Internet of Things-Based Device Operating Status Monitoring Method and System

Abstract

The present invention relates to the technical field of data processing, and particularly to a method and system for monitoring the operating state of devices based on the Internet of Things. The method includes the steps of: obtaining the oxygen concentration time series and temperature time series of the device; obtaining a plurality of preset local ranges of the oxygen concentration data at each moment in the oxygen concentration time series, calculating the noise performance degree of the oxygen concentration data, calculating the corrected noise performance degree of the oxygen concentration data according to the noise performance degree, and setting the decomposition level in the wavelet filtering algorithm by using the corrected noise performance degree to implement denoising processing to assist in monitoring the operating state of the device. Thus, efficient and accurate denoising processing is achieved through an adaptive decomposition level.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for monitoring the operating state of devices 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, etc. It is applicable to industries such as touch screens, wafers, LEDs, PCB boards, and ITO glass. In order to ensure the effective operation of the anaerobic oven equipment, it is necessary to monitor the operating state of the anaerobic oven equipment in real time. When there are abnormalities in the operating state of the anaerobic oven equipment, the oxygen concentration and temperature in the equipment will be abnormal. Therefore, the operating state of the equipment can be judged by analyzing the abnormal conditions of information such as the oxygen concentration and temperature of the equipment. In order to analyze the operating state of the equipment, data such as oxygen concentration and temperature need to be collected. 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 is likely to cause misjudgment of the equipment operating state. Therefore, before analyzing the operating state of the equipment, it is necessary to denoise the collected data.

[0003] The existing patent document CN116775408B discloses an intelligent monitoring method for the operating state of an energy storage device. In this method, the abnormal operating state is judged by means of threshold comparison. The influence of noise is not considered in this document, so it is impossible to accurately judge the operating state of the equipment by using the method in this patent document.

[0004] Wavelet threshold denoising is a commonly used time-series data denoising method. This method uses the multi-scale analysis ability of wavelet transform to separate the 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 components, 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 accurately separate the noise information and useful information, it is necessary to set appropriate decomposition levels for the wavelet filtering algorithm. Traditionally, the decomposition levels are set according to human experience, so the decomposition levels set by this method cannot separate the noise information and useful information. Summary of the Invention

[0005] To solve the problem of setting the decomposition levels, the present invention provides a method and system for monitoring the operating state of devices based on the Internet of Things.

[0006] In the first aspect, the present invention provides a method for monitoring the operating state of devices based on the Internet of Things, adopting the following technical solution:

[0007] The method for monitoring the operating state of devices based on the Internet of Things includes the steps:

[0008] Obtain the time series of oxygen concentration and the time series of temperature of the device;

[0009] Obtain several preset local ranges of the oxygen concentration data at each moment in the time series of oxygen concentration, and calculate the noise performance degree of the oxygen concentration data, where the noise performance degree is positively correlated with both the information entropy and the fitting error of the data within the preset local range;

[0010] The relational expression for calculating the corrected noise performance degree of the oxygen concentration data is:

[0011] ;

[0012] where, represents the noise performance degree of the oxygen concentration data at the i-th moment, represents the temperature data at the i-th moment in the time series of temperature, represents the fitting relational expression obtained by using the temperature data and the noise performance degree of the oxygen concentration data at the corresponding moment, and the fitting value of the noise performance degree of the temperature data at the i-th moment, represents the exponential function with the natural constant as the base, represents the corrected noise performance degree of the oxygen concentration data at the i-th moment;

[0013] Set the decomposition level in the wavelet filtering algorithm by using the corrected noise performance degree to implement denoising processing to assist in monitoring the operating state of the device, where the decomposition level is positively correlated with the corrected noise performance degree.

[0014] The present invention accurately describes the noise situation of the data within the local range of each oxygen concentration data by introducing data analysis means such as information entropy, providing a basis for accurately setting the decomposition level subsequently; at the same time, it also corrects the noise index for describing each oxygen concentration data through the correlation relationship between temperature and thermal noise, preventing the phenomenon of noise misjudgment caused by data fluctuations and other phenomena within the local range, further improving the accuracy of describing the noise situation of each oxygen concentration data, providing further guarantee for accurately setting the decomposition level, so that based on the accurately set decomposition level, noise data interference can be removed more efficiently and accurately, providing a basis for accurately monitoring the operating state of the device.

