighting factor for the vacancy rate coefficient, with values ​​ranging from -0.2 to 0.1. When the vacancy rate is high, a negative value is used to reduce rent. This is the competitor price coefficient, with values ​​ranging from -0.3 to 0.3. It is obtained by subtracting 1 from the ratio of the average rent in the surrounding area to the base rent. The competitor's price coefficient is used as a weight, with values ​​ranging from [0.3, 0.5]. When the competitor's price is high, the rent is increased. This is a macroeconomic index, representing the year-on-year increase in CPI, with values ​​ranging from -0.1 to 0.2. This represents the weight of the macroeconomic index, with values ​​ranging from [0.1, 0.2].

[0043] The rental data will then be transmitted to the blockchain storage layer for evidence preservation.

[0044] Furthermore, when the edge intelligence layer outputs a device fault warning ( When the percentage is ≥50%, the specific business chain triggered by the intelligent central layer includes:

[0045] Based on the device ID and fault type associated with the knowledge graph event bus, locate the faulty device.

[0046] Generate a repair work order and dispatch it to the nearest repair personnel;

[0047] After the repair is completed, the electronic repair voucher is retrieved from the blockchain storage layer, and the discounted repair cost is calculated using a formula based on the tenant's credit score G:

[0048]

[0049] In the formula, This is the repair cost after the discount. This is the benchmark price for repair services. This is the credit impact coefficient. The higher the tenant's credit score, the greater the discount.

[0050] Furthermore, the digital twin engine also performs financial flow simulation using equipment depreciation formulas:

[0051]

[0052] In the formula, For the equipment Annual depreciation Original value of the equipment. For equipment residual value, The equipment's service life. For the expected service life of the equipment, Failure frequency, which is the ratio of the annual number of failures to the industry average number of failures. This is the maintenance impact coefficient, with values ​​ranging from [0.05, 0.2]. The more maintenance is performed, the larger the value becomes.

[0053] The simulation results are transmitted to the intelligent central layer, which is used by the cross-dimensional cost association module to calculate the binding relationship between equipment maintenance costs and tenant credit scores.

[0054] This invention has the following features and effects:

[0055] 1. By working together with the edge intelligence layer, the intelligent hub layer, the blockchain evidence storage layer and the distributed cloud, the system efficiency is improved through adaptive transmission protocols and dynamic resource scheduling, enabling the prediction of hidden equipment faults and the identification of tenant behavior patterns, while protecting data privacy.

[0056] 2. Construct an event bus based on knowledge graphs to realize semantic data association and self-optimizing business chains, thereby improving the flexibility and adaptability of business processes.

[0057] 3. The blockchain evidence storage layer adopts a hybrid chain architecture, combining zero-knowledge proofs to achieve encrypted data storage and de-identified sharing, ensuring data immutability and privacy security. Through virtual simulation of apartment energy consumption and financial flow, it supports dynamic decision-making. At the same time, it enhances cross-regional data collaborative analysis capabilities through a federated learning framework, integrates a metaverse display module to achieve data synchronization between virtual and physical apartments, and provides immersive viewing and virtual decoration preview functions to enhance the tenant experience.

[0058] 4. Based on the dynamic pricing mechanism and cross-dimensional cost correlation calculation of reinforcement learning, optimize rental strategies and cost management to maximize revenue and control costs. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the structure of the apartment intelligent management system of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figure 1 This invention provides an apartment intelligent management system, comprising an edge intelligence layer, an intelligent central layer, a blockchain evidence storage layer, and a distributed cloud, wherein the data transmission and processing methods are as follows:

[0062] The edge intelligence layer serves as the system's data acquisition and preliminary processing center. It is responsible for collecting data on the operation of apartment equipment and tenant behavior, and then transmitting this data to the intelligent central layer after preprocessing it through a federated learning edge model.

[0063] The intelligent central layer constructs an event bus of knowledge graph based on preprocessed data, performs semantic association and analysis on the data, triggers self-optimizing business chains, and feeds back business chain optimization instructions to the edge intelligent layer to guide the edge intelligent layer's device control and data processing.

