Equipment operation state monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to an equipment operation state monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring an oxygen concentration time sequence and a temperature time sequence of equipment; acquiring a plurality of preset local ranges of the oxygen concentration data at each moment in the oxygen concentration time sequence, calculating the noise expression degree of the oxygen concentration data, and calculating the corrected noise expression degree of the oxygen concentration data according to the noise expression degree, the number of decomposition layers in a wavelet filtering algorithm is set according to the corrected noise expression degree, and denoising processing is achieved so as to assist equipment operation state monitoring. Therefore, efficient and accurate de-noising processing is realized through adaptive decomposition of the number of layers.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for monitoring the operating status of equipment based on the Internet of Things. Background Art

[0002] Anaerobic oven equipment is mainly used for silver glue curing, photoresist curing, PI glue curing and ITO high temperature annealing. It is suitable for touch screen, wafer, LED, PCB board, ITO glass and other industries. In order to ensure the effective operation of anaerobic oven equipment, the operating status of anaerobic oven equipment needs to be monitored in real time. When the operating status of anaerobic oven equipment is abnormal, the oxygen concentration and temperature in the equipment will be abnormal. Therefore, the operating status of the equipment can be judged by analyzing the abnormal conditions of the oxygen concentration and temperature of the equipment. In order to analyze the operating status of the equipment, it is necessary to collect data such as oxygen concentration and temperature. When using sensors to collect data such as oxygen concentration and temperature, the sensors are easily affected by thermal noise, resulting in noise data in the collected data. The interference of noise data can easily cause misjudgment of the operating status of the equipment. Therefore, before analyzing the operating status of the equipment, the collected data needs to be denoised.

[0003] The existing patent document CN116775408B shows an intelligent monitoring method for the operating status of energy storage equipment. In this method, the abnormal operating status is judged by threshold comparison. The document does not consider the influence of noise, so the method in this patent document cannot achieve accurate equipment operating status determination.

[0004] Wavelet threshold denoising is a commonly used method for denoising time series data. This method uses the multi-scale analysis capability of wavelet transform to separate noise information and useful information in the data. The basic idea of ​​this method is to perform wavelet transform on the data, then perform threshold processing on the transformed wavelet coefficients to remove the noise component, and finally reconstruct the denoised data through inverse wavelet transform. Therefore, to achieve accurate denoising, it is necessary to accurately separate the noise information and useful information in the data, and to achieve accurate separation of noise information and useful information, it is necessary to set a suitable number of decomposition layers for the wavelet filtering algorithm. Traditionally, the number of decomposition layers is mostly set based on human experience, so the number of decomposition layers set by this method cannot separate noise information from useful information. Summary of the invention

[0005] In order to solve the problem of setting the number of decomposition layers, the present invention provides a device operation status monitoring method and system based on the Internet of Things.

[0006] In the first aspect, the present invention provides a method for monitoring the operation status of an equipment based on the Internet of Things, which adopts the following technical solutions: The device operation status monitoring method based on the Internet of Things includes the following steps: Obtain the oxygen concentration timing sequence and temperature timing sequence of the device; Acquire several preset local ranges of oxygen concentration data at each moment in the oxygen concentration time series, and calculate the noise performance degree of the oxygen concentration data, wherein the noise performance degree is positively correlated with the information entropy and fitting error of the data in the preset local range; The relationship for calculating the corrected noise performance of the oxygen concentration data is: ; in, Indicates the noise level of the oxygen concentration data at the i-th moment, Represents the temperature data at the i-th moment in the temperature time series, represents the fitting relationship obtained by using the noise performance degree of the temperature data and the oxygen concentration data at the corresponding moment, and the noise performance degree fitting value of the temperature data at the i-th moment is fitted, represents an exponential function with a natural constant as base, Indicates the corrected noise performance of the oxygen concentration data at the i-th moment; The number of decomposition layers in the wavelet filtering algorithm is set according to the corrected noise performance level to achieve denoising processing to assist in equipment operation status monitoring, wherein the number of decomposition layers is positively correlated with the corrected noise performance level.

