Remote monitoring and fault diagnosis method and system for environment simulation system

Abstract

The invention relates to the field of simulation environment monitoring, in particular to a remote monitoring and fault diagnosis method and system for an environment simulation system. The method comprises the following steps: identifying all distributed multi-dimensional sensor nodes, carrying out spatial topology distribution analysis, and constructing a plurality of node region models; carrying out omnibearing environment simulation operation state monitoring on the environment simulation sample box, and carrying out environment state nonlinear correlation mining to obtain nonlinear operation state correlation characteristics; performing dynamic state rendering on the plurality of node area models based on the nonlinear operation state association features, and constructing a plurality of environment simulation data twin models; and performing operation simulation monitoring processing on the plurality of environment simulation data twin models, and performing parameter difference analysis between the models to generate parameter difference characteristics of different models. According to the invention, efficient and accurate fault analysis and diagnosis of the environment simulation system are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of simulated environment monitoring, and in particular to a remote monitoring and fault diagnosis method and system for an environmental simulation system. Background Art

[0002] With the continuous development of industrial automation and intelligent technology, the application of environmental simulation systems in multiple fields has gradually increased, especially in manufacturing, energy management, aerospace and other industries, and has become an indispensable key technology. Environmental simulation systems are mainly used to simulate and simulate various physical environmental conditions, such as temperature, humidity, pressure, vibration, electromagnetic interference and other factors on the operation of equipment. By simulating real or extreme environments, the performance and stability of equipment can be predicted and verified, thereby providing a scientific basis for equipment design, optimization and fault prediction.

[0003] However, with the increasing complexity and automation level of industrial equipment, traditional environmental simulation systems often have problems such as single monitoring means, poor real-time performance, and frequent manual intervention. Existing environmental simulation systems often rely on manual inspections and regular checks, and are unable to remotely monitor the status of equipment in a timely and effective manner. Due to the particularity of the working environment, they often face situations where manual monitoring is incomplete and data processing is delayed. In addition, the large amount of data generated during the environmental simulation process often needs to rely on expert experience for judgment, and lacks systematic and automated fault diagnosis methods, resulting in inaccurate equipment failure predictions, high maintenance costs, and even affecting production safety. In order to adapt to the development needs of intelligent production and industrial Internet, how to achieve real-time remote monitoring and fault diagnosis of environmental simulation systems has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a remote monitoring and fault diagnosis method and system for an environmental simulation system to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a remote monitoring and fault diagnosis method for an environmental simulation system, comprising the following steps: Step S1: Identify all distributed multi-dimensional sensor nodes, perform spatial topological distribution analysis, and construct multiple node area models; Step S2: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box, and performing nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Step S3: dynamically rendering multiple node area models based on nonlinear operating state association characteristics, and constructing multiple environmental simulation data twin models; Step S4: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and performing parameter difference analysis between models to generate parameter difference characteristics of different models; Step S5: performing a local model abnormality feature analysis based on parameter difference features of different models, and performing a potential environmental simulation fault analysis to obtain potential environmental simulation fault data of the abnormal area; Step S6: Infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

[0006] The present invention helps to fully understand the structure of the environmental monitoring network by identifying and analyzing the spatial topological distribution of distributed multidimensional sensor nodes, constructing multiple node area models can provide a detailed description of the status of each area of ​​the environment, and provide a basis for subsequent monitoring and analysis. Performing all-round environmental simulation operation status monitoring helps to understand the state changes of the simulated environment in real time, and obtaining nonlinear operation state association characteristics can reveal the potential association between environmental states and provide characteristics for subsequent analysis. Dynamic state rendering of multiple node area models based on nonlinear association characteristics can more intuitively present the state changes of the models. Constructing an environmental simulation data twin model helps to simulate the operation conditions under different environmental conditions and provide a basis for comparative analysis. Performing operation simulation monitoring on the environmental simulation data twin model can compare the operation conditions between different models, and generating parameter difference characteristics of different models helps to discover the differences between models and provide a basis for anomaly detection. Performing local model abnormal feature analysis based on parameter difference characteristics of different models can locate abnormal areas, and obtaining potential environmental simulation fault data of abnormal areas helps to identify potential fault points in the environmental simulation system. Inferring the fault factors of the environmental simulation system helps to quickly locate the root cause of the fault. Constructing an adaptive fault diagnosis strategy can improve the accuracy and efficiency of fault diagnosis and ensure the smooth progress of remote monitoring and fault diagnosis operations.

[0007] Preferably, step S1 comprises the following steps: Step S11: Identify the distributed multi-dimensional sensors in the environmental simulation sample box and mark all the distributed multi-dimensional sensor nodes; Step S12: Calculate the node positions of all distributed multi-dimensional sensor nodes to generate the position information of all sensor nodes; Step S13: Perform spatial topological distribution analysis on the location information of all sensor nodes to generate node spatial topological distribution data; Step S14: performing spatial topological distribution modeling on the node spatial topological distribution data to construct a node spatial topological distribution map; Step S15: dividing the node space topology distribution map into regions to obtain multiple node topology regions; Step S16: Perform three-dimensional point cloud modeling on multiple node topology areas to construct multiple node area models.

[0008] The present invention helps to accurately monitor environmental parameters by identifying distributed multidimensional sensors in an environmental simulation sample box and marking nodes. Marking sensor nodes can provide accurate identification for subsequent position calculation and data analysis. Calculating the position information of sensor nodes can provide the specific position of each sensor in the sample box. Generating sensor node position information helps to establish spatial relationships and provides a basis for subsequent analysis. Analyzing the spatial topological distribution of sensor nodes can reveal the positional relationship between sensors. Generating node spatial topological distribution data helps to understand the structure and layout of the sensor network. Establishing a node spatial topological distribution map can intuitively display the connection relationship between sensor nodes. Constructing a node spatial topological distribution map helps to form an overall sensor network model. Regional division of the node spatial topological distribution map can group and manage sensor nodes. Obtaining multiple node topological regions helps to independently analyze and monitor different regions. Performing three-dimensional point cloud modeling can more vividly present the spatial structure of the node region. Constructing multiple node region models helps to deeply understand the sensor layout of each region and provide visualization support for remote monitoring and fault diagnosis.

[0009] Preferably, step S2 comprises the following steps: Step S21: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box based on the distributed multi-dimensional sensor, and extracting multi-dimensional environmental simulation operation monitoring parameters, wherein the multi-dimensional environmental simulation operation monitoring parameters include environmental temperature parameters, environmental humidity parameters, and environmental wind pressure parameters; Step S22: performing temperature fluctuation analysis on the ambient temperature parameters to generate ambient temperature fluctuation characteristics; Step S23: calculating the fluctuation amplitude of the ambient temperature fluctuation characteristics to generate the ambient temperature fluctuation amplitude; Step S24: performing temperature fluctuation amplitude fitting based on the ambient temperature fluctuation amplitude to construct an ambient temperature fluctuation amplitude curve; Step S25: Perform nonlinear correlation mining of the environmental state on the environmental temperature fluctuation amplitude curve, the environmental humidity parameter and the environmental wind pressure parameter to obtain nonlinear operating state correlation characteristics.

[0010] The present invention uses distributed multi-dimensional sensors to conduct all-round monitoring of the environmental simulation sample box, and can obtain environmental parameter data in real time. Extracting multi-dimensional parameters such as environmental temperature, humidity and wind pressure is helpful for a comprehensive understanding of the internal environmental state of the sample box. Analyzing environmental temperature fluctuations can reveal the laws and trends of environmental temperature changes. Generating environmental temperature fluctuation characteristics is helpful for identifying the pattern and periodicity of temperature fluctuations. Calculating the environmental temperature fluctuation amplitude can quantify the size and intensity of temperature fluctuations. Generating fluctuation amplitude data is helpful for further analyzing the changes in temperature fluctuations. By fitting the temperature fluctuation amplitude, a fluctuation amplitude model can be established. Constructing an environmental temperature fluctuation amplitude curve is helpful for visually displaying the changing trends and laws of temperature fluctuations. Analyzing the correlation between the environmental temperature fluctuation amplitude curve and humidity and wind pressure parameters can reveal the nonlinear relationship between different parameters. Extracting nonlinear operating state correlation characteristics is helpful for identifying the complex interactions between environmental parameters, providing a deeper understanding for environmental state analysis and fault diagnosis.

