Intelligent equipment management system based on cloud platform

Abstract

The invention relates to the technical field of intelligent equipment management, in particular to an intelligent equipment management system based on a cloud platform, which comprises a data acquisition module, a data processing module, a feature extraction module, a prediction analysis module and an optimization decision module, wherein the data acquisition module is used for acquiring operation data of each intelligent device; the data processing module is used for preprocessing the operation data; the feature extraction module is used for extracting related features from the preprocessed operation data and generating feature vectors of equipment states; the prediction analysis module is used for classifying the operation state of the equipment; and the optimization decision module is used for formulating an optimization operation strategy of the equipment. According to the invention, through real-time data acquisition and intelligent analysis, accurate equipment state evaluation and dynamic optimization of an operation strategy are realized, and the equipment management efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent device management, and in particular to an intelligent device management system based on a cloud platform. Background Art

[0002] With the continuous development of intelligent technology, the widespread application of various intelligent devices, especially in the fields of industry, energy and construction, has promoted the upgrading of equipment management and maintenance needs; traditional equipment management methods often rely on manual operation and lack efficient monitoring and intelligent decision-making capabilities, resulting in poor equipment operation efficiency and maintenance effects, and even unexpected downtime, losses and safety hazards caused by unreasonable operating parameters or equipment failures; in order to improve equipment operation efficiency, reduce energy consumption, and predict and prevent faults in a timely manner, cloud-based equipment management systems have gradually become a mainstream solution; however, existing intelligent equipment management systems usually only rely on traditional rule-based control and early warning mechanisms, and lack intelligent strategies for real-time optimization and adjustment based on the equipment operation status.

[0003] Most of the intelligent equipment management systems in the existing technology have certain limitations in equipment failure prediction, status assessment and maintenance decision-making. First, many systems use models based on historical data for prediction, ignoring the sudden behavior and real-time data fluctuations that may occur during equipment operation, resulting in inaccurate prediction results. Secondly, although some systems can provide fault warnings, they lack sufficient processing capabilities to effectively distinguish between normal and potential fault states of equipment and propose accurate optimization operation strategies. The adjustment of equipment's optimized operating parameters, operating schedules, and preventive maintenance plans lacks systematic intelligent decision-making based on real-time status changes of equipment, and often can only rely on human judgment, resulting in low management efficiency. Summary of the invention

[0004] Based on the above objectives, the present invention provides a cloud platform-based intelligent device management system.

[0005] The intelligent equipment management system based on the cloud platform includes data acquisition module, data processing module, feature extraction module, prediction analysis module and optimization decision module; among which:

[0006] Data acquisition module: used to collect the operating data of each smart device in real time through a variety of sensors and network interfaces, including the working status, environmental parameters, energy consumption data and operation records of the device, and upload the collected operating data to the cloud platform in a structured format;

[0007] Data processing module: used to pre-process the operating data in the cloud platform, including data cleaning, missing value filling and data normalization;

[0008] Feature extraction module: Utilizes statistical analysis and signal processing algorithms to extract relevant features from pre-processed operation data, including trend features, periodic features, and mutation features of equipment operation, and generates feature vectors of equipment status;

[0009] Prediction and analysis module: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to classify the operating status of the equipment and identify whether the equipment is in a normal state or a potential fault state;

[0010] Optimization decision module: Based on the operating status identified by the predictive analysis module, formulate an optimized operation strategy for the equipment, including adjusting the operating parameters of the equipment, optimizing the operating schedule of the equipment, and formulating a preventive maintenance plan.

[0011] Optionally, the data acquisition module includes a sensor acquisition unit, an environmental data acquisition unit, an energy consumption monitoring unit, an operation record acquisition unit and a data upload unit; wherein:

[0012] Sensor acquisition unit: including temperature sensor, humidity sensor, pressure sensor, current sensor and vibration sensor, used to collect working status data of smart devices in real time, including temperature, humidity, pressure, current and vibration parameters of the devices;

[0013] Environmental data acquisition unit: including environmental sensors, used to collect parameter data of the environment where the equipment is located in real time, including temperature, humidity and light intensity;

[0014] Energy consumption monitoring unit: including energy consumption sensors, used to monitor the energy consumption data of the equipment in real time, including the power usage, working hours and load changes of the equipment;

[0015] Operation record collection unit: used to record the operation information of the equipment in real time, including equipment startup, shutdown and operation mode switching;

[0016] Data upload unit: used to upload all operating data collected by each unit, including the working status of the equipment, environmental parameters, energy consumption data and operation records, to the cloud platform for storage in a structured data format through the network interface.

