Remote monitoring system for magnetic suspension air feeder based on Internet

Abstract

The invention discloses an internet-based remote monitoring system for a magnetic suspension blower, which relates to the technical field of remote control, and comprises the following steps: analyzing an air duct structure adaptation set, dynamically matching the operation mode of the magnetic suspension blower, and outputting the operation state parameters of the blower; structured packaging and encryption processing are conducted on the operation state parameters of the air feeder through the multi-frequency-band wireless communication integration equipment, a communication frequency band is selected according to the network state to be uploaded, and a frequency band encryption data set is generated; and the cloud monitoring center carries out decryption and format analysis on the frequency band encrypted data set, carries out comparative analysis on the frequency band encrypted data set and historical operation data of the magnetic suspension blower, and generates an operation state diagnosis report. According to the method, the air duct parameters are dynamically regulated and controlled based on the airflow environment optimization instruction, the air duct configuration parameter set is continuously optimized in combination with the feedback data, real-time self-adaptive adjustment of the air duct structure is achieved, and a solid foundation is provided for efficient and intelligent remote monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote control, and particularly relates to an internet-based remote monitoring system for a magnetic levitation air supply fan. BACKGROUND

[0002] In modern industrial and building environments, magnetic levitation air supply fans are widely used in air purification, ventilation and temperature control systems due to their advantages of high efficiency, low noise and long service life. To realize remote monitoring of the running state of the air supply equipment, the traditional method usually relies on local sensors to collect key parameters and uploads the data to the monitoring center through wired or single-band wireless communication. On this basis, the running mode of the fan is adjusted in combination with the experience-based control logic to adapt to the influence of environmental changes. In addition, some monitoring centers have introduced basic data encryption and historical data analysis functions to improve communication security and equipment running stability.

[0003] However, the existing technology still has limitations in dynamic environment adaptation capability and remote control precision. On the one hand, the traditional method mostly uses static or semi-static air duct structure setting, which is difficult to respond to complex and changeable airflow environment in real time; on the other hand, in the remote control strategy, there is generally a lack of dynamic matching mechanism based on high-precision CFD simulation and multi-objective optimization algorithm generation, which leads to insufficient fine adjustment of the running state of the air supply fan, affecting the overall energy efficiency and stability. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an internet-based remote monitoring system for a magnetic levitation air supply fan to solve the problems of insufficient dynamic adaptation of air duct structure and inaccurate remote running state adjustment.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The application provides an internet-based magnetic levitation air supply machine remote monitoring system, which comprises a collection module, an adjustment module, a matching module, an encryption module and an analysis module.

[0008] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the environmental parameters of the magnetic levitation air supply machine include airflow speed and pressure distribution, temperature and humidity and spatial structure information.

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

[0010] The generation of the airflow environment optimization instruction is achieved by using aerodynamic modeling and a multi-objective optimization algorithm to analyze the preprocessed environmental parameters.

[0011] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the adjustment module adjusts the air duct parameters based on the airflow environment optimization instruction, generates an air duct structure adaptation set, and comprises the following specific steps.

[0012] The parameter difference between the target parameters in the airflow environment optimization instruction and the actual operation data of the magnetic levitation air supply machine is analyzed to generate the air duct parameters.

[0013] The airflow environment optimization instruction is analyzed and mapped by using a rule-based decision tree mapping method, and the control logic is formulated by using a rule-based reasoning method to generate an air duct structure adjustment strategy.

[0014] Based on the air duct structure adjustment strategy, the air duct parameters are dynamically adapted and controlled by using the air duct control component to generate an air duct configuration parameter set.

[0015] Collect feedback data inside the air duct, compare the feedback data with the target parameters in the air flow environment optimization instruction, generate a control deviation, and dynamically adjust the air duct configuration parameter set according to the control deviation to form an updated air duct configuration parameter set;

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

[0017] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the CFD method is used to analyze the air duct structure adaptation set, and the specific steps are,

[0018] The air duct structure adaptation set is imported into the CFD modeling to establish the three-dimensional structure of the air duct, and the grid is divided, and the air duct grid data set is output;

[0019] The CFD solver is used to simulate and analyze the air duct grid data set, generate the air duct CFD flow field data, and analyze the air duct CFD flow field data through the CFD simulation comprehensive processing method to generate the air duct CFD data.

