Graphical time series data processing method and device and computer equipment

Abstract

The invention relates to a graphical time series data processing method and device and computer equipment. The method comprises the following steps: acquiring time sequence data, and performing time sequence alignment and vectorization on the time sequence data to obtain a time sequence data vector; constructing an operator resource library at the cloud; values in the time sequence data vector serve as tokens in the Petri net, operators serve as operators in the Petri net, and the sequence of the values in the time sequence data vector before and after processing serves as directed arcs in the Petri net; when time series data processing is carried out, a drag instruction of a user for constructing a Petri net is received on a visual modeling interface, operator code resources are called from an operator resource library of a cloud end according to operators required for constructing the Petri net, and input time series data are calculated in real time according to operator logic in the Petri net; and a calculation result is fed back to a visual modeling interface in real time. By adopting the method, the data processing transparency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of time series data processing, and in particular to a graphical time series data processing method, apparatus and computer equipment. Background Art

[0002] Time series data is widely used in many fields, such as smart cities, the Internet of Things, the Internet of Vehicles, the Industrial Internet, and finance. Its data characteristics of being recorded and indexed in chronological order make it comparable and can be used for a variety of analytical operations. However, the existing time series data processing and analysis methods, such as Excel, Python programs, and databases, are efficient, but the processing logic is hidden in the code and formulas, making it difficult for data users to understand and verify the processing logic, affecting data sharing and utilization. Especially in scenarios such as financial data analysis that require high logical rationality, a method and tool for graphically expressing the processing process is urgently needed. Summary of the invention

[0003] Based on this, it is necessary to provide a graphical time series data processing method, device and computer equipment to address the above technical problems.

[0004] A graphical time series data processing method, the method comprising:

[0005] Acquire time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector;

[0006] Constructing an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data;

[0007] The values ​​in the time series data vector are used as tokens in the Petri net, the operators are used as operators in the Petri net, and the order of processing of the values ​​in the time series data vector is used as directed arcs in the Petri net;

[0008] When processing time series data, the user's drag-and-drop instructions for building a Petri net are received on the visual modeling interface, and the operator code resources are called from the operator resource library in the cloud according to the operators required to build the Petri net. The input time series data is calculated in real time according to the operator logic in the Petri net, and the calculation results are immediately fed back on the visual modeling interface.

[0009] In one of the embodiments, it also includes: using at least one of a linear interpolation algorithm, a spline interpolation algorithm, a mean aggregation algorithm, and a sum aggregation algorithm to interpolate the time series data, and performing time series alignment based on a preset time dimension.

[0010] In one of the embodiments, it also includes: mapping each data value in the time series data to the corresponding position of the vector in sequence according to the time order to form a one-dimensional time series data sequence; wherein the length of the time series data sequence is equal to the length of the time series data, and the index of the time series data sequence corresponds to the time order of the data value.

[0011] In one of the embodiments, the operator resource library includes operator code resources of a translation operator, a frequency conversion operator, a mathematical calculation operator, a data conversion operator, and a data screening operator.

[0012] In one of the embodiments, the method further includes: updating, adding and deleting the operator resource library according to user feedback information.

[0013] In one of the embodiments, it also includes: displaying node icons for representing time series data vectors, transition icons for representing operators, and directed arc drawing tools for connecting nodes and transitions in a visual modeling interface; detecting the legality of the user's dragging instructions in real time, and when there are illegal dragging instructions, prompting and correcting the illegal dragging instructions.

[0014] In one of the embodiments, it also includes: calculating the input time series data in real time according to the operator logic in the Petri net, caching the intermediate results in the calculation process, and when the user adjusts the parameters of the operator or the structure of the Petri net, recalculating the affected part according to the cached intermediate results.

[0015] In one of the embodiments, it also includes: displaying computing logic information on the connection line between the operator and the data node; the computing logic information includes the time when the calculation occurs, the name of the operator used, the operator parameters, the source of the input data, and the result of the previous calculation step; receiving the user's operation instruction by clicking on the connection line, displaying information or backtracking button, and viewing the evolution path of the data in the processing process and detailed information of each step.