[0007] The present invention introduces data analysis methods such as information entropy to accurately describe the noise situation of data in the local range of each oxygen concentration data, providing a basis for the subsequent accurate setting of the number of decomposition layers; at the same time, the noise index describing each oxygen concentration data is corrected through the correlation between temperature and thermal noise to prevent noise misjudgment caused by data fluctuations in the local range, so as to further improve the accuracy of the description of the noise situation of each oxygen concentration data, and further provide a guarantee for accurately setting the number of decomposition layers, so that based on the accurately set number of decomposition layers, the noise data interference can be removed more efficiently and accurately, providing a basis for accurate monitoring of the equipment operation status.

[0008] Preferably, the noise performance degree of the oxygen concentration data satisfies the relationship: ; in, represents the jth oxygen concentration data within the zth preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the jth oxygen concentration data in the zth preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data in the zth preset local range of oxygen concentration data at the i-th moment, represents the fitting error of all oxygen concentration data within the zth preset local range of the oxygen concentration data at the ith moment, represents the number of preset local ranges of oxygen concentration data at the i-th moment, Indicates the degree of noise in the oxygen concentration data at the i-th moment.

[0009] The present invention introduces information entropy, fitting error and other analytical methods to accurately describe the data regularity in the local range of each oxygen concentration data, and then accurately reflects the noise situation of each oxygen concentration data according to the negative correlation mapping of the data regularity in the local range. Accurately describing the noise situation of each oxygen concentration data provides a basis for the subsequent accurate setting of the number of decomposition layers.

[0010] Preferably, the fitting error acquisition method of all oxygen concentration data is: A polynomial is fitted to all oxygen concentration data within a preset local range of the oxygen concentration data at a moment using the least squares method to obtain a fitting value of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and an absolute value of a difference between each oxygen concentration data within the preset local range of the oxygen concentration data at the moment and the corresponding fitting value is used as a fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and a cumulative sum of the fitting errors of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment is used as a fitting error of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

[0011] Preferably, the method for obtaining the occurrence probability is: Within a preset local range of the oxygen concentration data at a moment, the number of data having the same value as that of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment is obtained, and the ratio of the number of data having the same value to the number of all data within the preset local range of the oxygen concentration data at the moment is used as the occurrence probability of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

[0012] Preferably, the method for obtaining the fitting relationship is: The temperature data with the same value at all moments are taken as a temperature category, the noise performance degree of the oxygen concentration data at the same moment as the temperature data in a temperature category is taken as the corresponding noise performance degree of each temperature data in the temperature category, the mean of the corresponding noise performance degrees of all temperature data in the temperature category is taken as the comprehensive noise performance degree of the temperature category, and a coordinate system is constructed with the temperature category as the horizontal axis and the comprehensive noise performance degree as the vertical axis. All temperature categories and the corresponding comprehensive noise performance degrees are mapped to the coordinate system to obtain a number of data points; the polynomial obtained by fitting all the data points in the coordinate system using the least squares method is used as the fitting relationship.

[0013] The present invention accurately and quickly describes the correlation between the temperature at all times and the noise performance degree of the oxygen concentration data by fitting the relationship, thereby providing a basis for subsequently correcting the noise performance degree of each oxygen concentration data.

[0014] Preferably, the method for obtaining the noise performance degree fitting value is: The temperature category to which the temperature data at each moment belongs is obtained, and the temperature category to which the temperature data at each moment belongs is input into a fitting equation to obtain a noise performance degree fitting value of the temperature data at each moment.

[0015] Preferably, the preset local range acquisition method is: An area consisting of a preset number of data centered on the oxygen concentration data at each moment in the oxygen concentration time series is used as a preset local range.

[0016] The present invention can simply and quickly grasp the situation of the data around each oxygen concentration data by setting a preset local range for each oxygen concentration data, thereby providing a basis for accurately analyzing the noise performance degree of each oxygen concentration data.

[0017] Preferably, before using the corrected noise performance level to set the number of decomposition levels in the wavelet filtering algorithm, the method further includes: According to the corrected noise performance of the oxygen concentration data at each moment, the Fisher optimal solution method is used to segment the oxygen concentration time series to obtain several data segments.

[0018] The present invention performs segmented processing on the oxygen concentration time series based on the corrected noise performance level, and can segment oxygen concentration data with similar noise conditions together, providing a basis for subsequent setting of the number of decomposition layers.

[0019] Preferably, the method of setting the number of decomposition layers in the wavelet filtering algorithm according to the corrected noise performance level includes: The average of the corrected noise performance levels of the oxygen concentration data at all moments in the data segment is used as the corrected noise performance level of each data segment, and the cumulative sum of the corrected noise performance level of each data segment and 1 is multiplied by the preset reference decomposition level and then rounded down to obtain the decomposition level of each data segment; The decomposition level number of each data segment is used as the decomposition level number of the wavelet filtering algorithm of the corresponding data segment.

