A business configuration-based outlet intelligent analysis and AI interaction method

Abstract

The application relates to the technical field of network point management, and discloses a network point intelligent analysis and AI interaction method based on service configuration. The method comprises the following steps: receiving a query request, performing multidimensional analysis, wherein the multidimensional analysis comprises the following steps: performing feature embedding and graph structure construction, regarding the features of each business board of a network point star level, time features and region features as nodes in a graph structure, and learning the interaction between the nodes; then inputting corresponding features of a current network point, calculating attention weights, deeply fusing each board feature, time sequence features and region features after attention weighting, forming a high-dimensional diagnosis vector, inputting the high-dimensional diagnosis vector into a multi-task output layer, obtaining a plurality of specific suggestions, outputting corresponding natural language suggestions according to the specific suggestions, so as to solve the problem that an existing AI can only provide data query or static rule explanation and cannot fuse multidimensional business data to perform personalized diagnosis, cause analysis and strategy suggestion generation.

Description

Technical Field

[0001] This invention relates to the field of branch management technology, and in particular to a branch intelligent analysis and AI interaction method based on business configuration. Background Technology

[0002] AI assistants in existing enterprise applications (such as branch management systems) typically provide a unified question-and-answer entry point in the form of a fixed floating window, which has the following problems: Superficial analytical capabilities: For comprehensive business indicators such as "branch star rating", existing AI can only provide data queries or static rule interpretations, and cannot integrate multi-dimensional business data (service quality, business coverage, investment scale, etc.) to conduct personalized diagnosis, attribution analysis and generate strategic recommendations.

[0003] Therefore, there is an urgent need to provide a business configuration-based intelligent analysis and AI interaction method for branches. This method constructs an analysis chain of "indicator acquisition - automatic attribution - mapping suggestion - text structure generation", which deterministically associates discrete business segment scores with a pre-maintained natural language-based suggestion text library, and finally generates a coherent, logical and actionable analysis report. Summary of the Invention

[0004] The main objective of this invention is to address the problem that existing AI can only provide data queries or static rule interpretations, and cannot integrate multi-dimensional business data to perform personalized diagnosis, attribution analysis, and generate strategic recommendations.

[0005] The first aspect of this invention provides a business configuration-based intelligent analysis and AI interaction method for business outlets, comprising: receiving a query request, performing multi-dimensional analysis, the multi-dimensional analysis including feature embedding and graph structure construction, treating the various business segment features, time features, and regional features of the business outlet star rating as nodes in the graph structure, and learning the interactions between nodes; then inputting the corresponding features of the current business outlet, calculating attention weights, and deeply fusing the attention-weighted features of each segment with time series features and regional features to form a high-dimensional diagnostic vector, inputting the high-dimensional diagnostic vector into a multi-task output layer, the output of the multi-task output layer including the main cause category of the current star rating and a strategy encoding vector, performing similarity matching between the strategy encoding vector and a predefined suggestion action space to obtain several specific suggestions, and outputting corresponding natural language suggestions based on the specific suggestions.

[0006] Optionally, in the first implementation of the first aspect of the present invention, it further includes: after detecting that the user enters the application system, performing dynamic retraction control, including starting a timer, and if no click or drag operation of the AI ​​floating window is detected within a set time, controlling the AI ​​floating window to automatically shrink from the first display area to the second display area and attach to the side of the screen.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the method further includes: after detecting that a user has entered the application system, performing hierarchical priority control, including setting the display level of the AI ​​floating window to be lower than that of the keyboard component when the keyboard component is activated.

[0008] Optionally, in the third implementation of the first aspect of the present invention, it further includes: configuring AI bubbles and AI preset questions in the background. The AI ​​bubble configuration includes inputting prompt text and displaying the prompt text in the form of a bubble next to the AI ​​floating window. The AI ​​preset questions include several preset questions, which are directly sent or displayed on the corresponding cards for the user to select.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the interaction between the learning nodes includes: pre-training a graph neural network model using historical data to capture the nonlinear coupling relationship between the features of each segment.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, the input of the corresponding features of the current branch includes: obtaining the current time and the branch code to which the user belongs, and obtaining business segment features including service quality, business coverage, investment scale, and deduction items based on the branch code to which the user belongs.

