Internet-based remote monitoring system for magnetic levitation air supply machine

Abstract

The application discloses an internet-based remote monitoring system for a magnetic levitation air blower, and relates to the technical field of remote control.The system comprises the following steps: analyzing air duct structure adaptation sets, dynamically matching the operation mode of the magnetic levitation air blower, and outputting air blower operation state parameters; performing structured packaging and encryption processing on the air blower operation state parameters through a multi-frequency wireless communication integration device, selecting a communication frequency band for uploading according to a network state, and generating frequency band encryption data sets; decrypting and format-analyzing the frequency band encryption data sets in a cloud monitoring center, comparing and analyzing the frequency band encryption data sets with magnetic levitation air blower operation history data, and generating an operation state diagnosis report.The application dynamically regulates and controls air duct parameters based on airflow environment optimization instructions, continuously optimizes air duct configuration parameter sets in combination with feedback data, realizes real-time self-adaptive adjustment of air duct structures, and provides a solid foundation for efficient and intelligent remote monitoring.

Description

Technical Field

[0001] This invention relates to the field of remote control technology, and in particular to an Internet-based remote monitoring system for magnetic levitation blowers. Background Technology

[0002] In modern industrial and building environments, magnetic levitation fans are widely used in air purification, ventilation, and temperature control systems due to their advantages such as high efficiency, low noise, and long lifespan. To achieve remote monitoring of the operating status of these fans, traditional methods typically rely on local sensors to collect key parameters and then upload the data to a monitoring center via wired or single-band wireless communication. Based on this, empirical control logic is used to adjust the fan's operating mode to adapt to environmental changes. Furthermore, some monitoring centers have introduced basic data encryption and historical data analysis functions to improve communication security and equipment operational stability.

[0003] However, existing technologies still have limitations in terms of adaptability to dynamic environments and precision of remote control. On the one hand, traditional methods mostly use static or semi-static duct structure settings, which are difficult to respond to complex and ever-changing airflow environments in real time. On the other hand, in terms of remote control strategies, there is a general lack of dynamic matching mechanisms based on high-precision CFD simulation and multi-objective optimization algorithms, resulting in insufficient precision in the adjustment of the blower's operating status, which affects the overall energy efficiency and stability. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an Internet-based remote monitoring system for magnetic levitation fans to solve the problems of insufficient dynamic adaptation of duct structure and inaccurate remote operation status adjustment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an internet-based remote monitoring system for a magnetic levitation blower, comprising: a data acquisition module for acquiring and preprocessing environmental parameters of the magnetic levitation blower to generate airflow environment optimization commands; an adjustment module for adjusting duct parameters using duct control components based on the airflow environment optimization commands to generate a duct structure adaptation set; a matching module for parsing the duct structure adaptation set and dynamically matching the operating mode of the magnetic levitation blower to output blower operating status parameters; an encryption module for structurally encapsulating and encrypting the blower operating status parameters using a multi-band wireless communication integration device, and uploading the data by selecting a communication frequency band based on network conditions to generate a frequency band encrypted dataset; a parsing module for decrypting and format parsing the frequency band encrypted dataset at a cloud monitoring center, comparing and analyzing it with historical operating data of the magnetic levitation blower to generate an operating status diagnostic report; and an evaluation module for evaluating the operating status of the magnetic levitation blower using the Free Time Adaptive Test (FTA) method based on the operating status diagnostic report, and performing remote adjustment and maintenance updates based on the evaluation results.

[0008] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation fans described in this invention, the environmental parameters of the magnetic levitation fans include airflow velocity and pressure distribution, temperature and humidity, and spatial structure information.

[0009] The preprocessing includes filtering and denoising, data correction, time alignment, interpolation completion, and feature extraction;

[0010] The generated airflow environment optimization command is generated by analyzing the preprocessed environmental parameters using aerodynamic modeling and multi-objective optimization algorithms.

[0011] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation fans described in this invention, the steps of adjusting duct parameters using a duct control component based on airflow environment optimization commands to generate a duct structure adaptation set are as follows:

[0012] Analyze the parameter differences between the target parameters in the airflow environment optimization command and the actual operating data of the magnetic levitation fan, and generate duct parameters;

[0013] The rule-based decision tree mapping method is used to parse and map the airflow environment optimization command, and the control logic is formulated through rule reasoning to generate the air duct structure adjustment strategy.

[0014] Based on the air duct structure adjustment strategy, the air duct control component is used to dynamically adapt and control the air duct parameters to generate an air duct configuration parameter set.

[0015] The system collects feedback data from inside the duct, compares the feedback data with the target parameters in the airflow environment optimization command, generates control deviation, and dynamically adjusts the duct configuration parameter set based on the control deviation to form an updated duct configuration parameter set.

[0016] The updated duct configuration parameter set is normalized to generate a duct structure adaptation set.

[0017] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation fans described in this invention, the specific steps of using CFD to analyze the duct structure fit set are as follows:

[0018] Import the duct structure adapter set into CFD modeling to create a 3D duct structure, perform mesh generation, and output the duct mesh data set.

[0019] The CFD solver is used to simulate and analyze the duct mesh data set to generate duct CFD flow field data. The duct CFD flow field data is then analyzed using the CFD simulation comprehensive processing method to generate duct CFD data.