[0020] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the operation mode of the magnetic levitation air supply machine is dynamically matched, and the specific steps are,

[0021] The air duct CFD data and the operation parameter in the operation mode parameter library of the magnetic levitation air supply machine are matched and screened to generate an operation mode matching result;

[0022] The operation mode matching result and the air duct CFD data are integrated to generate the air supply machine operation state parameter.

[0023] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the specific steps for the multi-frequency wireless communication integration device to structure and encrypt the air supply machine operation state parameter are,

[0024] The air supply machine operation state parameter is field-mapped and structured to form structured transmission data;

[0025] The structured transmission data is encrypted by the AES-256 encryption algorithm to generate encrypted operation state data.

[0026] As a preferred scheme of the internet-based magnetic levitation air supply machine remote monitoring system, the specific steps for the multi-frequency wireless communication integration device to structure and encrypt the air supply machine operation state parameter are,

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

[0028] The encrypted running state data is sent to the cloud monitoring center using the selected communication frequency band, and a frequency band encrypted data set is output.

[0029] As a preferred scheme of the Internet-based magnetic levitation air supply machine remote monitoring system, the cloud monitoring center decrypts and analyzes the format of the frequency band encrypted data set, which means using a symmetric key encryption mechanism to decrypt the frequency band encrypted data set according to a preset security key, generating a frequency band decrypted data set, and performing format analysis to output the analyzed running state data.

[0030] As a preferred scheme of the Internet-based magnetic levitation air supply machine remote monitoring system, the cloud monitoring center decrypts and analyzes the format of the frequency band encrypted data set, which means using a symmetric key encryption mechanism to decrypt the frequency band encrypted data set according to a preset security key, generating a frequency band decrypted data set, and performing format analysis to output the analyzed running state data.

[0031] Extract and analyze the running history data matched with the analyzed running state data from the magnetic levitation air supply machine historical running database, and compare the analyzed running state data with the running history data to generate a comparison result.

[0032] Based on the comparison result, combined with the fault judgment rule and the health degree evaluation standard, the running state diagnosis report is output by the fault judgment rule driven diagnosis generation method.

[0033] As a preferred scheme of the Internet-based magnetic levitation air supply machine remote monitoring system, the cloud monitoring center decrypts and analyzes the format of the frequency band encrypted data set, which means using a symmetric key encryption mechanism to decrypt the frequency band encrypted data set according to a preset security key, generating a frequency band decrypted data set, and performing format analysis to output the analyzed running state data.

[0034] Analyze and evaluate the running state diagnosis report to obtain a running state evaluation result.

[0035] Analyze the risk and fault in the running state evaluation result to generate a remote operation decision.

[0036] Based on the remote operation decision, the speed of the magnetic levitation air supply machine is adjusted, and the running parameters after remote adjustment are output.

[0037] According to the running abnormal characteristics in the running state evaluation result and the remote operation decision, the magnetic levitation air supply machine is completed maintenance and update.

[0038] The application has the beneficial effects that: by matching, screening and integrating the air duct CFD data with the operation mode parameter library, the running state parameters of the air supply machine that are highly matched with the current air duct state can be accurately identified and output, and the adaptability and control accuracy of the system to the complex air flow environment are significantly improved. By dynamically regulating the air duct parameters based on the air flow environment optimization instruction, and continuously optimizing the air duct configuration parameter set combining with the feedback data, real-time adaptive adjustment of the air duct structure is realized, which provides a solid foundation for efficient and intelligent remote monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0040] Fig. 1 Flowchart of the internet-based magnetic suspension air supply machine remote monitoring system.

[0041] Fig. 2 Schematic diagram of the internet-based magnetic suspension air supply machine remote monitoring system.

[0042] Fig. 3 CFD analysis and matching flowchart.

[0043] Fig. 4 Dynamic adjustment and maintenance flowchart. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0045] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0046] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0047] REFERENCE Figs. 1-4For an embodiment of the present application, the embodiment provides an Internet-based remote monitoring system for a magnetic levitation air supply fan, comprising the following steps:

[0048] A collection module collects environmental parameters of the magnetic levitation air supply fan and performs preprocessing to generate airflow environment optimization instructions.

