A multi-protocol data communication method and system for intelligent buildings

Abstract

The present invention discloses a multi-protocol data communication method and system for intelligent buildings, which relates to the field of data communication technology. A multi-protocol data communication system for intelligent buildings includes: a communication scenario construction module, a communication path output module and a communication data optimization module. The present invention constructs a semantic relay for various types of heterogeneous building communication equipment, completes the ternary mapping of communication behavior intention, service interface and protocol instruction, and forms a unified semantic neutral expression method, so that devices that originally adopted different protocols can achieve interoperability based on unified semantics, significantly improving the scalability and compatibility in protocol heterogeneous environments; by generating optimal paths and adaptation strategies, it effectively avoids communication bottlenecks such as network congestion, device response failure or excessive energy consumption, and improves the robustness and task success rate in fluctuating environments.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a multi-protocol data communication method and system for intelligent buildings. Background Art

[0002] In existing smart buildings, various communication devices often use different data communication protocols, resulting in a lack of unified interaction standards between devices. Protocol incompatibility and data interoperability are particularly prominent issues, especially in scenarios such as the renovation of older buildings, cross-vendor integration, or when protocols are not publicly available. Furthermore, the building communication environment is significantly affected by factors such as user behavior and external environmental fluctuations. Problems such as poor communication path stability, fixed policies, and inaccurate energy control are common, making it difficult to meet the actual needs of high robustness and low energy consumption. Traditional methods often rely on static configuration or fixed middleware, lacking semantic layer understanding and dynamic perception capabilities, and are unable to effectively achieve cross-protocol, predictable, and schedulable intelligent communication. Summary of the Invention

[0003] The present invention aims to provide a multi-protocol data communication method and system for intelligent buildings, which significantly improves the compatibility, stability and energy efficiency of intelligent building communications by implementing semantically unified communication and dynamic optimization scheduling in a multi-protocol heterogeneous equipment environment.

[0004] A multi-protocol data communication method for intelligent buildings, comprising the following steps:

[0005] Based on the data communication environment of intelligent buildings, it includes several data communication protocols and multiple building communication devices; establishes corresponding semantic relays based on building communication devices; obtains real-time monitoring of user behavior, external environment and building automatic tasks in the current building communication scene in real time, and matches the fluctuation prediction factor for the current building communication scene;

[0006] In the current building communication scenario, building communication events are acquired; semantic understanding is performed based on building communication events to obtain communication semantic neutral expressions; analysis is performed based on fluctuation prediction factors and communication semantic neutral expressions to output building communication paths and building communication strategies;

[0007] Among them, the device connection status and device sensitive characteristics are continuously monitored; the fluctuation communication optimization strategy is output based on the device connection status and device sensitive characteristics; the building communication events are communicated based on the building communication path, building communication strategy and corresponding fluctuation communication optimization strategy to obtain the final building equipment communication result.

[0008] As a preferred technical solution of the present invention, the specific steps of establishing a corresponding semantic relay based on building communication equipment include:

[0009] Detect the communication behavior intention of building communication equipment and establish a common intention set for building equipment;

[0010] Identify the capabilities of building communication equipment and obtain the equipment service interface;

[0011] Sampling historical data communication protocols of building communication equipment to obtain a set of equipment protocols;

[0012] Align the device protocol set, device service interface, and building device general intent set to establish building communication device protocol mapping rules;

[0013] Define semantic relays based on building communication equipment protocol mapping rules;

[0014] Traverse multiple building communication devices and complete the construction of semantic relay bodies corresponding to all building communication devices.

[0015] As a preferred technical solution of the present invention, the specific steps of matching the fluctuation prediction factor for the current building communication scenario include:

[0016] The real-time monitored user behavior, external environment and building automatic tasks are extracted according to time slices to obtain the building communication feature T k , k=1, 2, ..., K; K is the total number of time slices for real-time monitoring in the current building communication scenario, T k represents the building communication features extracted at the kth time slice;

[0017] For all building communication features T k Perform feature splitting to obtain the first building communication feature T k1 and the second building communication feature T k2 ;

[0018] Based on all first building communication features T k1 Clustering is performed to obtain the first communication environment perception matrix; based on all the second building communication features T k2 Perform clustering to obtain a second communication result perception matrix;

[0019] Combining the first communication environment perception matrix and the second communication result perception matrix to perform calculations to obtain a comprehensive communication scenario fluctuation matrix;

[0020] Communication fluctuation prediction is performed based on the comprehensive communication scenario fluctuation matrix to obtain the fluctuation prediction factor.

[0021] As a preferred technical solution of the present invention, the specific steps of analyzing the fluctuation prediction factor and the communication semantic neutral expression and outputting the building communication path and building communication strategy include:

[0022] Establish a building communication task graph for nodes based on the communication semantic neutral expression;

[0023] Introducing fluctuation prediction factors into the building communication task graph to establish a communication spatiotemporal state graph;

[0024] In order to express the communication semantics neutrally, several candidate paths are extracted from the communication spatiotemporal state graph. Based on the fluctuation prediction factor, a path objective function is established to score the candidate paths, and the candidate path corresponding to the maximum path objective function is output as the building communication path. At the same time, the building communication strategy corresponding to the building communication path is output based on the semantic relay.

