Data analysis-based manual and tourist clothes work order distribution method and system

Abstract

The invention discloses a hand tourist service work order distribution method and system based on data analysis, and the method comprises the steps: obtaining a hand tourist service work order, carrying out the text semantic analysis, generating a work order feature vector, collecting customer service real-time state data, generating a customer service state portrait, and carrying out the reverse work order recommendation based on the customer service state portrait, and generating work order preference data; complexity prediction is carried out based on the work order feature vectors, and work order preference data are combined and published to an auction platform to obtain an auction result sequence; constructing a preliminary distribution scheme according to an auction result, and executing an intentional mismatch distribution operation to obtain abnormal condition data; constructing a chaotic test work order injection processing flow to verify the system stability; performing deep mining on the abnormal condition data to identify hidden skill features, and re-evaluating the comprehensive capability; a distribution scheme is adjusted according to the optimized capability evaluation data, and real-time monitoring is started; and performing stability verification on the processing quality based on the system stability data, generating a distribution strategy optimization instruction, and realizing intelligent chemical order distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent customer service management, in particular to a hand game customer service work order distribution method and system based on data analysis. BACKGROUND

[0002] With the vigorous development of the hand game industry and the continuous expansion of the user scale, hand game customer service has become an important link in maintaining user experience and ensuring game operation. Traditional customer service work order distribution mainly relies on simple load balancing and rule matching, lacking deep understanding and accurate assessment of customer service individual ability, making it difficult to achieve optimal matching of work orders and customer service skills.

[0003] The current work order distribution method generally has the problem of single customer service ability evaluation system, which is based only on explicit skill indicators for evaluation, ignoring the hidden skill characteristics of customer service in abnormal processing and innovative solutions. At the same time, the distribution strategy lacks self-adaptive adjustment capability, cannot be dynamically optimized according to real-time processing results, the work order complexity recognition accuracy is insufficient, and the distribution process stability verification mechanism is imperfect. These technical bottlenecks seriously restrict the overall service level and operation effect of hand game customer service, and there is an urgent need to develop a new generation of work order distribution technology with intelligent recognition, self-adaptive optimization, and quality assurance capability. SUMMARY

[0004] The present application provides a hand game customer service work order distribution method and system based on data analysis, aiming to solve the technical problems of inaccurate ability evaluation, fixed distribution strategy, and unstable processing quality in traditional customer service work order distribution. By integrating work order semantic analysis, customer service ability deep mining, bidding distribution mechanism, mismatch test verification, chaos stability detection, and hidden skill recognition, an intelligent distribution system is constructed to realize accurate matching of customer service skills and work order demand and adaptive adjustment of distribution strategy.

[0005] The present application provides a hand game customer service work order distribution method and system based on data analysis, aiming to solve the technical problems of inaccurate ability evaluation, fixed distribution strategy, and unstable processing quality in traditional customer service work order distribution. By integrating work order semantic analysis, customer service ability deep mining, bidding distribution mechanism, mismatch test verification, chaos stability detection, and hidden skill recognition, an intelligent distribution system is constructed to realize accurate matching of customer service skills and work order demand and adaptive adjustment of distribution strategy.

[0006] Obtain hand game customer service work order, perform text semantic analysis on the hand game customer service work order to generate work order feature vector, collect real-time state data of customer service to generate customer service state portrait, and perform reverse work order recommendation based on the customer service state portrait to generate customer service work order preference data;

[0007] Based on the work order feature vector, the complexity is predicted to generate a work order complexity level, and based on the customer service work order preference data, the work order complexity level is published to a customer service bidding platform to obtain a bidding result ranking;

[0008] construct a preliminary distribution scheme according to the auction result ranking, perform an intentional mismatch distribution operation on the preliminary distribution scheme to generate a mismatch distribution record, and obtain customer service processing exception condition data based on the mismatch distribution record;

[0009] construct a chaotic test work order, inject the chaotic test work order into the mobile game customer service work order processing flow for distribution to generate a chaotic distribution record, and perform system anti-interference capability verification through the chaotic distribution record to generate system stability data;

[0010] deeply mine and analyze the exception condition data to identify customer service hidden skill characteristics, reevaluate customer service comprehensive ability based on the hidden skill characteristics to generate optimized ability evaluation data;

[0011] adjust the preliminary distribution scheme according to the optimized ability evaluation data to obtain distribution execution records, and start a real-time monitoring mechanism to track the distribution execution records to generate processing quality monitoring data;

[0012] based on the system stability data, perform stability verification on the processing quality monitoring data to generate verification result data, perform comprehensive analysis on the verification result data to generate distribution strategy optimization instructions, and complete mobile game customer service work order distribution.

[0013] The second aspect of the present application proposes a mobile game customer service work order distribution system based on data analysis, comprising:

[0014] A data acquisition module is configured to obtain mobile game customer service work orders, perform text semantic analysis on the mobile game customer service work orders to generate work order feature vectors, collect real-time state data of customer service to generate a customer service state portrait, perform reverse work order recommendation based on the customer service state portrait to generate customer service work order preference data.

[0015] An auction distribution module is configured to perform complexity prediction based on the work order feature vectors to generate work order complexity levels, publish the work order complexity levels to a customer service auction platform based on the customer service work order preference data to obtain an auction result ranking.

[0016] A test verification module is configured to construct a preliminary distribution scheme according to the auction result ranking, perform an intentional mismatch distribution operation on the preliminary distribution scheme to generate a mismatch distribution record, and obtain customer service processing exception condition data based on the mismatch distribution record.

[0017] A chaotic injection module is configured to construct a chaotic test work order, inject the chaotic test work order into the mobile game customer service work order processing flow for distribution to generate a chaotic distribution record, and perform system anti-interference capability verification through the chaotic distribution record to generate system stability data.

[0018] The capability optimization module is used for deep mining analysis of the abnormal condition data to identify hidden skill characteristics of the customer service, and re-evaluates the comprehensive capability of the customer service based on the hidden skill characteristics to generate optimized capability evaluation data.

[0019] The execution monitoring module is used for adjusting the preliminary distribution scheme according to the optimized capability evaluation data to obtain distribution execution records, and starting a real-time monitoring mechanism to track and generate processing quality monitoring data of the distribution execution records.

[0020] The strategy optimization module is used for stability checking of the processing quality monitoring data based on the system stability data to generate checking result data, comprehensively analyzing the checking result data to generate distribution strategy optimization instructions, and completing the distribution of the mobile game customer service work order.

[0021] The beneficial effects of the present application are embodied in the following points: first, by establishing a work order distribution basic framework based on text semantic analysis and customer service state portrait, combining reverse work order recommendation and bidding distribution mechanism, intelligent recognition of work order content, accurate grasp of customer service capability characteristics and autonomous selection of the distribution process are realized, the technical problems of rough work order analysis, insufficient customer service capability cognition and blind distribution decision in traditional methods are solved, the accuracy and efficiency of work order and customer service matching are improved, and the customer service can participate in work order distribution decision based on their own expertise and work willingness.

[0022] Secondly, by the innovative intentional mismatch test and chaotic work order injection verification technology, a complete customer service capability deep mining and system stability verification system is established, the hidden skill characteristics that cannot be found by traditional evaluation methods are successfully identified by observing the response of the customer service under abnormal conditions, and the anti-interference ability of the system under interference environment is verified, the core problems of one-sided customer service capability evaluation and missing system stability verification in existing methods are solved, and a technical breakthrough from surface skill evaluation to deep capability discovery is realized.

[0023] Finally, by constructing a closed-loop optimization mechanism based on real-time monitoring and stability checking, integrating processing quality monitoring, stability data checking, strategy dynamic adjustment and other key technologies, continuous improvement of the distribution strategy and continuous improvement of the system performance are realized, the technical limitations of lacking quality feedback and strategy adjustment lag in traditional distribution methods are solved, a complete technical closed loop of "test discovery-capability optimization-quality monitoring-strategy improvement" is formed, and the processing quality and system stability of the mobile game customer service work order distribution are improved.

[0024] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] The drawings here show the specific examples of the technical solutions of the present application, and constitute a part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0026] Unless specifically stated or otherwise understood, the same reference signs in different drawings represent the same or similar technical features, and different reference signs can also be used to represent the same or similar technical features.

[0027] Figure 1 is a flow diagram of a mobile game customer service ticket distribution method based on data analysis.

[0028] Figure 2 is a structural block diagram of a mobile game customer service ticket distribution system based on data analysis. DETAILED DESCRIPTION

[0029] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the application. However, persons having ordinary skill in the art will appreciate that embodiments of the application can be practiced without the specific details, and that the scope of the present application is not limited to the embodiments described herein. In other instances, well-known features are not described in detail in order to not unnecessarily obscure the present application.

[0030] It should be understood that the term "comprises" as used in the specification and the appended claims indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0031] It should also be understood that the term "and / or" as used herein refers to any combination of associated listed items, and all possible combinations thereof, and includes these combinations.

[0032] As used in the specification and the appended claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to the determination" or "once [a described condition or event] is detected" or "in response to detecting [a described condition or event]" depending on the context.

[0033] In addition, in the description of the specification and the appended claims, the terms "first", "second", "third", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0034] Reference throughout this application to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places in the specification are not necessarily all referring to the same embodiment, however, but can refer to one or more but not all embodiments. The terms "including," "comprising," "having" and variations thereof as used herein are meant to be equivalent to the term "consisting of."

[0035] The technical solutions of the embodiments of the application are described below.

[0036] As shown in Figure 1 The application provides a hand game customer service work order distribution method based on data analysis, which comprises the following steps S110-S170:

[0037] In step S110, a hand game customer service work order is obtained, text semantic analysis is performed on the hand game customer service work order to generate a work order feature vector, real-time state data of the customer service is collected to generate a customer service state portrait, and reverse work order recommendation is performed based on the customer service state portrait to generate customer service work order preference data.

