System for monitoring and managing real estate assets

Abstract

The invention relates to the field of computing. A system for monitoring and managing real estate assets contains the following interconnected elements: a database of equipment installed on an asset, said database being capable of storing data and receiving data from equipment sensors; a database of equipment-related technical data sheets; a set of sensors for monitoring the state of equipment, and at least one video camera with computer vision technology, which is disposed inside and / or outside a real estate asset; a telemetry server capable of collecting and storing in the equipment database information from the sensors for monitoring the state of equipment and from the video camera with computer vision technology, monitoring the sensor readings for conformity with normative values, and transmitting information to a predictive analytics module when readings reach the boundaries of said normative values; and a predictive analytics module capable of generating recommendations relating to the management of real estate assets, the predictive analytics module being implemented using a neural network that makes it possible to calculate reading-based criteria when generating recommendations.

Description

[0001] REAL ESTATE MONITORING AND MANAGEMENT SYSTEM

[0002] AREA OF TECHNOLOGY

[0003] This technical solution relates to the field of computing, in particular to real estate monitoring and management systems.

[0004] LEVEL OF TECHNOLOGY

[0005] A prior art solution, selected as the closest analog, is known: US 2022294217 (A1). This analog relates to systems and methods for predictive power system control using a dynamic power flow model and linear optimization. Specifically, the systems and methods of the previously mentioned analog can predict circuit states under various changes in control variables and control components in the power system based on a data-driven, real-time dynamic power flow model.

[0006] The proposed technical solution is aimed at eliminating the shortcomings of current technology and differs from known solutions in that it provides high-quality and advanced control of the property, as well as effective monitoring of the operation of various equipment located on the property.

[0007] ESSENCE OF THE INVENTION

[0008] The technical problem addressed by the claimed solution is the creation of a system for monitoring and managing real estate assets. Additional embodiments of the present invention are presented in the dependent claims.

[0009] The technical result consists of achieving the intended purpose. An additional technical result consists of improving the quality of monitoring and management of real estate assets.

[0010] The stated result is achieved through the implementation of a system for monitoring and managing real estate objects, containing: a database of equipment installed at the facility, designed with the ability to store data and receive data from equipment sensors; a database of equipment process maps;

[0011] 1

[0012] SUBSTITUTE SHEET (RULE 26) a set of equipment condition monitoring sensors, at least one video camera with machine vision technology, located inside and / or outside the real estate facility; a telemetry server configured to: collect and accumulate in a database information from the equipment condition monitoring sensors and the video camera with machine vision technology; monitor the compliance of the sensor readings with standard values; transmit information to the predictive analytics module on reaching the indicators within the standard value limits; a predictive analytics module configured to generate recommendations related to the management of real estate facilities, wherein the predictive analytics module is implemented using a neural network that allows for the calculation of criterion indicators when generating recommendations.

[0013] In a particular embodiment of the described system, the set of sensors for monitoring the equipment condition includes: a vibration sensor; an ultrasonic microphone; a dust sensor; a temperature sensor; an infrared sensor; a power sensor.

[0014] DETAILED DESCRIPTION OF THE INVENTION

[0015] The following detailed description of the invention includes numerous implementation details intended to provide a clear understanding of the present invention. However, one skilled in the art will readily appreciate how the present invention may be utilized with or without these implementation details. In other instances, well-known methods, procedures, and components have not been described in detail to avoid unnecessarily obscuring the features of the present invention.

[0016] Furthermore, it will be clear from the foregoing description that the invention is not limited to the embodiment described. Numerous possible modifications, changes, variations, and substitutions, while preserving the spirit and form of the present invention, will be apparent to those skilled in the art.

[0017] Terms and definitions.

[0018] 2

[0019] SUBSTITUTE SHEET (RULE 26) Equipment performance sensors - sensors that can detect equipment malfunctions.

[0020] Spare parts kits (SPTA) – spare parts, tools and accessories required for scheduled preventive maintenance of equipment.

