Data management system and method for OPS computer terminal

Abstract

The invention discloses a data management system and method for an OPS computer terminal, and relates to the technical field of OPS terminal data management, the system comprises a data processing module, a feature analysis module, a sampling frequency adjustment module and a cache management optimization module; the data processing module divides all state parameter data collected by the OPS computer terminal into reference data and data to be analyzed through sampling of different frequencies; the feature analysis module performs curve fitting on the reference data and the to-be-analyzed data, and calculates a data feature deviation rate between the to-be-analyzed data and the reference data; the sampling frequency adjusting module calculates a sampling frequency correction value of each state parameter, and optimizes and adjusts the sampling frequency of each state parameter; and the cache management optimization module is used for predicting the predicted storage data volume of each state parameter at the future time point, calculating the real-time storage loss of each state parameter and determining the cache data cleaning time point and the cleaning sequence of each state parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of OPS terminal data management, in particular to a data management system and method for an OPS computer terminal. Background Art

[0002] With the widespread application of OPS computer terminals in many fields, real-time monitoring and analysis of operating status parameters are particularly important to ensure the stable and efficient operation of its increasingly complex functional modules; However, in actual applications, the collection and management of the status parameters of the OPS computer terminal have the following technical defects: on the one hand, since the OPS computer terminal has high demand targeting and functional integration, there are obvious differences in the status parameters selected for monitoring and analysis by different OPS terminals. Therefore, the use of a unified fixed sampling frequency will inevitably lead to inconsistencies in the collection efficiency and use effects of different parameters in actual use; on the other hand, when setting the sampling frequency, the higher data acquisition speed associated with high-frequency sampling will undoubtedly bring great data storage pressure to the OPS terminal, while the use of low-frequency sampling will bring great challenges to the retention of data features; in addition, due to the real-time requirements for adjusting the sampling frequency of various types of parameters, the commonly used frequency domain analysis cannot obtain sufficient data support, and the lack of data feature and change trend analysis is inevitable; Therefore, a data management system and method for OPS computer terminals are needed to solve the above technical defects. Summary of the invention

[0003] The object of the present invention is to provide a data management system and method for an OPS computer terminal to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A data management method for an OPS computer terminal, the method comprising the following analysis steps: Step S100: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter data as reference data, and extract data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies; Step S200: performing curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies, and calculating the data feature deviation rate between the data to be analyzed and the reference data according to the sampling frequency of the data to be analyzed; Step S300: Screening reference data by time division, and optimizing the sampling frequency of each state parameter in real time according to the data feature deviation rate between the signal to be analyzed and the reference data at different sampling frequencies of each state parameter; Step S400: monitor the cache data of each state parameter in the OPS computer terminal in real time, predict the expected storage data volume of each state parameter in the OPS computer terminal at a future time point, calculate the real-time storage loss of each state parameter and determine the cache data cleaning time point and cache data cleaning priority of each state parameter.

[0005] In the above technical solution, the step S100 is divided into the following steps: Step S101: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter as reference data for encrypted storage; for any state parameter , and record the reference data as: ; in, is the state parameter An array of reference data, is the state parameter obtained by dense sampling Reference data, Number the densely sampled reference data; Step S102: extracting data from the reference data at different sampling frequencies to obtain the data to be analyzed at different sampling frequencies; for any state parameter , the state parameter At sampling frequency The data to be analyzed is recorded as follows: ; in, is the state parameter At sampling frequency The array of data to be analyzed is as follows: For state parameters At sampling frequency The data to be analyzed below, For the sampling frequency The number of the data to be analyzed extracted below; By generating reference data that is closer to the original parameter data through dense sampling, and classifying the parameter data of each state according to the sampling frequency, comparative analysis can be performed more intuitively, accurately reflecting the degree of retention of data features at different sampling frequencies.

