A data storage method for humidity online monitoring equipment

Abstract

The present invention relates to the technical field of data storage, and more specifically, to a data storage method for humidity online monitoring equipment, the method comprising: obtaining humidity values ​​collected by multiple monitoring equipment, determining the probability of each humidity value in all humidity values ​​of the corresponding monitoring equipment; calculating the similarity of humidity data distribution between monitoring equipment; treating monitoring equipment corresponding to a similarity greater than a similarity threshold as similar equipment, and performing associated storage; wherein, when performing associated storage, the gradient value and inflection point of the humidity data sequence that overlaps between similar equipment are obtained; determining the difference value between the humidity data sequences of similar equipment; and the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point, and the difference value between similar equipment in the humidity data sequence of any monitoring equipment among the similar equipment are stored in an associated manner, thereby effectively improving the efficiency of humidity data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and more specifically, to a data storage method for humidity online monitoring equipment. Background Art

[0002] Humidity online monitoring equipment plays an important role in many fields, including meteorology, environmental monitoring, agriculture, manufacturing, healthcare, etc. With the development of sensor technology and the Internet of Things, these devices are becoming more and more popular. Through monitoring equipment, humidity changes in the environment can be monitored and recorded in real time. The study of data storage methods for humidity online monitoring equipment aims to effectively manage and utilize humidity data, improve data reliability and security, thereby reducing data storage consumption and effectively improving storage efficiency.

[0003] The patent application document with publication number CN106017575A discloses a method for automatically collecting temperature and humidity in a tea factory. The method uses server configuration software to control multiple temperature and humidity sensors to monitor the temperature and humidity data of the tea factory in real time, and saves the temperature and humidity data in the server. The temperature and humidity monitoring software of the client monitors the collected temperature and humidity data in real time, and issues an alarm when the temperature and humidity data is greater than the set upper limit or lower than the set lower limit. Multiple temperature and humidity sensors are used to monitor the temperature and humidity values ​​of various places in the tea factory in real time, and then they are transmitted to the server for storage through the gateway.

[0004] The above-mentioned prior art directly stores a large amount of humidity data collected by multiple monitoring devices in the server when automatically collecting humidity data. Under normal circumstances, the humidity in the environment will be in a relatively regular state of change, and multiple monitoring devices are used for online monitoring at different locations. Therefore, there will be a large amount of repeated humidity data. If a large amount of repeated humidity data is directly stored without processing, it will cause a waste of storage system space, resulting in low storage efficiency of the data collected by the monitoring equipment. Summary of the invention

[0005] In order to solve the above technical problem of how to process and store a large amount of repeated humidity data to reduce the waste of storage space, the present invention proposes a data storage method for a humidity online monitoring device, which includes the following steps:

[0006] A data storage method for humidity online monitoring equipment, comprising:

[0007] Obtain humidity values ​​collected by multiple monitoring devices, determine the probability of each humidity value in all humidity values ​​of the corresponding monitoring device; calculate the similarity of humidity data distribution between monitoring devices, and the similarity is negatively correlated with the probability of humidity value; regard monitoring devices corresponding to similarities greater than a similarity threshold as similar devices, and store them in association; wherein, when storing in association, by obtaining the overlapping humidity data sequences between similar devices, the difference between each humidity value and the humidity value at the previous moment is taken as the gradient value of the humidity value, and determine the inflection point in the humidity data sequence, and the inflection point satisfies the relationship ; In the formula, Indicates the humidity value corresponding to the inflection point in the humidity data sequence, Represents the humidity value at time t+1 in the humidity data sequence, Represents the humidity value at time t-1 in the humidity data sequence, Indicates the tolerance for storage information loss; determines the difference value between humidity data sequences of similar devices, wherein the difference value is positively correlated with the humidity value; associates and stores the starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point, and the difference value between similar devices in the humidity data sequence of any monitoring device among the similar devices.

[0008] The effect is that the data storage method for humidity online monitoring equipment provided by the present invention analyzes the similar characteristics of humidity data sequences of similar equipment at key points such as the start, end and inflection points, and stores the difference values ​​between the key points and similar equipment without storing the complete humidity data sequence; and because the key points and their gradient values ​​are stored, when subsequent new data enters, its change trend with the historical data can be quickly compared, thereby making it easier to monitor possible anomalies or faults; at the same time, with the addition of new monitoring equipment, the method can also be easily expanded to include new equipment, and continue to search for and associate equipment with similar humidity change patterns, effectively improving the efficiency of humidity data storage.

