Thought chain data generation method and device, computer device, and storage medium

Abstract

The application discloses a thinking chain data generation method and device, computer equipment and a storage medium, relates to the technical field of data processing and the field of financial technology, wherein business data of a target business is collected; a standard operation process of the target business is constructed; a standard operation process template of the target business is generated based on the standard operation process; the standard operation process template is optimized to obtain a target standard operation process template; and the business data is filled into the target standard operation process template to generate thinking chain data of the target business. The application can automatically, efficiently and accurately generate thinking chain data corresponding to the target business.

Description

Technical Field

[0001] This invention relates to the fields of data processing technology and financial technology, specifically to a method, apparatus, computer device, and computer-readable storage medium for generating thought chain data. Background Technology

[0002] Currently, in the field of artificial intelligence, especially in the development of Natural Language Processing (NLP) technology, particularly in AI customer service applications within financial institutions (such as banks), building high-quality training datasets is crucial for achieving efficient and accurate AI models. With the rise of Large Language Models (LLMs), the demand for training data that reflects deep-seated human thought processes is increasing. This type of data, known as "thought chain data," is essential for improving the logical reasoning ability and understanding of complex contexts in large language models. Thought chain data goes beyond simple question-and-answer pairs; it encompasses the complete thought process from question to answer, including but not limited to information retrieval, logical reasoning, hypothesis testing, and conclusion generation. This data enables large language models to learn how to solve problems step-by-step, thus simulating human thinking when faced with new and unseen problems, and providing users with more accurate and reasonable answers.

[0003] Currently, the primary method for constructing thought chain data for fine-tuning training of large language models relies on manual annotation. Annotators need to manually write detailed thought processes and possible tool calls based on the problem, a process that is not only time-consuming and labor-intensive but also faces the following challenges:

[0004] 1. Efficiency issues: Manual annotation is a labor-intensive process, especially when fine-tuning large language models that require a lot of data support. The workload of annotation is huge, which leads to a significant increase in labor costs.

[0005] 2. Professional knowledge requirements: Mind chain data often involves professional knowledge in specific fields, such as finance, the Internet, and real estate. Annotators need to have professional knowledge in the corresponding fields in order to accurately understand the problem and provide reasonable solutions, which increases the cost and difficulty of constructing mind chain data.

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

[0007] Therefore, how to provide a method, apparatus, computer device, and non-volatile computer-readable storage medium for generating thought chain data, which can automatically, efficiently, and accurately generate thought chain data corresponding to the target business, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method, apparatus, computer device and computer-readable storage medium for generating thought chain data, aiming to solve the problem of how to automatically, efficiently and accurately generate thought chain data corresponding to target business.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

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

[0011] Collect business data for the target business;

[0012] Establish a standard operating procedure for the target business;

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

[0014] The standard operating procedure template is optimized to obtain the target standard operating procedure template;

[0015] The business data is populated into the target standard operating procedure template to generate the mind chain data of the target business.

[0016] Secondly, the present invention provides a mind chain data generation device, wherein the device comprises:

[0017] The collection module is used to collect business data for the target business.

[0018] The building module is used to build the standard operating procedures for the target business.

[0019] The generation module is used to generate a standard operation process template for the target business based on the standard operation process.

[0020] The optimization module is used to optimize the standard operating procedure template to obtain the target standard operating procedure template.

[0021] The population module is used to populate the business data into the target standard operation process template to generate the mind chain data of the target business.

[0022] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the thought chain data generation method as described above.

[0023] Fourthly, 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 effects: Compared with the prior art, the present invention provides a method, apparatus, computer device, and computer-readable storage medium for generating thought chain data. The method involves: collecting business data of a target business; constructing a standard operating procedure for the target business; generating a standard operating procedure template for the target business based on the standard operating procedure; optimizing the standard operating procedure template to obtain a target standard operating procedure template; and filling the target standard operating procedure template with the business data to generate thought chain data for the target business. This enables the automatic, efficient, and accurate generation of thought chain data corresponding to the target business. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram illustrating the application environment of a mind chain data generation method provided in an embodiment of the present invention.

[0027] Figure 2 This is a flowchart illustrating a method for generating thought chain data according to an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of the program modules of a mind chain data generation device provided in an embodiment of the present invention.

[0029] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention.