[0015] Preferably, the noise performance degree of the oxygen concentration data satisfies the relational expression:

[0016] ;

[0017] where, represents the j-th oxygen concentration data in the z-th preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the j-th oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the fitting error of all oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the number of preset local ranges of the oxygen concentration data at the i-th moment, represents the degree of noise manifestation of the oxygen concentration data at the i-th moment.

[0018] The present invention accurately describes the data law situation within the local range of each oxygen concentration data by introducing analysis means such as information entropy and fitting error, and then accurately reflects the noise situation of each oxygen concentration data according to the negative correlation mapping of the data law situation within the local range. Accurately describing the noise situation of each oxygen concentration data provides a basis for accurately setting the decomposition layer number subsequently.

[0019] Preferably, the method for obtaining the fitting error of all oxygen concentration data is as follows:

[0020] Using the least squares method to perform polynomial fitting on all oxygen concentration data within a preset local range of the oxygen concentration data at a moment, obtaining the fitting values of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment, taking the absolute value of the difference between each oxygen concentration data within the preset local range of the oxygen concentration data at this moment and the corresponding fitting value as the fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment, and taking the cumulative sum of the fitting errors of all oxygen concentration data within the preset local range of the oxygen concentration data at this moment as the fitting error of all oxygen concentration data within the preset local range of the oxygen concentration data at this moment.

[0021] Preferably, the method for obtaining the occurrence probability is as follows:

[0022] Within a preset local range of the oxygen concentration data at a moment, obtain the number of data with the same value as each oxygen concentration data within the preset local range of the oxygen concentration data at this moment, and take the ratio of the number of data with the same value to the total number of all data within the preset local range of the oxygen concentration data at this moment as the occurrence probability of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment.

[0023] Preferably, the method for obtaining the fitting relationship formula is as follows:

[0024] Take the temperature data with the same value at all times as a temperature category. Take the noise performance degree of the oxygen concentration data at the same time as that of each temperature data in a temperature category as the corresponding noise performance degree of each temperature data in this temperature category. Take the mean value of the corresponding noise performance degrees of all temperature data in this temperature category as the comprehensive noise performance degree of this temperature category. Use the temperature category as the horizontal axis and the comprehensive noise performance degree as the vertical axis to construct a coordinate system, and map all temperature categories and the corresponding comprehensive noise performance degrees into the coordinate system to obtain several data points; Use the polynomial obtained by fitting all data points in the coordinate system by the least squares method as the fitting relationship.

[0025] The present invention accurately and quickly describes the correlation between the temperature and the noise performance degree of the oxygen concentration data at all times through the fitting relationship, providing a basis for subsequent correction of the noise performance degree of each oxygen concentration data.

[0026] Preferably, the method for obtaining the fitting value of the noise performance degree is:

[0027] Obtain the temperature category to which the temperature data at each time belongs, and input the temperature category to which the temperature data at each time belongs into the fitting relationship to obtain the fitting value of the noise performance degree of the temperature data at each time.

[0028] Preferably, the method for obtaining the preset local range is:

[0029] Take the area composed of a preset number of data centered on the oxygen concentration data at each time in the oxygen concentration time series as the preset local range.

[0030] The present invention can simply and quickly grasp the data situation around each oxygen concentration data by setting a preset local range for each oxygen concentration data, providing a basis for accurately analyzing the noise performance degree of each oxygen concentration data.

[0031] Preferably, before setting the decomposition level in the wavelet filtering algorithm using the corrected noise performance degree, it further includes:

[0032] According to the corrected noise performance degree of the oxygen concentration data at each time, use the Fisher optimal solution method to segment the oxygen concentration time series to obtain several data segments.

[0033] The present invention segments the oxygen concentration time series based on the corrected noise performance degree, which can divide the oxygen concentration data with similar noise situations together, providing a basis for subsequent setting of the decomposition level.

[0034] Preferably, setting the decomposition level in the wavelet filtering algorithm using the corrected noise performance degree includes:

[0035] The mean of the corrected noise performance degrees of the oxygen concentration data at all times in the data segment is used as the corrected noise performance degree of each data segment. After adding 1 to the corrected noise performance degree of each data segment and multiplying the sum by the preset reference decomposition level, and then taking the floor value, the decomposition level of each data segment is obtained;

[0036] The decomposition level of each data segment is used as the decomposition level of the wavelet filtering algorithm for the corresponding data segment.

[0037] The present invention sets the decomposition level according to the corrected noise performance degree, and can set different decomposition levels for data with different noise conditions, thereby providing a basis for fast and accurate noise removal.