[0011] Preferably, step S25 specifically comprises the following steps: Perform spatial humidity distribution analysis on environmental humidity parameters to obtain spatial humidity distribution data of the sample box; Conduct long-term operation cycle change analysis on the spatial humidity distribution data of the sample box to generate humidity distribution cycle change data; The humidity distribution periodic change data is subjected to humidity change trend evolution, thereby generating humidity change trend evolution characteristics; Perform multi-time-frequency decomposition on the environmental wind pressure parameters to obtain wind pressure spectra of different frequencies; Perform main frequency component analysis on wind pressure spectra of different frequencies one by one, and extract the main frequency component of each spectrum; Calculate the power spectrum density of the main frequency component of each spectrum graph to generate the power spectrum density of each spectrum graph; Perform power spectrum density distribution identification on the power spectrum density of each spectrum graph to generate wind pressure density distribution data; The nonlinear correlation of environmental states is mined by using the humidity change trend evolution characteristics, ambient temperature fluctuation amplitude curve and wind pressure density distribution data to obtain the nonlinear operating state correlation characteristics.

[0012] The present invention can identify the existing humidity non-uniformity by analyzing the spatial distribution of environmental humidity parameters in the sample box, which helps to adjust the humidity control equipment to ensure uniform humidity distribution. Obtaining the spatial humidity distribution data of the sample box can be used to detect potential humidity abnormal areas and discover humidity problems in advance. By analyzing the long-term periodic changes in humidity distribution, the trend of humidity changes over time can be identified, which helps to predict the periodic law of humidity changes. Extracting the characteristics of humidity periodic changes helps to understand the periodic pattern of humidity changes and provide a more accurate reference basis for humidity control and regulation. Extracting the evolution characteristics of humidity change trends can deeply understand the historical trends of humidity changes and help predict the direction of future humidity changes. Analyzing the evolution characteristics of humidity change trends helps to identify the potential laws of humidity changes and provide a reference for timely adjustment of the environmental conditions of the sample box. By multi-time-frequency decomposition and processing of environmental wind pressure parameters, the wind pressure change laws at different frequencies can be identified, which helps to understand the frequency domain characteristics of wind pressure. Obtaining a wind pressure spectrum can help analyze the frequency distribution of wind pressure and provide a basis for evaluating the environmental state under the influence of wind. Analyzing the main frequency components of wind pressure spectrums of different frequencies can identify key wind pressure frequency components and help to understand the main characteristics of wind pressure at different frequencies. Extracting wind pressure density distribution data can describe the density distribution of wind pressure at different frequencies, providing support for the quantitative analysis of wind pressure characteristics. Combining the evolution characteristics of humidity change trends, temperature fluctuation amplitude curves and wind pressure density distribution data for nonlinear association mining can help to deeply understand the complex relationship between different environmental parameters and provide a comprehensive environmental status assessment. Extracting nonlinear operating status association features can help monitor changes in environmental status, identify potential problems in a timely manner, and provide more accurate information support for fault diagnosis and prediction.

[0013] Preferably, step S3 specifically comprises the following steps: Step S31: performing environmental simulation feature evolution based on nonlinear operating state association features, thereby generating all-round environmental operating state evolution data; Step S32: performing regional node state matching on the omnidirectional environment operation state evolution data according to the location information of all sensor nodes, thereby obtaining corresponding state evolution data of the node area; Step S33: Use the corresponding state evolution data of the node area to dynamically render the multiple node area models and construct multiple environmental simulation data twin models.

[0014] The present invention can capture the complex nonlinear relationship between environmental parameters through the nonlinear operation state association characteristics, provide a more accurate description for the evolution of environmental simulation characteristics, generate all-round environmental operation state evolution data, which can help monitor the subtle changes of environmental parameters and give early warning of environmental anomalies, thereby ensuring the stability and reliability of system operation. Regional node state matching is performed on the all-round environmental operation state evolution data according to the sensor node position information, which helps to correspond environmental parameters with specific locations, realize monitoring and diagnosis of environmental states in different regions, and obtain the corresponding state evolution data of the node region, which can provide detailed evolution information of the environmental state of each region, providing an accurate basis for subsequent fault diagnosis and problem location. Regional node state matching is performed on the all-round environmental operation state evolution data according to the sensor node position information, which helps to correspond environmental parameters with specific locations, realize monitoring and diagnosis of environmental states in different regions, and obtain the corresponding state evolution data of the node region, which can provide detailed evolution information of the environmental state of each region, providing an accurate basis for subsequent fault diagnosis and problem location.

[0015] Preferably, the specific steps of step S4 are: Step S41: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and extracting simulation monitoring data of each twin model; Step S42: performing time series state change trend analysis on the simulated monitoring data of each twin model to generate the time series state change trend of each twin model; Step S43: performing situation evolution prediction on the time series state change trend of each twin model, thereby obtaining situation evolution prediction data of each twin model; Step S44: Perform inter-model parameter difference analysis on each twin model situation evolution prediction data to generate parameter difference characteristics of different models.

[0016] The present invention can monitor the running status of the models in real time by performing operation simulation monitoring processing on multiple environmental simulation data twin models, discover abnormal situations in time, and ensure the normal operation of the environmental simulation system. Extracting the simulation monitoring data of each twin model helps to understand the performance of each model and provide data support for subsequent analysis and evaluation. The time-series state change trend analysis of the simulation monitoring data of each twin model can reveal the change law of the model state over time and help identify potential state evolution trends. Generating the time-series state change trend of each twin model helps to understand the evolution process of the model state and provide a basis for subsequent situation evolution prediction. By performing situation evolution prediction on the time-series state change trend of each twin model, the future development trend of the model state can be predicted, which helps to take measures to deal with the problems in advance. Obtaining the situation evolution prediction data of each twin model can provide early warning information for the remote monitoring system and strengthen the real-time monitoring and management of the environmental simulation system. The inter-model parameter difference analysis of the situation evolution prediction data of each twin model helps to compare the state change characteristics between different models and provide a reference for model performance evaluation and optimization. Generating parameter difference characteristics of different models can help identify the differences between models and provide key information support for system fault diagnosis and performance optimization.

[0017] Preferably, the specific steps of step S5 are: Step S51: performing local model abnormality feature analysis based on parameter difference features of different models, and extracting local model abnormality feature data; Step S52: locating abnormal features of the abnormal feature data of the local model and extracting the abnormal node area model; Step S53: Preview the environment parameters at multiple time points on the abnormal node area model to obtain the environment preview parameters at multiple time points; Step S54: Perform potential environmental simulation fault analysis on the environmental rehearsal parameters at multiple time points to obtain potential environmental simulation fault data of abnormal areas.

[0018] The present invention can help identify abnormal situations in the model and discover potential problem points in advance by performing abnormal feature analysis of local models based on parameter difference characteristics of different models. Extracting abnormal feature data of local models can provide key data support for subsequent abnormal detection and diagnosis, which helps to improve the stability and reliability of the system. Abnormal feature positioning of abnormal feature data of local models helps to accurately locate the abnormal node area model, quickly locate the problem, and shorten the fault diagnosis and repair time. Extracting the abnormal node area model can deeply analyze the changes in environmental parameters in the area and provide basic data for the next step of fault analysis and prediction. Previewing the abnormal node area model at multiple time points can simulate the development trend of abnormal situations at different time points and help understand the evolution process of the problem. Obtaining environmental preview parameters at multiple time points can provide detailed data support for subsequent fault analysis and prediction, which helps to accurately assess potential risks. Performing potential environmental simulation fault analysis on environmental preview parameters at multiple time points can help identify potential fault points in abnormal areas and predict the fault modes that occur. Obtaining potential environmental simulation fault data in abnormal areas can provide clues and directions for fault diagnosis and problem solving, and improve the system's fault response capability and efficiency.

[0019] Preferably, the specific steps of step S6 are: Step S61: inferring environmental fault factors from potential environmental simulation fault data of the abnormal area to obtain environmental fault factor data; Step S62: generating a fault warning signal based on the environmental fault factor data; Step S63: making a dynamic adaptive fault diagnosis decision according to the fault warning signal and constructing an adaptive fault diagnosis strategy; Step S64: Upload the adaptive fault diagnosis strategy to the cloud server to perform remote monitoring and fault diagnosis operations.

