Flying dust noise monitoring data intelligent analysis method based on deep learning

Abstract

The invention relates to the technical field of environment monitoring and analysis, and discloses a flying dust noise monitoring data intelligent analysis method based on deep learning. According to the method, real-time data of dust and noise are collected through monitoring equipment on an edge computing platform. And carrying out feature extraction on the clean time series data by adopting a deep belief network to obtain deep feature representation of the flying dust noise. And detecting an abnormal mode in the flying dust noise data based on the depth feature representation by using an isolated forest algorithm, analyzing a causal relationship in the abnormal mode through an attention mechanism enhanced recurrent neural network, and identifying key influence factors. And establishing a normal behavior model of the flying dust noise by using a generative adversarial network, calculating a deviation score between the real-time data and the normal behavior model, and evaluating the risk level of the flying dust noise by using a convolutional neural network. And according to the abnormal mode, the key influence factors and the risk level, applying a sequence-to-sequence model to forecast potential events of the flying dust noise, and outputting an optimization management strategy.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and analysis technology, specifically to an intelligent analysis method for dust and noise monitoring data based on deep learning. Background Technology

[0002] With the rapid development of urbanization, construction and road traffic activities are increasing daily. In cities, skyscrapers are constantly rising, road construction and municipal engineering projects are ongoing, and construction sites are ubiquitous. At the same time, the number of motor vehicles continues to climb, making road traffic increasingly congested. These factors make dust and noise pollution significant contributors to urban environmental pollution. Dust not only reduces air quality and causes respiratory diseases but also damages buildings and vegetation. Noise interferes with people's lives, work, and studies; long-term exposure to high-noise environments can damage hearing and may also lead to cardiovascular diseases, insomnia, and other health problems. Therefore, effective monitoring and analysis of dust and noise pollution are crucial.

[0003] Traditional methods for monitoring dust and noise have several problems. In terms of data acquisition, they typically rely on simple sensors, resulting in poor accuracy and stability, and are easily affected by environmental factors such as temperature, humidity, and electromagnetic interference. Furthermore, the low frequency of data acquisition makes it impossible to capture instantaneous changes in dust and noise. Regarding data preprocessing, traditional methods are often ineffective, failing to remove noise and outliers, leading to low-quality time-series data for subsequent analysis.

[0004] In the feature extraction stage, traditional techniques struggle to extract effective features from complex dust and noise data. For example, traditional methods cannot accurately distinguish and extract the characteristics of dust and noise generated at different construction stages, leaving the understanding and analysis of dust and noise at a superficial level.

[0005] In anomaly detection, traditional methods mainly rely on simple threshold judgments, which have low accuracy. When the environment changes, fixed thresholds cannot adapt to new situations, easily leading to false alarms and missed alarms. For example, in windy weather, dust concentration naturally increases, but traditional detection methods may misjudge this as an anomaly.

[0006] In terms of causal analysis, traditional methods struggle to deeply analyze the causal relationships within dust and noise data and cannot accurately identify key influencing factors. For changes in dust and noise caused by the combined effects of multiple factors, traditional methods cannot clearly elucidate the correlations and degrees of influence among these factors.

[0007] In terms of risk assessment, traditional methods rely mainly on human experience, lacking scientific rigor and objectivity. They cannot accurately assess the risk level to the environment and human health based on actual dust and noise data.

[0008] In terms of event forecasting, traditional methods are relatively weak and cannot accurately forecast potential dust and noise events based on historical and real-time monitoring data, making it difficult to take effective preventive measures in advance. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent analysis method for dust and noise monitoring data based on deep learning, so as to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, this invention provides a deep learning-based intelligent analysis method for dust and noise monitoring data, the method comprising: Real-time data of dust and noise is collected by monitoring devices on an edge computing platform, and the real-time data is preprocessed to obtain time-series data on cleanliness. A deep belief network is used to extract features from the time-series data of the cleaning process to obtain a deep feature representation of dust noise. The isolated forest algorithm is used to detect abnormal patterns in dust noise data based on the aforementioned deep feature representation. The causal relationships in the abnormal patterns are analyzed using an attention-enhanced recurrent neural network to identify key influencing factors; Based on the aforementioned key influencing factors, a normal behavior model of dust noise is established using generative adversarial networks, and the deviation score between real-time data and the normal behavior model is calculated. Convolutional neural networks are then used to assess the risk level of dust noise. Based on the aforementioned anomaly patterns, key influencing factors, and risk levels, a sequence-to-sequence model is applied to predict potential dust noise events and output optimized management strategies.

[0011] Preferably, the preprocessing operation on the real-time data to obtain clean time-series data includes: The collected real-time dust and noise data are processed by moving average filtering to smooth short-term fluctuations; Outliers were identified and removed using box plots to obtain cleaned data; Differential operations are performed on the purified data to eliminate trend components and obtain stationary time series data.

[0012] Preferably, the step of using a deep belief network to extract features from the time-series data of the cleanup to obtain a deep feature representation of dust noise includes: Deep belief networks are constructed using stacked restricted Boltzmann machines. A deep belief network was trained using a contrastive divergence algorithm to learn the hierarchical characteristics of dust noise data. Extract hidden layer features from a trained deep belief network as deep feature representations.

[0013] Preferably, the method of using the isolated forest algorithm to detect abnormal patterns in dust noise data based on the deep feature representation includes: Construct an isolated forest tree structure, randomly selecting features and split points; Calculate the path length for each data point; the shorter the path length, the more abnormal the data point. Anomaly scores are determined based on path length, and a threshold is set to identify a set of anomaly patterns.

[0014] Preferably, the analysis of causal relationships in the abnormal patterns and identification of key influencing factors through the attention-enhanced recurrent neural network includes: Using a long short-term memory network as the base model, an attention layer is added; The network is trained to predict dust noise sequences, and the attention weights represent the degree of influence at different time points. Based on attention weighting, key time points were extracted as influencing factors.

[0015] Preferably, the step of combining the key influencing factors, using a generative adversarial network to establish a normal behavior model of dust noise, calculating the deviation score between real-time data and the normal behavior model, and using a convolutional neural network to assess the risk level of dust noise includes: A generative adversarial network is trained to generate normal dust noise data, and a discriminator distinguishes between real and generated data. Using the generator as the normal behavior model, the reconstruction error between real-time data and generated data is calculated as the deviation score. The bias score is input into a one-dimensional convolutional neural network to classify the risk level.

