Time series data management method and device, equipment, storage medium and program product

Abstract

The invention discloses a time series data management method and device, equipment, a storage medium and a program product, and relates to the field of data processing. The method comprises the steps of obtaining time sequence data collected in a historical time period; based on a data acquisition type of measuring point data in the time sequence data, determining a coding data type adopted when coding the time sequence data; and by taking the coded data type as a data coding reference, combining and coding the measuring point data corresponding to the plurality of timestamps respectively, and generating a data coding result corresponding to the time sequence data. According to the invention, multiple measuring point data can be merged and coded from the dimension of the data acquisition type, the generation of a data coding result with less redundancy is facilitated, and the storage and utilization efficiency of time series data is improved. The method can be applied to various scenes such as the field of Internet of Things, the financial field and the industrial field.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing, and in particular to a time series data management method, apparatus, device, storage medium and program product. Background Art

[0002] Time series data is a string of data indexed by time dimension, usually used to describe the measurement value of a measured subject at each timestamp within a time range. Analyzing time series data along time changes can reveal potential regularities, trends, periodic changes or anomalies.

[0003] In the related art, a large amount of time series data is often stored in a time series database, and each time series data usually includes multiple measurement point data. The data information corresponding to the multiple measurement point data is serialized along the time series dimension to obtain a serialization result corresponding to the time series data; when querying the time series database based on query conditions, it is necessary to analyze the data information corresponding to the multiple measurement point data in the serialization result to obtain the time series data that meets the query conditions.

[0004] However, the data information corresponding to multiple measurement points in the serialization results are processed separately, which occupies a large amount of data. The process of querying time series data through query conditions also consumes a large amount of memory, resulting in long query time and low query efficiency. Summary of the invention

[0005] The embodiments of the present application provide a time series data management method, device, equipment, storage medium and program product, which can merge and encode multiple measurement point data from the dimension of data acquisition type, help generate data encoding results with less redundancy, and improve the storage utilization efficiency of time series data. The technical solution is as follows.

[0006] In one aspect, a time series data management method is provided, the method comprising:

[0007] Acquire time series data collected within a historical time period, where the time series data is used to describe, in a time series dimension, a change trend of measurement point data corresponding to a plurality of timestamps within the historical time period;

[0008] Determine, based on the data collection type of the measurement point data in the time series data, a coding data type used when encoding the time series data, wherein the coding data type is used to characterize the data type of encoding the time series data;

[0009] Taking the encoding data type as the data encoding benchmark, the measurement point data corresponding to the multiple timestamps are merged and encoded to generate a data encoding result corresponding to the time series data. The data encoding benchmark is used to merge and encode the measurement point data with the same data acquisition type, and the data encoding result is used for a single decoding to obtain the measurement point data corresponding to the multiple timestamps.

[0010] In another aspect, a time series data management device is provided, the device comprising:

[0011] An acquisition module, used to acquire time series data collected within a historical time period, wherein the time series data is used to describe, in a time series dimension, a change trend of measurement point data corresponding to a plurality of timestamps within the historical time period;

[0012] A determination module, configured to determine a coding data type used when encoding the time series data based on a data collection type of the measurement point data in the time series data, wherein the coding data type is used to characterize a data type for encoding the time series data;

[0013] A generation module is used to combine and encode the measurement point data corresponding to the multiple timestamps based on the encoded data type as a data encoding benchmark to generate a data encoding result corresponding to the time series data. The data encoding benchmark is used to combine and encode the measurement point data with the same data acquisition type, and the data encoding result is used for a single decoding to obtain the measurement point data corresponding to the multiple timestamps.

[0014] On the other hand, a computer device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a timing data management method as described in any of the above-mentioned embodiments of the present application.

[0015] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a timing data management method as described in any of the above-mentioned embodiments of the present application.

[0016] On the other hand, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the time series data management method described in any of the above embodiments.

[0017] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0018] The data collection type of the time series data collected in the historical time period is used to analyze the encoding data type used when encoding the time series data, so as to generate data encoding results corresponding to the time series data through the encoding data type and multiple measuring point data in the time series data. Compared with the process of separately processing the data information of the measuring point data included in the sequence data to realize the storage and analysis of the serialization results, the encoding data type used when encoding the time series data is more specifically determined through the data collection type, so as to merge and encode the measuring point data corresponding to multiple timestamps from the dimension of data collection type, which is helpful to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, which not only reduces the data storage volume, but also helps to improve the storage efficiency of the time series data and the subsequent analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 is a structural block diagram of a management system provided by an exemplary embodiment of the present application;

[0021] Figure 2 is a flowchart of a time series data management method provided by an exemplary embodiment of the present application;

[0022] Figure 3 is a flowchart of a time series data management method provided by another exemplary embodiment of the present application;

[0023] Figure 4 is a flowchart of a time series data management method provided by another exemplary embodiment of the present application;

[0024] Figure 5 is a flowchart of a time series data management method provided by another exemplary embodiment of the present application;

[0025] Figure 6 is a schematic diagram of generating a data encoding result provided by an exemplary embodiment of the present application;

[0026] Figure 7 It is a schematic diagram of a generated data encoding result provided by an exemplary embodiment of the present application, where the encoding data type is an integer type;

[0027] Figure 8It is a schematic diagram of data query after the encoding data type is integer encoding time series data provided by an exemplary embodiment of the present application;

[0028] Fig. 9 It is a schematic diagram of a generated data encoding result when the encoding data type is a floating point type provided by an exemplary embodiment of the present application;

[0029] Fig.10 It is a schematic diagram of data query after the encoding data type is floating-point encoding time series data provided by an exemplary embodiment of the present application;

[0030] Fig.11 It is a schematic diagram of a generated data encoding result when the encoding data type is a string type provided by an exemplary embodiment of the present application;

[0031] Fig.12 is a schematic diagram of a data encoding result including various information provided by an exemplary embodiment of the present application;

[0032] Fig.13 is a structural block diagram of a time series data management device provided by an exemplary embodiment of the present application;

[0033] Fig.14 It is a structural block diagram of a server provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0035] In the related art, a large amount of time series data is often stored in a time series database. Each time series data usually includes multiple measurement point data. The data information corresponding to the multiple measurement point data is serialized along the time series dimension to obtain the serialization result corresponding to the time series data. When querying the time series database based on the query conditions, it is necessary to analyze the data information corresponding to the multiple measurement point data in the serialization result to obtain the time series data that meets the query conditions for data analysis. However, the data information corresponding to the multiple measurement point data in the serialization result are stored separately, which occupies a large amount of data. The process of querying the time series data through the query conditions also consumes a large amount of memory, resulting in a long query time.

[0036] In an embodiment of the present application, a time series data management method is introduced, which can merge and encode multiple measurement point data from the dimension of data acquisition type, help generate data encoding results with less redundancy, and improve the storage utilization efficiency of time series data. The time series data management method proposed in the embodiment of the present application can be applied to time series data management in various fields such as Internet of Things (IoT) scenarios, financial scenarios, industrial scenarios, etc., which are not limited here.

[0037] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant regions. For example, the time series data, data collection types and other contents involved in this application are all obtained with full authorization.

[0038] The management system involved in the embodiment of the present application is explained. The time series data management method executed by the management system provided in the embodiment of the present application can be executed by a terminal, or by a server, or by the terminal and the server interactively, and the embodiment of the present application is not limited to this.

[0039] For illustration, please refer to Figure 1 The management system involves a terminal 110 and a server 120 , and the terminal 110 and the server 120 implement communication interaction through a communication network 130 .

[0040] In some embodiments, terminal 110 is used to obtain time series data collected within a historical time period.

[0041] Among them, time series data is used to describe the change trend of measurement point data corresponding to multiple timestamps in a historical time period in the time series dimension.

[0042] Schematically, the historical time period is a previous preset time period, and the time series data includes wind data (such as wind speed, power generation), hydraulic data (such as water volume, power generation), etc.; one time series data includes measurement point data collected at multiple timestamps, that is, one time series data includes multiple measurement point data, and multiple timestamps are arranged and distributed along the time series dimension. Therefore, multiple measurement point data arranged along the time series dimension can reflect the changing trend.

[0043] In some embodiments, the terminal sends the time series data to the server 120 via a communication network, and the server 120 performs analysis based on the time series data.

[0044] Optionally, the server 120 determines the encoding data type used when encoding the time series data based on the data collection type of the measurement point data in the time series data.

[0045] The encoding data type is used to represent the data type of the encoding time series data.

[0046] Illustratively, the data collection type of the measuring point data is used to characterize the type of the collected measuring point data, and the data collection type is at least one of multiple types such as wind speed type, rotation speed type, power generation type, temperature type, etc., which are not limited here.

[0047] The coded data type collected when encoding the time series data including the measurement point data is assisted in determining according to the data collection type, the coded data type being at least one of a plurality of data types such as integer type, floating point type, character string type, etc.

[0048] In some embodiments, the server 120 uses the encoding data type as a data encoding basis, merges and encodes the measurement point data corresponding to multiple timestamps, and generates a data encoding result corresponding to the time series data.

[0049] The data encoding reference is used to merge and encode the measurement point data with the same data acquisition type, and the data encoding result is used for single decoding to obtain the measurement point data corresponding to multiple timestamps.

[0050] Optionally, the server 120 sends the data encoding result to other terminals (such as the terminal 140 that needs to obtain the measurement point data) through the communication network 130, and the terminal 140 receives the data encoding result and decodes it to obtain the measurement point data.

[0051] It is worth noting that the above-mentioned terminals include but are not limited to various energy equipment such as wind power generation equipment, hydropower generation equipment, solar power generation equipment, meteorological data detection equipment, hydrogen power generation equipment, etc.; they can also include edge devices (such as sensors, gateways, etc.); they can also include mobile terminals such as mobile phones, tablets, portable laptops, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, etc., and can also be implemented as desktop computers, etc.; the above-mentioned servers can be independent physical servers, or they can be server clusters or distributed systems composed of multiple physical servers, or they can be cloud servers that provide basic cloud computing services, which are not limited here.