[0006] In the above technical solution, step S200 is divided into the following steps: Step S201: performing curve fitting on reference data of each state parameter and data to be analyzed at different sampling frequencies to obtain reference data curves and data curves to be analyzed of each state parameter; Step S202: using curve matching to obtain data in the reference data curve and the to-be-analyzed data curve at corresponding time points of each data in the reference data, and calculating the data feature deviation rate between the to-be-analyzed data and the reference data; For any state parameter , at the sampling frequency The calculation formula for the data feature deviation rate between the data to be analyzed and the reference data is as follows: ; ; in, is the state parameter At sampling frequency The data feature deviation rate between the data to be analyzed and the reference data under is the state parameter At sampling frequency The data feature deviation distance between the data to be analyzed and the reference data is For the state parameter Reference data volume, For state parameters The reference data curve and the data curve to be analyzed are respectively matched with the data numbers obtained by curve matching. is the state parameter The reference data curve is obtained by curve matching. Item data, is the state parameter At sampling frequency The next data curve to be analyzed is obtained by curve matching. Item data, is the natural logarithm; By comparing and analyzing with reference data, the lack of reliability in frequency domain analysis due to small data volume and lack of obvious periodic characteristics is avoided. By evaluating the collection effect in each time period, the degree of improvement in data quality when the system adjusts the sampling frequency is more intuitively reflected.

[0007] In the above technical solution, step S300 is divided into the following steps: Step S301: Classify the reference data of each state parameter by time period division, and obtain the reference data of each state parameter in the previous time period at the time when the time period alternates; Step S302: Calculate the data feature deviation rate between the data to be analyzed and the reference data at different sampling frequencies for each state parameter, and use curve fitting to obtain a sampling data-data feature deviation rate curve; Step S303: Setting the data feature deviation rate calibration value , use the sampling data-data feature deviation rate curve to obtain the sampling frequency calibration value through curve matching , determine the sampling frequency correction value of each state parameter, and adjust the sampling frequency of each state parameter at the alternating point of the time period; For any state parameter , calculated according to the formula: ; in, is the state parameter Sampling frequency correction value, is the state parameter Sampling frequency correction factor, is the state parameter Current sampling frequency, is the state parameter Sampling frequency calibration value; The data feature deviation is used to participate in the data sampling frequency adjustment, which reduces the analysis interference of other factors in the adjustment process, directly links the cause and effect relationship of the data sampling frequency adjustment, and then corrects the state parameter sampling frequency, which reduces redundant calculation processing, reduces calculation complexity, and further improves the system operation efficiency.

[0008] In the above technical solution, step S400 is divided into the following steps: Step S401: Real-time monitoring of the real-time cache data of each state parameter in the OPS computer terminal, and obtaining the sampling frequency data of each state parameter at the same time, according to the formula: , calculate any state parameter The time interval from the current time point The estimated amount of stored data at a future point in time ;in, is the state parameter Sampling frequency correction value, i.e. state parameter The sampling frequency at the current time point, is the state parameter Single sampling storage data size, is the time difference between the future time point and the current time point, The amount of data cached in the OPS computer at the current time point for the status parameter; Step S402: Using the sampled data-data feature deviation rate curve to obtain the state parameter data feature deviation rate through curve matching , and then calculate the storage loss of each state parameter; For any state parameter , calculate the interval time from the current time point according to the formula State parameters at future time points Storage loss : ; in, is the state parameter Cache occupancy loss coefficient, The maximum cache capacity of all status parameters monitored by the OPS computer terminal, is the state parameter Data sampling effectiveness coefficient; Step S403: Calculate and analyze the storage consumption of each state parameter in real time, and predict and calculate the space occupation of all state parameters, analyze the time interval between the future time point when the space occupation of all state parameters is equal to the state parameter cache space occupation threshold in the OPS computer terminal and the current time point, and use this time point as the next cache clearing time point; When the cache of the OPS computer terminal is cleared, the storage loss of each state parameter at the cache clearing time point is calculated using the method in step S402, and cache data of each state parameter is cleared in order from high to low according to the storage loss; Comprehensively evaluate the cache data of each state parameter to ensure that all state parameters are analyzed in a coordinated manner when the cache occupancy rate is high. While ensuring the effectiveness of system operation status analysis, the system data storage efficiency is taken into account, and the system state parameter cache management is further optimized and adjusted.

[0009] A data management system for OPS computer terminals using a data management method for OPS computer terminals in the above technical solution, the system comprising: a data processing module, a feature analysis module, a sampling frequency adjustment module, and a cache management optimization module; The data processing module divides all state parameter data collected by the OPS computer terminal into reference data and data to be analyzed by sampling at different frequencies; the feature analysis module performs curve fitting on the reference data and the data to be analyzed, and calculates the data feature deviation rate between each data to be analyzed and the reference data; the sampling frequency adjustment module calculates the sampling frequency correction value of each state parameter, and optimizes and adjusts the sampling frequency of each state parameter; the cache management optimization module is used to predict the expected storage data volume of each state parameter at a future time point, calculate the real-time storage loss of each state parameter, and determine the time point and order for clearing the cache data of each state parameter.

