Real-time database adaptive compression parameter calculation method and related device

Abstract

This invention discloses a real-time database adaptive compression parameter calculation method and related apparatus, comprising: acquiring a numerical set of each measuring point; sampling the numerical set of each measuring point to obtain a sample sequence of each measuring point; classifying each measuring point into a cumulative / constant value class and a non-cumulative / constant value class according to the sample sequence of each measuring point; taking the non-cumulative / constant value class measuring points as normal fluctuation measuring points; constructing a first-order difference sequence of each normal fluctuation measuring point; calculating and outputting the basic compression deviation D and the exception deviation E according to the first-order difference sequence of each normal fluctuation point. This method and related apparatus can accurately acquire the compression deviation and realize the automated, intelligent and batch update of the measuring point range.

Description

Technical Field

[0001] This invention belongs to the field of database storage and data compression technology, and relates to a method and related apparatus for calculating real-time adaptive compression parameters for databases. Background Technology

[0002] Real-time databases are the foundational software platform for industrial data monitoring systems, widely used in industries such as energy and power, petrochemicals, and intelligent manufacturing. They play a crucial role in the secure storage, rapid retrieval, and real-time analysis of large-scale time-series data. With the continuous advancement of Industry 4.0 and intelligent manufacturing, the number of field-based intelligent sensors, PLC controllers, and other equipment is increasing rapidly, and the total amount of time-series data to be stored has climbed to the petabyte (PB) or even exabyte (EB) level. For example, the SIS system of a large thermal power generating unit collects over 5 billion process parameter samples daily; while on a modern metallurgical continuous casting production line, more than 20,000 sensor data points need to be processed per second.

[0003] To cope with such massive data inflows, industry often uses time-series compression algorithms, such as the "revolving door" algorithm, for online data compression and storage. The compression performance of these algorithms—especially the trade-off between data restoration accuracy and compression ratio—is highly dependent on the value of their core parameter, compression deviation. This parameter is typically set based on a fixed percentage of the measurement point's range. Therefore, an accurate measurement range that conforms to actual operating conditions is crucial for improving compression efficiency. However, when dealing with millions of measurement points, relying on manual experience for batch parameter configuration or periodic updates is not only complex and inefficient but also fails to respond to real-time changes in production conditions. Against this backdrop, automating, intelligently updating, and batching measurement point ranges has become an urgent technical requirement for optimizing the performance of industrial real-time databases. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a real-time database adaptive compression parameter calculation method and related device. This method and related device can accurately obtain compression deviation and realize automated, intelligent and batch updates of the measurement range of the measurement point.

[0005] To achieve the above objectives, this invention discloses a method for calculating adaptive compression parameters for real-time databases, comprising: Obtain the numerical data set for each measuring point; The numerical data sets of each measuring point are sampled to obtain the sample sequence of each measuring point. Based on the sample sequence of each measuring point Each measuring point is divided into cumulative / constant value category and non-cumulative / constant value category, and the measuring points in the non-cumulative / constant value category are used as normal fluctuation measuring points; Construct the first-order difference sequence of each normal fluctuation measurement point ; The basic compression deviation D and the exception deviation E are calculated and output based on the first-order difference sequence of each normal fluctuation point.

[0006] Furthermore, the specified time-series measurement point table can be obtained directly from the internal database, or historical measurement point data files can be imported through an external interface to obtain the numerical set of each measurement point.

[0007] Furthermore, based on the measuring points The lifecycle and storage density of the measurement points The numerical set is sampled at equal intervals k to obtain the sample sequence. Sample sequence Internally continuous element points are For sample sequences .

[0008] Furthermore, statistical first-order difference sequences Positive difference quantity and negative difference quantity The total number of effective changes is When the following conditions are met:

[0009] The corresponding measurement points are then classified into the cumulative / constant value category, where This represents the threshold for the proportion of unidirectional changes.

[0010] Furthermore, for Take the n-1 elements in the sequence and sort them in ascending order of their values ​​to obtain a new sequence. When the number of elements is odd, then When the number of elements is even, then Calculate the absolute median Then the basic compression deviation .

[0011] Furthermore, exception bias E ,in, for The 95th percentile, for The median of It is a sensitivity modulator.

[0012] Furthermore, it also includes a streaming data compression engine that dynamically distributes the basic compression bias D and the exception bias E to the database.

[0013] This invention discloses a real-time database adaptive compression parameter calculation system, comprising: The acquisition module is used to acquire the numerical data set of each measuring point; The classification module is used to sample the numerical data sets of each measuring point to obtain the sample sequence of each measuring point. Based on the sample sequence of each measuring point Each measuring point is divided into cumulative / constant value category and non-cumulative / constant value category, and the measuring points in the non-cumulative / constant value category are used as normal fluctuation measuring points; The module is used to construct the first-order difference sequence for each normal fluctuation measurement point. ; The calculation module is used to calculate and output the basic compression deviation D and the exception deviation E based on the first-order difference sequence of each normal fluctuation point.

