Intelligent building full life cycle monitoring management method and system

Abstract

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent building management, and in particular to a full life cycle monitoring and management method and system for an intelligent building. Background Art

[0002] In the operation and management of hydraulic facilities such as reservoirs and drainage stations, the application of intelligent building technology is gradually increasing. Such buildings (such as the pump house of the reservoir and lake sluice station) need to closely monitor multiple parameters such as water level, flow, ambient temperature and humidity, equipment vibration, etc. during operation to ensure the safety and operation efficiency of hydraulic facilities in complex environments. The full life cycle management of intelligent buildings not only requires the system to have real-time data collection and analysis capabilities, but also needs to cover equipment status assessment, risk warning and remote control functions to achieve full process monitoring and management of facilities from construction to use and maintenance.

[0003] However, existing technologies face some technical difficulties in the full life cycle management of hydraulic intelligent buildings such as reservoirs, lakes and sluice stations. First, the data collection and processing capabilities of traditional systems are relatively limited, making it difficult to integrate real-time data from a variety of sensors, resulting in isolated data and difficulty in achieving accurate dynamic analysis, and inability to identify potential risks in a timely manner. Second, existing systems lack an automated feedback control mechanism and are unable to dynamically adjust operating parameters based on real-time data, resulting in slow response speed and low operating efficiency when environmental conditions change, and may even pose safety risks due to lagging parameter adjustments. Summary of the invention

[0004] Based on the above objectives, the present invention provides a method and system for monitoring and managing the entire life cycle of an intelligent building.

[0005] A monitoring and management method for the entire life cycle of an intelligent building comprises the following steps:

[0006] S1, data collection: by deploying a variety of sensors in smart buildings, the operation data of smart buildings can be collected in real time. The operation data of smart buildings includes environmental parameters and equipment operation parameters;

[0007] S2, multi-source data fusion and analysis: data cleaning, fusion and anomaly filtering of operation data from multiple sensors, using multi-source data analysis algorithms to extract key operation indicators, including temperature, pressure, speed and vibration frequency, perform trend analysis and abnormal pattern recognition, and reveal potential risk factors and system weaknesses;

[0008] S3, full life cycle management model construction: Based on data fusion and analysis results, a full life cycle management model for smart buildings is constructed, covering building structure health, equipment wear and tear conditions, and environmental influencing factors, and the model parameters are dynamically updated through real-time operation data;

[0009] S4, intelligent early warning mechanism: Based on the results of the full life cycle management model and multi-source data fusion and analysis, risk assessment is conducted on key operating indicators of intelligent buildings, and early warning signals are generated based on the output of trend analysis and abnormal pattern recognition;

[0010] S5, Adaptive feedback control: Based on the results of multi-source data fusion and analysis and the results of the full life cycle management model prediction, the changes of key operating parameters are monitored in real time, and the operating status of the intelligent building is adaptively controlled;

[0011] S6, historical data storage and trend analysis: store the operation data and historical operation data of the smart building in the data storage module, perform trend analysis regularly to generate historical performance curves, and provide data support for the long-term operation status evaluation of the facilities in the smart building;

[0012] S7, remote visual monitoring: Displays real-time operation data, predictive analysis results and early warning information of smart buildings through a visual interface, supports remote monitoring, operation and multi-party collaboration, and ensures the transparency and real-time monitoring and management of smart buildings throughout their life cycle.

[0013] Optionally, the S1 includes:

[0014] S11, sensor layout planning: according to the structural layout and monitoring requirements of the intelligent building, plan the collection points of environmental parameters and equipment operation status data, determine the layout location and type of sensors, and the sensors to be laid include temperature sensors, humidity sensors, air pressure sensors, wind speed sensors, water level sensors, flow sensors, vibration sensors and equipment temperature sensors;

[0015] S12, sensor installation and calibration: Install sensors at planned collection points to ensure accurate positioning and stable fixation of sensors. After installation, calibrate each sensor to ensure the accuracy of collected environmental parameters and equipment operation status data, reduce initial deviations, and improve the reliability of data collection;

[0016] S13, data collection frequency configuration: set the collection frequency for different types of data. The collection frequency of environmental parameters is set according to the rate of environmental change. The collection frequency of equipment operation status data is configured according to the equipment operation requirements.

[0017] S14, data transmission network configuration: deploy a data transmission network in the smart building, including wired and wireless transmission lines, to transmit the operating data collected by sensors to the multi-source data fusion and analysis module.

[0018] S15, preliminary data integrity check: After the data is transmitted to the multi-source data fusion and analysis module, a preliminary data integrity check is performed.

[0019] Optionally, S2 includes:

[0020] S21, multi-source data cleaning: data cleaning of collected environmental parameters and equipment operation status data;

[0021] S22, multi-source data fusion: the cleaned environmental parameters and equipment operation status data are integrated to form a unified intelligent building operation data set;

[0022] S23, key operation indicator extraction: extract key operation indicators from the smart building operation data set to support the operation analysis and trend prediction of smart buildings;

[0023] S24, Trend analysis and abnormal pattern recognition: Perform trend analysis and abnormal pattern recognition on key operating indicators to identify potential risk factors and system weaknesses.