[0030] Figure 5 This is another structural schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

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

[0033] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0034] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0035] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0037] It should be understood that the sequence number of each step in the following embodiments does not imply the order 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] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0039] An embodiment of the present invention provides a method for generating thought chain data, which can be applied to, for example... Figure 1In the application environment shown, the client communicates with the server via a network. The server can collect business data of the target business sent by the client, construct a standard operating procedure for the target business, generate a standard operating procedure template for the target business based on the standard operating procedure, optimize the standard operating procedure template to obtain a target standard operating procedure template, and finally fill the target standard operating procedure template with the business data to generate the thought chain data of the target business. In this way, this embodiment can automatically, efficiently and accurately generate the thought chain data corresponding to the target business. At the same time, the generated thought chain data can be used to fine-tune and train a large language model, thereby generating a target large language model that is better adapted to the intelligent customer service business scenario of financial institutions. This target large language model can improve the service capabilities of intelligent customer service in financial institutions. The client includes, but is not limited to, PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs), and other computer devices. The server can be a standalone 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 communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

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

[0041] S100, Collect business data for the target business;

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

[0043] S300. Based on the standard operating procedure, generate a standard operating procedure template for the target business;

[0044] S400. Optimize the standard operating procedure template to obtain the target standard operating procedure template;

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

[0046] In practical implementation, the mind chain data generation method of this embodiment first needs to collect business data related to the target business. This business data may include historical records, transaction data, customer information, etc., which form the basis for subsequent steps. Then, based on the collected business data, a standard operating procedure (SOP) for the target business is constructed. This step involves analyzing business activities to determine best practices and process efficiency. Next, the constructed SOP is converted into a template format so that it can be used in different scenarios and cases. This template serves as the framework for subsequent steps. Then, the SOP template is optimized to ensure it can adapt to different business scenarios and effectively support business objectives. Optimization may include simplifying processes, enhancing flexibility, or improving efficiency. Finally, actual business data is populated into the optimized target SOP template. This step concretizes the template and generates mind chain data specific to the target business. The mind chain data generated through the above steps can then be used to support complex decision-making processes, improve the transparency and traceability of business processes, and provide a foundation for continuous improvement. Thus, the method of this embodiment can automatically, efficiently, and accurately generate mind chain data corresponding to the target business.

[0047] Furthermore, in the aforementioned method for generating mind chain data, step S100, collecting business data of the target business, specifically includes the following steps:

[0048] Based on the business database of the target business, collect 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. Furthermore, preprocessing can eliminate data inconsistencies and convert the data into a uniform format and unit, which is crucial for subsequent data processing.

[0051] Furthermore, the aforementioned method for generating thought chain data, wherein the preprocessing of the data information to obtain the business data of the target business specifically includes the following steps:

[0052] The data information is analyzed to identify and extract key data of the target business.

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

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

[0055] 1) Data collection

[0056] Based on the defined needs and scope, collect data related to the target business from the business database; this may involve technical means such as database queries, API calls, and data import; ensure compliance with relevant data privacy and security regulations during the collection process;

[0057] 2) Data cleaning and integration

[0058] The collected data is cleaned and formatted, removing duplicate records and correcting errors and inconsistencies. If the data comes from multiple sources, it is also necessary to integrate the data to ensure consistency in data format and standards.

[0059] 3) Data analysis and key data identification

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

[0061] 4) Key data extraction

[0062] Based on the analysis results, key data is extracted. This may include sifting through a large amount of data to find the most relevant subsets or extracting specific information from complex data structures. By accurately extracting data that is closely related to business objectives, decision-makers can quickly obtain information that has a significant impact on the business, thereby making more informed and effective decisions in a shorter time. At the same time, it can also reduce decision-making errors caused by inaccurate or incomplete information. Furthermore, 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 utilization but also helps to reduce costs.

[0063] 5) Data preprocessing

[0064] Further preprocessing of the extracted key data yields business data for the target business, facilitating subsequent use. This may include data transformation (such as normalization and standardization), data encoding (such as converting text data into numerical representation), and feature engineering (such as creating new features or modifying existing features to improve model performance).

[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, highly relevant, and can provide strong support for business analysis and decision-making.

[0066] Furthermore, in the aforementioned method for generating thought chain data, step S200, constructing the standard operating procedure for the target business, specifically includes the following steps:

[0067] Obtain the existing original standard operating procedures for the target service;

[0068] The original standard operating procedure is organized to obtain the standard operating procedure for the target business.

[0069] In this embodiment, by acquiring and organizing the existing standard operating procedures of the target business, errors and repetitive work caused by human factors can be reduced, unnecessary time waste can be avoided, overall work efficiency can be improved, resource allocation can be optimized, resource waste can be reduced, and standardized workflows can 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 existing original standard operating procedures of the target service, organizes the original standard operating procedures, and obtains the specific implementation process of the standard operating procedures of the target service 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, flowcharts and other related documents; the purpose of the review is to understand the details, 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 standard operating procedures to determine whether they still meet current business needs and best practices; the evaluation may include aspects such as alignment with business objectives, process efficiency, employee compliance, and customer satisfaction.