[0016] A graphical time series data processing device, the device comprising:

[0017] A data processing module is used to obtain time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector;

[0018] An operator construction module, used to construct an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data;

[0019] A Petri net definition module, used to use the values ​​in the time series data vector as tokens in the Petri net, operators as operators in the Petri net, and the order of processing of each value in the time series data vector as directed arcs in the Petri net;

[0020] The Petri net construction module is used to receive the user's drag-and-drop instructions for constructing a Petri net in the visual modeling interface when processing time series data, call the operator code resources from the operator resource library in the cloud according to the operators required to construct the Petri net, and calculate the input time series data in real time according to the operator logic in the Petri net, and immediately feed back the calculation results on the visual modeling interface.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0022] Acquire time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector;

[0023] Constructing an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data;

[0024] The values ​​in the time series data vector are used as tokens in the Petri net, the operators are used as operators in the Petri net, and the order of processing of the values ​​in the time series data vector is used as directed arcs in the Petri net;

[0025] When processing time series data, the user's drag-and-drop instructions for building a Petri net are received on the visual modeling interface, and the operator code resources are called from the operator resource library in the cloud according to the operators required to build the Petri net. The input time series data is calculated in real time according to the operator logic in the Petri net, and the calculation results are immediately fed back on the visual modeling interface.

[0026] The above-mentioned graphical time series data processing method, device and computer equipment, by acquiring time series data and performing time series alignment and vectorization processing, convert it into a vector form suitable for calculation, laying the foundation for subsequent processing; constructing an operator resource library containing various functional operator code resources and classified storage in the cloud, which is easy to manage and call; cleverly mapping time series data with Petri net elements, using the visual modeling interface to receive user drag and drop instructions to build a model, and can dynamically call operator code resources from the cloud according to the needs of the model, calculate in real time and immediately feedback the results. It effectively solves the problems of opaque time series data processing logic and low efficiency in the prior art, realizes the visualization, interactivity and efficiency of the time series data processing process, enhances data analysis capabilities, improves the accuracy and flexibility of data processing, and improves user experience. It has significant practical value and innovation in the field of time series data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1A schematic diagram of a process flow of a graphical time series data processing method in one embodiment;

[0028] Figure 2 is a structural block diagram of a graphical time series data processing device in one embodiment;

[0029] Figure 3 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] In one embodiment, Figure 1 As shown, a graphical time series data processing method is provided, comprising the following steps:

[0032] Step 102, acquiring time series data, and performing time series alignment and vectorization on the time series data to obtain a time series data vector.

[0033] In terms of acquiring time series data, data is collected from a variety of possible data sources, such as sensors, monitoring equipment, and databases, to ensure that the data is accurate and complete. In the time series alignment phase, a variety of methods are used: directly aligning data with the same timestamp based on timestamps; using similarity metrics (such as the acquisition standard and target audio signal in signal time series alignment, determining candidate alignment points, calculating correlation and selecting target alignment points) to achieve data alignment in terms of features; for repeated experimental data, alignment is achieved by aligning key nodes (such as movement onset) and processing duration (calculating the mean or median and then stretching or compressing the trial); for multi-satellite data, a data queue is established, data is cached according to the satellite ID, and a specific dequeue strategy is implemented (such as the processing method of other satellite queues when a satellite data queue is full) to ensure time synchronization.

[0034] The vectorization process is to convert the aligned time series data into vector form according to certain rules (if the abstract does not mention it, it can be assumed to be in reasonable order such as time sequence or data characteristics), so as to provide a data basis for subsequent modeling and calculation operations based on Petri nets with the support of the operator resource library. This process involves operations such as processing missing data values ​​(using zero filling, forward filling, backward filling or filling based on statistical eigenvalues) to ensure vector integrity, and finally obtain time series data vectors that can be used for in-depth analysis and processing.

[0035] Step 104: construct an operator resource library in the cloud.

[0036] The operator resource library includes operator code resources for processing time series data.

[0037] In this step, a cloud server is established and the corresponding computing resources and storage capabilities are configured to pre-set and manage the operator resource library. The server is installed with the Python runtime environment to ensure that the operator code can be successfully executed in the cloud.

[0038] Specifically, when the user chooses to use a certain type of operator for calculation in the front-end interface, the system obtains the corresponding operator code resources from the cloud through asynchronous on-demand loading, and performs dynamic calculations in the browser based on WebAssembly (wasm) technology.

[0039] Step 106, using the values ​​in the time series data vector as tokens in the Petri net, the operators as operators in the Petri net, and the order of processing the values ​​in the time series data vector as directed arcs in the Petri net.