[0020] The present invention can help identify the root cause of the fault by inferring the environmental fault factor of the potential environmental simulation fault data of the abnormal area, deeply analyze the occurrence mechanism of the problem, and obtain the environmental fault factor data, which can provide important reference for subsequent fault warning and diagnosis, and help to accurately predict the fault situation. The generation of fault warning signals based on the environmental fault factor data can discover potential fault risks in advance, warn system operation and maintenance personnel, and help avoid the serious impact of the fault on the system. The generation of fault warning signals can improve the real-time monitoring capability of the system, take measures to prevent the occurrence of faults in time, and ensure the stable operation of the system. Dynamic adaptive fault diagnosis decisions made according to the fault warning signals can flexibly adjust the diagnosis strategy according to the real-time situation, improve the accuracy and efficiency of fault diagnosis, and construct an adaptive fault diagnosis strategy to help take corresponding measures according to different situations, quickly respond to fault events, and reduce the losses caused by faults. Uploading the adaptive fault diagnosis strategy to the cloud server can realize remote monitoring and fault diagnosis operations, improve the response speed and flexibility of the system, and execute the fault diagnosis strategy through the cloud server to realize remote collaboration and management, ensuring that the system can obtain timely fault diagnosis support at any time and any place.

[0021] In this specification, a remote monitoring and fault diagnosis system of an environmental simulation system is provided, which is used to execute the remote monitoring and fault diagnosis method of the environmental simulation system as described above, including: The spatial topology distribution module is used to identify all distributed multi-dimensional sensor nodes, perform spatial topology distribution analysis, and build multiple node area models; The nonlinear mining module is used to conduct all-round environmental simulation operation status monitoring of the environmental simulation sample box and to conduct nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Dynamic state rendering module, used to perform dynamic state rendering on multiple node area models based on nonlinear operation state association characteristics, and build multiple environmental simulation data twin models; The parameter difference module is used to perform operation simulation monitoring on multiple environmental simulation data twin models and perform parameter difference analysis between models to generate parameter difference characteristics of different models; The potential fault analysis module is used to analyze the abnormal characteristics of local models based on the parameter difference characteristics of different models, and to analyze the potential environmental simulation faults to obtain the potential environmental simulation fault data of the abnormal area; The fault diagnosis decision module is used to infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

[0022] The present invention helps to establish the cognition of the overall structure of the system by identifying distributed multidimensional sensor nodes and performing spatial topological distribution analysis, improves the visualization and management efficiency of the system, and constructs multiple node area models to help understand the correlation between nodes, providing a basis for subsequent environmental simulation and monitoring. Nonlinear correlation mining of environmental states can reveal the complex nonlinear relationship between environmental parameters and help understand the deep-level characteristics of the system operation state. Obtaining nonlinear operation state correlation characteristics can provide important clues for subsequent model establishment and fault diagnosis. Dynamic state rendering based on nonlinear operation state correlation characteristics can more intuitively display the operation state changes of multiple node area models and help identify abnormal situations. Constructing an environmental simulation data twin model helps to simulate the system operation state under different conditions, provide a reference for fault prediction and analysis, and improve the environmental simulation data. Parameter difference analysis based on the twin model can help compare the operating conditions of different models, identify potential abnormal features, and provide support for fault diagnosis. Generating parameter difference features for different models helps to understand the performance differences of the system under different conditions and guide subsequent fault analysis and repair. Local model abnormal feature analysis based on parameter difference features can effectively identify local abnormal conditions and help discover potential fault points in advance. Potential environmental simulation fault analysis can help predict fault conditions in abnormal areas and provide guidance for fault troubleshooting and repair. Environmental fault factor inference and dynamic adaptive fault diagnosis decision-making can help quickly and accurately diagnose the cause of the fault and reduce the impact of system failures. Building an adaptive fault diagnosis strategy and performing remote monitoring and fault diagnosis operations can improve the stability and reliability of the system and reduce the interference of failures on the normal operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of the steps of a remote monitoring and fault diagnosis method for an environmental simulation system of the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0024] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0025] The present application example provides a remote monitoring and fault diagnosis method and system for an environmental simulation system. The execution subjects of the remote monitoring and fault diagnosis method and system for the environmental simulation system include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0026] See also Figures 1 to 4 The present invention provides a remote monitoring and fault diagnosis method for an environmental simulation system, the remote monitoring and fault diagnosis method for an environmental simulation system comprising the following steps: Step S1: Identify all distributed multi-dimensional sensor nodes, perform spatial topological distribution analysis, and construct multiple node area models; Step S2: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box, and performing nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Step S3: dynamically rendering multiple node area models based on nonlinear operating state association characteristics, and constructing multiple environmental simulation data twin models; Step S4: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and performing parameter difference analysis between models to generate parameter difference characteristics of different models; Step S5: performing a local model abnormality feature analysis based on parameter difference features of different models, and performing a potential environmental simulation fault analysis to obtain potential environmental simulation fault data of the abnormal area; Step S6: Infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

[0027] The present invention helps to fully understand the structure of the environmental monitoring network by identifying and analyzing the spatial topological distribution of distributed multidimensional sensor nodes, constructing multiple node area models can provide a detailed description of the status of each area of ​​the environment, and provide a basis for subsequent monitoring and analysis. Performing all-round environmental simulation operation status monitoring helps to understand the state changes of the simulated environment in real time, and obtaining nonlinear operation state association characteristics can reveal the potential association between environmental states and provide characteristics for subsequent analysis. Dynamic state rendering of multiple node area models based on nonlinear association characteristics can more intuitively present the state changes of the models. Constructing an environmental simulation data twin model helps to simulate the operation conditions under different environmental conditions and provide a basis for comparative analysis. Performing operation simulation monitoring on the environmental simulation data twin model can compare the operation conditions between different models, and generating parameter difference characteristics of different models helps to discover the differences between models and provide a basis for anomaly detection. Performing local model abnormal feature analysis based on parameter difference characteristics of different models can locate abnormal areas, and obtaining potential environmental simulation fault data of abnormal areas helps to identify potential fault points in the environmental simulation system. Inferring the fault factors of the environmental simulation system helps to quickly locate the root cause of the fault. Constructing an adaptive fault diagnosis strategy can improve the accuracy and efficiency of fault diagnosis and ensure the smooth progress of remote monitoring and fault diagnosis operations.

