Business handling failure scenario-oriented dynamic adaptive recommendation strategy optimization method

Abstract

The application discloses a kind of dynamic self-adapting recommendation strategy optimization methods for business handling failure scene, it is related to business handling recommendation and intelligent decision-making technical field, first, it is collected to handle the whole process of business multi-class data, with 100 millisecond / second frequency guarantee complete real-time;With decision tree and bayesian network fusion algorithm attribution, direct and indirect reasons are clear;Integrate data to build user portrait, mine potential and subsequent demand;Based on attribution and portrait generation recommendation scheme set, priority and form are adjusted in combination with scene characteristics;Feedback data is introduced, and the strategy weight is optimized using gradient descent algorithm, and the scheme is updated regularly;Set multi-dimensional index weighted evaluation effect, not up to standard then emergency optimization;Establish distributed strategy library, and reuse similar scene optimal strategy using K nearest neighbor algorithm.The application realizes accurate positioning and personalized recommendation of failure causes, and the recommendation effect is continuously optimized with data accumulation, and is suitable for multiple business types and user groups.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of business handling recommendation and intelligent decision-making, and in particular to a dynamic adaptive recommendation strategy optimization method for business handling failure scenarios. BACKGROUND

[0002] In the process of business handling, frequent failure scenarios have become a core pain point affecting user experience and business efficiency, covering multiple fields such as government services, financial handling, e-commerce platforms, and medical appointments. Current business handling systems lack systematic solutions for failure scenarios, relying only on fixed error prompts or simple manual customer service intervention, which cannot quickly locate the root cause of failure. Limited data collection dimensions focus on business result data, ignoring key process data such as user operation behavior, system running state, and network environment, leading to superficial failure attribution and difficulty in distinguishing between direct causes such as missing materials and format errors and indirect causes such as ambiguous operation guidelines and complex processes, thus failing to provide targeted solutions.

[0003] Existing recommendation strategies generally have the problems of static and homogenization, and fail to fully combine user individual differences and real-time scenario characteristics. User portrait construction relies on static personal information, lacks dynamic integration of historical failure records, operation habits, and demand preferences, making it difficult for recommended solutions to match users with different operation capabilities and business needs. Meanwhile, the recommendation strategy does not consider real-time scenario changes such as business urgency, handling time, system load, and network quality. Whether it is peak or off-peak, or whether the network is good or poor, the same type of solution is pushed, causing a conflict between operation convenience and handling efficiency. For example, pushing a complex process solution during peak hours will increase user waiting time, and pushing an online-dependent solution when the network is poor will lead to secondary failure, severely affecting user acceptance.

[0004] In addition, existing methods lack a perfect feedback loop and strategy reuse mechanism. The evaluation index of the recommendation effect is single, mainly focusing on success rate, without considering user satisfaction, operation complexity, and secondary failure rate, making it difficult to fully measure the effectiveness of the strategy. Strategy optimization relies on manual iteration, lacks dynamic adjustment based on real-time feedback data, and lags behind changes in business scenarios and user needs. At the same time, there is no effective strategy library and similar scenario matching mechanism, and the same or similar failure scenarios need to generate recommended strategies repeatedly, wasting system resources and reducing response speed. In extreme abnormal scenarios, such as large-scale system failure and sudden peak traffic, there is a lack of emergency recommendation solutions, which can easily lead to user loss and restrict the scale operation and efficient service of business systems. SUMMARY

[0005] The dynamic adaptive recommendation strategy optimization method for business handling failure scenarios proposed in the present application solves the problems mentioned in the above prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic adaptive recommendation strategy optimization method for business processing failure scenarios, comprising the following steps:

[0007] Failure data is collected from multiple dimensions and accurately attributed. Data from the entire business process is collected, and a multi-factor attribution algorithm based on the fusion of decision trees and Bayesian networks is used to perform hierarchical analysis on the collected data. First, the core influencing factors are screened out through decision trees, and then the correlation probability of each factor is calculated using Bayesian networks to clarify the direct and indirect causes of business failure.

[0008] Dynamic user profile construction and in-depth demand mining: Integrate users' historical business processing, failure records, personal basic information and preference settings data to build multi-dimensional user profiles; Based on association rule mining algorithms, analyze the mapping relationship between profiles and failure scenarios, uncover potential needs, and predict subsequent processing needs;

[0009] Recommendation strategies are dynamically generated and adapted to different scenarios. An initial set of recommendation schemes is built based on failure attribution results and user profiles. The recommendation schemes are adjusted in combination with real-time business scenario characteristics, and the priority and presentation of the schemes are dynamically adjusted according to scenario characteristics.

[0010] The recommendation strategy is adaptively optimized and dynamically adjusted by incorporating real-time user feedback data and recommendation performance data. Gradient descent algorithm is used to dynamically adjust the weight coefficients of the recommendation scheme. The recommendation scheme set is updated regularly in combination with the changing trends of business scenarios, and the presentation of the schemes is optimized.

[0011] A closed-loop system for real-time evaluation and feedback of recommendation effectiveness is established, a multi-dimensional evaluation index system is set, and a weighted summation method is used to calculate the comprehensive recommendation effectiveness score; real-time monitoring of score changes, and triggering an emergency strategy optimization mechanism when the score is lower than a preset threshold;

[0012] Recommendation strategy storage and similar scenario reuse: A dynamic recommendation strategy library is established to store relevant strategies and feature data; a matching model is built based on the K-nearest neighbor algorithm to calculate the similarity between new scenarios and historical scenarios, and the best historical strategies are reused or new strategies are generated and added to the library according to the similarity.

[0013] Furthermore, it also includes a dynamic calculation step for the weights of failure causes, taking into account the time decay effect and changes in the intensity of the impact of failure causes. The weight calculation formula is as follows: ,in Reasons for failure The overall weight, f for Reasons for failure at any time The impact on business processing intensity, k is the time decay coefficient, and T is the time window for weight calculation. It is the integral variable.

[0014] Further, it also includes a user operation behavior sequence analysis step, which uses a long short-term memory network to deeply mine user operation behavior sequences, extracts operation habit features and behavior preference rules; based on the analysis results, the presentation form of the recommended scheme is optimized, the interface, content and recommended priority are adjusted according to different user characteristics, and the recommended timing is adjusted according to the number of operation failures, the basic scheme is pushed for the first failure, and the deep scheme and tool are pushed for the second failure.

[0015] Further, in the recommendation strategy dynamic generation step, a recommendation scheme priority sorting mechanism is added, a multi-dimensional sorting model is constructed based on the user demand urgency, solution implementation difficulty, business handling success rate, and user preference fit degree, and the sorting formula is , wherein is the priority score of the recommended scheme , is the business handling success rate of the scheme , is the implementation difficulty coefficient of the scheme , is the preference fit degree of the scheme and the user , is the demand urgency of the user for the scheme , , , , are weight coefficients.

