AI-driven online intelligent Internet of Things monitoring and evaluation method, system and device for building group service and medium

Abstract

The invention relates to an AI-driven online intelligent Internet of Things monitoring and evaluation method, system, equipment and medium for building group services, and the method comprises the steps: collecting and preprocessing environment parameters, equipment states and user data through a sensor network and a user interface, carrying out the clustering analysis through a dynamic Gaussian mixture model, and obtaining a clustering result; identifying and dividing a plurality of clusters or groups; analyzing and processing clusters or groups through a deep learning network to obtain an environment data analysis result, model prediction output and a user behavior analysis report, comprehensively evaluating the state of the building group through fuzzy synthesis operation, and generating a control strategy; and constructing an online sharing platform, and migrating a control strategy to other building groups by utilizing transfer learning to realize sharing of models, strategies and experience. According to the invention, many problems existing in a traditional Internet of Things system are solved, real-time data analysis, intelligent decision and system integration are realized, and the management efficiency of the building group and the user experience are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent Internet of Things for building complexes, and in particular to an AI-driven online intelligent Internet of Things monitoring and evaluation method, system, device and medium for building complex services. Background Art

[0002] With the continuous advancement of Internet of Things (IoT) technology, more and more devices are connected to the Internet, enabling real-time data collection and exchange. These devices are widely used in many fields such as home automation, industrial monitoring, and environmental monitoring. However, traditional IoT systems mainly focus on data collection and transmission functions, and fail to conduct in-depth analysis and intelligent control of the collected data, which to some extent limits the potential and application value of IoT systems.

[0003] Existing IoT solutions usually perform operations based on preset rules, which are relatively rigid and difficult to flexibly respond to dynamic changes in the environment or meet the personalized needs of users. Although some systems have tried to use basic data analysis techniques, these analysis processes are often performed offline, unable to achieve real-time response to environmental changes, and have not achieved a truly intelligent control level.

[0004] The development of artificial intelligence (AI) technology has brought new opportunities to IoT systems. By integrating advanced AI algorithms, IoT systems can not only analyze data in real time, but also make intelligent decisions and controls based on the analysis results. However, the relevant solutions currently on the market still have significant deficiencies in how to effectively integrate IoT monitoring, AI algorithm models, AI control, and comprehensive evaluation. These systems often fail to organically integrate these components into an overall system that works together, but instead handle each part in isolation, which leads to problems such as low system efficiency and long response time.

[0005] In addition, there are several key issues with existing technologies in real-time data monitoring and processing. First, many systems cannot achieve real-time data processing and response, especially when processing large amounts of data or when network conditions are not ideal, there is often a significant delay in data processing, which limits the application effect of the system in emergency or critical situations. Second, the decision-making process of current systems mostly relies on predefined rules, lacks adaptive capabilities, and cannot automatically adjust according to environmental changes or user behavior patterns, so it performs poorly when dealing with unforeseen or non-standard situations. Furthermore, the poor integration between IoT devices, AI algorithms, and control systems leads to inefficient transmission of data flows and control commands between system components. This segmented architecture has a negative impact on the overall performance and reliability of the system. Finally, existing technologies are usually under-optimized in terms of energy consumption and operating costs, resulting in inefficient system operation, which in turn increases unnecessary energy waste and maintenance costs. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation method, system, equipment and medium for building complex services, which solves the technical problems that traditional Internet of Things systems lack real-time data analysis, intelligent decision-making and system integration, resulting in low efficiency, slow response, and inability to meet personalized needs.

[0007] (II) Technical solution In order to achieve the above object, the main technical solutions adopted by the present invention include: In a first aspect, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services, including: Collect and pre-process environmental parameters, equipment status, and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex; Performing cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model to identify and divide multiple clusters or groups each representing a specific comfort zone; Through deep learning network analysis and processing of clusters or groups, we can obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports; Based on the environmental data analysis results, model prediction output and user behavior analysis report, a comprehensive status evaluation of the building complex services is obtained through fuzzy synthesis operation, and at least one control strategy for the building complex is generated based on the comprehensive status evaluation; Build an online sharing platform between building complexes, and use transfer learning to migrate the control strategies of the building complexes generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

[0008] Optionally, through a dynamically configurable sensor network and user interface pre-deployed in the building complex, environmental parameters, device status and user data are collected and pre-processed, including: Divide the space of the building complex into grids or areas, introduce the grid importance index, assign initial sensor deployment nodes to each grid or area, and preset a basic activation strategy for each node; A hybrid optimization algorithm framework is constructed based on simulated annealing algorithm and genetic algorithm. Iterative search is performed based on the hybrid optimization algorithm framework. In combination with the introduced innovative operators including node position adjustment and activation strategy reorganization, new sensor deployment schemes and new activation strategies are generated. In each iteration, the deployment location and activation strategy of the sensor nodes are evaluated according to the fitness function; The sensor deployment scheme and activation strategy are dynamically updated using the simulated annealing probability acceptance criterion, and the grid importance index and fitness function parameters are updated synchronously. After the dynamic sensor network is deployed based on the new sensor deployment node, the sensors in the corresponding area are dynamically activated or dormant based on the activation strategy or the real-time needs input by the user to collect environmental parameters, device status and user behavior data; By analyzing user behavior data, we can obtain hot spots of user activity and determine the deployment location of the user interface based on environmental characteristics. After the user interface is deployed, receiving and storing user feedback data through the user interface; Pre-process the collected environmental parameters, equipment status, user behavior data, and user feedback data; in, The grid importance index is: ; In the formula, E G is the rate of change of environmental parameters, S is the space complexity including the number of spatial elements and the number of connected elements, P is the density of people flow, E n is the rate of change of environmental parameters, α , β , c is the basic weight coefficient, is an exponential coefficient, which is used to adjust the nonlinear effect of the corresponding factor. Ais the activity level within the grid, including the number of business transactions and social interactions. A max is the maximum activity level, or is the weight coefficient of the activity level, T It's the time factor. T max is the maximum value of the time factor, i is the weight coefficient of the time factor, D is the distance between the grid and a specific location, l is the distance attenuation coefficient, is the distance decay function; The fitness function is: ; In the formula, F is the total value of the fitness function, is the weight coefficient of each indicator, and satisfies , n is the number of indicators of target coverage, m is the number of indicators of target accuracy, C ti It is i The actual value of the target coverage, C mi It is i The maximum or expected value of target coverage, A dj It is j The actual data value of the target accuracy, A mj It is j The maximum or expected value of the target accuracy, E c is the actual value of energy efficiency, S t is the actual value of communication stability, R s is the actual value of the response time.

[0009] Optionally, cluster analysis is performed on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model to identify and divide a plurality of clusters or groups each representing a specific comfort zone, including: Selecting at least one of environmental parameters, device status, and user data as an object to be analyzed; Determine the number of Gaussian components in the Gaussian mixture model corresponding to the number of clusters or groups formed K , and initialize the parameter configuration of the Gaussian mixture model; The object to be analyzed is divided into multiple continuous time windows, and the time windows are moved according to the set step size to cover the entire time series of the object to be analyzed. Each new time window contains a part of new data and a part of data overlapping with the previous time window; In each new time window, the initialized Gaussian mixture model is used to fit the object to be analyzed, and the expectation step and the maximization step are performed alternately using the expectation maximization method until the preset number of iterations is reached or the model parameter change is less than the threshold. In the expectation step, the responsibility of each data point belonging to each Gaussian component is calculated according to the current model parameters, and in the maximization step, the parameter configuration of the Gaussian mixture model is updated according to the responsibility. Through the fitting process, the data points of the object to be analyzed are divided into K clusters or groups, and determine the clusters or groups representing specific comfort zones corresponding to the data points in each time window; Among them, the degree of responsibility is: ; In the formula, z pq For the p Data points for q The responsibility of the Gaussian component, w q It is q The weights of the Gaussian components, s q It is q The standard deviation of the Gaussian components, x p It is p data points, m q It is q The mean of the Gaussian components, For the p Data points and q The square of the Euclidean distance between the means of the Gaussian components, K is the total number of Gaussian components, w k It is k The weights of the Gaussian components, s k It is k The standard deviation of the Gaussian components, m k It is k The mean of the Gaussian components, x It is a sharpening parameter, which is an adjustable parameter used to control the sharpening degree of responsibility calculation.