[0024] Optionally, the S24 includes:

[0025] S241, Trend analysis model construction: Use time series analysis methods to perform trend analysis on key operating indicators and identify historical change trends of operating data;

[0026] S242, Seasonal and cyclical pattern identification: Identify seasonal and cyclical patterns in data by decomposing the time series of key operating indicators;

[0027] S243, anomaly detection model construction: Use clustering-based anomaly detection algorithms (such as K-means clustering) to identify abnormal patterns of key operating indicators and build an anomaly detection model to identify abnormal values ​​that deviate from the normal data range;

[0028] S244, Risk pattern identification and early warning: Identify potential risk patterns in key operating indicators based on anomaly detection results and trend analysis.

[0029] Optionally, the S3 includes:

[0030] S31, model variable determination: Based on the results of multi-source data fusion and analysis, determine the core variables of the full life cycle management model, including key operating indicators and their related environmental parameters and equipment operating status data.

[0031] S32, model type selection: Combined with the operation data characteristics and monitoring requirements of intelligent buildings, multiple regression model and Bayesian network model are selected as the basis of the life cycle management model;

[0032] S33, model expression construction: construct a multivariate regression model to quantify the impact of key operating indicators on the status of intelligent buildings;

[0033] S34, dynamic update of model parameters: dynamically update the parameters of the multivariate regression model and Bayesian network model regularly using real-time operation data.

[0034] Optionally, the S4 includes:

[0035] S41, Risk level assessment: Risk level assessment is conducted based on the full life cycle management model and the results of multi-source data fusion and analysis. The risk level is divided into three levels: low, medium and high based on the results of key operating indicators and trend analysis and abnormal pattern recognition.

[0036] S42, early warning signal generation: Generate corresponding early warning signals for each risk level, including abnormal indicators, risk levels and recommended intervention measures;

[0037] S43, early warning signal distribution and notification: the generated early warning signal is sent to relevant managers through the visualization platform and communication network.

[0038] S44, dynamic update of early warning information: by continuously monitoring the real-time operation data of the intelligent building, the early warning information is dynamically updated;

[0039] S45, early warning record and feedback storage: the generation time, risk level, processing measures and feedback results of each early warning signal are stored in the data storage module to form a historical early warning record.

[0040] Optionally, the S5 includes:

[0041] S51, real-time parameter adjustment and monitoring: dynamically adjust key operating parameters of intelligent buildings based on the results of multi-source data fusion and analysis and the results predicted by the full life cycle management model;

[0042] S52, abnormal state identification and control strategy optimization: When the real-time operating data deviates from the set range, the system will identify it as an abnormal state and automatically adjust the control strategy through an optimization algorithm (such as gradient descent method) to restore the operating data to the normal range;

[0043] S53, feedback control mechanism and multi-level feedback loop: Use multi-level feedback control mechanism to achieve closed-loop regulation, monitor the effect of each data adjustment in real time, and further adjust and control if the adjustment effect does not meet expectations.

[0044] S54, feedback result storage and subsequent optimization: all feedback control results are recorded in the data storage module to form a historical feedback database.

[0045] Optionally, the S6 includes:

[0046] S61, historical data storage and archiving: regularly transmit and store the real-time operation data and environmental parameters of the intelligent building to the data storage module.

[0047] S62, data cleaning and organization: before data storage, the operation data is cleaned and organized to ensure the quality and consistency of the stored data;

[0048] S63, long-term trend analysis: regularly perform trend analysis on stored historical operation data to identify long-term change trends of key operation indicators of smart buildings

[0049] S64, historical performance curve generation: Generate the historical performance curve of the intelligent building based on the trend analysis results to show the changes of operating indicators over time.

[0050] S65, Long-term operating status evaluation: Use historical performance curves and trend analysis results to evaluate the long-term operating status of intelligent buildings.

[0051] Optionally, the S7 includes:

[0052] S71, Visualization Display: Through the visualization interface, the real-time operation data, prediction analysis results and early warning information of the intelligent building are centrally displayed;

[0053] S72, real-time operation data display: the real-time operation data of the intelligent building (including temperature, pressure, vibration frequency, flow, etc.) is displayed on the visual interface through charts and digital displays;

[0054] S73, display of forecast analysis results: display the forecast analysis results obtained based on the full life cycle management model and trend analysis on a visual interface;

[0055] S74, warning information display and notification: display the warning information generated by the system on the visual interface and send notifications to relevant personnel;

[0056] S75, remote monitoring and multi-party collaboration: supports managers to conduct remote monitoring, operation and multi-party collaboration through a visual interface.

[0057] An intelligent building full life cycle monitoring and management system, used to implement the above-mentioned intelligent building full life cycle monitoring and management method, includes the following modules:

[0058] Data acquisition module: collects real-time operation data of smart buildings through various sensors deployed in smart buildings, including environmental parameters and equipment operation status data;

[0059] Multi-source data fusion and analysis module: cleans, fuses and filters the collected real-time operation data, extracts key operation indicators, and performs trend analysis and abnormal pattern recognition to provide data support for risk identification;

[0060] Full life cycle management model building module: Build a full life cycle management model based on the integrated data, covering the health of building structures, equipment wear and tear, and environmental factors, and dynamically update and optimize the model through real-time operation data;

[0061] Intelligent early warning module: Combines the analysis results of the full life cycle management model with the multi-source data fusion and analysis module to assess the risk level and generate early warning signals, notify managers in real time, and provide risk assessment and intervention suggestions;

[0062] Adaptive feedback control module: Based on real-time operation data and abnormal detection results, it dynamically adjusts key operation parameters, maintains the operation of intelligent buildings within the normal range through adaptive feedback control, and optimizes the control parameters using a self-learning mechanism;

[0063] Data storage module: used to store the operation data and historical operation data of intelligent buildings, conduct regular trend analysis, generate historical performance curves, and provide data support for the long-term operation status evaluation of intelligent buildings;

[0064] Remote visual monitoring module: displays the real-time operation data, predictive analysis results and early warning information of intelligent buildings through a visual interface, and supports remote monitoring, operation and multi-party collaboration.