[0016] Preferably, the application of sequence-to-sequence modeling to predict potential dust noise events and output optimized management strategies includes: Use an encoder-decoder architecture to process dust noise time-series data; The model is trained to predict future sequences using a teacher-mandated method. Management strategies are generated based on the prediction results, including adjusting the intensity of operations or activating dust suppression equipment.

[0017] Preferably, the real-time data collection of dust noise through monitoring devices on the edge computing platform includes: Deploy lightweight sensors at edge nodes to collect dust and noise data in real time; Use an edge computing gateway to compress and encrypt the data; The processed data is then transmitted to a cloud platform for further analysis.

[0018] Preferably, the method further includes: Regularly fine-tune the deep learning model using new dust and noise data; An online learning algorithm is used to adapt to changes in data distribution and update model parameters.

[0019] Preferably, the output optimization management strategy includes: The warning level is determined based on the forecast results; Specific control measures should be formulated for different warning levels, such as adjusting construction time or increasing monitoring frequency.

[0020] Compared with the prior art, the beneficial effects of the present invention are: During the real-time data acquisition phase, monitoring devices on the edge computing platform efficiently collect real-time data on dust and noise, providing a rich data foundation for subsequent analysis. Preprocessing this real-time data effectively removes noise, outliers, and other interfering factors, resulting in clean time-series data. This makes the data more accurate and reliable, providing high-quality data support for subsequent in-depth analysis and avoiding analytical errors caused by poor raw data quality.

[0021] The application of deep belief networks has significantly improved the feature extraction capabilities of cleaning time-series data. It can automatically learn complex patterns and features in the data, uncovering latent features that are difficult to discover using traditional methods. Compared to traditional feature extraction methods, deep belief networks can more comprehensively and deeply understand dust noise data, transforming it into more representative deep feature representations. These deep features contain more essential information about dust noise, providing more valuable information for subsequent anomaly detection, causal analysis, and risk assessment, making the entire analysis process more accurate and in-depth.

[0022] The Isolation Forest algorithm, based on deep feature representation, detects anomalous patterns in dust and noise data, offering both high efficiency and accuracy. It requires no assumptions about the data distribution, adapts to various complex data distributions, and effectively detects anomalies in any data type. Compared to traditional anomaly detection methods based on simple threshold judgments, the Isolation Forest algorithm can more accurately locate anomalies, reducing false positives and false negatives. When encountering sudden abnormal increases in dust concentration or noise levels, it can quickly and accurately identify these anomalous patterns, providing strong support for timely countermeasures.

[0023] Attention-enhanced recurrent neural networks (RNNs) play a crucial role in analyzing causal relationships and identifying key influencing factors in anomaly patterns. They can capture long-term dependencies in time-series data, perform weighted analysis of various factors within anomaly patterns, and highlight the impact of key factors. This approach allows for a deeper understanding of the causes of dust and noise anomalies, clarifying which factors are the primary and secondary causes. This provides clear direction for developing targeted solutions, enabling us to take effective measures to address dust and noise problems rather than engaging in indiscriminate remediation.

[0024] Generative adversarial networks (GANs) are used to build normal behavior models of dust noise, accurately simulating its behavior patterns under normal conditions. By calculating the deviation score between real-time data and the normal behavior model, the degree of deviation between real-time data and the normal state can be clearly determined. This method can promptly identify potential risks, providing an objective and accurate basis for risk assessment. Convolutional neural networks (CNNs) further scientifically assess the risk level of dust noise. They can comprehensively consider multiple factors, such as dust concentration, noise intensity, and duration, to quantify the risk and provide a specific risk level. This allows relevant departments and personnel to intuitively understand the risk level of dust noise, enabling them to make more scientific and reasonable decisions, such as whether emergency measures are needed for control or whether monitoring frequency needs to be adjusted.

[0025] Sequence-to-sequence models, which predict potential dust and noise events based on anomaly patterns, key influencing factors, and risk levels, have significant practical implications. They can predict potential dust and noise events in advance, providing relevant departments and personnel with ample time to prepare. When a large-scale construction project leading to increased dust and noise is predicted, nearby residents can be notified in advance to take protective measures. Relevant departments can also strengthen supervision of construction sites and require construction companies to implement effective dust and noise reduction measures. Furthermore, the optimized management strategies output from the prediction results provide scientific guidance for environmental management. These strategies, based on comprehensive analysis of dust and noise data and accurate prediction of potential events, are highly targeted and operable, effectively improving the foresight and effectiveness of environmental management, achieving precise governance and effective control of dust and noise, thereby improving urban environmental quality and ensuring the health and comfort of residents. Attached Figure Description

[0026] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent analysis method for dust and noise monitoring data based on deep learning described in this invention. Figure 2 A flowchart for real-time data preprocessing of dust and noise; Figure 3This is a flowchart of the deep feature extraction of dust noise based on deep belief networks; Figure 4 This is a deep feature extraction map of dust noise data based on deep belief networks; Figure 5 A quantitative analysis chart of key influencing factors of dust and noise pollution. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 This invention provides an intelligent analysis method for dust and noise monitoring data based on deep learning. The method includes: integrating edge computing, deep learning algorithms, and sequence modeling techniques to achieve real-time monitoring, anomaly detection, causal analysis, risk assessment, and event prediction of dust and noise data. The overall process involves monitoring equipment deployed on an edge computing platform collecting real-time dust and noise data. The collected data undergoes preprocessing to remove noise and outliers, generating clean time-series data. This clean time-series data is input into a deep belief network for feature extraction, obtaining a high-level deep feature representation. This deep feature representation is used in an isolated forest algorithm to detect abnormal patterns in the data. These abnormal patterns are further analyzed using an attention-enhanced recurrent neural network to identify causal relationships and key factors affecting dust and noise. These key factors are combined with a generative adversarial network to establish a normal behavior model, calculating the deviation score between the real-time data and the model. This deviation score is input into a convolutional neural network to assess the risk level. Finally, the sequence-to-sequence model predicts potential events based on abnormal patterns, key factors, and risk levels, and outputs optimized management strategies.