[0010] Optionally, clusters or groups are processed through deep learning network analysis to obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports including: Build a deep learning network architecture consisting of a multi-layer perceptron and a recursive neural network, and initialize the parameters of the network architecture; Through forward propagation calculation, the activation function is used to learn nonlinear relationships from the input clusters or groups, and the network parameters are adjusted through the back-propagation algorithm and optimizer to minimize the prediction error to complete the training of the multi-layer perceptron of the deep learning network architecture; Training a recurrent neural network of a deep learning network architecture based on clustering or grouping of inputs and sequential backpropagation; Apply the trained deep learning network architecture to cluster or group analysis tasks, and output the analysis results of environmental parameters, device status, and user data through the collaborative analysis and processing of multi-layer perceptron and recurrent neural network; Based on the analysis results of environmental parameters and equipment status, combined with expert experience, an environmental data analysis report is output; Generate user behavior analysis reports based on the analysis results of user data; Use the trained deep learning network architecture to predict future environmental parameters, device status, or user behavior to obtain model prediction output.

[0011] Optionally, based on the environmental data analysis results, the model prediction output and the user behavior analysis report, a comprehensive status evaluation of the building complex service is obtained through fuzzy synthesis operation, and at least one control strategy for the building complex is generated based on the comprehensive status evaluation, including: Align and integrate environmental data analysis results, model prediction outputs, and user behavior analysis reports in time and space dimensions, and eliminate information redundancy and conflict based on a predefined set of rules; Analyze the data after eliminating information redundancy and conflict, and select evaluation indicators that can reflect the environmental status of the building complex, predict trends and user needs to build a comprehensive status indicator system; The fuzzy evaluation matrix and weight vector determined by integrating the evaluations of all experts are used to perform fuzzy synthesis operations on the data in the comprehensive status indicator system to generate a comprehensive status assessment of the building complex services; Analyze the control objectives and control requirements of the building complex based on the comprehensive status assessment of the building complex services; Construct a reinforcement learning model and use the overall state of the building complex services as the state space of the reinforcement learning environment. Integrate one or more intelligent agents in the reinforcement learning environment. Each intelligent agent can perceive the state of the environment and make decisions based on the learned strategy. According to the control objectives and control requirements of the building complex, combined with predefined performance indicators, the operation process of the building complex service is simulated in the constructed environment model, so that the intelligent agent can perceive the environmental state and select control actions according to the current state. After executing the action, the change of the environmental state and the reward signal obtained are observed, and the control strategy is continuously adjusted according to the received reward signal, with the goal of maximizing the cumulative reward. By allowing the agent to continuously interact and trial and error in the simulated environment, the agent's strategy is gradually converged to obtain at least one control strategy; The rule set includes data validation rules, redundancy detection rules, and conflict detection and resolution rules: Data validation rules include: checking whether the data is within a threshold range derived from at least one statistical calculation; cross-validating the data format using statistical validation, logical validation, expert review, and preset format templates; Redundancy detection rules include: if two data sources provide the same type of data and the timestamp difference is within a preset time window, it is considered redundant data; for redundant data, the data source with the latest timestamp is selected as the valid data; The conflict detection and resolution rules include: building an association graph model based on data provided by multiple data sources, traversing each node and edge of the association graph model, and triggering conflict detection when differences are found that contain inconsistent data values, contradictory association relationships, or abnormal data patterns; when a conflict occurs, recording the detected conflicting nodes and edges, and performing self-processing of data correction, merging, or deletion, feeding back the results of the conflict resolution to the association graph model, updating the status of the nodes and edges, and if the same problem still exists in the next round, requesting manual intervention.

[0012] Optionally, after obtaining a comprehensive status assessment and generating a control strategy for the building complex based on the environmental data analysis results, model prediction output, and user behavior analysis report, the following is also included: Create a digital twin building complex model that can simulate the state of the building complex under various conditions in real time; Mapping the generated control strategies to the control instructions of the building complex; Apply the mapped control instructions to the digital twin building complex model to observe and track the environmental parameter change information, equipment status change information and user feedback information within the building complex; The environmental parameter change information, equipment status change information and user feedback information within the building complex are fed into the constructed reinforcement learning model to adaptively optimize the control strategy and obtain the optimal control strategy.

[0013] Optionally, an online sharing platform between building clusters is constructed, and the control strategy of the building cluster generated on the current building cluster is migrated to other building clusters by using transfer learning, so that the learned models, control strategies and optimization experiences can be exchanged and shared between different building clusters, including: Build an online sharing platform among building complexes based on cloud computing platform; In response to a migration instruction input by a user, determining a source building complex and a target building complex; Analyze the control action sets of the source and target building complexes, identify common actions and specific actions, directly map common actions, and cut or expand specific actions according to the control requirements of the target building complex to ensure that the migrated strategy can adapt to the action space of the target building complex; Use the policy function learned on the source building cluster as the starting point to build the basis of the migration strategy; On the target building complex, adjust the policy function or continue to learn based on the migration formula and the set optimization goal. During the migration process, continuously evaluate the performance of the migrated policy on the target building complex. According to the evaluation results received in each round, adjust the parameters in the migration formula and repeat multiple times until the preset migration performance requirements or the maximum number of iterations are met; The migration formula is: ; In the formula, J m is the total return of the migration process, T b is the total number of time steps of the migration process, t is the discount factor used to balance the importance of immediate returns and future returns, In the target building complex T BG Medium status s t Take action a t Immediate returns, l is the state difference weight, which is used to adjust the impact of state space differences on the migration process. Is the source complex status The target building status The difference measure between .

[0014] In a second aspect, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation system for building complex services, including: A collection and processing module for collecting and preprocessing environmental parameters, device status, and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex; A cluster analysis module, for performing cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model, so as to identify and divide a plurality of clusters or groups each representing a specific comfort zone; Deep learning module, used to process clusters or groups through deep learning network analysis, obtain environmental data analysis results, model prediction output and user behavior analysis report; A state assessment and strategy output module is used to obtain a comprehensive state assessment of building complex services through fuzzy synthesis operations based on environmental data analysis results, model prediction outputs, and user behavior analysis reports, and to generate at least one control strategy for the building complex based on the comprehensive state assessment; The strategy sharing module is used to build an online sharing platform between building complexes. It uses transfer learning to migrate the control strategy of the building complex generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

[0015] In a third aspect, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation device for building complex services, including: A cloud computing platform is used to connect multiple building clusters into a network and use transfer learning to achieve data sharing and collaboration between building clusters; A central control platform for the AI-driven online intelligent IoT monitoring and evaluation method for building complex services as described above; and The database is connected to the central control platform and is used to store the monitoring data of the building complex, the constructed models, control strategies and optimization experience.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, which, when executed by a processor, implements the AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services as described above.

[0017] (III) Beneficial effects The beneficial effects of the present invention are: Through the dynamically configurable sensor network and user interface pre-deployed in the building complex, the present invention can comprehensively and accurately collect and pre-process environmental parameters, equipment status and user data. This measure provides a rich and high-quality information foundation for subsequent data analysis and intelligent decision-making, thereby ensuring the accuracy and timeliness of system response. This not only provides a rich data source for subsequent data analysis, but also greatly improves the reliability of the data.

