Thinking chain data generation method and device, computer equipment and storage medium

Abstract

The invention discloses a thinking chain data generation method and device, computer equipment and a storage medium, and relates to the technical field of data processing and the field of financial science and technology. Constructing a standard operation process of the target business; generating a standard operation process template of the target business based on the standard operation process; performing optimization processing on the standard operation process template to obtain a target standard operation process template; and filling the target standard operation process template with the service data to generate thinking chain data of the target service. Through the method and the device, the thinking chain data corresponding to the target business can be automatically, efficiently and accurately generated.

Description

Technical Field

[0001] The present invention relates to the fields of data processing technology and financial technology, and in particular to a method, device, computer equipment and computer-readable storage medium for generating thinking chain data. Background Art

[0002] At present, in the field of artificial intelligence, especially in the development of natural language processing (NLP) technology, especially in AI customer service application scenarios related to financial institutions (such as banks, etc.), building high-quality training data sets is the key to achieving efficient and accurate AI models. With the rise of large language models (LLMs), there is a growing demand for training data that can reflect the deep human thinking process. This type of data, the so-called "thinking chain data", is crucial to improving the logical reasoning ability and ability to understand complex contexts of large language models. Among them, thinking chain data is not just a simple question and answer pair, it contains the complete thinking process from question to answer, including but not limited to information retrieval, logical reasoning, hypothesis verification and conclusion generation; this data enables large language models to learn how to solve problems step by step, so that when faced with new and unseen problems, they can simulate the way humans think and provide users with more accurate and reasonable answers.

[0003] Currently, the main way to construct thought chain data for fine-tuning large language models relies on manual annotation. Annotators need to manually write detailed thought processes and possible tool calls based on the questions. This process is not only time-consuming and labor-intensive, but also faces the following challenges:

[0004] 1. Efficiency issue: Manual labeling is a labor-intensive process, especially when fine-tuning a large language model that requires a lot of data support. The labeling workload is huge, resulting in a significant increase in labor costs.

[0005] 2. Professional knowledge requirements: Thinking Chain data often involves professional knowledge in specific fields, such as finance, Internet, real estate, etc. The labelers need to have professional knowledge in the corresponding fields to accurately understand the problems and provide reasonable solutions, which increases the cost and difficulty of building Thinking Chain data;

[0006] 3. Subjectivity and errors: Since the construction of thought chain data involves complex logical reasoning and professional knowledge, subjective judgment and errors of labelers are difficult to avoid, which may affect the accuracy of thought chain data.

[0007] Based on this, how to provide a thinking chain data generation method, device, computer equipment and non-volatile computer-readable storage medium that can automatically, efficiently and accurately generate thinking chain data corresponding to the target business is a problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0008] In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a thought chain data generation method, device, computer equipment and computer-readable storage medium, aiming to solve the problem of how to automatically, efficiently and accurately generate thought chain data corresponding to the target business.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides a method for generating thought chain data, wherein the method comprises:

[0011] Collect business data of target business;

[0012] Establish standard operating procedures for the target business;

[0013] Based on the standard operating procedure, generating a standard operating procedure template for the target business;

[0014] Optimizing the standard operating procedure template to obtain a target standard operating procedure template;

[0015] Fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

[0016] In a second aspect, the present invention provides a thought chain data generating device, wherein the device comprises:

[0017] A collection module, used to collect business data of target business;

[0018] A construction module for constructing a standard operating procedure for the target business;

[0019] A generating module, used to generate a standard operating procedure template for the target business based on the standard operating procedure;

[0020] An optimization module, used for optimizing the standard operation process template to obtain a target standard operation process template;

[0021] A filling module is used to fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

[0022] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the thought chain data generation method as described above when executing the computer program.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the thought chain data generation method as described above.

[0024] Beneficial effect: Compared with the prior art, the present invention provides a method, device, computer equipment and computer-readable storage medium for generating thinking chain data, wherein the business data of the target business is collected; a standard operating procedure of the target business is constructed; based on the standard operating procedure, a standard operating procedure template of the target business is generated; the standard operating procedure template is optimized to obtain a target standard operating procedure template; the business data is filled into the target standard operating procedure template to generate the thinking chain data of the target business, thereby realizing automatic, efficient and accurate generation of thinking chain data corresponding to the target business. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of the application environment of a method for generating thought chain data provided by one embodiment of the present invention.

