Circuit schematic connection data processing method, computer equipment and storage medium

Abstract

The present invention provides a circuit schematic wiring data processing method, computer device, and storage medium, including: configuring two-dimensional grid data used to express circuit schematic wiring data into four sub-data types; dividing the data of each sub-data type into internal attribute data and external attribute data, with the internal attribute data storing data with read and write permissions and the external attribute data not distinguishing between read and write permissions; providing a data router to achieve grouped distribution of data of each sub-data type; configuring data processing operations to access or modify target data using data of any sub-data type as input; dividing the data processing operations into multiple ordered operations, and scheduling the ordered operations through an operation pipeline. This solution can achieve efficient access and processing of wiring data of large-scale circuit schematics, has universal applicability and scalability, and is conducive to improving the market competitiveness of products.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic design automation, and in particular to a circuit schematic connection data processing method, computer equipment and storage medium. Background Art

[0002] Processing schematic wiring data is a crucial step in electronic design automation (EDA). Its purpose is to effectively organize, analyze, and optimize the components and wiring within a schematic. Advanced automated wiring technology can help designers quickly and accurately complete circuit wiring tasks, reducing repetitive tasks and improving design efficiency.

[0003] A graph is a common data structure used to represent relationships between objects. It consists of vertices and edges, with vertices representing objects and edges representing relationships between them. Two-dimensional grid graph data is often used to represent data distribution in two-dimensional space, such as in geographic data, image processing, and scientific computing. It is also suitable for representing circuit schematic wiring data.

[0004] Currently, mainstream graph data formats include adjacency matrices, adjacency lists, and edge lists. However, these formats all face the same problem: as circuit design complexity increases, the computational effort required to process graph data increases exponentially. Therefore, a method is urgently needed to efficiently access and process large-scale circuit schematic wiring data. Summary of the Invention

[0005] The purpose of the present invention is to provide a circuit schematic connection data processing method, computer equipment and storage medium, which can realize efficient access and processing of connection data of large-scale circuit schematics, and has universality and scalability, which is conducive to improving the market competitiveness of products.

[0006] The technical solutions provided by the present invention are as follows:

[0007] In a first aspect, the present application provides a method for processing circuit schematic connection data, comprising the steps of:

[0008] The two-dimensional grid data used to express the circuit schematic connection data is set to four sub-data types, including nodes, segments, paths and multi-paths;

[0009] Dividing the data of each sub-data type into internal attribute data and external attribute data, wherein the internal attribute data is used to store data with read and write permissions, and the external attribute data is used to store data that does not distinguish between read and write permissions;

[0010] Setting a data router to realize group distribution of data of each sub-data type;

[0011] configuring data processing operations, each of the data processing operations taking data of any one of the sub-data types as input to access or modify target data;

[0012] The data processing operation is divided into a plurality of ordered operations, and the ordered operations are scheduled through an operation pipeline to perform parallel processing of target data.

[0013] In some embodiments, the nodes correspond to vertices of a graph;

[0014] The line segment corresponds to the edge of the graph, and the line segment is composed of nodes and is used to describe the connection relationship between multiple nodes;

[0015] The path corresponds to a graph, wherein the path is composed of nodes and line segments and is used to describe all nodes with dependency relationships;

[0016] The multiple paths correspond to a set of multiple graphs, and any node in any path will not be connected to any node in another path.

[0017] In some implementations, data of each sub-data type is divided into internal attribute data and external attribute data, including:

[0018] Storing corresponding data in each sub-data type in a key-value pair mapping table, and marking each object data with a unique identifier and data type;

[0019] Storing the unique identifier and the data type in the internal attribute data, wherein the internal attribute data is also used to store data characteristics, indexes, graph data, and cache data, and the data in the internal attribute data is set to be unmodifiable in read mode;

[0020] The external attribute data is used to store data status, labels, tags, cache data and custom data, and the data in the external attribute data is set to be modifiable in read mode.

