System for real-time tracking of the value development and maintenance history of real estate assets

Abstract

A system for real-time tracking of the performance and maintenance history of real estate assets, consisting of: a) a plurality of embedded sensor nodes distributed across structural, environmental and utility subsystems of a property asset, each sensor node configured to acquire localized telemetry data including at least one of the following: strain, vibration, temperature, humidity, gas concentration, occupancy, energy consumption or acoustic signal; b) a Real Estate Performance Tracking Unit (REPTU) comprising a microcontroller with embedded firmware, a real-time operating system (RTOS), a sensor interface module, non-volatile memory and a wireless communication transceiver, wherein the REPTU is operationally coupled with the sensor nodes to query, aggregate and preprocess the telemetry data; c) an edge inferencing module embedded in the REPTU, configured to perform localized anomaly detection using threshold filters and machine learning models trained on historical performance baselines, wherein the edge inferencing module generates event triggers when it detects a deviation from the nominal operating patterns; d) a secure technician authentication subsystem comprising at least a fingerprint scanner, an NFC identity tag reader or a biometric voice module integrated into the REPTU, the subsystem being configured to validate the identity of personnel during maintenance interactions; e) a maintenance ledger engine that is communicatively coupled to the REPTU and configured to generate cryptographically anchored entries representing maintenance events, each entry containing a timestamp, the technician's identity, the plant component ID, the service type, and diagnostic metadata; f) a cloud analytics platform comprising an engine for storing time-series data, an algorithm module for predictive maintenance and an interface layer for stakeholder access, wherein the platform is configured to receive encrypted telemetry and event data from the REPTU, perform long-term deterioration modeling and output maintenance plans and compliance reports; g) a role-based access control unit that enables differentiated data visibility and operating permissions for at least four categories of stakeholders: property owners, facility managers, certified technicians and regulatory authorities; h) an API interface unit configured for the integration of the system with third-party Building Information Modeling (BIM) platforms, municipal compliance registers and tenant-oriented applications, thus supporting cross-system data interoperability and contextual traceability.

Description

Field of invention:

[0001] The present invention relates to the field of real estate asset management and, in particular, to a system and device for the real-time tracking of physical and operational performance parameters of real estate, including building condition, usage metrics, and maintenance history. The invention integrates sensor networks, IoT gateways, edge computing, data analytics, and predictive maintenance algorithms to enable automated condition monitoring and the historical traceability of service interventions. Background of the invention:

[0002] Real estate such as commercial buildings, residential complexes, institutional properties, and industrial facilities requires continuous performance monitoring to ensure structural integrity, occupant safety, energy efficiency, and financial viability. Traditional property management practices rely primarily on manual inspections, periodic maintenance logs, and fragmented service records. These approaches often result in suboptimal maintenance schedules, unexpected downtime, undetected structural defects, and a lack of transparency during ownership changes or regulatory audits. Furthermore, most existing property management software systems lack the capability to process real-time sensor data. This leads to information delays between physical events (e.g., HVAC failure or structural cracks) and administrative actions.

[0003] Furthermore, the lack of a unified, tamper-proof maintenance history significantly impacts lifecycle planning, resale valuation, and insurance takeover of properties. Therefore, there is an urgent need for a unified, sensor-driven, real-time system that continuously monitors the operational and structural performance of buildings while logging all maintenance events, anomalies, and condition trends in a secure, timestamped digital ledger accessible to all relevant stakeholders.

[0004] Historically, real estate asset management relied on manual inspection logs, analog records, and regular maintenance schedules, which often failed to adequately reflect actual performance degradation or usage patterns. From commercial offices and multi-family dwellings to public buildings, operational maintenance and lifecycle management of physical structures and installed equipment present significant challenges. In traditional systems, maintenance is either reactive—intervention only occurs after a failure—or time-based, with maintenance activities scheduled at fixed intervals regardless of the actual condition of the component or system. This approach fails to account for the variable stresses that different buildings face depending on their geographic location, environmental factors, usage intensity, or design characteristics.As a result, critical systems such as heating, ventilation and air conditioning, elevators, sanitary pipes and structural elements are either not adequately or unnecessarily maintained, leading to inefficiencies, higher operating costs and increased risk.

