CN118940968A

CN118940968A - A smart office area management system and method based on the Internet of Things

Info

Publication number: CN118940968A
Application number: CN202411142396.3A
Authority: CN
Inventors: 麦广荣; 冯淑娴; 廖宇
Original assignee: Shenzhen Yuxi Technology Co ltd
Current assignee: Shenzhen Yuxi Technology Co ltd
Priority date: 2024-08-20
Filing date: 2024-08-20
Publication date: 2024-11-12

Abstract

The invention provides an intelligent office area management system and method based on the Internet of things, wherein the method comprises the following steps: building a BIM model according to three-dimensional data, design data and function description data of an office area; an intelligent sensor is arranged in an office area, regional data are collected in real time and transmitted to a cloud service platform; the cloud service platform performs statistical analysis on the data, establishes a digital model and analyzes the environmental parameter state, the personnel flow distribution and the resource use state; according to the analysis result, the intelligent control equipment is used for adjusting environmental facilities and intelligently managing shared resources, so that efficient utilization is realized; if an abnormal event is identified, pushing alarm information, and generating an abnormal processing measure according to the real-time area data and the digital model. According to the scheme, data acquisition, digital modeling and intelligent management based on the Internet of things technology are realized, and the operation efficiency and the use experience of an office area are improved.

Description

Intelligent office area management system and method based on Internet of things

Technical Field

The invention relates to the technical field of the Internet of things, in particular to an intelligent office area management system and method based on the Internet of things.

Background

The internet of things is an important component of a new generation of information technology, and is widely applied to networks through intelligent sensing and recognition technologies and pervasive computing. With the development of the internet of things and the progress of communication technology, the requirements of people on the environment are increasingly improved, and a comfortable, convenient and safe office environment becomes a pursuit target of people; however, the existing office area management scheme is not intelligent enough and low in efficiency, and an intelligent office area management system based on the Internet of things is needed.

Disclosure of Invention

Based on the above problems, the invention provides an intelligent office area management system and method based on the Internet of things, the data acquisition, the digital modeling and the intelligent management based on the internet of things technology are realized, and the operation efficiency and the use experience of an office area are improved.

In view of this, an aspect of the present invention proposes an intelligent office area management system based on the internet of things, including: the Internet of things server and the cloud service platform; wherein,

The internet of things server is configured to:

Acquiring first three-dimensional data, first design data and first function description data of a first office area;

Establishing a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first function description data;

configuring a plurality of first intelligent sensors in the first office area according to the first BIM model;

collecting first area data in the first office area in real time through the first intelligent sensor;

Transmitting the first region data to the cloud service platform;

The cloud service platform is configured to:

Carrying out statistical analysis on the collected first area data, and establishing a first digital model of the first office area;

analyzing and identifying the environmental parameter state, personnel flow distribution and resource use state of each area in the first office area according to the first digital model to obtain a first area analysis result;

according to the analysis result of the first area, intelligent control equipment is used for intelligently controlling and adjusting the environmental facilities in the first office area so as to ensure that environmental parameters are in a preset range and intelligently managing various shared resources in the first office area so as to realize efficient configuration and utilization of resources;

Identifying whether an abnormal event exists in the first area analysis result;

if an abnormal event exists, pushing alarm information and acquiring first real-time area data acquired by the first intelligent sensor;

and generating an exception handling measure according to the first real-time area data and the first digital model.

Another aspect of the present invention provides an intelligent office area management method based on the internet of things, including:

transmitting the first region data to a cloud service platform;

the cloud service platform performs statistical analysis on the collected first area data and establishes a first digital model of the first office area;

Identifying whether an abnormal event exists in the first area analysis result;

Optionally, the step of building a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first functional description data includes:

constructing an initial BIM model according to the first three-dimensional data;

Establishing entity object classes for each house object and/or facility object according to the names and the sizes in the first design data;

matching and associating the three-dimensional geometry in the initial BIM model with the entity object class;

inputting function labels in the first design data in the initial BIM model, associating correct function attributes for each entity object class, and determining attribute names and measurement units of each entity object class to be consistent with the first design data;

Importing the first function description data into the initial BIM model to serve as detailed text description of each entity object class;

performing graph management on the initial BIM model to realize scene simulation and analysis query;

checking whether the initial BIM model is consistent with a design plan in the first design data and has complete information;

If yes, the initial BIM model is used as the first BIM model;

If not, the initial BIM model is adjusted according to the design plan in the first design data, and the first BIM model is obtained.

Optionally, the step of configuring a plurality of first intelligent sensors in the first office area according to the first BIM model includes:

In the first BIM model, planning sensor types, the number of sensors of each type, parameter requirements of each sensor and theoretical arrangement positions of each sensor according to the function area division condition and the equipment layout condition, and establishing a first corresponding relation between each arrangement position and the sensor types, and between the sensor parameters and the number of sensors;

Combining reference office area data of other office areas based on time sequence association, and respectively determining a first type, a first parameter, a first quantity and a first arrangement position of each first intelligent sensor according to the sensor category, the number of each sensor category, the parameter requirement, the theoretical arrangement position and the first corresponding relation;

Installing each first intelligent sensor in the first office area according to the first type, the first parameters, the first quantity and the first arrangement positions, and recording the number of each first intelligent sensor and the corresponding identification of the entity object class in the first BIM model;

Acquiring real-time data of each first intelligent sensor, and testing whether the connection function of the first intelligent sensor and the house object and/or the facility object is normal or not;

If the sensor object is normal, uploading and writing the serial numbers and parameters of the first intelligent sensors into corresponding sensor object attributes in the first BIM model through a gateway so as to realize real-time bidirectional synchronous updating of the sensor objects and the entity sensors in the first BIM model.

Optionally, the step of performing statistical analysis on the collected first area data by the cloud service platform and establishing a first digital model of the first office area includes:

the first region data are tidied, and time sequence parameters and classification characteristics are extracted;

Establishing a time sequence analysis model of each index change of the first office area according to the time sequence parameters and the classification characteristics;

establishing a first physical model according to the relation between the object attribute of the first office area and the data characteristic of the entity object described in the first BIM model;

Predicting time-space distribution data of personnel activities and resource utilization in the first office area in different time periods according to the time sequence analysis model;

Extracting sample data from the reference office area data, training the sample data through a machine learning algorithm, and establishing a simulation model of the first office area;

Integrating and fusing the first physical model, the time-space distribution data and the simulation model to construct a first digital model;

and checking the first digital model, and adjusting parameters until the degree of matching with the real data reaches a preset matching value.

