Data processing method, system, device and storage medium

Abstract

The present disclosure relates to a data processing method and system, a device and a storage medium. The data processing method comprises: collecting operation data of a target device in a computer room system within a preset time period, wherein the operation data comprises a timestamp, and the preset time period comprises a plurality of time intervals; calculating the timestamp distribution of the operation data of the target device in the plurality of time intervals; calculating the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device; determining the target timestamp according to the total timestamp distribution of the operation data of all target devices; calculating the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data, and being able to process the operation data of different target devices under different timestamps into the operation data of different target devices under the same timestamp, so as to provide support for the joint analysis of multiple operation data in the computer room system.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a data processing method and system, device and storage medium. Background Art

[0002] During operation, each component of the computer room system will generate status information such as temperature, pressure, flow, and power consumption. At present, in the process of collecting this status information, most computer rooms still adopt the manual "meter reading" recording method. However, this recording method is inefficient and not convenient for subsequent algorithms to analyze and process the status data. In addition, in order to improve the management of each component of the computer room system, it is necessary to process the operating data of different components of the computer room system for joint analysis. For example, timestamp alignment processing generally uses a fixed-spacing timestamp sequence T0, and then samples the signals to be aligned to these timestamps respectively. However, this method is not effective when processing signals with uneven time distribution.

[0003] Therefore, how to achieve efficient collection of equipment room operation data and processing for joint analysis has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a data processing method and system, a device and a storage medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a data processing method, comprising the following steps:

[0006] Collecting operation data of target equipment in the computer room system within a preset time period, wherein the operation data includes a timestamp, and the preset time period includes multiple time intervals;

[0007] Calculate the timestamp distribution of the operation data of the target device in the plurality of time intervals;

[0008] Calculate the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device;

[0009] Determine a target timestamp based on the total timestamp distribution of the operation data of all target devices;

[0010] Calculate the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data.

[0011] In a possible implementation, the operating data is instantaneous sampling data, and the calculation of the timestamp distribution of the operating data of the target device in the multiple time intervals includes:

[0012] Count the number of instantaneous sampling data of the target device in each time interval;

[0013] The quantities of instantaneous sampling data corresponding to multiple time intervals are collected in chronological order to obtain the timestamp distribution of the operation data of the target device.

[0014] In a possible implementation manner, the operating data is interval sampling data, and calculating the timestamp distribution of the operating data of the target device according to the multiple time intervals includes:

[0015] Count the coverage duration of interval sampling data of the target device in each time interval;

[0016] The coverage durations of the interval sampling data corresponding to the multiple time intervals are aggregated in chronological order to obtain the timestamp distribution of the operation data of the target device.

[0017] In a possible implementation manner, calculating the total timestamp distribution of the operating data of all target devices according to the timestamp distribution of the operating data of each target device includes:

[0018] Normalize the timestamp distribution of the operation data of each target device;

[0019] The normalized timestamp distribution values ​​of different target devices corresponding to the same time interval are summed up to obtain the total timestamp distribution value corresponding to each time interval as the total timestamp distribution of the running data of all target devices.

[0020] In a possible implementation manner, determining the target timestamp according to the total timestamp distribution of the operation data of all target devices includes:

[0021] Determine the number of target timestamps according to a preset rule, wherein the number of target timestamps is less than the number of time intervals;

[0022] Select the value of the total timestamp distribution of the number of target timestamps according to the value of the total timestamp distribution;

[0023] Any timestamp in the time interval corresponding to the selected value of the total timestamp distribution is used as the target timestamp.

[0024] In a possible implementation manner, the operating data is instantaneous sampling data, and calculating the operating data of each target device corresponding to the same target timestamp includes:

[0025] Determine whether there is known instantaneous sampling data at the target timestamp:

[0026] When there is no known instantaneous sampling data at the target timestamp, the instantaneous sampling data at the target timestamp is interpolated according to the known instantaneous sampling data to obtain the operation data of the target device corresponding to the target timestamp.

