A Data Analysis-Based Mobile Customer Service Work Order Distribution Method and System

Abstract

This invention discloses a mobile customer service work order distribution method and system based on data analysis. The method involves: acquiring mobile customer service work orders, performing textual semantic analysis to generate work order feature vectors, collecting real-time customer service status data to generate customer service status profiles, and generating work order preference data through reverse work order recommendation based on these profiles. The method then predicts complexity based on the work order feature vectors and, combined with the work order preference data, publishes the work orders to an auction platform to obtain auction result rankings. Based on the auction results, a preliminary distribution plan is constructed, and an intentional mismatch distribution operation is performed to obtain abnormal situation data. A chaotic test work order injection processing flow is constructed to verify system stability. Deep mining is performed on the abnormal situation data to identify hidden skill features and reassess overall capabilities. The distribution plan is adjusted based on the optimized capability assessment data, and real-time monitoring is initiated. Finally, based on system stability data, the processing quality is verified for stability, and distribution strategy optimization instructions are generated to achieve intelligent work order distribution.

Description

Technical Field

[0001] This invention relates to the field of intelligent customer service management technology, and in particular to a mobile customer service work order distribution method and system based on data analysis. Background Technology

[0002] With the booming development of the mobile game industry and the continuous expansion of its user base, mobile game customer service has become a crucial link in maintaining user experience and ensuring game operation. Traditional customer service ticket distribution mainly relies on simple load balancing and rule matching, lacking in-depth understanding and accurate assessment of individual customer service personnel's capabilities, making it difficult to achieve optimal matching between ticket information and customer service skills.

[0003] Current ticket distribution methods generally suffer from a lack of simplistic customer service capability assessment systems, relying solely on explicit skill indicators and neglecting implicit skill characteristics such as exception handling and innovative problem-solving. Furthermore, distribution strategies lack adaptive adjustment capabilities, failing to dynamically optimize based on real-time processing results, exhibiting insufficient accuracy in ticket complexity identification, and lacking robust stability verification mechanisms for the distribution process. These technical bottlenecks severely restrict the overall service level and operational effectiveness of mobile game customer service, urgently necessitating the development of a new generation of ticket distribution technology with intelligent identification, adaptive optimization, and quality assurance capabilities. Summary of the Invention

[0004] This invention provides a data analysis-based method and system for distributing mobile customer service work orders, aiming to solve technical problems such as inaccurate capability assessment, rigid distribution strategies, and unstable processing quality in traditional customer service work order distribution. By integrating core technologies such as work order semantic analysis, in-depth mining of customer service capabilities, auction-based distribution mechanisms, mismatch testing and verification, chaotic stability detection, and hidden skill identification, an intelligent distribution system is constructed to achieve accurate matching between customer service skills and work order requirements and adaptive adjustment of distribution strategies.

[0005] The first aspect of this invention proposes a mobile customer service work order distribution method based on data analysis, comprising the following steps:

[0006] 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 recommendation based on the customer service status profiles to generate customer service work order preference data.

[0007] Based on the work order feature vector, complexity is predicted to generate a work order complexity level. Based on the customer service work order preference data, the work order complexity level is published to the customer service auction platform to obtain the auction results ranking.

[0008] A preliminary distribution plan is constructed based on the auction results. An intentional mismatch distribution operation is performed on the preliminary distribution plan to generate mismatch distribution records. Customer service handling exception data is obtained based on the mismatch distribution records.

[0009] 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 chaos distribution records, and use the chaos distribution records to verify the system's anti-interference capability and generate system stability data.

[0010] Deeply mine and analyze the abnormal data to identify hidden skills of customer service personnel, and re-evaluate the comprehensive capabilities of customer service personnel based on the hidden skills characteristics to generate optimized capability evaluation data.

[0011] Based on the optimized capability assessment data, the initial distribution plan is adjusted to obtain distribution execution records, and a real-time monitoring mechanism is activated to track the distribution execution records and generate processing quality monitoring data.

[0012] 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 then comprehensively analyzed to generate distribution strategy optimization instructions, thereby completing the distribution of mobile customer service work orders.

[0013] A second aspect of this invention proposes a mobile customer service work order distribution system based on data analysis, comprising:

[0014] The data acquisition module is used to acquire 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 recommendation based on the customer service status profiles to generate customer service work order preference data.

[0015] The auction distribution module is used to predict the complexity of the work order based on the work order feature vector, generate a work order complexity level, and publish the work order complexity level to the customer service auction platform based on the customer service work order preference data to obtain the auction results ranking.

[0016] The testing and verification module is used to construct a preliminary distribution plan based on the auction results, perform an intentional mismatch distribution operation on the preliminary distribution plan to generate mismatch distribution records, and obtain customer service handling abnormal situation data based on the mismatch distribution records.

[0017] The 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 use the chaos distribution record to verify the system's anti-interference capability and generate system stability data.

[0018] The capability optimization module is used to perform in-depth mining and analysis on the abnormal situation data to identify hidden skill characteristics of customer service personnel, and to re-evaluate the comprehensive capabilities of customer service personnel based on the hidden skill characteristics to generate optimized capability evaluation data.

[0019] The execution monitoring module is used to adjust the initial distribution plan based on the optimized capability assessment data, obtain distribution execution records, and start a real-time monitoring mechanism to track the distribution execution records and generate processing quality monitoring data.

[0020] The strategy optimization module is used to perform stability verification on the processing quality monitoring data based on the system stability data, 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.

[0021] The beneficial effects of this invention are reflected in the following points: First, by establishing a basic framework for work order distribution based on text semantic analysis and customer service status profiling, and combining reverse work order recommendation and auction distribution mechanisms, intelligent identification of work order content, accurate grasp of customer service capability characteristics, and autonomous selection of the distribution process are achieved. This solves the technical problems of crude work order analysis, insufficient understanding of customer service capabilities, and blind allocation decisions in traditional methods, improves the accuracy of matching work orders with customer service representatives and distribution efficiency, and enables customer service representatives to participate in work order allocation decisions based on their own strengths and work willingness.

[0022] Secondly, through innovative deliberate mismatch testing and chaotic work order injection verification technology, a complete system for in-depth mining of customer service capabilities and verification of system stability was established. By observing the response performance of customer service under abnormal conditions, hidden skill characteristics that traditional assessment methods could not discover were successfully identified. At the same time, the system's anti-interference capability under interference environment was verified, solving the core problems of one-sided customer service capability assessment and lack of system stability verification in existing methods. This achieved a technological breakthrough from surface skill assessment to deep capability discovery.

[0023] Finally, by constructing a closed-loop optimization mechanism based on real-time monitoring and stability verification, and integrating key technologies such as processing quality monitoring, stability data verification, and dynamic strategy adjustment, continuous improvement of distribution strategies and continuous enhancement of system performance have been achieved. This has solved the technical limitations of traditional distribution methods, such as lack of quality feedback and lagging strategy adjustment, and formed a complete technical closed loop of "test discovery - capability optimization - quality monitoring - strategy improvement", thereby improving the processing quality and system stability of mobile customer service work order distribution.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0026] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0027] Figure 1 This is a flowchart illustrating a mobile customer service work order distribution method based on data analysis according to the present invention.

[0028] Figure 2 This is a structural block diagram of a mobile customer service work order distribution system based on data analysis according to the present invention. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0031] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

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

[0033] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0035] The technical solutions of the embodiments of this application will be described below.

[0036] like Figure 1 As shown, this embodiment of the invention provides a mobile customer service work order distribution method based on data analysis, including the following steps S110-S170:

[0037] Step S110: Obtain mobile customer service work orders, perform text semantic analysis on 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 recommendation based on customer service status profiles to generate customer service work order preference data.

[0038] Specifically, the mobile game customer service ticket acquisition relies on a distributed ticket collection system, configured with multi-channel data access capabilities. It supports unified access from in-game reporting systems, official customer service platforms, third-party complaint platforms, and social media monitoring. The high concurrency processing capacity ensures timely response to tickets during peak periods. The ticket data structure includes complete fields such as ticket ID, player ID, complaint time, issue type, urgency, text content, attachment information, and channel source. Data standardization ensures consistent format across different channels. Text semantic analysis employs deep learning natural language processing technology, integrating a pre-trained language model and a domain-specific game customer service corpus for fine-tuning and optimization. The semantic analysis preprocessing stage cleans and filters the ticket text, removing special symbols, emoticons, and meaningless characters. Chinese word segmentation technology decomposes the text into word sequences. The sentiment analysis module uses multi-dimensional sentiment recognition to identify player anger, urgency, satisfaction, and emotional intensity. The intent recognition module uses classification algorithms to identify the core demands of the ticket, mainly covering categories such as account issues, recharge issues, game bugs, cheat reports, and feedback. The work order feature vector generation employs high-dimensional vector representation, constructed through a multi-level fusion technique involving word embedding, sentence embedding, and document embedding. The word embedding layer uses the Word2Vec algorithm to convert keywords in the work order into dense vectors. The sentence embedding layer utilizes a pre-trained model to encode the core sentences of the work order, generating sentence-level semantic vectors. The document embedding layer uses an attention mechanism to perform global semantic encoding on the entire work order document, forming document-level feature vectors. These feature vectors also integrate structured information from the work order, including problem type encoding, urgency level, timestamp features, and channel identifiers. For example, when a work order concerns "the balance issue of the in-game economic system, with players reporting that the difficulty of obtaining certain equipment is too high, affecting the game experience," text semantic analysis identifies high anger, high urgency, and the core intent as a game balance issue. Key entities include the equipment system, difficulty design, and user experience, generating a work order feature vector that accurately reflects the essential characteristics of the work order.