[0027] Figure 2 A flowchart of a method for generating thought chain data provided in accordance with an embodiment of the present invention.

[0028] Figure 3 A schematic diagram of a program module of a thought chain data generating device provided in one embodiment of the present invention.

[0029] Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention.

[0030] Figure 5 Another structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0033] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0034] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0035] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0037] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0038] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0039] A method for generating thought chain data provided by an embodiment of the present invention can be applied in the following aspects: Figure 1In the application environment shown, the client communicates with the server through the network. The server can collect the business data of the target business sent by the client, and construct the standard operation process of the target business, and then generate the standard operation process template of the target business based on the standard operation process, and then optimize the standard operation process template to obtain the target standard operation process template, and finally fill the business data into the target standard operation process template to generate the thinking chain data of the target business; in this way, this embodiment can realize the automatic, efficient and accurate generation of the thinking chain data corresponding to the target business. At the same time, the generated thinking chain data can also be used to fine-tune the large language model, so as to generate a target large language model that is better adapted to the intelligent customer service business scenario of the financial institution, and the service capability of the intelligent customer service of the financial institution can be improved through the target large language model. Among them, the client includes but is not limited to a handheld computer, a desktop computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a cloud computer device, a personal digital assistant (PDA) and other computer devices. The server can be an independent server 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, domain name services, security services, content delivery networks (CDNs), as well as big data and artificial intelligence platforms.

[0040] See also Figure 2 An embodiment of the present invention provides a method for generating thought chain data, wherein the method specifically comprises the following steps:

[0041] S100, collecting business data of the target business;

[0042] S200, constructing a standard operating procedure for the target business;

[0043] S300, generating a standard operation process template for the target business based on the standard operation process;

[0044] S400, optimizing the standard operation process template to obtain a target standard operation process template;

[0045] S500: Fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

[0046] When implemented, the method for generating thinking chain data of this embodiment first needs to collect business data related to the target business. These business data may include historical records, transaction data, customer information, etc., which are the basis for subsequent steps; then, based on the collected business data, the standard operating procedure (SOP) of the target business is constructed. This step involves the analysis of business activities to determine best practices and process efficiency; then, the constructed SOP is converted into a template form so that it can be used in different situations and cases. This template will serve as the framework for subsequent steps; then, the SOP template is optimized to ensure that it can adapt to different business scenarios and can effectively support business goals. The optimization may include simplifying the process, enhancing flexibility or improving efficiency; then, the actual business data is filled into the optimized target SOP template. This step makes the template specific and generates thinking chain data for the target business; the thinking chain data generated by the above steps can be used to support complex decision-making processes, improve the transparency and traceability of business processes, and also provide a basis for continuous improvement. In this way, the method of this embodiment can realize automatic, efficient and accurate generation of thinking chain data corresponding to the target business.

[0047] Furthermore, the method for generating thought chain data, wherein the step S100, collecting the business data of the target business, specifically comprises the steps of:

[0048] Based on the business database of the target business, collecting data information related to the target business;

[0049] The data information is preprocessed to obtain the business data of the target business.

[0050] In this embodiment, by preprocessing the data information, errors can be eliminated, missing values ​​can be estimated, and inconsistencies can be corrected, thereby improving the accuracy and reliability of the data. Preprocessing can eliminate data inconsistencies and convert data into a unified format and unit, which is crucial for subsequent data processing.

[0051] Furthermore, the method for generating thinking chain data, wherein the data information is preprocessed to obtain the business data of the target business, specifically comprises the steps of:

[0052] Analyze the data information to identify and extract key data of the target business;

[0053] The key data is preprocessed to obtain business data of the target business.