[0075] 3) Collect feedback and suggestions

[0076] Gather feedback and suggestions from employees, management, and customers who perform the original standard operating procedures; this helps identify problems, ambiguities, or areas for improvement in the original standard operating procedures.

[0077] 4) Organize the original standard operating procedures

[0078] Based on the evaluation results and collected feedback, the original standard operating procedures are reorganized to obtain the target business's standard operating procedure (SOP). This may include updating outdated information, simplifying complex processes, adding new steps, or deleting unnecessary steps. The revised standard operating procedure is made clearer, easier to understand, and easier to execute. The standard operating procedure (SOP) is a document that describes a specific operation or process in detail. Its purpose is to ensure that, under specific conditions, each task is performed in a consistent and predictable manner.

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

[0080] Furthermore, in the aforementioned method for generating thought chain data, step S300, generating a standard operating procedure template for the target business based on the standard operating procedure, specifically includes the following steps:

[0081] The structure, content, and execution process of the standard operating procedure are analyzed, and the analysis results are obtained.

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

[0083] In this embodiment, by analyzing and templated the standard operating procedure (SOP), each step and requirement of the SOP becomes clearly visible, making the process more transparent, easier to monitor and audit. Moreover, the standardized SOP template ensures that all employees follow the same steps when performing tasks, thereby guaranteeing the consistency of operations and the predictability of results. At the same time, a clear SOP template helps reduce operational errors and process deviations, which can greatly improve 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 these analysis results, the specific implementation process for generating the standard operating procedure template for 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 for the target business, analyze its structure, content, and execution process, and understand the purpose, execution 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 descriptions, designing tables and charts, etc.; ensure that the designed and generated standard operating procedure template has a clear structure, strong logic, and is easy to understand and execute;

[0089] This embodiment ensures that the design and generation of standard operating procedure templates are based on in-depth analysis and actual needs through the above process, thereby improving the efficiency and quality of business processes.

[0090] Furthermore, in the aforementioned method for generating thought chain data, step S400, optimizing the standard operating procedure template to obtain a target standard operating procedure template, specifically includes the following steps:

[0091] The standard operating procedure template is evaluated according to preset evaluation indicators to obtain evaluation results;

[0092] Based on the evaluation results, the standard operating procedure template is generalized and variables are set to obtain the target standard operating procedure template.

[0093] In this embodiment, by evaluating and optimizing the standard operating procedure template, it can be ensured that each step of the process is executed efficiently and consistently, reducing deviations and errors, improving overall work efficiency. Moreover, the generalization and variable setting enable the standard operating procedure template to adapt to different scenarios and needs, providing a wider range of applications, while maintaining the consistency of core steps and principles.

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

[0095] 1) Evaluate the standard operating procedure template.

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

[0097] 2) Determine the requirements for generalization and variable setting.

[0098] Based on the evaluation results, determine which parts of the standard operating procedure template need to be generalized and where variables need to be introduced. Generalization means making the standard operating procedure template more universal and adaptable to different business processes and environments. Variable setting means introducing adjustable parameters into the standard operating procedure template to adapt to different operating conditions and needs.

[0099] 3) Design generalization structure and variables

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

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

[0102] Based on the designed generalization structure and variables, develop the target standard operating procedure template; during the development process, ensure that the target standard operating procedure template is logically clear, easy to understand and execute; after development, conduct testing to verify the effectiveness and applicability of the target standard operating procedure template; testing may include simulating different business scenarios and collecting user feedback on the target standard operating procedure template;

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

[0104] Based on test results and user feedback, the target standard operating procedure template will be optimized and improved. This may include adjusting the generalization structure, modifying variable settings, and improving the template's description and format. The goal is to ensure that the target standard operating procedure template provides clear guidance in practical applications and meets the needs of different users.

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

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

[0107] The business data is populated into the corresponding fields within the target standard operating procedure template to obtain initial data;

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

[0109] In this implementation, by filling business data into the corresponding fields in the target standard operating procedure template and post-processing the obtained initial data to generate mind chain data, we can ensure the close integration of data and process. At the same time, the post-processing steps optimize the usability and accuracy of the data. This process helps to improve the quality and usability of the data, ensure the effectiveness of the data in practical applications, and thus 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. The specific implementation process of post-processing the initial data to generate the target business's thought chain data is roughly as follows:

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

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

[0113] 2) Automated data population

[0114] Using programming scripts or specialized data population tools, business data can be automatically populated into the corresponding fields of the target standard operating procedure template based on the established mapping relationship; automated population can improve efficiency and reduce human error.

