Compressor air system management and control system based on internet of things wisdom cloud platform

Abstract

The application provides a compressor air system management and control system based on an Internet of Things intelligent cloud platform, comprising an edge computing unit, an intelligent cloud platform and an intelligent terminal. The edge computing unit collects the operating parameters of the compressor in real time through Internet of Things sensors, processes the collected data, generates pre-processed data, and executes high-priority local rapid response control tasks. The intelligent cloud platform receives the data uploaded by the edge computing unit, uses a neural network model to predict the operating state of the compressor, dynamically calculates optimized operating parameters, and sends the optimization results to the edge computing unit for execution adjustment. The intelligent terminal interacts with the intelligent cloud platform and the edge computing unit, displays the real-time operating state of the compressor and the fault prediction results; in an emergency, the intelligent terminal cooperates with the edge computing unit to execute localized emergency management through direct communication. The application improves the operating efficiency, fault response capability and energy consumption optimization level of the compressor system.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation systems, and in particular to a management and control system for a compressor air system based on an Internet of Things smart cloud platform. Background Art

[0002] Compressor air systems are a vital component of industrial production and the energy sector, and their operating status directly impacts overall equipment performance and energy consumption. With the development of IoT technology, more and more compressor systems are being equipped with IoT sensors to collect real-time operating parameters (such as pressure, temperature, vibration, and current). These sensors then analyze this data using cloud platforms to improve operational efficiency and fault management. These systems typically rely on centralized data processing architectures, analyzing historical and real-time data to achieve basic predictive maintenance and energy efficiency optimization.

[0003] However, existing technologies have the following problems in compressor air system management and control: First, the centralized data processing architecture leads to delays in tasks with high real-time requirements, especially difficulty responding to emergencies when the network is interrupted or delayed; second, the accuracy of fault prediction and the optimization level of operating parameters are limited by simple rules or traditional algorithms, which cannot effectively process complex multi-dimensional dynamic data; in addition, the interaction between users and the system is often relatively limited, lacking flexible control methods and real-time feedback mechanisms, which reduces the intelligence level of the system.

[0004] Therefore, it is necessary to develop a new type of compressor air system control system based on the Internet of Things smart cloud platform. Summary of the Invention

[0005] This application provides a compressor air system management and control system based on the Internet of Things smart cloud platform to improve the operating efficiency, fault response capability and energy consumption optimization level of the compressor system.

[0006] This application provides a compressor air system management and control system based on the Internet of Things smart cloud platform, including:

[0007] The edge computing unit is configured to collect the operating parameters of the compressor in real time through the configured IoT sensors, including pressure, temperature, vibration, and current; perform data denoising, anomaly detection, and pattern recognition on the collected operating parameters to generate pre-processed data; transmit the pre-processed data to the smart cloud platform; and execute high-priority local rapid response control tasks based on the pre-processed data to respond to emergencies;

[0008] The smart cloud platform is configured to receive pre-processed data uploaded by the edge computing unit; use a pre-trained neural network model to perform fault prediction on the compressor's operating status and generate a fault prediction result; dynamically calculate optimized operating parameters for the compressor based on the pre-processed data and the fault prediction result, including pressure setpoint, duty cycle, and energy consumption balance strategy; and feed the calculated optimized operating parameters back to the edge computing unit for it to make corresponding operational adjustments;

[0009] The smart terminal interacts with the smart cloud platform and edge computing unit to display the real-time operating status information of the compressor and the fault prediction results generated by the smart cloud platform. In an emergency, it performs localized emergency operation management and fault handling by directly communicating with the edge computing unit. It supports users to input control commands through the smart terminal and transmit them to the smart cloud platform or edge computing unit.

[0010] Furthermore, the neural network model used in the smart cloud platform includes an input layer, a spatiotemporal nested encoding layer, a multi-scale feature extraction layer, a cyclic prediction layer, and a fault classification layer;

[0011] The input layer is used to receive the multidimensional time series data uploaded by the edge computing unit, and process the received data in segments according to fixed time windows to generate initial input tensors; wherein the multidimensional time series data includes pre-processed pressure, temperature, vibration and current data;

[0012] The spatiotemporal nested coding layer is used to receive the initial input tensor provided by the input layer, and the spatiotemporal nested coding layer first uses a one-dimensional convolutional neural network to extract local trend features from the initial input tensor; the spatiotemporal nested coding layer then uses an attention mechanism to adjust the weights of the extracted local trend features to generate a spatiotemporal feature tensor;

[0013] The multi-scale feature extraction layer is used to receive the spatiotemporal feature tensor provided by the spatiotemporal nested coding layer, extract short-term, medium-term and long-term time series features using multiple parallel multi-scale convolution units with different convolution kernel sizes, and combine the extracted results into a unified multi-scale feature tensor through a splicing operation;

[0014] The cyclic prediction layer is used to receive the data provided by the multi-scale feature extraction layer and combine the extraction results into a unified multi-scale feature tensor through a splicing operation; the cyclic prediction layer uses a two-layer long short-term memory network to process the received data and generate a prediction feature vector; wherein the first layer of the two-layer long short-term memory network learns short-term temporal dependencies, and the second layer learns long-term dependencies; each layer captures the temporal variation trend of the compressor operating state by dynamically updating the hidden state vector;

[0015] The fault classification layer is used to receive the prediction feature vector provided by the cyclic prediction layer, and use a fully connected neural network to perform linear transformation and nonlinear activation on the prediction feature vector to obtain a predicted probability distribution of the fault type.

[0016] This application has the following beneficial technical effects:

[0017] (1) Through the local processing capabilities of the edge computing unit, real-time preprocessing and anomaly detection of collected data are achieved, and high-priority rapid response control tasks can be executed in emergency situations, effectively reducing the system response lag caused by network delays or interruptions, and significantly improving the real-time performance and operating efficiency of the system. (2) The pre-trained neural network model on the smart cloud platform is used to predict the operating status of the fault, and the operating parameters of the compressor (including pressure set value, working cycle and energy consumption balance strategy) are dynamically optimized by combining historical data and real-time data, improving the accuracy of fault prediction and the intelligent level of operating parameter optimization, extending the life of the equipment and reducing energy consumption. (3) The smart terminal realizes the visualization of real-time operating status information and fault prediction results, and supports user input control instructions. It can directly communicate with the edge computing unit in an emergency and perform local emergency operation management operations, ensuring the high reliability of the system and the flexibility of user control. (4) Through the layered collaborative architecture formed by the edge computing unit, the smart cloud platform and the smart terminal, the system can efficiently switch between cloud intelligent analysis, local rapid processing and user interaction, which not only ensures the global optimization capability of complex tasks, but also enhances the local independent operation capability in emergency situations, and improves the robustness and stability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of a compressor air system control system based on the Internet of Things smart cloud platform provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0019] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present application. Therefore, the present application is not limited to the specific implementations disclosed below.

[0020] The first embodiment of this application provides a compressor air system control system based on the Internet of Things smart cloud platform. Figure 1 , which is a schematic diagram of the first embodiment of this application. Figure 1 The first embodiment of the present application provides a compressor air system control system based on the Internet of Things smart cloud platform for detailed description.

[0021] The compressor air system management and control system based on the Internet of Things smart cloud platform includes an edge computing unit 101, a smart cloud platform 102, and a smart terminal 103.

[0022] The edge computing unit 101 is used to collect the operating parameters of the compressor in real time through the configured Internet of Things sensors, and the operating parameters include pressure, temperature, vibration, and current; perform data denoising, anomaly detection, and pattern recognition on the collected operating parameters to generate preprocessed data; transmit the preprocessed data to the smart cloud platform; and execute high-priority local rapid response control tasks based on the preprocessed data to deal with emergencies.

