Multi-protocol data communication method and system for intelligent building

Abstract

The invention discloses a multi-protocol data communication method and system for an intelligent building, and relates to the technical field of data communication. A multi-protocol data communication system for an intelligent building comprises a communication scene construction module, a communication path output module and a communication data optimization module. According to the method, the semantic relay body is constructed for various heterogeneous building communication devices, ternary mapping of communication behavior intentions, service interfaces and protocol instructions is completed, a unified semantic neutral expression mode is formed, devices originally adopting different protocols can achieve interoperability based on unified semantics, and the interoperability of the devices is improved. The expandability and the compatibility in a protocol heterogeneous environment are obviously improved; by generating the optimal path and the adaptation strategy, the communication bottleneck problems such as network congestion, equipment response failure or overhigh energy consumption are effectively avoided, and the robustness and the task success rate in the fluctuation environment are improved.

Description

Technical Field

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

[0002] In existing intelligent buildings, various communication devices often adopt different data communication protocols, resulting in a lack of a unified interaction standard between devices. The problems of protocol incompatibility and difficult data interconnection are particularly prominent, especially in scenarios such as renovation of old buildings, cross-vendor integration, or where protocols are not publicly available. In addition, the building communication environment is significantly affected by factors such as user behavior and external environment fluctuations. Problems such as poor communication path stability, fixed strategies, and inaccurate energy consumption control frequently occur, making it difficult to meet the actual requirements of high robustness and low energy consumption. Traditional methods mostly rely on static configuration or fixed middleware, lacking semantic layer understanding and dynamic perception capabilities, and 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 level of intelligent building communication through semantic unified communication and dynamic optimization scheduling in a multi-protocol heterogeneous device environment.

[0004] A multi-protocol data communication method for intelligent buildings includes the following steps: Based on the data communication environment of an intelligent building, which includes several data communication protocols and multiple building communication devices; establishing corresponding semantic relay bodies based on the building communication devices; and obtaining in real time the real-time monitored user behavior, external environment, and building automatic tasks in the current building communication scenario, and matching fluctuation prediction factors for the current building communication scenario; In the current building communication scenario, obtaining building communication events; performing semantic understanding based on the building communication events to obtain a neutral expression of communication semantics; and analyzing based on the fluctuation prediction factors and the neutral expression of communication semantics to output a building communication path and a building communication strategy; Among them, continuously monitoring the device connection status and device sensitive features; outputting a fluctuation communication optimization strategy based on the device connection status and device sensitive features; and performing communication on 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 device communication result.

[0005] As a preferred technical solution of the present invention, the specific steps of establishing corresponding semantic relay bodies based on building communication devices include: Detecting the communication behavior intention of building communication devices to establish a general intention set of building devices; Identifying the device capabilities of building communication devices to obtain device service interfaces; Sample the historical data communication protocols of building communication devices to obtain a set of device protocols; Align the set of device protocols, device service interfaces, and the set of general intentions of building devices to establish protocol mapping rules for building communication devices; Define semantic relay entities based on the protocol mapping rules of building communication devices; Traverse multiple building communication devices to complete the construction of semantic relay entities corresponding to all building communication devices.

[0006] As a preferred technical solution of the present invention, the specific steps for matching a fluctuation prediction factor to the current building communication scenario include: Extract features of real-time monitored user behavior, external environment, and building automatic tasks by time slices to obtain building communication features T k , k = 1, 2,..., K; K is the total number of time slices real-time monitored in the current building communication scenario, T k represents the building communication features extracted in the k-th 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 the first building communication features T k1 Perform clustering to obtain the first communication environment perception matrix; based on all the second building communication features T k2 Perform clustering to obtain the second communication result perception matrix; Combine the first communication environment perception matrix and the second communication result perception matrix for calculation to obtain a comprehensive communication scenario fluctuation matrix; Perform communication fluctuation prediction based on the comprehensive communication scenario fluctuation matrix to obtain a fluctuation prediction factor.