[0052] In combination with the above-mentioned noun introduction and application scenarios, the time series data management method provided by the present application is described, and the application of the method to energy equipment is used as an example for description. Figure 2 As shown, the method includes the following steps 210 to 230.

[0053] Step 210, obtaining time series data collected within a historical time period.

[0054] Illustratively, the historical time period is a preset time period that has passed, for example: the historical time period is the past day, the past week, the past month, etc.; or, the current time is 10:09, the historical time period is the time period of 08:00-10:00, etc., which is not limited here.

[0055] Among them, time series data is used to describe the change trend of measurement point data corresponding to multiple timestamps in a historical time period in the time series dimension.

[0056] Schematically, the historical time period includes multiple timestamps, and the multiple timestamps are arranged in the time series dimension according to the time series development of the historical time period. For example, the multiple timestamps are arranged in the time series dimension as: timestamp 1, timestamp 2, timestamp 3, etc.

[0057] Optionally, the time series data includes a plurality of measurement point data, where the measurement point data is collected data and is used to characterize data measurement conditions at each time stamp.

[0058] Indicatively, the measurement point data is energy data detected by energy equipment. For example, the measurement point data is at least one of wind speed data, power generation data, and other data detected by wind power generation equipment; or the measurement point data is at least one of water flow velocity data, power generation data, and other data detected by hydropower generation equipment.

[0059] Schematically, each of the multiple timestamps corresponds to at least one type of measurement point data. For example, the measurement point data corresponding to timestamp 1 is wind speed data A1, and the measurement point data corresponding to timestamp 2 is wind speed data A2, etc.; or, the measurement point data corresponding to timestamp 1 is wind speed data A1, and the measurement point data corresponding to timestamp 2 is power generation data B2, etc.; or, the measurement point data corresponding to timestamp 1 includes wind speed data A1 and power generation data B1, and the measurement point data corresponding to timestamp 2 includes wind speed data A2 and power generation data B2, etc.

[0060] Step 220 , based on the data collection type of the measurement point data in the time series data, determine the encoding data type used when encoding the time series data.

[0061] Schematically, the data collection type is used to characterize the type of data collected at the measuring point, which is usually defined based on different measurement targets and application requirements; each measuring point data usually corresponds to a data collection type. For example: the data collection type is the wind speed type; or the data collection type is the speed type; or the data collection type is the power generation type; or the data collection type is the temperature type; or the data collection type is the humidity type; or the data collection type is the pressure type; or the data collection type is the light intensity type, etc., which are not limited here.

[0062] In some embodiments, the data acquisition type is determined for the measurement point data in the acquired time series data, and type information, quantity information, and other information of the data acquisition type indicated by the plurality of measurement point data are analyzed.

[0063] In schematic form, if the measurement point data corresponding to multiple timestamps are all wind speed data, then the data collection type representing the measurement point data is "wind speed type", and the quantity is 1; if the measurement point data corresponding to multiple timestamps include wind speed data and power generation data, then the data collection type representing the measurement point data is "wind speed type" and "power generation type", and the quantity is 2, and so on.

[0064] In some embodiments, the encoding data type used when performing an encoding process on time series data is determined according to the data acquisition type.

[0065] The coding data type is used to characterize the data type of the coded time series data. In an illustrative manner, for the data acquisition type represented by the measuring point data, the corresponding coding data type is used to encode the measuring point data in the time series data, thereby achieving the purpose of encoding the time series data; or, for different data acquisition types, the corresponding coding data type is used to encode the measuring point data in the time series data, etc.

[0066] Schematically, data type refers to the category of operations used to describe the properties, structure, representation and executable operations of measuring point data in computer program design; the data type can be used to specifically define the storage form and processing operation form of measuring point data. Different data types have different characteristics and uses, thus determining the value range of the measuring point data, the memory space occupied and the executable operations on it.

[0067] Optionally, the representation form of the measuring point data is determined according to the data collection type of the measuring point data, so that a corresponding data type is selected as the encoding data type based on the data collection type.

[0068] For example: if the data collection type is "wind speed type", considering that the measurement point data of the "wind speed type" is usually implemented in integer or decimal form, the floating point type can be selected as the encoding data type; if the data collection type includes "wind speed type" and "power generation type", considering that the measurement point data of the "wind speed type" and the measurement point data of the "power generation type" are usually implemented in integer or decimal form, the floating point type can be selected as the encoding data type; or, if the data collection type is "light intensity type", considering that the measurement point data of the "light intensity type" is usually selected in integer form, the integer type can be selected as the encoding data type, etc.

[0069] Step 230 , taking the coded data type as a data coding basis, merging and coding the measurement point data corresponding to a plurality of time stamps, and generating a data coding result corresponding to the time series data.

[0070] Illustratively, after determining the encoding data type for encoding the time series data, the encoding data type is used as a data encoding benchmark, and the data encoding benchmark is used to merge and encode the measurement point data with the same data acquisition type.

[0071] Optionally, after taking the encoding data type as the data encoding basis, under the condition that the encoding data type remains unchanged, the measurement point data corresponding to multiple timestamps are analyzed, and the measurement point data with the same data acquisition type at different timestamps are determined; then, merge encoding is performed on the measurement point data with the same data acquisition type at multiple timestamps to generate a data encoding result corresponding to the time series data.

[0072] For example: if the encoding data type is a floating point type, and under the condition that the floating point type remains unchanged, if the measurement point data corresponding to multiple timestamps are all wind speed data of the "wind speed type", the wind speed data corresponding to the multiple timestamps are determined, and the multiple wind speed data are merged and encoded, thereby obtaining a data encoding result encoded together with the multiple wind speed data and the data encoding benchmark.

[0073] Alternatively, the encoding data type is a floating point type. Under the condition that the floating point type remains unchanged, if the measurement point data corresponding to multiple timestamps respectively include wind speed data of the "wind speed type" and power generation data of the "power generation type", then the wind speed data corresponding to the multiple timestamps respectively are determined, and merge coding is performed on the multiple wind speed data, and the power generation data corresponding to the multiple timestamps respectively are determined, and merge coding is performed on the multiple power generation data, and then the results of the two merge codings are aggregated to obtain the data coding result corresponding to the time series data.

[0074] The data encoding result is used for a single decoding to obtain the measurement point data corresponding to multiple timestamps.

[0075] Illustratively, the data encoding result includes measurement point data corresponding to multiple timestamps respectively, and by decoding the data encoding result, the measurement point data corresponding to multiple timestamps respectively under the same data acquisition type can be obtained.

[0076] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0077] In summary, compared with the process of separately processing the data information of the measuring point data included in the sequence data to realize the storage and analysis of the serialization results, the encoding data type used when encoding the time series data is more specifically determined by the data acquisition type, so that the measuring point data corresponding to multiple timestamps are merged and encoded from the dimension of data acquisition type, which helps to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, which not only reduces the data storage volume, but also helps to improve the storage efficiency of time series data and the subsequent analysis efficiency.

[0078] In an optional embodiment, when multiple measurement point data in the time series data indicate the same data acquisition type, the encoding data type used when encoding the time series data is determined based on the data meaning represented by the data acquisition type. Figure 3 As shown above Figure 2 The illustrated embodiment may also be implemented as steps 310 to 350 as follows; wherein step 220 may also be implemented as step 320 as follows, and step 230 may also be implemented as steps 330 to 350 as follows.

[0079] Step 310, obtaining time series data collected within a historical time period.

[0080] Schematically, the historical time period is a preset time period that has passed; the time series data is used to describe the change trend of the measurement point data corresponding to multiple timestamps in the historical time period in the time series dimension.

[0081] Optionally, the measurement point data is log data detected by the terminal. For example, the terminal detects the running status of a certain program in real time or periodically, and multiple timestamps correspond to the historical time period of the detection, each timestamp corresponds to the measurement point data, and the multiple timestamps are comprehensively analyzed to determine the change trend of the measurement point data.

[0082] Optionally, the measurement point data is energy data detected by energy equipment. For example, the measurement point data is at least one of multiple data such as wind speed data and power generation data detected by wind power generation equipment; or the measurement point data is at least one of multiple data such as water flow velocity data and power generation data detected by hydropower generation equipment.

[0083] Schematically, the time series data is a collection of measurement point data collected by the first device during a historical time period, wherein the measurement point data represents the results collected by the first device at each timestamp of the historical time period; each of the multiple timestamps corresponds to at least one measurement point data.

[0084] For example, the first device is a wind power generation device (abbreviated as: wind turbine). As shown in Table 1, the time series data is a set of measurement point data detected by the wind turbine F in a historical time period, and the historical time period includes timestamp 1 and timestamp 2.

[0085] Table 1: Time series data of fan F

[0086] Speed Timestamp 1 A1 Timestamp 2 A2

[0087] Among them, the measurement point data corresponding to timestamp 1 is speed A1; the measurement point data corresponding to timestamp 2 is speed A2; that is, the time series data collected in the historical time period shown in Table 1 includes 2 measurement point data. Therefore, based on the change trend of speed A1 and speed A2 in the time series dimension, the change of speed can be determined.

[0088] Step 320 , when multiple measurement point data in the time series data indicate the same data acquisition type, based on the data meaning represented by the data acquisition type, select a coding data type used when encoding the time series data from at least two data types.

[0089] Schematically, the time series data includes multiple measuring point data, and each timestamp corresponds to at least one measuring point data. Optionally, the data acquisition types corresponding to the multiple measuring point data included in the time series data are determined, and each measuring point data usually corresponds to a data acquisition type; the data acquisition type is used to characterize the type of the collected measuring point data, which is usually a type defined based on different measurement objectives and application requirements.