[0020] The present invention sets the number of decomposition layers according to the degree of noise performance after correction, and can set different numbers of decomposition layers for data with different noise conditions, thereby providing a basis for fast and accurate denoising.

[0021] In a second aspect, the present invention provides an equipment operation status monitoring system based on the Internet of Things, which adopts the following technical solutions: The device operation status monitoring system based on the Internet of Things includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned device operation status monitoring method based on the Internet of Things is implemented.

[0022] By adopting the above technical solution, the above-mentioned device operation status monitoring method based on the Internet of Things is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0023] The present invention has the following technical effects: The present invention introduces data analysis methods such as information entropy to accurately describe the noise situation of data in the local range of each oxygen concentration data, providing a basis for the subsequent accurate setting of the number of decomposition layers; at the same time, the noise index describing each oxygen concentration data is corrected through the correlation between temperature and thermal noise to prevent noise misjudgment caused by data fluctuations in the local range, thereby further improving the accuracy of the description of the noise situation of each oxygen concentration data, and further providing a guarantee for accurately setting the number of decomposition layers, so that based on the accurately set number of decomposition layers, efficient and accurate removal of noise data interference can be achieved, providing a basis for accurate monitoring of the equipment operation status. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0025] Figure 1 It is a method flow chart of a method for monitoring device operation status based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0028] The embodiment of the present invention discloses a device operation status monitoring method based on the Internet of Things, referring to Figure 1 , comprising steps S1 to S4: S1: Obtain the oxygen concentration timing sequence and temperature timing sequence of the device.

[0029] Specifically, the corresponding sensor is used to collect oxygen concentration data and temperature data once every t seconds, and the data is collected N times. The oxygen concentration data collected at all times are arranged in time sequence to obtain an oxygen concentration time series sequence, and the temperature data collected at all times are arranged in time sequence to obtain a temperature time series sequence. t represents a preset time interval, and N represents a preset number of collections. This embodiment is described by taking t as 0.5 and N as 600 as an example. Other embodiments may take other values, and this embodiment does not specifically limit it.

[0030] S2: Acquire several preset local ranges of oxygen concentration data at each moment in the oxygen concentration time series, and calculate the noise performance degree of the oxygen concentration data.

[0031] It should be noted that the larger the number of decomposition layers in the wavelet filtering algorithm, the more detailed the data decomposition is, and the more accurate the denoising analysis based on it is. The smaller the number of decomposition layers, the coarser the data decomposition is, and the less accurate the denoising analysis based on it is. The larger the number of decomposition layers, the longer the processing time required, and the smaller the number of decomposition layers, the shorter the processing time required. Therefore, in order to achieve accurate and efficient denoising, it is necessary to set an appropriate number of decomposition layers. In areas with less noise content, it is not necessary to perform too detailed decomposition to remove noise well. In areas with higher noise content, detailed decomposition is required to remove noise in the data. Therefore, in order to set an appropriate number of decomposition layers, it is necessary to analyze the noise content in the data.

[0032] S20: Acquire several preset local ranges of oxygen concentration data at each moment in the oxygen concentration time series.

[0033] Preferably, as an example, the method for obtaining the preset local range includes: Ten preset numbers A1, A2, A3, ..., A10 are set. This embodiment is described by taking A1 as 9, A2 as 11, A3 as 13, A4 as 15, A5 as 17, A6 as 19, A7 as 21, A8 as 23, A9 as 25, and A10 as 27 as an example. Other embodiments may take other values, and this embodiment does not make any specific limitation.

[0034] An area consisting of a preset number of data centered on the oxygen concentration data at each moment in the oxygen concentration time series is used as a preset local range.

[0035] In particular, for the data at the edge, if the amount of data on one side of the oxygen concentration data cannot meet the quantity requirement, only as much data as possible needs to be obtained on that side, and the remaining data is obtained on the other side, so that the total amount of data in the local range is the preset amount. For example, for the oxygen concentration time series [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], there are only two data "1,2" on the left side of "3" in the sequence, and A1=9 data are obtained with "3" as the center, and 4 data need to be obtained on the left side. Therefore, the amount of data on the left side is insufficient, so at this time, only as much data as possible can be obtained on the left side, that is, the two data "1,2". The remaining data is obtained on the right side, that is, the six data "4,5,6,7,8,9" are obtained on the right side of "3", so that the total amount of data is equal to A1=9. Therefore, the area composed of a preset number of data with "3" as the center is [1,2,3,4,5,6,7,8,9].