[0027] In a possible implementation manner, the operating data is interval sampling data, and the calculating of the operating data of each target device corresponding to the same target timestamp includes:

[0028] In the time interval where the target timestamp is located, the ratio of the total value of the interval sampling data in the time interval to the coverage duration is used as the operation data of the target device corresponding to the target timestamp.

[0029] In a second aspect, an embodiment of the present disclosure provides a data processing system, including:

[0030] A sensor, which is used to collect operating data of a target device in a computer room system within a preset time period, wherein the operating data includes a timestamp, and the preset time period includes a plurality of time intervals;

[0031] A distribution station is used to calculate the timestamp distribution of the operation data of the target device in the multiple time intervals; calculate the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device; determine the target timestamp according to the total timestamp distribution of the operation data of all target devices; calculate the operation data of each target device corresponding to the same target timestamp.

[0032] In a third aspect, an embodiment of the present disclosure provides an electronic device, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0033] Memory, used to store computer programs;

[0034] The processor is used to implement the above-mentioned data processing method when executing the program stored in the memory.

[0035] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program implements the above-mentioned data processing method when executed by a processor.

[0036] The above technical solution provided by the embodiment of the present disclosure has at least some or all of the following advantages compared with the prior art:

[0037] Apply the data processing method of the embodiment of the present disclosure to collect operation data of target devices in a computer room system within a preset time period, wherein the operation data includes a timestamp, and the preset time period includes multiple time intervals; calculate the timestamp distribution of the operation data of the target devices in the multiple time intervals; calculate the total timestamp distribution of the operation data of all target devices based on the timestamp distribution of the operation data of each target device; determine the target timestamp based on the total timestamp distribution of the operation data of all target devices; calculate the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data, and can process the operation data of different target devices under different timestamps into the operation data of different target devices under the same timestamp, so as to provide support for the joint analysis of multiple operation data in the computer room system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown;

[0041] Figure 2 The detailed flow chart of step S2 according to the embodiment of the present disclosure is schematically shown;

[0042] Figure 3 Another detailed flow chart of step S2 according to an embodiment of the present disclosure is schematically shown;

[0043] Figure 4 The detailed flow chart of step S3 according to the embodiment of the present disclosure is schematically shown;

[0044] Figure 5 The detailed flow chart of step S4 according to the embodiment of the present disclosure is schematically shown;

[0045] Figure 6 The detailed flow chart of step S5 according to the embodiment of the present disclosure is schematically shown;

[0046] Figure 7 Another detailed flow chart of step S5 according to an embodiment of the present disclosure is schematically shown;

[0047] Figure 8 A flowchart schematically shows a data processing method according to another embodiment of the present disclosure; and

[0048] Fig. 9 The structural block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0050] See also Figure 1 , an embodiment of the present disclosure provides a data processing method, comprising the following steps:

[0051] S1, collecting operation data of target equipment in the computer room system within a preset time period, wherein the operation data includes a timestamp, and the preset time period includes multiple time intervals;

[0052] In practical applications, various sensors can be used to collect operating data of various target devices in the computer room system for a preset period of time, such as one week or one month, where the types of operating data collected include temperature, voltage, flow, power consumption, etc. Sensors can preferably be MEMS (Microelectro Mechanical Systems) sensors, which are used to work stably and long-term in a complex and changeable computer room environment.

[0053] In practical applications, the collected operation data is formatted in a standardized manner. For example, the operation data includes device meta-information, timestamp and measurement data, ensuring that the device meta-information, timestamp and measurement data are output simultaneously.

