Task processing method for logic system design and related equipment

Abstract

The invention provides a task processing method for logic system design and related equipment. The method comprises the following steps: receiving a task processing request designed for the logic system; searching in a command database based on the task processing request to obtain candidate commands associated with the task processing request; determining a target command from the candidate commands based on semantics of the task processing request; and performing task processing on the logic system design based on the target command to obtain a task processing result.

Description

Technical Field

[0001] The present disclosure relates to the field of chip verification, and in particular to a task processing method for logic system design and related equipment. Background Art

[0002] In existing Electronic Design Automation (EDA) tools, executing corresponding tasks for logic system design usually relies on human experience and manual operation, which leads to low efficiency of task processing. In addition, manual operation is prone to missing certain details or steps, resulting in low accuracy or failure of task processing. Summary of the invention

[0003] The present disclosure proposes a task processing method and related equipment for logic system design, so as to solve the technical problems of low efficiency and low accuracy in task processing of logic system design to a certain extent.

[0004] In a first aspect, the present disclosure provides a task processing method for logic system design, comprising:

[0005] receiving a task processing request designed by a user for the logic system;

[0006] Searching a command database based on the task processing request to obtain a candidate command associated with the task processing request;

[0007] determining a target command from the candidate commands based on the semantics of the task processing request;

[0008] Task processing is performed on the logic system design based on the target command to obtain a task processing result.

[0009] In a second aspect of the present disclosure, a task processing device for logic system design is provided, comprising:

[0010] A receiving module, used for receiving a task processing request designed for the logic system;

[0011] A search module, configured to search in a command database based on the task processing request to obtain candidate commands associated with the task processing request;

[0012] a semantic module, configured to determine a target command from the candidate commands based on the task processing request;

[0013] The task processing module is used to perform task processing on the logic system design based on the target command to obtain a task processing result.

[0014] In a third aspect of the present disclosure, an electronic device is provided, comprising one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the programs include instructions for executing the method described in the first aspect.

[0015] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium containing a computer program is provided. When the computer program is executed by one or more processors, the processors execute the method described in the first aspect.

[0016] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0017] From the above, it can be seen that the task processing method and related equipment for logic system design provided by the present disclosure searches for relevant candidate commands in the command database based on the task processing request input by the user, determines the target command that best meets the user's intention from the candidate commands through natural language processing technology, and then automatically executes the target command to perform task processing on the logic system design and returns the result. The task processing efficiency is significantly improved by automating the execution of task processing commands, the dependence on personal experience and skills is reduced, human errors are reduced, and user friendliness is enhanced, thereby improving the efficiency and accuracy of task processing, reducing the cost of task processing, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of a task processing architecture designed for a logic system according to an embodiment of the present disclosure.

[0020] Figure 2 The figure is a schematic diagram of the hardware structure of an exemplary electronic device according to an embodiment of the present disclosure.

[0021] Figure 3 A flowchart of a task processing method designed for a logic system of an embodiment of the present disclosure.

[0022] Figure 4 A schematic diagram of the principles of a task processing method designed for a logic system of an embodiment of the present disclosure.

[0023] Figure 5 A schematic diagram of a task processing device designed for a logic system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0025] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0026] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0027] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0028] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0029] Figure 1 FIG. 1 is a schematic diagram showing a task processing architecture of a logic system design according to an embodiment of the present disclosure. Figure 1The task processing architecture 100 designed by the logic system may include a server 110, a terminal 120, and a network 130 providing a communication link. The server 110 and the terminal 120 may be connected via a wired or wireless network 130. The server 110 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and CDN.

[0030] The terminal 120 may be implemented in hardware or software. For example, when the terminal 120 is implemented in hardware, it may be various electronic devices having a display screen and supporting page display, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. When the terminal 120 device is implemented in software, it may be installed in the electronic devices listed above; it may be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0031] It should be noted that the task processing method of the logic system design provided in the embodiment of the present application can be executed by the terminal 120 or by the server 110. It should be understood that Figure 1 The number of terminals, networks and servers in the embodiment is only for illustration and is not intended to limit the number of terminals, networks and servers. Any number of terminals, networks and servers may be provided as required.

