Business process processing method and device based on large model, storage medium and equipment

Abstract

The invention provides a business process processing method and device based on a large model, a storage medium and equipment, and the method comprises the steps: analyzing all node types and connection branches in a business process, recognizing a large model node, a branch node, a result merging node and a text output type node, receiving a business process processing instruction, the large model node executes the business process processing instruction according to the configuration content, judges whether the output result of the large model is quoted in the output text of the business process according to the configuration content, and if the output result of the large model is quoted in the output text of the business process, the large model node executes the business process processing instruction according to the configuration content. And compiling the output text of the business process to obtain a final output text of the business process. According to the method, the output text of the business processing flow can be dynamically managed, the information transmission efficiency is improved, and the response capability of the system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method, device, storage medium, and equipment for business process processing based on a large model. Background Art

[0002] With the rapid development of artificial intelligence technology, its influence has penetrated into various industries and fields, and the application scenarios based on business process orchestration have also been increasing continuously. These application scenarios usually require efficient, flexible, and customizable business processes to meet complex and changing business needs. In these applications, the demand for streaming text output is particularly important, especially in those application scenarios involving large models.

[0003] Large models, such as deep learning models, natural language processing models, etc., often consume a large amount of computing resources and time during execution due to their complex structures and large parameter scales. In business process orchestration, when these large models are used as nodes to execute tasks, their time-consuming nature often becomes a bottleneck in the entire business process. Users often need to wait for a long time to see the output results, which not only reduces work efficiency but also seriously affects the user experience.

[0004] Therefore, how to achieve timely output of text to shorten the waiting time of users and improve the user experience has become a key problem to be solved urgently at present. Summary of the Invention

[0005] In view of this, the present invention provides a method, device, storage medium, and equipment for business process processing based on a large model, which can dynamically manage the output text of the business processing process, improve the information transmission efficiency, and enhance the response ability of the system.

[0006] In a first aspect, an embodiment of the present invention provides a method for business process processing based on a large model, the method including:

[0007] Analyze all node types and their connection branches in the business process to identify large model nodes, branch nodes, result merging nodes, and text output type nodes;

[0008] Receive a business process processing instruction, and obtain configuration content corresponding to the business processing instruction. The large model node executes the business process processing instruction according to the configuration content, and determines whether the output result of the large model is referenced in the output text of the business process according to the configuration content;

[0009] When it is determined that the output result of the large model is referenced in the output text of the business process, perform compilation processing on the output text of the business process to obtain the final output text of the business process.

[0010] Further, the compilation process of the output text of the business process to obtain the final output text of the business process includes:

[0011] Segment the output text of the business process according to text blocks to obtain multiple constant text blocks and variable text blocks;

[0012] Sort the multiple variable text blocks according to their respective dependency relationships;

[0013] Distribute the multiple constant text blocks and the sorted variable text blocks to corresponding nodes for compilation processing to obtain the final output text of the business process.

[0014] Further, the variable text block includes a common variable text and a large model variable text.

[0015] Further, distributing the multiple constant text blocks and the sorted variable text blocks to corresponding nodes for compilation processing to obtain the final output text of the business process:

[0016] When the attribute of the text block is a constant text, output it before execution at the corresponding node;

[0017] When the attribute of the text block is a common variable text, output the variable text content after execution at the corresponding node;

[0018] When the attribute of the text block is a large model variable text, perform streaming text output during the execution process of the large model node.

[0019] Further, sorting the multiple variable text blocks according to the dependency relationship includes:

[0020] Identify all variable text blocks and the dependency relationships between the variable text blocks, determine the dependency direction according to the dependency relationships, and construct a dependency relationship graph;

[0021] When there is no circular dependency in the dependency relationship graph, use the topological sorting algorithm to sort the multiple variable text blocks.

[0022] Further, the method further includes: optimizing the sorting relationship of the multiple variable text blocks.

[0023] Further, the method further includes:

[0024] Real-time monitor the execution status of the business process, including the execution order, execution time, and output content of the nodes, and adjust and optimize the business process according to user feedback or system requirements.

[0025] In a second aspect, an embodiment of the present invention provides a business process processing device based on a large model, and the device includes:

[0026] An analysis module for analyzing all node types and their connection branches in a business process to identify large model nodes, branch nodes, result merging nodes, and text output type nodes;

[0027] A receiving and judging module for receiving a business process processing instruction and obtaining configuration content corresponding to the business processing instruction. The large model node executes the business process processing instruction according to the configuration content and judges whether the output text of the business process quotes the output result of the large model according to the configuration content;

[0028] A compilation module for, when it is judged that the output text of the business process quotes the output result of the large model, performing compilation processing on the output text of the business process to obtain the final output text of the business process.

