Model optimization method, apparatus, device, medium, and program product

Abstract

The present disclosure provides a model optimization method, device, equipment, medium and program product, which can be applied to the fields of artificial intelligence and finance. The method comprises: acquiring basic business data corresponding to a plurality of different business scenarios; generating rewriting rules for the basic business data in each business scenario; rewriting the basic business data based on the rewriting rules by using a natural language processing model to generate target business data suitable for each business scenario, wherein the semantics of the target business data and the basic business data are consistent; evaluating a to-be-optimized model by using the target business data to obtain an evaluation result; and optimizing the to-be-optimized model according to the evaluation result to obtain a target optimization model.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence and finance, and more specifically to a model optimization method, apparatus, device, medium, and program product. Background Art

[0002] In the financial field, large-scale machine learning models are often used for risk prediction, business management and other operations. Among them, model evaluation and optimization are crucial in the process of model application.

[0003] Related technologies often utilize fixed dataset evaluation methods and resampling techniques to evaluate models, with model optimization based on the evaluation results. However, using a pre-set, fixed dataset to test models carries the risk of selection bias and struggles to adapt to dynamically changing environments. Especially in the rapidly changing financial sector, this often exhibits poor adaptability, resulting in inaccurate evaluation results and, in turn, poor model optimization results. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a model optimization method, apparatus, device, medium and program product.

[0005] According to a first aspect of the present disclosure, a model optimization method is provided, including: obtaining basic business data corresponding to a plurality of different business scenarios; generating rewriting rules for the basic business data in each business scenario; based on the rewriting rules, rewriting the basic business data using a natural language processing model to generate target business data applicable to each of the business scenarios, wherein the semantics of the target business data and the basic business data are consistent; evaluating the model to be optimized using the target business data to obtain an evaluation result; and optimizing the model to be optimized based on the evaluation result to obtain a target optimization model.

[0006] According to an embodiment of the present disclosure, the rewriting rules include replacement sub-rules, structure sub-rules and scenario sub-rules; among them, the replacement sub-rules are used to determine the synonyms of the terms of the basic business data; the structure sub-rules are used to determine the adjustment method of the structural order of the basic business data; and the scenario sub-rules are used to determine the scenario terms corresponding to the basic business data.

[0007] According to an embodiment of the present disclosure, based on the rewriting rules, the basic business data is rewritten using a natural language processing model to generate target business data, including: based on the replacement sub-rule, the natural language processing model is used to replace the terms of the basic business data with synonyms to generate the first business data; based on the structure sub-rule, the natural language processing model is used to adjust the structural order of the first business data to generate the second business data; based on the scenario sub-rule, the natural language processing model is used to change the scenario terminology of the second business data to produce the target business data.

[0008] According to an embodiment of the present disclosure, the above method also includes: optimizing the rewriting rules according to the evaluation results to obtain optimized rewriting rules; and optimizing and rewriting the basic business data using a natural language processing model based on the optimized rewriting rules.

[0009] According to an embodiment of the present disclosure, the rewriting rules are optimized according to the evaluation results, including: determining the optimization target that the evaluation results are intended to meet according to the evaluation results; and optimizing the rewriting rules based on the optimization target using an optimization algorithm to obtain optimized rewriting rules.

[0010] According to an embodiment of the present disclosure, the target business data is used to evaluate the model to be optimized to obtain an evaluation result, including: determining a target evaluation operation corresponding to the business scenario based on the business scenario of the model to be optimized; and evaluating the model to be optimized based on the target evaluation operation to obtain an evaluation result.

[0011] According to an embodiment of the present disclosure, the above method also includes: using a task scheduling tool to determine the update cycle; based on the update cycle, using a natural language processing model to update and rewrite the basic business data to obtain updated target business data; based on the updated target business data, updating and evaluating the model to be optimized to obtain updated evaluation results.

[0012] According to an embodiment of the present disclosure, the model to be optimized is optimized according to the evaluation results to obtain a target optimization model, including: performing quantitative analysis on the evaluation results to obtain analysis results; and optimizing the model to be optimized according to the analysis results to obtain a target optimization model.