Optionally, the step of analyzing and identifying the environmental parameter status, the personnel flow distribution and the resource usage status of each area in the first office area according to the first digital model to obtain a first area analysis result includes:

Setting an analysis index system of each region in the first digital model;

obtaining statistical distribution data of each index in the analysis index system according to a historical data algorithm built in the first digital model;

Respectively comparing the statistical distribution data with a first preset standard corresponding to the environment parameter state, a second preset standard corresponding to the personnel flow distribution and a third preset standard corresponding to the resource use state, and determining an environment index abnormal value and an environment development trend, a people flow index abnormal value and a people flow development trend, and a resource use abnormal value and a resource use trend;

and generating a first area analysis result for marking the problem points and optimizing suggestions according to the environment index abnormal value and the environment development trend, the people stream index abnormal value and the people stream development trend, and the resource use abnormal value and the resource use trend.

Optionally, the step of intelligently controlling and adjusting the environmental facilities in the first office area by using the intelligent control device according to the analysis result of the first area to ensure that the environmental parameters are within a preset range and intelligently managing various shared resources in the first office area to realize efficient configuration and utilization of the resources includes:

According to the first area analysis result, determining a potential problem area of the environment and the resource utilization efficiency, and generating a corresponding tuning suggestion aiming at the potential problem area;

issuing the tuning advice to intelligent control equipment through an Internet of things;

the intelligent control equipment adjusts parameters of environmental facilities in the potential problem area according to the tuning advice;

acquiring real-time monitoring data of the first intelligent sensor;

judging whether the environmental parameters are in a preset range and/or whether various shared resources in the first office area accord with preset utilization rate according to the real-time monitoring data;

And if the environment parameters are not in the preset range and/or various shared resources in the first office area do not accord with the preset utilization rate, continuously optimizing the management measures of the first office area through the intelligent control equipment.

Optionally, the step of identifying whether the first area analysis result has an abnormal event includes:

extracting historical abnormal event data from the reference office area data;

establishing an anomaly identification model according to the historical anomaly event data;

preprocessing the analysis result of the first region, and extracting a first characteristic parameter;

Comparing the first characteristic parameter with an abnormal characteristic parameter in the abnormal recognition model by using a machine learning classifier stored in the abnormal recognition model;

The machine learning classifier gives out the probability score of each abnormal event and gives out the abnormal event type and level early warning with the maximum probability value.

Optionally, the step of generating an exception handling measure according to the first real-time region data and the first digitized model includes:

Determining an abnormal event real-time state from the first real-time region data according to the abnormal recognition model;

determining a first office area real-time state of the first office area according to the first real-time area data;

retrieving a general processing flow aiming at the abnormal event in a preset knowledge base;

And revising specific operation steps in the general processing flow according to the real-time state of the abnormal event and the real-time state of the first office area to obtain the abnormal processing measure.

Optionally, the step of revising the specific operation steps in the general processing flow according to the real-time status of the abnormal event and the real-time status of the first office area to obtain the abnormal processing measure includes:

Determining the current state of the abnormal event and the development trend of the abnormal event according to the real-time state of the abnormal event;

determining the current abnormal state and the current personnel evacuation state of the first office area according to the real-time state of the first office area;

According to the current state of the abnormal event, the development trend of the abnormal event, the current abnormal state and the current personnel evacuation state, evaluating the potential influence of the abnormal event on the first office area, and identifying an abnormal area and an abnormal problem which need to be processed preferentially;

according to the potential influence, the abnormal region and the abnormal problem, an evacuation route is adjusted, a high-risk region is avoided, resource allocation is optimized, and an emergency response level is updated;

Making detailed execution steps, defining responsibility division of departments and personnel, setting key nodes and completion time limit, and obtaining the exception handling measures;

and adjusting the processing measures in real time according to the latest state, evaluating the executing effect of the measures, and carrying out corresponding correction.

By adopting the technical scheme of the invention, the intelligent office area management method based on the Internet of things comprises the following steps: acquiring first three-dimensional data, first design data and first function description data of a first office area; establishing a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first function description data; configuring a plurality of first intelligent sensors in the first office area according to the first BIM model; collecting first area data in the first office area in real time through the first intelligent sensor; transmitting the first region data to a cloud service platform; the cloud service platform performs statistical analysis on the collected first area data and establishes a first digital model of the first office area; analyzing and identifying the environmental parameter state, personnel flow distribution and resource use state of each area in the first office area according to the first digital model to obtain a first area analysis result; according to the analysis result of the first area, intelligent control equipment is used for intelligently controlling and adjusting the environmental facilities in the first office area so as to ensure that environmental parameters are in a preset range and intelligently managing various shared resources in the first office area so as to realize efficient configuration and utilization of resources; identifying whether an abnormal event exists in the first area analysis result; if an abnormal event exists, pushing alarm information and acquiring first real-time area data acquired by the first intelligent sensor; and generating an exception handling measure according to the first real-time area data and the first digital model. According to the scheme, data acquisition, digital modeling and intelligent management based on the Internet of things technology are realized, and the operation efficiency and the use experience of an office area are improved.

Drawings

FIG. 1 is a schematic block diagram of an intelligent office area management system based on the Internet of things according to one embodiment of the present invention;

Fig. 2 is a flowchart of an intelligent office area management method based on the internet of things according to an embodiment of the present invention.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced otherwise than as described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.

The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

An intelligent office area management system and method based on the internet of things according to some embodiments of the present invention are described below with reference to fig. 1 to 2.

As shown in fig. 1, an embodiment of the present invention provides an intelligent office area management system based on the internet of things, including: the Internet of things server and the cloud service platform; wherein,

The internet of things server is configured to:

Transmitting the first region data to the cloud service platform;

The cloud service platform is configured to:

Identifying whether an abnormal event exists in the first area analysis result;

It should be noted that the block diagram of the intelligent office area management system based on the internet of things shown in fig. 1 is only illustrative, and the number of the illustrated modules does not limit the protection scope of the present invention.