[0039] Real-time customer service status data is collected through a multi-dimensional sensor network and behavior monitoring technology. The collection frequency is set to high frequency to ensure the real-time nature and accuracy of status information. Basic status data includes online status, current workload, number of processed work orders, average response time, and current emotional state. Online status monitoring is achieved through a heartbeat detection mechanism on the customer service workbench, periodically checking to determine the online status of customer service representatives. Workload assessment is calculated based on the number of currently processed work orders, work order complexity, and estimated completion time. The load index is set within a reasonable range, and an overload protection mechanism is automatically triggered when the load exceeds a preset threshold. Behavioral characteristic data collection covers mouse click frequency, keyboard input speed, page dwell time, and operation sequence patterns. Mouse click frequency is monitored within a normal range, and keyboard input speed is quantified by words per minute. Skill performance data is mined and analyzed from historical work order processing records, covering the resolution rate of various issues, average processing time, customer satisfaction scores, and professional skill tags. The customer service status profile generation employs multi-dimensional data fusion and machine learning clustering techniques to construct a multi-dimensional profile model encompassing capability, status, preference, and potential dimensions. The capability dimension is represented by a skill tag cloud and capability radar chart, with skill tags covering professional skill areas and each tag having a corresponding weight score. The status dimension reflects the customer service representative's work and emotional state in real time. The preference dimension analyzes the customer service representative's handling preferences and success rates for different types of work orders, revealing their professional inclinations and interests. For example, the status profile of customer service representative Xiao Li shows that he possesses skill tags such as "ranking expert," "hero balance expert," and "communication expert," and is currently in a focused state with a reasonable workload. He excels at handling game balance complaints and ranking mechanism issues, but his efficiency in handling technical bugs is relatively low. This generates a complete customer service status profile covering multiple dimensions.

[0040] In some embodiments, generating customer service ticket preference data by performing reverse ticket recommendation based on the customer service status profile includes: extracting capability features from the customer service status profile to obtain customer service skill tags; constructing a reverse recommendation strategy based on the customer service skill tags; and generating customer service ticket preference data according to the reverse recommendation strategy.

[0041] Customer service status profiles are analyzed to extract capability features and obtain customer service skill tags. Weight analysis is performed on the four dimensions of the status profile data. Analysis of variance and information gain calculation are used to determine the contribution of each dimension to the representation of customer service capabilities, and features with higher contributions are selected as the primary extraction targets. Skill tag extraction is achieved through a combination of text mining algorithms and principal component analysis. The TF-IDF algorithm is used to calculate the weights of keywords in historical customer service work orders, identifying high-frequency operations and success stories in specific skill areas. Principal component analysis dimensionality reduction techniques are then used to extract the main components of skill features. The skill tag weight calculation uses a weighted average algorithm, comprehensively considering historical performance weight, recent performance weight, and learning curve trend weight. A time decay function is used to adjust the influence of data from different periods to ensure that skill tags reflect the current capability status of customer service personnel. The skill tagging system is constructed using a hierarchical classification method, establishing a multi-level skill classification system from a primary category to a tertiary subcategory. The primary tags cover broad capabilities such as technical support, customer service, and problem solving. The secondary tags are subdivided into specific professional fields, and the tertiary tags correspond to specific operational skills. Each skill tag is quantitatively described through proficiency scores, confidence scores, and timeliness weights. Proficiency scores are calculated based on success rate and efficiency, confidence scores are assessed through data sample size and consistency, and timeliness weights are determined based on skill usage frequency and recent usage time, forming a comprehensive and accurate set of customer service skill tags.

[0042] A reverse recommendation strategy is constructed based on customer service skill tags. A skill-oriented work order type mapping algorithm and preference prediction model are employed to predict the suitable work order types and preferences of customer service representatives based on their skills and strengths. Skill-work order type association analysis establishes association rules between skill tags and work order types through historical data mining, statistically analyzing the success rate, efficiency, and satisfaction of customer service representatives with specific skill tags on different work order types, and constructing a mapping matrix from skill tags to work order types. The reverse recommendation model predicts the processing ability and interest level of customer service representatives for various types of work orders based on their skill tag combinations. Bayesian inference and decision tree algorithms are used to analyze the combined effect of skill tags, identifying the strengths and potential development directions of customer service representatives in multi-skill scenarios. Work order type preference prediction employs collaborative filtering to analyze the work order processing preference patterns of customer service groups with similar skill tags, predicting the intensity of interest of target customer service representatives for different work order types through similarity measurement and pattern matching. Capability boundary identification, based on skill tag proficiency scores and confidence scores, determines the core competency area, extended challenge area, and capability boundary range of customer service representatives, recommending work orders of appropriate difficulty and type to customer service representatives with different capability levels. For example, when a customer service representative has skill tags such as "game mechanics" and "data analysis", the reverse recommendation strategy will predict that the customer service representative is suitable for handling work orders such as game balance analysis, numerical design feedback, and system optimization suggestions. At the same time, it will recommend appropriate challenging work orders based on the customer service representative's skill maturity to promote ability improvement, thus establishing a reverse recommendation strategy from skill expertise to work order type preference.

[0043] Customer service ticket preference data is generated based on the reverse recommendation strategy. Multi-level data mining and machine learning techniques are employed to construct customer service ticket preference profiles based on the aforementioned reverse recommendation strategy. Preference type identification is based on the skill-ticket type mapping relationship in the reverse recommendation strategy. Advantageous ticket types corresponding to customer service skill tags are marked as high preference types, related ticket types covered by skill tags are marked as medium preference types, and ticket types not covered by skill tags are marked as low preference types or no preference types. Preference intensity quantification utilizes the capability matching evaluation results in the reverse recommendation strategy to convert customer service representatives' processing capabilities for different ticket types into preference intensity values. High capability matching corresponds to high preference intensity, medium capability matching corresponds to medium preference intensity, and insufficient capability corresponds to low preference intensity. Preference stability assessment is based on the confidence and timeliness weights of skill tags in the reverse recommendation strategy. Ticket preferences corresponding to skill tags with high confidence and strong timeliness have higher stability, while ticket preferences corresponding to skill tags with low confidence or weak timeliness have relatively lower stability. The preference development trend prediction, combined with capability boundary identification and challenging work order recommendation in the reverse recommendation strategy, analyzes the development direction of customer service skills and potential interest areas to predict future trends in their work order preferences. A dynamic adjustment mechanism synchronously adjusts the reverse recommendation strategy based on real-time updates of customer service skill tags. When a customer service representative acquires a new skill tag or their skill proficiency changes, their work order preference data is updated accordingly, ensuring that preference data remains synchronized with the development of customer service capabilities. Through multi-dimensional evaluation and dynamic updating mechanisms, customer service work order preference data with high-precision predictive capabilities and adaptive adjustment characteristics is generated.

[0044] Step S120: Based on the work order feature vector, perform complexity prediction to generate work order complexity level, and based on customer service work order preference data, publish the work order complexity level to the customer service auction platform to obtain the auction results ranking.

[0045] Specifically, a machine learning classification algorithm and a deep neural network model are used to predict the complexity of work orders based on their feature vectors, constructing an intelligent work order complexity assessment system. The complexity prediction model employs a multilayer perceptron neural network architecture. The input layer receives the previously generated high-dimensional work order feature vectors, and feature normalization and standardization preprocessing ensure the numerical stability of the input data. The hidden layers use a multilayer fully connected structure, with each layer containing multiple neurons. ReLU activation function and Dropout regularization techniques are used to prevent overfitting. The optimal configuration of the number of network layers and nodes is determined through grid search and cross-validation. Historical work order data is used as training samples. Work orders are divided into different complexity levels according to their actual processing difficulty and required time, and a complexity standard system is established through expert annotation and customer service feedback. The training process employs the Adam optimization algorithm and a learning rate decay strategy, optimizing model parameters through the dual objectives of minimizing the loss function and maximizing accuracy. The complexity prediction uses the following mathematical model: C(x)=σ(W3·σ(W2·σ(W1·x+b1)+b2)+b3), where x∈R^n is the input work order feature vector, which serves as the input to the first layer of the neural network; W1, W2, and W3 are the weight matrices of each hidden layer; b1, b2, and b3 are the bias vectors of each layer; and σ is the ReLU activation function σ(z)=max(0,z). The calculation process is as follows: the first layer calculates σ(W1·x+b1), the second layer calculates σ(W2·[first layer output]+b2), and the third layer output C(x)∈[0,1] is the final complexity probability distribution. The complexity level mapping rule is as follows: 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 validation employs stratified sampling and time-series validation to ensure the stability and generalization ability of prediction performance. Complexity level classification uses a hierarchical approach, establishing a multi-level complexity system from coarse-grained to fine-grained. The coarse-grained classification distinguishes three basic levels: simple, medium, and complex. The fine-grained classification further subdivides these basic levels into multiple sub-levels, each corresponding to specific expected processing time, skill requirements, and resource allocation. Prediction results are output in the form of a probability distribution, calculating the confidence probability for each complexity level and providing a more flexible and accurate complexity assessment through soft classification. For example, when a work order involves "in-game economic system balance issues, with players reporting that certain equipment is too difficult to obtain, affecting the game experience," the complexity prediction model, based on features such as the density of technical terms, logical reasoning complexity, and the scope of the systems involved in the work order's feature vector, predicts that the work order is of high complexity, requiring processing by senior planners and with an expected long processing time, thus generating work order complexity level data.