[0005] Several property management software solutions attempt to address this problem by digitizing service logs, maintenance schedules, and compliance documents. However, these software platforms are often disconnected from the physical systems they are meant to monitor. Most of these platforms rely on human input to update asset status, enter service details, and flag anomalies. This introduces subjectivity, delayed responses, and the risk of data manipulation. For example, a technician might forget to log a service task or, worse, misrepresent the nature of the intervention. These data gaps become critical when properties are transferred, audited for compliance, or assessed for insurance purposes. Furthermore, these solutions do not support real-time monitoring of environmental and structural conditions within the facility.This means that unexpected conditions such as penetrating moisture, structural stresses, or declining air quality may remain undetected until significant damage has occurred.

[0006] There are attempts to integrate basic sensor technology into building infrastructure, particularly in high-end commercial and smart home environments. These include occupancy sensors for lighting automation, energy meters for utility companies, and smart thermostats for climate control. However, such implementations are typically standalone and optimized for user comfort or energy efficiency rather than long-term asset management. Furthermore, coordination between the subsystems is lacking, and they are rarely integrated into a central analytics platform capable of holistically evaluating asset performance. Since there is also no long-term data storage or structured maintenance history, these systems provide no information for future maintenance planning and offer no audit trails for ownership changes and regulatory inspections.

[0007] Building Information Modeling (BIM) has become increasingly important in the architecture, engineering, and construction phases of real estate. BIM platforms offer a digital representation of a building's physical and functional characteristics and, linked to asset management tools, can theoretically support facility management throughout a building's entire lifecycle. However, BIM implementations typically become outdated shortly after construction is completed due to a lack of real-time updates. Integration between BIM models and live sensor data from the building is minimal, and there is no standardized method for dynamically displaying maintenance records, fault events, or usage trends. This limits the usefulness of BIM in asset performance management once a building is operational.

[0008] The advent of IoT (Internet of Things) technologies has opened up new possibilities for real-time facility monitoring. Smart building platforms now offer sensor integrations for temperature, humidity, CO2 levels, and motion, primarily aimed at optimizing space utilization and energy consumption. However, these platforms often operate in isolation and are not compatible with legacy building systems or third-party vendors. They are also frequently hosted on proprietary cloud platforms, limiting user control over data portability, privacy, and auditability. While they can issue alerts or alarms based on preset thresholds, few systems offer advanced analytics for condition-based monitoring or predictive maintenance, which are crucial for anticipating failures before they occur.Therefore, the potential of IoT to transform building maintenance remains untapped in most real-world applications.

[0009] Blockchain technology has also been proposed for recording real estate transactions and legal documents such as deeds, leases, or property transfers. These applications focus on legal transparency and fraud prevention mechanisms in the real estate sector. However, blockchain has not yet been comprehensively applied to the operational lifecycle of buildings themselves—particularly not for recording and securing maintenance history, performance analysis, or service interventions. Without such integration, the value of blockchain is limited to legal records and does not extend to performance tracking or predictive maintenance of properties.

[0010] Another challenge is the lack of accountability and traceability in current maintenance processes. External service providers often perform maintenance work without structured input / output documentation. Service quality, intervention time, and technical diagnostics are frequently summarized vaguely, and there is no mechanism to verify whether the prescribed tasks were actually completed or performed correctly. Furthermore, when ownership of a facility is transferred, the associated maintenance history—if it exists at all—is typically scattered across spreadsheets, emails, and paper reports. This obscures crucial historical insights into building performance, component wear, and maintenance patterns that would be invaluable for future planning, insurance validation, or resale valuation.