[0017] Optionally, the data processing module includes a data cleaning unit, a missing value filling unit and a data normalization unit; wherein:

[0018] Data cleaning unit: used to remove abnormal data and noise data from the operation data received from the cloud platform. Specifically, the upper and lower limit checking method is adopted. By setting the upper and lower limit ranges, it is checked whether the data points exceed the range. If the data points exceed the range, they are regarded as abnormal data and are removed.

[0019] Missing value filling unit: used to fill in the data with missing values, specifically through interpolation method, according to the existing adjacent data point values, through linear interpolation or polynomial interpolation method, to fill in the missing data values ​​to ensure the integrity of the data;

[0020] Data normalization unit: used to normalize the collected data, convert data of different dimensions into a unified standard range, and scale the data to the range of [0,1] or [-1,1] by normalizing the standard deviation of each data item or using the minimum-maximum normalization method to avoid comparison deviations between different dimensions.

[0021] Optionally, the feature extraction module includes a trend feature extraction unit, a periodic feature extraction unit, a mutation feature extraction unit and a feature vector generation unit; wherein:

[0022] Trend feature extraction unit: used to identify and extract the long-term trend features of equipment operation from the pre-processed operation data, determine the overall trend of equipment performance changes over time by fitting time series data, and generate trend parameters reflecting changes in equipment operation status;

[0023] Periodic feature extraction unit: uses Fourier transform algorithm to perform frequency domain analysis on pre-processed operation data, determines the periodic frequency and amplitude of the equipment operation by converting time series data into frequency spectrum, and generates frequency parameters reflecting the periodic behavior of the equipment;

[0024] Mutation feature extraction unit: used to detect and extract mutation features in equipment operation from pre-processed operation data, identify mutation points in data by calculating the moving average and differential of data series, and generate mutation parameters reflecting abnormal fluctuations of equipment;

[0025] Feature vector generation unit: used to integrate various feature parameters extracted by the trend feature extraction unit, the periodic feature extraction unit and the mutation feature extraction unit, and to form a feature vector of the device state by arranging the various feature parameters in a predetermined order.

[0026] Optionally, the mutation feature extraction unit includes:

[0027] Calculate the moving average: From the preprocessed running data sequence, select a fixed window size w and calculate the moving average of the data in the window. The calculation formula for the moving average is:

[0028] in, is the moving average at time t, x(i) is the original data at the i-th moment in the time series, and w is the size of the sliding window;

[0029] Calculate the difference: By calculating the difference of the data sequence, the severity of the data change, especially the mutation point, is detected. The difference formula is: Among them, Δx(t) is the difference value at time t, is the moving average at time t;

[0030] Identify mutation points: According to the calculated difference value, set a threshold δ. When the difference value is greater than the threshold δ, it is considered that the data has mutated, and the moment is marked as a mutation point.

[0031] Generate mutation parameters: Generate mutation parameters that reflect abnormal fluctuations of the equipment by comparing the detected mutation points with the difference between the original operating data and the moving average. The expression is: p(t) = |Δx(t)|, where p(t) is the mutation parameter at the mutation point t.

[0032] Optionally, the feature vector generating unit includes:

[0033] Feature parameter sorting: Let the trend parameter generated by the trend feature extraction unit be T, the frequency parameter generated by the periodic feature extraction unit be F, and the mutation parameter generated by the mutation feature extraction unit be P; and sort the feature parameters, the specific order is to arrange the trend feature parameter T first, then the periodic feature parameter F, and finally the mutation feature parameter P, to form an ordered feature sequence {T, F, P};

[0034] Normalization of feature parameters: Normalize the sorted feature parameters, using the minimum-maximum normalization method to scale each feature parameter to the range of [0, 1];

[0035] Feature vector construction: Arrange the normalized feature parameters in a predetermined order to construct the feature vector V of the device state.

[0036] Optionally, the prediction analysis module includes a data classification unit, a state identification unit and a result generation unit; wherein:

[0037] Data classification unit: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to perform unsupervised classification on the equipment's operating status data to form several categories, each of which represents a different mode of the equipment's operating status;

[0038] State recognition unit: Analyzes the feature center of each category based on the clustering results generated by the data classification unit, and determines whether the device is in a normal state or a potential fault state by comparing the distance between the real-time feature vector of the device and the feature center of each category;

[0039] Result generation unit: used to output the classification results in the form of structured data for reference by the optimization decision module.

[0040] Optionally, the data classification unit includes:

[0041] Initialize cluster centers: randomly select k feature vectors as initial cluster centers, denoted as μ1, μ2, …, μ j ;

[0042] Iterate to assign eigenvectors: For each eigenvector V i , calculate its relationship with each cluster center μ j The Euclidean distance is: Among them, V i,m is the feature vector V i The mth feature, μ j,m is the cluster center μ j The mth feature of V, where M is the dimension of the feature vector. i Assign to the cluster with the nearest cluster center;

[0043] Update cluster center: For each cluster j, calculate the average value of all feature vectors assigned to the cluster and update the cluster center μ j , the formula is: Among them, N j is the number of eigenvectors in cluster j, V i is the i-th feature vector assigned to the corresponding cluster j;

[0044] Convergence condition: Repeat the above steps of iteratively assigning feature vectors and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached to form the final k cluster categories.