[0028] In the embodiment of the present invention, refer to Figure 1 , is a flowchart of the steps of a remote monitoring and fault diagnosis method for an environmental simulation system of the present invention. In this example, the steps of the remote monitoring and fault diagnosis method for an environmental simulation system include: Step S1: Identify all distributed multi-dimensional sensor nodes, perform spatial topological distribution analysis, and construct multiple node area models; In this embodiment, in the environmental simulation system, the sensor nodes are usually distributed, and each sensor is responsible for collecting specific types of environmental data, such as temperature, humidity, pressure, gas composition, flow rate, vibration, etc. Each node contains one or more sensor modules, which collect corresponding environmental parameters and transmit data to the central control platform through the network. Each sensor node has different functions and measurement accuracy, so it is necessary to classify each node by type. For example, the nodes can be divided into temperature and humidity sensor nodes, pressure sensor nodes, flow sensor nodes, etc. Through the node identification algorithm, the specific type and deployment location of each node are identified, and the multi-dimensional data collected by it is obtained. Sensor nodes usually transmit data through standard communication protocols (such as LoRa, Zigbee, Wi-Fi, Bluetooth or Modbus). In this process, it is necessary to identify the communication protocol used by each node so that the data can be correctly decoded and processed in the subsequent data collection and analysis process. Accurately identify the spatial position of each sensor node. The node position can be determined by the global positioning system (GPS) or by indoor positioning technology (such as Wi-Fi positioning, Bluetooth positioning, ultra-wideband (UWB) positioning). This information will provide the necessary coordinate data for subsequent topological analysis and spatial modeling. After identifying and locating all sensor nodes, spatial topological analysis needs to be performed based on the physical locations of these nodes. Spatial topological relationships usually refer to the connection relationship between nodes and their relative position layout. In order to perform topological analysis, it is necessary to analyze the geometric position of the nodes (i.e., X, Y, and Z coordinates) and build a spatial graph model on this basis. By calculating the Euclidean distance between nodes, the proximity relationship between nodes can be identified. This step can use clustering algorithms such as K-means clustering algorithm and DBSCAN (density-based clustering algorithm) to classify similar nodes into the same category to form several regions or sub-regions. In spatial topological analysis, node density is an important indicator. By calculating the number of sensor nodes in each area, it can help identify which areas are data collection intensive areas and which areas are relatively sparse. On this basis, spatial division can be performed to divide the environment into multiple areas with similar monitoring density and functions. For example, one area is an area where temperature and humidity change frequently, while another area is an area where air pressure changes more drastically. Optimize the spatial topological relationship to ensure that the sensor nodes in each area can communicate and collaborate effectively. Here, we can use the shortest path algorithm and minimum spanning tree algorithm in graph theory to optimize the connection relationship between nodes and ensure the efficiency and robustness of information transmission. At the same time, factors such as signal attenuation and communication delay between sensor nodes need to be considered in network analysis.The spatial topological distribution results can be presented in a visual way. Geographical Information System (GIS) or 3D modeling software (such as Blender, Unity) can be used to generate a spatial layout diagram of the nodes in the region. This can help understand the spatial relationship between nodes and their distribution. Based on the results of the spatial topological distribution analysis, the sensor nodes in the region are divided into different regional models according to factors such as their physical location, measurement data type, and distribution density. Each regional model contains the spatial distribution of sensor nodes in the region, measurement data, and the interaction between nodes. In the model construction process, finite element models (FEM), physics-based modeling methods (such as CFD (computational fluid dynamics) models) or data-driven modeling methods can be used to establish simulation models for each region according to the environmental characteristics of different regions. For example, for areas with dense temperature and humidity sensors, a model simulating heat conduction and airflow can be constructed; for areas with significant pressure changes, a fluid dynamics model can be constructed. In the process of building multiple regional models, it is necessary to ensure that the data between the nodes in the region can work together. Data fusion technology (such as Kalman filtering and weighted average method) can be used to integrate the monitoring data of each sensor node in the region to form a more accurate regional status assessment. Finally, multiple regional models need to be integrated into the overall environmental monitoring system. A hierarchical modeling approach can be used to treat each regional model as an independent module, and the overall model simulates the dynamic changes of the environment by connecting the relationships between different regions. This integration process can also be optimized with the help of model integration methods in system engineering (such as system dynamics models).

[0029] Step S2: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box, and performing nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; In this embodiment, the environmental parameters to be monitored are determined, such as temperature, humidity, air pressure, light intensity, gas concentration, etc. Suitable sensors are selected for data collection according to the experimental objectives. Sensors are arranged reasonably in the environmental simulation sample box to ensure that the monitoring area can be fully covered. Sensors should be evenly distributed to obtain representative environmental data. A data acquisition system is set up to ensure that it can collect and store data output by sensors in real time. Microcontrollers (such as Arduino or RaspberryPi) are usually used to collect data and send the data to a central database through a communication interface (such as Wi-Fi, Bluetooth). The monitoring system is started to start all-round environmental status monitoring. Ensure that the system runs stably and can continuously record data. The monitoring system should record the environmental parameters of each sensor at different time points in real time. Set a timed sampling mechanism, such as sampling once per minute, to ensure the timeliness of the data. The collected data is stored in a database, usually using a relational database (such as MySQL) or a time series database (such as InfluxDB) to meet the storage requirements of time series data. The collected environmental data is cleaned, missing values ​​and outliers are processed, and data quality is ensured. Standardize or normalize the data for subsequent analysis. Select appropriate nonlinear association mining methods, such as: Neural Network: Use deep learning models (such as multilayer perceptron, LSTM) to capture nonlinear relationships in data. Support Vector Regression (SVR): Capture nonlinear features in data. Random Forest: Analyze nonlinear relationships between features through ensemble learning methods. Use the sorted data set to train the selected model, take environmental parameters as input, and the model outputs nonlinear association features. Usually divided into training set and validation set for cross-validation. Extract nonlinear operating state association features from the trained model, such as the degree of mutual influence between parameters, the importance of key features, etc. Use data visualization tools (such as Matplotlib, Seaborn) to display the extracted nonlinear association features, and display the relationship between different environmental parameters in the form of charts. Record the analysis results of nonlinear operating state association features in the database for subsequent analysis and application. At the same time, feedback the monitoring system based on the results to optimize sensor layout or monitoring parameters.

[0030] Step S3: dynamically rendering multiple node area models based on nonlinear operating state association characteristics, and constructing multiple environmental simulation data twin models; In this embodiment, the nonlinear operating state associated features extracted in the previous step are checked to ensure the integrity and accuracy of the data, including the characteristic information of different node areas. According to the goal of the experiment, the environmental parameters that need to be dynamically rendered (such as temperature, humidity, air pressure, etc.) are selected, and the rendering effects (such as color changes, dynamic graphics, etc.) are designed. A suitable rendering engine is selected, such as: Unity: suitable for complex three-dimensional environment simulation and dynamic interaction, Three.js: for three-dimensional visualization on web pages, Blender: for high-quality visual effects and animation production, and a rendering tool is used to build a three-dimensional model of each node area. According to the location and functional characteristics of the node, the corresponding environmental scene is designed, and the nonlinear operating state associated features are mapped to the three-dimensional model. The specific method includes: color coding: using color gradients to represent different states according to parameters such as temperature or humidity (for example, red Blue indicates high temperature, and blue indicates low temperature), shape change: dynamically adjust the shape or size of the model according to the change of parameters to enhance the visualization effect, write scripts so that the environmental status can be updated in real time. For example, set a timer to regularly obtain the latest environmental parameters from the data source, and update the appearance of the model in real time to achieve dynamic effects. For example, use animation to show the airflow and temperature changes in the node area to enhance the user's perception of environmental changes. Determine the structure of the twin model, including input (environmental parameters), output (rendering effect) and internal logic (data processing and rendering algorithm), integrate the environmental simulation data of multiple node areas into the twin model, ensure that the model can fully represent the overall environmental status, optimize the parameters of the twin model by comparing the simulation results with the actual environmental data, to improve its accuracy and reliability, and conduct verification tests to ensure that the model can accurately reflect changes under different environmental conditions.

[0031] Step S4: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and performing parameter difference analysis between models to generate parameter difference characteristics of different models; In this embodiment, the input parameters of each environmental simulation data twin model are collected, including environmental conditions, sensor data and other related variables, so as to perform a unified simulation operation, determine the key parameters that need to be monitored, such as temperature, humidity, air flow velocity, etc., to ensure that these parameters can reflect the operating status and performance of the model, and select appropriate software or tools for operation simulation monitoring. Commonly used tools include: MATLAB / Simulink: for system simulation and dynamic model analysis, Python: combined with NumPy, Pandas and Matplotlib for data processing and visualization, start the environmental simulation data twin model, and perform an operation simulation. Each model should be run under the same initial conditions to ensure the effectiveness of the comparison. During the simulation operation, the monitoring data output by each model is collected in real time, and the time series data of key parameters are recorded for subsequent analysis. Evaluate the operating status of each model, monitor its stability, response speed and parameter changes, ensure that the model operates as expected, organize the collected output parameters of each model to form a structured data set for subsequent analysis. The data set should include the parameter names, timestamps and corresponding values ​​of different models, and select appropriate statistical analysis methods for parameter difference analysis. Commonly used methods include: Analysis of variance (ANOVA): compare parameter differences among multiple models, t-test: compare two groups of model parameters, cluster analysis: identify similarities and differences between models, principal component analysis (PCA): dimensionality reduction analysis to identify the main components of parameter differences, and based on the results of parameter difference analysis, put forward model optimization suggestions to support subsequent environmental monitoring and management decisions, collect user feedback on the analysis results to evaluate the effectiveness and operability of the results, and make further model adjustments and optimizations based on the feedback.