[0011] Optionally, in the sixth implementation of the first aspect of the present invention, the step of outputting the corresponding natural language suggestion based on the specific suggestion includes: inputting the specific suggestion into a text generation model to directly generate a natural language suggestion; or, using the specific suggestion as a key to retrieve the most matching copywriting template in a preset "AI suggestion copywriting library" and then dynamically filling it in to obtain a natural language suggestion.

[0012] The second aspect of this invention provides a business configuration-based intelligent analysis and AI interaction device for business outlets, comprising: an AI floating window module for receiving query requests; a multi-dimensional analysis module, including: a feature embedding and graph structure construction unit for treating the business segment features, time features, and regional features of the business outlet star rating as nodes in a graph structure and learning the interactions between nodes; a dynamic weight allocation unit for inputting the corresponding features of the current business outlet and calculating attention weights; a multi-source feature fusion and diagnostic output unit for deeply fusing the attention-weighted features of each business segment with time series features and regional features to form a high-dimensional diagnostic vector, and inputting the high-dimensional diagnostic vector into a multi-task output layer; the output of the multi-task output layer includes the main cause category of the current star rating, a strategy encoding vector, and performs similarity matching between the strategy encoding vector and a predefined suggestion action space to obtain several specific suggestions; and a natural language generation module for outputting corresponding natural language suggestions based on the specific suggestions.

[0013] Optionally, in a first implementation of the second aspect of the present invention, the AI ​​floating window module further includes a dynamic retraction control unit, which controls the AI ​​floating window to automatically shrink from the first display area to the second display area and attach to the side of the screen when no click or drag operation of the AI ​​floating window is detected within a set time.

[0014] Optionally, in a second implementation of the second aspect of the present invention, the AI ​​floating window module further includes a hierarchy priority control unit, used to set the display hierarchy of the AI ​​floating window to be lower than that of the keyboard component when the keyboard component is activated.

[0015] Optionally, in a third implementation of the second aspect of the present invention, the AI ​​floating window module further includes a background configuration unit for configuring AI bubbles and AI preset questions. Configuring AI bubbles includes inputting prompt text and displaying the prompt text in the form of a bubble next to the AI ​​floating window. Configuring AI preset questions includes setting several preset questions and sending them directly or displaying them on the corresponding cards for the user to select.

[0016] Optionally, in a fourth implementation of the second aspect of the present invention, the feature embedding and graph structure construction unit is further used to obtain the current time and the network code to which the user belongs, and to obtain business segment features including service quality, business coverage, investment scale, and deduction items based on the network code to which the user belongs.

[0017] Optionally, in a fifth implementation of the second aspect of the present invention, the feature embedding and graph structure building unit is further used to pre-train a graph neural network model using historical data to capture the nonlinear coupling relationship between the features of each segment.

[0018] Optionally, in the sixth implementation of the second aspect of the present invention, the natural language generation module inputs the specific suggestion into the text generation model to directly generate natural language suggestions; or, using the specific suggestion as a key, it retrieves the most matching copywriting template in the preset "AI suggestion copywriting library" and then dynamically fills it in to obtain natural language suggestions.

[0019] A third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor invokes the instructions in the memory to cause the electronic device to execute the above-described service configuration-based intelligent branch network analysis and AI interaction method.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described business configuration-based intelligent branch network analysis and AI interaction method.

[0021] In the technical solution of this invention, cross-segment causal relationship modeling is realized. Traditional methods treat each segment as independent, while this model can learn causal relationships such as "deterioration in service quality → shrinkage in business coverage", thereby proposing more fundamental improvement suggestions (such as "first repair service reputation, then expand business"). By dynamically focusing on key weaknesses, the attention mechanism enables the model to automatically determine which area to focus on analyzing based on the unique situation of each branch, avoiding a "one-size-fits-all" approach. For example, for branches with extremely poor service quality but excellent performance in other areas, the model will focus on detailed plans for improving service quality; for branches with consistently low scores, the model will provide a systematic improvement plan. It is highly interpretable, and the attention weight can be directly visualized, showing users that "AI believes the most important problem at present is the decline in business coverage score, because it is the biggest factor causing the overall ranking to drop", which enhances users' trust in AI suggestions; It solves the problems of existing technologies, such as fixed interaction methods, floating windows that remain on the screen and obscure the operation interface, and the inability to adapt the form according to user behavior; and superficial analysis capabilities. For comprehensive business indicators such as "branch star rating", existing AI can only provide data query or static rule interpretation, and cannot integrate multi-dimensional business data (service quality, business coverage, investment scale, etc.) to conduct personalized diagnosis, attribution analysis and generate strategic suggestions. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a branch network intelligent analysis and AI interaction method based on business configuration, provided as an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a service configuration-based intelligent branch network analysis and AI interaction device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] This invention provides a business-configurable intelligent analysis and AI interaction method, apparatus, device, and storage medium for branch offices. It deterministically associates discrete business segment scores with a pre-maintained, natural language-based suggestion library, ultimately generating a coherent, logical, and actionable analysis report. A single AI entry point allows for dynamic adjustment of interaction strategies based on different business stages. A multi-source feature fusion deep neural network model based on an attention mechanism, combining graph neural networks and attention mechanisms, automatically determines which segment to focus on analyzing based on the unique circumstances of each branch office, improving branch office management efficiency. This invention solves the problems of rigid interaction methods, inflexible capability configuration, and superficial analytical capabilities.