[0010] In the above technical solution, the data processing module includes: a data acquisition unit, a reference data sampling unit, and a data extraction unit; The data acquisition unit is used to obtain all status parameter information monitored and collected by the OPS computer terminal; the reference data sampling unit intensively samples each status parameter data and divides the sampled data into reference data; the data extraction unit extracts data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies.

[0011] In the above technical solution, the feature analysis module includes: a curve fitting unit and a feature deviation analysis unit; The curve fitting unit performs curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies to obtain the reference data curve and the data curve to be analyzed of each state parameter; the characteristic deviation analysis unit obtains the data in the reference data curve and the data curve to be analyzed at the corresponding time point of each data in the reference data, and calculates the data characteristic deviation rate between the data to be analyzed and the reference data.

[0012] In the above technical solution, the sampling frequency adjustment module includes: a sampling data selection unit and a sampling frequency correction unit; The sampling data selection unit classifies the reference data of each state parameter by time period division, and calculates the data feature deviation rate between the data to be analyzed and the reference data of each state parameter in each time period at different sampling frequencies, and obtains the sampling data-data feature deviation rate curve by fitting; the sampling frequency correction unit determines the sampling frequency correction value of each state parameter in each time period according to the preset data feature deviation rate calibration value.

[0013] In the above technical solution, the cache management optimization module includes: a cache prediction unit, a storage loss analysis unit, and a cache cleaning decision unit; The cache prediction unit calculates the estimated storage data volume of each state parameter in the OPS computer terminal at a future time point based on the real-time cache data and sampling frequency data of each state parameter in the OPS computer terminal; the storage loss analysis unit is used to calculate the storage loss of each state parameter at a future time point; the cache cleaning decision unit determines the cache cleaning time and the cache cleaning order of each state parameter based on the cache space capacity of the OPS computer terminal and the storage loss of each state parameter.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, by analyzing the characteristic deviation rate between the data sampled at different frequencies and the reference data sampled densely, the management allocation of OPS terminal data sampling and data storage resources is effectively balanced to ensure the balance between data collection effect and data storage efficiency; In the present invention, the difference between data of different sampling frequencies and reference data is compared and analyzed by combining curve fitting with data extraction, which can also effectively avoid the data limitation of frequency domain analysis, greatly reduce the complexity of data analysis, reduce the pressure of system data processing and analysis, improve the operation efficiency of the system, and ensure the practicality of the system; In the present invention, the data feature deviation rate and the data cache occupancy rate are combined to comprehensively evaluate the storage loss of each state parameter in the OPS terminal, and then the OPS terminal cache is intelligently managed and optimized to effectively balance the comprehensive requirements of data collection effect and data storage efficiency. In the face of multiple types of state parameters, a unified and standardized evaluation and analysis is adopted to further improve the practicality and reusability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a data management method for an OPS computer terminal according to the present invention; Figure 2 The present invention discloses an organizational structure diagram of a data management system for an OPS computer terminal. DETAILED DESCRIPTION

[0016] 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 only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example: See Figure 1-Figure 2 , the present invention provides the following technical solutions: like Figure 1 As shown, the present invention provides a data management method for an OPS computer terminal, the method comprising the following analysis steps: Step S100: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter data as reference data, and extract data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies; Step S200: performing curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies, and calculating the data feature deviation rate between the data to be analyzed and the reference data according to the sampling frequency of the data to be analyzed; Step S300: Screening reference data by time division, and optimizing the sampling frequency of each state parameter in real time according to the data feature deviation rate between the signal to be analyzed and the reference data at different sampling frequencies of each state parameter; Step S400: monitor the cache data of each state parameter in the OPS computer terminal in real time, predict the expected storage data volume of each state parameter in the OPS computer terminal at a future time point, calculate the real-time storage loss of each state parameter and determine the cache data cleaning time point and cache data cleaning priority of each state parameter.

