Intelligent quality inspection method and device

Abstract

The invention discloses an intelligent quality inspection method and device, relates to the technical field of financial quality inspection, and mainly aims to intelligently improve the quality inspection accuracy and efficiency of service data in the field. According to the main technical scheme, the method comprises the steps of obtaining financial service data of at least one data mode generated in the process that a target object provides service for a customer; and for each quality inspection dimension applicable to the target object, determining a target data mode matched with the current quality inspection dimension in the at least one data mode, allocating a currently applicable target quality inspection model for each target data mode, and calling each target quality inspection model to perform quality inspection processing on financial service data of the corresponding target data mode, obtaining quality inspection result data corresponding to the target object in the current quality inspection dimension; and summarizing the quality inspection result data corresponding to each quality inspection dimension of the target object to generate final quality inspection result data of the target object.

Description

Technical Field

[0001] This application relates to the field of financial quality inspection technology, and in particular to an intelligent quality inspection method and device. Background Technology

[0002] Remote communication methods such as telephone, online chat, and video conferencing have become the mainstream way for financial professionals to serve customers and conduct business. In order to ensure compliant operation, prevent financial risks, and improve service quality, it is necessary to conduct quality inspections on the financial service data generated by financial professionals' accounts in the process of providing services to customers. Clarifying the service quality of the financial professionals corresponding to the accounts has become a key link in the risk control and operation management of financial institutions.

[0003] Traditional quality control methods primarily rely on manual inspection of financial service data (such as telephone recordings or chat logs) generated during the account service process of financial professionals. However, limitations such as the single data modality, labor costs, and the individual experience and expertise of quality inspectors result in low accuracy and efficiency. This not only makes it difficult to handle massive amounts of financial service data but also hinders the effective identification of violations.

[0004] Therefore, how to intelligently improve the accuracy and efficiency of quality inspection of service data in this field has become an urgent problem to be solved. Summary of the Invention

[0005] This application proposes an intelligent quality inspection method and apparatus, the main purpose of which is to intelligently improve the accuracy and efficiency of quality inspection of service data in this field.

[0006] To achieve the above objectives, this application mainly provides the following technical solutions:

[0007] Firstly, this application provides an intelligent quality inspection method applied to a financial quality inspection system. The intelligent quality inspection method provided in this embodiment may include at least: acquiring financial service data of at least one data modality generated by a target object during the process of providing services to customers; for each quality inspection dimension applicable to the target object, determining the target data modality that matches the current quality inspection dimension in the at least one data modality, assigning a currently applicable target quality inspection model to each target data modality, and calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality to obtain the quality inspection result data of the target object corresponding to the current quality inspection dimension; and summarizing the quality inspection result data of the target object corresponding to each quality inspection dimension to generate the final quality inspection result data of the target object.

[0008] Secondly, this application provides an intelligent quality inspection device for use in a financial quality inspection system. The intelligent quality inspection device provided in this embodiment may include at least:

[0009] The acquisition module is used to acquire financial service data of at least one data modality generated by the target object during the process of providing services to customers;

[0010] The quality inspection module is used to determine the target data mode that matches the current quality inspection dimension in the at least one data mode for each quality inspection dimension applicable to the target object, assign the currently applicable target quality inspection model to each target data mode, and call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data mode to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0011] The processing module is used to summarize and process the quality inspection result data of the target object in each quality inspection dimension, and generate the final quality inspection result data of the target object.

[0012] Thirdly, this application provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device where the storage medium is located to execute the intelligent quality inspection method described in the first aspect.

[0013] Fourthly, this application provides an electronic device, the electronic device comprising: a memory for storing a program; and a processor coupled to the memory for running the program to perform the intelligent quality inspection method described in the first aspect.

[0014] Fifthly, this application provides a computer program product, which includes: a computer program / computer executable instructions, wherein the computer program / computer is executable to perform the intelligent quality inspection method described in the first aspect.

[0015] The intelligent quality inspection method and apparatus provided in this application, when it is determined that quality inspection of a target object is required, acquires financial service data of at least one data modality generated by the target object during the process of providing services to customers. Then, for each quality inspection dimension applicable to the target object, it determines the target data modality that matches the current quality inspection dimension in at least one data modality, assigns a currently applicable target quality inspection model to each target data modality, and calls each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, obtaining the quality inspection result data of the target object corresponding to the current quality inspection dimension. Finally, it summarizes and processes the quality inspection result data of the target object corresponding to each quality inspection dimension to generate the final quality inspection result data of the target object. It can be seen that the solution provided in this embodiment, in this quality inspection process, uses financial service data of multiple data modalities generated during the process of providing services to customers by financial practitioners' accounts, and uses quality inspection models for automated quality inspection. This not only efficiently and objectively replaces traditional manual quality inspection, greatly improving quality inspection efficiency, but also, through in-depth analysis of financial service data of multiple data modalities, achieves a comprehensive and refined evaluation of the service quality of financial practitioners, improving the accuracy of quality inspection.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of an intelligent quality inspection method provided in one embodiment of this application is shown;

[0019] Figure 2 A flowchart of an intelligent quality inspection method provided in another embodiment of this application is shown;

[0020] Figure 3 A schematic diagram of an interactive interface provided in one embodiment of this application is shown;

[0021] Figure 4 This illustration shows a structural schematic diagram of an intelligent quality inspection device according to an embodiment of this application;

[0022] Figure 5A schematic diagram of the structure of an intelligent quality inspection device provided in another embodiment of this application is shown. Detailed Implementation

[0023] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0024] Currently, quality control of financial service data generated during the provision of intelligent services to customers through financial professionals' accounts, and the determination of the service quality of the financial professionals corresponding to those accounts, has become a crucial aspect of risk control and operational management for financial institutions. However, traditional quality control methods rely on manual inspection, which suffers from low accuracy and efficiency due to limitations such as the limited scope of financial service data, high labor costs, and the individual experience and expertise of inspectors. This not only struggles to handle massive amounts of financial service data but also fails to effectively identify violations (such as misleading sales, promises of returns, and leaks of customer privacy), creating potential risks of both misconduct and reputational damage for financial institutions.

[0025] Research has shown that by using an AI-based quality inspection model to inspect financial service data of at least one data modality generated during the process of providing services to customers through financial professionals' accounts, and clarifying the service quality of the financial professionals corresponding to those accounts, the accuracy and efficiency of quality inspection of service data in this field can be fundamentally and intelligently improved.

[0026] Based on the above findings, this embodiment provides an intelligent quality inspection technology solution. Specifically, it includes: acquiring financial service data of at least one data modality generated during the service provided to customers by a target object (e.g., the financial professional account corresponding to customer service, account managers, financial advisors, etc.); for each quality inspection dimension applicable to the target object, determining the target data modality that matches the current quality inspection dimension in at least one data modality, assigning a currently applicable target quality inspection model to each target data modality, and calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, obtaining the quality inspection result data corresponding to the target object in the current quality inspection dimension; summarizing the quality inspection result data corresponding to the target object in each quality inspection dimension to generate the final quality inspection result data of the target object. In this quality inspection process, by using financial service data of multiple data modalities generated during the service provided to customers by financial professional accounts, and utilizing quality inspection models for automated quality inspection, it not only efficiently and objectively replaces traditional manual quality inspection, significantly improving quality inspection efficiency, but also achieves a comprehensive and refined evaluation of the service quality of financial professionals through in-depth analysis of financial service data of multiple data modalities, thereby improving the accuracy of quality inspection.

[0027] Based on the above-mentioned quality inspection technical solution, this embodiment specifically provides an intelligent quality inspection method and device. The intelligent quality inspection method and device provided in this embodiment will be described in detail below.

[0028] This application provides an intelligent quality inspection method, which is applied to a financial quality inspection system to intelligently improve the accuracy and efficiency of quality inspection of service data in this field. This embodiment does not limit the type of financial quality inspection system, which can be flexibly selected based on business needs. For example, the financial quality inspection system can be a local system serving a specific financial institution, or it can be a cloud platform system serving multiple financial institutions.

[0029] like Figure 1 As shown, the intelligent quality inspection method provided in this embodiment may include at least the following steps 101 to 103.

[0030] 101. Obtain financial service data of at least one data modality generated by the target object in the process of providing services to customers.

[0031] The intelligent quality inspection method provided in this embodiment mainly relies on financial service data generated during the process of providing services to customers through financial practitioners' accounts. It performs quality inspection on the intelligent service process provided by financial practitioners to customers through designated service systems or communication tools. Specifically, financial service data such as chat logs or telephone recordings generated during the process of financial practitioners providing services to customer service through designated service systems or communication tools are stored in designated storage devices, and the financial service data is identified by unique identifiers. Thus, when quality inspection of the financial practitioner's service process is required, the financial service data can be extracted and used based on the identifiers. The identifiers can be any of the following: one, the identifiers are generated based on the identifier corresponding to the financial practitioner's account and the start time of the financial practitioner's account providing services to the customer; two, the identifiers are generated based on the identifier corresponding to the financial practitioner's account, the start time of the financial practitioner's account providing services to the customer, and the customer's identifier.

[0032] In some embodiments, considering that in today's financial institutions, natural persons such as customer service representatives, account managers, and financial advisors, as well as robots such as customer service representatives, can all provide services to customers as financial practitioners through designated service systems or communication tools, the target object in this embodiment can be any one of the financial practitioner accounts corresponding to the aforementioned natural persons or the financial practitioner accounts corresponding to the robots.

[0033] In some embodiments, the financial quality inspection system is associated with at least one financial institution to provide quality inspection services to the associated financial institution. Based on this, the method for determining the implementation of the target object can at least include the following methods A1 to A2.