[0049] The environmental parameters of the magnetic levitation air supply fan include airflow velocity and pressure distribution, temperature and humidity, and spatial structure information;

[0050] It should be noted that the speed of air flow is collected by a wind speed sensor, and the airflow velocity distribution at different cross-sectional positions is recorded; the pressure sensor is used to measure the pressure change of the internal and surrounding environment of the air duct to form pressure distribution data; a plurality of temperature and humidity sensors are arranged in the operating area of the air supply fan to collect air temperature values and relative humidity values, for example, temperature of 25 degrees Celsius and humidity of 60%, and a laser ranging device is used to obtain the spatial size and obstacle layout information of the air supply fan installation area to construct spatial structure information for describing 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 method is used to process the airflow velocity and pressure distribution data to remove high-frequency noise interference, for example, attenuating the components with a frequency higher than 10 Hz in the airflow velocity signal; the offset correction and sensitivity adjustment of the environmental parameters are performed according to the sensor calibration parameters, for example, correcting the 26 degrees Celsius measured by the temperature sensor to 25.3 degrees Celsius under the standard reference condition; the environmental parameters are processed synchronously with a unified time reference to eliminate the time deviation caused by the sampling time sequence difference, for example, aligning the airflow velocity, temperature and humidity signals to the same time stamp sequence; the linear interpolation is used to supplement the complete data sequence in the period with missing data, for example, the missing humidity value in a certain second is linearly estimated and filled by the values before and after two seconds; the statistical features and frequency domain features of the environmental parameters are extracted from the time sequence data, for example, the mean and variance of the airflow velocity and the main frequency component of the pressure signal.

[0053] The preprocessed environmental parameters are analyzed using aerodynamic modeling and multi-objective optimization algorithm to generate airflow environment optimization instructions.

[0054] Further, an aerodynamic model of the air duct and the surrounding environment is constructed to describe the flow characteristics of the airflow under different boundary conditions; based on the pre-processed environmental parameters, the geometric boundaries and obstacle layout of the air duct are determined, the boundary conditions of the airflow inlet and outlet are set, including the inlet wind speed and outlet pressure value, the calculation grid is divided in the three-dimensional space, and the local encryption grid method is used for the internal region of the air duct to improve the resolution of the key flow region; the standard k-ε turbulence model is used to describe the airflow behavior, and the steady-state simulation is performed through the 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, and under the given air supply angle, wind speed range, and spatial boundary conditions, the NSGA-II multi-objective optimization algorithm is used to iteratively calculate the control parameters, for example, the inlet wind speed is varied within the range of 6 to 10 meters / second as a constraint condition, and multiple non-dominated Pareto optimal solutions are obtained;

[0056] The NSGA-II multi-objective optimization algorithm is used to iteratively calculate the control parameters, and the expression is,

[0057]

[0058] wherein f1(x) represents the airflow uniformity score value under the current control parameter combination x, represents the spatial variance of the airflow velocity field, and ε represents a very small normal number.

[0059] The control parameters refer to adjustable physical quantities that affect the airflow characteristics, including the air supply angle, inlet wind speed, and air supply pressure. Based on the obtained multiple Pareto optimal solutions, the actual values of temperature and humidity, airflow velocity and pressure distribution in the current operating state are combined, and a multi-attribute decision method is used to evaluate the performance of each Pareto optimal solution in different performance indicators; the weighted evaluation refers to assigning weights according to the importance of different performance indicators, and performing weighted statistics on the performance of each Pareto optimal solution in each indicator to obtain a comprehensive score, thereby realizing the sorting and optimization of all solutions. By comparing the differences in comprehensive scores of each evaluation, the air supply strategy that best meets the current environmental conditions and operating requirements is selected; the air supply strategy includes control parameters such as air supply angle, inlet wind speed, and air supply pressure, and finally the air supply environment optimization instruction is obtained.

[0060] The adjustment module adjusts the air duct parameters based on the air supply environment optimization instruction using the air duct control components to generate an air duct structure adaptation set.