[0045] Optionally, the state identification unit includes:

[0046] Feature vector acquisition: Get the real-time feature vector V of the device real , the feature vector is generated by a feature vector generating unit;

[0047] Distance calculation: For each cluster center, calculate the real-time feature vector V real The Euclidean distance D from the cluster center j , the formula is: Among them, D j is the real-time feature vector V real and cluster center μ j The Euclidean distance between them; M is the dimension of the feature vector; V real,m is the real-time feature vector V real The mth feature of j,m is the cluster center μ j The mth feature of

[0048] Shortest distance recognition: Determine the feature vector V in real time real The cluster center with the shortest distance μ min , which is expressed as: min =argmin j D j ;

[0049] State judgment: According to the cluster center μ min The category label belongs to and determines the current operating status of the equipment. The category label is pre-defined by the data classification unit in the K-means clustering process, including a normal category and a fault category, which correspond to a normal state and a potential fault state, respectively.

[0050] Optionally, the optimization decision module includes an operation parameter adjustment unit, an operation schedule optimization unit, a preventive maintenance plan formulation unit, and a strategy integration implementation unit; wherein:

[0051] Operation parameter adjustment unit: used to adjust the operation parameters of the equipment according to the equipment operation status identified by the prediction analysis module; when the equipment is in a normal state, the power and speed of the equipment are optimized to improve the energy efficiency and work efficiency of the equipment; when the equipment is in a potential fault state, the operation load of the equipment is limited or the operation parameters of the equipment are temporarily reduced to ensure the safe operation of the equipment;

[0052] Operation schedule optimization unit: used to optimize the operation schedule of the equipment according to the operation status and usage requirements of the equipment. When the equipment is in a normal state, the operation time of the equipment is arranged by maximizing the utilization rate of the equipment; when the equipment is in a potential failure state, the continuous operation load of the equipment is reduced by adjusting the operation time of the equipment;

[0053] Preventive maintenance plan formulation unit: formulates a preventive maintenance plan based on the identification result of the equipment being in a potential failure state. When the equipment is in a potential failure state, specifies the specific maintenance content, including arranging equipment inspection, maintenance and replacement of parts; when the equipment is in a normal state, formulates a regular maintenance plan based on the maintenance history of the equipment;

[0054] Strategy integration and implementation unit: used to integrate various optimization strategies formulated by the operation parameter adjustment unit, the operation schedule optimization unit and the preventive maintenance plan formulation unit, and implement specific optimization operations through the cloud platform.

[0055] Beneficial effects of the present invention:

[0056] The present invention can accurately evaluate the health status of the equipment through real-time collection, preprocessing and feature extraction of equipment operation data, and effectively distinguish the normal state and potential fault state of the equipment by using the K-means clustering algorithm; the system not only improves the accuracy of equipment status judgment, but also can dynamically adjust the equipment's operating parameters, optimize the equipment's operating schedule, and formulate a more scientific preventive maintenance plan, thereby minimizing the occurrence of equipment failures and improving equipment management efficiency.

[0057] The present invention, by combining real-time equipment data with intelligent analysis technology, breaks through the limitations of traditional equipment management systems that rely solely on static rules and manual judgment; its intelligent prediction and optimization strategies can respond to changes in equipment operating status in real time, ensuring that the equipment always maintains the best working state under different working conditions, reducing unnecessary energy consumption and downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A schematic diagram of an intelligent device management system according to an embodiment of the present invention;

[0060] Figure 2 Schematic diagram of a prediction analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0062] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0063] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0064] like Figure 1-Figure 2 As shown, the intelligent equipment management system based on the cloud platform includes a data acquisition module, a data processing module, a feature extraction module, a prediction analysis module and an optimization decision module; among which:

[0065] Data acquisition module: used to collect the operating data of each smart device in real time through a variety of sensors and network interfaces, including the working status, environmental parameters, energy consumption data and operation records of the device, and upload the collected operating data to the cloud platform in a structured format;

[0066] Data processing module: used to pre-process the operating data in the cloud platform, including data cleaning, missing value filling and data normalization. The processed data will serve as the basis for subsequent analysis;

[0067] Feature extraction module: Utilizes statistical analysis and signal processing algorithms to extract relevant features from pre-processed operation data, including trend features, periodic features, and mutation features of equipment operation, and generates feature vectors of equipment status;

[0068] Prediction and analysis module: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to classify the operating status of the equipment and identify whether the equipment is in a normal state or a potential fault state;

[0069] Optimization decision module: Based on the operating status identified by the predictive analysis module, formulate an optimized operation strategy for the equipment, including adjusting the operating parameters of the equipment (such as power, speed, etc.), optimizing the equipment's operating schedule, and formulating a preventive maintenance plan to improve the equipment's operating efficiency and extend the equipment's service life.