Referring to fig. 2, another embodiment of the present invention provides an intelligent office area management method based on the internet of things, including:

in the step, three-dimensional point cloud data of all house structures and equipment layouts in a first office area are obtained by carrying out three-dimensional scanning on the first office area; carrying out fine processing on the scanning result, and extracting three-dimensional model file data of each house and equipment; according to the original design drawing, reducing structural design parameters such as personnel planning, area design and the like in the area; determining and recording or labeling functional usage descriptions for each region within the region by original design requirements, design materials, and field observations and interviews; the three-dimensional model data, the design data and the function description data are stored in a cloud database. Through the data collected in the step, a first digital twin of the first office area can be constructed, and a first entity space is truly restored; a first fine three-dimensional model can be provided to support a new round of design work; the first metadata can be input, and a first intelligent management basis is constructed; the access of the first data mobile phone end can be realized, and the remote visual management is supported; and can lay the foundation of the first intelligent operation and lay the foundation for intelligent operation.

collecting first area data (such as environment data, personnel activity data, resource use data and the like) in the first office area in real time through the first intelligent sensor;

In the step, a first intelligent sensor periodically and automatically collects data according to parameters, and the data is uploaded to a sensor object in which the data is located in a digital packet form; the sensor object converts the acquired data into a corresponding data standard format and stores the data in a corresponding attribute field of the first BIM model; the first BIM model integrates the collected data of a plurality of sensors into data analysis of each region, and identifies the change trend of the region; performing multivariate analysis on the collected environmental data, people stream data and resource use data, and drawing a regional space time dynamic map; the regional data is transparently presented to the administrative user, supporting query statistics and visual analysis. Through the step, unified data acquisition management based on BIM can be realized, multi-source physical indexes during real operation of the region are obtained, regional space decision references based on data analysis are provided, evidence basis for achieving regional resource operation and environment adjustment is provided, and a data foundation is laid for future intelligent facility control; the step is based on BIM, and the whole-course sensing of the state in the management area is realized.

Transmitting the first region data to a cloud service platform;

In the step, the first BIM module encapsulates and integrates the data acquired in each first area, selects a safe and reliable communication mode and sends the area data from the BIM module to the Internet of things platform; after receiving the data, the internet of things platform performs format verification and stores the data into a cloud service platform (cloud database server); the cloud service platform carries out association integration management on the regional physical data and BIM model information; developing cloud service application to realize online query and analysis of regional data by a cloud; the cloud service support issues the analysis result to the management end through the App. In the step, the centralized storage and management of the regional data are realized by utilizing the cloud technology, the capacity limitation of the local data storage and reasoning is solved, the network access query of any equipment to the regional data is realized, a uniform and normative development environment is provided for data application, and the regional operation decision support based on big data is supported. In the step, regional data are uploaded to the cloud, and the intelligent power-assisted management is converted to cloud service.

According to the analysis result of the first area, intelligent control and adjustment are performed on environmental facilities (such as lighting, ventilation, air conditioning and other equipment) in the first office area through intelligent control equipment so as to ensure that environmental parameters are within a preset range and intelligent management is performed on various shared resources (such as meeting rooms, multimedia equipment and the like) in the first office area so as to realize efficient allocation and utilization of resources;

Identifying whether an abnormal event exists in the first area analysis result;

In the step, after the abnormality recognition model recognizes that an abnormal event exists in the first office area, the state of the area is marked as abnormal at the cloud end immediately; the cloud pushes the abnormal event type and the regional position information to the intelligent control terminal through a short message or an application program, and the intelligent control terminal issues an instruction to a first intelligent sensor related to a first office area; the related first intelligent sensor starts to collect important monitoring parameters in the first office area at a higher sampling frequency; the first intelligent sensor reports the acquired first real-time area data to the cloud end at a higher frequency; the cloud end stores the first real-time region data and provides basis for further analysis and evaluation. In the step, the alarm can be pushed in time when the abnormality occurs, the monitoring frequency is raised after the alarm to acquire key data, an important reference is provided for subsequent event analysis and processing, the response and adjustment are timely carried out, the loss is reduced, and the automatic intelligent management under the digital environment is realized.

By adopting the technical scheme of the embodiment, the first three-dimensional data, the first design data and the first function description data of the first office area are acquired; establishing a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first function description data; configuring a plurality of first intelligent sensors in the first office area according to the first BIM model; collecting first area data in the first office area in real time through the first intelligent sensor; transmitting the first region data to a cloud service platform; the cloud service platform performs statistical analysis on the collected first area data and establishes a first digital model of the first office area; analyzing and identifying the environmental parameter state, personnel flow distribution and resource use state of each area in the first office area according to the first digital model to obtain a first area analysis result; according to the analysis result of the first area, intelligent control equipment is used for intelligently controlling and adjusting the environmental facilities in the first office area so as to ensure that environmental parameters are in a preset range and intelligently managing various shared resources in the first office area so as to realize efficient configuration and utilization of resources; identifying whether an abnormal event exists in the first area analysis result; if an abnormal event exists, pushing alarm information and acquiring first real-time area data acquired by the first intelligent sensor; and generating an exception handling measure according to the first real-time area data and the first digital model. According to the scheme, data acquisition, digital modeling and intelligent management based on the Internet of things technology are realized, and the operation efficiency and the use experience of an office area are improved. According to the scheme, data acquisition, digital modeling and intelligent management based on the Internet of things technology are achieved, and the operation efficiency and the use experience of an office area are improved.

In some possible embodiments of the present invention, the step of building a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first functional description data includes:

In this step, attribute information such as name, type, size, etc. of each house or facility object is extracted from the first design data; selecting a corresponding standard object template for each object in BIM software according to the object type library; designating a name and a type attribute value for each object template according to the attribute information; according to the position and azimuth information specified by the design document, adjusting the position and direction of the object template in the three-dimensional space; inputting the three-dimensional size attribute value of the length, width and depth of the object according to the real size; imparting material and surface properties to the object according to design requirements; and checking whether the object attribute value is consistent with the design document, and storing the object attribute value into an object class. Through the step, an object class system which truly corresponds to the design and has complete information can be constructed, a standardized object classification system is provided for information management and statistical calculation, and position and topology information is provided for future maintenance and embedded Internet of things equipment.

In the step, each established object class is exported to be three-dimensional geometric bodies with different colors, the initial model geometric bodies in the initial BIM model are classified, and different object types are represented by different colors; placing object geometry into initial model geometry of corresponding type and position one by one; checking the consistency of the object class geometry and the initial model geometry in position, shape and direction; establishing a topological relation between the object class and the initial model geometry; checking whether all geometric bodies are matched or not, and correcting necessary matching relation; writing the matching relation into the object attribute, and optimizing the BIM model structure organization. Through the step, the integration between the initial geometric information and the object attribute can be realized, the corresponding relation between each part of the model and the object attribute parameter is established, and a unified framework is provided for information inquiry, statistics and management.