[0065] Beneficial effects of the present invention:

[0066] The present invention acquires and analyzes the key operating indicators and environmental parameters of intelligent buildings in real time through the data acquisition module and the multi-source data fusion and analysis module. Through the intelligent early warning module, the system can timely identify potential risk factors, send early warning signals to managers based on multi-level risk levels, and provide intervention suggestions to ensure that abnormal conditions are effectively handled at an early stage. This real-time monitoring and precise early warning mechanism greatly improves the operational safety and risk prevention capabilities of buildings.

[0067] In this invention, the adaptive feedback control module automatically optimizes key operating parameters according to the changes in real-time operating data, ensuring that the building maintains an efficient and stable operating state under different environmental conditions, and continuously adjusts the control parameters through a self-learning mechanism, making the system highly adaptive. This closed-loop feedback control mechanism significantly improves the overall operating efficiency and durability of the building.

[0068] In the present invention, the remote visualization monitoring module presents the real-time data, prediction analysis results and early warning information of the intelligent building in a graphical manner, supports managers to monitor and operate remotely, and realizes multi-party collaborative management through multi-user access. This function ensures that managers at all levels can understand the operating status of the building in real time at different locations, make decisions and respond quickly, improve the transparency and real-time nature of management, and reduce the risks caused by human intervention delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0071] Figure 2 Schematic diagram of a system flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0073] like Figure 1 As shown, a monitoring and management method for the entire life cycle of an intelligent building comprises the following steps:

[0074] S1, data collection: by deploying a variety of sensors in smart buildings, the operation data of smart buildings can be collected in real time. The operation data of smart buildings includes environmental parameters and equipment operation parameters;

[0075] S2, multi-source data fusion and analysis: data cleaning, fusion and anomaly filtering of operation data from multiple sensors, using multi-source data analysis algorithms to extract key operation indicators, including temperature, pressure, speed and vibration frequency, perform trend analysis and abnormal pattern recognition, and reveal potential risk factors and system weaknesses;

[0076] S3, full life cycle management model construction: Based on data fusion and analysis results, a full life cycle management model for smart buildings is constructed, covering building structure health, equipment wear and tear conditions, and environmental influencing factors, and the model parameters are dynamically updated through real-time operation data;

[0077] S4, intelligent early warning mechanism: Based on the results of the full life cycle management model and multi-source data fusion and analysis, risk assessment is conducted on key operating indicators of intelligent buildings, and early warning signals are generated based on the output of trend analysis and abnormal pattern recognition;

[0078] S5, Adaptive feedback control: Based on the results of multi-source data fusion and analysis and the results of the full life cycle management model prediction, the changes of key operating parameters are monitored in real time, and the operating status of the intelligent building is adaptively controlled;

[0079] S6, historical data storage and trend analysis: store the operation data and historical operation data of the smart building in the data storage module, perform trend analysis regularly to generate historical performance curves, and provide data support for the long-term operation status evaluation of the facilities in the smart building;

[0080] S7, remote visual monitoring: Displays real-time operation data, predictive analysis results and early warning information of smart buildings through a visual interface, supports remote monitoring, operation and multi-party collaboration, and ensures the transparency and real-time monitoring and management of smart buildings throughout their life cycle.

[0081] S1 includes:

[0082] S11, sensor layout planning: according to the structural layout and monitoring requirements of the intelligent building, plan the collection points of environmental parameters and equipment operation status data, determine the layout location and type of sensors, and the deployed sensors include temperature sensors, humidity sensors, air pressure sensors, wind speed sensors, water level sensors, flow sensors, vibration sensors and equipment temperature sensors, among which;

[0083] Temperature sensors are used to monitor temperature changes inside and outside smart buildings;

[0084] Humidity sensors are used to monitor air humidity;

[0085] Air pressure sensors are used to monitor changes in atmospheric pressure;

[0086] Wind speed sensors are used to monitor wind speed and direction;

[0087] Water level sensors are used to monitor the water level of hydraulic equipment such as reservoirs and pump houses;

[0088] Flow sensors are used to monitor water flow and flow rate;

[0089] Vibration sensors are used to monitor the vibration frequency of equipment to assess the health status of the equipment;

[0090] Equipment temperature sensors are used to monitor the temperature of key equipment to ensure that the equipment operates within the normal temperature range;

[0091] S12, sensor installation and calibration: Install sensors at planned collection points to ensure accurate positioning and stable fixation of sensors. After installation, calibrate each sensor to ensure the accuracy of collected environmental parameters and equipment operation status data, reduce initial deviations, and improve the reliability of data collection;

[0092] S13, data collection frequency configuration: set the collection frequency for different types of data. The collection frequency of environmental parameters (such as temperature, humidity, air pressure and wind speed) is set according to the rate of environmental change. The collection frequency of equipment operation status data (such as water level, flow rate and vibration frequency) is configured according to the equipment operation requirements.