[0009] According to a data storage method for a humidity online monitoring device provided by the present invention, before obtaining humidity values ​​collected by multiple monitoring devices, it also includes: collecting data through the monitoring device, performing denoising preprocessing on the data, and obtaining the humidity value of the monitoring device at each moment.

[0010] The effect is that: the present invention takes into account that when the online monitoring device collects humidity data, there may be noise data in the collected data due to the device itself or the environment, thereby affecting the storage effect. Therefore, by performing denoising preprocessing on the collected data to eliminate or reduce the influence of the noise data, the overall quality of the data can be significantly improved.

[0011] According to a data storage method for a humidity online monitoring device provided by the present invention, the probability of each humidity value in the corresponding humidity data sequence satisfies the relationship: ; In the formula, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, It represents the number of times humidity value i appears in all humidity values ​​collected by monitoring device j. Represents the total number of humidity values ​​collected by monitoring device j.

[0012] According to a data storage method for a humidity online monitoring device provided by the present invention, the similarity satisfies the relationship:

[0013] ;

[0014] In the formula, represents the similarity between monitoring device j and monitoring device k, Indicates based on The minimum divergence value obtained is represents the difference between the humidity values ​​of monitoring device j and monitoring device k, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, Indicates monitoring equipment The humidity value of all collected humidity values ​​is The probability of Represents the maximum value of all humidity values ​​in monitoring device j and monitoring device k.

[0015] The effect is that the present invention provides an accurate similarity calculation method. By analyzing the data distribution of humidity values ​​between different monitoring devices, the similarity between different monitoring devices can be accurately obtained. Subsequently, the difference value between the monitoring devices can also be obtained based on the similarity between the monitoring devices.

[0016] According to a data storage method for humidity online monitoring equipment provided by the present invention, the monitoring equipment corresponding to a similarity greater than a similarity threshold is regarded as a similar equipment and is stored in association, including: after determining the similar equipment according to the similarity threshold, the similarities between the similar equipment are sorted from large to small; when storing in association, in response to the similarity arrangement order of the similar equipment, the similar equipment corresponding to the similarity is stored in association in sequence.

[0017] The effect is that: the present invention takes into account that the data distribution overlap in similar devices with higher similarity is higher and the difference value is smaller, and therefore provides priority management for the storage order of humidity data. Ordered associative storage helps to optimize the organization of data. By giving priority to storing similar devices with relatively small differences, the storage of redundant data can be reduced, further improving storage efficiency.

[0018] According to a data storage method for humidity online monitoring equipment provided by the present invention, the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point, and the difference values ​​between similar devices in the humidity data sequence of any monitoring device among the similar devices are stored in association, and also includes: taking the difference between the gradient value at a moment after the inflection point and the inflection point gradient value as the gradient change value at the inflection point; and storing the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point, and the difference values ​​between similar devices in the humidity data sequence that overlaps the similar devices in association.

[0019] According to a data storage method for humidity online monitoring equipment provided by the present invention, the monitoring equipment corresponding to the similarity greater than the similarity threshold is regarded as a similar equipment and is stored in association. Then, when using the stored humidity data of the monitoring equipment, the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point / the gradient value at a moment after the inflection point in the similar equipment are differentially restored to obtain the overlapping humidity data sequence between the similar equipment, and the humidity data of the monitoring equipment before storage is finally obtained by combining the difference values ​​between the similar equipment.

[0020] The effect is that the associative storage method provided by the present invention allows similar equipment groups with similar humidity change patterns to be quickly located through differential restoration during query and analysis, thereby facilitating further comparison and analysis, and can also quickly locate key locations during subsequent abnormal analysis.

[0021] According to a data storage method for a humidity online monitoring device provided by the present invention, the step of obtaining humidity values ​​collected by multiple monitoring devices includes: obtaining humidity values ​​of multiple monitoring devices based on the same sampling parameters, wherein the sampling parameters include one or more of a sampling period, a sampling frequency, a sampling time, and a number of sampling points.