[0021] In some embodiments, the setting of a data router to implement group distribution of data of each sub-data type further includes:

[0022] Check the validity of each data;

[0023] After data processing is performed through the data processing operation, the output results of each group are fused.

[0024] In some embodiments, the step of configuring a data router to distribute data of each sub-data type in groups further includes:

[0025] A data filter is set to filter the data distributed in packets by the data router.

[0026] In some embodiments, the data filter takes data of any one of the sub-data types as input and outputs a Boolean value, and performs read mode access on the input data;

[0027] The data filter is further used to perform validity verification and custom checks on the input data, and is configured to allow marking and modification in the external attribute data.

[0028] In some embodiments, when modifying target data, data processing operations are configured to implement traversal of the data in the corresponding sub-data type, and the data that needs to be modified is marked in the external attribute data, and the marked data is modified in read mode.

[0029] In some embodiments, the data backend of the data processing operation includes a shared backend and a private backend, the shared backend is used to store constants and shared variables, and the private backend is used to store temporary variables.

[0030] In a second aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the circuit schematic connection data processing method described in the first aspect.

[0031] In a third aspect, the present application provides a computer storage medium having a computer program or instruction stored thereon, which, when executed by a processor, implements the steps of the circuit schematic connection data processing method described in the first aspect.

[0032] According to the present invention, a circuit schematic connection data processing method, computer device, and storage medium have at least the following technical effects:

[0033] 1) This solution divides the two-dimensional grid data used to represent circuit schematic wiring data into four sub-data types: nodes, segments, paths, and multi-paths. This data is then divided into multiple, independent subsets, which are then assigned to different computing nodes for parallel access and processing. Given sufficient hardware, this solution enables efficient access to large-scale schematic wiring data, effectively enhancing product market competitiveness.

[0034] 2) This solution divides data processing into multiple, ordered operations and organizes and schedules them in a pipelined manner, enabling parallel data access and processing at their respective granularities, further improving data processing efficiency. Furthermore, by defining targeted operations, data processing can be adapted to heterogeneous computing systems, demonstrating good universality and scalability.

[0035] 3) This solution can dynamically identify data, filter out target data for processing, and summarize the results. By using data filtering, it can save computing resources by avoiding invalid calculations, ultimately reducing computing costs. By predefining diverse data routing methods, it can avoid repeated development of data access logic and significantly shorten the R&D cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The preferred implementation scheme will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of this solution.

[0037] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings and other embodiments can be obtained based on these drawings without inventive work.

[0039] To simplify the drawings, only the parts relevant to the present invention are schematically shown in each figure. They do not represent the actual structure of the product. Furthermore, to simplify the drawings and facilitate understanding, in some figures, only one of the components with the same structure or function is schematically depicted or labeled. As used herein, "one" not only means "only one" but also "more than one."

[0040] Processing schematic wiring data is a crucial step in electronic design automation (EDA). Its purpose is to effectively organize, analyze, and optimize the components and wiring within a schematic. Advanced automated wiring technology can help designers quickly and accurately complete circuit wiring tasks, reducing repetitive tasks and improving design efficiency.

[0041] A graph is a common data structure used to represent relationships between objects. It consists of vertices and edges, with vertices representing objects and edges representing relationships between them. Two-dimensional grid graph data is often used to represent data distribution in two-dimensional space, such as in geographic data, image processing, and scientific computing. It is also suitable for representing circuit schematic wiring data.