[0034] Method A1, the process of determining the target object may include: upon receiving a quality inspection instruction from a financial institution, identifying the financial practitioner's account specified in the quality inspection instruction as the target object. Under this method, the financial institution issuing the quality inspection instruction is identified as the quality inspection requester, and financial service data of at least one data modality generated by the target object during the process of providing services to customers is collected from the database corresponding to the quality inspection request.

[0035] Method A2 involves the associated financial institution, when a quality inspection requirement arises, sending data to be inspected to the financial quality inspection system as the requesting party, or periodically sending such data as the requesting party. This data includes financial service data of at least one data modality generated during the service provided to customers by the financial professionals' accounts of the requesting party. Based on this, the process of determining the target object can include: identifying all financial professional accounts involved in the data to be inspected as target objects. Under this method, financial service data of at least one data modality generated during the service provided to customers by the target objects is obtained from the data to be inspected.

[0036] The above methods A1 to A2 can be flexibly selected and used based on business needs, and this embodiment does not limit this.

[0037] In some embodiments, financial service data of at least one data modality generated during the process of the target object providing services to customers serves as the basis for quality inspection of the interaction process between the target object and the customer. The method for determining the implementation of the data modality required for the quality inspection of the target object may include at least the following methods B1 and B2.

[0038] Method B1 identifies the target positions of the target objects, each position having at least one corresponding data modality; it then identifies this at least one data modality as the data modality required for quality inspection of the target objects. This method can quickly determine the data modality required for quality inspection of the target objects based on their target positions.

[0039] For example, considering that customer service primarily provides services to customers through online chat tools or telephone, the preset data modalities for customer service include text data modalities and audio data modalities. Therefore, when the target position of the target object is a customer service account, the data modalities required for target object quality inspection are determined to include text data modalities and audio data modalities.

[0040] Method B2, upon receiving a modality specification instruction for the target object, determines the data modality specified in the modality specification instruction as the data modality required for the quality inspection of the target object. This approach allows the quality inspection requester to flexibly select the data modality based on its own needs, thereby improving the flexibility of quality inspection operations.

[0041] The above methods B1 and B2 can be used flexibly based on business needs, and this embodiment does not limit the choice of at least one method.

[0042] In some embodiments, the data modality may include, but is not limited to, at least one of the following: text data modality, audio data modality, video data modality, and marketing material data modality. Text data modality financial service data refers to chat logs generated during the process of a target customer providing services to a client through text interaction functions of tools such as online chat (e.g., WeChat for Business). Audio data modality financial service data refers to voice data generated during the process of a target customer providing services to a client through the audio functions of telephone or online chat tools. Video data modality financial service data refers to video data generated during the process of a target customer providing services to a client through video tools. Marketing material data modality financial service data refers to the content data corresponding to marketing materials sent by a target customer during the process of providing services to a client through email or online chat tools. Marketing materials refer to various promotional materials or marketing plans designed and produced in advance by financial professionals for financial products (such as deposits, loans, credit cards, wealth management, insurance, etc.) or services (such as transfer discounts, VIP benefits, online business handling guidelines, etc.) and suitable for use in communication scenarios with customers (including online chat, offline reception, etc.). These marketing materials are usually presented in the form of documents such as text scripts, graphic cards, short videos, and product manuals. Their core function is to help customer service representatives more clearly and intuitively introduce key information such as product advantages, activity rules, and handling methods to customers, reduce customers' understanding costs, standardize promotional messages, and help promote customers' understanding and selection of financial products or services.

[0043] 102. For each quality inspection dimension applicable to the target object, determine the target data mode that matches the current quality inspection dimension in at least one data mode, assign the currently applicable target quality inspection model to each target data mode, and call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data mode to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0044] After acquiring financial service data of at least one data modality generated by the target entity in providing services to customers, it is necessary to determine the applicable quality inspection dimensions for the target entity, so as to conduct quality inspection on the service process provided by the target entity to customer service based on the financial service data from the evaluation perspective corresponding to the quality inspection dimensions.

[0045] In some embodiments, a quality inspection dimension refers to a measurable and specific evaluation angle set during the quality inspection process to comprehensively assess the interaction between financial professionals and customers. Methods for determining the applicable quality inspection dimensions for the target object may include at least the following methods C1 and C2.

[0046] Method C1, where each type of financial business has corresponding quality inspection dimensions, involves determining the applicable quality inspection dimensions for a target object. This process can include identifying the quality inspection dimensions corresponding to the financial business type to which the target object belongs as the applicable quality inspection dimensions. This not only allows for the rapid identification of applicable quality inspection dimensions for the target object but also ensures that the identified dimensions accurately match the business to which the target object belongs, laying the foundation for efficient and accurate quality inspection in the future.

[0047] To enhance the targeted nature of quality inspection, corresponding quality inspection dimensions are pre-defined for each type of financial business. Financial business types may include, but are not limited to, at least one of the following: banking business, securities business, insurance business, and payment and settlement business. Specifically, banking business types may include, but are not limited to, at least one of the following: personal loan business, personal deposit business, and corporate business. Securities business types may include, but are not limited to, at least one of the following: securities brokerage business, investment banking business, and securities investment consulting business. Insurance business types may include, but are not limited to, at least one of the following: life insurance business, property insurance business, and insurance asset management business. Payment and settlement business types may include, but are not limited to, at least one of the following: third-party payment business and cross-border payment business.

[0048] Pre-define corresponding quality inspection dimensions for each type of financial business to establish a pre-defined correspondence between financial business types and quality inspection dimensions. After determining the financial business type of the target object, based on the pre-defined correspondence, the quality inspection dimensions corresponding to the target object's financial business type are determined as the applicable quality inspection dimensions for the target object.

[0049] Method C2, the process of determining the quality inspection dimension applicable to the target object may include: upon receiving a dimension specification instruction for the target object, determining the quality inspection dimension specified by the dimension specification instruction as the quality inspection dimension applicable to the target object.

[0050] To enable quality inspection operations to flexibly meet the quality inspection needs of requesters, permission to specify dimensions is granted to these requesters. This allows them to flexibly specify the required quality inspection dimensions based on these instructions. Therefore, upon receiving a dimension specification instruction for a target object, the quality inspection dimensions specified in the instruction are determined as the applicable quality inspection dimensions for that target object.

[0051] The above methods C1 and C2 can be used flexibly based on business needs, and this embodiment does not limit the choice of at least one method.

[0052] In some embodiments, the quality inspection dimensions applicable to the identified target objects may include, but are not limited to, at least one of the following: service compliance dimension, service professionalism dimension, customer satisfaction dimension, and customer virtual product acquisition preference dimension. The service compliance dimension refers to the evaluation perspective on whether the service behavior, processes, and content provided by financial practitioners strictly comply with laws and regulations, industry regulatory requirements, internal corporate systems, and service standards, ensuring no violations (such as information leakage, misleading promises, or procedural irregularities). The service professionalism dimension refers to the evaluation perspective on whether financial practitioners possess the professional knowledge (such as product knowledge and business rules) and skills (such as problem-solving abilities and communication skills) required to complete the service, and can use these abilities to accurately and efficiently meet the reasonable needs of customers. The customer satisfaction dimension refers to the evaluation perspective on assessing the degree to which the customer's actual experience matches their expectations throughout the entire service process (such as service response speed, problem-solving effectiveness, and service attitude) through subjective customer feedback, primarily reflecting the customer's recognition and satisfaction level with the services provided by financial practitioners. The customer virtual product acquisition preference dimension refers to the assessment of a customer's preference for acquiring a particular virtual product after receiving services. It represents the likelihood of a user recommending or acquiring a specific virtual product; the higher the likelihood, the higher the value of the customer virtual product acquisition preference dimension; conversely, the lower the likelihood, the lower the value. Virtual products can be business products within a specific field, such as financial products or software. For example, the customer virtual product acquisition preference dimension can be used to indicate the likelihood of consuming, purchasing, or exchanging related products / services, repeat purchases, or recommending others to consume, purchase, or exchange. It reflects the positive impact and conversion potential of financial professionals' accounts on customer consumption, purchase, or exchange decisions.

[0053] In some embodiments, after determining the quality inspection dimensions applicable to the target object, the following steps 102A to 102C are performed for each quality inspection dimension applicable to the target object to perform quality inspection on the target object from each quality inspection dimension applicable to the target object.

[0054] 102A. Determine the target data modality that the current quality inspection dimension matches in at least one of the acquired data modalities.

[0055] Each quality inspection dimension has a matching data modality. Under each quality inspection dimension, quality inspection needs to be performed on financial service data based on the matching data modality. Therefore, it is necessary to perform a step to determine the applicable data modality for each quality inspection dimension. This step can be implemented using any of the following methods.

[0056] One approach is to pre-define the applicable data modal for each quality inspection dimension. Based on this prior knowledge of the applicable data modal for each quality inspection dimension, the target data modal that matches the current quality inspection dimension in at least one data modal obtained in step 101 can be quickly determined.

[0057] For example, the pre-defined data modalities applicable to each quality inspection dimension include: for the service compliance dimension, text data modalities, audio data modalities, video data modalities, and marketing material data modalities; for the service professionalism dimension, text data modalities, audio data modalities, video data modalities, and marketing material data modalities; for the customer satisfaction dimension, text data modalities, audio data modalities, and video data modalities; and for the customer virtual product acquisition preference dimension, text data modalities, audio data modalities, and video data modalities. If the current quality inspection dimension is the service compliance dimension, and at least one data modality obtained in step 101 includes text data modalities, audio data modalities, video data modalities, and marketing material data modalities, then the target data modalities matched by the current quality inspection dimension among the at least one data modalities obtained in step 101 are determined to include text data modalities, audio data modalities, video data modalities, and marketing material data modalities.