[0021] Condition monitoring is the monitoring of machinery or production assets using sensors to diagnose their current condition.

[0022] PPR (preventive maintenance) is a set of organizational and technical preventative measures carried out on a scheduled basis to ensure the operability of mechanisms and equipment. PPRs of varying scopes, including replacement of varying quantities of spare parts, may be recommended for equipment at different stages of its operation.

[0023] Limit state is a state of equipment / premises in which its further operation is unacceptable or impractical, or restoration of its working condition is impossible or impractical.

[0024] If the operation of a property requires the constant presence of people, analyzing human behavior enables the development of effective forecasts and recommendations for property management. Examples of such properties include technology parks, business and entertainment centers, shopping and office centers, industrial facilities, and residential buildings. The scope of the invention also includes populated areas and cities (smart cities).

[0025] This technical solution enables the system to collect and efficiently process various data related to a property, including data related to the behavior of people inside and / or outside the property. This is accomplished using computing equipment (including cloud servers and databases), video cameras with machine vision technology, and various sensors installed on the property (vibration sensor, ultrasonic microphone, dust sensor, temperature sensor, infrared sensor, and power sensor).

[0026] Based on accumulated data on human behavior and its correlation with data on the functioning of the facility, a forecast is made and recommendations are formed for the effective and optimal management of the facility.

[0027] 3

[0028] SUBSTITUTE SHEET (RULE 26) This technical solution is intended for:

[0029] • ensuring the operability of equipment at facilities;

[0030] • increasing the service life of equipment through timely unscheduled and scheduled repairs;

[0031] • minimum downtime for maintenance and repair;

[0032] • reducing the costs of maintaining a spare parts warehouse by reducing inventory and ordering spare parts and tools in advance.

[0033] Equipment may not reach its intended service life. This may be due to improper operation or poor maintenance.

[0034] Predictive maintenance allows for scheduled repairs and prevents unscheduled downtime. Early detection of equipment failures through predictive maintenance also helps identify and repair inefficient equipment, thereby increasing productivity, asset availability, and extending its service life.

[0035] Predictive maintenance involves a combination of methods such as condition monitoring, machine learning, and analytics to predict potential failures of machinery or production assets. Predictive maintenance methods are applied to a wide range of rotary (motors, gearboxes, pumps, turbines) and non-rotary (valves, circuit breakers, cables) mechanisms.

[0036] This solution includes high-quality sensors for predictive maintenance, allowing for the timely detection of potential faults.

[0037] Some sensors can detect specific faults, real-time operation, and additional indicators characterizing potential problems (noise, vibration, etc.). The sensors most commonly used for early fault detection are accelerometers and microphones. Most predictive maintenance systems use only a subset of these sensors, so it's crucial to understand critical system faults and the sensors best suited for their detection.

[0038] 4

[0039] SUBSTITUTE SHEET (RULE 26) An additional technical element that allows for high-quality detection of faults is a machine vision system, which, based on information received from surveillance cameras and processed by a neural network, generates indications of the equipment’s condition.

[0040] A machine vision system is used as one of the operational monitoring devices. It processes equipment images in real time using a neural network and generates operational indicators. During the neural network training phase, an image is selected that reflects:

[0041] • operational condition of the equipment;

[0042] • working, but already with deviations from the norm, and requiring planning of replacement work;

[0043] • critical, which requires stopping the equipment and replacing it.

[0044] For example, at real estate properties, the condition of the cable system of elevator equipment can be monitored using machine vision systems.

[0045] This technical solution also utilizes artificial intelligence technology. A set of data is fed into a neural network, which is accumulated and processed to generate criteria that the neural network then uses to calculate the potential mean time between failures and the remaining service life of a specific piece of equipment.

[0046] The neural network calculates equipment benchmarks based on information received, including from sensors. These benchmarks are used by the telemetry server to generate signals indicating changes in the equipment's technical condition.