[0042] Currently, mainstream graph data formats include adjacency matrices, adjacency lists, and edge lists. The adjacency matrix uses a two-dimensional matrix to represent graph connectivity, facilitating fast querying of relationships between any two vertices. However, it suffers from low storage efficiency for sparse graphs. Each vertex in the adjacency list maintains a list of all adjacent vertices and their edge weights, facilitating traversal of a vertex's neighbors. However, it is inefficient when querying whether an edge exists between any two vertices. The edge list stores all edges in the graph as a list, with each edge consisting of a start point, an end point, and a weight. This facilitates adding and removing edges and is suitable for dynamically changing graph data. However, it is inefficient when querying the neighbors of a specific vertex. Furthermore, all of these formats face the problem that the computational effort required to process graph data increases exponentially with increasing circuit design complexity. Therefore, a method for efficiently accessing and processing large-scale circuit schematic wiring data is urgently needed. This proposal proposes a hierarchical two-dimensional grid graph data format and a corresponding high-performance computational method. By rationally splitting large graph data into multiple small subgraphs, data parallel processing capabilities are improved. Furthermore, by splitting a single data processing action into multiple sub-operations (operations) to form an operation pipeline (Pipeline), the parallelization of actions is improved, accelerating the efficiency of data access and editing. Furthermore, through dynamic data filtering and routing technology, operations can be automatically identified and acted on target data, making it easier to define a rich data processing operation library and improving the horizontal scalability of the action set. This solution is described in detail below with reference to the accompanying figures:

[0043] In one embodiment, the reference Figure 1 , the present application provides a method for processing circuit schematic connection data, comprising the steps of:

[0044] S100, setting the two-dimensional grid graph data used for expressing circuit schematic connection data into four sub-data types, including nodes, segments, paths, and multi-paths;

[0045] S200, dividing the data of each sub-data type into internal attribute data and external attribute data, wherein the internal attribute data is used to store data with read and write permissions, and the external attribute data is used to store data without distinguishing between read and write permissions;

[0046] S300, setting a data router to achieve group distribution of data of each sub-data type;

[0047] S400, configuring data processing operations, each data processing operation uses data of any sub-data type as input to access or modify target data;

[0048] S500: Divide the data processing operation into multiple ordered operations, and schedule the ordered operations through an operation pipeline to perform parallel processing of target data.

[0049] This solution divides the two-dimensional grid data used to express circuit schematic connection data into four sub-data types: nodes, segments, paths, and multi-paths. This allows the data to be divided into multiple unrelated subsets, and these subsets can be allocated to different computing nodes for parallel access and processing. Under sufficient hardware conditions, large-scale schematic connection data can be efficiently accessed. At the same time, data processing is divided into multiple ordered operations and organized and scheduled in a pipeline manner, so that different operations can perform parallel data access and processing at their respective granularities, further improving data processing efficiency.

[0050] Specifically, this solution abstracts the entire two-dimensional grid graph data into four sub-data formats: nodes (corresponding to graph vertices), segments (corresponding to graph edges), paths (corresponding to graphs), and multi-paths (corresponding to a collection of multiple graphs). Nodes correspond to graph vertices; segments correspond to graph edges, and segments are composed of nodes, used to describe the connection relationships between multiple nodes; paths correspond to graphs, and paths are composed of nodes and segments, used to describe all nodes with dependencies; multi-paths correspond to a collection of multiple graphs, and no node in any path has a connection relationship with any node in another path. Under this constraint, there are no dependencies between the paths in a multi-path, thereby increasing the opportunities for data parallel computing.

[0051] The four sub-data formats defined above each consist of two parts:

[0052] Internal Attribute: used to describe the unique identifier, type, characteristics, graph data, cache data, etc. of data, and distinguish read and write permissions (data cannot be modified under read permission, and data can be modified under write permission);

[0053] External Attribute Data: This data is used to describe data status, labels, tags, cached data, custom data, and more. It does not distinguish between read and write permissions and is locked as needed during concurrent access. Cached data is generated during data processing operations. Due to the division between internal and external attributes, each data processing operation corresponds to either an internal or external attribute, so both internal and external attributes store cached data.

[0054] Under the above constraints, in read mode, only the modification of attribute data outside the data is allowed for nodes and segments in the path, thereby increasing the opportunity for lock-free thread parallel computing.