[0011] The lack of a central and unchanging performance history also poses operational risks. For example, a building suffering from recurring structural vibrations or mold problems may have undergone several short-term repairs by different contractors over the years. Without a continuous digital record, it becomes difficult to analyze patterns, identify root causes, or hold service providers accountable. Furthermore, the lack of insight into historical defects and repairs makes it harder for future occupants or buyers to make informed decisions about the long-term profitability and safety of the property.

[0012] Another limitation of current systems is the lack of real-time decision-making capabilities at the network edge. Most sensor-based platforms rely entirely on centralized cloud services to perform calculations and detect anomalies. This results in latency and dependence on stable internet connections. In critical scenarios such as gas leaks, fire detection, or elevator malfunctions, the system must react immediately, even if the cloud connection is interrupted. Therefore, there is a need for edge-based intelligence that can execute localized response actions, such as closing valves or triggering alarms, without requiring cloud-based coordination.

[0013] Finally, user engagement and transparency remain limited on current platforms. Property owners, tenants, regulators, and facility managers often work within siloed information systems, each with access to only fragments of building data. A truly effective asset tracking and maintenance system must support role-based access, ensuring that each stakeholder receives tailored transparency based on their role, responsibilities, and access rights. For example, tenants should be able to check the air quality and efficiency of the heating, ventilation, and air conditioning (HVAC) systems in their apartments, while regulators should be able to view the maintenance history of fire protection equipment.This level of transparency is difficult to achieve in conventional property management systems due to architectural fragmentation, lack of secure authentication, and the absence of a common data backbone.

[0014] Given these gaps and inefficiencies, a next-generation system is urgently needed that unifies real-time sensor data, predictive analytics, technician-verified service history, and secure auditability within a single framework. Such a system must operate at the intersection of embedded hardware, edge computing, AI-driven analytics, cryptographic logging, and cross-platform integration, thereby transforming the monitoring, maintenance, and valuation of real estate assets over time. Summary of the invention:

[0015] The invention describes a comprehensive system and hardware device that tracks, analyzes, and stores real-time operational and maintenance-related data from buildings. The system comprises a distributed network of embedded multimodal sensors installed throughout the building, a central IoT gateway for signal aggregation, and a device controller with embedded firmware responsible for edge-level processing and communication. The collected data includes structural loads, environmental conditions, occupancy levels, and energy consumption patterns.

[0016] This data is continuously processed locally and transferred to a cloud-based platform. There, advanced analytics modules perform pattern recognition, anomaly detection, and predictive modeling to assess the condition of the facilities. The system also includes a maintenance log module that records all maintenance activities, inspection reports, emergency repairs, and upgrades in a verifiable and tamper-proof structure using blockchain or cryptographic hashing. The device also features NFC or biometric technician identification for audit trails. Real-time dashboards, compliance reports, and alert mechanisms are made available to facility managers, tenants, and property owners via mobile and web interfaces.

[0017] The main objective of the present invention is to provide a comprehensive real-time tracking system for monitoring the operational performance and maintenance history of building assets. This ensures proactive, data-driven asset management throughout the entire building lifecycle. The invention aims to overcome the inefficiencies of conventional reactive maintenance and fragmented documentation by enabling continuous condition monitoring through embedded sensors, edge computing, and cloud analytics. A further objective of the invention is to create a unified platform that seamlessly integrates environmental sensing, structural diagnostics, consumption measurement, and predictive maintenance algorithms into a framework that autonomously detects anomalies, predicts failures, and recommends timely service measures.

[0018] Another objective of the invention is to create a tamper-proof digital register for all maintenance activities performed on the property. By integrating secure technician authentication mechanisms and cryptographic anchoring methods such as blockchain or hash chaining, the system ensures that every maintenance event—including inspection, repair, upgrade, or component replacement—is recorded with an immutable timestamp and service metadata. This increases transparency and long-term traceability. This supports compliance audits, resale valuations, and insurance claims, while simultaneously preventing fraudulent or undocumented maintenance practices.

