Time series data consanguinity map construction method based on topological relation

Abstract

The invention relates to the technical field of data processing, in particular to a time series data consanguinity map construction method based on a topological relation, which comprises the following steps of: designing a quantization function of a governance attribute through a data modeling language, modeling a business index into the governance attribute, and expressing business calculation logic by using the packaged quantization function. According to business requirements, a time series data governance model is constructed, and the relation between governance attributes is defined through a quantization function. And analyzing the model by using a data relation graph engine, and extracting the topological relation between the governance attributes. Based on the topological relationships, a time series data blood relationship map can be generated, and the map takes governance attributes as nodes and dependencies as edges. According to the scheme, the effective management of the business indexes and the clear display of the time sequence data relationship can be realized, and the improvement of the efficiency and accuracy of data management and application is facilitated.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method for constructing a pedigree map of time-series data based on topological relationships. Background Technology

[0002] Data lineage mapping is a crucial component in data analysis and data governance. Based on it, the entire flow path of data can be traced from the original data source to its final usage, and anomalies can be located and traced back to their source. Data lineage mapping effectively ensures data security and quality.

[0003] However, most existing data lineage maps are based on relational structured data, using tables, columns, and structured query languages ​​to break down dependencies between data and construct the lineage map. For time-series data, where dependencies are not obvious, effectively constructing lineage maps and performing data analysis has become a pressing problem in the field of data processing technology. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for constructing a lineage map of time series data based on topological relationships, which addresses the shortcomings of the prior art and solves the technical problem that it is difficult to construct a lineage map of time series data due to the lack of clear dependencies.

[0005] The objective of this invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for constructing a lineage map of time-series data based on topological relationships, comprising: Based on a data modeling language, a quantification function for governance attributes is designed, and business indicators are modeled as governance attributes. The encapsulated quantification function represents the business calculation logic; the business indicators are entity time-series data. Build a time-series data governance model based on business needs, and define the relationships between governance attributes through quantization functions; The quantization function in the time series data governance model is analyzed based on the data relationship graph engine, and the topological relationship between governance attributes is extracted based on the analyzed time series data governance model. Based on the aforementioned topological relationships, a temporal data lineage graph is obtained, with governance attributes as nodes and dependencies as edges.

[0006] As a further improvement of the present invention, the quantification function for designing governance attributes based on a data modeling language specifically includes: The data modeling language is a script for writing quantitative functions that supports Chinese variable definitions. The governance attributes include business indicator metadata, quantitative functions, and external data interfaces; The quantization function includes at least one nestable function module, which is used to perform multi-dimensional data calculations of mathematical, string, and time types.

[0007] As a further improvement of the present invention, the step of constructing a time-series data governance model based on business needs and defining the relationship between governance attributes through a quantization function specifically includes: Based on business needs, the relationship between basic entity indicators and application indicators in the business process is analyzed, and governance attributes containing quantitative functions are constructed based on the analysis results. Write corresponding quantization function scripts for each governance attribute, and define the input parameters as lower-level attributes or external data sources; A dynamic referencing mechanism for attribute components is established to support cross-level branch references, forming a three-level time-series data governance model, which includes a business library layer, an indicator layer, and an attribute layer.

[0008] As a further improvement of the present invention, the quantization function in the time-series data governance model is parsed based on the data relationship graph engine, and the topological relationship between governance attributes is extracted based on the parsed time-series data governance model, specifically including: The quantization function script is decomposed and converted into a bytecode instruction set through lexical decomposition, semantic parsing and AST syntax tree. At runtime, it is optimized into machine code through JIT compilation and sent to the computing server for computation. By using a data relationship graph engine to analyze the reference relationships between governance attributes in the quantitative function script and the upstream and downstream relationships of governance attributes in the indicator system, the temporal dependency paths and weights between governance attributes are extracted, and a topological relationship containing the time dimension is generated.