[0025] As a preferred technical solution of the present invention, the specific steps of outputting a fluctuation communication optimization strategy based on the device connection status and device sensitive characteristics include:

[0026] identifying a data emitting device and a data destination device based on a building communication path;

[0027] Obtain the device connection status and device sensitivity characteristics of the data sending device and the data target device, and establish a building communication environment prediction factor set based on the device connection status and device sensitivity characteristics; analyze and obtain the communication energy consumption prediction curve based on the building communication environment prediction factor set and the environment-energy supply regression model;

[0028] Design a fluctuating communication optimization strategy for the building communication strategy based on the building communication strategy and communication energy consumption prediction curve;

[0029] Specifically, it includes: outputting the protocol energy consumption budget based on the building communication path; monitoring the energy consumption according to the protocol energy consumption budget output and the communication energy consumption prediction curve in combination with the pulse time window to obtain the energy consumption status label; obtaining the communication emergency level label based on the communication semantic neutral expression; screening the building communication strategy according to the energy consumption status label and the communication emergency level label to obtain the fluctuating communication optimization strategy.

[0030] As a preferred technical solution of the present invention, the specific steps of communicating building communication events based on the building communication path, the building communication strategy and the corresponding fluctuation communication optimization strategy include:

[0031] Bind communication health monitoring mechanisms to building communication paths, building communication strategies, and corresponding fluctuating communication optimization strategies;

[0032] Prioritize communication of building communication events based on building communication paths and building communication strategies; output communication health monitoring scores based on the communication health monitoring mechanism;

[0033] If the communication health monitoring score is lower than the preset first score threshold, the building communication strategy is replaced with the latest fluctuating communication optimization strategy for communication;

[0034] If the communication health monitoring score is lower than the preset second score threshold, the path degradation mechanism is triggered, and the candidate path with a higher path objective function is switched as the backup building communication path for communication;

[0035] The communication health monitoring mechanism is used to continuously monitor communication until the building communication event completes communication and obtains the final building equipment communication result.

[0036] A multi-protocol data communication system for intelligent buildings, comprising:

[0037] The communication scenario construction module is used in the data communication environment of intelligent buildings and includes several data communication protocols and multiple building communication devices. It establishes corresponding semantic relays based on building communication devices. It obtains real-time monitoring of user behavior, external environment and building automation tasks in the current building communication scenario and matches the fluctuation prediction factor for the current building communication scenario.

[0038] The communication path output module is used to obtain building communication events in the current building communication scenario; perform semantic understanding based on building communication events to obtain communication semantic neutral expressions; perform analysis based on fluctuation prediction factors and communication semantic neutral expressions, and output building communication paths and building communication strategies;

[0039] The communication data optimization module is used to continuously monitor the device connection status and device sensitive characteristics; output the fluctuation communication optimization strategy based on the device connection status and device sensitive characteristics; communicate the building communication events based on the building communication path, building communication strategy and the corresponding fluctuation communication optimization strategy to obtain the final building equipment communication result.

[0040] The present invention has the following advantages:

[0041] 1. The present invention builds a semantic relay for various types of heterogeneous building communication equipment, completes the ternary mapping of communication behavior intention, service interface and protocol instruction, and forms a unified semantic neutral expression method, so that devices that originally adopted different protocols can achieve interoperability based on unified semantics, significantly improving the scalability and compatibility in protocol heterogeneous environments; by constructing time-slice communication characteristics, introducing fluctuation prediction factors, and establishing a spatiotemporal state map based on communication scenarios, it can dynamically perceive and predict the communication link status, task urgency and equipment energy consumption, thereby generating the optimal path and adaptation strategy, effectively avoiding communication bottlenecks such as network congestion, device response failure or excessive energy consumption, and improving the robustness and task success rate in a fluctuating environment.

[0042] 2. The present invention introduces a communication health monitoring mechanism during the communication process, outputs the communication score in real time, and has the ability to dynamically switch strategies and paths according to the current score. When the communication score drops, the fluctuation optimization strategy or degenerate path can be replaced in time to ensure uninterrupted communication and optimal operating efficiency, thereby greatly improving the stability and quality of the communication process. By utilizing the environment-energy supply regression model and the pulse time window mechanism, the energy consumption trend of the communication task can be accurately predicted, and strategy screening can be performed in combination with the communication emergency level to achieve a coordinated balance between low energy consumption and high response performance, thereby improving the overall energy efficiency ratio of the building communication system, and is particularly suitable for energy-sensitive scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a schematic structural diagram of a multi-protocol data communication system for intelligent buildings adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0045] Embodiment 1, a multi-protocol data communication method for intelligent buildings, comprising the following steps:

[0046] Based on the data communication environment of intelligent buildings, it includes several data communication protocols and multiple building communication devices; establishes corresponding semantic relays based on building communication devices; obtains real-time monitoring of user behavior, external environment and building automatic tasks in the current building communication scene in real time, and matches the fluctuation prediction factor for the current building communication scene;

[0047] The specific steps for establishing a corresponding semantic relay based on building communication equipment include:

[0048] Detect the communication behavior intention of building communication equipment and establish a common intention set for building equipment;

[0049] Through device usage log analysis, control instruction call history, manufacturer document parsing, and user operation behavior records, communication behavior intention detection is performed on each type of building communication equipment (such as light controllers, air conditioners, sensors, etc.). The purpose of communication behavior intention detection is to identify the basic actions that the equipment is commonly used to perform, such as switch operations, value adjustment, status feedback, etc. By summarizing these actions, a general intention set for building equipment is constructed. Each element of this set is a semantic action intention label, which is used to uniformly express the semantic essence of the communication behavior of different devices. The general intention set for building equipment is used to provide semantic standards for all subsequent building communication equipment.

[0050] Identify the capabilities of building communication equipment and obtain the equipment service interface;

[0051] Active capability discovery is performed on each building communication device, including protocol handshake, interface detection, function point (function code) reading, device control menu extraction, etc., to obtain the service interface it supports. For example, a smart socket may expose service functions; for devices that support standard protocols, it is based on the function code table; for private protocol devices, it needs to be obtained through packet capture or SDK interface parsing; the device service interface clearly defines the capability boundary of the building communication device, that is, what the device can do and what it can respond to, and is the basic data structure for the semantic relay to execute the mapping from intent to command.

[0052] Sampling historical data communication protocols of building communication equipment to obtain a set of equipment protocols;

[0053] Capture communication packets or call record sampling of building communication equipment to collect protocol data frame samples in actual operation; parse these samples to extract information such as the protocol header structure, command segment, data bit structure, and verification method, and construct the protocol set of the device. Each protocol set item represents a communication command format and its meaning. The device protocol set is used to parse the original protocol language and perform bidirectional mapping with semantic intent. It is one of the key inputs to realize the relay conversion function.

[0054] Align the device protocol set, device service interface, and building device general intent set to establish building communication device protocol mapping rules;

[0055] A semantic alignment engine is used to jointly model the device protocol set, device service interface and building equipment general intent set, aligning their functional meanings and behavioral triggers to support the correct execution of any semantic instructions through the protocol, or mapping the protocol response into semantic feedback.

[0056] A semantic relay body is defined based on the building communication device protocol mapping rule; an independent semantic relay body is created for each building communication device protocol mapping rule for receiving the communication semantic neutral expression;

[0057] Traverse multiple building communication devices and complete the construction of semantic relay bodies corresponding to all building communication devices.

[0058] The specific steps to match the fluctuation prediction factors for the current building communication scenario include:

[0059] The real-time monitored user behavior, external environment and building automatic tasks are extracted according to time slices to obtain the building communication feature T k , k=1, 2, ..., K; K is the total number of time slices for real-time monitoring in the current building communication scenario, T krepresents the building communication features extracted from the kth time slice; each time slice represents a continuous time window, such as 1 minute, 2 minutes, 5 minutes, etc., and the size of the time slice is set manually; when dividing, it is ensured that the time slices do not overlap with each other and can completely cover the process of real-time monitoring; feature extraction includes but is not limited to the following dimensions: total number of communications, protocol type distribution, average transmission rate, maximum and minimum bandwidth utilization, average communication success rate, average delay, energy consumption statistics, number of failures and protocol switching frequency, etc.; through normalization and time series aggregation processing, the communication behavior feature vector of the time slice is obtained, which is recorded as the building communication feature T k .

[0060] For all building communication features T k Perform feature splitting to obtain the first building communication feature T k1 and the second building communication feature T k2 ;

[0061] Based on all first building communication features T k1 Clustering is performed to obtain the first communication environment perception matrix; based on all the second building communication features T k2 Perform clustering to obtain a second communication result perception matrix;

[0062] Combining the first communication environment perception matrix and the second communication result perception matrix to perform calculations to obtain a comprehensive communication scenario fluctuation matrix;

[0063] Communication fluctuation prediction is performed based on the comprehensive communication scenario fluctuation matrix to obtain the fluctuation prediction factor;

[0064] In building communication scene modeling, different features have different influences on scene recognition, so the building communication feature T can be transformed into k Split into two subsets: First, building communication feature T k1 It usually includes features with strong environmental relevance or system-level regulatory significance, such as bandwidth utilization, system average transmission rate, protocol switching frequency, etc. These features reflect the macro communication behavior pattern; the second building communication feature T k2 It tends to focus on the communication result characteristics at the device level, such as average packet loss rate, average communication success rate, energy consumption index, delay distribution, etc., which represent the quality and stability of individual communications;

