A remote monitoring method and system for a natural convection constant temperature test chamber

Abstract

The present invention relates to the technical field of data processing, and particularly to a remote monitoring method and system for a natural convection constant temperature test chamber. The method includes the steps of: collecting monitoring data, when compressing the monitoring data, obtaining the current literal, obtaining the frequency preference degree of the current literal according to the frequency of the characters in the monitoring data that appear in the current literal, obtaining the predicted frequency of each character in the current literal; obtaining the matching preference degree of the current literal according to the predicted frequency of each character in the current literal; obtaining the preference degree of the current literal according to the matching preference degree and the frequency preference degree of the current literal, enhancing the tire repair grayscale image according to the bubble degree of each pixel point of each block in each target area to obtain a tire repair enhanced image, and then identifying the bubble area. The present invention improves the compression efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a remote monitoring method and system for a natural convection constant temperature test chamber. Background Art

[0002] Natural convection constant temperature test chambers are applied in multiple scenarios such as material testing, environmental simulation, and biological experiments to provide a stable temperature and humidity environment. During their use, the demand for precise control and real-time monitoring of the test process has gradually increased. Through a remote monitoring system, researchers and engineers can view the temperature and humidity changes, equipment operating status, and alarm information inside the test chamber in real time without being restricted by time and space. However, in order to improve the transmission efficiency of monitoring data, it is necessary to compress the monitoring data before transmission.

[0003] Currently, the patent application document with the publication number CN110209640A is a method for dynamically switching the LZ4 compression algorithm type under the operating state of a mobile phone system. When a file needs to be compressed on the Android platform, it calculates the current CPU occupancy rate; obtains the size of the current free RAM; dynamically sets the block size during compression according to the size of the current free RAM; and dynamically selects the type of the LZ4 compression algorithm according to the current CPU occupancy rate: if the current CPU occupancy rate is higher than a predetermined threshold, it selects the lz4 ordinary compression algorithm, otherwise, it selects the lZ4 high compression rate algorithm.

[0004] When using the traditional LZ4 compression algorithm and setting a fixed base length to compress the collected monitoring data, since there are many types of characters in the collected monitoring data, the situation where the literal length gradually becomes longer is likely to occur, and if the base length set in the LZ4 compression algorithm is longer, it will be difficult to match the literal, which not only reduces the compression effect but also greatly increases the matching calculation amount. Summary of the Invention

[0005] In order to solve the technical problem that when the LZ4 compression algorithm is used to compress monitoring data, the situation where the literal length gradually becomes longer occurs, and when the base length is longer, it will be difficult to match the literal, reducing the compression efficiency, the present invention provides a remote monitoring method and system for a natural convection constant temperature test chamber.

[0006] In a first aspect, the present invention provides a remote monitoring method for a natural convection constant temperature test chamber, adopting the following technical solution:

[0007] A remote monitoring method for a natural convection constant temperature test chamber includes the steps of:

[0008] Collect monitoring data; obtain the current literal when using the LZ4 compression algorithm to compress the monitoring data; obtain the frequency preference degree of the current literal ; Represents the length of the current literal; Represents the frequency of the j-th character of the current literal in the monitored data; Represents the frequency degree of the m-th target character combination of the current literal; Represents the number of target character combinations of the current literal;

[0009] Obtain the matching preference degree of the current literal ; Represents the number of character types in the current literal; Represents the frequency of the h-th character in the current literal in the current literal; Represents the corrected predicted frequency of the h-th character of the current literal;

[0010] According to the matching preference degree and frequency preference degree of the current literal, obtain the preference degree of the current literal; after adjusting the preset basic length according to the preference degree of the current literal, compress and transmit the characters in the remaining monitored data.

[0011] The innovation of the present invention lies in first obtaining the frequency preference degree of the current literal according to the frequency of the characters in the current literal, then predicting the subsequent occurrence probability of each character in the current literal, and then obtaining the matching preference degree of the current literal. Finally, according to the matching preference degree and frequency preference degree of the current literal, obtain the preference degree of the current literal. A literal with a low preference degree indicates that it is difficult to match. Therefore, for a literal with a low preference degree, appropriately reduce the basic length of the LZ4 compression algorithm to enable the current literal to be matched in time, preventing the calculation amount of subsequent matching from becoming too large due to the excessive length of the current literal. By dynamically adjusting the fixed basic length according to the preference degree of the literal, to a certain extent, the compression effect on the monitored data can still be maintained. At the same time, it also avoids a large number of complex matching operations when the literal is long, improving the compression efficiency of the monitored data.