[0007] As a preferred technical solution of the present invention, the specific steps for analyzing based on the fluctuation prediction factor and communication semantic neutral expression and 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; Introduce the fluctuation prediction factor into the building communication task graph to establish a communication spatio-temporal state map; Extract several candidate paths for the communication semantic neutral expression in the communication spatio-temporal state map; establish a path objective function based on the fluctuation prediction factor to calculate scores for the candidate paths, and output the candidate path corresponding to the maximum of the path objective function as the building communication path; at the same time, output the building communication strategy corresponding to the building communication path based on the semantic relay entity.

[0008] As a preferred technical solution of the present invention, the specific steps of outputting a fluctuating communication optimization strategy based on the device connection state and the device sensitive characteristics include: A data sending device and a data target device are issued based on the building communication path recognition data; Obtain the device connection state and the device sensitive 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 state and the device sensitive characteristics; according to the building communication environment prediction factor set and the environment - energy supply regression model, analyze and obtain the communication energy consumption prediction curve; Design a fluctuating communication optimization strategy for the building communication strategy based on the building communication strategy and the communication energy consumption prediction curve; Specifically include: outputting the protocol energy consumption budget based on the building communication path; performing energy consumption monitoring in combination with the pulsed time window according to the protocol energy consumption budget output and the communication energy consumption prediction curve to obtain the energy consumption status label; obtaining the communication emergency level label based on the neutral expression of communication semantics; 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.

[0009] As a preferred technical solution of the present invention, the specific steps of communicating a building communication event based on the building communication path, the building communication strategy, and the corresponding fluctuating communication optimization strategy include: Bind a communication health monitoring mechanism to the building communication path, the building communication strategy, and the corresponding fluctuating communication optimization strategy; Communicate the building communication event preferentially according to the building communication path and the building communication strategy; output the communication health monitoring score based on the communication health monitoring mechanism; If the communication health monitoring score is lower than the preset first score threshold, then replace the building communication strategy with the latest fluctuating communication optimization strategy for communication; If the communication health monitoring score is lower than the preset second score threshold, then trigger the path degradation mechanism and switch to the candidate path with a higher path objective function as the backup building communication path for communication; Use the communication health monitoring mechanism to continuously perform communication monitoring until the building communication event completes communication to obtain the final building device communication result.

[0010] A multi - protocol data communication system for intelligent buildings includes: A communication scenario construction module, which is used to, based on the data communication environment of the intelligent building, include several data communication protocols and multiple building communication devices; establish a corresponding semantic relay body based on the building communication devices; and real - time obtain the real - time monitored user behavior, external environment, and building automatic tasks in the current building communication scenario, and match the fluctuating prediction factors for the current building communication scenario; A communication path output module, which is used to obtain building communication events in the current building communication scenario; perform semantic understanding based on the building communication events to obtain a neutral communication semantic expression; perform analysis based on the fluctuation prediction factor and the neutral communication semantic expression, and output the building communication path and the building communication strategy; A communication data optimization module, which is used to continuously monitor the device connection status and device sensitive features; output a fluctuating communication optimization strategy based on the device connection status and device sensitive features; communicate the building communication events based on the building communication path, the building communication strategy and the corresponding fluctuating communication optimization strategy to obtain the final building device communication result.

[0011] The present invention has the following advantages: 1. By constructing a semantic relay body for various heterogeneous building communication devices, the present invention completes the triple mapping of communication behavior intention, service interface and protocol instruction, forms a unified neutral communication semantic expression, enables devices originally using different protocols to interoperate based on the unified semantics, and significantly improves the scalability and compatibility in a protocol heterogeneous environment; by constructing time-slice communication features, introducing a fluctuation prediction factor, and establishing a spatio-temporal state map based on the communication scenario, the communication link state, task urgency and device energy consumption can be dynamically perceived and predicted, so as to generate the optimal path and adaptation strategy, effectively avoid communication bottleneck problems such as network congestion, device response failure or excessive energy consumption, and improve the robustness and task success rate in a fluctuating environment.