[0024] The terms "first," "second," "third," "fourth," etc. (if present) 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 embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a 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.

[0025] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the intelligent branch network analysis and AI interaction method based on business configuration in this invention includes: 110. After the AI ​​floating window receives the user's conversational query request (such as "Help me analyze the star rating of my branch", "Why is my branch's star rating three stars?"), proceed to step 120; 120. First, verify whether the logged-in user is a Level 1 branch role and has "Branch Star Rating" menu permissions; after verification, automatically obtain the current time (as the analysis period) and the branch code to which the user belongs, and perform analysis through a multi-dimensional analysis engine; The input features of the multi-dimensional analysis engine include (from the branch star rating data interface corresponding to the branch code): overall star rating (1-5 stars), overall score (0-100 points), service quality score and star rating, business coverage score and star rating, investment scale score and star rating, deductions (0-10 points), historical 12-month score trends for each segment, average score and ranking of the province and city where the branch is located, and static attributes such as branch size and business volume. The output of the multi-dimensional analysis engine includes at least a multi-dimensional diagnostic vector (each dimension corresponds to a key issue that requires recommendations), a predictive "improvement potential score", and directly generated structured recommendation text (or the best match for retrieving a recommendation text library). Specifically, multi-dimensional analysis includes, 121. Perform feature embedding and graph structure construction, treating the characteristics of each business segment (service quality, business coverage, investment scale, deductions, etc.) and time and regional characteristics of the branch star rating as nodes in a graph structure. Learn the interactions between nodes through a graph neural network, for example: A decline in service quality score may affect business coverage score (due to a decrease in market share caused by a deteriorating reputation); when negative items exist, the weight of the investment scale segment should be automatically increased (because compliance issues will limit expansion). The system trains a graph neural network model using historical data of network points (including the final actual improvement effect) in advance, enabling the model to capture the non-linear coupling relationship between the features of each section; 122. Dynamic Weight Allocation Driven by Attention Mechanism: The system obtains the current time and the user's branch code. Based on the branch code, it derives business segment characteristics including service quality, business coverage, investment scale, and deductions. These characteristics are then input into the corresponding features of the current branch. Attention weights are calculated within the graph neural network model, specifically including: For each feature of a segment, the corresponding score is used to determine its "degree of abnormality" relative to the historical performance of that segment and the regional average level. The attention mechanism automatically identifies the 1-2 segments that need the most attention and assigns them higher analysis weights. For example, if a branch has a high service quality score but a sharp drop in business coverage score, the attention weight will be concentrated on the "business coverage" section, while the analysis of service quality will be weakened; at the same time, the negative items will be given a higher attention weight (because they have a great impact on the star rating).

[0026] 123. Multi-source feature fusion and diagnostic output: The attention-weighted features of each segment are deeply fused with time-series features and regional features to form a high-dimensional diagnostic vector, which is then input into the multi-task output layer. Task 1 (Attribution Classification): Output the main categories of causes for the current star rating (such as "service quality drag", "insufficient business coverage", "excessive deduction items", etc.) and give the confidence level; Task 2 (Improving Strategy Encoding): Output a strategy encoding vector. Match the strategy encoding vector with the predefined suggested action space (such as "strengthening customer relationship maintenance", "automating equipment inspection", "expanding pickup and delivery scale", etc.) to obtain several specific suggestions, such as selecting the best 1-3 specific suggestions.

[0027] 124. Output corresponding natural language suggestions based on specific recommendations (the original polishing mechanism can be retained).