[0038] In a second aspect, the present invention provides an equipment operation status monitoring system based on the Internet of Things, adopting the following technical solutions:

[0039] The equipment operation status monitoring system based on the Internet of Things includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned equipment operation status monitoring method based on the Internet of Things is implemented.

[0040] By adopting the above technical solutions, the above-mentioned equipment operation status monitoring method based on the Internet of Things is generated into a computer program and stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0041] The present invention has the following technical effects:

[0042] The present invention accurately describes the noise conditions of the data within the local range of each oxygen concentration data by introducing data analysis means such as information entropy, providing a basis for accurately setting the decomposition level subsequently; at the same time, it also corrects the noise index describing each oxygen concentration data through the correlation relationship between temperature and thermal noise, preventing the phenomenon of noise misjudgment caused by data fluctuations and other phenomena within the local range, and further improving the accuracy of the description of the noise conditions of each oxygen concentration data, providing further guarantee for accurately setting the decomposition level, so that based on the accurately set decomposition level, it is possible to efficiently and accurately remove the interference of noise data, providing a basis for accurately monitoring the equipment operation status. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0044] Figure 1It is the flowchart of the method in the method for monitoring the operating state of a device based on the Internet of Things according to an embodiment of the present invention. Detailed implementation manners

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] It should be understood that when the claims, the description and the drawings of the present invention use terms such as "first" and "second", etc., they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" 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 combinations.

[0047] An embodiment of the present invention discloses a method for monitoring the operating state of a device based on the Internet of Things. Referring to Figure 1 it includes steps S1 to S4:

[0048] S1: Obtain the oxygen concentration time series and the temperature time series of the device.

[0049] Specifically, use the corresponding sensors to collect the oxygen concentration data and the temperature data once every t seconds, and collect N times. Arrange the oxygen concentration data at all collected moments in time sequence to obtain the oxygen concentration time series, and arrange the temperature data at all collected moments in time sequence to obtain the temperature time series. t represents the preset time interval, and N represents the preset number of collections. In this embodiment, t is taken as 0.5 and N is taken as 600 as an example for description. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0050] S2: Obtain several preset local ranges of the oxygen concentration data at each moment in the oxygen concentration time series, and calculate the noise manifestation degree of the oxygen concentration data.

[0051] It should be noted that in the wavelet filtering algorithm, the larger the decomposition level, the more detailed the data decomposition, and the more accurate the denoising analysis based on it. The smaller the decomposition level, the coarser the data decomposition, and the less accurate the denoising analysis based on it. Among them, the larger the decomposition level, the longer the required processing time, and the smaller the decomposition level, the shorter the required processing time. Therefore, in order to achieve accurate and efficient denoising, an appropriate decomposition level needs to be set. Among them, in areas with less noise content, it is not necessary to perform too detailed decomposition, and the noise can be removed well. In areas with larger noise content, detailed decomposition is required to remove the noise in the data. Therefore, in order to set an appropriate decomposition level, the noise content in the data needs to be analyzed.

[0052] S20: Obtain several preset local ranges of the oxygen concentration data at each moment in the oxygen concentration time series.

[0053] Preferably, as an example, the method for obtaining the preset local range includes:

[0054] Set 10 preset quantities A1, A2, A3,..., A10. In this embodiment, A1 is taken as 9, A2 is taken as 11, A3 is taken as 13, A4 is taken as 15, A5 is taken as 17, A6 is taken as 19, A7 is taken as 21, A8 is taken as 23, A9 is taken as 25, and A10 is taken as 27 for description. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0055] Take the area composed of the preset number of data centered on the oxygen concentration data at each moment in the oxygen concentration time series as the preset local range.

[0056] Specifically, 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 obtain as much data as possible on that side, and the remaining data is obtained on the other side, so that the total number of data in the local range is the preset number. 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], for the "3" in the sequence, there are only two data "1, 2" on the left side, and taking A1 = 9 data centered on "3", 4 data need to be obtained on the left side. Therefore, the amount of data on the left side is not enough at this time, so 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 number of data is equal to A1 = 9. Therefore, the area composed of the preset number of data centered on "3" is [1, 2, 3, 4, 5, 6, 7, 8, 9].

[0057] S21: Calculate the noise manifestation degree of the oxygen concentration data.

[0058] It should be noted that the distribution of noise information has poor regularity. Therefore, it is possible to determine whether each data is noise according to the distribution law of the data. In this embodiment, the degree of noise manifestation is used to reflect the situation of each data being noise.