[0023] The edge computing unit 101 is one of the core components of the present invention. Its function is to collect, process and respond to real-time data during the operation of the compressor, provide high-quality data support for the smart cloud platform, and perform rapid local control in emergency situations. The unit is connected to the key components of the compressor through multiple IoT sensors. These sensors are used to collect operating parameters, including but not limited to pressure sensors, temperature sensors, vibration sensors, and current sensors. After the sensor converts the collected analog signal into a digital signal, it is transmitted to the edge computing unit through the data interface. The data interface can be a wireless communication module, an industrial bus interface, or a standard wired connection module to ensure the real-time and accuracy of data transmission.

[0024] The edge computing unit 101 is equipped with a high-performance microprocessor or embedded chip that can execute a variety of complex algorithms, including data denoising, anomaly detection, and pattern recognition. The data denoising module processes the collected sensor data based on filtering technology, such as Kalman filtering or wavelet filtering, to eliminate environmental interference signals. The anomaly detection module uses a preset threshold value to determine whether the change in operating parameters exceeds the normal range, and establishes a dynamic threshold model based on historical data to further improve the accuracy of detection. The pattern recognition module uses feature extraction and classification technology to classify the operating status of the compressor to identify potential abnormal operating modes or fault characteristics. These processing steps generate pre-processed data to ensure the integrity and applicability of the data.

[0025] After data preprocessing, the edge computing unit 101 uploads the processed data to the smart cloud platform through a wireless communication module or industrial Ethernet. In order to cope with network delays or interruptions, the edge computing unit has a cache management function, which can temporarily store data and upload it in batches after the network is restored to avoid data loss. In addition, in order to further improve the robustness of the system, the unit can automatically switch to a local fast response mode in an emergency and directly execute preset control tasks, such as forcibly reducing operating pressure or stopping equipment operation. The fast response control task is based on a pre-set response rule table and is automatically triggered after logical judgment of the operating parameters collected in real time.

[0026] The software architecture of the edge computing unit 101 adopts a modular design, primarily comprising a data acquisition module, a data processing module, a communication module, and a rapid response module, with each module interacting via a data bus. The data acquisition module is responsible for receiving raw data from sensors, the data processing module implements denoising, anomaly detection, and pattern recognition, the communication module manages two-way communication with the smart cloud platform and smart terminals, and the rapid response module takes priority in emergency situations to ensure system security and real-time performance. Furthermore, the edge computing unit supports remote firmware updates, enabling algorithm upgrades or optimization functions as needed during operation to maintain long-term applicability and efficiency.

[0027] Through the above functions and architectural design, the edge computing unit 101 can not only realize efficient processing of real-time data, but also operate independently in emergency situations to ensure the reliability and safety of the compressor air system.

[0028] The smart cloud platform 102 is used to receive the pre-processed data uploaded by the edge computing unit; use a pre-trained neural network model to predict the operating status of the compressor and generate a fault prediction result; based on the pre-processed data and the fault prediction result, dynamically calculate the optimized operating parameters of the compressor, the optimized operating parameters including the pressure setting value, the working cycle and the energy consumption balance strategy; and feed back the calculated optimized operating parameters to the edge computing unit for it to perform corresponding operation adjustments.

[0029] Smart cloud platform 102 is a crucial component of the present invention for achieving global analysis and optimized control. It receives preprocessed data uploaded by the edge computing unit and analyzes, predicts, and adjusts compressor parameters based on advanced artificial intelligence algorithms and dynamic optimization techniques. The platform comprises a data receiving module, an analysis module, an optimization module, and a feedback module, all working together to provide efficient management and control.

[0030] During the data reception phase, the smart cloud platform receives pre-processed data uploaded by edge computing units via a stable network interface (such as 5G, industrial Ethernet, or Wi-Fi). This data includes time series of key parameters such as compressor pressure, temperature, vibration, and current. The received data is first temporarily stored in a data cache unit to handle possible network fluctuations. The platform also has a built-in data synchronization mechanism that sorts and merges data uploaded by different edge computing units based on timestamps, ensuring global data consistency and integrity.

[0031] The analysis module is the core of the smart cloud platform, and is mainly used to perform fault prediction of the compressor operating status based on the received data. This module relies on a pre-trained neural network model to perform a fusion analysis of the compressor's historical data and real-time data. The neural network model is trained through a large data set of compressor operations before the platform is deployed, including common fault types (such as excessive pressure, abnormal temperature or abnormal vibration) and their corresponding operating modes. The platform will continuously learn and optimize the model during operation, and dynamically update the model weights through the input of incremental data to improve the accuracy and real-time performance of the prediction. When the data enters the analysis module, the platform first performs data normalization to standardize the range of each parameter to adapt to the input requirements of the neural network; then, the model generates a prediction result for the compressor fault based on the input data, including the fault type, probability of occurrence and urgency.

[0032] After generating the fault prediction results, the optimization module further calculates the dynamic optimization operating parameters of the compressor based on these results, including pressure set points, duty cycle, and energy consumption balance strategy. The optimization module uses a multi-objective optimization algorithm to dynamically adjust the operating parameters with the goals of maximizing energy efficiency, minimizing fault risks, and maintaining system stability. Specifically, the optimization module first determines the current operating priority (such as focusing on energy saving or fault avoidance) based on the fault prediction results, and then calculates the optimal operating parameter combination through built-in decision rules or optimization models. The optimization results are stored in the platform database in real time and are available for query by other modules or terminals.

[0033] The feedback module transmits the calculated optimized operating parameters to the edge computing unit via a stable communication link, enabling it to make corresponding operational adjustments. During the feedback process, the platform uses a confirmation mechanism to ensure that the instructions have successfully reached the edge unit and resends them when necessary to ensure accurate execution. Furthermore, the feedback module supports syncing optimization results to smart terminals for real-time user viewing.

[0034] The smart cloud platform is also highly scalable and compatible, enabling integration with other industrial management systems via APIs. It supports multiple communication protocols (such as MQTT and OPC UA) and is compatible with compressors of different brands and models. Furthermore, the platform supports multi-level permission management, providing tiered access for users with different roles, and offers a secure remote update mechanism for continuous optimization of models and algorithms.

[0035] Through the above functional design, the smart cloud platform 102 realizes global analysis and efficient optimization of the compressor operating status, while providing the system with powerful predictive capabilities and real-time control support, providing technical guarantees for the safety, stability and energy efficiency improvement of the compressor air system.

[0036] Furthermore, the neural network model used in the smart cloud platform includes an input layer, a spatiotemporal nested encoding layer, a multi-scale feature extraction layer, a cyclic prediction layer, and a fault classification layer;

[0037] The input layer is used to receive the multidimensional time series data uploaded by the edge computing unit, and process the received data in segments according to fixed time windows to generate initial input tensors; wherein the multidimensional time series data includes pre-processed pressure, temperature, vibration and current data;

[0038] The spatiotemporal nested coding layer is used to receive the initial input tensor provided by the input layer, and the spatiotemporal nested coding layer first uses a one-dimensional convolutional neural network to extract local trend features from the initial input tensor; the spatiotemporal nested coding layer then uses an attention mechanism to adjust the weights of the extracted local trend features to generate a spatiotemporal feature tensor;

[0039] The multi-scale feature extraction layer is used to receive the spatiotemporal feature tensor provided by the spatiotemporal nested coding layer, extract short-term, medium-term and long-term time series features using multiple parallel multi-scale convolution units with different convolution kernel sizes, and combine the extracted results into a unified multi-scale feature tensor through a splicing operation;

[0040] The cyclic prediction layer is used to receive the data provided by the multi-scale feature extraction layer and combine the extraction results into a unified multi-scale feature tensor through a splicing operation; the cyclic prediction layer uses a two-layer long short-term memory network to process the received data and generate a prediction feature vector; wherein the first layer of the two-layer long short-term memory network learns short-term temporal dependencies, and the second layer learns long-term dependencies; each layer captures the temporal variation trend of the compressor operating state by dynamically updating the hidden state vector;

[0041] The fault classification layer is used to receive the prediction feature vector provided by the cyclic prediction layer, and use a fully connected neural network to perform linear transformation and nonlinear activation on the prediction feature vector to obtain a predicted probability distribution of the fault type.