[0064] Key data generated during business processing by the intelligent central layer, such as lease information and equipment maintenance records, is transmitted to the blockchain evidence storage layer for encrypted storage. After the blockchain evidence storage layer verifies the key data, it feeds back the verification results to the intelligent central layer, ensuring the immutability and traceability of the data. When the intelligent central layer needs to verify the authenticity of data, it can retrieve relevant data from the blockchain evidence storage layer.

[0065] Meanwhile, the intelligent central layer transmits the processed aggregated data and business chain operation status to the distributed cloud. The distributed cloud uses this data to perform cross-apartment area collaborative analysis, virtual simulation and other operations, and feeds back the analysis results and optimization strategies to the intelligent central layer to help it optimize the business chain and management strategies.

[0066] The distributed cloud interacts with the edge intelligence layer through dynamic resource scheduling commands, allocating computing resources to the edge intelligence layer based on factors such as apartment density. Simultaneously, the distributed cloud transmits trained model parameters to the edge intelligence layer, updating its federated learning edge model and enhancing its data analysis and processing capabilities.

[0067] For data transmission and processing within the edge intelligence layer, the AIoT fusion terminal in the edge intelligence layer transmits the collected sensor data through the internal bus. The federated learning edge model on the AIoT fusion terminal performs local processing on the data, such as predicting hidden device faults and recognizing tenant behavior patterns. For model parameters that need to be shared, they are transmitted between terminals through federated learning, without sharing the original data, thus protecting data privacy.

[0068] The edge intelligence layer is located near the apartment equipment and deploys AIoT converged terminals with self-learning capabilities. These AIoT converged terminals can autonomously learn the patterns of equipment and tenant behavior. By integrating infrared thermal imaging, acoustic sensing modules, and vibration sensors, these terminals can perceive the environment and equipment status. They also integrate adaptive decision chips and access heterogeneous devices in the apartment (such as smart door locks, water and electricity meters, air conditioners, etc.) through quantum encryption protocols. They are equipped with federated learning edge models to predict hidden faults in apartment equipment and identify tenant behavior patterns.

[0069] Among them, the infrared thermal imaging sensor can be installed on the surface of apartment equipment (such as air conditioners, water heaters, and refrigerators) and the ceiling of the room to detect abnormal operating temperatures of the equipment (such as overheating of the outdoor unit of the air conditioner and abnormal temperature of the water heater pipes) and the overall temperature distribution of the room. It can not only meet the needs of predicting hidden equipment faults, but also help to adjust the room temperature to achieve energy saving, which is suitable for the scenario of dense equipment in apartments and the need for precise temperature control.

[0070] Acoustic sensing module: Deployed near apartment equipment (such as washing machine and air conditioner compressor) and around the door, it can predict faults by collecting the sound patterns of the equipment (such as abnormal noises from washing machines and abnormal noises from air conditioner fans). At the same time, it can capture abnormal sounds from the door (such as the sound of forcefully prying the lock). Combined with smart door lock data, it enhances security monitoring capabilities and is highly compatible with the needs of apartment equipment operation and maintenance and security management.

[0071] The adaptive decision chip is used to make decisions autonomously based on data collected by infrared thermal imaging sensors and acoustic sensing modules, without relying on real-time instructions from the cloud.

[0072] Federated learning edge models enable multiple devices to collaboratively train models without sharing raw data, thus protecting data privacy.

[0073] For predicting hidden faults in apartment equipment, combining sound pattern and vibration frequency data can improve the accuracy of fault prediction. The calculation formula is as follows:

[0074]

[0075] In the formula, The probability of equipment failure. The voiceprint anomaly coefficient is obtained by subtracting 1 from the ratio of the actual voiceprint frequency to the normal voiceprint frequency. The weight of the voiceprint anomaly coefficient, The vibration anomaly coefficient is obtained by subtracting 1 from the ratio of the actual vibration amplitude to the normal vibration amplitude. The weight of the vibration anomaly coefficient;

[0076] Regarding the identification of tenant behavior patterns, such as automatically triggering the room's energy-saving mode when the room is vacant for a long time and turning off unnecessary appliances, the analysis of tenants' entry and exit data, electricity consumption, and other data can identify behavior patterns and reduce operating costs by automatically saving energy when the room is vacant for a long time.