[0016] Further, in the failure data multi-dimensional collection step, a data quality verification and cleaning mechanism is added, a double verification method based on statistical threshold and rule matching is used, the normal value range of each data dimension is set, and data outside the range is marked as abnormal data; through rule matching, repeated data and invalid data are excluded, repeated data is identified through data characteristic hash value comparison, invalid data includes null data, format error data and logical contradiction data, and the interpolation method is used to repair the verified abnormal data.

[0017] Further, in the user portrait dynamic construction step, a portrait real-time updating mechanism is added, a portrait updating trigger condition is set, when the trigger condition is met, the portrait updating process is immediately started, an incremental learning algorithm is used to analyze the new data, and the feature parameters of the user portrait are updated; at the same time, a portrait decay mechanism is established, the weight of long-term unused user features is reduced, and the features that appear frequently recently are mainly retained.

[0018] Further, in the recommendation strategy self-adaptive optimization step, a strategy iteration optimization formula is introduced, the strategy parameters are dynamically adjusted based on the recommendation effect deviation, and the optimization formula is , wherein the strategy parameter after iteration, the current strategy parameter, the learning rate, the the recommended effect value corresponding to the current parameter at the moment, the the recommended effect target value at the moment, T is an iteration time window, and is set to 1 hour, the integral variable.

[0019] Further, in the real-time evaluation step of the recommended effect, a multi-scene effect comparison and analysis mechanism is added, different scenes are divided according to business types, failure reasons, user groups and handling time periods, and different evaluation index weights are set for different scenes. Government service business focuses on handling success rate and compliance, financial handling business focuses on safety and efficiency, novice users focus on operation convenience, and experienced users focus on personalization and flexibility.

[0020] Further, it also includes an emergency recommendation mechanism for abnormal scenes, preset extreme abnormal scenes, and emergency recommendation schemes are formulated for different extreme scenes. When a large-scale system failure occurs, offline handling channels and business postponement application options are recommended. When the network is paralyzed, recommend the cached version of the operation guide and subsequent handling reminder service. When the business rules are temporarily adjusted, push the latest rule interpretation and material supplement list. When a sudden peak flow occurs, preferentially recommend a simplified version of the handling process with low resource consumption. An abnormal scene rapid identification model is established, and the system load, network state and business rule change signals are monitored in real time. The emergency recommendation mechanism is triggered within 50 milliseconds after the abnormal scene occurs.

[0021] Further, in the strategy storage and reuse step, an intelligent iteration and update mechanism of the strategy is added. The historical recommended strategies in the strategy library are periodically reviewed for effect. The random forest algorithm is used to screen the strategy features that significantly affect the recommended effect, and the strategy feature weights are adjusted. Every 5000 valid business handling failure data and recommended feedback data are accumulated, and the similar scene strategies in the strategy library are retrained, the strategy parameters and the recommended logic are optimized, and the reinforcement learning algorithm is introduced. The recommended effect is used as a reward signal to guide the strategy library to iterate in a better direction. A strategy version management mechanism is established to record the content, time and applicable scene of each strategy update.

[0022] Compared with the prior art, the beneficial effects of the present application are:

[0023] Multi-dimensional data acquisition covers key parameters of the whole process of business handling. Combined with the attribution algorithm of the decision tree and the Bayesian network fusion, the failure reason is accurately located, the direct cause is determined, and the indirect cause is excavated, which provides a reliable basis for recommended strategy generation, and changes the existing fuzzy attribution and insufficient targeting status.

[0024] The user portrait dynamic construction mechanism integrates multi-source user data, and through real-time updating and attenuation mechanism, ensures that the portrait always matches the current needs and behavior characteristics of the user, avoiding the recommendation deviation caused by static portrait. The personalized recommendation scheme based on user portrait and failure attribution result covers solutions, alternative businesses, process optimization, auxiliary tools and other types, which can accurately match users with different operation ability and demand preference, and improve the acceptance and practicality of the recommendation.

[0025] The scene adaptation ability of the recommendation strategy is significantly enhanced, which combines real-time scene characteristics such as business urgency, handling period, system load, network quality, dynamically adjusts the priority and presentation form of the recommendation scheme, makes the recommendation service more suitable for the actual use scene, and solves the problems of homogenization and poor scene adaptation of the existing strategy. The adaptive optimization and feedback closed-loop mechanism dynamically adjusts the strategy parameters based on user feedback and recommendation effect data, continuously approaches the target effect through scientific iterative optimization formula, and ensures the timeliness and effectiveness of the recommendation strategy.

[0026] The multi-scene effect evaluation and strategy reuse mechanism improves the system running efficiency, divides different scenes according to business types, user groups and other factors, and sets evaluation weights to optimize the strategy performance; similar scene matching and strategy library reuse reduce repeated calculation and improve response speed. The extreme abnormal scene emergency recommendation mechanism can quickly identify sudden conditions and push adaptive solutions to reduce the risk of user loss. Overall, the present application greatly improves the repair efficiency and user experience after business handling failure, reduces the secondary failure rate, optimizes the business handling process, provides reliable technical support for efficient and intelligent operation of various business systems, has wide application prospect and practical value. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The schematic block diagram of the dynamic adaptive recommendation strategy optimization method for business handling failure scene proposed by the present application;

[0028] Figure 2 The repair success rate comparison column chart for different failure reasons;

[0029] Figure 3 The recommendation scheme adoption rate line chart for different user groups;

[0030] Figure 4 The pie chart of the importance distribution of the core features of the recommendation strategy;

[0031] Figure 5 The secondary failure rate column chart for different business scenes. DETAILED DESCRIPTION

[0032] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of the present application.

[0033] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0034] In addition, the terms "first", "second" are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or internal communication of two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail with reference to the drawings.

[0035] With reference to Figures 1 to 5 A dynamic adaptive recommendation strategy optimization method for business handling failure scenarios, comprising the following steps:

[0036] Multi-dimensional collection of failure data and accurate attribution, collection of business handling whole process data, covering user operation behavior data, system running state data, business parameter configuration data, failure prompt feedback data, network transmission data, user operation behavior data including click path, operation time length, input content integrity, interaction frequency, system running state data including server response delay, database read-write speed, interface call success rate, resource occupancy rate, business parameter configuration data including business handling required material type, filling format requirement, permission verification rule, failure prompt feedback data including error code, failure description text, user feedback label, network transmission data including bandwidth occupancy, data packet loss rate, network delay, data collection frequency is set to 100 milliseconds / time, ensuring data real-time and integrity; multi-factor attribution algorithm based on decision tree and Bayesian network fusion is adopted to analyze the collected data in layers, first screening out the core influencing factors through the decision tree, then calculating the correlation probability of each factor by using the Bayesian network, to clarify the direct and indirect causes of business handling failure, the direct causes including material missing, format error, insufficient permission, system failure, network anomaly, the indirect causes including unclear operation guide, complex business process, user cognitive bias, the attribution accuracy rate is controlled above 95%, providing basis for recommendation strategy generation;