[0020] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation blowers described in this invention, the specific steps for dynamically matching the operating mode of the magnetic levitation blower and outputting the blower's operating status parameters are as follows:

[0021] The CFD data of the air duct is matched and filtered with the operating parameters in the operating mode parameter library of the magnetic levitation fan to generate the operating mode matching results.

[0022] The operating mode matching results are integrated with the duct CFD data to generate the operating status parameters of the blower.

[0023] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation blowers described in this invention, the specific steps for performing structured encapsulation and encryption of the blower's operating status parameters through a multi-band wireless communication integration device are as follows:

[0024] Field mapping and structured organization of the blower's operating status parameters are performed to form structured transmission data;

[0025] The structured transmission data is encrypted using the AES-256 encryption algorithm to generate encrypted runtime status data.

[0026] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation fans described in this invention, the specific steps for selecting a communication frequency band for uploading based on network status and generating a frequency band encrypted dataset are as follows:

[0027] Collect network status information in the current communication environment and select the communication frequency band according to the dynamic frequency band adaptation method;

[0028] The encrypted operational status data is sent to the cloud monitoring center using the selected communication frequency band, and the frequency band encrypted dataset is output.

[0029] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation blowers described in this invention, the cloud monitoring center decrypts and parses the frequency band encrypted dataset by using a symmetric key encryption mechanism, decrypting the frequency band encrypted dataset according to a preset security key, generating a frequency band decrypted dataset, parsing the format, and outputting the parsed operating status data.

[0030] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation blowers described in this invention, the specific steps for comparing and analyzing historical operating data of the magnetic levitation blower to generate an operational status diagnostic report are as follows:

[0031] Extract historical operation data that matches the parsed operation status data from the historical operation database of the magnetic levitation fan, and compare and analyze the parsed operation status data with the historical operation data to generate comparison results;

[0032] Based on the comparison results, and combining the fault determination rules and health assessment standards, the operational status diagnostic report is output through the fault determination rule-driven diagnostic generation method.

[0033] As a preferred embodiment of the Internet-based remote monitoring system for magnetic levitation blowers described in this invention, the following steps are taken: Based on the operational status diagnostic report, the operating status of the magnetic levitation blower is evaluated using the Free Time Assessment (FTA) method, and remote adjustment and maintenance updates are performed according to the evaluation results.

[0034] Analyze and evaluate the operational status diagnostic report to obtain the operational status assessment results;

[0035] Analyze the risks and faults in the operational status assessment results to generate remote operation decisions;

[0036] Based on remote operation decisions, the rotational speed of the magnetic levitation blower is adjusted, and the remotely adjusted operating parameters are output.

[0037] Based on the abnormal operation characteristics and remote operation decisions in the operational status assessment results, the magnetic levitation fan was maintained and updated.

[0038] The beneficial effects of this invention are as follows: By matching, filtering, and integrating duct CFD data with an operating mode parameter library, it can accurately identify and output blower operating status parameters that highly match the current duct state, significantly improving the system's adaptability and control accuracy to complex airflow environments. By dynamically adjusting duct parameters based on airflow environment optimization commands and continuously optimizing the duct configuration parameter set using feedback data, real-time adaptive adjustment of the duct structure is achieved, providing a solid foundation for efficient and intelligent remote monitoring. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of an internet-based remote monitoring system for magnetic levitation blowers.

[0041] Figure 2 This is a schematic diagram of an internet-based remote monitoring system for magnetic levitation fans.

[0042] Figure 3 This is a flowchart of CFD parsing and matching.

[0043] Figure 4 This is a flowchart for dynamic adjustment and maintenance. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0047] Reference Figures 1-4As one embodiment of the present invention, this embodiment provides an Internet-based remote monitoring system for magnetic levitation fans, comprising the following steps:

[0048] The data acquisition module collects environmental parameters of the magnetic levitation blower and preprocesses them to generate airflow environment optimization instructions.

[0049] The environmental parameters of a magnetic levitation fan include airflow velocity and pressure distribution, temperature and humidity, and spatial structure information.

[0050] It should be noted that the airflow speed is collected by an anemometer, and the airflow speed distribution at different cross-sectional locations is recorded; the pressure changes inside the duct and the surrounding environment are measured by a pressure sensor to form pressure distribution data; multiple temperature and humidity sensors are arranged in the operating area of ​​the blower to collect air temperature and relative humidity values, for example, when the temperature is 25 degrees Celsius and the humidity is 60%; a laser rangefinder is used to obtain the spatial dimensions and obstacle layout information of the blower installation area to construct spatial structure information, which is used to describe the air supply path and environmental boundary conditions.

[0051] Preprocessing includes filtering and denoising, data correction, time alignment, interpolation completion, and feature extraction;

[0052] Specifically, low-pass filtering is used to process airflow velocity and pressure distribution data to remove high-frequency noise interference, such as attenuating components with frequencies higher than 10 Hz in the airflow velocity signal; offset correction and sensitivity adjustment of environmental parameters are performed based on sensor calibration parameters, such as correcting 26 degrees Celsius measured by the temperature sensor to 25.3 degrees Celsius under standard reference conditions; environmental parameters are synchronized using a unified time base to eliminate time deviations caused by differences in sampling timing, such as aligning airflow velocity, temperature, and humidity signals to the same timestamp sequence; for periods with missing data, linear interpolation is used to complete the data sequence, such as estimating and filling in missing humidity values ​​within a certain second using values ​​from two seconds before and after; statistical and frequency domain characteristics of environmental parameters are extracted from the time series data, such as the mean and variance of airflow velocity and the dominant frequency component of the pressure signal.

