Time correlation data processing method and device based on double time sequences

Abstract

The invention discloses a time correlation data processing method and device based on double time sequences. The method comprises the following steps: acquiring a plurality of pieces of time-related data from a plurality of data sources in real time, wherein each piece of time-related data comprises a first timestamp representing data occurrence time; for each piece of time-related data, recording the acquisition time of the piece of time-related data to obtain a second timestamp representing the data recording time; for each piece of time-related data, constructing a time identifier of the piece of time-related data according to the first timestamp and the second timestamp; based on the time identifiers of the multiple pieces of time-related data, multiple pieces of target time-related data with mutually matched time sequence characteristics are selected from the multiple pieces of time-related data; and processing the time-related business according to the multiple pieces of target time-related data. According to the data processing method disclosed by the embodiment of the invention, accurate and efficient processing of the time-related data is realized by utilizing the double-time-sequence index and the time-sequence feature matching.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically to a method and apparatus for processing time-related data based on dual time series, as well as a computing device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the continuous development of computer and network technology and the continuous improvement of social needs, the collection, analysis and management of time-related data (or time-varying data) are playing an increasingly important role in more and more application scenarios with high time-varying and uncertainty (such as financial market forecasting and decision-making, system operation and maintenance monitoring, and natural environment monitoring). Moreover, with the increasing diversification and complexity of various application scenarios, the processing of time-related data (such as data collection, access, analysis, etc.) and the specific time-related business based on it (such as real-time prediction of various business indicators, etc.) are becoming more and more complicated.

[0003] Taking the financial market as an example, the external sources of market data (i.e., time-related data) required by financial service institutions when making relevant forecasts and decisions on financial products are complex and diverse. Therefore, these externally received real-time market data contain information in different formats, different arrangements, and different processing logics, which makes the data look disorganized and lacks unified and accurate indexes or identifiers. Therefore, it is easy to cause data confusion and misuse during the data processing process, which in turn significantly affects the efficiency, accuracy and reliability of data processing operations such as data collection, analysis, and query, as well as subsequent forecasting or decision-making business. Summary of the invention

[0004] The purpose of the present application is to overcome at least one of the defects of the related art. To this end, the present application provides a time-related data processing method and apparatus based on dual time series, a computing device, a computer-readable storage medium and a computer program product.

[0005] According to one aspect of the present application, a method for processing time-related data based on dual time series is provided, including: acquiring multiple time-related data from multiple data sources in real time, each time-related data including a first timestamp indicating the time when the data occurred; for each time-related data, recording the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time; for each time-related data, constructing a time identifier of the time-related data according to the first timestamp and the second timestamp; based on the time identifiers of the multiple time-related data, selecting multiple target time-related data whose timing features match each other from the multiple time-related data; and processing time-related services according to the multiple target time-related data.

[0006] In a dual-time series-based time-related data processing method according to some embodiments of the present application, based on the time identifiers of the multiple time-related data, multiple target time-related data whose time series features match each other are selected from the multiple time-related data, including: for each time-related data, extracting a first time series feature from the time identifier of the time-related data, the first time series feature representing the time sequence of the first timestamp of the time-related data in the corresponding time series data; selecting multiple target time-related data from the multiple time-related data according to the first time series features of the multiple time-related data, so that the first time series feature of each target time-related data is the last one.

[0007] In a dual-time series-based time-related data processing method according to some embodiments of the present application, based on the time identifiers of the multiple time-related data, multiple target time-related data whose time series features match each other are selected from the multiple time-related data, including: for each time-related data, extracting a second time series feature from the time identifier of the time-related data, the second time series feature representing the time sequence of the second timestamp of the time-related data in the corresponding time series data; selecting multiple target time-related data from the multiple time-related data according to the second time series features of the multiple time-related data, so that the second time series feature of each target time-related data is the last one.

[0008] In the time-related data processing method based on dual time series according to some embodiments of the present application, the time-related data includes market data, the time-related business includes market-related predictions and / or decisions, and processing the time-related business according to the multiple target time-related data includes: performing real-time market-related data analysis based on the multiple target time-related data; and performing market-related predictions and / or decisions based on the results of the real-time market-related data analysis.

[0009] In the time-related data processing method based on dual time series according to some embodiments of the present application, the time-related data includes system operation and maintenance monitoring data, the time-related business includes system operation and maintenance status prediction, and the time-related business processed according to the multiple target time-related data includes: performing real-time operation and maintenance status data analysis according to the multiple target time-related data; and performing system operation and maintenance status prediction based on the real-time operation and maintenance status data analysis results.

[0010] In the time-related data processing method based on dual time series according to some embodiments of the present application, the time-related data includes environmental monitoring data, the time-related services include environment-related trend predictions, and processing the time-related services based on the multiple target time-related data includes: performing real-time environmental status data analysis based on the multiple target time-related data; and performing environment-related trend predictions based on the real-time environmental status data analysis results.

[0011] In a dual-time-series time-related data processing method according to some embodiments of the present application, acquiring multiple time-related data in real time from multiple data sources includes: acquiring multiple original time-related data in real time from an external data source; performing data integration on the multiple original time-related data to obtain multiple time-related data; and obtaining a first timestamp of each time-related data through time alignment processing for the multiple time-related data.

[0012] In the time-related data processing method based on dual time series according to some embodiments of the present application, data integration of the multiple original time-related data to obtain multiple time-related data includes: data cleaning of the multiple original time-related data to obtain multiple cleaned time-related data; formatting the multiple cleaned time-related data based on a predetermined mapping relationship to form multiple time-related data.

[0013] In the time-related data processing method based on dual time series according to some embodiments of the present application, it further includes: merging the multiple target time-related data into a target time-related data set; obtaining a third timestamp corresponding to the processing process of the time-related business; and constructing a time series index of the target time-related data set based on the third timestamp.

[0014] In the dual-time series-based time-related data processing method according to some embodiments of the present application, it further includes: based on the real-time processing results of the target time-related data set and the time-related business, presenting a dynamic visualization interface for displaying the real-time processing status of the time-related business.

[0015] In the time-related data processing method based on dual time series according to some embodiments of the present application, the dynamic visualization interface further includes a search box, and the method further includes: using the search box of the dynamic visualization interface to query the target time-related data set corresponding to the historical time-related business processing results.

[0016] In the time-related data processing method based on dual time series according to some embodiments of the present application, querying the target time-related data set corresponding to the historical time-related business processing results using the search box of the dynamic visualization interface includes: in response to the search box being clicked, displaying multiple third timestamps to be selected corresponding to multiple historical time-related business processing results in the search box; in response to one of the multiple third timestamps to be selected being selected, obtaining the time series index of the historical target time-related data set corresponding to the selected third timestamp; according to the time series index of the historical target time-related data set corresponding to the selected third timestamp, obtaining the historical target time-related data set corresponding to the time series index; and displaying the data in the historical target time-related data set.

[0017] According to another aspect of the present application, a time-related data processing device based on dual time series is provided, including: an acquisition module, which is configured to acquire multiple time-related data from multiple data sources in real time, each time-related data includes a first timestamp indicating the time when the data occurred; a recording module, which is configured to record, for each time-related data, the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time; a construction module, which is configured to construct, for each time-related data, a time identifier of the time-related data according to the first timestamp and the second timestamp; a screening module, which is configured to select, based on the time identifiers of the multiple time-related data, multiple target time-related data whose time series features match each other from the multiple time-related data; a processing module, which is configured to process time-related services according to the multiple target time-related data.

[0018] According to another aspect of the present application, a computing device is provided, including: a processor; and a memory having instructions stored thereon, which, when executed on the processor, cause the processor to execute a dual-time-series-based time-correlated data processing method according to some embodiments of the present application.

[0019] According to another aspect of the present application, one or more computer-readable storage media are provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed, the time-related data processing method based on dual-time series according to some embodiments of the present application is implemented.