[0022] The effect is that, in order to facilitate data processing, the present invention obtains the humidity value of each monitoring device through the same sampling parameters.

[0023] Based on the above scheme, when storing humidity data collected by online monitoring equipment, the present invention analyzes the similar characteristics of humidity data sequences of similar equipment at the starting, ending and inflection key points, and stores the difference values ​​between the key points and similar equipment without storing the complete humidity data sequence, thereby reducing the storage of redundant data to save data storage resources; further, with the addition of new monitoring equipment, the method can be easily expanded to include new equipment, and continue to search for and associate equipment with similar humidity change patterns, effectively improving the efficiency of humidity data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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 accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0025] Figure 1 A schematic flow chart of a data storage method for a humidity online monitoring device provided by an embodiment of the present invention;

[0026] Figure 2 A schematic flow chart of a method for associating and storing similar devices provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0028] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] The embodiment of the present invention provides a data storage method for a humidity online monitoring device, see Figure 1 As shown, Figure 1 A schematic flow chart of a data storage method for a humidity online monitoring device provided by an embodiment of the present invention, the method comprising the following steps S1-S3:

[0030] S1: Obtain humidity values ​​collected by multiple monitoring devices, and determine the probability of each humidity value among all humidity values ​​of the corresponding monitoring device.

[0031] The monitoring device may be a humidity sensor; the frequency of collecting humidity data may be once per second, which may be set according to actual needs.

[0032] Due to the collection process of the monitoring equipment or the influence of the surrounding environment, the collected humidity data may contain noise data, thus affecting the storage effect. Therefore, after collecting humidity data within the monitoring range through multiple monitoring devices in the environment, the collected data can be preprocessed:

[0033] For example, data is collected through monitoring equipment, and denoising preprocessing is performed on the data to obtain humidity values ​​at each moment, and a humidity data sequence is formed based on each humidity value and the corresponding moment.

[0034] After the humidity data sequence of each monitoring device is obtained based on the above method, the probability of each humidity value in the humidity data sequence is determined.

[0035] For example, in the embodiment of the present invention, the probability of each humidity value among all humidity values ​​of the corresponding monitoring device is determined, and the specific details can be referred to the following relationship:

[0036] ;

[0037] In the formula, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, It represents the number of times humidity value i appears in all humidity values ​​collected by monitoring device j. Represents the total number of humidity values ​​collected by monitoring device j.

[0038] Based on the above method, the probability of each humidity value can be obtained to characterize the data distribution of each humidity value in the humidity data collected by the corresponding monitoring device. In order to reduce the space required for humidity data storage, the data distribution of humidity values ​​in different monitoring devices can be analyzed to obtain the correlation between monitoring devices, and then the monitoring devices are associated and stored based on the correlation, that is, the following steps are performed:

[0039] S2: Calculate the similarity of humidity data distribution between monitoring devices.

[0040] Among them, the similarity is negatively correlated with the probability of humidity value.

[0041] It should be noted that the similarity between monitoring devices can be used to characterize the similarity of humidity data distribution between monitoring devices. If the data distribution between monitoring devices is highly similar, the same overlapping data in the monitoring devices can be stored only through one monitoring device during storage. Combined with the difference between the monitoring devices, all data in the monitoring devices with high similarity can be stored. The specific operation steps are described in detail below:

[0042] For example, in the embodiment of the present invention, when determining the similarity between monitoring devices, it can be obtained by analyzing the data distribution of humidity values ​​in the monitoring devices, and specifically, refer to the following formula:

[0043] ;

[0044] In the formula, represents the similarity between monitoring device j and monitoring device k, Indicates based on The minimum divergence value obtained is represents the difference between the humidity values ​​of monitoring device j and monitoring device k, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, Indicates monitoring equipment The humidity value of all collected humidity values ​​is The probability of Represents the maximum value of all humidity values ​​in monitoring device j and monitoring device k.

[0045] It should be noted that Represents the humidity value i of monitoring device j and the monitoring device Humidity value The divergence value between the monitoring device j and the monitoring device j is negatively correlated with the similarity. The smaller the value, the closer the monitoring device j and the monitoring device j are. The higher the distribution similarity of humidity value i between monitoring devices j and monitoring devices i, the higher the distribution similarity of humidity value i between monitoring devices j and monitoring devices i. The lower the distribution similarity of humidity value i.