[0038] Specifically, the hand game customer service order acquisition relies on a distributed order collection system, configures multi-channel data access capability, supports unified access of in-game reporting system, official customer service platform, third-party complaint platform and social media monitoring, and data access frequency reaches high concurrent processing capability to ensure timely response of orders during peak period. The order data structure includes complete fields such as order ID, player ID, complaint time, problem type, urgency, text content, attachment information and channel source, and through data standardization processing, the format uniformity of orders from different channels is ensured. The text semantic analysis adopts deep learning natural language processing technology, integrates pre-trained language model and domain-specific game customer service corpus for fine tuning optimization, the semantic analysis preprocessing stage filters the order text, removes special symbols, emoticons and meaningless characters, and through Chinese word segmentation technology, the text is decomposed into word sequence. The sentiment analysis module adopts multi-dimensional sentiment recognition to identify the player's anger, urgency, satisfaction and emotional intensity, and the intent recognition module identifies the core demands of the order based on classification algorithm, mainly covering account problems, recharge problems, game bugs, external plug-in reporting and suggestion feedback categories. The order feature vector generation adopts high-dimensional vector representation, which is constructed through multi-level fusion technology of word embedding, sentence embedding and document embedding. The word embedding layer uses Word2Vec algorithm to convert the keywords in the order into dense vectors, the sentence embedding layer uses pre-trained model to encode the core sentence of the order to generate sentence-level semantic vector, and the document embedding layer uses attention mechanism to globally encode the entire order document to form a document-level feature vector. The feature vector also integrates the structured information of the order, including problem type code, urgency value, timestamp feature and channel identifier. For example, when the order involves "in-game economic system balance problem, player feedback that the difficulty of obtaining certain equipment is too high to affect the game experience", the text semantic analysis identifies high anger, high urgency, core intent as game balance problem, and key entities include equipment system, difficulty design, user experience, etc., and generates an order feature vector that accurately reflects the essential characteristics of the order.

[0039] The customer service real-time state data collection is realized through a multi-dimensional sensor network and behavior monitoring technology, and the collection frequency is set to high-frequency collection to ensure the real-time and accuracy of the state information. The basic state data includes online state, current workload, number of processed work orders, average response time and current emotional state. The online state monitoring is realized through the heartbeat detection mechanism of the customer service console, and the online state of the customer service is determined through periodic detection. The workload evaluation is calculated based on the number of currently processed work orders, work order complexity and estimated completion time. The load index is set to a reasonable range, and the overload protection mechanism is automatically triggered when the load exceeds the preset threshold. The behavior characteristic data collection includes mouse click frequency, keyboard input speed, page dwell time and operation sequence pattern. The mouse click frequency is set to a normal range for monitoring. The keyboard input speed is quantified by the number of words per minute. The skill performance data is mined and analyzed through historical work order processing records, covering the solution rate of various problems, average processing time, customer satisfaction score and professional skill label. The customer service state portrait generation adopts multi-dimensional data fusion and machine learning clustering technology to build a multi-dimensional portrait model containing ability dimension, state dimension, preference dimension and potential dimension. The ability dimension is represented by skill label cloud and ability radar chart. The skill label covers professional skill fields, and each label has a corresponding weight score. The state dimension reflects the work state and emotional state of the customer service in real time. The preference dimension analyzes the processing preference and success rate of the customer service for different types of work orders, and finds the professional inclination and interest characteristics of the customer service. For example, the state portrait of customer service Li shows that he has skill labels such as ranking expert, hero balance, communication expert, etc. His current state is focused, and the workload is within a reasonable range. He is good at handling game balance complaints and ranking mechanism problems, and his processing efficiency for technical bug problems is relatively low. A complete customer service state portrait covering multiple dimensions is generated.

[0040] In some embodiments, the generating customer service work order preference data based on the customer service state portrait includes: extracting the ability characteristics of the customer service state portrait to obtain customer skill labels; constructing a reverse recommendation strategy based on the customer skill labels; and generating customer service work order preference data according to the reverse recommendation strategy.

[0041] The customer service state portrait is subjected to ability feature extraction to obtain a customer service skill label. Weight analysis is performed on the four-dimensional data of the state portrait, and the contribution of each dimension to the representation of customer service ability is determined through variance analysis and information gain calculation, and the features with higher contribution are selected as the main extraction objects. The skill label extraction is realized by combining the text mining algorithm and the principal component analysis method. The TF-IDF algorithm is used to calculate the weight of the keywords of the customer service historical processing work order, to identify the high-frequency operations and successful cases of the customer service in a specific skill field, and to extract the main components of the skill features by combining the principal component analysis dimension reduction technology. The skill label weight calculation adopts the weighted average algorithm, and comprehensively considers the historical performance weight, the recent performance weight and the learning curve trend weight, adjusts the influence of the data in different periods through the time decay function, and ensures that the skill label reflects the current ability state of the customer service. The skill label system construction adopts the hierarchical classification method, establishes a multi-layer skill classification system from the first-level category to the third-level subdivision, the first-level label covers the technical support, customer service, problem solving and other major abilities, the second-level label is subdivided into specific professional fields, and the third-level label corresponds to specific operation skills. Each skill label is quantitatively described by proficiency score, confidence score and timeliness weight, the proficiency score is calculated based on success rate and efficiency, the confidence score is evaluated by data sample size and consistency, and the timeliness weight is determined according to the skill usage frequency and the latest usage time, to form a comprehensive and accurate customer service skill label set.

[0042] Based on the customer service skill label, a reverse recommendation strategy is constructed. A skill-oriented work order type mapping algorithm and a preference prediction model are used to predict the work order types and preference tendencies that the customer service is suitable for handling from the customer service ability specialty. Skill-work order type association analysis establishes the association rules between skill labels and work order types through historical data mining, and statistics the success rate, efficiency and satisfaction of customer service with specific skill labels in different work order types, to construct a mapping matrix of skill labels to work order types. The reverse recommendation model predicts the processing ability and interest degree of each type of work order based on the skill label combination of the customer service, analyzes the combined effect of skill labels through Bayesian inference and decision tree algorithm, and identifies the advantage field and potential development direction of the customer service in the complex skill scene. Work order type preference prediction uses the idea of collaborative filtering to analyze the work order processing preference mode of the customer service group with similar skill labels, and predicts the interest intensity of the target customer service to different work order types through similarity measurement and pattern matching. Ability boundary recognition is based on the proficiency score and confidence score of skill labels to determine the core competence area, expansion challenge area and ability boundary range of the customer service, and recommend work orders of corresponding difficulty and type for customer services of different ability levels. For example, when the customer service has skill labels such as "game mechanism" and "data analysis", the reverse recommendation strategy will predict that the customer service is suitable for handling work orders such as game balance analysis, numerical planning feedback, system optimization suggestions, and at the same time recommend appropriate challenging work orders to promote ability improvement according to the skill maturity, and establish a reverse recommendation strategy from skill specialty to work order type preference.

[0043] The customer service work order preference data is generated according to the reverse recommendation strategy. A multi-level data mining and machine learning technology is adopted to construct the work order preference profile of the customer service based on the reverse recommendation strategy established in the foregoing. The preference type identification is based on the skill-work order type mapping relationship in the reverse recommendation strategy, and the advantage work order type corresponding to the customer service skill label is marked as a high preference type, the related work order type covered by the skill label is marked as a medium preference type, and the work order type not involved in the skill label is marked as a low preference type or a no preference type. The preference intensity quantification utilizes the ability matching degree evaluation result in the reverse recommendation strategy to convert the processing ability of the customer service for different work order types into a preference intensity numerical value, and high ability matching corresponds to high preference intensity, medium ability matching corresponds to medium preference intensity, and insufficient ability corresponds to low preference intensity. The preference stability evaluation is based on the skill label confidence and timeliness weight in the reverse recommendation strategy, and the work order preference corresponding to the skill label with high confidence and strong timeliness has high stability, and the work order preference corresponding to the skill label with low confidence or weak timeliness has relatively low stability. The preference development trend prediction combines the ability boundary identification and challenging work order recommendation in the reverse recommendation strategy to analyze the skill development direction and potential interest field of the customer service, and predict the future change trend of the work order preference. The dynamic adjustment mechanism synchronously adjusts the reverse recommendation strategy according to the real-time update of the customer service skill label, and when the customer service obtains a new skill label or the skill proficiency changes, the work order preference data is updated accordingly, so that the preference data is kept synchronized with the development of the customer service ability, and the customer service work order preference data with high precision prediction ability and self-adaptive adjustment characteristics is generated through the multi-dimensional evaluation and dynamic update mechanism.

[0044] In step S120, the complexity prediction is performed based on the work order feature vector to generate a work order complexity level, and the work order complexity level is published to a customer service bidding platform based on the customer service work order preference data to obtain a bidding result ranking.