[0065] When performing optimized cluster analysis, the FSND method and K-means method are used to process different feature sets respectively; the FSND method is an adaptive clustering method based on feature similarity and neighborhood density. It does not rely on the preset number of clusters and can discover naturally formed cluster structures in complex data distributions. It is particularly suitable for scenarios with local density differences. The specific steps include: first calculating the feature similarity matrix between each data point, then determining the density peak point based on the local density distribution of each point, completing adaptive partitioning by constructing a similarity-density map, and finally generating the first communication environment perception matrix; in comparison, The K-means method is a classic clustering algorithm based on the "distance minimization" principle. It requires a preset number of clusters. Its steps include: initializing the preset number of cluster centers, calculating the Euclidean distance between each point and each center, assigning each point to the nearest cluster center, updating the center point position, and repeating the iteration until convergence. K-means is suitable for scenarios where the data is relatively uniform, low-dimensional, and linearly separable, with clear cluster boundaries. The main difference between the two is that FSND does not rely on the initial number of clusters and is suitable for non-uniform density distributions and irregular cluster shapes. K-means is fast and simple to implement, but is sensitive to the initial number of clusters and data distribution.

[0066] The selection of two clustering methods is essentially a matching decision based on the different characteristics of the two types of features in terms of semantic properties, data distribution characteristics, clustering targets, etc. The first building communication feature T k1 These usually include network environment-aware features or system behavior features, such as protocol switching frequency, bandwidth occupancy fluctuations, protocol hybridity, and inter-device communication collaboration patterns. These features often exhibit strong nonlinear relationships, large variations in distribution density, unclear cluster boundaries, and may be highly heterogeneous in different time slices. In this case, the FSND method has obvious advantages: it does not rely on a preset number of clusters, can explore local density changes in data, is suitable for discovering complex structures and abnormal clustering forms, and can more accurately reveal the natural division of system-level communication behavior patterns.

[0067] In contrast, the second building communication feature T k2 These are more representative of communication results or performance characteristics, such as average latency, packet loss rate, communication success rate, and link stability. These characteristics typically have strong numerical continuity, stable statistical distribution, and good linear separability. Their clustering structures tend to be regular and spherical, making them well-suited for processing using the K-means clustering algorithm. K-means can efficiently classify these points into different performance intervals based on Euclidean distance, thereby quickly forming a communication quality hierarchy. This makes it suitable for feature sets with clear cluster centers.

[0068] The two feature types are actually complementary: the first building communication feature T k1Describes the evolutionary structure and control behavior of the communication environment, and is suitable for characterizing the potential network state transition pattern using density adaptive methods; the second building communication feature T k2 To describe the communication results and performance indicators, it is suitable to use the traditional distance measurement algorithm to characterize the classification boundary of the communication status; therefore, using the FSND and K-means methods to cluster these two sub-feature sets respectively is a targeted and structure-matching strategy.

[0069] Combine the first communication environment perception matrix and the second communication result perception matrix to calculate and obtain the comprehensive communication scenario fluctuation matrix. The specific steps are:

[0070] Using the formula Combining the first communication environment perception matrix and the second communication result perception matrix to perform calculations to obtain a comprehensive communication scenario fluctuation matrix;

[0071] in, represents the comprehensive communication scenario fluctuation matrix, represents the first communication environment perception matrix, represents the first category mapping matrix; represents the second communication result perception matrix, Represents the second category mapping matrix; α, β, γ are dynamic weight coefficients, satisfying α+β+γ=1, , Silhouette ( ) represents the calculation of silhouette coefficient; represents the Hadamard operation; the function of this formula is to fuse the communication scenario result matrices generated by the two clustering methods to construct a more accurate and generalizable communication scenario matrix; the category mapping matrix is ​​used to unify the communication scenario result matrix into a standard label encoding space.

[0072] The specific steps for predicting communication fluctuations based on the comprehensive communication scenario fluctuation matrix and obtaining the fluctuation prediction factor are as follows:

[0073] Use time series extrapolation models to model and predict the dynamic evolution trend of the volatility matrix of integrated communication scenarios. For example, the ARIMA (Autoregressive Integrated Moving Average) model is suitable for data series with long-term dependencies and nonlinear trends in the volatility matrix, and can capture complex time series changes for prediction.

[0074] The training process of the time series extrapolation model includes the following key elements: First, the training set comes from the historical data sequence arranged by time slice in the fluctuation matrix of the comprehensive communication scenario. The data sequence covers multiple building communication cycles, including normal conditions, high-incidence fluctuation periods and abnormal scenarios, to ensure that the model has the ability to generalize to different communication states; the training goal is to minimize the error between the predicted value and the actual observed value in the next time slice in the actual fluctuation matrix, and the mean square error or mean absolute error is often used as the loss function; during the training process, the model continuously learns the dynamic law of the fluctuation factor changing over time and optimizes the model parameters through back propagation; the training termination condition can be set as the loss on the validation set no longer decreases in several consecutive rounds of training, or reaches the preset maximum number of training rounds, or the validation set error reaches an acceptable threshold, so as to avoid overfitting and improve the prediction stability in the actual communication environment.