[0047] To train a neural network, use:

[0048] • history of readings at which equipment failures occurred (equipment entering a critical state);

[0049] • vendor-recommended equipment operating parameters;

[0050] • history of performed preventive maintenance (operating period, list of replaced spare parts)

[0051] • history of unscheduled repairs (operating period, list of replaced spare parts).

[0052] 5

[0053] SUBSTITUTE SHEET (RULE 26) The neural network analyzes the indicators of the same type of equipment installed at all facilities connected to the system and approximates the data received from different facilities and different regions.

[0054] By monitoring equipment performance sensors, calculating the equipment's service life, and comparing this period with the mean time between failures, the predictive analytics module provides recommendations on the dates for scheduled preventive maintenance.

[0055] If the equipment load is lower than recommended by the supplier, the module can provide recommendations to increase the service interval if the sensor readings are within the normal operating range of the equipment.

[0056] If the equipment load exceeds the recommended value, the module will provide recommendations for reducing the service interval to ensure proper operation.

[0057] The module also monitors the availability of spare parts for specific equipment in stock and the delivery time. If spare parts are unavailable, the module provides advance recommendations for their purchase, taking into account the possible delivery time of spare parts from the vendor or another supplier.

[0058] The module monitors the remaining life of the equipment and the moment the equipment enters its limit state. If the recommendation generation period occurs, the system provides recommendations for purchasing equipment for its replacement in the event of equipment failure.

[0059] This allows the equipment to be replaced before it physically fails, preventing damage to surrounding equipment, sensors, wiring, and other components that could otherwise occur during a real failure due to increased temperature, sparking, and possibly fire. This approach also reduces downtime, as only the primary equipment needs to be replaced, without replacing the surrounding technical infrastructure.

[0060] The solution is innovative, employing a neural network that continuously learns by processing information about equipment operation in real time. The neural network generates benchmarks for specific equipment at a specific facility based on

[0061] 6

[0062] SUBSTITUTE SHEET (RULE 26) from the actual load on the equipment, the operating environment of this equipment, recommendations of vendors and suppliers, as well as its technical condition.

[0063] The indicators will be modified in real time, as the results of breakdowns, downtime, and repairs of equipment at various facilities will be taken into account.

[0064] Recommendations for repair or maintenance of one type of equipment can and will vary depending on the facility, its operating conditions, and the vendor's recommendations.

[0065] Thus, the proposed system contains the following technical components that allow for effective monitoring of the facility and high-quality management of the real estate.

[0066] Equipment database. The equipment database stores information regarding: serial numbers of equipment located at the property; equipment models; equipment categories; equipment purchase date; equipment installation date; equipment warranty expiration date; information on the remaining life of the equipment - the total operating time of the equipment in hours / cubic meters / cycles before it reaches its limit state; equipment failure; shutdown date; list of faults; information on work performed on the equipment (scheduled or unscheduled repairs); date of work; list of work performed; list of components replaced or repaired as part of the work.

[0067] Equipment process chart database. The equipment process chart database stores information regarding:

[0068] • Equipment categories;

[0069] • Equipment models;

[0070] • Warranty period of equipment operation;

[0071] • Mean time between failures, in hours / cubic meters / cycles;

[0072] • Recommendation generation period - the period of time in hours before the possible occurrence of an event of equipment transition to a limit state or the occurrence of a failure event;

[0073] • List of scheduled preventive maintenance (SPM);

[0074] • Periods of scheduled maintenance;

[0075] 7

[0076] SUBSTITUTE SHEET (RULE 26) • Required spare parts, tools and accessories necessary (spare parts) for carrying out scheduled preventive maintenance of equipment for carrying out scheduled preventive maintenance;

[0077] • Vendor contacts;

[0078] • Delivery times for spare parts from the main supplier / vendor;

[0079] • Delivery times for equipment from the main supplier / vendor;

[0080] • Maximum number of equipment shutdowns;

[0081] • Maximum period between operational interruptions;

[0082] • Normal operating parameters (equipment and ambient temperature);

[0083] • Parameters of maximum exploitation.

[0084] Examples of using information stored in the system's databases.