[0029] Monitoring devices on the edge computing platform are responsible for data acquisition. These devices include lightweight sensors and edge computing gateways. Sensors collect dust and noise data in real time, while the edge computing gateways compress and encrypt the data before transmitting it to the cloud platform for further analysis. Preprocessing involves applying a moving average filter to smooth short-term fluctuations, using a box plot method to identify and remove outliers, and then performing a differential operation to eliminate trend components, resulting in stable time-series data. A deep belief network is constructed using a multi-layered restricted Boltzmann machine. The network is trained using a contrastive divergence algorithm to learn the hierarchical characteristics of the data, extracting features from the hidden layers as deep feature representations. An isolated forest algorithm constructs a tree structure, randomly selecting features and split points, calculating the path length of data points. Shorter path lengths indicate higher anomalies, and anomaly scores are determined based on path length. A threshold is set to identify a set of anomaly patterns. An attention-enhanced recurrent neural network, based on a long short-term memory network, incorporates an attention layer to train the network to predict dust and noise sequences. Attention weights represent the degree of influence at different time points, and key time points are extracted as influencing factors based on the order of attention weights. A generative adversarial network (GAN) is used to train a generator to produce normal dust noise data. A discriminator distinguishes between real and generated data. The generator serves as a normal behavior model, and the reconstruction error between real-time and generated data is calculated as a bias score. This bias score is input into a one-dimensional convolutional neural network to classify the risk level. A sequence-to-sequence model uses an encoder-decoder architecture to process time-series data. The model is trained to predict future sequences through a teacher-forced method. Management strategies are generated based on the prediction results, including adjusting work intensity or activating dust suppression equipment. The method periodically fine-tunes the deep learning model using new dust noise data, employing an online learning algorithm to adapt to changes in data distribution and update model parameters. When outputting optimized management strategies, warning levels are divided based on forecast results, and specific control measures are formulated for different warning levels, such as adjusting construction time or increasing monitoring frequency.

[0030] Example 1: See Figure 2The monitoring equipment on the edge computing platform is responsible for collecting real-time data on dust and noise. This equipment consists of two main components: lightweight sensors deployed at edge nodes and an edge computing gateway. The lightweight sensors employ laser scattering principle particle sensors and capacitive microphone noise sensors. These sensors are directly installed near dust and noise sources, such as construction site fences, mine entrances / exits, and road construction areas. The sensors collect PM2.5 and PM10 dust concentration data, as well as Leq equivalent continuous sound level noise data, once per second. The raw data is transmitted to the edge computing gateway via an RS485 bus. The edge computing gateway uses an ARM architecture processor and embeds a real-time operating system. The gateway has built-in data compression and encryption modules. The data compression module uses the LZ77 algorithm to perform lossless compression of the raw data, maintaining a compression ratio between 60% and 70%. The encryption module uses the AES-256 algorithm to generate a key for encrypting the compressed data. The encrypted data packets are transmitted to the cloud platform via a 4G / 5G network, using the MQTT protocol with a QoS level of 1 to ensure reliable and real-time data transmission.

[0031] The deployment plan for the monitoring equipment needs to consider environmental adaptability and power supply. The sensor housing meets IP65 protection standards to prevent dust and moisture intrusion. The power supply uses a hybrid system combining solar panels and lithium battery packs. The edge computing gateway is installed in a waterproof enclosure with an internal temperature control module to ensure stable operation in environments ranging from -20℃ to 60℃. The raw data collected by the sensors contains transient pulse interference and background noise. The edge computing gateway first applies a moving average filter to the raw data, with a moving window width of 30 data points. The filtering calculation uses a convolution algorithm, and each data point is weighted and averaged with its neighboring data points. The weighting coefficients are generated using a Gaussian distribution function. The filtered data then enters the outlier detection stage. Box plot analysis calculates the upper and lower quartiles and interquartile range of the dataset to detect outliers. Data points within the specified range are marked as outliers, and these outliers are replaced using linear interpolation. The cleaned data then undergoes a stabilization process; the difference between adjacent time points is calculated using a first-order difference formula. The difference operation is repeated until the autocorrelation coefficient function of the data decays to within the confidence interval, indicating that the data has reached a stationary state. The parameter settings in the preprocessing process need to be adjusted according to the actual monitoring environment. Dust data from construction sites fluctuates significantly, so the window width of the moving average filter can be expanded to 60 data points. Noise data from urban roads exhibits obvious periodicity, so the outlier detection range of box plot analysis can be narrowed down to... .

[0032] An internal quality control mechanism is established within the edge computing gateway to verify the integrity of data at each processing stage using a cyclic redundancy check (CRC) algorithm. Preprocessed cleaning time-series data is timestamped and tagged with location, stored as JSON data packets, and uploaded to the cloud platform's object storage service via HTTPS. Upon receiving the data packets, the cloud platform's parsing program extracts the time-series data and writes it to a time-series database. The database employs a sharding strategy, with data from each monitoring point stored independently in different tables.

[0033] The maintenance process for monitoring equipment includes regular calibration and fault diagnosis. Sensors undergo on-site calibration every three months, using a standard dust concentration detector and sound level calibrator for comparative measurements. Calibration data is recorded in the equipment maintenance log. The edge computing gateway has remote diagnostic capabilities, periodically sending device status information to the cloud platform, including CPU utilization, memory usage, and network connection quality indicators. When monitoring data is continuously missing or fluctuates abnormally, the cloud platform triggers an alarm mechanism, notifying maintenance personnel to conduct on-site equipment repair. The entire data acquisition and preprocessing system employs a redundant design, deploying backup sensors and gateways at critical nodes. When the primary device fails, it automatically switches to the backup device, ensuring the continuity of monitoring data. Security measures for data transmission include a two-way authentication mechanism and link encryption. When the edge computing gateway establishes a connection with the cloud platform, a TLS handshake is required, exchanging digital certificates to verify identity. Data packets employ a segmented encryption strategy during transmission, with each data segment encrypted using a different initialization vector to prevent replay attacks. The cloud platform establishes a data verification mechanism, performing signature verification and decryption verification on received data packets to ensure that the data has not been tampered with during transmission. The cleaning time-series data is stored in the database using a time-partitioned table structure, with each partition storing one day's worth of data. The partition key is the data collection timestamp. This storage structure facilitates subsequent querying and analysis. Collaboration between the edge computing platform and the cloud platform is achieved through asynchronous communication via message queues. The edge computing gateway sends processed data to the message queue topic, and the cloud platform's data consumption service pulls data from the topic for processing. This architecture effectively addresses data transmission interruptions caused by network fluctuations; when network connectivity is restored, the edge computing gateway automatically retransmits the backlogged data. The message queue is configured with a data retention policy, retaining copies of the most recent 7 days' data to prevent data loss. The cleaning time-series data is ultimately loaded into the distributed computing framework, providing input data for subsequent deep belief network feature extraction. The entire implementation constructs a complete data acquisition and preprocessing pipeline. From physical equipment deployment to data transmission and storage, quality control and security measures are implemented at each stage to ensure the reliability and availability of dust and noise monitoring data.