[0019] The invention also aims to provide an interoperable platform that supports integration with Building Information Modeling (BIM) systems, regulatory compliance registers, and tenant-oriented user interfaces, thereby enabling a common data environment with role-based access for various stakeholders. Property owners, facility managers, tenants, contractors, and regulatory authorities can each access context-relevant portions of the facility's performance data and maintenance history, improving operational collaboration and decision-making. Finally, a key objective of the invention is to enable adaptive learning over time.The system refines its maintenance prediction models and anomaly detection logic using real data, thus building a continuously improved knowledge base tailored to the individual stress factors, usage patterns and aging profiles of each property. BRIEF DESCRIPTION OF THE FIGURE

[0020] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a system for real-time tracking of the performance and maintenance history of real estate assets.

[0021] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0022] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0023] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0024] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0025] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0027] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0028] In the Fig.Figure 1 illustrates the block diagram of a system for real-time tracking of the performance and maintenance history of real estate assets. The system 100 comprises: a plurality of embedded sensor nodes (102) distributed across the structural, environmental, and utility subsystems of a real estate asset, each sensor node configured to acquire localized telemetry data, including at least one of the following: load, vibration, temperature, humidity, gas concentration, occupancy, energy consumption, or acoustic signal; a Real Estate Performance Tracking Unit (REPTU) (104) comprising a microcontroller with embedded firmware, a real-time operating system (RTOS), a sensor interface module, non-volatile memory, and a wireless communication transceiver, the REPTU being operationally coupled to the sensor nodes to query, aggregate, and preprocess the telemetry data;an edge inference module (106) embedded in the REPTU, configured to perform localized anomaly detection using threshold filters and machine learning models trained on historical performance baselines, wherein the edge inference module generates event triggers when deviations from nominal operating patterns are detected; a secure technician authentication subsystem (108) comprising at least one of the following: a fingerprint scanner, an NFC identity tag reader, or a biometric voice module integrated into the REPTU, wherein the subsystem is configured to validate the identity of personnel during maintenance interactions;a maintenance log engine (110) that is communicatively coupled with the REPTU and configured to generate cryptographically anchored entries representing maintenance events, each entry containing a timestamp, technician identity, plant component ID, service type, and diagnostic metadata; a cloud analytics platform (112) comprising a time-series data storage engine, a predictive maintenance algorithm module, and a stakeholder access interface layer, the platform being configured to receive encrypted telemetry and event data from the REPTU, run long-term degradation models, and output maintenance plans and compliance reports;a role-based access control unit (114) that enables differentiated data visibility and operating permissions for at least four categories of stakeholders: property owners, facility managers, certified technicians, and regulatory authorities; an API interface unit (116) configured to integrate the system with third-party Building Information Modeling (BIM) platforms, municipal compliance registers, and tenant-oriented applications, thereby supporting cross-system data interoperability and contextual traceability.

[0029] In one embodiment, the edge inference module (106) uses a recurrent neural network model consisting of Long Short-Term Memory (LSTM) cells trained on labeled fault and normal operating datasets specific to HVAC compressors, water pumps, and elevator motors, so that localized vibration and acoustic patterns are classified in real time into probable fault categories such as bearing wear, misalignment, or cavitation, thereby enabling warnings of a failure without dependence on cloud latency.

[0030] In one embodiment, the maintenance ledger engine (110) is also configured to anchor each ledger entry using a hash chain mechanism, wherein each event hash contains the cryptographic digest of the previous event, the technician's public key signature, and a system nonce, and wherein the hash chain is periodically checked against a distributed ledger maintained in an authorized blockchain network operated by a consortium of certified real estate compliance authorities, thereby ensuring tamper resistance and regulatory auditability.

[0031] In one embodiment, the REPTU (104) also includes a dual-mode power supply subsystem with a primary AC converter and a secondary, battery-backed solar micro-harvester with automatic failover logic. The subsystem is configured to maintain uninterrupted telemetry acquisition and event logging in the event of power outages or disaster scenarios, thus ensuring the resilience of the asset monitoring system under adverse operating conditions.