[0037] Dynamic construction of user portrait and deep mining of demand, integrating user historical business handling data, failure record data, personal basic information, preference setting data, personal basic information including user identity type, business handling frequency, historical handling scene, preference setting data including user selected handling channel, receiving notification mode, operation habit preference, constructing multi-dimensional user portrait, portrait dimension covering business demand type, operation ability level, preferred handling mode, risk tolerance, failure tolerance; based on association rule mining algorithm, analyzing the mapping relationship between user portrait and failure scene, mining user potential demand, potential demand including simplifying operation process, supplementing material guide, substituting business recommendation, temporarily opening permission, at the same time combining with user recent business handling intention, predicting user subsequent handling demand, providing support for personalized recommendation;

[0038] The recommended strategy is dynamically generated and adaptively adapted to the scene. Based on the failure attribution result and the user portrait, an initial recommendation scheme set is constructed. The scheme types include failure solution, alternative business option, operation process optimization suggestion, and auxiliary handling tool recommendation. The failure solution provides a precise repair path for the direct cause. The alternative business option filters the business that is consistent with the original business target and has a lower handling threshold. The operation process optimization suggestion simplifies the redundant steps. The auxiliary handling tool recommendation includes material template download, format verification tool, and automatic filling plug-in. The recommended scheme is adjusted in combination with real-time business scene characteristics. The scene characteristics include business emergency level, handling time period, system load state, and network environment quality. The business emergency level is divided into four levels: very high, high, medium, and low. The handling time period is divided into peak time period, flat peak time period, and trough time period. The system load state is divided into four levels: idle, normal, busy, and overloaded. The network environment quality is divided into four levels: excellent, good, general, and poor. The priority and presentation form of the recommended scheme are dynamically adjusted according to the scene characteristics. The scheme with convenient operation is preferentially recommended in the peak time period. The offline operable scheme is preferentially recommended when the network is poor.

[0039] The recommended strategy is adaptively optimized and dynamically adjusted. Real-time feedback data and recommended effect data of users are introduced. The real-time feedback data includes scheme click rate, adoption rate, completion handling rate, and satisfaction score. The recommended effect data includes failure repair success rate, business handling time length reduction ratio, and secondary failure rate. Gradient descent algorithm is used to dynamically adjust the weight coefficients of the recommended scheme. The weight coefficients include solution matching degree, user preference fit degree, operation complexity, and handling efficiency. The initial weight coefficients are set to 0.4, 0.3, 0.15, and 0.15, respectively. In combination with the business scene change trend, the recommended scheme set is regularly updated. Invalid schemes are deleted, new schemes are supplemented, and the presentation form of existing schemes is optimized to ensure the timeliness and effectiveness of the recommended strategy.

[0040] Real-time evaluation and feedback loop of recommended effect are constructed. A multi-dimensional evaluation index system is set, including failure repair success rate, business handling time length, user satisfaction, secondary failure rate, recommended scheme adoption rate, and operation complexity score. The failure repair success rate is the proportion of users who successfully handle the business after adopting the recommended scheme. The business handling time length is the total time from the presentation of the recommended scheme to the completion of the business handling. The user satisfaction is collected by 1-5 point scoring. The secondary failure rate is the proportion of users who still fail to handle the business after adopting the recommended scheme. The recommended scheme adoption rate is the proportion of users who click to view the recommended scheme. The operation complexity score is the subjective score of users on the operation difficulty of the recommended scheme. The weighted summation method is used to calculate the comprehensive recommended effect score. The weights of each index are set to 0.3, 0.2, 0.2, 0.15, 0.1, and 0.05, respectively. The change of the comprehensive score is monitored in real time. When the score is lower than the preset threshold, the strategy emergency optimization mechanism is triggered.

[0041] The system stores recommendation strategies and reuses them in similar scenarios. A dynamic recommendation strategy library is established to store optimized recommendation strategies, corresponding failure scenario features, user profile features, and recommendation performance data. The strategy library adopts a distributed storage architecture, supporting high-concurrency read / write and fast retrieval. A similar scenario matching model is built based on the K-nearest neighbor algorithm to calculate the similarity between new failure scenarios and historical scenarios in the strategy library. The similarity calculation dimensions include failure reasons, user profiles, and scenario features. The similarity threshold is set at 0.85. When the similarity is higher than the threshold, the historical best recommendation strategy is directly reused and fine-tuned. When the similarity is lower than the threshold, a new recommendation strategy is generated and added to the strategy library, improving the efficiency of strategy generation.

[0042] This invention also includes a step for dynamically calculating the weights of failure causes, taking into account the time decay effect and changes in the intensity of the impact of failure causes. The weight calculation formula is as follows: ,in Reasons for failure The overall weight, f for Reasons for failure at any time The impact strength on business processing ranges from 0 to 1, k is the time decay coefficient with a value of 0.02, and T is the time window for weight calculation, set to 24 hours. As an integral variable, this integral operation accurately quantifies the actual impact of different failure causes within a specific time window, making the attribution results more consistent with the dynamic scenarios of business processing, and providing a more scientific weighting basis for the accurate generation of recommendation strategies.

[0043] This invention also includes a user operation behavior sequence analysis step, which uses a long short-term memory network to deeply mine user operation behavior sequences, extracting user operation habit characteristics and behavioral preference patterns. Operation habit characteristics include operation rhythm, click preference areas, input speed, and error correction frequency. Behavioral preference patterns include the degree of dependence on guidance information, the intensity of demand for simplified operation, and the willingness to accept alternative solutions. Based on the analysis results, the presentation format of the recommended solutions is optimized. For users with a fast operation rhythm, a simplified recommendation interface is used to highlight the core solution. For users who rely on guidance information, detailed operation steps are added. For users with a high willingness to accept, alternative business options are recommended first. At the same time, the timing of recommendations is adjusted. If the user fails the first time, a basic solution is pushed immediately. If the user fails a second time, a deeply optimized solution and auxiliary tools are pushed to improve the adaptability of the recommendations and user acceptance.

[0044] In this invention, a priority ranking mechanism for recommendation schemes is added to the dynamic generation step of the recommendation strategy. A multi-dimensional ranking model is constructed based on the urgency of user needs, the difficulty of solution implementation, the success rate of business processing, and the fit of user preferences. The ranking formula is as follows: ,in Recommended solution Priority score, For the plan The corresponding business processing success rate, ranging from 0 to 1. For the plan The implementation difficulty coefficient ranges from 1 to 5, with higher values ​​indicating greater difficulty. For the plan With users The degree of preference matching, with a value ranging from 0 to 1. The value represents the urgency of the user's need for solution s, ranging from 0 to 1. , , , The weighting coefficients are set to 0.4, 0.25, 0.2, and 0.15 respectively. This multi-dimensional weighted calculation clarifies the presentation order of recommended solutions, prioritizing solutions with high success rates, low implementation difficulty, alignment with user preferences, and strong urgent needs, thereby shortening the user's decision-making time.