[0018] Secondly, the dynamic Gaussian mixture model is used to perform cluster analysis on the preprocessed data, and the present invention can identify and divide multiple clusters or groups representing specific comfort areas. This analysis method not only helps to understand the complexity and variability of the building complex environment more deeply, but also provides strong data support for the subsequent deep learning and intelligent control strategy generation. Furthermore, by deeply analyzing and processing the clusters or groups through the deep learning network, the present invention obtains accurate environmental data analysis results, model prediction outputs, and user behavior analysis reports. These analysis results not only reveal the inherent laws and trends of the building complex environment, but also provide a scientific basis for comprehensive status assessment and control strategy generation. In addition, based on the above analysis results, the present invention obtains a comprehensive status assessment of the building complex service through fuzzy synthesis operations, and generates targeted building complex control strategies accordingly. These control strategies not only take into account the real-time status of the environment, but also fully integrate the actual needs and behavior patterns of users, thereby ensuring that the building complex can maintain an efficient, comfortable and energy-saving operating state under various circumstances.

[0019] Furthermore, the present invention also constructs an online sharing platform between building clusters, and uses transfer learning technology to migrate the control strategy generated on the current building cluster to other building clusters. This cross-building cluster knowledge sharing and transfer learning not only accelerates the intelligent process of new building clusters, but also promotes the exchange and cooperation between different building clusters in terms of models, control strategies and optimization experience, thereby promoting continuous innovation and development in the field of intelligent control of the entire building cluster.

[0020] Therefore, the present invention significantly improves the response speed and accuracy of the system by integrating IoT monitoring, artificial intelligence algorithm models, intelligent control, comprehensive evaluation, and online sharing methods. Utilizing real-time data processing and online learning mechanisms, the present invention can quickly and accurately adapt to environmental changes and user needs. In addition, the intelligent control strategy of the present invention effectively optimizes energy use and reduces operating costs, while enhancing the system's adaptability and long-term stability to complex usage environments through continuous learning and adaptive updates. This highly adaptive and user-friendly design significantly improves user satisfaction and provides a solid foundation for the long-term operation and future upgrades of the system, demonstrating its important value and potential in technological innovation and practical applications. In general, this solution solves many problems existing in traditional IoT systems, realizes real-time data analysis, intelligent decision-making, and system integration, and significantly improves the management efficiency and user experience of building complexes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a flow chart of a method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a specific flow chart of step S1 of the method provided in an embodiment of the present invention; Figure 3 A schematic diagram of a specific flow chart of step S2 of the method provided in an embodiment of the present invention; Figure 4 A specific flow chart of step S3 of the method is provided for an embodiment of the present invention; Figure 5 A schematic diagram of a specific flow chart of step S4 of the method provided in an embodiment of the present invention; Figure 6 A schematic diagram of a specific flow chart of step S5 of the method provided in an embodiment of the present invention; Figure 7 A schematic diagram of the composition of a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0023] like Figure 1As shown, an AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services proposed in an embodiment of the present invention includes: collecting and preprocessing environmental parameters, device status and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex; using a dynamic Gaussian mixture model to perform cluster analysis on at least one of the preprocessed environmental parameters, device status and user data to identify and divide multiple clusters or groups each representing a specific comfort zone; processing clusters or groups through deep learning network analysis to obtain environmental data analysis results, model prediction outputs and user behavior analysis reports; based on the environmental data analysis results, model prediction outputs and user behavior analysis reports, obtaining a comprehensive status evaluation of the building complex service through fuzzy synthesis operations, and generating at least one control strategy for the building complex based on the comprehensive status evaluation; constructing an online sharing platform between building complexes, and using transfer learning to migrate the control strategy of the building complex generated on the current building complex to other building complexes, so that different building complexes can exchange and share learned models, control strategies and optimization experiences.

[0024] Through the dynamically configurable sensor network and user interface pre-deployed in the building complex, the present invention can comprehensively and accurately collect and pre-process environmental parameters, equipment status and user data. This measure provides a rich and high-quality information foundation for subsequent data analysis and intelligent decision-making, thereby ensuring the accuracy and timeliness of system response. This not only provides a rich data source for subsequent data analysis, but also greatly improves the reliability of the data.

[0025] Secondly, the dynamic Gaussian mixture model is used to perform cluster analysis on the preprocessed data, and the present invention can identify and divide multiple clusters or groups representing specific comfort areas. This analysis method not only helps to understand the complexity and variability of the building complex environment more deeply, but also provides strong data support for the subsequent deep learning and intelligent control strategy generation. Furthermore, by deeply analyzing and processing the clusters or groups through the deep learning network, the present invention obtains accurate environmental data analysis results, model prediction outputs, and user behavior analysis reports. These analysis results not only reveal the inherent laws and trends of the building complex environment, but also provide a scientific basis for comprehensive status assessment and control strategy generation. In addition, based on the above analysis results, the present invention obtains a comprehensive status assessment of the building complex service through fuzzy synthesis operations, and generates targeted building complex control strategies accordingly. These control strategies not only take into account the real-time status of the environment, but also fully integrate the actual needs and behavior patterns of users, thereby ensuring that the building complex can maintain an efficient, comfortable and energy-saving operating state under various circumstances.

[0026] Furthermore, the present invention also constructs an online sharing platform between building clusters, and uses transfer learning technology to migrate the control strategy generated on the current building cluster to other building clusters. This cross-building cluster knowledge sharing and transfer learning not only accelerates the intelligent process of new building clusters, but also promotes the exchange and cooperation between different building clusters in terms of models, control strategies and optimization experience, thereby promoting continuous innovation and development in the field of intelligent control of the entire building cluster.

[0027] Therefore, the present invention significantly improves the response speed and accuracy of the system by integrating IoT monitoring, artificial intelligence algorithm models, intelligent control, comprehensive evaluation, and online sharing methods. Utilizing real-time data processing and online learning mechanisms, the present invention can quickly and accurately adapt to environmental changes and user needs. In addition, the intelligent control strategy of the present invention effectively optimizes energy use and reduces operating costs, while enhancing the system's adaptability and long-term stability to complex usage environments through continuous learning and adaptive updates. This highly adaptive and user-friendly design significantly improves user satisfaction and provides a solid foundation for the long-term operation and future upgrades of the system, demonstrating its important value and potential in technological innovation and practical applications. In general, this solution solves many problems existing in traditional IoT systems, realizes real-time data analysis, intelligent decision-making, and system integration, and significantly improves the management efficiency and user experience of building complexes.

[0028] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0029] Specifically, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services, including: S1. Collect and pre-process environmental parameters, device status, and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex.

[0030] The sensor network covers all areas that need to be monitored to ensure the comprehensiveness and accuracy of the data. Environmental parameters include but are not limited to temperature, humidity, light intensity, etc., which are crucial for evaluating the comfort and energy efficiency of the building complex. At the same time, the sensors also monitor the status of various devices in real time, such as the switch status of air conditioners, lights, curtains, etc., to provide data support for subsequent intelligent control.

[0031] Furthermore, if Figure 2 As shown, step S1 includes: S11. Use the acquired building structure, usage frequency, and environmental parameter change gradient to divide the space of the building complex into grids or regions. The environmental parameter change gradient refers to the rate and direction of change of environmental parameters (such as temperature, humidity, light intensity, air quality, etc.) over a certain spatial range as the spatial position or time passes. This division must ensure that each grid or region can reflect its internal spatial characteristics, usage conditions, and environmental change characteristics.

[0032] S12. Introduce the grid importance index, assign an initial sensor deployment node to each grid or area, and preset a basic activation strategy for each node.

[0033] A grid importance index is introduced, which comprehensively considers the structural complexity, usage frequency and dynamic variability of environmental parameters within the grid.

[0034] According to the grid importance index, an initial sensor deployment node is assigned to each grid or area. The location of the node should be selected to maximize the monitoring effect.

[0035] At the same time, a basic activation strategy is preset for each initially deployed sensor node, including but not limited to working mode, data collection frequency and communication method, to ensure that the initial environmental monitoring and data collection functions can be realized.