[0054] In specific implementation, this embodiment collects data information related to the target business based on the business database of the target business, analyzes the data information, identifies and extracts key data of the target business, pre-processes the key data, and obtains the business data of the target business. The specific implementation process is roughly as follows:

[0055] 1) Data Collection

[0056] Collect data information related to the target business from the business database according to the determined needs and scope; this may involve technical means such as database query, API call, data import, etc.; ensure compliance with relevant laws and regulations on data privacy and security during the collection process;

[0057] 2) Data cleaning and integration

[0058] Clean and format the collected data information, remove duplicate records, correct errors and inconsistent data; if the data comes from multiple sources, data integration is also required to ensure consistency of data format and standards;

[0059] 3) Data analysis and key data identification

[0060] Use data analysis tools and techniques (such as statistical analysis, data mining, machine learning, etc.) to conduct in-depth analysis of cleaned data information; identify key data that have a significant impact on business decisions, such as key performance indicators (KPIs), trend changes, outliers, etc.;

[0061] 4) Extraction of key data

[0062] Extract key data based on the analysis results; this may include screening out the most relevant data subsets from a large amount of data, or extracting specific information from a complex data structure. By accurately extracting data that is closely related to business goals, decision makers can quickly obtain information that has a significant impact on the business, thereby making more informed and effective decisions in a shorter period of time, while also reducing decision-making errors caused by inaccurate or incomplete information. At the same time, the extraction of key data can identify which business areas or processes require the most resource investment, which not only improves the efficiency of resource use but also helps reduce costs;

[0063] 5) Data preprocessing

[0064] Further preprocess the extracted key data to obtain the business data of the target business for subsequent use; this may include data conversion (such as normalization, standardization), data encoding (such as converting text data into numerical representation), feature engineering (such as creating new features or modifying existing features to improve model performance), etc.

[0065] Through the above process, this embodiment can ensure that the business data of the target business collected from the business database is of high quality and highly relevant, and can provide strong support for business analysis and decision-making.

[0066] Furthermore, in the method for generating thinking chain data, the step S200, constructing the standard operating procedure of the target business, specifically comprises the steps of:

[0067] Obtaining the original standard operating procedures of the target business;

[0068] The original standard operating procedure is sorted to obtain the standard operating procedure of the target business.

[0069] In this embodiment, by obtaining the original standard operating procedures of the target business and organizing them, it is possible to reduce errors and duplication of work caused by human factors, avoid unnecessary waste of time, improve overall work efficiency, optimize resource allocation, and reduce resource waste. At the same time, standardized work processes help to better control and manage production costs, labor costs, etc., thereby reducing overall costs and improving profitability.

[0070] In specific implementation, this embodiment obtains the original standard operating procedures of the target business, sorts out the original standard operating procedures, and obtains the specific implementation process of the standard operating procedures of the target business as follows:

[0071] 1) Review the original standard operating procedures

[0072] First, it is necessary to collect and review the original standard operating procedures currently used by the target business; this includes all relevant operation manuals, work instructions, flow charts and other relevant documents; the purpose of the review is to understand the detailed content, structure and implementation of the original standard operating procedures;

[0073] 2) Evaluate the effectiveness and applicability of the original standard operating procedures

[0074] Evaluate the original SOP to determine if it still meets current business needs and best practices; the evaluation may include alignment with business objectives, efficiency of the process, employee compliance, and customer satisfaction.

[0075] 3) Collect feedback and suggestions

[0076] Gather feedback and suggestions from employees, management, and customers who implemented the original SOP; this helps identify problems, ambiguities, or areas that need improvement in the original SOP;

[0077] 4) Organize the original standard operating procedures

[0078] Based on the evaluation results and the feedback information collected, the original standard operating procedures are sorted out as necessary to obtain the standard operating procedures (SOP) for the target business; this may include updating outdated information, simplifying complex processes, adding new steps or deleting unnecessary steps; ensuring that the adjusted standard operating procedures are clearer, easier to understand and execute; among them, the standard operating procedure (SOP) is a document that describes a specific operation or process in detail, and its purpose is to ensure that each time a task is performed under specific conditions, it can be performed in a consistent and predictable manner;

[0079] Through the above process, this embodiment can obtain the current standard operating procedures of the target business, which can support the efficient operation and continuous improvement of the target business.