[0013] A second aspect of the present disclosure provides a model optimization device, including: an acquisition module for acquiring basic business data corresponding to multiple different business scenarios; a first generation module for generating rewriting rules for the basic business data in each business scenario; a second generation module for rewriting the basic business data using a natural language processing model based on the rewriting rules to generate target business data, wherein the target business data and the basic business data have consistent semantics; an evaluation module for evaluating the model to be optimized using the target business data to obtain an evaluation result; and a first optimization module for optimizing the model to be optimized based on the evaluation result to obtain a target optimization model.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0017] According to the embodiments of the present disclosure, basic business data corresponding to multiple different business scenarios are obtained; rewriting rules for the basic business data in each business scenario are generated; based on the rewriting rules, the basic business data are rewritten using a natural language processing model to generate target business data, wherein the target business data and the basic business data have the same semantics; the target business data are used to evaluate the model to be optimized to obtain an evaluation result; based on the evaluation result, the model to be optimized is optimized to obtain a target optimization model. Since specific rewriting rules are generated for different business scenarios, the target business data can be generated using a natural language processing model while the semantics remain unchanged, thus avoiding the problem in the prior art that the data is fixed and difficult to adapt to scenarios in the financial field, achieving high accuracy of the evaluation results, avoiding model evaluation deviation, and thus improving the technical effect of the optimization effect of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 Schematically illustrates an application scenario diagram of the model optimization method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

[0020] Figure 2 The following schematically shows a flow chart of a model optimization method according to an embodiment of the present disclosure;

[0021] Figure 3 A schematic diagram of a model optimization method according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 4 Schematically shows a structural block diagram of a model optimization device according to an embodiment of the present disclosure; and

[0023] Figure 5 A block diagram of an electronic device suitable for implementing a model optimization method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0028] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0029] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.

[0030] The embodiments of the present disclosure provide a model optimization method, which obtains basic business data corresponding to multiple different business scenarios; generates rewriting rules for the basic business data in each business scenario; based on the rewriting rules, rewrites the basic business data using a natural language processing model to generate target business data, wherein the target business data and the basic business data have consistent semantics; evaluates the model to be optimized using the target business data to obtain evaluation results; and optimizes the model to be optimized based on the evaluation results to obtain a target optimization model.

[0031] Figure 1 The application scenario diagram of the model optimization method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0032] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0033] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0034] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0035] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0036] It should be noted that the model optimization method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the model optimization device provided in the embodiment of the present disclosure can generally be set in the server 105. The model optimization method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the model optimization device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0037] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0038] The following will be based on Figure 1 The described scenario is described in detail about the model optimization method of the disclosed embodiment.

[0039] Figure 2 The flowchart of the model optimization method according to the embodiment of the present disclosure is schematically shown.

[0040] like Figure 2 As shown, the model optimization method of this embodiment includes operations S210 to S250.

[0041] In operation S210 , basic business data corresponding to a plurality of different business scenarios is acquired.

[0042] According to an embodiment of the present disclosure, basic business data can be financial statements for different businesses in the same business scenario, for example, statements such as "the company's profit increased by 20% in the second quarter of 2023" used to input models to implement risk prediction, information management and other operations based on the model. The source of basic business data can be market data, bank transaction records, debt and asset information, company financial reports, etc. In order to improve the timeliness of basic business data, it can be combined with a real-time data capture system to dynamically collect the latest changes in the financial market. In addition, by acquiring real-time financial news, news fact data can be introduced into basic business data to increase the responsiveness of basic business data to the latest financial events, ensuring that basic business data fully and timely reflects the actual situation of the current market, thereby improving the authenticity of the evaluation of the model to be optimized and the relevance of the application.

[0043] According to an embodiment of the present disclosure, the business scenario may be a banking service scenario such as "amount transaction" or "securities business".

[0044] In operation S220 , rewriting rules for basic business data in each business scenario are generated.