In this step, functional labels of the respective objects, such as "office", "conference room", etc., are extracted from the first design data; in a BIM object classification system, preparing corresponding function attribute fields for each object class; the function labels of the corresponding objects in the design file are input into the function attribute fields in the object class; checking whether all attribute names, attribute types and units of the object class are opposite to the attribute defined by the design document; if the attribute is inconsistent, modifying to enable the attribute to be consistent; repeating the steps for all the objects until the functional attribute labels and the association of all the objects are completed. Through the step, consistency of object attributes and design requirements can be realized, standardized data references are provided for information extraction and management, objects and attribute definitions thereof are effectively associated, and guarantee is provided for subsequent management.

In this step, in the BIM model management software, a specification attribute field is prepared for each entity object; and extracting detailed function description characters of each object from the first function description data, and importing character information into detailed description attribute fields of corresponding objects. And repeating the steps until all the objects are imported with related descriptions, checking whether the detailed descriptions of all the objects are correctly imported, and carrying out format unification and information supplementation to ensure the description quality. In the step, rich functional background information is added for object attribute information; effectively utilizing information collected in the design stage, and enriching a BIM model; providing reference for future operation and management personnel, and improving the working efficiency; and a high-quality digital twin model is built, so that the full life cycle requirement is met.

If yes, the initial BIM model is used as the first BIM model;

In the embodiment, a true manageable digital first BIM model can be constructed, data content with rich object attributes is provided, the function region division condition and the equipment layout condition are described in detail, integrated inquiry of three-dimensional data and attribute information is realized, reliable basis is provided for periodic management and transformation of an office area, and professional technical guarantee of fine adjustment management is provided; constructing a high quality first BIM model is the basis for managing digitization.

In some possible embodiments of the present invention, the step of configuring a plurality of first smart sensors in the first office area according to the first BIM model includes:

In the step, the division of different functional areas is obtained from the first BIM model, and the types of sensors (such as temperature and humidity, smoke feeling and the like) to be monitored are planned and monitored according to the functional requirements of the different areas; planning the theoretical quantity of each type of sensor according to the area and the importance degree of the area; determining parameter items and precision requirements monitored by each type of sensor according to the sensor model specification; in the BIM model, judging the theoretical installation position of each sensor according to the layout and the monitoring requirements; establishing sensor class in BIM object attribute, recording attribute such as type, parameter, quantity, etc.; each theoretical position is correlated with a corresponding sensor class. In the step, the sensor network is planned to be reasonably arranged, the corresponding relation between BIM and sensor attributes is established, standard reference is provided for installation and monitoring, intelligent monitoring closed-loop management is realized through layout optimization, and fine matching of BIM closed-loop and monitoring implementation is ensured.

Determining a first type, a first parameter, a first number and a first arrangement position of each first intelligent sensor (such as a temperature sensor, a humidity sensor, an illumination sensor, a micro sensor, an image sensor and the like) according to the sensor category, the number of various sensors, the parameter requirement, the theoretical arrangement position and the first corresponding relation respectively in combination with reference office area data (historical environment data, historical operation data, historical people stream data, historical overhaul and maintenance data, spatial layout/construction data, sensor arrangement data, sensor work data and the like) of other office areas based on time sequence association;

In the step, the sensor type, parameter setting and quantity planning conditions of the similar areas of the history are extracted from the data of the reference office area; selecting sensor reference data according to the similarity of the first office area and the reference area in functions, scales, structures and the like; determining the type requirement (namely the first type) of the corresponding sensor of the first office area by referring to the type specification of the same sensor in the sensor reference data and combining the sensor types; referring to the average value of the same sensor quantity in the sensor reference data, adjusting the quantity setting (namely the quantity of the sensors of each class) of the corresponding sensor of the first office area to obtain a first quantity; determining preliminary parameters of the corresponding sensor of the first office area according to common parameter settings of the same sensor in the sensor reference data and combining the parameter requirements; the parameters are adjusted according to the actual conditions of the first office area, so that the parameters are more finely adapted to obtain first parameters; according to the first correspondence, the types, numbers, and parameters are refined to corresponding sensor locations (i.e., first placement locations) in a first BIM model. Through the steps, the design precision can be improved, and the design period can be shortened; repeated careless omission is avoided, and the design quality is improved; similar case experience is fully utilized, and the application threshold is reduced; pushing the intelligent process to a standardized shaping stage; and the intelligent monitoring capability is improved by data deduction design.

According to the embodiment, the intelligent sensor is accurately deployed according to the BIM model, a digital double-entity-digital mutual mapping relation is formed, a unified intelligent management platform based on BIM is realized, a foundation is laid for information acquisition, and visual decision support is developed; and the Internet of things sensor is correctly arranged according to the BIM model, so that the intelligent monitoring closed loop is realized.

In some possible embodiments of the present invention, the step of the cloud service platform performing statistical analysis on the collected first area data to build a first digitized model of the first office area includes:

In the step, the sensor data can be arranged according to the monitoring time sequence to construct a time sequence data frame; performing time sequence analysis on the corresponding indexes of each sensor, and extracting parameters such as periodicity, trending and the like; classifying and coding various environmental influence factors (such as people flow, equipment use condition and the like) in the area; binding the classification codes into corresponding time spans according to the occurrence time of the influence factors; performing association analysis on the environmental parameters and the classification influence factors, and identifying characteristic influence rules; and checking whether the time sequence parameters and the classification features completely represent the original data features. In the step, the essential characteristics of the data can be extracted, the data scale is reduced, basic variables are provided for subsequent modeling and management, important rules of regional operation are mastered, the model establishment and decision-making efficiency is improved, and data mining is the basis for deeply understanding regional operation.