[0080] Furthermore, the method for generating thinking chain data, wherein the step S300, based on the standard operating procedure, generates a standard operating procedure template for the target business, specifically comprises the steps of:

[0081] Analyze the structure, content and execution process of the standard operating procedure to obtain analysis results;

[0082] Based on the analysis results, a standard operating process template for the target business is generated.

[0083] In this embodiment, through the analysis and templateization of the standard operating procedure, each step and requirement of the standard operating procedure can be clearly visible, making the process more transparent and easy to monitor and audit. The standardized standard operating procedure template can ensure that all employees follow the same steps when performing tasks, thereby ensuring consistency of operations and predictability of results. At the same time, a clear standard operating procedure template helps to reduce operational errors and process deviations, thereby greatly improving work quality and efficiency.

[0084] In specific implementation, this embodiment analyzes the structure, content and execution process of the standard operating procedure to obtain analysis results. Based on the analysis results, the specific implementation process of generating the standard operating procedure template of the target business is roughly as follows:

[0085] 1) Analyze the current standard operating procedures of the target business

[0086] Conduct a thorough review of the current standard operating procedures of the target business, analyze their structure, content, and implementation process, and understand the purpose, implementation method, required resources, and expected results of each step;

[0087] 2) Design and generate standard operating procedure templates

[0088] Based on the analysis results, design a standard operating procedure template for the target business; this may include creating flowcharts, writing step-by-step instructions, designing tables and charts, etc.; ensure that the designed and generated standard operating procedure template is clear in structure, logical, and easy to understand and execute;

[0089] Through the above process, this embodiment can ensure that the design and generation of the standard operation process template are based on in-depth analysis and actual needs, thereby improving the efficiency and quality of the business process.

[0090] Furthermore, the method for generating thinking chain data, wherein the step S400, optimizing the standard operation process template to obtain the target standard operation process template, specifically comprises the steps of:

[0091] Evaluate the standard operating procedure template according to preset evaluation indicators to obtain evaluation results;

[0092] Based on the evaluation result, the standard operating process template is generalized and variable settings are performed to obtain a target standard operating process template.

[0093] In this embodiment, by evaluating and optimizing the standard operating process template, it is possible to ensure that each step of the process is executed efficiently and consistently, reduce deviations and errors, and improve overall work efficiency. In addition, generalization and variable settings enable the standard operating process template to adapt to different scenarios and needs, providing a wider range of applications while maintaining consistency in core steps and principles.

[0094] In specific implementation, this embodiment evaluates the standard operating process template according to preset evaluation indicators to obtain evaluation results. Based on the evaluation results, the standard operating process template is generalized and variable settings are performed to obtain the target standard operating process template. The specific implementation process is as follows:

[0095] 1) Conduct standard operating procedure template assessment

[0096] First, conduct a comprehensive evaluation of the standard operating process template of the target business based on the preset evaluation indicators. This includes evaluating the readability, usability, comprehensiveness of the business scenarios covered, and whether it can meet the needs of different users. The evaluation can be conducted through internal review, user feedback, actual usage analysis, etc.

[0097] 2) Determine the need for generalization and variable settings

[0098] Based on the evaluation results, determine which parts of the standard operating process template need to be generalized and where variables need to be introduced; generalization means making the standard operating process template more universal and able to adapt to different business processes and environments; variable setting means introducing adjustable parameters in the standard operating process template to adapt to different operating conditions and requirements;

[0099] 3) Design generalization structure and variables

[0100] Design the generalized structure of the standard operating process template to ensure that the generalized structure can flexibly adapt to various business scenarios; at the same time, determine the variables that need to be set and define clear parameter ranges and options for these variables; this may involve working with business experts to ensure that the setting of variables can cover all possible business needs;

[0101] 4) Develop and test target standard operating procedure templates

[0102] Develop the target standard operating process template based on the designed generalized structure and variables; during the development process, ensure that the logic of the target standard operating process template is clear, easy to understand and execute; after the development is completed, conduct testing to verify the effectiveness and applicability of the target standard operating process template; testing can include simulating different business scenarios and collecting user feedback on the target standard operating process template;

[0103] 5) Optimize and improve the target standard operating process template

[0104] Optimize and improve the target standard operating procedure template based on test results and user feedback; this may include adjusting the generalization structure, modifying variable settings, improving the expression and format of the template, etc.; ensure that the target standard operating procedure template can provide clear guidance in actual application and meet the needs of different users;

[0105] Through the above process, this embodiment can ensure that the generalization and variable setting of the standard operating process template are based on in-depth evaluation and actual needs, thereby improving the flexibility and applicability of the obtained target standard operating process template and supporting efficient operation of the business.