[0009] As a further improvement of the present invention, the data relationship graph engine specifically includes: Syntax decomposition module: Used to decompose keywords, operators, and governance attribute references in a script; Semantic parsing module: Constructs an intermediate representation that includes variable scope and type constraints; JIT optimization coding module: optimizes the parsed results into an executable set of machine instructions; Topology extraction module: Generates a dependency matrix by tracing the function call chain in reverse.

[0010] As a further improvement of the present invention, the topology relationship extraction module specifically includes: Identify explicit attribute references and implicit data dependencies in quantization function scripts; Calculate the dependency strength values ​​between each governance attribute and generate a weighted topological edge structure; Establish a time-series dependency model across time windows and record historical version associations.

[0011] As a further improvement of the present invention, after obtaining the time-series data lineage graph with governance attributes as nodes and dependencies as edges, the method further includes visual analysis of the time-series data lineage graph: A force-directed layout algorithm is used to spatially distribute nodes in the pedigree graph of temporal data. The calculation logic of the quantization function is displayed by showing the edges of the pedigree graph of time-series data; Use heatmap overlays to display the dynamic changing trends of topological relationships; A lineage tracing tool is provided, which is used to expand the upstream and downstream dependency chain from any node.

[0012] Secondly, the present invention provides a time-series data lineage map construction system based on topological relationships, used to implement the above-mentioned time-series data lineage map construction method based on topological relationships, comprising: The modeling language interpretation module designs quantification functions for governance attributes based on a data modeling language, models business indicators as governance attributes, and represents business calculation logic through encapsulated quantification functions; the business indicators are entity time-series data. The governance attribute processing module constructs a time-series data governance model based on business needs and defines the relationships between governance attributes through quantification functions. The topology calculation module uses a data relationship graph engine to parse the quantization function in the time series data governance model and extracts the topology relationships between governance attributes based on the parsed time series data governance model. The kinship graph construction module obtains a time-series kinship graph with governance attributes as nodes and dependency relationships as edges based on the topological relationships.

[0013] Thirdly, the present invention provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described method for constructing a time-series data lineage map based on topological relationships.

[0014] Fourthly, the present invention provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for performing the above-described method for constructing a pedigree map of time-series data based on topological relationships.

[0015] The beneficial effects of this invention are as follows: This invention provides a method for constructing a lineage graph of time-series data based on topological relationships. This invention focuses on the characteristics of time-series data and the challenges in lineage graph construction. It uses the time-series data of entities as business indicators, designs quantification functions for governance attributes using a data modeling language, converts business indicators into governance attributes, and represents the business calculation logic through encapsulated quantification functions. This avoids the ambiguity of traditional qualitative assessments, enabling governance attributes to be accurately measured and tracked. A time-series data governance model is constructed according to business needs, and the relationships between governance attributes are defined through quantification functions. This leads to the identification of dependencies between time-series data, forming a topological structure, and ultimately, a time-series data lineage graph. This invention presents complex governance attribute relationships in an intuitive way, facilitating understanding and analysis. By analyzing the lineage graph, key governance attributes and key dependencies can be identified, improving work efficiency.

[0016] Furthermore, quantization function scripts are written using a data modeling language that supports Chinese variable definitions, allowing for a more intuitive understanding of function logic and functionality, and improving readability and maintainability. Quantization functions not only include mathematical calculations but also support multi-dimensional calculations such as string processing and time-type data calculations, thereby decomposing complex calculation logic into multiple simple sub-functions, further improving code readability and maintainability.

[0017] Furthermore, by clarifying the relationships between indicators, it becomes clear how data governance supports the achievement of business objectives, preventing a disconnect between governance efforts and business needs. Transforming abstract business indicators into quantifiable governance attributes makes governance goals more explicit and measurable. Simultaneously, defining input parameters clearly demonstrates the dependencies between governance attributes.