[0058] Another approach involves determining the target data modality that matches the current quality inspection dimension among at least one acquired data modality, based on the feature data corresponding to the target object and the current quality inspection dimension. The feature data may include, but is not limited to, at least one of the following: the type of financial business the target object belongs to, and the attribute information of the financial professional's account corresponding to the target object (e.g., account ID, age, education, major). This allows for the inference of frequently problematic data modalities of the target object based on its characteristics and the current quality inspection dimension, which can then be used as the target data modality for more targeted quality inspection. Specifically, the process of determining the target data modality that matches the current quality inspection dimension among at least one acquired data modality may include: inputting the identifier information of the current quality inspection dimension, the feature data, and the modality identifier information corresponding to the at least one acquired data modality into a specified model; the specified model processes the input data and outputs the modality identifier information corresponding to the target data modality matched by the current quality inspection dimension. The specified model is trained using multiple sets of data. Each set of data includes the identification information corresponding to the sample quality inspection dimension, the sample feature data corresponding to the sample object, and the modality identification information corresponding to the sample data modality, as well as the modality identification information corresponding to the sample target data modality that matches the sample object corresponding to the aforementioned data.

[0059] 102B. Assign the currently applicable target quality inspection model to each target data modality.

[0060] Assign a currently applicable target quality inspection model to each target data modality. The currently applicable target quality inspection model is the one that meets the current quality inspection requirements of the target object. The target quality inspection model applicable to the target data modality can accurately match the characteristics of the financial service data of the target data modality, thereby minimizing the occurrence of missed detections and false detections, and thus providing quality inspection assurance that meets the requirements for the financial service data of the target data modality, reducing quality inspection risks.

[0061] In some embodiments, the financial quality inspection system is configured with at least one quality inspection model. Each quality inspection model supports a corresponding data modality, quality inspection dimension, and financial business type. The process of assigning a currently applicable target quality inspection model to each target data modality is as follows: For each target data modality, the following steps 102B1 to 102B2 are executed respectively, so as to accurately assign the currently applicable target quality inspection model to the current target data modality through steps 102B1 to 102B2, thereby avoiding quality inspection failures or inaccuracies caused by quality inspection model adaptation issues as much as possible, and allowing the quality inspection model to perform at its best.

[0062] 102B1. Select the quality inspection model that supports the current target data modality, the current quality inspection dimension, and the financial business type to which the target object belongs as the candidate quality inspection model.

[0063] Only a quality inspection model that supports the current target data modality, the current quality inspection dimension, and the financial business type to which the target object belongs can accurately perform quality inspection from the corresponding evaluation perspective of the current quality inspection dimension, based on the corresponding financial service data of the current target data modality. Therefore, a quality inspection model that supports the current target data modality, the current quality inspection dimension, and the financial business type to which the target object belongs is selected as a candidate quality inspection model.

[0064] 102B2. Based on the current attributes of the candidate quality inspection models, assign the currently applicable target quality inspection model to the current target data modality from the candidate quality inspection models.

[0065] After identifying candidate quality control models, a secondary screening process is required to select the optimal model to be assigned as the target quality control model for the current target data modality. This secondary screening is based on the current attributes of the candidate quality control models. Current attributes may include, but are not limited to, at least one of the following: current load, current performance attributes, and current virtual resources. Current load can be represented by the amount of financial service data already assigned to the quality control model but not yet processed by it. The current performance attributes may include, but are not limited to, at least one of the following: the accuracy of the quality inspection model in processing financial service data in the current data format (the current data format is the format of the financial service data corresponding to the current target data modality; for example, different quality inspection models have different accuracy rates in recognizing data in formats such as mp3 and wav); the response speed of the quality inspection model in the most recent time period (the current time period is the time period from the point when the financial service data of the target object is obtained to the point when it is obtained, with a preset time interval between the start and the end); the quality inspection accuracy of the quality inspection model in the most recent time period (the current time period is the time period from the point when the financial service data of the target object is obtained to the point when it is obtained, with a preset time interval between the start and the end); the timbre parameters adapted by the quality inspection model (for example, timbre parameters such as timbre, speech rate, and sound quality that different quality inspection models are good at processing); and the stability of the quality inspection model in the most recent time period (the current time period is the time period from the point when the financial service data of the target object is obtained to the point when it is obtained, with a preset time interval between the start and the end). The current virtual resource refers to a non-physical data carrier constructed by integrating billing rules and pricing logic in the digital delivery scenario of the quality inspection model service. This non-physical data is used to measure, dynamically represent, and transmit the cost and charging standards for calling the quality inspection model. For example, the current virtual resources of Quality Inspection Model 1 are: 20 yuan for every 1 million text fragments (Tokens) input into Quality Inspection Model 1, and 30 yuan for every 1 million text fragments (Tokens) output by Quality Inspection Model 1.

[0066] The process of assigning a suitable target quality inspection model to the current target data modality from among the candidate quality inspection models, based on the current attributes of the candidate quality inspection models, may include:

[0067] For each candidate quality control model, the following steps are performed: Assign corresponding parameter values ​​to each current attribute of the current candidate quality control model; perform a weighted calculation on the assigned parameter values ​​for each current attribute based on its weight, obtaining the weighted calculation result for the current candidate quality control model. The candidate quality control model with the highest weighted calculation result is then selected as the applicable target quality control model for the current target data modality. Each current attribute has a corresponding evaluation model; inputting each current attribute into its respective evaluation model yields the corresponding parameter values ​​for that current attribute.

[0068] For example, based on the current attributes of the candidate quality inspection model, the following are selected: quality inspection accuracy, response speed, virtual resources, and stability. The weighted score corresponding to the current candidate quality inspection model can then be calculated using the following formula: Weighted Calculation Result = Accuracy Weight × Accuracy Parameter Value + Speed ​​Weight × Speed ​​Parameter Value + Virtual Resource Weight × Virtual Resource Parameter Value + Stability Weight × Stability Parameter Value. In some embodiments, the aforementioned parameter values ​​can all be scores, and when the parameter values ​​are scores, the weighted calculation result is the weighted calculation score.

[0069] In some embodiments, in order to ensure that the quality inspection requester can flexibly assign the currently applicable target quality inspection model to the current target data modality, the intelligent quality inspection method provided in this embodiment may also include the following Scheme 1 and Scheme 2.

[0070] Option 1: Considering that the quality inspection requesting party to which the target object belongs has its own quality inspection model usage requirements, and that the quality inspection requesting party can specify a quality inspection model that meets its quality inspection requirements through model specification instructions, the intelligent quality inspection method provided in this embodiment may further include the following step before step 102B1 above, in order to meet the quality inspection requirements of the quality inspection requesting party as much as possible: detecting whether a model specification instruction for the current target data modality has been received.

[0071] If no model specification instruction for the current target data mode is received, it means that the quality inspection requester has not specified a quality inspection model, so step 102B1 is executed.

[0072] If a model specification instruction for the current target data modality is received, and the quality inspection model specified by the model specification instruction has been configured in the financial quality inspection system, it means that the quality inspection requester has its own required quality inspection model for the current target data modality. Therefore, in order to meet the quality inspection requirements of the quality inspection requester, step 102B1 is no longer executed, and the specified quality inspection model is directly assigned as the target quality inspection model currently applicable to the current target data modality.

[0073] If a model specification instruction is received for the current target data modality, and the quality inspection model specified in the instruction is not configured in the financial quality inspection system, it indicates that the quality inspection requester has its own required quality inspection model for the current target data modality, and the currently configured quality inspection model in the financial quality inspection system does not meet the requester's needs. In this case, to meet the requester's needs, the specified quality inspection model is integrated into the financial quality inspection system as a plug-in through the model extension interface set by the financial quality inspection system. The integrated quality inspection model is then assigned as the target quality inspection model currently applicable to the current target data modality. With this approach, when a new quality inspection model needs to be added, there is no need for large-scale modifications to the core architecture of the financial quality inspection system. Only the specified quality inspection model needs to be integrated into the financial quality inspection system as a plug-in through the model extension interface, thereby reducing development and maintenance costs. Furthermore, plug-in integration ensures that the iteration of the quality inspection model does not interfere with the operation of the financial system, guaranteeing the stability of the quality inspection process.

[0074] Option 2: In some embodiments, at least some data modalities can be configured to have corresponding default quality inspection models in the financial quality inspection system based on virtual resource factors, technical factors, or the usage habits of the quality inspection requester. Based on this, before step 102B1 above, the intelligent quality inspection method provided in this embodiment may further include the following step: determining whether the quality inspection requester to which the target object belongs requests the current target data modality to use the default quality inspection model.

[0075] If the quality inspection requester to which the target object belongs does not request the use of the default quality inspection model for the current target data modality, it means that the quality inspection requester does not have a need to use the default quality inspection model, so step 102B1 is executed.

[0076] If the quality inspection requester to which the target object belongs requests the use of the default quality inspection model for the current target data modality, it means that the quality inspection requester has a requirement for the use of the default quality inspection model for the current target data modality. Therefore, in order to meet the quality inspection requirements of the quality inspection requester, step 102B1 is no longer executed, and the default quality inspection model is directly assigned as the target quality inspection model currently applicable to the current target data modality.

[0077] The above-mentioned Scheme 1 and Scheme 2 can be flexibly selected for use based on business needs, and this embodiment does not limit this choice. When both schemes are selected simultaneously, Scheme 1 can be executed first. If Scheme 1 determines that no model specification instruction for the current target data modality has been received, Scheme 2 can be executed. It should be understood that if there are other schemes that reasonably combine Scheme 1 and Scheme 2, those schemes should also be within the scope of protection of this application.