[0075] Through splitting and clustering analysis, complex communication time series data can be accurately divided into multiple building communication scenarios. The FSND method and K-means method are used to cluster different features respectively, which helps to analyze communication behavior from multiple perspectives, thereby achieving more refined scenario division, such as bandwidth, transmission rate, etc. By combining the first communication environment perception matrix and the second communication result perception matrix and adopting dynamic weight coefficients, the weights in different clustering results can be flexibly adjusted according to actual needs, achieving more accurate scenario division. This fusion strategy can avoid the limitations of single feature clustering and ensure more comprehensive and accurate classification results. Based on the scenario classification, the intelligent building digital twin model is used for simulation to obtain M groups of communication results, and a multi-objective optimal evaluation is performed on them to generate the optimal communication strategy, which can significantly improve the communication efficiency and resource allocation of various devices in the intelligent building and avoid resource waste. Through intelligent clustering analysis and multi-objective optimization, it is possible to avoid comprehensive calculation and evaluation in all possible communication scenarios, thereby saving computing resources and improving processing efficiency, which is particularly important for large-scale intelligent building systems.

[0076] In the current building communication scenario, building communication events are acquired; semantic understanding is performed based on building communication events to obtain communication semantic neutral expressions; analysis is performed based on fluctuation prediction factors and communication semantic neutral expressions to output building communication paths and building communication strategies;

[0077] The specific steps for semantic understanding based on building communication events include: in the current building communication scenario, after obtaining the building communication event, first identify the source of the event, including user behavior triggers (such as access control card swiping, movement trajectory), external environment changes (such as temperature and humidity increase, illumination change) or building automatic tasks (such as timed scheduling, preset script execution); then extract the key attributes of the event to construct an event feature vector, including event type, trigger time, trigger area, target device category and context information; then call the semantic parsing module to map the event features into standardized communication intent labels, and then combine the current building status, scene labels and device control capabilities to enhance the context and complete the parameters of the semantic expression; finally, generate a communication semantic neutral expression through a structured representation method (such as JSON-LD or RDF) to ensure that the expression can be recognized and called by the semantic relay body in a protocol-independent manner, so as to achieve consistent communication intent expression across devices and protocols.

[0078] Based on the analysis of the fluctuation prediction factors and the communication semantic neutral expression, the specific steps of outputting the building communication path and building communication strategy include:

[0079] Establish a building communication task graph for nodes based on the communication semantic neutral expression;

[0080] Based on the generated neutral expression of communication semantics, a building communication task graph is constructed. This graph uses each device instruction or control target in the neutral expression of communication semantics as a node. Directed edges are established between nodes based on operation dependencies, inter-device collaboration logic, or scenario-triggered relationships, forming a task execution process topology. Each node contains attributes such as instruction type, target device, execution priority, and delay tolerance, which are used to describe the execution structure of the communication intent. Subsequently, a fluctuation prediction factor is introduced based on the building communication task graph to construct a communication spatiotemporal state graph. This graph abstracts devices, routers, and relay nodes within the building space as graph nodes. Connections represent feasible communication links, and edge attributes include historical bandwidth, current congestion status, predicted packet loss rate, and environmental interference risk. This is a dynamic, multi-dimensional communication state graph model.

[0081] Introducing fluctuation prediction factors into the building communication task graph to establish a communication spatiotemporal state graph;

[0082] To express communication semantics neutrally, several candidate paths are extracted from the communication spatiotemporal state graph. A path objective function is established based on the fluctuation prediction factor to score the candidate paths, and the candidate path with the maximum corresponding path objective function is output as the building communication path. At the same time, the building communication strategy corresponding to the building communication path is output based on the semantic relay body.

[0083] For each source node and destination device in the spatiotemporal state graph, the system extracts several candidate paths. Each path consists of a sequence of hops and includes the state characteristics of the path links. The system constructs a path objective function based on the fluctuation prediction factor and the communication requirements (such as low latency and high success rate) contained in the semantically neutral expression. This objective function maps factors such as availability, security, real-time performance, and hop count penalty for each path into a comprehensive score. The path with the highest objective function score is selected as the final building communication path. Based on the node characteristics, semantic intent, and device capabilities involved in this path, the semantic relay is invoked to generate a corresponding building communication strategy. This strategy includes factors such as whether to enable link encryption, whether to use a retransmission mechanism, communication rhythm control method, and energy consumption optimization mode. This ensures that path execution adapts to the current communication fluctuation state of the building while meeting the task semantic objectives, thereby realizing intelligent and dynamic multi-protocol communication scheduling.