[0061] The deviation analysis method is used to analyze the parameter difference between the target parameters in the air supply environment optimization instruction and the actual operating data of the magnetic levitation air supply machine to generate the air duct parameters;

[0062] Specifically, the target parameters in the air flow environment optimization instruction are obtained, such as the expected air supply speed, pressure distribution, air flow uniformity, etc. At the same time, the actual running feedback data of the magnetic levitation air supply fan in the current running state is collected, including the real-time air supply speed, pressure value and temperature value measured by the sensor, etc. The target parameters are compared with the corresponding actual running feedback data item by item, and the difference between the two is calculated as the parameter difference. For example, if the air flow speed at a certain position in the target parameter is set to 8.0 m / s, and the actual running feedback data is 7.5 m / s, then the parameter difference is 0.5 m / s. According to the direction and size of the parameter difference, combined with the rule-based reasoning mechanism, the adjustment direction and amplitude of the air duct structure are determined. Finally, the specific parameters for guiding the adjustment of the air duct structure are output. The rule-based reasoning mechanism refers to the process of analyzing and judging the parameter difference according to the set logical conditions and parameter thresholds, and deriving the air duct structure adjustment strategy according to the established rules. The analysis and judgment of the parameter difference refers to: by comparing the target parameters in the air flow environment optimization instruction with the actual running feedback data of the magnetic levitation air supply fan, the parameter deviation value is obtained; according to the set logical conditions and parameter thresholds, it is judged whether the difference value is within the allowed 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 determined to be abnormal. The set logical conditions and parameter thresholds are set based on the equipment design specifications, historical running data and actual working condition requirements. The set logical conditions refer to the logical rules for judging whether the parameter state is normal, and the parameter threshold refers to the allowed range boundary value set for the key running parameters, which is used to measure whether the deviation between the actual parameters and the target parameters is within the 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 air flow environment optimization instruction is analyzed and mapped by using the rule-based decision tree mapping method, and the control logic is formulated by using the rule-based reasoning method to generate the air duct structure adjustment strategy.

[0064] It should be noted that the air supply angle, inlet air speed and air supply pressure values in the air flow environment optimization instruction are used as input conditions, and the input conditions are classified and path-matched in a hierarchical manner according to the decision tree structure, for example, when the inlet air speed is greater than 8 m / s and the air supply angle is 35 degrees, it is matched to the corresponding decision node. The decision tree structure refers to a logical framework composed of a series of nodes and branches with hierarchical relationship, which is used to judge and classify the input conditions layer by layer, and map to the corresponding output results. In each decision node, the rule-based reasoning method is applied, combined with the air duct geometric boundary and spatial structure information, to deduce the air duct structure adjustment mode that adapts to the current air flow state, for example, adjusting the air duct bending angle according to the obstacle position. Finally, the air duct structure adjustment strategy containing parameters such as air duct shape, cross-sectional size and guide component position is output.

[0065] Based on the air duct structure adjustment strategy, the air duct parameter is dynamically adapted and regulated by using the air duct regulation component to generate the air duct configuration parameter set;

[0066] Specifically, according to the information of the air duct shape, cross-sectional size and guide component position in the air duct structure adjustment strategy, combined with the actual layout and spatial structure information of the current air duct, the differences between the target parameters and the existing parameters in the strategy are matched one by one; According to the values in the air flow environment optimization instructions such as air supply angle, inlet air speed and air supply pressure, it is verified whether the selected geometric parameters meet the air flow dynamic matching requirements; The matching requirements refer to verifying and comparing whether the adjusted geometric parameters and installation positions meet the target parameters and control conditions specified in the air flow environment optimization instructions during the air duct structure adjustment process; Finally, the air duct geometric parameters and installation positions are determined;

[0067] Under the execution control of the air duct regulation component, the air duct bending angle, cross-sectional width and guide plate deflection angle are adjusted one by one, for example, the air 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 adjustment is completed, it is verified through the feedback mechanism whether the current setting meets the strategy requirements, which include the target setting of the air duct shape, cross-sectional size, guide component position and other parameters, as well as the dynamic control standards matched with the air flow environment optimization instructions; And all the adjusted parameters are integrated into the air duct configuration parameter set.

[0068] Collect feedback data inside the air duct, compare the feedback data with the target parameters in the air flow environment optimization instructions using the difference calculation method, generate control deviation, and dynamically adjust the air duct configuration parameter set according to the control deviation to form the updated air duct configuration parameter set;

[0069] Further, the actual air flow speed and pressure distribution, temperature and humidity and spatial structure information inside the air duct are collected by sensors as feedback data; The collected feedback data and the target parameters set in the air flow environment optimization instructions are compared one by one by difference calculation to generate control deviation, for example, the actual measured inlet air speed 8.2 meters / second is compared with the target value 8 meters / second, and the control deviation 0.2 meters / second is obtained; According to the size and direction of the control deviation, the corresponding air duct bending angle, cross-sectional width and guide plate deflection angle in the air duct configuration parameter set are corrected, for example, the air duct bending angle is adjusted from 30 degrees to 29 degrees to reduce the deviation; Finally, the updated air duct configuration parameter set is formed.