[0042] The neural network model used in the smart cloud platform consists of multiple functional layers to accurately analyze compressor operating status and predict faults. The model first receives multidimensional time series data uploaded by the edge computing unit through the input layer. This data includes preprocessed pressure, temperature, vibration, and current information. The input layer segments the received data according to fixed time windows and converts each segment into an initial input tensor with uniform dimensions, providing a standardized input format for subsequent neural network processing.

[0043] Following the input layer, the spatiotemporal nested encoding layer processes the initial input tensor. This layer first uses a one-dimensional convolutional neural network to extract local trend features from the time series data. One-dimensional convolutional neural networks can capture short-term patterns of change in time series, such as rapid fluctuations in compressor pressure or temperature. The spatiotemporal nested encoding layer then uses an attention mechanism to weight the extracted local trend features. The attention mechanism dynamically adjusts the weights of each feature based on its importance, ensuring that information with significant influence at a specific time point or parameter dimension is more prominently reflected in the spatiotemporal feature tensor. Through this processing, the spatiotemporal feature tensor generated by the spatiotemporal nested encoding layer can more accurately reflect the spatiotemporal correlations between compressor operating parameters.

[0044] The spatiotemporal feature tensor is then passed to the multi-scale feature extraction layer. This layer uses multiple parallel multi-scale convolutional units, each configured with a different convolution kernel size, to extract short-term, medium-term, and long-term time series features. Short-term features capture rapidly changing operating patterns, such as high-frequency fluctuations in vibration signals; medium-term features extract the changing trends of operating parameters on medium time scales, such as periodic fluctuations in pressure; and long-term features capture slowly changing operating patterns, such as the gradual increase in temperature. The outputs of each convolutional unit are concatenated into a unified multi-scale feature tensor, providing a comprehensive and multidimensional input for the subsequent recurrent prediction layer.

[0045] The multi-scale feature tensor is further processed in the recurrent prediction layer. This layer consists of a two-layer long short-term memory (LSTM) network. The first layer is used to learn short-term temporal dependencies, capturing rapid changes in compressor operating parameters, such as the changing pattern of vibration signals within seconds. The second layer focuses on learning long-term temporal dependencies, extracting operating trends over longer time spans, such as slow changes in temperature and current. By dynamically updating the hidden state vector, each layer can effectively integrate dynamic information in the time series, providing a highly accurate time series feature vector for fault prediction.

[0046] Finally, the fault classification layer receives the predicted feature vector generated by the recurrent prediction layer and further processes the feature vector using a fully connected neural network. The fully connected network first reduces or enhances the input features through linear transformation to adapt to the requirements of the classification task, and then enhances the classifier's ability to express complex patterns through nonlinear activation functions. The output of the fault classification layer is the predicted probability distribution of the fault type, such as the probability of occurrence of each fault type (such as abnormal pressure or excessive vibration). Through this probability distribution, the smart cloud platform can provide clear fault prediction results, providing a basis for compressor fault handling and optimized operation.

[0047] Through this layer-by-layer processing, the neural network model extracts multidimensional features of the compressor's operating parameters from the initial input tensor, accurately predicting the fault type and providing its probability of occurrence. The network's structural design combines spatiotemporal feature extraction, multiscale feature fusion, and long- and short-term temporal dependency modeling, providing reliable intelligent analysis and control support for compressor air systems.

[0048] Furthermore, the attention mechanism in the spatiotemporal nested encoding layer is used to adjust the weight relationship between different operating parameters in the initial input tensor, specifically including:

[0049] The operating parameter correlation score is calculated according to the following formula (1):

[0050]

[0051] Among them, e ij represents the attention correlation score between the i-th parameter and the j-th parameter; Z is the local trend feature vector, Where T is the time step, M is the number of operating parameters; α is the control coefficient of the prior weight, which is used to adjust the relative importance of physical characteristics and data-driven results; β is a hyperparameter that adjusts the decay rate of the relationship between parameters; represents the transpose of the vector Z[:,i]; ||·||2 represents the L2 norm of the vector;

[0052] The attention weight is calculated according to the following formula (2):

[0053]

[0054] Among them, A ij is the attention weight of the i-th parameter to the j-th parameter;

[0055] According to the following formula 3, the spatiotemporal feature tensor is calculated:

[0056]

[0057] Among them, H[t,i] is the spatiotemporal feature tensor at time step t and parameter i; Z[t,j] is the eigenvalue of the jth parameter in the local trend feature vector Z at time step t.

[0058] This embodiment proposes a spatiotemporal nested encoding layer based on an attention mechanism. By adjusting the weighted relationships between operating parameters, it generates a more accurate spatiotemporal feature tensor, thereby improving the dynamic analysis of compressor operating status. The core of the entire solution is to calculate attention relevance scores and attention weights based on the local trend characteristics of the initial input tensor, combined with the similarity between parameters and prior physical relationships. These weights are ultimately used to generate the spatiotemporal feature tensor.

[0059] First, the local trend feature vector Z is a T × M matrix, where T represents the time step and M represents the number of operating parameters (such as pressure, temperature, vibration, and current). Z contains the characteristic information of each parameter at different time steps. After being extracted by a one-dimensional convolutional network, it contains local trend features. The spatiotemporal nested encoding layer further optimizes these features through the following steps.

[0060] Formula (1) calculates the correlation score e between operating parameters ij , which is defined as follows:

[0061]

[0062] The relevance score consists of two parts: a data-driven similarity measure and prior physical properties.

[0063] Part 1 Represents the cosine similarity between the i-th parameter and the j-th parameter in terms of local trend characteristics. This value is calculated by vector dot product and indicates the directional similarity between parameters, with a value range of [-1, 1]. For example, if Z[:,i] and Z[:,j] represent the trend characteristics of pressure and temperature, the score is close to 1 when the two trends are similar.

[0064] Part 2 Physical priors are introduced to describe how the relationship between parameters decays with physical distance. For example, if the physical distance between pressure and temperature is close, the decay rate β controls how quickly their weight decays, while α controls the overall magnitude of the prior's influence.

[0065] By combining these two parts, e ij It can simultaneously reflect the data-driven dynamic similarity and the inherent physical correlation between parameters.

[0066] Formula (2) is obtained by calculating the correlation score e ij Perform softmax normalization and calculate attention weight:

[0067]

[0068] Among them, A ij is the normalized weight, representing the importance of the i-th parameter to the j-th parameter. The softmax normalization operation ensures that the sum of the weights is 1, effectively weighting different parameters. The physical significance of this step is that, through normalization, the relevance score of each parameter is converted into a weight for its influence on other parameters, highlighting important relationships and suppressing irrelevant features.

[0069] For example, when the correlation score between pressure and vibration is higher than the correlation score between pressure and temperature, softmax will assign higher weights to pressure and vibration, ensuring that vibration contributes more to pressure when generating spatiotemporal features later.

[0070] Formula (3) calculates the spatiotemporal feature tensor through weighted summation:

[0071]

[0072] Among them, H[t,i] represents the spatiotemporal feature value of time step t and parameter u. By using the attention weight A ij The local trend feature vector Z[t,j] is weighted to dynamically integrate the features of different parameters into a spatiotemporal feature with more global characteristics.

[0073] The significance of this step is to use the attention weight A ij To dynamically adjust the contribution of each parameter in generating spatiotemporal features. For example, at a certain time step, if the system operation shows that pressure and temperature have a high correlation, the weight A ij This will ensure that the pressure trend has a greater impact on the eigenvalues ​​of temperature, thereby generating a space-time characteristic tensor that is more in line with the actual situation.

[0074] Suppose there are four operating parameters M = 4 (pressure, temperature, vibration, and current) and a time step of T = 3. Assume that at a certain moment, the correlation score between pressure and temperature is high, while the correlation score with vibration is low. The attention mechanism dynamically adjusts these weights, making the pressure feature more sensitive to the temperature trend and suppressing the influence of vibration. The resulting spatiotemporal feature tensor H[t,i] not only contains the local trend of each parameter but also incorporates the dynamic adjustment information of other parameters to it, providing accurate input for subsequent fault analysis.