[0053] Aerodynamic modeling and multi-objective optimization algorithms are used to analyze the preprocessed environmental parameters and generate airflow environment optimization commands.

[0054] Furthermore, an aerodynamic model of the interior and surrounding environment of the duct is constructed to describe the flow characteristics of the airflow under different boundary conditions. Based on the preprocessed environmental parameters, the geometric boundaries and obstacle layout of the duct are determined, and the boundary conditions of the airflow inlet and outlet are set, including the inlet wind speed and outlet pressure values. The computational grid is divided in three-dimensional space, and the resolution of key flow regions is improved by using a local mesh refinement method for the interior region of the duct. The standard k-ε turbulence model is applied to describe the airflow behavior, and steady-state simulation is performed using a CFD solver to obtain the aerodynamic model.

[0055] Based on the aerodynamic model, the airflow uniformity, energy consumption minimization, and response time minimization are used as evaluation indicators for multi-objective optimization. Under given air supply angle, wind speed range, and spatial boundary conditions, the control parameters are iteratively calculated using the NSGA-II multi-objective optimization algorithm. For example, with the inlet wind speed varying in the range of 6 to 10 m / s as a constraint, multiple non-dominated Pareto optimal solutions are obtained.

[0056] The control parameters are iteratively calculated using the NSGA-II multi-objective optimization algorithm, and the expression is:

[0057]

[0058] Where f1(x) represents the airflow uniformity score under the current control parameter combination x. Let ε represent the spatial variance of the airflow velocity field, and let ε represent the smallest normal quantity.

[0059] Control parameters refer to adjustable physical quantities that affect the characteristics of the airflow, including air supply angle, inlet velocity, and air supply pressure. Based on obtaining multiple Pareto optimal solutions, and combining the actual values ​​of temperature and humidity, airflow velocity, and pressure distribution under the current operating conditions, a multi-attribute decision-making method is used to weighted evaluate the performance of each Pareto optimal solution on different performance indicators. Weighted evaluation involves assigning weights according to the importance of different performance indicators, performing weighted statistics on the performance of each Pareto optimal solution on each indicator, and obtaining a comprehensive score, thereby ranking and optimizing all solutions. By comparing the differences in the comprehensive scores of each evaluation, the air supply strategy that best suits the current environmental conditions and operational requirements is selected. The air supply strategy includes control parameters such as air supply angle, inlet velocity, and air supply pressure, and is ultimately compiled into airflow environment optimization instructions.

[0060] The adjustment module, based on the airflow environment optimization command, uses the air duct control component to adjust the air duct parameters and generate an air duct structure adaptation set.

[0061] Deviation analysis is used to analyze the parameter differences between the target parameters in the airflow environment optimization command and the actual operating data of the magnetic levitation fan, and to generate duct parameters.

[0062] Specifically, the process involves acquiring target parameters from the airflow environment optimization command, such as desired airflow velocity, pressure distribution, and airflow uniformity. Simultaneously, it collects actual operational feedback data from the magnetic levitation fan under its current operating state, including real-time airflow velocity, pressure, and temperature values ​​measured by sensors. The target parameters are then compared item by item with the corresponding actual operational feedback data, and the difference between the two is calculated as the parameter difference. For example, if the target airflow velocity at a certain location is set to 8.0 m / s, while the actual operational feedback data shows 7.5 m / s, the parameter difference is 0.5 m / s. Based on the direction and magnitude of the parameter difference, and combined with a rule-based reasoning mechanism, the process determines the direction and magnitude of adjustments needed to the duct structure. Finally, it outputs specific parameters to guide the duct structure adjustment. The rule-based reasoning mechanism refers to the process of analyzing and judging parameter differences based on set logical conditions and parameter thresholds, and deriving duct structure adjustment strategies according to predetermined rules. Analyzing and judging parameter differences involves comparing the target parameters in the airflow environment optimization command with the actual operational feedback data of the magnetic levitation blower to obtain parameter deviation values. Based on set logical conditions and parameter thresholds, it is determined whether the difference is within the allowable range. For example, if the air supply angle difference exceeds ±2 degrees or the pressure gradient change rate is higher than 0.5 kPa / m, it is considered abnormal. The set logical conditions and parameter thresholds are based on equipment design specifications, historical operating data, and actual operating conditions. The set logical conditions refer to the logical rules used to determine whether the parameter status is normal, and the parameter thresholds refer to the allowable range boundary values ​​set for key operating parameters, used to measure whether the deviation between the actual parameters and the target parameters is within an acceptable range. For example, the air supply angle deviation threshold is ±2 degrees, and the pressure gradient change rate threshold is 0.5 kPa / m.

[0063] The rule-based decision tree mapping method is used to parse and map the airflow environment optimization command, and the control logic is formulated through rule reasoning to generate the air duct structure adjustment strategy.