[0032] Step S5: performing a local model abnormality feature analysis based on parameter difference features of different models, and performing a potential environmental simulation fault analysis to obtain potential environmental simulation fault data of the abnormal area; In this embodiment, based on historical operation data and industry standards, the threshold of parameter abnormality characteristics is defined, for example, the standard deviation, Z-score or quantile based on data distribution is used to set the standard of abnormal values, and a suitable abnormality detection algorithm is selected, such as: Isolation Forest: used for outlier detection of high-dimensional data, Local Outlier Factor (LOF): suitable for detecting local anomalies, Support Vector Machine (SVM): boundary-based anomaly detection, the integrated parameter difference feature data is input into the selected anomaly detection model, the abnormal features are identified, and the abnormal detection results of each local model are recorded, including the type of anomaly, severity and scope of impact, and the detected abnormal features are organized into a data set, including node ID, anomaly type, detection time and related parameters, to facilitate subsequent analysis, and determine the appropriate potential fault analysis technology, Such as: Fault Tree Analysis (FTA): used to identify the causes and consequences of failures; Event Tree Analysis (ETA): used to analyze the consequences of failures and their paths; Data-based fault prediction models: use historical data for predictions, use selected fault analysis methods to associate abnormal features with failure modes, and identify potential causes of failures. For example, use fault tree analysis to build a fault model, identify the root cause of the abnormality, organize the analysis results, generate a potential environmental simulation fault data report on the abnormal area, record the fault type, potential cause, affected area and response measures, and based on the results of potential fault analysis, put forward improvement suggestions and preventive measures to support system maintenance and management decisions, collect user feedback on fault analysis results, evaluate its effectiveness, and continuously optimize the analysis process and model parameters based on feedback.

[0033] Step S6: Infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

[0034] In this embodiment, the potential environmental simulation fault data of the abnormal area is integrated, including the environmental parameters when the fault occurs (such as temperature, humidity, pressure, airflow, etc.), the time point of the fault and the corresponding parameter value are recorded to ensure the integrity of the data, and the key features related to the fault are identified through preliminary data analysis (such as correlation analysis and principal component analysis) for subsequent inference. The selected model is trained with the integrated data to infer the environmental factors related to the fault, and the model performance is evaluated to ensure accuracy (cross-validation and other methods can be used). The inference results of each fault factor are recorded, including the degree of impact and the probability of occurrence, and a fault diagnosis model that can be updated in real time is constructed. The appropriate algorithm is selected, such as: fuzzy logic system: the advantages of handling uncertainty and fuzzy information, enhanced learning: continuously optimize the decision-making strategy based on feedback, and real-time access to environmental monitoring data, Use it as dynamic input to ensure that the model is updated according to the latest data, use dynamic input data and fault factor inference results to generate fault diagnosis decisions, identify the current fault status, provide processing suggestions for different faults, support rapid response, and design adaptive fault diagnosis strategies based on the fault diagnosis results. The strategies include: Fault identification: clarify the characteristics and processing methods of different faults, response strategy: formulate corresponding response measures for different fault conditions (such as issuing alarms, automatically adjusting parameters, notifying maintenance personnel, etc.), evaluate the effectiveness of the diagnosis strategy through real-time monitoring and historical data, adjust and optimize the strategy based on feedback information, upload the constructed adaptive fault diagnosis strategy to the cloud server, ensure remote access and operation, integrate the cloud monitoring system, receive the status information of each environmental simulation model in real time, and use adaptive strategies to diagnose faults.

[0035] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Identify the distributed multi-dimensional sensors in the environmental simulation sample box and mark all the distributed multi-dimensional sensor nodes; Step S12: Calculate the node positions of all distributed multi-dimensional sensor nodes to generate the position information of all sensor nodes; Step S13: Perform spatial topological distribution analysis on the location information of all sensor nodes to generate node spatial topological distribution data; Step S14: performing spatial topological distribution modeling on the node spatial topological distribution data to construct a node spatial topological distribution map; Step S15: dividing the node space topology distribution map into regions to obtain multiple node topology regions; Step S16: Perform three-dimensional point cloud modeling on multiple node topology areas to construct multiple node area models.

[0036] In this embodiment, image processing technology or manual inspection is used to identify all distributed multidimensional sensors in the environmental simulation sample box, and the type, function and preliminary position of the sensor are recorded. A unique identifier (ID) is assigned to each identified sensor node to facilitate subsequent data processing and analysis. A node list is created, which contains basic information of each sensor (such as type, location, status, etc.). A laser rangefinder, GPS, or 3D scanner is used to accurately measure the spatial position (x, y, z coordinates) of each sensor node. The measured position data is recorded in a database or spreadsheet to ensure the accuracy and completeness of the data. The accuracy of the position calculation of each node is ensured by cross-validating the measured data. If necessary, re-measurement is performed and an appropriate topological analysis method is selected, such as: adjacency matrix: used to represent the connection relationship between nodes. Graph theory algorithm: such as Dijkstra algorithm, used to calculate the shortest path between nodes. Based on the node position data, the distance and connection relationship between nodes are calculated to generate topological distribution data. The analysis results are stored in a database to form a spatial topological distribution data set that can be used for subsequent analysis. Appropriate modeling tools and software are selected (such as MATLAB, Python's NetworkX library or Gephi) for spatial topology modeling, input the spatial topology distribution data into the selected modeling tool, create node and edge models, define node attributes (such as position, type) and edge weights (such as distance, connection strength), generate a node spatial topology distribution map, intuitively display the connection relationship and spatial layout between nodes, select a suitable area partitioning algorithm, for example: K-means clustering: divide the nodes into multiple regions, DBSCAN: a density-based clustering algorithm used to discover regions of arbitrary shapes, apply the selected algorithm to divide the node spatial topology distribution map into regions, generate multiple node topology regions, select a suitable 3D modeling tool (such as PCL (PointCloudLibrary), Blender or MATLAB) for point cloud modeling, convert the node position data after region division into point cloud data format, ensure that the location, attributes and other information of each point are complete, use the selected tool to generate a 3D point cloud model, visualize the spatial position and attributes of the node, optimize the generated point cloud model, and improve the visualization effect and rendering performance of the model.

[0037] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box based on the distributed multi-dimensional sensor, and extracting multi-dimensional environmental simulation operation monitoring parameters, wherein the multi-dimensional environmental simulation operation monitoring parameters include environmental temperature parameters, environmental humidity parameters, and environmental wind pressure parameters; Step S22: performing temperature fluctuation analysis on the ambient temperature parameters to generate ambient temperature fluctuation characteristics; Step S23: calculating the fluctuation amplitude of the ambient temperature fluctuation characteristics to generate the ambient temperature fluctuation amplitude; Step S24: performing temperature fluctuation amplitude fitting based on the ambient temperature fluctuation amplitude to construct an ambient temperature fluctuation amplitude curve; Step S25: Perform nonlinear correlation mining of the environmental state on the environmental temperature fluctuation amplitude curve, the environmental humidity parameter and the environmental wind pressure parameter to obtain nonlinear operating state correlation characteristics.