[0070] The data acquisition module includes a sensor acquisition unit, an environmental data acquisition unit, an energy consumption monitoring unit, an operation record acquisition unit, and a data upload unit; wherein:

[0071] Sensor acquisition unit: including temperature sensor, humidity sensor, pressure sensor, current sensor and vibration sensor, used to collect working status data of smart devices in real time, including temperature, humidity, pressure, current and vibration parameters of the devices;

[0072] Environmental data acquisition unit: including environmental sensors, used to collect parameter data of the environment where the equipment is located in real time, including temperature, humidity and light intensity;

[0073] Energy consumption monitoring unit: including energy consumption sensors, used to monitor the energy consumption data of the equipment in real time, including the power usage, working hours and load changes of the equipment;

[0074] Operation record collection unit: used to record the operation information of the equipment in real time, including equipment startup, shutdown and operation mode switching;

[0075] Data upload unit: used to upload all operating data collected by each unit, including the working status of the equipment, environmental parameters, energy consumption data and operation records, to the cloud platform for storage in a structured data format through the network interface to ensure the real-time and accuracy of data transmission; through the detailed configuration of the above data acquisition module, it is possible to accurately obtain multi-dimensional data during the operation of various types of equipment, ensuring the comprehensiveness and real-time nature of data input in the equipment management system. Different types of sensors obtain various data related to the equipment and environment through special acquisition units, and these data are structured and transmitted to the cloud platform in real time through the data upload unit, thereby providing high-quality basic data for subsequent data processing and analysis.

[0076] The data processing module includes a data cleaning unit, a missing value filling unit and a data normalization unit; wherein:

[0077] Data cleaning unit: used to remove abnormal data and noise data from the operation data received from the cloud platform. Specifically, the upper and lower limit checking method is adopted. By setting the upper and lower limit ranges, it is checked whether the data points exceed the range. If the data points exceed the range, they are regarded as abnormal data and removed to ensure data quality.

[0078] Missing value filling unit: used to fill in the data with missing values, specifically through interpolation method, according to the existing adjacent data point values, through linear interpolation or polynomial interpolation method, to fill in the missing data values ​​to ensure the integrity of the data;

[0079] Data normalization unit: used to normalize the collected data, convert data of different dimensions into a unified standard range, and scale the data to the range of [0,1] or [-1,1] by normalizing the standard deviation of each data item or using the minimum-maximum normalization method to avoid comparison deviations between different dimensions and ensure data consistency. The design of the above data processing module can efficiently clean and supplement the original data in the cloud platform, making the data more accurate and complete. These preprocessing measures provide a high-quality data foundation for subsequent feature extraction and predictive analysis.

[0080] The feature extraction module includes a trend feature extraction unit, a periodic feature extraction unit, a mutation feature extraction unit and a feature vector generation unit; wherein:

[0081] Trend feature extraction unit: used to identify and extract the long-term trend features of equipment operation from the preprocessed operation data, determine the overall trend of equipment performance changes over time by fitting time series data, and generate trend parameters reflecting changes in equipment operation status; trend features can be fitted by a linear regression model, the formula is as follows: y(t) = β0+β1t+(t), where y(t) is the equipment performance value at time t, β0 is the intercept, β1 is the trend coefficient, (t) is the error term, and the β1 parameter obtained by fitting reflects the long-term trend changes in equipment operation;

[0082] Periodic feature extraction unit: The Fourier transform algorithm is used to perform frequency domain analysis on the preprocessed operation data. By converting the time series data into a spectrum, the periodic frequency and amplitude of the equipment operation are determined, and the frequency parameters reflecting the periodic behavior of the equipment are generated. The periodic characteristics can be expressed by the Fourier transform formula: Among them, x(t) is the time series data, X(f) is the spectrum in the frequency domain, f is the frequency, and N is the length of the time series data. By analyzing the significant frequency components in the spectrum, the periodic characteristics of the equipment can be obtained;

[0083] Mutation feature extraction unit: used to detect and extract mutation features in equipment operation from pre-processed operation data, identify mutation points in data by calculating the moving average and differential of data series, and generate mutation parameters reflecting abnormal fluctuations of equipment;

[0084] Feature vector generation unit: used to integrate various feature parameters extracted by the trend feature extraction unit, the periodic feature extraction unit and the mutation feature extraction unit, and form a feature vector of the equipment state by arranging various feature parameters in a predetermined order for use by the subsequent prediction and analysis module; through the design of the above feature extraction module, the key features of the equipment operation can be systematically and comprehensively extracted from the preprocessed operation data; the trend feature extraction unit accurately captures the long-term change trend of the equipment performance, the periodic feature extraction unit effectively identifies the periodic behavior in the equipment operation, and the mutation feature extraction unit timely detects abnormal fluctuations in the equipment operation; the comprehensive generation of these features into feature vectors provides a high-dimensional, comprehensive and representative equipment state description for the subsequent prediction and analysis module.