[0029] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the method according to any one of the first aspect when running.

[0030] In a fourth aspect, an embodiment of the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method according to any one of the first aspect.

[0031] The technical solution provided by the present invention analyzes all node types and their connection branches in a business process to identify large model nodes, branch nodes, result merging nodes, and text output type nodes, receives a business process processing instruction, and obtains configuration content corresponding to the business processing instruction. The large model node executes the business process processing instruction according to the configuration content and judges whether the output text of the business process quotes the output result of the large model according to the configuration content. When it is judged that the output text of the business process quotes the output result of the large model, compilation processing is performed on the output text of the business process to obtain the final output text of the business process. Thus, in the first aspect of the present invention, by accurately identifying key node types such as large model nodes and branch nodes, it can ensure that each step of the business process is executed as expected, thereby improving the accuracy of the output text; in the second aspect, the business processing flow of the present invention introduces steps such as compilation processing to ensure that the finally output text meets the business requirements and improves the reliability of the entire business process; in the third aspect, it can effectively realize the dynamic management of text output and optimize the user experience in the business process; in the fourth aspect, timely text output not only improves the efficiency of information transmission but also enhances the response ability of the system, laying a good foundation for the subsequent execution of the business process. Description of the Drawings

[0032] Figure 1 It is a flowchart of a business process processing method based on a large model provided in the first embodiment of the present invention;

[0033] Figure 2 It is a schematic structural diagram of a business process processing device based on a large model provided in the second embodiment of the present invention;

[0034] Figure 3 It is a schematic structural diagram of an electronic device provided in the third embodiment of the present invention. Detailed implementation manners

[0035] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1

[0037] Refer to Figure 1 , Figure 1 which is a flowchart of a business process processing method based on a large model provided in an embodiment of the present invention. The method includes the following steps:

[0038] Step 11: Analyze all node types and their connection branches in the business process, and identify large model nodes, branch nodes, result merging nodes, and text output type nodes.

[0039] In a business process, nodes and connection branches form the backbone of the entire process. By deeply analyzing these nodes and branches, different types of nodes can be identified, including large model nodes, branch nodes, result merging nodes, and text output type nodes.

[0040] Large model nodes are nodes in a business process that perform complex calculation or reasoning tasks. They are usually responsible for processing a large amount of data and outputting calculation results or reasoning results. In artificial intelligence applications, large model nodes may involve deep learning models, natural language processing models, etc. After receiving input data, these models will perform corresponding calculation or reasoning tasks and output processing results.

[0041] Branch nodes are decision points in business processes. They divide processes into different paths or branches according to specific conditions or rules. Branch nodes will judge the input data according to preset conditions or rules, and direct the process to different branches according to the judgment results. According to the results of conditional judgment, branch nodes can select multiple different paths or branches for subsequent processing, and branch nodes can be adjusted and optimized according to changes in business needs to adapt to different business scenarios.

[0042] The result merge node is a node used to merge multiple branch results in a business process. In a business process, there may be multiple branches executing at the same time, and the results of these branches need to be merged into a whole after execution.

[0043] Text output type nodes are nodes used to output text results in business processes. They are usually located at the end of the process and are responsible for presenting the processing results to the user in text form.

[0044] In a business process, nodes are connected and communicated through connection branches. Connection branches can be divided into the following types: (1) Sequential connection: nodes are connected in a specific order to form a linear process; (2) Conditional connection: nodes are connected according to specific conditions or rules to form a branching process; (3) Loop connection: nodes form a loop structure, which can repeatedly execute a process segment.

[0045] Step 12: Receive a business process processing instruction and obtain configuration content corresponding to the business process instruction. The large model node executes the business process processing instruction according to the configuration content and determines whether the output text of the business process references the output result of the large model according to the configuration content.

[0046] In the automated processing of business processes, receiving processing instructions, obtaining configuration content, executing instructions, and determining whether the output text references the large model results is a coherent and critical process.

[0047] First, the system needs to be able to receive business process instructions from users or upstream systems. These instructions usually contain information such as the specific tasks to be performed, the required data input, and the expected output results.