[0032] In one embodiment, the sensor interface module within the REPTU supports the automatic detection and dynamic addressing of newly added sensor nodes using a user-defined extension of the Modbus RTU protocol with deterministic polling intervals and CRC integrity checks. This enables the scalable integration of additional sensor modalities, including but not limited to corrosion rate sensors, differential pressure transducers, and gas leak detectors, without requiring a firmware redeployment.

[0033] In one embodiment, the role-based access control unit (114) uses attribute-based encryption (ABE) to enforce stakeholder-specific decryption of telemetry and maintenance history records, so that a regulatory authority with the attribute 'fire protection' can only decrypt the ledger entries relating to fire extinguishing systems, while access to unrelated subsystems is cryptographically restricted.

[0034] In one embodiment, the cloud analytics platform (112) also includes an ensemble decision engine that combines outputs from multiple prediction models, including ARIMA, exponential smoothing, and gradient-boosted trees, each model being tailored to different sensor types and environmental exposure profiles, and the system dynamically selecting the most accurate model over time using a weighted performance evaluation matrix based on historical prediction error rates.

[0035] In one embodiment, the housing of the REPTU (104) device is mechanically equipped with a corrosion-resistant stainless steel chassis, an EMI shielding grille, IP67 seals, and vibration-damping rubber bushings. The housing also includes a removable sensor backplane that supports hot-swappable sensor interface cards, enabling long-term field use with minimal downtime and high environmental resistance.

[0036] In one embodiment, the interface layer (116) comprises a GIS-linked visual dashboard that overlays sensor data and maintenance events onto a digital floor plan or a 3D BIM model of the real estate asset, with interactive filtering by time window, component type, event severity and technician ID, thereby enabling spatially contextualized performance diagnostics and forensic analysis.

[0037] In one embodiment, the REPTU (104) includes a firmware integrity verification module based on the Trusted Platform Module (TPM) architecture. This module performs secure boot authentication and regular hash verification of firmware binaries against a cloud-signed manifest and initiates secure lockout protocols in the event of firmware tampering, thereby ensuring operational security against unauthorized reprogramming or cyberattacks.

[0038] The invention described herein relates to a comprehensive, sensor-driven system for real-time tracking of the performance and maintenance history of buildings. Its core functionality is enabled by a combination of embedded hardware, machine learning algorithms, edge analytics, secure authentication, and cryptographically anchored service history. The system architecture is multi-layered—from physical data acquisition and local inference at the building level to cloud-based forecasting and secure historical recording—thus providing a comprehensive vertical stack for intelligent building performance management.

[0039] The distributed network of multimodal sensor nodes is installed in key structural, mechanical, and environmental zones within the property. These include, among others, structural strain sensors, vibration and acoustic emission sensors for rotating machinery, temperature and humidity sensors, CO2 and particle sensors for indoor air quality, occupancy sensors, and smart meters for energy, water, and gas consumption. These sensors regularly transmit raw telemetry data to a central embedded device, the Real Estate Performance Tracking Unit (REPTU), which acts as the system's edge controller.

[0040] The REPTU runs a real-time operating system (RTOS) and integrates firmware logic for sensor querying, data filtering, temporal aggregation, and event tagging. The preprocessing algorithm first applies low-pass filtering, moving average smoothing, and outlier suppression to reduce noise in incoming sensor data. Each processed data point is then compared against dynamic threshold profiles derived from locally stored historical baselines. If deviations are detected—for example, abnormal vibration amplitudes in an elevator motor or sudden increases in humidity in a wall cavity—these data points are flagged and forwarded to the embedded edge inference module.

[0041] The edge inferencing module comprises lightweight machine learning models trained for condition classification and early anomaly detection. For rotating machinery such as air conditioning compressors or water pumps, the module utilizes a Long Short-Term Memory (LSTM) neural network implemented in quantized form on the microcontroller. The LSTM is trained using historical temporal patterns of vibration and acoustic data corresponding to various failure modes, such as bearing wear, rotor imbalance, or misalignment. Once real-time input sequences are fed into this network, it returns a probabilistic failure classification and a confidence score.If the confidence value exceeds a defined threshold, a local event trigger is generated, which causes the system to log the event and send a priority message to the cloud platform for further evaluation and alerting.