[0085] The mutation feature extraction unit includes:

[0086] Calculate the moving average: From the preprocessed running data sequence, select a fixed window size w and calculate the moving average of the data in the window. The calculation formula for the moving average is:

[0087] in, is the moving average at time t, x(i) is the original data at the i-th moment in the time series, w is the size of the sliding window, and the moving average is used to smooth the original data in order to better identify potential mutation points;

[0088] Calculate the difference: By calculating the difference of the data sequence, the severity of the data change, especially the mutation point, is detected. The difference formula is: Among them, Δx(t) is the difference value at time t, is the moving average at time t; a significant change in the differential value can indicate a sudden change in the operating status of the equipment;

[0089] Identify mutation points: According to the calculated difference value, set a threshold δ. When the difference value is greater than the threshold δ, it is considered that the data has mutated, and the moment is marked as a mutation point.

[0090] Generate mutation parameters: Generate mutation parameters reflecting abnormal fluctuations of the equipment by comparing the detected mutation points with the difference between the original operating data and the moving average. The expression is: p(t) = |Δx(t)|, where p(t) is the mutation parameter at the mutation point t, reflecting the amplitude of the change in the equipment state. Through the design of the above-mentioned mutation feature extraction unit, the mutation points in the equipment operation data can be accurately identified, and the drastic changes in the equipment state can be captured in time.

[0091] The feature vector generation unit includes:

[0092] Feature parameter sorting: Let the trend parameter generated by the trend feature extraction unit be T, the frequency parameter generated by the periodic feature extraction unit be F, and the mutation parameter generated by the mutation feature extraction unit be P; and sort the feature parameters, the specific order is to arrange the trend feature parameter T first, then the periodic feature parameter F, and finally the mutation feature parameter P, to form an ordered feature sequence {T, F, P};

[0093] Normalization of feature parameters: Normalize the sorted feature parameters, and use the minimum-maximum normalization method to scale each feature parameter to the range of [0, 1]. The normalization formula is as follows:

[0094] Among them, T norm ,F norm and P norm are the normalized trend parameter, frequency parameter and mutation parameter, T min ,T max ,F min ,F max ,Pmin and P max are the minimum and maximum values ​​of each characteristic parameter respectively;

[0095] Feature vector construction: Arrange the normalized feature parameters in a predetermined order to construct the feature vector V of the device state, which is expressed as: V = [T norm , F norm , P norm ], where V is the characteristic vector of the equipment status, which includes the trend characteristics, periodic characteristics and mutation characteristics of the equipment operation; through the design of the above-mentioned characteristic vector generation unit, the multi-dimensional characteristic parameters can be systematically integrated into a unified characteristic vector, ensuring the comprehensiveness and consistency of the equipment status description.

[0096] The prediction and analysis module includes a data classification unit, a state recognition unit, and a result generation unit; wherein:

[0097] Data classification unit: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to perform unsupervised classification on the equipment's operating status data to form several categories, each of which represents a different mode of the equipment's operating status;

[0098] State recognition unit: Analyzes the feature center of each category based on the clustering results generated by the data classification unit, and determines whether the device is in a normal state or a potential fault state by comparing the distance between the real-time feature vector of the device and the feature center of each category;

[0099] Result generation unit: used to output the classification results in the form of structured data for reference by the optimization decision module; through the design of the above-mentioned prediction and analysis module, the K-means clustering algorithm can be effectively used to accurately classify the equipment operation status, distinguish normal operation from potential fault status, enhance the fault prediction ability and operation reliability of the intelligent equipment management system, and reduce equipment maintenance costs.