In the step, the extracted time sequence parameters and classification features are imported into a time sequence analysis model template, and a proper time sequence analysis method (ARIMA, prophet and the like) is selected according to the index type (such as temperature and humidity and the like); inputting time sequence parameters, and training a model to learn index change rules; inputting classification characteristics, and bringing influence factors into the establishment of model relation; optimizing super parameters of the model, and optimizing fitting residual errors and prediction performance; repeating the steps for each index to establish a dedicated time sequence model; and (5) counting evaluation indexes of each model, and evaluating the overall modeling quality. In the step, the dynamic change rule of each index can be quantitatively described and predicted, the influence decomposition on complex factors is realized, the decision support and risk early warning capability are provided, the management decision driven by data is supported, the deep layer relation is extracted through time sequence modeling, and the intelligent decision is assisted.

in the step, each object attribute (such as name, position, etc.) can be extracted from the first BIM model, each object influence factor classification characteristic is extracted from data mining, and the object type (such as person, equipment, etc.) of the physical model to be modeled is determined; creating an instance for each object to be modeled by using the object attribute in the physical model software; defining classification features as different physical attributes, and endowing corresponding object instances; according to the object position relation in the BIM model, adjusting the position of the object instance in the physical space; establishing interaction rules, such as people stream behavior rules, among object examples; setting a physical simulation scene, running and debugging processing rules. The object relation in the BIM can be reflected through the step, the real running condition of the region is restored, the design of supporting equipment and people stream reporting is supported, the next physical construction and running is guided, and the intelligent operation planning and verification of physical modeling and boosting are performed.

In the step, according to each index time sequence model, predicting predicted environment parameter values at different time points (such as working day rush hour and working day rush hour); inputting predicted environment parameters into a physical model, and simulating environment scenes at different time points; operating a physical model, and predicting a people flow distribution rule by considering personnel behavior rules and environmental influence; predicting the activity intensity of personnel space under different time, such as the distribution of the people flow density in a certain area; combining BIM model resource attribute to further predict resource utilization condition distribution under different time; and comparing the prediction result with the real data, evaluating the prediction precision and improving the model. Through the method, people flow and resource use distribution in an important time period can be predicted, support is provided for operation scheduling and scene simulation decision, important area congestion and resource deficiency are effectively prevented, area operation efficiency and user experience are improved, and full-flow data driving management decision is supported. For people stream analysis, it may include: human body model creation, namely, creating standard human body digital models of different types (such as men, women and the like) by using three-dimensional modeling software according to human body dimension statistical data; behavior rule design, namely, formulating behavior rule parameters such as human walking speed, sight line range, avoiding distance and the like, and describing action characteristics of people flow in different scenes; the path planning algorithm is that an optimal path is selected to plan a people stream walking route by using a Jackson algorithm, an A algorithm and the like; the people stream is imported, namely, a corresponding number of digital human models are input into a three-dimensional scene according to the people flow of an actual office scene; dynamically simulating, namely dynamically simulating real time of the people stream model based on a set behavior rule, and visualizing a people stream movement track; parameter tuning, namely, adjusting behavior parameters according to experimental results, such as increasing the avoiding distance to simulate people flow behavior in crowded scenes; analyzing and identifying, namely counting the number of people passing through different areas per unit time, and identifying the people flow tide time and possible congestion points; feedback optimization, namely feeding back an analysis result to scene configuration, redesigning a diversion area and the like for verification optimization; repeating simulation, namely repeating simulation to check whether the people flow is smooth under different design schemes.

In this step, sample data similar to the first office area design parameter is extracted from the reference office area; dividing sample data into input factors (such as people stream, environmental parameters and the like) and output results (such as resource utilization rate and the like); selecting a suitable machine learning algorithm, such as a decision tree or neural network; the input factors and the output results are imported into a machine learning algorithm for training; training by using part of sample data, and using the rest data to verify the model effect; and adjusting the super parameters, repeating training verification to optimize the performance of the model, and establishing a simulation model of the first office area. In the step, the large-scale and generalized capacity of the model can be ensured, support is provided for future real-time simulation and scheduling decision, data-driven digital management is realized, simulation capacity and management level are continuously improved, machine learning energization modeling is realized, and full-flow intelligent operation is assisted.

In this step, the first physical model may be imported into a digital twin simulation platform; importing the time-space distribution data into a physical model as environment input and weight constraint; importing a simulation model into a platform as a rule core and a decision engine; integrating data transmission interfaces among the three, setting simulation scene rules to drive physical behaviors, and running an integration model on a platform to debug and verify; and analyzing the abnormal result to further optimize the model. In the step, the real operation scene and behavior rule can be restored, the simulation precision is improved, the application range is enlarged, the whole-flow digital management and decision support are realized, the power-assisted resource scheduling and event response optimization are realized, the continuous improvement of intelligent operation capability is supported, and the integrated digital modeling is realized.

In the embodiment, panoramic digital description of the operation of the first office area is realized, a statistical analysis report of each index of the first office area is provided, future dynamic change rules of the first office area are predicted, comprehensive digital description of the state of the first office area is realized, and a foundation of a later management level is laid.

In some possible embodiments of the present invention, the step of analyzing and identifying the environmental parameter status, the personnel flow distribution and the resource usage status of each area in the first office area according to the first digital model to obtain a first area analysis result includes:

Setting an analysis index system of each region in the first digital model;

In the step, various index types (such as environmental indexes, safety indexes and the like) possibly needing to be analyzed are preliminarily determined according to the regional functional characteristics and the management requirements; further subdividing each index type to determine specific index items (such as temperature, light intensity and the like); comparing the reference data/cases, and confirming the specific measurement content and calculation method of each index; designing an index system data receiving interface, and connecting with sensor data contained in the model; establishing a corresponding attribute field for each region in the model, and corresponding to each index; the index statistics period is standardized, and a threshold value parameter taking range is set; after the system is constructed, whether various indexes are complete and reasonable is reviewed. In the step, a standard system can be provided for subsequent regional assessment, analysis dimensionality is customized for each functional region, intelligent management work guidelines are guided, fine operation under data guidance is realized, and an index system is established as a basis for digital management.

In this step, the historical data algorithm may be: a time sequence analysis algorithm (analyzing the time sequence relation of variables by utilizing an autocorrelation function of time sequence data, identifying a circulation mode and a trend change rule), a cluster analysis algorithm (carrying out unsupervised clustering on historical data samples according to characteristic dimensions, identifying an intrinsic classification mode of the data), an abnormality detection algorithm (establishing a normal value range through statistical learning of the historical data, giving an index threshold value, identifying abnormal value points in new data), a prediction algorithm (training a historical data model by using algorithms such as ARIMA, holt-windows and the like, carrying out short-term prediction of a time sequence), a correlation analysis algorithm (analyzing the correlation strength degree among indexes by calculating correlation coefficients or mutual information and the like), a distribution fitting algorithm (carrying out probability distribution of fitting index values by utilizing probability distribution functions such as normal distribution, deflection distribution and the like), a characteristic extraction algorithm (extracting data main components based on a dimension reduction method, grasping core characteristics) and the like; the historical data algorithm acquires the statistical distribution mode and the management rule of the index by training the historical acquisition data.