[0036] S21: Calculate the noise performance level of the oxygen concentration data.

[0037] It should be noted that the distribution regularity of noise information is poor, so the data may be judged as noise based on the distribution regularity of the data. In this embodiment, the degree of noise performance is used to reflect the noise of each data.

[0038] Preferably, as an example, the noise performance degree of the oxygen concentration data satisfies the relationship: ; in, represents the jth oxygen concentration data within the zth preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the jth oxygen concentration data in the zth preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data in the zth preset local range of oxygen concentration data at the i-th moment, The information entropy of the data in the zth preset local range of the oxygen concentration data at the i-th moment is represented. The larger the value is, the more chaotic the data distribution in the zth preset local range of the oxygen concentration data at the i-th moment is. Therefore, the data regularity in the zth preset local range of the oxygen concentration data at the i-th moment is poor. Therefore, the possibility that the oxygen concentration data at the i-th moment is noise data is greater. Represents the fitting error of all oxygen concentration data within the zth preset local range of oxygen concentration data at the ith moment. The larger the value, the worse the regularity of the data within the local range. represents the number of preset local ranges of oxygen concentration data at the i-th moment, Indicates the degree of noise in the oxygen concentration data at the i-th moment.

[0039] Furthermore, the above embodiments involve occurrence probability, and the following describes how to determine the occurrence probability, including: Within a preset local range of the oxygen concentration data at a moment, the number of data having the same value as that of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment is obtained, and the ratio of the number of data having the same value to the number of all data within the preset local range of the oxygen concentration data at the moment is used as the occurrence probability of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

[0040] Furthermore, the above embodiments involve fitting errors of all oxygen concentration data. The following describes how to determine the fitting errors of all oxygen concentration data, including: A polynomial is fitted to all oxygen concentration data within a preset local range of the oxygen concentration data at a moment using the least squares method to obtain a fitting value of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and a difference between each oxygen concentration data within the preset local range of the oxygen concentration data at the moment and the corresponding fitting value is used as a fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and a cumulative sum of the fitting errors of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment is used as the fitting error of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

[0041] S3: Calculate the corrected noise performance level of the oxygen concentration data.

[0042] It should be noted that, under normal circumstances, thermal noise is related to temperature. Therefore, when the noise description of oxygen concentration data is accurate, the noise performance level of each oxygen concentration data should conform to the correlation between the temperature and the noise performance level of all oxygen concentration data. On the contrary, when the noise description of oxygen concentration data is inaccurate, the deviation between the noise performance level of each oxygen concentration data and the correlation between the temperature and the noise performance level of all oxygen concentration data is large. Therefore, the noise performance level of each oxygen concentration data can be corrected according to the deviation between the noise performance level of each oxygen concentration data and the correlation between the temperature and the noise performance level of all oxygen concentration data, so as to accurately describe the noise performance level of each oxygen concentration data.

[0043] Preferably, as an example, the corrected noise performance degree of the oxygen concentration data satisfies the relationship: ; in, Indicates the noise level of the oxygen concentration data at the i-th moment, Represents the temperature data at the i-th moment in the temperature time series, represents the fitting relationship obtained by using the noise performance degree of the temperature data and the oxygen concentration data at the corresponding moment, and the noise performance degree fitting value of the temperature data at the i-th moment is fitted, It reflects the deviation between the noise performance of the oxygen concentration data at the i-th moment and the correlation between the temperature and noise performance of all oxygen concentration data. The larger the value, the less the noise performance of the oxygen concentration data at the i-th moment conforms to the correlation between the temperature and noise performance. Therefore, the noise situation of the oxygen concentration data at the i-th moment is not accurately described and needs to be corrected to a greater extent. represents an exponential function with a natural constant as base, Indicates the degree of noise expression after correction of the oxygen concentration data at the i-th moment.