[0020] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement the dual-time-series-based time-correlated data processing method according to some embodiments of the present application.

[0021] In the dual-time series-based time-related data processing method and device according to some embodiments of the present application, for time-related businesses with high time variability and uncertainty (such as market-related product prediction and decision-making, system operation and maintenance monitoring, environmental assessment and prediction, etc.), dual-time series indexes and corresponding time series feature matching are used to achieve accurate and efficient processing of time-related data and its businesses. Specifically, firstly, the method obtains dual time series information (i.e., a first timestamp and a second timestamp) of the data through data acquisition and time recording steps for various time-related data in different formats from different sources, and uses the dual time series information as the time index or identifier of the data, thereby ensuring the uniqueness and identification accuracy of the data index, avoiding data confusion and misuse, and improving the efficiency and accuracy of data processing operations such as data query and analysis, thereby ensuring the integrity, consistency, reliability and security of time-related data; secondly, for multiple time-related data with dual time series identifiers, multiple target time-varying data are screened based on the matching of time series features extracted therefrom so as to realize time-related business processing based on these screened data, thereby ensuring the data consistency of subsequent data business processing processes (such as distributed computing processes), so that the corresponding business processing results are more accurate, more in line with the actual situation, and more real and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The various aspects, features and advantages of the present application will be readily understood from the following detailed description and accompanying drawings, in which:

[0023] Figure 1 Schematically illustrating an example application environment of a dual-time series-based time-correlated data processing method according to some embodiments of the present application;

[0024] Figure 2 Schematically illustrating an example interaction flow chart of a dual-time series-based time-related data processing method in an example application environment according to some embodiments of the present application;

[0025] Figure 3 A flowchart schematically illustrates a method for processing time-related data based on dual time series according to some embodiments of the present application;

[0026] Figure 4 A flowchart schematically illustrates a method for processing time-related data based on dual time series according to some embodiments of the present application;

[0027] Figure 5 Schematically illustrating an example process of data acquisition steps in a dual-time series-based time-correlated data processing method according to some embodiments of the present application;

[0028] Figure 6 and Figure 7The exemplary processes of the data screening step in the time-related data processing method based on dual time series according to some embodiments of the present application are schematically shown respectively;

[0029] Figure 8 Schematically illustrating an example process of a data query step in a time-related data processing method based on dual time series according to some embodiments of the present application;

[0030] Fig. 9 Schematically showing an exemplary structural block diagram of a dual-time-series-based time-correlated data processing device according to some embodiments of the present application;

[0031] Fig.10 An example block diagram of a computing device according to some embodiments of the present application is schematically shown.

[0032] It should be noted that the above drawings are merely schematic and illustrative and are not necessarily drawn to scale. DETAILED DESCRIPTION

[0033] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. However, the present application can be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the present application comprehensive and complete, and will fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0034] It will be understood that, although the terms first, second, third, etc. may be used to describe various elements, components and / or parts in this article, these elements, components and / or parts should not be limited by these terms. These terms are only used to distinguish one element, component or part from another element, component or part. Therefore, the first element, component or part discussed below may be referred to as the second element, component or part without departing from the teaching of the application.

[0035] The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the application. As used herein, the singular forms "one", "one" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. It will be further understood that the terms "include" and / or "comprise" specify the existence of the features, integral bodies, steps, operations, elements and / or parts when used in this specification, but do not exclude the existence of one or more other features, integral bodies, steps, operations, elements, parts and / or their groups or add one or more other features, integral bodies, steps, operations, elements, parts and / or their groups. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and / or this specification, and will not be interpreted in an idealized or overly formal sense, unless explicitly defined as such herein.

[0037] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity:

[0038] 1. Time-related data (or time-varying data): refers to information or values ​​related to time in a database or data set. These data are usually used to record and analyze the state or changes of things at different time points, which is of great significance for understanding the evolution of things, predicting future trends, and making decisions. According to different application scenarios, time-related data can include the following types: market data, operation and maintenance monitoring data, natural environment monitoring data, traffic data, production control data, etc. Specific examples include: temperature data recorded every hour or every day, stock trading prices recorded every minute, every hour or every day, monthly, quarterly or annual sales, real-time recording of biomedical signals (such as electrocardiograms, electroencephalograms, etc.), traffic flow data recorded every hour or every day, etc.

[0039] 2. Time-related business: refers to business that can be performed based on time-related data (for example, batch processing of time-related data to obtain predictions or estimates of related indicators). Time-related business may include, for example: (1) Revealing the changing patterns and trends of things at different time points by analyzing time-related data. (2) Time-related data can be used to build prediction models to predict future trends. Based on the prediction results, corresponding decision plans can be made to cope with future changes and challenges. For example, by analyzing historical stock price data, a stock price prediction model can be built to provide decision support for investors. (3) In system logs and monitoring, time-related data is used to record the time and sequence of events. For example, by analyzing these log data, abnormal behavior and potential problems of the system can be identified and repaired and optimized in a timely manner. In addition, time-related business can be triggered by the following methods: scheduled tasks, by setting a scheduled task plan to regularly trigger time-related business (such as the model batch process), this method is suitable for scenarios that require regular data analysis; event triggering, in some cases, model batch can be triggered by specific events; distributed computing, for large-scale data processing and analysis tasks, distributed computing technology can be used to implement model batch. This helps improve processing speed and efficiency and reduce computing costs.

[0040] 3. Time series data (sequence of time-related data): also known as time series data, refers to a collection of data arranged in chronological order, that is, a data column of the same indicator recorded in chronological order, with temporal continuity between each data point. Time series data reflects the dynamic change process of a phenomenon and is used to reveal the evolution process and trend of a phenomenon (such as a periodic change trend). Each piece of data in a time series data can be called a piece of time-related data, which usually includes information such as a timestamp and a corresponding value or event.

[0041] 4. Dual time series: usually refers to the ability to simultaneously process and store two different times (stamps) associated with each data item (i.e., each time-related data) in data processing and management, especially in database systems. These two different times include: real time (also called wall clock time, business occurrence time) and system time (also called transaction time, recording time, and saving time). Among them, real time (also called first timestamp in this article): refers to the time when the data is actually generated or modified, which reflects the time attribute of the data in the real world; system time (also called second timestamp in the text): indicates the time when the data is stored or changed in the system, which reflects the processing process of the data within the system. Dual time series database is a powerful data storage and processing tool that can handle large amounts of time series data and historical data, and provide accurate and complete data identification and indexing.

[0042] Figure 1 An example application scenario of a dual-time-series-based time-correlated data processing method according to some embodiments of the present application is schematically illustrated.

[0043] like Figure 1 As shown, the application scenario 100 of the dual-time-series based time-related data processing method according to some embodiments of the present application may include a server 110, and may optionally include multiple (different external) data sources or databases 120, a network 130 and a terminal device 140, wherein the terminal device 140 may be controlled by a user 150.

[0044] In some embodiments, the time-related data processing method based on dual time series according to some embodiments of the present application can be deployed on the server 110 and implemented by the server 110. For example, the server 110 can be deployed with a corresponding program or instruction for executing the data processing method provided by some embodiments of the present application. Optionally, the time-related data processing method based on dual time series according to some embodiments of the present application can also be deployed on the terminal device 140 and implemented by the terminal device 140. Further optionally, the combination of the server 110 and the terminal device 140 can also be used to implement the data processing method according to some embodiments of the present application.

[0045] Exemplarily, server 110 can store and run instructions that can execute the various methods described herein. Server 110 can be a single server or a server cluster, or can be a cloud server or cloud server cluster that can provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. It should be understood that the server mentioned herein can typically be a server computer with a large amount of memory and processor resources, but other embodiments are also possible. In addition, server 110 is shown only as an example. In fact, other devices or combinations of devices with computing power and storage capacity can also be used alternatively or additionally to provide corresponding services.