[0078] 102C. Call each target quality inspection model to perform quality inspection on the financial service data of the corresponding target data modality, and obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0079] There is at least one applicable target quality inspection model for each target data modality. Based on this, the process of calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality and obtaining the quality inspection result data of the target object in the current quality inspection dimension may include the following steps 102C1 to 102C3.

[0080] 102C1. Call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality.

[0081] Before invoking the target quality inspection model, the intelligent quality inspection method provided in this embodiment may further include the following implementation steps: allocating a corresponding thread for each target quality inspection model, so as to perform quality inspection processing on the financial service data of the corresponding target data modality by invoking each target quality inspection model in parallel based on the thread. In this way, the quality inspection efficiency is improved by invoking each target quality inspection model in parallel.

[0082] In some embodiments, if the amount of financial service data is large, the quality inspection speed of the target quality inspection model will inevitably decrease. Therefore, before calling each target quality inspection model in step 102C1 to perform quality inspection processing on the financial service data of the corresponding target data modality, the intelligent quality inspection method provided in this embodiment may further include the following steps: determining whether there is financial service data to be split in the financial service data of the target object, wherein the financial service data to be split contains multiple service records, and the different service records differ in the corresponding service time period and / or the customers served.

[0083] If the target object's financial service data contains data that needs to be broken down, it means that directly providing this data to the target quality inspection model would overwhelm it with the sheer volume of data, making rapid quality inspection impossible. Therefore, based on the corresponding data modality of the data to be broken down, each service record needs to be decomposed into an independent data unit, and then converted into a format that the target quality inspection model can process. This improves quality inspection efficiency when the converted data units are provided to the target quality inspection model.

[0084] Based on the data modality corresponding to the financial service data to be decomposed, the specific implementation methods for decomposing each service record of the financial service data to be decomposed into independent data units can include the following four methods. First, if the data modality corresponding to the financial service data to be decomposed is an audio data modality, and the financial service data to be decomposed includes multiple audio service records, and the different audio service records differ in service time, then each audio service record of the financial service data to be decomposed is decomposed into an independent data unit. Second, if the data modality corresponding to the financial service data to be decomposed is a text data modality, and the financial service data to be decomposed includes multiple chat service records, and the different chat service records differ in service time and the customers served, then each chat service record of the financial service data to be decomposed is decomposed into an independent data unit. For example, if the financial service data to be decomposed is an Excel file, and each row of the Excel file records chat service records between the target object and a customer during a service time, then each chat service record corresponding to each row in each sheet of the Excel file is decomposed into an independent data unit. The data unit can be in Excel format or CSV format; this embodiment does not limit this. Third, if the data modality of the financial service data to be decomposed is video data modality, and the financial service data to be decomposed includes multiple video service records, and the different video service records differ in service time and the customers served, then each video service record of the financial service data to be decomposed will be decomposed into an independent data unit. Fourth, if the data modality of the financial service data to be decomposed is marketing material data modality, and the financial service data to be decomposed includes multiple marketing material service records, then in this case, at least one of the following decomposition operations can be adopted: (1) Determine the marketing material service records belonging to the same financial object in the service data to be decomposed, where the financial object is a financial product or financial service; for each financial object, decompose the current financial object's marketing material service record into an independent data unit; in this way, the data in each data unit belongs to the same financial object. For example, financial product 1 has a PDF format marketing material service record 1 and a short video MP4 format marketing material service record 2 in the service data to be decomposed, and the PDF format marketing material service record 1 and the short video MP4 format marketing material service record 2 will be decomposed into a data unit. By conducting quality inspections using different forms of marketing materials for the same financial product, we can accurately test the target audience's comprehensive understanding of the product information, the adaptability of the wording under different forms of marketing materials, and the consistency of information transmission. (2) Each marketing material service record of the financial service data to be decomposed is decomposed into an independent data unit, and each data unit corresponds to a single marketing material.For example, the service data to be broken down might include service record 1 (PDF format) for marketing materials of financial product 1, and service record 2 (PDF format) for marketing materials of financial product 2. These records can be separated into independent data units. This allows for more precise identification of the target audience's individualized issues regarding their understanding of financial products, content conversion, and communication skills across different forms of marketing materials. This provides a clearer direction for subsequent targeted training and skills enhancement, thereby more effectively driving service quality optimization.

[0085] After the data is split, the data units are converted into a format that can be processed by the target quality inspection model corresponding to the financial service data to be split. This process first involves identifying the data format of the data unit, then matching it with a conversion tool that converts the data format to a format that the target quality inspection model can process. The successfully matched conversion tool is then used to convert the data unit into a format that the target quality inspection model can process.

[0086] For data units corresponding to text data modalities, methods for identifying the data format of the data unit may include: First, if the identified data unit contains HTML tags, then the data unit is determined to be in HTML format; second, if it contains Markdown tags, then the data unit is determined to be in Markdown format; third, if it contains the word "json," or if the data unit can be directly converted to JSON format by a sample conversion tool without errors, then the data unit is determined to be in JSON format; fourth, if the identified data unit does not contain HTML tags, Markdown tags, or the word "json," then the data unit is determined to be in plain text format. After identifying the data format, a conversion tool is matched to convert the data format to a format that the target quality inspection model can process. The conversion tool is then used to convert the data unit to a format that the target quality inspection model corresponding to the current financial service data can process. The conversion tool can be flexibly selected based on business needs, and this embodiment does not limit it.

[0087] For data units corresponding to audio data modalities, methods for identifying the data format of a data unit can include: First, identifying the file extension of the data unit and determining the data format of the data unit based on the file extension. For example, if the file extension of the data unit is .mp3, then the data format of the data unit is determined to be MP3. Second, examining the metadata of the data unit (metadata is the built-in identity information of the audio data and includes key information such as format, encoding, and sampling rate), and determining the data format of the data unit based on the format information recorded in the metadata. For example, if the format information recorded in the metadata is MPEG-1 Audio Layer 3, then the data format of the data unit is determined to be MP3.

[0088] For data units corresponding to video data modalities, methods for identifying the data format of a data unit can include: First, identifying the file extension of the data unit and determining the data format of the data unit based on the file extension. For example, if the file extension of the data unit is .mp4, then the data format of the data unit is determined to be MP4. Second, examining the metadata of the data unit (metadata is the identity information built into the video data and includes key information such as format, encoding, and resolution), and determining the data format of the data unit based on the format information recorded in the metadata. For example, if the format information recorded in the metadata is MPEG-4, then the data format of the data unit is determined to be MP3.

[0089] For each data unit corresponding to a marketing material data modality, regardless of which of the above-mentioned splitting operations (1) and (2) is used, the marketing material service record in the data unit has its own corresponding file extension. Based on this, the method for identifying the data format of the data unit may include: identifying the file extension corresponding to the marketing material service record in the data unit, and determining the data format matching the file extension as the data format of the corresponding marketing material service record in the data unit. For example, if the file extension of marketing material service record 1 in the data unit is .pdf, then the data format of marketing material service record 1 in the data unit is determined to be PDF format. For example, if the file extension of marketing material service record 2 in the data unit is .pptx / .ppt, then the data format of marketing material service record 1 in the data unit is determined to be PPT format.

[0090] In some embodiments, considering the possibility that the target quality inspection model may fail to perform quality inspection, the intelligent quality inspection method provided in this embodiment may further include the following steps to address this situation: During the process of calling each target quality inspection model to perform quality inspection on the financial service data of the corresponding target data modality, if it is detected that the target financial service data has been processed by the corresponding target quality inspection model a target number of times, and the corresponding target quality inspection model has failed to successfully complete the quality inspection on the target financial service data, then a substitute quality inspection model is used to replace the corresponding target quality inspection model to perform quality inspection on the target financial service data. This not only avoids the waste of time and computing power caused by excessive repeated attempts, but also improves the quality inspection success rate by replacing the previous target quality inspection model with a substitute quality inspection model to perform quality inspection on the target financial service data.

[0091] For each target quality inspection model, during the process of calling the target quality inspection model to perform quality inspection on the financial service data of the corresponding target data modality, each time a financial service data is input into the target quality inspection model, the system monitors whether the target quality inspection model outputs quality inspection result data for the financial service data within a preset time period, starting from the time point of input of the financial service data. If output is output, it is determined that the target quality inspection model has successfully completed the quality inspection of the financial service data. If no output is output, it is determined that the target quality inspection model has failed to successfully complete the quality inspection of the financial service data, and the number of times the financial service data has failed to complete the quality inspection is recorded. The financial service data is then re-input into the target quality inspection model until the target number of times is reached or the quality inspection is successfully completed.

[0092] If, after monitoring indicates that the target financial service data has been processed a target number of times by the corresponding target quality inspection model, the model fails to complete the quality inspection, it suggests a potential malfunction that cannot be resolved quickly. Therefore, to avoid impacting the quality inspection progress, an alternative quality inspection model is used to replace the original target model in processing the target financial service data. The alternative quality inspection model undergoes data modality matching with the corresponding target financial service data.

[0093] 102C2. For each target data modality, the first quality inspection result data of the corresponding financial service data of each target quality inspection model currently applicable to the current target data modality is summarized and processed to generate the second quality inspection result data corresponding to the current target data modality.

[0094] After calling each target quality inspection model to perform quality inspection on the financial service data of the corresponding target data modality, the first quality inspection result data of each target quality inspection model for the corresponding financial service data of the current target data modality is obtained. At this time, in order to clarify the quality inspection status of each target data modality, it is necessary to perform a step of summarizing the first quality inspection result data of each target quality inspection model currently applicable to the current target data modality for the corresponding financial service data of the current target data modality, and generating the second quality inspection result data corresponding to the current target data modality. The implementation methods of this step can include the following methods D1 and D2.