[0077] For data transmission and processing between the edge intelligence layer and the intelligent central layer, the two layers adopt adaptive transmission protocols for data transmission. For example, high-frequency small data such as door opening and closing status is transmitted via LoRaWAN, while low-frequency big data such as monthly water and electricity usage records is transmitted via 5G slicing. The edge intelligence layer transmits pre-processed data, such as equipment fault warnings and tenant behavior analysis results, to the intelligent central layer. The intelligent central layer uses a knowledge graph-based event bus to semantically associate the data, build a dynamic business knowledge network, and trigger a self-optimizing business chain.

[0078] The event bus of a knowledge graph can semantically associate various types of information to form a structured knowledge network. It can semantically associate apartment equipment events, environmental data, and tenant behavior (e.g., associating "tenant leaving" with "air conditioner turned off") to form a dynamic business knowledge network. The construction methods of the event bus of a knowledge graph include:

[0079] By constructing and dynamically updating a semantic network in the field of asset operation through a knowledge graph engine, entities such as equipment ID, tenant identity, and environmental parameters are associated with events such as "door lock opening and closing" and "water and electricity fluctuations". For example, triple relationships such as "tenant A - resides in - room 101 - equipped with - air conditioner KFR-35GW" and "air conditioner malfunction - associated with - repair work order type - corresponding - cost standard" are established.

[0080] Natural Language Processing (NLP) and pattern recognition technologies are used to semantically transform the raw event data uploaded by the edge intelligence layer. For example, "door lock vibration frequency 80Hz + lasting 10 seconds" is parsed as "suspicious unlocking attempt" event and associated with the "security risk" category in the knowledge graph.

[0081] Based on the association rules of the knowledge graph, the processing nodes corresponding to the event are automatically matched to form the initial business process. For example, the "equipment failure" event will trigger the basic chain of "fault location → work order generation → repair dispatch → cost calculation".

[0082] For self-optimizing business chains: By collecting execution efficiency data of each node in real time, such as work order response time and cost calculation accuracy, the order of nodes in the business chain can be dynamically adjusted or nodes can be added or removed. For example, when the efficiency of the "manual review" node is below the threshold for three consecutive times, the "AI pre-review" node is automatically inserted for pre-filtering.

[0083] For data transmission and processing between the intelligent central layer and the blockchain evidence storage layer, the intelligent central layer transmits data that needs to be stored, such as core lease data and electronic equipment maintenance certificates, to the blockchain evidence storage layer through an encrypted channel. The blockchain evidence storage layer adopts a hybrid chain architecture, with the consortium chain storing the core lease data and connecting to the government leasing filing system, and the private chain storing the micro-data of equipment operation and maintenance. Data desensitization and sharing are achieved through zero-knowledge proofs. When the intelligent central layer needs to verify data, it sends a query request to the blockchain evidence storage layer. The blockchain evidence storage layer returns the encrypted evidence storage result, which the intelligent central layer then decrypts and verifies.

[0084] Micro-level data on equipment operation and maintenance, such as repair details and parts replacement information, are stored in a private blockchain to protect internal enterprise information. The consortium blockchain is jointly maintained by the apartment management and government departments, while the private blockchain is controlled only by the apartment management, thus balancing data sharing and privacy protection.

[0085] Taking equipment failure repair as an example, electronic certificates for apartment equipment repair, which include timestamps of the repair personnel's biometrics, will be automatically uploaded to the blockchain.

[0086] For data transmission and processing between the intelligent central layer and the distributed cloud, the intelligent central layer transmits the aggregated business data and business chain operation data to the distributed cloud. The distributed cloud uses a federated learning framework to conduct collaborative analysis of data across apartment areas. Its integrated digital twin engine performs virtual simulations of apartment energy consumption, financial flow, etc. The analysis results and simulation conclusions are fed back to the intelligent central layer through a secure channel. Based on this information, the intelligent central layer optimizes the node sequence and management strategy of the business chain.