[0040] Specifically, the tokens in the Petri net are defined to represent time series data (indicators), that is, each token represents the time series data value at a specific time point; transitions represent operators, which means that when a transition is triggered, the corresponding operator will process the input time series data; directed arcs represent the order of processing, and clarify the flow path of data between operators. For example, starting from the data source node, it is connected to the transition representing the translation operator through a directed arc, and then connected to the next operator or data storage node through the output directed arc of the transition, thus building a complete data processing process model.

[0041] Step 108, when processing time series data, the user's drag and drop instructions for building a Petri net are received on the visual modeling interface, the operator code resources are called from the operator resource library in the cloud according to the operators required to build the Petri net, and the input time series data is calculated in real time according to the operator logic in the Petri net, and the calculation results are immediately fed back on the visual modeling interface.

[0042] In this embodiment, for example, when a user connects a node representing a time series data sequence to a translation operator transition and sets the translation amount, the system immediately performs a translation calculation on the data sequence and displays the resulting sequence after the translation on the interface, thus achieving a what-you-see-is-what-you-get interactive experience.

[0043] In the above-mentioned graphical time series data processing method, by acquiring time series data and performing time series alignment and vectorization processing, it is converted into a vector form suitable for calculation, laying the foundation for subsequent processing; an operator resource library containing various functional operator code resources and classified storage is built in the cloud for easy management and calling; the time series data is cleverly mapped with Petri net elements, and the model is constructed by receiving user drag and drop instructions with the help of a visual modeling interface, and the operator code resources can be dynamically called from the cloud according to the needs of the model, and the results are calculated in real time and fed back immediately. It effectively solves the problems of opaque logic and low efficiency of time series data processing in the existing technology, realizes the visualization, interactivity and efficiency of the time series data processing process, enhances data analysis capabilities, improves the accuracy and flexibility of data processing, and improves user experience. It has significant practical value and innovation in the field of time series data processing.

[0044] In one of the embodiments, at least one of a linear interpolation algorithm, a spline interpolation algorithm, a mean aggregation algorithm, and a sum aggregation algorithm is used to interpolate the time series data, and the time series alignment is performed based on a preset time dimension.

[0045] In this step, when processing time series data, at least one of the linear interpolation algorithm, spline interpolation algorithm, mean aggregation algorithm, and sum aggregation algorithm is selected for interpolation operation. The main purpose is to make the time series data unified and complete in the time dimension to meet the requirements of subsequent analysis and processing. Different algorithms are suitable for different data features and processing scenarios. By flexibly using these algorithms, time series data in various complex situations can be better processed.

[0046] In one of the embodiments, each data value in the time series data is mapped to the corresponding position of the vector in chronological order to form a one-dimensional time series data sequence; wherein the length of the time series data sequence is equal to the length of the time series data, and the index of the time series data sequence corresponds to the chronological order of the data value.

[0047] In one embodiment, the operator resource library includes: operator code resources of translation operators, frequency conversion operators, mathematical calculation operators, data conversion operators, and data screening operators. These operators cover common operations of time series data processing, such as translation operators (which can be used to translate data on the time axis to achieve data alignment or lag processing in time series prediction, etc.), frequency conversion operators (used to change the sampling frequency of data, such as converting high-frequency data to low-frequency data for trend analysis), mathematical calculation operators (including basic mathematical operations such as addition, subtraction, multiplication, and division, as well as complex function calculations, used to achieve data transformation and combination, such as calculating the difference and ratio of two time series data sequences, etc.), and possible combination operators for processing the result output calculation between operators (used to further integrate and calculate the output results of multiple operators, such as first performing translation and frequency conversion operations on two time series data, and then multiplying the results), etc.

[0048] In one of the embodiments, the operator resource library is updated, added, and deleted based on user feedback information.

[0049] In this embodiment, in the operator resource library in the cloud, the operator code resources are updated and optimized regularly, the performance of commonly used operators is improved based on user usage frequency and feedback information, and new operator types are added based on emerging data processing needs. Emerging data processing needs include the application of deep learning algorithms in time series data, professional data processing rules in specific industry fields, etc.

[0050] In one of the embodiments, node icons for representing time series data vectors, transition icons for representing operators, and directed arc drawing tools for connecting nodes and transitions are displayed on a visual modeling interface; the legality of the user's dragging instructions is detected in real time, and when there are illegal dragging instructions, the illegal dragging instructions are prompted and corrected.