[0014] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the real-time database adaptive compression parameter calculation method.

[0015] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the real-time database adaptive compression parameter calculation method.

[0016] The present invention has the following beneficial effects: The real-time database adaptive compression parameter calculation method and related device described in this invention, in specific operation, effectively solves the problem that traditional algorithms are easily affected by sensor sampling pulses and abnormal jump points by introducing differential sequences and robust statistics based on the median (MAD). This ensures that the calculated compression gate width always matches the real background noise level, avoiding over-compression or under-compression. By calculating exception deviations, it can keenly identify and forcibly retain abrupt feature points that deviate from statistical regularities (such as equipment tripping or instantaneous failures), making up for the defect of traditional rotating door algorithms that easily smooth out key abnormal details, and realizing automated, intelligent, and batch updates of the measurement range of measurement points. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0023] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0024] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0027] Example 1 refer to Figure 1 The real-time database adaptive compression parameter calculation method of the present invention includes the following steps: 1) Data access and strong type validation; It provides a two-way data access channel, supporting direct access from the internal database to obtain a specified time-series measurement point table, or importing historical measurement point data files through an external interface. After acquiring the data, it iterates through the target measurement point column and obtains its data type identifier, performing a strict type filtering mechanism. When the measurement point data type is Boolean or string, the system skips it directly and does not include it in the compression parameter calculation process; only numerical measurement point data (such as integer, floating-point, etc.) is allowed, and its corresponding timestamp field is extracted simultaneously. The monotonically increasing nature of the timestamp and the basic sampling resolution are verified to prepare a cleaned standard input stream for subsequent data feature extraction.

[0028] 2) Data sampling and initial feature screening; According to the measuring points Lifecycle and storage density, from measurement points The numerical set is sampled at equal intervals k to obtain the sample sequence. Sample sequence Internally continuous element points are .

[0029] First, a constant value judgment is performed. If the fluctuation range of the sample sequence within the window is extremely small, it is judged as a constant value. If it is not a constant value, a monotonic trend detection is performed to filter out normal fluctuation measurement points other than the cumulative / constant value category. Specifically, for the sample sequence When any All are less than a very small threshold ( If the value is 0,000001, then it is determined to be a constant value.

[0030] Then, monotonic trend detection is performed, and the sample sequences are statistically analyzed. First-order difference sequence Neutral difference quantity (when ) and negative difference quantity (when The total number of effective changes is... A pattern is considered to have a strong monotonic (cumulative) trend when the following conditions are met:

[0031] in This is the threshold for the percentage of unidirectional changes, with a default value of 5 / 6. Measurement points that meet any of the above conditions will be classified as "cumulative / constant value" and subsequent calculations of compressed parameters will be terminated, with a note added to the log.

[0032] 3) Adaptive parameter calculation based on first-order difference and robust statistics; For the normal fluctuation measurement points not classified into the "cumulative / constant value category" in 2), robust statistical methods are used to extract compression parameters that reflect the true noise level of the measurement points. It represents the incremental change of the measurement point in adjacent sampling periods. By using differential operations, the linear trend and offset in the time series data are eliminated, transforming it into an approximately stationary fluctuation sequence.

[0033] Subsequently, the median calculation logic is introduced. For Take the n-1 elements in the sequence and sort them in ascending order of their values ​​to obtain a new sequence. When the number of elements is odd, then When the number of elements is even, then Using the median as a measure of central location provides stronger resistance to outlier interference compared to the mean.

[0034] The formula for calculating the absolute median deviation (MAD) is as follows: Based on this, the basic compression deviation D is calculated using a normality consistency factor of 0.6745. The physical meaning of this normality consistency factor is: within a standard normal distribution... In, its cumulative distribution function is The probability coverage at this point is approximately 50%, which corresponds to an interquartile range of approximately 0.6745 for the standard normal distribution. The robust volatility measure based on the median is then transformed to be comparable to the standard deviation. The equivalent order of magnitude leads to the fundamental compression bias that can cover typical physical noise fluctuations. .

[0035] Finally, the exception deviation E is calculated. It is defined as the threshold value at which a sudden change in data points is allowed outside of the revolving door compression logic. If the absolute value of the difference between the current point and the previous point exceeds E, it is considered an "exceptional" situation (such as sudden equipment tripping, sensor failure, or critical state switching), requiring forced storage. The calculation formula is as follows: ,in, for The 95th percentile, for The median of As a sensitivity adjustment factor, this design ensures that E is always greater than D, which ensures that conventional noise is efficiently compressed while enabling abrupt feature points that significantly deviate from the statistical distribution to be accurately captured and directly persisted into the database, bypassing the rotating door logic.