[0038] In this embodiment, distributed multi-dimensional sensors including temperature sensors, humidity sensors and wind pressure sensors are arranged in the environmental simulation sample box to ensure that the sensors are evenly distributed so as to comprehensively monitor the environmental status. A suitable data acquisition system (such as Arduino, RaspberryPi, LabVIEW, etc.) is selected, the connection between the sensor and the data acquisition device is set, and a program is written to collect data at a regular time, for example, the sensor data is read once every minute to ensure that the collection frequency can capture environmental changes. The collected environmental monitoring parameters (temperature, humidity, wind pressure) are stored in a database (such as MySQL, MongoDB) in real time. Each data record should include information such as timestamp, sensor ID, parameter value, etc., and the implementation Data backup mechanism to prevent data loss, clean the ambient temperature data, remove outliers caused by sensor failure or environmental interference, you can use methods such as: Z-score method: calculate the Z-score of each data point, set a threshold (such as ±3), and treat data outside this range as outliers, apply smoothing techniques (such as moving average) to reduce noise, ensure the smoothness of temperature data, calculate the statistical characteristics of temperature data, such as: mean: reflects the overall temperature level, standard deviation: reflects the degree of temperature fluctuation, maximum and minimum values: identify extreme temperature values, record the time series characteristics of temperature fluctuations, extract the fluctuation pattern within the time window (for example, using sliding window technology), extract the temperature fluctuation features (such as mean, standard deviation) The temperature fluctuation range (standard deviation, fluctuation range, etc.) is stored in the database to form a data set for subsequent analysis. The calculation formula for the fluctuation range is usually defined as: Amplitude = Maximum Value - Minimum Value, or the standard deviation is used as a measure of the fluctuation range. The above formula is applied to calculate the temperature fluctuation characteristics and generate the temperature fluctuation range for each time period. Select a suitable fitting model: According to the characteristics of the fluctuation range data, select a suitable fitting method: Linear regression model: suitable for simple linear relationships, polynomial regression: suitable for data with nonlinear relationships, better capture fluctuation trends, smoothing spline: used to process complex nonlinear relationships in the data, use statistical software (such as Python's SciPy or R) to fit the temperature fluctuation range. Fit the degree, use visualization libraries (such as Matplotlib) to draw the temperature fluctuation curve, collect data sets of ambient temperature fluctuation, humidity parameters, and wind pressure parameters, ensure that these data are aligned in time to form a complete feature set, and select suitable nonlinear association mining methods. Commonly used methods include: Support Vector Machine Regression (SVR): suitable for capturing complex nonlinear relationships, neural networks: use multi-layer perceptrons (MLP) to model nonlinear relationships, random forests: evaluate nonlinear relationships between features, and mine important features. Use the prepared data set to train the selected model, use cross-validation to evaluate model performance, ensure the robustness and accuracy of the model, and extract nonlinear operating state association features from the trained model.Identify the relationship between ambient temperature, humidity, and wind pressure, and record key indicators such as importance scores.

[0039] In this embodiment, the specific steps of step S25 are: Perform spatial humidity distribution analysis on environmental humidity parameters to obtain spatial humidity distribution data of the sample box; Conduct long-term operation cycle change analysis on the spatial humidity distribution data of the sample box to generate humidity distribution cycle change data; The humidity distribution periodic change data is subjected to humidity change trend evolution, thereby generating humidity change trend evolution characteristics; Perform multi-time-frequency decomposition on the environmental wind pressure parameters to obtain wind pressure spectra of different frequencies; Perform main frequency component analysis on wind pressure spectra of different frequencies one by one, and extract the main frequency component of each spectrum; Calculate the power spectrum density of the main frequency component of each spectrum graph to generate the power spectrum density of each spectrum graph; Perform power spectrum density distribution identification on the power spectrum density of each spectrum graph to generate wind pressure density distribution data; The nonlinear correlation of environmental states is mined by using the humidity change trend evolution characteristics, ambient temperature fluctuation amplitude curve and wind pressure density distribution data to obtain the nonlinear operating state correlation characteristics.

[0040] In this embodiment, environmental humidity parameters are collected from distributed multidimensional sensors to ensure that the data includes timestamps and sensor locations. A spatial interpolation method (such as Kriging interpolation or inverse distance weighting) is used to perform spatial analysis on the humidity data to generate a spatial distribution map of humidity. The humidity value of each area is recorded to form spatial humidity distribution data of the sample box. A visualization tool (such as Matplotlib or Plotly) is used to display the humidity spatial distribution map to intuitively display the change of humidity in the sample box. The humidity distribution data is sorted in time series to ensure the continuity and integrity of the data. A frequency domain analysis is performed on the humidity data using Fourier transform or wavelet transform to identify periodic changes and generate humidity distribution periodic change data. The humidity changes in different time periods are recorded and the generated humidity periodic change data is stored in a database for subsequent analysis. A regression analysis method (such as linear regression or polynomial regression) is used to analyze the humidity distribution periodic change data to identify the change trend. Important humidity change trend evolution characteristics are extracted from the regression model. The direction and amplitude of the trend are recorded. The wind pressure parameter data is denoised to ensure data quality. A short-time Fourier transform is used to analyze the humidity data. Transform (STFT) or wavelet transform is used to perform multi-time-frequency decomposition of wind pressure data, generate wind pressure spectra of different frequencies, record the frequency components and amplitudes of each spectra, prepare for main frequency component analysis, use Welch method or adaptive spectrum estimation method, calculate power spectral density (PSD) for each spectra, obtain the power spectral density of each spectra, store the calculated power spectral density data in the database for subsequent analysis, identify the distribution of power spectral density of each spectra, use statistical analysis methods (such as histogram or kernel density estimation) to generate wind pressure density distribution data, collect humidity change trend evolution characteristics, ambient temperature fluctuation amplitude curve and wind pressure density distribution data, ensure data format consistency, select suitable nonlinear association mining methods, such as: Random forest regression: used to capture complex nonlinear relationships between features, neural network: analyze nonlinear relationships between features through multi-layer perceptron (MLP), use prepared data sets to train the selected model, evaluate model performance through cross-validation, extract nonlinear operating state association features from the trained model, and record key indicators related to environmental status.

[0041] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing environmental simulation feature evolution based on nonlinear operating state association features, thereby generating all-round environmental operating state evolution data; Step S32: performing regional node state matching on the omnidirectional environment operation state evolution data according to the location information of all sensor nodes, thereby obtaining corresponding state evolution data of the node area; Step S33: Use the corresponding state evolution data of the node area to dynamically render the multiple node area models and construct multiple environmental simulation data twin models.

[0042] In this embodiment, a suitable mathematical model (such as a state space model or a Markov model) is selected to describe the evolution of the environmental state. The model should be able to capture nonlinear relationships and dynamic changes. For example, a recursive neural network (RNN) or a long short-term memory network (LSTM) is used to process time series data. A data set based on nonlinear operating state association features is collected and prepared, including parameters such as temperature, humidity, and wind force. The model is trained using a deep learning framework (such as TensorFlow or PyTorch). The input is historical state data, and the output is the predicted future state. The model parameters are optimized by a back propagation algorithm to minimize the prediction error. The trained evolution model is used to predict the future environmental state, and all-round environmental operating state evolution data is generated, including temperature, humidity, and wind pressure at different time points in the time series. The location information of all sensor nodes is extracted from the previous step to ensure that the coordinates of each node correspond to its function. A spatial interpolation method (such as Kriging interpolation or inverse distance weighting) is used to interpolate the all-round environmental operating state evolution data with the sensor nodes. The generated environmental operation state evolution data is matched with the node location, the corresponding state evolution data of each regional node is determined, and a database containing the state evolution of the node area is generated, including the state information of each node at each time point. A 3D visualization tool (such as Unity, Blender or Three.js) is used to create a dynamic state rendering model, and the state evolution data corresponding to the node area is imported into the selected rendering tool to ensure that the data format meets the requirements of the model. Different visualization effects are set according to the node state evolution data. For example, temperature is represented by color changes, red represents high temperature, blue represents low temperature, humidity and wind pressure are visualized through different graphic effects (such as flow animation), and the rendering model is run to observe whether the dynamic effect accurately reflects the state evolution process. The rendering parameters are adjusted as needed to optimize the visual effect to make it more intuitive and easy to understand. The rendering results are displayed to relevant personnel to facilitate environmental monitoring and decision support. The user can view the environmental status at different time points through an interactive interface.

[0043] In this embodiment, step S4 includes the following steps: Step S41: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and extracting simulation monitoring data of each twin model; Step S42: performing time series state change trend analysis on the simulated monitoring data of each twin model to generate the time series state change trend of each twin model; Step S43: performing situation evolution prediction on the time series state change trend of each twin model, thereby obtaining situation evolution prediction data of each twin model; Step S44: Perform inter-model parameter difference analysis on each twin model situation evolution prediction data to generate parameter difference characteristics of different models.