[0032] Figure 2 FIG. 2 shows a schematic diagram of the hardware structure of an exemplary electronic device 200 provided in an embodiment of the present disclosure. Figure 2 As shown, the electronic device 200 may include: a processor 202, a memory 204, a network module 206, a peripheral interface 208 and a bus 210. The processor 202, the memory 204, the network module 206 and the peripheral interface 208 are connected to each other in communication within the electronic device 200 through the bus 210.

[0033] Processor 202 may be a central processing unit (CPU), a neural network processor (NPU), a microcontroller (MCU), a programmable logic device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or one or more integrated circuits. Processor 202 may be used to perform functions related to the technology described in this disclosure. In some embodiments, processor 202 may also include multiple processors integrated into a single logical component. For example, Figure 2 As shown, processor 202 may include a plurality of processors 202a, 202b, and 202c.

[0034] The memory 204 may be configured to store data (eg, instructions, computer code, etc.). Figure 2 As shown, the data stored in the memory 204 may include program instructions (e.g., program instructions for implementing the task processing method of the logic system design of the embodiment of the present disclosure) and data to be processed (e.g., the memory may store configuration files of other modules, etc.). The processor 202 may also access the program instructions and data stored in the memory 204, and execute the program instructions to operate on the data to be processed. The memory 204 may include a volatile storage device or a non-volatile storage device. In some embodiments, the memory 204 may include a random access memory (RAM), a read-only memory (ROM), an optical disk, a magnetic disk, a hard disk, a solid-state drive (SSD), a flash memory, a memory stick, etc.

[0035] The network module 206 can be configured to provide communication with other external devices to the electronic device 200 via a network. The network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, near field communication (NFC) etc.), a cellular network, the Internet or a combination thereof. It is understood that the type of network is not limited to the above specific examples. In some embodiments, the network module 206 can include any number of network interface controllers (NICs), radio frequency modules, transceivers, modems, routers, gateways, adapters, cellular network chips, etc., in any combination.

[0036] The peripheral interface 208 can be configured to connect the electronic device 200 to one or more peripheral devices to achieve information input and output. For example, the peripheral devices can include input devices such as a keyboard, a mouse, a touch pad, a touch screen, a microphone, and various sensors, and output devices such as a display, a speaker, a vibrator, and an indicator light.

[0037] The bus 210 can be configured to transmit information between various components of the electronic device 200 (e.g., the processor 202, the memory 204, the network module 206, and the peripheral interface 208), such as an internal bus (e.g., a processor-memory bus), an external bus (USB port, PCI-E bus), etc.

[0038] It should be noted that, although the architecture of the electronic device 200 only shows the processor 202, the memory 204, the network module 206, the peripheral interface 208 and the bus 210, in the specific implementation process, the architecture of the electronic device 200 may also include other components necessary for normal execution. In addition, it can be understood by those skilled in the art that the architecture of the electronic device 200 may also only include the components necessary for implementing the embodiments of the present disclosure, and does not necessarily include all the components shown in the figure.

[0039] The existing task processing logic system design usually relies on the experience and manual operation of the task processor. The task processor needs to manually input commands, check the code line by line, and locate errors, which makes the task processing process time-consuming and inefficient. Moreover, manual task processing is prone to missing certain details or steps, resulting in reduced task processing accuracy. Therefore, how to improve the efficiency and accuracy of task processing in logic system design has become a technical problem that needs to be solved urgently.

[0040] In view of this, the embodiment of the present disclosure provides a task processing method and related equipment for logical system design. Based on the task processing request input by the user, the command database is searched for relevant candidate commands, and the target command that best meets the user's intention is determined from the candidate commands through natural language processing technology, and then the target command is automatically executed to perform task processing on the logical system design and return the result. The task processing efficiency is significantly improved by automating the execution of task processing commands, the dependence on personal experience and skills is reduced, human errors are reduced, and user friendliness is enhanced, thereby improving the efficiency and accuracy of task processing, reducing the cost of task processing, and improving the user experience.