[0045] According to an embodiment of the present disclosure, the rewriting rules are used to ensure that the semantics of basic business data remain unchanged during the rewriting process.

[0046] In operation S230 , the basic business data is rewritten based on the rewriting rules using a natural language processing model to generate target business data, wherein the target business data and the basic business data have the same semantics.

[0047] According to an embodiment of the present disclosure, the natural language processing model may be a deep learning model architecture.

[0048] According to the embodiments of the present disclosure, the natural language processing model can extract relevant financial events, market sentiment and other information from financial news. These data can be dynamically added to the existing data set, so that the generated data set is closer to the current market status, thereby improving the real-time and representativeness of the evaluation data.

[0049] According to the embodiments of the present disclosure, based on rewriting rules, the basic business data is rewritten using a natural language processing model, and the integrity of the original financial information can be maintained when rewriting the text. Especially in scenarios involving complex financial logic, the rewritten text is guaranteed to be highly semantically consistent with the original text, thereby generating target business data.

[0050] In operation S240 , the model to be optimized is evaluated using the target business data to obtain an evaluation result.

[0051] According to an embodiment of the present disclosure, for the models to be optimized in different financial scenarios, the target business data is used to evaluate the models to be optimized to obtain evaluation results that are suitable for different financial scenarios.

[0052] In operation S250 , the model to be optimized is optimized according to the evaluation result to obtain a target optimization model.

[0053] According to an embodiment of the present disclosure, an optimization target associated with the model to be optimized is set based on the evaluation result to optimize the model to be optimized so that the obtained target optimization model can meet the optimization target.

[0054] According to the embodiments of the present disclosure, basic business data corresponding to multiple different business scenarios are obtained; rewriting rules for the basic business data in each business scenario are generated; based on the rewriting rules, the basic business data are rewritten using a natural language processing model to generate target business data, wherein the target business data and the basic business data have the same semantics; the target business data are used to evaluate the model to be optimized to obtain an evaluation result; based on the evaluation result, the model to be optimized is optimized to obtain a target optimization model. Since specific rewriting rules are generated for different business scenarios, the target business data can be generated using a natural language processing model while the semantics remain unchanged, thus avoiding the problem in the prior art that the data is fixed and difficult to adapt to scenarios in the financial field, achieving high accuracy of the evaluation results, avoiding model evaluation deviation, and thus improving the technical effect of the optimization effect of the model.

[0055] According to an embodiment of the present disclosure, the rewriting rules include replacement sub-rules, structure sub-rules and scenario sub-rules; among them, the replacement sub-rules are used to determine the synonyms of the terms of the basic business data; the structure sub-rules are used to determine the adjustment method of the structural order of the basic business data; and the scenario sub-rules are used to determine the scenario terms corresponding to the basic business data.

[0056] According to an embodiment of the present disclosure, the replacement sub-rule is used to replace financial terms and common words in the basic business data with synonyms to enrich the expression while maintaining semantic consistency. For example, "return on investment" is replaced with "yield rate".

[0057] According to embodiments of the present disclosure, structural sub-rules are used to adjust the grammatical structure or sentence order of basic business data to increase the complexity and diversity of the data. For example, "The company's profits increased by 20% in the second quarter of 2023" can be rewritten as "The company achieved a 20% profit growth in the second quarter of 2023."

[0058] According to an embodiment of the present disclosure, scenario sub-rules are used to appropriately adjust the use of financial terms in basic business data for different business application scenarios to better conform to the financial background of specific business application scenarios. For example, "debt repayment capacity" can be replaced with "debt repayment capacity" to ensure the accuracy of terminology used in different financial contexts. For another example, if the basic business data is designed to involve "exchange rate", for the business scenario of "only one exchange rate for one foreign country", "exchange rate" is changed to "single rate", and for the business scenario of "multiple exchange rates for one foreign country", "exchange rate" is changed to "multiple rate".