[0051] In addition, after calling the operator code resources from the cloud according to the operators required to build the Petri net, the operator code is dynamically compiled and loaded based on the WebAssembly technology in the front-end browser environment to achieve efficient execution of the operator in the browser. During the execution process, the computing resource usage of the operator is monitored and optimized. When the resource usage exceeds the preset threshold, measures such as pausing or adjusting other parallel computing tasks are taken to ensure the stability and response speed of the system.

[0052] In one of the embodiments, the input time series data is calculated in real time according to the operator logic in the Petri net, and the intermediate results in the calculation process are cached. When the user adjusts the parameters of the operator or the structure of the Petri net, the affected part is recalculated according to the cached intermediate results.

[0053] In another embodiment, computing logic information is displayed on the connection line between the operator and the data node; the computing logic information includes the time when the calculation occurs, the name of the operator used, the operator parameters, the source of the input data, and the result of the previous calculation step; the user is received to click the operation instruction on the connection line, display the information or backtrack button to view the evolution path of the data in the processing process and the detailed information of each step.

[0054] In some other embodiments, a model saving step is also included. After the user completes the Petri net construction and data processing, the entire model (including model structure, operator parameters, data source, calculation logic and other information) is saved to the local file system or cloud storage. The saved model file format is a custom binary format or an extended definition based on a general data exchange format such as XML and JSON to ensure the integrity and recoverability of the model information. In the model reuse step, when the user loads the saved model from the local or cloud, the system automatically parses the model file, restores the structure and parameter settings of the model, and prompts the user to import the corresponding time series data according to the data source information. The user can directly use the restored model to process the new data, or make further modifications and extensions based on the original model. The modification and extension operations include adding or deleting operators, adjusting operator parameters, changing data connection relationships, etc. During the modification process, the system updates the model's visual display and related information in real time to ensure the consistency and accuracy of the model.

[0055] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0056] In one embodiment, Figure 2 As shown, a graphical time series data processing device is provided, including: a data processing module 202, an operator construction module 204, a Petri net definition module 206 and a Petri net construction module 208, wherein:

[0057] The data processing module 202 is used to obtain time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector;

[0058] An operator construction module 204 is used to construct an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data;

[0059] A Petri net definition module 206, used to use the values ​​in the time series data vector as tokens in the Petri net, operators as operators in the Petri net, and the order of processing of each value in the time series data vector as directed arcs in the Petri net;

[0060] The Petri net construction module 208 is used to receive the user's drag and drop instructions to construct the Petri net in the visual modeling interface when processing time series data, call the operator code resources from the operator resource library in the cloud according to the operators required to construct the Petri net, and calculate the input time series data in real time according to the operator logic in the Petri net, and immediately feed back the calculation results on the visual modeling interface.

[0061] In one embodiment, the data processing module 202 is further used to interpolate the time series data using at least one of a linear interpolation algorithm, a spline interpolation algorithm, a mean aggregation algorithm, and a sum aggregation algorithm, and perform time series alignment based on a preset time dimension.

[0062] In one of the embodiments, the data processing module 202 is also used to map each data value in the time series data to the corresponding position of the vector in sequence according to the time sequence to form a one-dimensional time series data sequence; wherein the length of the time series data sequence is equal to the length of the time series data, and the index of the time series data sequence corresponds to the time sequence of the data value.

[0063] In one of the embodiments, the operator resource library includes operator code resources of a translation operator, a frequency conversion operator, a mathematical calculation operator, a data conversion operator, and a data screening operator.

[0064] In one embodiment, the operator construction module 204 is further used to update, add and delete the operator resource library according to user feedback information.

[0065] In one of the embodiments, the Petri net definition module 206 is also used to display node icons for representing time series data vectors, transition icons for representing operators, and directed arc drawing tools for connecting nodes and transitions in a visual modeling interface; and to detect the legality of the user's dragging instructions in real time, and when there are illegal dragging instructions, prompt and correct the illegal dragging instructions.

[0066] In one embodiment, the Petri net construction module 208 is also used to calculate the input time series data in real time according to the operator logic in the Petri net, cache the intermediate results in the calculation process, and when the user adjusts the parameters of the operator or the structure of the Petri net, recalculate the affected part according to the cached intermediate results.

