Electromechanical equipment state management method and system based on Internet of Things, and storage medium

Abstract

The invention discloses an electromechanical equipment state management method and system based on the Internet of Things, and a storage medium, and relates to the technical field of industrial Internet of Things. According to the invention, the sensor is arranged on the electromechanical equipment and the time sequence data of the equipment is acquired in real time, so that the running state of the equipment is monitored in real time. The system can timely detect abnormal conditions in equipment operation, such as temperature exceeding and pressure abnormity, so as to trigger early warning. Equipment faults can be avoided, the downtime is shortened, and the production efficiency is improved; through an internet of things protocol, cleaning, preprocessing and further time domain or frequency domain analysis are carried out by using time sequence data, data support is provided for equipment state diagnosis, the accuracy of an electromechanical state management data analysis process is improved, and equipment management is more refined and intelligent; by periodically collecting, cleaning, and analyzing the operating data of the device, the system can provide data-based maintenance and management decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things, and in particular to an Internet of Things-based electromechanical equipment state management method, system and storage medium. Background Art

[0002] With the development of industrial automation and intelligent manufacturing, the Industrial Internet of Things (IIoT) has become one of the key technologies to improve production efficiency, reduce costs, improve equipment reliability and optimize operations management. The Industrial Internet of Things combines sensors, equipment, control systems with cloud computing, big data and artificial intelligence technologies to achieve real-time monitoring, status prediction and intelligent management of equipment. IIoT technology can help companies achieve remote monitoring, health assessment, fault warning and life cycle management of equipment, thereby optimizing maintenance strategies, reducing downtime and improving production efficiency.

[0003] However, the existing industrial Internet of Things technology still has many shortcomings in the management of the status of electromechanical equipment. First, the application of the existing electromechanical equipment status management for the industrial Internet of Things is often limited to the data collection stage. The specific data management and equipment monitoring mostly rely on manual inspection or regular maintenance. There is a lack of real-time data support, and it is difficult to detect abnormal conditions of equipment in time. It is often impossible to warn of potential failures at an early stage, resulting in maintenance only after the equipment fails, affecting production efficiency.

[0004] Secondly, the fault warnings of current equipment monitoring systems mostly rely on preset thresholds or simple rules, lack adaptive analysis capabilities based on real-time data and intelligent algorithms, are unable to cope with the noise, data missing and errors generated during data collection and transmission, and cannot dynamically adjust the warning strategy according to the actual operating status of the equipment, thereby reducing the accuracy of fault prediction.

[0005] In view of the above problems, it is necessary to propose a method, system and storage medium for electromechanical equipment status management based on the Internet of Things. Summary of the invention

[0006] The purpose of the present invention is to solve the problems existing in the background technology and to propose a method, system and storage medium for electromechanical equipment status management based on the Internet of Things.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for managing the state of electromechanical equipment based on the Internet of Things, comprising: Step 1: Mechanical and electrical equipment data collection; All electromechanical equipment are numbered with the number symbol , ;in is the total number of electromechanical equipment; The sensor group installed on each electromechanical device i collects the timing operation data of the electromechanical device in real time. The timing operation data includes but is not limited to temperature ,pressure ,vibration , Current , speed and power .in, It is the data timestamp, that is, the time when each data is recorded.

[0008] Every preset statistical period , count all time series running data and generate a time series data set: , in, It is the time series running data number. ; The data timestamp is the time when each time series running data is collected; .

[0009] Based on the Internet of Things protocol, the time series data set is sent to the data preprocessing module for data cleaning and gap filling.

[0010] Step 2: Time series data cleaning and preprocessing; Data missing detection is used to identify data gaps in time series data sets caused by information transmission delays, packet loss, and communication interference, and linear interpolation operations are used to fill in the missing data.

[0011] The data missing detection is specifically as follows: Check the data timestamps of each time series running data in the time series dataset . Define the data timestamp interval .in The timestamp number of the data. .

[0012] Calculate the timestamp interval to traverse the electromechanical equipment number symbol , timing operation data number and data timestamp number ; When a certain time series running data is identified exist and The timestamp interval between Greater than the preset maximum preset interval , then it is determined that the timing operation data is and There are missing data, and and Mark as data missing points; The linear interpolation operation is specifically as follows: Through the linear interpolation formula: ; Estimate the time when the missing data is ,as well as Imputed value for missing data at time ; Perform linear interpolation on all missing data points and , calculates estimates of the data between points where data are missing.