[0085] 1. The deadline for carrying out planned maintenance work has arrived.

[0086] The system checks the availability of the required spare parts in stock, taking into account the information loaded into the database. If the information is missing, the system generates a recommendation to place an order for spare parts with the vendor (or creates an order and sends it to the vendor automatically). Typical delivery times are taken into account.

[0087] 2. Control of residual resource.

[0088] The system monitors the remaining lifespan. When the replacement deadline arrives, the system generates a recommendation to the manager regarding the need for equipment replacement. The system monitors the availability of equipment in the warehouse; if it is unavailable, it recommends an order to the manager or automatically orders it from the vendor. Typical delivery times are taken into account.

[0089] 3. Monitoring the number and frequency of failures.

[0090] The system monitors the number and duration of shutdowns. When threshold values ​​are reached, the system generates a recommendation for repair or replacement.

[0091] A set of equipment condition monitoring sensors. The sensors provide real-time data on equipment performance and enable predictions of potential mean time between failures and remaining service life. The equipment condition monitoring sensor set includes: a vibration sensor; an ultrasonic microphone; a dust sensor; a temperature sensor; an infrared sensor; and a power sensor.

[0092] 8

[0093] SUBSTITUTE SHEET (RULE 26) An infrared sensor or temperature sensor records the temperature of the equipment (e.g., a motor). A dust sensor records the dustiness of the room, and a vibration sensor records the magnitude and frequency of vibration of the equipment (e.g., a motor).

[0094] When a sensor value is exceeded, the system automatically generates an event (logs the event). If a critical value is reached, the system generates a notification for the person responsible.

[0095] Telemetry server. The telemetry server provides:

[0096] • collection and accumulation of information from performance sensors in the database;

[0097] • monitoring the compliance of sensor readings with standard values ​​and transmitting signals to the predictive analytics server about reaching the limits of standard values.

[0098] The telemetry server records the moment the equipment fails:

[0099] • collection of information from performance sensors at the moment preceding failure;

[0100] • equipment performance indicators;

[0101] • operating time;

[0102] • equipment temperature;

[0103] • ambient temperature;

[0104] • energy consumption of equipment;

[0105] • time intervals of equipment downtime between periods of operation;

[0106] • compliance of recommended indicators from vendors with actual ones.

[0107] Predictive analytics module. The module is implemented using computing hardware and is linked to all components of the proposed system. It is capable of generating recommendations related to real estate management. The predictive analytics module is implemented using a neural network, which enables the calculation of criteria for generating recommendations.

[0108] Although the invention has been described with reference to the disclosed embodiments, it will be apparent to those skilled in the art that the specific experiments described in detail are provided for the purpose of illustration only.

[0109] 9

[0110] SUBSTITUTE SHEET (RULE 26) illustrations of the present invention, and they should not be considered as in any way limiting the scope of the invention. It should be understood that various modifications are possible without departing from the essence of the present invention.

[0111] 10

[0112] SUBSTITUTE SHEET (RULE 26)

Claims

Formula 1. A system for monitoring and managing real estate objects, comprising the following, interconnected: a database of equipment installed at the object, configured to store data and receive data from equipment sensors; a database of equipment process charts; a set of sensors for monitoring the condition of the equipment, at least one video camera with machine vision technology, located inside and / or outside the real estate object; a telemetry server configured to: collect and accumulate in the database information from the sensors for monitoring the condition of the equipment and the video camera with machine vision technology; monitor the compliance of the sensor readings with standard values; transmit information to the predictive analytics module on reaching the indicators beyond the limits of standard values;a predictive analytics module capable of generating recommendations related to the management of real estate assets, wherein the predictive analytics module is implemented using a neural network that allows for the calculation of criterion indicators when generating recommendations.

2. The system according to paragraph 1, in which the set of sensors for monitoring the condition of the equipment includes: a vibration sensor; an ultrasonic microphone; a dust sensor; a temperature sensor; an infrared sensor; a power sensor. 11 SUBSTITUTE SHEET (RULE 26)