Coating storage environment monitoring method and system based on data processing

Abstract

The invention relates to the technical field of data processing, in particular to a paint storage environment monitoring method and system based on data processing. The method comprises the following steps: acquiring a plurality of temperature data, and dividing a temperature data sequence into a plurality of data intervals; acquiring the temperature data change degree of each data interval; according to the difference condition of sampling moments between adjacent temperature data in the temperature data sequence of each data interval, the confidence coefficient of the temperature data change degree of each data interval is obtained, and iterative correction is carried out on each data interval according to the confidence coefficient of the temperature data change degree of each data interval and the temperature data change degree to obtain the temperature data sequence of each data interval. The optimal correction interval of each data interval is obtained, the coating storage environment is monitored through the optimal correction intervals, and the accuracy of abnormal temperature data monitoring in the coating storage environment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method and system for monitoring the storage environment of coatings based on data processing. Background Art

[0002] Too high temperature will accelerate the evaporation rate of the solvent in the coating, resulting in an increase in the viscosity of the coating, a deterioration in fluidity, and an impact on the construction performance of the coating. For example, some water-based coatings may dry too quickly in a high-temperature environment, forming an uneven coating, reducing the flatness and gloss of the coating. Too low temperature may cause the water in the coating to freeze, damaging the structure of the coating, resulting in phenomena such as demulsification and delamination of the coating. For example, latex paint may freeze at low temperature, and the emulsion particles will aggregate after thawing, leading to a decline in the performance of the coating and even rendering it unusable.

[0003] Currently, the patent application document with the publication number CN116608904A discloses a method and system for real-time monitoring of the safety status of a hydrogen storage tank. By collecting the temperature data sequence and the strain force data sequence of the monitoring points in the hydrogen storage tank during the initial monitoring period; determining the compression moment when hydrogen starts to be compressed in the hydrogen storage tank based on the strain force data sequence of the monitoring points during the initial monitoring period; calculating the first interval division quantity based on the temperature anomaly degree corresponding to the temperature data sequence after the compression moment; calculating the second interval division quantity based on the strain force anomaly degree corresponding to the strain force data sequence after the compression moment; using the HBOS anomaly monitoring algorithm to perform anomaly monitoring on the temperature data sequence based on the first interval division quantity, and using the HBOS anomaly monitoring algorithm to perform anomaly monitoring on the strain force data sequence based on the second interval division quantity.

[0004] In the monitoring scenario of the coating storage temperature, the HBOS (Histogram-based Outlier Score) algorithm divides all temperature data into different data intervals by sorting the temperature data and according to a fixed interval width, and obtains the density of each temperature data in the data interval. The higher the density of the data interval, the lower the anomaly score of the temperature data. However, in the coating storage environment, the influence of temperature changes is often due to temperature anomalies caused by weather changes or malfunctions of temperature control equipment. When such an abnormal situation just occurs, the change in abnormal data is often relatively small, and its difference from the change in normal and stable temperature data is not obvious. This results in that the data intervals divided by the fixed interval width of the HBOS (Histogram-based Outlier Score) algorithm may contain both normal and abnormal data, and subsequent abnormal data is identified as normal data. Summary of the Invention

[0005] To solve the technical problem that the change difference between abnormal data generated when an abnormal situation just occurs and normal and stable temperature data is not obvious, resulting in that the data intervals divided by the fixed interval width of the HBOS (Histogram-based Outlier Score) algorithm may contain normal and abnormal data, making the abnormal detection inaccurate, the present invention provides a method and system for monitoring the paint storage environment based on data processing.

[0006] In the first aspect, the present invention provides a method for monitoring the paint storage environment based on data processing, adopting the following technical solution: A method for monitoring the paint storage environment based on data processing includes the steps: Collect a number of temperature data, divide the temperature data sequence into a number of data intervals; obtain the degree of change of the temperature data in the data interval ; n represents the number of all temperature data in the data interval; represents the i-th temperature data in the temperature data sequence of the data interval; represents the j-th temperature data in the temperature data sequence of the data interval; norm() represents the linear normalization function; obtain the confidence of the degree of change of the temperature data in the data interval: , n represents the number of all temperature data in the data interval; represents the sampling moment corresponding to the i-th temperature data in the temperature data sequence of the data interval; represents the sampling moment corresponding to the (i + 1)-th temperature data in the temperature data sequence of the data interval; represents taking the absolute value; exp() represents the exponential function with the natural constant as the base; Multiply the confidence of the degree of change of the temperature data in the data interval by the degree of change of the temperature data as the true degree of change of the temperature in the data interval; according to the true degree of change of the temperature, iteratively correct the data interval to obtain the optimal correction interval of the data interval; according to the optimal correction interval, obtain the abnormal score of each temperature data, and monitor the paint storage environment.