[0064] It should be noted that the air supply angle, inlet velocity, and air supply pressure values ​​in the airflow environment optimization command are used as input conditions. The input conditions are then classified and matched hierarchically according to a decision tree structure. For example, when the inlet velocity is greater than 8 m / s and the air supply angle is 35 degrees, the corresponding decision node is matched. The decision tree structure is a logical framework composed of a series of hierarchical nodes and branches, used to judge and classify the input conditions layer by layer and map them to the corresponding output results. In each decision node, rule-based reasoning is applied, combining the duct's geometric boundaries and spatial structure information to derive a duct structure adjustment method adapted to the current airflow state, such as adjusting the duct's bending angle based on the location of obstacles. The final output is a duct structure adjustment strategy containing parameters such as duct shape, cross-sectional dimensions, and guide component positions.

[0065] Based on the air duct structure adjustment strategy, the air duct control component is used to dynamically adapt and control the air duct parameters to generate an air duct configuration parameter set.

[0066] Specifically, based on information such as duct shape, cross-sectional dimensions, and guide component positions in the duct structure adjustment strategy, and combined with the current actual duct layout and spatial structure information, the differences between the target parameters in the strategy and the existing parameters are matched one by one; based on the values ​​in the airflow environment optimization instructions such as air supply angle, inlet velocity, and air supply pressure, it is verified whether the selected geometric parameters meet the airflow dynamic matching requirements; the matching requirements refer to the verification and comparison of whether the adjusted geometric parameters and installation positions meet the target parameters and control conditions specified in the airflow environment optimization instructions during the duct structure adjustment process; finally, the duct geometric parameters and installation positions are determined.

[0067] Under the execution control of the duct control component, the duct bending angle, cross-sectional width, and guide plate deflection angle are adjusted item by item. For example, the duct bending angle is adjusted from 25 degrees to 30 degrees, and the cross-sectional width is adjusted from 0.5 meters to 0.6 meters. After each parameter is adjusted, the feedback mechanism verifies whether the current setting meets the strategy requirements. The strategy requirements include target settings for parameters such as duct shape, cross-sectional size, and guide component position, as well as dynamic control standards that match the airflow environment optimization instructions. All adjusted parameters are then integrated into a duct configuration parameter set.

[0068] The system collects feedback data from inside the duct, uses the difference calculation method to compare the feedback data with the target parameters in the airflow environment optimization command, generates control deviation, and dynamically adjusts the duct configuration parameter set according to the control deviation to form an updated duct configuration parameter set.

[0069] Furthermore, sensors collect actual airflow velocity and pressure distribution, temperature and humidity, and spatial structure information inside the duct as feedback data. The collected feedback data is then compared with the target parameters set in the airflow environment optimization command to calculate the difference and generate control deviation. For example, the actual measured inlet wind speed of 8.2 m / s is compared with the target value of 8 m / s to obtain a control deviation of 0.2 m / s. Based on the magnitude and direction of the control deviation, parameters such as the duct bending angle, cross-sectional width, and guide plate deflection angle corresponding to the duct configuration parameter set are corrected. For example, the duct bending angle is adjusted from 30 degrees to 29 degrees to reduce the deviation. Finally, an updated duct configuration parameter set is formed.

[0070] The updated duct configuration parameter set is normalized using the parameter template filling method to generate a duct structure adaptation set.

[0071] It should be noted that the updated duct configuration parameter set is categorized and arranged according to the parameter template format. The parameter template format refers to the standardized structure used to standardize the arrangement order, field names, and data types of duct configuration parameters, ensuring that duct parameters from different sources or under different conditions can be organized and processed in a unified manner. The updated duct configuration parameter set is then subjected to unit unification and numerical range standardization processing. For example, all angle parameters are converted to relative values ​​within the range of 0 to 45 degrees, and all length parameters are converted to standard values ​​in meters. Finally, a duct structure adaptation set with a consistent structure and standardized format is output.

[0072] The matching module parses the duct structure adaptation set and dynamically matches the operating mode of the magnetic levitation blower, outputting the blower's operating status parameters.

[0073] Import the duct structure adapter set into CFD modeling to establish the 3D structure of the duct, and use the local mesh refinement method to divide the mesh and output the duct mesh data set.

[0074] Specifically, the main outline of the air duct is defined based on the duct structure parameters. These parameters are physical quantities used to describe and define the geometry and internal structural features of the air duct, including specific values ​​such as the duct bending angle, cross-sectional width, and guide plate deflection angle. The inlet and outlet positions are then set, and guiding components are arranged within the duct's geometric space, forming a complete geometric model of the internal flow region of the air duct.

[0075] Based on the geometric model of the internal flow region of the duct, the inlet, outlet and wall boundary conditions of the duct are defined in the CFD modeling environment to complete the digital reconstruction of the duct's three-dimensional structure. In areas with large airflow velocity gradients in the duct's three-dimensional structure, such as duct bends and near guide plates, a local mesh refinement method is used to increase the mesh density of the geometric model to improve the accuracy of flow feature capture. Finally, a duct mesh data set is generated.