[0075] Through the above steps and formulas, the generation of each spatiotemporal eigenvalue is based on the dynamic characteristics of the operating data and combined with physical prior relationships, ensuring that the generated spatiotemporal feature tensor has high credibility and accuracy, and can effectively support intelligent analysis of the compressor operating status.

[0076] Furthermore, the multi-scale convolution unit in the multi-scale feature extraction layer includes three parallel convolution channels, including a short-term convolution channel, a medium-term convolution channel, and a long-term convolution channel;

[0077] Among them, the convolution kernel size used by the short-term convolution channel is 3, which is used to capture the local characteristics of rapid fluctuations in the compressor operating parameters; the convolution kernel size used by the medium-term convolution channel is 7, which is used to extract the changing trend of medium frequency; the convolution kernel size used by the long-term convolution channel is 15, which is used to capture the slow changes and periodic characteristics of the compressor operating parameters.

[0078] The multi-scale feature extraction layer is constructed using three parallel convolutional channels: short-term, medium-term, and long-term. These channels use convolution kernels of different sizes to extract features of compressor operating parameters at different time scales. The multi-scale convolutional unit is designed to comprehensively capture the dynamic characteristics of operating parameters, providing rich information support for subsequent fault analysis and optimization.

[0079] First, the short-term convolution channel is configured with a convolution kernel size of 3. This means that during each convolution operation, the channel performs a sliding window of three consecutive time steps, performing a local analysis of the operating parameter data. Due to the small receptive field of the convolution kernel, the short-term convolution channel focuses on extracting rapidly fluctuating local features, such as high-frequency variations in vibration signals or transient current fluctuations. These features are often closely related to the real-time dynamics of the compressor and are sensitive indicators of the equipment's operational health.

[0080] The medium-term convolutional channel uses a kernel size of 7. Compared to the short-term convolutional channel, it has a larger receptive field and can capture the changing trends of operating parameters over longer timeframes. For example, pressure and temperature data may exhibit smooth increasing or decreasing trends over medium timescales, and these trends are important for the overall operational efficiency and potential risks of the equipment. The intermediate-frequency features extracted by the medium-term convolutional channel can reflect the stability and continuity of parameter changes.

[0081] The long-term convolutional channel uses a kernel size of 15 to maximize its receptive field, capturing slowly changing and periodic characteristics of operating parameters. For example, a gradual increase in compressor temperature during operation may take a long time to manifest, while periodic fluctuations in vibration characteristics reflect the inherent mechanical properties of the equipment or potential failure modes. Capturing these low-frequency features helps identify chronic equipment issues, such as component aging or declining energy efficiency.

[0082] In the multi-scale convolutional unit, these three channels operate in parallel, each independently performing convolution on the input operational parameter data. The output of each channel is a feature map matrix, where the time dimension corresponds to the time step of the input data, while the feature dimension reflects the channel's multi-level abstraction of the input features. To generate a unified feature tensor, the system combines the outputs of the three channels through a concatenation operation, resulting in a multi-scale feature tensor that simultaneously incorporates short-term, medium-term, and long-term feature information.

[0083] Through this multi-scale design, the multi-scale feature extraction layer simultaneously captures the rapid dynamics, stable trends, and cyclical behavior of compressor operating parameters, covering the diverse frequency characteristics of the operating data and providing comprehensive and accurate feature input for subsequent prediction and classification. This layered convolutional architecture ensures the system's efficient extraction of multi-dimensional features of operating parameters, giving it exceptional adaptability and reliability when processing complex industrial data.

[0084] Furthermore, the first layer of the long short-term memory network of the recurrent prediction layer dynamically optimizes the hidden state according to the following formula (4):

[0085]

[0086] Among them, h t represents the hidden state vector at the current time step t, which is used to capture the dynamic characteristics of the compressor operating parameters in the short-term time dependence; t is the output gate, which is calculated by the output gate of the standard LSTM unit and is used to control the information in the hidden state of the current time step that needs to be output to the next step; c t is the unit state, indicating the memory state of the current time step; M is the number of operating parameters; is the convolution feature of the mth parameter at time step t; γ is the weight parameter.

[0087] The first layer of the recurrent prediction layer, a long short-term memory (LSTM) network, achieves efficient modeling of short-term temporal dependencies through dynamically optimized hidden state calculations. The core of this solution is to use Equation (4) to generate the hidden state vector at the current time step by combining the weighted fusion of the output gate, cell state, and convolutional features, thereby capturing the dynamic characteristics of the compressor operating parameters.

[0088] Formula (4) is defined as follows:

[0089]

[0090] In the formula, the hidden state h t It is the core output of the current time step t, which not only carries the system's memory of short-term time dependencies, but also provides input for the next time step.

[0091] The implementation logic of the entire solution is first based on the standard LSTM unit, which processes each time step in the input sequence in turn and combines the convolution features with the specific operating parameters of the current time step. Perform dynamic optimization. According to formula (4), the hidden state h t The generation not only relies on the memory mechanism of LSTM, but also integrates the real-time information from the multi-parameter convolution features, so that the output of each time step is more in line with the actual changes in the operating status of the compressor.

[0092] In the formula, the output gate o t Calculated by a standard LSTM unit, it controls which information in the hidden state of the current time step is output to the next step. The output gate is typically calculated dynamically based on the input data and the hidden state of the previous time step, using a set of weight matrices and activation functions. For example, at a given time step, if the input data indicates that the compressor is currently in a stable state, the output gate may filter out irrelevant short-term fluctuations to ensure that the information being transmitted is of practical value.

[0093] Cell state c t The memory state of the current time step is another core component of the LSTM cell, responsible for preserving important information between time steps. The cell state is updated through the combined action of the forget gate and the input gate. For example, if the input data indicates that the trend of a compressor parameter remains consistent over multiple time steps, the cell state will prioritize preserving this trend information to avoid losing long-term correlations.

[0094] In the formula, the convolution feature It is the feature value generated by performing a one-dimensional convolution operation on each operating parameter. Here M represents the number of operating parameters, such as pressure, temperature, vibration, and current. By performing local convolution on each parameter at time step t, the rapidly changing local features can be extracted. By averaging the convolution features of M parameters, a mean feature term is obtained. Used to comprehensively reflect the overall dynamics of all parameters at the current time step.

[0095] The weight parameter γ controls the cell state c t and the balance between the convolution feature mean. In some cases, the dynamic characteristics of the compressor may rely more on the historical memory (given by c t denoted by , in which case γ is closer to 1. However, in scenarios requiring real-time response to rapid changes (such as sudden pressure fluctuations), γ may be lower, resulting in a greater contribution of the convolutional feature mean to the hidden state. Weight parameters can be obtained from experimental data or directly set using expert knowledge.

[0096] Finally, the hidden state ht By applying the tanh function to the above weighted results for nonlinear transformation and combining the output gate o t Nonlinear transformations can constrain the range of hidden states, ensuring the numerical stability and expressiveness of the model. For example, at a given time step, if the output gate indicates that a feature is of low importance, the contribution of that feature in the hidden state is suppressed to avoid interfering with predictions at the next time step.

[0097] For example, assuming that the operating parameters include pressure, temperature and vibration (M=3), the convolution feature of the current time step t is By taking the average, we get =0.833. If the cell state c t = 0.9 and γ = 0.7, the weighted result is γ·c t +(1-γ)·0.833=0.7·0.9+0.3·0.833=0.882. Combined with tanh and output gate o t , hidden state h t Optimized to a value that better fits the current operating status of the compressor.

[0098] By applying the above calculation logic and formulas, the first-layer LSTM can effectively capture the short-term time dependency characteristics of the compressor operating parameters, laying the foundation for long-term time modeling in subsequent layers.