[0054] In practical applications, in order to determine a reasonable sampling interval, after obtaining a certain amount of densely sampled data, it is necessary to measure and examine the variation amplitude of the parameter per unit time and the estimated frequency to avoid data reconstruction distortion. Usually the sampling rate is about five times greater than the signal frequency. In reality, data fluctuates at a certain frequency. Low-frequency information is relatively stable, while the oscillation of high-frequency information may be regular. In order to obtain more realistic data as much as possible, it is necessary to reduce the sampling interval as much as possible within the sensor processing range, so that discrete acquisition can be close to continuous time domain signals. For instantaneous sampling, it is necessary to reduce the sampling interval to achieve high-frequency sampling; for interval sampling, it is necessary to compress the sampling interval.

[0055] In practical applications, the data storage format of the running data after format standardization is standardized: storage structure, metadata discard, maximum time limit redundancy, etc., to balance data fidelity and storage capacity to obtain the best storage performance. The storage structure refers to what type of data is used to store the sampled data. The various information in the sampled data, such as the sampling location number, sampling device number, sampling value, etc., are not all useful. In practice, it will be decided which ones are discarded and which ones are retained. The maximum time limit redundancy refers to how long the collected data is discarded, such as six months or one year. Considering that too old data is not very meaningful for current data analysis, it is set. All data needs to have identifications such as sampling location number, sampling device number, sampling value, unit, etc. Instantaneous sampling requires a sampling timestamp, and interval sampling requires interval start and end timestamps.

[0056] S2, calculating the timestamp distribution of the operation data of the target device in the multiple time intervals;

[0057] In practical applications, the timestamp distribution of the operating data of different devices at multiple preset time intervals is calculated to achieve the timestamp alignment of the operating parameters of different devices.

[0058] S3, calculating the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device;

[0059] In practical applications, the total timestamp distribution of the operating data of all target devices may be calculated by summing the timestamp distribution of the operating data of different target devices.

[0060] S4, determining a target timestamp according to a total timestamp distribution of the operation data of all target devices;

[0061] In practical applications, a larger value may be selected from the values ​​of the total timestamp distribution, so that the timestamp in the time interval corresponding to the larger value is used as the target timestamp.

[0062] S5, calculating the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data.

[0063] In practical applications, the operation data of multiple target devices corresponding to the same target timestamp are aggregated into one group of data, and multiple groups of data corresponding to multiple target timestamps are aggregated to support the joint analysis of multiple operation data in the equipment management of the entire computer room system.

[0064] In practical applications, a dynamic timestamp alignment method is used. For the two signals to be aligned, assuming they are instantaneous sampling signal A and interval sampling signal B, first calculate the timestamp distribution. The timestamp distribution calculation requires two parameters, the unit time interval T0 and the number of samples N0. Starting from time 0, a timestamp sequence is filled in with the unit time interval T0. For the instantaneous sampling signal A, the number of signal points of signal A falling in each unit time period can be calculated to obtain the timestamp distribution N of signal A. A ; For the interval sampling signal B, the total time length of signal B in each unit time period can be calculated to obtain the timestamp distribution N of signal B B The total timestamp distribution N can be obtained by normalizing the timestamp distribution of different signals and summing them up. AB =N A '+N B ', where N A ' is the normalized timestamp distribution of signal A, N B ' is the normalized timestamp distribution of signal B. According to the total timestamp distribution N AB , the highest density can be selected in each N0 unit time period as the final timestamp sequence T anchor . Get the timestamp sequence T anchor Afterwards, different interpolation methods can be used to resample the instantaneous sampling signal to each time stamp. According to the characteristics of the signal, linear interpolation, Lagrange interpolation, linear regression, multiple interpolation, harmonic fitting and other methods can be selected to obtain the reconstructed instantaneous sampling signal; for interval sampling signals, such as flow rate and power consumption, the data near each time stamp can be used to calculate the instantaneous flow velocity and power to obtain the reconstructed interval sampling signal, so as to resample the reconstructed data and achieve the effect of timestamp alignment.

[0065] See also Figure 2 In this embodiment, in step S2, the operation data is instantaneous sampling data, and the timestamp distribution of the operation data of the target device in the multiple time intervals is calculated, including:

[0066] S21, counting the number of instantaneous sampling data of the target device in each time interval;

[0067] S22, collecting the quantities of instantaneous sampling data corresponding to a plurality of time intervals in chronological order to obtain a timestamp distribution of the operation data of the target device.