[0090] Schematically, as shown in Table 1, the data collection type of the two measuring point data included in the time series data is both the rotation speed type, which means that the two measuring point data are both data collected from the rotation speed angle of the fan F.

[0091] Optionally, the data collection types respectively corresponding to the plurality of measuring point data are compared, and if the plurality of measuring point data indicate the same data collection type, the coded data type is selected according to the data meaning represented by the data collection type.

[0092] Schematically, the data meaning is used to indicate the expression mode of the measuring point data under the data acquisition type; at least two data types are predetermined data types, and at least two data types include at least two of multiple data types such as integer type, floating point type, string type, Boolean type, custom type, etc. The data meaning represented by the data acquisition type is used to select the corresponding encoding data type to achieve the purpose of encoding the measuring point data in the time series data.

[0093] In an optional embodiment, when the data acquisition type characterizes that the measurement point data is represented by an integer, an integer type is selected from at least two data types as the encoding data type.

[0094] Indicatively, multiple measuring point data indicate the same data collection type as "count type". The meaning of data based on the count type usually indicates the current number of operations, cumulative number of times, etc. Therefore, the measuring point data represented by the "count type" is usually in integer form, and the "integer type" (int type) is selected as the encoding data type from at least two data types. For example: the measuring point data represents the number of times the device detects an abnormal state at the current timestamp. Based on the number of times, it is usually implemented in integer form, so the "integer type" is selected as the encoding data type from at least two data types. That is, the integer type is usually used to represent integer data such as quantity, count, status code, etc.

[0095] In some embodiments, when the data acquisition type characterizes the measurement point data by an integer, based on the data value range corresponding to the data acquisition type, an integer type corresponding to the data value range is selected from at least two data types as the encoding data type.

[0096] Indicatively, the data value range is used to represent the value range of the measurement point data of the data acquisition type. For example, the value of the measurement point data of the counting type is usually within 0 to 10 times (such as issuing an early warning and clearing after 10 times, etc.), then the data value range is (0, 10).

[0097] Optionally, in the case where the data acquisition type is characterized by representing the measurement point data by an integer, if the data acquisition type corresponds to a first data value range, a first integer type corresponding to the first data value range is selected from at least two data types as the encoded data type; if the data acquisition type corresponds to a second data value range, a second integer type corresponding to the second data value range is selected from at least two data types as the encoded data type.

[0098] The first data value range is different from the second data value range, and the first integer type and the second integer type usually represent data representation forms running on different operating systems.

[0099] Illustratively, the first integer type is int32, the second integer type is int64, the first data value range corresponding to the first integer type is -231 to 231-1; the second data value range corresponding to the second integer type is -263 to 263-1.

[0100] In an optional embodiment, when the data acquisition type characterizes that the measurement point data is represented by decimals, a floating point type is selected from at least two data types as the encoding data type.

[0101] Indicatively, multiple measuring point data indicate the same data collection type of "rotation speed type". The data meaning based on the rotation speed type usually indicates the rotation speed of the equipment or the rotation speed of the equipment component, etc. Therefore, the measuring point data represented by the "rotation speed type" is usually in decimal form, and the "floating point type" (float type) is selected from at least two data types as the encoding data type. The floating point type is usually used to represent data that needs to represent a decimal part and has a certain precision requirement.

[0102] In some embodiments, when the data acquisition type characterizes the measurement point data by representing decimals, based on the data precision corresponding to the data acquisition type, a floating point type corresponding to the data precision is selected from at least two data types as the encoding data type.

[0103] Indicatively, data precision is used to indicate the number of digits after the decimal point during data analysis. For example, the larger the number of digits after the decimal point, the greater the data precision; the smaller the number of digits after the decimal point, the smaller the data precision.

[0104] Optionally, in the case where the data acquisition type characterizes the measured point data by representing them through decimals, if the data acquisition type corresponds to a first data precision, a first floating-point type corresponding to the first data precision is selected from at least two data types as the encoded data type; if the data acquisition type corresponds to a second data precision, a second floating-point type corresponding to the second data precision is selected from at least two data types as the encoded data type.

[0105] The first data precision is different from the second data precision, and the first floating point type is different from the second floating point type. For example, the second data precision is higher than the first data precision, and the second floating point type corresponding to the second data precision is float64, and the second data precision is usually about 15-16 significant digits (the number of digits after the decimal point is also relatively large), and the first floating point type corresponding to the first data precision is float32, and the first data precision is usually about 7 significant digits (the number of digits after the decimal point is also relatively small).

[0106] In an optional embodiment, when the data acquisition type characterizes that the measurement point data is represented by a character string, a character string type is selected from at least two data types as the encoding data type.

[0107] Indicatively, multiple measuring point data indicate the same data collection type of "equipment status type". The data meaning based on the equipment status type usually represents the equipment operating status (such as normal, faulty, stopped, running, etc.). Therefore, the measuring point data expressed in the "equipment status type" is usually descriptive and textual information. Then, "string" (string type) is selected from at least two data types as the encoding data type; that is, the string is usually used to represent descriptive and categorical text content.

[0108] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0109] Step 330 , for the jth measuring point data corresponding to the jth timestamp, a first data array is formed by the jth timestamp and the jth measuring point data to obtain first data arrays corresponding to a plurality of timestamps respectively, where j is a positive integer.

[0110] Schematically, the time series data includes multiple measuring point data, and each timestamp corresponds to at least one measuring point data; if multiple measuring point data indicate the same data acquisition type, the measuring point data corresponding to each timestamp is the same data acquisition type. Since the measuring point data under the timestamp represents the data information collected at a certain moment, each timestamp usually corresponds to one measuring point data, such as the speed data under timestamp 1 is A1, etc.

[0111] The jth timestamp is any one of the multiple timestamps, and the measuring point data corresponding to the jth timestamp is the jth measuring point data; the jth timestamp and the jth measuring point data are combined to obtain a first data array; and multiple timestamps and corresponding measuring point data are combined to obtain first data arrays corresponding to the multiple timestamps respectively.

[0112] Optionally, taking the jth timestamp and the corresponding jth measuring point data as an example, the jth measuring point data is spliced ​​after the jth timestamp to obtain a first data array corresponding to the jth timestamp, and first data arrays corresponding to multiple timestamps are obtained.

[0113] Step 340 , concatenate first data arrays corresponding to a plurality of measurement point data respectively along the time series dimension to obtain a second data array.

[0114] Schematically, multiple timestamps within a historical time period are determined along the time series dimension, and the multiple timestamps are arranged in sequence. Since each timestamp corresponds to a first data array, the multiple first data arrays can be arranged based on the arrangement order of the multiple timestamps, and then the first data arrays corresponding to the multiple timestamps are spliced ​​to obtain a second data array; the second data array is used to represent the merged encoding results of multiple measurement point data under the same data acquisition type.

[0115] Step 350, write the encoded data type at the first position serving as a data encoding reference, and write the second data array at the second position after the first position, to generate a data encoding result corresponding to the timing data.

[0116] Schematically, taking the data encoding benchmark implemented as the character writing position as an example, the first position is used as the data encoding benchmark, the encoded data type is written at the first position, and the spliced ​​second data array is written at the second position after the first position, thereby obtaining a data encoding result corresponding to the timing data.

[0117] Optionally, the first position is a pre-selected character writing position written during encoding, and the encoding data type is written at the first position to guide the reading method of the second data array written at the second position after the first position, such as: the encoding data type is a floating point type, and the second data array is the encoding result of merging the measurement point data in decimal form of the same data acquisition type under multiple timestamps.

[0118] In some embodiments, the device name of the first device that collects timing data is obtained, the device name is written in the third position before the first position, the encoded data type is written in the first position, and the second data array is written in the second position to generate a data encoding result corresponding to the timing data.

[0119] Schematically, the device name is used to uniquely characterize the first device, the third position is a pre-selected character writing position written during encoding, the third position is located before the first position, and the contents written in the third position, the first position and the second position are combined to generate a data encoding result corresponding to the timing data.

[0120] Optionally, the device name length, device name and device acquisition data volume of the first device are written at the third position to obtain a device array corresponding to the first device; the type name length, type name, encoded data type and measurement point data volume corresponding to the encoded data type of the data acquisition type are written at the first position to obtain a measurement point array corresponding to the data acquisition type; the second data array is written at the second position; the device array, the measurement point array and the second data array are spliced ​​in the order of the third position, the first position and the second position to obtain a data encoding result corresponding to the timing data.

[0121] Among them, the device name length is used to represent the number of characters in the device name (such as the device name length of fan F is 3); the device collected data volume is used to represent the number of measurement point data collected by the first device; the type name length is used to represent the number of characters in the type name of the data collection type (such as the type name length of the speed is 2); the measurement point data volume is used to represent the number of measurement point data of the data collection type corresponding to the coded data type.

[0122] In an illustrative manner, data encoding processing is performed on the time series data shown in Table 1. By writing the device name length (3) of fan F, the device name (fan F), and the device acquisition data volume (2) at the third position, the device array corresponding to the first device is obtained as follows: 3——fan F——2; by writing the type name length (2) of the data acquisition type, the type name (rotation speed), the encoding data type (floating point type), and the measurement point data volume (2) corresponding to the encoding data type at the first position, the measurement point array corresponding to the data acquisition type is obtained as follows: 2——rotation speed——floating point type——2; and writing the second data array as follows: timestamp 1——A1——timestamp 2——A2 at the second position, thereby obtaining a data encoding result corresponding to the time series data.

[0123] In some embodiments, the timestamp is a digital representation of the collection time when the measurement point data is collected, and the collection time is the recording time point when the measurement point data is collected, and there is a preset conversion relationship between the two. In the process of obtaining the second data array, multiple timestamps are converted into corresponding collection times based on the conversion relationship, and the first data array is obtained based on the timestamp, the collection time and the measurement point data, so as to splice the first data arrays corresponding to the multiple timestamps respectively along the time series dimension to obtain the second data array.