[0028] This embodiment deterministically correlates discrete business segment scores (such as service quality score of 40) with a pre-maintained, natural language-based suggestion library, ultimately generating a coherent, logical, and actionable analysis report. Utilizing a single AI entry point, it dynamically adjusts interaction strategies based on different business stages. A multi-source feature fusion deep neural network model based on an attention mechanism, combining graph neural networks and attention mechanisms, automatically determines which segment to focus on analyzing based on the unique circumstances of each branch. This approach significantly improves branch management efficiency.

[0029] A second embodiment of the intelligent branch network analysis and AI interaction method based on business configuration in this invention includes: 100. Add two configurable items, "AI Bubble" and "AI Preset Questions," to the menu management interface. In the backend, dynamically configure AI bubbles and AI preset questions based on different business stages (such as the monthly star-rating assessment period). AI bubble configuration includes allowing operators to enter a prompt text of up to 20 characters. The system displays this prompt text as a bubble next to the AI ​​floating window, automatically closing after a 3-second countdown. If the user manually closes it, it will not pop up again within the same login period. AI preset questions allow operators to dynamically configure 1-3 preset questions. After entering the AI ​​assistant, the system executes different logic based on the number of configured questions: if only one question is configured, it is automatically sent; if multiple questions are configured, cards corresponding to the preset questions are displayed for the user to select. Upon detecting a user entering the application system (PC / App), dynamic revocation control, hierarchical priority control, and gray-scale jump control are implemented. The dynamic retraction control includes starting a timer; if no click or drag operation of the AI ​​floating window is detected within a set time (5 seconds), the AI ​​floating window is controlled to automatically shrink from the first display area (e.g., 100%) to the second display area (e.g., 60%) and attach to the side of the screen. The hierarchy priority control includes listening to system focus events and setting the display hierarchy of the AI ​​floating window to be lower than that of the keyboard component when the keyboard component is activated, so as to avoid obstructing input. The gray-scale jump control includes support for gray-scale strategies based on business province, administrative city, and branch code for jump links associated with the AI ​​floating window. This allows users within the configured range to access the new version of the AI ​​link, while users outside the range access the old link.

[0030] 110. After the AI ​​floating window receives the user's conversational query request (such as "Help me analyze the star rating of my branch", "Why is my branch's star rating three stars?"), proceed to step 120; 120. Conduct multi-dimensional analysis; including, 121. Perform feature embedding and graph structure construction, and regard the features of each business segment (service quality, business coverage, investment scale, deduction items, etc.) and time and regional features of the branch star rating as nodes in the graph structure, and learn the interaction between nodes through graph neural network; The system trains a graph neural network model using historical data of network points (including the final actual improvement effect) in advance, enabling the model to capture the non-linear coupling relationship between the features of each section; 122. Attention-driven dynamic weight allocation: Input the corresponding features of the current node and calculate the attention weights, specifically including: For each feature of a segment, the corresponding score is used to determine its "degree of abnormality" relative to the historical performance of that segment and the regional average level. The attention mechanism automatically identifies the 1-2 segments that need the most attention and assigns them higher analysis weights. For example, if a branch has a high service quality score but a sharp drop in business coverage score, the attention weight will be concentrated on the "business coverage" section, while the analysis of service quality will be weakened; at the same time, the negative items will be given a higher attention weight (because they have a great impact on the star rating).

[0031] 123. Multi-source feature fusion and diagnostic output: The attention-weighted features of each segment are deeply fused with time-series features and regional features to form a high-dimensional diagnostic vector, which is then input into the multi-task output layer. Task 1 (Attribution Classification): Output the main categories of causes for the current star rating (such as "service quality drag", "insufficient business coverage", "excessive deduction items", etc.) and give the confidence level; Task 2 (Improving Strategy Encoding): Output a strategy encoding vector. Match the strategy encoding vector with the predefined suggested action space (such as "strengthening customer relationship maintenance", "automating equipment inspection", "expanding pickup and delivery scale", etc.) to obtain several specific suggestions, such as selecting the best 1-3 specific suggestions.

[0032] 124. Output corresponding natural language suggestions based on specific recommendations (the original polishing mechanism can be retained).