[0034] Example 2: See Figure 3 The deep belief network is constructed using multi-layer Restricted Boltzmann Machines (RBMs). An RBM is a randomly generated neural network with a two-layer structure, containing one visible layer and one hidden layer. Neurons in the visible layer and hidden layer are bidirectionally connected, but there are no connections between neurons within the same layer. When constructing the deep belief network, the network depth is first determined to be five layers. The number of neurons in the visible layer of each RBM matches the dimension of the input data. The clean time-series data includes two feature dimensions: dust concentration and noise intensity. The visible layer has two neurons, and the number of neurons in the hidden layers decreases layer by layer: the first hidden layer has 128 neurons, the second has 64 neurons, the third has 32 neurons, the fourth has 16 neurons, and the fifth has 8 neurons. The neuron activation function is the Sigmoid function, the weights are initialized using a Gaussian distribution, and the bias term is initialized to zero.

[0035] The deep belief network was trained using the contrastive divergence algorithm. Training data came from cleaning time-series data stored on a cloud platform. Before training, the data was normalized, mapping dust concentration values ​​to the [0,1] interval and noise intensity values ​​to the [-1,1] interval. The training process of the contrastive divergence algorithm consisted of two stages: pre-training and fine-tuning. The pre-training stage employed a layer-by-layer greedy training strategy. First, the first Restricted Boltzmann Machine (RBM) was trained, with normalized time-series data as input to the visible layer. The weight matrix and bias vector were updated using the contrastive divergence-1 algorithm, with stochastic gradient descent used for weight updates. The learning rate was set to 0.01, and the momentum coefficient to 0.9. After the first RBM was trained, its hidden layer activation values ​​were used as the visible layer input for the second RBM, and this process was repeated layer by layer for all RBMs. In the fine-tuning phase, the backpropagation algorithm is used to train the entire deep belief network end-to-end. The mean squared error function is used as the loss function, the number of training iterations is set to 1000, and the batch size is set to 256 samples. Training is terminated early when the validation set loss no longer decreases for 10 consecutive iterations. Deep feature representations are extracted from the trained deep belief network. The feature extraction location is the activation values ​​of neurons in the fifth hidden layer. The outputs of the eight neurons in the fifth hidden layer constitute an 8-dimensional feature vector. The feature extraction process is implemented through forward propagation. Clean time-series data is input into the visible layer of the deep belief network. The data undergoes a nonlinear transformation through five layers of restricted Boltzmann machines, and finally, the deep feature representation is output in the fifth hidden layer. The deep feature representation captures the high-level abstract characteristics of dust noise data, including temporal correlation, periodic patterns, and nonlinear dynamic features. The extracted deep feature representations are stored in the feature database in the form of floating-point vectors. Each feature vector corresponds to a time window of the original data. The time window length is set to 30 minutes, and the sliding step size is set to 5 minutes.

[0036] The Deep Belief Network (DBN) is implemented using the TensorFlow deep learning framework, version 2.9.0, and programmed in Python 3.8. Network training was performed on a server equipped with an NVIDIA Tesla V100 GPU, using approximately one million time-series records, and took about 12 hours. During training, weight decay regularization was implemented to prevent overfitting, with a weight decay coefficient set to 0.001. Dropout regularization was applied to each hidden layer with a dropout rate of 0.2. After training, the DBN model was saved as a Protocol Buffer file and deployed to a model inference service on a cloud platform. The model inference service provides a gRPC interface, receiving clean time-series data and returning deep feature representations, with an interface response time controlled within 100 milliseconds. Hyperparameter tuning of the DBN employed a grid search method. The grid search evaluated the performance of different hyperparameter combinations on the validation set, using reconstruction error and feature discriminancy as evaluation metrics. The optimal hyperparameter combination is: learning rate 0.01, batch size 256, number of hidden layers 5, and neuron count sequence [128, 64, 32, 16, 8]. The quality of deep feature representations is evaluated using visualization methods. The 8-dimensional features are reduced to 2-dimensionality using the t-distributed random neighborhood embedding algorithm, and the clustering of feature distribution is observed on a two-dimensional plane. The deep belief network model is retrained periodically, with a retraining cycle of three months. The retraining data includes the latest collected dust noise monitoring data. The retraining process uses incremental learning, fine-tuning the original model parameters.

[0037] Deep feature representations are subsequently input into the Isolation Forest algorithm for anomaly detection. Specifically, multiple isolated trees are generated to form a forest structure. The construction process of each isolated tree recursively partitions the data space starting from the root node. The algorithm randomly selects a subset of features and split points to divide the data. After the data points represented by deep features are input into the Isolation Forest, the algorithm calculates the path length of each data point in the tree. The path length is accumulated from the root node to the leaf node, and data points with shorter path lengths are considered anomalies. The dimension of the feature vector matches the input requirements of the Isolation Forest algorithm. The feature extraction process of the Deep Belief Network is fully automated, requiring no manual feature engineering. The network automatically discovers the inherent representation of the data through unsupervised learning. The Deep Belief Network supports processing large-scale time-series data, and its feature representation capability can be improved by expanding the network depth and width. The network architecture is flexible, and the number of layers and neurons can be adjusted according to different dust and noise monitoring scenarios. A shallower network architecture can be used for urban road monitoring scenarios, while a deeper network architecture can be used for industrial plant monitoring scenarios. The training stability of the Deep Belief Network is ensured through gradient pruning technology, with the gradient pruning threshold set to 1.0 to prevent gradient explosion. Performance monitoring of the feature extraction module includes feature dimension consistency and feature extraction latency. Monitoring data is recorded in the system log for performance optimization. The feature extraction performance of the Deep Belief Network depends on the quality of clean time-series data; the accuracy of the data preprocessing stage directly affects the distinguishability of the feature representation. The Deep Belief Network forms a pipeline with the subsequent Isolation Forest algorithm, and the pipeline's data flow achieves asynchronous communication through a message queue. Deep feature representations undergo serialization during transmission, using the Apache Avro serialization format. The Avro schema defines the data structure and metadata of the feature vectors. An indexing mechanism is established for the feature database, with index fields including timestamps and monitoring point numbers, supporting fast retrieval of feature data by time range and spatial location. Deep Belief Network model version management uses semantic version numbers; each model update corresponds to a version number, and version information is recorded in the model metadata. The fault tolerance mechanism of the feature extraction service includes automatic retries and failover. When feature extraction fails, it automatically retryes three times, and after a retry fails, it switches to a backup model service instance.