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

[0071] It should be noted that the updated air duct configuration parameter set is classified and arranged according to the parameter template format, the parameter template format refers to a standardized structure for specifying the arrangement order, field name and data type of the air duct configuration parameters, ensuring that the air duct parameters from different sources or states can be organized and processed in a unified manner; the updated air duct configuration parameter set is subjected to dimension unification and numerical range standardization processing, for example, all angle parameters are converted into relative values within the 0-45 degree interval, and all length parameters are converted into standard numerical values in meters; finally, the air duct structure adaptation set with consistent structure and standardized format is output.

[0072] The matching module parses the air duct structure adaptation set and dynamically matches the running mode of the magnetic levitation air supply fan, and outputs the air supply fan running state parameters.

[0073] The air duct structure adaptation set is imported into the CFD modeling to establish the air duct three-dimensional structure, and the local encryption grid method is used for grid division, and the air duct grid data set is output;

[0074] Specifically, the air duct main contour is defined according to the air duct structure parameters, the air duct structure parameters refer to physical quantities used to describe and define the geometric shape and internal structure characteristics of the air duct, including specific values such as air duct bending angle, cross-sectional width and deflection angle of guide plate. Set the inlet and outlet positions, and arrange the guide components inside the air duct geometric space; form a complete geometric model of the air duct internal flow region;

[0075] Based on the geometric model of the air duct internal flow region, define the air duct inlet, outlet and wall boundary conditions in the CFD modeling environment, complete the digital reconstruction of the air duct three-dimensional structure; in the area where the air flow velocity gradient of the air duct three-dimensional structure is large, such as the air duct bending part and the vicinity of the guide plate, the local encryption grid method is used to increase the grid density of the geometric model, to improve the flow feature capture accuracy; finally, the air duct grid data set is generated.

[0076] The CFD solver is used to simulate and analyze the air duct grid data set, generate the air duct CFD flow field data, and analyze the air duct CFD flow field data through the CFD simulation comprehensive processing method to generate the air duct CFD data.

[0077] Further, the air duct grid data set is imported into the CFD solver, and the standard k-ε turbulence model is applied to numerically solve the air flow process in the air duct under the set boundary conditions; the set boundary condition refers to the physical quantity constraint set on the geometric boundary of the air duct model in the CFD simulation analysis, which is used to describe the behavior characteristics of the air flow at the inlet, outlet and wall surface.

[0078] The velocity field and pressure field distribution of each position inside the air duct are obtained by iterative calculation to generate the air duct CFD flow field data; the CFD simulation comprehensive processing method is used to extract and structure the air duct CFD flow field data, including extracting the key parameters such as the average air flow velocity and the pressure gradient change rate; and finally the air duct CFD data are generated.

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

[0080] It should be noted that multiple sets of operating parameters in the operating mode parameter library of the magnetic levitation air supply fan are extracted, including different combinations of air supply angles, inlet air speeds and air supply pressure values; the NSGA-II algorithm is used to perform non-dominated sorting and fitness evaluation on all operating parameter combinations, for example, finding the closest matching item in the current air flow distribution in the combination of an air supply angle of 35 degrees and an inlet air speed of 8 meters / second; and finally outputting multiple sets of operating mode matching results with the highest adaptation degree.

[0081] The operating mode matching result and the air duct CFD data are integrated by using the multi-parameter weighted mapping method to generate the air supply fan operating state parameters.

[0082] Specifically, the air supply angle, inlet air speed and air supply pressure values in the operating mode matching result and the key parameters such as the average air flow velocity and the pressure gradient change rate in the air duct CFD data are normalized, and a corresponding weight coefficient is set for each parameter. The corresponding weight coefficient refers to the importance proportion value allocated to each parameter in the operating mode matching result and the air duct CFD data, which is used to reflect the relative priority of different parameters in the final air supply fan operating state in the multi-parameter weighted mapping process.