[0036] Specifically, the grid importance index is: ; In the formula, E G is the rate of change of environmental parameters, S It is the spatial complexity that includes the number of spatial elements and the number of connected elements. For example, a simple single-story space may be assigned a lower S value, such as 1 or 2, while a multi-story, multi-channel, and complex building may be assigned a higher S value, such as 7 or 8; P is the density of people flow, E n is the rate of change of environmental parameters, α , β , c is the basic weight coefficient, is an exponential coefficient used to adjust the nonlinear effects of various factors. A is the activity level within the grid, including the number of business transactions and social interactions. Amax is the maximum activity level, or is the weight coefficient of the activity level, T is a time factor, which can be quantified by timestamps or time periods, such as peak hours of the day or off / high seasons, Tmax is the maximum value of the time factor, i is the weight coefficient of the time factor, D is the distance between the grid and a specific location, l is the distance attenuation coefficient. If distance has a great influence on the importance of the grid, λ can be set relatively large, so that the importance of grids that are slightly farther away will decrease rapidly. is a distance decay function that simulates the effect of mesh importance decreasing as distance increases.

[0037] S13, construct a hybrid optimization algorithm framework based on simulated annealing algorithm and genetic algorithm. The present invention combines the advantages of simulated annealing algorithm and genetic algorithm to construct a hybrid optimization algorithm framework. The framework aims to improve search efficiency and solution quality by combining global search with local optimization. In this framework, the initial parameters of the algorithm are clearly set, including but not limited to initial temperature, annealing rate, population size, crossover probability, mutation probability and selection strategy.

[0038] S14, based on the hybrid optimization algorithm framework, iterative search is performed, combined with the introduced innovative operators including node position adjustment and activation strategy reorganization, to generate new sensor deployment schemes and new activation strategies; based on the hybrid optimization algorithm framework, iterative search is performed. Then, in each iteration, the deployment position and activation strategy of the sensor node are evaluated according to the fitness function.

[0039] In the search process, innovative operators are combined, such as fine-tuning algorithms for node positions and reorganization mechanisms for activation strategies, to generate new sensor deployment schemes and activation strategies. The design of these innovative operators should aim to increase the diversity and depth of the search, thereby finding a better solution.

[0040] In each iteration, the deployment location and activation strategy of the current sensor node are evaluated according to a comprehensive fitness function. The fitness function is: ; In the formula, F is the total value of the fitness function, is the weight coefficient of each indicator, and satisfies , C ti It is i The actual value of the target coverage, C mi It is i The maximum or expected value of target coverage, A dj It is j The actual data value of the target accuracy, A mj It isj The maximum or expected value of the target accuracy, E c is the actual value of energy efficiency. The inverse is taken here to make the energy consumption lower and the fitness higher. S t It is the actual value of communication stability. The higher the value, the more stable the communication. R s It is the actual value of the system response time. The lower the value, the faster the system responds.

[0041] S15. Use the probability acceptance criterion of simulated annealing, adjust the acceptance probability according to the temperature parameter, balance the global and local search, to decide whether to accept the new deployment plan and information activation plan, so as to obtain the optimal sensor deployment node and activation strategy.

[0042] S16. If a new deployment plan and information activation plan are received, the grid importance index and fitness function are dynamically updated according to the operation data under the new deployment plan and information activation plan.

[0043] S17. After the dynamic sensor network is deployed based on the optimal sensor deployment node, the sensors in the corresponding area are dynamically activated or dormant based on the activation strategy or the real-time needs input by the user to collect environmental parameters, device status and user behavior data.

[0044] S18. Analyze user behavior data to obtain hot spots of user activities, and determine the deployment location of the user interface based on environmental characteristics.

[0045] Specifically, collect and analyze user behavior data, including but not limited to the user's movement trajectory, dwell time, interaction frequency, etc. in a specific space, to determine the hot spots and preferences of user activities. At the same time, combine environmental characteristics such as space layout, lighting conditions, noise level, etc. to comprehensively evaluate and determine the best deployment location of the user interface. This location should be convenient for users to observe and operate, while ensuring the visibility and ease of use of the user interface in the environment, thereby improving user experience and interaction efficiency.

[0046] S19. After the user interface is deployed, user feedback data is received and stored through the user interface, and the collected environmental parameters, device status, and user feedback data are cleaned to remove duplicate, invalid, or erroneous data entries; noise filtering is performed on the cleaned data; outlier detection is performed on the noise-filtered data to identify and process data points that significantly deviate from the normal range, and the data after outlier detection is normalized or standardized, and data from different sensors are synchronized and aligned in time according to the collected timestamps.

[0047] During the data collection process, duplicate, invalid or erroneous data entries may be mixed in. In order to ensure data quality, the present invention cleans all collected data. The cleaning process includes removing duplicate data, identifying and deleting invalid data (such as format errors, values ​​outside a reasonable range, etc.), and correcting or marking erroneous data.

[0048] Although the data has been cleaned, it may still contain some noise. The noise may be caused by the sensor's own errors, environmental interference, or problems in the data transmission process. In order to further improve the accuracy of the data, the present invention performs noise filtering on the cleaned data.

[0049] Outliers refer to values ​​that significantly deviate from the normal range in the data, which may be caused by abnormal conditions, failures or misoperation. If these outliers are directly used for analysis without processing, they may cause deviations or misleading results. Therefore, the present invention implements outlier detection on the data after noise filtering.

[0050] In order to facilitate subsequent data analysis and model training, the present invention normalizes or standardizes the data after outlier detection. Normalization is to scale the data to a specified range (such as between 0 and 1), while standardization is to convert the data into a distribution with a mean of 0 and a standard deviation of 1. These processes help to eliminate the dimensional differences and numerical range differences between different features and improve the comparability of the data. At the same time, according to the collected timestamp information, the data from different sensors are synchronized and aligned in time to ensure their consistency in the time dimension. This step is crucial for subsequent time series data analysis.

[0051] S2. Performing cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model to identify and divide multiple clusters or groups, each representing a specific comfort zone.

[0052] Furthermore, if Figure 3 As shown, step S2 includes: S21. Select at least one of environmental parameters, device status, and user data as an object to be analyzed.

[0053] S22, determining the number of Gaussian components in the Gaussian mixture model corresponding to the number of clusters or groups formed K , K The value corresponds to the number of clusters or groups that you want to form and initializes the parameter configuration of the Gaussian mixture model, such as the mixing coefficients, mean vector, and covariance matrix.

[0054] Determine the number of Gaussian components K in the Gaussian mixture model, that is, the number of clusters or groups to be formed. Initialize the mixing coefficient, mean vector, and covariance matrix of each Gaussian component. These parameters will be used to describe the data distribution characteristics of each potential comfort region.

[0055] S23, dividing the object to be analyzed into multiple continuous time windows, and moving the time windows according to the set step length to cover the entire time series of the object to be analyzed, each new time window contains a part of new data and a part of data overlapping with the previous time window. The specific range of overlap = window size - step length.

[0056] S24. In each new time window, the initialized Gaussian mixture model is used to fit the object to be analyzed, and the expectation maximization method is used to alternately perform the expectation step and the maximization step until the preset number of iterations is reached or the model parameter change is less than the threshold. In the expectation step, the responsibility of each data point belonging to each Gaussian component is calculated according to the current model parameters. In the maximization step, the parameter configuration of the Gaussian mixture model is updated according to the responsibility.

[0057] Among them, the degree of responsibility is: ; In the formula, z pq For the p Data points for q The responsibility of the Gaussian component, w q It is q The weights of the Gaussian components, s q It is q The standard deviation of the Gaussian components, x p It is p data points, m q It is q The mean of the Gaussian components, For the p Data points and q The square of the Euclidean distance between the means of the Gaussian components, K is the total number of Gaussian components, w k It is k The weights of the Gaussian components, s k It is k The standard deviation of the Gaussian components, m k It is k The mean of the Gaussian components, xis the sharpening parameter, which is an adjustable parameter used to control the sharpening degree of responsibility calculation. When the sharpening parameter = 1, the responsibility formula degenerates into the standard Gaussian responsibility calculation; when the sharpening parameter > 1, the responsibility calculation will be sharper, making the data points more inclined to belong to the Gaussian component closer to it; when 0 < sharpening parameter < 1, the responsibility calculation will be smoother.