[0099] Furthermore, the internal state of the second-layer long short-term memory network of the recurrent prediction layer is propagated between time steps in a recursive manner, and a forgetting mechanism is used to dynamically filter irrelevant information within a long time span, while retaining long-term features that affect the current operating state.

[0100] The second layer of the recurrent prediction layer, a long short-term memory (LSTM) network, recursively propagates its internal state between time steps. Combined with a forgetting mechanism, it dynamically filters out irrelevant information while effectively retaining long-term features that are crucial to the current operating state. This design aims to address the long-term dependency modeling problem of complex time series data during compressor operation, ensuring that key patterns in historical data are captured while eliminating redundant information, thereby improving the accuracy of fault prediction and parameter optimization.

[0101] The core of the second-layer LSTM lies in its recursive propagation mechanism for internal states. Specifically, at each time step, the network uses the hidden state and cell state from the previous time step as input, and combines this with the input data from the current time step to calculate the new hidden state and cell state. This mechanism enables LSTM to capture the changing trends of data in a time series and maintain consistency across multiple time steps. Specifically, the propagation of LSTM cell states between time steps is achieved through a set of dynamic gating mechanisms, including a forget gate, an input gate, and an output gate. The forget gate is responsible for filtering out no longer relevant historical information, the input gate determines which new information needs to be added to the current memory, and the output gate controls which information needs to be output as part of the hidden state.

[0102] The forgetting mechanism is a key component of the second-layer LSTM. It uses a dynamic weight matrix to weight the values ​​of the cell state at the previous time step, determining which historical information should be retained and which should be discarded. For example, if a compressor's operating parameters exhibited periodic fluctuations over a period of time, and the input data at the current time step no longer reflects this periodic trend, the forgetting mechanism automatically reduces the weight of information related to this periodicity, gradually filtering it out of the cell state. This process ensures that the LSTM's memory capacity remains focused on the historical information most relevant to the current operating state, avoiding memory redundancy caused by excessive irrelevant information.

[0103] At the same time, the second-layer LSTM integrates the current time step data into the cell state through the input gate, ensuring that the model can dynamically adapt to real-time changes in operating conditions. The input gate filters the input data for the current time step, allowing only important information to influence the cell state. For example, if the compressor's pressure data rises slowly over a certain period of time, the input gate will increase the weight of pressure-related information based on this trend, giving it a larger proportion in the cell state. This dynamic weight distribution mechanism enables the second-layer LSTM to flexibly integrate long-term and short-term information, supporting the modeling of the compressor's long-term operating characteristics.

[0104] Ultimately, at each time step, the cell state undergoes a nonlinear transformation to generate a new hidden state. This hidden state contains both a dynamic memory of historical information and reflects the important features of the current time step. As the output of the LSTM, the hidden state not only guides compressor fault prediction but also provides the necessary input for subsequent processing layers.

[0105] The steps to train the neural network model are as follows:

[0106] First, prepare multidimensional time series data as input. This data includes preprocessed parameters such as pressure, temperature, vibration, and current. Each sample consists of a fixed time window and is labeled with the corresponding fault category distribution or optimized parameter value. The data should be divided into training, validation, and test sets, and normalized to standardize the parameter value range.

[0107] Next, a neural network model is defined, consisting of an input layer, a spatiotemporal nested encoding layer, a multi-scale feature extraction layer, a recurrent prediction layer, and a fault classification layer. The model weights can be initialized using common initialization methods to ensure numerical stability.

[0108] Next, choose an appropriate loss function, such as the cross entropy loss function for classification tasks or the mean squared error loss function for parameter optimization tasks. Also, set up an optimizer, such as Adam, and configure a dynamic learning rate scheduler to enhance training performance.

[0109] During training, data is fed into the model in small batches. The model then performs a forward pass to calculate the output, compares the loss with the true labels, and finally optimizes the weights through backpropagation. At the end of each training cycle (epoch), the model's performance is evaluated using the validation set to monitor for overfitting. If validation set performance stops improving, consider terminating training early.

[0110] Finally, after completing all training cycles, the model's final performance is verified on a test set, and it is deployed and debugged in real-world application scenarios. Through this process, the model effectively captures the dynamic characteristics of compressor operation, supporting real-time fault prediction and operating parameter optimization.

[0111] Furthermore, the intelligent cloud platform dynamically calculates the optimal operating parameters of the compressor by minimizing the objective function shown in the following formula (5): Implemented:

[0112]

[0113] in, To optimize the operating parameter vector, including the pressure setting value P set , working cycle T cycle and energy balance strategy S balance ; P represents the fault prediction result; λ1,λ2 are weight parameters; Energy(u) is the energy consumption estimation function of the compressor; FaultRisk(u,P) is the risk cost function based on the fault prediction result;

[0114] The energy consumption estimation function Energy(u) is implemented using the following formula (6):

[0115]

[0116] Among them, η eff is the efficiency factor of the compressor;

[0117] The risk cost function is implemented using the following formula (7):

[0118]

[0119] Where P[c] is the probability of occurrence of fault prediction category c; N is the total number of fault prediction categories; φ c (u) is the failure risk cost function, which is implemented using the following formula (8):

[0120] φ c (u)=α c ·|P set ―P opt,c |+β c ·|T cycle ―T opt,c | (8)

[0121] Among them, P set The current optimized pressure setting value; P opt,c is the optimal pressure value associated with fault category c; T cycle The current optimized working cycle; T opt,c is the optimal duty cycle associated with fault category c; c and β c is the sensitivity coefficient.

[0122] The smart cloud platform dynamically optimizes the objective function To calculate the optimal operating parameters of the compressor. This objective function comprehensively considers the energy consumption and failure risk of the compressor, and achieves the pressure setting value P by weighing the impact of the two. set , working cycle T cycle and energy balance strategy S balarce optimal settings, thereby improving the overall performance of the compressor system.

[0123] Objective function The form is as follows:

[0124]

[0125] Among them, the optimization operation parameter vector represents the current compressor operating parameters; λ1 and λ2 are weight parameters used to adjust the relative importance of energy consumption and fault risk; Energy(u) is an energy consumption estimation function that evaluates the operating energy consumption of the compressor; FaultRisk(u,P) is a risk cost function based on the fault prediction results, reflecting the contribution of operating parameters to fault risk.

[0126] The energy consumption estimation function Energy(u) is calculated by the following formula (6):

[0127]

[0128] Here, the efficiency factor η of the compressor is eff It is a proportional parameter that reflects the energy conversion efficiency of the compressor; P set is the pressure setting value, which indicates the target pressure maintained by the compressor during the current operating cycle; S balnnce It is an energy consumption balance strategy used to dynamically adjust energy consumption distribution. The integral calculation represents the energy consumption in the entire working cycle T cycle For example, when P set =8bar, S balance =0.9 and η eff =0.85, if T cycle =10s, then the estimated energy consumption for this cycle is:

[0129]

[0130] Energy balance strategy S balance It is represented by a numerical value (usually in the interval [0,1]) and is used to dynamically adjust the compressor's operating energy consumption distribution and energy-saving priority. Specifically, this value reflects the compressor's trade-off between performance and energy saving:

[0131] S balance =1 means that performance is given full priority. In this case, the compressor will provide the target pressure and operating parameters at the highest energy consumption to ensure that the performance indicators are achieved, regardless of energy conservation needs.

[0132] S balance =0 means that energy saving is given full priority. In this case, the compressor may reduce the target pressure setpoint or reduce the operating cycle to minimize energy consumption, but this may have a certain impact on performance.

[0133] 0 balance <1 indicates a dynamic balance between performance and energy saving. For example, when S balance =0.9, the compressor considers performance with 90% weight and energy saving with 10%. Specifically, this may cause the compressor to slightly reduce the duty cycle frequency or adjust other parameters to reduce energy consumption while maintaining the target pressure.

[0134] The closer the value is to 1, the more emphasis is placed on performance; the closer it is to 0, the more emphasis is placed on energy saving. For example, under high load or emergency conditions, the compressor may balance ​Set it close to 1 to ensure that the device performance meets the requirements. During light load or non-critical operation, the system can reduce S balance, More emphasis is placed on optimizing energy consumption to achieve economical operation.