[0042] Simultaneously, the REPTU supports interaction with technicians through a secure authentication subsystem. This subsystem integrates biometric fingerprint readers or NFC-based identity token scanners, enabling the system to verify the maintenance personnel's credentials before granting access to the internal maintenance interface. Following authentication, the technician logs the service activity via a connected input module or mobile application, entering service metadata such as component ID, fault type, replaced parts, and remarks. This data is structured according to a predefined schema and combined with the authenticated technician's ID, GPS location (if available), and timestamp.

[0043] The maintenance ledger engine, embedded in REPTU or hosted on the edge gateway, creates a cryptographic hash of this service event. This hash contains the SHA-256 digest of the event data, the technician's public key signature, and a nonce derived from the previous event hash on the chain. This structure forms a hash-chained ledger, similar in design to a blockchain but optimized for local sequential writes. A checkpoint of this local chain is periodically uploaded to an authorized blockchain network managed by regulatory authorities. This secures the property's maintenance history in an immutable, verifiable format.

[0044] The encrypted telemetry data and hash-validated maintenance records are transmitted to a cloud analytics platform via secure MQTT or HTTPS protocols. There, a time-series data engine captures and indexes incoming streams based on sensor type, location, and time. The analytics engine performs long-term performance modeling and predictive maintenance planning. The modeling stack uses an ensemble of forecasting algorithms: ARIMA for linear temporal trends, exponential smoothing for periodic fluctuations, and gradient-enhanced decision trees for nonlinear variable interactions. These models are trained on cumulative operational data and regularly validated against historical service results. The ensemble forecasting mechanism uses a weighted tuning strategy based on the mean absolute error (MAE) and mean squared error (RMSE) values ​​over a moving time window.This allows the system to continuously adapt to the evolving plant behavior and refine its ability to predict future component failures, optimal service intervals, and likely types of errors.

[0045] For data access, the system enforces granular, role-based access control using attribute-based encryption (ABE). For example, a tenant can view energy efficiency reports and air quality trends for their unit but has no access to structural stress analyses or technician identities. Facility managers, on the other hand, can access comprehensive diagnostic and service planning interfaces, while inspectors can only access maintenance history logs for fire protection systems, plumbing compliance, or elevator certifications. All access logs are recorded with audit trails and session metadata.

[0046] The system's graphical user interface includes a GIS-connected digital twin of the building and supports 2D and 3D visualizations that overlay real-time sensor data and maintenance events onto the architectural floor plan. Users can interactively navigate between building zones, filter events by time period, severity, or technician, and view analysis trends contextualized with physical locations. Integration with BIM systems enables live updates of component status and maintenance intervals directly within digital construction models.

[0047] The system also features integrated failover functions for continuous operation in emergencies. The REPTU offers redundant power supply via a solar trickle charge module and a supercapacitor-based backup system. During power outages, it can continue to autonomously collect sensor data, time-stamp it, and log it locally. Once the connection is restored, it automatically synchronizes the buffered data with the cloud. The firmware is protected by a TPM-based (Trusted Platform Module) secure boot and hash verification architecture. This ensures that only authenticated firmware binaries can be executed, thus protecting the system from unauthorized manipulation.

[0048] Through this multi-layered integration of embedded edge processing, machine learning-based diagnostics, secure service authentication, cryptographically anchored history logging, and cloud-based lifecycle analysis, the system offers a holistic solution for intelligent, transparent, and actionable real estate asset management.

[0049] The system comprises a device integrated into real estate assets for continuous data acquisition and operational monitoring. The core device, the Real Estate Performance Tracking Unit (REPTU), consists of a robust housing with the following embedded components: a microcontroller with a real-time operating system (RTOS), a sensor array interface module, local memory, a wireless communication module (Wi-Fi, ZigBee, LoRa, or 5G), and a secure cryptographic coprocessor.