[0076] The CFD solver is used to simulate and analyze the duct mesh data set to generate duct CFD flow field data. The duct CFD flow field data is then analyzed using the CFD simulation comprehensive processing method to generate duct CFD data.

[0077] Furthermore, the duct mesh data set is imported into the CFD solver, and the standard k-ε turbulence model is applied to numerically solve the airflow process inside the duct under the set boundary conditions. The set boundary conditions refer to the physical quantity constraints set on the geometric boundary of the duct model in the CFD simulation analysis, which are used to describe the behavior characteristics of the airflow at the inlet, outlet and wall.

[0078] The velocity and pressure field distributions at various locations inside the duct are obtained through iterative calculations, generating CFD flow field data for the duct. The CFD simulation comprehensive processing method is used to extract features and structure the CFD flow field data of the duct, including extracting key parameters such as the mean airflow velocity and the rate of change of pressure gradient. Finally, the duct CFD data is generated.

[0079] The NSGA-II algorithm is used to match and filter the CFD data of the air duct with the operating parameters in the operating mode parameter library of the magnetic levitation fan to generate operating mode matching results.

[0080] It should be noted that multiple sets of operating parameters are extracted from the operating mode parameter library of the magnetic levitation fan, including different combinations of air supply angle, inlet velocity, and air supply pressure. The NSGA-II algorithm is used to perform non-dominated sorting and fitness evaluation on all combinations of operating parameters. For example, in the combination of air supply angle of 35 degrees and inlet velocity of 8 m / s, the matching item that is closest to the current airflow distribution is found. Finally, multiple sets of operating mode matching results with the highest degree of fit are output.

[0081] The operating mode matching results are integrated with the duct CFD data using a multi-parameter weighted mapping method to generate the operating status parameters of the blower.

[0082] Specifically, the key parameters such as the air supply angle, inlet velocity, and air supply pressure values ​​in the operation mode matching results, and the mean airflow velocity and pressure gradient change rate in the duct CFD data are normalized respectively. A corresponding weight coefficient is set for each parameter. The corresponding weight coefficient refers to the importance ratio value assigned to each parameter in the operation mode matching results and the duct CFD data, which is used to reflect the relative priority of the influence of different parameters on the final operation status of the blower in the multi-parameter weighted mapping process.

[0083] By linearly weighting the comprehensive scores of each parameter and combining the set parameters in the operation mode matching results with the flow characteristics in the duct CFD data, a mapping relationship between airflow output performance and duct response behavior is established. The airflow output performance and duct response behavior are then integrated to generate blower operation status parameters, indicating the overall operational performance of the magnetic levitation blower under a specific duct environment. Airflow output performance refers to the airflow characteristics generated by the magnetic levitation blower under its current operating state, including the actual performance of parameters such as air delivery angle, outlet velocity, airflow uniformity, and dynamic pressure distribution. Duct response behavior refers to the internal flow state of the duct system under the influence of the magnetic levitation blower's airflow, including dynamic characteristics such as pressure field distribution, velocity field changes, vortex region formation, and pressure gradient change rate.

[0084] The comprehensive score of each parameter is calculated by linear weighting, and the expression is as follows:

[0085]

[0086] Where i represents the parameter number, w i x represents the actual value of the i-th running parameter. i represents the weight coefficient of the i-th parameter, n represents the total number of parameters involved in the weighted calculation, and S represents the linear weighted composite score of all parameters.

[0087] The encryption module performs structured encapsulation and encryption processing of the blower's operating status parameters through a multi-band wireless communication integration device, and selects the communication frequency band for uploading based on the network status to generate a frequency band encrypted dataset.

[0088] The field template matching method is used to map and organize the operating status parameters of the blower into fields, forming structured transmission data;

[0089] It should be noted that, according to the field template format, the airflow output characteristics and duct response characteristics in the blower operating status parameters are matched one by one according to field name, data type, and arrangement order. Each blower operating status parameter undergoes format standardization processing; for example, the airflow angle value is converted to degrees and retained to one decimal place, and the average airflow velocity is expressed in meters per second. At the field level, the parameters are matched with their corresponding fields in the field template to ensure that all parameter content conforms to the structural specifications defined in the template. The structural specifications defined in the field template refer to the unified rules set for data organization in the field template, including field name, data type, numerical format, arrangement order, and allowed value range. Finally, structured transmission data with a unified format and clear field identifiers is generated.

[0090] The structured transmission data is encrypted using the AES-256 encryption algorithm to generate encrypted runtime status data.

[0091] Specifically, the field names and values ​​in the structured transmission data are sequentially converted into their corresponding byte representations using binary encoding. The AES-256 encryption algorithm is then used for block encryption in a 256-bit key mode. For example, CBC mode is used in conjunction with an initialization vector to encrypt the data blocks segment by segment. The encrypted data is then encoded and encapsulated to ensure compatibility and integrity in different communication environments. Finally, the encrypted running status data is output.

[0092] Collect network status information in the current communication environment and select the communication frequency band according to the dynamic frequency band adaptation method;

[0093] It should be noted that network status information such as signal strength, channel interference level, and available frequency band list in the communication environment is obtained through the wireless communication interface. For example, it is detected that the channel occupancy rate of the 2.4GHz band is 65% and the channel occupancy rate of the 5GHz band is 30%. The communication quality of each available frequency band is evaluated and ranked according to the dynamic frequency band adaptation method, and the optimal transmission frequency band is determined by comprehensively considering the signal stability and bandwidth utilization indicators. The communication frequency band with the least channel interference and the highest transmission rate is selected.