[0068] In practical applications, assume that the instantaneous sampling signal A lasts for 12 seconds and contains 5 sampling points, among which the 5 sampling points are unevenly distributed. When the time interval is 3 seconds, the timestamp distribution N of signal A is AThe length of is easy to calculate to 4. Count the number of points of signal A in each time interval of 3s, that is, there is 1 sampling point in the first time interval, 2 sampling points in the second time interval, 1 sampling point in the third time interval, and 1 sampling point in the fourth time interval. The timestamp distribution N of signal A can be obtained. A [1, 2, 1, 1].

[0069] See also Figure 3 In this embodiment, in step S2, the operation data is interval sampling data, and the timestamp distribution of the operation data of the target device is calculated according to the multiple time intervals, including:

[0070] S31, counting the coverage duration of interval sampling data of the target device in each time interval;

[0071] S32, collecting the coverage durations of the interval sampling data corresponding to the multiple time intervals in chronological order to obtain the timestamp distribution of the operation data of the target device.

[0072] In practical applications, assume that the total duration of interval sampling signal B is 12s, which contains 2 sampling sub-intervals. The 2 sub-intervals are unevenly distributed and of different lengths. When the time interval is 3s, the timestamp distribution N of signal B is B The length of is also 4. The duration of signal B covered in each time interval of 3s is counted. That is, the signal coverage duration in the first time interval is 1s, the signal coverage duration in the second time interval is 2s, the signal coverage duration in the third time interval is 3s, and the signal coverage duration in the fourth time interval is 1s. The timestamp distribution N of signal B can be obtained. B [1, 2, 3, 1].

[0073] See also Figure 4 In this embodiment, in step S3, the total timestamp distribution of the operation data of all target devices is calculated according to the timestamp distribution of the operation data of each target device, including:

[0074] S41, normalizing the timestamp distribution of the operation data of each target device;

[0075] S42, summing up the normalized timestamp distribution values ​​of different target devices corresponding to the same time interval to obtain a total timestamp distribution value corresponding to each time interval as the total timestamp distribution of the operation data of all target devices.

[0076] In practical applications, the timestamp distribution N A [1, 2, 1, 1] is normalized to get the timestamp distribution N A '[0.2, 0.4, 0.2, 0.2].

[0077] Distribute timestamps N B [1, 2, 3, 1] is normalized to get the timestamp distribution N B '[1 / 7, 2 / 7, 3 / 7, 1 / 7].

[0078] Total timestamp distribution N AB That is N A '+N B ', [12 / 35, 24 / 35, 22 / 35, 12 / 35].

[0079] See also Figure 5 In this embodiment, in step S4, determining the target timestamp according to the total timestamp distribution of the operation data of all target devices includes:

[0080] S51, determining the number of target timestamps according to a preset rule, wherein the number of target timestamps is less than the number of time intervals;

[0081] In actual applications, the preset rule may be to take a target timestamp every two time intervals. Then, when there are four time intervals, the number of target timestamps is two.

[0082] S52, selecting a value of the total timestamp distribution of the number of target timestamps according to the value of the total timestamp distribution;

[0083] In practical applications, when the number of target timestamps is 2, two larger values ​​can be selected from the values ​​of the total timestamp distribution.

[0084] S53: taking any timestamp in the time interval corresponding to the selected value of the total timestamp distribution as a target timestamp.

[0085] In practical applications, any one of the start timestamp, middle timestamp, end timestamp, etc. in the time interval corresponding to the selected numerical value is selected as the target timestamp. It is worth noting that the positions of the target timestamps selected in different time intervals are the same. For example, if the start timestamp is selected as the target timestamp in the first time interval, the start timestamp is also selected as the target timestamp in the second time interval.