[0007] The innovation of the present invention lies in introducing a strategy for adjusting the width of the dynamic data interval. First, according to the fixed data interval width, the temperature data is divided using the traditional HBOS algorithm to obtain several data intervals. Then, the degree of change and the credibility of the degree of change of the temperature data in the data intervals are evaluated, and based on this, the width of the data interval is iteratively adjusted to obtain the optimal correction interval for each data interval, ensuring that the distribution of the temperature data in the optimal correction interval can reasonably distinguish abnormal data changes, effectively reducing the possibility of some abnormal data being misdetected; the abnormal scores of each temperature data obtained based on the distribution of the temperature data in the optimal correction interval are more accurate, making the subsequent abnormal monitoring of the coating storage environment more accurate.

[0008] Preferably, the dividing the temperature data sequence into several data intervals includes: The initial width of the preset data interval is Y, and according to the initial width of the data interval, the temperature data sequence is input into the HBOS algorithm to divide the temperature data sequence into several data intervals.

[0009] It is convenient for subsequent analysis of the degree of change of the temperature data in the data interval.

[0010] Preferably, the obtaining of the temperature data sequence of the data interval includes: The temperatures in the data interval are sorted in ascending order according to their sampling times to obtain the temperature data sequence of the data interval.

[0011] Preferably, the iteratively correcting the data interval according to the true temperature change degree to obtain the optimal correction interval width of the data interval includes: Preset the correction parameter a and the threshold parameter T1. The first correction interval of the first data interval , where Y represents the initial width of the data interval; represents the true temperature change degree of the first data interval; if the difference between the true temperature change degree of the first data interval and the first correction interval of the first data interval is greater than the threshold parameter T1, according to the first correction interval and the true temperature change degree of the first correction interval, obtain the second correction interval of the first data interval; and so on, until the difference between the true temperature change degree of the (H - 1)-th correction interval and the H-th correction interval of the first data interval is less than or equal to the threshold parameter T1, and take the H-th correction interval as the optimal correction interval of the first data interval; according to the optimal correction interval, obtain several optimal correction intervals.

[0012] Ensure that the distribution of the temperature data in the optimal correction interval can reasonably distinguish abnormal data changes, effectively reducing the possibility of some abnormal data being misdetected.

[0013] Preferably, obtaining several optimal correction intervals according to the optimal correction interval includes: For the second data interval, use the optimal correction interval of the first data interval as the initial width of the second data interval, and obtain the optimal correction interval of the second data interval according to the method for obtaining the optimal correction interval of the first data interval, and so on, to obtain the optimal correction interval of each data interval.

[0014] Preferably, obtaining the anomaly score of each temperature data according to the optimal correction interval and monitoring the paint storage environment includes: Input all temperature data within all optimal correction intervals into the HOBS algorithm to obtain the anomaly score of each temperature data, and use the anomaly score of each temperature data as the smoothing parameter value of each temperature data, and use the exponential smoothing algorithm to obtain the predicted value of each temperature data; Preset an early warning coefficient C. For any temperature data, if the absolute value of the difference between the predicted value and the actual value of the temperature data is greater than the early warning coefficient C, trigger the early warning mechanism to notify relevant personnel in a timely manner.

[0015] Improve the accuracy of anomaly monitoring of the paint storage environment.

[0016] Preferably, collecting several temperature data includes: Preset the sampling time as 1 minute / time, and collect for 24 hours in total. Place the temperature sensor in the paint storage area to collect each temperature data in the paint storage environment in real time.

[0017] Facilitate subsequent analysis of temperature data.

[0018] In a second aspect, the present invention provides a paint storage environment monitoring system based on data processing, adopting the following technical solution: A paint storage environment monitoring system based on data processing 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 paint storage environment monitoring method based on data processing is implemented.

[0019] By adopting the above technical solution, the above-mentioned paint storage environment monitoring method based on data processing 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.

[0020] The present invention has the following technical effects: The object of the present invention is to introduce a strategy for adjusting the width of the dynamic data interval. First, according to the fixed data interval width, the traditional HBOS algorithm is used to divide the temperature data to obtain several data intervals. The change degree and the credibility of the change degree of the temperature data in the data interval are evaluated, and the width of the data interval is iteratively adjusted accordingly to obtain the optimal correction interval for each data interval, ensuring that the distribution of the temperature data in the optimal correction interval can reasonably distinguish abnormal data changes, effectively reducing the possibility of misdetection of some abnormal data; the abnormal scores of each temperature data obtained based on the distribution of the temperature data in the optimal correction interval are more accurate, making the subsequent abnormal monitoring of the paint storage environment more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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.