[0059] Preferably, as an example, the degree of noise manifestation of the oxygen concentration data satisfies the relational expression:

[0060] ;

[0061] where represents the j-th oxygen concentration data in the z-th preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the j-th oxygen concentration data in the z-th preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data in the z-th preset local range of the oxygen concentration data at the i-th moment, represents the information entropy of the data in the z-th preset local range of the oxygen concentration data at the i-th moment. The larger this value is, the more chaotic the data distribution in the z-th preset local range of the oxygen concentration data at the i-th moment is. Therefore, the regularity of the data in the z-th preset local range of the oxygen concentration data at the i-th moment is poor, and the greater the possibility that the oxygen concentration data at the i-th moment is noise data; represents the fitting error of all oxygen concentration data in the z-th preset local range of the oxygen concentration data at the i-th moment. The larger this value is, the more difficult it is to accurately describe the regular characteristics in this local range using the regular function. Therefore, the regularity of the data in this local range is worse; represents the number of preset local ranges of the oxygen concentration data at the i-th moment, represents the degree of noise manifestation of the oxygen concentration data at the i-th moment.

[0062] Furthermore, the occurrence probability is involved in the above embodiments. Now, an explanation of how to determine the occurrence probability is given, including:

[0063] In a preset local range of the oxygen concentration data at a moment, obtain the number of data with the same value as each oxygen concentration data in this preset local range of the oxygen concentration data at this moment, and use the ratio of the number of data with the same value to the total number of all data in this preset local range of the oxygen concentration data at this moment as the occurrence probability of each oxygen concentration data in this preset local range of the oxygen concentration data at this moment.

[0064] Furthermore, the fitting error of all oxygen concentration data is involved in the above embodiments. Now, an explanation of how to determine the fitting error of all oxygen concentration data is given, including:

[0065] Use the least squares method to fit polynomial processing for all oxygen concentration data within a preset local range of the oxygen concentration data at a certain moment, obtain the fitting values of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment, take the difference between each oxygen concentration data within the preset local range of the oxygen concentration data at this moment and the corresponding fitting value as the fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment, and take the cumulative sum of the fitting errors of all oxygen concentration data within the preset local range of the oxygen concentration data at this moment as the fitting error of all oxygen concentration data within the preset local range of the oxygen concentration data at this moment.

[0066] S3: Calculate the corrected noise performance degree of the oxygen concentration data.

[0067] It should be noted that under normal circumstances, thermal noise is related to temperature. Therefore, when the noise description of the oxygen concentration data is accurate, the noise performance degrees of each oxygen concentration data should conform to the correlation relationship between the temperature and noise performance degrees of all oxygen concentration data. On the contrary, when the noise description of the oxygen concentration data is inaccurate, the deviation of the noise performance degrees of each oxygen concentration data from the correlation relationship between the temperature and noise performance degrees of all oxygen concentration data is relatively large. Therefore, the noise performance degrees of each oxygen concentration data can be corrected according to the deviation of the noise performance degrees of each oxygen concentration data from the correlation relationship between the temperature and noise performance degrees of all oxygen concentration data, so as to accurately describe the noise performance degrees of each oxygen concentration data.

[0068] Preferably, as an example, the corrected noise performance degree of the oxygen concentration data satisfies the relational expression:

[0069] ;

[0070] Where represents the noise performance degree 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 value of the noise performance degree of the temperature data at the i-th moment obtained by using the temperature data and the noise performance degree of the oxygen concentration data at the corresponding moment, and is obtained by fitting the relational expression, reflects the deviation of the noise performance degree of the oxygen concentration data at the i-th moment from the correlation relationship between the temperature and noise performance degrees of all oxygen concentration data. The larger this value is, the more inaccurate the description of the noise situation of the oxygen concentration data at the i-th moment is, indicating that the noise performance degree of the oxygen concentration data at the i-th moment does not conform to the temperature-noise performance degree correlation relationship, and thus a greater degree of correction is required. represents the exponential function with the natural constant as the base, represents the degree of corrected noise performance of the oxygen concentration data at the i-th moment.