[0046] In some embodiments, 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 the auction result ranking includes: filtering qualified auction customer service representatives based on the customer service work order preference data; pushing the work order complexity level to the qualified auction customer service representatives to obtain push confirmation information; collecting customer service auction response data based on the push confirmation information to generate auction intention statistics; and performing sorting processing based on the auction intention statistics to obtain the auction result ranking.

[0047] Customer service representatives are selected based on their preferences in customer service work orders. A multi-dimensional matching algorithm and intelligent filtering mechanism are employed to achieve accurate matching and efficient filtering between customer service representatives and work orders. The customer service work order preference data is structured and analyzed to extract key dimensions such as customer service representatives' skill preferences, complexity preferences, processing time preferences, and workload status. Skill matching uses semantic similarity calculation and a vector space model to calculate the cosine similarity between the skill requirements of the work order and the customer service representatives' skill preferences. A similarity threshold is set to filter out candidate customer service representatives with high skill matching. Complexity matching is achieved through complexity preference curve analysis. Each customer service representative has a preference intensity distribution for different complexity levels, and customer service representatives with higher preference intensity are matched according to the complexity level of the work order. Workload assessment uses real-time status monitoring and a load prediction model to filter online customer service representatives with moderate current workload and the ability to accept orders. For example, when a high-complexity work order related to account security needs to be distributed, the filtering mechanism will match experienced customer service representatives who are skilled in account security issues and currently have a light workload, avoiding pushing such work orders to novice or overloaded customer service representatives. The multi-dimensional scoring system employs a weighted scoring mechanism to calculate the comprehensive suitability score for each customer service representative. Based on the score ranking, the top-ranked customer service representatives are selected as eligible bidding representatives.

[0048] The complexity level of work orders is pushed to qualified bidding customer service representatives to obtain confirmation. Based on the selected qualified customer service representatives, the complexity level of the work orders is personalized and targeted to the customer service bidding platform. High-complexity work orders are prioritized for senior expert customer service representatives, medium-complexity work orders are pushed to experienced ordinary customer service representatives, and low-complexity work orders are pushed to novice customer service representatives for skills training. The complexity level information is customized and the presentation is adjusted according to the skill background of the customer service representatives. Technical expert customer service representatives are shown the key technical difficulties and system complexity, communication expert customer service representatives are shown the customer's emotions and handling skills requirements, and novice customer service representatives are provided with detailed handling guidance and learning resource links. The timing of the push is intelligently optimized based on the work patterns and response characteristics of qualified customer service representatives. High-complexity work orders are pushed when the customer service representatives are most focused, and learning-type complexity work orders are pushed when the customer service representatives are more relaxed. For example, when pushing the "Recharge Anomaly - High Complexity" level to a senior customer service representative who is good at recharge issues, 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 representative of the key points of handling such issues. The confirmation mechanism is designed with differentiated confirmation processes for different complexity levels. High complexity levels require customer service representatives to conduct a dual confirmation of their capabilities and willingness to handle the issue. Medium and low complexity levels adopt a simplified confirmation process. By accurately matching the complexity level with the customer service representative's capabilities, the confirmation efficiency and success rate are improved.

[0049] Based on push confirmation information, customer service bidding response data is collected to generate bidding intention statistics. Confirmation information analysis focuses on the differences in confirmation speed among customer service representatives for different complexity levels. Rapid confirmation usually indicates strong confidence and willingness to handle the complexity level, while hesitant or delayed confirmation reflects uncertainty or concern about the complexity level. Confirmation behavior pattern analysis tracks customer service actions during the confirmation process, including the number of times complexity level information is viewed, dwell time, and detail expansion behavior, to gain a deeper understanding of customer service focus and decision-making basis for work orders of different complexity levels. Bidding response data collection focuses on customer service response behavior for specific complexity levels, recording behavioral data directly related to complexity levels, such as the duration of viewing complexity descriptions, self-assessment of skill requirement matching, and estimated expected processing time. Intention strength quantification specifically considers customer service adaptability assessments for complexity levels, comprehensively calculating a complexity matching intention score by combining factors such as historical complexity handling success rate, current complexity handling ability, and willingness to accept complexity challenges. The auction enthusiasm analysis distinguishes the differences in customer service participation enthusiasm at different complexity levels, statistically analyzes the auction frequency, success rate and satisfaction of customer service at each complexity level, identifies the complexity preference range and capability boundary of customer service, and generates auction willingness statistics based on the complexity dimension.

[0050] The auction results are ranked based on the auction willingness statistics. The analysis focuses on the distribution of customer service representatives' willingness to participate in the current work order complexity level, using willingness intensity as the core weighting factor for ranking. Customer service representatives with high willingness intensity are given priority, those with medium willingness intensity are considered as alternatives, and those with low willingness intensity have their ranking priority reduced. Complexity matching is assessed based on the historical complexity handling performance of customer service representatives in the auction willingness statistics, calculating the matching coefficient between customer service representatives and the current work order complexity level. Customer service representatives with high matching coefficients gain a ranking advantage under the same willingness conditions. Willingness stability analysis uses time-series data from the auction willingness statistics to assess the consistency and reliability of customer service representatives' expressed willingness. Customer service representatives with stable willingness receive reliability bonuses in the ranking, while those with fluctuating willingness are appropriately ranked lower. Response quality weights are adjusted based on the confirmation quality and response depth of customer service representatives in the auction willingness statistics. Customer service representatives who deeply participate in the auction and confirm complete information receive quality weights, while those who participate only briefly or provide incomplete information have their weights reduced accordingly. The bidding ranking adopts a comprehensive scoring model: S(i)=α·W(i)+β·A(i)+γ·L(i)+δ·R(i), where W(i) is the bidding intention intensity of customer service i and W(i)∈[0,1], A(i) is the ability matching degree of customer service i calculated by A(i=cos(V_skill,V_requirement), V_skill is the customer service skill vector containing the score values ​​of various skills such as technical support ability, communication and coordination ability, and problem solving ability, and V_requirement is the work order demand vector containing the demand intensity of corresponding skills. The matching degree is reflected by the cosine similarity of the two vectors to show the degree of consistency of skill requirements. L(i) is the load factor of customer service i 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 complexity weight of each work order, and Max_capacity is the maximum work order equivalent that the customer service can process per unit time. R(i) represents the timeliness of customer service representative i's response, calculated as R(i) = e^(-Response_delay / τ). Response_delay is the time interval in minutes between receiving the auction notification and confirming participation. τ is a time decay constant set to 30 minutes as a response time sensitivity parameter. Weight allocation satisfies the constraint α + β + γ + δ = 1, specifically set as follows: α = 0.4 emphasizes the importance of willingness, β = 0.3 reflects capability matching, γ = 0.2 considers workload, and δ = 0.1 balances response speed. The final auction results are sorted in descending order by S(i).For high-complexity work orders related to technical bugs, the ranking algorithm prioritizes customer service representatives who have demonstrated a strong interest in high-complexity technical issues in the willingness statistics and have a high historical success rate in handling such issues. It also considers the customer service representative's current workload and response timeliness, using a multi-dimensional ranking process to ensure the most suitable representative receives the work order allocation opportunity. Dynamic ranking adjustments are based on real-time changes in bidding willingness; when a customer service representative's willingness status is updated, the ranking results are adjusted synchronously to obtain a ranking that reflects the true matching of customer service representatives' willingness and capabilities.

[0051] Step S130: Construct a preliminary distribution plan based on the auction results, perform an intentional mismatch distribution operation on the preliminary distribution plan to generate mismatch distribution records, and obtain customer service handling exception data based on the mismatch distribution records.