[0012] 2. By introducing a communication health monitoring mechanism in the communication process, the present invention can output 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 fluctuating optimization strategy or degraded path can be replaced in time to ensure that the communication is not interrupted and the operation efficiency is optimal, greatly improving the stability and quality of the communication process; using the environment-energy supply regression model and the pulsed time window mechanism, the energy consumption trend of the communication task can be accurately predicted, and the strategy can be screened in combination with the communication emergency level to achieve the coordinated balance between low energy consumption and high response performance, and improve the overall energy efficiency ratio of the building communication system, which is particularly suitable for energy consumption sensitive scenarios. Description of the Drawings

[0013] Figure 1 It is a schematic structural diagram of a multi-protocol data communication system for intelligent buildings adopted in an embodiment of the present invention. Detailed Embodiments

[0014] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention.

[0015] Embodiment 1, a multi - protocol data communication method for intelligent buildings, includes the following steps: Based on the data communication environment of an intelligent building, there are several data communication protocols and multiple building communication devices; establish corresponding semantic relay entities based on the building communication devices; obtain in real - time the real - time monitored user behaviors, external environments, and building automation tasks in the current building communication scenario, and match fluctuation prediction factors for the current building communication scenario. The specific steps for establishing corresponding semantic relay entities based on the building communication devices include: Detect the communication behavior intentions of the building communication devices and establish a general intention set for building devices. Through device usage log analysis, control instruction call history, manufacturer document parsing, and based on user operation behavior records, detect the communication behavior intentions of each type of building communication device (such as light controllers, air conditioners, sensors, etc.). The purpose of detecting communication behavior intentions is to identify the basic actions that the device is usually used to perform, such as switch operations, adjusting values, status feedback, etc. By summarizing these actions, a general intention set for building devices is constructed. Each element of this set is a type of semantic action intention label, which is used to uniformly express the semantic essence of different device communication behaviors. The general intention set for building devices is used to provide a semantic standard for all subsequent building communication devices.

[0016] Identify the device capabilities of the building communication devices to obtain device service interfaces. Perform active capability discovery on each building communication device, including protocol handshakes, interface detection, function point (function code) reading, device control menu extraction, etc., to obtain the service interfaces it supports. For example, a certain 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 is necessary to obtain through packet capture or SDK interface parsing. The device service interface defines the capability boundaries 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 entity to perform the mapping from intention to command.

[0017] Sample the historical data communication protocols of the building communication devices to obtain a device protocol set. Perform communication packet capture or call record sampling on the building communication devices, collect the protocol data frame samples during their actual operation; parse these samples to extract information such as protocol header structure, command section, data bit structure, and verification method, and construct the protocol set for 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 a two - way mapping with semantic intentions, and is one of the key inputs for implementing the relay conversion function.

[0018] Align the device protocol set, device service interfaces, and the general intent set of building devices to establish the protocol mapping rules for building communication devices; Use a semantic alignment engine to jointly model the device protocol set, device service interfaces, and the general intent set of building devices, aligning their functional meanings and behavior triggers to support the correct execution of any semantic instruction through the protocol, or mapping the protocol response to a semantic feedback.

[0019] Define semantic relays based on the protocol mapping rules for building communication devices; create independent semantic relays for each protocol mapping rule of building communication devices to receive neutral expressions of communication semantics; Traverse multiple building communication devices to complete the construction of semantic relays corresponding to all building communication devices.

[0020] The specific steps to match the fluctuation prediction factor for the current building communication scenario include: Extract features from the user behavior, external environment, and building automation tasks monitored in real time by time slices to obtain the building communication feature T k , k = 1, 2, …, K; K is the total number of time slices monitored in real time within the current building communication scenario, T k represents the building communication feature extracted in the k-th 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; ensure that the time slices do not overlap with each other and can completely cover the process of one real-time monitoring; feature extraction includes, but is not limited to, the following dimensions: total communication times, protocol type distribution, average transmission rate, maximum and minimum bandwidth utilization rates, average communication success rate, average latency, energy consumption statistical value, number of failures, and protocol switching frequency, etc.; through normalization and time series aggregation processing, obtain the communication behavior feature vector of this time slice, denoted as the building communication feature T k .