In the step, threshold judgment can be performed on the environmental parameter data according to a first preset standard based on the statistical distribution data, abnormal values are identified, clustering analysis is performed on the abnormal data, and environmental parameter change trend is identified; judging abnormal values of the people stream distribution data according to a second preset standard, carrying out time sequence analysis on the people stream abnormal data, and identifying a people stream change trend; and judging abnormal values of the resource use data according to a third standard, carrying out association analysis on the abnormal data of the resource, and identifying the use trend of the resource. Through the step, abnormal values of key data can be accurately identified, potential change rules of various indexes are revealed, basis is provided for subsequent risk early warning and adjustment decision making, digital management level and response capability are improved, and learning and treating capability of boosting problem rules are improved; the step takes the standard as a benchmark to improve the analysis capability of the abnormal event.

In this embodiment, by comprehensively evaluating the current running state of each area of the first office area, providing the basis and reference sample for the adjustment of the first office area, identifying the potential problem and the point to be improved of the first office area, playing a guiding role in improving the management level of the first office area, and laying a foundation for the intelligent management of the office area.

In some possible embodiments of the present invention, the steps of intelligently controlling and adjusting, by an intelligent control device, the environmental facilities in the first office area according to the analysis result of the first area to ensure that the environmental parameters are within a preset range and intelligently managing various shared resources in the first office area to realize efficient configuration and utilization of resources include:

In the step, searching a region with environment parameters and resource utilization rate abnormally high or low (such as a preset duration is exceeded and the utilization rate exceeds/is lower than a preset value) for a long term in the first region analysis result; judging a potential problem area through the numerical value and the time distribution characteristics; detecting corresponding influence factors (people stream change and the like) of the problem area; analyzing the cause of the problem by referring to the case and expert opinion of other areas; designing a related adjustment scheme according to the cause; evaluating the tuning effect of each scheme in the model; generating tuning advice including description of the problem, adjustment measures and expected effects. In the step, the hidden efficiency trouble can be pertinently solved, the resource utilization and the environmental quality are improved, the next design and implementation improvement are guided, the digital management level and the service level are improved, the power-assisted intelligent process is converted into qualitative management and control, the data is led to be accurately optimized, and the management efficiency is improved.

the intelligent control device adjusts parameters (such as illumination brightness and air-conditioning temperature set points) of environmental facilities in the potential problem area according to the tuning advice;

acquiring real-time monitoring data of the first intelligent sensor;

In the embodiment, the environment parameters are accurately adjusted and the resource allocation and scheduling are realized, the management efficiency is improved, the use environment is improved, the intelligent operation capability under the guidance of data is started, a replicable sample of management experience is provided, and a foundation for improving the management level is laid; through data driving intelligent management, the maximization of management efficiency is realized.

In some possible embodiments of the present invention, the step of identifying whether the first area analysis result has an abnormal event includes:

extracting historical abnormal event data from the reference office area data;

establishing an anomaly identification model according to the historical anomaly event data (the anomaly identification model can identify an anomaly event according to anomaly characteristic parameters such as temperature rise, local light brightening, smoke concentration rising and the like during a fire alarm);

In this step, related index data during different types of abnormal events are extracted from the historical abnormal event data, and the data is preprocessed, such as standardized processing; marking event types as classification features, taking index data as input features, and selecting a proper machine learning classification algorithm according to model types (such as decision trees, SVMs and the like); randomly dividing the data into training and testing data sets; training a model on a training data set, and finding out an optimal super-parameter combination; evaluating model classification effects, such as accuracy, on the test dataset; and further optimizing the model according to the evaluation result. Through the method, the type of the abnormal event can be identified in real time, the occurrence of the abnormal event can be early warned in advance, and the security response efficiency is improved; identifying novel anomalies, perfecting a model, and realizing intelligent management of data driving; the abnormal recognition model can effectively improve the office area safety management level.

in the step, an established abnormal recognition model is activated, and the extracted first characteristic parameters are classified and recognized by using a classifier; inputting the first characteristic parameters into a classifier to perform prediction classification; judging whether the predicted result is of an abnormal event type or not; judging the reliability of the identification result according to the confidence coefficient of the classifier; if the abnormal condition exists, extracting abnormal details to generate a report. Through the step, real-time abnormal monitoring and identification can be realized, possible safety events can be early warned in time, the response efficiency of safety production is improved, loss is effectively prevented and controlled and reduced, the digital security management level is improved, and technical support is provided for comprehensive operation monitoring.

After the characteristic parameters monitored in real time are input into the classifier for prediction in the last step, the classifier gives a probability value of each abnormal event type; searching a predicted probability value of each event type, and searching the largest event type; judging the prediction reliability by combining the degree of the maximum probability value; classifying the credibility through threshold setting, and identifying the grade; semantic early warning information is given according to the grade; recording the identification result, and facilitating later consulting and analysis. In the step, the identification effect can be quantified, and the false alarm rate is reduced; generating early warning response calls of different grades; the novel abnormality is effectively identified, and the model is perfected; unified data output is easy to integrate and apply; and the safety operation automation level is improved.

In the embodiment, the hidden abnormal change in the area can be timely found, potential safety hazards and problem points are early warned in advance, management response efficiency is effectively improved, consequences of abnormal events are prevented and lightened, and area operation monitoring and safety guarantee capability is improved. Regional potential safety hazards are identified through the digital model, and intelligent safety management is achieved.

In some possible embodiments of the present invention, the step of generating an exception handling measure according to the first real-time area data and the first digitized model includes:

determining an abnormal event real-time state (such as a fire development state, a water accumulation depth state and the like) from the first real-time region data according to the abnormal recognition model;

Determining a first office area real-time state (such as an office area disaster degree, a personnel evacuation state and the like) of the first office area according to the first real-time area data;

In the present embodiment, a countermeasure scheme can be scientifically generated based on real data; the method has the advantages that historical experience is utilized efficiently, an optimal solution is designed, powerful reference support is provided for management decision making, the influence of artificial subjective factors on the solution is reduced to the maximum extent, the autonomous processing capability under a digital working scene is realized, and specialized abnormal treatment measures are generated by data driving.