[0046] like Figure 1 As shown, optionally, the application scenario 100 may further include multiple (external) data sources or databases 120 and a network 130. The server 110 may be connected to each external data source or database 120 via the network 130, so as to obtain various data to be collected for time-related services (i.e., multiple time-related data) from multiple external data sources or databases 120. Exemplarily, the external data source or database 120 may be an independent data storage device or device group, or may also be a backend data storage device or device group related to other online services.

[0047] like Figure 1 As shown, optionally, the application scenario 100 may further include a terminal device 140, which may be connected to the server 110 and / or the external data source 120 via the network 130. Figure 1 As shown, the user 150 of the terminal device 140 can access the server 110 through the terminal device 140 via the network 130 to obtain the services provided by the server 110. For example, the user 150 can input instructions through the user interface provided by the terminal device 140, such as through a physical input device (such as a keyboard and / or a mouse, etc.) or a virtual key (such as a touch screen), through voice or gesture instructions, etc., so as to start the dual-time-series-based time-related data processing solution deployed on the server 110, send the time-related service to be processed, receive the time-related service processing result, etc.

[0048] Exemplarily, examples of the network 130 include a local area network (LAN), a wide area network (WAN), a personal area network (PAN), and / or a combination of communication networks such as the Internet. Each of the server 110, the database 120, and the terminal device 140 may include at least one communication interface (not shown) capable of communicating through the network 130. Such a communication interface may be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), a wired or wireless (such as an IEEE 802.11 wireless LAN (WLAN)) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, etc.

[0049] like Figure 1 As shown, the terminal device 140 can be any type of mobile computing device, including a mobile computer (e.g., a personal digital assistant (PDA), a laptop computer, a notebook computer, a tablet computer, a netbook, etc.), a mobile phone (e.g., a cellular phone, a smart phone, etc.), a wearable computing device (e.g., a smart watch, a head-mounted device, including smart glasses, etc.) or other types of mobile devices. In some embodiments, the terminal device 140 can also be a fixed computing device, such as a desktop computer, a game console, a smart TV, etc. In addition, when the application scenario 100 includes multiple terminal devices 140, the multiple terminal devices 140 can be computing devices of the same or different types.

[0050] Exemplarily, the terminal device 140 may include a display screen and a terminal application that can interact with the terminal user via the display screen. The terminal application may be a local application, a web page (Web) application, or a small program as a lightweight application (e.g., a mobile phone small program, a WeChat small program). In the case where the terminal application is a local application that needs to be installed, the terminal application can be installed in the user terminal. In the case where the terminal application is a Web application, the terminal application can be accessed through a browser. In the case where the terminal application is a small program, the terminal application can be directly opened on the terminal device 140 by searching for relevant information of the terminal application (such as the name of the terminal application, etc.), scanning a graphic code of the terminal application (such as a barcode, a QR code, etc.), etc., without installing the terminal application.

[0051] It should be understood that although the server 110, the data source or database 120 and the terminal device 140 are shown and described as separate structures in this document, they can also be different components of the same computing device. For example, the server 110 can provide background computing functions, the data source database 120 can provide data exchange, storage, and acquisition functions, and the terminal device 140 can provide foreground functions for interacting with users, such as receiving user input and providing output to users through terminal applications. Optionally, the time-related data processing method based on dual time series according to some embodiments of the present application is not limited to Figure 1 The illustrated implementation is on the server 110 side, but may also be implemented on the terminal 140 side, or may also be implemented on both the terminal 140 side and the server 110 side.

[0052] Figure 2 Shows Figure 1 The example interaction diagram of each entity in the example application scenario 100 of the time-related data processing method based on dual time series according to some embodiments of the present application is shown. Figure 2 The example interaction diagram shown briefly describes the working principle of the dual-time series-based time-correlated data processing method according to some embodiments of the present application.

[0053] exist Figure 2 In the application scenario 100 shown, the server 110 can be configured to: first, acquire multiple time-related data from multiple data sources 120 in real time, each time-related data including a first timestamp indicating the time when the data occurred; second, for each time-related data, record the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time; third, for each time-related data, construct a time identifier of the time-related data based on the first timestamp and the second timestamp; then, based on the time identifiers of the multiple time-related data, select multiple target time-related data whose timing features match each other from the multiple time-related data; finally, process time-related services based on the multiple target time-related data.

[0054] like Figure 2 As shown, optionally, before acquiring multiple time-related data from an external source, the server 110 may be further configured to receive a time-related data processing request from the user 150 from the terminal device 140, and trigger a data processing process including data collection, recording, analysis, etc. based on the request. For example, the server 110 may pay attention to the interaction between the user and the terminal device 140 (such as the client application therein) in real time, and trigger a data processing request in response to the user performing a specific interactive operation (such as starting the terminal application or clicking a data processing button), thereby starting a subsequent data processing process (starting from acquiring time-related data from an external source) based on the request.

[0055] like Figure 2 As shown, optionally, in Figure 2 In the application scenario 100 shown, after completing the time-related service processing process, the terminal device 140 can be configured to: receive the processing result of the time-related service from the server 110; and display the processing result of the time-related service to the user 150.

[0056] Figure 1 and Figure 2 The example environment and interaction process are merely illustrative, and the dual-time-series-based time-related data processing method according to the present application is not limited to the example environment and interaction process shown.

[0057] Figure 3 The flowchart of the method for processing time-related data based on dual time series according to some embodiments of the present application is schematically shown. In some embodiments, Figure 3 The time-related data processing method based on dual time series according to the present application can be shown in Figure 1 The application scenario shown in the figure can be implemented by the execution subject Figure 1 Server 110 is shown.

[0058] like Figure 3 As shown, the time-related data processing method based on dual time series according to some embodiments of the present application may include the following steps: S310, data acquisition step; S320, time recording step; S330, identification construction step; S340, data screening step; and S350, business processing step. Steps S310-S350 are described in detail below.

[0059] In step S310 (data acquisition step), multiple pieces of time-related data are acquired from multiple data sources in real time, and each piece of time-related data includes a first timestamp indicating the time when the data occurred.

[0060] In some embodiments, Figure 2As shown, according to the concept of the present application, in order to realize the dual time series identification or indexing of time-related data (such as market data) and process time-related services (such as market-related forecasts) based on the dual time series data to provide decision-making references for users, it is necessary to first obtain the original time-related data (i.e., data with only actual time) associated with the corresponding time-related services from an external source in real time. Here, the external source refers to a non-local device or system. For example, taking the application scenario of the financial market as an example, when financial service institutions conduct market-related forecasts (time-related business processing), they often need to use a variety of real-time market data, such as transaction market data or quotation market data of related trading markets, as well as exchange rates and interest rates of related currencies, etc., and these data come from different channels. For example, trading market market data can be provided by exchanges (such as the Shanghai Stock Exchange, Shenzhen Stock Exchange, Beijing Stock Exchange, etc.); and exchange rates and interest rates may come from foreign exchange trading centers or central banks.

[0061] In some embodiments, the acquired original time-related data only has the actual time information in the dual time series information before being received, that is, the actual occurrence time or the first timestamp of the corresponding data, which reflects the time attribute of the data in the real world. For example, the actual occurrence time of the transaction market data in the financial market is the actual transaction time of the market data. The system time (recording or saving time) or the second timestamp in the dual time series needs to be obtained by automatic recording in the subsequent time recording step S320, which reflects the recording or processing process of the data within the system.

[0062] In some embodiments, the "real-time acquisition" in step S310 can be understood as acquiring multiple time-related data within the current (i.e., the same) time-related business processing cycle, that is, the "real-time" therein represents the current time-related business processing cycle. Because, as described above, the time-related business according to the present application can be triggered periodically, for example, it can be triggered by a scheduled task or a specific event (such as a user request, etc.). In some embodiments, taking the time-related business triggered by a scheduled task as an example, assuming that it is predetermined that the time-related business is triggered every 1 minute, the process of acquiring time-related data within the current business processing cycle (i.e., within 1 minute) is real-time acquisition.