[0115] 3) Generate initial data

[0116] After data population is completed, the initial data is obtained; this initial data is a specific instance of the target standard operating procedure template, reflecting the specific steps and requirements of the business operation; the initial data is preliminarily checked to ensure the correctness and completeness of the data population;

[0117] 4) Post-processing initial data

[0118] Post-processing of the initial data may include further data validation, logical verification, format adjustment, etc. The purpose of post-processing is to ensure the accuracy and usability of the data, as well as that the data can meet the needs of subsequent analysis and application.

[0119] 5) Generate mind chain data

[0120] Based on the post-processing results, the final thought chain data of the target business is generated; these data not only include the specific steps of business operations, but may also include 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 business data can be effectively integrated into the target standard operating procedure template and ultimately generate high-quality mind chain data for the target business.

[0122] Finally, the large language model can be fine-tuned and trained using the generated target business's thought chain data, 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 fintech. By using the thought chain data in the intelligent customer service business scenario generated by the method of the present invention, the thought chain data is then used to fine-tune and train the large language model, thereby generating a better target large language model that adapts to the intelligent customer service business scenario. The service capability of intelligent customer service in financial institutions can be improved through this target large language model.

[0124] As can be seen from the above method embodiments, the thought chain data generation method provided by the present invention includes: collecting business data of a target business; constructing a standard operating procedure for the target business; generating a standard operating procedure template for the target business based on the standard operating procedure; optimizing the standard operating procedure template to obtain a target standard operating procedure template; and filling the target standard operating procedure template with the business data to generate thought chain data for the target business. Thus, the method of the present invention can automatically, efficiently, and accurately generate thought chain data corresponding to a target business.

[0125] Specifically, the thought chain data generation method provided in this embodiment of the invention can achieve at least 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. 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 thought chain data (training samples) for fine-tuning training of large language models in a short period of time.

[0127] 2. The generation process of the thought chain data by the method of the present invention involves reasonable definition of grammar and context through templates, resulting in more accurate thought chain data. This helps large language models learn more accurate and language-rule-compliant expressions, thereby improving the generalization ability of the target large language model.

[0128] 3. In the method of this invention, researchers can design templates and define variables as needed, thereby enabling precise control over the generated data and helping to build customized thought chain data (training samples) for specific tasks or scenarios.

[0129] It should be understood that although this application provides the method operation steps as described in the embodiments or flowcharts, conventional or non-inventive labor may include more or fewer operation steps, and these operation steps are not necessarily executed sequentially according to the order of the embodiments or flowcharts. The order of steps listed in the embodiments or flowcharts is merely one way of executing many steps and does not represent the only execution order. It should be noted that there is no necessary sequential order between the above steps. Those skilled in the art can understand from the description of the embodiments of the present invention that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in exchange, etc. Moreover, at least some steps in the embodiments or flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, 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 embodiments, please refer to Figure 3 Another embodiment of the present invention also provides a mind chain data generation device, wherein the device specifically includes:

[0131] Collection module 11 is used to collect business data of the target business;

[0132] Module 12 is used to build the standard operating procedures for the target business.

[0133] The generation module 13 is used to generate a standard operation process template for the target business based on the standard operation process.

[0134] Optimization module 14 is used to optimize the standard operating procedure template to obtain a target standard operating procedure template;

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

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

[0137] The collection unit is used to collect data information related to the target business based on the target business's business database.

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

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

[0140] The data information is analyzed to identify and extract key data of the target business.

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

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

[0143] The acquisition unit is used to acquire the existing original standard operating procedures of the target service;

[0144] The sorting unit is used to sort out the original standard operating procedures to obtain the standard operating procedures for the target business.

[0145] Furthermore, in the aforementioned thought chain data generation device, the generation module 13 specifically includes:

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

[0147] The generation unit is used to generate a standard operating procedure template for the target business based on the analysis results.

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

[0149] An evaluation unit is used to evaluate the standard operating procedure template according to preset evaluation indicators and obtain evaluation results.

[0150] The generalization and variable setting unit is used to generalize and set variables for the standard operating procedure template based on the evaluation results to obtain the target standard operating procedure template.

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

[0152] A fill unit is 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 device embodiments of the present invention, the information interaction and execution process between the above modules are based on the same concept as in the method embodiments of the present invention. For details on their specific functions and the resulting technical effects, please refer to the aforementioned method embodiments section, which will not be repeated here.