[0086] See also Figure 6 In this embodiment, in step S5, the operation data is instantaneous sampling data, and the operation data of each target device corresponding to the same target timestamp is calculated, including:

[0087] S61, determine whether the current target device has known instantaneous sampling data at the target timestamp:

[0088] If yes, execute step S62;

[0089] If not, execute step S63;

[0090] S62, using the instantaneous sampling data of the current target device at the target timestamp as the operation data of the target device corresponding to the target timestamp;

[0091] S63, interpolating the instantaneous sampling data at the target timestamp according to the known instantaneous sampling data to obtain the operation data of the target device corresponding to the target timestamp.

[0092] In practical applications, the interpolation method may be linear interpolation, Lagrange interpolation, linear regression, multiple interpolation, harmonic fitting and the like.

[0093] See also Figure 7 In this embodiment, in step S5, the operation data is interval sampling data, and the operation data of each target device corresponding to the same target timestamp is calculated, including:

[0094] S71, within the time interval where the target timestamp is located, the ratio of the total value of the interval sampling data within the time interval to the coverage duration is used as the operation data of the target device corresponding to the target timestamp.

[0095] like Figure 8 As shown in the figure, the preset duration is 12s, the number of time intervals is 4, and the time interval is 3s as an example to explain the process of timestamp alignment:

[0096] Assume that the instantaneous sampling signal A lasts for 12 seconds and contains 5 sampling points, among which the 5 sampling points are unevenly distributed. When the time interval is 3 seconds, the timestamp distribution N of signal A is A The length of is easy to calculate 4. By counting the number of points of signal A in each time interval of 3s, the timestamp distribution N of signal A can be obtained. A [1, 2, 1, 1]. N timestamp distribution A Normalize and get the timestamp distribution N A '[0.2, 0.4, 0.2, 0.2].

[0097] Assume that the total duration of interval sampling signal B is 12s, and it contains 2 sampling sub-intervals. The 2 sub-intervals are unevenly distributed and of different lengths. When the time interval is 3s, the timestamp distribution N of signal B is B The length of is also 4. By counting the duration of signal B covered in each time interval of 3s, the timestamp distribution N of signal B can be obtained. B [1, 2, 3, 1]. N timestamp distribution B Normalize and get the timestamp distribution N B'[1 / 7, 2 / 7, 3 / 7, 1 / 7].

[0098] Total timestamp distribution N AB That is N A '+N B ', [12 / 35, 24 / 35, 22 / 35, 12 / 35].

[0099] Given two signals, the total timestamp distribution N AB =[12 / 35, 24 / 35, 22 / 35, 12 / 35]. If a timestamp is taken for every two units of time, then obviously two timestamps need to be selected for the four time periods. In the above four time periods [t1, t2, t3, t4], since the values ​​of t2 and t3 are large, [t2, t3] can be selected as the final timestamp sequence T anchor .

[0100] Get the timestamp sequence T anchor After that, the signal values ​​at the two time points [t2, t3] can be calculated. Instantaneous signals can be obtained by interpolating nearby signal points. Interval signals can be obtained by summing the values ​​of a nearby interval divided by time. For interval signal B (assuming it is flow), the value at time t2 can be calculated by dividing the total flow of 2s covered by signal b1 in t2 by 2s to get the flow velocity at t2. Compared with using the entire signal b1 to calculate the flow velocity, the result of this method is closer to the moment value of the calculated timestamp, which is convenient for improving the accuracy of subsequent multi-signal joint analysis.

[0101] The data processing system of this embodiment can directly collect the operating data of the target device, standardize the data format and storage method, and distinguish between instantaneous sampling data and interval sampling data, so as to automate, standardize and ensure the fidelity of the operating data collection, and ensure that the operating data can be further fitted and reconstructed for subsequent algorithm reading and calling.