[0095] Method D1 determines the first weight corresponding to each target quality inspection model currently applicable to the current target data modality; based on the first weight, the first quality inspection result data of the corresponding financial service data of the current target data modality is weighted for each target quality inspection model currently applicable to the current target data modality to obtain the second quality inspection result data corresponding to the current target data modality. The first quality inspection result data can be expressed by the first quality inspection score, and the second quality inspection result data can be expressed by the second quality inspection score.

[0096] The first weight indicates the confidence level of the quality inspection results of the target quality inspection model under the current target data modality. The first weight is evaluated based on the historical quality inspection performance of each target quality inspection model under the current target data modality and is updated as the quality inspection performance of each target quality inspection model under the current target data modality changes. Different target quality inspection models have varying accuracy in inspecting the same target data modality. The results of a single target quality inspection model may be biased due to limited perspective or subject to noise interference, leading to misjudgments. Therefore, by weighting and integrating the results of each target quality inspection model and mitigating the risk of misjudgments caused by the bias of a single quality inspection model, the final second quality inspection result data is more closely aligned with the actual quality inspection situation, improving the reliability and application value of the quality inspection results.

[0097] Method D2 involves summing the first quality inspection results of the corresponding financial service data for each applicable target quality inspection model in the current target data modality to obtain the second quality inspection results for the current target data modality.

[0098] The above methods D1 and D2 can be used flexibly based on business needs, and this embodiment does not limit the choice of at least one method.

[0099] 102C3. Summarize the second quality inspection result data of each target data modality matched in the current quality inspection dimension to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0100] The method for summarizing and processing the second quality inspection result data of each target data modality matched in the current quality inspection dimension to obtain the quality inspection result data of the target object in the current quality inspection dimension can include the following methods E1 and E2.

[0101] Method E1 determines the second weight corresponding to each target data modality matched by the current quality inspection dimension; based on the second weight, the second quality inspection result data of each target data modality matched by the current quality inspection dimension is weighted to obtain the quality inspection result data of the target object corresponding to the current quality inspection dimension.

[0102] The second weight is used to indicate the importance of the target data modality in the current quality inspection dimension. This second weight is assessed based on the historical quality inspection performance of each target data modality in the current quality inspection dimension and is updated as the quality inspection performance of each target data modality in the current quality inspection dimension changes. Different target data modalities have varying accuracy in the same quality inspection dimension, and the quality inspection results of a single target data modality may be biased due to limited perspective. Therefore, by weighting and integrating the results of each target data modality and mitigating the risk of misjudgment caused by the bias of a single target data modality, the final quality inspection result data corresponding to the current quality inspection dimension is more closely aligned with the actual quality inspection situation.

[0103] Method E2 sums the second quality inspection result data of each target data modality matched in the current quality inspection dimension to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0104] The above methods E1 and E2 can be flexibly selected for use based on business needs, and this embodiment does not limit this.

[0105] 103. Summarize and process the quality inspection results data corresponding to each quality inspection dimension of the target object to generate the final quality inspection result data of the target object.

[0106] Quality inspection results from a single dimension only reflect the quality performance of the target object from a particular evaluation perspective. Viewing these results in isolation can easily lead to a biased judgment of the overall quality. Therefore, it is necessary to aggregate and process the quality inspection results for each dimension to generate the final quality inspection results for the target object. This results in a more comprehensive, systematic, and decision-oriented final quality inspection report, helping financial institutions quickly grasp the overall quality status of the target object.

[0107] The process of summarizing and processing the quality inspection result data corresponding to each quality inspection dimension of the target object to generate the final quality inspection result data of the target object may include: determining the corresponding third weight for each quality inspection dimension, and weighting the quality inspection result data corresponding to each quality inspection dimension of the target object based on the third weight to obtain the final quality inspection result data of the target object.

[0108] The third weight is used to indicate the importance of the quality inspection dimension to the quality inspection target. The third weight is based on the historical quality inspection performance of a large number of objects (whose positions and financial business types are the same as the target object) in the corresponding quality inspection dimension, and it is updated as the quality inspection performance of these objects in the current quality inspection dimension changes. Different quality inspection dimensions may have varying accuracy for the same object, and the results of a single quality inspection dimension may be biased due to limited perspective. Therefore, by weighting and integrating the results of each quality inspection dimension, and mitigating the risk of misjudgment caused by the bias of a single quality inspection dimension, the final quality inspection results for the target object are more closely aligned with the actual quality inspection situation.

[0109] The intelligent quality inspection method provided in this application, when it is determined that quality inspection of a target object is required, acquires financial service data of at least one data modality generated by the target object during the process of providing services to customers. Then, for each quality inspection dimension applicable to the target object, it determines the target data modality that matches the current quality inspection dimension in at least one data modality, assigns a currently applicable target quality inspection model to each target data modality, and calls each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, obtaining the quality inspection result data of the target object corresponding to the current quality inspection dimension. Finally, it summarizes and processes the quality inspection result data of the target object corresponding to each quality inspection dimension to generate the final quality inspection result data of the target object. It can be seen that the solution provided in this embodiment, in this quality inspection process, uses financial service data of multiple data modalities generated during the process of providing services to customers by financial practitioners' accounts, and uses quality inspection models for automated quality inspection. This not only efficiently and objectively replaces traditional manual quality inspection, greatly improving quality inspection efficiency, but also, through in-depth analysis of financial service data of multiple data modalities, achieves a comprehensive and refined evaluation of the service quality of financial practitioners, improving the accuracy of quality inspection.

[0110] In some embodiments of this application, one embodiment also provides an intelligent quality inspection method. This intelligent quality inspection method is applied to a financial quality inspection system, such as... Figure 2 As shown, the intelligent quality inspection method provided in this embodiment may include at least the following steps 201 to 210.

[0111] 201. Obtain financial service data of at least one data modality generated by the target object in the process of providing services to customers, and execute steps 202, 204 and 208.

[0112] 202. For each quality inspection dimension applicable to the target object, determine the target data mode that matches the current quality inspection dimension in at least one data mode, assign the currently applicable target quality inspection model to each target data mode, and call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data mode to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0113] 203. Summarize and process the quality inspection results data corresponding to each quality inspection dimension of the target object to generate the final quality inspection result data of the target object, and continue to execute step 207.

[0114] For a detailed explanation of steps 201 to 203, please refer to steps 101 to 103 above, which will not be repeated here.

[0115] 204. Assign the currently applicable evaluation model for assessing operational stability to each data modality.

[0116] The final quality inspection results of the target entity reflect the quality of its services provided to clients. This quality determines the professionalism and ultimate effectiveness of the services offered to clients, and is the core factor in judging "service quality." Job stability can be assessed from the perspective of "service capability," and it can also assist in quality inspection. Therefore, it is necessary to assign a suitable assessment model for evaluating job stability to each data modality.

[0117] In some embodiments, the financial quality inspection system is further configured with at least one evaluation model, each evaluation model having a corresponding data modality and financial business type. Based on this, for each data modality, the evaluation model corresponding to the current data modality and the financial business type to which the target object belongs is selected as a candidate evaluation model. Based on the current attributes of the candidate evaluation models, an applicable evaluation model is assigned to the current data modality from among the candidate evaluation models. The current attributes may include, but are not limited to, at least one of the following: current load, current performance attributes, and current virtual resources. The specific details of the current attributes are the same as those of the quality inspection models; therefore, please refer to the detailed explanation of the quality inspection models, which will not be repeated here.

[0118] It should be noted that if the quality inspection model configured in the financial quality inspection system is compatible with the work stability assessment function, then the quality inspection model can also be used as an assessment model to maximize the role of model resources.

[0119] 205. For each data modality, extract target data related to job stability from the financial service data of the current data modality, call the applicable evaluation model to evaluate the job stability of the target data, and obtain the job stability evaluation data of the target object in the current data modality.

[0120] In some embodiments, the target data related to operational stability are associated with data modes.

[0121] Given that the current data modality is text-based, the target financial service data records each conversation and the time it appears. Therefore, the target data related to job stability includes at least one of the following: First, the target data includes the communication duration between the target and the customer (e.g., the time of the first and last conversation in the chat log) and the number of times inappropriate content appears. Second, the target data includes the time between the time of the first conversation and the time of the conversation indicating the customer's preference for acquiring virtual products.

[0122] Given that the current data modality is audio data, the target data related to job stability includes at least one of the following: First, the target data includes the communication duration between the target and the customer (i.e., the total duration of the audio data) and the number of times inappropriate content appears. Second, the target data includes the duration between the start time of the audio recording and the point in time when the customer's virtual product acquisition preference appears. The appearance of the customer's virtual product acquisition preference indicates the likelihood that the customer will consume, purchase, or exchange related products / services, repurchase, or recommend others to consume, purchase, or exchange them. A higher preference value indicates a higher likelihood; a lower preference value indicates a lower likelihood.

[0123] Given that the current data modality is video data, the target data related to work stability includes at least one of the following: First, the target data includes the communication duration between the target and the customer (i.e., the total duration of the video data) and the number of times inappropriate content appears. Second, the target data includes the duration between the start time of the video and the time when the customer's virtual product acquisition preference dialogue content or the customer's virtual product acquisition preference action appears.

[0124] Given that the current data modality is the marketing creative data modality, the target data related to job stability includes: the duration of writing marketing creative data.

[0125] In some embodiments, after extracting target data related to job stability from the financial services data of the current data modality, an applicable evaluation model is invoked to evaluate the job stability of the target data, thereby obtaining job stability evaluation data for the target object in the current data modality. The evaluation model is a pre-trained model used to evaluate job stability, which takes the target data of the corresponding data modality as input and outputs job stability evaluation data (e.g., job stability evaluation data represented by job stability evaluation scores).