[0124] In an illustrative manner, data encoding processing is performed on the time series data shown in Table 1, and the device array is: 3-fan F-2; the measurement point array is: 2-speed-floating point type-2; the second data array written at the second position is: timestamp 1-collection time 1-A1-timestamp 2-collection time 2-A2, thereby obtaining the data encoding result corresponding to the time series data shown in Table 1.

[0125] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0126] In summary, compared with the process of separately processing the data information of the measuring point data included in the sequence data to realize the storage and analysis of the serialization results, the encoding data type used when encoding the time series data is more specifically determined by the data acquisition type, so that the measuring point data corresponding to multiple timestamps are merged and encoded from the dimension of data acquisition type, which helps to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, which not only reduces the data storage volume, but also helps to improve the storage efficiency of time series data and the subsequent analysis efficiency.

[0127] In the embodiment of the present application, it is introduced that when multiple measuring point data indicate the same data acquisition type, the content of the coded data type is obtained. If multiple measuring point data indicate the same data acquisition type, multiple measuring point data of the same data acquisition type can be merged and encoded based on the coded data type to obtain a data encoding result. In the data encoding result, one data acquisition type can be used to express multiple measuring point data, avoiding the cumbersome expression problem that each measuring point data needs to correspond to one data acquisition type. In addition, if multiple measuring point data correspond to the same device name and other information, multiple measuring point data with the same information can also be merged and encoded, thereby further reducing the storage amount of the data encoding result, which helps to improve data retrieval efficiency.

[0128] In an optional embodiment, when multiple measurement point data in the time series data indicate different data acquisition types, at least one encoding data type is determined as the encoding data type used when encoding the time series data based on the data meanings represented by at least two data acquisition types. Figure 4 As shown above Figure 2 The illustrated embodiment may also be implemented as steps 410 to 450 as follows; wherein step 220 may also be implemented as steps 420 to 440 as follows; wherein step 230 may also be implemented as step 450 as follows.

[0129] Step 410, obtaining time series data collected within a historical time period.

[0130] Schematically, the historical time period is a preset time period that has passed; the time series data is used to describe the change trend of the measurement point data corresponding to multiple timestamps in the historical time period in the time series dimension.

[0131] Schematically, the time series data is a collection of measurement point data collected by the first device within a historical time period, and one timestamp corresponds to at least one measurement point data; since the measurement point data under the timestamp represents the data information collected at a certain moment, if one timestamp corresponds to multiple measurement point data, the data collection types of the multiple measurement point data are different. For example, the measurement point data corresponding to timestamp 1 includes wind speed data A1 and power generation data B1. Regardless of whether it is wind speed data or power generation data, usually only a single one will be collected under the timestamp instead of multiple ones.

[0132] For example: the first device is a wind power generation device (abbreviated as: wind turbine). As shown in Table 2, the time series data is a set of measurement point data detected by wind turbine M in a historical time period, and the historical time period includes timestamp 1, timestamp 2 and timestamp 3.

[0133] Table 2: Time series data of fan M

[0134] Speed Power generation Timestamp 1 A1 B1 Timestamp 2 A2 B2 Timestamp 3 A3 B3

[0135] Among them, the measurement point data corresponding to timestamp 1 includes speed A1 and power generation B1; the measurement point data corresponding to timestamp 2 includes speed A2 and power generation B2; the measurement point data corresponding to timestamp 3 includes speed A3 and power generation B3. That is, the time series data collected in the historical time period shown in Table 2 includes 6 measurement point data. Based on the change trends of speed A1, speed A2 and speed A3 in the time series dimension, the change of speed in the historical time period can be determined, and based on the change trends of power generation B1, power generation B2 and power generation B3 in the time series dimension, the change of power generation in the historical time period can be determined.

[0136] Step 420 : when multiple measurement point data in the time series data indicate different data collection types, determine at least two data collection types.

[0137] Illustratively, the time series data includes multiple measurement point data, and each timestamp corresponds to at least one measurement point data. Optionally, the data acquisition types corresponding to the multiple measurement point data included in the time series data are determined, and the data acquisition type is used to characterize the type of the collected measurement point data, which is usually defined based on different measurement objectives and application requirements.

[0138] Schematically, as shown in Table 2, the time series data includes 6 measuring point data, among which the data collection type of the rotation speed A1, rotation speed A2 and rotation speed A3 is "rotation speed type", which means that the data of these 3 measuring points are collected from the rotation speed angle of the wind turbine F; the data collection type of the power generation B1, power generation B2 and power generation B3 is "power generation type", which means that the data of these 3 measuring points are collected from the power generation angle of the wind turbine F, etc.

[0139] Optionally, the data acquisition types respectively corresponding to the plurality of measurement point data are compared, and if the plurality of measurement point data indicate different data acquisition types, at least two data acquisition types corresponding to the time series data are determined.

[0140] As shown in Table 2, the data collection types corresponding to the time series data include "speed type" and "power generation type".

[0141] Step 430 , based on the data meanings respectively represented by the at least two data acquisition types, select at least one data type from the at least two data types as the encoding data type used when encoding the time series data.

[0142] Schematically, the data meaning is used to indicate the expression mode of the measuring point data under the data acquisition type; at least two data types are predetermined data types, and at least two data types include at least two of multiple data types such as integer type, floating point type, string type, Boolean type, custom type, etc. The data meaning represented by the data acquisition type is used to select the corresponding encoding data type to achieve the purpose of encoding the measuring point data in the time series data.

[0143] Optionally, although there are at least two data acquisition types, different data acquisition types may select the same data type as the encoding data type, that is, at least one data type may be selected as the encoding data type through at least two data acquisition types.

[0144] In an optional embodiment, when the data acquisition type is characterized by representing the measured point data by an integer, an integer type is selected as the encoding data type from at least two data types; when the data acquisition type is characterized by representing the measured point data by a decimal, a floating point type is selected as the encoding data type from at least two data types; when the data acquisition type is characterized by representing the measured point data by a character string, a character string type is selected as the encoding data type from at least two data types.

[0145] In addition, the integer type may further include a first integer type and a second integer type; the floating point type may further include a first floating point type and a second floating point type, etc. For related contents, refer to the contents shown in the above step 320 and are not limited here.

[0146] Optionally, if at least two data acquisition types are characterized by representing the measured point data through integers, integer type is selected as the encoding data type; if at least two data acquisition types are characterized by representing the measured point data through decimals, floating point type is selected as the encoding data type; if at least two data acquisition types are characterized by representing the measured point data through strings, string type is selected as the encoding data type.

[0147] Optionally, if at least two data acquisition types represent that the measured point data are represented in different forms, at least two data types are selected as the coded data types. Schematically, data acquisition type 1 of at least two data acquisition types represents that the measured point data are represented by integers, and data acquisition type 2 represents that the measured point data are represented by decimals, then the at least two data types selected are integers and floating point types; if data acquisition type 1 of at least two data acquisition types represents that the measured point data are represented by integers, and data acquisition type 2 represents that the measured point data are represented by strings, then the at least two data types selected are integers and strings, etc., which are not limited here.

[0148] Step 440, when the at least two data collection types include a first data collection type and a second data collection type, determine at least one first measurement point data corresponding to the first data collection type from the plurality of measurement point data, and determine at least one second measurement point data corresponding to the second data collection type.

[0149] Illustratively, taking the at least two data collection types as a first data collection type and a second data collection type as an example, the first data collection type and the second data collection type are different.

[0150] The measuring point data with the first data acquisition type is determined from the multiple measuring point data included in the time series data, which is called the first measuring point data, and the multiple measuring point data include at least one first measuring point data; similarly, the measuring point data with the second data acquisition type is determined from the multiple measuring point data included in the time series data, which is called the second measuring point data, and the multiple measuring point data include at least one second measuring point data.

[0151] In the time series data shown in Table 2, taking the first data acquisition type as "speed type" and the second data acquisition type as "power generation type" as an example, at least one first measuring point data corresponding to the first data acquisition type is determined from the 6 measuring point data to include speed A1, speed A2 and speed A3; at least one second measuring point data corresponding to the second data acquisition type is determined from the 6 measuring point data to include power generation B1, power generation B2 and power generation B3.

[0152] Step 450, taking the first data type corresponding to the first data acquisition type as the data encoding basis, merge and encode at least one first measuring point data to generate a first data encoding result corresponding to the first data acquisition type, and taking the second data type corresponding to the second data acquisition type as the data encoding basis, merge and encode at least one second measuring point data to generate a second data encoding result corresponding to the second data acquisition type.

[0153] Illustratively, the first data type is the encoded data type indicated by the first data collection type, and the second data type is the encoded data type indicated by the second data collection type. The first data type and the second data type may be the same (such as both are floating point types, etc.), or the first data type and the second data type may be different (such as the first data type is an integer type, the second data type is a floating point type, etc.), which is not limited here.

[0154] Optionally, the first data type is determined based on the first data acquisition type, so that when merging and encoding measurement point data with the encoded data type as the data encoding basis, the first data type is used as the data encoding basis to merge and encode at least one first measurement point data with the first data acquisition type, and a first data encoding result corresponding to the first data acquisition type is generated.

[0155] In an optional embodiment, for the mth first measuring point data in at least one first measuring point data, an mth data array is formed by using the mth timestamp corresponding to the mth first measuring point data and the mth first measuring point data to obtain data arrays corresponding to the at least one first measuring point data, where m is a positive integer.

[0156] Schematically, the mth first measuring point data is any one of the at least one first measuring point data, and the mth first measuring point data is the first measuring point data corresponding to the mth timestamp; the mth timestamp and the mth first measuring point data are combined to obtain the mth data array; multiple timestamps and corresponding first measuring point data are combined to obtain data arrays corresponding to at least one first measuring point data respectively.

[0157] Optionally, taking the mth first measuring point data and the corresponding mth timestamp as an example, the mth first measuring point data is spliced ​​after the mth timestamp to obtain the mth data array corresponding to the mth first measuring point data, and obtain data arrays corresponding to at least one first measuring point data.