[0100] Data classification units include:

[0101] Initialize cluster centers: randomly select k feature vectors as initial cluster centers, denoted as μ1, μ2, …, μ j ;

[0102] Iterate to assign eigenvectors: For each eigenvector V i , calculate its relationship with each cluster center μ j The Euclidean distance is: Among them, V i,m is the feature vector V i The mth feature, μ j,m is the cluster center μ jThe mth feature of V, where M is the dimension of the feature vector. i Assign to the cluster with the nearest cluster center;

[0103] Update cluster center: For each cluster j, calculate the average value of all feature vectors assigned to the cluster and update the cluster center μ j , the formula is: Among them, N j is the number of eigenvectors in cluster j, V i is the i-th feature vector assigned to the corresponding cluster j;

[0104] Determine convergence conditions: repeat the above steps of iteratively assigning feature vectors and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached, forming the final k cluster categories; through the detailed design of the above data classification unit, the K-means clustering algorithm can be used to efficiently and accurately classify the equipment operation status.

[0105] The state recognition unit includes:

[0106] Feature vector acquisition: Get the real-time feature vector V of the device real ,The feature vector is generated by the feature vector generating unit, including the trend feature, periodicity feature and mutation feature of the equipment;

[0107] Distance calculation: For each cluster center, calculate the real-time feature vector V real The Euclidean distance D from the cluster center j , the formula is: Among them, D j is the real-time feature vector V real and cluster center μ j The Euclidean distance between them; M is the dimension of the feature vector; V real,m is the real-time feature vector V real The mth feature of j,m is the cluster center μ j The mth feature of

[0108] Shortest distance recognition: Determine the feature vector V in real time real The cluster center with the shortest distance μ min , which is expressed as: min =argmin j D j , where μ min is the real-time feature vector V real The cluster center with the shortest distance;

[0109] State judgment: According to the cluster center μ minThe category label belongs to judge the current operating status of the equipment. The category label is pre-defined by the data classification unit in the K-means clustering process to clearly distinguish normal operation from potential fault status. The category label includes normal category and fault category, which correspond to normal status and potential fault status respectively. Through the design of the above-mentioned status recognition unit, the real-time operating status of the equipment can be accurately matched with the pre-defined normal and fault categories to ensure accurate judgment of the equipment status.

[0110] The optimization decision module includes an operation parameter adjustment unit, an operation schedule optimization unit, a preventive maintenance plan formulation unit, and a strategy integration implementation unit; among which:

[0111] Operation parameter adjustment unit: used to adjust the operation parameters of the equipment according to the equipment operation status identified by the prediction analysis module; when the equipment is in a normal state, the power and speed of the equipment are optimized to improve the energy efficiency and work efficiency of the equipment; when the equipment is in a potential fault state, the operation load of the equipment is limited or the operation parameters of the equipment are temporarily reduced to ensure the safe operation of the equipment;

[0112] Operation schedule optimization unit: used to optimize the operation schedule of the equipment according to the operation status and usage requirements of the equipment. When the equipment is in a normal state, the operation time of the equipment is arranged by maximizing the utilization rate of the equipment; when the equipment is in a potential failure state, the operation time of the equipment is adjusted to reduce the continuous operation load of the equipment, avoid the aggravation of failures caused by excessive use, and reserve time for maintenance work;

[0113] Preventive maintenance plan formulation unit: Based on the identification results of equipment in potential failure states, formulate preventive maintenance plans to extend the service life of equipment and reduce the occurrence of failures. When the equipment is in a potential failure state, specify specific maintenance content, including arranging equipment inspection, maintenance and replacement of parts; when the equipment is in a normal state, formulate a regular maintenance plan based on the maintenance history of the equipment to ensure the long-term stable operation of the equipment;

[0114] Strategy integration and implementation unit: used to integrate various optimization strategies formulated by the operating parameter adjustment unit, the operating schedule optimization unit and the preventive maintenance plan formulation unit, and implement specific optimization operations through the cloud platform; when the equipment is in a normal state, the optimized operating parameters and operating schedule are integrated, and adjustment instructions are sent to the equipment through the cloud platform to achieve efficient operation of the equipment; when the equipment is in a potential fault state, the adjusted operating parameters, optimized operating schedule and preventive maintenance plan are integrated, and corresponding instructions and notifications are sent to the equipment and maintenance personnel through the cloud platform to ensure safe operation and timely maintenance of the equipment; through the detailed design of the above-mentioned optimization decision-making module, the system can formulate scientific and effective optimization operation strategies according to the real-time operating status of the equipment.

[0115] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0116] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Intelligent equipment management system based on cloud platform, characterized by: It includes data acquisition module, data processing module, feature extraction module, prediction analysis module and optimization decision module; among which: Data acquisition module: used to collect the operating data of each smart device in real time through a variety of sensors and network interfaces, including the working status, environmental parameters, energy consumption data and operation records of the device, and upload the collected operating data to the cloud platform in a structured format; Data processing module: used to pre-process the operating data in the cloud platform, including data cleaning, missing value filling and data normalization; Feature extraction module: Utilizes statistical analysis and signal processing algorithms to extract relevant features from pre-processed operation data, including trend features, periodic features, and mutation features of equipment operation, and generates feature vectors of equipment status; Prediction and analysis module: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to classify the operating status of the equipment and identify whether the equipment is in a normal state or a potential fault state; Optimization decision module: Based on the operating status identified by the predictive analysis module, formulate an optimized operation strategy for the equipment, including adjusting the operating parameters of the equipment, optimizing the operating schedule of the equipment, and formulating a preventive maintenance plan.