[0021] 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 the first building communication features T k1 Perform clustering to obtain the first communication environment perception matrix; based on all the second building communication features T k2 Perform clustering to obtain the second communication result perception matrix; Combine the first communication environment perception matrix and the second communication result perception matrix for calculation to obtain the comprehensive communication scenario fluctuation matrix; Based on the comprehensive communication scenario fluctuation matrix, perform communication fluctuation prediction to obtain the fluctuation prediction factor; In building communication scene modeling, different features have different influences on scene recognition. Therefore, building communication features T can be transformed into k Split into two subsets: First, building communication features T k1 Usually includes features with strong environmental relevance or system-level regulation significance, such as bandwidth utilization, system average transmission rate, protocol switching frequency, etc. These features reflect the macro communication behavior pattern; while 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; When performing optimized clustering 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 find 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 the adaptive division by constructing a similarity-density map, and finally generating the first communication environment perception matrix; in contrast, The K-means method is a classic clustering algorithm based on the "distance minimization" principle. It requires a preset number of clusters. The 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, and the cluster boundaries are clear; the main difference between the two is that FSND does not depend on the initial number of clusters, is suitable for non-uniform density distribution, and has irregular cluster shapes; while K-means is fast and easy to implement, but is sensitive to the initial number of clusters and data distribution.

[0022] 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 mixing, and inter-device communication collaboration patterns. These features often exhibit strong nonlinear relationships, large changes 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 mine 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.

[0023] In contrast, the second building communication feature Tk2 More features represent communication results or performance characteristics, such as average latency, packet loss rate, communication success rate, link stability, etc.; these features usually have strong numerical continuity, stable statistical distribution, and good linear separability. The clustering structure tends to be regular and spherical, so it is very suitable to use the K-means clustering algorithm for processing; K-means can efficiently classify these points into different performance intervals according to the Euclidean distance, thus quickly forming a hierarchical division of communication quality, which is applicable to such a feature set with clear clustering centers.

[0024] The feature types of the two are actually complementary: the first building communication feature T k1 Describes the evolution structure and regulation behavior of the communication environment, and is suitable for using density adaptive methods to depict potential network state transition patterns; the second building communication feature T k2 Describes communication results and performance indicators, and is suitable for using traditional distance metric algorithms to depict the classification boundary of communication states; therefore, using the FSND and K-means methods to cluster these two sub-feature sets respectively is a targeted and structure-matching strategy.

[0025] Calculate by combining the first communication environment perception matrix and the second communication result perception matrix to obtain the comprehensive communication scenario fluctuation matrix. The specific steps are as follows: Use the formula Calculate by combining the first communication environment perception matrix and the second communication result perception matrix to obtain the comprehensive communication scenario fluctuation matrix; Among them, 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 the 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 coding space.

[0026] Based on the comprehensive communication scenario fluctuation matrix, perform communication fluctuation prediction to obtain the specific steps of the fluctuation prediction factor: Model and predict the dynamic evolution trend of the comprehensive communication scenario fluctuation matrix using a time series extrapolation model. For example, the ARIMA (AutoRegressive Integrated Moving Average) model can be selected, which is suitable for data sequences with long-term dependencies and non-linear trends in the fluctuation matrix, and can capture complex time series changes for prediction; 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 slices in the comprehensive communication scenario fluctuation matrix. This data sequence covers multiple building communication cycles, including normal states, high-fluctuation periods, and abnormal scenarios, ensuring that the model has generalization ability for different communication states; the training objective is to minimize the error between the predicted value and the true observed value of the next time slice in the actual fluctuation matrix. 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 factors changing with time, and optimizes the model parameters through backpropagation; the training termination condition can be set as the loss on the validation set no longer decreases in several consecutive training rounds, or reaches the preset maximum number of training rounds, or the validation set error reaches an acceptable threshold, to avoid overfitting and improve the prediction stability in the actual communication environment.