[0084] Among them, the device connection status and device sensitive characteristics are continuously monitored; based on the device connection status and device sensitive characteristics, a fluctuation communication optimization strategy is output; based on the building communication path, building communication strategy and corresponding fluctuation communication optimization strategy, building communication events are communicated to obtain the final building equipment communication result;

[0085] The specific steps of outputting a fluctuating communication optimization strategy based on the device connection status and device sensitive characteristics include:

[0086] identifying a data emitting device and a data destination device based on a building communication path;

[0087] obtaining device connection states and device sensitive features of a data-emitting device and a data-target device, and establishing a building communication environment prediction factor set based on the device connection states and the device sensitive features;

[0088] Based on the determined building communication paths, the data-issuing devices and data-target devices in the communication tasks are identified, that is, the physical nodes responsible for sending and receiving instructions in the current semantic communication. The real-time monitoring module is called to obtain the device connection status of these two types of devices, including the current link connectivity, signal quality, communication stability score and routing integrity. At the same time, device sensitive features are extracted. These features reflect the resource constraint degree, response urgency and energy consumption sensitivity of the device during the communication process, such as whether it is a low-power device, whether it is located in a high-interference area, whether it is in standby or wake-up state, etc. These connection states and sensitive features jointly constitute a set of building communication environment prediction factors, which are used to comprehensively model the dynamic environmental background of device communication.

[0089] Based on the building communication environment prediction factor set and the environment-energy supply regression model, the communication energy consumption prediction curve is obtained through analysis;

[0090] Taking the building communication environment prediction factor set as input and combining it with historical building operation data, a trained environment-energy supply regression model is used to analyze its impact on communication energy consumption. The resulting output is a communication energy consumption prediction curve for the building communication path under current conditions. This curve shows the expected energy consumption fluctuations of devices in each time slice during the communication process, characterizing energy consumption trends under different devices, different protocols, and different load conditions. It is a key reference for optimizing communication rhythm and protocol selection.

[0091] The training process of the environment-energy supply regression model includes the following key steps: the training set is derived from historical data collected in the building communication system at different times, spaces, and device states, including input features such as device connection status, communication protocol type, bandwidth occupancy, signal quality, and environmental interference level, and the corresponding actual communication energy consumption as the output label; the training goal is to establish a mapping relationship between feature variables and communication energy consumption, minimizing the error between predicted energy consumption and actual measured energy consumption, usually using mean squared error or mean absolute error as the loss function; the model can be trained using methods such as linear regression, support vector regression, random forest regression, or lightweight neural network; training termination conditions include: when the loss function on the validation set no longer decreases significantly over several consecutive rounds, or reaches a preset maximum number of iterations, or the error falls below a set threshold, training is terminated to ensure that the model has good generalization ability and adapts to the energy consumption prediction needs in dynamic building communication scenarios. The environment-energy supply regression model outputs a charge and discharge prediction curve for the current device in the future time period. This curve is used to analyze the fluctuation trend of the device's power supply level in different time periods, including charging speed, power consumption rate, and remaining power trend.

[0092] Design a fluctuating communication optimization strategy for the building communication strategy based on the building communication strategy and communication energy consumption prediction curve;

[0093] Specifically, it includes: outputting a protocol energy consumption budget based on the building communication path; monitoring energy consumption based on the protocol energy consumption budget output and the communication energy consumption prediction curve, combined with a pulse time window, to obtain an energy consumption status label; obtaining a communication emergency level label based on the communication semantic neutral expression; screening the building communication strategy based on the energy consumption status label and the communication emergency level label to obtain a fluctuating communication optimization strategy;

[0094] Based on the communication energy consumption prediction curve and the initially generated building communication strategy, a more adaptive fluctuating communication optimization strategy is designed. The operational process includes: first, based on the selected communication path and the target device protocol stack, the protocol energy consumption budget of different protocols during the communication process is calculated, that is, the estimated energy consumption value per unit time or unit data transmission. Then, combined with the prediction curve, a pulsed time window monitoring mechanism is used to monitor the actual energy consumption status of the device, generating energy consumption status labels for each time window, such as normal, overloaded, and frequency reduction required. At the same time, based on the task characteristics carried in the neutral expression of communication semantics, communication urgency level labels such as high priority, low delay tolerance, regular, and cacheable are extracted. Finally, the energy consumption status label and the communication urgency level label are combined as a dual-label input condition to screen feasible solutions from the initial communication strategy set, eliminating high-energy consumption or low-urgency combinations and prioritizing low-energy consumption and high-priority compatible strategy structures. Ultimately, a set of optimized fluctuating communication strategies is output, which can be used to dynamically adapt to energy consumption status and task urgency during actual communication, thereby improving overall communication efficiency and building system stability.