[0055] In a specific implementation, the data of each sub-data type is divided into internal attribute data and external attribute data, including:

[0056] The corresponding data is stored in each sub-data type in the form of a key-value pair mapping table, and each object data is labeled with a unique identifier and data type; the unique identifier and data type are stored in the internal attribute data, which is also used to store data characteristics, indexes, graph data and cached data. The data in the internal attribute data is set to be unmodifiable in read mode; the external attribute data is used to store data status, labels, tags, cached data and custom data. The data in the external attribute data is set to be modifiable in read mode.

[0057] Specifically, paths, segments, and nodes are all stored in a key-value mapping table (KV Map). The key (K) of the mapping table always uses a 32-bit integer as the unique identifier (UID).

[0058] In this solution, each object (Item) data has a UID and type. Cells inherit from Items, combining data into two parts: internal attribute data and external attribute data. Internal attribute data includes data indexes, characteristics, and other data, and requires read permission to modify this data. External attribute data includes status, tags, labels, and custom data. Both internal and external attribute data can store cached data, which generally includes a dirty bit, refresh mechanism, and data. Each of the four data types implements its own internal and external attribute data through inheritance. Cells can be specialized into four data types based on different internal and external attribute data: node, segment, path, and multi-path.

[0059] A cell holds its root item. The root item of a node or segment is a path, and the root item of a path is a multi-path.

[0060] The UID of an object (item) must be assigned by its root object (root item), is generally not 0, and must be guaranteed to be unique within the root object (root item).

[0061] A node represents any point in a grid graph, and its basic data includes the node type and coordinate data. A segment represents a line segment formed by any two points in a grid graph, and its basic data includes the segment type and the UIDs of the two nodes. A path represents a path formed by continuous segments in a grid graph, and its basic data includes the path type and the nodes and segments that constitute the path. Specifically, this solution can establish a path with a UID of 0, with 0 segments and 0 or more nodes. When the UID of a node, segment, or path is less than 0, it indicates that the object (item) has an image relationship with the object (item) corresponding to the absolute value of its UID, meaning that the two have some kind of connection. The specific nature of this connection is determined by its attributes (related to the specific application scenario) and is not restricted by this application.

[0062] In a specific implementation, the data processing operation of the present application is applicable to accessing or modifying the data format described in the above embodiments, and is specifically described as follows:

[0063] 1) Data processing operations can be applied to one or more data types such as nodes, segments, paths, and multi-paths to implement functions such as data access, modification, or process control.

[0064] 2) Operations are divided into data processing operations and process control operations:

[0065] (a) Data processing operations: can take one of four data formats (node, segment, path, and multi-path) as input, and access or modify one or more types of data. Modification permissions can be divided into read operations and write operations.

[0066] (b) Process control operations: mainly divided into conditional control and loop control, providing opportunities for implementing diverse parallel computing.

[0067] 3) Operation execution (Call) generally includes the following steps:

[0068] (a) Initialization operation;

[0069] (b) Loading parameters;

[0070] (c) commence the operation;

[0071] (d) call operation;

[0072] (e) terminate the operation;

[0073] (f) Post-processing.

[0074] In a specific implementation, the operation pipeline of this application is suitable for managing and scheduling the operations described in the data processing operation, as described below:

[0075] 1) Operation pipeline is a process control operation that includes:

[0076] (a) Operation set: a series of ordered operations;

[0077] (b) Runtime: Indicates the working environment and status of the current pipeline, and can store progress, signals, exceptions, logs, etc.

[0078] 2) The execution (Call) process of the operation pipeline implements the scheduling of the operation set, which generally includes the following steps:

[0079] (a) Initialize the operation runtime and backend;

[0080] (b) Traverse the operations in the operation set one by one;

[0081] (c) Check the runtime signal and jump to step (g) if it fails the check;

[0082] (d) Execute the operation and generate an error code for the current operation;

[0083] (e) update the runtime;

[0084] (f) when running, accepting the error code generated in step (d) and attempting to generate a signal;

[0085] (g) If the operation set traversal is not completed, jump to step (c);

[0086] (h) post-processing;

[0087] (i) Returns the final error code.

[0088] In addition, the operation pipeline of this solution can work in a nested form to achieve operation stage division and shared backend isolation.