[0045] Specifically, the complexity prediction based on the ticket feature vector adopts a machine learning classification algorithm and a deep neural network model to construct an intelligent ticket complexity evaluation system. The complexity prediction model adopts a multi-layer perceptron neural network architecture, the input layer receives the aforementioned generated high-dimensional ticket feature vector, and the feature normalization and standardization preprocessing ensure the numerical stability of the input data. The hidden layer adopts a multi-layer fully connected structure, each layer contains multiple neuron nodes, and the ReLU activation function and Dropout regularization technique are used to prevent model overfitting, and the network layer and node number are determined by grid search and cross-validation to determine the optimal configuration. The model training uses historical ticket data as training samples, and the tickets are divided into different complexity levels according to the actual processing difficulty and required time, and the complexity standard system is established through expert annotation and customer feedback. The training process adopts the Adam optimization algorithm and the learning rate decay strategy, and the model parameters are optimized through the dual objectives of loss function minimization and accuracy maximization. The complexity prediction adopts the following mathematical model: C(x) = σ(W3·σ(W2·σ(W1·x+b1)+b2)+b3), where x ∈ R^n is the input ticket feature vector as the first layer input of the neural network, W1, W2, W3 are the weight matrices of each hidden layer, b1, b2, b3 are the bias vectors of each layer, and σ is the ReLU activation function σ(z) = max(0, z). The calculation process is: the first layer calculates σ(W1·x+b1), the second layer calculates σ(W2·[first layer output]+b2), and the third layer outputs C(x) ∈ [0, 1] as the final complexity probability distribution. The complexity level mapping rule is: simple level corresponds to C(x) ∈ [0, 0.33), medium level corresponds to C(x) ∈ [0.33, 0.67), and complex level corresponds to C(x) ∈ [0.67, 1.0]. Model verification adopts stratified sampling and time series verification to ensure the stability and generalization ability of the prediction performance. The complexity level division adopts a hierarchical classification method to establish a multi-level complexity system from coarse granularity to fine granularity. The coarse granularity classification distinguishes three basic levels: simple, medium, and complex, and the fine granularity classification further subdivides the basic levels into multiple sub-levels, each level corresponds to a specific processing time expectation, skill requirement, and resource allocation. The prediction result output adopts a probability distribution form, calculates the confidence probability for each complexity level, and provides a more flexible and accurate complexity evaluation through soft classification. For example, when a ticket involves "game economy system balance problem, player feedback that the difficulty of obtaining certain equipment is too high and affects the game experience", the complexity prediction model predicts that the ticket is of high complexity level based on the technical term density, logical reasoning complexity, and system range involved in the ticket feature vector, and that it needs to be handled by a senior planner and the processing time is expected to be long, and generates ticket complexity level data.

[0046] In some embodiments, the publishing the work order complexity level to the customer service bidding platform based on the customer service work order preference data to obtain a bidding result ranking includes: screening qualified bidding customer services based on the customer service work order preference data; pushing the work order complexity level to the qualified bidding customer services to obtain push confirmation information; collecting customer service bidding response data based on the push confirmation information to generate bidding willingness statistics; and performing sorting processing according to the bidding willingness statistics to obtain a bidding result ranking.

[0047] The qualified bidding customer services are screened based on the customer service work order preference data. A multi-dimensional matching algorithm and an intelligent filtering mechanism are adopted to realize accurate matching and efficient screening of customer services and work orders. The customer service work order preference data is structurally analyzed to extract key dimensional information such as skill preference, complexity preference, processing time preference, and work load state of the customer service. The skill matching adopts semantic similarity calculation and a vector space model to calculate the cosine similarity between the skill demand of the work order and the skill preference of the customer service, and to screen candidate customer services with high skill matching degree by setting a similarity threshold. The complexity matching is realized through complexity preference curve analysis. Each customer service has a preference intensity distribution for different complexity levels, and the customer service group with high preference intensity is matched according to the complexity level of the work order. The work load is evaluated through real-time state monitoring and a load prediction model to screen online customer services with moderate current work load and the ability to accept orders. For example, when a high complexity work order of account security needs to be distributed, the screening mechanism matches experienced customer services who are good at account security problems and have light current load, avoiding pushing such work orders to novice customer services or overloaded customer services. A weighted scoring mechanism is adopted for multi-dimensional score fusion to calculate the comprehensive adaptation score of each customer service, and the customer services with high ranking are selected as the qualified bidding customer services according to the score ranking.

[0048] The work order complexity level is pushed to the eligible bidding customer service to obtain push confirmation information. According to the screened eligible customer service, the work order complexity level is individually packaged and directed to the customer service bidding platform. High complexity level work orders are preferentially pushed to experienced expert customer service, medium complexity level work orders are pushed to experienced ordinary customer service, and low complexity level work orders are pushed to novice customer service for ability training. The complexity level information is customized and displayed according to the skill background of the customer service. The technical difficulties and system complexity are highlighted to technical expert customer service, the customer emotion and processing skill requirements are highlighted to communication expert customer service, and detailed processing guidance and learning resource links are provided to novice customer service. The push timing is intelligently optimized based on the work rules and response characteristics of the eligible customer service. High complexity work orders are pushed during the period when the customer service is most attentive, and learning complexity work orders are pushed when the customer service is in a relaxed state. For example, when a senior customer service expert who is good at recharge problems is pushed a "recharge exception-high complexity" level, the push content will highlight the complexity of the payment channel involved in the work order, the estimated processing time, the reward coefficient, and remind the customer service of the key processing points of such problems. The confirmation mechanism designs different confirmation processes for different complexity levels. High complexity level requires customer service to perform ability self-evaluation and processing willingness double confirmation, and medium and low complexity level adopts a simplified confirmation process to improve confirmation efficiency and success rate through precise matching of complexity level and customer service ability.

[0049] The customer service bidding response data is collected based on the push confirmation information to generate bidding willingness statistics. The confirmation information analysis focuses on the confirmation speed difference of the customer service for different complexity levels. Fast confirmation usually indicates that the customer service has strong confidence and processing willingness for the complexity level, and hesitant confirmation or delayed confirmation reflects the uncertainty or ability concerns of the customer service for the complexity level. The confirmation behavior pattern analysis tracks the operation track of the customer service in the confirmation process, including the number of times of viewing complexity level information, the length of stay, the detail expansion behavior, etc., to deeply understand the customer service's focus and decision basis for different complexity work orders. The bidding response data collection focuses on the response behavior of the customer service for a specific complexity level, records the length of time the customer service views the complexity description, the skill requirement matching degree self-evaluation, the expected processing time estimation, and other behavior data directly related to the complexity level. The willingness intensity quantification particularly considers the adaptability evaluation of the customer service for the complexity level, and comprehensively calculates the complexity matching willingness score by combining factors such as the historical complexity processing success rate of the customer service, the current complexity processing ability, and the complexity challenge acceptance willingness. The bidding enthusiasm analysis distinguishes the participation enthusiasm difference of the customer service for different complexity levels, and statistics the bidding frequency, success rate and satisfaction of the customer service in each complexity level, identifies the complexity preference interval and ability boundary of the customer service, and generates bidding willingness statistics based on the complexity dimension.

[0050] The auction result ranking is obtained by sorting according to the auction willingness statistics. The willingness intensity distribution of the customer service for the current work order complexity level is analyzed, and the willingness intensity is taken as the core weight factor of the ranking. The customer service with high willingness intensity has priority in the ranking, the customer service with medium willingness intensity is the alternative solution, and the customer service with low willingness intensity reduces the priority in the ranking. The complexity matching degree evaluation is based on the historical complexity processing performance of the customer service in the auction willingness statistics, and the matching coefficient of the customer service and the current work order complexity level is calculated. The customer service with high matching degree has an advantage in ranking under the same willingness condition. The willingness stability analysis uses the time series data in the auction willingness statistics to evaluate the consistency and reliability of the customer service willingness expression. The customer service with stable willingness obtains a reliability score in the ranking, and the customer service with large willingness fluctuation appropriately reduces the ranking position. The response quality weight is adjusted according to the confirmation quality and response depth of the customer service in the auction willingness statistics. The customer service that deeply participates in the auction and has complete confirmation information obtains a quality weight, and the customer service that simply participates or has incomplete information correspondingly reduces the weight. The auction ranking adopts a comprehensive scoring model: S(i) = α·W(i) + β·A(i) + γ·L(i) + δ·R(i), wherein W(i) is the auction willingness intensity of the customer service i and W(i) ∈ [0, 1], A(i) is the ability matching degree of the customer service i, which is calculated by A(i) = cos(V_skill, V_requirement), V_skill is the customer service skill vector containing the scores of technical support ability, communication and coordination ability, problem solving ability and other skills, V_requirement is the work order demand vector containing the demand intensity of each skill, and the matching degree is reflected by the cosine similarity of the two vectors. L(i) is the load factor of the customer service i, which is calculated by L(i) = 1-(Current_load / Max_capacity), Current_load is the weighted sum of the number of work orders currently processed by the customer service and the weight of each work order complexity, and Max_capacity is the maximum work order equivalent that the customer service can process in a unit of time. R(i) is the response timeliness of the customer service i, which is calculated by R(i) = e^(-Response_delay / τ), Response_delay is the time interval from receiving the auction notice to confirming the participation of the customer service in minutes, and τ is the time attenuation constant set to 30 minutes as the response time sensitivity parameter. The weight allocation satisfies the constraint condition α+β+γ+δ=1, and the specific setting is α=0.4 to highlight the importance of willingness, β=0.3 to reflect the ability matching, γ=0.2 to consider the work load, and δ=0.1 to weigh the response speed. Finally, the auction result ranking is obtained by descending order arrangement of S(i).For technical BUG type high complexity work orders, the sorting algorithm will give priority to customer service that shows strong interest in high complexity technical problems in willingness statistics and has a high historical success rate of processing, while also considering the current workload and response timeliness of the customer service, and through multi-dimensional fusion sorting, ensure that the most suitable customer service obtains the opportunity to assign work orders. Dynamic sorting adjustment is based on real-time changes in bidding willingness, and when the willingness status of the customer service is updated, the sorting result is adjusted synchronously to obtain a bidding result sorting that reflects the real willingness and ability matching of the customer service.

[0051] Step S130, according to the bidding result sorting, a preliminary distribution scheme is constructed, a deliberate mismatch distribution operation is performed on the preliminary distribution scheme to generate a mismatch distribution record, and customer service processing abnormal situation data is obtained based on the mismatch distribution record.