[0052] Specifically, a preliminary distribution plan is constructed based on the auction results ranking. Based on the auction results ranking, work orders are assigned to customer service representatives according to their priority, with higher-ranked representatives receiving priority in work order allocation. For representatives with similar rankings, the allocation is refined based on specific score details. The distribution plan design considers the customer service representative's ability matching degree, willingness intensity, and workload status reflected in the auction results ranking, ensuring that work order allocation meets both the processing capacity requirements of customer service representatives and their work willingness and workload capacity. The work order allocation strategy uses a differentiated allocation based on the multi-dimensional scores of the auction results ranking. High-scoring customer service representatives are assigned work orders with higher complexity or importance, medium-scoring customer service representatives are assigned routine work orders, and novice customer service representatives are assigned work orders suitable for their ability level. The load balancing mechanism dynamically adjusts based on the workload information in the auction results ranking, avoiding excessive concentration of work orders among high-ranking customer service representatives, and ensuring overall processing efficiency and quality through reasonable work order distribution. The distribution plan is validated by using historical performance data and quality indicators from the auction results ranking to conduct feasibility tests and risk assessments, ensuring the scientific and reasonable nature of the allocation decisions. For example, if the auction results show that Customer Service A ranks first in account security-related work orders, has a high overall score, and has a moderate current workload, the initial distribution plan will prioritize assigning the most important account security work orders to that customer service representative, while reserving appropriate processing time and resource support for them, and generating an initial distribution plan.

[0053] In some embodiments, the step of performing intentional mismatch distribution on the preliminary distribution plan to generate a mismatch distribution record includes: designing a mismatch strategy for the preliminary distribution plan to generate a mismatch execution plan; intentionally assigning work orders to customer service representatives other than the qualified bidding customer service representatives according to the mismatch execution plan to obtain mismatch distribution results; monitoring the mismatch distribution process based on the mismatch distribution results to obtain mismatch execution data; and generating a mismatch distribution record based on the mismatch execution data.

[0054] A mismatch strategy is designed to generate a mismatch execution plan based on the initial distribution plan. Mismatches are categorized into four main types: skill mismatch, experience mismatch, workload mismatch, and preference mismatch. Skill mismatch involves assigning work orders to customer service representatives whose skills are not a perfect match; experience mismatch involves assigning high-difficulty work orders to customer service representatives with relatively insufficient experience; workload mismatch involves assigning additional work orders to customer service representatives with heavy workloads; and preference mismatch involves assigning work orders to customer service representatives with low interest in that type of work order. Mismatch intensity is graded based on the difference in matching degree between customer service representatives and work orders in the initial distribution plan. Mild mismatch involves assigning work orders to customer service representatives with moderate matching degree; moderate mismatch involves assigning work orders to customer service representatives with low matching degree; and severe mismatch involves assigning work orders to customer service representatives with no matching degree. This tiered control ensures the safety and controllability of mismatch operations. Mismatch targets are selected based on customer service resources not selected in the initial distribution plan, focusing on customer service representatives with potential but lower rankings, as well as those who perform well in certain dimensions but have low overall scores. Mismatch testing is used to discover the hidden strengths and development potential of these customer service representatives. For example, when the initial distribution plan shows that a certain technical work order should be assigned to a professional technical customer service representative, the mismatch strategy will design 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 the execution plan will detail the specific implementation steps, time schedule, monitoring focus and risk control measures for each mismatch operation, and generate a mismatch execution plan.

[0055] According to the mismatch execution plan, work orders are intentionally assigned to customer service representatives other than those who meet the bidding criteria to obtain mismatch distribution results. The mismatch distribution operation strictly follows the strategy type and intensity requirements in the mismatch execution plan to reallocate work orders, transferring work orders that should originally be assigned to highly matched customer service representatives to the mismatch target customer service representatives specified in the plan. The allocation process is transparent and traceable, with detailed records of the basis and process for each allocation decision. A differentiated communication strategy is used to notify the target customer service representatives, explaining the test nature and learning objectives of the work order allocation, providing necessary support resources and guidance information, and reducing anxiety and resistance caused by skill mismatch. The allocation result record includes basic information about the mismatched work order, allocation time, target customer service representative's response, acceptance status, and initial handling performance, establishing a complete mismatch distribution file. For example, according to the mismatch execution plan, a complex payment exception work order that should have been assigned to a recharge issue expert is assigned to a customer service representative specializing in game mechanics. The initial response of the customer service representative is recorded, including their confused expression when they receive the work order, their behavior of consulting payment process documents, and the number of times they consult colleagues. The assignment confirmation mechanism ensures that the mismatch work order is successfully communicated to the target customer service representative and receives a processing commitment. The effective execution of the mismatch assignment is verified by confirming the status, and the mismatch distribution result is obtained.

[0056] Based on the mismatch distribution results, the monitoring of the mismatch distribution process acquires mismatch execution data. Mismatch handling behavior monitoring focuses on customer service representatives' initial reactions, handling strategy selection, resource seeking behavior, and problem-solving paths when faced with mismatched work orders. Fine-grained behavior tracking identifies customer service representatives' adaptability and learning ability. Processing time analysis compares the time customer service representatives take to process mismatched work orders with those of normally matched work orders, analyzing the specific impact of mismatch on processing efficiency and identifying customer service representatives' time management and efficiency adjustment capabilities in challenging tasks. Quality performance evaluation employs a multi-dimensional quality evaluation system to assess the quality of work order processing under mismatch conditions, including problem-solving accuracy, customer satisfaction, processing completeness, and professional performance. Quality comparative analysis reveals the capability boundaries and potential strengths of customer service representatives. Help-seeking behavior analysis records the frequency, recipients, content, and effectiveness of help requests made by customer service representatives during the handling of mismatched work orders, assessing their learning willingness, teamwork ability, and knowledge acquisition ability. For example, the monitoring showed that when a customer service representative was handling an account security ticket that was not their area of ​​expertise, they first paused for more than ten seconds, then quickly consulted the security handling manual, and then took the initiative to contact a security expert colleague to consult on the key points of the handling. The whole process showed a strong learning initiative and awareness of seeking help. The emotional state monitoring monitors the changes in the customer service representative's emotions, work enthusiasm and coping resilience under the pressure of mismatch through work status analysis and feedback collection, and obtains mismatch execution data.

[0057] Mismatch distribution records are generated based on mismatch execution data. Behavioral data, duration data, quality data, request data, and emotional data from the mismatch process are structured, organized, and correlated to establish a correspondence between mismatch operations and customer service performance. The execution data is integrated. Mismatch operation records detail the operation type, intensity level, target customer service representative, work order type, execution time, and operation result for each mismatch instance, forming a complete mismatch operation profile. Customer service performance profiles, based on multi-dimensional performance indicators from the mismatch execution data, record the specific performance and coping behaviors of customer service representatives under mismatch conditions, establishing a profile of their capabilities under suboptimal conditions. Anomaly identification identifies and records special abilities, innovative handling, and adaptive behaviors demonstrated by customer service representatives during the mismatch process by identifying and recording abnormal and prominent performances in the mismatch execution data. Standardized record formats ensure the consistency and comparability of mismatch distribution records, establishing a unified data structure and recording standards to facilitate subsequent data retrieval, analysis, and comparison. For example, mismatched distribution records show that Customer Service A demonstrated rapid learning ability when handling work orders with mismatched skills, Customer Service B was able to maintain processing quality under high load conditions, and Customer Service C showed unexpected innovative solutions when handling unfavorable work orders, thus generating mismatched distribution records.

[0058] Based on data obtained from mismatched distribution records, this study extracts and analyzes customer service performance characteristics and behavioral patterns under abnormal working conditions using deep data mining and pattern recognition techniques. The extraction of abnormal situation data focuses on customer service anomalies, innovative coping strategies, stress adaptability, and the display of hidden skills recorded in the mismatched distribution records. These anomalies, scattered across various mismatched records, are centrally organized and deeply analyzed. Abnormal behavior pattern recognition identifies typical coping patterns and unique performance characteristics of customer service representatives when facing challenges through cluster analysis and association mining of customer service behavior data in the mismatched distribution records. This reveals the potential capabilities and development directions of customer service representatives, ultimately yielding data on customer service handling of abnormal situations.

[0059] Step S140: 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 chaos distribution records, and use the chaos distribution records to verify the system's anti-interference capability and generate system stability data.

[0060] Specifically, chaotic test work orders are constructed using work order simulation technology and interference pattern design to create virtual work orders with specific interference characteristics and test objectives. These are used to verify the stability and anti-interference capability of the customer service work order processing flow. The construction of chaotic test work orders is based on the data structure and content characteristics of real recharge anomaly work orders, creating test samples by simulating various abnormal situations and boundary conditions. Chaotic work order types are designed with multiple interference variations around recharge anomaly issues, including contradictory logic work orders such as "recharge failed but payment was successful while requesting an immediate refund and continued recharge," formatted abnormal work orders such as "￥#@*recharged 500 yuan but not received#&@", redundant information work orders with excessively long text descriptions of the recharge process, and incomplete work orders lacking key payment information. Work order content generation uses natural language generation technology to create semantically ambiguous, logically contradictory, and incomplete test texts around the recharge scenario. The complexity and unpredictability of the work orders are increased by randomly combining elements such as payment channels, amount data, and time information. The chaos intensity design sets different levels of interference intensity based on the complexity of the recharge problem. Mild chaos work orders include common issues such as "delayed recharge arrival," moderate chaos work orders have obvious logical conflicts such as "successful deduction but failed recharge," and severe chaos work orders have serious logical contradictions and missing information such as "requesting a refund while continuing to recharge and applying for account cancellation." The work order identification mechanism identifies chaotic test work orders through implicit tagging technology, ensuring that test work orders can be identified and tracked in the process, while avoiding interference with the processing of normal recharge problems.