[0158] In an optional embodiment, data arrays corresponding to at least one first measurement point data are spliced ​​along a time series dimension to obtain a first type data array corresponding to the first data acquisition type.

[0159] Schematically, multiple timestamps within a historical time period are determined along a time series dimension, and the multiple timestamps are arranged in sequence. Since there is at least one timestamp corresponding to the first measuring point data, the data arrays corresponding to the at least one first measuring point data can be arranged based on the arrangement order of the multiple timestamps, and then the data arrays corresponding to the at least one first measuring point data are spliced ​​to obtain a first type of data array corresponding to the first data acquisition type.

[0160] Illustratively, the first type data array describes the combined encoding result of the measurement point data of the first data acquisition type.

[0161] In an optional embodiment, a first data type corresponding to a first data acquisition type is written at a first position serving as a data encoding reference, and a first type data array is written at a second position after the first position to generate a first data encoding result corresponding to the first data acquisition type.

[0162] Schematically, taking the data encoding benchmark implemented as the character writing position as an example, the first position is used as the data encoding benchmark, the first data type is written at the first position, and the spliced ​​first type data array is written at the second position after the first position, thereby obtaining a first data encoding result corresponding to the first data acquisition type.

[0163] Optionally, the first position is a pre-selected character writing position written during encoding, and a first data type is written at the first position to guide the reading method of the first type data array written at a second position after the first position, such as: the first data type is a floating point type, and the first type data array is an encoding result after merging at least one first measurement point data expressed in decimal form under the first data acquisition type.

[0164] In some embodiments, the device name of the first device that collects timing data is obtained, the device name is written in the third position before the first position, the first data type is written in the first position, and the first type data array is written in the second position to generate a first data encoding result corresponding to the first data collection type.

[0165] Schematically, the device name is used to uniquely characterize the first device, the third position is a pre-selected character writing position written during encoding, the third position is located before the first position, and the contents written in the third position, the first position, and the second position are combined to generate a first data encoding result corresponding to the first data acquisition type.

[0166] Optionally, the device name length, device name and device collected data volume of the first device are written at the third position to obtain a device array corresponding to the first device; the type name length, type name, first data type and the measurement point data volume corresponding to the first data type of the first data collection type are written at the first position to obtain a measurement point array corresponding to the first data collection type; the first type data array is written at the second position; the device array, the measurement point array and the first type data array are spliced ​​in the order of the third position, the first position and the second position to obtain a first data encoding result corresponding to the first data collection type.

[0167] Schematically, when data encoding processing is performed on the time series data shown in Table 2, taking the first data acquisition type as "speed type" as an example, by writing the device name length (3) of fan M, the device name (fan M) and the device acquisition data volume (6) at the third position, the device array corresponding to the first device is obtained as follows: 3-fan M-6; by writing the type name length (2), type name (speed), first data type (floating point type) and the measurement point data volume (3) corresponding to the first data acquisition type at the first position, the measurement point array corresponding to the first data acquisition type is obtained as follows: 2-speed-floating point type-3; the first type data array written at the second position is as follows: timestamp 1-A1-timestamp 2-A2-timestamp 3-A3, thereby obtaining the first data encoding result corresponding to the first data acquisition type.

[0168] In some embodiments, there is a preset conversion relationship between the timestamp and the acquisition moment. In the process of obtaining the first type of data array, the timestamp is converted into the corresponding acquisition moment based on the conversion relationship, so as to obtain the first type of data array including the timestamp and the acquisition moment along the time series dimension.

[0169] In an illustrative manner, data encoding processing is performed on the time series data shown in Table 2, and the device array is: 3-fan M-6; the measurement point array is: 2-speed-floating point type-3; the first type of data array written at the second position is: timestamp 1-collection time 1-A1-timestamp 2-collection time 2-A2-timestamp 3-collection time 3-A3, thereby obtaining the first data encoding result corresponding to the first data collection type in the time series data shown in Table 2.

[0170] In an optional embodiment, for the nth second measuring point data in the at least one second measuring point data, an nth data array is formed by using the nth timestamp corresponding to the nth second measuring point data and the nth second measuring point data to obtain a data array corresponding to the at least one second measuring point data, where n is a positive integer.

[0171] Schematically, the nth second measuring point data is any one of the at least one second measuring point data, and the nth second measuring point data is the second measuring point data corresponding to the nth timestamp; the nth timestamp and the nth second measuring point data are combined to obtain the nth data array; multiple timestamps and corresponding second measuring point data are combined to obtain data arrays corresponding to at least one second measuring point data respectively.

[0172] Optionally, taking the nth second measuring point data and the corresponding nth timestamp as an example, the nth second measuring point data is spliced ​​after the nth timestamp to obtain the nth data array corresponding to the nth second measuring point data, and obtain data arrays corresponding to at least one second measuring point data.

[0173] In an optional embodiment, data arrays corresponding to at least one second measuring point data are spliced ​​along the time series dimension to obtain a second type of data array corresponding to the second data acquisition type.

[0174] Schematically, multiple timestamps within a historical time period are determined along a time series dimension, and the multiple timestamps are arranged in sequence. Since there is at least one timestamp corresponding to the second measuring point data, the data arrays corresponding to the at least one second measuring point data can be arranged based on the arrangement order of the multiple timestamps, and then the data arrays corresponding to the at least one second measuring point data are spliced ​​to obtain a second type of data array corresponding to the second data acquisition type.

[0175] Illustratively, the second type data array describes the combined encoding result of the measurement point data of the second data acquisition type.

[0176] In an optional embodiment, a second data type corresponding to the second data acquisition type is written at a first position serving as a data encoding reference, and a second type data array is written at a second position after the first position to generate a second data encoding result corresponding to the second data acquisition type.

[0177] Schematically, taking the data encoding benchmark implemented as the character writing position as an example, the first position is used as the data encoding benchmark, the second data type is written at the first position, and the concatenated second type data array is written at the second position after the first position, thereby obtaining a second data encoding result corresponding to the second data acquisition type.

[0178] Optionally, the first position is a pre-selected character writing position written during encoding, and the second data type is written at the first position to guide the reading method of the second type data array written at the second position after the first position, such as: the second data type is a floating point type, and the second type data array is the encoding result of merging at least one second measurement point data expressed in decimal form under the first data acquisition type.

[0179] In some embodiments, the device name of the first device that collects timing data is obtained, the device name is written at a third position before the first position, the second data type is written at the first position, and the second type data array is written at the second position to generate a second data encoding result corresponding to the second data collection type.

[0180] Schematically, the device name is used to uniquely characterize the first device, the third position is a pre-selected character writing position written during encoding, the third position is located before the first position, and the contents written in the third position, the first position and the second position are combined to generate a second data encoding result corresponding to the second data acquisition type.

[0181] Optionally, the device name length, device name and device collected data volume of the first device are written at the third position to obtain a device array corresponding to the first device; the type name length, type name, second data type and the measurement point data volume corresponding to the second data type of the second data collection type are written at the first position to obtain a measurement point array corresponding to the second data collection type; the second type data array is written at the second position; the device array, the measurement point array and the second type data array are spliced ​​in the order of the third position, the first position and the second position to obtain a second data encoding result corresponding to the second data collection type.

[0182] Schematically, when data encoding processing is performed on the time series data shown in Table 2, taking the first data acquisition type as "power generation type" as an example, by writing the device name length (3) of the fan M, the device name (fan M) and the device acquisition data volume (6) at the third position, the device array corresponding to the first device is obtained as follows: 3-fan M-6; by writing the type name length (3), type name (power generation), second data type (floating point type) and the measurement point data volume (3) corresponding to the second data type at the first position, the measurement point array corresponding to the second data acquisition type is obtained as follows: 3-power generation-floating point type-3; the second type data array written at the second position is as follows: timestamp 1-B1-timestamp 2-B2-timestamp 3-B3, thereby obtaining a second data encoding result corresponding to the second data acquisition type.

[0183] In some embodiments, there is a preset conversion relationship between the timestamp and the acquisition moment. In the process of obtaining the second type of data array, the timestamp is converted into the corresponding acquisition moment based on the conversion relationship, so as to obtain the first type of data array including the timestamp and the acquisition moment along the time series dimension.

[0184] In an illustrative manner, data encoding processing is performed on the time series data shown in Table 2, and the device array is: 3-wind turbine M-6; the measurement point array is: 3-power generation-floating point type-3; the second type data array written at the second position is: timestamp 1-collection time 1-B1-timestamp 2-collection time 2-B2-timestamp 3-collection time 3-B3, thereby obtaining the first data encoding result corresponding to the second data collection type in the time series data shown in Table 2.

[0185] The data encoding result corresponding to the time series data includes a first data encoding result and a second data encoding result. That is, based on the number of data acquisition types, the time series data obtains at least one data encoding result as the data encoding result corresponding to the time series data.

[0186] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0187] In summary, compared with the process of separately processing the data information of the measuring point data included in the sequence data to realize the storage and analysis of the serialization results, the encoding data type used when encoding the time series data is more specifically determined by the data acquisition type, so that the measuring point data corresponding to multiple timestamps are merged and encoded from the dimension of data acquisition type, which helps to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, which not only reduces the data storage volume, but also helps to improve the storage efficiency of time series data and the subsequent analysis efficiency.

[0188] In the embodiment of the present application, the content of obtaining the coded data type when multiple measuring point data indicate different data acquisition types is introduced. If multiple measuring point data indicate different data acquisition types, the measuring point data of the same data acquisition type can be merged and encoded based on the coded data type, so as to obtain the data encoding result of the time series data by combining the data encoding results corresponding to at least two data acquisition types respectively; by finding the commonality of data information of multiple measuring point data, the measuring point data with some of the same data information are merged and encoded, avoiding the problem of repeated expression of information, reducing the amount of data storage, and helping to improve data retrieval efficiency through the data encoding results corresponding to the time series data.