[0045] In this invention, a data quality verification and cleaning mechanism is added to the multi-dimensional collection step of failed data. A dual verification method based on statistical thresholds and rule matching is adopted, setting normal value ranges for each data dimension. The normal range for server response latency is 0-500 milliseconds, the normal range for database read / write speed is above 1000 times / second, the normal range for interface call success rate is above 99%, the normal range for bandwidth usage is set to 1-10 Mbps according to the business type, the normal range for data packet loss rate is 0-1%, and the normal range for network latency is 0-100 milliseconds. Data outside the range is marked as abnormal data. Duplicate and invalid data are removed through rule matching. Duplicate data is identified by comparing data feature hash values, and invalid data includes null data, data with incorrect format, and data with logical contradictions. The abnormal data after verification is repaired using interpolation, ensuring the accuracy and reliability of the collected data and laying a data foundation for subsequent attribution analysis and strategy generation.

[0046] In this invention, a real-time profile update mechanism is added to the user profile dynamic construction step. Profile update trigger conditions are set, including new user business processing records, changes in failure scenarios, changes in operating habits, adjustments to preference settings, and updates to personal information. When the trigger conditions are met, the profile update process is immediately initiated. An incremental learning algorithm is used to analyze the new data and update the user profile's feature parameters, reducing the decrease in the suitability of recommendation strategies due to profile lag. Simultaneously, a profile decay mechanism is established to reduce the weight of user features that have not been used for a long time, while retaining frequently occurring features. This ensures that the user profile always aligns with the user's current actual needs and behavioral characteristics, improving the personalization of the recommendation strategy.

[0047] In the adaptive optimization step of the recommended strategy, a strategy iteration optimization formula is introduced, the strategy parameters are dynamically adjusted based on the recommended effect deviation, the optimization formula is , wherein is the policy parameter after iteration, is the current policy parameter, is the learning rate, and the value is 0.01, is the recommended effect value corresponding to the current parameter at the moment, is the recommended effect target value at the moment, T is the iteration time window, and is set to 1 hour, is the integral variable, the deviation cumulative value of the current strategy and the target effect is quantified through the integral operation, the strategy parameters are accurately adjusted based on the deviation direction and size, the recommended effect continuously approaches the target value, and the scientificity and efficiency of the strategy optimization are improved.

[0048] In the recommended effect real-time evaluation step, a multi-scene effect comparison and analysis mechanism is added, different scenes are divided according to business types, failure reasons, user groups and handling time periods, the business types include government service business, financial handling business, e-commerce platform business and medical appointment business, the user groups are divided into novice users, skilled users and experienced users according to operation ability, and the handling time periods are divided into working day peak, working day flat peak, holiday peak and holiday flat peak; different evaluation index weights are set for different scenes, the government service business focuses on handling success rate and compliance, the financial handling business focuses on safety and efficiency, the novice user focuses on operation convenience, and the skilled user focuses on individualization and flexibility; through multi-scene comparison and analysis, the advantages and disadvantages of the recommended strategy in different scenes are identified, targeted optimization and adjustment are carried out, and the scene adaptation ability of the strategy is improved.

[0049] In the application, an emergency recommendation mechanism for abnormal scenes is also included, extreme abnormal scenes are preset, including large-scale system failure, network paralysis, temporary adjustment of business rules and sudden peak traffic, emergency recommendation schemes are developed for different extreme scenes, offline handling channels and business postponement application options are recommended during large-scale system failure, cache version operation guide and subsequent handling reminder service are recommended during network paralysis, the latest rule interpretation and material supplement list are pushed during temporary adjustment of business rules, and a simplified version of the handling process with low resource consumption is preferentially recommended during sudden peak traffic; an abnormal scene rapid identification model is established, system load, network state and business rule change signals are monitored in real time, and the emergency recommendation mechanism is triggered within 50 milliseconds after the abnormal scene occurs, so that the users who fail to handle the business can obtain timely and effective recommended services, and the user loss rate is reduced.

[0050] In the present application, in the strategy storage and reuse step, the intelligent iteration updating mechanism of the strategy is added, the effect of the historical recommended strategy in the strategy library is reviewed regularly, the random forest algorithm is used to screen the strategy features that have a significant impact on the recommendation effect, and the weight of the strategy features is adjusted; every 5000 effective business handling failure data and recommendation feedback data are accumulated, the similar scene strategy in the strategy library is retrained, the strategy parameter and the recommendation logic are optimized, and the reinforcement learning algorithm is introduced, the recommendation effect is taken as the reward signal, the strategy library is guided to iterate in a better direction; the strategy version management mechanism is established, the content, time and applicable scene of each strategy update are recorded, the strategy version rollback and comparative analysis are supported, the continuous optimization and stable operation of the recommended strategy are ensured, and the changing business scene and user demand are adapted.

[0051] The specific implementation of the present system is further illustrated by two embodiments as follows:

[0052] Embodiment one: application of government service enterprise registration business handling failure scene

[0053] This embodiment is aimed at the enterprise registration business of the government service platform, the daily handling volume of this business is 5000, the failure scenes mainly concentrate in five categories of material missing, format error, insufficient authority, system load too high and network fluctuation, involve three groups of novice users, skilled users and intermediary institution users, and the adaptability and effectiveness of the verification method in the high-frequency business and multi-user type scene are verified.

[0054] The failure data multi-dimensional collection and accurate attribution stage collects full-process data at a frequency of 100 milliseconds / time. User operation behavior data includes click path, such as registration entry→information filling→material uploading→submission for review, operation time length information filling 3-20 minutes material uploading 1-5 minutes, input content integrity unified social credit code, legal person information, business scope field filling completeness, interaction frequency same page refresh number button click number; system running state data includes server response delay 0-500 milliseconds, database read-write speed 1000-3000 times / second, interface call success rate 98%-100%, resource occupancy rate CPU usage rate 20%-80% memory usage rate 30%-70%; business parameter configuration data includes required material type business license scan legal identity card photo company charter and the like 8 types, filling format requirement picture resolution not less than 300 dpi file size not more than 5 MB text field no special character, permission verification rule legal real name authentication agent authorization letter; failure prompt feedback data includes error code, such as 1001-material missing 1002-format error, failure description text, user feedback label operation complex unclear system lag; network transmission data includes bandwidth occupancy 2-10 Mbps, data packet loss rate 0-2%, network delay 0-150 milliseconds. In the data quality verification and cleaning stage, the server response delay normal range is set to 0-500 milliseconds, the database read-write speed is 1000 times / second or more, the interface call success rate is 99% or more, the bandwidth occupancy is 2-8 Mbps, the data packet loss rate is 0-1%, and the network delay is 0-100 milliseconds. Data outside the range is marked as abnormal. Through hash value comparison, duplicate submission of material data is identified, null legal person information data, format error code data, and logically contradictory business scope data are excluded, and linear interpolation method is used to repair abnormal network delay data. The attribution is attributed by using the fusion algorithm of decision tree and Bayesian network. The decision tree selects three core influencing factors of material uploading, format verification, and system response, the Bayesian network calculates the correlation probability of each factor, and when a certain intermediary user fails, the correlation probability of material missing is 0.7, the correlation probability of format error is 0.2, and the correlation probability of system load is 0.1. The direct cause is that the company charter is not uploaded due to material missing, and the indirect cause is that the operation guide is not clear.