[0038] The training data for the Deep Belief Network (DBN) needs to cover different seasons and weather conditions. The training set includes data samples from spring, summer, autumn, and winter, as well as data samples from sunny days, rainy days, and windy days. Data augmentation techniques are applied to prepare the training data, including time warp, adding noise, and scaling, increasing the amount of training data by five times. The DBN uses a Bernoulli-Bernoulli energy function, with the states of the visible and hidden layers following a Bernoulli distribution. The Markov chain length of the contrast divergence algorithm is set to one step to balance training efficiency and model accuracy. Histogram analysis of the hidden layer activation values ​​shows a bimodal distribution, indicating that the network has learned meaningful feature representations. Computational complexity analysis of the feature extraction module shows that the forward propagation computation for a single sample requires approximately 100,000 floating-point operations, meeting the time constraints of real-time processing. The DBN model size is approximately 50MB, and after loading into memory, it occupies approximately 200MB of memory.

[0039] See Figure 4 This figure visually presents the feature extraction results of the Deep Belief Network on time-series data of dust and noise pollution. The vertical axis covers eight deep feature dimensions, which are learned from multi-layer Restricted Boltzmann Machines and represent a hierarchical abstraction of the dust and noise data. The horizontal axis covers a 24-hour monitoring period, fully presenting the feature evolution of daily data. The color gradient from dark purple to bright yellow corresponds to feature values ​​from low to high, with warm-colored areas representing high feature activation and cool-colored areas representing low activation. The figure shows significant differences in feature responses at different time points, indicating that the deep patterns contained in the data during these periods are more complex. The presence of dark purple areas in some periods reflects the sparsity of feature representation, reflecting the specificity of the data in that dimension. This figure quantitatively demonstrates the nonlinear dynamic features such as temporal correlation and periodic patterns mined by the Deep Belief Network, providing a highly discriminative deep feature representation for subsequent isolated forest anomaly detection. It is a key step from raw data to intelligent analysis, and visually verifies the effectiveness of unsupervised feature learning in complex dust and noise data.

[0040] Example 3: The Isolation Forest algorithm is an unsupervised anomaly detection method. Its core lies in constructing multiple isolated trees to form an isolated forest. These isolated trees recursively and randomly partition the data space to isolate outliers. The deep feature representation is an 8-dimensional feature vector extracted from a deep belief network. These feature vectors are stored in a cloud platform feature database, with each vector corresponding to dust noise data within a 30-minute time window. When implementing the Isolation Forest algorithm, the number of isolated trees is initially set to 100, with each tree having a fixed training sample size of 256 data points. Samples are randomly drawn from the deep feature representation with replacement. The construction of the isolated trees begins at the root node. The node's data range covers the entire feature space. For each node, the algorithm randomly selects a feature dimension, choosing a subset from eight features. The subset size is set to the square root of the number of features, i.e., three features. The split point is randomly selected uniformly between the minimum and maximum values ​​of this feature dimension. Data points are assigned to either the left or right subtree based on the split point. The rule is that if the feature value is less than the split point, the data point goes to the left subtree; otherwise, it goes to the right subtree. The isolated tree growth process proceeds recursively until a termination condition is met. Termination conditions include a node containing a single data point or a node depth reaching a preset maximum value, limited to 8 levels to control computational complexity. Path length calculation measures the number of edges from the root node to a leaf node in the isolated tree. A shorter path length indicates a data point is more likely to be isolated, i.e., a higher probability of an anomaly. Path length calculation is achieved by traversing the isolated tree, accumulating the number of edges traversed from the root node to a leaf node. The anomaly score is derived based on the path length, and the formula for the anomaly score is:

[0041] in: It is an abnormal fraction, a dimensionless quantity because it is a ratio or fraction. (Character) Represents data points, Represents the number of isolated trees. This represents the average path length of data point x across all isolated trees. The normalization factor represents a function of the number of isolated trees, N. Outlier scores range from 0 to 1, with scores closer to 1 indicating a higher degree of anomaly. Threshold settings employ statistical methods, with a threshold of 0.6 determined based on the percentiles of historical normal data distribution. Data points with outlier scores greater than 0.6 are categorized as anomaly patterns. The anomaly pattern set includes anomaly timestamps and corresponding feature vectors, and is stored in an anomaly database for subsequent analysis.

[0042] The Isolation Forest algorithm is implemented using the Scikit-learn machine learning library, version 1.0.2, and Python 3.8. Training is performed on a cloud platform distributed computing framework, with approximately 500,000 deep feature representations as training data, taking about 30 minutes. Model parameter tuning uses cross-validation, with a validation set containing 100,000 labeled data points. The evaluation metric is F1 score. The Isolation Forest model is saved as a PKL format file and deployed on a real-time inference service, which receives streaming deep feature representations and processes 1000 data points per second. Anomaly detection results are pushed to a message queue in real time, with messages in JSON format containing data point identifiers, anomaly scores, and timestamps. An attention-enhanced recurrent neural network analyzes causal relationships in anomaly patterns. The recurrent neural network architecture uses a Long Short-Term Memory (LSTM) network, which has an input gate, forget gate, and output gate structure to handle long-term dependencies in sequence data. The attention mechanism is integrated on top of the LSM network output layer, calculating the weight distribution at each time step in the sequence. The input data comes from time-series data of anomaly pattern sets, with a fixed sequence length of 60 time steps corresponding to 5 hours of monitoring data. Data preprocessing included sequence alignment and padding; short sequences were padded with zero values, while long sequences were segmented using a sliding window. The Long Short-Term Memory (LSTM) network was set to 3 layers with 128 hidden units per layer, and the attention layer dimension was matched to the number of hidden units with 128 dimensions. The network training objective was to predict the next time step value of the dust noise sequence, and the training data consisted of a mixed dataset of historical abnormal pattern sequences and normal sequences.

[0043] The training process employs a teacher-mandated strategy, where the teacher mandates the use of true values ​​as decoder input during training to improve convergence speed. The loss function chosen is the mean squared error function, and the optimizer uses the Adam algorithm with a learning rate of 0.001 and a batch size of 64. A maximum of 1000 training iterations is implemented, and an early stopping mechanism monitors the validation set loss, terminating training if there is no improvement after 10 consecutive iterations. Attention weights are calculated using a query key-value mechanism. The query vector comes from the final hidden state of the Long Short-Term Memory network, and the key-value vector comes from the hidden states at all time steps. Attention weights are normalized using the softmax function, and the weight value represents the contribution of each time step to the current prediction. After training, the network outputs an attention weight vector for each input sequence, with the vector dimension matching the sequence length, and each element corresponding to the weight value of a time step. Key influencing factors are identified based on attention weight sorting, with the top 10% of time points selected as key time points in descending order of weight values. Key time points correspond to the occurrence times of dust and noise events, and time point metadata includes environmental parameters such as temperature, humidity, and wind speed. Key influencing factors are stored as a list of time points, which is linked to the original anomaly pattern for causal analysis. The attention-enhanced recurrent neural network implementation is based on the TensorFlow deep learning framework, version 2.9.0, and the model is saved in SavedModel format. The inference service is deployed on a GPU server, with inference latency controlled within 50 milliseconds and supporting 100 concurrent requests. The model update cycle is set to once a month, with updated data including the latest anomalous pattern sequences.