[0093] S14, data transmission network configuration: Deploy a data transmission network in the intelligent building, including wired and wireless transmission lines, to transmit the operating data collected by sensors to the multi-source data fusion and analysis module. The data transmission network uniformly encodes environmental parameters and equipment operating status data through a protocol converter to ensure the stability, real-time and integrity of data transmission.

[0094] S15, preliminary data integrity check: After the data is transmitted to the multi-source data fusion and analysis module, a preliminary data integrity check is performed to detect and mark missing data or abnormal data, providing a basis for subsequent further analysis. The integrity check includes:

[0095] Timestamp synchronization: ensure that data is transmitted synchronously according to the set acquisition frequency;

[0096] Range detection: Verify whether environmental parameters and equipment operating status data are within a reasonable range to filter out extreme outliers;

[0097] Through reasonable sensor layout, data collection frequency configuration, stable data transmission network and preliminary data integrity check, the real-time, completeness and accuracy of environmental parameters and equipment operating status data are ensured, providing a reliable data foundation for the operation monitoring of intelligent buildings.

[0098] S2 includes:

[0099] S21, multi-source data cleaning: perform data cleaning on the collected environmental parameters and equipment operation status data to ensure the quality of input data. Data cleaning includes:

[0100] Missing value filling: Use linear interpolation to fill missing values ​​to ensure data continuity;

[0101] Outlier removal: Outliers are removed based on the Z-score method, expressed as:

[0102] ;

[0103] in is the data point, is the mean of the data, The standard deviation of the data is used to mark and remove abnormal data whose Z scores exceed the set threshold to improve data accuracy;

[0104] S22, multi-source data fusion: The cleaned environmental parameters and equipment operation status data are fused to form a unified intelligent building operation data set. The weighted average fusion algorithm is used to integrate the multi-source data. The calculation formula for the fused data is:

[0105] ;

[0106] in, represents the fused data set, is the temperature data, is the humidity data, is the air pressure data, is the vibration frequency data, For flow data, are the weights of each data, and the weights are assigned according to the importance and frequency of the data;

[0107] S23, key operating indicator extraction: Extract key operating indicators from the smart building operation data set to support the operation analysis and trend prediction of smart buildings. The key operating indicators include:

[0108] Temperature: used to monitor temperature changes inside buildings and key equipment;

[0109] Pressure: used to detect the impact of atmospheric pressure on building structures and equipment;

[0110] Vibration frequency: used to evaluate equipment health and vibration anomalies;

[0111] Flow: used to monitor water flow and flow rate of hydraulic equipment;

[0112] S24, Trend Analysis and Abnormal Pattern Identification: Perform trend analysis and abnormal pattern identification on key operating indicators to identify potential risk factors and system weaknesses;

[0113] Through multi-source data fusion and analysis, the uniformity, integrity and reliability of environmental parameters and equipment operating status data are ensured, providing accurate basic data support for the subsequent construction of full life cycle management models and early warning mechanisms.

[0114] S24 includes:

[0115] S241, trend analysis model construction: Use time series analysis methods to perform trend analysis on key operating indicators (such as temperature, pressure, vibration frequency and flow) to identify the historical change trend of operating data. The trend analysis model is constructed based on historical operating data. The model expression is:

[0116] ;

[0117] in, Indicates at time The running data at is a constant term, is the trend coefficient, is a random error. This model can be used to predict the future trend of operating data to evaluate the performance of smart buildings under different environmental conditions;

[0118] S242, seasonal and cyclical pattern identification: By decomposing the time series of key operating indicators, the seasonal and cyclical change patterns of the data are identified. The decomposition model expression is:

[0119] ;

[0120] in, For time series data, represents the seasonal component, represents the periodic component, Represents the random component. This model can effectively separate seasonal and cyclical factors, making it easier for managers to understand the regularity of operating data;

[0121] S243, Anomaly detection model construction: Use clustering-based anomaly detection algorithms (such as K-means clustering) to identify abnormal patterns of key operating indicators and build anomaly detection models to identify abnormal values ​​that deviate from the normal data range. The model process includes:

[0122] (1) Data standardization: Standardize key operating indicators to ensure that data of different dimensions can be analyzed uniformly;

[0123] (2) Cluster analysis: divide the standardized data into normal group and abnormal group;

[0124] (3) Anomaly score: Assign an anomaly score to each data point. The scoring formula is expressed as:

[0125] ;

[0126] in is the data point, is the center value of the normal group, is the standard deviation of the data. Points with scores higher than the set threshold are marked;

[0127] S244, Risk pattern identification and early warning: Identify potential risk patterns in key operating indicators based on anomaly detection results and trend analysis;

[0128] Through trend analysis and abnormal pattern recognition, we can effectively grasp the changing trends of the operating status of intelligent buildings, identify potential risk factors, and provide managers with accurate risk information so that they can take preventive measures in time to ensure the safe and stable operation of the building.

[0129] S3 includes:

[0130] S31, model variable determination: Based on the results of multi-source data fusion and analysis, determine the core variables of the full life cycle management model, including key operating indicators (such as temperature, pressure, vibration frequency and flow) and their related environmental parameters and equipment operating status data. These variables are used to describe the health status, equipment wear status and environmental influencing factors of the smart building during operation, and serve as the basic input for model construction.

