Business processing method, device, equipment, medium and product

Abstract

The invention discloses a service processing method, device and equipment, a medium and a product. The method comprises the steps of determining a request text in response to a service request sent by a target user, and determining a request type corresponding to the request text by adopting a preset semantic analysis strategy; determining service data associated with the request text, and determining a feedback strategy for the service request based on a preset large language model according to the service data and the request type; and according to the feedback strategy, displaying the query content to the target user or interacting with the user to realize automatic optimization so as to respond to the service request. According to the technical scheme, the service data can be comprehensively analyzed in response to the request of the user, so that automatic content display and optimization operation are realized, the service processing efficiency is improved, and the service handling experience of the user is improved.

Description

Technical Field

[0001] This invention relates to the fields of big data and artificial intelligence, and in particular to a business processing method, apparatus, equipment, medium, and product. Background Technology

[0002] Existing cloud-based financial operations and maintenance solutions primarily rely on rule engines and predefined policies for cost monitoring and optimization suggestions. These tools typically provide data dashboards, cost reports, and basic alerting functions. However, they are essentially static and mechanical analyses of historical data, lacking an understanding of business semantics and failing to directly correlate cost fluctuations with specific business activities (such as marketing promotions and user growth), thereby reducing the efficiency of business processing.

[0003] Therefore, how to respond to user requests and conduct comprehensive analysis of business data to achieve automated content display and optimization operations, thereby improving business processing efficiency, is an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a business processing method, apparatus, device, medium, and product to comprehensively analyze business data in response to user requests, achieve automated content display and optimized operation, improve business processing efficiency, and enhance the user's business handling experience.

[0005] According to one aspect of the present invention, a business processing method is provided, comprising:

[0006] In response to a business request from a target user, determine the request text and use a pre-defined semantic analysis strategy to determine the request type corresponding to the request text;

[0007] Determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request based on a pre-defined large language model;

[0008] Based on the feedback strategy, the system automatically optimizes the display of query content to target users or the interaction with users to respond to business requests.

[0009] According to another aspect of the present invention, a business processing apparatus is provided, comprising:

[0010] The type determination module is used to respond to business requests issued by target users, determine the request text, and use a preset semantic analysis strategy to determine the request type corresponding to the request text;

[0011] The strategy determination module is used to determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request according to a preset large language model.

[0012] The response module is used to display query content to the target user or interact with the user to achieve automated optimization in response to business requests, based on the feedback strategy.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the business processing method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the business processing method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program, which, when executed by a processor, implements the business processing method of any embodiment of the present invention.

[0019] The technical solution of this invention, in response to a business request issued by a target user, determines the request text and, using a preset semantic analysis strategy, determines the request type corresponding to the request text; determines the business data associated with the request text, and, based on the business data and request type, determines a feedback strategy for the business request based on a preset large language model; and, according to the feedback strategy, displays query content to the target user or interacts with the user to achieve automated optimization in response to the business request. By comprehensively analyzing business data in response to user requests, automated content display and optimization operations can be achieved, improving business processing efficiency and enhancing the user's business handling experience.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a business processing method provided in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a business processing method provided in an embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a business processing device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the invention described herein can be practiced in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant laws and regulations.

[0028] It should be noted that the user information collected in this invention is information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, and necessary confidentiality measures have been taken. This process does not violate public order and good morals, and corresponding operation entry points are provided for users to choose to authorize or reject automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making process. In other words, the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of this data comply with the relevant laws, regulations, and standards of the relevant regions.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a business processing method provided in an embodiment of the present invention. This embodiment is applicable to situations where a business system analyzes business data based on the type of user's business request to automatically display query content to the target user or achieve automated optimization through user interaction. This method can be executed by a business processing device, which can be implemented in hardware and / or software and can be configured in an electronic device, such as... Figure 1 As shown, the business processing method includes:

[0031] S101. In response to a business request issued by the target user, determine the request text and use a preset semantic analysis strategy to determine the request type corresponding to the request text.

[0032] Among them, business request refers to query request or optimization request issued by target user, request text refers to natural speech text issued by target user, semantic analysis strategy refers to strategy for analyzing the semantics of text, and request type is query request or optimization request.