[0055] The failure reason weight dynamic calculation adopts the formula , wherein is the material missing reason, The influence intensity in different time periods within 24 hours: peak period 9:00-11:00 and 14:00-16:00 is 0.9, flat peak period is 0.7, and trough period is 0.5, k=0.02, T=24 hours, and the integral calculation is , =12.8, which is higher than the format error = 8.5 and system load = 4.2, becoming the core basis for recommended policy making.

[0056] In the user portrait dynamic construction and demand deep mining stage, the user historical data is integrated: the intermediary user handles 300 enterprise registration businesses per year, with 15 historical failure records, 10 times of missing materials, 5 times of format errors, personal basic information for enterprise service type, high-frequency handling scene, preference setting for PC handling, SMS notification, and batch operation function. A multi-dimensional portrait is constructed: the business demand type is batch registration, the operation ability level is proficient, the preferred handling method is efficient batch processing, the risk tolerance is low, and the failure tolerance is low. Based on association rule mining, the mapping relationship between the portrait and the failure scene is analyzed, and the potential demand is mined for material template batch download, format batch verification, and missing material reminder. Combined with the user's browsing of batch registration guidelines in the past 3 days in recent business handling intention, it is predicted that the subsequent demand is a multi-enterprise information batch import function. The portrait real-time updating mechanism triggers the condition to be met, and after the user adds 1 failure record, the failure tolerance parameter in the portrait is immediately updated through incremental learning algorithm, the weight of the single registration preference feature which is not used for a long time is reduced, and the batch operation demand feature is strengthened.

[0057] In the recommended strategy dynamic generation and scene self-adaptive adaptation stage, based on the attribution results and the user portrait, an initial recommended scheme set is constructed: the failure solution is company charter template download and missing material quick upload channel; there is no alternative business option; the operation process optimization suggestion is to open the batch material upload function; the auxiliary handling tool recommendation is batch format verification plug-in and enterprise information automatic filling tool. Adjust the scheme combined with real-time scene characteristics: the current is the working day peak period 10:00, the system load state is busy, the network environment quality is good, and the business urgency is high. Dynamically adjust the priority: preferentially recommend batch format verification plug-in operation convenient adaptation peak period, company charter template download high-frequency demand, and present the form as pop-up window display core scheme and fold display auxiliary scheme. The recommended scheme priority ranking adopts the formula , where scheme 1 is batch format verification plug-in, = 0.95 success rate, = 2 difficulty coefficient, = 0.9 preference fit degree, = 0.9 urgency, = 0.4, = 0.25, = 0.2, = 0.15, = 0.4*0.95+0.25*(1 / 2)+0.2*0.9+0.15*0.9=0.38+0.125+0.18+0.135=0.82; scheme Download the company charter template, = 0.4 x 0.9 + 0.25 x (1 / 1) + 0.2 x 0.8 + 0.15 x 0.9 = 0.36 + 0.25 + 0.16 + 0.135 = 0.905, final ranking Priority Consistent with the scene adaptation adjustment result.

[0058] In the adaptive optimization and dynamic adjustment stage of the recommendation strategy, real-time feedback data of users is introduced: scheme s2 click rate 85%, adoption rate 78%, completion rate 70%, satisfaction 4.5 points; recommendation effect data: failure repair success rate 70%, business handling time shortened by 30%, secondary failure rate 5%. Gradient descent algorithm is used to adjust the weight coefficient, the solution matching degree weight is increased from 0.4 to 0.45, the user preference fit degree is adjusted from 0.3 to 0.28, the operation complexity is reduced from 0.15 to 0.12, and the handling efficiency is increased from 0.15 to 0.15. The formula for policy iteration optimization is , where is the current solution matching degree weight 0.4, = 0.01, is the recommended effect value within 1 hour 0.7, is the target effect value 0.8, = 1 hour, and the integral is calculated as , = 0.4 - 0.01 x (-0.1) = 0.401, and the precise adjustment strategy parameters. Regularly update the recommended scheme set, supplement the new scheme of batch import tool of multi-enterprise information, and optimize the presentation form of the scheme according to the user operation rhythm to automatically expand / fold details.

[0059] In the real-time evaluation and feedback loop construction stage of the recommended effect, multi-dimensional evaluation indicators are set: failure repair success rate, business handling time, user satisfaction, secondary failure rate, recommended scheme adoption rate, and operation complexity score. The weighted sum method is used to calculate the comprehensive score, and the weight of each indicator is 0.3, 0.2, 0.2, 0.15, 0.1, and 0.05. The comprehensive score in a certain period is 0.7 x 0.3 + 0.8 x 0.2 + 0.9 x 0.2 + 0.95 x 0.15 + 0.85 x 0.1 + 0.8 x 0.05 = 0.21 + 0.16 + 0.18 + 0.1425 + 0.085 + 0.04 = 0.8175, which is higher than the preset threshold 0.7, and the emergency optimization is not triggered. Multi-scene effect comparison and analysis are divided into scenes according to business type enterprise registration, user group intermediary agency, and handling time peak, and the weight of the evaluation indicators in this scene is set: success rate 0.35, efficiency 0.25, and satisfaction 0.2. The batch operation related recommended scheme is optimized.

[0060] The recommended strategy storage and similar scene reuse stage establishes a distributed storage dynamic recommended strategy library, stores the optimized recommended strategy, the failure scene characteristic material missing peak period skilled user, the user portrait characteristic, and the recommended effect data. A similar scene matching model is constructed based on a K nearest neighbor algorithm. A new failure scene is a batch registration material missing peak period of an intermediary agency user, the similarity calculation with a historical scene in the strategy library is 0.92, which is higher than a threshold value 0.85, the historical optimal recommended strategy is directly reused, and the latest version of the format verification plug-in is supplemented. A strategy intelligent iterative updating mechanism accumulates 5000 data, adopts a random forest algorithm to select a batch operation tool recommended template download as a core strategy characteristic, adjusts the weight, introduces a reinforcement learning algorithm, takes 70% of the failure repair success rate as a reward signal, and optimizes the recommended strategy logic. An abnormal scene emergency recommended mechanism presets a large-scale system failure scene, when the server response delay is more than 500 milliseconds, the interface call success rate is less than 95%, and the emergency recommendation is triggered within 50 milliseconds, the offline material filling template and the business postponement application option are pushed.