[0067] In one embodiment, the Petri net construction module 208 is also used to display computing logic information on the connection line between the operator and the data node; the computing logic information includes the time when the calculation occurs, the name of the operator used, the operator parameters, the source of the input data, and the result of the previous calculation step; receive the user's operation instructions by clicking on the connection line, display information or backtrack button, and view the evolution path of the data in the processing process and detailed information of each step.

[0068] For the specific definition of the graphical timing data processing device, please refer to the definition of the graphical timing data processing method above, which will not be repeated here. Each module in the above-mentioned graphical timing data processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0069] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a graphical time series data processing method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0070] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0071] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program,

[0072] In one embodiment, a computer-readable storage medium is provided on which a computer program is stored. When the computer program is executed by a processor,

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0074] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A graphical time series data processing method, characterized in that: The method comprises: Acquire time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector; Constructing an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data; The values ​​in the time series data vector are used as tokens in the Petri net, the operators are used as operators in the Petri net, and the order of processing of the values ​​in the time series data vector is used as directed arcs in the Petri net; When processing time series data, the user's drag-and-drop instructions for building a Petri net are received on the visual modeling interface, and the operator code resources are called from the operator resource library in the cloud according to the operators required to build the Petri net. The input time series data is calculated in real time according to the operator logic in the Petri net, and the calculation results are immediately fed back on the visual modeling interface.

2. The method according to claim 1, characterized in that Performing time series alignment on the time series data includes: At least one of a linear interpolation algorithm, a spline interpolation algorithm, a mean aggregation algorithm, and a sum aggregation algorithm is used to interpolate the time series data, and time series alignment is performed based on a preset time dimension.

3. The method according to claim 1, characterized in that The time series data is processed, including: Map each data value in the time series data to the corresponding position of the vector in chronological order to form a one-dimensional time series data sequence; wherein the length of the time series data sequence is equal to the length of the time series data, and the index of the time series data sequence corresponds to the chronological order of the data value.

4. The method according to any one of claims 1 to 3, characterized in that: The operator resource library includes operator code resources for translation operators, frequency conversion operators, mathematical calculation operators, data conversion operators, and data screening operators.

5. The method according to claim 4, characterized in that The method further comprises: According to the user's feedback information, the operator resource library is updated, added and deleted.

6. The method according to claim 1, characterized in that When receiving a user's drag and drop instruction to construct a Petri net on the visual modeling interface, the method includes: Display node icons for representing time series data vectors, transition icons for representing operators, and directed arc drawing tools for connecting nodes and transitions in the visual modeling interface; The legitimacy of the user's dragging instruction is detected in real time, and when there is an illegal dragging instruction, the illegal dragging instruction is prompted and corrected.

7. The method according to claim 6, characterized in that The input time series data is calculated in real time according to the operator logic in the Petri net, and the calculation results are immediately fed back on the visual modeling interface, including: The input time series data is calculated in real time according to the operator logic in the Petri net, and the intermediate results in the calculation process are cached. When the user adjusts the parameters of the operator or the structure of the Petri net, the affected part is recalculated according to the cached intermediate results.

8. The method according to any one of claims 5 to 7, characterized in that: The calculation results are immediately fed back on the visual modeling interface, including: Displaying computational logic information on the connection line between the operator and the data node; the computational logic information includes the time when the computation occurred, the name of the operator used, the operator parameters, the source of the input data, and the result of the previous computation; Receive user operation instructions by clicking on the connection line, display information or click the backtrack button to view the evolution path of the data during the processing and detailed information of each step.

9. A graphical time series data processing device, characterized in that: The device comprises: A data processing module is used to obtain time series data, and perform time series alignment and vectorization on the time series data to obtain a time series data vector; An operator construction module, used to construct an operator resource library in the cloud; the operator resource library includes operator code resources for processing time series data; A Petri net definition module, used to use the values ​​in the time series data vector as tokens in the Petri net, operators as operators in the Petri net, and the order of processing of each value in the time series data vector as directed arcs in the Petri net; The Petri net construction module is used to receive the user's drag-and-drop instructions for constructing a Petri net in the visual modeling interface when processing time series data, call the operator code resources from the operator resource library in the cloud according to the operators required to construct the Petri net, and calculate the input time series data in real time according to the operator logic in the Petri net, and immediately feed back the calculation results on the visual modeling interface.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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