[0052] Specifically, a preliminary distribution scheme is constructed according to the bidding result sorting. Based on the bidding result sorting, the corresponding work orders are assigned to the customer service in order of sorting priority, and the customer service with a higher ranking has priority to obtain the assignment right of the work order. The customer service with a similar ranking is fine-tuned and allocated according to the specific scoring details. The distribution scheme design takes into account the customer service ability matching degree, willingness strength and workload status reflected in the bidding result sorting, to ensure that the work order assignment meets the processing capacity requirements of the customer service and conforms to its work willingness and load bearing capacity. The work order assignment strategy is based on multi-dimensional scoring of the bidding result sorting for differential allocation, high-scoring customer service is assigned work orders with high complexity or importance, medium-scoring customer service is assigned regular type work orders, and novice customer service is assigned work orders suitable for its ability level. The load balancing mechanism dynamically adjusts according to the workload information in the bidding result sorting to avoid excessive concentration of high-ranking customer service, and ensures the overall processing efficiency and quality through reasonable dispersion of work orders. The distribution scheme is verified by historical performance data and quality indicators in the bidding result sorting to conduct feasibility test and risk assessment on the allocation scheme, to ensure the scientificity and rationality of the allocation decision. For example, when the bidding result sorting shows that customer service A ranks first in account security type work orders, the comprehensive score is high and the current workload is moderate, the preliminary distribution scheme will preferentially assign the most important account security work order to the customer service, and reserve appropriate processing time and resource support for it, to generate a preliminary distribution scheme.

[0053] In some embodiments, the deliberate mismatch distribution operation performed on the preliminary distribution scheme to generate a mismatch distribution record includes: mismatch strategy design is performed on the preliminary distribution scheme to generate a mismatch execution plan; according to the mismatch execution plan, work orders are deliberately assigned to other customer services except the qualified bidding customer service to obtain mismatch distribution results; mismatch distribution process is monitored based on the mismatch distribution results to obtain mismatch execution data; and the mismatch distribution record is generated according to the mismatch execution data.

[0054] A mismatch strategy is designed for the preliminary distribution plan to generate a mismatch execution plan. Based on the preliminary distribution plan, mismatches are categorized into four main types: skill mismatch, experience mismatch, load mismatch, and preference mismatch. Skill mismatch refers to assigning tickets to agents with incompletely matched skills. Experience mismatch refers to assigning difficult tickets to agents with relatively insufficient experience. Load mismatch refers to assigning additional tickets to agents with a heavier workload. Preference mismatch refers to assigning tickets to agents with less interest in that type of ticket. Mismatch intensity is graded based on the difference in matching between agents and tickets in the preliminary distribution plan. Mild mismatch refers to assigning tickets to agents with a medium match, moderate mismatch refers to assigning tickets to agents with a low match, and severe mismatch refers to assigning tickets to agents with a completely unmatched match. This tiered control ensures the safety and controllability of mismatch operations. Mismatch targets are selected based on agent resources not selected in the preliminary distribution plan, focusing on agents with low rankings but potential, as well as agents with outstanding performance in certain dimensions but low overall scores. Mismatch testing uncovers the hidden strengths and development potential of these agents. For example, when the preliminary distribution plan shows that a technical work order should be assigned to a professional technical customer service representative, the mismatch strategy will be designed to assign the work order to a customer service representative whose main expertise is customer communication, observe their technical learning ability and cross-domain adaptability, and execute the plan to detail the specific implementation steps, time schedule, monitoring focus and risk control measures for each mismatch operation to generate a mismatch execution plan.

[0055] According to the mismatch execution plan, the work order is intentionally assigned to other customer service representatives other than the eligible bidding customer service representatives to obtain the mismatch distribution results. The mismatch allocation operation strictly reallocates the work orders in accordance with the strategy type and strength requirements in the mismatch execution plan, and transfers the work orders that should have been assigned to highly matched customer service representatives to the mismatch target customer service representatives specified in the plan. The allocation process maintains transparency and traceability, and records the basis and process of each allocation decision in detail. The target customer service notification adopts a differentiated communication strategy to explain the test nature and learning objectives of the work order allocation to the mismatched customer service representatives, provide necessary support resources and guidance information, and reduce the anxiety and resistance of the customer service representatives due to the mismatch of capabilities. The allocation result record covers the basic information of the mismatched work order, allocation time, target customer service response, acceptance status and initial processing performance, and establishes a complete record of the mismatch allocation. For example, according to the mismatch execution plan, a complex payment exception work order that should have been assigned to a recharge problem expert is assigned to a customer service representative who specializes in game mechanics. The customer service representative's initial response, such as his confused expression when receiving the work order, his behavior of consulting payment process documents, and the number of times he consulted colleagues, is recorded. The allocation confirmation mechanism ensures that the mismatch work order is successfully conveyed to the target customer service representative and obtains a commitment to processing. The effective execution of the mismatch allocation is verified by confirming the status, and the mismatch distribution result is obtained.

[0056] Mismatch handling behavior monitoring focuses on the initial reaction, processing strategy selection, resource seeking behavior, and problem solving path of the customer service when facing mismatched work orders. Through fine-grained behavior tracking, the adaptability and learning ability of the customer service are identified. Processing time analysis compares the time difference between the customer service processing mismatched work orders and normally matched work orders, analyzes the specific impact of mismatch on processing efficiency, and identifies the time management and efficiency adjustment ability of the customer service in challenging tasks. Quality performance evaluation adopts a multi-dimensional quality evaluation system to evaluate the work order processing quality of the customer service under mismatched conditions, including problem solving accuracy, customer satisfaction, processing integrity and professional performance. Through quality comparison analysis, the ability boundary and potential advantages of the customer service are found. Help-seeking behavior analysis records the help-seeking frequency, help-seeking object, help-seeking content and help-seeking effect of the customer service in the process of processing mismatched work orders, and evaluates the learning willingness, team cooperation ability and knowledge acquisition ability of the customer service. For example, the monitoring shows that a customer service first hesitates for more than ten seconds when processing a non-expert account security work order, then quickly consults the security processing manual, and then actively contacts a security expert colleague for consultation on processing points. The whole process shows strong learning initiative and help-seeking consciousness. Emotion state monitoring collects feedback through work state analysis and feedback collection, monitors the emotional changes, work enthusiasm and coping resilience of the customer service under mismatched pressure, and obtains mismatch execution data.

[0057] Mismatch distribution records are generated according to mismatch execution data. The behavior data, time data, quality data, help-seeking data and emotion data in the mismatch process are structured and analyzed to establish the corresponding relationship between mismatch operation and customer service performance. Mismatch operation records record the operation type, intensity level, target customer, work order type, execution time and operation result of each mismatch instance, forming a complete mismatch operation file. The customer service performance file records the specific performance and coping behavior of the customer service under mismatched conditions based on the multi-dimensional performance indicators in the mismatch execution data, and establishes the ability performance file of the customer service under non-optimal conditions. Abnormal situation identification identifies and records the special ability, processing innovation and adaptation behavior of the customer service in the mismatch process through abnormal performance and outstanding performance in the mismatch execution data. Standardized record format ensures the consistency and comparability of the mismatch distribution records, establishes a unified data structure and record standard, and facilitates subsequent data retrieval and analysis comparison. For example, the mismatch distribution record shows that customer service A shows rapid learning ability when processing skill mismatched work orders, customer service B can still maintain processing quality under high load conditions, and customer service C shows unexpected innovative solutions when processing non-preferred work orders, generating mismatch distribution records.

[0058] Based on the mismatch distribution record, the customer service processing abnormal situation data is obtained, and the performance characteristics and behavior patterns of the customer service under abnormal working conditions are extracted and analyzed from the mismatch distribution record through deep data mining and pattern recognition technology. The abnormal situation data extraction focuses on the abnormal performance, coping strategy innovation, stress adaptation ability and hidden skill display of the customer service recorded in the mismatch distribution record. These abnormal situations scattered in each mismatch record are centralized and deeply analyzed. The abnormal behavior pattern recognition identifies the typical coping patterns and unique performance characteristics of the customer service when facing challenges through clustering analysis and association mining of the customer service behavior data in the mismatch distribution record, discovers the potential ability and development direction of the customer service, and finally obtains the customer service processing abnormal situation data.

[0059] In step S140, a chaotic test work order is constructed, the chaotic test work order is injected into the mobile game customer service work order processing flow for distribution to generate a chaotic distribution record, and system stability data is generated through system anti-interference capability verification by the chaotic distribution record.

[0060] Specifically, the chaotic test work order is constructed by work order simulation technology and interference mode design to create virtual work orders with specific interference characteristics and test targets for verifying the stability and anti-interference ability of the customer service work order processing flow. The chaotic test work order construction is based on the data structure and content characteristics of the real recharge abnormal work order, and test samples are created by simulating various abnormal situations and boundary conditions. The chaotic work order type designs multiple interference variants around the recharge abnormal problem, including "recharge failure but successful deduction with immediate refund request and continue to recharge" contradictory logic work order, "recharge 500 yuan not arrived" format abnormal work order, super-long text description of recharge process redundancy information work order, incomplete work order with missing key payment information, and multiple interference types. The work order content generation adopts natural language generation technology to create test texts with semantic ambiguity, logical contradiction and incomplete information around the recharge scenario, and increases the complexity and unpredictability of the work order by randomly combining payment channels, amount data, time information and other elements. The chaotic intensity design sets different levels of interference intensity according to the complexity of the recharge problem, the light chaotic work order contains "recharge account delay" and other common problems, the medium chaotic work order has "successful deduction but recharge failure" obvious logical conflict, and the heavy chaotic work order has "request for refund while continuing to recharge and applying for account cancellation" and other serious logical contradictions and information missing. The work order identification mechanism identifies the chaotic test work order through implicit marking technology to ensure that the test work order can be identified and tracked in the flow, while avoiding interference to normal recharge problem processing.