[0012] Preferably, the acquisition of the target character combination of the current literal includes:

[0013] Preset the number of consecutive characters N, record any N consecutive characters in the current literal as a character combination of the current literal, obtain several character combinations of the current literal, multiply the frequencies of the characters in each character combination of the current literal and then take the N-th root to obtain the frequency degree of each character combination of the current literal; if the frequency degree of any character combination of the current literal is greater than , Represents the number of character types in the monitored data, and record this character combination of the current literal as the target character combination of the current literal.

[0014] Preferably, the obtaining of the correction prediction frequency of the h-th type of character in the current literal includes:

[0015] Obtaining the prediction frequency of each type of character in the current literal; taking the ratio of the prediction frequency of the h-th type of character in the current literal to the sum of the prediction frequencies of each type of character in the current literal as the correction prediction frequency of the h-th type of character in the current literal.

[0016] It is convenient to subsequently obtain the matching preference degree of the current literal.

[0017] Preferably, the obtaining of the prediction frequency of each type of character in the current literal includes:

[0018] ;

[0019] In the formula, represents the prediction frequency of the h-th type of character in the current literal; represents the frequency of occurrence of the h-th type of character in the current literal in the current literal; represents the frequency of occurrence of the h-th type of character in its previous literal; || represents the absolute value symbol; max() represents the maximum value function; exp() represents the exponential function with the natural constant as the base.

[0020] Based on the prediction frequency of each type of character in the current literal, it is judged whether the current literal is likely to be matched subsequently.

[0021] Preferably, the obtaining of the preference degree of the current literal includes:

[0022] ;

[0023] In the formula, represents the preference degree of the current literal; represents the matching preference degree of the current literal; represents the frequency preference degree of the current literal; represents the length of the current literal.

[0024] A literal with a low preference degree indicates that it is difficult to match. Therefore, it is convenient to subsequently adjust the preset base length according to the preference degree of the current literal to make the literal easier to be matched.

[0025] Preferably, after adjusting the preset base length according to the preference degree of the current literal, compressing and transmitting the characters in the remaining monitoring data includes:

[0026] Obtaining a preference degree threshold , Denote the number of character types in the monitoring data. When the preference degree of the current literal is less than the preference degree threshold, set the base length to 5, continue to compress the characters in the remaining monitoring data, and analyze the next literal according to the analysis method of the current literal to obtain the final compressed data, and then transmit the final compressed data.

[0027] The compression efficiency is improved.

[0028] Preferably, when using the LZ4 compression algorithm to compress the monitoring data to obtain the current literal, it includes:

[0029] Preset the base length as V and the literal length as L. During the process of using the LZ4 compression algorithm to compress the monitoring data, the literal with a length greater than L is denoted as the current literal.

[0030] In a second aspect, the present invention provides a remote monitoring system for a natural convection constant temperature test chamber, adopting the following technical solutions:

[0031] A remote monitoring system for a natural convection constant temperature test chamber 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 remote monitoring method for a natural convection constant temperature test chamber is implemented.

[0032] By adopting the above technical solutions, the above-mentioned remote monitoring method for a natural convection constant temperature test chamber 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.

[0033] The present invention has the following technical effects: The purpose of the present invention is to first obtain the frequency preference degree of the current literal according to the frequency of character occurrences in the current literal, then predict the subsequent occurrence probability of each character in the current literal, and then obtain the matching preference degree of the current literal. Finally, according to the matching preference degree and the frequency preference degree of the current literal, obtain the preference degree of the current literal. For the literal with a low preference degree, appropriately reduce the base length of the LZ4 compression algorithm to make the current literal match in time, preventing the subsequent matching calculation amount from being too large due to the excessive length of the current literal. By dynamically adjusting the fixed base length according to the preference degree of the literal, to a certain extent, the compression effect on the monitoring data can still be maintained. At the same time, it also avoids a large number of complex matching operations in the case of a long literal, and improves the compression efficiency of the monitoring data. Description of the Drawings

[0034] 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 understandable. 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.