[0126] For example, if the target data includes a communication duration of 3 minutes between the target and the customer and 0 instances of violations, the corresponding evaluation model would output a job stability assessment score of 1. If the target data includes a communication duration of 30 minutes between the target and the customer and 0 instances of violations, the corresponding evaluation model would output a job stability assessment score of 2. A job stability assessment score of 2 is greater than a job stability assessment score of 1, indicating that the longer the call duration and the fewer the instances of violations, the stronger the job stability.

[0127] 206. Summarize and process the operational stability assessment data of the target object for each data mode to generate the final operational stability assessment data of the target object.

[0128] The methods for summarizing and processing the operational stability assessment data of the target object for each data mode to generate the final operational stability assessment data of the target object can include the following methods F1 and F2.

[0129] Method F1 determines the fourth weight corresponding to each data modality; based on the fourth weight, the job stability assessment data corresponding to each data modality are weighted to obtain the final job stability assessment data of the target object. The final job stability assessment data can be represented by a job stability assessment score.

[0130] The fourth weight is used to indicate the importance of data modalities to job stability assessment. Different data modalities have varying degrees of accuracy in assessing job stability, and the results of job stability assessments based on a single data modality may be biased due to limitations in perspective. Therefore, by weighting and integrating job stability assessment data from various data modalities and mitigating the risk of misjudgment caused by the bias of a single data modality, the final job stability assessment data for the target object can be made to better reflect the actual job stability situation.

[0131] Method F2 sums the operational stability assessment data corresponding to each data modality to obtain the final operational stability assessment data of the target object.

[0132] The above methods F1 and F2 can be flexibly selected for use based on business needs, and this embodiment does not limit this.

[0133] 207. Based on the final operational stability assessment data, correct the final quality inspection results data of the target object and execute 210.

[0134] The final work stability assessment data is the target assessment score, and the final quality inspection result data is the target quality inspection score. In order to make the quality inspection result of the target object more accurate, the final quality inspection result data of the target object is corrected based on the final work stability assessment data. The specific implementation process of this correction may include the following steps: input the target assessment score and the target quality inspection score into the correction formula, and obtain the corrected target quality inspection score through the correction formula.

[0135] The correction formula is used to define the association rules between the target evaluation score and the target quality inspection score, and to calculate the corrected target quality inspection score based on the association rules. The correction formula can be flexibly set according to business needs, and this embodiment does not limit it. For example, the correction formula is: Corrected target quality inspection score = Target evaluation score + Target quality inspection score.

[0136] Based on the final job stability assessment data, the final quality inspection results of the target object are revised. This makes the quality inspection conclusions more closely reflect real-world work scenarios and more accurate, avoiding biases or misjudgments caused by judging from a single quality inspection dimension. Job stability data reflects the target object's consistent performance, risk tolerance, and adaptability over a long period of work. Revision can prevent the overall capability of the target object from being overly negated due to occasional errors. Conversely, if the target object passes a single quality inspection, but the job stability assessment data reveals frequent minor deviations and large fluctuations in compliance, revision can identify potential risks in advance. In this way, the revised final quality inspection results not only assess the target object's current performance but also its long-term capabilities.

[0137] It should be noted that if the party requesting quality inspection needs to use the final operational stability assessment data of the target object separately, the final operational stability assessment data can also be fed back to the party requesting quality inspection for their use.

[0138] 208. Assign at least one applicable target analysis model to each data modality.

[0139] In the process of providing services to customers, financial professionals may encounter customers expressing new preferences for virtual products. Therefore, to accurately capture customer needs, improve marketing conversion efficiency, and enhance customer loyalty and service experience, targeted analysis of financial service data can be conducted. Targeted analysis may include, but is not limited to, at least one of the following: marketing lead analysis and customer virtual product acquisition preference analysis. Marketing lead analysis refers to the process of screening, classifying, and evaluating financial service data to determine its match with bank products / services, conversion potential, and ultimately forming actionable business opportunities. For example, if a customer mentions "planning to change houses soon" in the financial service data, a marketing lead related to mortgage business can be obtained. Based on the analyzed marketing leads, targeted financial services can be provided to customers to avoid lead loss or missed marketing opportunities. Customer virtual product acquisition preference analysis refers to interpreting the needs, questions, or preferences actively expressed by customers in the financial service data to determine the strength of their virtual product acquisition preference and core concerns regarding specific bank products / services. For example, if a customer mentions "flexible and stable wealth management" in the financial service data, a customer's virtual product acquisition preference for wealth management products can be obtained.

[0140] Therefore, to avoid capturing marketing leads and customer virtual product acquisition preferences, it is necessary to assign at least one applicable analytical model to each data modality for the target analysis. The analytical model is a pre-trained model used to perform the corresponding target analysis, taking financial service data of the corresponding data modality as input and the target analysis results as output.

[0141] 209. For each data modality, call each analysis model applicable to the current data modality to perform target analysis on the corresponding financial service data of the current data modality, and obtain the target analysis result data of the target object in the current data modality.

[0142] Each analysis model is assigned a corresponding thread to perform target analysis on the corresponding financial service data of the current data model by calling each analysis model applicable to the current data model in parallel based on the thread, thereby improving analysis efficiency.

[0143] The target analysis results of marketing lead analysis mainly include lead source, matching bank products, conversion probability, priority, and initial follow-up direction, used to identify effective leads that require focused follow-up. The target analysis results of customer virtual product acquisition preference analysis mainly include the types of products customers want to buy, the strength of their virtual product acquisition preference, key concerns, and decision-making time, providing support for precise product promotion and communication strategy development.

[0144] 210. Feed back the target analysis results data and the final quality inspection results data to the quality inspection initiator to which the target object belongs.

[0145] The target analysis results serve as the basis for financial institutions' work. Based on this, the target analysis results, along with the final quality inspection results, are fed back to the quality inspection initiator to which the target entity belongs. This allows financial institutions to control the work quality of the target entity and specify the subsequent work to be carried out in a targeted manner.

[0146] It should be noted that steps 202, 204, and 208 can be executed in parallel to improve efficiency.

[0147] In some embodiments of this application, in order to enable the quality inspection requester to intuitively view the quality inspection results, when the quality inspection dimensions applicable to the target object include the service compliance dimension, the target quality inspection model under the service compliance dimension is used to indicate the non-compliant content in the financial service data. In this embodiment, the intelligent quality inspection method may further include the following steps: for the financial service data with non-compliant content indicated by the target quality inspection model, the non-compliant content in the current financial service data is highlighted using a display method corresponding to the data modality of the current financial service data, so that the personnel of the quality inspection requester can clearly and intuitively locate which non-compliant content has occurred in the target object.

[0148] For example, such as Figure 3 As shown, Figure 3 An interactive interface is shown. After quality inspection of the financial service data of the target object's audio modality, the target quality inspection model indicates that there is illegal content "basically following this curve online." To clarify this illegal content, through... Figure 3 The interactive interface shown displays financial service data in the audio modality (i.e. Figure 3 The system uses 11 minutes and 26 seconds of audio data to illustrate violations. To clearly identify violations, the financial services data is converted into conversational text and displayed using a downslope (or, in some embodiments, highlighted) to emphasize that the violations "basically follow this curve upwards." To clarify the reasons for the violations, the reasons and recommended responses are displayed below the violations. Additionally, the left side of the interface lists other violations from the financial services data; selecting these violations will display them on the right side of the interface.

[0149] In some embodiments of this application, each quality inspection model has a corresponding data modality, quality inspection dimension, and financial business type. Each quality inspection model performs quality inspection on the financial service data of the corresponding data modality generated by financial practitioners providing services to customers during the process of providing services to customers in the corresponding financial business type, under the corresponding quality inspection dimension. It takes financial service data as input and corresponding quality inspection result data as output.

[0150] The quality inspection model inspects financial service data according to relevant rules. For example, the rules for the quality inspection model corresponding to the service compliance dimension may include, but are not limited to, at least one of the following: First, deducting 10 points for phrases such as "XX Securities is mediocre," "XX Bank was fined," "Those in the know don't use XX platform," or "garbage platform." Second, deducting 10 points for phrases such as "guaranteed principal and interest," "guaranteed profit," "you can come to me if you lose money," "guaranteed return rate of XX%," "safe," "guaranteed," "promise," "insurance," "secure," "high return," "risk-free," or "enjoy wealth growth." Third, deducting 3 points for phrases such as "stable performance," "excellent," "top-ranked," "ranked among the best (without evidence)," "unique," "first," "most," "exclusive," "unique," "unprecedented," "certain," "absolutely," "buying is guaranteed profit," or "strongest functionality." Fourth, deducting 6 points for phrases such as "buy now," "sell immediately," or "sell at 10 yuan."

[0151] The corresponding rules for other quality inspection dimensions can be flexibly set based on business needs and industry standards, and will not be elaborated on in this embodiment.

[0152] Furthermore, one embodiment of this application also provides an intelligent quality inspection device, which is applied to a financial quality inspection system, such as... Figure 4 As shown, the intelligent quality inspection device provided in this embodiment may include at least:

[0153] The acquisition module 31 is used to acquire financial service data of at least one data modality generated by the target object in the process of providing services to customers;

[0154] The quality inspection module 32 is used to determine the target data mode that the current quality inspection dimension matches in the at least one data mode for each quality inspection dimension applicable to the target object, assign the currently applicable target quality inspection model to each target data mode, and call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data mode to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0155] The processing module 33 is used to summarize and process the quality inspection result data of the target object in each quality inspection dimension, and generate the final quality inspection result data of the target object.