[0018] The step S100 is divided into the following steps: Step S101: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter as reference data for encrypted storage; for any state parameter , and record the reference data as: ; in, is the state parameter An array of reference data, is the state parameter obtained by dense sampling Reference data, Number the densely sampled reference data; Step S102: extracting data from the reference data at different sampling frequencies to obtain the data to be analyzed at different sampling frequencies; for any state parameter , the state parameter At sampling frequency The data to be analyzed is recorded as follows: ; in, is the state parameter At sampling frequency The array of data to be analyzed is as follows: For state parameters At sampling frequency The data to be analyzed below, For the sampling frequency The number of the data to be analyzed extracted below; In the specific implementation, at the physical level, due to the highly integrated miniaturized structure of the OPS terminal, multiple components have obvious state interference during operation. At the software level, due to the targeted and highly coupled functional module distribution of the OPS terminal, there is a more obvious influence between system parameters during operation. Therefore, real-time monitoring and collection of the operating status parameters of the OPS terminal is of great significance to the stable operation of its functional modules. When collecting data, since densely sampled data is needed as a reference, edge computing can be used to collect more critical environmental parameters and system parameters as analysis objects, and pre-process the collected data, such as data cleaning and removal of outliers. The step S200 is divided into the following steps: Step S201: performing curve fitting on reference data of each state parameter and data to be analyzed at different sampling frequencies to obtain reference data curves and data curves to be analyzed of each state parameter; Step S202: using curve matching to obtain data in the reference data curve and the to-be-analyzed data curve at corresponding time points of each data in the reference data, and calculating the data feature deviation rate between the to-be-analyzed data and the reference data; For any state parameter , at the sampling frequency The calculation formula for the data feature deviation rate between the data to be analyzed and the reference data is as follows: ; ; in, is the state parameter At sampling frequency The data feature deviation rate between the data to be analyzed and the reference data under is the state parameter At sampling frequency The data feature deviation distance between the data to be analyzed and the reference data is For the state parameter Reference data volume, For state parameters The reference data curve and the data curve to be analyzed are respectively matched with the data numbers obtained by curve matching. is the state parameter The reference data curve is obtained by curve matching. Item data, is the state parameter At sampling frequency The next data curve to be analyzed is obtained by curve matching. Item data, is the natural logarithm; In the specific implementation, since frequency domain analysis has requirements on data volume and data periodicity characteristics when used, in the scenario of real-time adjustment and optimization of the sampling frequency, if relatively rich data is collected as the basis for analysis, the timeliness of the data will inevitably be lost, and the real-time nature of the adjustment strategy will be reduced. Therefore, a data extraction and comparison analysis method with a lower threshold and more in line with actual needs is adopted to analyze the degree of loss of characteristic information of different frequency sampling data compared to the original data. In common OPS terminal usage scenarios, state parameters will hardly fluctuate greatly. Therefore, the above method combined with commonly used smoothing operations can basically meet the data processing and analysis needs in all OPS usage scenarios.

[0019] The step S300 is divided into the following steps: Step S301: Classify the reference data of each state parameter by time period division, and obtain the reference data of each state parameter in the previous time period at the time when the time period alternates; Step S302: Calculate the data feature deviation rate between the data to be analyzed and the reference data at different sampling frequencies for each state parameter, and use curve fitting to obtain a sampling data-data feature deviation rate curve; Step S303: Setting the data feature deviation rate calibration value , use the sampling data-data feature deviation rate curve to obtain the sampling frequency calibration value through curve matching , determine the sampling frequency correction value of each state parameter, and adjust the sampling frequency of each state parameter at the alternating point of the time period; For any state parameter , calculated according to the formula: ; in, is the state parameter Sampling frequency correction value, is the state parameter Sampling frequency correction factor, is the state parameter Current sampling frequency, is the state parameter Sampling frequency calibration value; In the specific implementation, considering the unavoidable errors and interferences in data collection and processing, the commonly used threshold judgment method is not adopted when analyzing the adjustment strategy of data sampling frequency. Instead, the calibration value is used as a reference, and combined with common sense, linear adjustment is adopted, such as: In the following problem, different sampling frequencies are used to adjust the deviation: When it is larger, the final sampling frequency correction value With calibration value The absolute difference also increases linearly, so a nonlinear method is used to ensure that the final sampling frequency correction value can fit the preset calibration value in different adjustment deviations.