[0046] Minimum function It can be used as a conservative estimate to avoid the situation where the overall similarity measure is too high due to the outliers of one or some data. The minimum value of the divergence of all humidity values ​​in the monitoring device is used as the similarity between the corresponding monitoring devices. That is, as long as the two distributions are similar enough at any humidity value, it can be considered that monitoring device j and monitoring device The data distributions are similar overall.

[0047] To illustrate this formula, if the difference between the humidity values ​​of monitoring device j and monitoring device k is is 0, 1, 2, respectively. When the divergence value is 0, 1, or 2, the minimum value among all the divergence values ​​is taken as the divergence value between monitoring device j and monitoring device k. The divergence value is added with 0.01 and the reciprocal is taken to finally obtain the similarity between monitoring device j and monitoring device k.

[0048] After obtaining the similarity between monitoring devices based on the above formula, the humidity data sequences can be associated and stored based on the similarity between monitoring devices:

[0049] S3: The monitoring devices corresponding to the similarities greater than the similarity threshold are regarded as similar devices and are associated and stored.

[0050] For example, in the embodiment of the present invention, the similarity threshold can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions here.

[0051] When storing monitoring devices with high similarity in association, see Figure 2 As shown, Figure 2 A schematic flow chart of a method for associating and storing similar devices provided in an embodiment of the present invention, the method comprises the following steps S301-S303:

[0052] S301: Obtain overlapping humidity data sequences between similar devices, take the difference between each humidity value and the humidity value at the previous moment as the gradient value of the humidity value, and determine the inflection point in the humidity data sequence.

[0053] For example, when determining the gradient value of the humidity value at the current moment, it can be determined by the difference between the humidity value at the current moment and the humidity value at the previous moment. For details, refer to the following formula:

[0054] ;

[0055] In the formula, represents the gradient value of the humidity value at the tth moment in the humidity data sequence, represents the humidity value at the tth moment in the humidity data sequence, Represents the humidity value at time t-1 in the humidity data sequence.

[0056] In the above formula, if , indicating that the relationship between the humidity value and the corresponding time is positively correlated; if , indicating that the relationship between the humidity value and the corresponding time is negatively correlated; if , indicating that the humidity value remains unchanged as the corresponding time changes.

[0057] It should be noted that the gradient value of the humidity value reflects the change in humidity before and after the humidity data is collected. If the gradient value remains unchanged over a period of time, only the starting humidity value and the ending humidity value in the humidity data sequence need to be stored, which can effectively reduce the storage amount of humidity data.

[0058] However, as time changes, the correlation between humidity and the corresponding time will change. The point where the correlation changes is the inflection point. Since the correlation between humidity and the corresponding time before and after the inflection point is different, the storage content of the overlapping humidity data before and after the inflection point changes. Based on this, the inflection point in the humidity data sequence can be found by changing this characteristic through the correlation of humidity data before and after the inflection point:

[0059] For example, in the embodiment of the present invention, the inflection point of the humidity data sequence can be determined by referring to the following formula:

[0060] ;

[0061] In the formula, Indicates the humidity value corresponding to the inflection point in the humidity data sequence, Represents the humidity value at time t+1 in the humidity data sequence, Represents the humidity value at time t-1 in the humidity data sequence, Indicates the tolerance for storage information loss.

[0062] In the above formula, the storage information loss tolerance The larger the value, the higher the tolerable information loss is during the storage process.

[0063] For example, in the embodiment of the present invention, the storage information loss tolerance can be specifically set according to the amount of data to be stored, and the embodiment of the present invention does not impose too many restrictions here.

[0064] After obtaining the inflection point of the humidity data sequence of each monitoring device based on the above formula, continue to determine the difference value between similar devices:

[0065] S302: Determine the difference value between humidity data sequences of similar devices.

[0066] Among them, the difference value is positively correlated with the humidity value.

[0067] For example, in the embodiment of the present invention, the difference value between humidity data sequences of similar devices is determined, and the specific formula may be as follows:

[0068] ;

[0069] In the formula, represents the difference value between similar devices, i represents the humidity value i of one of the similar devices, Indicates the humidity value of another similar device .