[0061] Table 1 Comparison of recommended strategy effect of government service enterprise registration business

[0062]

[0063] Table 1 shows that the application has obvious advantages in the government service enterprise registration business. The existing method has a failure repair success rate of only 62%, and due to the ambiguity of attribution and the homogenization of recommended solutions, it cannot accurately match the needs of batch handling users; the application collects multi-dimensional data and accurately attributes, clearly identifies the core reasons for material missing, and pushes targeted solutions combined with user batch operation preferences, so that the repair success rate is improved to 88%. The user satisfaction is increased from 65 points to 90 points, which is due to personalized recommendation and scene adaptation, and the peak period pushes the batch tool with convenient operation, which meets the efficient handling needs of intermediary agency users. The secondary failure rate is reduced from 28% to 6%, which benefits from the adaptive optimization of the recommended strategy and the deep solution pushing after the secondary failure, which effectively avoids the repeated occurrence of similar errors. The business handling time is shortened by 40%, which is due to the recommendation of batch verification, automatic filling and other auxiliary tools, which reduces the user operation steps. The recommended solution adoption rate is improved to 82%, which reflects the high fit between the solution and the user needs and the scene characteristics, and improves the government service handling experience.

[0064] Embodiment two: Application of financial institution personal loan application business failure scene

[0065] This embodiment is aimed at the personal loan application business of financial institutions, with an average daily handling volume of 3000, failure scenarios including mismatched qualifications, incomplete materials, format errors, network abnormalities, system failures, user groups covering young users, middle-aged users, and elderly users, involving online APP handling and offline branch auxiliary handling channels, and verifying the performance of the method in multi-channel, multi-age layer, and high-risk control scenarios.

[0066] The failure data multi-dimensional collection and accurate attribution stage collects data at a frequency of 100 milliseconds / second. User operation behavior data includes click path APP homepage → loan application → basic information filling → income proof uploading → qualification review, operation duration information filling 5-30 minutes material uploading 2-8 minutes, input content integrity ID number income information contact information, etc. Field, interaction frequency button click number page return number; System running state data includes server response delay 0-400 milliseconds, database read-write speed 1500-2500 times / second, interface call success rate 99%-100%, resource occupancy rate CPU usage rate 30%-75% memory usage rate 40%-65%; Business parameter configuration data includes required material types ID photo income proof credit report, etc. 6 types, filling format requirement clear picture without occlusion file format JPG / PNG text information consistent with certificate, permission verification rule credit authorization real name verification; Failure prompt feedback data includes error code such as 2001-mismatched qualifications 2002-incomplete materials, failure description text, user feedback label unclear qualification requirements, cumbersome operation, network lag; Network transmission data includes bandwidth occupancy 1-8 Mbps, data packet loss rate 0-1.5%, network delay 0-120 milliseconds. Data quality verification sets server response delay 0-500 milliseconds, database read-write speed 1000 times / second or more, interface call success rate 99% or more, bandwidth occupancy 1-6 Mbps, data packet loss rate 0-1%, network delay 0-100 milliseconds, and removes duplicate income proof data and invalid blurred picture data by rule matching. Attribute using decision tree and Bayesian network fusion algorithm, decision tree selects three core factors of qualification review, material uploading, and network transmission, Bayesian network calculates correlation probability, when an elderly user fails to handle, the correlation probability of mismatched qualifications is 0.6, the correlation probability of incomplete materials is 0.3, and the correlation probability of network abnormalities is 0.1, the direct cause is mismatched qualifications and the indirect cause is user's cognitive bias on qualification requirements.

[0067] The failure reason weight dynamic calculation uses the formula , λ is the mismatched qualification reason, Within 24 hours: 0.8 in working day flat peak period 10:00-11:00 15:00-16:00, 0.9 in peak period, 0.6 in trough period, = 0.02, = 24 hours, integral calculation , = 11.5, higher than the material is not complete = 7.8 and network anomalies = 3.6, become the core basis of recommended strategy.

[0068] In the user portrait dynamic construction and demand depth mining stage, the user historical data is integrated: the old user is 60 years old, the personal loan application history is 2 times, both of which fail due to credit problems, the personal basic information is a retiree, the low-frequency handling scene, the preference setting is the APP end simplified operation, the telephone notification, the voice guidance. Build multi-dimensional portrait: the business demand type is small consumer loan, the operation ability level is novice, the preferred handling method is simplified process, the risk tolerance is low, and the failure tolerance is medium. Based on the association rule mining, the mapping relationship between the portrait and the failure scene is analyzed, and the potential demand is mined for clear interpretation of credit requirements, simplified application process, and manual auxiliary guidance. Combined with the recent business handling intention, the small loan product introduction is browsed, and the follow-up demand is predicted for low-threshold small loan recommendation. The portrait update trigger condition is met, the user adds a failure record, and the operation ability level parameter is updated by incremental learning algorithm, the self-fill preference feature weight is reduced, and the simplified process demand feature is strengthened.

[0069] In the recommendation strategy dynamic generation and scene self-adaptive adaptation stage, based on the attribution results and the user portrait, the initial recommendation scheme set is constructed: the failure solution is the credit requirement step-by-step interpretation, the low-threshold small loan recommendation; the alternative business option is the consumption loan A income requirement lower than the original business; the operation process optimization suggestion is to simplify the application fill-in field to retain the core 3 items; the auxiliary handling tool recommendation is the voice fill-in helper, the manual customer service online consultation. Adjust the scheme combined with real-time scene characteristics: the current is the working day peak period 15:30, the system load state is normal, the network environment quality is general, and the business emergency degree is medium. Dynamically adjust the priority: prioritize the core needs of credit requirement interpretation and low-threshold small loan alternative scheme, present the form as large font, voice broadcast prompt core scheme, and step-by-step display operation process. The priority of the recommended scheme is sorted by the formula , the scheme 1 is the credit requirement step-by-step interpretation, = 0.9 success rate, = 1 difficulty coefficient, = 0.92 preference fit degree, = 0.7 emergency degree, = 0.4, = 0.25, = 0.2, = 0.15, = 0.4 x 0.9 + 0.25 x 1 + 0.2 x 0.92 + 0.15 x 0.7 = 0.36 + 0.25 + 0.184 + 0.105 = 0.899; the scheme s2 is a low threshold micro loan recommendation, = 0.4 x 0.85 + 0.25 x 1 + 0.2 x 0.88 + 0.15 x 0.7 = 0.34 + 0.25 + 0.176 + 0.105 = 0.871, ranking priority , which meets the scene adaptation requirements.