[0087] The distributed cloud consists of multiple regional nodes. The federated learning framework enables different apartment areas to share analysis results while protecting data privacy, thereby improving overall analysis capabilities. It automatically allocates computing resources based on apartment density to achieve dynamic computing resource scheduling. The virtual image built by the digital twin engine is synchronized with the physical apartment in real time to simulate and optimize operational strategies.

[0088] The distributed cloud-based digital twin engine predicts energy consumption under different operating modes through virtual simulation of apartment energy consumption, such as the impact of adjusting air conditioning temperature on energy consumption, which facilitates the development of energy-saving strategies. The energy consumption calculation formula is as follows:

[0089]

[0090] In the formula, Total energy consumption of the apartment For the total number of devices, For the first The rated power of each device For the first The actual running time of each device For the first Energy efficiency coefficient of each device For the first The usage scenario coefficient for each device is calculated as follows: for example, the usage scenario coefficient decreases by 0.05 for every 1°C increase in air conditioning temperature.

[0091] The distributed cloud-based digital twin engine also simulates the impact of rent adjustments on apartment cash flow in real time through virtual mapping of apartment financial flows. By virtually mapping cash flow changes, it allows for quick viewing of short-term and long-term cash flow conditions after rent adjustments. By simulating various operational scenarios, such as different vacancy rates and rent levels, it provides comprehensive reference for decision-making.

[0092] For data transmission and processing between the distributed cloud and the edge intelligent layer, the distributed cloud generates dynamic resource scheduling instructions based on factors such as apartment density, and transmits them to the edge intelligent layer through a control channel. The edge intelligent layer adjusts its own computing resource allocation according to the instructions. The distributed cloud packages the trained model parameters and transmits them to the edge intelligent layer. The edge intelligent layer updates its local federated learning edge model, improving its local data processing capabilities.

[0093] To further explain, this system also utilizes virtual reality technology through the Metaverse Display Module to provide tenants with an immersive apartment viewing experience. Tenants can enter the virtual space through VR devices to view apartment rooms. The virtual space data is synchronized with the status of the physical apartment rooms in real time. For example, the deviation between the virtual water and electricity meter readings and the physical meter readings is ≤0.1%. The data in the virtual space and the physical room are highly consistent, ensuring that the information that tenants understand is true and accurate.

[0094] The system allows tenants to customize their furniture layout through virtual room renovation previews. Based on the layout results, the system calculates space utilization and additional rent. Tenants can design furniture placement in the virtual space, and the system analyzes the space utilization and calculates the additional rent generated by the customized renovation, thus meeting the personalized needs of tenants.

[0095] To further explain, the intelligent central layer uses reinforcement learning for dynamic apartment pricing. Through reinforcement learning, the system continuously optimizes its pricing strategy, adjusting rents based on market supply and demand, competitor prices, and the economic environment to maximize revenue. This dynamic apartment pricing includes:

[0096]

[0097] In the formula, For the final rent, Basic rent, This is the vacancy rate coefficient, with values ​​ranging from [0, 1]. The higher the vacancy rate, the higher the vacancy rate. The larger, This is the weighting factor for the vacancy rate coefficient, with values ​​ranging from -0.2 to 0.1. When the vacancy rate is high, a negative value is used to reduce rent. This is the competitor price coefficient, with values ​​ranging from -0.3 to 0.3. It is obtained by subtracting 1 from the ratio of the average rent in the surrounding area to the base rent. The competitor's price coefficient is used as a weight, with values ​​ranging from [0.3, 0.5]. When the competitor's price is high, the rent is increased. This is a macroeconomic index, representing the year-on-year increase in CPI, with values ​​ranging from -0.1 to 0.2. This represents the weight of the macroeconomic index, with values ​​ranging from [0.1, 0.2].

[0098] By linking apartment room equipment failure rates to tenant credit scores through cross-dimensional cost correlation calculations, tenants with excellent credit scores enjoy discounts on repair costs. By linking data from different dimensions, this incentivizes tenants to take care of the equipment, reducing repair costs, and providing preferential treatment to tenants with good credit. The formula for calculating equipment depreciation in repair costs is as follows:

[0099]

[0100] In the formula, For the equipment Annual depreciation Original value of the equipment. For equipment residual value, The equipment's service life. For the expected service life of the equipment, Failure frequency, which is the ratio of the annual number of failures to the industry average number of failures. This is the maintenance impact coefficient, with values ​​ranging from [0.05, 0.2]. The more maintenance is performed, the larger the value becomes.