[0058] It should be clear that the present invention introduces an adjustable sharpening parameter, which can flexibly adjust the calculation method of the responsibility degree as needed. By adjusting the value of the sharpening parameter, a better balance can be achieved between the fitting ability and generalization ability of the model. For example, when processing data with obvious clustering structure, the value of the sharpening parameter can be increased to emphasize the affiliation between the data point and its nearest Gaussian component; when processing data with more uniform distribution or more noise, we can reduce the value of the sharpening parameter to obtain a smoother responsibility distribution.

[0059] S25. Divide the data points of the object to be analyzed into K clusters or groups through a fitting process, and determine the cluster or group representing a specific comfort zone corresponding to the data points in each time window.

[0060] Through the fitting process, the data points of the object to be analyzed are divided into K clusters or groups. The classification of the cluster or group corresponding to the data points in each time window is determined, that is, each data point is assigned to the cluster corresponding to the Gaussian component with the highest probability density. Therefore, each cluster or group represents a specific comfort area or other related characteristic area.

[0061] Next, the present invention uses a Gaussian mixture model to construct the comfort range division. The Gaussian mixture model can be a weighted combination of multiple Gaussian distributions, which is used to fit the data, and its mathematical formula is as follows: ; In the formula, It is a data point x The probability density of K is the total number of Gaussian components, that is, the number of different Gaussian distributions in the model, It is k The mixing coefficient of the Gaussian distribution satisfies and . These coefficients are the weights of each Gaussian distribution. is a multivariate normal distribution with a mean of , the covariance matrix is .

[0062] Next, each Gaussian component The probability density function of can be: ; In the formula, x yes d dimensional data vector, It is k The mean vector of the components, It is k The covariance matrix of the component is the spread and shape of the component data, is the determinant of the covariance matrix.

[0063] Therefore, the Gaussian mixture model is a model composed of multiple Gaussian distributions, each of which has its own mean and standard deviation (or covariance matrix), and the contribution of each Gaussian distribution to the overall model is determined by the weight coefficient. This model can fit any type of distribution, especially when the data in the same set contains multiple different distributions.

[0064] S3. Process clusters or groups through deep learning network analysis to obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports.

[0065] Furthermore, if Figure 4 As shown, step S3 includes: S31. Build a deep learning network architecture consisting of a multi-layer perceptron and a recursive neural network, and initialize the parameters of the network architecture.

[0066] S32. Through forward propagation calculation, the activation function is used to learn nonlinear relationships from the input clusters or groups, and the network parameters are adjusted through the back-propagation algorithm and optimizer to minimize the prediction error to complete the training of the multi-layer perceptron of the deep learning network architecture.

[0067] S33. The recursive neural network of the deep learning network architecture is trained according to the clustering or grouping of the input and the time-series back propagation method.

[0068] S34. Apply the trained deep learning network architecture to clustering or group analysis tasks, and output the analysis results of environmental parameters, device status and user data through the collaborative analysis and processing of multi-layer perceptron and recursive neural network.

[0069] S35. Based on the analysis results of environmental parameters and equipment status and combined with expert experience, an environmental data analysis report is output.

[0070] S36. Generate a user behavior analysis report based on the analysis results of the user data.

[0071] S37. Use the trained deep learning network architecture to predict future environmental parameters, device status or user behavior to obtain model prediction output.

[0072] In another specific embodiment, a deep learning network is used to analyze and process data, and the network design includes a multi-layer perceptron and a recursive neural network to capture the complex patterns and dependencies of time series data.

[0073] Design a multi-layer perceptron network MLP, which includes multiple hidden layers, each layer consists of multiple neurons. Initialize the network weight matrix W and the bias vector b Through forward propagation calculation, the activation function is used to learn nonlinear relationships from input features, and the network parameters are adjusted through the back-propagation algorithm and optimizer to minimize the prediction error. Its forward propagation calculation is: ; In the formula, W and b are the weight matrix and bias vector respectively, x is the input vector, h is the output after the activation function, is an activation function, such as ReLU or Sigmoid.

[0074] Next, we design a recurrent neural network, specifically a long short-term memory network (LSTM), to capture the complex patterns and dependencies in time series data. Initialize the weight matrix of the LSTM (e.g. W xh and W hh ) and the bias vector b h . Use the LSTM hidden state update formula, combined with the activation function (usually tanh), at each time step t Processing input vector x t , and update the hidden state h t The LSTM network is trained by the back propagation through time (BPTT) algorithm to optimize the network parameters to capture the long-term dependency of time series data. The hidden state update formula is: .

[0075] Then, the trained deep learning model is applied to the clustering or group analysis task and the analysis results are output.

[0076] S4. Based on the environmental data analysis results, model prediction output and user behavior analysis report, a comprehensive status evaluation of the building complex services is obtained through fuzzy synthesis operation, and at least one control strategy for the building complex is generated based on the comprehensive status evaluation.

[0077] Furthermore, if Figure 5 As shown, step S4 includes: S41. Align and integrate the environmental data analysis results, model prediction outputs, and user behavior analysis reports in time and space dimensions, and eliminate information redundancy and conflict based on a predefined set of rules.

[0078] Specifically, the rule set includes data validation rules, redundancy detection rules, and conflict detection and resolution rules: Data validation rules include: checking whether the data is within a threshold range obtained by at least one statistical calculation; cross-validating the data format using statistical validation, logical validation, expert review, and preset format templates.

[0079] Redundancy detection rules include: if two data sources provide the same type of data and the timestamp difference is within a preset time window, it is considered redundant data; for redundant data, the data source with the latest timestamp is selected as the valid data.

[0080] The conflict detection and resolution rules include: building an association graph model based on data provided by multiple data sources, traversing each node and edge of the association graph model, and triggering conflict detection when differences are found that contain inconsistent data values, contradictory association relationships, or abnormal data patterns; when a conflict occurs, recording the detected conflicting nodes and edges, and performing self-processing of data correction, merging, or deletion, feeding back the results of the conflict resolution to the association graph model, updating the status of the nodes and edges, and if the same problem still exists in the next round, requesting manual intervention.

[0081] S42. Analyze the data after eliminating information redundancy and conflict, and select evaluation indicators that can reflect the environmental status of the building complex, predicted trends and user needs to construct a comprehensive status indicator system.

[0082] S43. Use the fuzzy evaluation matrix and weight vector determined by integrating the evaluations of all experts to perform fuzzy synthesis operations on the data in the comprehensive status indicator system to generate a comprehensive status evaluation of the building complex services.

[0083] In a specific embodiment, first, all evaluation factors involved in the building complex status assessment need to be clarified, and these factors should cover various aspects such as environment, equipment, operation, user satisfaction, etc. Each evaluation factor can be subdivided into more specific sub-factors, thereby forming a hierarchical evaluation factor set.

[0084] For each evaluation factor, experts are invited or relevant data are used to make fuzzy evaluations. Fuzzy evaluation can be done using membership functions, that is, to determine the degree to which each factor belongs to different evaluation levels (such as excellent, good, medium, and poor). By integrating the evaluations of all experts or data analysis results, a fuzzy evaluation matrix can be constructed, in which each row represents an evaluation factor, each column represents an evaluation level, and the elements in the matrix are the degree to which the factor belongs to the corresponding level.

[0085] The importance of different evaluation factors in the assessment of the status of building complexes is different, so a weight needs to be assigned to each factor. The weight can be determined by subjective weighting methods (such as the Delphi method and the hierarchical analysis method) or objective weighting methods (such as the entropy weight method and the principal component analysis method). By determining the weight vector, the influence of each factor in the comprehensive evaluation can be quantified.

[0086] Fuzzy synthesis operation is performed using fuzzy evaluation matrix and weight vector. The purpose of this step is to combine the fuzzy evaluation results of various factors to obtain an overall evaluation result. Fuzzy synthesis operation can use different operators, such as weighted average operator, take the greater or less operator, etc., and select the appropriate operator for operation according to the specific situation.