[0050] The REPTU is installed in the building's main supply shaft or control room, where it is physically connected to a sensor network consisting of environmental sensors (e.g., temperature, CO2, particulate matter), structural integrity monitoring sensors (e.g., strain gauges, vibration accelerometers, acoustic emission sensors), occupancy sensors (e.g., PIR, ultrasound), consumption measurement sensors (electricity, water, gas), and maintenance interaction sensors (e.g., RFID readers, service keypads).

[0051] Once activated, the REPTU continuously queries these sensors at defined intervals and performs local, threshold-based filtering to detect anomalies (e.g., abnormal vibrations indicating mechanical wear or temperature spikes suggesting an air conditioning malfunction). Each data point is time-stamped and its spatial location within the building, and then processed using integrated inference models such as logistic regression classifiers for anomaly categorization and Kalman filters for smoothing.

[0052] The data is encrypted and transmitted to the cloud platform via MQTT or HTTPS protocols. The cloud system comprises a distributed storage module for time-series sensor data, an AI-based analytics engine, and a service management subsystem. Using time-series forecasting algorithms such as ARIMA and LSTM neural networks, the analytics engine can generate heatmaps, fault prediction curves, and maintenance planning recommendations.

[0053] In parallel, the REPTU prompts the technician for authentication during each service activity. This is done via a secure mechanism such as biometric verification (e.g., an integrated fingerprint reader) or NFC identification token. After successful authentication, the parameters of the maintenance task—such as task type, component serviced, parts replaced, time required, and technician notes—are entered via a connected handheld interface or a mobile app.

[0054] These maintenance events are cryptographically hashed and written to a blockchain-based ledger, which is stored either on a private, authorized blockchain network or as cryptographically hashed logs on a secure server. This ensures verifiable traceability and auditable records. The entire lifecycle of an HVAC system, elevator, structural slab, or plumbing system is thus tracked and managed in a searchable, immutable format.

[0055] The device also features firmware updates and edge inference logic that enables localized decision-making, such as immediately shutting down a faulty pump or alerting the fire department when excessive CO levels are detected.

[0056] An additional structure includes a tamper-proof enclosure with electromagnetic shielding and redundant power supplies (solar trickle charger and supercapacitor) to maintain functionality during power outages. The device's mechanical mounting includes vibration-damping grommets and an IP67-rated seal to withstand harsh indoor environments.

[0057] In terms of interoperability, the system includes an API layer for integration with Building Information Modeling (BIM) platforms, tenant-oriented dashboards, and municipal e-compliance registers. Authorized stakeholders can access filtered subsets of the data via secure, role-based access controls. For example, a tenant can view their unit's HVAC efficiency and air quality metrics, while being denied access to structural integrity data, which is restricted to civil engineers or building inspectors.

[0058] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0059] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A system for real-time tracking of the value development and maintenance history of real estate. 102 Embedded Sensor Nodes 104 Units for Tracking Real Estate Performance 106-edge inference module 108 Secure Technician Authentication Subsystem 110 Maintenance Manual - Engine 112 Cloud analytics platform 114 Role-based access control unit 116 API interface unit