[0033] For example, a target user can issue voice commands to the business system, such as "Why did our S3 costs increase by 15% last quarter?" or "Please find the 10 services with the lowest value density." When the business system detects a voice command, it considers it a business request from the target user and can perform speech recognition processing to obtain the corresponding request text. Specifically, the request type for "Why did our S3 costs increase by 15% last quarter? Please provide optimization suggestions?" is an optimization request, while the request type for "Please find the 10 services with the lowest value density" is a query request.

[0034] Optionally, a preset semantic analysis strategy can be adopted to match common keywords of different request types in the request text and determine the request type corresponding to the request text based on the matching results. Alternatively, natural language processing technology can be used to parse the request text to determine the request type corresponding to the request text.

[0035] S102. Determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request according to the preset large language model.

[0036] The business data may include at least one of the following: cloud billing and usage data, resource tags and metadata, business metric data, and operation and maintenance change record data. Cloud billing and usage data includes basic data on cost and resource usage. Resource tags and metadata include business context data of resources (e.g., project, environment, and responsible person). Business metric data includes production data such as order volume, active users, and transaction amount obtained directly from the business database or data warehouse. Operation and maintenance change record data includes the latest code deployment and configuration change information obtained from the distributed version control system. The feedback strategy is to display query content or interact with users to achieve automated optimization.

[0037] It's worth noting that by employing a large language model as the interface for natural language understanding, the barrier to entry is significantly lowered, enabling financial personnel without a technical background to directly interact with the system and gain the necessary insights. By leveraging the natural language processing capabilities, multimodal data association, and logical reasoning abilities of the large language model, patterns and causal relationships can be found in heterogeneous data to generate query content or display content.

[0038] Optionally, based on business data and request type, and using a pre-defined large language model, a feedback strategy for the business request is determined, including: if the request type is a query request, the corresponding query content is determined, and the feedback strategy for the business request is to display the query content; if the request type is an optimization request, the target user is interacted with to determine the target optimization strategy corresponding to the business request, and the feedback strategy for the business request is to achieve automated optimization through user interaction.

[0039] Optionally, for query requests, a pre-defined large language model can be used to analyze and determine the corresponding query content, while for optimization requests, a pre-defined large language model can be used to initially analyze the corresponding display content, and then interact with the target user to achieve automated optimization.

[0040] Optionally, if the request type is a query request, the query content corresponding to the business request is determined, including: if the request type is a query request, the business data is analyzed based on a preset large language model to determine the query content corresponding to the business request.

[0041] Optionally, based on a pre-defined large language model, the business data can be filtered according to the content of the request text corresponding to the query request to determine the query content corresponding to the business request. For example, the resource utilization rate of each business service involved in the business system can be determined based on the business data, and the 10 services with the lowest resource utilization rate can be filtered out to determine the query content of the query request "help me find the 10 services with the lowest resource utilization rate".

[0042] It should be noted that for query analysis, the corresponding output query content can be an in-depth attribution report that includes the root causes, rather than just a data report.

[0043] Optionally, if the request type is an optimization request, then interact with the target user to determine the target optimization strategy corresponding to the business request, including: if the request type is an optimization request, perform comparative analysis on the business data based on a preset large language model to determine the indicators to be optimized, and determine the content to be displayed to the target user based on the indicators to be optimized; display the content to the target user to instruct the target user to determine the target optimization indicator from the indicators to be optimized, and determine the target optimization strategy corresponding to the target optimization indicator.

[0044] Among them, the indicators to be optimized refer to those whose resource consumption or cost consumption has reached a preset threshold. These indicators could be, for example, projects whose cost consumption has reached a preset cost threshold, or services or processes whose resource consumption has reached a preset resource threshold. The displayed content should at least include information indicating to the target user which indicators can be optimized, such as the indicator itself, its corresponding optimization strategy, optimization benefits, and potential risks. The target optimization indicator refers to the indicator that is ultimately determined to require optimization, and the target optimization strategy refers to the execution strategy for optimizing the target indicator.

[0045] For example, if the target optimization metric is cloud services, the corresponding target optimization strategy could be to achieve cost optimization through preemptive instances.