[0036] 4) Adaptive revolving door compression dual-line execution; The calculated compression deviation D and exception deviation E are dynamically sent to the streaming data compression engine in the database. When receiving real-time measurement point data, the engine prioritizes performing direct storage verification of abnormal points: calculating the numerical difference between the current arrival point and the previous archived retention point. If the absolute value of this difference is greater than the exception deviation E, the current point is determined to be a valid mutation point, and the system forces the previous time point and the current mutation point to be archived into the database simultaneously, and resets the revolving door reference point; if it does not exceed the exception deviation E, it enters the standard revolving door compression logic. Using the previous archived point as the reference and the compression deviation D as the upper and lower door width boundaries, the upper and lower offset slopes of the current data window are calculated in real time. When the upper door slope is less than the lower door slope (i.e., an opening sector intersection occurs), a closing door archiving command is triggered, and the data point of the previous time point is stored into the database, thereby achieving the removal of high-frequency noise and the accurate retention of key feature points.

[0037] 5) Persistent storage and periodic closed-loop parameter updates; The valid data points filtered in step 4) are persistently stored in the underlying files of the time-series database according to time series. Simultaneously, to prevent the original parameters from becoming invalid due to equipment aging or changes in operating conditions, the system establishes a closed-loop parameter update mechanism. Based on a set time period or by monitoring data drift characteristics such as sudden drops in compression ratio as trigger signals, the system will automatically recall the corresponding measurement point, re-execute the sampling and calculation process in steps 2) and 3), and dynamically refresh the compression deviation D and exception deviation E of that measurement point. Through this adaptive closed-loop mechanism, it is ensured that the revolving door compression parameters remain highly adapted to the actual physical characteristics of the underlying industrial data throughout the long-term operation of the database.

[0038] Confirmatory Experiment This embodiment uses the steam temperature measuring point at the boiler outlet of a thermal power plant (the measuring point is marked as follows) as an example. Taking (e.g.) as an example, the specific process of calculating the adaptive compression parameters of the present invention is shown.

[0039] 1) The data access and verification system obtains measurement points through direct internal database connection. The historical data is of floating-point type (Float), which meets the requirements for numerical data. The system extracts sample points from the most recent 20 sampling periods, with a sampling interval of 1 second. The sample point sequence Y (unit: ℃) is shown in Table 1: Table 1

[0040] 2) Initial screening based on features; 21) Constant value judgment: Calculate the maximum difference between two adjacent points. The data was determined to be non-constant value data.

[0041] 22) Monotonic trend detection: Calculate the difference sequence

[0042]

[0043] Count the number of positive differences Count the number of negative differences Total number of effective changes Calculate the percentage: If it is determined that there is no strong monotonic trend, proceed to step 3).

[0044] 3) Adaptive parameter calculation; 31) Median calculation: For difference sequences Sort in ascending order:

[0045]

[0046] The sequence has 19 elements, and the median is... .

[0047] 32) Calculation of MAD and door width D: Calculate the absolute deviation sequence :

[0048] The median of this sequence is calculated as follows: .

[0049] Calculating compression bias using the normality uniformity factor .

[0050] 33) Calculation of exception deviation E; Set sensitivity factor The 95th percentile of the absolute difference sequence , The median is 0.1.

[0051]

[0052] 4) Compressed execution processing; The system will and Send to the engine. Under normal operating conditions, when subsequent real-time data fluctuations occur... Within the specified range, the revolving door algorithm remains active, eliminating high-frequency noise. Anomaly detection: If the measuring point experiences a sudden jump due to severe electromagnetic interference (e.g., a single jump value reaching 6.0), due to... The engine will identify it as a "valid mutation point" and force the archiving of the point and its predecessor to prevent the loss of critical abnormal information.

[0053] 5) Store and update compressed valid data points persistently to the underlying file. The system enables periodic monitoring; if a change in boiler operating conditions causes a trend in background noise levels (e.g., noise levels shift from low to high), the system will monitor the data. Increase to The system will trigger a recalculation mechanism to automatically adjust the gate width D, ensuring that the compression ratio remains within the preset optimization range.

[0054] This invention automates the entire process from data access and initial feature screening to parameter calculation and closed-loop update. It can automatically match the optimal parameters for measurement points with different ranges, sampling frequencies, and operating conditions, greatly reducing manual configuration costs and improving the long-term operational stability of the database.