Claims

[1] A system for real-time tracking of the performance and maintenance history of real estate assets, consisting of: a) a plurality of embedded sensor nodes distributed across structural, environmental and utility subsystems of a property asset, each sensor node configured to acquire localized telemetry data including at least one of the following: strain, vibration, temperature, humidity, gas concentration, occupancy, energy consumption or acoustic signal; b) a Real Estate Performance Tracking Unit (REPTU) comprising a microcontroller with embedded firmware, a real-time operating system (RTOS), a sensor interface module, non-volatile memory and a wireless communication transceiver, wherein the REPTU is operationally coupled with the sensor nodes to query, aggregate and preprocess the telemetry data; c) an edge inferencing module embedded in the REPTU, configured to perform localized anomaly detection using threshold filters and machine learning models trained on historical performance baselines, wherein the edge inferencing module generates event triggers when it detects a deviation from the nominal operating patterns; d) a secure technician authentication subsystem comprising at least a fingerprint scanner, an NFC identity tag reader or a biometric voice module integrated into the REPTU, the subsystem being configured to validate the identity of personnel during maintenance interactions; e) a maintenance ledger engine that is communicatively coupled to the REPTU and configured to generate cryptographically anchored entries representing maintenance events, each entry containing a timestamp, the technician's identity, the plant component ID, the service type, and diagnostic metadata; f) a cloud analytics platform comprising an engine for storing time-series data, an algorithm module for predictive maintenance and an interface layer for stakeholder access, wherein the platform is configured to receive encrypted telemetry and event data from the REPTU, perform long-term deterioration modeling and output maintenance plans and compliance reports; g) a role-based access control unit that enables differentiated data visibility and operating permissions for at least four categories of stakeholders: property owners, facility managers, certified technicians and regulatory authorities; h) an API interface unit configured for the integration of the system with third-party Building Information Modeling (BIM) platforms, municipal compliance registers and tenant-oriented applications, thus supporting cross-system data interoperability and contextual traceability. [2] System according to claim 1, wherein the edge inference module uses a recurrent neural network model consisting of Long Short-Term Memory (LSTM) cells trained on characterized fault and normal operating data sets specific to HVAC compressors, water pumps and elevator motors, such that localized vibration and acoustic patterns are classified in real time into probable fault categories such as bearing wear, misalignment or cavitation, thereby enabling warnings of a failure without dependence on cloud latency. [3] System according to claim 1, wherein the maintenance ledger engine is further configured to anchor each ledger entry using a hash chain mechanism, wherein each event hash contains the cryptographic digest of the previous event, the technician's public key signature and a system nonce, and wherein the hash chain is periodically checked against a distributed ledger maintained in an authorized blockchain network operated by a consortium of certified real estate compliance authorities, thereby ensuring tamper resistance and regulatory auditability. [4] System according to claim 1, wherein the REPTU further comprises a dual-mode power supply subsystem comprising a primary AC converter and a secondary battery-backed solar microharvester with automatic failover logic, wherein the subsystem is configured to maintain uninterrupted telemetry acquisition and event logging in the event of power outages or disaster scenarios, thereby ensuring the resilience of the asset tracking system under adverse operating conditions. [5] System according to claim 1, wherein the sensor interface module within the REPTU supports automatic detection and dynamic addressing of newly added sensor nodes using a user-defined extension of the Modbus RTU protocol with deterministic polling intervals and CRC integrity checks, thereby enabling scalable integration of additional sensor modalities, including but not limited to corrosion rate sensors, differential pressure transducers and gas leak detectors, without requiring firmware redeployment. [6] System according to claim 1, wherein the role-based access control unit uses attribute-based encryption (ABE) to enforce stakeholder-specific decryption of telemetry and maintenance history records, so that a regulatory authority with the attribute 'fire protection' can only decrypt the ledger entries relating to fire extinguishing systems, while access to unrelated subsystems is cryptographically restricted. [7] System according to claim 1, wherein the cloud analytics platform further comprises an ensemble decision engine that combines outputs from multiple prediction models, including ARIMA, exponential smoothing and gradient-boosted trees, each model being tailored to different sensor types and environmental exposure profiles, and wherein the system dynamically selects the most accurate model over time using a weighted performance evaluation matrix based on historical forecast error rates. [8] System according to claim 1, wherein the housing of the REPTU device is mechanically equipped with a corrosion-resistant stainless steel chassis, an EMI shielding grille, IP67 seals, and vibration-damping rubber bushings. The housing also includes a removable sensor backplane with hot-swappable sensor interface cards, enabling long-term field use with minimal downtime and high environmental resistance. [9] System according to claim 1, wherein the interface level comprises a GIS-linked visual dashboard that displays sensor data and maintenance events overlaid on a digital floor plan or a 3D BIM model of the real estate asset, with interactive filtering by time window, component type, event severity and technician ID, thereby enabling spatially contextualized performance diagnostics and forensic analysis.

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