[0070] In the adaptive optimization and dynamic adjustment stage of the recommendation strategy, real-time feedback data of users are introduced: the click rate of scheme s1 is 78%, the adoption rate is 70%, the completion rate is 65%, and the satisfaction is 4.3 points; the recommendation effect data: the success rate of failure repair is 65%, the business handling time is shortened by 25%, and the secondary failure rate is 8%. The weight coefficient is adjusted by using gradient descent algorithm, the matching degree of the solution is improved from 0.4 to 0.42, the user preference fit degree is adjusted from 0.3 to 0.32, the operation complexity is reduced from 0.15 to 0.1, and the handling efficiency is improved from 0.15 to 0.16. The strategy iteration optimization adopts the formula , is the current user preference fit degree weight 0.3, = 0.01, is the recommended effect value within 1 hour 0.65, is the target effect value 0.75, = 1 hour, and the integral calculation is , = 0.3 - 0.01 x (-0.1) = 0.301, the precise optimization strategy parameter. The recommendation scheme set is updated regularly, the new scheme is supplemented with artificial auxiliary video guidance, and the presentation form is optimized to adapt to the large font and high contrast interface of the elderly users.

[0071] In the real-time evaluation and feedback closed loop construction stage of the recommendation effect, the comprehensive score of multi-dimensional evaluation index is 0.65 x 0.3 + 0.75 x 0.2 + 0.86 x 0.2 + 0.92 x 0.15 + 0.78 x 0.1 + 0.85 x 0.05 = 0.195 + 0.15 + 0.172 + 0.138 + 0.078 + 0.0425 = 0.7755, which is higher than the preset threshold 0.7. Multi-scene effect comparison analysis is divided into scenes according to user groups of elderly users and business types of personal loans, and evaluation index weights are set: operation convenience 0.3, satisfaction 0.25, success rate 0.25, and related schemes of process simplification and voice assistance are optimized in a targeted manner.

[0072] The recommended strategy storage and similar scene reuse stage stores the recommended strategy, the failure scene feature, the unqualified old user in the peak period, the user portrait feature, and the recommended effect data in the distributed strategy library. The similarity calculation between the new failure scene and the historical scene is 0.88, which is higher than the threshold 0.85, the historical strategy is reused, and the latest low threshold loan product information is supplemented. The strategy intelligent iterative updating mechanism accumulates 5000 data, adopts the random forest algorithm to select the core feature of the qualification interpretation simplification process, and adjusts the weight; the reinforcement learning algorithm takes the repair success rate of 65% as the reward signal, and optimizes the recommendation logic. The abnormal scene emergency recommendation mechanism monitors that the network is paralyzed, the packet loss rate is more than 5%, and the delay is more than 200 milliseconds, triggers the emergency recommendation within 50 milliseconds, and pushes the cached version of the qualification interpretation document and the offline network point reservation service.

[0073] Table 2 Financial institution personal loan application business recommended strategy effect comparison

[0074]

[0075] Table 2 shows that the present application performs outstandingly in the financial personal loan application business. The existing method has a failure repair success rate of only 55%, because it does not accurately identify the operation ability and cognitive bias of the old user, the recommended scheme is complex and lacks pertinence; the present application pushes the adaptation scheme such as qualification interpretation and simplification process through multi-dimensional user portrait and accurate attribution, and the repair success rate is improved to 83%. The user satisfaction is increased from 60 points to 88 points, which is due to the fact that the recommended scheme fits the operation habit and cognitive level of the old user, and the large font, voice guidance and other presentation forms improve the convenience of use. The secondary failure rate is reduced from 35% to 7%, which benefits from the adaptive optimization of the strategy and the deep auxiliary scheme after the secondary failure, effectively solving the cognitive bias of the user to the qualification requirements. The business handling time is shortened by 35%, which is attributed to the recommendation of tools such as simplified filling field and voice filling assistant, reducing the operation difficulty and time consumption. The adoption rate of the recommended scheme reaches 79%, which reflects that the scheme is highly matched with the user demand and scene feature, and at the same time meets the risk control requirements of the financial business, realizing the win-win of user experience and business compliance.

[0076] Referring to Figure 2 The histogram clearly shows the efficient repair capability of the present application for various failure scenes. The repair success rate of the existing method for different failure reasons is generally lower than 65%, especially for complex reasons such as unqualified and system failure, the repair effect is poor, because the attribution is ambiguous and the recommended scheme lacks pertinence. The present application accurately locates the direct and indirect reasons for various failures through multi-dimensional data collection and decision tree-Bayesian network fusion attribution algorithm, pushes the template and fast uploading channel for material missing, provides low threshold alternative business and clear interpretation for unqualified, so that the repair success rate of various reasons is more than 83%. The repair success rate of material missing and format error is more than 90%, which fully embodies the effectiveness of the targeted recommended strategy.

[0077] Referring to Figure 3 The broken line graph highlights the personalized recommendation adaptation capability of the present application. The adoption rate of the existing method shows significant differentiation. Novice users and elderly users have an adoption rate of less than 45% due to limited operational ability, while skilled users and intermediary institution users have a relatively high adoption rate, reflecting the lack of adaptation of the recommended solution to the needs of different groups. The present application dynamically constructs a multi-dimensional user portrait, integrates operational ability, preference habits, business needs and other features, and pushes simplified processes and detailed instructions to novice users, provides voice assistance and large font interface for elderly users, and recommends batch operation tools for intermediary institution users, so that the adoption rate of each group is more than 82%. Especially the adoption rate of elderly users increases from 38% to 82%, which reflects the core value of user portrait and personalized recommendation, and solves the problem of insufficient adaptability caused by homogeneous recommendation of existing methods.

[0078] Referring to Figure 4 The pie chart reveals the scientific construction logic of the recommendation strategy of the present application. The failure attribution accuracy accounts for 30%, which is the highest, because accurately locating the failure source is the basis for subsequent recommendations. The present application ensures the accuracy of attribution through multi-dimensional data collection and fusion attribution algorithm; the user portrait matching degree accounts for 28%, which reflects the core position of personalized recommendation, and the dynamically updated user portrait ensures that the recommended solution meets the user's needs; the scene adaptation weight is 22%, which highlights the key influence of scene awareness on the recommendation effect, and the real-time scene feature adjustment makes the recommendation more suitable for the actual use environment; the feedback optimization coefficient is 12% and the emergency mechanism adaptation is 8%, which respectively guarantee the continuous iteration of the strategy and the stable service of the extreme scene. This balanced and focused weight distribution of multiple features avoids the one-sidedness of recommendation caused by the dominance of a single feature, so that the strategy has precision, personalization, scene and robustness, which is superior to the limitations of existing methods relying on a single feature.