[0106] Furthermore, the method for generating thought chain data, wherein the step S500, filling the business data into the target standard operation process template to generate the thought chain data of the target business, specifically comprises the steps of:

[0107] Filling the business data into corresponding fields in the target standard operating process template to obtain initial data;

[0108] The initial data is post-processed to generate thought chain data of the target business.

[0109] In this implementation, by filling business data into the corresponding fields in the target standard operating process template and post-processing the obtained initial data to generate thinking chain data, the close integration of data and process can be ensured. At the same time, the availability and accuracy of data are optimized through post-processing steps. This process helps to improve the quality and availability of data and ensure the effectiveness of data in practical applications, so as to better support subsequent decision-making, analysis and processing processes.

[0110] In specific implementation, this embodiment fills the business data into the corresponding fields in the target standard operation process template to obtain initial data, and post-processes the initial data to generate the thinking chain data of the target business. The specific implementation process is roughly as follows:

[0111] 1) Map data to target standard operating procedure template

[0112] Analyze the structure of the target standard operating process template and identify the fields that need to be filled with data; then, establish a mapping relationship between data and template fields based on the characteristics of business data and the requirements of template fields; this step is the key to ensure that business data can be correctly filled into the target standard operating process template;

[0113] 2) Automatically fill in data

[0114] Use programming scripts or specialized data filling tools to automatically fill business data into the corresponding fields of the target standard operating process template based on the established mapping relationship; automated filling can improve efficiency and reduce human errors;

[0115] 3) Generate initial data

[0116] After completing the data filling, you will get the initial data. These initial data are specific instances of the target standard operating process template, reflecting the specific steps and requirements of the business operation. Perform a preliminary check on the initial data to ensure the correctness and completeness of the data filling.

[0117] 4) Post-processing of initial data

[0118] Post-process the initial data, which may include further data verification, logic check, format adjustment, etc. The purpose of post-processing is to ensure the accuracy and usability of the data and that the data can meet the needs of subsequent analysis and application;

[0119] 5) Generate thought chain data

[0120] Based on the post-processing results, the final thought chain data of the target business is generated; this data includes not only the specific steps of the business operation, but also the decision logic, condition judgment, exception handling, etc., to reflect the complete business logic and process;

[0121] Through the above process, this embodiment can ensure that the business data can be effectively integrated into the target standard operating process template, and ultimately generate high-quality thinking chain data for the target business.

[0122] Finally, the large language model can be fine-tuned and trained using the thought chain data of the target business, thereby generating a target large language model that can adapt to the target business.

[0123] Specifically, the method of the present invention can be applied to fine-tuning scenarios of large language models related to financial services, such as intelligent (AI) customer service application scenarios in the field of financial technology. By adopting the method of the present invention to generate thinking chain data in the intelligent customer service business scenario, and then fine-tuning the large language model with the thinking chain data, a target large language model that is better adapted to the intelligent customer service business scenario can be generated. The target large language model can improve the service capabilities of the intelligent customer service of financial institutions.

[0124] It can be seen from the above method embodiments that the method for generating thinking chain data provided by the present invention includes: collecting business data of the target business; constructing the standard operating procedure of the target business; generating the standard operating procedure template of the target business based on the standard operating procedure; optimizing the standard operating procedure template to obtain the target standard operating procedure template; filling the business data into the target standard operating procedure template to generate the thinking chain data of the target business. In this way, the method of the present invention can realize automatic, efficient and accurate generation of thinking chain data corresponding to the target business.