[0131] S32, model type selection: Combined with the operation data characteristics and monitoring requirements of intelligent buildings, multiple regression model and Bayesian network model are selected as the basis of the life cycle management model. The multiple regression model is suitable for establishing the relationship between key operation indicators and building health status, and the Bayesian network model is suitable for dealing with the dependency and uncertainty between variables to ensure the robustness of the model;

[0132] S33, model expression construction: A multivariate regression model is constructed to quantify the impact of key operating indicators on the state of intelligent buildings. The model expression is as follows:

[0133] ;

[0134] in, Output for the full life cycle model (health status of smart buildings), , , and They are temperature, pressure, vibration frequency and flow data respectively. is the regression coefficient, is a constant term. In addition, the Bayesian network model expresses the relationship between environmental parameters and equipment operating status through conditional probability distribution. The conditional probability is calculated as:

[0135] ;

[0136] in, Indicates that under the given conditions Lower variable The probability of inference is used to infer the state change of smart buildings under specific environmental conditions;

[0137] S34, Dynamic update of model parameters: Use real-time operation data to regularly dynamically update the parameters of the multivariate regression model and Bayesian network model to improve the prediction accuracy and adaptability of the model. The parameter update method uses the least squares method and the Bayesian update algorithm;

[0138] The least squares method is used to update the regression coefficients in the multiple regression model ;

[0139] Bayesian updating is used to dynamically adjust the conditional probability distribution in the Bayesian network to adapt to new data changes;

[0140] Through the construction, dynamic updating and optimization of the full life cycle management model, it can comprehensively reflect the health status and operating conditions of smart buildings under different environmental conditions, and provide reliable predictive support for risk assessment and early warning.

[0141] S4 includes:

[0142] S41, Risk level assessment: Based on the full life cycle management model and the results of multi-source data fusion and analysis, risk level assessment is carried out. Based on key operating indicators (such as temperature, pressure, vibration frequency and flow) and the results of trend analysis and abnormal pattern recognition, the risk level is divided into three levels: low, medium and high. Specifically, it includes:

[0143] Low risk: operating parameters are within the normal range, and only monitoring is prompted;

[0144] Medium risk: some operating parameters are close to the threshold, and regular inspection is recommended;

[0145] High risk: operating parameters exceed safety thresholds and immediate maintenance is recommended.

[0146] S42, early warning signal generation: Generate corresponding early warning signals for each risk level, including abnormal indicators, risk levels and recommended intervention measures. The early warning signal information includes:

[0147] Abnormal indicators: mark key operating indicators that exceed or approach thresholds;

[0148] Risk level: indicates the risk level of the current status;

[0149] Intervention recommendations: maintenance or monitoring recommendations are given based on the risk level, such as immediate maintenance, regular inspections, or monitoring observations;

[0150] S43, early warning signal distribution and notification: The generated early warning signal is sent to relevant managers through the visualization platform and communication network. The specific distribution methods include:

[0151] Real-time visual display: Display real-time warning information on the monitoring system interface;

[0152] Push notification: Push notifications to managers via SMS, email or mobile apps to ensure timely delivery of warning information.

[0153] S44, dynamic update of early warning information: by continuously monitoring the real-time operation data of intelligent buildings, the early warning information is dynamically updated. If the risk level changes, the system will automatically adjust the content of the early warning signal and redistribute it to ensure the accuracy and timeliness of the early warning information;

[0154] S45, early warning record and feedback storage: the generation time, risk level, treatment measures and feedback results of each early warning signal are stored in the data storage module to form a historical early warning record to support future risk pattern identification and analysis. At the same time, the feedback results of managers can be used to further optimize the early warning mechanism and improve the reliability of the system;

[0155] Through the risk level assessment, warning signal generation and dynamic update of the intelligent early warning mechanism, timely warning and intervention of intelligent buildings under different risk conditions are achieved, providing important guarantees for building safety and stability.

[0156] S5 includes:

[0157] S51, real-time parameter adjustment and monitoring: based on the results of multi-source data fusion and analysis and the results predicted by the full life cycle management model, dynamically adjust the key operating parameters of the intelligent building, including temperature, pressure, vibration frequency and flow, to ensure that the equipment remains within the normal operating range. Real-time parameter adjustment includes:

[0158] Adjust the temperature: If the temperature is outside the normal range, reduce the operating speed of the equipment or increase the cooling function to avoid overheating.

[0159] Pressure regulation: When it is detected that the pressure is too high, the load will be appropriately reduced or a pressure buffer device will be added to prevent overload operation.

[0160] Vibration frequency monitoring and adjustment: When the vibration frequency exceeds the set threshold, adjust the operating frequency or load of the equipment to reduce wear and extend the life of the equipment;

[0161] S52, abnormal state identification and control strategy optimization: When the real-time operating data deviates from the set range, the system will identify it as an abnormal state and automatically adjust the control strategy through an optimization algorithm (such as gradient descent method) to restore the operating data to the normal range. The specific steps include:

[0162] Anomaly detection: Identify key parameters that deviate from normal values ​​based on the results of data analysis and model prediction;

[0163] Objective function optimization: Establish the objective function to minimize the deviation value, expressed as:

[0164] ;

[0165] in is the real-time parameter value, is the target value, is the regularization coefficient, To control the parameters;

[0166] Adjust parameters: adjust the operating parameters of the equipment through the optimized control strategy to ensure that the system status returns to a safe range;

[0167] S53, Feedback control mechanism and multi-level feedback loop: Use multi-level feedback control mechanism to achieve closed-loop regulation, monitor the effect of each data adjustment in real time, and further adjust if the adjustment effect does not meet expectations. The feedback control mechanism includes:

[0168] First-level feedback: real-time monitoring of the deviation between the adjusted parameters and the expected values. If the deviation is within the allowable range, the current state is maintained.