[0059] According to an embodiment of the present disclosure, based on the rewriting rules, the basic business data is rewritten using a natural language processing model to generate target business data, including: based on the replacement sub-rule, the natural language processing model is used to replace the terms of the basic business data with synonyms to generate the first business data; based on the structure sub-rule, the natural language processing model is used to adjust the structural order of the first business data to generate the second business data; based on the scenario sub-rule, the natural language processing model is used to change the scenario terms of the second business data to generate the target business data.

[0060] According to an embodiment of the present disclosure, based on the above-mentioned replacement sub-rules, replacement sub-rules and scenario sub-rules, the basic business data is rewritten using a natural language processing model, which can maintain the integrity of the basic business data, especially in scenarios involving complex financial logic, and ensure that the rewritten text is highly semantically consistent with the original text.

[0061] According to an embodiment of the present disclosure, after the basic business data is rewritten using a natural language processing model, in order to ensure that the rewritten or dynamically generated target business data maintains its compliance and validity, it is necessary to perform strict compliance verification on the target business data after it is generated to ensure that the target business data complies with actual financial standards and industry norms, and can reflect the dynamic environment of the current financial market.

[0062] According to an embodiment of the present disclosure, the rewriting order of the above-mentioned replacement sub-rules, replacement sub-rules and scenario sub-rules can be randomly set.

[0063] According to the embodiments of the present disclosure, by rewriting basic business data based on rewriting rules using a natural language processing model and dynamically generating target business data, the security and confidentiality of the data can be significantly improved. In addition, the generated data sets are all unique, thereby avoiding the risk of leakage caused by fixed data sets and improving the security of model evaluation and optimization. At the same time, the generation mechanism of the natural language processing model ensures the freshness and diversity of the target business data, so that the generated target business data can more realistically reflect changes in the financial market, improve the accuracy of the evaluation results, and avoid the problem of model evaluation bias.

[0064] According to an embodiment of the present disclosure, the above method also includes: optimizing the rewriting rules according to the evaluation results to obtain optimized rewriting rules; and optimizing and rewriting the basic business data using a natural language processing model based on the optimized rewriting rules.

[0065] According to an embodiment of the present disclosure, when the evaluation results are abnormal, such as when they differ greatly from historical evaluation results, it is determined that the current rewriting rules are not applicable to the model to be optimized, and the rewriting rules need to be optimized to update the target business data.

[0066] According to an embodiment of the present disclosure, the rewriting rules are optimized according to the evaluation results, including: determining the optimization target that the evaluation results are intended to meet according to the evaluation results; and optimizing the rewriting rules based on the optimization target using an optimization algorithm to obtain optimized rewriting rules.

[0067] According to the embodiments of the present disclosure, the optimization target may be determined based on historical evaluation results, and may also be obtained by further adjusting the evaluation results by those skilled in the art.

[0068] According to an embodiment of the present disclosure, the rewriting rules are optimized using an optimization algorithm to obtain new rewriting rules, so that the target business data generated based on the new rewriting rules and the evaluation results obtained by evaluating the optimization model meet the optimization goals.

[0069] According to an embodiment of the present disclosure, the target business data is used to evaluate the model to be optimized to obtain an evaluation result, including: determining a target evaluation operation corresponding to the business scenario based on the business scenario of the model to be optimized; and evaluating the model to be optimized based on the target evaluation operation to obtain an evaluation result.

[0070] According to an embodiment of the present disclosure, in order to measure the performance of the model to be optimized in the prediction and classification task, the "Accuracy" target evaluation operation is used to evaluate the model to be optimized to obtain an evaluation result.

[0071] According to an embodiment of the present disclosure, for business scenarios such as financial risk prediction, the "F1 score" target evaluation operation is used to evaluate the optimization model to obtain an evaluation result.

[0072] According to an embodiment of the present disclosure, for business scenarios such as predicting market prices or financial indicators, the "mean square error (MSE)" target evaluation operation is used to evaluate the optimization model to obtain an evaluation result.

[0073] According to the embodiments of the present disclosure, for business scenarios such as binary classification problems such as credit rating, the "ROC curve" target evaluation operation is used to evaluate the optimization model to obtain the evaluation results.