[0044] The integration of the Isolation Forest algorithm with an attention-enhanced recurrent neural network is achieved through a data pipeline. The pipeline steps include serializing anomaly detection results using an Avro schema. Data flows through a message queue middleware, which employs a Kafka cluster with topic partitioning based on monitoring point numbers. Causal relationship analysis results are visualized using a Grafana dashboard, displaying attention weight time-series curves and keypoint annotations. System monitoring metrics include anomaly detection accuracy and causal relationship analysis latency; monitoring data is recorded in a time-series database. Dust and noise monitoring scenarios are adapted to different environmental conditions: the anomaly threshold for construction sites is adjusted to 0.55 due to large data fluctuations; the attention weight ranking ratio for urban roads is adjusted to 15% due to short event durations. The algorithm parameter configuration file uses YAML format and supports hot reload without requiring service restarts. Performance optimization measures include memoization caching to cache frequently queried anomaly patterns and reduce computational overhead. Fault tolerance mechanisms include data validation, checking input sequence length and value range, logging invalid data, and skipping processing. Isolation Forest algorithm model version management uses semantic version numbers; each parameter change corresponds to a new version, with version information embedded in the model metadata. The attention-enhanced recurrent neural network model is evaluated using a hold-out method. The evaluation set data is not used in training. Evaluation metrics include attention weights and consistency coefficients. Model interpretability analysis employs attribution methods to calculate feature importance, and the importance ranking is fed back to the feature engineering stage. The entire implementation emphasizes algorithmic collaboration. The isolated forest algorithm locates outliers, and the attention-enhanced recurrent neural network reveals causal chains, forming a closed-loop analysis system.

[0045] See Figure 5 This chart uses a horizontal bar graph to visually present eight key factors affecting dust and noise, along with their quantified impact. The vertical axis encompasses eight core factors: construction intensity, wind speed, temperature, humidity, traffic flow, time of day, equipment status, and surrounding environment. The horizontal axis represents the degree of impact. Construction intensity, with the highest value, is the most critical influencing factor, perfectly aligning with the actual scenario where construction directly generates dust and machinery operations cause noise. Traffic flow and wind speed follow closely behind; high traffic flow leads to a simultaneous increase in road dust and traffic noise, while wind speed exacerbates dust dispersion and alters noise propagation patterns. Humidity and time of day are considered moderate influencing factors; humidity determines dust settling efficiency, and differences in day and night construction / traffic times cause fluctuations in dust and noise. Temperature and the surrounding environment have relatively mild impacts, while equipment status has the lowest impact, indicating that normal equipment operation and maintenance have a weaker influence on dust and noise. This figure shows the results of analyzing the causal relationships of abnormal patterns using an attention-enhanced recurrent neural network. It quantifies the weights of environmental and human factors associated with key time points, providing priority guidance for risk assessment and management strategy formulation of dust and noise pollution. By focusing on high-impact factors such as construction intensity and traffic flow to formulate control measures, the precise and efficient implementation of dust and noise pollution control can be achieved.

[0046] Example 4: The Generative Adversarial Network (GAN) comprises two core components: a generator and a discriminator. The generator learns the distribution characteristics of normal dust noise data, while the discriminator distinguishes between real and generated data. The generator network structure uses a combination of fully connected layers and deconvolutional layers. The input layer receives a random noise vector of dimension 100, sampled from a standard normal distribution. The first hidden layer of the generator contains 512 neurons using the ReLU activation function, and the second hidden layer contains 256 neurons using the ReLU activation function. The output layer's dimension matches the dimension of the real data, generating a 2D vector corresponding to the dust concentration and noise intensity values ​​using the Tanh activation function. The discriminator network structure uses a convolutional neural network architecture. The input layer receives a 2D data sequence, with a convolutional kernel size of 3. The first convolutional layer contains 32 filters with a stride of 2 using the LeakyReLU activation function, and the second convolutional layer contains 64 filters with a stride of 2 using the LeakyReLU activation function. The fully connected layer outputs a scalar using the Sigmoid activation function to represent the true probability of the data. The Generative Adversarial Network (GAN) was trained using historical normal dust noise data, with training data derived from labeled normal behavior samples stored in a database. An alternating optimization strategy was employed during training: first, the generator parameters were fixed while the discriminator parameters were updated, and the discriminator loss function calculated the cross-entropy loss between real and generated data. Then, the discriminator parameters were fixed again while the generator parameters were updated, and the generator loss function measured the probability that generated data was classified as real data. The training hyperparameters were set with a learning rate of 0.0002, a batch size of 128, and 10,000 training iterations. After training, the generator was able to generate synthetic data conforming to normal behavior patterns, and the generator was saved as a normal behavior model in TensorFlowSavedModel format.

[0047] Deviation scores are calculated by comparing real-time data with the output of the normal behavior model. Real-time data, after preprocessing and feature extraction, is input into the normal behavior model generator, which outputs synthetic data for the corresponding time point. Deviation scores are calculated using reconstruction error, which is the Euclidean distance between the real-time and synthetic data. The deviation score range is mapped to 0 to 1, with larger values ​​indicating greater deviation from normal behavior. The deviation score sequence is input into a one-dimensional convolutional neural network (CNN) for risk level classification. This CNN is specifically designed for handling temporal deviation scores. The network input layer receives a deviation score sequence of length 30. The first convolutional layer contains 16 filters with a kernel size of 5 using ReLU activation, followed by a max-pooling layer with a pooling size of 2. The second convolutional layer contains 32 filters with a kernel size of 3 using ReLU activation. A global average pooling layer is followed by a fully connected layer, outputting three nodes using Softmax activation to correspond to low-risk, medium-risk, and high-risk categories.

[0048] The one-dimensional convolutional neural network was trained using a labeled bias score dataset containing historical bias score sequences for different risk levels. The training process employed a classification cross-entropy loss function, and the optimizer used the Adam algorithm with a learning rate of 0.001. Five-fold cross-validation was used for model evaluation to ensure classification accuracy. See Table 1 for the correspondence between risk level classification results and bias score values.