[0013] The sliding equal weight algorithm is used to perform noise reduction and cleaning on the time series data set after the gaps are filled, removing abnormal data caused by sensor failure, environmental interference and other factors.

[0014] The sliding equalization algorithm is specifically as follows: Retrieve the preset sliding window size W, through the formula: ; Computing time series data Sliding average in each sliding window ,in is the sliding window index; the sliding average is recorded as the smoothed data output, and the denoised preprocessed time series data set is obtained through statistics: .

[0015] Step 3: Extracting time series features; Obtain the preprocessed time series data set of each electromechanical device and extract its time series feature value. The specific process is as follows: Get each electromechanical device Preprocessed time series dataset; By formula: ; Calculate the mean of each time series running data ; By formula: , Calculate the standard deviation of each time series running data ; By formula: , Calculate the skewness of each time series run data ; By formula: , Calculate the kurtosis of each time series running data .

[0016] As a preferred method of the present invention, a time series feature vector is generated: .

[0017] Step 4: Frequency domain analysis and frequency domain feature extraction; The preprocessed time series data set of each electromechanical device i is obtained, and all data in the preprocessed time series data set are transformed from the time domain to the frequency domain by discrete Fourier transform, wherein the discrete Fourier transform formula is: ; in, is the frequency domain signal obtained after transformation, is the frequency, and ; is a negative exponential kernel, where is an imaginary unit and ; As a preferred embodiment of the present invention, frequency domain features are extracted from the frequency domain data obtained by discrete Fourier transform.

[0018] As a preferred embodiment of the present invention, by formula: , calculate the frequency spectral density ; By formula: , calculate the main frequency energy ; By formula: , calculate the effective frequency bandwidth ; As a preferred method of the present invention, a frequency sequence feature vector is generated: .

[0019] Step 5: Eigenvalue analysis; By weighted Euclidean distance formula: ; Calculate each electromechanical device About time series operation data Time domain anomaly parameters and frequency domain anomaly parameters , in, , , , ..., are preset weight factors respectively; is the preset mean standard value, is the preset standard deviation standard value, is the preset standard value of skewness, is the preset kurtosis standard value, is the preset frequency spectrum density standard value, is the preset main frequency energy standard value, It is the preset standard value of effective frequency bandwidth.

[0020] Every preset statistical period, the weighted Euclidean distance formula is used to traverse all electromechanical equipment number symbols. , get each electromechanical equipment About time series operation data Time domain anomaly parameters and frequency domain anomaly parameters ; when When it is greater than the preset threshold, the first status symbol The value is 1, otherwise the first status symbol The value of is 0; when When it is greater than the preset threshold, the second status symbol The value is 1, otherwise the second status symbol The value of is 0; Step 6: State management based on eigenvalues; Get each electromechanical device The first status symbol and the second status symbol , through the formula: ; Determine the status symbol of each electromechanical device i with respect to the timing operation data j ; and is the logical symbol “and”; or is the logical symbol “or”; Normal represents the normal state; Warning represents the warning state; Fault represents the fault state.

[0021] Real-time monitoring of the status of electromechanical equipment; if the equipment status changes to Fault, an electromechanical equipment alarm is immediately issued and an equipment maintenance notice is generated. If the equipment status changes to Warning, an electromechanical equipment early warning signal is immediately issued.

[0022] Record the status symbols, alarm records and response measures of all devices in real time and generate regular reports.