[0189] In an optional embodiment, the above operations are performed on multiple time series data in the time series database to obtain data encoding results corresponding to the multiple time series data, and the time series data is stored in the data encoding results. The purpose of transmitting the time series data can also be achieved by transmitting the data encoding results. Schematically, as shown in Figure 5 As shown above Figure 2 The illustrated embodiment may further include the following steps 510 to 530 .

[0190] Step 510, obtaining a time series database.

[0191] The time series database includes multiple time series data, each of which corresponds to at least one data encoding result. The data encoding result is a result obtained by merging and encoding the measurement point data corresponding to multiple timestamps under the same data acquisition type.

[0192] Optionally, after obtaining the time series database, the above-mentioned data encoding process is performed separately for multiple time series data in the time series database; each time series data includes multiple measurement point data, and based on the data acquisition type of the measurement point data in the time series data, the measurement point data with the same data acquisition type are merged and encoded to obtain data encoding results corresponding to the multiple time series data respectively.

[0193] like Figure 6 As shown, it is a schematic diagram of generating data encoding results. The encoded data type (such as referred to as Value_type) is written at the first position 610 indicated by the first byte, and the encoded data type occupies 1 byte; the second data array consisting of a timestamp (such as referred to as Timestamp) and measurement point data (such as referred to as value) is written at the second position 620 starting from the second byte. For example, the timestamp in the second data array occupies 8 bytes, and the bytes occupied by the measurement point data are determined according to the measurement point data itself (such as the larger the measurement point data, the more bytes it occupies, etc.).

[0194] According to different data collection types, different data types are sampled from the measurement point data as encoding data types to perform encoding. The data types include at least one of multiple types such as integer type (int, int32, int64), floating point type (float32, float64) and string (string).

[0195] like Figure 7 As shown, a schematic diagram of the generated data encoding result with the encoding data type being an integer type is shown. Taking the integer type int32 as an example, the encoding data type (such as referred to as Value_type for short) written at the first position 710 indicated by the first byte is an integer type, occupying 1 byte, and a second data array consisting of a timestamp (such as referred to as Timestamp for short) and measurement point data (such as referred to as value for short) is written at the second position 720 starting from the second byte. For example, the timestamp in the second data array occupies 8 bytes, and the measurement point data occupies 4 bytes.

[0196] As shown in Table 3, it is a schematic table of data query conditions after encoding time series data with the encoding data type being integer type.

[0197] Table 3

[0198]

[0199] like Figure 8 As shown, it is a schematic diagram of data query after encoding time series data with the encoding data type being integer type, corresponding to Table 3. Among them, the line graph 810 is a schematic diagram of the comparison of query time consumption before and after optimization, the line graph 811 before optimization indicates the query time consumption before encoding the time series data, and the line graph 812 after optimization indicates the query time consumption after encoding the time series data; among them, the line graph 820 is a schematic diagram of the comparison of query traffic before and after optimization, the line graph 821 before optimization indicates the query traffic consumption before encoding the time series data, and the line graph 822 after optimization indicates the query traffic consumption after encoding the time series data.

[0200] like Fig. 9 As shown, a schematic diagram of the data encoding result generated when the encoding data type is a floating point type is shown. Taking the floating point type as float64 as an example, the encoding data type (such as referred to as Value_type for short) written at the first position 910 indicated by the first byte is a floating point type, occupying 1 byte, and a second data array consisting of a timestamp (such as referred to as Timestamp for short) and measurement point data (such as referred to as value for short) is written at the second position 920 starting from the second byte. For example, the timestamp in the second data array occupies 8 bytes, and the measurement point data occupies 8 bytes (the precision is higher, so it occupies more bytes).

[0201] Table 4 is a schematic table of data query conditions when the encoding data type is floating-point encoding time series data.

[0202] Table 4

[0203]

[0204]

[0205] like Fig.10 As shown, it is a schematic diagram of data query after encoding time series data with the encoding data type as floating point type, corresponding to Table 4. Among them, the line graph 1010 is a comparative schematic diagram of query time consumption before and after optimization, the line graph 1011 before optimization indicates the query time consumption before encoding the time series data, and the line graph 1012 after optimization indicates the query time consumption after encoding the time series data; among them, the line graph 1020 is a comparative schematic diagram of query traffic before and after optimization, the line graph 1021 before optimization indicates the query traffic consumption before encoding the time series data, and the line graph 1022 after optimization indicates the query traffic consumption after encoding the time series data.

[0206] In some embodiments, as shown in Figure 5, it is a transmission flow comparison table of serializing time series data and performing custom encoding processing based on float64 data type.

[0207] Table 5

[0208]

[0209] As shown in Table 5, if serialization processing is performed on multiple measurement point data under the time series data (6000 data or 60,000 data or 600,000 data) respectively, the current number of bytes is larger; if merge encoding processing is performed on multiple measurement point data under the time series data, the optimized number of bytes is smaller, which helps to reduce the amount of data storage and also helps to reduce the amount of data transmission.

[0210] like Fig.11 As shown, a schematic diagram of the generated data encoding result with the encoding data type being a string type is shown, the encoding data type (such as referred to as Value_type for short) written at the first position 1110 indicated by the first byte is a floating point type, occupying 1 byte, and a second data array consisting of a timestamp (such as referred to as Timestamp for short), a string length (such as referred to as Length for short) and measurement point data (such as referred to as value for short) is written at the second position 1120 starting from the second byte, such as the timestamp in the second data array occupies 8 bytes, the string length occupies 2 bytes, and the bytes occupied by the measurement point data (represented by a string) are determined according to the measurement point data itself.

[0211] In some embodiments, as shown in Figure 6, it is a transmission flow comparison table for serializing time series data and performing custom encoding processing based on string type.

[0212] Table 6

[0213]

[0214] As shown in Table 6, if serialization processing is performed on multiple measurement point data under the time series data (6000 data or 60,000 data or 600,000 data) respectively, the current number of bytes is larger; if merge encoding processing is performed on multiple measurement point data under the time series data, the optimized number of bytes is smaller, which helps to reduce the amount of data storage and also helps to reduce the amount of data transmission.

[0215] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0216] In some embodiments, for the ath time series data among multiple time series data, when multiple measurement point data in the ath time series data indicate the same data acquisition type, a second data array is obtained by merging and encoding first data arrays corresponding to the multiple measurement point data in the ath time series data, thereby combining the encoded data type and the second data array to obtain a data encoding result corresponding to the ath time series data, where a is a positive integer.

[0217] Illustratively, the a-th time series data is any one of multiple time series data. When encoding to obtain a data encoding result corresponding to the a-th time series data, if multiple measurement point data in the a-th time series data indicate the same data acquisition type, the measurement point data corresponding to different timestamps can be merged and encoded through the data acquisition type to obtain a data encoding result corresponding to the a-th time series data, and the data encoding result also corresponds to the data acquisition type.

[0218] In some embodiments, for the bth time series data among multiple time series data, when multiple measurement point data in the bth time series data indicate different data acquisition types, the measurement point data corresponding to at least two data acquisition types are determined, such as determining at least one first measurement point data with a first data acquisition type from the multiple measurement point data included in the bth time series data, and determining at least one second measurement point data with a second data acquisition type from the multiple measurement point data included in the bth time series data; taking the data type corresponding to the data acquisition type as the data encoding reference, by merging and encoding the measurement point data under the same data acquisition type, data encoding results corresponding to at least two data acquisition types are obtained, such as a first data encoding result corresponding to the first data acquisition type and a second data encoding result corresponding to the second data acquisition type; the data encoding results corresponding to the at least two data acquisition types are collectively referred to as the data encoding results corresponding to the bth time series data.

[0219] Illustratively, the bth time series data is any one of multiple time series data. When encoding to obtain a data encoding result corresponding to the bth time series data, if multiple measurement point data in the bth time series data indicate different data acquisition types, the measurement point data corresponding to different timestamps under the same data acquisition type can be merged and encoded to obtain data encoding results corresponding to at least two data acquisition types, so that the at least two data encoding results can be used as the data encoding result corresponding to the bth time series data.

[0220] That is: when the multiple measurement point data included in the time series data indicate the same data acquisition type, the data encoding result corresponding to the time series data is an encoding result corresponding to the data acquisition type; when the multiple measurement point data included in the time series data indicate at least two different data acquisition types, the data encoding result corresponding to the time series data includes encoding results corresponding to at least two data acquisition types respectively.

[0221] The data encoding result is marked with a data tag, and the data tag is used to uniquely indicate the data encoding result.

[0222] Illustratively, when generating a data encoding result corresponding to time series data, a data tag corresponding to the data encoding result is generated, and the data encoding result is uniquely indicated by the data tag, so the data encoding result can also be uniquely determined based on the data tag.

[0223] Optionally, when the measurement point data included in the time series data indicates the same data acquisition type, the time series data corresponds to a data encoding result, and the data encoding result corresponds to a unique data label, and the time series data can be uniquely identified from multiple time series data through the data label.

[0224] Optionally, when the measurement point data included in the time series data indicates different data acquisition types, the time series data corresponds to at least two data encoding results, and the at least two data encoding results correspond one-to-one to at least two data acquisition types, such as data acquisition type 1 corresponds to data encoding result A, and data acquisition type 2 corresponds to data encoding result B; each data encoding result corresponds to a unique data label, such as data encoding result A corresponds to data label a, and data label result B corresponds to data label b. The data encoding result can be uniquely determined from multiple data encoding results through the data label.

[0225] Step 520, receiving search information.

[0226] The search information is used to select the measurement point data with the target data collection type.

[0227] Illustratively, the target data collection type is a pre-selected data collection type, such as the target data collection type is "rotation speed type", "power generation type" and the like.