[0089] In one embodiment, based on the above embodiment, a data router is provided to implement group distribution of data of each sub-data type, further comprising:

[0090] Check the validity of each data; after processing the data through data processing operations, merge the output results of each group.

[0091] In a specific implementation, setting a data router to distribute data of each sub-data type in groups further includes: setting a data filter to filter the data distributed in groups by the data router.

[0092] Data filters take data of any sub-data type as input and output Boolean values, and perform read mode access to the input data; data filters are also used to perform validity verification and custom checks on input data, and are configured to allow marking and modification in external attribute data.

[0093] This application provides a data filter (Filter) suitable for filtering the above-mentioned four data formats: node (Node), segment (Segment), path (Path), and multi-path (Multi-path). Specifically:

[0094] 1) The filter takes one of the four data formats mentioned above, node, segment, path, or multi-path, as input and outputs a Boolean value;

[0095] 2) The filter accesses the input data in read mode;

[0096] 3) The filter also contains the following steps:

[0097] (a) Verify the validity of input data;

[0098] (b) Data customization check, which allows modifications such as marking in data attributes outside the data.

[0099] This application also provides a data router (Router), which is suitable for grouping and distributing the above-mentioned four data formats of node, segment, path, and multi-path, and fusing the results. Specifically:

[0100] 1) A router is a data processing operation;

[0101] 2) The router must be bound to a non-routing operation to work properly;

[0102] 3) The router can bind filters for specific data formats. When a filter is not bound, no data is filtered by default;

[0103] 4) The router includes the following steps:

[0104] (a) Checking data validity;

[0105] (b) data packet distribution;

[0106] (c) filtering the data using data filters;

[0107] (d) Apply the operation to the data;

[0108] (e) Fusing each set of output results;

[0109] 5) Steps (c) and (d) create a favorable environment for fine-grained parallel computing;

[0110] 6) Routers can work in a nested manner to achieve multi-level group management.

[0111] Based on the four hierarchical data formats and data routers defined above, namely node, segment, path, and multi-path, the default form of the data processing operation calling process is further described, including the following steps:

[0112] 1) Processing operation mode and operation objectives;

[0113] 2) Data processing, which generally includes the following steps:

[0114] (a) Sending operations to the target data level by level through the data router;

[0115] (b) Apply the operation to the target data;

[0116] (c) Uploading and integrating the operation results step by step through the data router;

[0117] 3) Return the running results.

[0118] In addition, as a process-controlled data processing operation, the main functions of the router are:

[0119] (a) Traverse or distribute all sub-data of the input data in groups;

[0120] (b) Filter out target data;

[0121] (c) applying another data processing operation to the target data;

[0122] (d) Integrate the processing results of all target data.

[0123] Different routers can implement their own data forwarding methods, allowing for nested routers to group data forwarding. Routers use filters to filter out target data. A filter takes a certain type of data as input and returns a Boolean value. Different routers can also implement their own method for fusing operation results to obtain specific output content.

[0124] For example, an application example of a router has the following steps:

[0125] (a) (Forward) Router 1 takes Multi-path as input and processes all its child data paths.

[0126] (b) (Forward) Router 1 sends Operation 1 to all target paths for processing.

[0127] (c) (Forward) Operation's processing of Path is: Router2 continues to access its child data;

[0128] (d) (Forward) Router2 takes Path as input and processes all its child data Node and Segment;

[0129] (e) (Forward) Router 2 sends Operation 2 to all target nodes and segments for processing.

[0130] (f) (Reverse direction) Router 2 collects the processing results of Operation 2.

[0131] (g) (Reverse direction) Router 2 merges the results and uses them as the processing result of Operation 1;

[0132] (h) (Reverse direction) Router 1 collects the processing results of Operation 1.

[0133] (i) (Reverse) Router 1 merges the results as the final processing result.

[0134] The above steps can complete the processing of all data organized by Multi-path. By designing and developing different routers, different levels of data concurrent processing can be achieved.