[0046] Optionally, determine the content to be displayed to the target users based on the metrics to be optimized, including: for each metric to be optimized, determine the corresponding optimization strategy, and determine the optimization benefits and potential risks of each optimization strategy; and determine the content to be displayed to the target users based on the metrics to be optimized, the optimization strategy, the optimization benefits and potential risks.

[0047] For example, for optimization requests, the output is value-driven advice. It can not only provide optimization strategies for each metric to be optimized (such as switching to preemptive instances), but also provide the expected benefits after optimization, such as the expected amount of money saved. It can also assess the potential risks that may exist after optimization, such as the probability of interruption increasing to 5%. In this way, the traditional "advice" is upgraded to "decision support", providing users with all the information to weigh the pros and cons, which greatly improves the credibility and adoption rate of the advice.

[0048] S103. Based on the feedback strategy, display the query content to the target user or interact with the user to achieve automated optimization in order to respond to business requests.

[0049] Optionally, if the feedback strategy is to display the query content, then the query content is displayed to the target user, thereby responding to the business request; if the feedback strategy is to achieve automated optimization through user interaction, then automated optimization is achieved through user interaction, thereby responding to the business request.

[0050] Optionally, based on the feedback strategy, the system can display query content to the target user or interact with the user to achieve automated optimization in response to business requests, including: when the request type is a query request, displaying the query content corresponding to the business request to the target user in response to the business request.

[0051] Optionally, when the request type is a query request, the corresponding query content can be directly displayed to the target user to realize the response to the business request.

[0052] Optionally, based on the feedback strategy, the query content can be displayed to the target user or the user can be interacted with to achieve automated optimization, including: when the request type is an optimization request, generating a target execution work order and target execution code according to the target optimization strategy; connecting the target execution work order to the downstream system through a preset interface, and performing optimization operations based on the target execution code using continuous integration and continuous delivery tools to achieve automated optimization.

[0053] The target execution work order can be an ITSM work order (Information Technology Service Management).

[0054] Optionally, after interacting with the target user and determining the target optimization metrics, the business system can automatically complete the optimization operation based on the target optimization strategy. Specifically, it can use a preset large language model to automatically generate ITSM work orders, initiate approval processes, and generate Terraform code snippets that conform to the specifications and can be executed immediately. Terraform refers to the Infrastructure as Code (IaC) tool.

[0055] Optionally, business systems can automatically connect target execution tickets to downstream systems (systems configured with Jira and Jenkins tools) via pre-defined APIs (Application Programming Interfaces), triggering the automatic execution of changes in the CI (Continuous Integration) / CD (Continuous Delivery) pipeline. Here, Jira refers to the pre-defined project management and issue tracking tool, and Jenkins refers to the pre-defined continuous integration and continuous delivery (CI / CD) tool.

[0056] It's important to note that this invention, through the correlation analysis capabilities of a large language model, can transform cost data from purely financial figures into insights with business semantics. This is the fundamental reason why users can directly obtain cost changes using natural language, significantly improving their decision-making efficiency. By including business impact and risk assessments in optimization suggestions, decision-makers (such as operations managers and CFOs) can make scientific trade-offs, avoiding the business risks that traditional tools may bring, and improving the credibility and adoption rate of optimization solutions. Through automated optimization based on target optimization strategies, end-to-end automation from analysis and suggestion to execution is achieved. This significantly reduces manual intervention, shortening the cost optimization cycle from days to hours or even minutes, greatly improving the efficiency and responsiveness of Fin-Ops (Financial Operations, a best practice framework integrating financial management and engineering) practices.

[0057] The technical solution of this invention, in response to a business request issued by a target user, determines the request text and, using a preset semantic analysis strategy, determines the request type corresponding to the request text; determines the business data associated with the request text, and, based on the business data and request type, determines a feedback strategy for the business request based on a preset large language model; and, according to the feedback strategy, displays query content to the target user or interacts with the user to achieve automated optimization in response to the business request. By comprehensively analyzing business data in response to user requests, automated content display and optimization operations can be achieved, improving business processing efficiency and enhancing the user's business handling experience.

[0058] Example 2

[0059] Figure 2 This is a flowchart of a business processing method provided in an embodiment of the present invention; based on the above embodiments, this embodiment provides a preferred example of a business system that analyzes business data according to the type of user's business request to automatically display query content to the target user or interact with the user to achieve automated optimization, such as... Figure 2 As shown, the method includes:

[0060] S201. In response to a business request issued by the target user, determine the request text and use a preset semantic analysis strategy to determine the request type corresponding to the request text.