[0022] Figure 1 It is a flowchart of a method for monitoring a paint storage environment based on data processing in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] It should be understood that when the claims, specifications and drawings of the present invention use terms such as "first" and "second", they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the specifications 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.

[0025] An embodiment of the present invention discloses a method for monitoring a paint storage environment based on data processing, referring to Figure 1 , including steps S1 - S4: S1: Collect a plurality of temperature data.

[0026] In the implementation of the present invention, the preset sampling time is once per minute, and a total of 24 hours are collected. The temperature sensor is placed in the paint storage area to collect each temperature data in the paint storage environment in real time, and each temperature data is sorted in turn to obtain a temperature data sequence.

[0027] S2: Divide the temperature data sequence into several data intervals; according to the difference situation between the temperature data within each data interval, obtain the temperature data change degree of each data interval; according to the difference situation of the sampling time between adjacent temperature data in the temperature data sequence of each data interval, obtain the confidence level of the temperature data change degree of each data interval.

[0028] It should be noted that when monitoring the temperature data of paint storage, it is easily affected by factors such as weather interference and internal temperature control equipment failures, resulting in abnormal data in the temperature data. It is necessary to detect abnormalities in a timely manner. In the temperature monitoring scenario of paint storage, the traditional histogram anomaly detection algorithm sorts the temperature data and divides all temperature data into different data intervals according to a fixed interval width, and obtains the density of each data interval where the temperature data is located. The higher the density of the data interval, the lower the anomaly score of the temperature data. However, in the paint storage environment, the temperature change is often caused by temperature anomalies such as weather changes or temperature control equipment failures. When such abnormal situations first occur, the changes in the abnormal data generated are often relatively small, and the difference in changes from normal and stable temperature data is not obvious. This leads to the possibility that the data divided by the fixed interval width may contain both normal and abnormal data, making the subsequent anomaly detection inaccurate. Since the temperature data is relatively stable under normal circumstances, after dividing the temperature data into several data intervals by using a fixed interval width, it is first necessary to analyze the temperature data change degree of the data interval. If the temperature data change degree in a certain data interval is large, it may mean that there may be abnormal change data in the temperature data of this data interval, that is, the temperature data change degree of this data interval is large.

[0029] Specifically, the initial width of the preset data interval is Y. The temperature data sequence is input into the HBOS algorithm according to the initial width of the data interval, and the temperature data sequence is divided into several data intervals; among them, the HBOS algorithm is a prior art, and no more details are described here in this embodiment. In the embodiment of the present invention, the initial width of the preset data interval is Y = 20 degrees Celsius. In other embodiments, the implementer can preset the value of Y according to the specific implementation method.

[0030] The specific operation of obtaining the temperature data change degree of each data interval according to the difference situation between the temperature data within each data interval is as follows: This embodiment is described by taking any one data interval as an example; Sort the temperatures in the data interval in ascending order according to their sampling times to obtain the temperature data sequence of the data interval; ; In the formula, S represents the degree of change of temperature data in the data interval; n represents the number of all temperature data in the data interval; represents the i-th temperature data in the temperature data sequence of the data interval; represents the j-th temperature data in the temperature data sequence of the data interval; norm() represents the linear normalization function.

[0031] It should be noted that the greater the degree of change of temperature data in the data interval, the greater the difference between the temperature data in the data interval, that is, the temperature data in the data interval changes in a non-stationary state, and there may be temperature data that deviates from the stationary state suddenly.

[0032] Thus, the degree of change of temperature data for each data interval is obtained.

[0033] It should be noted that since the data interval division divides the temperature data distributed within a temperature range into one interval, the temperature data in the data interval may be for different time periods of a day; then the degree of change of temperature data in the data interval represents the temperature data fluctuation situation in the overall data interval; since the characteristics of abnormal data distribution are ignored, that is, abnormal data caused by factors such as weather interference and equipment failure often appear continuously in a short period of time, which means that the abnormal temperature change data is relatively concentrated in a certain local time period; if the time interval between any one temperature data and the previous temperature data in the temperature data sequence of the data interval is smaller, and at this time if the degree of change of temperature data in the data interval is larger, the greater the possibility of abnormal data in this data interval, indicating that the temperature data in the data interval is more likely to be the data collected continuously for a period of time. Preferably, in some embodiments of the present invention, according to the difference situation of the sampling times between adjacent temperature data in the temperature data sequence of each data interval, the specific formula for obtaining the confidence level of the degree of change of temperature data in each data interval is: ; In the formula, represents the confidence level of the degree of change of temperature data in the data interval; n represents the number of all temperature data in the data interval; represents the sampling time corresponding to the i-th temperature data in the temperature data sequence of the data interval; represents the sampling time corresponding to the i + 1-th temperature data in the temperature data sequence of the data interval; represents taking the absolute value; exp() represents the exponential function with the natural constant as the base.