[0135] The risk cost function FaultRisk(u,P) is calculated by the following formula (7):

[0136]

[0137] Here, P[c] is the probability of occurrence of category c in the fault prediction result; φ c (u) is the risk cost function of fault category c, which is used to evaluate the impact of current operating parameters on the specific fault risk.

[0138] N is the total number of fault categories.

[0139] Failure risk cost function φ c (u) is calculated by the following formula (8):

[0140] φ c (u)=α c ·|P set ―P opt,c |+β c ·|T cycle ―T opt,c |

[0141] Here, P opt,c is the optimal pressure setting value for fault category c. For example, to avoid abnormal vibration, the pressure setting value P opt,1 =7.5bar; T opt,c is the optimal duty cycle for fault category c. For example, for a fault with excessive temperature, the optimal duty cycle T opt,2 =12s;α c and β c is the sensitivity coefficient, controlling the relative impact of pressure and cycle on the risk of failure.

[0142] Through the comprehensive objective function By minimizing calculations, the smart cloud platform can dynamically optimize the operating parameters of the compressor and achieve an effective trade-off between energy consumption and failure risk, thereby ensuring the safety, efficiency and stability of the compressor system.

[0143] The smart terminal 103 interacts with the smart cloud platform and the edge computing unit to display the real-time operating status information of the compressor and the fault prediction results generated by the smart cloud platform. In an emergency, it performs localized emergency operation management and fault handling by directly communicating with the edge computing unit. It supports users to input control instructions through the smart terminal and transmit them to the smart cloud platform or the edge computing unit.

[0144] Smart terminal 103 is a key interface for user interaction with the system. It features display, communication, command input, and emergency management functions, providing users with real-time monitoring of compressor operating status, fault response, and control command transmission. The smart terminal is equipped with a high-resolution touchscreen display. Its built-in hardware includes a processor, communication module, and input module, supporting two-way data communication with the smart cloud platform and edge computing unit.

[0145] The intelligent terminal maintains a wireless or wired connection with the smart cloud platform and edge computing unit, and can receive and display the operating status information of the compressor and the fault prediction results generated by the smart cloud platform in real time. The operating status information includes multi-dimensional operating parameters such as pressure, temperature, vibration and current. This information is presented in a visual form, including line graphs, bar graphs and color-coded status indicators to reflect the dynamic changes and health status of the operating parameters. The fault prediction results are classified and displayed according to the fault type, predicted probability and urgency. Different colors and icons are used to identify the risk level of potential faults to help users quickly understand the current operating status of the system. The display interface supports multi-level view switching. Users can freely switch between the overall monitoring view and the detailed view of a single parameter to obtain the required system information in different scenarios.

[0146] In an emergency, when the smart cloud platform is unable to respond promptly, the smart terminal establishes a direct connection with the edge computing unit through its built-in communication module for point-to-point communication. Through this mechanism, the smart terminal can receive real-time data feedback from the edge computing unit and execute localized emergency management processes, such as prompting the user to activate the compressor's backup mode, reduce operating pressure, or perform an emergency shutdown. Emergency operation instructions are guided by the smart terminal's built-in logic rules and combined with user input confirmation to ensure the reliability and security of decision-making. After executing the emergency process, the smart terminal will also update the operation log in real time and feedback the current system status, so that users can understand the latest operating conditions.

[0147] The smart terminal also supports users to transmit control instructions to the smart cloud platform or edge computing unit through the input interface. The input interface includes virtual buttons, sliders and voice recognition modules on the touch screen. Users can input instructions to adjust operating parameters through simple and intuitive operations, such as changing the pressure setting value, adjusting the working cycle or enabling energy-saving mode. After receiving the instruction, the smart terminal will first perform a legitimacy check to ensure that the instruction is compatible with the current system operating status; then, the instruction will be classified. Emergency instructions will be sent directly to the edge computing unit to ensure a quick response, while adjustment instructions will be transmitted to the smart cloud platform through the communication module. The cloud will generate an adjustment plan based on the global optimization strategy and then send it to the edge computing unit.

[0148] In addition, the smart terminal features user rights management, allowing different users to be assigned different access and operation permissions based on their roles, such as viewing status information, adjusting operating parameters, or performing emergency operations. The smart terminal also integrates local storage and logging capabilities to record operation history, system alarm information, and troubleshooting procedures, and supports uploading this data to the cloud for further analysis.

[0149] Through the above functional design, the intelligent terminal 103 can not only provide users with comprehensive and intuitive system operation information, but also ensure the real-time and reliability of operations in complex operating environments, thereby realizing efficient interaction and intelligent control between users and the compressor air system.

[0150] Furthermore, the smart terminal displays the real-time operating status information of the compressor and the fault prediction results generated by the smart cloud platform through the following steps:

[0151] Receive real-time operating status data, including time series data of key parameters such as compressor pressure, temperature, vibration, current, as well as current operating mode and duty cycle;

[0152] The received data is parsed by the processing module of the intelligent terminal to generate dynamic visual charts, including line charts showing time series data trends, status indicators indicating the current operating mode, and bar charts showing real-time changes in parameter values;

[0153] In the fault prediction result display, a graded display is generated according to the fault type, fault probability and urgency, and color coding is used to distinguish different levels of fault risks.

[0154] Through multiple steps, the smart terminal intuitively displays real-time compressor operating status information and fault prediction results generated by the smart cloud platform, allowing users to quickly understand system performance and promptly identify potential issues. During implementation, the smart terminal receives real-time operating status data from the smart cloud platform through its communication module. This data includes time series information on key compressor parameters such as pressure, temperature, vibration, and current, as well as the current operating mode and duty cycle. This data is stored in a structured format in the smart terminal's memory for subsequent processing.

[0155] The processing module of the intelligent terminal parses and processes the received data, ensuring that the data format matches the display requirements of the visualization component. Time series data is used to generate dynamic line charts, which clearly show the changing trends of operating parameters in recent time periods, helping users quickly detect fluctuations and anomalies. The current operating mode is indicated by status indicators. Different colored indicators indicate the operating status of the system, such as green for normal operation, yellow for precautions, and red for possible failures or abnormal conditions. At the same time, the intelligent terminal displays real-time changes in parameter values ​​through bar charts, allowing users to intuitively compare the relative levels of different parameters, such as whether the current temperature is significantly higher than the pressure value within the normal range. The combination of these charts and indicators provides users with a multi-level information presentation, which not only meets overall monitoring needs but also facilitates detailed analysis of single parameters.

[0156] When displaying fault prediction results, the intelligent terminal categorizes them by fault type, probability of occurrence, and urgency. The fault type clearly identifies the predicted problem, such as bearing wear or motor overload. The probability of the fault occurring is displayed visually as a percentage, helping users assess the likelihood of the risk. The urgency level is color-coded, using a standardized color scheme to distinguish fault risk levels—for example, low risk is displayed in green, medium risk is displayed in yellow, and high risk is displayed in red. This grading mechanism allows users to clearly understand potential system issues and prioritize urgent matters.

[0157] The display interface layout is optimized to ensure a positive user experience across various screen sizes. The interface supports multiple views, allowing users to monitor the trends and status of all parameters in an overall view or select individual parameters or specific fault results for detailed analysis. For enhanced interactivity, users can directly access detailed explanations of the relevant data by clicking on charts or indicators, such as the logic of the fault prediction model and comparisons between historical and current data.

[0158] Through this complete implementation process, the smart terminal can not only display the compressor operating status and fault prediction results in an intuitive, dynamic and hierarchical manner, but also help users quickly judge the system operating status through the integration of multi-dimensional data and the clear presentation of charts, thereby reducing the risk of failure and improving management efficiency.