[0095] The specific steps for communicating building communication events based on building communication paths, building communication strategies, and corresponding fluctuation communication optimization strategies include:

[0096] Bind communication health monitoring mechanisms to building communication paths, building communication strategies, and corresponding fluctuating communication optimization strategies;

[0097] Before communication is executed, a communication health monitoring mechanism is uniformly bound to the selected building communication path, building communication strategy and corresponding fluctuating communication optimization strategy. This mechanism is a real-time dynamic evaluation component used to continuously evaluate the link status and device response quality during the communication process. Monitoring parameters include packet arrival rate, average round-trip time (RTT), packet loss rate, energy consumption change trend, protocol response delay, etc.; all monitoring indicators are weightedly combined to form a communication health monitoring score, which is used to quantify the overall reliability and stability of the current communication behavior; then, according to the default priority, based on the current building communication path and building communication strategy, actual data transmission operations are performed on the building communication events described by the communication semantic neutral expression, that is, device command sending, data reception or control execution, etc. The communication health monitoring mechanism is started synchronously during the whole process to collect and calculate the communication health monitoring score in real time.

[0098] Prioritize communication of building communication events based on building communication paths and building communication strategies; output communication health monitoring scores based on the communication health monitoring mechanism;

[0099] If the communication health monitoring score is lower than the preset first score threshold, the building communication strategy is replaced with the latest fluctuating communication optimization strategy for communication;

[0100] If the communication health monitoring score is lower than the preset second scoring threshold, the path degradation mechanism is triggered, and the candidate path with a higher path objective function is switched as the backup building communication path for communication; the preset first scoring threshold and the preset second scoring threshold are set manually;

[0101] When the communication health monitoring score falls below the set first scoring threshold (indicating mild communication fluctuation or primary anomaly) during communication execution, the previously generated fluctuating communication optimization strategy will be automatically called to replace the current building communication strategy, giving priority to using a configuration with lower energy consumption, faster response, or more stable performance to perform communication operations, thereby improving communication stability and preventing further degradation. If the communication health monitoring score continues to decline and falls below the lower second scoring threshold (indicating severe communication anomaly or path instability), the path degradation mechanism will be immediately triggered. This mechanism will reselect the alternative path with the highest objective function score from the candidate paths generated by the previous scoring function as the backup building communication path, and perform communication switching in combination with the bound fluctuating communication optimization strategy to ensure uninterrupted tasks.

[0102] Use the communication health monitoring mechanism to continuously monitor communication until the building communication event completes communication and obtain the final building equipment communication results;

[0103] The entire communication process will run under the condition that the communication health monitoring mechanism is continuously enabled. The system will update the score in real time and determine whether to maintain the current path and strategy based on the threshold until the communication event completes all the scheduled data interaction processes and generates the final building equipment communication result. The result includes task execution status (success / failure), response delay, energy consumption data, strategy switching records, etc., which is an important data source for subsequent feedback learning and adaptive update of semantic relays.

[0104] Example 2, a multi-protocol data communication system for intelligent buildings, see Figure 1 Shown, including:

[0105] The communication scenario construction module is used in the data communication environment of intelligent buildings and includes several data communication protocols and multiple building communication devices. It establishes corresponding semantic relays based on building communication devices. It obtains real-time monitoring of user behavior, external environment and building automation tasks in the current building communication scenario and matches the fluctuation prediction factor for the current building communication scenario.

[0106] The communication path output module is used to obtain building communication events in the current building communication scenario; perform semantic understanding based on building communication events to obtain communication semantic neutral expressions; perform analysis based on fluctuation prediction factors and communication semantic neutral expressions, and output building communication paths and building communication strategies;

[0107] The communication data optimization module is used to continuously monitor the device connection status and device sensitive characteristics; output the fluctuation communication optimization strategy based on the device connection status and device sensitive characteristics; communicate the building communication events based on the building communication path, building communication strategy and the corresponding fluctuation communication optimization strategy to obtain the final building equipment communication result.

[0108] It should be understood that those skilled in the art may make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the scope of protection of the appended claims. Any portion of this specification not described in detail is prior art known to those skilled in the art.

Claims

1. A multi-protocol data communication method for intelligent buildings, characterized in that: The following steps are involved: The data communication environment based on intelligent buildings includes several data communication protocols and multiple building communication devices; Establish corresponding semantic relays based on building communication equipment; obtain real-time monitoring of user behavior, external environment and building automatic tasks in the current building communication scene in real time, and match fluctuation prediction factors for the current building communication scene; In the current building communication scenario, obtain building communication events; Based on the semantic understanding of building communication events, a neutral expression of communication semantics is obtained; based on the analysis of fluctuation prediction factors and neutral expression of communication semantics, the building communication path and building communication strategy are output; Among them, the device connection status and device sensitive characteristics are continuously monitored; the fluctuation communication optimization strategy is output based on the device connection status and device sensitive characteristics; the building communication events are communicated based on the building communication path, building communication strategy and corresponding fluctuation communication optimization strategy to obtain the final building equipment communication result.

2. A multi-protocol data communication method for intelligent buildings according to claim 1, characterized in that: The specific steps for establishing a corresponding semantic relay based on building communication equipment include: Detect the communication behavior intention of building communication equipment and establish a common intention set for building equipment; Identify the capabilities of building communication equipment and obtain the equipment service interface; Sampling historical data communication protocols of building communication equipment to obtain a set of equipment protocols; Align the device protocol set, device service interface, and building device general intent set to establish building communication device protocol mapping rules; Define semantic relays based on building communication equipment protocol mapping rules; Traverse multiple building communication devices and complete the construction of semantic relay bodies corresponding to all building communication devices.