[0023] In a second aspect, the present invention provides an electromechanical equipment state management system based on the Internet of Things, which adopts an electromechanical equipment state management method based on the Internet of Things as described in any one of the first aspects, and the management system further includes: Data perception module: The data perception module obtains the timing operation data of each electromechanical device through the sensor group, performs high-frequency data collection and real-time transmission through the Internet of Things protocol, and sends the collected data to the data preprocessing module; Data preprocessing module: The data preprocessing module is responsible for preprocessing, automatically processing missing values, and cleaning and filtering the data sent by the data perception module to ensure data quality for subsequent analysis; Time domain analysis module,The time domain analysis module performs preliminary analysis on the processed time series data,,mainly including trend analysis and anomaly detection; Frequency domain analysis module: The frequency domain analysis module converts the timing data of electromechanical equipment into frequency domain through Fourier transform method, analyzes the frequency characteristics of the timing operation data of electromechanical equipment, and assists in fault diagnosis; Electromechanical equipment data center: The electromechanical equipment data center is responsible for centrally managing the real-time monitoring data, historical data and analysis results of all electromechanical equipment; Database: The database is responsible for storing all collected time series data, analysis results, and equipment operation history, and provides efficient data retrieval and query functions; The execution module executes relevant instructions according to the analysis results of the electromechanical equipment data center.

[0024] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, an Internet of Things-based electromechanical equipment status management method as described in any one of the first aspects is implemented.

[0025] The beneficial effects of the present invention are: 1. The present invention realizes real-time monitoring of the equipment's operating status by installing sensors on electromechanical equipment and collecting the equipment's time series data in real time. The system can detect abnormal conditions in the equipment's operation in a timely manner, such as excessive temperature, abnormal pressure, etc., thereby triggering early warnings. This helps to avoid equipment failures, reduce downtime, and improve production efficiency; 2. Through the Internet of Things protocol, all collected time series data are transmitted to the data center for centralized management and analysis. The data center uses the time series data for cleaning, preprocessing and further time domain or frequency domain analysis to provide data support for equipment status diagnosis; data feature extraction is performed in both time domain and frequency domain to improve the accuracy of the electromechanical status management data analysis process, making equipment management more refined and intelligent; 3. The present invention can provide maintenance and management decision support based on data by regularly collecting, cleaning and analyzing the operation data of the equipment. For example, through the accumulation of historical data, the operation law of the equipment can be identified, the possible failure cycle of the equipment can be predicted, and predictive maintenance can be achieved. This strategy can not only extend the service life of the equipment, but also reduce the occurrence of sudden failures, reduce maintenance costs, and optimize resource allocation and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall process of a method for managing the state of electromechanical equipment based on the Internet of Things provided in an embodiment of the present invention; Figure 2 The present invention is a schematic diagram of the system architecture of an Internet of Things-based electromechanical equipment status management system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0029] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0031] Reference Figure 1 As shown, in order to solve the above-mentioned prior art problems, the present invention provides a method for managing the state of electromechanical equipment based on the Internet of Things in a first aspect. The management method comprises: step 1, collecting data of electromechanical equipment; All electromechanical equipment are numbered with the number symbol , ;in is the total number of electromechanical equipment; The sensor group installed on each electromechanical device i collects the timing operation data of the electromechanical device in real time. The timing operation data includes but is not limited to temperature ,pressure ,vibration , Current , speed and power .in, It is the data timestamp, that is, the time when each data is recorded.

[0032] Every preset statistical period , count all time series running data and generate a time series data set: , in, It is the time series running data number. ; The data timestamp is the time when each time series running data is collected; .

[0033] Then, based on the Internet of Things protocol, the time series data set is sent to the data preprocessing module for data cleaning and gap filling.

[0034] Step 2: Time series data cleaning and preprocessing; Data missing detection is used to identify data gaps in time series data sets caused by information transmission delays, packet loss, and communication interference, and linear interpolation operations are used to fill in the missing data.

[0035] The data missing detection is specifically as follows: Check the data timestamps of each time series running data in the time series dataset . Define the data timestamp interval .in is the data timestamp number, .

[0036] Calculate the timestamp interval to traverse the electromechanical equipment number symbol , timing operation data number and data timestamp number ; When a certain time series running data is identified exist and The timestamp interval between Greater than the preset maximum preset interval , then it is determined that the timing operation data is and There are missing data, and and Mark as data missing points; The linear interpolation operation is specifically as follows: Through the linear interpolation formula: ; Estimate the time when the missing data is ,as well as Imputed value for missing data at time ; Perform linear interpolation on all missing data points and , calculates estimates of the data between points where data are missing.

[0037] The sliding equal weight algorithm is used to perform noise reduction and cleaning on the time series data set after the gaps are filled, removing abnormal data caused by sensor failure, environmental interference and other factors.