[0027] Through splitting and clustering analysis, complex communication time series data can be accurately divided into multiple building communication scenarios. Using the FSND method and the K-means method to cluster different features respectively helps to analyze communication behaviors from multiple perspectives, thus achieving a 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 using dynamic weight coefficients, the weights can be flexibly adjusted according to actual needs in different clustering results to achieve a more accurate scenario division; this fusion strategy can avoid the limitations brought by single-feature clustering and ensure that the classification results are more comprehensive and accurate; on the basis of scenario classification, use the intelligent building digital twin model for simulation to obtain M groups of communication results and conduct multi-objective optimal evaluation on them, so as 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 waste of resources; through intelligent clustering analysis and multi-objective optimization, it is possible to avoid overall calculation and evaluation in all possible communication scenarios, thus saving computing resources and improving processing efficiency, which is particularly important for large-scale intelligent building systems; In the current building communication scenario, obtain building communication events; perform semantic understanding based on the building communication events to obtain a neutral expression of communication semantics; analyze based on the fluctuation prediction factors and the neutral expression of communication semantics, and output the building communication path and the building communication strategy; 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 event source, including user behavior triggers (such as access card swiping, movement trajectory), external environment changes (such as increased temperature and humidity, illumination changes), 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, etc.; next, call the semantic parsing module to map the event features to standardized communication intent labels, and then enhance the semantic expression and complete the parameters in context by combining the current building state, scene labels, and device control capabilities; finally, generate a communication semantic neutral expression through a structured representation method (such as JSON-LD or RDF) to ensure that this expression can be recognized and called by the semantic relay body regardless of the protocol, realizing consistent communication intent expression across devices and protocols.

[0028] The specific steps for analyzing based on the fluctuation prediction factor and the communication semantic neutral expression and 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; Construct a building communication task graph based on the generated communication semantic neutral expression. This graph takes each device instruction or control target in the communication semantic neutral expression as a node, and directed edges are established between the nodes according to the operation sequence dependence, device cooperation logic, or scene trigger relationship to form a topological structure of the task execution process; 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, introduce the fluctuation prediction factor on the basis of the building communication task graph to construct a communication spatio-temporal state map. This map abstracts devices, routers, relay nodes, etc. in the building space as graph nodes, the connection relationship represents a feasible communication link, and the edge attributes include historical bandwidth, current congestion status, predicted packet loss rate, environmental interference risk, etc. It is a dynamic and multi-dimensional communication state graph model.

[0029] Introduce the fluctuation prediction factor in the building communication task graph to establish a communication spatio-temporal state map; Extract several candidate paths for the communication semantic neutral expression in the communication spatio-temporal state map; establish a path objective function based on the fluctuation prediction factor to calculate the scores of the candidate paths, and output the candidate path corresponding to the maximum of the path objective function as the building communication path; at the same time, output the building communication strategy corresponding to the building communication path based on the semantic relay body; For each source node and target device of the communication semantic expression in the spatio-temporal state map, a number of candidate paths are extracted. Each path is composed of a sequence of hop points and is accompanied by the state characteristics of the path link. The system constructs a path objective function based on the fluctuation prediction factor and the communication requirements (such as low latency, high success rate) carried in the semantic neutral expression. The objective function maps factors such as the availability, security, real-time performance, and hop count penalty of each path to a comprehensive score value. The path with the maximum score value of the objective function is selected as the final building communication path. Based on the node characteristics, semantic intentions, and device capabilities involved in this path, a semantic relay body is called to generate a corresponding building communication strategy, which includes: whether to enable link encryption, whether to use a retransmission mechanism, the communication rhythm control method, the energy consumption optimization mode, etc., to ensure that the path execution adapts to the current communication fluctuation state of the building while meeting the task semantic goal, so as to achieve intelligent and dynamic multi-protocol communication scheduling.

[0030] Among them, continuously monitor the device connection status and device sensitive characteristics; output a fluctuating communication optimization strategy based on the device connection status and device sensitive characteristics; communicate with the building communication event based on the building communication path, building communication strategy, and corresponding fluctuating communication optimization strategy to obtain the final building device communication result; The specific steps for outputting a fluctuating communication optimization strategy based on the device connection status and device sensitive characteristics include: Identify the data sending device and data target device based on the building communication path recognition data; Obtain the device connection status and device sensitive characteristics of the data sending device and data target device, and establish a building communication environment prediction factor set based on the device connection status and device sensitive characteristics; Based on the determined building communication path, identify the data sending device and data target device in the communication task, that is, the physical nodes responsible for sending and receiving instructions in the current semantic communication; call the real-time monitoring module 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, extract the device sensitive characteristics, which 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 a standby or wake-up state, etc. These connection statuses and sensitive characteristics jointly constitute the building communication environment prediction factor set, which is used to comprehensively model the dynamic environmental background of device communication.