[0070] In the above formula, Indicates the difference between humidity values ​​of similar devices. If all humidity values ​​of similar devices and humidity value If there is a one-to-one correspondence, it means that the data distribution in similar devices is exactly the same.

[0071] Take the formula as an example. The similar devices are monitoring device j and monitoring device k. If, among the multiple humidity values ​​of monitoring device j, each humidity value of 30% can find the corresponding humidity value of 30% in monitoring device k, then there is no difference in humidity value of 30% between monitoring device j and monitoring device k. Similarly, all humidity values ​​of monitoring device j and monitoring device k are compared to obtain all the differences between similar devices.

[0072] S303: The starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point, and the difference value between similar devices in the humidity data sequence of any monitoring device among similar devices are stored in association.

[0073] After obtaining the inflection point and gradient value of the humidity data sequence based on the above method, the humidity data can be stored according to the correlation between similar devices. It should be noted that the higher the correlation, the higher the overlap of data distribution between the corresponding monitoring devices. Therefore, when associating and storing, similar devices with higher similarity should be given priority:

[0074] For example, in an embodiment of the present invention, when similar devices are stored in association, the similarities between the similar devices may be sorted from large to small; when stored in association, similar devices corresponding to the similarities are stored in association in sequence in response to the similarity arrangement order of the similar devices.

[0075] In this way, by providing priority management for the storage order of humidity data, the ordered associative storage helps to optimize the organization of data. By preferentially storing similar devices with relatively small differences, the storage of redundant data can be reduced, further improving storage efficiency.

[0076] After sorting the similarities based on the above method, the starting humidity value, end humidity value, inflection point humidity value, inflection point gradient value, gradient value at a moment after the inflection point in the humidity data sequence of any monitoring device among the similar devices and the difference values ​​between similar devices can be associated and stored.

[0077] In another possible implementation, the gradient change value may be obtained by using the gradient value at the inflection point and the gradient value at a moment after the inflection point, and then stored based on the gradient change value.

[0078] For example, in an embodiment of the present invention, when storing in association, the difference between the gradient value at a moment after the inflection point and the gradient value at the inflection point can also be used as the gradient change value at the inflection point; the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point and the difference value between similar devices in the humidity data sequence overlapping with similar devices are stored in association.

[0079] For example, obtain humidity data series of similar equipment , the inflection point of the sequence is calculated as The humidity gradient before the inflection point is The humidity gradient after the inflection point is , gradient change value , the stored content can be , , , and ; or it can be , , , and , both methods process a humidity data sequence of length T, and only need to store five values ​​and other difference values.

[0080] It can be seen that in an embodiment of the present invention, when storing data of an online humidity monitoring device, humidity values ​​collected by multiple monitoring devices can be obtained, and the probability of each humidity value in all humidity values ​​of the corresponding monitoring device can be determined; the similarity of the humidity data distribution between the monitoring devices is calculated, and the similarity is negatively correlated with the probability of the humidity value; the monitoring devices corresponding to the similarity greater than the similarity threshold are regarded as similar devices, and are stored in association; wherein, when storing in association, by obtaining the overlapping humidity data sequences between similar devices, the difference between each humidity value and the humidity value at the previous moment is taken as the gradient value of the humidity value, and the inflection point in the humidity data sequence is determined; the difference value between the humidity data sequences of similar devices is determined, and the difference value is positively correlated with the humidity value; the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at the moment after the inflection point, and the difference value between similar devices in the humidity data sequence of any monitoring device among the similar devices are stored in association.

[0081] In this way, by analyzing the similar characteristics of humidity data sequences of similar devices at key points (such as starting, ending, inflection points, etc.), the difference values ​​between key points and similar devices are stored without storing the complete humidity data sequence, reducing the storage of redundant data to save data storage resources; further, with the addition of new monitoring devices, the method can be easily expanded to include new devices, and continue to find and associate devices with similar humidity change patterns, effectively improving the efficiency of humidity data storage.

[0082] In the above embodiment, after the data of the humidity online monitoring device is stored, the humidity data sequence of each monitoring device can be obtained by differential restoration when used:

[0083] For example, in an embodiment of the present invention, when using the stored humidity data of a monitoring device, by performing differential restoration on the starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point / the gradient value at one moment after the inflection point in similar devices, a humidity data sequence overlapping between similar devices is obtained, and combined with the difference values ​​between similar devices, the humidity data of the monitoring device before storage is finally obtained.