[0079] Referring to Figure 5 The column chart highlights the scene self-adaptation capability of the present application. The secondary failure rate of the existing method is significantly affected by the scene, which is more than 30% in peak hours and poor network, because the recommended strategy does not adapt to the scene characteristics, such as pushing complex solutions in peak hours and pushing online dependent solutions in poor network. The present application dynamically adjusts the recommended solution combined with real-time scene features, prioritizes the operation of convenient solutions in peak hours, and pushes offline operable tools and cached version instructions in poor network, so that the secondary failure rate of each scene is controlled within 10%. Even in poor network and peak hours, the secondary failure rate is only 9% and 8%, which is much lower than the existing method. This advantage is due to the real-time perception of scene features and the dynamic adaptation of the recommended strategy, effectively solving the problem of secondary failure caused by the insufficient scene adaptation of existing methods.

[0080] The above merely describes preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes within the technical scope disclosed by the present application and according to the technical solutions and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A dynamic adaptive recommendation strategy optimization method for business handling failure scenarios, characterized in that, Comprise the following steps: Multi-dimensional collection of failure data and accurate attribution, collecting data throughout the business handling process, using a multi-factor attribution algorithm based on the fusion of decision trees and Bayesian networks to perform hierarchical analysis on the collected data, first filtering out the core influencing factors through decision trees, then calculating the correlation probability of each factor using Bayesian networks to identify the direct and indirect causes of business failure; Dynamic construction of user portraits and deep mining of user needs, integrating user historical business handling, failure records, personal basic information, and preference setting data to construct multi-dimensional user portraits; Based on the association rule mining algorithm, analyze the mapping relationship between the portrait and the failure scene, mine potential needs, and predict subsequent handling needs; Dynamic generation of recommendation strategies and scene self-adaptive adaptation, based on the failure attribution results and user portraits to construct an initial recommendation scheme set; Adjust the recommendation scheme according to the real-time business scene characteristics, dynamically adjust the priority and presentation form of the scheme according to the scene characteristics; Self-adaptive optimization and dynamic adjustment of recommendation strategies, introduce real-time feedback data and recommendation effect data, and use gradient descent algorithm to dynamically adjust the weight coefficient of the recommendation scheme; combined with the trend of business scene changes, regularly update the recommendation scheme set and optimize the presentation form of the scheme; Real-time evaluation and feedback loop construction of recommendation effect, set up a multi-dimensional evaluation index system, and calculate the comprehensive recommendation effect score by using the weighted summation method; real-time monitoring of score changes, score below the preset threshold triggers the strategy emergency optimization mechanism; Storage of recommendation strategies and reuse of similar scenes, establish a dynamic recommendation strategy library to store related strategies and feature data; based on the K nearest neighbor algorithm to construct a matching model, calculate the similarity between new scenes and historical scenes, reuse the historical optimal strategy or generate a new strategy and store it in the library according to the similarity; The dynamic calculation steps for failure cause weights take into account the time decay effect and changes in the intensity of influence of failure causes. The weight calculation formula is as follows: ,in Reasons for failure The overall weight, f for Reasons for failure at any time The impact on business processing intensity, k is the time decay coefficient, and T is the time window for weight calculation. For integration variables; In the step of dynamically generating the recommendation strategy, a recommendation scheme priority ranking mechanism is added, a multi-dimensional ranking model is constructed based on the user demand urgency, the solution implementation difficulty, the business handling success rate, and the user preference fit degree, and the ranking formula is , wherein is the priority score of the recommendation scheme , is the business handling success rate corresponding to the scheme , is the implementation difficulty coefficient of the scheme , is the preference fit degree of the scheme and the user , is the demand urgency of the user for the scheme , , , are weight coefficients. In the multi-dimensional collection of failure data steps, add data quality verification and cleaning mechanism, use dual verification method based on statistical threshold and rule matching, set the normal value range of each data dimension, data beyond the range is marked as abnormal data; eliminate duplicate data, invalid data through rule matching, duplicate data is identified by data feature hash value comparison, invalid data includes null data, format error data, logical contradiction data, repair the abnormal data after verification by interpolation method; Emergency recommendation mechanism for abnormal scenes, preset extreme abnormal scenes, develop emergency recommendation schemes for different extreme scenes, recommend offline handling channels and business postponement application options when there is a large-scale system failure, recommend cache version operation guide and subsequent handling reminder service when the network is paralyzed, push the latest rule interpretation and material supplement list when the business rules are temporarily adjusted, and preferentially recommend simplified version of handling process with low resource consumption when there is a sudden peak traffic; establish a rapid identification model for abnormal scenes, monitor system load, network status, and business rule change signals in real time, and trigger the emergency recommendation mechanism within 50 milliseconds after the abnormal scene occurs; In the strategy storage and reuse step, an intelligent iterative updating mechanism is added to regularly review the effects of historical recommended strategies in the strategy library, filter the strategy features that significantly affect the recommendation effect using the random forest algorithm, and adjust the weights of the strategy features. Every 5000 valid business handling failure data and recommendation feedback data are accumulated to retrain the similar scene strategies in the strategy library, optimize the strategy parameters and recommendation logic, and introduce a reinforcement learning algorithm to guide the strategy library to iterate in a better direction. A strategy version management mechanism is established to record the content, time, and applicable scenarios of each strategy update.

2. The method of claim 1, wherein, It also includes a user operation behavior sequence analysis step, which uses a long short-term memory network to deeply mine user operation behavior sequences and extract operation habit features and behavior preference rules. Based on the analysis results, the presentation form of the recommended solution is optimized, the interface, content, and recommendation priority are adjusted according to different user characteristics, and the recommendation timing is adjusted according to the number of operation failures. The first failure pushes the basic solution, the second failure pushes the deep solution and tools. 3.The method of claim 1, wherein, In the user portrait dynamic construction step, an incremental learning algorithm is used to analyze the new data and update the feature parameters of the user portrait when the trigger condition is met. At the same time, an image decay mechanism is established to reduce the weight of long-term unused user features, focusing on retaining recent high-frequency features.

4. The method of claim 1, wherein, In the adaptive optimization step of the recommendation strategy, a policy iteration optimization formula is introduced to dynamically adjust the policy parameters based on the recommendation effect deviation. The optimization formula is wherein is the policy parameter after iteration, is the current policy parameter, is the learning rate, is is the recommendation effect value corresponding to the current parameter at the moment, is is the target value of the recommendation effect at the moment, and T is the iteration time window, which is set to 1 hour, is the integral variable.

5. The method for dynamically adaptive recommendation policy optimization for business transaction failure scenarios as claimed in claim 1 wherein, In the real-time evaluation of the recommendation effect step, a multi-scenario effect comparison and analysis mechanism is added to divide different scenarios according to business types, failure reasons, user groups, and handling time periods. Differentiated evaluation index weights are set for different scenarios. Government service business focuses on handling success rate and compliance, financial handling business focuses on safety and efficiency, novice users focus on operation convenience, and experienced users focus on personalization and flexibility.

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