[0018] Furthermore, this method achieves intelligent, real-time, and refined data governance by accurately parsing quantification functions, deeply analyzing the relationships between governance attributes, and generating topological relationships that include a time dimension. This provides strong support for improving the efficiency and effectiveness of data governance. By extracting the temporal dependency paths and weights between governance attributes and generating topological relationships that include a time dimension, it is possible to analyze the changing trends of the relationships between governance attributes over time, quantify the dependency strength between governance attributes, and more accurately assess their impact.

[0019] Furthermore, a heatmap layer is overlaid on the graph, using variations in color depth or brightness to represent the dynamic trends of topological relationships. This visually demonstrates the changes in dependencies between governance attributes over different time periods, as well as the activity level of data flow. By providing a lineage tracing tool, users can start from any node and trace upstream and downstream dependency chains to gain a deeper understanding of the data flow paths and transformation processes within the system. This invention, through the use of visualized graphs and heatmap matrices, makes complex data relationships and dynamic trends more intuitive and easier to understand, lowering the barrier to comprehension. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the process for constructing a pedigree map of time-series data based on topological relationships in this invention.

[0022] Figure 2 This is a schematic diagram of the data governance indicator system in this invention.

[0023] Figure 3 This is a schematic diagram of the bloodline graph constructed by the data relationship graph engine in this invention.

[0024] Figure 4 This is a schematic diagram of the time-series data lineage map in this invention.

[0025] Figure 5 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0026] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] Example 1 like Figure 1 As for Figure 4As shown, this embodiment provides a method for constructing a time-series data lineage graph based on topological relationships. It involves designing quantification function scripts for governance attributes using a data modeling language; establishing a time-series data governance model according to business requirements; and constructing a data lineage graph by extracting the topological relationships between governance attributes using a data relationship graph engine. The specific implementation method is as follows.

[0029] Based on a data modeling language, a quantification function for governance attributes is designed, and business indicators are modeled as governance attributes. The encapsulated quantification function represents the business calculation logic.

[0030] Specifically, business metrics are entity time-series data, that is, entity objects that need to be modeled, such as actual production indicators in the power generation field, such as "power generation" and "plant power consumption rate".

[0031] The data modeling language allows for the writing of quantitative function scripts that support Chinese variable definitions. Specifically, the syntax of the data modeling language is similar to JavaScript, and variable definitions support Chinese characters. In this embodiment, the data modeling language can also employ tools such as entity-relationship modeling, unified modeling language, and object-relational mapping.

[0032] Governance attributes refer to the smallest independent governance unit in the data governance process, which includes business metrics and quantitative function scripts.

[0033] Business calculation logic refers to the calculation formulas for modeled entity objects, such as "power generation" obtained through integration at measurement points. Quantization functions, on the other hand, refer to code scripts composed of functions, which can reference other attributes, manual data, and other data entities. "Quantization functions" are primarily encapsulated from various highly specialized and complex algorithms, used to quantify "business calculation logic" into specific function models. They support nested combinations and branch references, and support calculations on multiple data types such as numbers, strings, and time, facilitating the resolution of complex business quantification problems. Functions and data modeling languages ​​together constitute the quantization function scripts for governance attributes.

[0034] A time-series data governance model is built based on business needs, and the relationships between governance attributes are defined through quantization functions.

[0035] First, based on business requirements, the relationships between basic entity indicators and application indicators in the business process are analyzed. Based on the analysis results, governance attributes containing quantification functions are constructed. For each governance attribute, corresponding quantification function scripts are written, defining input parameters as lower-level attributes or external data sources. A dynamic referencing mechanism for attribute components is established, supporting cross-level branch references, forming a three-level time-series data governance model. For example... Figure 2 As shown, the time-series data governance model includes a business database layer, an indicator layer, and an attribute layer.