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

[0062] Based on chaotic test work orders, the timing and proportion of chaotic injection were determined to generate injection strategies. Based on chaotic work orders related to recharge anomalies, the runtime and load patterns of the mobile game customer service work order processing flow were analyzed to identify key test nodes and sensitive time windows in the recharge issue handling process. Since recharge issues typically occur during game events and peak evening hours, a peak-hour injection strategy was specifically designed to test the process's ability to handle abnormal work orders when processing a large number of real recharge issues. An off-peak injection strategy was used to observe the process's basic analytical capabilities regarding complex recharge logic. Injection ratio control was based on the interference intensity of chaotic work orders related to recharge anomalies and the resilience of the recharge issue handling process. A reasonable ratio between chaotic work orders and normal recharge work orders was set to ensure sufficient testing of the process's stability without affecting the resolution of real recharge issues. When a work order with highly contradictory logic, such as "recharge failed but payment was successful, and an immediate refund and continued recharge are requested," is detected, the injection strategy will select a small proportion of injections during periods when there are relatively few recharge issues. This avoids conflicts with handling real, complex recharge issues and ensures that customer service's ability to handle contradictory recharge logic can be fully observed, thus forming an injection strategy for abnormal recharge scenarios.

[0063] Following the injection strategy, chaotic test work orders were inserted into the mobile customer service work order processing flow to obtain the injection execution status. The work order insertion operation strictly adhered to the timing and proportion requirements of the injection strategy, inserting abnormal recharge chaotic work orders into different process stages such as work order classification, customer service assignment, and processing execution. The focus was on observing the differences in the response of each stage to the abnormal recharge logic. When an abnormal work order in the format "￥#@*Recharged 500 Yuan Not Received#&@" entered the work order classification stage, the insertion status monitoring recorded in real time the classification algorithm's reaction to special characters, whether the format filtering mechanism was triggered, and the accuracy of the final classification result. Process response observation focused on the immediate response of the recharge problem handling process to the insertion of chaotic work orders, including key response indicators such as whether the recharge amount was accurately identified, whether the payment channel was correctly determined, and whether the processing priority was reasonably set. When a work order containing contradictory logic of "requesting a refund while continuing to recharge" enters the customer service allocation process, the monitoring records whether the allocation algorithm can identify the logical conflict and whether it will allocate such complex issues to senior customer service staff with experience in handling recharge problems. Although the entire insertion process is successful, it significantly increases the complexity of allocation decisions and processing time, forming an injection execution state under recharge abnormality scenarios.

[0064] Distribution tracking data is obtained based on the injection execution status. According to the injection execution status, the transmission path and processing nodes of chaotic recharge orders in the distribution process are tracked, recording the complete trajectory of the order from insertion to final assignment to a recharge problem expert. Processing latency analysis compares the time consumption of chaotic recharge orders with normal recharge orders at each processing stage, identifying additional analysis time and decision delays caused by contradictory recharge logic. Distribution result observation focuses on which type of customer service representative the chaotic recharge order is ultimately assigned to, whether the allocation algorithm accurately identifies the complexity of the recharge problem, and the initial confusion and subsequent logical reasoning strategies of experienced customer service representatives upon receiving a "successful deduction but failed recharge with a refund request" order. Anomaly records detail abnormal events occurring during the distribution process, such as incorrect recharge amount recognition, payment channel judgment errors, and processing timeouts, analyzing the causes and propagation impact of these anomalies in the recharge problem handling chain. The process stability assessment uses statistical analysis methods to evaluate the impact of chaotic recharge anomaly work orders on the stability of the entire recharge problem handling process, quantifies the process's anti-interference ability and recovery speed when facing complex recharge logic, and obtains distribution tracking data for recharge anomaly scenarios.

[0065] Chaotic distribution records are generated based on distribution tracking data. In-depth analysis of the distribution tracking data identifies the specific impact patterns and degrees of different types of chaotic recharge anomaly work orders on the distribution process. Chaotic work order performance evaluation, based on the processing trajectory and result information in the distribution tracking data, assesses the interference effects and testing value of different types of recharge anomaly work orders, such as those with contradictory logic, formatting errors, and missing information, summarizing the characteristic performance of various types of recharge anomaly work orders. Process response pattern identification, through pattern analysis of the distribution tracking data, identifies typical response patterns and coping strategies of the recharge problem handling process to different types of interference, discovering the adaptability of the process when handling complex recharge logic. Anomaly event classification categorizes and organizes the anomaly records in the distribution tracking data according to the type, severity, and scope of impact of the recharge problem, establishing a classification system and statistical archive for recharge anomaly events. When contradictory work orders such as "recharge failed but payment was successful, and a refund was requested while continuing to recharge" enter the processing flow, the chaotic distribution record shows that such work orders significantly increase the logical analysis time for customer service, causing customer service to need to additionally consult recharge policy documents and seek guidance from the finance department. On the other hand, abnormal work orders in the format "￥#@*recharged 500 yuan but not received#&@" significantly reduce the accuracy of automatic classification, requiring more manual intervention to identify the recharge amount and the nature of the problem, thus forming a structured chaotic distribution record of recharge anomalies.

[0066] In some embodiments, the step of verifying the system's anti-interference capability and generating system stability data through the chaotic distribution record includes: identifying abnormal events in the chaotic distribution record to obtain interference event data; analyzing the system response mechanism based on the interference event data to obtain response mechanism evaluation results; evaluating the system stability performance based on the response mechanism evaluation results to generate stability indicators; and constructing system stability data based on the stability indicators.

[0067] Anomaly identification is performed on chaotic distribution records to obtain interference event data. Based on the chaotic distribution records of recharge anomalies, anomalies are classified into multiple dimensions according to their occurrence stage, impact degree, and recovery difficulty, establishing an anomaly event classification system covering types such as recharge amount recognition delay, payment channel allocation error, recharge logic processing interruption, and financial data anomalies. Interference intensity assessment quantifies the degree of interference and scope of impact of each recharge anomaly event on the normal process by analyzing indicators such as increased latency, error frequency, and recovery time in the chaotic distribution records. When a contradictory logic work order of "recharge failed but deduction was successful, and a refund was requested while continuing to recharge" enters the customer service allocation stage, it will trigger a significant decision delay anomaly event. Customer service needs to repeatedly confirm the recharge status and deduction details, and in most cases, it needs to be transferred to specialized recharge problem experts and financial verification personnel, forming a significant processing bottleneck. Event correlation analysis identifies the correlation and propagation path between different recharge anomalies in the chaotic distribution records, and discovers how recharge logic conflicts lead to a chain reaction of customer service confusion, increased financial inquiries, and extended processing time. Interference pattern recognition identifies typical recharge problem interference patterns and anomaly propagation rules by analyzing the patterns of abnormal events in chaotic distribution records. It summarizes the characteristics and impact patterns of different types of abnormal recharge work orders, thereby identifying key interference event data.

[0068] The response mechanism evaluation results are obtained through data analysis of interference events. By analyzing data on recharge anomaly interference events, the detection speed and response latency of the recharge problem handling process for different types of interference events are calculated, evaluating the process's sensitivity and responsiveness when facing complex recharge logic. Response strategy identification involves analyzing the processing trajectory and recovery process in the interference event data to identify the response strategies and methods adopted by the process when facing recharge anomalies, evaluating the success and applicability of these strategies. When faced with a complex and contradictory work order of "successful deduction but failed recharge requiring an immediate refund, yet the customer still wants to recharge," the response mechanism analysis shows that customer service needs a relatively short time to identify the complexity of the recharge logic. The main response strategy adopted is to first verify the deduction and recharge status and then gradually clarify the customer's actual needs. Most customer service representatives with recharge processing experience will proactively provide step-by-step solutions for the customer to choose from, including processing the refund first and then arranging a new recharge process. Recovery capability evaluation, based on the recovery time and recovery effect in the interference event data, quantifies the ability and speed of the recharge problem handling process to recover from an abnormal state to a normal state, evaluating the process's resilience and stability when handling complex recharge logic. The fault tolerance mechanism analysis evaluates the fault tolerance capability and error recovery mechanism of the recharge problem handling process by studying the error handling and anomaly management process in the interference event data. The process can recover to normal within a reasonable time, showing a good ability to deal with recharge anomalies, and thus the response mechanism evaluation results are obtained.