[0094] The encrypted operational status data is sent to the cloud monitoring center using the selected communication frequency band, and the frequency band encrypted dataset is output.

[0095] Furthermore, the encrypted operational status data is encapsulated into a data packet format conforming to the wireless communication protocol. The data packets are modulated and transmitted according to the set communication parameters using a multi-band wireless communication integration device, ensuring stable data transmission within the selected frequency band. The set communication parameters refer to the various technical parameters pre-configured and used to control the operating status of the wireless transmission module during wireless data transmission, based on the selected communication frequency band and network environment requirements. Finally, the complete data content under the selected frequency band is received and recorded at the cloud monitoring center, forming a frequency band encrypted dataset.

[0096] The parsing module decrypts and parses the encrypted dataset of the frequency band in the cloud monitoring center, compares and analyzes it with the historical operating data of the magnetic levitation fan, and generates an operating status diagnostic report.

[0097] The frequency band encrypted dataset is decrypted using a symmetric key encryption mechanism and a preset security key, generating a frequency band decrypted dataset. The dataset is then parsed using a predefined format parsing method, and the parsed running status data is output.

[0098] Specifically, a symmetric key encryption mechanism is adopted. At the cloud monitoring center, a pre-configured 256-bit security key is used to decrypt the data packets in the frequency band encrypted dataset block by block. For example, CBC mode is used with the corresponding initialization vector to restore the byte sequence. The decrypted byte sequence is then processed for character encoding recognition and field segmentation according to a predefined format parsing method. Based on the field template matching rules, the airflow output characteristics and duct response characteristics in the blower's operating status parameters are extracted. Finally, parsed operating status data with clear field identifiers and numerical representations is generated.

[0099] The operating condition similarity matching method is used to extract historical operating data that matches the parsed operating status data from the historical operating database of the magnetic levitation fan. The parsed operating status data and the historical operating data are compared and analyzed by multi-dimensional data analysis to generate comparison results.

[0100] Furthermore, multiple key parameters in the parsed operational status data are organized into a set of numerical features according to rules to construct a feature vector for the current operational status. For example, parameters include an air supply angle of 35 degrees and an average airflow velocity of 8.2 m / s. In the historical operational database of the magnetic levitation fan, the operating condition similarity matching method is used to retrieve historical records, calculate the cosine similarity between the current feature vector and each historical data, and select the historical operational data with the highest similarity as the matching sample. Rule organization refers to the process of integrating multiple key parameters in the parsed operational status data into a structured feature vector according to a predefined parameter arrangement order, data format, and weight allocation method.

[0101] Multidimensional data analysis is applied to compare the parsed operational status data with the matched historical operational data in multiple dimensions such as time series, parameter distribution and change trend, to identify differences. For example, it was found that the current airflow speed fluctuation amplitude is 15% higher than that of historical data. The final output includes comparison results containing difference indicators and matching degree.

[0102] Based on the comparison results, and combining the fault determination rules and health assessment standards, the operational status diagnostic report is output through the fault determination rule-driven diagnostic generation method.

[0103] It should be noted that the comparison results are used as input to extract the deviation values ​​and trend change characteristics of key parameters. For example, the current airflow velocity fluctuation is 15% higher than historical data, and the pressure gradient change rate exceeds the airflow pressure anomaly judgment threshold of 0.5 kPa / m. The airflow pressure anomaly judgment threshold is the maximum pressure change rate within the allowable range determined by the design specifications of the duct system and the statistical analysis of historical operating data under stable operation of the magnetic levitation fan. It is used to judge whether the current airflow state is abnormal, and the reasonable range is 0.1 kPa / m to 0.5 kPa / m. The parsed operating status data is judged according to the fault judgment rules to determine whether it exceeds the normal range, and the judgment result is generated. If the air supply angle deviation exceeds ±2 degrees, it is marked as abnormal. The fault judgment rules refer to the pre-set logical conditions used to judge whether the operating parameters of the magnetic levitation fan exceed the normal range and to identify abnormal or fault states. The normal range refers to the reasonable value range of each key operating parameter allowed under stable operation of the magnetic levitation fan, which is usually set according to the equipment design specifications and historical operating data.

[0104] A health assessment standard is applied to classify and evaluate the overall operational status of the magnetic levitation fan. The analyzed operational status data is compared with health assessment indicators, and a weighted comprehensive scoring method is used to derive the assessment indicators. This method calculates a comprehensive score reflecting the overall operational health level of the magnetic levitation fan based on the degree of conformity between the analyzed operational status data and the health assessment standard, combined with weighting coefficients. The health assessment indicators are based on the magnetic levitation fan's design parameters, historical operational data, and HVAC industry standards. A rule-driven diagnostic generation method integrates all judgment results and assessment indicators into a structured text report, outputting an operational status diagnostic report that includes operational status conclusions, explanations of anomalies, and the health level.

[0105] The assessment module uses the FTA method to assess the operating status of the magnetic levitation blower based on the operating status diagnostic report, and performs remote adjustment and maintenance updates based on the assessment results.