[0048] After receiving a processing instruction, the system needs to search for and obtain the configuration content corresponding to the instruction. This configuration content may include specific parameters required for executing the instruction, model selection, data preprocessing rules, output format, and other information. The configuration content is usually stored in a database, configuration file, or remote service, and the system needs to obtain them through appropriate interfaces or query statements. The configuration content may need to be dynamically adjusted as the business requirements and model updates change. Therefore, the system needs to be able to flexibly obtain and update the configuration content.

[0049] After obtaining the configuration content, the large model node will execute the processing instruction according to these configurations. This includes steps such as loading the specified model, preparing the input data, performing model inference or calculation, etc. The large model node usually has high flexibility and scalability and can handle various complex tasks and scenarios.

[0050] After executing the processing instruction, the system needs to determine whether the output text of the business process references the output result of the large model. This can be achieved by checking specific markers, keywords, or formats in the output text. For example, if the output text contains text or data in a specific format generated by the large model, then it can be considered that the output text references the output result of the large model.

[0051] In some other embodiments of the present invention, in order to ensure the quality and accuracy of the output text, the system needs to perform verification and validation on the output text. This may include steps such as grammar checking, data consistency verification, logical correctness evaluation, etc.

[0052] In some other embodiments of the present invention, when processing the business process, the system needs to ensure the security and privacy protection of the data. This includes measures such as data encryption, access control, data masking, etc. to prevent data leakage and abuse.

[0053] Step 13: When it is determined that the output text of the business process references the output result of the large model, perform compilation processing on the output text of the business process to obtain the final output text of the business process.

[0054] In the automated processing flow of the business process, after it is determined that the output text references the output result of the large model, the next step is to perform compilation processing on these output texts to generate the final business process output text that meets the requirements.

[0055] First, the system needs to accurately identify which parts of the output text of the business process reference the output results of the large model. This can usually be achieved by searching for specific markers, identifiers, or formats. For example, if the output results of the large model are embedded in the text in a specific JSON structure, the system can identify the referenced content by parsing this JSON structure. Once the referenced content is identified, the system needs to compile and process this content. After the compilation and processing, the system can generate the final output text of the business process. This text should clearly show the results of the business process and accurately reflect the output results of the large model. At the same time, it should also meet the business requirements and format requirements.

[0056] The implementation methods of each step in the embodiments of the present invention will be elaborated in detail below:

[0057] In another embodiment of the present invention, step 13 can be implemented through the following steps:

[0058] Step 131: Split the output text of the business process into text blocks to obtain multiple constant text blocks and variable text blocks.

[0059] In this step, the system needs to cut the output text of the business process into smaller and more manageable text blocks. These text blocks can be divided into two categories: constant text blocks and variable text blocks.

[0060] Constant text blocks: These text blocks are fixed in the business process and will not change with the change of input data or business logic. They may include titles, explanatory texts, fixed formats, or templates, etc.

[0061] Variable text blocks: These text blocks contain dynamic content, and their content depends on input data, business logic, or model output results. For example, a variable text block may contain customer names, order details, or model prediction results, etc.

[0062] Step 132: Sort the multiple variable text blocks according to their respective dependency relationships.

[0063] In the business process, there may be dependency relationships between variable text blocks. For example, the content of one variable text block may depend on the output results of another variable text block. Therefore, before distributing the text blocks to the nodes for compilation and processing, the system needs to determine these dependency relationships and sort the variable text blocks.

[0064] When sorting, the system needs to consider the direct and indirect dependency relationships between the text blocks to ensure that each text block can obtain all the input data or preconditions it needs during the compilation and processing.

[0065] Step 133: Distribute multiple constant text blocks and the sorted variable text blocks to corresponding nodes for compilation processing to obtain the final output text of the business process.

[0066] In this step, the system needs to distribute the segmented and sorted text blocks to corresponding nodes for compilation processing. These nodes may be processing programs, computing units, or template engines on the server, etc.

[0067] Constant text blocks: Since the content of constant text blocks is fixed, they can be directly distributed to nodes for formatting and typesetting processing.

[0068] Variable text blocks: For variable text blocks, the system needs to associate them with corresponding input data or model output results, and perform necessary calculations or conversion operations on the nodes. Then, these processed text blocks can be integrated into the final output text.

[0069] During the distribution and compilation processing, the system needs to ensure that each text block can be correctly processed, and the final output text meets the business requirements and format requirements.

[0070] In some other embodiments of the present invention, step 132 can be implemented through the following steps:

[0071] Step 1321: Identify all variable text blocks and the dependency relationships between the variable text blocks, determine the dependency direction according to the dependency relationships, and construct a dependency relationship graph.