[0071] Furthermore, in the above embodiments, a fitting relationship is involved. Now, an explanation on how to determine the fitting relationship is given, including:

[0072] Take the temperature data with the same value at all moments as a temperature category. Take the degree of noise performance of the oxygen concentration data at the same moment as the corresponding degree of noise performance of each temperature data in a temperature category. Take the mean value of the corresponding degrees of noise performance of all temperature data in this temperature category as the comprehensive degree of noise performance of this temperature category. Use the temperature category as the horizontal axis and the comprehensive degree of noise performance as the vertical axis to construct a coordinate system. Map all temperature categories and the corresponding comprehensive degrees of noise performance into the coordinate system to obtain several data points; The polynomial obtained by fitting all data points in the coordinate system using the least squares method is used as the fitting relationship.

[0073] Furthermore, in the above embodiments, the fitting value of the noise performance degree is involved. Now, an explanation on how to determine the fitting value of the noise performance degree is required, including:

[0074] Obtain the temperature category to which the temperature data at each moment belongs, and input the temperature category to which the temperature data at each moment belongs into the fitting relationship to obtain the fitting value of the noise performance degree of the temperature data at each moment.

[0075] S4: Use the corrected noise performance degree to set the decomposition level in the wavelet filtering algorithm to achieve denoising processing to assist in monitoring the operating state of the device.

[0076] It should be noted that there are more noise data in the data with a large amount of noise. The noise data needs to be deeply decomposed to decompose it. Therefore, a larger decomposition level is set for the data with a large amount of noise to remove all noise data. There are fewer noise data in the data with a small amount of noise. The noise data can be decomposed by shallow decomposition of the data. Therefore, a smaller decomposition level is set for the data with a small amount of noise to remove all noise data. Therefore, the decomposition level needs to be set according to the content of the noise data.

[0077] Preferably, as an example, before using the corrected noise performance degree to set the decomposition level in the wavelet filtering algorithm, it further includes:

[0078] According to the corrected noise performance degree of the oxygen concentration data at each moment, use the Fisher optimal solution method to segment the oxygen concentration time series to obtain several data segments.

[0079] It should be noted that, based on the corrected noise level, segmenting the oxygen concentration time series can group the oxygen concentration data with the same noise level together, providing a basis for subsequent accurate and rapid denoising.

[0080] Preferably, as an example, the decomposition level in the wavelet filtering algorithm is set using the corrected noise performance level, including:

[0081] The mean of the corrected noise performance levels of the oxygen concentration data at all times in the data segment is used as the corrected noise performance level of each data segment. After adding 1 to the corrected noise performance level of each data segment and multiplying by the preset reference decomposition level, the result is rounded down to obtain the decomposition level of each data segment. In this embodiment, the preset reference decomposition level is taken as 3 for description, and other values can be taken in other embodiments, which are not specifically limited in this embodiment.

[0082] The decomposition level of each data segment is used as the decomposition level of the wavelet filtering algorithm for the corresponding data segment.

[0083] Preferably, as an example, the denoising process is implemented, including:

[0084] The wavelet filtering algorithm with the set decomposition level is used to perform filtering denoising on each data segment to obtain the denoised data segment.

[0085] It should be noted that after excluding 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 indicates that there is an abnormality in the sealing of the anaerobic oven equipment or insufficient nitrogen supply. Therefore, the operating condition of the anaerobic oven equipment can be determined based on the oxygen concentration.

[0086] Preferably, as an example, to assist in monitoring the operating state of the equipment, including:

[0087] The oxygen concentration data at each time in the denoised data segment is compared with a preset concentration threshold. If there is oxygen concentration data greater than the preset concentration threshold in the denoised data segment, a warning is issued.

[0088] The embodiment of the present invention also discloses an equipment operating state monitoring system based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the equipment operating state monitoring method based on the Internet of Things according to the present invention is implemented.

[0089] 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 settings and functions are known in the art, so they will not be elaborated here.

[0090] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program, which 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 memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0091] Although this specification has shown and described multiple 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 think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

[0092] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A method for monitoring the operating state of a device based on the Internet of Things, characterized in that, Including the steps: Obtain the time series of oxygen concentration and the time series of temperature of the device; Obtain a number of preset local ranges of the oxygen concentration data at each moment in the time series of oxygen concentration, and calculate the noise performance degree of the oxygen concentration data. The noise performance degree is positively correlated with both the information entropy and the fitting error of the data within the preset local range; The noise performance degree of the oxygen concentration data satisfies the relationship: ； Among them, represents the j-th oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the occurrence probability of the j-th oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the number of oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the fitting error of all oxygen concentration data within the z-th preset local range of the oxygen concentration data at the i-th moment, represents the number of preset local ranges of the oxygen concentration data at the i-th moment, represents the noise performance degree of the oxygen concentration data at the i-th moment; The relationship for calculating the corrected noise performance degree of the oxygen concentration data is: ； Among them, represents the noise performance degree 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 temperature data and the noise performance degree of the oxygen concentration data at the corresponding moment, and the fitting value of the noise performance degree of the temperature data at the i-th moment obtained by fitting, represents the exponential function with the natural constant as the base, represents the corrected noise performance degree of the oxygen concentration data at the i-th moment; Use the corrected noise performance degree to set the decomposition level in the wavelet filtering algorithm to achieve denoising processing to assist in monitoring the operating state of the device, where the decomposition level is positively correlated with the corrected noise performance degree.