[0044] Furthermore, the above embodiments involve a fitting relationship. The following describes how to determine the fitting relationship, including: The temperature data with the same value at all moments are taken as a temperature category, the noise performance degree of the oxygen concentration data at the same moment as the temperature data in a temperature category is taken as the corresponding noise performance degree of each temperature data in the temperature category, the mean of the corresponding noise performance degrees of all temperature data in the temperature category is taken as the comprehensive noise performance degree of the temperature category, and a coordinate system is constructed with the temperature category as the horizontal axis and the comprehensive noise performance degree as the vertical axis. All temperature categories and the corresponding comprehensive noise performance degrees are mapped to the coordinate system to obtain a number of data points; the polynomial obtained by fitting all the data points in the coordinate system using the least squares method is used as the fitting relationship.

[0045] Furthermore, the above embodiments involve a noise performance degree fitting value. The following describes how to determine the noise performance degree fitting value, including: The temperature category to which the temperature data at each moment belongs is obtained, and the temperature category to which the temperature data at each moment belongs is input into a fitting equation to obtain a noise performance degree fitting value of the temperature data at each moment.

[0046] S4: Use the corrected noise performance level to set the number of decomposition layers in the wavelet filtering algorithm to achieve denoising processing to assist in equipment operation status monitoring.

[0047] It should be noted that data with high noise content contains more noise data, and deep decomposition of the data is required to decompose the noise data. Therefore, a larger decomposition layer number is set for the data with high noise content to remove all noise data. Data with low noise content contains less noise data, and shallow decomposition of the data can decompose the noise data. Therefore, a smaller decomposition layer number is set for the data with low noise content to remove all noise data. Therefore, the number of decomposition layers needs to be set according to the content of the noise data.

[0048] Preferably, as an example, before using the corrected noise performance level to set the number of decomposition layers in the wavelet filtering algorithm, the method further includes: According to the corrected noise performance of the oxygen concentration data at each moment, the Fisher optimal solution method is used to segment the oxygen concentration time series to obtain several data segments.

[0049] It should be noted that segmenting the oxygen concentration time series based on the corrected noise level can segment the oxygen concentration data with the same noise level together, providing a basis for subsequent accurate and rapid denoising.

[0050] Preferably, as an example, the number of decomposition layers in the wavelet filtering algorithm is set using the corrected noise performance level, including: The average of the corrected noise performance levels of the oxygen concentration data at all moments in the data segment is used as the corrected noise performance level of each data segment, and the cumulative sum of the corrected noise performance level of each data segment and 1 is multiplied by the preset reference decomposition level and then rounded down to obtain the decomposition level of each data segment. This embodiment is described by taking 3 as the preset reference decomposition level as an example, and other embodiments may take other values, which are not specifically limited in this embodiment.

[0051] The decomposition level number of each data segment is used as the decomposition level number of the wavelet filtering algorithm of the corresponding data segment.

[0052] Preferably, as an example, the denoising process is implemented, including: The wavelet filtering algorithm after setting the decomposition layer number is used to filter and denoise each data segment to obtain the denoised data segment.

[0053] It should be noted that after eliminating the noise interference in the oxygen concentration data, when the anaerobic oven equipment is operating normally, the oxygen concentration is low. When the oxygen concentration is high, it means that there is an abnormality in the sealing of the anaerobic oven equipment or the nitrogen supply is insufficient. Therefore, the operating status of the anaerobic oven equipment can be judged according to the oxygen concentration height.

[0054] Preferably, as an example, the auxiliary equipment operation status monitoring includes: The oxygen concentration data at each moment in the denoised data segment is compared with the preset concentration threshold. If there is oxygen concentration data greater than the preset concentration threshold in the denoised data segment, an early warning is issued.

[0055] An embodiment of the present invention also discloses an IoT-based device operation status monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an IoT-based device operation status monitoring method according to the present invention is implemented.

[0056] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0057] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0058] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0059] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A device operation status monitoring method based on the Internet of Things, characterized in that: Includes steps: Obtain the oxygen concentration timing sequence and temperature timing sequence of the device; Acquire several preset local ranges of oxygen concentration data at each moment in the oxygen concentration time series, and calculate the noise performance degree of the oxygen concentration data, wherein the noise performance degree is positively correlated with the information entropy and fitting error of the data in the preset local range; The relationship for calculating the corrected noise performance of the oxygen concentration data is: ； in, Indicates the noise level of the oxygen concentration data at the i-th moment, Represents the temperature data at the i-th moment in the temperature time series, represents the fitting relationship obtained by using the noise performance degree of the temperature data and the oxygen concentration data at the corresponding moment, and the noise performance degree fitting value of the temperature data at the i-th moment is fitted, represents an exponential function with a natural constant as base, Indicates the corrected noise performance of the oxygen concentration data at the i-th moment; The number of decomposition layers in the wavelet filtering algorithm is set according to the corrected noise performance level to achieve denoising processing to assist in equipment operation status monitoring, wherein the number of decomposition layers is positively correlated with the corrected noise performance level.