[0063] In step S320 (time recording step), for each piece of time-related data, the acquisition time of the time-related data is recorded to obtain a second timestamp indicating the data recording time.

[0064] Based on the concept of the present application, in order to construct a dual-series index or identifier, it is necessary to record the data acquisition time after acquiring the time-related data as a second timestamp. In this way, for each time-related data, a dual-series time identifier or time index of the time-related data can be established based on a first timestamp representing the data occurrence time (actual time) and a second timestamp representing the data recording time (system time). Subsequently, such time-related data with dual-series indexes can be used for the processing of time-related services. Regarding the recording of the second timestamp, the data reception or storage time can be recorded as a second timestamp while receiving the data from an external source. Optionally, when the data may already contain a second timestamp (i.e., the time when the external source database stores the data) before receiving a time-related data from the outside, the second timestamp can be obtained from the data.

[0065] In step S330 (identifier construction step), for each piece of time-related data, a time identifier of the piece of time-related data is constructed according to the first timestamp and the second timestamp.

[0066] According to the concept of the present application, in order to construct a dual-series time identifier, the first timestamp representing the actual time and the second timestamp representing the system time can be directly used as the time identifier of the time-related data. During the collection and storage process of real-time time-related data, each piece of data is assigned a unique identifier based on the dual-series timestamp (the first timestamp and the second timestamp). Since the time identifier is composed of the recording time and the occurrence time of the data, it is ensured that each piece of data has a unique identity in the system. This unique design not only guarantees the accuracy of the data, avoids data confusion and misuse, but also enables the visible range of the data to be accurately determined by its unique identifier. Whether it is data query, analysis or management operation, specific data items can be quickly located, greatly improving the efficiency and accuracy of data processing. This design plays a vital role in ensuring the integrity, reliability and security of dual-series data.

[0067] In the financial market application scenario embodiment, the first timestamp can be the real transaction time: recording the time when the market data actually occurred, that is, the precise timestamp of the transaction. This is an important attribute of market data, reflecting the real-time dynamics of the market; the second timestamp can be the system storage time: recording the time when the data is received, processed and saved by the system. This helps to track the processing flow of the data and ensure the integrity and accuracy of the data. In the financial market application scenario, the time identifier construction process in S330 can be implemented in the following way: first, based on the real transaction time and the system storage time, a two-dimensional coordinate system is constructed. In this coordinate system, each data point has two dimensions: the horizontal axis represents the real transaction time, and the vertical axis represents the system storage time. Such a coordinate system not only intuitively shows the spatiotemporal distribution of the data, but also ensures the uniqueness of the data. Secondly, the time identifier of the data is determined as the coordinates in the above coordinate system (real transaction time, system storage time), so through the positioning of the coordinate system, the market data at any time point and the processing of these data in the system can be accurately found.

[0068] In some embodiments, dual-series time identifiers have many advantages. First, dual-series time identifiers can ensure data immutability and uniqueness, that is, once the data is stored, it will not be overwritten or tampered with. At the same time, each data item has a unique identifier, that is, its corresponding recording time and occurrence time, which ensures the uniqueness and traceability of the data. Secondly, dual-series time identifiers can contribute to efficient data storage and query. For example, dual-series data can adopt a special data storage and index structure, which can efficiently store and query time series data. Through compression algorithms and data partitioning technology, data storage space can be effectively reduced and query performance can be improved. Thirdly, dual-series identifiers can improve real-time data processing capabilities. Dual-series databases support real-time data processing and analysis, and can quickly process large-scale real-time data streams. It has high concurrent processing capabilities and low-latency data writing capabilities, which can meet the needs of real-time data processing. Dual-series identifiers have a wide range of application scenarios, such as in finance, Internet of Things, industrial manufacturing, energy management, transportation, environmental monitoring, e-commerce and other fields. It can process a large amount of time series data and historical data, providing users with efficient data processing and analysis tools.

[0069] In step S340 (data screening step), based on the time identifiers of the multiple time-related data, multiple target time-related data whose timing features match each other are selected from the multiple time-related data.

[0070] According to the concept of the present application, in order to perform time-related services (such as trend prediction or decision-making), it is necessary to filter out target time-related data for the above-mentioned services from the multiple time-related data obtained. In this data screening process, it can be implemented based on the dual time series time identifier of each time-related data received or obtained. Specifically, time-related data with the same or similar time series characteristics (i.e., time series characteristics matching) can be screened from a large number of data used for time-related services, and used as target time-related data for processing time-related services.

[0071] In some embodiments, the time series features in S340 refer to various time characteristics and regularities presented by each time-related data in the time series data, including basic features such as trend, periodicity, randomness and comprehensiveness. Therefore, in some embodiments, the time series features of each time-related data obtained in S310 can be defined as the time identifier features of each in the corresponding time series data, such as the specific time of the first timestamp and / or the second timestamp, or the time sequence or reverse sequence of at least one of the two in the respective time series data. Therefore, the time series features of the multiple time-related data obtained can be extracted based on the dual time series time identifier, for example, determined based on the first timestamp and / or the second timestamp in the identifier and their order in the respective time series data.

[0072] Generally speaking, time-related services are usually processed based on various time-related data (or various time series data) with actual occurrence time or recording time close to each other, especially the latest data. Therefore, the time series characteristics of time-related data can be limited to the sorting position of the first timestamp (actual occurrence time) or / and the second timestamp (system recording time) of its time identifier in the corresponding time series data. The matching of timing characteristics in two different types of time-related data means that the sorting positions of the first timestamp and / or the second timestamp are the same or close, especially the last position (i.e. the last position, which means that both are the latest data currently acquired).

[0073] In some embodiments, the timing feature may include a first timing feature and a second timing feature, wherein the first timing feature corresponds to a first timestamp in the time identifier, and the second timing feature corresponds to a second timestamp in the time identifier.

[0074] Table 1 - Example of timing characteristics of time-dependent data

[0075]

[0076] Table 1 shows an example of the timing characteristics of time-related data according to some embodiments of the present application. As shown in Table 1, in the current time-related business processing cycle (for example, starting from the completion of the last time-related business processing), the timing data A (i.e., the time-related data sequence A) includes 5 time-related data, which are A1-A5 in the order of the second timestamp (data recording time) in the time identifier; the timing data B (i.e., the time-related data sequence B) includes 4 time-related data, which are B1-B4 in the order of the second timestamp (data recording time) in the time identifier. As shown in Table 1, the specific moments of the first timestamp (data occurrence time) and the second timestamp (data recording time) of the time identifiers of these time-related data sequences A1-A5 and B1-B5 are shown in Table 1 respectively.

[0077] Table 1 shows the timing characteristics of each time-related data A1-A5 extracted from the time identifier, that is, the first timing characteristics corresponding to the time sequence of the first timestamp in the timing data A and the second timing characteristics corresponding to the time sequence of the second timestamp in the timing data A. As shown in Table 1, the first timing characteristics and the second timing characteristics of each time-related data in A1-A5 are exactly the same, which are 1-5 respectively. Because under normal circumstances, according to the principle of receiving one by one according to the actual occurrence time of the data, the data occurrence time is proportional to the data reception or recording time, that is, the first data that occurs first is also received or recorded the earliest, and vice versa, the last data that occurs is recorded the latest.