[0031] Analyze and obtain the communication energy consumption prediction curve according to the building communication environment prediction factor set and the environment-energy supply regression model; Taking the building communication environment prediction factor set as the input, combining with the historical building operation data, and using the trained environment - energy regression model to analyze its impact on communication energy consumption, the communication energy consumption prediction curve of the building communication path under the current conditions is output. This curve shows the expected energy consumption fluctuations of devices in each time slice during the communication process, depicts the energy consumption trends under different devices, different protocols, and different load conditions, and is a key reference for optimizing communication rhythm and protocol selection; The training process of the environment - energy regression model includes the following key aspects: The training set comes from the historical data collected in the building communication system under different times, spaces, and device states, including input features such as device connection status, communication protocol type, bandwidth occupancy rate, signal quality, environmental interference level, etc., and the corresponding actual communication energy consumption as the output label; The training goal is to construct the mapping relationship between feature variables and communication energy consumption, and minimize the error between the predicted energy consumption and the actual measured energy consumption. Usually, the mean square error or mean absolute error is used 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; The training termination conditions include: stopping training when the loss function on the validation set no longer decreases significantly for several consecutive rounds, or reaching the preset maximum number of iterations, or the error is lower than the set threshold, to ensure that the model has good generalization ability and can adapt to the energy consumption prediction requirements in the dynamic building communication scenario. The environment - energy regression model outputs the charge - discharge prediction curve of the current device in the next time period, and this curve is used to analyze the power supply level fluctuation trend of the device at different time periods, including charging speed, power consumption rate, and remaining power trend.

[0032] Design a fluctuating communication optimization strategy for the building communication strategy based on the building communication strategy and the communication energy consumption prediction curve; Specifically, it includes: outputting the protocol energy consumption budget based on the building communication path; performing energy consumption monitoring based on the protocol energy consumption budget output and the communication energy consumption prediction curve, combined with the pulsed time window, to obtain the energy consumption status label; obtaining the communication emergency level label based on the neutral expression of communication semantics; 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; Based on the communication energy consumption prediction curve and the initially generated building communication strategy, design a more adaptable fluctuating communication optimization strategy. The operation process includes: First, according to the selected communication path and the target device protocol stack, calculate the protocol energy consumption budget of different protocols during communication, that is, the estimated energy consumption value per unit time or per unit data transmission. Then, combined with the prediction curve, use the pulsed time window listening mechanism to monitor the actual energy consumption status of the device, and generate energy consumption status labels for each time window, such as normal, overloaded, frequency reduction required, etc. At the same time, according to the task characteristics carried in the neutral expression of communication semantics, extract communication emergency level labels, such as high priority, low latency tolerance, regular, cacheable, etc. Finally, combine the energy consumption status label and the communication emergency level label as the input condition of the double label, screen the feasible solutions in the initial communication strategy set, exclude the combinations of high energy consumption or low emergency, and preferentially select the strategy structure with low energy consumption and compatible with high priority. Finally, output a set of optimized fluctuating communication strategies for dynamically adapting the energy consumption status and task urgency in the actual communication process, and improving the overall communication efficiency and building system stability.

[0033] The specific steps for communicating building communication events based on the building communication path, building communication strategy, and the corresponding fluctuating communication optimization strategy include: Bind a communication health monitoring mechanism to the building communication path, building communication strategy, and the corresponding fluctuating communication optimization strategy. Before communication execution, uniformly bind a communication health monitoring mechanism to the selected building communication path, building communication strategy, and the 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 communication. The 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 combined through weighting to form a communication health monitoring score, which is used to quantify the overall reliability and stability of the current communication behavior. Subsequently, according to the default priority, based on the current building communication path and building communication strategy, perform actual data transmission operations on the building communication events described by the neutral expression of communication semantics, that is, device instruction sending, data reception, or control execution, etc. During the whole process, start the communication health monitoring mechanism synchronously to collect and calculate the communication health monitoring score in real time.