[0035] Figure 1 is a flowchart of a method in a remote monitoring method for a natural convection constant temperature test chamber according to an embodiment of the present invention. Detailed implementation manners

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0037] It should be understood that when terms such as "first" and "second" are used in the claims, the description, and the drawings of the present invention, 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.

[0038] An embodiment of the present invention discloses a remote monitoring method for a natural convection constant temperature test chamber. Referring to Figure 1 , it includes steps S1 - S4:

[0039] S1: Collect monitoring data.

[0040] In an embodiment of the present invention, the monitoring data of the natural convection constant temperature test chamber is collected from a database. The monitoring data includes: temperature, humidity, air velocity, the status of equipment components, the duration of the experiment, and other data.

[0041] S2: When compressing the monitoring data, obtain the current literal, and according to the frequency of the characters in the current literal appearing in the monitoring data, obtain the frequency preference degree of the current literal.

[0042] It should be noted that during the remote monitoring of the convection constant temperature test chamber, the LZ4 compression algorithm is used, and a fixed base length is set to compress the collected monitoring data. Since there are many types of characters in the collected monitoring data, the literal length is likely to gradually increase. Moreover, when the base length set in the LZ4 compression algorithm is longer, it is difficult to match the literal, which not only reduces the compression effect but also greatly increases the matching calculation amount. Therefore, the present invention analyzes the frequency of characters in the literal and the literal length to obtain the preference degree of the literal. When the preference degree of the literal is low, it indicates that the literal is not easily matched subsequently, and a lower base length needs to be set so that the literal can be matched as early as possible and then the compression analysis of the monitoring data can continue, that is, analyze the next literal. By dynamically adjusting the fixed base length according to the preference degree of the literal, to a certain extent, the compression effect of the monitoring data can still be maintained. At the same time, it also avoids a large number of complex matching operations when the literal is long, improving the compression efficiency of the monitoring data.

[0043] It should be further noted that when the length of the literal obtained during the compression process is too large, it is considered that the literal may belong to characters that are difficult to match. Therefore, the literal is analyzed. When the frequency of the characters in the literal in the monitoring data is higher, it is considered that the probability of the characters in the literal appearing and being matched subsequently is also higher, and then the preference degree of the frequency of the literal is greater.

[0044] In the embodiment of the present invention, the preset base length is V, the literal length is L, and during the process of using the LZ4 compression algorithm to compress the monitoring data, the literal with a length greater than L is recorded as the current literal. It should be noted that obtaining the literal during the compression process is a well-known technology and will not be elaborated too much in the present invention. The literal length is also the number of characters in the literal; the preset base length is V = 10, and the literal length is L = 20. In the embodiment of the present invention, the implementer can preset the values of the base length and the literal length according to the specific implementation method.

[0045] The preset number of consecutive characters N = 5. In other embodiments, the implementer can preset the value of the number of consecutive characters N according to the specific implementation method. Any N consecutive characters in the current literal are recorded as a character combination of the current literal, and several character combinations of the current literal are obtained. Multiply the frequencies of the characters in each character combination of the current literal and then take the Nth root to obtain the frequency degree of each character combination of the current literal; if the frequency degree of any character combination of the current literal is greater than , represents the number of character types in the monitoring data, and this character combination of the current literal is recorded as the target character combination of the current literal;

[0046] Obtain the frequency preference degree of the current literal:

[0047] ;

[0048] In the formula, represents the frequency preference degree of the current literal; represents the length of the current literal; represents the frequency at which the j-th character of the current literal appears in the monitoring data; represents the frequency degree of the m-th target character combination of the current literal; represents the number of target character combinations of the current literal; represents the average frequency at which all characters in the current literal appear in the monitoring data. The larger its value, the greater the frequency preference degree of the current literal; The larger the value of, it indicates that there are characters with relatively large consecutive frequencies in the current literal. Then the probability that the characters of the current literal appear and match subsequently is also greater, and thus the frequency preference degree of the current literal is greater.