[0038] The sliding equalization algorithm is specifically as follows: Retrieve the preset sliding window size W, through the formula: ; Computing time series data Sliding average in each sliding window ,in is the sliding window index; the sliding average is recorded as the smoothed data output, and the denoised preprocessed time series data set is obtained through statistics: .

[0039] Step 3: Extracting time series features; Obtain the preprocessed time series data set of each electromechanical device and extract its time series feature value. The specific process is as follows: Get each electromechanical device Preprocessed time series dataset; By formula: ; Calculate the mean of each time series running data ; By formula: , Calculate the standard deviation of each time series running data ; By formula: , Calculate the skewness of each time series run data ; By formula: , Calculate the kurtosis of each time series running data .

[0040] As a preferred method of the present invention, a time series feature vector is generated: .

[0041] Step 4: Frequency domain analysis and frequency domain feature extraction; The preprocessed time series data set of each electromechanical device i is obtained, and all data in the preprocessed time series data set are transformed from the time domain to the frequency domain by discrete Fourier transform, wherein the discrete Fourier transform formula is: ; in, is the frequency domain signal obtained after transformation, is the frequency, and ; is a negative exponential kernel, where is an imaginary unit and ; As a preferred embodiment of the present invention, frequency domain features are extracted from the frequency domain data obtained by discrete Fourier transform.

[0042] As a preferred embodiment of the present invention, by formula: , calculate the frequency spectral density ; By formula: , calculate the main frequency energy ; By formula: , calculate the effective frequency bandwidth ; As a preferred method of the present invention, a frequency sequence feature vector is generated: .

[0043] Step 5: Eigenvalue analysis; By weighted Euclidean distance formula: ; Calculate each electromechanical device About time series operation data Time domain anomaly parameters and frequency domain anomaly parameters , in, , , , ..., are preset weight factors respectively; is the preset mean standard value, is the preset standard deviation standard value, is the preset standard value of skewness, is the preset kurtosis standard value, is the preset frequency spectrum density standard value, is the preset main frequency energy standard value, It is the preset standard value of effective frequency bandwidth.

[0044] Every preset statistical period, the weighted Euclidean distance formula is used to traverse all electromechanical equipment number symbols. , get each electromechanical equipment About time series operation data Time domain anomaly parameters and frequency domain anomaly parameters ; when When it is greater than the preset threshold, the first status symbol The value is 1, otherwise the first status symbol The value of is 0; when When it is greater than the preset threshold, the second status symbol The value is 1, otherwise the second status symbol The value of is 0; Step 6: State management based on eigenvalues; Get each electromechanical device The first status symbol and the second status symbol , through the formula: ; Determine the status symbol of each electromechanical device i with respect to the timing operation data j ; and is the logical symbol “and”; or is the logical symbol “or”; Normal represents the normal state; Warning represents the warning state; Fault represents the fault state.

[0045] Real-time monitoring of the status of electromechanical equipment; if the equipment status changes to Fault, an electromechanical equipment alarm is immediately issued and an equipment maintenance notice is generated. If the equipment status changes to Warning, an electromechanical equipment early warning signal is immediately issued.

[0046] Record the status symbols, alarm records and response measures of all devices in real time and generate regular reports.

[0047] See also Figure 2 As shown, in the second aspect, the present invention provides an electromechanical equipment state management system based on the Internet of Things, including a data perception module, a data preprocessing module, a time domain analysis module, a frequency domain analysis module, an electromechanical equipment data center, a database and an execution module. The data perception module obtains the time series operation data of each electromechanical equipment through a sensor group, performs high-frequency data collection and real-time transmission through the Internet of Things protocol, and sends the collected data to the data preprocessing module; the data preprocessing module is responsible for preprocessing, automatic missing value processing and cleaning and filtering the data sent by the data perception module to ensure data quality for subsequent analysis. The time domain analysis module performs a preliminary analysis on the processed time series data, mainly including trend analysis and anomaly detection. The frequency domain analysis module performs frequency domain conversion on the time series data of the electromechanical equipment through the Fourier transform method, analyzes the frequency characteristics of the time series operation data of the electromechanical equipment, and assists in fault diagnosis. The electromechanical equipment data center is responsible for centralized management of the real-time monitoring data, historical data and analysis results of all electromechanical equipment. The database is responsible for storing all collected time series data, analysis results and equipment operation history, and provides data retrieval and query functions. The execution module executes relevant instructions according to the analysis results of the electromechanical equipment data center.