[0055] Example 2 The real-time database adaptive compression parameter calculation system of the present invention includes: The acquisition module is used to acquire the numerical data set of each measuring point; The classification module is used to sample the numerical data sets of each measuring point to obtain the sample sequence of each measuring point. Based on the sample sequence of each measuring point Each measuring point is divided into cumulative / constant value category and non-cumulative / constant value category, and the measuring points in the non-cumulative / constant value category are used as normal fluctuation measuring points; The module is used to construct the first-order difference sequence for each normal fluctuation measurement point. ; The calculation module is used to calculate and output the basic compression deviation D and the exception deviation E based on the first-order difference sequence of each normal fluctuation point.

[0056] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0057] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the real-time database adaptive compression parameter calculation method, for example, including: acquiring a set of values ​​for each measurement point; sampling the set of values ​​for each measurement point to obtain a sample sequence for each measurement point. Based on the sample sequence of each measuring point The measurement points are divided into cumulative / constant value categories and non-cumulative / constant value categories. The measurement points in the non-cumulative / constant value category are used as normal fluctuation measurement points. A first-order difference sequence is constructed for each normal fluctuation measurement point. The fundamental compression bias D and exceptional bias E are calculated and output based on the first-order difference sequence of each normal fluctuation point. The memory may include main memory, such as high-speed random access memory, or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which can be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus can be divided into address bus, data bus, control bus, etc. The memory is used to store programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0058] Example 4 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the real-time database adaptive compression parameter calculation method, for example including: acquiring a set of values ​​for each measuring point; sampling the set of values ​​for each measuring point to obtain a sample sequence for each measuring point. Based on the sample sequence of each measuring point The measurement points are divided into cumulative / constant value categories and non-cumulative / constant value categories. The measurement points in the non-cumulative / constant value category are used as normal fluctuation measurement points. A first-order difference sequence is constructed for each normal fluctuation measurement point. The fundamental compression bias D and the exceptional bias E are calculated and output based on the first-order difference sequence of each normal fluctuation point. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0059] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0064] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0065] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for calculating adaptive compression parameters for real-time databases, characterized in that, include: Obtain the numerical data set for each measuring point; The numerical data sets of each measuring point are sampled to obtain the sample sequence of each measuring point. Based on the sample sequence of each measuring point Each measuring point is divided into cumulative / constant value category and non-cumulative / constant value category, and the measuring points in the non-cumulative / constant value category are used as normal fluctuation measuring points; Construct the first-order difference sequence of each normal fluctuation measurement point ; The basic compression deviation D and the exception deviation E are calculated and output based on the first-order difference sequence of each normal fluctuation point.

2. The method for calculating real-time database adaptive compression parameters according to claim 1, characterized in that, You can obtain a table of specified time-series measurement points by directly connecting to the database or by importing historical measurement point data files through an external interface to obtain the numerical set of each measurement point.

3. The method for calculating adaptive compression parameters for real-time databases according to claim 1, characterized in that, According to the measuring points The lifecycle and storage density of the measurement points The numerical set is sampled at equal intervals k to obtain the sample sequence. Sample sequence Internally continuous element points are For sample sequences .

4. The method for calculating real-time database adaptive compression parameters according to claim 3, characterized in that, Statistical first-order difference sequences Positive difference quantity and negative difference quantity The total number of effective changes is When the following conditions are met: The corresponding measurement points are then classified into the cumulative / constant value category, where This represents the threshold for the proportion of unidirectional changes.

5. The method for calculating real-time database adaptive compression parameters according to claim 4, characterized in that, for Take the n-1 elements in the sequence and sort them in ascending order of their values ​​to obtain a new sequence. When the number of elements is odd, then ; When the number of elements is even, then Calculate the absolute median Then the basic compression deviation .

6. The method for calculating real-time database adaptive compression parameters according to claim 5, characterized in that, Exception Deviation E ,in, for The 95th percentile, for the median of It is a sensitivity modulator.

7. The method for calculating adaptive compression parameters for real-time databases according to claim 1, characterized in that, Also includes: The basic compression bias D and the exception bias E are dynamically distributed to the streaming data compression engine in the database.

8. A real-time database adaptive compression parameter calculation system, characterized in that, include: The acquisition module is used to acquire the numerical data set of each measuring point; The classification module is used to sample the numerical data sets of each measuring point to obtain the sample sequence of each measuring point. Based on the sample sequence of each measuring point Each measuring point is divided into cumulative / constant value category and non-cumulative / constant value category, and the measuring points in the non-cumulative / constant value category are used as normal fluctuation measuring points; The module is used to construct the first-order difference sequence for each normal fluctuation measurement point. ; The calculation module is used to calculate and output the basic compression deviation D and the exception deviation E based on the first-order difference sequence of each normal fluctuation point.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the real-time database adaptive compression parameter calculation method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the real-time database adaptive compression parameter calculation method as described in any one of claims 1-7.

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