[0044] In this embodiment, it is ensured that all environmental simulation data twin models have been configured and have all the input parameters and initial conditions required for operation. According to the characteristics of different models, the time step, simulation cycle and other relevant parameters of the simulation operation are set, and the simulation operation of each twin model is started. The monitoring process is usually implemented through programming languages ​​​​(such as Python, MATLAB) or special software (such as Simulink). During the simulation operation, the output monitoring data of each twin model is recorded in real time, including key parameters such as temperature, humidity, and wind speed. The data is stored in the form of a time series to ensure that the state data of each time step is complete. The extracted monitoring data is cleaned, missing values ​​​​and outliers are processed to ensure the quality of the data. The monitoring data of each twin model is organized into a time series format for subsequent analysis. Statistical methods (such as moving average, exponential smoothing method) are used to analyze the state change trend of each twin model, and the change rate and growth rate of each time period are calculated to identify trends. Use data visualization tools (such as Matplotlib, Seaborn) to draw a state change trend chart of each twin model to show the changes in key parameters, ensure that the chart is clear and easy to understand, and mark important change points and trends. According to the characteristics of the data, select a suitable time series prediction model, such as :Autoregressive integrated moving average (ARIMA) model: suitable for linear time series data, Long short-term memory network (LSTM): suitable for processing complex nonlinear relationships and long-term dependent data, divide the state change trend data into training set and test set, usually use 70% of the data for training and 30% of the data for verification, use the selected prediction algorithm to train the training data, optimize the model parameters, ensure that the model can accurately reflect the change trend of the data, use the trained model to predict the future state, generate the situation evolution prediction data of each twin model, and use visualization tools to draw the situation evolution prediction of each twin model Figure 3 shows the comparison between the prediction results and the actual data. The situation evolution prediction data of all twin models are organized into a comprehensive data set, including the key parameters of each model. Statistical analysis methods (such as t-test, analysis of variance ANOVA) or machine learning methods (such as cluster analysis) are used to compare the parameter differences between different models. The parameters of each model are compared, and the features of significant differences are identified. The parameter difference features are recorded, and a difference analysis report is generated, including the parameter mean, standard deviation and significance of each model. Visualization tools are used to draw charts of the difference analysis results (such as box plots and bar charts) to intuitively display the parameter differences between different models.

[0045] In this embodiment, step S5 includes the following steps: Step S51: performing local model abnormality feature analysis based on parameter difference features of different models, and extracting local model abnormality feature data; Step S52: locating abnormal features of the abnormal feature data of the local model and extracting the abnormal node area model; Step S53: Preview the environment parameters at multiple time points on the abnormal node area model to obtain the environment preview parameters at multiple time points; Step S54: Perform potential environmental simulation fault analysis on the environmental rehearsal parameters at multiple time points to obtain potential environmental simulation fault data of abnormal areas.

[0046] In this embodiment, the parameter difference feature data of different models are integrated into a data set, including the key parameters of each model and their differences. The threshold of abnormal features, such as standard deviation multiples, Z-score, etc., is determined based on historical data to define under what circumstances the parameters are considered abnormal. A machine learning algorithm (such as an isolation forest, a local outlier factor (LOF), or a support vector machine (SVM)) is used to detect the abnormal features of the local model. The integrated data is input into the selected anomaly detection algorithm to identify the abnormal features and extract the abnormal feature data of the local model. The type and severity of the abnormal features are recorded for subsequent analysis. According to the severity and type of the abnormal features, positioning standards are set, such as the threshold of the outlier value, the scope of influence, etc. According to the extracted abnormal feature data, the specific abnormal node area is identified. The abnormal features are associated with the node area using a geographic information system (GIS) tool or a spatial analysis method to determine the affected area, and a model data set containing the abnormal node area is generated. According to the data, the environmental parameters and status information of these areas are recorded for subsequent processing. According to the required accuracy and complexity, a suitable rehearsal model is selected, such as a physics-based simulation model or a data-driven prediction model. The necessary initial conditions and parameters, including the environmental status data within the time period, are collected to input into the rehearsal model. Multi-point environmental parameter rehearsals are performed on the selected abnormal node area model, usually in units of hours, days or weeks. The changes in environmental parameters at each time point, such as temperature, humidity, wind speed, etc., are monitored and recorded. Fault tree analysis (FTA), event tree analysis (ETA) or data-driven methods, such as anomaly detection and prediction models, are used to analyze the potential factors leading to failures based on multi-point environmental parameter rehearsal data, determine which parameter changes will trigger environmental simulation failures, and record the characteristics of these potential failures. The analysis results are sorted out to generate a data report on potential environmental simulation failures in abnormal areas, including failure types, impact scope and severity assessments.

[0047] In this embodiment, step S6 includes the following steps: Step S61: inferring environmental fault factors from potential environmental simulation fault data of the abnormal area to obtain environmental fault factor data; Step S62: generating a fault warning signal based on the environmental fault factor data; Step S63: making a dynamic adaptive fault diagnosis decision according to the fault warning signal and constructing an adaptive fault diagnosis strategy; Step S64: Upload the adaptive fault diagnosis strategy to the cloud server to perform remote monitoring and fault diagnosis operations.

[0048] In this embodiment, the potential environmental simulation fault data extracted from the previous step is integrated to ensure data integrity and consistency, including the time of fault occurrence, environmental parameter changes and abnormal characteristics, and a suitable fault factor inference model is selected, such as a causal inference model, a Bayesian network or a decision tree, to analyze the relationship between potential faults and environmental factors. The integrated potential fault data is input into the selected inference model, and how each environmental factor affects the occurrence of the fault is analyzed, key factors are identified, and the degree of influence of each environmental factor on the occurrence of the fault is evaluated. Environmental fault factor data is generated, and factors with greater influence and their weights are recorded. The inference results are organized into a structured data set, including the name of each fault factor, the degree of influence and the corresponding parameter value. According to historical data and industry standards, an early warning threshold is set for each environmental fault factor, and the circumstances under which the early warning is triggered are defined. The data inferred from the fault factor is integrated with the early warning standard to prepare to generate an early warning signal. A monitoring system is developed to monitor the environmental fault factor data in real time. When a factor exceeds the set threshold, a fault early warning signal is generated. The generated early warning signal is formatted into an easy-to-understand format, including a signal timestamp, a fault type and a severity. The system collects the generated fault warning signals and combines them with the historical fault data of the system. Based on machine learning algorithms (such as decision trees, random forests or neural networks), it designs a dynamic adaptive fault diagnosis strategy framework. It uses historical fault data and warning signals to train adaptive diagnosis models and optimize model parameters to improve diagnostic accuracy. After receiving new fault warning signals, it automatically triggers the diagnosis model for real-time analysis and generates corresponding fault diagnosis decisions (such as recommended repair measures, emergency response plans, etc.). It records each generated fault diagnosis decision in the system database for subsequent tracking and auditing. It ensures that the cloud server has been configured with the necessary software environment and can process the uploaded diagnostic strategies and data. It organizes the adaptive fault diagnosis strategy into a format suitable for cloud storage to ensure the clarity and completeness of the data structure. It uses API or data transmission protocol (such as HTTP POST request) to upload the adaptive fault diagnosis strategy to the cloud server. It confirms that the uploaded data is correct on the cloud server. It ensures that the strategy can be accessed by the remote monitoring and fault diagnosis system. It sets a monitoring mechanism in the cloud system to track the implementation effect of fault warning signals and diagnostic decisions in real time to ensure the efficient operation of the system.

[0049] In this embodiment, a remote monitoring and fault diagnosis system for an environmental simulation system is provided, which is used to execute the remote monitoring and fault diagnosis method for the environmental simulation system as described above, including: The spatial topology distribution module is used to identify all distributed multi-dimensional sensor nodes, perform spatial topology distribution analysis, and build multiple node area models; The nonlinear mining module is used to conduct all-round environmental simulation operation status monitoring of the environmental simulation sample box and to conduct nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Dynamic state rendering module, used to perform dynamic state rendering on multiple node area models based on nonlinear operation state association characteristics, and build multiple environmental simulation data twin models; The parameter difference module is used to perform operation simulation monitoring on multiple environmental simulation data twin models and perform parameter difference analysis between models to generate parameter difference characteristics of different models; The potential fault analysis module is used to analyze the abnormal characteristics of local models based on the parameter difference characteristics of different models, and to analyze the potential environmental simulation faults to obtain the potential environmental simulation fault data of the abnormal area; The fault diagnosis decision module is used to infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