2. The device operation status monitoring method based on the Internet of Things according to claim 1 is characterized in that: The noise performance of the oxygen concentration data satisfies the relationship: ； in, represents the jth oxygen concentration data within the zth preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the jth oxygen concentration data in the zth preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data in the zth preset local range of oxygen concentration data at the i-th moment, represents the fitting error of all oxygen concentration data within the zth preset local range of the oxygen concentration data at the ith moment, represents the number of preset local ranges of oxygen concentration data at the i-th moment, Indicates the degree of noise in the oxygen concentration data at the i-th moment.

3. The device operation status monitoring method based on the Internet of Things according to claim 2 is characterized in that: The fitting error acquisition method of all oxygen concentration data is: A polynomial is fitted to all oxygen concentration data within a preset local range of the oxygen concentration data at a moment using the least squares method to obtain a fitting value of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and an absolute value of a difference between each oxygen concentration data within the preset local range of the oxygen concentration data at the moment and the corresponding fitting value is used as a fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment, and a cumulative sum of the fitting errors of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment is used as a fitting error of all oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

4. The device operation status monitoring method based on the Internet of Things according to claim 2 is characterized in that: The method for obtaining the occurrence probability is: Within a preset local range of the oxygen concentration data at a moment, the number of data having the same value as that of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment is obtained, and the ratio of the number of data having the same value to the number of all data within the preset local range of the oxygen concentration data at the moment is used as the occurrence probability of each oxygen concentration data within the preset local range of the oxygen concentration data at the moment.

5. The device operation status monitoring method based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the fitting relationship is: The temperature data with the same value at all moments are taken as a temperature category, the noise performance degree of the oxygen concentration data at the same moment as the temperature data in a temperature category is taken as the corresponding noise performance degree of each temperature data in the temperature category, the mean of the corresponding noise performance degrees of all temperature data in the temperature category is taken as the comprehensive noise performance degree of the temperature category, and a coordinate system is constructed with the temperature category as the horizontal axis and the comprehensive noise performance degree as the vertical axis. All temperature categories and the corresponding comprehensive noise performance degrees are mapped to the coordinate system to obtain a number of data points; the polynomial obtained by fitting all the data points in the coordinate system using the least squares method is used as the fitting relationship.

6. The device operation status monitoring method based on the Internet of Things according to claim 5 is characterized in that: The method for obtaining the noise performance degree fitting value is: The temperature category to which the temperature data at each moment belongs is obtained, and the temperature category to which the temperature data at each moment belongs is input into a fitting equation to obtain a noise performance degree fitting value of the temperature data at each moment.

7. The method for monitoring the operation status of an equipment based on the Internet of Things according to claim 1, characterized in that: The preset local range acquisition method is: An area consisting of a preset number of data centered on the oxygen concentration data at each moment in the oxygen concentration time series is used as a preset local range.

8. The method for monitoring the operation status of an equipment based on the Internet of Things according to claim 1, characterized in that: Before using the corrected noise performance level to set the number of decomposition levels in the wavelet filtering algorithm, it also includes: According to the corrected noise performance of the oxygen concentration data at each moment, the Fisher optimal solution method is used to segment the oxygen concentration time series to obtain several data segments.

9. The method for monitoring the operation status of an equipment based on the Internet of Things according to claim 8, characterized in that: The method of setting the number of decomposition layers in the wavelet filtering algorithm by using the corrected noise performance level includes: The average of the corrected noise performance levels of the oxygen concentration data at all moments in the data segment is used as the corrected noise performance level of each data segment, and the cumulative sum of the corrected noise performance level of each data segment and 1 is multiplied by the preset reference decomposition level and then rounded down to obtain the decomposition level of each data segment; The decomposition level number of each data segment is used as the decomposition level number of the wavelet filtering algorithm of the corresponding data segment.

10. The equipment operation status monitoring system based on the Internet of Things is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating status of an IoT-based device is implemented according to any one of claims 1 to 9.

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