[0169] Secondary feedback: If the primary feedback does not achieve the expected effect, secondary parameter adjustment is performed to re-optimize key operating parameters through a more sophisticated control algorithm;

[0170] Level 3 feedback: After the level 2 feedback adjustment, if the deviation still does not meet the standard, an alarm is triggered and the management personnel are notified to conduct further inspection or manual intervention.

[0171] S54, feedback result storage and subsequent optimization: all feedback control results are recorded in the data storage module to form a historical feedback database;

[0172] Through adaptive feedback control, the system can monitor and adjust key operating parameters in real time under changing environmental conditions, ensuring the safe and stable operation of smart buildings, and improving the system's adaptability, control accuracy and feedback response speed.

[0173] S6 includes:

[0174] S61, Historical Data Storage and Archiving: The real-time operation data of the intelligent building (including key operation indicators such as temperature, pressure, vibration frequency and flow) and environmental parameters are regularly transmitted and stored in the data storage module. The data storage includes:

[0175] Real-time data archiving: Archive the collected real-time operation data at set time intervals to ensure data integrity and continuity;

[0176] Data classification storage: historical data is classified according to data type (such as temperature data, equipment operation status data and environmental parameters) to facilitate subsequent retrieval and analysis.

[0177] S62, data cleaning and organization: before data storage, the operation data is cleaned and organized to ensure the quality and consistency of the stored data;

[0178] S63, long-term trend analysis: regularly perform trend analysis on stored historical operation data to identify long-term change trends of key operation indicators of smart buildings

[0179] S64, historical performance curve generation: Generate the historical performance curve of the intelligent building based on the trend analysis results to show the changes of the operating indicators over time. The specific steps include:

[0180] Curve drawing: key operating indicators such as temperature, pressure, vibration frequency and flow rate are drawn into historical performance curves to facilitate observation of the changing trends of different indicators;

[0181] Data comparison: Compare performance curves at different periods to identify the changing patterns of operating data and determine potential performance degradation or equipment aging trends.

[0182] S65, Long-term operation status evaluation: Use historical performance curves and trend analysis results to evaluate the long-term operation status of smart buildings and provide data support for facility operation and maintenance decisions. The evaluation content includes:

[0183] Equipment wear assessment: Evaluate equipment wear based on historical vibration frequency and temperature data;

[0184] Environmental adaptability analysis: Combine environmental parameters to evaluate the long-term adaptability of buildings to environmental changes;

[0185] Performance prediction: Use historical data models to predict future performance trends of smart buildings so that maintenance planning can be done in advance;

[0186] Through historical data storage and trend analysis, the system can continuously accumulate operating data, generate historical performance curves and provide long-term status assessment support, providing a scientific basis for long-term operation and maintenance decisions of smart buildings.

[0187] S7 includes:

[0188] S71, Visualization Display: Through the visualization interface, the real-time operation data, prediction analysis results and early warning information of the intelligent building are centrally displayed;

[0189] S72, real-time operation data display: The real-time operation data of the intelligent building (including temperature, pressure, vibration frequency, flow, etc.) is displayed on the visual interface in the form of charts and digital displays, so that managers can intuitively understand the real-time status of the system. The display content includes:

[0190] Data Overview Panel: summarizes and displays the latest values ​​of key operating indicators and environmental parameters;

[0191] Real-time trend chart: A trend chart updated in real time shows the changes of key operating indicators over time, helping to identify abnormal situations;

[0192] S73, Prediction analysis results display: The prediction analysis results based on the full life cycle management model and trend analysis are displayed on a visual interface. The display content includes:

[0193] Future status prediction chart: Based on trend analysis and prediction models, it shows the future trends of key operating indicators to help managers predict equipment status;

[0194] Performance degradation curve: Displays the long-term performance forecast of the equipment, facilitating early planning of maintenance work;

[0195] S74, warning information display and notification: Display the warning information generated by the system on the visual interface and send notifications to relevant personnel to ensure timely response. The display and notification content includes:

[0196] Alert dashboard: lists all active alerts, including abnormal indicators, risk levels, and recommended interventions;

[0197] Notification push: push warning information to managers via SMS, email or mobile application to ensure timely delivery of important warning information;

[0198] S75, remote monitoring and multi-party collaboration: supports managers to conduct remote monitoring, operation and multi-party collaboration through a visual interface to ensure the real-time and transparency of the system. Specific collaboration functions include:

[0199] Remote operation: Authorized managers can perform basic control operations through the platform, such as adjusting key operating parameters, turning equipment on or off, etc.

[0200] Multi-user collaboration: Allow multiple users to access the platform at the same time. Users with different permissions can view, operate and record corresponding data information to achieve team collaborative management.