[0069] The stability performance is evaluated based on the response mechanism assessment results, generating stability indices. Based on the response mechanism assessment results, the average response time, maximum response latency, and standard deviation of the recharge issue handling process for interfering events are calculated to quantify the timeliness of the process in handling recharge anomalies, resulting in a response latency index. The processing success rate index, based on the response mechanism assessment results' analysis of coping strategies and recovery capabilities, calculates the proportion of recharge anomaly interference events successfully handled, the success rate of completely resolving recharge issues, and the proportion of partially resolving them, quantifying the process's effectiveness. The recovery time index, based on the recovery capability assessment in the response mechanism assessment results, calculates the average time, shortest recovery time, and longest recovery time for the recharge issue handling process to recover from an abnormal state to a normal state, quantifying the process's recovery efficiency. The fault tolerance level index, based on the fault tolerance mechanism analysis in the response mechanism assessment results, evaluates the recharge issue handling process's tolerance and error handling capabilities under different recharge anomaly intensities, quantifying the process's robustness. For extreme recharge anomaly work orders where "the recharge amount is displayed incorrectly but the actual deduction is correct, and compensation and refund are requested", the stability indicators show that the response latency is short, the processing success rate remains at a high level, and full recovery can be completed within a reasonable time. The recharge problem handling process can still maintain basic service capabilities even when faced with complex financial logic deficiencies. Based on this, corresponding stability indicators were established.

[0070] Stability data is constructed based on stability indicators. Response latency, processing success rate, recovery time, and fault tolerance indicators are structurally integrated to establish a multi-dimensional stability data matrix for recharge issue handling. A comprehensive stability score is generated by combining various stability indicators using weighted averaging and comprehensive evaluation methods, reflecting the overall stability level of the customer service ticket processing flow under recharge anomaly scenarios. Stability level classification is based on the comprehensive stability score and the performance of each indicator, classifying the stability level of recharge issue handling into different levels and establishing stability level standards and judgment rules. Stability feature analysis identifies the performance characteristics and fluctuation patterns of stability indicators through comparative analysis of multiple recharge anomaly test results, assessing the basic stability characteristics of the process when handling recharge issues. Risk identification analysis, based on the stability data analysis results, identifies stability risk points and weak links in the recharge issue handling process, establishing a risk assessment file. In a stability test following a large-scale recharge event, when faced with complex and chaotic recharge complaint tickets such as "the recharge discount for the new event has not been credited but the deduction was successful, requesting immediate processing and additional compensation," the stability data showed that the overall stability score of the current customer service ticket processing process reached a good level, belonging to the good stability category. Among them, the response latency was excellent, the processing success rate was moderate, and the recovery time needed improvement. The main risk points were concentrated on the processing delays caused by the complexity of the recharge discount rules during the event, which ultimately constituted the comprehensive system stability data.

[0071] Step S150: Conduct in-depth mining and analysis of abnormal situation data to identify hidden skill characteristics of customer service personnel, and re-evaluate the comprehensive ability of customer service personnel based on the hidden skill characteristics to generate optimized ability evaluation data.

[0072] Specifically, by using a multi-level analysis framework to deeply mine abnormal data, we can identify the potential capabilities and innovative behaviors of customer service staff in challenging environments, and discover hidden skill characteristics that traditional assessment systems have failed to identify.

[0073] In some embodiments, the step of performing in-depth mining and analysis on the abnormal situation data to identify hidden customer service skills includes: performing pattern recognition on the abnormal situation data to obtain abnormal handling patterns; analyzing customer service response strategies based on the abnormal handling patterns; extracting unconventional skill performances from the customer service response strategies; and identifying hidden customer service skills based on the unconventional skill performances.

[0074] Pattern recognition is used to identify anomaly handling patterns in abnormal situation data. Pattern recognition algorithms and data mining techniques are employed to identify and extract typical behavioral patterns and handling characteristics of customer service representatives when processing abnormal work orders. The pattern recognition process is based on the processing flow, operation sequence, time distribution, and result characteristics in the abnormal situation data. Cluster analysis and sequence pattern mining are used to identify the behavioral patterns and handling habits of customer service representatives. Anomaly handling patterns are categorized into typical patterns such as innovative solution patterns, collaborative assistance patterns, gradual trial patterns, intuitive judgment patterns, and resource integration patterns based on the handling method, solution path, and effect performance. Pattern feature extraction quantifies the key characteristic parameters of each handling pattern through statistical analysis and feature engineering techniques, including indicators such as processing time, operation steps, resource usage, success rate, and customer feedback. For example, pattern recognition of abnormal situation data reveals that a certain customer service representative consistently exhibits a three-stage handling pattern of "rapid learning - proactive assistance - innovative attempt" when handling work orders with mismatched skills. The processing time is longer than average, but the success rate is significantly improved, demonstrating a strong learning drive and problem-solving orientation, forming a clear anomaly handling pattern.

[0075] This analysis of customer service response strategies is based on anomaly handling patterns. Utilizing strategy analysis techniques and behavioral assessment methods, it deeply analyzes anomaly handling patterns to identify the specific response strategies and solutions adopted by customer service representatives when facing challenges. Response strategy identification is based on the behavioral sequences and decision nodes within the anomaly handling patterns, analyzing the strategy selection, adjustment, and execution processes of customer service representatives when handling abnormal work orders. Strategy type analysis categorizes customer service response strategies according to their main characteristics, including different types such as technology-oriented strategies, communication-oriented strategies, collaboration-oriented strategies, innovation-oriented strategies, and resource-oriented strategies. Strategy effectiveness evaluation assesses the effectiveness and applicability of various response strategies by comparing the execution results of different strategies and customer feedback, identifying the strategic advantages and areas for improvement of customer service representatives. For example, the analysis shows that when handling the complex security issue of "account stolen and all bound information modified," a customer service representative adopted a comprehensive response strategy of "emotional reassurance - layered verification - third-party confirmation - compensation negotiation," which not only solved the technical problem but also maintained customer relationships, demonstrating an effective combination of technical capabilities and communication skills, and providing a deep understanding of the characteristics of customer service response strategies.

[0076] For example, the step of extracting unconventional skill performance from the customer service response strategy includes: performing standardized comparative analysis on the customer service response strategy to generate strategy deviation data; identifying abnormal operation records from the strategy deviation data; and evaluating the effectiveness of the abnormal operation records to obtain unconventional skill performance.

[0077] First, a standardized comparative analysis of customer service response strategies is conducted. These strategies are compared with standard processing procedures and routine operating procedures to identify deviations and innovations in strategy selection, execution methods, and problem-solving paths. Deviation measurement and difference analysis quantify the gap between customer service behavior and the standard model, establishing quantitative indicators for strategy deviation and generating strategy deviation data reflecting unique customer service approaches. Next, operational behavior identification is performed on the strategy deviation data. Through behavioral sequence analysis and operational pattern recognition techniques, specific operational details and behavioral characteristics of customer service in deviation behaviors are extracted. This identifies innovative operations beyond standard procedures, unique problem analysis methods, unconventional resource utilization, and special communication skills, obtaining detailed records of non-standard customer service operations. Finally, the effectiveness of these abnormal operation records is evaluated. Through result analysis and effectiveness quantification techniques, the effectiveness and value of these abnormal operation records in actual problem-solving are assessed. This analyzes whether these unconventional operations produced positive results, improved problem-solving efficiency, and enhanced customer satisfaction. The effectiveness evaluation identifies truly valuable unconventional skill performances. For example, when a customer service representative was handling a complex financial issue of "recharge failure but successful deduction," standardized comparative analysis revealed that they used a "three-party simultaneous verification method," which deviated from the standard single-channel query process. Operational behavior identification showed that they proactively contacted the payment platform, game finance, and bank customer service for parallel verification. The effect evaluation showed that this unconventional operation significantly shortened the processing time, greatly improved customer satisfaction, and identified valuable unconventional skill performance.

[0078] Identifying hidden customer service skills based on unconventional skill performance. Utilizing skill recognition algorithms and feature modeling techniques, a feature model and capability profile of hidden customer service skills are constructed based on unconventional skill performance. Hidden skills are categorized into multiple types based on the type and characteristics of unconventional skill performance, including cognitive skills, communication skills, innovation skills, collaboration skills, and adaptive skills. Each category is further subdivided into specific skill items and capability dimensions. Skill intensity assessment evaluates customer service representatives' capability level and performance intensity in various hidden skills by quantitatively analyzing the frequency, quality, and effectiveness of unconventional skill performance, establishing a skill intensity scoring system and grading standards. Hidden skill intensity is calculated using a comprehensive assessment model: H(j) = w1·F(j) + w2·Q(j) + w3·E(j), where F(j) is the performance frequency of skill j calculated using F(j) = N_skill_j / N_total, N_skill_j is the number of times the customer service representative demonstrated skill j during the assessment period, and N_total is the total number of work orders processed by the customer service representative during the assessment period, reflecting the activity level of skill use. Q(j) represents the performance quality of skill j, calculated as Q(j) = (Score_j - Score_avg) / Score_avg. Score_j is the average performance score given by customer service representatives for skill j, and Score_avg is the average performance score given by all customer service representatives for skill j, reflecting the relative advantage of skill level. E(j) represents the effectiveness evaluation of skill j, quantified by the mean customer satisfaction score, reflecting the actual effect of skill application and customer recognition. Weights are set as follows: w1 = 0.3 to reflect the importance of frequency, w2 = 0.4 to highlight the core of quality, and w3 = 0.3 to balance the effect orientation. Skill confidence is calculated using C(j) = min(1, N_samples_j / N_threshold) × Consistency_factor, where N_samples_j is the number of samples observed for skill j, N_threshold is set to 20 as the minimum sample requirement for confidence, and Consistency_factor is the consistency coefficient, measured by the ratio of the performance standard deviation to the mean, which measures performance stability. The final hidden skill score is calculated using Hidden_Skill_Score(j) = H(j) × C(j) × Time_decay_factor, where Time_decay_factor is the time decay factor calculated using e^(-Δt / T), Δt is the time interval since the most recent skill performance (in days), and T is set to 90 days as the skill's time-effect decay constant. Skill levels are categorized as follows: potential skills correspond to 0 ≤ Hidden_Skill_Score < 0.3, manifest skills correspond to 0.3 ≤ Hidden_Skill_Score < 0.7, and core skills correspond to 0.7 ≤ Hidden_Skill_Score ≤ 1.0.Skill stability analysis assesses the consistency and repeatability of customer service hidden skill performance, identifies core hidden skills and occasional performance, and ensures the reliability and effectiveness of hidden skill identification. For example, by analyzing a customer service representative's performance in multiple exception handling cases, a hidden skill of "cross-domain knowledge integration ability" was identified. This skill allows the representative to organically combine game mechanics knowledge, psychological principles, and business negotiation skills to solve complex customer problems. This skill was not detected in traditional assessments but performed outstandingly in exception handling. The skill intensity was assessed as advanced, with good stability, accurately identifying the characteristics of the customer service representative's hidden skills.