[0034] Communicate building communication events preferentially according to the building communication path and building communication strategy; output the communication health monitoring score based on the communication health monitoring mechanism. If the communication health monitoring score is lower than the preset first score threshold, replace the building communication strategy with the latest fluctuating communication optimization strategy for communication. If the communication health monitoring score is lower than the preset second score threshold, a path degradation mechanism is triggered to switch to a candidate path with a higher objective function of the path as the backup building communication path for communication; the preset first score threshold and the preset second score threshold are set manually; During the communication execution process, when the communication health monitoring score is lower than the set first score threshold (indicating mild communication fluctuations or primary anomalies), the previously generated fluctuation communication optimization strategy is automatically called to replace the current building communication strategy, and the communication operation is preferentially executed with a configuration that consumes less energy, responds faster, or is more stable, so as to improve communication stability and prevent further degradation; if the communication health monitoring score continues to decline and is lower than the lower second score threshold (indicating severe communication anomalies or path instability), the path degradation mechanism will be immediately triggered. This mechanism reselects 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 combines the bound fluctuation communication optimization strategy to perform communication switching to ensure that the task is not interrupted; The communication health monitoring mechanism is used to continuously monitor the communication until the building communication event completes the communication to obtain the final building equipment communication result; The entire communication process will run under the condition that the communication health monitoring mechanism is continuously enabled. The system updates the score in real time and judges whether to maintain the current path and strategy according to the threshold until the communication event completes all the scheduled data interaction processes and generates the final building equipment communication result. This result includes the task execution status (success / failure), response delay, energy consumption data, strategy switching records, etc., and is an important data source for subsequent feedback learning and semantic relay body adaptive update.

[0035] Embodiment 2, a multi-protocol data communication system for an intelligent building, see Figure 1 as shown, including: A communication scenario construction module, which is used to, based on the data communication environment of the intelligent building, include several data communication protocols and multiple building communication devices; establish a corresponding semantic relay body based on the building communication devices; and obtain the real-time monitored user behavior, external environment, and building automatic tasks in the current building communication scenario in real time to match the fluctuation prediction factors for the current building communication scenario; A communication path output module, which is used to, in the current building communication scenario, obtain the building communication event; perform semantic understanding based on the building communication event to obtain the neutral expression of communication semantics; and analyze based on the fluctuation prediction factors and the neutral expression of communication semantics to output the building communication path and the building communication strategy; A communication data optimization module is used to continuously monitor the device connection status and device sensitive features; output a fluctuating communication optimization strategy based on the device connection status and device sensitive features; perform communication on building communication events based on the building communication path, building communication strategy, and the corresponding fluctuating communication optimization strategy to obtain the final building device communication result.

[0036] It should be understood that those of ordinary skill in the art can make improvements or transformations according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention. The parts not described in detail in this specification belong to the prior art well-known to those of ordinary skill in the art.

Claims

1. A multi-protocol data communication method for intelligent buildings, characterized in that, Including the following steps: Based on the data communication environment of intelligent buildings, it includes several data communication protocols and multiple building communication devices; Based on the building communication devices, establish corresponding semantic relay entities; obtain in real time the real-time monitored user behaviors, external environments, and building automatic tasks in the current building communication scenario, and match fluctuation prediction factors for the current building communication scenario; In the current building communication scenario, obtain building communication events; Perform semantic understanding based on the building communication events to obtain a neutral expression of communication semantics; analyze based on the fluctuation prediction factors and the neutral expression of communication semantics, and output the building communication path and the building communication strategy; Among them, continuously monitor the device connection status and device sensitive features; output a fluctuation communication optimization strategy based on the device connection status and device sensitive features; communicate the building communication events based on the building communication path, the building communication strategy, and the corresponding fluctuation communication optimization strategy to obtain the final building device communication result.

2. The multi-protocol data communication method for intelligent buildings according to claim 1, characterized in that The specific steps for establishing corresponding semantic relay entities based on the building communication devices include: Detect the communication behavior intentions of the building communication devices and establish a general intention set for the building devices; Identify the device capabilities of the building communication devices to obtain device service interfaces; Sample the historical data communication protocols of the building communication devices to obtain a device protocol set; Align the device protocol set, the device service interfaces, and the general intention set of the building devices to establish a protocol mapping rule for the building communication devices; Define the semantic relay entity based on the protocol mapping rule of the building communication devices; Traverse multiple building communication devices to complete the construction of the semantic relay entities 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 for matching fluctuation prediction factors for the current building communication scenario include: Extract features of the user behavior, external environment, and building automation tasks monitored in real time by time slice to obtain building communication feature T k , k = 1, 2, …, K; K is the total number of time slices monitored in real time in the current building communication scenario, and T k represents the building communication feature extracted in the k-th 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 perform clustering to obtain a first communication environment perception matrix; Based on all second building communication features T k2 perform clustering to obtain a second communication result perception matrix; Calculate by combining the first communication environment perception matrix and the second communication result perception matrix to obtain a comprehensive communication scenario fluctuation matrix; Perform communication fluctuation prediction based on the comprehensive communication scenario fluctuation matrix to obtain the fluctuation prediction factors.