[0087] After fuzzy synthesis operation, a comprehensive evaluation value or evaluation vector can be obtained, which reflects the comprehensive status of the building complex in all aspects.

[0088] S44. Based on the comprehensive status assessment of building complex services, analyze the control objectives and control requirements of the building complex.

[0089] The control objectives of the building complex include: improving energy efficiency, improving comfort, extending equipment life, etc., and formulating specific control requirements based on these objectives. Based on these objectives, the system formulates specific control requirements, such as adjusting the operation strategy of the air conditioning system to reduce energy consumption, or optimizing the lighting system to improve user comfort.

[0090] S45. Build a reinforcement learning model and use the comprehensive state of the building complex services as the state space of the reinforcement learning environment.

[0091] S46. Integrate one or more intelligent agents in a reinforcement learning environment, where each agent can perceive the state of the environment and make decisions based on the learned strategy.

[0092] S47. Based on the control objectives and control requirements of the building complex, combined with predefined performance indicators, the operation process of the building complex service is simulated in the constructed environmental model, so that the intelligent agent can perceive the environmental state and select control actions according to the current state. After executing the action, the changes in the environmental state and the reward signals obtained are observed, and the control strategy is continuously adjusted according to the received reward signals, with the goal of maximizing the cumulative reward.

[0093] Among them, predefined performance indicators, such as energy consumption reduction percentage, comfort index, etc., are used to quantitatively evaluate the effect of the control strategy. In the embodiment of the present invention, the control strategy of the intelligent agent is iteratively updated and optimized by using the optimization mechanism of the reinforcement learning algorithm, such as gradient descent, policy gradient, etc.

[0094] S48. By allowing the intelligent agent to continuously interact and trial and error in a simulated environment, the strategy of the intelligent agent is gradually converged to obtain at least one control strategy.

[0095] After a sufficient number of interactions and trial and error, the agent will gradually converge to one or more optimal control strategies, which can maximize or minimize performance indicators while meeting the control objectives and requirements of the building complex.

[0096] In addition, after step S4, it also includes: creating a digital twin building complex model that can simulate the state of the building complex under various conditions in real time; mapping the generated control strategy to the control instructions of the building complex, and then applying the mapped control instructions to the digital twin building complex model, observing and tracking the environmental parameter change information, equipment status change information and user feedback information in the building complex; feeding the environmental parameter change information, equipment status change information and user feedback information in the building complex into the constructed reinforcement learning model to adaptively optimize and adjust the control strategy to obtain the optimal control strategy.

[0097] In yet another specific embodiment, based on the current environment state s t , the control strategy is implemented through the strategy function π ( s t ) to get the control command: ; Where π(⋅) is the policy function, which may be based on reinforcement learning or neural networks. a t For the time step t control actions such as adjusting temperature, turning lights on and off, etc.

[0098] Create a digital twin building complex model that can simulate the state of the building complex under various conditions in real time, and configure it with a user agent that can simulate user input instructions and feedback information. After that, the mapped control instructions are applied to the digital twin building complex model to enter the observation and tracking phase. The goal of this phase is to collect data on environmental changes (such as temperature, humidity, light, etc.) and feedback information from user agents (such as whether the user manually adjusted the device settings, or expressed satisfaction or dissatisfaction with the environmental status in some way). This information is crucial for evaluating the effectiveness of the control strategy and is also the basis for subsequent strategy optimization.

[0099] Finally, the collected environmental parameter change information, equipment status change information, and user feedback information are fed into the reinforcement learning model. This information is used as input data for model training to help the model learn how to better generate control strategies based on the current environmental status and user preferences. Through continuous iteration and optimization, the model can gradually adapt to different environments and user needs, thereby achieving more intelligent and efficient control while updating the comfort range. This can be the update rule for the strategy: ; In the formula, is the learning rate, controlling the step size of the policy update, is a loss function used to quantify the deviation of policy generation from performance indicators, such as energy efficiency optimization or user satisfaction maximization.

[0100] S5. Build an online sharing platform between building complexes, and use transfer learning to migrate the control strategies of the building complexes generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

[0101] Furthermore, if Figure 6 As shown, step S5 includes: S51. Construct an online sharing platform among building complexes based on the cloud computing platform.

[0102] S52: In response to the migration instruction input by the user, determine the source building complex and the target building complex.

[0103] S53. Analyze the control action sets of the source building complex and the target building complex, identify common actions and specific actions, directly map the common actions, and cut or expand the specific actions according to the control requirements of the target building complex to ensure that the migrated strategy can adapt to the action space of the target building complex.

[0104] S54. Use the strategy function learned on the source building complex as an initial point to build the basis of the migration strategy.

[0105] S55. Based on the migration formula and the set optimization goal, adjust the policy function or continue to learn on the target building complex, and iteratively update the migration strategy on the target building complex through the reinforcement learning algorithm to maximize the performance index. At the same time, during the migration process, continuously evaluate the performance of the migrated strategy on the target building complex.

[0106] S56. Adjust the parameters in the migration formula according to the evaluation results received in each round, and repeat multiple times until the preset migration performance requirements or the maximum number of iterations are reached.

[0107] According to the evaluation results, adjust the parameters in the migration formula, such as the state difference weight and discount factor, to optimize the migration effect. Repeat the above steps until the preset migration performance requirements or the maximum number of iterations are met. The migration formula is: ; In the formula, J m is the total return of the migration process, T b is the total number of time steps of the migration process, t is the discount factor used to balance the importance of immediate returns and future returns, In the target building complex T BG Medium status s t Take action a t Immediate returns, l is the state difference weight, which is used to adjust the impact of state space differences on the migration process. Is the source complex status The target building status The difference measure between .

[0108] It should be emphasized that the present invention also provides a user-friendly interface that allows users to view environmental status, control device operation and provide feedback. The interface design is simple and intuitive, and supports access from multiple devices. It also has a continuous learning mechanism that can continuously optimize its performance based on historical data and continuous user interaction to adapt to the changing needs of users and dynamic changes in environmental conditions.

[0109] In a specific embodiment, the IoT sensor is used to obtain various indoor environmental parameters in real time, such as temperature, humidity, light intensity, etc. The thermal sensation of the current occupants is evaluated by a dynamic Gaussian mixture model. This model divides different thermal sensation intervals by modeling the joint distribution of temperature and humidity, and determines whether the current state is in a comfortable range. After receiving the feedback, the control system uses a machine learning algorithm (such as reinforcement learning) to calculate the optimal control strategy to adjust the temperature and humidity parameters so that the indoor environment re-enters the comfortable range. This process includes controlling the operation of equipment such as air conditioners, humidifiers, and dehumidifiers to ensure comfort through real-time response.

[0110] In addition, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation system for building complex services, such as Figure 7 As shown, including: The collection and processing module is used to collect and pre-process environmental parameters, device status and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex.

[0111] The cluster analysis module is used to perform cluster analysis on at least one of the pre-processed environmental parameters, device status and user data using a dynamic Gaussian mixture model to identify and divide a plurality of clusters or groups each representing a specific comfort zone.

[0112] The deep learning module is used to process clusters or groups through deep learning network analysis to obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports.

[0113] The status assessment and strategy output module is used to obtain a comprehensive status assessment of building complex services through fuzzy synthesis operations based on environmental data analysis results, model prediction outputs and user behavior analysis reports, and to generate at least one control strategy for the building complex based on the comprehensive status assessment.

[0114] The strategy sharing module is used to build an online sharing platform between building complexes. It uses transfer learning to migrate the control strategy of the building complex generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

[0115] Next, an embodiment of the present invention provides an AI-driven online intelligent Internet of Things monitoring and evaluation device for building complex services, including: a cloud computing platform, used to connect multiple building complexes into a network, and use transfer learning to achieve data sharing and collaboration between building complexes; a central control platform, used for the AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services as described above; and a database, which is communicated with the central control platform and is used to store the monitoring data of the building complex, the constructed model, control strategies and optimization experience.