[0072] In the processing of the business process output text, it is crucial to ensure that the variable text blocks are compiled in the correct order, which depends on their dependency relationships.

[0073] First, the system needs to be able to identify all variable text blocks from the output text of the business process. This can be achieved by analyzing the text structure, tags, or specific formats.

[0074] Next, the system needs to determine the dependency relationships between these variable text blocks. This usually means that the content or generation of one variable text block depends on the result of another variable text block. Such dependency relationships can be determined by analyzing the business logic, data flow, or model output.

[0075] Once the dependency relationships are determined, the system can construct a dependency relationship graph. In this graph, each variable text block is a node, and the dependency relationship is a directed edge connecting these nodes. The direction of the directed edge represents the dependency direction, that is, from the dependency source to the dependency target.

[0076] Step 1322: When there is no cyclic dependency in the dependency relationship graph, use the topological sorting algorithm to sort the multiple variable text blocks.

[0077] Before attempting to sort variable text blocks, the system first needs to check whether there are circular dependencies in the dependency graph. A circular dependency means that there is one or more closed loops, where at least one variable text block directly or indirectly depends on itself. If there are circular dependencies, the system may need to take additional measures to resolve this issue, such as redesigning the business process or modifying the dependencies between variable text blocks.

[0078] If there are no circular dependencies in the dependency graph, the system can use a topological sorting algorithm to sort the variable text blocks. Topological sorting is a linear sorting method that applies to directed acyclic graphs (DAGs). This algorithm can ensure that for every directed edge (u, v) in the graph, vertex u appears before vertex v in the sorting. In this way, the system can process the variable text blocks in the sorted order.

[0079] In some other embodiments of the present invention, the method may further include step 1323:

[0080] Step 1323: Optimize the sorting relationship of multiple variable text blocks.

[0081] Some variable text blocks may be more important or urgent than others and need to be processed preferentially. For example, certain key data or decision results may need to be output as early as possible. A priority can be assigned to each variable text block, and these priorities can be considered during the sorting process. A weighted topological sorting or other priority sorting algorithms can be used to achieve this.

[0082] When processing multiple variable text blocks, the execution time and resource consumption of each text block need to be considered. Some text blocks may take longer or require more resources to process, which may affect the efficiency of the entire business process. Adjust the sorting relationship according to the execution time and resource limitations. For example, those text blocks with shorter execution times and less resource consumption can be processed first to obtain partial results more quickly. At the same time, parallel processing of multiple text blocks can also be considered to further improve efficiency.

[0083] The variable text blocks in the business process may change over time, input data, or business logic. Therefore, a fixed sorting relationship may not meet the requirements of all situations. Introduce a dynamic adjustment mechanism that allows the system to adjust the sorting relationship at runtime according to the current situation. For example, a feedback loop or a real-time monitoring system can be used to detect and handle changes and adjust the sorting relationship accordingly.

[0084] In some other embodiments of the present invention, the variable text blocks include ordinary variable text and large model variable text, and step 133 can be implemented through the following steps:

[0085] Step 1331: When the attribute of the text block is constant text, output it before the corresponding node is executed.

[0086] The constant text block is a fixed part of the business process output text, and its content will not change with the change of input data or business logic. Therefore, when processing the constant text block, the system can directly output it before the corresponding node is executed. That is to say, the constant text block does not need to wait for the processing results of any variable text blocks and can be directly used as part of the output text.

[0087] Step 1332: When the attribute of the text block is ordinary variable text, output the variable text content after the corresponding node is executed.

[0088] The ordinary variable text block contains dynamic content, and its content depends on the input data, business logic, or the processing results of other variable text blocks. Therefore, when processing the ordinary variable text block, the system needs to wait for the variable text blocks (if any) it depends on to be processed before proceeding with its own processing. Once the processing is completed, the system will output the variable text content after the corresponding node is executed. This ensures that the content of the variable text block in the output text is accurate and consistent.

[0089] Step 1333: When the attribute of the text block is large model variable text, perform streaming text output during the execution of the large model node.

[0090] The processing of the large model variable text block may involve complex calculations or model inferences, which may take a long time. To optimize the user experience and improve processing efficiency, the system can perform streaming text output during the execution of the large model node. That is, the system can gradually output the processing results of the large model variable text block instead of waiting to output all at once after the entire processing is completed. In this way, users can see partial results during the processing without having to wait for all to be completed.