[0101] Repair fee discounts are calculated based on the tenant's credit score.

[0102]

[0103] In the formula, This is the repair cost after the discount. This is the benchmark price for repair services. This is the credit impact coefficient. The higher the tenant's credit score, the greater the discount.

[0104] Scene 1

[0105] The air conditioner in apartment 1003 on the 10th floor of a certain apartment building experienced a hidden malfunction, and the system automatically triggered an early warning and maintenance procedure:

[0106] S1, Edge Intelligent Layer Data Acquisition and Processing

[0107] The infrared thermal imaging sensor installed on the outdoor unit of the air conditioner detects the temperature in real time. It finds that the temperature exceeds the normal threshold (85℃) for 3 consecutive hours. At the same time, the acoustic sensing module collects the sound wave frequency of the compressor operation, which deviates from the normal range (normal frequency 45Hz, actual 58Hz). The federated learning edge model of the edge intelligence layer combines the sound wave anomaly coefficient of 0.289 and the vibration anomaly coefficient of 0.15. The calculation result is 52.2% by the fault probability formula, which determines the fault risk to be medium and generates a warning message.

[0108] S2, Intelligent Central Layer Business Chain Trigger

[0109] The warning information is transmitted to the intelligent central layer via LoRaWAN. The knowledge graph event bus associates "abnormal air conditioner temperature + abnormal soundprint" with the "potential compressor failure" tag, triggering the initial business chain: fault location → work order generation → repair dispatch → cost calculation.

[0110] The self-optimizing controller detects that the historical average response time of the "manual dispatch" node is 40 minutes, and automatically inserts the "AI pre-dispatch" node, prioritizing the matching of the nearest maintenance personnel (within 3 kilometers), thus reducing the response time to 15 minutes.

[0111] S3, Blockchain Evidence Storage and Distributed Cloud Collaboration

[0112] After the repair personnel complete the repair, they upload an electronic certificate containing a fingerprint and timestamp to the blockchain storage layer. The private chain records micro data such as "replacing the compressor capacitor", and the consortium chain updates the "repair completed" status simultaneously.

[0113] The distributed cloud-based digital twin engine updates the equipment health model based on maintenance records and simulates the energy consumption formula E. After maintenance, the daily energy consumption of the air conditioner decreased from 8.2 kWh to 5.7 kWh, with an energy saving rate of approximately 30.5%.

[0114] Scene 2

[0115] Tenant Wang completed his check-in application for room 1202 on the 12th floor through the system. The system automatically processed the pricing, contract signing, and equipment activation. The steps are as follows:

[0116] S1, Metaverse House Viewing and Virtual Decoration

[0117] Wang entered the Metaverse display module through VR device, viewed the virtual image of room 1202, customized the furniture layout, and after the system calculated the space utilization rate, it determined that the layout was reasonable and generated the result of "no additional rent".

[0118] S2, Dynamic Rent Calculation

[0119] With a base rent of 3500 yuan / month, an occupancy rate of 75%, a current vacancy rate of 0.25, a price coefficient of surrounding competitors C = (3600 / 3500) - 1 ≈ 0.0286, a macroeconomic index of 0.03, and a CPI increase of 3% year-on-year, the final rent is generated using the formula R = 3500 × (1 + (-0.1) × 0.25 + 0.4 × 0.0286 + 0.1 × 0.03) ≈ 3463 yuan / month.