[0084] It should be noted that the associative storage method provided in the embodiment of the present invention enables the rapid location of equipment groups with similar humidity change patterns through differential restoration during query and analysis, thereby facilitating further comparison and analysis, and allowing key locations to be quickly located during subsequent abnormal analysis.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data storage method for humidity online monitoring equipment, characterized in that: include: Obtain humidity values ​​collected by multiple monitoring devices, and determine the probability of each humidity value among all humidity values ​​of the corresponding monitoring device; Calculate the similarity of humidity data distribution between monitoring devices, where the similarity is negatively correlated with the probability of humidity value; consider monitoring devices corresponding to similarities greater than a similarity threshold as similar devices, and store them in association; Among them, when associating and storing, by obtaining the overlapping humidity data sequences between similar devices, the difference between each humidity value and the humidity value at the previous moment is used as the gradient value of the humidity value, and the inflection point in the humidity data sequence is determined, and the inflection point satisfies the relationship ; In the formula, Indicates the humidity value corresponding to the inflection point in the humidity data sequence, Represents the humidity value at time t+1 in the humidity data sequence, Represents the humidity value at time t-1 in the humidity data sequence, Indicates the tolerance for storage information loss; Determine the difference value between humidity data sequences of similar devices, where the difference value is positively correlated with the humidity value; associate and store the starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point in the humidity data sequence of any monitoring device among the similar devices, and the difference value between the similar devices.

2. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: Before obtaining the humidity values ​​collected by the plurality of monitoring devices, the method further includes: The monitoring device collects data, performs denoising preprocessing on the data, and obtains the humidity value of the monitoring device at each moment.

3. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: The probability of each humidity value among all humidity values ​​of the corresponding monitoring device satisfies the relationship: ； In the formula, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, It represents the number of times humidity value i appears in all humidity values ​​collected by monitoring device j. Represents the total number of humidity values ​​collected by monitoring device j.

4. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: The similarity satisfies the relationship: ； In the formula, represents the similarity between monitoring device j and monitoring device k, Indicates based on The minimum divergence value obtained is represents the difference between the humidity values ​​of monitoring device j and monitoring device k, represents the probability that the humidity value is i among all the humidity values ​​collected by monitoring device j, Indicates monitoring equipment The humidity value of all collected humidity values ​​is The probability of Represents the maximum value of all humidity values ​​in monitoring device j and monitoring device k.

5. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: The monitoring devices corresponding to the similarities greater than the similarity threshold are regarded as similar devices and are stored in association, including: After similar devices are determined according to the similarity threshold, the similarities between the similar devices are sorted from large to small; During the associated storage, in response to the similarity arrangement order of the similar devices, the similar devices corresponding to the similarities are sequentially associated and stored.

6. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: The storing of the starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient value at a moment after the inflection point and the difference value between the similar devices in the humidity data sequence of any monitoring device among the similar devices in association also includes: The difference between the gradient value at a moment after the inflection point and the gradient value at the inflection point is taken as the gradient change value at the inflection point; The starting humidity value, the ending humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point and the difference value between the similar devices in the humidity data sequence overlapping the similar devices are associated and stored.

7. The data storage method of a humidity online monitoring device according to claim 1, characterized in that: The monitoring devices corresponding to the similarities greater than the similarity threshold are regarded as similar devices and associated and stored, and then further include: When using the stored humidity data of the monitoring device, the starting humidity value, the end humidity value, the inflection point humidity value, the inflection point gradient value, the gradient change value at the inflection point / the gradient value at the moment after the inflection point in similar devices are differentially restored to obtain the overlapping humidity data sequence between similar devices, and combined with the difference values ​​between similar devices, finally obtain the humidity data of the monitoring device before storage.

8. The data storage method for humidity online monitoring equipment according to claim 1, characterized in that: The step of obtaining humidity values ​​collected by multiple monitoring devices includes: Based on the same sampling parameters, humidity values ​​of multiple monitoring devices are obtained, wherein the sampling parameters include one or more of a sampling period, a sampling frequency, a sampling time, and a number of sampling points.

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