[0041] See also Figure 3 , Figure 3 A schematic flow chart of a task processing method designed by a logic system according to an embodiment of the present disclosure is shown. The task processing method designed by a logic system according to an embodiment of the present disclosure can be at least partially deployed on a server and / or a terminal. Figure 3 In the task processing method 300 of logic system design, the task processing method 300 may further include the following steps.

[0042] In step S310, a task processing request designed by a user for the logic system is received.

[0043] The logical system design may refer to a design with logical structure and function, including but not limited to hardware design (such as integrated circuit, FPGA design), etc. The task processing request may refer to a description of a specific processing task or action (such as debugging, etc.) that the user wants the system to perform, and the task processing request may be a natural language request. For example, the user enters a natural language request in the task processing tool:

[0044] "Find out why the module outputs an error when the input signal is 1010." In this way, users do not need to remember complex commands or manually write task processing scripts, they can initiate task processing requests using natural language or simple operations. A variety of input methods are provided to users, so that users of different technical levels can easily use task processing tools. The interactive efficiency and ease of use of the task processing process can be improved, thereby improving the overall task processing efficiency, reducing task processing time and cost, and improving user experience. In some embodiments, multiple input methods (such as command line, graphical interface operation) can also be provided to users, so that users can flexibly choose input methods to issue task processing requests according to their own habits and needs.

[0045] Specifically, see Figure 4 , Figure 4 A schematic diagram showing a principle of a task processing method designed according to a logic system of an embodiment of the present disclosure is shown. Figure 4 In the process, the user can send a task processing request for the logic system design to the digital verification task processing system. The agent module in the digital verification task processing system receives the task processing request.

[0046] In step S320, a search is performed in a command database based on the task processing request to obtain candidate commands associated with the task processing request.

[0047] Among them, the command database can be a database that stores multiple commands (such as Tcl (Tool Command Language) commands, or other types of commands) and their related information. For example, the command database may include one or more commands and description files about the commands. The command description file may refer to a file that describes the command function, parameters, usage scenarios and other information. These files can be in plain text, XML or JSON format, etc. For example, software configuration file: a file containing application configuration options. API document: a document that describes the API interface and its usage. Database schema file: a file that describes the database structure and table definition. Command manual: a document that describes the commands and parameters of the command line tool. According to the content of the user's task processing request, it can be based on a variety of search methods such as keyword matching and semantic understanding, so as to quickly find relevant commands from the command database and improve the accuracy of command matching.

[0048] In some embodiments, searching in a command database based on the task processing request to obtain a candidate command associated with the task processing request includes:

[0049] Searching the command database for commands related to the task processing request based on at least one search dimension, obtaining search results corresponding to the search dimension;

[0050] Perform mixed sorting on all the search results to obtain a mixed sorting result;

[0051] The top n search results in the mixed sorting result are determined as the candidate commands, where n is a positive integer.

[0052] Among them, the search dimension may refer to different standards or conditions for searching commands, such as keyword index, inverted index, embedding index, etc. Mixed sorting may refer to the comprehensive sorting of search results obtained from different search dimensions. For example, the search results obtained from different dimensions may be comprehensively considered and mixed sorted according to certain rules or algorithms to produce a comprehensive sorting result. The first n (n is a positive integer) search results are selected from the mixed sorting results as candidate commands, which are the commands most likely to meet the user's task processing request.

[0053] Specifically, Figure 4 As shown, the agent can perform embedded retrieval and inverted retrieval in the Tcl command file based on the task processing request request in the Tcl command retrieval system by the Tcl command retrieval engine, and perform mixed sorting on the retrieval results to obtain candidate Tcl commands. The Tcl command retrieval engine returns the candidate Tcl command to the agent. In this way, by searching based on at least one search dimension, it is possible to more comprehensively cover all aspects of the task processing request and improve the relevance of the search results. The mixed sorting can comprehensively consider the results of different dimensions to ensure that the final candidate command is the best choice. Through multi-dimensional search and mixed sorting, the matching degree between the candidate command and the user's task processing request can be improved, thereby improving the accuracy of task processing.