[0155] Specifically, the device of the present invention can be applied to scenarios involving fine-tuning of large language models related to financial services, such as intelligent (AI) customer service applications in the fintech field. By using the thought chain data generated in the intelligent customer service business scenario by the device of the present invention, and then using the thought chain data to fine-tune and train the large language model, a better target large language model adapted to the intelligent customer service business scenario can be generated. Through this target large language model, the service capabilities of intelligent customer service in financial institutions can be improved.

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

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

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

[0159] The processor referred to herein can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0160] The memory includes readable storage media, internal memory, etc., where internal memory can be the RAM of a computer device. Internal memory provides an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of the computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal storage units and external storage devices of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. The memory can also be used to temporarily store data that has been output or will be output.

[0161] Based on the above method embodiments, another embodiment of 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 in any of the above method embodiments. The computer-readable storage medium may be non-volatile or volatile.

[0162] It should be noted that the functions or steps that can be achieved by the computer-readable storage medium or computer device, and the technical effects brought about by the functions / steps, can be referred to the relevant descriptions in the foregoing 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 applied to scenarios involving fine-tuning of large language models related to financial services, such as intelligent (AI) customer service applications in the fintech field. By using the thought chain data generated in the intelligent customer service business scenario using the computer-readable storage medium or computer device of the present invention, the thought chain data can be used to fine-tune and train the large language model, thereby generating a better target large language model that adapts to the intelligent customer service business scenario. This target large language model can improve the service capabilities of financial institutions' intelligent customer service.

[0164] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, 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. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc. The disclosed memory components or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0165] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as 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 recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0167] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

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

[0169] It should be noted that if any software tools or components not belonging to this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use. The above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for generating thought chain data, characterized in that, The method includes: Collect business data for the target business; Establish a standard operating procedure for the target business; Based on the aforementioned standard operating procedure, a standard operating procedure template for the target business is generated; The standard operating procedure template is optimized to obtain the target standard operating procedure template; The business data is populated into the target standard operating procedure template to generate the mind chain data of the target business; The collection of business data for the target business includes: Based on the business database of the target business, collect data information related to the target business; The data information is preprocessed to obtain the business data of the target business; The preprocessing of the data information to obtain the business data of the target service includes: The data information is analyzed to identify and extract key data of the target business. The key data is preprocessed to obtain the business data of the target business; The analysis of the data information to identify and extract key data of the target business includes: The collected data is cleaned and formatted. The data information after cleaning and formatting is analyzed, and the key data of the target business is identified and extracted based on the analysis results; The preprocessing of the key data to obtain the business data of the target business includes: The extracted key data is subjected to data transformation, data encoding, and feature engineering to generate the business data of the target business.

2. The method for generating thought chain data according to claim 1, characterized in that, The standard operating procedure for constructing the target service includes: Obtain the existing original standard operating procedures for the target service; The original standard operating procedure is organized to obtain the standard operating procedure for the target business.

3. 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: The structure, content, and execution process of the standard operating procedure are analyzed, and the analysis results are obtained. Based on the analysis results, a standard operating procedure template for the target business is generated.

4. The method for generating thought chain data according to claim 1, characterized in that, The optimization of the standard operating procedure template to obtain the target standard operating procedure template includes: The standard operating procedure template is evaluated according to preset evaluation indicators to obtain evaluation results; Based on the evaluation results, the standard operating procedure template is generalized and variables are set to obtain the target standard operating procedure template.

5. 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 operating procedure template to generate the target business's thought chain data includes: The business data is populated into the corresponding fields within the target standard operating procedure template to obtain initial data; The initial data is post-processed to generate the thought chain data for the target business.

6. A mind chain data generation device, characterized in that, The device includes: The collection module is used to collect business data for the target business. The building module is used to build the standard operating procedures for the target business. The generation module is used to generate a standard operation process template for the target business based on the standard operation process. The optimization module is used to optimize the standard operating procedure template to obtain the target standard operating procedure template. The population module is used to populate the business data into the target standard operation process template to generate the mind chain data of the target business. The collection of business data for the target business includes: Based on the business database of the target business, collect data information related to the target business; The data information is preprocessed to obtain the business data of the target business; The preprocessing of the data information to obtain the business data of the target service includes: The data information is analyzed to identify and extract key data of the target business. The key data is preprocessed to obtain the business data of the target business; The analysis of the data information to identify and extract key data of the target business includes: The collected data is cleaned and formatted. The data information after cleaning and formatting is analyzed, and the key data of the target business is identified and extracted based on the analysis results; The preprocessing of the key data to obtain the business data of the target business includes: The extracted key data is subjected to data transformation, data encoding, and feature engineering to generate the business data of the target business.

7. 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, it implements the thought chain data generation method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the thought chain data generation method as described in any one of claims 1-5.

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