[0228] Optionally, the retrieval information is information generated in other terminals based on the target data collection type. For example, the terminal used to collect and manage time series data is a wind power generation device, and the other terminal is a mobile terminal, which displays multiple data collection types. Upon receiving a selection operation for a target data collection type among multiple data collection types, type retrieval information is generated based on the target data collection type and sent to the wind power generation device to obtain measurement point data with the target data collection type from the wind power generation device.

[0229] In some embodiments, the search information is used to select measurement point data that meets the search conditions, and the search conditions include the target data collection type.

[0230] Indicatively, the search condition is a pre-selected data search condition, and the search condition includes at least one of multiple conditions such as target data collection type, device name, device type, etc. For example, the search information is used to select the measurement point data of "speed type" collected by "fan F", etc.

[0231] Step 530, searching for multiple data tags based on the search information, and obtaining a data encoding result corresponding to the target data collection type as a data search result.

[0232] Optionally, at least one data collection type under a time series data corresponds to a data encoding result, and each data encoding result corresponds to a data label; therefore, multiple data labels will be generated based on multiple time series data.

[0233] For example: the time series database includes the time series data shown in Table 1 and the time series data shown in Table 2. The "speed type" under the time series data shown in Table 1 corresponds to a data encoding result P1 and a data label p1; the "speed type" under the time series data shown in Table 2 corresponds to a data encoding result P2 and a corresponding data label p2, and the "power generation type" corresponds to a data encoding result P3 and a corresponding data label p3. Then the multiple data labels included in the time series database include data label p1, data label p2 and data label p3.

[0234] In an optional embodiment, the search condition indicated by the search information is determined, and the data encoding result that meets the search condition is obtained as the data search result.

[0235] Illustratively, at least one of the target data collection type, device type, device name, and other information is determined based on the search conditions; if the data encoding result includes at least one of the data collection type, device type, device name, and other information, multiple data tags can be searched specifically based on the search conditions to obtain the data encoding result.

[0236] like Fig.12 As shown, it is a schematic diagram of a data encoding result including a variety of information, including a device array 1210 [information used to characterize the device for collecting data, such as: device name length (abbreviated as Assetid_cnt), device name (abbreviated as Assetid) and device collected data volume (abbreviated as Assetbytes)], a measuring point array 1220 [information used to characterize the type of measuring point data, such as: type name length of the data collection type (abbreviated as Pointid_cnt), type name (abbreviated as Pointid), encoding data type (abbreviated as Pointid_value_type) and the amount of measuring point data corresponding to the encoding data type (abbreviated as Pointbytes)] and a data array 1230 (information used to characterize the measuring point data, such as: timestamp (abbreviated as Timestamp), collection time (abbreviated as Localtime) and the value of the measuring point data (abbreviated as Value)], which are not limited here.

[0237] Optionally, the target data collection type indicated by the retrieval information is determined, and a data encoding result corresponding to the target data collection type is obtained as a data retrieval result.

[0238] For example: if the target data acquisition type indicated by the retrieval information is "speed type", then after searching multiple data tags (data tag p1, data tag p2, and data tag p3), the data encoding results corresponding to the target data acquisition type are obtained, including the data encoding result P1 corresponding to the data tag p1 and the data encoding result P2 corresponding to the data tag p2, and the data encoding result P1 and the data encoding result P2 are used as the data retrieval results determined based on the retrieval information.

[0239] Optionally, the target data acquisition type and device name indicated by the retrieval information are determined, and a data encoding result corresponding to the target data acquisition type is obtained as a data retrieval result.

[0240] For example: the target data collection type indicated by the retrieval information is "speed type" and the equipment name is "fan M", that is, the target of the retrieval information is to find the measurement point data of the "speed type" collected by fan M. After searching for multiple data tags (data tag p1, data tag p2, and data tag p3), the data encoding result that meets the target data collection type and equipment name is obtained as the data encoding result P2 corresponding to the data tag p2, and the data encoding result P2 is used as the data retrieval result determined based on the retrieval information.

[0241] The data retrieval result is used to decode the measuring point data indicated by the retrieval information.

[0242] Illustratively, based on the search information sent by the other terminals, the data search result is sent to the other terminals, so that the other terminals can obtain at least one measuring point data indicated by the search information by decoding the data search result.

[0243] In schematic form, compared to the need to perform serialization and deserialization operations on time series data, which consumes computing resources and leads to large data traffic and time-consuming network transmission, the merged encoding process can store time series data through data encoding results with smaller resource usage, greatly reducing redundant fields (such as fields corresponding to data acquisition type, fields corresponding to device name, etc.), and can also reduce the transmission traffic of time series data by transmitting data encoding results, thereby improving data transmission efficiency.

[0244] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0245] In an optional embodiment, in the process of acquiring the measurement point data based on the time series database query, the metadata query and the time series data query may be processed separately.

[0246] Metadata is data used to describe the data structure, type, source, storage location, relationship and other information of measurement point data; metadata query usually does not involve the measurement point data itself, but focuses on the definition, structure, storage method and other information of the measurement point data.

[0247] In some embodiments, when querying the measurement point data corresponding to multiple time series data in the time series database, if it is necessary to query the definition, structure, storage method and other information of the measurement point data, considering that the metadata query process consumes less traffic and the query frequency is low, a serialized query method can be used to achieve the purpose of metadata query 1210; if it is necessary to query the measurement point data itself, considering that the transmission traffic of the measurement point data is large and the transmission frequency is high, a coded query method can be used to achieve the purpose of time series data query, so as to realize the process of efficient data transmission by transmitting the data encoding result after merging the encoding.

[0248] It is worth noting that the above are merely illustrative examples and are not limited to the embodiments of the present application.

[0249] In summary, compared with the process of separately processing the data information of the measuring point data included in the sequence data to realize the storage and analysis of the serialization results, the encoding data type used when encoding the time series data is more specifically determined by the data acquisition type, so that the measuring point data corresponding to multiple timestamps are merged and encoded from the dimension of data acquisition type, which helps to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, which not only reduces the data storage volume, but also helps to improve the storage efficiency of time series data and the subsequent analysis efficiency.

[0250] In an embodiment of the present application, it is introduced to retrieve and transmit the content of the data encoding result while storing the data encoding result. The data volume of the data encoding result is small, which makes the efficiency of transmitting the data encoding result higher; in addition, the terminal sending the retrieval information can quickly decode the data encoding result with a small data volume to obtain the time series data, thereby improving data utilization efficiency while ensuring data integrity.

[0251] Fig.13 is a structural block diagram of a time series data management device provided by an exemplary embodiment of the present application, such as Fig.13 As shown, the device includes the following parts:

[0252] An acquisition module 1310 is used to acquire time series data collected in a historical time period, where the time series data is used to describe, in a time series dimension, a change trend of measurement point data corresponding to a plurality of time stamps in the historical time period;

[0253] A determination module 1320, configured to determine a coding data type used when encoding the time series data based on a data collection type of the measurement point data in the time series data, wherein the coding data type is used to characterize a data type for encoding the time series data;

[0254] The generation module 1330 is used to combine and encode the measurement point data corresponding to the multiple timestamps based on the encoded data type as the data encoding basis, and generate a data encoding result corresponding to the time series data. The data encoding basis is used to combine and encode the measurement point data with the same data acquisition type, and the data encoding result is used for a single decoding to obtain the measurement point data corresponding to the multiple timestamps.

[0255] In an optional embodiment, the determination module 1320 is also used to select the encoding data type used when encoding the time series data from at least two data types based on the data meaning represented by the data acquisition type when multiple measurement point data in the time series data indicate the same data acquisition type.

[0256] In an optional embodiment, the determination module 1320 is further used to select an integer type as the encoding data type from at least two data types when the data acquisition type characterizes that the measurement point data is represented by an integer; or, when the data acquisition type characterizes that the measurement point data is represented by a decimal, select a floating point type as the encoding data type from at least two data types; or, when the data acquisition type characterizes that the measurement point data is represented by a character string, select a string type as the encoding data type from at least two data types.

[0257] In an optional embodiment, the generation module 1330 is also used to form a first data array with the jth timestamp and the jth measurement point data for the jth measurement point data among the multiple measurement point data, to obtain first data arrays corresponding to the multiple timestamps respectively, where j is a positive integer; splicing the first data arrays corresponding to the multiple timestamps respectively along the time series dimension to obtain a second data array; writing the encoded data type at a first position serving as the data encoding reference, and writing the second data array at a second position after the first position, to generate the data encoding result corresponding to the time series data.

[0258] In an optional embodiment, the determination module 1320 is also used to determine at least two data acquisition types when multiple measurement point data in the time series data indicate different data acquisition types; based on the data meanings respectively represented by the at least two data acquisition types, select at least one data type from at least two data types as the encoded data type used when encoding the time series data.

[0259] In an optional embodiment, the generation module 1330 is also used to determine at least one first measurement point data corresponding to the first data acquisition type from the multiple measurement point data, and to determine at least one second measurement point data corresponding to the second data acquisition type when the at least two data acquisition types include a first data acquisition type and a second data acquisition type; using the first data type corresponding to the first data acquisition type as a data encoding reference, generating a first data encoding result corresponding to the first data acquisition type based on the at least one first measurement point data; and using the second data acquisition type as a data encoding reference, generating a second data encoding result corresponding to the second data acquisition type based on the at least one second measurement point data; the data encoding results corresponding to the time series data include the first data encoding result and the second data encoding result.

[0260] In an optional embodiment, the generating module 1330 is further used to form an mth data array with the mth timestamp corresponding to the mth first measuring point data and the mth first measuring point data for the mth first measuring point data in the at least one first measuring point data, to obtain data arrays corresponding to the at least one first measuring point data, where m is a positive integer; to splice the data arrays corresponding to the at least one first measuring point data along the time dimension to obtain a first type data array corresponding to the first data acquisition type; to write the first data acquisition type at a first position serving as the data encoding reference, and to write the first type data array at a second position after the first position, to generate the first data encoding result corresponding to the first data acquisition type.