[0054] In some embodiments, all the search results are mixed and sorted to obtain mixed sort results, including:

[0055] Scoring the search results based on at least one scoring dimension to obtain a scoring result corresponding to the scoring dimension;

[0056] Obtaining a total score of the search result based on the score weights corresponding to the score dimensions and the score results;

[0057] All the search results are sorted from high to low based on the total score to obtain the mixed sorting result.

[0058] Among them, the scoring dimension can refer to different standards or conditions used to evaluate the relevance and applicability of search results, such as keyword matching, semantic similarity, command usage frequency, etc. Scoring search results through at least one scoring dimension can comprehensively evaluate the relevance and applicability of search results and improve the quality of search results. The scoring weight can refer to the importance coefficient of each scoring dimension, which reflects the proportion of the scoring dimension in the total score. By setting the weight of the scoring dimension, the scoring criteria can be flexibly adjusted according to the actual situation, making the scoring results more reasonable.

[0059] Specifically, the search results can be scored according to the preset scoring dimensions to obtain the scoring results under each scoring dimension. The scoring results of each scoring dimension are multiplied by the corresponding weights, and then summed up to obtain the total score of each search result. The search results are sorted according to the total score of each search result to obtain the final mixed sorting result. It can be seen that multi-dimensional scoring and comprehensive sorting can improve the matching degree between search results and user requests, thereby improving the accuracy of task processing.

[0060] In some embodiments, searching the command database for commands related to the task processing request based on at least one search dimension to obtain search results corresponding to the search dimension includes:

[0061] Based on the index structure corresponding to the search dimension, the command database is searched for commands related to the task processing request to obtain search results corresponding to the search dimension; wherein the index structure is constructed according to the search dimension based on a description file of the commands in the command database.

[0062] Among them, the index structure may refer to a data structure used to speed up the search speed for commands in the command database, such as a keyword index, an inverted index, an attribute index, etc. The pre-built index structure can be used to search in the command database according to the task processing request provided by the user to find the command related to the task processing request. The index structure is pre-built according to the description file of the command in the command database and according to a specific search dimension to facilitate efficient search. For example, keyword index: the search can be accelerated by extracting keywords and building an index. For example, in the API document, the name, parameters and return value of each API can be extracted and an index can be built. Inverted index: in the full-text search, by recording the words that have appeared in the document and their locations, documents containing specific vocabulary can be quickly found. For example, in the command manual, the location where each command and its description appear can be recorded. Attribute index: for structured description files (such as JSON or XML), an index can be built by extracting attributes (such as key-value pairs) in the file. For example, in the database schema file, the name, field name and type of each table can be recorded. Path index: in the XML or JSON file, the path of the element or object can be recorded for quick positioning. For example, in a configuration file, you can record the full path of each configuration item.

[0063] In some embodiments, method 300 further includes:

[0064] Updating the commands in the command database and the corresponding description files to obtain updated commands and updated description files;

[0065] The index structure is updated based on the updated description file.

[0066] In actual applications, commands in the command database may be updated over time (for example, added, deleted, or modified), and the corresponding description files will also be updated accordingly. The index structure can be updated based on the updated commands and description files. In this way, even if the number of commands for the task processing tool continues to increase, the task processing process can be expanded by simply updating the index structure. Figure 4 As shown, the number of commands in the Tcl command database of the task processing module (such as EDA tools) continues to increase. The Tcl command retrieval system implemented by the present disclosure adopts RAG (Retrieval-Augmented Generation) technology, which can ensure that only the index structure is updated to achieve rapid expansion of new commands, ensure that the commands are the latest version, and improve the accuracy and effectiveness of task processing. Through real-time updates and efficient index maintenance, the latest and most relevant command suggestions can be provided to enhance user experience.