[0061] S202. If the request type is a query request, the business data is analyzed based on the preset large language model to determine the query content corresponding to the business request, and the feedback strategy for the business request is to display the query content.

[0062] S203. When the request type is a query request, display the query content corresponding to the business request to the target user in order to respond to the business request.

[0063] S204. If the request type is an optimization request, the business data is compared and analyzed based on the preset large language model to determine the indicators to be optimized, and for each indicator to be optimized, the corresponding optimization strategy is determined.

[0064] S205. When the request type is an optimization request, determine the optimization benefits and potential risks corresponding to each optimization strategy, and determine the content to be displayed to the target user based on the metrics to be optimized, optimization strategies, optimization benefits and potential risks.

[0065] S206. When the request type is an optimization request, the content is displayed to the target user to instruct the target user to determine the target optimization metric from the metrics to be optimized.

[0066] S207. When the request type is an optimization request, determine the target optimization strategy corresponding to the target optimization metric, and determine the feedback strategy for the business request as automated optimization through user interaction.

[0067] S208. When the request type is an optimization request, generate a target execution work order and target execution code according to the target optimization strategy.

[0068] S209. When the request type is an optimization request, the target execution work order is connected to the downstream system through a preset interface, and optimization operations are performed based on the target execution code using continuous integration and continuous delivery tools to achieve automated optimization.

[0069] Example 3

[0070] Figure 3This is a structural block diagram of a business processing device provided in an embodiment of the present invention. This embodiment is applicable to situations where a business system analyzes business data based on the type of user's business request to automatically display query content to the target user or achieve automated optimization through user interaction. The business processing device provided by the present invention can execute the business processing methods provided in any embodiment of the present invention, possessing the corresponding functional modules and beneficial effects of the execution method. This business processing device can be implemented in hardware and / or software and configured in an electronic device with business processing functions, such as... Figure 3 As shown, the service processing device may specifically include:

[0071] The type determination module 301 is used to respond to the business request issued by the target user, determine the request text, and use a preset semantic analysis strategy to determine the request type corresponding to the request text;

[0072] The strategy determination module 302 is used to determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request based on a preset large language model.

[0073] The response module 303 is used to display query content to the target user or interact with the user to achieve automated optimization in response to business requests, based on the feedback strategy.

[0074] The technical solution of this invention, in response to a business request issued by a target user, determines the request text and, using a preset semantic analysis strategy, determines the request type corresponding to the request text; determines the business data associated with the request text, and, based on the business data and request type, determines a feedback strategy for the business request based on a preset large language model; and, according to the feedback strategy, displays query content to the target user or interacts with the user to achieve automated optimization in response to the business request. By comprehensively analyzing business data in response to user requests, automated content display and optimization operations can be achieved, improving business processing efficiency and enhancing the user's business handling experience.

[0075] Furthermore, the request type is a query request or an optimization request; the feedback strategy is to display the query content or interact with the user to achieve automated optimization.

[0076] The strategy determination module 302 is specifically used for:

[0077] If the request type is a query request, then determine the query content corresponding to the business request, and determine the feedback strategy for the business request as displaying the query content.

[0078] If the request type is an optimization request, then interact with the target user to determine the target optimization strategy corresponding to the business request, and determine the feedback strategy for the business request as automated optimization through user interaction.

[0079] Furthermore, the strategy determination module 302 is also used for:

[0080] If the request type is a query request, the business data will be analyzed based on the preset large language model to determine the query content corresponding to the business request.

[0081] Accordingly, response module 303 is specifically used for:

[0082] When the request type is a query request, the query content corresponding to the business request is displayed to the target user in order to respond to the business request.

[0083] Furthermore, the strategy determination module 302 is also used for:

[0084] If the request type is an optimization request, the business data is compared and analyzed based on the preset large language model to determine the indicators to be optimized, and the content to be displayed to the target users is determined according to the indicators to be optimized.

[0085] The content is displayed to the target users to instruct them to identify the target optimization metrics from the metrics to be optimized, and to determine the target optimization strategy corresponding to the target optimization metrics.