[0020] The step S400 is divided into the following steps: Step S401: Real-time monitoring of the real-time cache data of each state parameter in the OPS computer terminal, and obtaining the sampling frequency data of each state parameter at the same time, according to the formula: , calculate any state parameter The time interval from the current time point The estimated amount of stored data at a future point in time ;in, is the state parameter Sampling frequency correction value, i.e. state parameter The sampling frequency at the current time point, is the state parameter Single sampling storage data size, is the time difference between the future time point and the current time point, The amount of data cached in the OPS computer at the current time point for the status parameter; Step S402: Using the sampled data-data feature deviation rate curve to obtain the state parameter data feature deviation rate through curve matching , and then calculate the storage loss of each state parameter; For any state parameter , calculate the interval time from the current time point according to the formula State parameters at future time points Storage loss : ; in, is the state parameter Cache occupancy loss coefficient, The maximum cache capacity of all status parameters monitored by the OPS computer terminal, is the state parameter Data sampling effectiveness coefficient; Step S403: Calculate and analyze the storage consumption of each state parameter in real time, and predict and calculate the space occupation of all state parameters, analyze the time interval between the future time point when the space occupation of all state parameters is equal to the state parameter cache space occupation threshold in the OPS computer terminal and the current time point, and use this time point as the next cache clearing time point; When the cache of the OPS computer terminal is cleared, the storage loss of each state parameter at the cache clearing time point is calculated using the method in step S402, and cache data of each state parameter is cleared in order from high to low according to the storage loss; In the specific implementation, data is collected, stored, used and cleaned in the OPS terminal. Data use is an unavoidable functional requirement, and data cleaning is a means to improve storage efficiency. The impact of remaining data collection and storage on the efficiency of OPS terminal use can be managed and optimized. Therefore, when calculating the state parameter data storage loss, the analysis of cache capacity occupancy rate and data feature deviation rate is included respectively, and the two data are unified into In the range, the cleanup priority of cache data of each status parameter can be evaluated more intuitively.

[0021] like Figure 2As shown, the present invention also provides a data management system for an OPS computer terminal, the system comprising: a data processing module, a feature analysis module, a sampling frequency adjustment module, and a cache management optimization module; The data processing module divides all state parameter data collected by the OPS computer terminal into reference data and data to be analyzed by sampling at different frequencies; the feature analysis module performs curve fitting on the reference data and the data to be analyzed, and calculates the data feature deviation rate between each data to be analyzed and the reference data; the sampling frequency adjustment module calculates the sampling frequency correction value of each state parameter, and optimizes and adjusts the sampling frequency of each state parameter; the cache management optimization module is used to predict the expected storage data volume of each state parameter at a future time point, calculate the real-time storage loss of each state parameter, and determine the time point and order for clearing the cache data of each state parameter.

[0022] The data processing module includes: a data acquisition unit, a reference data sampling unit, and a data extraction unit; The data acquisition unit is used to obtain all status parameter information monitored and collected by the OPS computer terminal; the reference data sampling unit intensively samples each status parameter data and divides the sampled data into reference data; the data extraction unit extracts data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies.

[0023] The feature analysis module includes: a curve fitting unit and a feature deviation analysis unit; The curve fitting unit performs curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies to obtain the reference data curve and the data curve to be analyzed of each state parameter; the characteristic deviation analysis unit obtains the data in the reference data curve and the data curve to be analyzed at the corresponding time point of each data in the reference data, and calculates the data characteristic deviation rate between the data to be analyzed and the reference data.

[0024] The sampling frequency adjustment module includes: a sampling data selection unit and a sampling frequency correction unit; The sampling data selection unit classifies the reference data of each state parameter by time period division, and calculates the data feature deviation rate between the data to be analyzed and the reference data of each state parameter in each time period at different sampling frequencies, and obtains the sampling data-data feature deviation rate curve by fitting; the sampling frequency correction unit determines the sampling frequency correction value of each state parameter in each time period according to the preset data feature deviation rate calibration value.