[0049] Table 1: Correspondence between Deviation Score and Risk Level

[0050] The training data preparation phase of the Generative Adversarial Network (GAN) requires rigorous data cleaning and standardization. Normal behavior data is selected based on historical event records, excluding data from all abnormal event periods. Data augmentation techniques are applied to expand the training samples, including adding Gaussian noise and time series distortion. The training stability of the GAN is improved through gradient penalty, with the gradient penalty coefficient set to 10. The normal behavior model update mechanism employs a sliding window strategy, retraining the GAN every three months using the latest normal data. The bias score calculation service is deployed on edge computing nodes, requiring a computation latency of less than 100 milliseconds. A lightweight serialization protocol is used for bias score sequence transmission, based on a Protocol Buffer-defined data structure. One-dimensional convolutional neural network (1D) model quantization technology reduces the model size, decreasing it by 75% to suit edge device deployment. Risk level assessment results are pushed to the monitoring center in real time, with alarm thresholds dynamically adjusted based on seasonal factors: a 0.1 decrease during the rainy season and a 0.1 increase during the dry season. The collaborative operation of the GAN and the 1D convolutional neural network is achieved through a message middleware, using RabbitMQ for asynchronous communication. Specifically, after calculating the deviation score between real-time data and the normal behavior model, the Generative Adversarial Network (GAN) encapsulates the deviation score along with a timestamp and monitoring point identifier into a JSON message. The message structure includes fields such as the deviation score sequence, data acquisition time, and device number. The message is published to a designated queue via RabbitMQ's producer interface, with queue names determined by the monitoring region to ensure accurate message routing. System performance monitoring includes deviation score calculation accuracy and risk classification response time, with monitoring metrics recorded in the Prometheus time-series database. A fault-tolerant mechanism is designed with a model degradation scheme, allowing the GAN to revert to a statistical baseline value for deviation score calculation when it fails.

[0051] The regional adaptability of the normal behavior model is achieved through multi-center training, with independent generative adversarial network (GAN) models trained in different geographical regions. Model version management employs a blue-green deployment strategy, gradually replacing older models after validation. Bias score calculation considers multi-sensor data fusion, with the fusion algorithm using a weighted average method where weights are allocated based on sensor accuracy. The interpretability of risk level assessment results is enhanced through an attention mechanism, with an attention layer added to the one-dimensional convolutional neural network to highlight key time points. Assessment reports are automatically generated, including bias score curves and risk classification criteria, and the report format conforms to environmental monitoring standards. System integration testing covers various extreme scenarios, with test data simulating equipment failure and extreme weather conditions. The quality assessment of the data generated by the GAN uses multi-dimensional indicators, including distribution similarity and feature consistency. The one-dimensional convolutional neural network model is periodically retrained to adapt to changes in data distribution, with a retraining cycle set at six months. This implementation constructs a complete chain from normal behavior modeling to risk level assessment. The GAN captures complex data distributions, the convolutional neural network provides efficient classification, and the bias score acts as a bridge connecting the two modules. The system design meets real-time requirements, and the accuracy of risk assessment is continuously improved through optimization.

[0052] Example 5: The encoder consists of three layers of bidirectional gated recurrent units. The input sequence length is fixed at 60 time steps, and each time step includes five feature dimensions: dust concentration, noise intensity, abnormal pattern markers, key influencing factor identifiers, and risk level. The encoder's gated recurrent units have 256 hidden layer units, with an initial state of zero vectors. The output is the hidden state of the last time step, which serves as the context vector. The decoder consists of three layers of unidirectional gated recurrent units, with the same number of hidden layer units as the encoder. The initial state uses the context vector generated by the encoder. The output layer is a fully connected layer, using the Softmax activation function to generate the probability distribution of the predicted sequence. The training process employs a teacher-forced strategy, and the training data comes from a historical monitoring database containing 100,000 complete time series records. The loss function is classification cross-entropy, the optimizer is the Adam algorithm, the initial learning rate is set to 0.001, and cosine annealing scheduling is used. The training batch size is set to 64, and the model converges after approximately 48 hours of training on a server equipped with four V100 graphics cards. After training, the model is saved in an open neural network exchange format for easy cross-platform deployment. The model inference service is deployed on a cloud container cluster. Each container instance, after loading model weights, occupies approximately 2GB of memory, and the inference latency is required to be controlled within 200 milliseconds. The forecast function is implemented based on a sliding window mechanism, acquiring the latest time-series data every five minutes and inputting it into the sequence model. The model outputs forecast values ​​for dust noise data within the next 30 minutes, maintaining a forecast time resolution of one data point per minute. The forecast results are compared with preset thresholds to generate warning levels, which are divided into four levels: blue, yellow, orange, and red. A blue warning corresponds to a forecast value below 70% of the standard limit, a yellow warning corresponds to 70% to 85%, an orange warning corresponds to 85% to 100%, and a red warning corresponds to a value exceeding the standard limit.

[0053] The optimized management strategy generation module integrates an expert knowledge base, which contains control measures corresponding to different warning levels. During a blue warning, the system automatically sends a normal monitoring report to management personnel; during a yellow warning, the system prompts increased inspection frequency and checks the operating status of dust suppression equipment; during an orange warning, the system automatically activates the spray dust suppression device and restricts high-noise operation periods; and during a red warning, the system immediately issues a production stoppage and rectification order and activates the emergency response plan. The strategy output format adopts a standardized JSON structure, including fields for warning level, forecast curve, suggested measures, and execution time window. The online learning mechanism is implemented through model fine-tuning, with fine-tuning data derived from the deviation records between the latest monitoring data and forecast results. Cases with large forecast errors are automatically selected daily to form a fine-tuning dataset, with the dataset size controlled to around a thousand records. Fine-tuning training uses a small learning rate of 0.0001, and the number of training epochs is limited to 50 to prevent overfitting. Model version management adopts a canary release strategy, with new models gradually replacing older models after a week of trial operation and verification. A version rollback mechanism ensures rapid service recovery in case of model update failure. A specific example is a monitoring point at a construction site. Time-series data collected at 8:00 AM shows a continuous upward trend in dust concentration. The sequence-to-sequence model takes the most recent 60 minutes of data as input. The encoder processes the data and generates a context vector containing current environmental characteristics. The decoder, based on the context vector, progressively outputs forecast values ​​for the next 30 minutes. The forecast curve shows that the dust concentration will exceed 85% of the standard limit in 15 minutes. The system generates an orange alert based on the forecast results, including the specific time of exceedance and the expected peak concentration. The management strategy module automatically generates a list of control measures: suspend earthwork excavation from 9:00 AM, activate the perimeter sprinkler system, reschedule concrete pouring to the afternoon, and increase the monitoring frequency to once every five minutes. These measures are pushed to the site's intelligent management platform via an application programming interface (API), and the platform automatically executes equipment start / stop commands and adjusts the work plan.