[0156] The intelligent quality inspection device provided in this application, when it is determined that quality inspection of a target object is required, acquires financial service data of at least one data modality generated by the target object during the process of providing services to customers. Then, for each quality inspection dimension applicable to the target object, it determines the target data modality that matches the current quality inspection dimension in at least one data modality, assigns a currently applicable target quality inspection model to each target data modality, and calls each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, obtaining the quality inspection result data of the target object corresponding to the current quality inspection dimension. Finally, it summarizes and processes the quality inspection result data of the target object corresponding to each quality inspection dimension to generate the final quality inspection result data of the target object. It can be seen that the solution provided in this embodiment, in this quality inspection process, uses financial service data of multiple data modalities generated during the process of providing services to customers by financial practitioners' accounts, and uses quality inspection models for automated quality inspection. This not only efficiently and objectively replaces traditional manual quality inspection, greatly improving quality inspection efficiency, but also, through in-depth analysis of financial service data of multiple data modalities, achieves a comprehensive and refined evaluation of the service quality of financial practitioners, improving the accuracy of quality inspection.

[0157] In some embodiments of this application, such as Figure 5 As shown, the financial quality inspection system is configured with at least one quality inspection model. Each quality inspection model supports the corresponding data modality, quality inspection dimension and financial business type. The quality inspection module 32 may include a first allocation unit 321 to allocate the currently applicable target quality inspection model to each target data modality through the first allocation unit 321.

[0158] The first allocation unit 321 is used to, for each target data modality, select a quality inspection model that supports the current target data modality, the current quality inspection dimension, and the financial business type to which the target object belongs as a candidate quality inspection model, and allocate a currently applicable target quality inspection model from the candidate quality inspection models based on the current attributes of the candidate quality inspection models; the current attributes include at least one of the following: current load, current performance attributes, and current virtual resources.

[0159] In some embodiments of this application, such as Figure 5As shown, the quality inspection module 32 may further include: a second allocation unit 322, configured to, if a model specification instruction for the current target data modality is received, and the quality inspection model specified by the model specification instruction is already configured in the financial quality inspection system, allocate the specified quality inspection model as the target quality inspection model currently applicable to the current target data modality; if a model specification instruction for the current target data modality is received, and the quality inspection model specified by the model specification instruction is not configured in the financial quality inspection system, integrate the specified quality inspection model into the financial quality inspection system in the form of a plug-in through the model extension interface set by the financial quality inspection system, and allocate the integrated quality inspection model as the target quality inspection model currently applicable to the current target data modality.

[0160] In some embodiments of this application, such as Figure 5 As shown, at least some data modalities have corresponding default quality inspection models in the financial quality inspection system. In this case, the quality inspection module 32 may further include a third allocation unit 323, which is used to allocate the default quality inspection model as the target quality inspection model currently applicable to the current target data modal if the quality inspection requester to which the target object belongs requests the current target data modal to use the default quality inspection model.

[0161] In some embodiments of this application, such as Figure 5 As shown, if there is at least one target quality inspection model applicable to each target data modality, then the quality inspection module 32 may include a calling unit 324, a first processing unit 325, and a second processing unit 326, so as to call each target quality inspection model through the calling unit 324, the first processing unit 325, and the second processing unit 326 to perform quality inspection processing on the financial service data of the corresponding target data modality, and obtain the quality inspection result data corresponding to the target object in the current quality inspection dimension.

[0162] Calling unit 324 is used to call each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality;

[0163] The first processing unit 325 is used to summarize and process the first quality inspection result data of the corresponding financial service data of the current target data mode for each target data mode currently applicable to the current target data mode, and generate the second quality inspection result data corresponding to the current target data mode.

[0164] The second processing unit 326 is used to summarize the second quality inspection result data of each target data modality matched in the current quality inspection dimension to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0165] In some embodiments of this application, such as Figure 5As shown, the first processing unit 325 is specifically used to determine the first weight corresponding to each target quality inspection model currently applicable to the current target data mode. The first weight is used to indicate the confidence level of the quality inspection result data of the target quality inspection model under the current target data mode. Based on the first weight, the first quality inspection result data of the corresponding financial service data of the current target data mode is weighted by each target quality inspection model currently applicable to the current target data mode to obtain the second quality inspection result data corresponding to the current target data mode.

[0166] In some embodiments of this application, such as Figure 5 As shown, the second processing unit 326 is specifically used to determine the second weight corresponding to each target data modality matched in the current quality inspection dimension. The second weight is used to indicate the importance of the target data modality in the current quality inspection dimension. Based on the second weight, the second quality inspection result data of each target data modality matched in the current quality inspection dimension is weighted to obtain the quality inspection result data of the target object in the current quality inspection dimension.

[0167] In some embodiments of this application, such as Figure 5 As shown, the intelligent quality inspection device provided in this embodiment may further include:

[0168] The evaluation module 34 is used to assign a currently applicable evaluation model for evaluating job stability to each data modality; for each data modality, it extracts target data related to job stability from the financial service data of the current data modality, calls the applicable evaluation model to evaluate the job stability of the target data, and obtains the job stability evaluation data of the target object in the current data modality; it summarizes and processes the job stability evaluation data of the target object in each data modality to generate the final job stability evaluation data of the target object.

[0169] The correction module 35 is used to correct the final quality inspection result data of the target object based on the final operational stability assessment data.

[0170] In some embodiments of this application, such as Figure 5 As shown, the final work stability assessment data is the target assessment score, and the final quality inspection result data is the target quality inspection score. Then, the correction module 35 is specifically used to input the target assessment score and the target quality inspection score into the correction formula, and obtain the corrected target quality inspection score through the correction formula. The correction formula is used to limit the association rules of the target assessment score and the target quality inspection score, and calculate the corrected target quality inspection score according to the association rules.

[0171] In some embodiments of this application, such as Figure 5 As shown, the intelligent quality inspection device provided in this embodiment may further include:

[0172] Analysis module 36 is used to assign at least one applicable analysis model to each data modality, wherein the target analysis includes at least one of the following: marketing lead analysis and customer virtual product acquisition preference analysis; for each data modality, each analysis model applicable to the current data modality is called to perform target analysis on the corresponding financial service data of the current data modality, and the target analysis result data of the target object in the current data modality is obtained;

[0173] The sending module 37 is used to send the target analysis result data and the final quality inspection result data back to the quality inspection initiator to which the target object belongs.

[0174] In some embodiments of this application, such as Figure 5 As shown, the processing module 33 is specifically used to determine the corresponding third weight for each quality inspection dimension, and to perform weighted processing on the quality inspection result data of the target object corresponding to each quality inspection dimension based on the third weight to obtain the final quality inspection result data of the target object; the third weight is used to indicate the importance of the quality inspection dimension to the quality inspection target object.

[0175] In some embodiments of this application, such as Figure 5 As shown, each type of financial business has a corresponding quality inspection dimension. Therefore, the intelligent quality inspection device provided in this embodiment may further include: a determination module 38, used to determine the quality inspection dimension corresponding to the financial business type to which the target object belongs as the quality inspection dimension applicable to the target object.

[0176] In some embodiments of this application, such as Figure 5 As shown, the intelligent quality inspection device provided in this embodiment may further include: an allocation module 39, used to allocate a corresponding thread to each target quality inspection model, so as to perform quality inspection processing on the financial service data of the corresponding target data mode by calling each target quality inspection model in parallel based on the thread.

[0177] In some embodiments of this application, such as Figure 5 As shown, the quality inspection module 32 is also used to, during the process of calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, if it is detected that the target financial service data has been processed by the corresponding target quality inspection model a target number of times and the corresponding target quality inspection model has failed to successfully complete the quality inspection processing for the target financial service data, then a substitute quality inspection model is used to take over the quality inspection processing of the target financial service data, and the substitute quality inspection model is matched with the corresponding data modality of the target financial service data.

[0178] In some embodiments of this application, such as Figure 5As shown, the quality inspection module 32 is further configured to, before calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, determine that there is financial service data to be decomposed in the financial service data of the target object, wherein the financial service data to be decomposed contains multiple service records, and the different service records differ in the corresponding service time period and / or the customers served, then based on the corresponding data modality of the financial service data to be decomposed, decompose each service record of the financial service data to be decomposed into an independent data unit; and convert the data unit into a format that can be processed by the target quality inspection model corresponding to the financial service data to be decomposed.

[0179] In some embodiments of this application, the data modalities mentioned in these embodiments may include, but are not limited to, at least one of the following: text data modalities, audio data modalities, video data modalities, and marketing material data modalities.

[0180] In some embodiments of this application, the quality inspection dimensions applicable to the target object mentioned in this embodiment may include, but are not limited to, at least one of the following: service compliance dimension, service professionalism dimension, customer satisfaction dimension, and customer virtual product acquisition preference dimension.

[0181] In some embodiments of this application, such as Figure 5 As shown, when the quality inspection dimensions applicable to the target object include the service compliance dimension, the target quality inspection model under the service compliance dimension is used to indicate the non-compliant content in the financial service data. Therefore, the intelligent quality inspection device provided in this embodiment may further include: a display module 40, which is used to highlight the non-compliant content in the current financial service data indicated by the target quality inspection model using a display method corresponding to the data modality of the current financial service data.

[0182] For a detailed explanation of the operation of each functional module in the intelligent quality inspection device provided in this application embodiment, please refer to the corresponding detailed explanation of the intelligent quality inspection method embodiment above, which will not be repeated here.

[0183] Furthermore, one embodiment of this application also provides a computer-readable storage medium, the storage medium including a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the above-described intelligent quality inspection method.

[0184] Furthermore, one embodiment of this application also provides an electronic device, the electronic device comprising: a memory for storing a program; and a processor coupled to the memory for running the program to perform the above-described intelligent quality inspection method.