[0159] Furthermore, in an emergency, the smart terminal directly communicates with the edge computing unit to perform localized emergency operation management and fault handling, specifically including the following steps:

[0160] When a network interruption or cloud command delay is detected, the smart terminal directly establishes a point-to-point communication connection with the edge computing unit through the built-in communication module to ensure the real-time transmission of critical data;

[0161] Receive compressor operating status and emergency description data from the edge computing unit, including current pressure, temperature, and vibration limit information, as well as the timestamp and type of abnormal events;

[0162] Based on the emergency information received, trigger the local preset emergency response process, including reducing operating pressure, switching to standby compressors, or forced shutdown;

[0163] After executing the emergency command, the emergency processing results are displayed to the user through the terminal feedback interface, including the start time of the emergency processing, the operation steps executed, and the current system status;

[0164] The execution records of local processing are stored in the log system of the smart terminal and uploaded to the smart cloud platform for global analysis after the network is restored.

[0165] In an emergency, the smart terminal directly communicates with the edge computing unit to implement specific steps for localized emergency operation management and troubleshooting, ensuring the safety and reliability of the system in the event of cloud unavailability or delays. First, when the smart terminal detects a network interruption or the cloud command cannot respond in a timely manner, its built-in communication module automatically switches to point-to-point communication mode and establishes a direct connection with the edge computing unit. This communication connection can use wireless protocols (such as Wi-Fi Direct) or wired industrial interfaces (such as Modbus) to ensure the real-time and reliability of data transmission. Through this mechanism, the smart terminal can bypass dependence on the cloud and obtain critical data in emergency situations directly from the edge computing unit.

[0166] The data received by the smart terminal from the edge computing unit includes the compressor's current operating status and emergency situation descriptions. This data is transmitted in a structured format and includes real-time values ​​of operating parameters such as pressure, temperature, and vibration, as well as clear abnormal event information, such as the specific parameter that exceeded the limit, the timestamp that triggered the abnormality, and a description of the abnormality type. The processing module within the smart terminal parses this data, matching it with a built-in pre-set rule library to quickly determine the appropriate emergency response measures.

[0167] Upon confirming an emergency, the smart terminal triggers localized emergency response procedures, pre-stored in the terminal's control logic. These procedures can include reducing operating pressure to alleviate equipment load, switching to a backup compressor to maintain production continuity, or, in extremely high-risk situations, forcing a shutdown to protect equipment and the environment. Each emergency response action is executed by the smart terminal, sending control commands to the edge computing unit, ensuring real-time and accurate operation.

[0168] After emergency response is complete, the intelligent terminal displays the results to the user through its feedback interface. These results are presented in a clear format, including the time the emergency response was initiated, the specific steps performed, and the current system status (such as whether the operating pressure has dropped to a safe range or the backup compressor has been activated). Users can review this feedback information on the touchscreen and perform further operations as needed.

[0169] At the same time, the smart terminal stores all localized processing records in a log system. These records include not only the original data and processing results of the abnormal event, but also detailed information about the operation process, such as the time each instruction was sent and the execution confirmation status. Once the network is restored, the smart terminal automatically uploads this log data to the smart cloud platform for global analysis and further optimization of the system's control strategy.

[0170] Through the above steps, the smart terminal can rely on direct communication with the edge computing unit to respond and handle emergencies in a timely manner in the event of network anomalies or cloud command delays, ensuring the operational safety of the compressor air system and providing complete data support for subsequent system optimization.

[0171] Furthermore, the smart terminal supports the user to input control instructions and transmit them to the smart cloud platform or edge computing unit. The specific implementation includes the following steps:

[0172] The user inputs control commands through the input interface of the intelligent terminal, which includes touch screen menu selection, virtual keyboard input or voice command entry;

[0173] The intelligent terminal parses and verifies the legitimacy of the control commands input by the user, including checking the command format, parameter range, and whether it is compatible with the current operating status;

[0174] If the command is verified, the intelligent terminal selects the target receiving end according to the command type:

[0175] The instructions for adjusting operating parameters are sent to the smart cloud platform through the communication module. The cloud platform will globally optimize the instructions and then send them to the edge computing unit.

[0176] Send emergency control instructions directly to the edge computing unit to ensure real-time execution of instructions;

[0177] After the command is executed, the intelligent terminal receives and displays the execution results in real time, including confirmation that the command has been adopted, the current update status of the compressor operating parameters, and whether the execution is successful;

[0178] If the instruction fails to pass the verification, the intelligent terminal generates a prompt message and feeds back the specific error cause to the user.

[0179] Smart terminals support user input control commands through a comprehensive input and processing mechanism. These commands are selectively transmitted to the smart cloud platform or edge computing unit based on the command type, ensuring efficient command execution and feedback. During implementation, users can issue control commands through the smart terminal's various input interfaces. These interfaces include touchscreen menus, where users tap or swipe to select operation options; virtual keyboards, where users can directly enter specific parameter values; and voice recognition modules, where users input control commands via voice. Smart terminal input interfaces support a variety of interaction methods to suit different usage scenarios and user preferences.

[0180] When the user completes the command input, the intelligent terminal will parse and verify the legitimacy of the input command. The parsing process converts the command into a standardized format that the system can recognize, including the target of the parsed command, the type of operation, and related parameters. The legitimacy verification step includes checking whether the format of the command is correct, whether the parameter value is within the preset range, and whether the command is compatible with the current operating status of the compressor. For example, if the current system is running in emergency mode, the input adjustment command may be marked as illegal. Through these checks, the intelligent terminal can filter out invalid commands that may cause system failures.

[0181] For instructions that pass verification, the smart terminal selects the appropriate target receiver based on its type. If the instruction is to adjust operating parameters, such as modifying the pressure setting value or changing the working cycle, the smart terminal will send the instruction to the smart cloud platform through the communication module. The smart cloud platform performs global optimization processing on the instructions, and after comprehensively considering the overall operating status and historical data of the system, it generates a specific execution plan, and then sends the optimized parameters to the edge computing unit for execution. For emergency control instructions, such as immediate shutdown or switching to a standby compressor, the smart terminal will send the instruction directly to the edge computing unit through the communication module to ensure that the instruction can take effect immediately to respond to emergencies.

[0182] After the instruction is executed, the smart terminal receives feedback information from the edge computing unit or the smart cloud platform through the communication module, and displays the execution results to the user in real time. The display content includes whether the instruction has been successfully adopted, the latest status of the current compressor operating parameters, and the actual execution effect of the instruction. For example, if the user modifies the pressure setting value, the smart terminal will display the new pressure value and the stability of the parameters after adjustment in actual operation. If the instruction fails to pass the verification, the smart terminal will generate a prompt message to clearly inform the user of the specific cause of the error, such as "the parameter is out of the allowable range" or "the instruction is incompatible with the current operating mode", so that the user can correct the input in time.

[0183] Through the above implementation steps, the intelligent terminal not only flexibly receives control commands input by users, but also ensures accurate transmission and efficient execution of commands through parsing, verification, and target selection. It also provides a comprehensive feedback mechanism to help users understand system status and operation results. This mechanism provides reliable user interaction support for the intelligent operation of compressor air systems.