[0083] The mapping relationship between the air flow output performance and the air duct response behavior is established by linearly weighting the comprehensive scores of each parameter, combining the set parameters in the operating mode matching result and the flow characteristics in the air duct CFD data, and integrating the air flow output performance and the air duct response behavior to generate the air supply fan operating state parameters to indicate the overall operating performance of the current magnetic levitation air supply fan in a specific air duct environment. The air flow output performance refers to the air flow characteristics generated by the magnetic levitation air supply fan under the current operating state, including the actual performance of parameters such as the air supply angle, outlet air speed, air flow uniformity and dynamic pressure distribution; the air duct response behavior refers to the internal flow state of the air duct system under the action of the air flow of the magnetic levitation air supply fan, including dynamic characteristics such as pressure field distribution, velocity field change, vortex area formation and pressure gradient change rate.

[0084] The comprehensive scores of each parameter are calculated by linear weighting, and the expression is,

[0085]

[0086] wherein i represents the number of the parameter, w i represents the actual value of the i-th operating parameter, x i represents the weight coefficient of the i-th parameter, n represents the total number of parameters participating in the weighted calculation, and S represents the linearly weighted comprehensive score of all parameters.

[0087] The encryption module performs structured packaging and encryption processing on the air supply fan operating state parameters through the multi-band wireless communication integration device, and uploads the data according to the network state and the selected communication frequency band to generate frequency band encrypted data sets.

[0088] The field template matching method is used to map and structure the air supply fan operating state parameters, forming structured transmission data.

[0089] It should be noted that according to the field template format, the air supply fan operating state parameters are matched one by one according to the field name, data type and arrangement order. Each air supply fan operating state parameter is processed according to the format standard, such as converting the air supply angle value to degrees and keeping one decimal place, and expressing the air flow speed average in meters per second. The parameters are matched with the corresponding fields in the field template at the field level to ensure that all parameter contents meet the structure specifications defined by the template. The structure specification defined by the field template refers to the unified rules set by the field template for data organization form, including field name, data type, value format, arrangement order and allowed value range. Finally, structured transmission data with unified format and explicit field identification is generated.

[0090] The structured transmission data is encrypted by the AES-256 encryption algorithm to generate encrypted operating state data.

[0091] Specifically, in binary encoding mode, the field name and value in the structured transmission data are converted into corresponding byte representation form one by one, and the AES-256 encryption algorithm is used for group encryption processing in the encryption mode with a key length of 256 bits, such as using CBC mode and cooperating with initialization vector to encrypt data blocks segment by segment. The encrypted data is encoded and packaged to ensure compatibility and integrity in different communication environments. Finally, the encrypted operating state data is output.

[0092] Network state information in the current communication environment is collected, and the communication frequency band is selected according to the dynamic frequency band adaptation method.

[0093] It should be noted that the network state 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 2.4GHz frequency band is 65%, and the channel occupancy rate of 5GHz frequency band is 30%; the communication quality of each available frequency band is evaluated and sorted 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 rate index; the communication frequency band with the smallest channel interference and the highest transmission rate is selected.

[0094] The encrypted running state data is sent to the cloud monitoring center using the selected communication frequency band, and the frequency band encrypted data set is output.

[0095] Further, the encrypted running state data is packaged into a data packet format conforming to the wireless communication protocol; the data packet is modulated and transmitted according to the set communication parameters through the multi-frequency wireless communication integration device, ensuring stable transmission of the data in the selected frequency band; the set communication parameters refer to various technical parameters for pre-configuring and controlling the working state of the wireless transmission module during wireless data transmission according to the selected communication frequency band and network environment requirements. Finally, the complete data content in the selected frequency band is received and recorded in the cloud monitoring center, forming a frequency band encrypted data set,

[0096] The analysis module decrypts and formats the frequency band encrypted data set, and compares and analyzes it with the magnetic levitation air supply machine running history data to generate a running state diagnosis report.

[0097] The symmetric key encryption mechanism is used, and the frequency band encrypted data set is decrypted according to the pre-configured security key to generate a frequency band decrypted data set, and the pre-defined format analysis method is used for format analysis to output the parsed running state data;

[0098] Specifically, the symmetric key encryption mechanism is used, and the pre-configured 256-bit security key is used in the cloud monitoring center to decrypt the data packets in the frequency band encrypted data set block by block, for example, using the CBC mode with the corresponding initialization vector to restore the byte sequence; the byte sequence obtained after decryption is processed according to the pre-defined format analysis method, such as character encoding recognition and field segmentation, and the airflow output characteristics and air duct response characteristics in the air supply machine running state parameters are extracted according to the field template matching rule; finally, the parsed running state data with clear field identification and numerical representation is generated.