[0091] In some other embodiments of the present invention, the method may further include the following steps:

[0092] Step 14: Monitor the execution status of the business process in real time, including the execution order, execution time, and output content of the nodes, and adjust and optimize the business process according to user feedback or system requirements.

[0093] In this step, monitor the execution order of each node in the business process to ensure that they proceed in the expected order. If any problems are found in the execution order (such as node skipping, repeated execution, etc.), error handling or manual intervention should be carried out immediately.

[0094] Alternatively, you can record the execution time of each node to analyze whether there are performance bottlenecks or timeouts. For nodes that take too long to execute, you can consider optimizing their internal logic, increasing parallel processing, or adjusting resource allocation.

[0095] Alternatively, you can check whether the output content of each node meets expectations, including data format, data quality, business logic, etc. If the output content is found to be incorrect, error handling or data correction should be performed immediately.

[0096] Collect user feedback on business processes through user surveys, satisfaction ratings, problem reports, etc. Analyze the problems and suggestions in user feedback to understand users' expectations and needs for business processes. Based on business development and market demand, evaluate whether the system needs to add new functions, optimize existing functions, or adjust business processes.

[0097] The technical solution provided by the present invention analyzes all node types and their connection branches in the business process, identifies the large model node, branch node, result merging node and text output type node, receives the business process processing instruction, and obtains the configuration content corresponding to the business processing instruction. The large model node executes the business process processing instruction according to the configuration content, and judges whether the output result of the large model is quoted in the output text of the business process according to the configuration content. When it is judged that the output result of the large model is quoted in the output text of the business process, the output text of the business process is compiled and processed to obtain the final output text of the business process. Therefore, the first aspect of the present invention can ensure that each step of the business process is executed as expected by accurately identifying key node types such as large model nodes and branch nodes, thereby improving the accuracy of the output text; secondly, the business processing flow of the present invention introduces steps such as compilation processing to ensure that the final output text meets the business requirements and improves the reliability of the entire business process; thirdly, it can effectively realize the dynamic management of text output and optimize the user experience in the business process; fourthly, timely text output not only improves the efficiency of information transmission, but also enhances the responsiveness of the system, laying a good foundation for subsequent business process execution.

[0098] Embodiment 2

[0099] See also Figure 2 , Figure 2 : is a schematic diagram of a structure diagram of a business process processing device based on a large model provided in Embodiment 2 of the present invention, wherein the device comprises:

[0100] Analysis module 21, used to analyze all node types and their connection branches in the business process, and identify large model nodes, branch nodes, result merging nodes and text output type nodes;

[0101] A receiving and judging module 22, configured to receive a service process processing instruction, and obtain configuration content corresponding to the service processing instruction. The large model node executes the service process processing instruction according to the configuration content, and judges whether the output text of the service process references the output result of the large model according to the configuration content;

[0102] A compiling module 23, configured to perform a compiling process on the output text of the service process to obtain a final output text of the service process when it is judged that the output text of the service process references the output result of the large model.

[0103] Wherein, the compiling module 23 may include:

[0104] A splitting unit 231, configured to split the output text of the service process into text blocks, obtaining a plurality of constant text blocks and variable text blocks;

[0105] A sorting unit 232, configured to sort the plurality of variable text blocks according to their respective dependency relationships;

[0106] A compiling unit 233, configured to distribute the plurality of constant text blocks and the sorted variable text blocks to corresponding nodes for compiling processing, obtaining a final output text of the service process.

[0107] Wherein, the variable text block includes an ordinary variable text and a large model variable text, and the compiling unit 233 may further be configured to:

[0108] When the attribute of the text block is a constant text, output it before the corresponding node executes;

[0109] When the attribute of the text block is an ordinary variable text, output the variable text content after the corresponding node executes;

[0110] When the attribute of the text block is a large model variable text, perform streaming text output during the execution process of the large model node.

[0111] Wherein, the sorting unit 232 may further include:

[0112] A constructing subunit 2321, configured to identify all variable text blocks and the dependency relationships between the variable text blocks, determine the dependency direction according to the dependency relationships and construct a dependency graph;

[0113] A sorting subunit 2322, configured to sort the plurality of variable text blocks using a topological sorting algorithm when there is no circular dependency in the dependency graph.

[0114] In other embodiments of the present invention, the sorting unit 232 may further include an optimization subunit 2323, configured to optimize the sorting relationships of the plurality of variable text blocks.