[0048] In some embodiments, the management system can communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0049] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that may be used include, without limitation, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), etc.

[0050] The third aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein the program is executed by a processor to implement a method for managing the state of an electromechanical device based on the Internet of Things as described in any one of the first aspects. The computer-readable medium in this embodiment can be written in one or more programming languages ​​or a combination thereof to write computer program codes for performing the operations of some embodiments of the present disclosure, wherein the programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0051] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0052] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flowchart.

[0053] The fourth aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement a method for managing the status of electromechanical equipment based on the Internet of Things as described in the first aspect. The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist separately, that is, it is not assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device can implement a method for managing the status of electromechanical equipment based on the Internet of Things as described in the first aspect.

[0054] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements a method for managing the status of electromechanical equipment based on the Internet of Things as described in the first aspect.

[0055] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for managing the state of electromechanical equipment based on the Internet of Things, characterized in that: include: Step 1: Mechanical and electrical equipment data collection; All electromechanical equipment are numbered with the number symbol , ;in The total number of electromechanical equipment; every preset statistical period , by installing in various electromechanical equipment The sensor group on the device collects the time series operation data of the electromechanical equipment in real time and generates a time series data set; Step 2: Time series data cleaning and preprocessing: Use data missing detection to identify data gaps in the time series data set caused by information transmission delay, packet loss, and communication interference, and fill the gaps through linear interpolation operations; Use the sliding equal weight algorithm to perform noise reduction and cleaning on the time series data set after the gaps are filled, and remove abnormal data caused by sensor failures and environmental interference factors; Step 3: Extracting time series features; Obtain the preprocessed time series data set of each electromechanical device, extract its time series feature value, and generate a time series feature vector; Step 4: Frequency domain analysis and frequency domain feature extraction; Obtain a preprocessed time series data set of each electromechanical device i, transform all data in the preprocessed time series data set from the time domain to the frequency domain through discrete Fourier transform, and perform frequency domain feature extraction on the frequency domain data obtained through discrete Fourier transform; Step 5: Eigenvalue analysis: Calculate the time domain abnormality parameters and frequency domain abnormality parameters of each electromechanical device for each timing operation data by using the weighted Euclidean distance formula; Step 6: Status management based on characteristic values; obtain the status symbol of each electromechanical device regarding each timing operation data through conditional judgment operation; monitor the status of electromechanical equipment in real time; if the device status changes to Fault, immediately issue an electromechanical equipment alarm and generate an equipment maintenance notice; if the device status changes to Warning, immediately issue an electromechanical equipment early warning signal; record the status symbols, alarm records and response measures of all devices in real time, and generate regular reports.

2. According to the method for managing the state of electromechanical equipment based on the Internet of Things according to claim 1, it is characterized in that: The time series data set is specifically: ； in, It is the time series running data number. ; The data timestamp is the time when each time series running data is collected; .

3. According to the method for managing the state of electromechanical equipment based on the Internet of Things as claimed in claim 1, it is characterized in that: The data missing detection and linear interpolation operations are specifically as follows: Check the data timestamps of each time series running data in the time series dataset ; Define the data timestamp interval ;in is the data timestamp number, ; Calculate the timestamp interval to traverse the electromechanical equipment number symbol , timing operation data number and data timestamp number ; When a certain time series running data is identified exist and The timestamp interval between Greater than the preset maximum preset interval , then it is determined that the timing operation data is and There are missing data, and and Mark as data missing points; Through the linear interpolation formula: ； Estimate the time when the missing data is ,as well as Imputed value for missing data at time ; Perform linear interpolation on all missing data points and , calculates estimates of the data between points where data are missing.