[0078] Table 1 also shows the timing characteristics of each time-related data B1-B4 extracted from the time identifier, that is, the first timing characteristics corresponding to the time sequence of the first timestamp in the timing data B and the second timing characteristics corresponding to the time sequence of the second timestamp in the timing data B. Compared with A1-A5, for each time-related data in B1-B4, the first timing characteristics and the second timing characteristics are not exactly the same, that is, the first timing characteristics of B3 and B4 are 4 and 3 respectively, but the second timing characteristics are 3 and 4 respectively. This is because in the case of data communication failure (such as loss or disorder of order), the data generation time and the data reception or recording time may not be synchronized, that is, the data that occurs first may be received or recorded later, and the data that occurs later may be recorded earlier. Therefore, in order to obtain the target time-related data, using the first timing feature for matching can more accurately filter out the latest time-related data (i.e., the data sorted last by the first timestamp); however, generally speaking, since the recording time and order corresponding to the second timestamp are easier to obtain, matching using the second timing feature is more efficient, and the probability of communication failure is lower, so the accuracy of filtering out the latest data is less affected.

[0079] In some embodiments, when the timing characteristics of each time-related data obtained in S310 are limited to the specific time indicated by the time identifier, such as the specific time of the first timestamp and / or the second timestamp, the timing characteristics of two different types of time-related data can be matched to each other by being limited to the first or second timestamps being the same or similar (for example, the time difference is less than a predetermined time threshold), which indicates that the two data belong to roughly the same time and match each other. The target time-related data obtained in this way makes subsequent business processing or model calculation results more accurate.

[0080] In step S350 (business processing step), time-related business is processed according to the plurality of target time-related data.

[0081] Based on the concept of the present application, after the target time-related data (such as the latest various time-related data) are screened out, corresponding time-related business processing can be performed based on these data. For example, the latest market data obtained can be used to perform real-time valuation or prediction of relevant financial products based on the valuation model of the financial market.

[0082] In this article, time-related services can refer to services that are implemented by analyzing time-related data or time series data. Specific time-related services may include, for example:

[0083] Monitor and track changes: Time series data can continuously record the changes in the state of a system or process over time, thereby enabling monitoring and tracking of system performance.

[0084] Predict future trends: By analyzing historical patterns and trends in time series data, future behaviors or events can be predicted to support decision making.

[0085] Anomaly detection and problem diagnosis: Establish a baseline for normal operations and detect abnormal behavior through time series data to help quickly locate problems.

[0086] Resource optimization and efficiency improvement: Time series data helps organizations better manage resources and improve efficiency by optimizing resource allocation.

[0087] Support automation and intelligent decision-making: Time series data is the core of many automation systems and intelligent decision-making systems, providing real-time data support for the systems.

[0088] In some embodiments, time-related data includes market data, time-related business includes market-related predictions and / or decisions, and the business processing step S350 may include: performing real-time market-related data analysis based on multiple target time-related data; performing market-related predictions and / or decisions based on the results of the real-time market-related data analysis.

[0089] In some embodiments, time-related data includes system operation and maintenance monitoring data, time-related services include system operation and maintenance status prediction, and the service processing step S350 may include: performing real-time operation and maintenance status data analysis based on multiple target time-related data; and performing system operation and maintenance status prediction based on the real-time operation and maintenance status data analysis results.

[0090] In some embodiments, time-related data includes environmental monitoring data, time-related services include environment-related trend forecasting, and the service processing step S350 may include: performing real-time environmental status data analysis based on multiple target time-related data; performing environment-related trend forecasting based on the real-time environmental status data analysis results.

[0091] In a time-related data processing method based on dual time series according to some embodiments of the present application, firstly, for various time-related data of different sources and different formats, the dual time series information of the data (i.e., the first timestamp and the second timestamp, the data occurrence time and the data recording time) is obtained through data acquisition and time recording steps, and the dual time series information is used as the time index or identifier of the data, thereby ensuring the uniqueness and identification accuracy of the data index, avoiding data confusion and misuse, and improving the efficiency and accuracy of data processing operations such as data query and analysis, thereby ensuring the integrity, consistency, reliability and security of time-related data; secondly, for multiple time-related data with dual time series characteristics, multiple target time-related data are screened based on the matching of the time series features extracted therefrom so as to realize the processing of time-related business based on these screened data, thereby ensuring the data consistency of the business processing process (such as a distributed computing process), so that the corresponding business processing results are more accurate, more in line with reality, and more real and effective.

[0092] Figure 4 The flowchart of the dual-time series-based time-correlated data processing method according to some embodiments of the present application is schematically shown.

[0093] like Figure 4 As shown, in addition to steps S310-S350, the time-related data processing method based on dual time series according to some embodiments of the present application may further include the following steps:

[0094] S360-data merging step: merging multiple target time-related data into a target time-related data set;

[0095] S370-time acquisition step: obtaining a third timestamp corresponding to the processing process of the time-related service;

[0096] S380 - index building step: building a temporal index of the target time-related data set according to the third timestamp.

[0097] According to the concept of the present application, after completing the processing of time-related business based on multiple target-related data selected from multiple time-related data obtained (using the matching of time series features), a target-related data set for querying the data source of the business processing process can be constructed for the processing process of this time-related business. As shown in steps S370-390, firstly, the multiple target-related data used in this business processing are merged into a data set to form a target-related data set; then, in order to facilitate the query, it is necessary to obtain the business processing time corresponding to this business processing process, wherein the business processing time can be called a third timestamp, which can be determined, for example, according to the start time, end time and / or the time interval of the entire process of this business processing; finally, a time index is constructed for the target-related data set based on the third timestamp obtained, so that the user can use the time index constructed based on the third timestamp of the business processing to accurately query the target time-related data set (or corresponding time-related data) used in this business processing afterwards (after the business processing is completed). In this way, it is convenient to trace back historical data, analyze the performance changes of the system or model used for time-related business processing, and find possible problems or anomalies. The specific data query process of the business personnel can be implemented by using the dynamic visualization interface presented in the subsequent step S390 for real-time display of time-related business processing status.

[0098] In some embodiments, the time-related business processing process may be a data batch processing process, i.e., a batch process, and therefore the third timestamp may be a batch timestamp, i.e., a timestamp used to identify the execution time of a batch task. This timestamp is usually used to record information such as the start time, end time, and time range of data processing of a batch task. The batch timestamp is used to identify the task execution time, and therefore the recording process of the batch timestamp includes, for example: at the start of the batch task, the current system time is recorded as the start timestamp; at the end of the batch task, the current system time is recorded again as the end timestamp; and according to business needs, key time nodes may be recorded in the data processing process in order to perform more sophisticated time management. Therefore, the third timestamp, especially the recording and management of the batch timestamp, can achieve efficient and accurate processing of a large amount of business data, and provide strong support for the business analysis and decision-making of the enterprise.

[0099] like Figure 4 As shown, the dual-time series-based time-correlated data processing method according to some embodiments of the present application may further include the following steps:

[0100] S390 - Interface presentation step: Based on the target time-related data set and the real-time processing results of the time-related business, a dynamic visualization interface for displaying the real-time processing status of the time-related business is presented.

[0101] In some embodiments, based on the acquired time-related data, the target time-related data set, and the real-time processing results of the corresponding time-related business, a dynamic visualization interface is constructed to provide real-time and intuitive data display for the time-related business. For example, this dynamic visualization interface can display the real-time data processing status or batch running status of the model used for time-related business processing, including at least one of the key indicators such as the running status of the model (such as the real-time processing results and its corresponding target time-related data set, etc.), processing speed, and data quality.

[0102] like Figure 4 As shown, the dual-time series-based time-correlated data processing method according to some embodiments of the present application may further include the following steps:

[0103] S400 - Data query step: using the search box of the dynamic visualization interface to query the target time-related data set corresponding to the historical time-related business processing results.

[0104] In some embodiments, the dynamic visualization interface according to the present application may further include a search box for querying a target time-related data set corresponding to a historical time-related business processing result. The search box may be a control for manually inputting a search text (e.g., the third timestamp of a single historical business processing time) to retrieve a target time-related data set, or it may be a control in the form of a drop-down menu option, for example, a user may query a target-related data set corresponding to a corresponding business processing time or a corresponding (original) time-related data by clicking on an option (the third timestamp corresponding to each historical business processing process). The specific query process can be found in Figure 8 .