[0099] The running history data matching the parsed running state data is extracted from the magnetic levitation air supply machine historical running database using the working condition similarity matching method, and the parsed running state data and the running history data are compared and analyzed using the multi-dimensional data analysis method to generate a comparison result;

[0100] Further, the multiple key parameters in the parsed running state data are organized into a set of numerical features according to rules to construct a feature vector of the current running state, for example, including a supply air angle of 35 degrees and an air flow speed average of 8.2 meters per second; in the historical running database of the magnetic levitation air supply fan, a working condition similarity matching method is used to search the historical records, the cosine similarity between the current feature vector and each piece of historical data is calculated, and the historical running data with the highest similarity is selected as the matching sample; the rule organization refers to the process of integrating the multiple key parameters in the parsed running state data into a structured feature vector according to the pre-defined parameter arrangement order, data format and weight distribution method.

[0101] The parsed running state data and the matched historical running data are compared in multiple dimensions such as time sequence, parameter distribution and change trend using a multi-dimensional data analysis method, and difference features are identified, for example, it is found that the current air flow speed fluctuation amplitude is 15% higher than the historical data; and finally, a comparison result containing difference indicators and matching degree is output.

[0102] Based on the comparison result, a running state diagnosis report is output by a fault diagnosis rule driven diagnosis generation method combining fault diagnosis rules and health degree evaluation standards.

[0103] It should be noted that the comparison result is taken as input to extract key parameter deviation values and trend change features, for example, the current air flow speed fluctuation amplitude is 15% higher than the historical data, and the pressure gradient change rate exceeds the air flow pressure abnormality determination threshold of 0.5 kilopascal per meter; the air flow pressure abnormality determination threshold is the maximum pressure change rate within the allowed range determined based on the design specifications of the air duct system of the magnetic levitation air supply fan in a stable running state and statistical analysis of historical running data, used to determine whether the current air flow state is abnormal, and the reasonable range is 0.1 kilopascal per meter to 0.5 kilopascal per meter; whether the parsed running state data exceeds the normal range is determined according to the fault diagnosis rule to generate a judgment result; for example, if the supply air angle deviation exceeds ±2 degrees, it is marked as abnormal; the fault diagnosis rule refers to a pre-set logical condition for determining whether the running parameters of the magnetic levitation air supply fan exceed the normal range and identifying abnormal or fault states; the normal range refers to the reasonable numerical interval allowed for each key running parameter of the magnetic levitation air supply fan in a stable running state, which is usually set according to the equipment design specifications and historical running data.

[0104] The overall operation state of the magnetic levitation air supply fan is evaluated by using the health degree evaluation standard, the analyzed operation state data is compared with the health degree evaluation index, and the evaluation index is obtained by using the weighted comprehensive score method; the evaluation index obtained by the weighted comprehensive score method refers to that according to the degree of conformity between the analyzed operation state data and the health degree evaluation standard, the weight coefficient is combined, and the comprehensive score reflecting the overall operation health level of the magnetic levitation air supply fan is counted. The health degree evaluation index is based on the magnetic levitation air supply fan equipment design parameters, historical operation data and the HVAC industry standard. All the judgment results and the evaluation index are integrated into a structured text report by using the rule-driven diagnosis generation method, and an operation state diagnosis report containing operation state conclusion, abnormal item description and health level is output.

[0105] The evaluation module uses the FTA method to evaluate the operation state of the magnetic levitation air supply fan based on the operation state diagnosis report, and performs remote adjustment and maintenance update according to the evaluation result.

[0106] The operation state diagnosis report is analyzed and evaluated by using the fault tree analysis method, and the operation state evaluation result is obtained;

[0107] Further, the abnormal item description, parameter deviation value and health score recorded in the operation state diagnosis report are used as input information, and the fault tree analysis method is used to construct a logical causal relationship diagram of the magnetic levitation air supply fan operation fault; according to the hierarchical structure of the fault tree analysis method, starting from the top event (such as air flow output abnormality), the logical causal relationship diagram is decomposed into intermediate events and basic events layer by layer, for example, the air flow output abnormality is decomposed into air supply angle deviation too large and pressure gradient change rate exceeding the standard, etc. Sub-events, and judge the occurrence probability of each basic event by combining fault determination rules and historical statistical data; the occurrence probability refers to the numerical representation of the possibility of a basic event or intermediate event in the fault tree analysis under certain operating conditions and historical statistical data, which is usually defined as a real number between 0 and 1. Finally, through qualitative analysis, the reliability level and fault risk level of the current operation state of the magnetic levitation air supply fan are obtained, and a structured operation state evaluation result is generated.