[0261] In an optional embodiment, the generating module 1330 is further used to form an nth data array with an nth timestamp corresponding to the nth second measuring point data and the nth second measuring point data for the nth second measuring point data in the at least one second measuring point data, so as to obtain data arrays corresponding to the at least one second measuring point data, where n is a positive integer; splicing the data arrays corresponding to the at least one second measuring point data along the time series dimension to obtain a second type data array corresponding to the second data acquisition type; writing the second data acquisition type at a first position serving as the data encoding reference, and writing the second type data array at a second position after the first position, so as to generate the second data encoding result corresponding to the second data acquisition type.

[0262] In an optional embodiment, the generation module 1330 is also used to obtain a time series database, which includes multiple time series data, each of which corresponds to at least one data encoding result, and the data encoding result is marked with a data tag, and the data tag is used to uniquely indicate the data encoding result; receive retrieval information, and the retrieval information is used to select measurement point data with a target data acquisition type; search for multiple data tags based on the retrieval information, and obtain the data encoding result corresponding to the target data acquisition type as a data retrieval result.

[0263] In summary, compared with the process of converting each measuring point data of the time series data into a specific sequence format through serialization to achieve storage and transmission, the encoding data type used when encoding the time series data is more specifically determined by the data acquisition type, thereby merging and encoding multiple measuring point data from the dimension of data acquisition type, which helps to generate data encoding results with less redundancy through the encoding data type and multiple measuring point data, thereby improving the storage utilization efficiency of the time series data.

[0264] It should be noted that the time series data management device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the time series data management device provided in the above embodiment and the time series data management method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0265] Fig.14 The schematic diagram of the structure of a server provided by an exemplary embodiment of the present application is shown. The server 1400 includes a central processing unit (CPU) 1401 and a system memory 1402, wherein the system memory 1402 includes a random access memory (RAM) 1403 and a read-only memory (ROM) 1404; the server 1400 also includes a system bus 1405 connecting the system memory 1402 and the central processing unit 1401. In addition, the server 1400 also includes a large-capacity storage device 1406 for storing an operating system 1413, an application program 1414, and other program modules 1415.

[0266] The mass storage device 1406 is connected to the central processing unit 1401 through a mass storage controller (not shown) connected to the system bus 1405. The mass storage device 1406 and its associated computer readable media provide non-volatile storage for the server 1400. That is, the mass storage device 1406 may include a computer readable medium (not shown) such as a hard disk or a compact disc read only memory (CD-ROM) drive.

[0267] Without loss of generality, computer readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. The above-mentioned system memory 1402 and mass storage device 1406 can be collectively referred to as memory.

[0268] According to various embodiments of the present application, the server 1400 can also be connected to a remote computer on the network through a network such as the Internet. That is, the server 1400 can be connected to the network 1412 through the network interface unit 1411 connected to the system bus 1405, or the network interface unit 1411 can be used to connect to other types of networks or remote computer systems (not shown).

[0269] The above-mentioned memory also includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU.

[0270] An embodiment of the present application also provides a computer device, which includes a processor and a memory, in which at least one instruction, at least one program, code set or instruction set is stored, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the timing data management method provided by the above-mentioned method embodiments.

[0271] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the timing data management method provided by the above-mentioned method embodiments.

[0272] The embodiments of the present application also provide a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the time series data management method described in any of the above embodiments.

[0273] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A time series data management method, characterized in that: The method comprises: Acquire time series data collected within a historical time period, where the time series data is used to describe, in a time series dimension, a change trend of measurement point data corresponding to a plurality of timestamps within the historical time period; Determine, based on the data collection type of the measurement point data in the time series data, a coding data type used when encoding the time series data, wherein the coding data type is used to characterize the data type of encoding the time series data; Taking the encoding data type as the data encoding benchmark, the measurement point data corresponding to the multiple timestamps are merged and encoded to generate a data encoding result corresponding to the time series data. The data encoding benchmark is used to merge and encode the measurement point data with the same data acquisition type, and the data encoding result is used for a single decoding to obtain the measurement point data corresponding to the multiple timestamps.

2. The method according to claim 1, characterized in that: The step of determining the encoding data type used when encoding the time series data based on the data collection type of the measurement point data in the time series data includes: In the case where multiple measurement point data in the time series data indicate the same data acquisition type, the encoded data type used when encoding the time series data is selected from at least two data types based on the data meaning represented by the data acquisition type.

3. The method according to claim 2, characterized in that The selecting, based on the data meaning represented by the data acquisition type, the encoding data type used when encoding the time series data from at least two data types includes: In the case where the data acquisition type characterizes that the measuring point data is represented by an integer, an integer type is selected from at least two data types as the encoding data type; or, In the case where the data acquisition type characterizes that the measurement point data is represented by a decimal, a floating point type is selected from at least two data types as the encoding data type; or, In the case where the data collection type characterizes that the measurement point data is represented by a character string, a character string type is selected from at least two data types as the encoding data type.

4. The method according to claim 2, characterized in that: The step of taking the coded data type as a data coding reference, merging and coding the measurement point data corresponding to the multiple timestamps, and generating a data coding result corresponding to the time series data includes: For the j-th measuring point data among the multiple measuring point data, a first data array is formed by using the j-th timestamp and the j-th measuring point data to obtain first data arrays corresponding to the multiple timestamps respectively, where j is a positive integer; splicing the first data arrays corresponding to the multiple timestamps respectively along the time series dimension to obtain a second data array; The encoded data type is written at a first position serving as a reference for the data encoding, and the second data array is written at a second position subsequent to the first position, thereby generating the data encoding result corresponding to the timing data.

5. The method according to claim 1, characterized in that The step of determining the encoding data type used when encoding the time series data based on the data collection type of the measurement point data in the time series data includes: In the case where the plurality of measurement point data in the time series data indicate different data acquisition types, determining at least two data acquisition types; Based on the data meanings respectively represented by the at least two data acquisition types, at least one data type is selected from the at least two data types as the encoding data type used when encoding the time series data.

6. The method according to claim 5, characterized in that The step of taking the coded data type as a data coding reference, merging and coding the measurement point data corresponding to the multiple timestamps, and generating a data coding result corresponding to the time series data includes: In a case where the at least two data acquisition types include a first data acquisition type and a second data acquisition type, determining at least one first measurement point data corresponding to the first data acquisition type from the plurality of measurement point data, and determining at least one second measurement point data corresponding to the second data acquisition type; Taking the first data type corresponding to the first data acquisition type as the data encoding reference, a first data encoding result corresponding to the first data acquisition type is generated based on the at least one first measuring point data, and taking the second data acquisition type as the data encoding reference, a second data encoding result corresponding to the second data acquisition type is generated based on the at least one second measuring point data; the data encoding results corresponding to the time series data include the first data encoding result and the second data encoding result.

7. The method according to claim 6, characterized in that The method of taking the first data type corresponding to the first data collection type as a data encoding reference and generating a first data encoding result corresponding to the first data collection type based on the at least one first measuring point data includes: For the mth first measuring point data in the at least one first measuring point data, an mth data array is formed by using the mth timestamp corresponding to the mth first measuring point data and the mth first measuring point data to obtain data arrays respectively corresponding to the at least one first measuring point data, where m is a positive integer; splicing the data arrays corresponding to the at least one first measuring point data respectively along the time series dimension to obtain a first type data array corresponding to the first data acquisition type; The first data acquisition type is written at a first position serving as a reference for the data encoding, and the first type data array is written at a second position after the first position, to generate the first data encoding result corresponding to the first data acquisition type.

8. The method according to claim 6, characterized in that The step of taking the second data collection type as a data encoding reference and generating a second data encoding result corresponding to the second data collection type based on the at least one second measuring point data includes: For an nth second measuring point data in the at least one second measuring point data, an nth data array is formed by using an nth timestamp corresponding to the nth second measuring point data and the nth second measuring point data to obtain data arrays respectively corresponding to the at least one second measuring point data, where n is a positive integer; splicing the data arrays corresponding to the at least one second measuring point data respectively along the time series dimension to obtain a second type data array corresponding to the second data acquisition type; The second data acquisition type is written at a first position serving as a reference for the data encoding, and the second type data array is written at a second position after the first position, to generate the second data encoding result corresponding to the second data acquisition type.

9. The method according to any one of claims 1 to 8, characterized in that: After generating the data encoding result corresponding to the time series data, the method further includes: Acquire a time series database, wherein the time series database includes a plurality of time series data, each of the plurality of time series data corresponds to at least one data encoding result, and the data encoding result is marked with a data tag, and the data tag is used to uniquely indicate the data encoding result; Receiving search information, wherein the search information is used to select measurement point data having a target data collection type; Based on the search information, multiple data tags are searched to obtain a data encoding result corresponding to the target data collection type as a data search result.

10. A time series data management device, characterized in that: The device comprises: An acquisition module, used to acquire time series data collected within a historical time period, wherein the time series data is used to describe, in a time series dimension, a change trend of measurement point data corresponding to a plurality of timestamps within the historical time period; A determination module, configured to determine a coding data type used when encoding the time series data based on a data collection type of the measurement point data in the time series data, wherein the coding data type is used to characterize a data type for encoding the time series data; A generation module is used to combine and encode the measurement point data corresponding to the multiple timestamps based on the encoded data type as a data encoding benchmark to generate a data encoding result corresponding to the time series data. The data encoding benchmark is used to combine and encode the measurement point data with the same data acquisition type, and the data encoding result is used for a single decoding to obtain the measurement point data corresponding to the multiple timestamps.

11. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one program, and the at least one program is loaded and executed by the processor to implement the time series data management method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The storage medium stores at least one program, and the at least one program is loaded and executed by the processor to implement the time series data management method as described in any one of claims 1 to 9.

13. A computer program product, characterized in that It comprises computer instructions, which, when executed by a processor, implement the time series data management method as described in any one of claims 1 to 9.

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