[0049] S3: Obtain the previous literal of the current literal. According to the frequency at which each character of the current literal appears in the current literal and the frequency at which each character of the current literal appears in its previous literal, obtain the predicted frequency of each character in the current literal; According to the difference between the predicted frequency of each character in the current literal and the frequency at which each character of the current literal appears in the current literal, obtain the matching preference degree of the current literal; According to the matching preference degree and frequency preference degree of the current literal, obtain the preference degree of the current literal.

[0050] It should be noted that since the monitoring data is transmitted in real time and it is impossible to perform character frequency statistics at every moment, it is necessary to predict the subsequent appearance frequency of each character in the current literal. If the predicted frequency of each character in the current literal is close to the frequency at which each character of the current literal appears in the current literal, it is considered that the probability of the current literal being matched is relatively large.

[0051] In the embodiment of the present invention, the characters before the current literal are used as the previous literal of the current literal, represents the length of the current literal.

[0052] Obtain the predicted frequency of each character in the current literal:

[0053] ;

[0054] In the formula, represents the predicted frequency of the h-th character in the current literal; represents the frequency of occurrence of the h-th type of character in the current literal within the current literal; represents the frequency of occurrence of the h-th type of character in the current literal within its previous literal; || represents the absolute value symbol; max() represents the maximum value function; exp() represents the exponential function with the natural constant as the base;

[0055] represents the degree of fluctuation of the frequency of occurrence of the h-th type of character in the current literal within the current literal and its previous literal. The larger its value, the lower the reference degree of the frequency of occurrence of the h-th type of character in the current literal within the current literal. At this time, the frequency of occurrence of the h-th type of character in the current literal within the current literal should be adjusted downward to obtain the predicted frequency. Therefore, through the negative correlation value of the result is adjusted and added to the frequency of occurrence of the h-th type of character in the current literal within the current literal to obtain the predicted frequency of the h-th type of character in the current literal; the maximum value function is used to prevent the value from being less than 0.

[0056] It should be noted that the predicted frequency of any type of character in the current literal represents the frequency of occurrence of that type of character in the subsequent characters. If the difference between the frequency of occurrence of that type of character in the current literal within the current literal and the predicted frequency of that type of character in the current literal is smaller, it is considered that the current literal has a higher matchability in the subsequent, that is, the matching preference degree of the current literal is greater.

[0057] In the embodiments of the present invention, the ratio of the predicted frequency of the h-th type of character in the current literal to the sum of the predicted frequencies of each type of character in the current literal is used as the corrected predicted frequency of the h-th type of character in the current literal;

[0058] Obtain the matching preference degree of the current literal:

[0059] ;

[0060] In the formula, represents the matching preference degree of the current literal; represents the number of types of characters in the current literal; represents the frequency of occurrence of the h-th type of character in the current literal within the current literal; represents the corrected predicted frequency of the h-th type of character in the current literal;

[0061] The larger the value of , the less matching the frequency of occurrence of the h-th type of character in the current literal within the current literal is with the corrected predicted frequency of the h-th type of character in the current literal. Then, the probability of the current literal being matched subsequently is smaller, and the matching preference degree of the current literal is also lower. Add the value of to prevent the numerator from being 0; and The larger the value of

[0062] It should be noted that when the matching preference degree of the current literal and the frequency preference degree of the current literal are larger, it means that the previous literal is more likely to be matched subsequently, so the preference degree of the current literal is larger; when the length of the current literal is larger, the amount of computation required for the current literal to match is more, indicating that the preference degree of the current literal is lower.

[0063] In the embodiments of the present invention, obtain the preference degree of the current literal:

[0064] ;

[0065] In the formula, represents the preference degree of the current literal; represents the matching preference degree of the current literal; represents the frequency preference degree of the current literal; represents the length of the current literal; when the length of the current literal is larger, the amount of computation required for the current literal to match is more, indicating that the preference degree of the current literal is lower; and The larger the value of

[0066] S4: After adjusting the base length according to the preference degree of the current literal, perform compression analysis on the remaining monitoring data to obtain the final compressed data for transmission.