2. The cloud platform-based intelligent device management system according to claim 1, characterized in that: The data acquisition module includes a sensor acquisition unit, an environmental data acquisition unit, an energy consumption monitoring unit, an operation record acquisition unit and a data upload unit; wherein: Sensor acquisition unit: including temperature sensor, humidity sensor, pressure sensor, current sensor and vibration sensor, used to collect working status data of smart devices in real time, including temperature, humidity, pressure, current and vibration parameters of the devices; Environmental data acquisition unit: including environmental sensors, used to collect parameter data of the environment where the equipment is located in real time, including temperature, humidity and light intensity; Energy consumption monitoring unit: including energy consumption sensors, used to monitor the energy consumption data of the equipment in real time, including the power usage, working hours and load changes of the equipment; Operation record collection unit: used to record the operation information of the equipment in real time, including equipment startup, shutdown and operation mode switching; Data upload unit: used to upload all operating data collected by each unit, including the working status of the equipment, environmental parameters, energy consumption data and operation records, to the cloud platform for storage in a structured data format through the network interface.

3. The cloud platform-based intelligent device management system according to claim 1, characterized in that: The data processing module includes a data cleaning unit, a missing value filling unit and a data normalization unit; wherein: Data cleaning unit: used to remove abnormal data and noise data from the operation data received from the cloud platform. Specifically, the upper and lower limit checking method is adopted. By setting the upper and lower limit ranges, it is checked whether the data points exceed the range. If the data points exceed the range, they are regarded as abnormal data and are removed. Missing value filling unit: used to fill in the data with missing values, specifically through interpolation method, according to the existing adjacent data point values, through linear interpolation or polynomial interpolation method, to fill in the missing data values ​​to ensure the integrity of the data; Data normalization unit: used to normalize the collected data, convert data of different dimensions into a unified standard range, and scale the data to the range of [0,1] or [-1,1] by normalizing the standard deviation of each data item or using the minimum-maximum normalization method to avoid comparison deviations between different dimensions.

4. The cloud platform-based intelligent device management system according to claim 1, characterized in that: The feature extraction module includes a trend feature extraction unit, a periodic feature extraction unit, a mutation feature extraction unit and a feature vector generation unit; in: Trend feature extraction unit: used to identify and extract the long-term trend features of equipment operation from the pre-processed operation data, determine the overall trend of equipment performance changes over time by fitting time series data, and generate trend parameters reflecting changes in equipment operation status; Periodic feature extraction unit: uses Fourier transform algorithm to perform frequency domain analysis on pre-processed operation data, determines the periodic frequency and amplitude of the equipment operation by converting time series data into frequency spectrum, and generates frequency parameters reflecting the periodic behavior of the equipment; Mutation feature extraction unit: used to detect and extract mutation features in equipment operation from pre-processed operation data, identify mutation points in data by calculating the moving average and differential of data series, and generate mutation parameters reflecting abnormal fluctuations of equipment; Feature vector generation unit: used to integrate various feature parameters extracted by the trend feature extraction unit, the periodic feature extraction unit and the mutation feature extraction unit, and to form a feature vector of the device state by arranging the various feature parameters in a predetermined order.

5. The cloud platform-based intelligent device management system according to claim 4, characterized in that: The mutation feature extraction unit comprises: Calculate the moving average: From the preprocessed running data sequence, select a fixed window size w and calculate the moving average of the data in the window. The calculation formula for the moving average is: in, is the moving average at time t, x(i) is the original data at the i-th moment in the time series, and w is the size of the sliding window; Calculate the difference: By calculating the difference of the data sequence, the severity of the data change, especially the mutation point, is detected. The difference formula is: Among them, Δx(t) is the difference value at time t, is the moving average at time t; Identify mutation points: According to the calculated difference value, set a threshold δ. When the difference value is greater than the threshold δ, it is considered that the data has mutated, and the moment is marked as a mutation point. Generate mutation parameters: Generate mutation parameters that reflect abnormal fluctuations of the equipment by comparing the detected mutation points with the difference between the original operating data and the moving average. The expression is: p(t) = |Δx(t)|, where p(t) is the mutation parameter at the mutation point t.