[0102] An embodiment of the present disclosure provides a data processing system, including:

[0103] A sensor, which is used to collect operating data of a target device in a computer room system within a preset time period, wherein the operating data includes a timestamp, and the preset time period includes a plurality of time intervals;

[0104] A distribution station is used to calculate the timestamp distribution of the operation data of the target device in the multiple time intervals; calculate the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device; determine the target timestamp according to the total timestamp distribution of the operation data of all target devices; calculate the operation data of each target device corresponding to the same target timestamp.

[0105] In practical applications, the number of sensors is the same as the number of target devices, usually multiple. Information transmission lines are laid between sensors and hubs, so that the communication between sensors and hubs includes: wireless signals: Zig-Bee, Bluetooth, Wi-Fi, UWP, etc.; wired cables. The hub can be a high-performance and high-reliability server.

[0106] The implementation process of the functions and effects of each hardware unit in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0107] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network model units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0108] Reference Fig. 9 As shown, the electronic device provided by the third exemplary embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0109] Memory 1130, used for storing computer programs;

[0110] The processor 1110 is used to implement the following data processing method when executing the program stored in the memory 1130:

[0111] Collecting operation data of target equipment in the computer room system within a preset time period, wherein the operation data includes a timestamp, and the preset time period includes multiple time intervals;

[0112] Calculate the timestamp distribution of the operation data of the target device in the plurality of time intervals;

[0113] Calculate the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device;

[0114] Determine a target timestamp based on the total timestamp distribution of the operation data of all target devices;

[0115] Calculate the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data.

[0116] The communication bus 1140 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0117] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0118] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0119] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network model processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0120] The fourth exemplary embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data processing method described above is implemented.

[0121] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the data processing method according to the embodiment of the present disclosure is implemented.

[0122] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0123] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0124] The foregoing is merely a specific embodiment of the present disclosure, which enables those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A data processing method, characterized in that: The following steps are involved: Collecting operation data of a target device in a computer room system within a preset time period, wherein the operation data includes a timestamp, and the preset time period includes multiple time intervals; Calculate the timestamp distribution of the operation data of the target device in the plurality of time intervals; Calculate the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device; Determine a target timestamp based on the total timestamp distribution of the operation data of all target devices; Calculate the operation data of each target device corresponding to the same target timestamp for subsequent joint analysis of multiple operation data. For the two signals to be aligned, assume that they are instantaneous sampling signal A and interval sampling signal B. First, calculate the timestamp distribution. The timestamp distribution calculation requires two parameters, unit time interval T0 and sampling number N0. Take time 0 as the starting point and unit time interval T0 as the unit to fill a timestamp sequence. For the instantaneous sampling signal A, calculate the number of signal points of signal A falling in each unit time period to obtain the timestamp distribution N of signal A. A ; For the interval sampling signal B, calculate the total time length of signal B in each unit time period to obtain the timestamp distribution N of signal B B , the timestamp distribution of different signals is normalized and summed up to get the total timestamp distribution N AB =N A '+N B ', where N A ' is the normalized timestamp distribution of signal A, N B ' is the normalized timestamp distribution of signal B, according to the total timestamp distribution N AB , in each N0 unit time period, select the one with the highest density as the final timestamp sequence T anchor , get the timestamp sequence T anchor Finally, different interpolation methods are used to resample the instantaneous sampling signal to each time stamp. According to the characteristics of the signal, linear interpolation, Lagrange interpolation, linear regression, multiple interpolation, and harmonic fitting methods are selected to obtain the reconstructed instantaneous sampling signal; for the interval sampling signal, the reconstructed interval sampling signal is obtained to resample the reconstructed data and achieve the effect of timestamp alignment.

2. The method according to claim 1, characterized in that The operation data is instantaneous sampling data, and the timestamp distribution of the operation data of the target device in the plurality of time intervals is calculated, including: Count the number of instantaneous sampling data of the target device in each time interval; The quantities of instantaneous sampling data corresponding to multiple time intervals are collected in chronological order to obtain the timestamp distribution of the operation data of the target device.