[0074] According to the embodiments of the present disclosure, a comprehensive performance evaluation of the model to be optimized is conducted. By testing the model in various business scenarios, it is ensured that the model can operate stably and effectively under different market conditions, thereby verifying its generalization and practicality. This evaluation not only includes an assessment of predictive accuracy but also focuses on the adaptability and robustness of the model to be optimized in different financial scenarios, thereby improving the accuracy of the evaluation results of the model to be optimized in different business scenarios.

[0075] According to an embodiment of the present disclosure, the above method also includes: using a task scheduling tool to determine the update cycle; based on the update cycle, using a natural language processing model to update and rewrite the basic business data to obtain updated target business data; based on the updated target business data, updating and evaluating the model to be optimized to obtain updated evaluation results.

[0076] According to the embodiments of the present disclosure, a task scheduling tool is used to achieve regular generation and update of target business data, and an intelligent generation strategy is used to reduce manual intervention, improve the degree of automation of the generation process, and ensure the diversity and timeliness of the target business data.

[0077] According to the embodiments of the present disclosure, the rewriting rules are regularly optimized in combination with market feedback and changes in economic conditions to ensure that the target business data can still reflect key characteristics when facing drastic market fluctuations (such as economic crises, etc.), for example, data related to market volatility and risk management can be added during economic crises.

[0078] According to the embodiments of the present disclosure, through the automated data generation and optimization mechanism, the evaluation process is made more efficient and flexible, and can respond to market changes in a timely manner, ensuring that the evaluation of the model to be optimized keeps pace with the times.

[0079] According to an embodiment of the present disclosure, the model to be optimized is optimized according to the evaluation results to obtain a target optimization model, including: performing quantitative analysis on the evaluation results to obtain analysis results; and optimizing the model to be optimized according to the analysis results to obtain a target optimization model.

[0080] According to an embodiment of the present disclosure, quantitative analysis is a method of analyzing financial data such as financial markets and securities using mathematical models, statistical methods, and algorithms, and involves using historical evaluation results to predict future market behavior and obtain analysis results.

[0081] According to the embodiments of the present disclosure, after the evaluation is complete, the results are quantitatively analyzed and fed back to the model to be optimized. Based on the performance differences discovered during the evaluation, the data generation strategy and model parameters are adjusted. This feedback mechanism ensures that the generated data is continuously improved, thereby driving the model to achieve better performance in future tests.

[0082] Figure 3 A schematic diagram of a model optimization method according to an embodiment of the present disclosure is schematically shown.

[0083] According to an embodiment of the present disclosure, the model optimization method of the embodiment of the present disclosure is as follows Figure 3 As shown, execute operation S310 to obtain basic business data; then execute operation S320 to generate rewriting rules corresponding to the basic business data; execute operation S330 to rewrite the basic business data using a natural language processing model based on the rewriting rules generated above to generate target business data; execute operation S340 to determine the target evaluation operation according to the business scenario of the model to be optimized, and then execute operation S350 to evaluate the model to be optimized based on the target evaluation operation to obtain the evaluation result; execute operation S360 to determine the optimization target based on the evaluation result; execute operation S370 to optimize the rewriting rules, and the optimized rewriting rules obtained make the evaluation result of the model to be optimized meet the optimization target; execute operation S380 to optimize the model to be optimized based on the evaluation result to obtain the target optimization model.

[0084] Based on the above model optimization method, the present disclosure also provides a model optimization device. Figure 4 The device is described in detail.

[0085] Figure 4 The structural block diagram of the model optimization device according to an embodiment of the present disclosure is schematically shown.

[0086] like Figure 4 As shown, the model optimization device 400 of this embodiment includes an acquisition module 410 , a first generation module 420 , a second generation module 430 , an evaluation module 440 and a first optimization module 450 .

[0087] The acquisition module 410 is configured to acquire basic business data corresponding to a plurality of different business scenarios. In one embodiment, the acquisition module 410 may be configured to execute the operation S210 described above, which will not be described in detail here.