[0033] In this embodiment, by fully exposing the AI's guidance behavior (bubble prompts, default questions) to the operations backend, and combining it with adaptive UI shrinking and canary release capabilities, the same AI entry point can dynamically adjust its interaction strategy according to different business stages (such as the star rating assessment period at the beginning of the month). This solves the pain points of "one-size-fits-all" AI functions in enterprise software and the inability of operations to intervene (rigid configuration of structural function improvement capabilities, AI assistant's question guidance, default questions, etc. are hard-coded, and operations personnel cannot quickly configure and canary release according to different business cycles or activities, such as star rating).

[0034] A third embodiment of the intelligent branch network analysis and AI interaction method based on business configuration in this invention includes: 110. After the AI ​​floating window receives the user's conversational query request (such as "Help me analyze the star rating of my branch", "Why is my branch's star rating three stars?"), proceed to step 120; 120. Conduct multi-dimensional analysis; including, 121. Perform feature embedding and graph structure construction, and regard the features of each business segment (service quality, business coverage, investment scale, deduction items, etc.) and time and regional features of the branch star rating as nodes in the graph structure, and learn the interaction between nodes through graph neural network; The system trains a graph neural network model using historical data of network points (including the final actual improvement effect) in advance, enabling the model to capture the non-linear coupling relationship between the features of each section; 122. Dynamic weight allocation driven by attention mechanism: Input the corresponding features of the current node, calculate the attention weight, specifically including: For each feature of a segment, the corresponding score is used to determine its "degree of abnormality" relative to the historical performance of that segment and the regional average level. The attention mechanism automatically identifies the 1-2 segments that need the most attention and assigns them higher analysis weights. For example, if a branch has a high service quality score but a sharp drop in business coverage score, the attention weight will be concentrated on the "business coverage" section, while the analysis of service quality will be weakened; at the same time, the negative items will be given a higher attention weight (because they have a great impact on the star rating).

[0035] 123. Multi-source feature fusion and diagnostic output: The attention-weighted features of each segment are deeply fused with time-series features and regional features to form a high-dimensional diagnostic vector, which is then input into the multi-task output layer. Task 1 (Attribution Classification): Output the main categories of causes for the current star rating (such as "service quality drag", "insufficient business coverage", "excessive deduction items", etc.) and give the confidence level; Task 2 (Improving Strategy Encoding): Output a strategy encoding vector. The strategy encoding vector is matched with the predefined suggested action space (such as "strengthening customer relationship maintenance", "automating equipment inspection", "expanding pickup and delivery scale", etc.) to obtain several specific suggestions, such as selecting the best 1-3 specific suggestions. Task 3 (Predictive Scoring): Predict the potential increase in scores for each section over the next 1-3 months if model recommendations are adopted.

[0036] 124. Output corresponding natural language suggestions based on specific recommendations (the original polishing mechanism can be retained); this can be used in two ways: 1. Direct text generation: Input specific suggestions into a lightweight text generation model (such as a Transformer-based decoder) to directly generate natural language suggestions; Second, using specific suggestions as keys, the most matching copywriting template is retrieved from the pre-set "AI suggestion copywriting library" and then dynamically populated (such as replacing "service quality" with specific scores and rankings) to obtain natural language suggestions. This method can ensure the accuracy of suggestions and avoid illusions. In some embodiments, a structured response is finally output: the above data, the analysis conclusions of the multi-task output layer, and the text of natural language suggestions are polished and spliced ​​using natural language through comprehensive evaluation templates and other methods to finally generate a structured response that includes "overall performance, section score and suggestions, deduction items, comprehensive evaluation, and warm reminders", and then fed back to the AI ​​dialogue interface.

[0037] In this embodiment, a closed loop of prediction and suggestion is realized. The model can not only provide suggestions, but also predict the score improvement effect after implementing the suggestions. This allows the AI ​​assistant to answer complex questions such as "If I follow your suggestion, how many stars can I improve next month?" This approach greatly improves the value and credibility of decision assistance.