[0079] Based on hidden skill features, optimized capability assessment data is generated by re-evaluating customer service capabilities. A multi-dimensional weighted fusion technique is employed to integrate hidden skill features with existing capability data from customer service status profiles, constructing a more comprehensive and accurate customer service capability assessment system. Hidden skill feature quantification converts hidden skill features into standardized numerical vectors, achieving organic integration with existing capability data through weight allocation and feature alignment. A new comprehensive capability profile for customer service staff is established, including assessment results across multiple dimensions such as skill breadth, skill depth, adaptability, innovation, and development potential. For example, a customer service representative initially categorized as "technical support" at an intermediate level may be re-evaluated and upgraded to "multi-skilled expert" level after hidden skill feature fusion reveals excellent "user psychology analysis" and "innovative problem-solving" abilities. The scope of work order matching expands from a single technical category to multiple fields, generating optimized capability assessment data that includes changes in capability level, reclassification of professional types, and hidden skill tags.

[0080] Step S160: Adjust the preliminary distribution plan based on the optimized capability assessment data, obtain distribution execution records, and start a real-time monitoring mechanism to track the distribution execution records and generate processing quality monitoring data.

[0081] Specifically, based on the optimized capability assessment data, the work order allocation in the initial distribution plan is reassessed and adjusted. The distribution adjustment algorithm reads the changes in customer service capability levels and professional type reclassification information from the capability assessment data, and recalculates the matching score between qualified bidding customer service representatives and work orders. When capability assessment data shows that a customer service representative has been reassessed from a "technical support" intermediate level to a "composite expert" level, and new hidden skills of "user psychology analysis" and "innovative problem solving" are discovered, the distribution adjustment expands the scope of work order matching from a single technical category to composite work orders that include multiple areas such as customer relationship management and complaint resolution. The adjustment process is based on the hidden skill feature tags in the capability assessment data and the comprehensive capability profile reconstruction results. When a significant improvement in a customer service representative's capability dimension is detected, challenging work orders that match the newly discovered skill features are prioritized to ensure that hidden skills are fully utilized and verified. After the adjustment operation is completed, a distribution execution record is automatically generated, detailing which specific capability profile changes were made, the changes in customer service representative classification before and after the adjustment, and the reassignment decision process based on the discovery of hidden skills.

[0082] A multi-layered real-time monitoring system is established to continuously track and evaluate the entire work order processing process based on distribution execution records. The monitoring mechanism uses distribution execution records as a benchmark, triggering data collection and real-time analysis through key events such as work order status changes, customer service actions, and customer feedback. When the distribution execution record shows that a "account security issue" work order has been reassigned to a multi-skilled expert with hidden "user psychology analysis" skills, the monitoring system focuses on verifying whether the customer service representative fully utilizes their professional capabilities, recording in detail their analytical depth and logical reasoning during problem diagnosis, innovative ideas in the solution development stage, and the effectiveness of psychological guidance techniques used in customer communication. Customer service behavior monitoring, based on the customer service capability level and specific skill characteristics marked in the distribution execution record, verifies whether the customer service representative's actual work performance matches their optimized capability assessment data through operation log analysis and processing path tracing, identifying excellent handling cases where hidden skills are fully utilized and potential areas for improvement where capabilities are not fully utilized. Customer feedback monitoring, combining the adjustment basis and matching score recorded in the distribution execution record, uses sentiment analysis technology to analyze changes in customer satisfaction with the reassignment results and improvements in service experience, assessing whether the distribution adjustments based on hidden skills have indeed produced the expected results. By systematically comparing and analyzing the data collected in real-time monitoring with the expected processing performance in the distribution execution records, the accuracy and effectiveness of distribution adjustment decisions are verified, and finally, processing quality monitoring data that comprehensively reflects the processing quality level and the effect of allocation optimization is generated.

[0083] Step S170: Based on 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 the distribution of mobile customer service work orders.

[0084] Specifically, a verification benchmark is established through system stability data to verify the stability of actual processing quality, identify the optimization space and improvement direction of the distribution strategy, and finally generate strategy optimization instructions to complete the entire distribution process.

[0085] In some embodiments, the step of 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 a consistency comparison analysis on the processing quality monitoring data based on the stability verification benchmark to generate a quality stability assessment; and generating verification result data based on the quality stability assessment.

[0086] Based on system stability data, response latency, processing success rate, recovery time, and fault tolerance indicators are extracted. Data mining and statistical modeling techniques are used to construct a multi-dimensional stability verification benchmark system. The benchmark establishment process comprehensively considers the system performance baseline under complex scenarios such as recharge anomalies. By analyzing the processing patterns and performance of different types of recharge problems in the system stability data, key performance benchmarks are identified. When system stability data shows that the average response latency is at a reasonable level and the processing success rate remains high when facing contradictory work orders such as "recharge failure but successful deduction," these performance levels are set as reference benchmarks for quality verification, while also considering the differences in the impact of different customer service capability levels on processing effectiveness. Benchmark thresholds are set using statistical analysis methods. By calculating the mean, standard deviation, and confidence interval of various indicators in the system stability data, a reasonable fluctuation range under normal operating conditions is determined, and a dynamic adjustment mechanism is introduced to adapt to performance changes under different time periods and load conditions. Fault tolerance boundaries are defined based on the maximum tolerance capacity and recovery time limits in the system stability data, combined with the business characteristics and risk levels of recharge problem handling, to set corresponding fault tolerance thresholds and anomaly detection rules for work order processing of different complexities. The calibration benchmark system includes a multi-level verification mechanism. Cross-validation and sensitivity analysis are used to ensure the rationality and applicability of the benchmark setting, and finally a complete stability calibration benchmark is established.

[0087] Multi-dimensional comparative analysis is employed to systematically compare the threshold values ​​of various indicators in the stability verification benchmark with specific indicators such as actual response time, resolution success rate, and customer satisfaction recorded in the processing quality monitoring data. When the stability verification benchmark shows the standard performance range of abnormal recharge work orders, and the actual processing time of a customer service representative in the processing quality monitoring data exceeds the benchmark range, consistency analysis identifies performance deviations and quantifies the degree of deviation. The comparison process extracts specific performance data of customer service representatives handling recharge issues from the processing quality monitoring data and matches them with the fault tolerance level limits in the verification benchmark to evaluate the degree of conformity between the actual operating effect and the system's capabilities under the test environment. Deviation calculation, through numerical comparison, performs difference analysis between the processing timeliness, problem resolution accuracy, and customer satisfaction in the processing quality monitoring data and the corresponding threshold standards in the stability verification benchmark to identify abnormal fluctuations exceeding the benchmark range. Trend analysis, based on time series data, compares the consistency changes between the processing quality monitoring data and the verification benchmark at different time periods to discover the development trend of quality stability and potential risk points. When it is found that the actual performance of the work order distribution adjusted based on hidden skills in 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 generates a quality stability assessment.

[0088] The quality stability assessment is structured and comprehensively evaluated to form a complete verification result data system. Verification results are categorized based on the consistency of the quality stability assessment, into different levels such as stable and consistent, basically consistent, with deviations, and significant anomalies. Anomaly summaries, based on performance deviations identified in the quality stability assessment, detail the specific manifestations and impact range of discrepancies between processing quality and the verification benchmark. The stability score is calculated using the comprehensive results of the quality stability assessment, generating a comprehensive stability score through a weighted average method to quantify the overall verification pass rate. Improvement suggestions are generated based on the problems and deficiencies identified in the quality stability assessment, proposing targeted improvement suggestions and optimization directions. When the quality stability assessment shows that the performance of customer service staff with hidden skills deviates from the benchmark expectations in actual handling, the verification result data records these differences and possible improvement measures in detail, generating complete verification result data.