[0061] In some embodiments, the injecting the chaos test work order into the mobile game customer service work order processing flow for distribution generates a chaos distribution record, including: determining a chaos injection time and an injection ratio based on the chaos test work order to generate an injection strategy; inserting the chaos test work order into the mobile game customer service work order processing flow according to the injection strategy, obtaining an injection execution state; based on the injection execution state, performing distribution processing to obtain distribution tracking data; and generating a chaos distribution record according to the distribution tracking data.

[0062] An injection strategy is generated based on the chaos test work order, the running period and load mode of the mobile game customer service work order processing flow are analyzed based on the recharge anomaly chaos work order, and the key test nodes and sensitive time windows in the recharge problem processing flow are identified. Since recharge problems usually occur during game activities and at night during peak hours, the peak injection strategy is specifically designed to test the ability of the flow to handle a large number of abnormal work orders when processing a large number of real recharge problems, and the low injection strategy is used to observe the basic analysis ability of the flow to complex recharge logic. The injection ratio controls the interference intensity of the recharge anomaly chaos work order and the bearing capacity of the recharge problem processing flow, sets a reasonable proportion relationship between the chaos work order and the normal recharge work order, and ensures that the stability of the flow is fully tested without affecting the solution of real recharge problems. When detecting "recharge failure but successful deduction and immediate refund and continue to recharge" such heavy contradictory logic work orders, the injection strategy will choose to inject a small proportion in a period when the recharge problem is relatively small, avoiding conflicts with real complex recharge problem processing, while ensuring that the ability of the customer service to handle contradictory recharge logic can be fully observed, forming an injection strategy for recharge anomaly scenarios.

[0063] According to the injection strategy, the chaos test work order is inserted into the hand game customer service work order processing flow, and the injection execution state is obtained. The work order insertion operation strictly follows the time arrangement and proportion requirements in the injection strategy to insert the recharge exception chaos work order into different process links such as work order classification, customer service allocation, processing execution, etc., and focuses on observing the response differences of each link to abnormal recharge logic. When the "￥#@* recharge 500 yuan not arrived #&" format abnormal work order enters the work order classification link, the insertion state monitoring records the processing reaction of the classification algorithm to special characters, whether the format filtering mechanism is triggered, and the accuracy of the final classification result. Process response observation focuses on the immediate reaction of the recharge problem processing flow to the chaos work order insertion, including whether the recharge amount identification is accurate, whether the payment channel judgment is correct, whether the processing priority setting is reasonable, and other key response indicators. When the work order containing the contradictory logic of "requesting refund while continuing to recharge" enters the customer service allocation link, the monitoring records whether the allocation algorithm can identify the logical conflict and whether it will allocate such complex problems to experienced customer service personnel with recharge problem processing experience. Although the entire insertion process is successful, it significantly increases the complexity and processing time of the allocation decision, forming the injection execution state under the recharge exception scenario.

[0064] Based on the injection execution state, the distribution tracking data is obtained. According to the injection execution state, the transmission path and processing nodes of the recharge exception chaos work order in the distribution process are tracked, and the complete trajectory of the work order from insertion to final allocation to the recharge problem expert is recorded. Processing delay analysis compares the time consumption of recharge exception chaos work orders and normal recharge work orders in each processing link, identifies the additional analysis time and decision delay caused by contradictory recharge logic. Allocation result observation focuses on which type of customer service the recharge exception chaos work order is finally allocated to, whether the allocation algorithm accurately identifies the complexity of the recharge problem, and the initial confusion reaction and subsequent logic analysis strategy of the customer service personnel with recharge processing experience after receiving the "successful deduction but failed recharge while requesting refund" work order. Abnormal situation records detailed records of recharge amount identification errors, payment channel judgment errors, processing time timeouts, and other abnormal events that occur during the distribution process, analyzes the causes and propagation effects of these abnormalities in the recharge problem processing chain. Process stability evaluation evaluates the influence of recharge exception chaos work orders on the stability of the entire recharge problem processing flow through statistical analysis methods, quantifies the anti-interference ability and recovery speed of the flow when facing complex recharge logic, and obtains the distribution tracking data of the recharge exception scenario.

[0065] Chaotic distribution records are generated according to distribution tracking data. Deep analysis is performed on the distribution tracking data to identify specific influence patterns and degrees of different types of abnormal charging chaotic work orders on the distribution process. Chaotic work order performance evaluation is based on the processing track and result information in the distribution tracking data to evaluate the interference effect and test value of different types of contradictory logic charging work orders, format abnormal charging work orders, information missing charging work orders, and other types of interference, and to summarize the characteristic performance of various types of abnormal charging work orders. Process response mode identification identifies typical response modes and coping strategies of the charging problem processing flow to different interference types through pattern analysis of the distribution tracking data, and discovers the adaptability characteristics of the flow in processing complex charging logic. Abnormal event classification classifies and organizes abnormal records in the distribution tracking data according to charging problem types, severity and impact range, establishes a classification system and statistical archives of abnormal charging events, and forms structured abnormal charging chaotic distribution records. When the contradictory work order of "charging failure but successful deduction, requiring refund and continuing to charge" enters the processing flow, the chaotic distribution record shows that such work orders significantly increase the logic analysis time of customer service, resulting in additional reference to charging policy documents and seeking guidance from the finance department, while the format abnormal work order of "￥#@*500 yuan charge not arrived #&@" significantly reduces the accuracy of automatic classification, requiring more manual intervention to identify the charging amount and problem nature.

[0066] In some embodiments, the system stability data generated by verifying the system anti-interference capability through the chaotic distribution record includes: obtaining interference event data by performing abnormal event identification on the chaotic distribution record; obtaining response mechanism evaluation results by analyzing the system response mechanism based on the interference event data; generating stability indicators by evaluating the system stability performance according to the response mechanism evaluation results; and constructing system stability data based on the stability indicators.

[0067] Abnormal event identification on chaotic distribution records obtains interference event data. Based on the abnormal chaotic distribution records of recharge, the abnormal events are classified in multiple dimensions according to the occurrence link, influence degree and recovery difficulty, and an abnormal event classification system covering recharge amount identification delay, payment channel allocation error, recharge logic processing interruption, financial data anomaly and other types is established. Interference intensity evaluation quantifies the interference degree and influence range of each recharge abnormal event on the normal process by analyzing indicators such as time delay increase, error frequency and recovery time in chaotic distribution records. When the contradictory logic work order of "recharge failure but successful deduction and request for refund and continue to recharge" enters the customer service distribution link, it will cause significant decision delay abnormal events. The customer service needs to repeatedly confirm the recharge status and deduction, and in most cases needs to transfer to a special recharge problem expert and financial verification personnel, forming an obvious processing bottleneck. Event correlation analysis identifies the correlation between different recharge abnormal events and the propagation path in chaotic distribution records, and finds out how the recharge logic conflict leads to a chain of customer service confusion, financial query increase and processing time extension and other amplification effects. Interference mode identification identifies typical recharge problem interference modes and abnormal propagation rules by analyzing the mode of abnormal events in chaotic distribution records, summarizes the characteristic performance and influence mode of different types of recharge abnormal work orders, and thus identifies the key interference event data.

[0068] Response mechanism based on interference event data analysis obtains response mechanism evaluation results. Through the recharge abnormal interference event data, the detection speed and response time of the recharge problem processing flow to different types of interference events are calculated, and the sensitivity and reaction ability of the flow in the face of complex recharge logic are evaluated. Response strategy identification identifies the response strategy and processing method adopted by the flow in the face of recharge abnormal interference by analyzing the processing track and recovery process in the interference event data, and evaluates the success and applicability of the strategy. When faced with the complex contradictory work order of "successful deduction but failed recharge, need to refund immediately but want to continue to recharge", the response mechanism analysis shows that the customer service needs a short time to identify the complexity of the recharge logic, and the main response strategy adopted is to first verify the deduction and recharge status and then gradually sort out the real needs of the customer. Most customer services with recharge processing experience will actively provide step-by-step solutions for the customer to choose, including processing refund first and then arranging a new recharge process. Recovery capability evaluation quantifies the ability and speed of the recharge problem processing flow to recover from abnormal state to normal state based on the recovery time and recovery effect in the interference event data, and evaluates the resilience and stability of the flow in processing complex recharge logic. Fault tolerance mechanism analysis evaluates the fault tolerance capability and error recovery mechanism of the recharge problem processing flow by studying the error processing and abnormal management process in the interference event data. The flow can recover to normal in a reasonable time, showing good recharge abnormal response ability, and thus the response mechanism evaluation results are obtained.

[0069] The stability performance is evaluated according to the response mechanism evaluation result, and a stability index is generated. According to the response mechanism evaluation result, the average response time, the maximum response time delay and the response time standard deviation of the recharge problem processing flow to the interference event are calculated, the response timeliness of the flow in processing the recharge exception is quantified, and the response time delay index is obtained. The processing success rate index is based on the coping strategy and recovery capability analysis in the response mechanism evaluation result, the proportion of successfully processing recharge abnormal interference events, the success rate of completely solving recharge problems and the proportion of partial solution are calculated, and the processing effectiveness of the flow is quantified. The recovery time index is based on the recovery capability evaluation in the response mechanism evaluation result, the average time, the shortest recovery time and the longest recovery time of the recharge problem processing flow from the abnormal state to the normal state are calculated, and the recovery efficiency of the flow is quantified. The fault tolerance level index is based on the fault tolerance mechanism analysis in the response mechanism evaluation result, the tolerance and error handling capability of the recharge problem processing flow under different recharge abnormal intensity are evaluated, and the robustness of the flow is quantified. For the extreme recharge abnormal work order of "the recharge amount display error but the actual deduction is correct and requires compensation and refund", the stability index shows that the response time delay is short, the processing success rate remains at a high level, the complete recovery can be completed within a reasonable time, and the recharge problem processing flow can still maintain basic service ability in the face of complex financial logic loss, and accordingly the corresponding stability index is established.