[0185] Furthermore, one embodiment of this application also provides a computer program product, the computer program product comprising: a computer program / computer executable instructions, the computer program / computer executable to perform the above-described intelligent quality inspection method.

[0186] In the above embodiments, the account, financial service data, attribute data and other data involved in each embodiment are all authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0187] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0188] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0190] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of this application.

[0191] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0192] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0193] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data cutover device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data cutover device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0194] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data cutover device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data cutover device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0196] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0197] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0198] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0199] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0200] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An intelligent quality inspection method, characterized in that, The method is applied to a financial quality inspection system, and comprises the following steps: acquiring financial service data of at least one data modality generated in a service process of a target object for a customer; for each quality inspection dimension applicable to the target object, determining a target data modality matched by a current quality inspection dimension in the at least one data modality, assigning a target quality inspection model currently applicable to each target data modality, and calling each target quality inspection model to perform quality inspection processing on financial service data of the corresponding target data modality, to obtain quality inspection result data corresponding to the current quality inspection dimension of the target object; performing summary processing on the quality inspection result data corresponding to each quality inspection dimension of the target object, to generate final quality inspection result data of the target object.

2. The method of claim 1, wherein, The financial quality inspection system is configured with at least one quality inspection model, each quality inspection model supports a corresponding data modality, quality inspection dimension and financial business type, and therefore, assigning a target quality inspection model currently applicable to each target data modality comprises the following steps: for each target data modality, taking a quality inspection model supporting a current target data modality, a current quality inspection dimension and a financial business type to which the target object belongs as a candidate quality inspection model, assigning a target quality inspection model currently applicable to the current target data modality from the candidate quality inspection model based on a current attribute of the candidate quality inspection model; the current attribute comprises at least one of the following: current load, current performance attribute and current virtual resource.

3. The method of claim 2, wherein, The method further comprises the following steps: if a model specifying instruction for the current target data modality is received, and a quality inspection model specified by the model specifying instruction is configured in the financial quality inspection system, assigning the specified quality inspection model as the target quality inspection model currently applicable to the current target data modality; if a model specifying instruction for the current target data modality is received, and a quality inspection model specified by the model specifying instruction is not configured in the financial quality inspection system, integrating the specified quality inspection model into the financial quality inspection system in the form of a plug-in through a model extension interface set by the financial quality inspection system, and assigning the integrated quality inspection model as the target quality inspection model currently applicable to the current target data modality; and / or, at least part of the data modalities has a corresponding default quality inspection model in the financial quality inspection system, and therefore, the method further comprises the following step: if it is determined that a quality inspection request party to which the target object belongs requests the current target data modality to use a default quality inspection model, assigning the default quality inspection model as the target quality inspection model currently applicable to the current target data modality.

4. The method of claim 1, wherein, The target quality inspection model currently applicable to each target data modality is at least one, and therefore, calling each target quality inspection model to perform quality inspection processing on financial service data of the corresponding target data modality, to obtain quality inspection result data corresponding to the current quality inspection dimension of the target object, comprises the following steps: calling each target quality inspection model to perform quality inspection processing on financial service data of the corresponding target data modality; for each target data modality, performing summary processing on first quality inspection result data of the corresponding financial service data of the current target data modality by each target quality inspection model currently applicable to the current target data modality, to generate second quality inspection result data corresponding to the current target data modality; The second quality inspection result data of each target data modality matched with the current quality inspection dimension is summarized to obtain the quality inspection result data corresponding to the current quality inspection dimension of the target object.

5. The method of claim 4, wherein, The first quality inspection result data of the financial service data corresponding to the current target data modality is summarized by each target quality inspection model currently applicable to the current target data modality to generate the second quality inspection result data corresponding to the current target data modality, including: determining the first weight corresponding to each target quality inspection model currently applicable to the current target data modality, the first weight being used to indicate the confidence of the quality inspection result data of the target quality inspection model under the current target data modality; and performing weighted processing on the first quality inspection result data of the financial service data corresponding to the current target data modality by each target quality inspection model currently applicable to the current target data modality based on the first weight to obtain the second quality inspection result data corresponding to the current target data modality. And / or, The second quality inspection result data of each target data modality matched with the current quality inspection dimension is summarized to obtain the quality inspection result data corresponding to the current quality inspection dimension of the target object, including: determining the second weight corresponding to each target data modality matched with the current quality inspection dimension, the second weight being used to indicate the quality inspection importance of the target data modality under the current quality inspection dimension; and performing weighted processing on the second quality inspection result data of each target data modality matched with the current quality inspection dimension based on the second weight to obtain the quality inspection result data corresponding to the current quality inspection dimension of the target object.

6. The method of claim 1, wherein, The method further includes: allocating an evaluation model currently applicable to each data modality for evaluating work stability; extracting target data related to work stability from the financial service data of the current data modality for each data modality, calling the applicable evaluation model to evaluate the work stability of the target data, and obtaining the work stability evaluation data corresponding to the current data modality of the target object; performing summary processing on the work stability evaluation data corresponding to each data modality of the target object to generate the final work stability evaluation data of the target object. Based on the final work stability evaluation data, the final quality inspection result data of the target object is corrected.

7. The method of claim 6, wherein, When the final work stability evaluation data is a target evaluation score and the final quality inspection result data is a target quality inspection score, based on the final work stability evaluation data, the final quality inspection result data of the target object is corrected, including: inputting the target evaluation score and the target quality inspection score into a correction formula to obtain a corrected target quality inspection score through the correction formula, the correction formula being used to define the correlation rule of the target evaluation score and the target quality inspection score, and the corrected target quality inspection score being calculated according to the correlation rule.

8. The method of claim 1, wherein, The method further includes: allocating an analysis model corresponding to at least one target analysis to each data modality, the target analysis including at least one of the following: marketing clue analysis, customer virtual product acquisition preference analysis. For each data modality, call each analysis model applicable to the current data modality to perform target analysis on the financial service data corresponding to the current data modality, and obtain target analysis result data of the target object corresponding to the current data modality; The target analysis result data and the final quality inspection result data are fed back to the quality inspection initiator to which the target object belongs.

9. The method according to any one of claims 1-8, characterized in that, The quality inspection result data of the target object corresponding to each quality inspection dimension is summarized to generate the final quality inspection result data of the target object, including: determining a third weight corresponding to each quality inspection dimension, and performing weighted processing on the quality inspection result data of the target object corresponding to each quality inspection dimension based on the third weight to obtain the final quality inspection result data of the target object; the third weight is used to indicate the importance of the quality inspection dimension to the quality inspection target object; And / or, Each financial business type has a corresponding quality inspection dimension, and the method further includes: determining the quality inspection dimension corresponding to the financial business type to which the target object belongs as the quality inspection dimension applicable to the target object; And / or, The method further includes: assigning a corresponding thread to each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality based on the thread in parallel; And / or, The method further includes: in the process of calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, if it is monitored that the target financial service data is processed by the corresponding target quality inspection model for a target number of times, and the corresponding target quality inspection model fails to successfully complete the quality inspection processing on the target financial service data, then a replacement quality inspection model is used to replace the corresponding target quality inspection model to perform quality inspection processing on the target financial service data, and the replacement quality inspection model matches the data modality corresponding to the target financial service data; And / or, Before calling each target quality inspection model to perform quality inspection processing on the financial service data of the corresponding target data modality, the method further includes: if it is determined that there is to-be-decomposed financial service data in the financial service data of the target object, the to-be-decomposed financial service data contains multiple service records, and different service records differ in corresponding service time periods and / or served customers, then each service record of the to-be-decomposed financial service data is decomposed into an independent data unit based on the data modality corresponding to the to-be-decomposed financial service data; and the data unit is converted into a format that can be processed by the target quality inspection model corresponding to the to-be-decomposed financial service data; And / or, The data modality includes at least one of the following: text data modality, audio data modality, video data modality, and marketing material data modality; And / or, The quality inspection dimension applicable to the target object includes at least one of the following: service compliance dimension, service professionalism dimension, customer satisfaction dimension, and customer virtual product acquisition preference dimension; And / or, In the case that the service compliance dimension is included in the quality inspection dimensions applicable to the target object, the target quality inspection model under the service compliance dimension is used to prompt the illegal content in the financial service data, and the method further includes: for the financial service data with illegal content prompted by the target quality inspection model, the illegal content in the current financial service data is highlighted in a display mode corresponding to the data modality of the current financial service data.

10. An intelligent quality inspection device, characterized in that, The device is applied to a financial quality inspection system, and the device comprises: An acquisition module is configured to acquire financial service data in at least one data modality generated in a service process of a target object for a client. A quality inspection module is configured to determine, for each quality inspection dimension applicable to the target object, a target data modality matched by a current quality inspection dimension in the at least one data modality, assign a target quality inspection model currently applicable to each target data modality, and call each target quality inspection model to perform quality inspection processing on financial service data of the corresponding target data modality, to obtain quality inspection result data corresponding to the current quality inspection dimension of the target object. A processing module is configured to perform summary processing on the quality inspection result data corresponding to each quality inspection dimension of the target object, to generate final quality inspection result data of the target object.

11. A computer readable storage medium, characterized in that, The storage medium includes a stored program, wherein the program controls a device in which the storage medium is located to perform the intelligent quality inspection method of any one of claims 1 to 9 when the program is running.

12. An electronic device, comprising: The electronic device includes a memory for storing a program and a processor coupled to the memory for running the program to perform the intelligent quality inspection method of any one of claims 1 to 9.

13. A computer program product, characterised in that, The computer program product includes computer program / computer executable instructions for performing the intelligent quality inspection method of any one of claims 1 to 9.

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