[0089] Utilizing multi-dimensional analysis and decision support technologies, the system deeply analyzes verification result data to identify the strengths and weaknesses of the current distribution strategy. When verification results show that a customer service representative has achieved an excellent level in handling abnormal recharge orders, and their performance confirms the full utilization of their hidden skills, the strategy effectiveness evaluation confirms the successful implementation of the distribution adjustment strategy. When verification results show that customer service representatives with "user psychology analysis" skills are less efficient in handling simple technical issues, and improvement suggestions indicate that the accuracy of capability matching needs to be improved, optimization opportunities are identified to generate corresponding improvement directions. Decision rules are formulated based on specific problems and suggestions recorded in the verification result data. When data shows improved quality in handling abnormal recharge orders but some customer service representatives' capabilities are not fully utilized, specific execution instructions such as capability assessment weight adjustments and matching threshold optimizations are generated. Strategy adjustment plans include measures such as refining capability assessment dimensions, enhancing the targeting of customer service training, and optimizing the work order allocation time strategy. A feedback mechanism establishes a continuous improvement process based on verification result data. Through regular analysis of verification results, the strategy is dynamically optimized, ultimately generating distribution strategy optimization instructions that include specific adjustment measures, execution schedules, and effectiveness evaluation plans, completing the entire process optimization of mobile customer service work order distribution.

[0090] To implement the data analysis-based mobile customer service work order distribution method corresponding to the above method embodiments, and to achieve the corresponding functions and technical effects. See also Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a mobile customer service work order distribution system 200 based on data analysis, according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The mobile customer service work order distribution system 200 based on data analysis provided in this embodiment includes:

[0091] Data acquisition module 201 is used to acquire 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 recommendation based on the customer service status profiles to generate customer service work order preference data.

[0092] The auction distribution module 202 is used to predict the complexity of the work order based on the work order feature vector to generate a work order complexity level, and publish the work order complexity level to the customer service auction platform based on the customer service work order preference data to obtain the auction results ranking.

[0093] Test and verification module 203 is used to construct a preliminary distribution plan based on the auction results, perform an intentional mismatch distribution operation on the preliminary distribution plan to generate mismatch distribution records, and obtain customer service handling abnormal situation data based on the mismatch distribution records;

[0094] The chaos injection module 204 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 use the chaos distribution record to verify the system's anti-interference capability and generate system stability data.

[0095] Capability optimization module 205 is used to perform in-depth mining and analysis on the abnormal situation data to identify hidden skill characteristics of customer service personnel, and to re-evaluate the comprehensive capabilities of customer service personnel based on the hidden skill characteristics to generate optimized capability evaluation data.

[0096] The execution monitoring module 206 is used to adjust the preliminary distribution plan based on the optimized capability assessment data, obtain distribution execution records, and start a real-time monitoring mechanism to track the distribution execution records and generate processing quality monitoring data.

[0097] The strategy optimization module 207 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.

[0098] The aforementioned data analysis-based mobile customer service work order distribution system 200 can implement the data analysis-based mobile customer service work order distribution method of the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0099] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0100] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for distributing mobile game customer service tickets based on data analysis, characterized in that, The method comprises the following steps: Obtaining a mobile game customer service work order, performing text semantic analysis on the mobile game customer service work order to generate a work order feature vector, collecting real-time state data of the customer service to generate a customer service state portrait, and performing reverse work order recommendation based on the customer service state portrait to generate customer service work order preference data; Based on the work order feature vector, the complexity prediction is generated to generate the work order complexity level, and based on the customer service work order preference data, the work order complexity level is published to the customer service bidding platform to obtain the bidding result ranking; According to the bidding result ranking, 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 based on the mismatch distribution record, customer service processing abnormal situation data is obtained; Constructing a chaotic test work order, injecting the chaotic test work order into the mobile game customer service work order processing flow for distribution to generate a chaotic distribution record, and verifying the system anti-interference ability through the chaotic distribution record to generate system stability data; Deeply mining and analyzing the abnormal situation data to identify customer service hidden skill characteristics, re-evaluating the comprehensive ability of the customer service based on the hidden skill characteristics to generate optimized ability evaluation data; According to the optimized ability evaluation data, the preliminary distribution scheme is adjusted 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; 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 subjected to comprehensive analysis to generate distribution strategy optimization instructions, and the distribution of the mobile game customer service work order is completed.

2. The method of claim 1, wherein, The method comprises the following steps: Ability feature extraction is performed on the customer service state portrait to obtain customer service skill labels; Based on the customer service skill labels, a reverse recommendation strategy is constructed; According to the reverse recommendation strategy, customer service work order preference data is generated.

3. The method of claim 1, wherein, The method comprises the following steps: Based on the customer service work order preference data, the customer service that meets the conditions is filtered; The work order complexity level is pushed to the customer service that meets the conditions to obtain push confirmation information; Based on the push confirmation information, customer service bidding response data is collected to generate bidding willingness statistics; According to the bidding willingness statistics, the sorting processing is performed to obtain the bidding result ranking.

4. The method of claim 3, wherein, The method comprises the following steps: The preliminary distribution scheme is subjected to mismatch strategy design to generate a mismatch execution plan; According to the mismatch execution plan, the work order is deliberately assigned to other customer services except the customer service that meets the conditions to obtain a mismatch distribution result; Based on the mismatch distribution result, the mismatch distribution process is monitored to obtain mismatch execution data; According to the mismatch execution data, a mismatch distribution record is generated.

5. The method of claim 1, wherein, The method comprises the following steps: Based on the chaotic test work order, a chaotic injection time and an injection ratio are determined to generate an injection strategy; According to the injection strategy, the chaos test work order is inserted into the mobile game customer service work order processing flow, and an injection execution state is obtained; Based on the injection execution state, distribution processing is performed to obtain distribution tracking data; According to the distribution tracking data, a chaos distribution record is generated.

6. The method of claim 1, wherein, The system stability data generated by the system anti-interference ability verification through the chaos distribution record includes: Abnormal event identification is performed on the chaos distribution record to obtain interference event data; Based on the interference event data, the system response mechanism is analyzed to obtain a response mechanism evaluation result; According to the response mechanism evaluation result, a system stability performance is evaluated to generate a stability index, and the stability index includes a response time delay index, a processing success rate index, and a system recovery time index; Based on the stability index, system stability data is constructed.

7. The method of claim 1, wherein, The deep mining analysis of the abnormal situation data to identify the hidden skill characteristics of the customer service includes: Pattern recognition is performed on the abnormal situation data to obtain an abnormal processing mode; Based on the abnormal processing mode, a customer service coping strategy is analyzed; Unconventional skill performance is extracted from the customer service coping strategy; According to the unconventional skill performance, the hidden skill characteristics of the customer service are identified.

8. The method of claim 1, wherein, The stability verification of the processing quality monitoring data based on the system stability data generates verification result data, including: A stability verification benchmark is established using the system stability data; Based on the stability verification benchmark, a consistency comparison analysis is performed on the processing quality monitoring data to generate a quality stability evaluation; Based on the quality stability evaluation, verification result data is generated.

9. The method of claim 7, wherein, The unconventional skill performance extracted from the customer service coping strategy includes: Standardized comparative analysis is performed on the customer service coping strategy to generate strategy deviation data; Abnormal operation records are obtained by operation behavior identification of the strategy deviation data; The unconventional skill performance is obtained by effect evaluation of the abnormal operation records.

10. A data analysis based mobile tourist customer service ticket distribution system, characterized in that, It includes: A data acquisition module is used to acquire mobile game customer service work orders, perform text semantic analysis on the mobile game customer service work orders to generate work order feature vectors, acquire customer service real-time state data to generate customer service state portraits, and perform reverse work order recommendation based on the customer service state portraits to generate customer service work order preference data; An auction distribution module is used to generate work order complexity levels based on the work order feature vectors, and 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; A test verification module is used to construct a preliminary distribution scheme according to the auction result ranking, perform intentional mismatch distribution operations to generate mismatch distribution records, and obtain customer service processing abnormal situation data based on the mismatch distribution records; A chaos injection module is used to construct chaos test work orders, inject the chaos test work orders into the mobile game customer service work order processing flow for distribution to generate chaos distribution records, and generate system stability data by verifying the system anti-interference ability through the chaos distribution records; The capability optimization module is configured to perform deep mining analysis on the abnormal situation data to identify hidden skill characteristics of the customer service personnel, reevaluate the comprehensive capability of the customer service personnel based on the hidden skill characteristics, and generate optimized capability evaluation data. The execution monitoring module is configured to adjust the preliminary distribution scheme based on the optimized capability evaluation data to obtain distribution execution records, start a real-time monitoring mechanism to track the distribution execution records, and generate processing quality monitoring data. The strategy optimization module is configured to perform stability checking on the processing quality monitoring data based on the system stability data to generate checking result data, perform comprehensive analysis on the checking result data to generate distribution strategy optimization instructions, and complete the distribution of the hand game customer service work orders.

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