[0070] The stability data is constructed based on the stability index. The response time delay index, the processing success rate index, the recovery time index and the fault tolerance level index are structured and integrated to establish a multi-dimensional recharge problem processing stability data matrix. The comprehensive stability score is obtained by weighted average and comprehensive evaluation method, which integrates each stability index into the overall stability score, reflecting the overall stability level of the customer service work order processing flow in the recharge abnormal scene. The stability level is divided based on the comprehensive stability score and the performance of each index, and the stability level of the recharge problem processing is divided into different levels, and the stability level standard and determination rule are established. The stability feature analysis identifies the performance characteristics and fluctuation rules of the stability index through comparative analysis of multiple recharge abnormal test results, and evaluates the basic characteristics of the stability of the flow in processing the recharge problem. The risk identification analysis identifies the stability risk points and weak links in the recharge problem processing flow based on the stability data analysis result, and establishes the risk evaluation file. In the stability test after a large recharge activity, facing the complex recharge complaint chaos work order such as "new activity recharge discount not arrived but successful deduction requires immediate processing and additional compensation", the stability data shows that the comprehensive stability score of the current customer service work order processing flow reaches a good level, belongs to the good stability level, among which the response time delay performance is excellent, the processing success rate is medium, the recovery time needs to be improved, and the main risk points are concentrated in the processing link delay caused by the complexity of the activity period recharge discount rules, and finally a comprehensive system stability data is formed.

[0071] Step S150, the abnormal situation data is deeply mined and analyzed to identify the hidden skill characteristics of the customer service, and the comprehensive ability of the customer service is re-evaluated based on the hidden skill characteristics to generate optimized ability evaluation data.

[0072] Specifically, the abnormal situation data is deeply mined through a multi-level analysis framework to identify the potential ability and innovative behavior of the customer service in a challenging environment, and to discover hidden skill characteristics that are not identified by the traditional evaluation system.

[0073] In some embodiments, the deep mining and analysis of the abnormal situation data to identify the hidden skill characteristics of the customer service includes: performing pattern recognition on the abnormal situation data to obtain an abnormal handling mode; analyzing the customer service coping strategy based on the abnormal handling mode; extracting unconventional skill performance from the customer service coping strategy; and identifying the hidden skill characteristics of the customer service according to the unconventional skill performance.

[0074] The abnormal situation data is pattern-recognized to obtain an abnormal handling mode. A pattern recognition algorithm and data mining technology are used to identify and extract typical behavior patterns and handling characteristics of the customer service when handling abnormal work orders from the customer service handling abnormal situation data. The pattern recognition process is based on the handling process, operation sequence, time distribution and result characteristics in the abnormal situation data, and identifies the behavior rules and handling habits of the customer service through clustering analysis and sequence pattern mining. The abnormal handling mode classification divides the mode into typical modes such as innovative solution mode, collaborative help-seeking mode, step-by-step trial mode, intuitive judgment mode and resource integration mode according to the handling method, solution path and effect. The mode characteristics are extracted by statistical analysis and feature engineering technology to quantify the key feature parameters of each handling mode, including handling time, operation steps, resource usage, success rate and customer feedback. For example, through pattern recognition of abnormal situation data, it is found that a customer service always shows a three-stage handling mode of "quick learning - active help-seeking - innovative attempt" when handling work orders with mismatched skills, the handling time is longer than the average level but the success rate is significantly improved, showing strong learning drive and problem solving orientation, forming a clear abnormal handling mode.

[0075] Based on the analysis of the abnormal handling mode, the customer service coping strategy is analyzed. By using the strategy analysis technology and behavior evaluation method, the abnormal handling mode is deeply analyzed, and the specific coping strategies and solutions adopted by the customer service when facing challenges are identified. Coping strategy identification is based on the behavior sequence and decision node in the abnormal handling mode, and the strategy selection, strategy adjustment and strategy execution process of the customer service when handling abnormal work orders are analyzed. Strategy type analysis classifies the coping strategies of the customer service according to the main characteristics, including technical-oriented strategy, communication-oriented strategy, cooperation-oriented strategy, innovation-oriented strategy and resource-oriented strategy, etc. Strategy effect evaluation compares the execution results and customer feedback of different strategies to evaluate the effectiveness and scope of application of various coping strategies, and identifies the strategy advantages and improvement space of the customer service. For example, the analysis shows that when dealing with the complex security problem of "account stolen and all bound information modified", a customer service adopts a comprehensive coping strategy of "emotional pacification-layered verification-third party confirmation-compensation negotiation", which not only solves the technical problem but also maintains customer relationship, showing the effective combination of technical ability and communication skills, and deep understanding of the characteristics of customer service coping strategy.

[0076] For example, the extraction of unconventional skill performance from the customer service coping strategy includes: performing standardized comparative analysis on the customer service coping strategy to generate strategy deviation data; performing operation behavior identification on the strategy deviation data to obtain abnormal operation records; and performing effect evaluation on the abnormal operation records to obtain unconventional skill performance.

[0077] First, the customer service coping strategy is standardized and compared. The customer service coping strategy is compared with the standard processing flow and the regular operation specification. The deviation and innovation of the customer service in the strategy selection, the execution mode and the solution path are identified. The gap between the customer service behavior and the standard mode is quantified through the deviation measurement and the difference analysis. The quantification index of the strategy deviation is established. The strategy deviation data reflecting the unique processing mode of the customer service is generated. Then, the operation behavior of the strategy deviation data is identified. The specific operation details and the behavior characteristics of the customer service in the deviation behavior are extracted through the behavior sequence analysis and the operation mode identification technology. The abnormal operation behaviors such as the innovative operation, the unique problem analysis method, the unconventional resource utilization mode and the special communication skill beyond the standard flow are identified. The abnormal operation records recording the non-standard operation of the customer service are obtained. Finally, the effect of the abnormal operation record is evaluated. The effect of the abnormal operation record in the actual problem solving is evaluated and the value is quantified through the result analysis and the effect quantification technology. Whether the unconventional operation produces a positive result, whether the unconventional operation improves the problem solving efficiency and whether the unconventional operation improves the customer satisfaction are analyzed. The unconventional skill performance with real value is screened out through the effect evaluation. For example, a customer service handles the complex financial problem of "recharge failure but successful deduction". The standardized comparison analysis shows that the customer service adopts the "three-party synchronous verification method" deviating from the standard single channel query flow. The operation behavior identification shows that the customer service actively contacts the payment platform, the game finance and the bank customer service for parallel verification. The effect evaluation shows that the unconventional operation significantly shortens the processing time and greatly improves the customer satisfaction. The valuable unconventional skill performance is identified.

[0078] Hidden skill features are identified from unconventional skill performance. Based on unconventional skill performance, a feature model and ability profile of hidden skills are constructed using skill identification algorithms and feature modeling techniques. According to the types and characteristics of unconventional skill performance, hidden skills are divided into cognitive skills, communication skills, innovation skills, collaboration skills, and adaptability skills, and each category is further subdivided into specific skill items and ability dimensions. Skill strength assessment evaluates the ability level and performance strength of the customer service in each hidden skill by quantitatively analyzing the frequency, quality, and effect of unconventional skill performance, and establishes a scoring system and rating standard for skill strength. The calculation of hidden skill strength uses a comprehensive evaluation model: H(j) = w1·F(j) + w2·Q(j) + w3·E(j), where F(j) is the performance frequency of skill j, calculated by F(j) = N_skill_j / N_total, N_skill_j is the number of times the customer service exhibits skill j during the evaluation period, and N_total is the total number of orders handled by the customer service during the evaluation period, reflecting the activity level of skill use. Q(j) is the performance quality of skill j, calculated by Q(j) = (Score_j - Score_avg) / Score_avg, Score_j is the average performance score of the customer service in skill j, and Score_avg is the average performance score of all customer services in skill j, reflecting the relative advantage of skill level. E(j) is the effect evaluation of skill j, quantified by the mean of customer satisfaction, reflecting the actual effect of skill use and the degree of customer recognition. The weights are set as w1 = 0.3 to reflect the importance of frequency, w2 = 0.4 to highlight the core nature of quality, and w3 = 0.3 to balance the effect-oriented. Skill confidence calculation uses C(j) = min(1, N_samples_j / N_threshold) × Consistency_factor, where N_samples_j is the number of samples observed for skill j performance, N_threshold is set to 20 as the minimum sample requirement for confidence, and Consistency_factor is a consistency coefficient that measures performance stability by the ratio of standard deviation to mean. The final hidden skill score is calculated by Hidden_Skill_Score(j) = H(j) × C(j) × Time_decay_factor, where Time_decay_factor is a time decay factor calculated by e^(-Δt / T), Δt is the time interval from the last skill performance in days, and T is set to 90 days as the skill time effectiveness decay constant. Skill level division is set as potential skill corresponds to 0 ≤ Hidden_Skill_Score < 0.3, apparent skill corresponds to 0.3 ≤ Hidden_Skill_Score < 0.7, and core skill corresponds to 0.7 ≤ Hidden_Skill_Score ≤ 1.0.The skill stability analysis evaluates the consistency and repeatability of the hidden skill performance of the customer service, identifies the core hidden skills of the customer service and incidental performance, and ensures the reliability and effectiveness of the hidden skill identification. For example, by analyzing the performance of a customer service in multiple abnormal handling cases, it is identified that the customer service has strong "cross-domain knowledge integration ability" hidden skill, which can combine game mechanism knowledge, psychological principles and business negotiation skills to solve complex customer problems. This skill was not found in traditional evaluation but performed outstandingly in abnormal situation handling. The skill strength evaluation is at an advanced level, and the stability is good. The hidden skill characteristics of the customer service are accurately identified.