3. A multi-protocol data communication method for intelligent buildings according to claim 2, characterized in that: The specific steps to match the fluctuation prediction factors for the current building communication scenario include: The real-time monitored user behavior, external environment and building automatic tasks are extracted according to time slices to obtain the building communication feature T k , k=1, 2, ..., K; K is the total number of time slices for real-time monitoring in the current building communication scenario, T k represents the building communication features extracted at the kth time slice; For all building communication features T k Perform feature splitting to obtain the first building communication feature T k1 and the second building communication feature T k2 ; Based on all first building communication features T k1 Clustering is performed to obtain the first communication environment perception matrix; based on all the second building communication features T k2 Perform clustering to obtain a second communication result perception matrix; Combining the first communication environment perception matrix and the second communication result perception matrix to perform calculations to obtain a comprehensive communication scenario fluctuation matrix; Communication fluctuation prediction is performed based on the comprehensive communication scenario fluctuation matrix to obtain the fluctuation prediction factor.

4. A multi-protocol data communication method for intelligent buildings according to claim 3, characterized in that: Based on the analysis of the fluctuation prediction factors and the neutral expression of communication semantics, the specific steps of outputting the building communication path and building communication strategy include: Establish a building communication task graph for nodes based on the communication semantic neutral expression; Introducing fluctuation prediction factors into the building communication task graph to establish a communication spatiotemporal state graph; In order to express the communication semantics neutrally, several candidate paths are extracted from the communication spatiotemporal state graph. Based on the fluctuation prediction factor, a path objective function is established to score the candidate paths, and the candidate path corresponding to the maximum path objective function is output as the building communication path. At the same time, the building communication strategy corresponding to the building communication path is output based on the semantic relay.

5. A multi-protocol data communication method for intelligent buildings according to claim 4, characterized in that: The specific steps of outputting a fluctuating communication optimization strategy based on the device connection status and device sensitive characteristics include: identifying a data emitting device and a data destination device based on a building communication path; Obtain the device connection status and device sensitivity characteristics of the data sending device and the data target device, and establish a building communication environment prediction factor set based on the device connection status and device sensitivity characteristics; analyze and obtain the communication energy consumption prediction curve based on the building communication environment prediction factor set and the environment-energy supply regression model; Design a fluctuating communication optimization strategy for the building communication strategy based on the building communication strategy and communication energy consumption prediction curve; Specifically, it includes: outputting the protocol energy consumption budget based on the building communication path; monitoring the energy consumption according to the protocol energy consumption budget output and the communication energy consumption prediction curve in combination with the pulse time window to obtain the energy consumption status label; obtaining the communication emergency level label based on the communication semantic neutral expression; screening the building communication strategy according to the energy consumption status label and the communication emergency level label to obtain the fluctuating communication optimization strategy.

6. A multi-protocol data communication method for intelligent buildings according to claim 5, characterized in that: The specific steps for communicating building communication events based on building communication paths, building communication strategies, and corresponding fluctuation communication optimization strategies include: Bind communication health monitoring mechanisms to building communication paths, building communication strategies, and corresponding fluctuating communication optimization strategies; Prioritize communication of building communication events based on building communication paths and building communication strategies; output communication health monitoring scores based on the communication health monitoring mechanism; If the communication health monitoring score is lower than the preset first score threshold, the building communication strategy is replaced with the latest fluctuating communication optimization strategy for communication; If the communication health monitoring score is lower than the preset second score threshold, the path degradation mechanism is triggered, and the candidate path with a higher path objective function is switched as the backup building communication path for communication; The communication health monitoring mechanism is used to continuously monitor communication until the building communication event completes communication and obtains the final building equipment communication result.

7. A multi-protocol data communication system for intelligent buildings, characterized in that: The system applies a multi-protocol data communication method for intelligent buildings according to any one of claims 1 to 6, comprising: The communication scenario construction module is used in the data communication environment of intelligent buildings and includes several data communication protocols and multiple building communication devices. It establishes corresponding semantic relays based on building communication devices. It obtains real-time monitoring of user behavior, external environment and building automation tasks in the current building communication scenario and matches the fluctuation prediction factor for the current building communication scenario. The communication path output module is used to obtain building communication events in the current building communication scenario; perform semantic understanding based on building communication events to obtain communication semantic neutral expressions; perform analysis based on fluctuation prediction factors and communication semantic neutral expressions, and output building communication paths and building communication strategies; The communication data optimization module is used to continuously monitor the device connection status and device sensitive characteristics; output the fluctuation communication optimization strategy based on the device connection status and device sensitive characteristics; communicate the building communication events based on the building communication path, building communication strategy and the corresponding fluctuation communication optimization strategy to obtain the final building equipment communication result.

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