[0184] Although the present application is disclosed as above with the preferred embodiments, it is not intended to limit the present application. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A compressor air system management and control system based on the Internet of Things smart cloud platform, characterized in that: include: An edge computing unit is used to collect compressor operating parameters in real time through configured IoT sensors, including pressure, temperature, vibration, and current; perform data denoising, anomaly detection, and pattern recognition on the collected operating parameters to generate pre-processed data; Transmit the pre-processed data to the smart cloud platform; According to the preprocessed data; Based on the pre-processed data, high-priority local rapid response control tasks are executed to deal with emergencies; Smart cloud platform, used to receive pre-processed data uploaded by edge computing units; Use a pre-trained neural network model to predict the operating status of the compressor and generate fault prediction results; Based on the pre-processed data and fault prediction results, dynamically calculate the optimized operating parameters of the compressor, including the pressure setting value, duty cycle and energy consumption balance strategy; Feedback the calculated optimized operating parameters to the edge computing unit for it to make corresponding operational adjustments; The smart terminal interacts with the smart cloud platform and edge computing unit to display the compressor's real-time operating status and fault prediction results generated by the smart cloud platform. In emergency situations, it directly communicates with the edge computing unit to perform localized emergency operation management and fault handling. Users can input control commands through the smart terminal and transmit them to the smart cloud platform or edge computing unit. The neural network model used in the smart cloud platform includes an input layer, a spatiotemporal nested coding layer, a multi-scale feature extraction layer, a cyclic prediction layer, and a fault classification layer; The input layer is used to receive the multidimensional time series data uploaded by the edge computing unit, and process the received data in segments according to fixed time windows to generate initial input tensors; wherein the multidimensional time series data includes pre-processed pressure, temperature, vibration and current data; The spatiotemporal nested coding layer is used to receive the initial input tensor provided by the input layer. The spatiotemporal nested coding layer first uses a one-dimensional convolutional neural network to extract local trend features from the initial input tensor; the spatiotemporal nested coding layer then uses an attention mechanism to adjust the weights of the extracted local trend features to generate a spatiotemporal feature tensor.

2. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 1 is characterized in that: The multi-scale feature extraction layer is used to receive the spatiotemporal feature tensor provided by the spatiotemporal nested coding layer, and extract short-term, medium-term and long-term time series features using multiple parallel multi-scale convolution units with different convolution kernel sizes; The extracted results are combined into a unified multi-scale feature tensor through splicing operations; The cyclic prediction layer is used to receive the data provided by the multi-scale feature extraction layer and combine the extraction results into a unified multi-scale feature tensor through a splicing operation; the cyclic prediction layer uses a two-layer long short-term memory network to process the received data and generate a prediction feature vector; wherein the first layer of the two-layer long short-term memory network learns short-term temporal dependencies, and the second layer learns long-term dependencies; each layer captures the temporal variation trend of the compressor operating state by dynamically updating the hidden state vector; The fault classification layer is used to receive the prediction feature vector provided by the cyclic prediction layer, and use a fully connected neural network to perform linear transformation and nonlinear activation on the prediction feature vector to obtain a predicted probability distribution of the fault type.

3. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 2 is characterized in that: The attention mechanism in the spatiotemporal nested encoding layer is used to adjust the weight relationship between different operating parameters in the initial input tensor, specifically including: The operating parameter correlation score is calculated according to the following formula (1): in, Indicates the Parameters and The attention relevance score of each parameter; is the local trend eigenvector, ,in is the time step, is the number of operating parameters; is the control coefficient of the prior weight, which is used to adjust the relative importance of physical characteristics and data-driven results; It is a hyperparameter that adjusts the decay rate of the relationship between parameters; Represents a vector The transpose of Represents a vector norm; The attention weight is calculated according to the following formula (2): in, For the Parameter pair The attention weight of parameters; According to the following formula 3, the spatiotemporal feature tensor is calculated: in, is the time step and parameters The spatiotemporal feature tensor on ; is the local trend eigenvector Middle Parameters in time step The eigenvalue of .

4. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 2 is characterized in that: The multi-scale convolution unit in the multi-scale feature extraction layer includes three parallel convolution channels, including a short-term convolution channel, a medium-term convolution channel, and a long-term convolution channel; Among them, the convolution kernel size used by the short-term convolution channel is 3, which is used to capture the local characteristics of rapid fluctuations in the compressor operating parameters; the convolution kernel size used by the medium-term convolution channel is 7, which is used to extract the changing trend of medium frequency; the convolution kernel size used by the long-term convolution channel is 15, which is used to capture the slow changes and periodic characteristics of the compressor operating parameters.

5. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 2 is characterized in that: The first layer of the long short-term memory network of the recurrent prediction layer dynamically optimizes the hidden state according to the following formula (4): in, Indicates the current time step The hidden state vector is used to capture the dynamic characteristics of the compressor operating parameters in the short-term time dependence; The output gate is calculated by the output gate of the standard LSTM unit and is used to control the information in the hidden state of the current time step that needs to be output to the next step; is the unit state, which represents the memory state of the current time step; is the number of operating parameters; For the Parameters in time step Convolutional features of is the weight parameter.

6. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 2 is characterized in that: The internal state of the second-layer long short-term memory network of the recurrent prediction layer is propagated between time steps in a recursive manner, and a forgetting mechanism is used to dynamically filter irrelevant information within a long time span, while retaining long-term features that have an impact on the current operating state.

7. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 1 is characterized in that: The intelligent cloud platform dynamically calculates the optimal operating parameters of the compressor by minimizing the objective function shown in the following formula (5): Implemented: in, To optimize the operating parameter vector, including the pressure setpoint , working cycle and energy balance strategies ; Indicates the fault prediction result; is the weight parameter; is the energy consumption estimation function of the compressor; is the risk cost function based on the failure prediction results; The energy consumption estimation function This is achieved using the following formula (6): in, is the efficiency factor of the compressor; The risk cost function is implemented using the following formula (7): in, Is the fault prediction category Probability of occurrence; is the total number of fault prediction categories; is the failure risk cost function, which is implemented using the following formula (8): in, The currently optimized pressure setting value; For fault category The relevant optimal pressure value; Optimized duty cycle for the current situation; For fault category The relevant optimal duty cycle; and is the sensitivity coefficient.

8. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 1 is characterized in that: The smart terminal displays the real-time operating status information of the compressor and the fault prediction results generated by the smart cloud platform through the following steps: Receive real-time operating status data, including time series data of key parameters of the compressor, such as pressure, temperature, vibration, and current, as well as the current operating mode and duty cycle; The received data is parsed by the processing module of the intelligent terminal to generate dynamic visual charts, including line charts showing time series data trends, status indicators indicating the current operating mode, and bar charts showing real-time changes in parameter values; In the fault prediction result display, a graded display is generated according to the fault type, fault probability and urgency, and color coding is used to distinguish different levels of fault risks.

9. The control system of the compressor air system based on the Internet of Things smart cloud platform according to claim 1 is characterized in that: The specific implementation of the smart terminal performing localized emergency operation management and fault handling by directly communicating with the edge computing unit in an emergency includes the following steps: When a network interruption or cloud command delay is detected, the smart terminal directly establishes a point-to-point communication connection with the edge computing unit through the built-in communication module to ensure the real-time transmission of critical data; Receive compressor operating status and emergency description data from the edge computing unit, including current pressure, temperature, and vibration limit information, as well as the timestamp and type of abnormal events; Based on the emergency information received, trigger the local preset emergency response process, including reducing operating pressure, switching to standby compressors, or forced shutdown; After executing the emergency command, the emergency processing results are displayed to the user through the terminal feedback interface, including the start time of the emergency processing, the operation steps executed, and the current system status; The execution records of local processing are stored in the log system of the smart terminal and uploaded to the smart cloud platform for global analysis after the network is restored.

10. The compressor air system management and control system based on the Internet of Things smart cloud platform according to claim 1 is characterized in that: The specific implementation of the intelligent terminal supporting the user to input control instructions and transmit them to the smart cloud platform or edge computing unit includes the following steps: The user inputs control commands through the input interface of the intelligent terminal, which includes touch screen menu selection, virtual keyboard input or voice command entry; The intelligent terminal parses and verifies the legitimacy of the control commands input by the user, including checking the command format, parameter range, and whether it is compatible with the current operating status; If the command is verified, the intelligent terminal selects the target receiving end according to the command type: The instructions for adjusting operating parameters are sent to the smart cloud platform through the communication module. The cloud platform will globally optimize the instructions and then send them to the edge computing unit. Send emergency control instructions directly to the edge computing unit to ensure real-time execution of instructions; After the command is executed, the intelligent terminal receives and displays the execution results in real time, including confirmation that the command has been adopted, the current update status of the compressor operating parameters, and whether the execution is successful; If the instruction fails to pass the verification, the intelligent terminal generates a prompt message and feeds back the specific error cause to the user.

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