[0105] Figure 5 An example flow of a data acquisition step in a dual-time series-based time-correlated data processing method according to some embodiments of the present application is schematically illustrated.

[0106] like Figure 5 As shown, Figure 3 The data acquisition step S310 shown may include:

[0107] S311, acquiring multiple pieces of original time-related data from an external data source in real time;

[0108] S312, integrating the plurality of original time-related data to obtain a plurality of time-related data;

[0109] S313, obtaining a first timestamp of each piece of time-related data through time alignment processing for the plurality of time-related data.

[0110] As described above, since the external data sources of time-related data are complex and diverse, and the formats and arrangements of the data are different and disorganized, the original time-related data received from various external data sources need to be pre-processed or integrated (for example, including data cleaning and formatting operations) before use. As described in S311-S313, firstly, various original time-related data from different external data sources are received; secondly, data integration operations are performed on the data from these different sources to form complete and unified time-related data; finally, the actual occurrence time of the original data is aligned to obtain a first timestamp with a consistent format.

[0111] Here, time alignment refers to aligning time series data from different data sources or different devices in time for subsequent data analysis or comparison. The main purpose of time alignment is to ensure the consistency and comparability of different time series data in the same time dimension. In the time series data generated by multiple data sources or devices, due to factors such as sampling frequency, data transmission delay, clock deviation, etc., there may be differences in data in terms of time representation, benchmark, accuracy, etc. Through time alignment, these data can be adjusted to the same time benchmark, so that the data can be analyzed and compared more accurately.

[0112] The time alignment method can be selected according to the specific application scenario and data characteristics. The following are some common time alignment methods including: interpolation, nearest neighbor matching, dynamic time warping, etc. Among them, when the time nodes of two time series data do not match completely, the missing time node data can be estimated by interpolation. Interpolation methods include linear interpolation, polynomial interpolation, spline interpolation, etc. The specific choice depends on the characteristics and requirements of the data. The nearest neighbor matching is to find the closest matching node for each time node during the time alignment process. Dynamic time warping (DTW) is an algorithm for measuring the similarity between two time series data. Through the dynamic programming algorithm, the optimal alignment path between the two time series data is found to achieve alignment. The DTW algorithm is suitable for processing time series data with nonlinear changes.

[0113] In some embodiments, the data integration step S312 may include the following steps: performing data cleaning on the multiple original time-related data to obtain multiple cleaned time-related data; formatting the multiple cleaned time-related data based on a predetermined mapping relationship to form multiple time-related data.

[0114] In this article, data cleaning, which can include operations such as removing noise, filling missing values, and data smoothing, aims to identify and correct errors, duplications, missing values, etc. in the data set; while data formatting is used to process inconsistent data, identify and unify data formats, such as date format, currency format, etc., and standardize data, such as converting measurements in different units into unified units.

[0115] Table 2 shows an example of a predetermined mapping relationship for formatting processing according to some embodiments of the present application.

[0116] Table 2 - Examples of predetermined mapping relationships for formatting

[0117] Market Name Market ID suffix SHSE A 0 .SH Shanghai B 2 .SH Deep A 1 .SZ Deep B 3 .SZ Shenzhen Stock Options Y Hong Kong Stock Connect (Shenzhen Stock Exchange) H .HK Hong Kong Stock Connect (Shanghai Stock Exchange) H Hong Kong stocks H Shanghai Futures Exchange F

[0118] In some embodiments, the mapping relationship example shown in Table 2 is applied to a financial market application scenario. For example, according to the predetermined mapping relationship shown in Table 2, the format conversion of the following data segments can be achieved:

[0119] "strCode":"01225"->01225.HK;

[0120] "strCategory":"S"->EQPRICE;

[0121] "fPreClose":0.226->close_1.

[0122] In some embodiments, the data integration process can also be performed after the identification construction step S330, for example, using a two-dimensional coordinate system based on real transaction time and system storage time, the data in different result tables can be integrated into a unified view. By matching the real transaction time and system storage time, data from different sources and formats can be associated to form a complete data set. Subsequently, based on the integrated data, a dynamic visualization interface can be constructed to provide real-time and intuitive data display for the business.

[0123] Figure 6 and Figure 7 The example processes of the data screening step in the dual-time series-based time-correlated data processing method according to some embodiments of the present application are respectively shown.

[0124] like Figure 6 As shown, in some embodiments, the data screening step S340 (selecting a plurality of target time-related data whose timing features match each other from the plurality of time-related data based on the time identifiers of the plurality of time-related data) may include:

[0125] S341, for each piece of time-related data, extract a first time series feature from a time identifier of the time-related data, where the first time series feature represents a sequence of a first timestamp of the time-related data in the corresponding time series data;

[0126] S342: Select multiple target time-related data from the multiple time-related data according to the first time series features of the multiple time-related data, so that the first time series feature of each target time-related data is the last one.

[0127] like Figure 7 As shown, the data screening step S340 may also include:

[0128] S341', for each piece of time-related data, extract a second time series feature from the time identifier of the time-related data, the second time series feature representing the time sequence of the second timestamp of the time-related data in the corresponding time series data;

[0129] S342', selecting a plurality of target time-related data from the plurality of time-related data according to the second time series features of the plurality of time-related data, so that the second time series feature of each target time-related data is the last one.

[0130] Taking the financial market as an example, assume that the market data as time-related data includes two types: stock transaction data and exchange rate data. The stock transaction data sequence obtained during the current business processing (for example, starting from the last business processing or calculation) is used as the time series data A in Table 1, and a total of 5 data A1-A5 are received, while the exchange rate data is used as the time series data B in Table 1, and a total of 4 data B1-B4 are received. Figure 7 As shown in Table 1, if sorted according to the second time series feature (i.e., sorted by the second timestamp), A5 and B4 match in position and are both ranked last, that is, as the latest data in their respective types of data (i.e., target time-related data), they can be screened out for pricing or valuation calculations of related financial products (i.e., time-related business).

[0131] Alternatively, if Figure 6 As shown in Table 1, the first time series feature (i.e., sorting by the first timestamp) can also be used to filter out A5 and B3, both of which are ranked last in time, and their positions match, so the two can be used as the latest targets in stock trading data and exchange rate data (i.e., target time-related data).

[0132] Therefore, in order to obtain the latest stock trading data and exchange rate data, using the first time series feature for matching can more accurately screen out the stock trading data A5 and exchange rate data B3 with the latest actual data occurrence time (that is, the data sorted at the end of the first timestamp), while when using the second time series feature for matching, the screened B4 is not the data with the latest actual occurrence time; however, generally speaking, since the record time and sequence corresponding to the second timestamp are easier to obtain, the work efficiency is higher when using the second time series feature for matching, and with the development of communication technology, the probability of current communication failures is low, so in general, the second timestamp is basically synchronized with the first timestamp, so that the screening process using the second timestamp for feature matching can ensure the accuracy of screening out the latest data at the actual occurrence time while being significantly more efficient, especially for big data batch computing and distributed computing. The computing efficiency advantage is obvious.

[0133] Figure 8 An example flow of a data query step in a dual-time series-based time-correlated data processing method according to some embodiments of the present application is schematically illustrated.

[0134] like Figure 8 As shown, Figure 4 The data query step S400 shown may include:

[0135] S410, in response to the search box being clicked, displaying a plurality of third timestamps to be selected corresponding to a plurality of historical time-related business processing results in the search box;

[0136] S420, in response to one of the plurality of third timestamps to be selected being selected, obtaining a time series index of a historical target time-related data set corresponding to the selected third timestamp;

[0137] S430, according to the time series index of the historical target time-related data set corresponding to the selected third timestamp, acquiring the historical target time-related data set corresponding to the time series index;

[0138] S440, displaying the data in the historical target time related data set.