[0086] Furthermore, the strategy determination module 302 is also used for:

[0087] For each metric to be optimized, determine the corresponding optimization strategy, and determine the optimization benefits and potential risks of each optimization strategy;

[0088] Based on the metrics to be optimized, optimization strategies, optimization benefits, and potential risks, determine the content to be displayed to the target users.

[0089] Furthermore, the response module 303 is also used for:

[0090] When the request type is an optimization request, generate a target execution work order and target execution code according to the target optimization strategy;

[0091] By connecting the target execution work order to the downstream system through a preset interface, and by using continuous integration and continuous delivery tools, the target execution code is optimized to achieve automated optimization.

[0092] Example 4

[0093] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Figure 4A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0094] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0095] Multiple components in electronic device 10 are connected to input / output 15, including: input unit 16, such as a keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as a disk, optical disk, etc.; and communication unit 19, such as a network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as business processing methods.

[0097] In some embodiments, the business processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into random access memory 13 and executed by processor 11, one or more steps of the business processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the business processing method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), system-on-a-chip (SoCs), complex programmable logic devices (PLCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (e.g., voice input, speech input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0103] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual reality services, such as high management difficulty and weak business scalability.

[0104] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the business processing method of any embodiment of the present invention.

[0105] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0106] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A business processing method, characterized in that, include: In response to a business request from a target user, determine the request text and use a pre-defined semantic analysis strategy to determine the request type corresponding to the request text; Determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request based on a pre-defined large language model; Based on the feedback strategy, the system automatically optimizes the display of query content to target users or the interaction with users to respond to business requests.

2. The method according to claim 1, characterized in that, The request type is either a query request or an optimization request; the feedback strategy is to display the query content or interact with the user to achieve automated optimization. Accordingly, based on business data and request type, and using a pre-defined large language model, a response strategy for business requests is determined, including: If the request type is a query request, then determine the query content corresponding to the business request, and determine the feedback strategy for the business request as displaying the query content. If the request type is an optimization request, then interact with the target user to determine the target optimization strategy corresponding to the business request, and determine the feedback strategy for the business request as automated optimization through user interaction.

3. The method according to claim 2, characterized in that, If the request type is a query request, then determine the query content corresponding to the business request, including: If the request type is a query request, the business data will be analyzed based on the preset large language model to determine the query content corresponding to the business request. Accordingly, based on the feedback strategy, automated optimization is implemented to display query content to target users or interact with users to respond to business requests, including: When the request type is a query request, the query content corresponding to the business request is displayed to the target user in order to respond to the business request.

4. The method according to claim 2, characterized in that, If the request type is an optimization request, then interact with the target user to determine the target optimization strategy corresponding to the business request, including: If the request type is an optimization request, the business data is compared and analyzed based on the preset large language model to determine the indicators to be optimized, and the content to be displayed to the target users is determined according to the indicators to be optimized. The content is displayed to the target users to instruct them to identify the target optimization metrics from the metrics to be optimized, and to determine the target optimization strategy corresponding to the target optimization metrics.

5. The method according to claim 4, characterized in that, The content displayed to the target users is determined based on the metrics to be optimized, including: For each metric to be optimized, determine the corresponding optimization strategy, and determine the optimization benefits and potential risks of each optimization strategy; Based on the metrics to be optimized, optimization strategies, optimization benefits, and potential risks, determine the content to be displayed to the target users.

6. The method according to claim 1, characterized in that, Based on the feedback strategy, automated optimization is achieved by displaying query content to target users or interacting with users, including: When the request type is an optimization request, generate a target execution work order and target execution code according to the target optimization strategy; By connecting the target execution work order to the downstream system through a preset interface, and by using continuous integration and continuous delivery tools, the target execution code is optimized to achieve automated optimization.

7. A business processing device, characterized in that, include: The type determination module is used to respond to business requests issued by target users, determine the request text, and use a preset semantic analysis strategy to determine the request type corresponding to the request text; The strategy determination module is used to determine the business data associated with the request text, and based on the business data and request type, determine the feedback strategy for the business request according to a preset large language model. The response module is used to display query content to the target user or interact with the user to achieve automated optimization in response to business requests, based on the feedback strategy.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the business processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the business processing method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the business processing method according to any one of claims 1-6.

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