[0025] The cache management optimization module includes: a cache prediction unit, a storage loss analysis unit, and a cache cleaning decision unit; The cache prediction unit calculates the estimated storage data volume of each state parameter in the OPS computer terminal at a future time point based on the real-time cache data and sampling frequency data of each state parameter in the OPS computer terminal; the storage loss analysis unit is used to calculate the storage loss of each state parameter at a future time point; the cache cleaning decision unit determines the cache cleaning time and the cache cleaning order of each state parameter based on the cache space capacity of the OPS computer terminal and the storage loss of each state parameter.

[0026] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A data management method for an OPS computer terminal, characterized in that , the method comprises the following analysis steps: Step S100: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter data as reference data, and extract data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies; Step S200: performing curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies, and calculating the data feature deviation rate between the data to be analyzed and the reference data according to the sampling frequency of the data to be analyzed; Step S300: Screening reference data by time division, and optimizing the sampling frequency of each state parameter in real time according to the data feature deviation rate between the signal to be analyzed and the reference data at different sampling frequencies of each state parameter; Step S400: monitor the cache data of each state parameter in the OPS computer terminal in real time, predict the expected storage data volume of each state parameter in the OPS computer terminal at a future time point, calculate the real-time storage loss of each state parameter and determine the cache data cleaning time point and cache data cleaning priority of each state parameter.

2. A data management method for an OPS computer terminal according to claim 1, characterized in that: The step S100 is divided into the following steps: Step S101: Obtain all state parameter information monitored and collected by the OPS computer terminal, and perform intensive sampling on each state parameter as reference data for encrypted storage; for any state parameter , and record the reference data as: ; in, is the state parameter An array of reference data, is the state parameter obtained by dense sampling Reference data, Number the densely sampled reference data; Step S102: extracting data from the reference data at different sampling frequencies to obtain the data to be analyzed at different sampling frequencies; for any state parameter , the state parameter At sampling frequency The data to be analyzed is recorded as follows: ; in, is the state parameter At sampling frequency The array of data to be analyzed is as follows: For state parameters At sampling frequency The data to be analyzed below, For the sampling frequency The number of the data to be analyzed.

3. A data management method for an OPS computer terminal according to claim 1, characterized in that: The step S200 is divided into the following steps: Step S201: performing curve fitting on reference data of each state parameter and data to be analyzed at different sampling frequencies to obtain reference data curves and data curves to be analyzed of each state parameter; Step S202: using curve matching to obtain data in the reference data curve and the to-be-analyzed data curve at corresponding time points of each data in the reference data, and calculating the data feature deviation rate between the to-be-analyzed data and the reference data; For any state parameter , at the sampling frequency The calculation formula for the data feature deviation rate between the data to be analyzed and the reference data is as follows: ； ； in, is the state parameter At sampling frequency The data feature deviation rate between the data to be analyzed and the reference data under is the state parameter At sampling frequency The data feature deviation distance between the data to be analyzed and the reference data is For the state parameter Reference data volume, For state parameters The reference data curve and the data curve to be analyzed are matched with the data numbers obtained by curve matching. is the state parameter The reference data curve is obtained by curve matching. Item data, is the state parameter At sampling frequency The next data curve to be analyzed is obtained by curve matching. Item data, is the natural logarithm.

4. A data management method for an OPS computer terminal according to claim 1, characterized in that: The step S300 is divided into the following steps: Step S301: Classify the reference data of each state parameter by time period division, and obtain the reference data of each state parameter in the previous time period at the time when the time period alternates; Step S302: Calculate the data feature deviation rate between the data to be analyzed and the reference data at different sampling frequencies for each state parameter, and use curve fitting to obtain a sampling data-data feature deviation rate curve; Step S303: Setting the data feature deviation rate calibration value , use the sampling data-data feature deviation rate curve to obtain the sampling frequency calibration value through curve matching , determine the sampling frequency correction value of each state parameter, and adjust the sampling frequency of each state parameter at the alternating point of the time period; For any state parameter , calculated according to the formula: ； in, is the state parameter Sampling frequency correction value, is the state parameter Sampling frequency correction factor, is the state parameter Current sampling frequency, is the state parameter Sampling frequency calibration value.