[0139] In some embodiments, as shown in S410, the dynamic visualization interface for real-time presentation of time-related business processing conditions according to the present application may further include a search box for querying a target time-related data set corresponding to a historical time-related business processing result. The search box may be in the form of a time selector or a text input box. As shown in S410, the search box may display a third timestamp option (e.g., a third timestamp of a single historical business processing time) in response to a user click for the user to select to trigger the query process, such as a control in the form of a drop-down menu option. As shown in S420, after the user selects one of the third timestamps, the time series index of the historical target time-related data set (of the business processing process) corresponding to it may be searched; subsequently, as shown in S430, the corresponding historical target time-related data set is obtained in the background database based on the time series index; finally, as shown in S440, the corresponding data queried is displayed to the user on, for example, a display screen of a terminal device.

[0140] In the above embodiment, the user or business personnel can select or enter the third timestamp (or batch timestamp) through the time selector or search box on the time-related business real-time processing interface to accurately query the target time-related data used when the model used for business processing is running at that time. In this way, it is convenient to trace back historical data, analyze the performance changes of the model, and find possible problems or anomalies.

[0141] Fig. 9 The exemplary block diagram of a dual-time series-based time-correlated data processing device 900 according to some embodiments of the present application is schematically shown. The dual-time series-based time-correlated data processing device 900 may include an acquisition module 910 , a recording module 920 , a construction module 930 , a screening module 940 , and a processing module 940 .

[0142] The acquisition module 910 may be configured to acquire multiple pieces of time-related data from multiple data sources in real time, each piece of time-related data including a first timestamp indicating the time when the data occurred.

[0143] The recording module 920 may be configured to record, for each piece of time-related data, the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time.

[0144] The construction module 930 may be configured to construct, for each piece of time-related data, a time identifier of the piece of time-related data according to the first timestamp and the second timestamp.

[0145] The screening module 940 may be configured to select, based on the time identifiers of the plurality of time-related data, a plurality of target time-related data whose timing features match each other from the plurality of time-related data.

[0146] The processing module 950 may be configured to process time-related services according to the plurality of target time-related data.

[0147] In a time-related data processing device based on dual time series according to some embodiments of the present application, firstly, for various time-related data of different sources and different formats, the dual time series information of the data (i.e., the first timestamp and the second timestamp, the data occurrence time and the data recording time) is obtained through data acquisition and time recording steps, and the dual time series information is used as the time index or identifier of the data, thereby ensuring the uniqueness and identification accuracy of the data index, avoiding data confusion and misuse, and improving the efficiency and accuracy of data processing operations such as data query and analysis, thereby ensuring the integrity, consistency, reliability and security of time-related data; secondly, for multiple time-related data with dual time series characteristics, multiple target time-related data are screened based on the matching of the time series features extracted therefrom so as to realize the processing of time-related services based on these screened data, thereby ensuring the data consistency of the business processing process (such as a distributed computing process), so that the corresponding business processing results are more accurate, more in line with reality, and more real and effective.

[0148] Fig.10 An example block diagram of a computing device 1000 according to some embodiments of the present application is schematically shown. The computing device 1000 may represent a device for implementing the various means or modules described herein and / or performing the various methods described herein. The computing device 1000 may be, for example, a server, a desktop computer, a laptop computer, a tablet, a smart phone, a smart watch, a wearable device, or any other suitable computing device or computing system, which may include various levels of devices ranging from full-resource devices with a large amount of storage and processing resources to low-resource devices with limited storage and / or processing resources. In some embodiments, the above description of the computing device 1000 may be a computer system that can be used to implement the various means or modules described herein and / or perform the various methods described herein. Fig. 9 The described dual-timing-based time-correlated data processing apparatus 900 may be implemented in one or more computing devices 1000 .

[0149] As shown, example computing device 1000 includes a processing system 1001, one or more computer-readable media 1002, and one or more I / O interfaces 1003 that are communicatively coupled to each other. Although not shown, computing device 1000 may also include a system bus or other data and command transmission system that couples various components to each other. The system bus may include any one or combination of different bus structures, which may be a processor or local bus such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and / or any of a variety of bus architectures. Alternatively, it may also include control and data lines.

[0150] Processing system 1001 represents the functionality of performing one or more operations using hardware. Thus, processing system 1001 is illustrated as including hardware elements 1004 that may be configured as processors, functional blocks, etc. This may include other logic devices implemented in hardware as application specific integrated circuits or formed using one or more semiconductors. Hardware elements 1004 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, a processor may be composed of (multiple) semiconductors and / or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor executable instructions may be electronic executable instructions.

[0151] Computer-readable medium 1002 is illustrated as including memory / storage 1005. Memory / storage 1005 represents a memory / storage associated with one or more computer-readable media. Memory / storage 1005 may include volatile media (such as random access memory (RAM)) and / or non-volatile media (such as read-only memory (ROM), flash memory, optical disk, magnetic disk, etc.). Memory / storage 1005 may include fixed media (e.g., RAM, ROM, fixed hard drive, etc.) and removable media (e.g., flash memory, removable hard drive, optical disk, etc.). Exemplarily, memory / storage 1005 may be used to store various data mentioned in the above embodiments, etc. Computer-readable medium 1002 may be configured in various other ways as further described below.

[0152] One or more I / O (input / output) interfaces 1003 represent functionality that allows a user to enter commands and information into the computing device 1000 and also allows information to be displayed to the user and / or sent to other components or devices using various input / output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone (e.g., for voice input), a scanner, touch functionality (e.g., a capacitive or other sensor configured to detect physical touch), a camera (e.g., that can detect motion that does not involve touch as gestures using visible or invisible wavelengths (such as infrared frequencies)), a network card, a receiver, and the like. Examples of output devices include a display device (e.g., a monitor or projector), a speaker, a printer, a tactile response device, a network card, a transmitter, and the like.

[0153] The computing device 1000 also includes a time-dependent data processing strategy 1006. The time-dependent data processing strategy 1006 may be stored as a computing program instruction in the memory / storage device 1005. The time-dependent data processing strategy 1006 may be implemented in conjunction with the processing system 1001, etc. Fig. 9 The entire functions of each module of the dual-timing-based time-correlated data processing device 900 are described.

[0154] Various techniques may be described herein in the general context of software, hardware, elements, or program modules. Typically, these modules include routines, programs, objects, elements, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The terms "module," "function," etc., as used herein, generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that these techniques can be implemented on a variety of computing platforms with a variety of processors.

[0155] An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. Computer-readable media may include various media accessible by computing device 1000. By way of example and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media”.

[0156] In contrast to a simple signal transmission, carrier wave, or signal itself, "computer-readable storage medium" refers to a medium and / or device capable of persistently storing information, and / or a tangible storage device. Therefore, a computer-readable storage medium refers to a non-signal-bearing medium. Computer-readable storage media include hardware such as volatile and non-volatile, removable and non-removable media and / or storage devices implemented in a method or technology suitable for storing information (such as computer-readable instructions, data structures, program modules, logic elements / circuits, or other data). Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage device, hard disk, cassette, magnetic tape, magnetic disk storage device or other magnetic storage device, or other storage device, tangible medium, or article of manufacture suitable for storing desired information and accessible by a computer.

[0157] "Computer readable signal media" refers to signal bearing media that is configured to send instructions to the hardware of the computing device 1000, such as via a network. Signal media typically can embody computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, data signal, or other transport mechanism. Signal media also includes any information transfer media. By way of example and not limitation, signal media include wired media such as a wired network or direct connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

[0158] As previously described, hardware elements 1004 and computer-readable media 1002 represent instructions, modules, programmable device logic, and / or fixed device logic implemented in hardware form, which in some embodiments can be used to implement at least some aspects of the technology described herein. Hardware elements may include integrated circuits or systems on a chip, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and other implementations in silicon or components of other hardware devices. In this context, hardware elements can be used as processing devices for executing program tasks defined by instructions, modules, and / or logic embodied by hardware elements, as well as hardware devices for storing instructions for execution, such as the computer-readable storage media described previously.