[0120] S3, Lease Agreement Preservation and Equipment Activation

[0121] After Wang signed the lease online, the core data of the lease (lease term of 1 year, rent of 3463 yuan / month) was uploaded to the consortium blockchain and connected to the government rental registration system; the edge intelligence layer activated the smart door lock, water and electricity meters and other devices in the room through quantum encryption protocol, and simultaneously opened the "occupied" mode, and the energy consumption scenario coefficient was adjusted to 1.0;

[0122] S4, Cross-dimensional Cost Association

[0123] The system indicates that Wang's credit score is 9 out of 10. If equipment repairs are subsequently required, the repair costs will be calculated according to the formula. Calculate and enjoy a 72% discount.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent management system for apartments, characterized in that, It includes an edge intelligence layer, an intelligent central layer, a blockchain evidence storage layer, and a distributed cloud; The edge intelligence layer is used to collect apartment equipment operation data and tenant behavior data. After preprocessing by the federated learning edge model, it outputs equipment fault warnings and tenant behavior analysis results to the intelligent central layer. The intelligent central layer is used to construct an event bus of knowledge graph based on the equipment fault warning and tenant behavior analysis results, trigger self-optimizing business chain through semantic association, and feed back the business chain optimization instructions to the edge intelligent layer to control the operation of apartment equipment; The intelligent central layer is used to transmit key data generated during the business chain processing to the blockchain evidence storage layer for encrypted storage. The blockchain evidence storage layer returns the evidence storage result to the intelligent central layer for the intelligent central layer to call in the business chain verification process. The intelligent central layer also transmits the aggregated data and business chain operation status to the distributed cloud. The distributed cloud is used to perform cross-regional collaborative analysis and virtual simulation based on the aggregated data and business chain operation status transmitted by the intelligent central layer, and to generate optimization strategies to be fed back to the intelligent central layer to adjust the business chain. The distributed cloud is also used to dynamically schedule the computing resources of the edge intelligence layer according to the apartment density, and update the trained model parameters to the edge intelligence layer to update the federated learning edge model of the edge intelligence layer, thereby improving the data analysis and processing capabilities of the edge intelligence layer.

2. The system according to claim 1, characterized in that, The edge intelligence layer includes an AIoT fusion terminal, which integrates an infrared thermal imaging sensor, an acoustic sensing module, a vibration sensor, and an adaptive decision chip, and accesses heterogeneous devices in the apartment through a quantum encryption protocol. The infrared thermal imaging sensor detects temperature anomalies and room temperature distribution, the acoustic sensing module collects device acoustic signatures and door sounds, and the vibration sensor collects device vibration data. Based on the aforementioned sensor data, the adaptive decision chip calculates the probability of device failure and identifies tenant behavior patterns through a federated learning edge model, and outputs the calculation results as preprocessed data to the intelligent central layer.

3. The system according to claim 2, characterized in that, The preprocessing process of the federated learning edge model includes: The formula for calculating the equipment failure probability based on the acoustic signature anomaly coefficient A and the vibration anomaly coefficient B is as follows: ； In the formula, The probability of equipment failure. The voiceprint anomaly coefficient is obtained by subtracting 1 from the ratio of the actual voiceprint frequency to the normal voiceprint frequency. The weight of the voiceprint anomaly coefficient, The vibration anomaly coefficient is obtained by subtracting 1 from the ratio of the actual vibration amplitude to the normal vibration amplitude. The weight of the vibration anomaly coefficient; Based on tenant entry and exit records and electricity consumption data, behavioral patterns are identified. When there are no entries or exits for 7 consecutive days and the electricity consumption is below the threshold, a "long-term vacancy" behavior analysis result is generated, triggering the edge intelligence layer to shut down unnecessary appliances.

4. The system according to claim 1, characterized in that, The intelligent central layer and the edge intelligent layer adopt an adaptive transmission protocol: high-frequency small data such as device fault warnings are transmitted via LoRaWAN, and low-frequency big data such as tenant monthly electricity consumption records are transmitted via 5G slicing. The process of constructing and applying the event bus of the knowledge graph is as follows: Based on the preprocessed data transmitted from the edge intelligence layer, a triple association of "device ID-tenant identity-environmental parameters-event" is established through a knowledge graph engine; Natural language processing technology is used to semantically transform the raw event data and associate it with the "security risk" category of the knowledge graph; The system automatically matches and processes nodes based on the association results to form an initial business chain, and dynamically adjusts the node order according to the node execution efficiency data.