[0201] like Figure 2 As shown, a smart building full life cycle monitoring and management system is used to implement the above-mentioned smart building full life cycle monitoring and management method, including the following modules:

[0202] Data acquisition module: collects real-time operation data of smart buildings through various sensors deployed in smart buildings, including environmental parameters and equipment operation status data;

[0203] Multi-source data fusion and analysis module: cleans, fuses and filters the collected real-time operation data, extracts key operation indicators, and performs trend analysis and abnormal pattern recognition to provide data support for risk identification;

[0204] Full life cycle management model building module: Build a full life cycle management model based on the integrated data, covering the health of building structures, equipment wear and tear, and environmental factors, and dynamically update and optimize the model through real-time operation data;

[0205] Intelligent early warning module: Combines the analysis results of the full life cycle management model with the multi-source data fusion and analysis module to assess the risk level and generate early warning signals, notify managers in real time, and provide risk assessment and intervention suggestions;

[0206] Adaptive feedback control module: Based on real-time operation data and abnormal detection results, it dynamically adjusts key operation parameters, maintains the operation of intelligent buildings within the normal range through adaptive feedback control, and optimizes the control parameters using a self-learning mechanism;

[0207] Data storage module: used to store the operation data and historical operation data of intelligent buildings, conduct regular trend analysis, generate historical performance curves, and provide data support for the long-term operation status evaluation of intelligent buildings;

[0208] Remote visual monitoring module: displays the real-time operation data, predictive analysis results and early warning information of intelligent buildings through a visual interface, and supports remote monitoring, operation and multi-party collaboration.

[0209] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0210] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A monitoring and management method for the entire life cycle of an intelligent building, characterized in that: The following steps are involved: S1, data collection: by deploying a variety of sensors in smart buildings, the operation data of smart buildings can be collected in real time. The operation data of smart buildings includes environmental parameters and equipment operation parameters; S2, multi-source data fusion and analysis: data cleaning, fusion and anomaly filtering of operation data from multiple sensors, using multi-source data analysis algorithms to extract key operation indicators, including temperature, pressure, speed and vibration frequency, perform trend analysis and abnormal pattern recognition, and reveal potential risk factors and system weaknesses; S3, full life cycle management model construction: Based on data fusion and analysis results, a full life cycle management model for smart buildings is constructed, covering building structure health, equipment wear and tear conditions, and environmental influencing factors, and the model parameters are dynamically updated through real-time operation data; S4, intelligent early warning mechanism: Based on the results of the full life cycle management model and multi-source data fusion and analysis, risk assessment is conducted on key operating indicators of intelligent buildings, and early warning signals are generated based on the output of trend analysis and abnormal pattern recognition; S5, Adaptive feedback control: Based on the results of multi-source data fusion and analysis and the results of the full life cycle management model prediction, the changes of key operating parameters are monitored in real time, and the operating status of the intelligent building is adaptively controlled; S6, historical data storage and trend analysis: store the operation data and historical operation data of the smart building in the data storage module, perform trend analysis regularly to generate historical performance curves, and provide data support for the long-term operation status evaluation of the facilities in the smart building; S7, remote visual monitoring: Displays real-time operation data, predictive analysis results and early warning information of smart buildings through a visual interface, supporting remote monitoring, operation and multi-party collaboration.

2. According to claim 1, a monitoring and management method for the entire life cycle of an intelligent building is characterized in that: The S1 includes: S11, sensor layout planning: According to the structural layout and monitoring requirements of the intelligent building, plan the collection points of environmental parameters and equipment operation status data. The deployed sensors include temperature sensors, humidity sensors, air pressure sensors, wind speed sensors, water level sensors, flow sensors, vibration sensors and equipment temperature sensors; S12, sensor installation and calibration: install sensors at the planned collection points, and calibrate each sensor after installation; S13, data collection frequency configuration: set the collection frequency for different types of data. The collection frequency of environmental parameters is set according to the rate of environmental change. The collection frequency of equipment operation status data is configured according to the equipment operation requirements. S14, data transmission network configuration: deploying a data transmission network in the smart building, including wired and wireless transmission lines, to transmit the operating data collected by sensors to a multi-source data fusion and analysis module; S15, preliminary data integrity check: After the data is transmitted to the multi-source data fusion and analysis module, a preliminary data integrity check is performed.

3. The intelligent building full life cycle monitoring and management method according to claim 2 is characterized in that: The S2 includes: S21, multi-source data cleaning: data cleaning of collected environmental parameters and equipment operation status data; S22, multi-source data fusion: the cleaned environmental parameters and equipment operation status data are integrated to form a unified intelligent building operation data set; S23, key operation indicator extraction: extract key operation indicators from the smart building operation data set to support the operation analysis and trend prediction of smart buildings; S24, Trend analysis and abnormal pattern recognition: Perform trend analysis and abnormal pattern recognition on key operating indicators to identify potential risk factors and system weaknesses.

4. The intelligent building full life cycle monitoring and management method according to claim 3 is characterized in that: The S24 includes: S241, Trend analysis model construction: Use time series analysis methods to perform trend analysis on key operating indicators and identify historical change trends of operating data; S242, Seasonal and cyclical pattern identification: Identify seasonal and cyclical patterns in data by decomposing the time series of key operating indicators; S243, anomaly detection model construction: using a clustering-based anomaly detection algorithm to identify abnormal patterns of key operating indicators and construct an anomaly detection model to identify abnormal values ​​that deviate from the normal data range; S244, Risk pattern identification and early warning: Identify potential risk patterns in key operating indicators based on anomaly detection results and trend analysis.