[0159] The aforementioned combination may also be used to implement various techniques and modules described herein. Therefore, software, hardware or program modules and other program modules may be implemented as one or more instructions and / or logic embodied on some form of computer-readable storage medium and / or by one or more hardware elements 1004. Computing device 1000 may be configured to implement specific instructions and / or functions corresponding to software and / or hardware modules. Therefore, for example, by using a computer-readable storage medium and / or hardware element 1004 of a processing system, a module may be implemented as a module executable by computing device 1000 as software, at least in part, in hardware. Instructions and / or functions may be executed / operable by, for example, one or more computing devices 1000 and / or processing system 1001 to implement the techniques, modules and examples described herein.

[0160] The techniques described herein may be supported by these various configurations of computing device 1000 and are not limited to the specific examples of the techniques described herein.

[0161] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer program. For example, an embodiment of the present application provides a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing at least one step in the method embodiment of the present application.

[0162] In some embodiments of the present application, one or more computer-readable storage media are provided, on which computer-readable instructions are stored, and when the computer-readable instructions are executed, the time-related data processing method based on dual time series according to some embodiments of the present application is implemented. The various steps of the time-related data processing method based on dual time series according to some embodiments of the present application can be converted into computer-readable instructions through programming, thereby being stored in a computer-readable storage medium. When such a computer-readable storage medium is read or accessed by a computing device or a computer, the computer-readable instructions therein are executed by a processor on the computing device or the computer to implement the method described in some embodiments of the present application.

[0163] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other.

[0164] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a sequence not shown or discussed (including in a substantially simultaneous manner or in reverse order depending on the functions involved), which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0165] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0166] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combinational logic gate circuit, a programmable gate array (Programmable Gate Array), a field programmable gate array (Field Programmable Gate Array), etc.

[0167] A person skilled in the art may understand that all or part of the steps of the above-mentioned embodiment method may be completed by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, which, when executed, includes executing one or a combination of the steps of the method embodiment.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

Claims

1. A time-related data processing method based on dual time series, characterized in that: The method comprises: Acquire multiple pieces of time-related data from multiple data sources in real time, each piece of time-related data including a first timestamp indicating the time when the data occurred; For each piece of time-related data, record the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time; For each piece of time-related data, construct a time identifier of the piece of time-related data according to the first timestamp and the second timestamp; Based on the time identifiers of the multiple time-related data, selecting multiple target time-related data whose timing features match each other from the multiple time-related data; The time-related services are processed according to the plurality of target time-related data.

2. The method according to claim 1, characterized in that The selecting, based on the time identifiers of the plurality of time-related data, a plurality of target time-related data whose timing features match each other from the plurality of time-related data comprises: For each piece of time-related data, extract a first time series feature from a time identifier of the time-related data, where the first time series feature represents a time sequence of a first timestamp of the time-related data in the corresponding time series data; A plurality of target time-related data are selected from the plurality of time-related data according to the first time series features of the plurality of time-related data, so that the first time series feature of each target time-related data is the last one.

3. The method according to claim 1, characterized in that The selecting, based on the time identifiers of the plurality of time-related data, a plurality of target time-related data whose timing features match each other from the plurality of time-related data comprises: For each piece of time-related data, extract a second time series feature from a time identifier of the time-related data, where the second time series feature represents a time sequence of a second timestamp of the time-related data in the corresponding time series data; A plurality of target time-related data are selected from the plurality of time-related data according to the second time series features of the plurality of time-related data, so that the second time series feature of each target time-related data is the last one.

4. The method according to claim 2 or 3, characterized in that: The time-related data includes market data, the time-related business includes market-related forecasts and / or decisions, and the processing of the time-related business according to the plurality of target time-related data includes: Conduct real-time market-related data analysis based on multiple target time-related data; Market-related predictions and / or decisions are made based on the results of the real-time market-related data analysis.

5. The method according to claim 2 or 3, characterized in that: The time-related data includes system operation and maintenance monitoring data, the time-related services include system operation and maintenance situation prediction, and the processing of time-related services according to the plurality of target time-related data includes: Conduct real-time operation and maintenance status data analysis based on multiple target time-related data; Based on the real-time operation and maintenance status data analysis results, the system operation and maintenance status is predicted.

6. The method according to claim 2 or 3, characterized in that: The time-related data includes environmental monitoring data, the time-related services include environmental trend prediction, and the processing of the time-related services according to the plurality of target time-related data includes: Conduct real-time environmental status data analysis based on multiple target time-related data; Based on the real-time environmental status data analysis results, environmental-related trend forecasts are made.

7. The method according to claim 1, characterized in that Acquiring multiple time-related data from multiple data sources in real time includes: Acquire multiple pieces of raw time-related data from external data sources in real time; Performing data integration on the multiple pieces of original time-related data to obtain multiple pieces of time-related data; For the multiple pieces of time-related data, a first timestamp of each piece of time-related data is obtained through time alignment processing.

8. The method according to claim 7, characterized in that The integrating the plurality of original time-related data to obtain the plurality of time-related data comprises: Performing data cleaning on the plurality of original time-related data to obtain a plurality of cleaned time-related data; The cleaned time-related data are formatted based on a predetermined mapping relationship to form a plurality of time-related data.

9. The method according to claim 1, characterized in that: The method further comprises: merging the plurality of target time-related data into a target time-related data set; Obtaining a third timestamp corresponding to the processing process of the time-related service; A temporal index of the target time-related data set is constructed according to the third timestamp.

10. The method according to claim 9, characterized in that The method further comprises: Based on the target time-related data set and the real-time processing results of the time-related services, a dynamic visualization interface for displaying the real-time processing status of the time-related services is presented.

11. The method according to claim 10, characterized in that The dynamic visual interface further includes a search box, and the method further includes: The target time-related data set corresponding to the historical time-related business processing results is queried using the search box of the dynamic visualization interface.

12. The method according to claim 11, characterized in that The method of using the search box of the dynamic visualization interface to query the target time-related data set corresponding to the historical time-related business processing result includes: In response to the search box being clicked, displaying a plurality of third timestamps to be selected corresponding to a plurality of historical time-related business processing results in the search box; In response to one of the plurality of third timestamps to be selected being selected, obtaining a time series index of a historical target time-related data set corresponding to the selected third timestamp; According to the time series index of the historical target time-related data set corresponding to the selected third timestamp, acquiring the historical target time-related data set corresponding to the time series index; Displays data from the historical target time-related data set.

13. A time-related data processing device based on dual time series, comprising: An acquisition module configured to acquire multiple pieces of time-related data from multiple data sources in real time, each piece of time-related data including a first timestamp indicating a time when the data occurred; a recording module configured to record, for each piece of time-related data, the acquisition time of the time-related data to obtain a second timestamp indicating the data recording time; A construction module configured to construct, for each piece of time-related data, a time identifier of the piece of time-related data according to the first timestamp and the second timestamp; A screening module configured to select, based on time identifiers of the plurality of time-related data, a plurality of target time-related data whose timing features match each other from the plurality of time-related data; A processing module is configured to process time-related services according to the plurality of target time-related data.

14. A computing device comprising: a memory configured to store computer executable instructions; A processor, configured to perform the method according to any one of claims 1 to 12 when the computer executable instructions are executed by the processor. 15 . A computer-readable storage medium having computer-executable instructions stored thereon, wherein when the computer-executable instructions are executed, the method according to any one of claims 1 to 12 is executed.

16. A computer program product comprising computer executable instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 12.

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