3. The method according to claim 1, characterized in that The operation data is interval sampling data, and the timestamp distribution of the operation data of the target device is calculated according to the multiple time intervals, including: Count the coverage duration of interval sampling data of the target device in each time interval; The coverage durations of the interval sampling data corresponding to the multiple time intervals are aggregated in chronological order to obtain the timestamp distribution of the operation data of the target device.

4. The method according to claim 1, characterized in that: The calculating the total timestamp distribution of the operation data of all target devices according to the timestamp distribution of the operation data of each target device includes: Normalize the timestamp distribution of the operation data of each target device; The normalized timestamp distribution values ​​of different target devices corresponding to the same time interval are summed up to obtain the total timestamp distribution value corresponding to each time interval as the total timestamp distribution of the running data of all target devices.

5. The method according to claim 1, characterized in that: The determining the target timestamp according to the total timestamp distribution of the operation data of all target devices includes: Determine the number of target timestamps according to a preset rule, wherein the number of target timestamps is less than the number of time intervals; Select the value of the total timestamp distribution of the number of target timestamps according to the value of the total timestamp distribution; Any timestamp in the time interval corresponding to the selected value of the total timestamp distribution is used as the target timestamp.

6. The method according to claim 1, characterized in that The operation data is instantaneous sampling data, and the operation data of each target device corresponding to the same target timestamp is calculated, including: Determine whether there is known instantaneous sampling data at the target timestamp: When there is no known instantaneous sampling data at the target timestamp, the instantaneous sampling data at the target timestamp is interpolated according to the known instantaneous sampling data to obtain the operation data of the target device corresponding to the target timestamp.

7. The method according to claim 1, characterized in that The operation data is interval sampling data, and the operation data of each target device corresponding to the same target timestamp is calculated, including: In the time interval where the target timestamp is located, the ratio of the total value of the interval sampling data in the time interval to the coverage duration is used as the operation data of the target device corresponding to the target timestamp.

8. A data processing system, characterized in that: include: A sensor, which is used to collect operating data of a target device in a computer room system within a preset time period, wherein the operating data includes a timestamp, and the preset time period includes a plurality of time intervals; The collecting and distributing station is used to calculate the timestamp distribution of the operation data of the target device in the multiple time intervals; calculate the total timestamp distribution of the operation data of all the target devices according to the timestamp distribution of the operation data of each target device; determine the target timestamp according to the total timestamp distribution of the operation data of all the target devices; calculate the operation data of each target device corresponding to the same target timestamp, For the two signals to be aligned, assume that they are instantaneous sampling signal A and interval sampling signal B. First, calculate the timestamp distribution. The timestamp distribution calculation requires two parameters, unit time interval T0 and sampling number N0. Take time 0 as the starting point and unit time interval T0 as the unit to fill a timestamp sequence. For the instantaneous sampling signal A, calculate the number of signal points of signal A falling in each unit time period to obtain the timestamp distribution N of signal A. A ; For the interval sampling signal B, calculate the total time length of signal B in each unit time period to obtain the timestamp distribution N of signal B B , the timestamp distribution of different signals is normalized and summed up to get the total timestamp distribution N AB =N A '+N B ', where N A ' is the normalized timestamp distribution of signal A, N B ' is the normalized timestamp distribution of signal B, according to the total timestamp distribution N AB , in each N0 unit time period, select the one with the highest density as the final timestamp sequence T anchor , get the timestamp sequence T anchor Finally, different interpolation methods are used to resample the instantaneous sampling signal to each time stamp. According to the characteristics of the signal, linear interpolation, Lagrange interpolation, linear regression, multiple interpolation, and harmonic fitting methods are selected to obtain the reconstructed instantaneous sampling signal; for the interval sampling signal, the reconstructed interval sampling signal is obtained to resample the reconstructed data and achieve the effect of timestamp alignment.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing the data processing method according to any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 7 is implemented.

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