[0050] The present invention helps to establish the cognition of the overall structure of the system by identifying distributed multidimensional sensor nodes and performing spatial topological distribution analysis, improves the visualization and management efficiency of the system, and constructs multiple node area models to help understand the correlation between nodes, providing a basis for subsequent environmental simulation and monitoring. Nonlinear correlation mining of environmental states can reveal the complex nonlinear relationship between environmental parameters and help understand the deep-level characteristics of the system operation state. Obtaining nonlinear operation state correlation characteristics can provide important clues for subsequent model establishment and fault diagnosis. Dynamic state rendering based on nonlinear operation state correlation characteristics can more intuitively display the operation state changes of multiple node area models and help identify abnormal situations. Constructing an environmental simulation data twin model helps to simulate the system operation state under different conditions, provide a reference for fault prediction and analysis, and improve the environmental simulation data. Parameter difference analysis based on the twin model can help compare the operating conditions of different models, identify potential abnormal features, and provide support for fault diagnosis. Generating parameter difference features for different models helps to understand the performance differences of the system under different conditions and guide subsequent fault analysis and repair. Local model abnormal feature analysis based on parameter difference features can effectively identify local abnormal conditions and help discover potential fault points in advance. Potential environmental simulation fault analysis can help predict fault conditions in abnormal areas and provide guidance for fault troubleshooting and repair. Environmental fault factor inference and dynamic adaptive fault diagnosis decision-making can help quickly and accurately diagnose the cause of the fault and reduce the impact of system failures. Building an adaptive fault diagnosis strategy and performing remote monitoring and fault diagnosis operations can improve the stability and reliability of the system and reduce the interference of failures on the normal operation of the system.

[0051] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0052] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A remote monitoring and fault diagnosis method for an environmental simulation system, characterized in that: The following steps are involved: Step S1: Identify all distributed multi-dimensional sensor nodes, perform spatial topological distribution analysis, and construct multiple node area models; Step S2: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box, and performing nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Step S3: dynamically rendering multiple node area models based on nonlinear operating state association characteristics, and constructing multiple environmental simulation data twin models; Step S4: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and performing parameter difference analysis between models to generate parameter difference characteristics of different models; Step S5: performing a local model abnormality feature analysis based on parameter difference features of different models, and performing a potential environmental simulation fault analysis to obtain potential environmental simulation fault data of the abnormal area; Step S6: Infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

2. The remote monitoring and fault diagnosis method of the environmental simulation system according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Identify the distributed multi-dimensional sensors in the environmental simulation sample box and mark all the distributed multi-dimensional sensor nodes; Step S12: Calculate the node positions of all distributed multi-dimensional sensor nodes to generate the position information of all sensor nodes; Step S13: Perform spatial topological distribution analysis on the location information of all sensor nodes to generate node spatial topological distribution data; Step S14: performing spatial topological distribution modeling on the node spatial topological distribution data to construct a node spatial topological distribution map; Step S15: dividing the node space topology distribution map into regions to obtain multiple node topology regions; Step S16: Perform three-dimensional point cloud modeling on multiple node topology areas to construct multiple node area models.

3. The remote monitoring and fault diagnosis method of the environmental simulation system according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Performing all-round environmental simulation operation status monitoring on the environmental simulation sample box based on the distributed multi-dimensional sensor, and extracting multi-dimensional environmental simulation operation monitoring parameters, wherein the multi-dimensional environmental simulation operation monitoring parameters include environmental temperature parameters, environmental humidity parameters, and environmental wind pressure parameters; Step S22: performing temperature fluctuation analysis on the ambient temperature parameters to generate ambient temperature fluctuation characteristics; Step S23: calculating the fluctuation amplitude of the ambient temperature fluctuation characteristics to generate the ambient temperature fluctuation amplitude; Step S24: performing temperature fluctuation amplitude fitting based on the ambient temperature fluctuation amplitude to construct an ambient temperature fluctuation amplitude curve; Step S25: Perform nonlinear correlation mining of the environmental state on the environmental temperature fluctuation amplitude curve, the environmental humidity parameter and the environmental wind pressure parameter to obtain nonlinear operating state correlation characteristics.

4. The method according to claim 3, characterized in that The specific steps of step S25 are: Perform spatial humidity distribution analysis on environmental humidity parameters to obtain spatial humidity distribution data of the sample box; Conduct long-term operation cycle change analysis on the spatial humidity distribution data of the sample box to generate humidity distribution cycle change data; The humidity distribution periodic change data is subjected to humidity change trend evolution, thereby generating humidity change trend evolution characteristics; Perform multi-time-frequency decomposition on the environmental wind pressure parameters to obtain wind pressure spectra of different frequencies; Perform main frequency component analysis on wind pressure spectra of different frequencies one by one, and extract the main frequency component of each spectrum; Calculate the power spectrum density of the main frequency component of each spectrum graph to generate the power spectrum density of each spectrum graph; Perform power spectrum density distribution identification on the power spectrum density of each spectrum graph to generate wind pressure density distribution data; The nonlinear correlation of environmental states is mined by using the humidity change trend evolution characteristics, ambient temperature fluctuation amplitude curve and wind pressure density distribution data to obtain the nonlinear operating state correlation characteristics.

5. The method according to claim 1, characterized in that The specific steps of step S3 are: Step S31: performing environmental simulation feature evolution based on nonlinear operating state association features, thereby generating all-round environmental operating state evolution data; Step S32: performing regional node state matching on the omnidirectional environment operation state evolution data according to the location information of all sensor nodes, thereby obtaining corresponding state evolution data of the node area; Step S33: Use the corresponding state evolution data of the node area to dynamically render the multiple node area models and construct multiple environmental simulation data twin models.

6. The remote monitoring and fault diagnosis method of the environmental simulation system according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing operation simulation monitoring processing on multiple environmental simulation data twin models, and extracting simulation monitoring data of each twin model; Step S42: performing time series state change trend analysis on the simulated monitoring data of each twin model to generate the time series state change trend of each twin model; Step S43: performing situation evolution prediction on the time series state change trend of each twin model, thereby obtaining situation evolution prediction data of each twin model; Step S44: Perform inter-model parameter difference analysis on each twin model situation evolution prediction data to generate parameter difference characteristics of different models.

7. The remote monitoring and fault diagnosis method of the environmental simulation system according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing local model abnormality feature analysis based on parameter difference features of different models, and extracting local model abnormality feature data; Step S52: locating abnormal features of the abnormal feature data of the local model and extracting the abnormal node area model; Step S53: Preview the environment parameters at multiple time points on the abnormal node area model to obtain the environment preview parameters at multiple time points; Step S54: Perform potential environmental simulation fault analysis on the environmental rehearsal parameters at multiple time points to obtain potential environmental simulation fault data of abnormal areas.

8. The remote monitoring and fault diagnosis method of the environmental simulation system according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: inferring environmental fault factors from potential environmental simulation fault data of the abnormal area to obtain environmental fault factor data; Step S62: generating a fault warning signal based on the environmental fault factor data; Step S63: making a dynamic adaptive fault diagnosis decision according to the fault warning signal and constructing an adaptive fault diagnosis strategy; Step S64: Upload the adaptive fault diagnosis strategy to the cloud server to perform remote monitoring and fault diagnosis operations.

9. A remote monitoring and fault diagnosis system for an environmental simulation system, characterized in that: A remote monitoring and fault diagnosis method for executing the environmental simulation system according to claim 1, comprising: The spatial topology distribution module is used to identify all distributed multi-dimensional sensor nodes, perform spatial topology distribution analysis, and build multiple node area models; The nonlinear mining module is used to conduct all-round environmental simulation operation status monitoring of the environmental simulation sample box and to conduct nonlinear correlation mining of the environmental status to obtain nonlinear operation status correlation features; Dynamic state rendering module, used to perform dynamic state rendering on multiple node area models based on nonlinear operation state association characteristics, and build multiple environmental simulation data twin models; The parameter difference module is used to perform operation simulation monitoring on multiple environmental simulation data twin models and perform parameter difference analysis between models to generate parameter difference characteristics of different models; The potential fault analysis module is used to analyze the abnormal characteristics of local models based on the parameter difference characteristics of different models, and to analyze the potential environmental simulation faults to obtain the potential environmental simulation fault data of the abnormal area; The fault diagnosis decision module is used to infer environmental fault factors from potential environmental simulation fault data in abnormal areas, make dynamic adaptive fault diagnosis decisions, and build adaptive fault diagnosis strategies to perform remote monitoring and fault diagnosis tasks.

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