[0088] The first generating module 420 is used to generate rewriting rules for basic business data in various business scenarios. In one embodiment, the first generating module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0089] The second generation module 430 is configured to rewrite the basic business data using a natural language processing model based on the rewriting rules to generate target business data, wherein the target business data and the basic business data have the same semantics. In one embodiment, the second generation module 430 can be configured to perform operation S230 described above, which will not be further described here.

[0090] The evaluation module 440 is used to evaluate the model to be optimized using the target business data to obtain an evaluation result. In one embodiment, the evaluation module 440 can be used to perform the operation S240 described above, which will not be repeated here.

[0091] The first optimization module 450 is configured to optimize the target optimization model based on the evaluation results. In one embodiment, the first optimization module 450 may be configured to perform the operation S250 described above, which will not be described in detail here.

[0092] According to the embodiments of the present disclosure, basic business data corresponding to multiple different business scenarios are obtained; rewriting rules for the basic business data in each business scenario are generated; based on the rewriting rules, the basic business data are rewritten using a natural language processing model to generate target business data, wherein the target business data and the basic business data have the same semantics; the target business data are used to evaluate the model to be optimized to obtain an evaluation result; based on the evaluation result, the model to be optimized is optimized to obtain a target optimization model. Since specific rewriting rules are generated for different business scenarios, the target business data can be generated using a natural language processing model while the semantics remain unchanged, thus avoiding the problem in the prior art that the data is fixed and difficult to adapt to scenarios in the financial field, achieving high accuracy of the evaluation results, avoiding model evaluation deviation, and thus improving the technical effect of the optimization effect of the model.

[0093] According to embodiments of the present disclosure, any multiple of the acquisition module 410, first generation module 420, second generation module 430, evaluation module 440, and first optimization module 450 can be combined into a single module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the acquisition module 410, first generation module 420, second generation module 430, evaluation module 440, and first optimization module 450 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the acquisition module 410, the first generation module 420, the second generation module 430, the evaluation module 440 and the first optimization module 450 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0094] According to an embodiment of the present disclosure, the second generation module 430 includes a first generation submodule, a second generation submodule, and a third generation submodule.

[0095] The first generating submodule is used to replace the terms of the basic business data with synonyms based on the replacement sub-rule using a natural language processing model to generate first business data.

[0096] The second generating submodule is used to adjust the structural sequence of the first business data based on the structural sub-rules and using the natural language processing model to generate second business data.

[0097] The third generating submodule is used to modify the scenario terminology of the second business data based on the scenario sub-rules using a natural language processing model to generate target business data.

[0098] According to an embodiment of the present disclosure, the model optimization device 400 further includes a second optimization module and a rewriting module.

[0099] The second optimization module is used to optimize the rewriting rules according to the evaluation results to obtain optimized rewriting rules.

[0100] The rewriting module is used to optimize and rewrite basic business data based on the optimized rewriting rules using the natural language processing model.

[0101] According to an embodiment of the present disclosure, the rewriting module includes a first determining submodule and a first optimizing submodule.

[0102] The first determination submodule is used to determine the optimization goal that the evaluation results are intended to meet based on the evaluation results.

[0103] The first optimization submodule is used to optimize the rewriting rules based on the optimization target by using an optimization algorithm to obtain optimized rewriting rules.

[0104] According to an embodiment of the present disclosure, the evaluation module 440 includes a second determination submodule and an evaluation submodule.

[0105] The second determination submodule is used to determine the target evaluation operation corresponding to the business scenario based on the business scenario of the model to be optimized.

[0106] The evaluation submodule is used to evaluate the model to be optimized based on the target evaluation operation and obtain the evaluation results.

[0107] According to an embodiment of the present disclosure, the model optimization device 400 further includes a first determination module, a first obtaining module and a second obtaining module.

[0108] The first determining module is used to determine the update cycle by using a task scheduling tool.

[0109] The first obtaining module is used to update and rewrite the basic business data based on the update cycle using the natural language processing model to obtain the updated target business data.

[0110] The second obtaining module is used to update and evaluate the optimization model based on the updated target business data to obtain updated evaluation results.