[0036] Specifically, users, based on actual business needs, analyze the various data elements involved in the business process and their inherent relationships. Taking the power generation sector as an example, this method not only considers basic entity indicators such as "power generation" and "load," but also associates highly aggregated application indicators such as "power generation coal cost" and "plant power consumption rate." Based on the analysis results, governance attributes containing quantitative function scripts are constructed. In this process, for different types of business data, corresponding quantitative function scripts are written using data modeling languages ​​to define corresponding governance attributes. For example, "plant power consumption rate" is obtained by calculating the power generation and grid connection power of the computer group using quantitative function scripts. Different governance attributes are interconnected and referenced. For example, the calculation of "power generation coal cost" can depend on governance attributes such as "coal price per unit" and "load." Through the interrelationships of different governance attributes, a comprehensive data governance indicator system is formed, resulting in a time-series data governance model. This time-series data governance model has a three-level structure: business database, indicators, and attributes. This three-level structure accurately reflects the data flow and calculation logic throughout the entire business process.

[0037] This paper utilizes a data relationship graph engine to parse the quantization functions in a time-series data governance model, and extracts the topological relationships between governance attributes based on the parsed model. According to these topological relationships, a time-series data lineage graph is obtained, with governance attributes as nodes and dependencies as edges. Leveraging the quantization function script analysis and decomposition capabilities of the data relationship graph engine, the paper traverses the time-series data governance model, extracting topological relationships from the quantization function scripts of governance attributes, and fusing and interweaving them to form a data lineage graph originating from business topology logic.

[0038] This mainly includes: a data relationship graph engine for analyzing and decomposing quantization function scripts, converting scripts into bytecode instruction sets through lexical decomposition, semantic parsing, and AST syntax trees, and optimizing them into machine code at runtime through JIT, which is then sent to the computing server for computation.

[0039] By using a data relationship graph engine to analyze the reference relationships between governance attributes in the quantitative function script and the upstream and downstream relationships of governance attributes in the indicator system, the temporal dependency paths and weights between governance attributes are extracted, and a topological relationship containing the time dimension is generated.

[0040] Specifically, such as Figure 3 As shown, the data relationship graph engine in this embodiment specifically includes: a syntax decomposition module, a semantic parsing module, an AST syntax tree, and a topological relationship extraction module. The specific functions of each module include: Syntax decomposition module: Used to decompose keywords, operators, and governance attribute references in a script; Semantic parsing module: Constructs an intermediate representation that includes variable scope and type constraints; JIT optimization coding module: optimizes the parsed results into an executable set of machine instructions; Topology extraction module: Generates a dependency matrix by tracing the function call chain in reverse.

[0041] The topology extraction module specifically includes: identifying explicit attribute references and implicit data dependencies in the quantization function script; calculating the dependency strength values ​​between each governance attribute to generate a weighted topology edge structure; establishing a time-series dependency model across time windows and recording historical version associations.

[0042] Specifically, the data relationship graph engine analyzes the reference relationships between governance attributes in the quantification function script and the upstream and downstream connections of governance attributes in the indicator system, extracting the topological relationships between governance attributes. For example, if the quantification function script for "power generation coal cost" references governance attributes such as "coal unit price" and "load," the topological relationship will clearly show the direction and degree of dependence among these three. These topological relationships are then visualized graphically to construct a time-series data lineage graph. Figure 4 As shown, in the time series data lineage graph, each governance attribute is a node, and the topological relationship between attributes is represented by lines. The node color can be used to distinguish different types of relationships.

[0043] Furthermore, after obtaining the time-series data pedigree, this embodiment also includes visual analysis of the time-series data pedigree, specifically including: A force-directed layout algorithm is used to spatially distribute nodes in the pedigree graph of time-series data; the calculation logic of the quantization function is displayed through the edges of the pedigree graph; a heatmap layer is used to display the dynamic trend of topological relationships; and a pedigree tracing tool is provided to expand upstream and downstream dependency chains from any node.