[0067] In step S330 , a target command is determined from the candidate commands based on the semantics of the task processing request.

[0068] Among them, semantic analysis can refer to understanding the meaning of task processing requests through natural language processing technology, including intent recognition, keyword extraction, context understanding, etc. Based on the semantic analysis of task processing requests, the intent and specific needs of user requests can be understood. Specifically, the key entities (such as module names, input values, etc.) and operations (such as analysis, inspection, etc.) involved can be extracted from the task processing request. According to the extracted key information, the most semantically consistent command among the candidate commands is matched. The command with the highest matching degree and the most consistent with the user's intent is selected as the target command. It can intelligently select the most appropriate command from the candidate commands according to the semantics of the user's request, improve the efficiency of task processing, and reduce errors in the task processing process. By accurately understanding the user's intent and providing the most appropriate command, it can provide a better user experience and make it easier for users to complete task processing tasks.

[0069] In some embodiments, determining a target command from the candidate commands based on the semantics of the task processing request includes:

[0070] The task processing request and the description files of the candidate commands are semantically analyzed based on a language processing model to determine the target command that matches the semantics of the task processing request from the candidate commands.

[0071] Among them, the language processing model can be a pre-trained large language model (LLM), such as GPT-4, LLaMA series, etc., which has strong natural language understanding capabilities. The language processing model can use the language processing model to understand the semantics of the task processing request and the candidate command description file, extract key information and perform semantic matching. Specifically, the task processing request can be input into the language processing model to obtain the embedding of the task processing request. The description file of the candidate command can also be input into the language processing model to obtain the embedding of each candidate command. The similarity between the embedding of the task processing request and the embedding of the candidate command description file can be calculated using cosine similarity or other distance measurement methods. One or more commands with the highest similarity to the task processing request embedding can be selected from all candidate commands as the target command.

[0072] Specifically, Figure 4 As shown, the agent inputs the candidate command and the task processing request into the language processing model for semantic analysis, and can obtain one or more target commands that best match the task processing request from the candidate command. The language processing model can return the target command as an answer to the agent in a pre-provided format.

[0073] It should be understood that the language processing model can be deployed locally and directly called locally; it can also be deployed in the cloud and called via the network, and there is no restriction here. The pre-trained language processing model already has a strong natural language understanding ability and can be used for semantic analysis without retraining or fine-tuning, saving the computing resources and time cost required for retraining or fine-tuning. Moreover, according to the method of the embodiment of the present disclosure, before this, the command database has been searched and screened based on the task processing request to obtain candidate commands. Even if the commands of the task processing tool are increased a lot, it is only necessary to use the command user manual of the task processing tool as a data source to update and train the retrieval part, without updating and training the large language model, to complete semantic analysis and command matching. The use of internal data can better control data quality and security, and avoid dependence on external data. By using a pre-trained large language model for semantic analysis, accurate understanding and efficient processing of user task processing requests are achieved, the accuracy of task processing and user experience are improved, and resources are saved at the same time.

[0074] In step S340, task processing is performed on the logic system design based on the target command to obtain a task processing result.

[0075] Among them, according to the user request and the target command determined by semantic analysis, the corresponding task processing operation is executed in the logical system design. In the process of executing the target command, the information generated in the task processing process is collected, such as variable values, log information, error reports, etc. Based on the collected task processing information, the final task processing result is generated and presented to the user.

[0076] In some embodiments, performing task processing on the logic system design based on the target command to obtain a task processing result includes:

[0077] Determining a command set required for the task processing based on the target command and an associated command of the target command;

[0078] Task processing is performed on the logic system design based on the command set to obtain the task processing result.