[0034] It should be noted that, the smaller the difference in sampling moments between adjacent temperature data in the temperature data sequence of a data interval, the shorter the time interval within which the adjacent temperature data are collected, indicating that the temperature change degree within the data interval is more likely to reflect the true temperature fluctuation, that is, the higher the credibility of the temperature data change degree within the data interval.

[0035] Thus, the confidence level of the temperature data change degree for each data interval is obtained.

[0036] S3: Using the confidence level of the temperature data change degree and the temperature data change degree for each data interval, perform iterative correction on each data interval to obtain the optimal correction interval for each data interval, and divide the temperature data sequence into several optimal correction intervals.

[0037] It should be noted that, the fixed-width data intervals in the traditional HBOS algorithm may not be able to fully reflect the complex change patterns that may occur in the actual environment. In particular, initial abnormal data is likely to be classified within the interval where normal data is located, resulting in inaccurate calculation of the anomaly score; if there are obvious temperature data changes within a data interval and the confidence level is higher, it indicates that there may be abnormally changing temperature data distributed within the data interval. Therefore, it is necessary to adjust the width of the current data interval. After each correction, it is necessary to analyze whether there are differences in the data changes within the data interval before and after the correction. If the differences are very small or equal, it means that the data changes within the data interval tend to be stable and have reached the optimal interval width. Then, through iterative calculation, the optimized data interval is obtained; the data distribution within the optimized data interval can reasonably distinguish abnormal data changes.

[0038] In the embodiment of the present invention, a correction parameter a = 0.5 and a threshold parameter T1 = 0.03 are preset. The product of the confidence level of the temperature data change degree of the first data interval and its temperature data change degree is denoted as the true temperature change degree of the first data interval; the sum of the true temperature change degree of the first data interval and the correction parameter a is denoted as the first sum value; the ceiling value of the ratio of the initial width of the preset data interval to the first sum value is used as the first correction interval of the first data interval; The specific formula is: ; In the formula, represents the first correction interval of the first data interval; Y represents the initial width of the data interval; represents the true temperature change degree of the first data interval; represents the ceiling symbol.

[0039] According to the method for obtaining the true temperature change degree of the data interval, obtain the true temperature change degree of the first correction interval; if the difference between the true temperature change degree of the first data interval and the true temperature change degree of the first correction interval is greater than the threshold parameter T1, record the sum of the true temperature change degree of the first correction interval and the correction parameter a as the second sum value, and take the ceiling value of the ratio of the first correction interval to the second sum value as the second correction interval of the first data interval; and so on, until the difference between the true temperature change degree of the (H - 1)-th correction interval and the true temperature change degree of the H-th correction interval of the first data interval is less than or equal to the threshold parameter T1, and take the H-th correction interval as the optimal correction interval of the first data interval; It should be noted that represents the true temperature change degree of the data interval. The larger this value is, the more fluctuations there are in the temperature data in the data interval and the more credible these fluctuations are. Then there are abnormal temperature data in the data interval, and it is necessary to reduce the interval width to distinguish the abnormal temperature data; on the contrary, it is necessary to increase the data interval width to ensure that more normal data are divided into this data interval.

[0040] Furthermore, it should be noted that if the difference between the true temperature change degree of the latest correction interval and the true temperature change degree of the previous correction interval is less than or equal to the threshold parameter T1, it means that the data change in the data interval before and after correction has tended to be stable and the optimal interval width has been reached; on the contrary, it means that there are still abnormal data in the data interval after correction, and it is necessary to continue iterative correction.

[0041] Specifically, for the second data interval, take the optimal correction interval of the first data interval as the initial width of the second data interval, and through the above method, obtain the optimal correction interval of the second data interval, and so on, obtain the optimal correction interval of each data interval, and use the optimal correction interval to divide the temperature data sequence into several optimal correction intervals.

[0042] S4: Monitor the coating storage environment through the optimal correction interval.

[0043] Specifically, input all temperature data in all optimal correction intervals into the HOBS algorithm to obtain the anomaly score of each temperature data, and take the anomaly score of each temperature data as the smoothing parameter value of each temperature data, and use the exponential smoothing algorithm to obtain the predicted value of each temperature data; Preset an early warning coefficient C = 1. For any temperature data, if the absolute value of the difference between the predicted value and the actual value of the temperature data is greater than the early warning coefficient, trigger the early warning mechanism to notify relevant personnel in time.