[0108] The risk and fault in the operation state evaluation result are analyzed by using the strategy matching determination method to generate a remote operation decision;

[0109] Specifically, based on the reliability level and fault risk contained in the operation state evaluation result, the risk classification standard is matched, for example, the events with high fault risk level are classified as priority processing items; the risk classification standard refers to a unified evaluation criterion used to divide and classify the fault risk level in the magnetic levitation air supply fan operation evaluation, which is set according to historical operation data, equipment performance boundary and fault influence degree, and usually includes multiple risk level intervals and corresponding determination conditions;

[0110] The fault features in the running state evaluation result are compared with the matching conditions in the risk level and rule library, and the strategy mapping is performed for different types of fault features and risk levels, such as matching the operation instruction of adjusting the control parameter and restarting the drive module for the guide plate control signal abnormality; finally, the remote operation decision suitable for the current running state is output.

[0111] Based on the remote operation decision, the speed of the magnetic levitation air supply fan is adjusted by using the parameter optimization adjustment method, and the running parameter after remote adjustment is output.

[0112] It should be noted that according to the operation instruction and target control value specified in the remote operation decision, the target speed of the magnetic levitation air supply fan to be adjusted is determined, such as increasing the current speed from 12000 rpm to 12500 rpm to improve the airflow output stability; the multi-objective optimization analysis of the acceleration curve, energy consumption change and vibration response in the speed regulation process is performed by using the parameter optimization adjustment method, and the optimal regulation step and transition time are set, such as gradually adjusting to the target value at a speed of 200 rpm per second; finally, the speed change is completed under the specified regulation strategy, and the running parameter after remote adjustment containing the actual regulation result and running state feedback is output. The specified regulation strategy refers to the specific control rules of the speed regulation mode, regulation step and transition time determined according to the remote operation decision and the parameter optimization adjustment method.

[0113] According to the running abnormality features in the running state evaluation result and the remote operation decision, the maintenance and update of the magnetic levitation air supply fan are completed.

[0114] Further, according to the fault risk level and abnormal item description recorded in the running state evaluation result, such as identifying the specific problems of guide plate control signal abnormality or airflow output stability decrease of 15%, and combining the operation instruction and target parameter adjustment range specified in the remote operation decision, the type of maintenance to be performed and the content of update are determined; the specified operation instruction refers to the specific control action required to be performed by the magnetic levitation air supply fan according to the remote operation decision, including parameter adjustment, mode switching or state reset; the target parameter adjustment range refers to the numerical span between the minimum value and the maximum value allowed to be adjusted during the remote adjustment process, such as the speed variation interval of ±500 rpm or the guide plate angle adjustment range of 30 to 35 degrees, indicating that the parameter can only be effectively adjusted within the specified boundary.

[0115] The maintenance and update scheme library is called to match the processing flow suitable for the current abnormal feature, such as starting the firmware upgrade program for sensor signal abnormality problem or performing control logic optimization for airflow instability phenomenon; finally, the parameter writing and hardware state reset operation are completed under the specified regulation strategy, and the running parameter after remote adjustment is output, realizing the maintenance and update process of the magnetic levitation air supply fan.

[0116] To sum up, by matching, screening and integrating the air duct CFD data with the operation mode parameter library, the running state parameters of the air supply machine that are highly matched with the current air duct state can be accurately identified and output, and the adaptability and control accuracy of the system to the complex air flow environment are significantly improved. By dynamically regulating the air duct parameters based on the air flow environment optimization instruction, and continuously optimizing the air duct configuration parameter set in combination with the feedback data, real-time adaptive adjustment of the air duct structure is realized, which provides 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 application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

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.

2. The Internet-based remote monitoring system for magnetic levitation fans as described in claim 1, characterized in that: 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.

3. The Internet-based remote monitoring system for magnetic levitation blowers 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.

4. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: 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.

5. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: 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.

6. 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.

7. 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.

8. The Internet-based remote monitoring system for magnetic levitation blowers as described in claim 1, characterized in that: 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.

9. 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.

10. 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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