[0079] Among them, the associated command may refer to other commands related to the target command, which are auxiliary commands that need to be executed in order to better complete the task processing task. It can be seen that the method of the embodiment of the present disclosure not only considers the target command itself, but also determines a command set including the target command and its associated commands according to the context and relevance of the target command. After determining the command set required for task processing, the system will execute the commands in these command sets, perform task processing on the logical system design, and finally obtain the task processing result. Specifically, Figure 4As shown, the agent generates a completed task processing program based on the target command and sends it to the task processing module for task processing, and obtains the corresponding task processing results to be displayed to the user.

[0080] It can be seen that according to the method of the embodiment of the present disclosure, the task processing efficiency is significantly improved by automating the execution of task processing commands, the dependence on personal experience and skills is reduced, human errors are reduced, and user-friendliness is enhanced, thereby improving the task processing efficiency and accuracy, reducing the task processing cost, and improving the user experience.

[0081] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0082] It should be noted that some embodiments of the present disclosure are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides a task processing device designed by a logic system, see Figure 5 , the task processing device designed by the logic system, the device comprising:

[0084] A receiving module, used for receiving a task processing request designed by a user for the logic system;

[0085] A search module, configured to search in a command database based on the task processing request to obtain candidate commands associated with the task processing request;

[0086] a semantic module, configured to determine a target command from the candidate commands based on the task processing request;

[0087] The task processing module is used to perform task processing on the logic system design based on the target command to obtain a task processing result.

[0088] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0089] The device of the above embodiment is used to implement the task processing method of the corresponding logic system design in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0090] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the task processing method of the logic system design as described in any of the above embodiments.

[0091] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0092] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the task processing method of the logic system design described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0093] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0094] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0095] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0096] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A task processing method for logic system design, comprising: receiving a task processing request designed for the logic system; Searching a command database based on the task processing request to obtain a candidate command associated with the task processing request; determining a target command from the candidate commands based on the semantics of the task processing request; Task processing is performed on the logic system design based on the target command to obtain a task processing result.

2. The method according to claim 1, wherein: Searching in a command database based on the task processing request to obtain candidate commands associated with the task processing request includes: Searching the command database for commands related to the task processing request based on at least one search dimension, obtaining search results corresponding to the search dimension; Perform mixed sorting on all the search results to obtain a mixed sorting result; The top n search results in the mixed sorting result are determined as the candidate commands, where n is a positive integer.

3. The method according to claim 2, wherein: All the search results are mixed and sorted to obtain mixed sort results, including: Scoring the search results based on at least one scoring dimension to obtain a scoring result corresponding to the scoring dimension; Obtaining a total score of the search result based on the score weights corresponding to the score dimensions and the score results; All the search results are sorted from high to low based on the total score to obtain the mixed sorting result.

4. The method according to claim 2, wherein: Searching the command database for commands related to the task processing request based on at least one search dimension, and obtaining search results corresponding to the search dimension, includes: Based on the index structure corresponding to the search dimension, the command database is searched for commands related to the task processing request to obtain search results corresponding to the search dimension; wherein the index structure is constructed according to the search dimension based on a description file of the commands in the command database.

5. The method according to claim 4, further comprising: Updating the commands in the command database and the corresponding description files to obtain updated commands and updated description files; The index structure is updated based on the updated description file.

6. The method according to claim 1, wherein: Determining a target command from the candidate commands based on the semantics of the task processing request includes: The task processing request and the description files of the candidate commands are semantically analyzed based on a language processing model to determine the target command that matches the semantics of the task processing request from the candidate commands.

7. The method according to claim 1, wherein: Performing task processing on the logic system design based on the target command to obtain a task processing result includes: Determining a command set required for the task processing based on the target command and an associated command of the target command; Task processing is performed on the logic system design based on the command set to obtain the task processing result.

8. A task processing device designed for a logic system, comprising: A receiving module, used for receiving a task processing request designed for the logic system; A search module, configured to search in a command database based on the task processing request to obtain candidate commands associated with the task processing request; a semantic module, configured to determine a target command from the candidate commands based on the task processing request; The task processing module is used to perform task processing on the logic system design based on the target command to obtain a task processing result.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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