[0044] An embodiment of the present invention also discloses a paint storage environment monitoring system based on data processing, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a paint storage environment monitoring method based on data processing according to the present invention is implemented.

[0045] The above system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0046] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can 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 program, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0047] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

[0048] The above are all preferred embodiments of the present invention. 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 should be covered within the protection scope of the present invention.

Claims

1. A coating storage environment monitoring method based on data processing, characterized in that: Includes steps: Collect a number of temperature data, divide the temperature data sequence into a number of data intervals; obtain the degree of change of temperature data in the data interval ; n represents the number of all temperature data in the data interval; Represents the i-th temperature data in the temperature data sequence of the data interval; Represents the jth temperature data in the temperature data sequence of the data interval; norm() represents the linear normalization function; obtains the confidence level of the temperature data change degree of the data interval: , n represents the number of all temperature data in the data interval; Represents the sampling time corresponding to the i-th temperature data in the temperature data sequence of the data interval; The sampling time corresponding to the i+1th temperature data in the temperature data sequence of the data interval; Indicates taking the absolute value; exp() indicates an exponential function with a natural constant as the base; The product of the confidence of the temperature data change degree of the data interval and the temperature data change degree is used as the real temperature change degree of the data interval; according to the real temperature change degree, the data interval is iteratively corrected to obtain the optimal correction interval of the data interval; According to the optimal correction interval, the abnormal score of each temperature data is obtained, and the paint storage environment is monitored.

2. A coating storage environment monitoring method based on data processing according to claim 1, characterized in that: The temperature data sequence is divided into several data intervals, including: The initial width of the preset data interval is Y, and the temperature data sequence is input into the HBOS algorithm according to the initial width of the data interval, so as to divide the temperature data sequence into a plurality of data intervals.

3. A coating storage environment monitoring method based on data processing according to claim 1, characterized in that: The acquisition of the temperature data sequence of the data interval includes: The temperatures in the data interval are sorted in ascending order according to their sampling times to obtain a temperature data sequence of the data interval.

4. A coating storage environment monitoring method based on data processing according to claim 1, characterized in that: The iterative correction of the data interval according to the actual temperature change degree to obtain the optimal correction interval width of the data interval includes: Preset correction parameter a and threshold parameter T1, the first correction interval of the first data interval , where Y represents the initial width of the data interval; Represents the actual temperature change degree of the first data interval; if If the difference between the actual temperature change degree of the first correction interval of the first data interval and the actual temperature change degree of the first correction interval is greater than the threshold parameter T1, the second correction interval of the first data interval is obtained according to the first correction interval and the actual temperature change degree of the first correction interval; and so on, until the difference between the actual temperature change degree of the H-1th correction interval of the first data interval and its Hth correction interval is less than or equal to the threshold parameter T1, the Hth correction interval is taken as the optimal correction interval of the first data interval; and according to the optimal correction interval, several optimal correction intervals are obtained.

5. A coating storage environment monitoring method based on data processing according to claim 4, characterized in that: The step of obtaining a plurality of optimal correction intervals according to the optimal correction interval comprises: For the second data interval, the optimal correction interval of the first data interval is used as the initial width of the second data interval. The optimal correction interval of the second data interval is obtained according to the method for obtaining the optimal correction interval of the first data interval. And so on, the optimal correction interval of each data interval is obtained.

6. The coating storage environment monitoring method based on data processing according to claim 1 is characterized in that: The method of obtaining an abnormal score of each temperature data according to the optimal correction interval and monitoring the paint storage environment includes: All temperature data within the optimal correction interval are input into the HOBS algorithm to obtain the abnormal score of each temperature data, and the abnormal score of each temperature data is used as the smoothing parameter value of each temperature data, and the exponential smoothing algorithm is used to obtain the predicted value of each temperature data; A warning coefficient C is preset. For any temperature data, if the absolute value of the difference between the predicted value and the actual value of the temperature data is greater than the warning coefficient C, the warning mechanism is triggered to notify relevant personnel in time.

7. The coating storage environment monitoring method based on data processing according to claim 1 is characterized in that: The collecting of a number of temperature data includes: The preset sampling time is 1 minute / time, and the total sampling time is 24 hours. The temperature sensor is placed in the paint storage area to collect every temperature data in the paint storage environment in real time.

8. A coating storage environment monitoring system based on data processing, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a coating storage environment monitoring method based on data processing according to any one of claims 1 to 7 is implemented.

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