5. The system according to claim 1, characterized in that, The blockchain evidence storage layer adopts a hybrid chain architecture: the consortium chain stores the core lease data and connects to the government leasing filing system, while the private chain stores the micro data of equipment maintenance. When signing a lease, the intelligent central layer uploads rental data generated based on the dynamic pricing results of the distributed cloud to the consortium blockchain; after the equipment is repaired, it uploads an electronic certificate containing the fingerprint and timestamp of the repair personnel to the private blockchain; when it is necessary to verify the authenticity of the lease or the repair record, the intelligent central layer retrieves the encrypted evidence storage results from the blockchain evidence storage layer for decryption and verification.

6. The system according to claim 1, characterized in that, The distributed cloud-integrated federated learning framework and digital twin engine have the following specific collaborative logic: The federated learning framework trains a cross-regional collaborative model based on the aggregated data transmitted from the intelligent central layer without sharing the original data, and transmits the model parameters to the edge intelligent layer to update the federated learning edge model. The digital twin engine uses energy consumption formulas to perform virtual simulations based on equipment operation data and tenant behavior analysis results. ； In the formula, Total energy consumption of the apartment The total number of devices. For the first The rated power of each device For the first The actual running time of each device For the first Energy efficiency coefficient of each device For the first The usage scenario coefficient of each device; The simulation results are fed back to the intelligent central layer to optimize the equipment control strategy.

7. The system according to claim 2, characterized in that, The AIoT fusion terminal of the edge intelligence layer also integrates a metaverse data interface, which drives the metaverse display module based on real-time data from infrared thermal imaging sensors and acoustic sensing modules. Immersive property viewing experience, with virtual water and electricity meter readings differing from physical meter readings by ≤0.1%; The virtual renovation preview calculates the space utilization rate based on the tenant's customized furniture layout and transmits the utilization rate results to the intelligent central layer for the dynamic pricing module to calculate additional rent.

8. The system according to claim 1, characterized in that, The intelligent central layer also includes a dynamic pricing module, which calculates the rent based on the following logic: Obtain the competitor price coefficient C and vacancy rate coefficient V output from the distributed cloud; Calculate the final rent using the formula: ； In the formula, For the final rent, Basic rent, This is the vacancy rate coefficient, with values ​​ranging from [0, 1]. The higher the vacancy rate, the higher the vacancy rate. The larger, This is the weighting factor for the vacancy rate coefficient, with values ​​ranging from -0.2 to 0.

1. When the vacancy rate is high, a negative value is used to reduce rent. This is the competitor price coefficient, with values ​​ranging from -0.3 to 0.

3. It is obtained by subtracting 1 from the ratio of the average rent in the surrounding area to the base rent. The competitor's price coefficient is used as a weight, with values ​​ranging from [0.3, 0.5]. When the competitor's price is high, the rent is increased. This is a macroeconomic index, representing the year-on-year increase in CPI, with values ​​ranging from -0.1 to 0.

2. This represents the weight of the macroeconomic index, with values ​​ranging from [0.1, 0.2]. The rental data will then be transmitted to the blockchain storage layer for evidence preservation.

9. The system according to claim 3, characterized in that, When the edge intelligence layer outputs a device fault warning, the specific business chain triggered by the intelligent central layer includes: Based on the device ID and fault type associated with the knowledge graph event bus, locate the faulty device. Generate a repair work order and dispatch it to the nearest repair personnel; After the repair is completed, the electronic repair voucher is retrieved from the blockchain storage layer, and the discounted repair cost is calculated using a formula based on the tenant's credit score G: ； In the formula, This is the repair cost after the discount. This is the benchmark price for repair services. This is the credit impact coefficient. The higher the tenant's credit score, the greater the discount.

10. The system according to claim 6, characterized in that, The digital twin engine also performs financial flow simulation using equipment depreciation formulas: ； In the formula, For the equipment Annual depreciation Original value of the equipment. For equipment residual value, The equipment's service life. For the expected service life of the equipment, Failure frequency, which is the ratio of the annual number of failures to the industry average number of failures. This is the maintenance impact coefficient, with values ​​ranging from [0.05, 0.2]. The more maintenance is performed, the larger the value becomes. The simulation results are transmitted to the intelligent central layer, which is used by the cross-dimensional cost association module to calculate the binding relationship between equipment maintenance costs and tenant credit scores.

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