[0125] Specifically, the thought chain data generation method provided by the embodiment of the present invention can at least achieve the following beneficial technical effects:

[0126] 1. Compared with the traditional manual annotation method, the method of the present invention can greatly reduce the annotation cost. The traditional manual annotation of data requires a lot of manpower and time, while the method of the present invention can quickly generate a large amount of high-quality thinking chain data (training samples) for fine-tuning training of large language models in a short time;

[0127] 2. The thought chain data generation process of the method of the present invention involves reasonable definition of grammar and context through the template, and the generated thought chain data is more accurate, which helps the large language model learn more accurate expressions that are more in line with language rules, thereby improving the generalization ability of the target large language model;

[0128] 3. In the method of the present invention, researchers can design templates and define variables as needed, so as to achieve precise control over the generated data, which helps to build customized thinking chain data (training samples) for specific tasks or scenarios.

[0129] It should be understood that, although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps may be included based on conventional or non-creative labor, and these operation steps are not necessarily performed in sequence according to the embodiment or flowchart. The order of steps listed in the embodiment or flowchart is only one way of executing the order of many steps, and does not represent the only execution order. It should be noted that there is not necessarily a certain order between the above steps. A person of ordinary skill in the art can understand from the description of the embodiment of the present invention that in different embodiments, the above steps may have different execution orders, that is, they may be executed in parallel, or they may be executed in exchange, etc. Moreover, at least a part of the steps in the embodiment or flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but may be executed in turn, alternately or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0130] Based on the above method embodiment, please refer to Figure 3 Another embodiment of the present invention further provides a device for generating thought chain data, wherein the device specifically comprises:

[0131] A collection module 11, used to collect business data of a target business;

[0132] A construction module 12 is used to construct a standard operating procedure for the target business;

[0133] A generating module 13, configured to generate a standard operating procedure template for the target business based on the standard operating procedure;

[0134] An optimization module 14 is used to optimize the standard operation process template to obtain a target standard operation process template;

[0135] The filling module 15 is used to fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

[0136] Furthermore, in the thought chain data generating device, the collection module 11 specifically includes:

[0137] A collecting unit, configured to collect data information related to the target business based on a business database of the target business;

[0138] The preprocessing unit is used to preprocess the data information to obtain the business data of the target business.

[0139] Furthermore, the thought chain data generating device, wherein the preprocessing of the data information to obtain the business data of the target business specifically includes:

[0140] Analyze the data information to identify and extract key data of the target business;

[0141] The key data is preprocessed to obtain business data of the target business.

[0142] Furthermore, in the thought chain data generating device, the construction module 12 specifically includes:

[0143] An acquisition unit, used to acquire the original standard operating procedure of the target business;

[0144] The arranging unit is used to arrange the original standard operating procedure to obtain the standard operating procedure of the target business.

[0145] Furthermore, in the thought chain data generating device, the generating module 13 specifically comprises:

[0146] An analysis unit, used to analyze the structure, content and execution process of the standard operating procedure to obtain analysis results;

[0147] A generating unit is used to generate a standard operating process template for the target business based on the analysis result.

[0148] Furthermore, in the thought chain data generating device, the optimization module 14 specifically includes:

[0149] An evaluation unit, used to evaluate the standard operating procedure template according to preset evaluation indicators to obtain an evaluation result;

[0150] A generalization and variable setting unit is used to generalize and set variables for the standard operating process template based on the evaluation result to obtain a target standard operating process template.

[0151] Furthermore, in the thought chain data generating device, the filling module 15 specifically includes:

[0152] A filling unit, used to fill the business data into the corresponding fields in the target standard operation process template to obtain initial data;

[0153] The post-processing unit is used to post-process the initial data to generate the thought chain data of the target business.

[0154] It should be noted that in the embodiment of the device of the present invention, the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the aforementioned method embodiment part and will not be repeated here.

[0155] Specifically, the device of the present invention can be applied to large language model fine-tuning scenarios related to financial services, such as intelligent (AI) customer service application scenarios in the field of financial technology. By adopting the thinking chain data in the intelligent customer service business scenario generated by the device of the present invention, and then fine-tuning the large language model with the thinking chain data, a target large language model that is better adapted to the intelligent customer service business scenario can be generated. The target large language model can improve the service capabilities of the intelligent customer service of financial institutions.