6. The cloud platform-based intelligent device management system according to claim 5, characterized in that: The feature vector generating unit comprises: Feature parameter sorting: Let the trend parameter generated by the trend feature extraction unit be T, the frequency parameter generated by the periodic feature extraction unit be F, and the mutation parameter generated by the mutation feature extraction unit be P; and sort the feature parameters, the specific order is to arrange the trend feature parameter T first, then the periodic feature parameter F, and finally the mutation feature parameter P, to form an ordered feature sequence {T, F, P}; Normalization of feature parameters: Normalize the sorted feature parameters, using the minimum-maximum normalization method to scale each feature parameter to the range of [0, 1]; Feature vector construction: Arrange the normalized feature parameters in a predetermined order to construct the feature vector V of the device state.

7. The cloud platform-based intelligent device management system according to claim 1, characterized in that: The prediction and analysis module includes a data classification unit, a state identification unit and a result generation unit; wherein: Data classification unit: Based on the feature vector generated by the feature extraction module, the K-means clustering algorithm is used to perform unsupervised classification on the equipment's operating status data to form several categories, each of which represents a different mode of the equipment's operating status; State recognition unit: Analyzes the feature center of each category based on the clustering results generated by the data classification unit, and determines whether the device is in a normal state or a potential fault state by comparing the distance between the real-time feature vector of the device and the feature center of each category; Result generation unit: used to output the classification results in the form of structured data for reference by the optimization decision module.

8. The cloud platform-based intelligent device management system according to claim 7, characterized in that: The data classification unit comprises: Initialize cluster centers: randomly select k feature vectors as initial cluster centers, denoted as μ1, μ2, …, μ j ; Iterate to assign eigenvectors: For each eigenvector V i , calculate its relationship with each cluster center μ j The Euclidean distance is: Among them, V i,m is the feature vector V i The mth feature, μ j,m is the cluster center μ j The mth feature of V, where M is the dimension of the feature vector. i Assign to the cluster with the nearest cluster center; Update cluster center: For each cluster j, calculate the average value of all feature vectors assigned to the cluster and update the cluster center μ j , the formula is: Among them, N j is the number of eigenvectors in cluster j, V i is the i-th feature vector assigned to the corresponding cluster j; Convergence condition: Repeat the above steps of iteratively assigning feature vectors and updating cluster centers until the cluster centers no longer change or the predetermined number of iterations is reached to form the final k cluster categories.

9. The cloud platform-based intelligent device management system according to claim 8, characterized in that: The state recognition unit comprises: Feature vector acquisition: Get the real-time feature vector V of the device real , the feature vector is generated by a feature vector generating unit; Distance calculation: For each cluster center, calculate the real-time feature vector V real The Euclidean distance D from the cluster center j , the formula is: Among them, D j is the real-time feature vector V real and cluster center μ j The Euclidean distance between them; M is the dimension of the feature vector; V real,m is the real-time feature vector V real The mth feature of j,m is the cluster center μ j The mth feature of Shortest distance recognition: Determine the feature vector V in real time real The cluster center with the shortest distance μ min , which is expressed as: min =argmin j D j ; State judgment: According to the cluster center μ min The category label belongs to and determines the current operating status of the equipment. The category label is pre-defined by the data classification unit in the K-means clustering process, including a normal category and a fault category, which correspond to a normal state and a potential fault state, respectively.

10. The cloud platform-based intelligent device management system according to claim 1, characterized in that: The optimization decision module includes an operation parameter adjustment unit, an operation schedule optimization unit, a preventive maintenance plan formulation unit, and a strategy integration implementation unit; wherein: Operation parameter adjustment unit: used to adjust the operation parameters of the equipment according to the equipment operation status identified by the prediction analysis module; when the equipment is in a normal state, the power and speed of the equipment are optimized to improve the energy efficiency and work efficiency of the equipment; when the equipment is in a potential fault state, the operation load of the equipment is limited or the operation parameters of the equipment are temporarily reduced to ensure the safe operation of the equipment; Operation schedule optimization unit: used to optimize the operation schedule of the equipment according to the operation status and usage requirements of the equipment. When the equipment is in a normal state, the operation time of the equipment is arranged by maximizing the utilization rate of the equipment; when the equipment is in a potential failure state, the continuous operation load of the equipment is reduced by adjusting the operation time of the equipment; Preventive maintenance plan formulation unit: formulates a preventive maintenance plan based on the identification result of the equipment being in a potential failure state. When the equipment is in a potential failure state, specifies the specific maintenance content, including arranging equipment inspection, maintenance and replacement of parts; when the equipment is in a normal state, formulates a regular maintenance plan based on the maintenance history of the equipment; Strategy integration and implementation unit: used to integrate various optimization strategies formulated by the operation parameter adjustment unit, the operation schedule optimization unit and the preventive maintenance plan formulation unit, and implement specific optimization operations through the cloud platform.

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