5. The intelligent building full life cycle monitoring and management method according to claim 4 is characterized in that: The S3 includes: S31, model variable determination: based on the results of multi-source data fusion and analysis, determine the core variables of the full life cycle management model, including key operating indicators and their related environmental parameters and equipment operating status data; S32, model type selection: Combined with the operation data characteristics and monitoring requirements of intelligent buildings, multiple regression model and Bayesian network model are selected as the basis of the life cycle management model; S33, model expression construction: construct a multivariate regression model to quantify the impact of key operating indicators on the status of intelligent buildings; S34, dynamic update of model parameters: dynamically update the parameters of the multivariate regression model and Bayesian network model regularly using real-time operation data.

6. The intelligent building full life cycle monitoring and management method according to claim 5 is characterized in that: The S4 includes: S41, Risk level assessment: Based on the full life cycle management model and the results of multi-source data fusion and analysis, risk level assessment is carried out. Based on the results of key operating indicators, trend analysis and abnormal pattern recognition, the risk level is divided into three levels: low, medium and high; S42, early warning signal generation: Generate corresponding early warning signals for each risk level, including abnormal indicators, risk levels and recommended intervention measures; S43, early warning signal distribution and notification: sending the generated early warning signal to relevant managers through the visualization platform and communication network; S44, dynamic update of early warning information: by continuously monitoring the real-time operation data of the intelligent building, the early warning information is dynamically updated; S45, early warning record and feedback storage: the generation time, risk level, processing measures and feedback results of each early warning signal are stored in the data storage module to form a historical early warning record.

7. The intelligent building full life cycle monitoring and management method according to claim 6 is characterized in that: The S5 includes: S51, real-time parameter adjustment and monitoring: dynamically adjust key operating parameters of intelligent buildings based on the results of multi-source data fusion and analysis and the results predicted by the full life cycle management model; S52, abnormal state identification and control strategy optimization: When the real-time operating data deviates from the set range, the system will identify it as an abnormal state and automatically adjust the control strategy through the optimization algorithm to restore the operating data to the normal range; S53, feedback control mechanism and multi-level feedback loop: use multi-level feedback control mechanism to achieve closed-loop regulation, monitor the effect of each data adjustment in real time, and further regulate if the adjustment effect does not meet expectations; S54, feedback result storage and subsequent optimization: all feedback control results are recorded in the data storage module to form a historical feedback database.

8. The intelligent building full life cycle monitoring and management method according to claim 7 is characterized in that: The S6 includes: S61, historical data storage and archiving: regularly transmit and store the real-time operation data and environmental parameters of the intelligent building to the data storage module; S62, data cleaning and organization: before data storage, the operation data is cleaned and organized to ensure the quality and consistency of the stored data; S63, long-term trend analysis: regularly perform trend analysis on stored historical operation data to identify long-term change trends of key operation indicators of smart buildings S64, historical performance curve generation: Generate the historical performance curve of the intelligent building based on the trend analysis results to show the changes of operating indicators over time; S65, Long-term operating status evaluation: Use historical performance curves and trend analysis results to evaluate the long-term operating status of intelligent buildings.

9. The intelligent building full life cycle monitoring and management method according to claim 8 is characterized in that: The S7 includes: S71, Visualization Display: Through the visualization interface, the real-time operation data, prediction analysis results and early warning information of the intelligent building are centrally displayed; S72, real-time operation data display: display the real-time operation data of the intelligent building on a visual interface in the form of charts and digital displays; S73, display of forecast analysis results: display the forecast analysis results obtained based on the full life cycle management model and trend analysis on a visual interface; S74, warning information display and notification: display the warning information generated by the system on the visual interface and send notifications to relevant personnel; S75, remote monitoring and multi-party collaboration: supports managers to conduct remote monitoring, operation and multi-party collaboration through a visual interface.

10. An intelligent building full life cycle monitoring and management system, used to implement an intelligent building full life cycle monitoring and management method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Data acquisition module: collects real-time operation data of smart buildings through various sensors deployed in smart buildings, including environmental parameters and equipment operation status data; Multi-source data fusion and analysis module: cleans, fuses and filters the collected real-time operation data, extracts key operation indicators, and performs trend analysis and abnormal pattern recognition to provide data support for risk identification; Full life cycle management model building module: Build a full life cycle management model based on the integrated data, covering the health of building structures, equipment wear and tear, and environmental factors, and dynamically update and optimize the model through real-time operation data; Intelligent early warning module: Combines the analysis results of the full life cycle management model with the multi-source data fusion and analysis module to assess the risk level and generate early warning signals, notify managers in real time, and provide risk assessment and intervention suggestions; Adaptive feedback control module: Based on real-time operation data and abnormal detection results, it dynamically adjusts key operation parameters, maintains the operation of intelligent buildings within the normal range through adaptive feedback control, and optimizes the control parameters using a self-learning mechanism; Data storage module: used to store the operation data and historical operation data of intelligent buildings, conduct regular trend analysis, generate historical performance curves, and provide data support for the long-term operation status evaluation of intelligent buildings; Remote visual monitoring module: displays the real-time operation data, predictive analysis results and early warning information of intelligent buildings through a visual interface, and supports remote monitoring, operation and multi-party collaboration.

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