[0115] In other embodiments of the present invention, the device may further include a monitoring module 24 for monitoring the execution status of the business process in real time, including the execution order, execution time, and output content of the nodes, and adjusting and optimizing the business process according to user feedback or system requirements.

[0116] The technical solution provided by the present invention analyzes all node types and their connection branches in the business process, identifies the large model node, branch node, result merging node and text output type node, receives the business process processing instruction, and obtains the configuration content corresponding to the business processing instruction. The large model node executes the business process processing instruction according to the configuration content, and judges whether the output result of the large model is quoted in the output text of the business process according to the configuration content. When it is judged that the output result of the large model is quoted in the output text of the business process, the output text of the business process is compiled and processed to obtain the final output text of the business process. Therefore, the first aspect of the present invention can ensure that each step of the business process is executed as expected by accurately identifying key node types such as large model nodes and branch nodes, thereby improving the accuracy of the output text; secondly, the business processing flow of the present invention introduces steps such as compilation processing to ensure that the final output text meets the business requirements and improves the reliability of the entire business process; thirdly, it can effectively realize the dynamic management of text output and optimize the user experience in the business process; fourthly, timely text output not only improves the efficiency of information transmission, but also enhances the responsiveness of the system, laying a good foundation for subsequent business process execution.

[0117] It should be noted that the business process processing device based on the big model in the embodiment of the present invention and the business process processing method based on the big model in the above embodiment belong to the same inventive concept. The technical details not described in detail in this device can be found in the previous description of the method and will not be repeated here.

[0118] In addition, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the aforementioned method when running.

[0119] Figure 3The structural schematic diagram of an electronic device 10 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0120] As Figure 3 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0122] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the idle detection method.

[0123] In some embodiments, the idle detection method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the idle detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the idle detection method by any other suitable means (e.g., by means of firmware).

[0124] The various embodiments of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0126] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0128] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0130] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0131] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A business process processing method based on a large model, characterized in that: The method comprises: Analyze all node types and their connecting branches in the business process, and identify large model nodes, branch nodes, result merging nodes, and text output type nodes; Receive a business process processing instruction and obtain configuration content corresponding to the business process instruction, the large model node executes the business process processing instruction according to the configuration content, and determines whether the output text of the business process references the output result of the large model according to the configuration content; When it is determined that the output result of the large model is referenced in the output text of the business process, the output text of the business process is compiled to obtain the final output text of the business process.

2. The method according to claim 1, characterized in that: The step of compiling the output text of the business process to obtain the final output text of the business process includes: The output text of the business process is segmented according to text blocks to obtain a plurality of constant text blocks and variable text blocks; Sorting the multiple variable text blocks according to their respective dependencies; Distribute multiple constant text blocks and sorted variable text blocks to corresponding nodes for compilation and processing to obtain the final output text of the business process.

3. The method according to claim 2, characterized in that The variable text block includes common variable text and large model variable text.

4. The method according to claim 3, characterized in that: Distribute multiple constant text blocks and sorted variable text blocks to corresponding nodes for compilation and processing to obtain the final output text of the business process: When the attribute of the text block is regular text, it is output before the corresponding node is executed; When the attribute of the text block is ordinary variable text, the variable text content is output after the corresponding node is executed; When the attribute of the text block is a large model variable text, streaming text output is performed during the execution of the large model node.

5. The method according to claim 3, characterized in that: Sorting the plurality of variable text blocks according to the dependency relationship includes: Identify all variable text blocks and dependency relationships between variable text blocks, determine dependency directions based on the dependency relationships, and construct a dependency graph; When there is no circular dependency in the dependency graph, a topological sorting algorithm is used to sort the multiple variable text blocks.

6. The method according to claim 5, characterized in that The method also includes: optimizing the sorting relationship of multiple variable text blocks.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Monitor the execution status of the business process in real time, including the execution order, execution time, and output content of the nodes, and adjust and optimize the business process based on user feedback or system requirements.

8. A business process processing device based on a large model, characterized in that: The device comprises: The analysis module is used to analyze all node types and their connection branches in the business process, and identify large model nodes, branch nodes, result merging nodes and text output type nodes; A receiving and judging module, used to receive a business process processing instruction and obtain configuration content corresponding to the business process instruction, wherein the large model node executes the business process processing instruction according to the configuration content and judges whether the output text of the business process references the output result of the large model according to the configuration content; The compiling module is used to compile the output text of the business process to obtain the final output text of the business process when it is determined that the output result of the large model is referenced in the output text of the business process.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 7.

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