[0038] The above describes a service configuration-based intelligent branch network analysis and AI interaction method according to an embodiment of the present invention. The following describes a service configuration-based intelligent branch network analysis and AI interaction device according to an embodiment of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the intelligent branch network analysis and AI interaction device based on business configuration in this invention includes: The adaptive AI floating window module 210 is used to receive query requests and perform multi-dimensional analysis. In some embodiments, the AI ​​floating window module 210 further includes a dynamic retraction control unit, a hierarchy priority control unit, a grayscale jump control unit, and a background configuration unit. The dynamic retraction control unit is used to detect events where the user enters the application system (PC / App). If no click or drag operation of the AI ​​floating window is detected within a set time (within 5 seconds), the AI ​​floating window is controlled to automatically shrink from the first display area (e.g., 100%) to the second display area (e.g., 60%) and attach to the side of the screen. The hierarchy priority control unit is used to listen for system focus events. When the keyboard component is activated, the display hierarchy of the AI ​​floating window is set to be lower than that of the keyboard component to avoid obstructing input. The grayscale jump control unit is used to implement grayscale strategies based on business province, administrative city, and branch code for jump links associated with floating windows. Users within the configuration range access the new version of AI links, while users outside the range access the old links. The backend configuration unit is used to configure AI bubbles and AI preset questions in the backend. Configuring AI bubbles includes entering prompt text and displaying the prompt text as a bubble next to the AI ​​floating window. Configuring AI preset questions includes setting several preset questions, which can be sent directly or displayed on the corresponding cards for users to select. Multi-dimensional analysis module 220, including, The feature embedding and graph structure construction unit 221 is used to obtain the current time and the network code of the user's network, and to obtain the business segment features including service quality, business coverage, investment scale, and deduction items based on the network code of the user's network. The various business segment features, time features, and regional features of the network star rating are regarded as nodes in the graph structure. The interaction between nodes is learned, and the non-linear coupling relationship between the features of each segment is captured by pre-training the graph neural network model using historical data. The dynamic weight allocation unit 222, driven by the attention mechanism, is used to input the corresponding features of the current network point and calculate the attention weight. By scoring each segment, it judges the "abnormality" of the segment relative to the historical performance and regional average level of the network point. The attention mechanism automatically identifies the 1-2 segments that need the most attention and assigns them higher analysis weights. The multi-source feature fusion and diagnostic output unit 223 is used to deeply fuse the attention-weighted features of each segment with time-series features and regional features to form a high-dimensional diagnostic vector, and then input the high-dimensional diagnostic vector into the multi-task output layer. Task 1 (Attribution Classification): Output the main categories of causes for the current star rating (such as "service quality drags down", "insufficient business coverage", "excessive deduction items", etc.) and give the confidence level.

[0039] Task 2 (Improving Strategy Encoding): Output a strategy encoding vector. Match this vector with the predefined suggested action space (such as "strengthen customer relationship maintenance", "automated equipment inspection", "expand pickup and delivery scale", etc.) and select the 1-3 best specific suggestions.

[0040] Task 3 (Predictive Scoring): Predict the potential increase in scores for each section if model recommendations are adopted within the next 1-3 months. The natural language generation module 224 (which can retain the original polishing mechanism) is used to output corresponding natural language suggestions based on specific suggestions. It directly generates natural language suggestions by inputting specific suggestions into a text generation model (such as a Transformer-based decoder); or, it uses specific suggestions as keys to retrieve the most matching copywriting template from a pre-set "AI suggestion copywriting library" and then dynamically fills it in (such as replacing "service quality" with specific scores and rankings) to obtain natural language suggestions, ensuring the accuracy of the suggestions and avoiding illusions. In some embodiments, a structured answer output module is also provided, which uses a preset comprehensive evaluation template to polish and splice the above data, analysis conclusions, and suggestions into natural language, and finally generates a structured answer that includes "overall performance, section score and suggestions, deduction items, comprehensive evaluation, and warm reminders", and feeds it back to the AI ​​dialogue interface.

[0041] In this embodiment, an analysis chain of "metric acquisition - automatic attribution - mapping suggestion - text structure generation" is constructed. In particular, in its degradation mode, discrete business segment scores are deterministically correlated with a pre-maintained, natural language-based suggestion text library, ultimately generating a coherent, logical, and actionable analysis report. This method of combining a business rule engine with AI natural language generation represents a significant technological advancement in the field of branch management. Simultaneously, it fully exposes AI's guiding behaviors (bubble prompts, default questions) to the operations backend, and combined with adaptive UI shrinking and canary release capabilities, allows the same AI entry point to dynamically adjust its interaction strategy according to different business stages (such as the initial star rating assessment period). This solves the pain points of "one-size-fits-all" AI functions in enterprise software and the inability of operations to intervene, representing a structural functional improvement.

[0042] above Figure 2 This invention provides a detailed description of a business configuration-based intelligent branch network analysis and AI interaction device from the perspective of modular functional entities. The electronic equipment in this invention is described in detail from the perspective of hardware processing.