5. A data management method for an OPS computer terminal according to claim 4, characterized in that: The step S400 is divided into the following steps: Step S401: Real-time monitoring of the real-time cache data of each state parameter in the OPS computer terminal, and obtaining the sampling frequency data of each state parameter at the same time, according to the formula: , calculate any state parameter The time interval from the current time point The estimated amount of stored data at a future point in time ;in, is the state parameter Sampling frequency correction value, i.e. state parameter The sampling frequency at the current time point, is the state parameter Single sampling storage data size, is the time difference between the future time point and the current time point, The amount of data cached in the OPS computer at the current time point for the status parameter; Step S402: Using the sampled data-data feature deviation rate curve to obtain the state parameter data feature deviation rate through curve matching , and then calculate the storage loss of each state parameter; For any state parameter , calculate the interval time from the current time point according to the formula State parameters at future time points Storage loss : ； in, is the state parameter Cache occupancy loss coefficient, The maximum cache capacity of all status parameters monitored by the OPS computer terminal, is the state parameter Data sampling effectiveness coefficient; Step S403: Calculate and analyze the storage consumption of each state parameter in real time, and predict and calculate the space occupation of all state parameters, analyze the time interval between the future time point when the space occupation of all state parameters is equal to the state parameter cache space occupation threshold in the OPS computer terminal and the current time point, and use this time point as the next cache clearing time point; When the cache of the OPS computer terminal is cleared, the method in step S402 is used to calculate the storage loss of each state parameter at the cache clearing time point, and the cache data of each state parameter is cleared in sequence from high to low according to the storage loss.

6. A data management system for OPS computer terminals using a data management method for OPS computer terminals according to any one of claims 1 to 5, characterized in that: The system includes: a data processing module, a feature analysis module, a sampling frequency adjustment module, and a cache management optimization module; The data processing module divides all state parameter data collected by the OPS computer terminal into reference data and data to be analyzed by sampling at different frequencies; the feature analysis module performs curve fitting on the reference data and the data to be analyzed, and calculates the data feature deviation rate between each data to be analyzed and the reference data; the sampling frequency adjustment module calculates the sampling frequency correction value of each state parameter, and optimizes and adjusts the sampling frequency of each state parameter; the cache management optimization module is used to predict the expected storage data volume of each state parameter at a future time point, calculate the real-time storage loss of each state parameter, and determine the time point and order for clearing the cache data of each state parameter.

7. A data management system for OPS computer terminals according to claim 6, characterized in that: The data processing module includes: a data acquisition unit, a reference data sampling unit, and a data extraction unit; The data acquisition unit is used to obtain all status parameter information monitored and collected by the OPS computer terminal; the reference data sampling unit intensively samples each status parameter data and divides the sampled data into reference data; the data extraction unit extracts data from the reference data at different sampling frequencies as data to be analyzed at different sampling frequencies.

8. A data management system for OPS computer terminals according to claim 6, characterized in that: The feature analysis module includes: a curve fitting unit and a feature deviation analysis unit; The curve fitting unit performs curve fitting on the reference data of each state parameter and the data to be analyzed at different sampling frequencies to obtain the reference data curve and the data curve to be analyzed of each state parameter; the characteristic deviation analysis unit obtains the data in the reference data curve and the data curve to be analyzed at the corresponding time point of each data in the reference data, and calculates the data characteristic deviation rate between the data to be analyzed and the reference data.

9. A data management system for an OPS computer terminal according to claim 6, characterized in that: The sampling frequency adjustment module includes: a sampling data selection unit and a sampling frequency correction unit; The sampling data selection unit classifies the reference data of each state parameter by time period division, and calculates the data feature deviation rate between the data to be analyzed and the reference data of each state parameter in each time period at different sampling frequencies, and obtains the sampling data-data feature deviation rate curve by fitting; the sampling frequency correction unit determines the sampling frequency correction value of each state parameter in each time period according to the preset data feature deviation rate calibration value.

10. A data management system for OPS computer terminals according to claim 6, characterized in that: The cache management optimization module includes: a cache prediction unit, a storage loss analysis unit, and a cache cleaning decision unit; The cache prediction unit calculates the estimated storage data volume of each state parameter in the OPS computer terminal at a future time point based on the real-time cache data and sampling frequency data of each state parameter in the OPS computer terminal; the storage loss analysis unit is used to calculate the storage loss of each state parameter at a future time point; the cache cleaning decision unit determines the cache cleaning time and the cache cleaning order of each state parameter based on the cache space capacity of the OPS computer terminal and the storage loss of each state parameter.

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