4. A multi-protocol data communication method for intelligent buildings according to claim 3, characterized in that, The specific steps for analyzing based on the fluctuation prediction factors and the neutral expression of communication semantics and outputting the building communication path and the building communication strategy include: Establish a building communication task graph for the nodes based on the neutral expression of communication semantics; Introduce the fluctuation prediction factors into the building communication task graph to establish a communication space-time state graph; Extract several candidate paths for the neutral expression of communication semantics in the communication space-time state graph; establish a path objective function based on the fluctuation prediction factors to calculate the scores for the candidate paths, and output the candidate path corresponding to the maximum of the path objective function as the building communication path; at the same time, output the building communication strategy corresponding to the building communication path based on the semantic relay entity.

5. A multi-protocol data communication method for intelligent buildings according to claim 4, characterized in that The specific steps for outputting a fluctuation communication optimization strategy based on the device connection status and device sensitive features include: Identify the data sending device and the data target device based on the building communication path; Obtain the device connection status and device sensitive features 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 sensitive features; analyze according to the building communication environment prediction factor set and the environment - energy supply regression model to obtain the communication energy consumption prediction curve; Design a fluctuating communication optimization strategy for building communication strategies based on building communication strategies and communication energy consumption prediction curves; Specifically, it includes: outputting protocol energy consumption budgets based on building communication paths; performing energy consumption monitoring based on the protocol energy consumption budgets and communication energy consumption prediction curves, combined with pulsed time windows, to obtain energy consumption status labels; obtaining communication emergency level labels based on neutral communication semantic expressions; screening building communication strategies according to the energy consumption status labels and communication emergency level labels to obtain a fluctuating communication optimization strategy.

6. A multi-protocol data communication method for an intelligent building 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 fluctuating communication optimization strategies include: Binding a communication health monitoring mechanism to the building communication path, building communication strategy, and corresponding fluctuating communication optimization strategy; Communicating building communication events preferentially according to the building communication path and building communication strategy; outputting a communication health monitoring score based on the communication health monitoring mechanism; If the communication health monitoring score is lower than a preset first score threshold, then replace the building communication strategy with the latest fluctuating communication optimization strategy for communication; If the communication health monitoring score is lower than a preset second score threshold, then trigger a path degradation mechanism and switch to a candidate path with a higher path objective function as the backup building communication path for communication; Continuously monitor communication using the communication health monitoring mechanism until the building communication event completes communication to obtain the final building equipment communication result.

7. A multi-protocol data communication system for intelligent buildings, characterized in that, The system applies the multi-protocol data communication method for intelligent buildings described in any one of claims 1-6 above, including: A communication scenario construction module, which is used to, based on the data communication environment of an intelligent building, include several data communication protocols and multiple building communication devices; establish corresponding semantic relays based on the building communication devices; and obtain real-time monitoring of user behavior, external environment, and building automatic tasks in the current building communication scenario in real time to match fluctuating prediction factors for the current building communication scenario; A communication path output module, which is used to obtain building communication events in the current building communication scenario; perform semantic understanding based on the building communication events to obtain neutral communication semantic expressions; analyze based on the fluctuating prediction factors and neutral communication semantic expressions, and output building communication paths and building communication strategies; A communication data optimization module, which is used to continuously monitor the device connection status and device sensitive characteristics; output a fluctuating communication optimization strategy based on the device connection status and device sensitive characteristics; communicate building communication events based on the building communication path, building communication strategy, and corresponding fluctuating communication optimization strategy to obtain the final building equipment communication result.

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