[0106] Fault tree analysis is used to analyze and evaluate the operational status diagnostic report to obtain operational status evaluation results;

[0107] Furthermore, using the anomaly descriptions, parameter deviations, and health scores recorded in the operational status diagnostic report as input information, a logical causal relationship diagram of the magnetic levitation blower's operational failures is constructed using fault tree analysis. Following the hierarchical structure of fault tree analysis, starting from the top-level event (such as abnormal airflow output), the logical causal relationship diagram is decomposed layer by layer into intermediate and basic events. For example, abnormal airflow output is decomposed into sub-events such as excessive airflow angle deviation and excessive pressure gradient change rate. The probability of occurrence of each basic event is determined by combining fault judgment rules and historical statistical data. The probability of occurrence refers to the numerical representation of the likelihood of a basic or intermediate event occurring in the fault tree analysis under specific operating conditions and historical statistical data, typically defined as a real number between 0 and 1. Finally, through qualitative analysis, the reliability level and failure risk level of the magnetic levitation blower's current operational status are determined, generating a structured operational status assessment result.

[0108] The strategy matching and judgment method is used to analyze the risks and faults in the operational status assessment results and generate remote operation decisions.

[0109] Specifically, based on the reliability level and failure risk included in the operational status assessment results, classification and matching are carried out according to the risk grading standard. For example, events with a high failure risk level are classified as priority items. The risk grading standard refers to the unified evaluation criteria used to classify and categorize failure risk levels in the operation assessment of magnetic levitation blowers. It is set based on historical operating data, equipment performance boundaries and failure impact, and usually includes multiple risk level ranges and corresponding judgment conditions.

[0110] The fault characteristics in the operational status assessment results are compared with the risk level and matching conditions in the rule base. Different types of fault characteristics are mapped to risk levels. For example, for abnormal matching of guide plate control signals, operation instructions are executed to adjust control parameters and restart the drive module. Finally, remote operation decisions adapted to the current operational status are output.

[0111] Based on remote operation decision-making, the rotational speed of the magnetic levitation blower is adjusted using the parameter optimization and adjustment method, and the remotely adjusted operating parameters are output.

[0112] It should be noted that, based on the operating instructions and target control values ​​specified in the remote operation decision, the target speed that the magnetic levitation blower should currently be adjusted to is determined. For example, the current speed may be increased from 12,000 rpm to 12,500 rpm to improve airflow output stability. A parameter optimization adjustment method is used to perform multi-objective optimization analysis on the acceleration curve, energy consumption changes, and vibration response during the speed adjustment process, setting the optimal adjustment step size and transition time, for example, gradually adjusting to the target value at a rate of 200 rpm per second. Finally, the speed change is completed under the specified adjustment strategy, and the remotely adjusted operating parameters, including the actual adjustment results and operating status feedback, are output. The specified adjustment strategy refers to the specific control rules, such as the speed adjustment method, adjustment step size, and transition time, determined according to the remote operation decision and the parameter optimization adjustment method.

[0113] Based on the abnormal operation characteristics and remote operation decisions in the operational status assessment results, the magnetic levitation fan was maintained and updated.

[0114] Furthermore, based on the fault risk level and anomaly descriptions recorded in the operational status assessment results, such as identifying specific problems like abnormal guide plate control signals or a 15% decrease in airflow output stability, and combining the operation instructions and target parameter adjustment range specified in the remote operation decision, the required maintenance type and update content are determined. The specified operation instructions refer to the specific control actions required of the magnetic levitation blower, as determined by the remote operation decision, including parameter adjustment, mode switching, or status reset. The target parameter adjustment range refers to the numerical span between the minimum and maximum values ​​that a certain operating parameter is allowed to be adjusted during remote adjustment. For example, a speed change range of ±500 rpm or a guide plate angle adjustment range of 30 to 35 degrees indicates that the parameter can only be effectively adjusted within the set boundaries.

[0115] The system calls upon the maintenance and update solution library and matches the appropriate handling procedures for the current abnormal characteristics. For example, it initiates a firmware upgrade program for sensor signal anomalies or performs control logic optimization for unstable airflow. Finally, under the specified adjustment strategy, it completes parameter writing and hardware status reset operations, outputs the remotely adjusted operating parameters, and realizes the maintenance and update process of the magnetic levitation blower.