4. According to the method for managing the state of electromechanical equipment based on the Internet of Things as claimed in claim 1, it is characterized in that: The sliding equalization algorithm is specifically as follows: Retrieve the preset sliding window size W, through the formula: ； Computing time series data Sliding average in each sliding window ,in is the sliding window index; the sliding average is recorded as the smoothed data output, and the denoised preprocessed time series data set is obtained through statistics: 。 5. The method for managing the state of electromechanical equipment based on the Internet of Things according to claim 4, characterized in that: The specific process of extracting its time series feature value is: Get each electromechanical device Preprocessed time series dataset; By formula: ； Calculate the mean of each time series running data ; By formula: , Calculate the standard deviation of each time series running data ; By formula: , Calculate the skewness of each time series run data ; By formula: , Calculate the kurtosis of each time series running data ; Generate time series feature vector And input to the weighted Euclidean distance formula.

6. The method for managing the state of electromechanical equipment based on the Internet of Things according to claim 1, characterized in that: The specific process of discrete Fourier transform and frequency domain data feature extraction is: Get each electromechanical device The preprocessed time series data set is transformed from the time domain to the frequency domain by discrete Fourier transform, where the discrete Fourier transform formula is: ; in, is the frequency domain signal obtained after transformation, is the frequency, and ; is a negative exponential kernel, where is an imaginary unit and ; Extract frequency domain features from the frequency domain data obtained by discrete Fourier transform; By formula: ， Calculate frequency spectral density ; By formula: ， Calculate the main frequency energy ; By formula: ， Generate frequency-order feature vector And input to the weighted Euclidean distance formula.

7. A method for managing the state of electromechanical equipment based on the Internet of Things according to claim 5 or 6, characterized in that: The weighted Euclidean distance formula is as follows: ； in, For electromechanical equipment About time series operation data Time domain anomaly parameters; For electromechanical equipment About time series operation data Frequency domain anomaly parameters of ; , , , ..., are preset weight factors respectively; is the preset mean standard value, is the preset standard deviation standard value, is the preset standard value of skewness, is the preset kurtosis standard value, is the preset frequency spectrum density standard value, is the preset main frequency energy standard value, It is the preset standard value of effective frequency bandwidth.

8. The method for managing the state of electromechanical equipment based on the Internet of Things according to claim 1, characterized in that: The specific process of obtaining the status symbol of each electromechanical device regarding each timing operation data through conditional judgment operation is as follows: when When it is greater than the preset threshold, the first status symbol The value is 1, otherwise the first status symbol The value of is 0; when When it is greater than the preset threshold, the second status symbol The value is 1, otherwise the second status symbol The value of is 0; Get all first status symbols of each electromechanical device i and the second status symbol ; By formula: ； Determine the status symbol of each electromechanical device i with respect to the timing operation data j ; and is the logical symbol "and"; or is the logical symbol "or"; Normal represents the normal state; Warning represents the warning state; Fault represents the fault state.

9. An electromechanical equipment status management system based on the Internet of Things, characterized in that: The method for managing the state of electromechanical equipment based on the Internet of Things as claimed in any one of claims 1 to 8 is adopted, and the management system further comprises: Data perception module: The data perception module obtains the timing operation data of each electromechanical device through the sensor group, performs high-frequency data collection and real-time transmission through the Internet of Things protocol, and sends the collected data to the data preprocessing module; Data preprocessing module: The data preprocessing module is responsible for preprocessing, automatically processing missing values, and cleaning and filtering the data sent by the data perception module to ensure data quality for subsequent analysis; Time domain analysis module,The time domain analysis module performs preliminary analysis on the processed time series data,,mainly including trend analysis and anomaly detection; Frequency domain analysis module: The frequency domain analysis module converts the timing data of electromechanical equipment into frequency domain through Fourier transform method, analyzes the frequency characteristics of the timing operation data of electromechanical equipment, and assists in fault diagnosis; Electromechanical equipment data center: The electromechanical equipment data center is responsible for centrally managing the real-time monitoring data, historical data and analysis results of all electromechanical equipment; Database: The database is responsible for storing all collected time series data, analysis results, and equipment operation history, and provides efficient data retrieval and query functions; The execution module executes relevant instructions according to the analysis results of the electromechanical equipment data center.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: in, When the program is executed by the processor, an electromechanical equipment status management method based on the Internet of Things as described in any one of claims 1 to 8 is implemented.

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