[0135] In one embodiment, based on the above embodiment, the data backend of the data processing operation includes a shared backend and a private backend, the shared backend is used to store constants and shared variables, and the private backend is used to store temporary variables.

[0136] Specifically, data processing is based on operations, which include two types of data backends:

[0137] (a) Public Backend: Used to store constants, shared variables, etc. It is passed in from the outside and is generally the private backend of its outer Operation.

[0138] (b) Private Backend: Used for temporary variables, etc. As a shared backend for a sub-operation, it is initialized based on the shared backend by default. If the shared backend does not exist, a new one is created. Upon a successful operation call, data from the private backend is synchronized to the shared backend by default.

[0139] A series of consecutive operations forms a special type of operation: a pipeline. A pipeline has its own runtime, which indicates the current pipeline's working environment and status. A pipeline can also become an operation within another pipeline. From a macro perspective, a series of operations within a pipeline share the same backend, which is known as sharing. Operation options can be used to determine the data synchronization mechanism between the two backends, achieving isolation of shared data between operations.

[0140] In a specific implementation, all data processing methods must use Operation as the base class. Operation has the following interface:

[0141] (a) Execution (Call): the main entry point for the operation;

[0142] (b) Invoke: Starts the specific content of this operation;

[0143] (c) Apply: access or modify input data;

[0144] (d) Initialize: Initialize the operation (load the shared backend, generate the private backend, check the operation status, etc.);

[0145] (e) Post-processing: Processing the results of the operation, synchronizing with the backend, triggering callbacks, throwing exceptions or signals, etc.

[0146] By overriding the virtual functions of an Operation, you can implement various data processing methods. An Operation contains basic information, options, a data backend, and input parameters (KWArguments). The data backend contains context, data modification information, and custom data. Pipelines inherit from Operations and contain data such as the operation list and runtime. The runtime contains data such as signals, exceptions, and traces.

[0147] The Router inherits from the Operation class and contains individual operations as well as filters for different data types. By overriding the Router's virtual functions, different data traversal methods can be implemented.

[0148] Data Accessor inherits from Operation and needs to implement the interface to process input data into target data. Data Querier obtains arbitrary target data through a given data accessor and router.

[0149] In one embodiment, when modifying target data, data processing operations are configured to implement traversal of data in corresponding sub-data types, and the data that needs to be modified is marked in the external attribute data, and the marked data is modified in read mode.

[0150] Specifically, data processing operations generally include three stages:

[0151] 1) Preprocessing:

[0152] (a) Input parameter check;

[0153] (b) input parameter preprocessing;

[0154] (c) 2D grid map data preprocessing: generating cache data, etc.;

[0155] 2) Data processing:

[0156] (a) Feature data marking: Traverse the data and mark the data that needs to be modified in the external attribute data. You can also directly generate a callback function for data modification so that the data itself can be held;

[0157] (b) Marking data to make it effective: processing the marked data;

[0158] 3) Post-processing:

[0159] (a) Data normalization: processing data into a form that meets requirements according to certain rules;

[0160] (b) Data self-checking and repair: Check and find abnormal data, and try to self-repair according to certain rules.

[0161] A circuit schematic connection data processing method provided by the present invention has at least the following technical effects:

[0162] 1) This solution divides the two-dimensional grid data used to represent circuit schematic wiring data into four sub-data types: nodes, segments, paths, and multi-paths. This data is then divided into multiple, independent subsets, which are then assigned to different computing nodes for parallel access and processing. Given sufficient hardware, this solution enables efficient access to large-scale schematic wiring data, effectively enhancing product market competitiveness.

[0163] 2) This solution divides data processing into multiple, ordered operations and organizes and schedules them in a pipelined manner, enabling parallel data access and processing at their respective granularities, further improving data processing efficiency. Furthermore, by defining targeted operations, data processing can be adapted to heterogeneous computing systems, demonstrating good universality and scalability.