[0116] In summary, this invention, by matching, filtering, and integrating duct CFD data with an operational mode parameter library, can accurately identify and output blower operational status parameters that highly match the current duct state, significantly improving the system's adaptability and control accuracy to complex airflow environments. By dynamically adjusting duct parameters based on airflow environment optimization commands and continuously optimizing the duct configuration parameter set using feedback data, real-time adaptive adjustment of the duct structure is achieved, providing a solid foundation for efficient and intelligent remote monitoring.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A remote monitoring system for a magnetic levitation blower based on the Internet, characterized in that: include, The data acquisition module collects environmental parameters of the magnetic levitation blower and preprocesses them to generate airflow environment optimization instructions. The adjustment module, based on the airflow environment optimization command, uses the air duct control component to adjust the air duct parameters and generate an air duct structure adaptation set; The matching module parses the duct structure adaptation set, dynamically matches the operating mode of the magnetic levitation blower, and outputs the operating status parameters of the blower. The encryption module performs structured encapsulation and encryption processing on the operating status parameters of the blower through a multi-band wireless communication integration device, and selects the communication frequency band according to the network status for uploading, generating a frequency band encrypted dataset; In the parsing module, the cloud monitoring center decrypts and parses the encrypted dataset of the frequency band, compares and analyzes it with the historical operating data of the magnetic levitation fan, and generates an operating status diagnostic report. The assessment module uses the FTA method to assess the operating status of the magnetic levitation blower based on the operating status diagnostic report, and performs remote adjustment and maintenance updates based on the assessment results. The environmental parameters of the magnetic levitation fan include airflow velocity and pressure distribution, temperature and humidity, and spatial structure information. The preprocessing includes filtering and denoising, data correction, time alignment, interpolation completion, and feature extraction; The generated airflow environment optimization command is generated by analyzing the preprocessed environmental parameters using aerodynamic modeling and multi-objective optimization algorithms. The specific steps for parsing the duct structure fit set are as follows: Import the duct structure adapter set into CFD modeling to create a 3D duct structure, perform mesh generation, and output the duct mesh data set. The CFD solver is used to perform simulation analysis on the duct mesh data set to generate duct CFD flow field data. The CFD flow field data of the air duct is analyzed by CFD simulation and comprehensive processing method to generate air duct CFD data. The specific steps for dynamically matching the operating mode of the magnetic levitation blower and outputting the blower's operating status parameters are as follows: The CFD data of the air duct is matched and filtered with the operating parameters in the operating mode parameter library of the magnetic levitation fan to generate the operating mode matching results. The operating mode matching results are integrated with the duct CFD data to generate the operating status parameters of the blower. The airflow output characteristics and duct response characteristics in the blower operating status parameters are mapped one by one according to field name, data type and sorting order; the format of each blower operating status parameter is standardized. At the field level, parameters are matched with corresponding fields in the field template to generate structured transmission data; The field names and values ​​in the structured transmission data are converted into their corresponding byte representations in sequence, and block encryption is performed using the AES-256 encryption algorithm in a 256-bit key encryption mode; the encrypted data is then encoded and encapsulated, and the encrypted running status data is output. The cloud monitoring center's decryption and format parsing of the frequency band encrypted dataset refers to using a symmetric key encryption mechanism to decrypt the frequency band encrypted dataset based on a preset security key, generating a frequency band decrypted dataset, parsing its format, and outputting the parsed running status data.

2. The Internet-based remote monitoring system for magnetic levitation fans as described in claim 1, characterized in that: The process involves using airflow environment optimization commands and airflow control components to adjust airflow parameters and generate an airflow structure adaptation set. The specific steps are as follows: Analyze the parameter differences between the target parameters in the airflow environment optimization command and the actual operating data of the magnetic levitation fan, and generate duct parameters; The rule-based decision tree mapping method is used to parse and map the airflow environment optimization command, and the control logic is formulated through rule reasoning to generate the air duct structure adjustment strategy. Based on the air duct structure adjustment strategy, the air duct control component is used to dynamically adapt and control the air duct parameters to generate an air duct configuration parameter set. The system collects feedback data from inside the duct, compares the feedback data with the target parameters in the airflow environment optimization command, generates control deviation, and dynamically adjusts the duct configuration parameter set based on the control deviation to form an updated duct configuration parameter set. The updated duct configuration parameter set is normalized to generate a duct structure adaptation set.

3. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: The specific steps for performing structured encapsulation and encryption of the blower's operating status parameters using a multi-band wireless communication integration device are as follows: Field mapping and structured organization of the blower's operating status parameters are performed to form structured transmission data; The structured transmission data is encrypted using the AES-256 encryption algorithm to generate encrypted runtime status data.

4. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: The steps for selecting a communication frequency band based on network conditions for uploading and generating a frequency band encrypted dataset are as follows: Collect network status information in the current communication environment and select the communication frequency band according to the dynamic frequency band adaptation method; The encrypted operational status data is sent to the cloud monitoring center using the selected communication frequency band, and the frequency band encrypted dataset is output.

5. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: The process involves comparing and analyzing historical operating data of the magnetic levitation fan to generate an operational status diagnostic report. The specific steps are as follows: Extract historical operation data that matches the parsed operation status data from the historical operation database of the magnetic levitation fan, and compare and analyze the parsed operation status data with the historical operation data to generate comparison results; Based on the comparison results, and combining the fault determination rules with the health assessment standards, the operational status diagnostic report is output through the fault determination rule-driven diagnostic generation method.

6. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: Based on the operational status diagnostic report, the FTA method is used to assess the operational status of the magnetic levitation blower, and remote adjustment and maintenance updates are performed according to the assessment results. The specific steps are as follows: Analyze and evaluate the operational status diagnostic report to obtain the operational status assessment results; Analyze the risks and faults in the operational status assessment results to generate remote operation decisions; Based on remote operation decisions, the rotational speed of the magnetic levitation blower is adjusted, and the remotely adjusted operating parameters are output. Based on the abnormal operation characteristics and remote operation decisions in the operational status assessment results, the magnetic levitation fan was maintained and updated.

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