[0079] Based on the hidden skill characteristics, the comprehensive ability of the customer service is re-evaluated to generate optimized ability evaluation data. A multi-dimensional weighted fusion technology is used to integrate the hidden skill characteristics of the customer service with the original ability data in the customer service state portrait, and a more comprehensive and accurate customer service ability evaluation system is constructed. The hidden skill characteristics are quantified to convert the hidden skill characteristics of the customer service into a standardized numerical vector, and the original ability data is organically integrated through weight distribution and feature alignment. A new comprehensive ability file of the customer service is established, which includes evaluation results of multiple dimensions such as skill breadth, skill depth, adaptability, innovation ability and development potential. For example, a customer service originally classified as "technical support type" at the intermediate level was found to have excellent "user psychological analysis" and "innovative problem solving" ability through hidden skill characteristic fusion, and was upgraded to "composite expert" level after re-evaluation. The scope of work order matching is expanded from a single technical category to multiple fields, and the optimized ability evaluation data including ability level change, professional type reclassification and hidden skill label is generated.

[0080] In step S160, the preliminary distribution scheme is adjusted according to the optimized ability evaluation data to obtain a distribution execution record, and a real-time monitoring mechanism is started to track the distribution execution record to generate processing quality monitoring data.

[0081] Specifically, based on the optimized capability assessment data, the assignment of work orders in the preliminary distribution scheme is re-evaluated and adjusted. The distribution adjustment algorithm reads the changes in the capability levels and re-classification of professional types of the customer service personnel in the capability assessment data, and recalculates the matching scores of the eligible bidding customer service personnel and work orders. When the capability assessment data shows that a customer service personnel is re-evaluated from a "technical support type" intermediate level to a "composite type expert" level, and new hidden skills such as "user psychological analysis" and "innovative problem solving" are discovered, the distribution adjustment will expand the matching range of its work orders from a single technical category to composite work orders covering customer relationship handling, complaint resolution, and other fields. The adjustment process is based on the hidden skill feature labels and comprehensive capability profile reconstruction results in the capability assessment data. When a significant improvement in the capability dimension of a customer service personnel is detected, it is given priority to assign challenging work orders that match the newly discovered skill features, ensuring that the hidden skills are fully utilized and verified. After the adjustment operation is completed, a distribution execution record is automatically generated, which details the specific capability profile changes that have been adjusted, the changes in the classification of customer service personnel before and after the adjustment, and the re-allocation decision-making process based on the discovery of hidden skills.

[0082] A multi-level real-time monitoring system is established to continuously track and evaluate the quality of the entire process of work order handling based on the distribution execution record. The monitoring mechanism takes the distribution execution record as the benchmark, and triggers monitoring data collection and real-time analysis through key events such as work order status changes, customer service operation behaviors, and customer feedback. When the distribution execution record shows that the "account security problem" work order is adjusted and assigned to a composite expert with "user psychological analysis" hidden skills, the monitoring system focuses on verifying whether the corresponding professional capabilities of the customer service personnel are fully utilized, and details the analysis depth and logical reasoning in the problem diagnosis process, the innovative ideas in the scheme development stage, and the application effect of psychological counseling skills in the customer communication link. The customer service behavior monitoring verifies whether the actual work performance of the customer service personnel is consistent with the optimized capability assessment data based on the customer service capability level and specific skill features marked in the distribution execution record, identifies excellent handling cases where hidden skills are fully utilized and potential improvement spaces where capabilities are not fully utilized. The customer feedback monitoring analyzes the changes in customer satisfaction and service experience improvement after the re-allocation using sentiment analysis technology, and evaluates whether the distribution adjustment based on the discovery of hidden skills has indeed produced the expected results. The data collected through real-time monitoring is systematically compared and analyzed with the expected handling performance in the distribution execution record to verify the accuracy and effectiveness of the distribution adjustment decision, and finally the handling quality monitoring data that comprehensively reflects the handling quality level and the effect of distribution optimization is generated.

[0083] In step S170, the processing quality monitoring data is checked for stability based on the system stability data to generate checking result data, the checking result data is comprehensively analyzed to generate distribution strategy optimization instructions, and the distribution of the hand game customer service work order is completed.

[0084] Specifically, a checking reference is established based on the system stability data, the actual processing quality is checked for stability, the optimization space and improvement direction of the distribution strategy are identified, and finally the strategy optimization instructions are generated to complete the entire distribution process.

[0085] In some embodiments, the processing quality monitoring data is checked for stability based on the system stability data to generate checking result data, including: establishing a stability checking reference using the system stability data; generating a quality stability evaluation based on the consistency comparison analysis of the processing quality monitoring data based on the stability checking reference; and generating checking result data based on the quality stability evaluation.

[0086] Based on the system stability data, the response time index, the processing success rate index, the recovery time index and the fault tolerance level index are extracted, and a multi-dimensional stability checking reference system is constructed using data mining and statistical modeling techniques. The checking reference establishment process comprehensively considers the system performance baseline under complex scenarios such as recharge anomalies, and by analyzing the processing mode and performance of different types of recharge problems in the system stability data, key performance reference points are identified. When the system stability data shows that the average response time is at a reasonable level when facing "recharge failure but successful deduction" type contradictory work orders, and the processing success rate remains at a high standard, these performance levels are set as the reference for quality checking, while considering the influence of different customer service ability levels on the processing effect. The reference threshold is set using statistical analysis methods, the mean, standard deviation and confidence interval of each index in the system stability data are calculated to determine the reasonable fluctuation range under normal operating conditions, and a dynamic adjustment mechanism is introduced to adapt to the performance changes under different time periods and load conditions. The fault tolerance boundary is defined based on the maximum bearing capacity and recovery time limit in the system stability data, combined with the business characteristics and risk level of recharge problem processing, to set the corresponding fault tolerance threshold and abnormal detection rules for work orders of different complexity. The checking reference system contains a multi-level verification mechanism, which ensures the rationality and applicability of the reference setting through cross-validation and sensitivity analysis, and finally establishes a complete stability checking reference.

[0087] The multi-dimensional comparative analysis technology is adopted to compare the index threshold in the stability verification benchmark with the actual response time, success rate of problem solving, customer satisfaction and other specific indexes recorded in the processing quality monitoring data. When the stability verification benchmark shows the standard performance range of the recharge abnormal work order, and the actual processing time of a customer service in the processing quality monitoring data exceeds the benchmark range, the consistency analysis identifies the performance deviation and quantifies the deviation degree. The comparison process extracts the specific performance data of the customer service processing recharge problems in the processing quality monitoring data, and matches and verifies with the fault tolerance level limit in the verification benchmark, to evaluate the conformity degree of the actual operation effect and the system capacity in the test environment. The deviation calculation compares the processing timeliness, problem solving accuracy and customer satisfaction in the processing quality monitoring data with the corresponding threshold standard in the stability verification benchmark through numerical comparison, to identify the abnormal fluctuation exceeding the benchmark range. The trend analysis is based on time series data, compares the consistency change of the processing quality monitoring data and the verification benchmark in different periods, finds the development trend and potential risk points of quality stability. When it is found that the actual performance of the work order distribution based on the hidden skill adjustment in the processing quality monitoring data is basically consistent with the expected level of the stability verification benchmark, it indicates the effectiveness of the distribution optimization strategy, and the quality stability evaluation is generated.

[0088] The quality stability evaluation is structured and comprehensively judged to form a complete verification result data system. The verification result classification divides the verification result into different levels such as stable consistency, basic consistency, deviation, significant abnormality according to the consistency degree of the quality stability evaluation. The abnormal situation summary records the specific performance and influence range of the processing quality inconsistent with the verification benchmark based on the performance deviation identified by the quality stability evaluation. The stability score calculation generates a comprehensive stability score by weighted average method based on the comprehensive result of the quality stability evaluation, to quantify the overall verification passing degree. The improvement suggestion is generated based on the problems and deficiencies found by the quality stability evaluation, to propose targeted improvement suggestions and optimization direction. When the quality stability evaluation shows that the performance of some customer services with hidden skills in actual processing deviates from the benchmark expectation, the verification result data records these differences and possible improvement measures in detail to generate complete verification result data.

[0089] The multi-dimensional analysis and decision support technology is used to read the verification result data for deep analysis, and the advantages and disadvantages of the current distribution strategy are identified. When the verification result data shows that a customer service processing recharge exception work order reaches an excellent level, and the performance confirms that the hidden skill is fully played, the strategy effect evaluation confirms the successful implementation of the distribution adjustment strategy. When the verification result data shows that the customer service with the "user psychological analysis" skill is inefficient in handling simple technical problems, and the improvement suggestion points out that the ability matching accuracy needs to be improved, the optimization opportunity identification generates the corresponding improvement direction. The decision rule is made based on the specific problems and suggestions recorded in the verification result data. When the data shows that the recharge exception work order processing quality is improved, but the ability of some customer services is not fully played, the ability evaluation weight adjustment, matching threshold optimization and other specific execution instructions are generated. The strategy adjustment scheme includes refining the ability evaluation dimension, strengthening the pertinence of customer service training, optimizing the work order distribution time strategy and other measures. The feedback mechanism is established based on the continuous improvement process of the verification result data. Through regular analysis of the verification result, the strategy is dynamically optimized, and finally the distribution strategy optimization instruction containing specific adjustment measures, execution time arrangement and effect evaluation plan is generated to complete the whole process optimization of the mobile game customer service work order distribution.