[0044] Based on data lineage graphs, the flow of time-series data throughout the entire business process is clearly and intuitively displayed, facilitating data governance personnel to perform functions such as source tracing analysis, snapshot analysis, and historical trend analysis of governance attributes. Following the above steps, constructing a lineage graph for time-series data can provide strong support for enterprise business decision-making, data management, and optimization.

[0045] Example 2 This embodiment provides a system for constructing a pedigree map of time-series data based on topological relationships, used to implement the method for constructing a pedigree map of time-series data based on topological relationships in Embodiment 1. The system specifically includes: The modeling language interpretation module designs quantification functions for governance attributes based on a data modeling language, models business indicators as governance attributes, and represents business calculation logic through encapsulated quantification functions; business indicators are entity time-series data. The governance attribute processing module constructs a time-series data governance model based on business needs and defines the relationships between governance attributes through quantification functions. The topology calculation module uses a data relationship graph engine to parse the quantization function in the time series data governance model and extracts the topology relationships between governance attributes based on the parsed time series data governance model. The kinship graph construction module generates a time-series kinship graph with governance attributes as nodes and dependencies as edges based on topological relationships.

[0046] Example 3 In one embodiment of the present invention, a computer-readable storage medium is provided, which belongs to the memory device of a terminal device and is mainly used for storing programs and data. The computer-readable storage medium includes both storage media built into the terminal and extended storage media supported by the terminal. Specifically, any tangible medium capable of storing programs and used by an instruction execution system, apparatus, or device falls within this category. The storage medium provides storage space for storing the terminal operating system and instructions (including one or more computer programs and their code) that can be loaded and executed by a processor. Examples include electrical connections, portable disks, hard disks, RAM, ROM, EPROM / flash memory, optical fibers, CD-ROMs, optical storage devices, magnetic storage devices, and combinations thereof.

[0047] Furthermore, computer-readable storage media also include data signals propagated in baseband or as carrier waves, carrying readable program code, which may be in the form of electromagnetic signals, optical signals, etc. Readable storage media are not limited to the types mentioned above, but also include other media capable of sending, propagating, or transmitting programs for use by instruction execution systems, devices, or apparatuses. Program code can be transmitted wirelessly, wired, optically, RF, etc.

[0048] The program code can be written in various programming languages, such as object-oriented languages ​​(Python, Java, C++, etc.) and procedural languages ​​(C, etc.). The code can be executed fully or partially on the user's device, or as a standalone software package, or partially / fully on a remote device. The remote device is connected to the user's device via a LAN, WAN, or Internet service provider.

[0049] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for constructing a time-series lineage map based on topological relationships in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Based on a data modeling language, a quantification function for governance attributes is designed, and business indicators are modeled as governance attributes. The encapsulated quantification function represents the business calculation logic; the business indicators are entity time-series data. Build a time-series data governance model based on business needs, and define the relationships between governance attributes through quantization functions; The quantization function in the time series data governance model is analyzed based on the data relationship graph engine, and the topological relationship between governance attributes is extracted based on the analyzed time series data governance model. Based on the topological relationships, a time-series data lineage graph is obtained, with governance attributes as nodes and dependencies as edges.

[0050] Example 4 Figure 5 This is a block diagram of an electronic device provided according to an embodiment of the present invention.

[0051] Please see Figure 5 The terminal device 600 is an electronic device, which takes the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0052] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0053] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0054] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0055] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0056] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

Claims

1. A method for constructing a pedigree map of time-series data based on topological relationships, characterized in that, include: Based on a data modeling language, a quantification function for governance attributes is designed, business indicators are modeled as governance attributes, and the business calculation logic is represented by the encapsulated quantification function. The business metrics are entity time-series data; Build a time-series data governance model based on business needs, and define the relationships between governance attributes through quantization functions; The quantization function in the time series data governance model is analyzed based on the data relationship graph engine, and the topological relationship between governance attributes is extracted based on the analyzed time series data governance model. Based on the aforementioned topological relationships, a temporal data lineage graph is obtained, with governance attributes as nodes and dependencies as edges.

2. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 1, characterized in that, The quantification function for governance attributes designed based on the data modeling language specifically includes: The data modeling language is a script for writing quantitative functions that supports Chinese variable definitions. The governance attributes include business indicator metadata, quantitative functions, and external data interfaces; The quantization function includes at least one nestable function module, which is used to perform multi-dimensional data calculations of mathematical, string, and time types.

3. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 1, characterized in that, The construction of a time-series data governance model based on business needs, and the definition of relationships between governance attributes through quantization functions, specifically includes: Based on business needs, the relationship between basic entity indicators and application indicators in the business process is analyzed, and governance attributes containing quantitative functions are constructed based on the analysis results. Write corresponding quantization function scripts for each governance attribute, and define the input parameters as lower-level attributes or external data sources; A dynamic referencing mechanism for attribute components is established to support cross-level branch references, forming a three-level time-series data governance model, which includes a business library layer, an indicator layer, and an attribute layer.

4. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 1, characterized in that, Based on the data relationship graph engine, the quantization function in the time series data governance model is parsed, and the topological relationships between governance attributes are extracted from the parsed time series data governance model, specifically including: The quantization function script is decomposed and converted into a bytecode instruction set through lexical decomposition, semantic parsing and AST syntax tree. At runtime, it is optimized into machine code through JIT compilation and sent to the computing server for computation. By using a data relationship graph engine to analyze the reference relationships between governance attributes in the quantitative function script and the upstream and downstream relationships of governance attributes in the indicator system, the temporal dependency paths and weights between governance attributes are extracted, and a topological relationship containing the time dimension is generated.

5. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 4, characterized in that, The data relationship graph engine specifically includes: Syntax decomposition module: Used to decompose keywords, operators, and governance attribute references in a script; Semantic parsing module: Constructs an intermediate representation that includes variable scope and type constraints; JIT optimization coding module: optimizes the parsed results into an executable set of machine instructions; Topology extraction module: Generates a dependency matrix by tracing the function call chain in reverse.

6. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 5, characterized in that, The topology relationship extraction module specifically includes: Identify explicit attribute references and implicit data dependencies in quantization function scripts; Calculate the dependency strength values ​​between each governance attribute and generate a weighted topological edge structure; Establish a time-series dependency model across time windows and record historical version associations.

7. The method for constructing a pedigree map of time-series data based on topological relationships according to claim 1, characterized in that, After obtaining the time-series data lineage graph with governance attributes as nodes and dependencies as edges, the process also includes visual analysis of the time-series data lineage graph: A force-directed layout algorithm is used to spatially distribute nodes in the pedigree graph of temporal data. The calculation logic of the quantization function is displayed by showing the edges of the pedigree graph of time-series data; Use heatmap overlays to display the dynamic changing trends of topological relationships; A lineage tracing tool is provided, which is used to expand the upstream and downstream dependency chain from any node.

8. A system for constructing a pedigree map of time-series data based on topological relationships, used to implement the method for constructing a pedigree map of time-series data based on topological relationships as described in any one of claims 1 to 7, characterized in that, include: The modeling language interpretation module designs quantification functions for governance attributes based on a data modeling language, models business indicators as governance attributes, and represents business calculation logic through encapsulated quantification functions; the business indicators are entity time-series data. The governance attribute processing module constructs a time-series data governance model based on business needs and defines the relationships between governance attributes through quantification functions. The topology calculation module uses a data relationship graph engine to parse the quantization function in the time series data governance model and extracts the topology relationships between governance attributes based on the parsed time series data governance model. The kinship graph construction module obtains a time-series kinship graph with governance attributes as nodes and dependency relationships as edges based on the topological relationships.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method for constructing a time-series pedigree map based on topological relationships as described in any one of claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the steps in the method for constructing a time-series pedigree map based on topology according to any one of claims 1 to 7.

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