[0156] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions or steps on the service side of the thinking chain data generation method in any of the above method embodiments are implemented.

[0157] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a client, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions or steps of the client side of the thinking chain data generation method in any of the above method embodiments are implemented.

[0158] Those skilled in the art will understand that Figure 4 and Figure 5The structural schematic diagram shown in the figure is only a schematic diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0159] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0160] Among them, the memory includes a readable storage medium, an internal memory, etc., wherein the internal memory can be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium can be a hard disk of a computer device, and in other embodiments, it can also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory can also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory can also be used to temporarily store data that has been output or is to be output.

[0161] Based on the above method embodiments, another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the method for generating thought chain data in any of the above method embodiments is implemented. The computer-readable storage medium may be non-volatile or volatile.

[0162] It should be noted that the above-mentioned functions or steps that can be implemented by the computer-readable storage medium or computer device, and the technical effects brought about by the functions / steps, can be found in the relevant descriptions in the aforementioned method embodiments. To avoid repetition, they will not be described one by one here.

[0163] Specifically, the computer-readable storage medium or computer device of the present invention can be specifically applied to large language model fine-tuning scenarios related to financial services, such as intelligent (AI) customer service application scenarios in the field of financial technology. By adopting the thinking chain data in the intelligent customer service business scenario generated by the computer-readable storage medium or computer device of the present invention, and then fine-tuning the large language model with the thinking chain data, a target large language model that is better adapted to the intelligent customer service business scenario can be generated. The target large language model can improve the service capabilities of the intelligent customer service of financial institutions.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM). The disclosed memory components or memories of the operating environments described herein are intended to comprise one or more of these and / or any other suitable types of memory.

[0165] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0166] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0167] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for generating thought chain data, characterized in that: The method comprises: Collect business data of target business; Establish standard operating procedures for the target business; Based on the standard operating procedure, generating a standard operating procedure template for the target business; Optimizing the standard operating procedure template to obtain a target standard operating procedure template; Fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

2. The method for generating thought chain data according to claim 1, characterized in that: The business data of the target business is collected, including: Based on the business database of the target business, collecting data information related to the target business; The data information is preprocessed to obtain the business data of the target business.

3. The method for generating thought chain data according to claim 2, characterized in that: The preprocessing of the data information to obtain the business data of the target business includes: Analyze the data information to identify and extract key data of the target business; The key data is preprocessed to obtain business data of the target business.

4. The method for generating thought chain data according to claim 1, characterized in that: The standard operating procedure for building the target business includes: Obtaining the original standard operating procedures of the target business; The original standard operating procedure is sorted to obtain the standard operating procedure of the target business.

5. The method for generating thought chain data according to claim 1, characterized in that: The step of generating a standard operating procedure template for the target business based on the standard operating procedure includes: Analyze the structure, content and execution process of the standard operating procedure to obtain analysis results; Based on the analysis results, a standard operating process template for the target business is generated.

6. The method for generating thought chain data according to claim 1, characterized in that: The step of optimizing the standard operating process template to obtain a target standard operating process template includes: Evaluate the standard operating procedure template according to preset evaluation indicators to obtain evaluation results; Based on the evaluation result, the standard operating process template is generalized and variable settings are performed to obtain a target standard operating process template.

7. The method for generating thought chain data according to claim 1, characterized in that: The step of filling the business data into the target standard operation process template to generate the thought chain data of the target business includes: Filling the business data into corresponding fields in the target standard operating process template to obtain initial data; The initial data is post-processed to generate thought chain data of the target business.

8. A thought chain data generating device, characterized in that: The device comprises: A collection module, used to collect business data of target business; A construction module for constructing a standard operating procedure for the target business; A generating module, used to generate a standard operating procedure template for the target business based on the standard operating procedure; An optimization module, used for optimizing the standard operation process template to obtain a target standard operation process template; A filling module is used to fill the business data into the target standard operation process template to generate the thinking chain data of the target business.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the thought chain data generating method as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating thought chain data as described in any one of claims 1 to 7 is implemented.

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