[0090] To implement the mobile game customer service work order distribution method based on data analysis corresponding to the above-mentioned method embodiment, so as to realize the corresponding functions and technical effects. Referring to Figure 2 , Figure 2 The structure block diagram of the mobile game customer service work order distribution system 200 based on data analysis provided by the embodiment of the application is shown. For the convenience of description, only the part related to the embodiment is shown. The mobile game customer service work order distribution system 200 based on data analysis provided by the embodiment of the application comprises:

[0091] The data acquisition module 201 is configured to acquire the mobile game customer service work order, perform text semantic analysis on the mobile game customer service work order to generate a work order feature vector, acquire real-time state data of the customer service to generate a customer service state portrait, and perform reverse work order recommendation based on the customer service state portrait to generate customer service work order preference data.

[0092] The bidding distribution module 202 is configured to perform complexity prediction based on the work order feature vector to generate a work order complexity level, publish the work order complexity level to a customer service bidding platform based on the customer service work order preference data to obtain a bidding result ranking, and perform reverse work order recommendation based on the customer service state portrait to generate customer service work order preference data.

[0093] The test verification module 203 is configured to construct a preliminary distribution scheme according to the bidding result ranking, perform a deliberate mismatch distribution operation on the preliminary distribution scheme to generate a mismatch distribution record, and acquire customer service processing abnormal situation data based on the mismatch distribution record.

[0094] The chaos injection module 204 is configured to construct a chaos test work order, inject the chaos test work order into the mobile game customer service work order processing flow for distribution, generate a chaos distribution record, and verify the system anti-interference capability through the chaos distribution record to generate system stability data.

[0095] The capability optimization module 205 is configured to perform deep mining analysis on the abnormal situation data to identify hidden skill characteristics of the customer service, reevaluate the comprehensive capability of the customer service based on the hidden skill characteristics, and generate optimized capability evaluation data.

[0096] The execution monitoring module 206 is configured to adjust the preliminary distribution scheme according to the optimized capability evaluation data to obtain distribution execution records, and start a real-time monitoring mechanism to track the distribution execution records to generate processing quality monitoring data.

[0097] The strategy optimization module 207 is configured to perform stability verification on the processing quality monitoring data based on the system stability data to generate verification result data, perform comprehensive analysis on the verification result data to generate distribution strategy optimization instructions, and complete the distribution of the mobile game customer service work order.

[0098] The above-described mobile game customer service work order distribution system 200 based on data analysis can implement the mobile game customer service work order distribution method based on data analysis of the above-described method embodiments. The optional items in the above-described method embodiments are also applicable to the present embodiment, and will not be described in detail herein. The remaining contents of the present embodiment can be referred to the contents of the above-described method embodiments, and will not be described in detail herein.

[0099] The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the present application, and to completely describe the technical solutions, purposes and effects of the present application. The purpose is to make the public understand the disclosure of the present application more thoroughly and comprehensively, and does not limit the protection scope of the present application.

[0100] The above embodiments are not based on an exhaustive enumeration of the present application, and there can be many other unlisted embodiments. Any replacement and improvement made without violating the concept of the present application is within the protection scope of the present application.

Claims

1. A method for distributing mobile customer service tickets based on data analysis, characterized in that: include: Obtain mobile customer service work orders, perform text semantic analysis on the mobile customer service work orders to generate work order feature vectors, collect real-time customer service status data to generate customer service status profiles, and perform reverse work order recommendations based on the customer service status profiles to generate customer service work order preference data; Performing complexity prediction based on the work order feature vector to generate a work order complexity level, and publishing the work order complexity level to the customer service auction platform based on the customer service work order preference data to obtain auction result ranking; constructing a preliminary distribution plan according to the order of the auction results, performing a deliberate mismatch distribution operation on the preliminary distribution plan to generate a mismatch distribution record, and obtaining customer service handling abnormality data based on the mismatch distribution record; Constructing a chaos test work order, injecting the chaos test work order into the mobile customer service work order processing flow for distribution to generate a chaos distribution record, and verifying the system's anti-interference capability through the chaos distribution record to generate system stability data; Conducting in-depth mining and analysis on the abnormal situation data to identify customer service personnel's hidden skill characteristics, and re-evaluating the customer service personnel's comprehensive capabilities based on the hidden skill characteristics to generate optimized capability evaluation data; Adjusting the preliminary distribution plan according to the optimized capability assessment data to obtain distribution execution records, and starting a real-time monitoring mechanism to track the distribution execution records to generate processing quality monitoring data; Based on the system stability data, the processing quality monitoring data is subjected to stability verification to generate verification result data, the verification result data is comprehensively analyzed to generate distribution strategy optimization instructions, and the distribution of mobile customer service work orders is completed.

2. The method according to claim 1, characterized in that The generating of customer service work order preference data by performing reverse work order recommendation based on the customer service status portrait includes: Extracting capability features from the customer service status profile to obtain a customer service skill label; Building a reverse recommendation strategy based on the customer service skill tags; Generate customer service work order preference data based on the reverse recommendation strategy.

3. The method according to claim 1, characterized in that The step of publishing the work order complexity level to the customer service auction platform based on the customer service work order preference data to obtain auction result ranking includes: Filtering qualified bidding customer service representatives based on the customer service ticket preference data; Push the work order complexity level to the qualified auction customer service to obtain push confirmation information; Collect customer service bidding response data based on the push confirmation information to generate bidding intention statistics; The bidding result ranking is obtained by performing a sorting process based on the bidding intention statistics.

4. The method according to claim 1, wherein The performing of the intentional mismatch distribution operation on the preliminary distribution plan to generate a mismatch distribution record includes: Performing mismatch strategy design on the preliminary distribution plan to generate a mismatch execution plan; Intentionally assigning the work order to other customer service personnel other than the qualified bidding customer service personnel according to the mismatch execution plan to obtain a mismatch distribution result; monitoring the mismatch distribution process based on the mismatch distribution result to obtain mismatch execution data; A mismatch distribution record is generated according to the mismatch execution data.

5. The method according to claim 1, wherein The step of injecting the chaos test work order into the mobile customer service work order processing flow for distribution and generating a chaos distribution record includes: Determine the chaos injection timing and injection ratio based on the chaos test work order to generate an injection strategy; Insert the chaos test work order into the mobile customer service work order processing flow according to the injection strategy, and obtain the injection execution status; Perform distribution processing based on the injection execution state to obtain distribution tracking data; A chaotic distribution record is generated based on the distribution tracking data.

6. The method according to claim 1, characterized in that The generating of system stability data by verifying the system anti-interference capability through the chaotic distribution record includes: Performing abnormal event identification on the chaotic distribution record to obtain interference event data; Analyzing the system response mechanism based on the interference event data to obtain a response mechanism evaluation result; Evaluate the system stability performance according to the response mechanism evaluation result to generate a stability index, wherein the stability index includes a response delay index, a processing success rate index, and a system recovery time index; System stability data is constructed based on the stability index.

7. The method according to claim 1, characterized in that The in-depth mining and analysis of the abnormal situation data to identify customer service hidden skill characteristics includes: Performing pattern recognition on the abnormal situation data to obtain an abnormality handling pattern; Analyze customer service response strategies based on the exception handling model; Extracting unconventional skill performance from the customer service response strategy; Identify customer service hidden skill characteristics based on the unconventional skill performance.

8. The method according to claim 1, characterized in that The performing stability verification on the processing quality monitoring data based on the system stability data to generate verification result data includes: establishing a stability verification benchmark using the system stability data; Performing consistency comparison analysis on the processing quality monitoring data based on the stability verification benchmark to generate a quality stability assessment; Calibration result data is generated based on the quality stability assessment.

9. The method according to claim 7, characterized in that Extracting unconventional skill performance from the customer service response strategy includes: Conduct standardized comparative analysis of the customer service response strategies to generate strategy deviation data; Performing operation behavior identification on the policy deviation data to obtain abnormal operation records; An effect evaluation is performed on the abnormal operation records to obtain unconventional skill performance.

10. A mobile customer service ticket distribution system based on data analysis, characterized in that: include: A data collection module is used to obtain mobile customer service work orders, perform text semantic analysis on the mobile customer service work orders to generate work order feature vectors, collect real-time customer service status data to generate customer service status profiles, and perform reverse work order recommendations based on the customer service status profiles to generate customer service work order preference data; An auction distribution module is used to generate a work order complexity level by performing complexity prediction based on the work order feature vector, and publish the work order complexity level to the customer service auction platform based on the customer service work order preference data to obtain an auction result ranking; a test verification module, configured to construct a preliminary distribution plan based on the order of the auction results, perform a deliberate mismatch distribution operation on the preliminary distribution plan to generate a mismatch distribution record, and obtain customer service handling exception data based on the mismatch distribution record; A chaos injection module is used to construct a chaos test work order, inject the chaos test work order into the mobile customer service work order processing flow for distribution to generate a chaos distribution record, and verify the system's anti-interference ability through the chaos distribution record to generate system stability data; A capability optimization module is used to conduct in-depth mining and analysis of the abnormal situation data to identify the customer service's hidden skill characteristics, and re-evaluate the customer service's comprehensive capabilities based on the hidden skill characteristics to generate optimized capability evaluation data; An execution monitoring module is configured to adjust the preliminary distribution plan according to the optimized capability assessment data, obtain distribution execution records, and initiate a real-time monitoring mechanism to track the distribution execution records and generate processing quality monitoring data; The strategy optimization module is used to perform stability verification on the processing quality monitoring data based on the system stability data to generate verification result data, perform comprehensive analysis on the verification result data to generate distribution strategy optimization instructions, and complete the distribution of mobile customer service work orders.

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