[0111] According to an embodiment of the present disclosure, the first optimization module 450 includes an analysis submodule and a third obtaining submodule.

[0112] The analysis submodule is used to perform quantitative analysis on the evaluation results and obtain the analysis results.

[0113] The third submodule is used to optimize the model to be optimized according to the analysis results to obtain the target optimization model.

[0114] Figure 5 A block diagram of an electronic device suitable for implementing a model optimization method according to an embodiment of the present disclosure is schematically shown.

[0115] like Figure 5As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0116] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 903. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0117] According to an embodiment of the present disclosure, electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to bus 504. Electronic device 500 may also include one or more of the following components connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or modem. Communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 510 as needed, so that computer programs read from the removable media can be installed into storage section 508 as needed.

[0118] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0119] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 402 and / or RAM 403 described above, and / or one or more memories other than ROM 402 and RAM 403.

[0120] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to cause the computer system to implement the model optimization method provided by the embodiments of the present disclosure.

[0121] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0122] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0123] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0124] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0127] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A model optimization method, characterized in that: The method comprises: Obtain basic business data corresponding to multiple different business scenarios; Generating rewriting rules for the basic business data in each of the business scenarios; Based on the rewriting rules, the basic business data is rewritten using a natural language processing model to generate target business data applicable to each of the business scenarios, wherein the target business data and the basic business data have consistent semantics; Using the target business data to evaluate the model to be optimized to obtain an evaluation result; Optimizing the model to be optimized according to the evaluation results to obtain a target optimization model; The rewriting rules include replacement sub-rules, structure sub-rules and scenario sub-rules; Wherein, the replacement sub-rule is used to determine synonyms of the terms of the basic business data; The structural sub-rule is used to determine the adjustment method of the structural sequence of the basic business data; The scenario sub-rule is used to determine the scenario term corresponding to the basic business data; The rewriting of the basic business data using a natural language processing model based on the rewriting rules to generate target business data applicable to each of the business scenarios includes: Based on the replacement sub-rule, using the natural language processing model, synonym replacement is performed on the terms of the basic business data to generate first business data; Based on the structural sub-rules, the natural language processing model is used to adjust the structural order of the first business data to generate second business data; Based on the scenario sub-rules, using the natural language processing model, modifying the scenario terminology of the second business data to generate the target business data; Optimizing the rewriting rules according to the evaluation results includes: Determining, based on the evaluation results, an optimization goal that the evaluation results are intended to meet; Based on the optimization target, the rewriting rule is optimized using an optimization algorithm to obtain the optimized rewriting rule.

2. The method according to claim 1, characterized in that The method of evaluating the optimization model using the target business data to obtain evaluation results includes: Determine, based on the business scenario of the model to be optimized, a target evaluation operation corresponding to the business scenario; Based on the target evaluation operation, the model to be optimized is evaluated to obtain the evaluation result.

3. The method according to claim 1, characterized in that The method further comprises: Use task scheduling tools to determine update cycles; Based on the update cycle, the basic business data is updated and rewritten using a natural language processing model to obtain updated target business data; Based on the updated target business data, the model to be optimized is updated and evaluated to obtain an updated evaluation result.

4. The method according to claim 1, wherein Optimizing the model to be optimized according to the evaluation results to obtain a target optimization model includes: Performing quantitative analysis on the evaluation results to obtain analysis results; The model to be optimized is optimized according to the analysis results to obtain the target optimization model.

5. A model optimization device for executing the method according to any one of claims 1 to 4, characterized in that: The device comprises: The acquisition module is used to obtain basic business data corresponding to multiple different business scenarios; A first generating module is used to generate rewriting rules for the basic business data in each of the business scenarios; A second generating module is configured to rewrite the basic business data based on the rewriting rules using a natural language processing model to generate target business data, wherein the target business data and the basic business data have the same semantics; An evaluation module, configured to evaluate the optimization model using the target business data to obtain an evaluation result; and The first optimization module is used to optimize the model to be optimized according to the evaluation results to obtain a target optimization model.

6. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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