2. The method for monitoring the operating state of a device based on the Internet of Things according to claim 1, characterized in that, The method for obtaining the fitting error of all the oxygen concentration data is: Use the least squares method to perform polynomial fitting on all the oxygen concentration data within a preset local range of the oxygen concentration data at one moment to obtain the fitting values of the oxygen concentration data within the preset local range of the oxygen concentration data at this moment. Take the absolute value of the difference between each oxygen concentration data within the preset local range of the oxygen concentration data at this moment and the corresponding fitting value as the fitting error of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment. Take the cumulative sum of the fitting errors of all the oxygen concentration data within the preset local range of the oxygen concentration data at this moment as the fitting error of all the oxygen concentration data within the preset local range of the oxygen concentration data at this moment.

3. The method for monitoring the operating state of a device based on the Internet of Things according to claim 1, wherein, The method for obtaining the occurrence probability is: Within a preset local range of the oxygen concentration data at one moment, obtain the number of data with the same value as each oxygen concentration data within the preset local range of the oxygen concentration data at this moment. Take the ratio of the number of data with the same value to the total number of all data within the preset local range of the oxygen concentration data at this moment as the occurrence probability of each oxygen concentration data within the preset local range of the oxygen concentration data at this moment.

4. The method for monitoring the operating state of a device based on the Internet of Things according to claim 1, wherein, The method for obtaining the fitting relationship is: Take the temperature data with the same value in all moments as a temperature category. Take the noise performance degree of the oxygen concentration data at the same moment as the corresponding noise performance degree of each temperature data in a temperature category. Take the average value of the corresponding noise performance degrees of all the temperature data in this temperature category as the comprehensive noise performance degree of this temperature category. Use the temperature category as the horizontal axis and the comprehensive noise performance degree as the vertical axis to construct a coordinate system. Map all the temperature categories and the corresponding comprehensive noise performance degrees into the coordinate system to obtain several data points; Use the polynomial obtained by fitting all the data points in the coordinate system by the least squares method as the fitting relationship.

5. The method for monitoring the operating state of a device based on the Internet of Things according to claim 4, characterized in that, The method for obtaining the fitting value of the noise performance degree is: Obtain the temperature category to which the temperature data at each moment belongs, and input the temperature category to which the temperature data at each moment belongs into the fitting relationship to obtain the fitting value of the noise performance degree of the temperature data at each moment.

6. The method for monitoring the operating state of a device based on the Internet of Things according to claim 1, characterized in that, The method for obtaining the preset local range is: Take the area composed of a preset number of data centered on the oxygen concentration data at each moment in the time series of oxygen concentration as the preset local range.

7. The method for monitoring the operating state of a device based on the Internet of Things according to claim 1, characterized in that, Before setting the decomposition level in the wavelet filtering algorithm using the corrected noise performance degree, it further includes: According to the corrected noise performance degree of the oxygen concentration data at each moment, the time series of oxygen concentration is segmented by using Fisher's optimal solution method to obtain several data segments.

8. The method for monitoring the operating state of a device based on the Internet of Things according to claim 7, wherein, The setting of the decomposition level in the wavelet filtering algorithm using the corrected noise performance degree includes: Taking the mean value of the corrected noise performance degrees of the oxygen concentration data at all moments in the data segment as the corrected noise performance degree of each data segment, multiplying the sum of the corrected noise performance degree of each data segment and 1 by the preset reference decomposition level and then performing a floor operation to obtain the decomposition level of each data segment; Taking the decomposition level of each data segment as the decomposition level of the wavelet filtering algorithm for the corresponding data segment.

9. An equipment operation status monitoring system based on the Internet of Things, characterized in that, It includes: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating state of a device based on the Internet of Things according to any one of claims 1-8 is implemented.

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