[0164] 3) This solution can dynamically identify data, filter out target data for processing, and summarize the results. By using data filtering, it can save computing resources by avoiding invalid calculations, ultimately reducing computing costs. By predefining diverse data routing methods, it can avoid repeated development of data access logic and significantly shorten the R&D cycle.

[0165] In one embodiment, based on the aforementioned embodiment, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the circuit schematic connection data processing method of the aforementioned embodiment.

[0166] In one embodiment, based on the aforementioned embodiment, the present application provides a computer storage medium on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of the circuit schematic connection data processing method of the aforementioned embodiment are implemented.

[0167] In one embodiment, based on the aforementioned embodiment, the present application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the steps of the circuit schematic wiring data processing method of the aforementioned embodiment.

[0168] The circuit schematic wiring data processing method of the present application can be implemented using program codes executable by a computing device. Thus, the program codes can be stored in a storage device and executed by the computing device, or can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0169] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention. It should be pointed out that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing circuit schematic connection data, characterized in that: Including steps: The two-dimensional grid data used to express the circuit schematic connection data is set to four sub-data types, including nodes, segments, paths and multi-paths; Dividing the data of each sub-data type into internal attribute data and external attribute data, wherein the internal attribute data is used to store data with read and write permissions, and the external attribute data is used to store data that does not distinguish between read and write permissions; Setting a data router to group the data of each sub-data type and distribute it to corresponding data processing operations; configuring data processing operations, each of the data processing operations taking data of any one of the sub-data types as input to access or modify target data; The data processing operation is divided into a plurality of ordered operations, and the ordered operations are scheduled through an operation pipeline to perform parallel processing of target data.

2. The circuit schematic connection data processing method according to claim 1, characterized in that: The nodes correspond to vertices of the graph; The line segment corresponds to the edge of the graph, and the line segment is composed of nodes and is used to describe the connection relationship between multiple nodes; The path corresponds to a graph, wherein the path is composed of nodes and line segments and is used to describe all nodes with dependency relationships; The multiple paths correspond to a set of multiple graphs, and any node in any path will not be connected to any node in another path.

3. The circuit schematic connection data processing method according to claim 1, characterized in that: The data of each sub-data type is divided into internal attribute data and external attribute data, including: Storing corresponding data in each sub-data type in a key-value pair mapping table, and marking each object data with a unique identifier and data type; The unique identifier and data type are stored in the internal attribute data, the internal attribute data is also used to store data characteristics, indexes, graph data and cache data, and the data in the internal attribute data is set to be unmodifiable in read mode; The external attribute data is used to store data status, labels, tags, cache data and custom data, and the data in the external attribute data is set to be modifiable in read mode.

4. The circuit schematic connection data processing method according to claim 1, characterized in that: The setting of the data router to realize the group distribution of the data of each sub-data type further includes: Check the validity of each data; After data processing is performed through the data processing operation, the output results of each group are fused.

5. The circuit schematic connection data processing method according to claim 1, characterized in that: The setting of the data router to achieve group distribution of the data of each sub-data type further includes: A data filter is set to filter the data distributed in packets by the data router.

6. The circuit schematic connection data processing method according to claim 5, characterized in that: The data filter takes data of any one of the sub-data types as input and outputs a Boolean value, and performs read mode access to the input data; The data filter is further used to perform validity verification and custom checks on the input data, and is configured to allow marking and modification in the external attribute data.

7. The circuit schematic connection data processing method according to claim 1, characterized in that: When modifying target data, data processing operations are configured to implement traversal of the data in the corresponding sub-data type, and the data to be modified is marked in the external attribute data, and the marked data is modified in the read mode.

8. The circuit schematic connection data processing method according to claim 1, characterized in that: The data backend of the data processing operation includes a shared backend and a private backend, the shared backend is used to store constants and shared variables, and the private backend is used to store temporary variables.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the circuit schematic connection data processing method according to any one of claims 1 to 8.

10. A computer storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the circuit schematic connection data processing method according to any one of claims 1 to 8 are implemented.

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