In some possible embodiments of the present invention, the step of revising the specific operation steps in the general processing flow according to the real-time status of the abnormal event and the real-time status of the first office area to obtain the abnormal processing measure includes:

Determining the current state (such as data of fire, water accumulation and the like) of the abnormal event and the development trend (such as the development trend of the abnormal event estimated by utilizing technologies such as video analysis and the like) of the abnormal event according to the real-time state of the abnormal event;

Determining a current abnormal state (such as a range affected by flame or water accumulation) and a current personnel evacuation state (such as the number of evacuated persons, the positions of the remaining persons and the like) of the first office area according to the real-time state of the first office area;

according to the potential influence, the abnormal region and the abnormal problem, adjusting evacuation routes, avoiding high-risk regions, optimizing resource allocation (such as increasing fire-fighting power to a region with larger fire), and updating emergency response levels (such as expanding the lifting response levels according to the fire);

Making detailed execution steps (such as specific fire extinguishing or water draining operation), defining responsibility division of each department and personnel, setting key nodes and completion time limit, and obtaining the exception handling measures;

The embodiment can improve pertinence and effectiveness of emergency response, realize dynamic optimization of treatment measures, improve flexibility of handling emergency, optimize resource allocation, improve emergency treatment efficiency, reduce casualties and property loss risks, and provide data support for follow-up emergency plan optimization. By the method, the emergency treatment flow can be flexibly adjusted according to actual conditions, and hysteresis possibly brought by executing a fixed plan is avoided, so that various emergency abnormal conditions can be better dealt with.

In some possible embodiments of the present invention, the method comprises: monitoring the development state of an event in real time, such as knowing the diffusion speed of fire, the water depth of accumulated water and the like; factors such as disaster tolerance range/personnel evacuation degree of an office area are monitored in real time; comparing the real-time data with a general flow, and evaluating the applicability of the measure; according to the difference between the two, adjusting the detailed operation of the flow: preferentially evacuating personnel in a wider area, and selecting drainage/fire extinguishing equipment which is easy to operate and more effective; feeding back the modified flow, and searching for expert review to ensure feasibility; notifying the field executive to implement the new flow. According to the embodiment, the real-time condition adjustment processing scheme is fully considered, the defect of a general flow in actual operation is overcome, the processing efficiency is improved, the secondary loss is reduced, the dynamic optimization of event countermeasures under digitization is realized, the defect of subjective factors of manual judgment is overcome, real-time revising measures under data support is realized, and the reaction capability is improved.

In some possible embodiments of the present invention, the method further comprises: the method comprises the steps of simulating a personnel evacuation line and time when an abnormal situation occurs by using a model, and finding out a possible bottleneck area, wherein the method comprises the following steps of:

Setting safety exits in different areas and compiling evacuation routes in a first digital model;

setting optimal and suboptimal evacuation paths for each person according to people flow density and equipment limit;

According to the attribute of sex, age and the like of the personnel, setting evacuation parameters such as walking speed and the like of the personnel;

simulating evacuation actions and positions of each person according to path scheduling at certain intervals;

Counting the personnel remaining density of different areas at each time point, and identifying possible stagnation points;

Searching for reasons causing stagnation such as people flow robbing or channel obstruction;

technical countermeasures such as adding emergency exits or optimizing instruction guides are proposed.

In the embodiment, potential safety hazards can be checked in advance, so that evacuation efficiency is improved; testing the scientificity and operability of the emergency plan; the protection level of the safety facility is upgraded in a targeted way; the scientificity and the clarity of the management decision are improved.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Although the present invention is disclosed above, the present invention is not limited thereto. Variations and modifications, including combinations of the different functions and implementation steps, as well as embodiments of the software and hardware, may be readily apparent to those skilled in the art without departing from the spirit and scope of the invention.

Claims

1. A smart office area management system based on the Internet of Things, characterized by comprising: an Internet of Things server and a cloud service platform; wherein,

The IoT server is configured as follows:

Acquire first three-dimensional data, first design data, and first functional description data of a first office area;

Establishing a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first functional description data;

According to the first BIM model, a plurality of first intelligent sensors are configured in the first office area;

Transmitting the first regional data to the cloud service platform;

The cloud service platform is configured as follows:

Performing statistical analysis on the collected data of the first area to establish a first digital model of the first office area;

Analyze and identify the environmental parameter status, personnel flow distribution and resource usage status of each area in the first office area according to the first digital model to obtain a first area analysis result;

Based on the analysis results of the first area, intelligently control and adjust the environmental facilities in the first office area through intelligent control equipment to ensure that the environmental parameters are within a preset range and intelligently manage various shared resources in the first office area to achieve efficient resource allocation and utilization;

Identify whether there is an abnormal event in the first area analysis result;

If an abnormal event occurs, an alarm message is pushed, and first real-time regional data collected by the first smart sensor is obtained;

An exception handling measure is generated according to the first real-time regional data and the first digital model.

2. A smart office area management method based on the Internet of Things, characterized by comprising:

Transmitting the first regional data to the cloud service platform;

The cloud service platform performs statistical analysis on the collected data of the first area to establish a first digital model of the first office area;

According to the analysis result of the first area, the environmental facilities in the first office area are intelligently controlled and adjusted through the intelligent control device to ensure that the environmental parameters are within the preset range and the various shared resources in the first office area are intelligently managed to achieve efficient allocation and utilization of resources;

Identify whether there is an abnormal event in the first area analysis result;

3. The method for managing a smart office area based on the Internet of Things according to claim 2, wherein the step of establishing a first BIM model of the first office area according to the first three-dimensional data, the first design data and the first functional description data comprises:

Establishing an entity object class for each house object and/or facility object according to the name and size in the first design data;

Matching and associating the three-dimensional geometric body in the initial BIM model with the entity object class;

Inputting the function annotations in the first design data into the initial BIM model, associating correct function attributes for each of the entity object classes, and ensuring that the attribute names and measurement units of each of the entity object classes are consistent with the first design data;

Importing the first functional description data into the initial BIM model as a detailed text description of each entity object class;

Performing layer management on the initial BIM model to achieve scenario simulation and analysis query;

Check whether the initial BIM model is consistent with the design plan in the first design data and whether the information is complete;

If yes, the initial BIM model is used as the first BIM model;

If not, the initial BIM model is adjusted according to the design plan in the first design data to obtain the first BIM model.