[0067] It should be noted that the known LZ4 compression algorithm traverses the original data to check whether there is a repeated string that can match the current literal among the characters following the current literal. Therefore, if the base length set in the LZ4 compression algorithm is longer, it is difficult for the current literal to match, which will cause the current literal to accumulate and become longer, affecting the compression performance of the algorithm. Therefore, in the present invention, the base length is adjusted according to the preference degree of the current literal, so that the current literal can be matched out as early as possible. It is known that when the preference degree of the current literal is larger, it means that the current literal is more likely to be matched subsequently. At this time, the base length does not need to be adjusted. If the preference degree of the current literal is smaller, it means that the current literal is not easy to be matched subsequently. At this time, the base length needs to be adjusted smaller and then the remaining monitoring data is compressed.

[0068] In the embodiments of the present invention, obtain the preference degree threshold , Representing the number of character types in the monitored data, when the preference degree of the current literal is less than the preference degree threshold, the basic length is set to 5, and the characters in the remaining monitored data are continuously compressed. And according to the analysis method of the current literal, the next literal is analyzed to obtain the final compressed data, and the final compressed data is transmitted, which improves the compression efficiency and thus improves the transmission efficiency.

[0069] An embodiment of the present invention also discloses a remote monitoring system for a natural convection constant temperature test chamber, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a remote monitoring method for a natural convection constant temperature test chamber according to the present invention is implemented.

[0070] The above system also includes a communication bus, a communication interface and other components well known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0071] 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 device. 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 a part of the device or accessible or connectable to the device.

[0072] 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 idea 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.

[0073] 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 should be covered within the protection scope of the present invention.

Claims

1. A remote monitoring method for a natural convection constant temperature test chamber, characterized in that, Including the steps: Collect monitoring data; obtain the current literal when compressing the monitoring data using the LZ4 compression algorithm; obtain the preferred degree of frequency of the current literal ; is the length of the current literal; is the frequency of the j-th character of the current literal in the monitoring data; is the frequency degree of the m-th target character combination of the current literal; is the number of target character combinations of the current literal; The obtaining of the target character combination of the current literal includes: presetting the number N of consecutive characters, taking any N consecutive characters in the current literal as a character combination of the current literal, obtaining several character combinations of the current literal, multiplying the frequencies of the characters in each character combination of the current literal and then taking the Nth root to obtain the frequency degree of each character combination of the current literal; if the frequency degree of any character combination of the current literal is greater than , is the number of character types in the monitoring data, and this character combination of the current literal is recorded as the target character combination of the current literal; Obtain the matching preference degree of the current literal ; is the number of character types in the current literal; is the frequency of the th character type in the current literal; is the corrected prediction frequency of the th character type in the current literal. The obtaining method is: obtain the prediction frequency of each character type in the current literal, and use the ratio of the prediction frequency of the th character type in the current literal to the sum of the prediction frequencies of each character type in the current literal as the corrected prediction frequency of the th character type in the current literal; Obtain the preference degree of the current literal according to the matching preference degree and frequency preference degree of the current literal, including: , is the preference degree of the current literal; after adjusting the preset basic length according to the preference degree of the current literal, compress and transmit the characters in the remaining monitoring data, including: obtaining a preference degree threshold , when the preference degree of the current literal is less than the preference degree threshold, set the basic length to 5 to continue compressing the characters in the remaining monitoring data, and analyze the next literal according to the analysis method of the current literal to obtain the final compressed data, and transmit the final compressed data.

2. The remote monitoring method for a natural convection constant temperature test chamber according to claim 1, wherein The obtaining of the predicted frequency of each type of character in the current literal includes: ； In the formula, represents the predicted frequency of the th character in the current literal; represents the frequency of occurrence of the th character in the current literal; represents the frequency of occurrence of the th character in the literal before it; || represents the absolute value symbol; max() represents the maximum value function; exp() represents the exponential function with the natural constant as the base.

3. A remote monitoring method for a natural convection constant temperature test chamber according to claim 1, characterized in that, When using the LZ4 compression algorithm to compress the monitoring data, the obtaining of the current literal includes: The preset base length is V, the literal length is L. During the process of using the LZ4 compression algorithm to compress the monitoring data, the literal with a length greater than L is recorded as the current literal.

4. A remote monitoring system for a natural convection constant temperature test chamber, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions. When the computer program instructions are executed by the processor, a remote monitoring method for a natural convection constant temperature test chamber according to any one of claims 1-3 is implemented.

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