[0054] The forecast results visualization interface uses time-series comparison charts, displaying both historical and forecast data curves simultaneously. The user interface provides early warning confirmation and strategy adjustment functions, allowing administrators to fine-tune the system-generated strategies based on actual conditions. All forecast records and strategy execution results are stored in a blockchain database, forming an immutable decision log. System performance monitoring includes forecast accuracy, strategy execution success rate, and response latency indicators, with a monitoring dashboard displaying the system's operational status in real time. The sequence-to-sequence model is adaptive to special weather conditions, automatically loading pre-trained rainfall correction parameters during rainy weather. Meteorological data such as precipitation, wind speed, and wind direction are added to the model input features to improve forecast accuracy under special conditions. The strategy knowledge base includes specific weather response plans, such as automatically covering dust nets during strong winds and strengthening drainage during heavy rain. The model update cycle is synchronized with the seasonal characteristics of weather forecasts, undergoing large-scale retraining twice a year, in spring and autumn. The system integrates a multi-source data verification mechanism, performing spatial consistency checks between forecast results and data from surrounding monitoring points. When the deviation between forecast data and actual conditions exceeds a threshold, the system automatically triggers a model diagnostic process. The diagnostic process includes feature importance analysis and error source tracing. After identifying the problem, the model parameters are automatically adjusted or manual intervention is triggered. The communication protocol uses MQTT to ensure data transmission reliability, and the message quality level is set to at least once.

[0055] The dust and noise forecasting system achieves data interoperability with the urban environmental monitoring platform, with forecast results uploaded to the monitoring platform's big data center in real time. The monitoring platform can generate regional pollution diffusion maps based on multi-site forecast results, providing decision support for urban environmental governance. The system regularly generates forecast accuracy assessment reports, including error distribution statistics and model performance trend analysis. The assessment results are used to optimize model parameters and adjust warning thresholds, forming a closed-loop optimization mechanism. The sequence-to-sequence model supports both multi-step and single-step prediction modes. Routine monitoring uses the computationally less demanding single-step prediction mode, while switching to the more accurate multi-step prediction mode during major events. Mode switching is completed with a single click through the management interface, and the system automatically reallocates computing resources. Prediction confidence is calculated based on the probability distribution output by the model; low-confidence prediction results are marked as requiring manual review. System operation and maintenance include a daily self-inspection process, with self-inspection items including model loading status, data access quality, and storage space usage. Maintenance reports are automatically sent to the technical support team, and an abnormal situation triggers a three-level alarm mechanism to ensure timely problem handling.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A deep learning-based intelligent analysis method for dust and noise monitoring data, characterized in that, include: Real-time data of dust and noise is collected by monitoring devices on an edge computing platform, and the real-time data is preprocessed to obtain time-series data on cleanliness. A deep belief network is used to extract features from the time-series data of the cleaning process to obtain a deep feature representation of dust noise. The isolated forest algorithm is used to detect abnormal patterns in dust noise data based on the aforementioned deep feature representation. The causal relationships in the abnormal patterns are analyzed using an attention-enhanced recurrent neural network to identify key influencing factors; Based on the aforementioned key influencing factors, a normal behavior model of dust noise is established using generative adversarial networks, and the deviation score between real-time data and the normal behavior model is calculated. Convolutional neural networks are then used to assess the risk level of dust noise. Based on the aforementioned anomaly patterns, key influencing factors, and risk levels, a sequence-to-sequence model is applied to predict potential dust noise events and output optimized management strategies.

2. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The preprocessing operation on the real-time data to obtain clean time-series data includes: The collected real-time dust and noise data are processed by moving average filtering to smooth short-term fluctuations; Outliers were identified and removed using box plots to obtain cleaned data; Differential operations are performed on the purified data to eliminate trend components and obtain stationary time series data.

3. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The step of using a deep belief network to extract features from the time-series data of the cleanup process to obtain a deep feature representation of dust noise includes: Deep belief networks are constructed using stacked restricted Boltzmann machines. A deep belief network was trained using a contrastive divergence algorithm to learn the hierarchical characteristics of dust noise data. Extract hidden layer features from a trained deep belief network as deep feature representations.

4. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The method of using the isolated forest algorithm to detect abnormal patterns in dust noise data based on the deep feature representation includes: Construct an isolated forest tree structure, randomly selecting features and split points; Calculate the path length for each data point; the shorter the path length, the more abnormal the data point. Anomaly scores are determined based on path length, and a threshold is set to identify a set of anomaly patterns.

5. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The recurrent neural network enhanced by the attention mechanism analyzes the causal relationships in the abnormal patterns and identifies key influencing factors, including: Using a long short-term memory network as the base model, an attention layer is added; The network is trained to predict dust noise sequences, and the attention weights represent the degree of influence at different time points. Based on attention weighting, key time points were extracted as influencing factors.

6. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The process involves combining the aforementioned key influencing factors, using generative adversarial networks to establish a normal behavior model for dust noise, calculating the deviation score between real-time data and the normal behavior model, and employing convolutional neural networks to assess the risk level of dust noise, including: A generative adversarial network is trained to generate normal dust noise data, and a discriminator distinguishes between real and generated data. Using the generator as the normal behavior model, the reconstruction error between real-time data and generated data is calculated as the deviation score. The bias score is input into a one-dimensional convolutional neural network to classify the risk level.

7. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The application of sequence-to-sequence models predicts potential dust noise events and outputs optimized management strategies, including: Use an encoder-decoder architecture to process dust noise time-series data; The model is trained to predict future sequences using a teacher-mandated method. Management strategies are generated based on the prediction results, including adjusting the intensity of operations or activating dust suppression equipment.

8. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The real-time data on dust and noise collected through monitoring devices on the edge computing platform includes: Deploy lightweight sensors at edge nodes to collect dust and noise data in real time; Use an edge computing gateway to compress and encrypt the data; The processed data is then transmitted to a cloud platform for further analysis.

9. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, Also includes: Regularly fine-tune the deep learning model using new dust and noise data; An online learning algorithm is used to adapt to changes in data distribution and update model parameters.

10. The intelligent analysis method for dust and noise monitoring data based on deep learning according to claim 1, characterized in that, The output optimization management strategy includes: The warning level is determined based on the forecast results; Specific control measures should be formulated for different warning levels, such as adjusting construction time or increasing monitoring frequency.

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