4. The method for managing a smart office area based on the Internet of Things according to claim 3, wherein the step of configuring a plurality of first smart sensors in the first office area according to the first BIM model comprises:

In the first BIM model, the sensor categories, the number of sensors of each category, the parameter requirements of each sensor and the theoretical layout positions of each sensor are planned according to the functional area division and the equipment layout, and a first corresponding relationship between each layout position and the sensor category, the sensor parameter and the number of sensors is established;

In combination with reference office area data based on time series association of other office areas, the first type, the first parameter, the first quantity and the first arrangement position of each first smart sensor are determined according to the sensor category, the number of sensors of each category, the parameter requirements, the theoretical arrangement position and the first corresponding relationship;

Installing each of the first smart sensors in the first office area according to the first type, the first parameter, the first quantity, and the first arrangement position, and recording the serial number of each of the first smart sensors and the corresponding identifier of the entity object class in the first BIM model;

Acquire real-time data of each of the first smart sensors, and test whether the connection function between the first smart sensor and the house object and/or facility object is normal;

If normal, the numbers and parameters of each of the first smart sensors are uploaded through the gateway and written into the corresponding sensor object attributes in the first BIM model to achieve real-time bidirectional synchronous update of the sensor objects and physical sensors in the first BIM model.

5. The method for managing a smart office area based on the Internet of Things according to claim 4, wherein the cloud service platform statistically analyzes the collected data of the first area to establish a first digital model of the first office area, comprising:

Arranging the data of the first region and extracting time series parameters and classification features;

Establishing a time series analysis model for changes in each indicator of the first office area according to the time series parameters and the classification characteristics;

Establishing a first physical model according to the relationship between the object attributes of the first office area described in the first BIM model and the data features of the physical object;

Predicting the time-space distribution data of personnel activities and resource utilization in the first office area in different time periods according to the time series analysis model;

Extracting sample data from the reference office area data, and training the sample data through a machine learning algorithm to establish a simulation model of the first office area;

Integrate and fuse the first physical model, the time-space distribution data and the simulation model to construct a first digital model;

The first digital model is tested and the parameters are adjusted to match the real data to a preset matching value.

6. The method for managing a smart office area based on the Internet of Things according to claim 5, characterized in that the step of analyzing and identifying the environmental parameter status, personnel flow distribution and resource usage status of each area in the first office area according to the first digital model to obtain the first area analysis result comprises:

Setting an analysis index system for each region in the first digital model;

Obtaining statistical distribution data of each indicator in the analysis indicator system according to the historical data algorithm built into the first digital model;

The statistical distribution data is respectively compared with a first preset standard corresponding to the environmental parameter state, a second preset standard corresponding to the personnel flow distribution, and a third preset standard corresponding to the resource usage state to determine abnormal values of environmental indicators and environmental development trends, abnormal values of personnel flow indicators and personnel flow development trends, and abnormal values of resource usage and resource usage trends;

Based on the abnormal values of environmental indicators and environmental development trends, the abnormal values of human flow indicators and human flow development trends, and the abnormal values of resource use and resource use trends, the first regional analysis results are generated to mark problem points and optimization suggestions.

7. The method for managing a smart office area based on the Internet of Things according to claim 6 is characterized in that the step of intelligently controlling and adjusting the environmental facilities in the first office area through intelligent control equipment to ensure that the environmental parameters are within a preset range and intelligently managing various shared resources in the first office area to achieve efficient resource allocation and utilization includes:

Determine potential problem areas of environment and resource utilization efficiency based on the first area analysis results, and generate corresponding tuning suggestions for the potential problem areas;

Sending the tuning suggestions to the intelligent control device via the Internet of Things;

The intelligent control device adjusts the parameters of the environmental facilities in the potential problem area according to the tuning suggestion;

Acquire real-time monitoring data of the first intelligent sensor;

Determining whether environmental parameters are within a preset range and/or whether various shared resources in the first office area meet preset usage rates according to the real-time monitoring data;

If the environmental parameters are not within a preset range and/or the various shared resources in the first office area do not meet the preset usage rate, the management measures of the first office area are continuously optimized through the intelligent control device.

8. The method for managing a smart office area based on the Internet of Things according to claim 7, wherein the step of identifying whether there is an abnormal event in the analysis result of the first area comprises:

Extracting historical abnormal event data from the reference office area data;

Establishing an anomaly recognition model based on the historical abnormal event data;

Preprocessing the first region analysis result to extract a first characteristic parameter;

Using a machine learning classifier stored in the abnormality recognition model, comparing the first feature parameter with the abnormal feature parameter in the abnormality recognition model;

The machine learning classifier gives a probability score for each abnormal event and issues a warning for the type and level of the abnormal event with the highest probability value.

9. The method for managing a smart office area based on the Internet of Things according to claim 8, wherein the step of generating an exception handling measure according to the first real-time area data and the first digital model comprises:

Determining a real-time status of an abnormal event from the first real-time regional data according to the abnormality recognition model;

determining a first office area real-time status of the first office area according to the first real-time area data;

Retrieving a common processing flow for the abnormal event in a preset knowledge base;

According to the real-time status of the abnormal event and the real-time status of the first office area, the specific operation steps in the general processing flow are revised to obtain the abnormal processing measures.

10. The method for managing a smart office area based on the Internet of Things according to claim 9, characterized in that the step of revising the specific operation steps in the general processing flow according to the real-time status of the abnormal event and the real-time status of the first office area to obtain the abnormal processing measures comprises:

Determine the current state of the abnormal event and the development trend of the abnormal event according to the real-time state of the abnormal event;

Determine the current abnormal state and current personnel evacuation state of the first office area according to the real-time state of the first office area;

According to the current state of the abnormal event, the development trend of the abnormal event, the current abnormal state and the current personnel evacuation state, evaluate the potential impact of the abnormal event on the first office area, and identify the abnormal areas and abnormal problems that need to be handled with priority;

According to the potential impact, the abnormal area and the abnormal problem, adjust the evacuation route, avoid high-risk areas, optimize resource allocation, and update the emergency response level;

Formulate detailed implementation steps, clarify the division of responsibilities of each department and personnel, set key nodes and completion deadlines, and obtain the aforementioned exception handling measures;

Adjust processing measures in real time according to the latest status, evaluate the effectiveness of the measures, and make corresponding corrections.