[0116] The equipment integrates advanced cloud computing platforms, central control platforms and databases to provide comprehensive and efficient intelligent management solutions for building complexes.

[0117] First of all, as one of the core components of the device, the cloud computing platform has powerful data processing and computing capabilities. It can closely connect multiple building complexes to build a huge network system. In this network, by using transfer learning technology, seamless data sharing and efficient collaboration can be achieved between building complexes. This not only breaks the limitations of data islands in traditional building management, but also greatly improves the utilization value of data resources, laying a solid foundation for the overall intelligent management of building complexes.

[0118] Secondly, the central control platform is responsible for executing the above-mentioned AI-driven online intelligent IoT monitoring and evaluation method for building complex services. Relying on powerful algorithm support, it can collect, analyze and process data information from various building complexes in real time, thereby achieving accurate monitoring and intelligent evaluation of the status of the building complex. Through the central control platform, users can easily grasp the operating status of the building complex, discover potential problems in a timely manner, and formulate effective response strategies.

[0119] As an important storage unit of the equipment, the database maintains a close communication connection with the central control platform. It is responsible for storing various monitoring data of the building complex, the constructed models, control strategies and optimization experience. These valuable data resources not only provide strong support for the continuous learning and optimization of the equipment, but also provide users with rich references and lessons in the subsequent building complex management.

[0120] Furthermore, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, and when the executable instructions are executed by a processor, the above-mentioned AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services is implemented.

[0121] The embodiment of the present invention further provides a computer-readable medium on which specific computer-executable instructions are stored. When these instructions are executed by the processor, the above-mentioned AI-driven online intelligent Internet of Things monitoring and evaluation method designed specifically for building complex services can be accurately implemented. This innovative medium not only provides a convenient carrier for the implementation of related methods, but also further broadens the application scope of AI in the field of intelligent Internet of Things monitoring and evaluation, thereby greatly improving the intelligent level of building complex management.

[0122] In summary, the embodiments of the present invention provide an AI-driven online intelligent IoT monitoring and evaluation method, system, device, and medium for building complex services. The core advantage of the present invention lies in its advanced learning mechanism, which is mainly reflected in the following aspects: First, there is the multimodal data fusion capability, which enables the system to comprehensively process data from different sources to obtain a more comprehensive view of operations.

[0123] Secondly, it is the ability of continuous learning and online learning. The system design of the present invention introduces an innovative continuous learning framework, which enables the system to continuously absorb new data and user feedback, and update and optimize the decision-making algorithm in real time. Compared with traditional static AI models, the model of the present invention has stronger environmental adaptability and the ability to cope with changes in behavior patterns, and can continuously improve decision-making quality and operational efficiency over time. At the same time, the present invention also emphasizes the importance of online learning, allowing the system to perform model training and optimization without interrupting service. This means that the system can learn new interactions, events, and data in real time during actual operation without offline processing or manual intervention.

[0124] Furthermore, a sharing mechanism is provided to utilize transfer learning to migrate the control strategy of the building complex generated on the current building complex to other building complexes, so that the learned models, control strategies and optimization experience can be exchanged and shared between different building complexes.

[0125] Through the above mechanisms, the present invention can provide a highly adaptive and intelligent solution, which is significantly better than traditional systems that rely on preset rules and periodic manual updates. This adaptive learning capability is a major innovation of the present invention, which brings significant benefits to users and operators, including higher system reliability, lower maintenance costs and better user experience.

[0126] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0128] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the technical solution should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0129] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the technical solution of the present invention and its equivalent technology, the present invention should also include these modifications and variations.

Claims

1. An AI-driven online intelligent IoT monitoring and evaluation method for building complex services, characterized in that: include: Collect and pre-process environmental parameters, equipment status, and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex; Performing cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model to identify and divide multiple clusters or groups each representing a specific comfort zone; Through deep learning network analysis and processing of clusters or groups, we can obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports; Based on the environmental data analysis results, model prediction output and user behavior analysis report, a comprehensive status evaluation of the building complex services is obtained through fuzzy synthesis operation, and at least one control strategy for the building complex is generated based on the comprehensive status evaluation; Build an online sharing platform between building complexes, and use transfer learning to migrate the control strategies of the building complexes generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

2. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: Through a dynamically configurable sensor network and user interface pre-deployed in the building complex, environmental parameters, device status and user data are collected and pre-processed, including: The space of the building complex is divided into grids or areas, the grid importance index is introduced, the initial sensor deployment node is allocated to each grid or area, and the basic activation strategy is preset for each node; A hybrid optimization algorithm framework is constructed based on simulated annealing algorithm and genetic algorithm. Iterative search is performed based on the hybrid optimization algorithm framework. In combination with the introduced innovative operators including node position adjustment and activation strategy reorganization, new sensor deployment schemes and new activation strategies are generated. In each iteration, the deployment location and activation strategy of the sensor nodes are evaluated according to the fitness function; The sensor deployment scheme and activation strategy are dynamically updated using the simulated annealing probability acceptance criterion, and the grid importance index and fitness function parameters are updated synchronously. After the dynamic sensor network is deployed based on the new sensor deployment node, the sensors in the corresponding area are dynamically activated or dormant based on the activation strategy or the real-time needs input by the user to collect environmental parameters, device status and user behavior data; By analyzing user behavior data, we can obtain hot spots of user activity and determine the deployment location of the user interface based on environmental characteristics. After the user interface is deployed, receiving and storing user feedback data through the user interface; Pre-process the collected environmental parameters, equipment status, user behavior data, and user feedback data; in, The grid importance index is: ； In the formula, E G is the rate of change of environmental parameters, S is the space complexity including the number of spatial elements and the number of connected elements, P is the density of people flow, E n is the rate of change of environmental parameters, α , β , γ is the basic weight coefficient, is an exponential coefficient, which is used to adjust the nonlinear effect of the corresponding factor. A is the activity level within the grid, including the number of business transactions and social interactions. A max is the maximum activity level, η is the weight coefficient of the activity level, T It's the time factor. T max is the maximum value of the time factor, θ is the weight coefficient of the time factor, D is the distance between the grid and a specific location, λ is the distance attenuation coefficient, is the distance decay function; The fitness function is: ； In the formula, F is the total value of the fitness function, is the weight coefficient of each indicator, and satisfies , n is the number of indicators of target coverage, m is the number of indicators of target accuracy, C ti It is i The actual value of the target coverage, C mi It is i The maximum or expected value of target coverage, A dj It is j The actual data value of the target accuracy, A mj It is j The maximum or expected value of the target accuracy, E c is the actual value of energy efficiency, S t is the actual value of communication stability, R s is the actual value of the response time.

3. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: A dynamic Gaussian mixture model is used to perform cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data to identify and divide multiple clusters or groups each representing a specific comfort zone, including: Selecting at least one of environmental parameters, device status, and user data as an object to be analyzed; Determine the number of Gaussian components in the Gaussian mixture model corresponding to the number of clusters or groups formed K , and initialize the parameter configuration of the Gaussian mixture model; The object to be analyzed is divided into multiple continuous time windows, and the time windows are moved according to the set step size to cover the entire time series of the object to be analyzed. Each new time window contains a part of new data and a part of data overlapping with the previous time window; In each new time window, the initialized Gaussian mixture model is used to fit the object to be analyzed, and the expectation step and the maximization step are performed alternately using the expectation maximization method until the preset number of iterations is reached or the model parameter change is less than the threshold. In the expectation step, the responsibility of each data point belonging to each Gaussian component is calculated according to the current model parameters, and in the maximization step, the parameter configuration of the Gaussian mixture model is updated according to the responsibility. Through the fitting process, the data points of the object to be analyzed are divided into K clusters or groups, and determine the clusters or groups representing specific comfort zones corresponding to the data points in each time window; Among them, the degree of responsibility is: ； In the formula, z pq For the p Data points for q The responsibility of the Gaussian component, w q It is q The weights of the Gaussian components, σ q It is q The standard deviation of the Gaussian components, x p It is p data points, μ q It is q The mean of the Gaussian components, For the p Data points and q The square of the Euclidean distance between the means of the Gaussian components, K is the total number of Gaussian components, w k It is k The weights of the Gaussian components, σ k It is k The standard deviation of the Gaussian components, μ k It is k The mean of the Gaussian components, χ It is a sharpening parameter, which is an adjustable parameter used to control the sharpening degree of responsibility calculation.

4. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: Through deep learning network analysis and processing of clusters or groups, we can obtain environmental data analysis results, model prediction outputs, and user behavior analysis reports including: Build a deep learning network architecture consisting of a multi-layer perceptron and a recursive neural network, and initialize the parameters of the network architecture; Through forward propagation calculation, the activation function is used to learn nonlinear relationships from the input clusters or groups, and the network parameters are adjusted through the back-propagation algorithm and optimizer to minimize the prediction error to complete the training of the multi-layer perceptron of the deep learning network architecture; Training a recurrent neural network of a deep learning network architecture based on clustering or grouping of inputs and sequential backpropagation; Apply the trained deep learning network architecture to cluster or group analysis tasks, and output the analysis results of environmental parameters, device status, and user data through the collaborative analysis and processing of multi-layer perceptron and recurrent neural network; Based on the analysis results of environmental parameters and equipment status, combined with expert experience, an environmental data analysis report is output; Generate user behavior analysis reports based on the analysis results of user data; Use the trained deep learning network architecture to predict future environmental parameters, device status, or user behavior and obtain model prediction output.

5. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: Based on the environmental data analysis results, model prediction output and user behavior analysis report, a comprehensive status evaluation of the building complex service is obtained through fuzzy synthesis operation, and at least one control strategy for the building complex is generated based on the comprehensive status evaluation, including: Align and integrate environmental data analysis results, model prediction outputs, and user behavior analysis reports in time and space dimensions, and eliminate information redundancy and conflict based on a predefined set of rules; Analyze the data after eliminating information redundancy and conflict, and select evaluation indicators that can reflect the environmental status of the building complex, predict trends and user needs to build a comprehensive status indicator system; The fuzzy evaluation matrix and weight vector determined by integrating the evaluations of all experts are used to perform fuzzy synthesis operations on the data in the comprehensive status indicator system to generate a comprehensive status assessment of the building complex services; Analyze the control objectives and control requirements of the building complex based on the comprehensive status assessment of the building complex services; Construct a reinforcement learning model and use the overall state of the building complex services as the state space of the reinforcement learning environment. Integrate one or more intelligent agents in the reinforcement learning environment. Each intelligent agent can perceive the state of the environment and make decisions based on the learned strategy. According to the control objectives and control requirements of the building complex, combined with predefined performance indicators, the operation process of the building complex service is simulated in the constructed environment model, so that the intelligent agent can perceive the environmental state and select control actions according to the current state. After executing the action, the change of the environmental state and the reward signal obtained are observed, and the control strategy is continuously adjusted according to the received reward signal, with the goal of maximizing the cumulative reward. By allowing the agent to continuously interact and trial and error in the simulated environment, the agent's strategy is gradually converged to obtain at least one control strategy; The rule set includes data validation rules, redundancy detection rules, and conflict detection and resolution rules: Data validation rules include: checking whether the data is within a threshold range derived from at least one statistical calculation; cross-validating the data format using statistical validation, logical validation, expert review, and preset format templates; Redundancy detection rules include: if two data sources provide the same type of data and the timestamp difference is within a preset time window, it is considered redundant data; for redundant data, the data source with the latest timestamp is selected as the valid data; The conflict detection and resolution rules include: building an association graph model based on data provided by multiple data sources, traversing each node and edge of the association graph model, and triggering conflict detection when differences are found that contain inconsistent data values, contradictory association relationships, or abnormal data patterns; when a conflict occurs, recording the detected conflicting nodes and edges, and performing self-processing of data correction, merging, or deletion, feeding back the results of the conflict resolution to the association graph model, updating the status of the nodes and edges, and if the same problem still exists in the next round, requesting manual intervention.

6. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: After obtaining a comprehensive status assessment and generating a control strategy for the building complex based on environmental data analysis results, model prediction output, and user behavior analysis reports, it also includes: Create a digital twin building complex model that can simulate the state of the building complex under various conditions in real time; Mapping the generated control strategies to the control instructions of the building complex; Apply the mapped control instructions to the digital twin building complex model to observe and track the environmental parameter change information, equipment status change information and user feedback information within the building complex; The environmental parameter change information, equipment status change information and user feedback information within the building complex are fed into the constructed reinforcement learning model to adaptively optimize the control strategy and obtain the optimal control strategy.

7. The AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services according to claim 1, characterized in that: Build an online sharing platform between building clusters, and use transfer learning to migrate the control strategy of the building cluster generated on the current building cluster to other building clusters, so that different building clusters can exchange and share the learned models, control strategies and optimization experience, including: Build an online sharing platform among building complexes based on cloud computing platform; In response to a migration instruction input by a user, determining a source building complex and a target building complex; Analyze the control action sets of the source and target building complexes, identify common actions and specific actions, directly map common actions, and cut or expand specific actions according to the control requirements of the target building complex to ensure that the migrated strategy can adapt to the action space of the target building complex; Use the policy function learned on the source building cluster as the starting point to build the basis of the migration strategy; On the target building complex, adjust the policy function or continue to learn based on the migration formula and the set optimization goal. During the migration process, continuously evaluate the performance of the migrated policy on the target building complex. According to the evaluation results received in each round, adjust the parameters in the migration formula and repeat multiple times until the preset migration performance requirements or the maximum number of iterations are met; The migration formula is: ； In the formula, J m is the total return of the migration process, T b is the total number of time steps of the migration process, τ is the discount factor used to balance the importance of immediate returns and future returns, In the target building complex T BG Medium status s t Take action a t Immediate returns, λ is the state difference weight, which is used to adjust the impact of state space differences on the migration process. Is the source complex status The target building status The difference measure between .

8. An AI-driven online intelligent IoT monitoring and evaluation system for building complex services, characterized in that: include: A collection and processing module for collecting and preprocessing environmental parameters, device status, and user data through a dynamically configurable sensor network and user interface pre-deployed in the building complex; A cluster analysis module, for performing cluster analysis on at least one of the preprocessed environmental parameters, device status, and user data using a dynamic Gaussian mixture model, so as to identify and divide a plurality of clusters or groups each representing a specific comfort zone; Deep learning module, used to process clusters or groups through deep learning network analysis, obtain environmental data analysis results, model prediction output and user behavior analysis report; A state assessment and strategy output module is used to obtain a comprehensive state assessment of building complex services through fuzzy synthesis operations based on environmental data analysis results, model prediction outputs, and user behavior analysis reports, and to generate at least one control strategy for the building complex based on the comprehensive state assessment; The strategy sharing module is used to build an online sharing platform between building complexes. It uses transfer learning to migrate the control strategy of the building complex generated on the current building complex to other building complexes, so that different building complexes can exchange and share the learned models, control strategies and optimization experiences.

9. An AI-driven online intelligent IoT monitoring and evaluation device for building complex services, characterized in that: include: A cloud computing platform is used to connect multiple building clusters into a network and use transfer learning to achieve data sharing and collaboration between building clusters; A central control platform for the AI-driven online intelligent IoT monitoring and evaluation method for building complex services as described in any one of claims 1 to 7; as well as, The database is connected to the central control platform and is used to store the monitoring data of the building complex, the constructed models, control strategies and optimization experience.

10. A computer-readable medium having computer-executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, the AI-driven online intelligent Internet of Things monitoring and evaluation method for building complex services is implemented as described in any one of claims 1-7.

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