[0043] Figure 3This is a schematic diagram of the structure of an electronic device 700 provided in an embodiment of the present invention. The electronic device 700 can vary significantly due to differences in configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the electronic device 300.

[0044] Electronic device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 350, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The illustrated electronic device structure does not constitute a limitation on electronic devices and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0045] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform steps of a business configuration-based intelligent branch network analysis and AI interaction method.

[0046] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A branch network intelligent analysis and AI interaction method based on business configuration, characterized in that, include: Receive query requests and perform multi-dimensional analysis, which includes: Feature embedding and graph structure construction are performed, treating the various business segment features, time features, and regional features of the branch star rating as nodes in the graph structure, and learning the interactions between nodes. Then, the corresponding features of the current branch are input, attention weights are calculated, and the attention-weighted features of each segment are deeply fused with the time series features and regional features to form a high-dimensional diagnostic vector. The high-dimensional diagnostic vector is input into the multi-task output layer. The output of the multi-task output layer includes the main cause category of the current star rating and the policy encoding vector. The policy encoding vector is similarly matched with the predefined suggestion action space to obtain several specific suggestions. Based on the specific suggestions, the corresponding natural language suggestions are output.

2. The branch network intelligent analysis and AI interaction method based on business configuration according to claim 1, characterized in that, Also includes: After detecting that a user has entered the application system, dynamic retraction control is implemented, including starting a timer. If no click or drag operation of the AI ​​floating window is detected within a set time, the AI ​​floating window is controlled to automatically shrink from the first display area to the second display area and attach to the side of the screen.

3. A branch network intelligent analysis and AI interaction method based on business configuration according to claim 1 or 2, characterized in that, Also includes: After detecting that a user has entered the application system, hierarchical priority control is implemented, including setting the display level of the AI ​​floating window to be lower than that of the keyboard component when the keyboard component is activated.

4. The branch network intelligent analysis and AI interaction method based on business configuration according to claim 1, characterized in that, Also includes: The backend configures AI bubbles and AI preset questions. Configuring AI bubbles includes inputting prompt text and displaying the prompt text as a bubble next to the AI ​​floating window. Configuring AI preset questions includes setting several preset questions, which can be sent directly or displayed on the corresponding cards for users to select.

5. The branch network intelligent analysis and AI interaction method based on business configuration according to claim 1, characterized in that, The interaction between the learning nodes includes: pre-training a graph neural network model using historical data to capture the nonlinear coupling relationship between the features of each segment.

6. A branch network intelligent analysis and AI interaction method based on business configuration according to claim 1 or 5, characterized in that, The input of the corresponding features of the current branch includes: obtaining the current time and the branch code to which the user belongs, and obtaining business segment features including service quality, business coverage, investment scale, and deduction items based on the branch code to which the user belongs.

7. The branch network intelligent analysis and AI interaction method based on business configuration according to claim 1, characterized in that, The step of outputting corresponding natural language suggestions based on specific recommendations includes: Specific suggestions can be input into the text generation model to directly generate natural language suggestions; or, specific suggestions can be used as keys to retrieve the most matching copywriting template from the pre-set "AI Suggestion Copywriting Library" and then dynamically populated to obtain natural language suggestions.

8. A branch network intelligent analysis and AI interaction device based on business configuration, characterized in that, include: The AI ​​floating window module is used to receive query requests; The multi-dimensional analysis module includes: The feature embedding and graph structure building unit is used to treat the various business segment features, time features, and regional features of the branch star rating as nodes in a graph structure, and to learn the interactions between the nodes. The dynamic weight allocation unit is used to input the corresponding features of the current node and calculate the attention weights. The multi-source feature fusion and diagnostic output unit is used to deeply fuse the attention-weighted features of each segment with time series features and regional features to form a high-dimensional diagnostic vector. The high-dimensional diagnostic vector is then input into the multi-task output layer. The output of the multi-task output layer includes the main cause category of the current star rating and the policy encoding vector. The policy encoding vector is matched with the predefined suggested action space for similarity and several specific suggestions are obtained. The natural language generation module is used to output corresponding natural language suggestions based on specific recommendations.

9. An electronic device comprising a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the service configuration-based intelligent branch network analysis and AI interaction method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement each step of the branch network intelligent analysis and AI interaction method based on business configuration as described in any one of claims 1-7.

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