A customer portrait dynamic construction and intelligent work order automatic flow transfer optimization system

Abstract

The application belongs to the technical field of computer data processing, and specifically discloses a customer portrait dynamic construction and intelligent work order automatic flow optimization system. The system acquires full heterogeneous data in real time, dynamically updates customer semantic vectors by using a time decay function, generates real-time portraits containing multiple dimensions such as consumption tendency, analyzes work order semantic intention by using NLP technology, combines the real-time load and skill state of the processing node, calculates the optimal flow path by a multi-objective constraint solving algorithm, analyzes the deviation degree based on the processing feedback to correct the weight, and realizes the closed-loop optimization of portrait accuracy and flow efficiency. The application solves the problems of portrait lag and work order flow rigidity, significantly shortens the turnover time, improves the resource utilization rate and service quality, and enables the system to have a strong self-evolution ability.

Description

Technical Field

[0001] This invention belongs to the field of computer data processing technology, specifically relating to a dynamic customer profile construction and intelligent work order automatic flow optimization system. Background Technology

[0002] In the field of enterprise digital operations and customer service management, building accurate customer profiles and efficient business flow mechanisms are core means to improve service response speed and customer satisfaction. By collecting, cleaning, and tagging multi-source business data, enterprises can achieve deep insights into customer preferences, behavioral patterns, and potential needs, thereby providing basic data support for personalized marketing and differentiated services.

[0003] Among these, the dynamic construction of customer profiles and the intelligent optimization of the work order system are key technological directions for improving service chain efficiency. This technology aims to seamlessly integrate real-time updated customer characteristics with the work order processing flow, and through continuous monitoring of business status and customer attributes, achieve precise matching of service resources and automated closed-loop management of business processes.

[0004] Existing technologies still face significant challenges in practical applications. Traditional customer profiling methods rely heavily on pre-set static models and offline batch processing, resulting in low profile update frequency and difficulty in capturing instantaneous changes in customer behavior, leading to a severe disconnect between profile information and actual business needs. Simultaneously, ticket processing mechanisms are primarily based on fixed logical rules or manual scheduling, lacking adaptability to massive business scenarios and failing to achieve optimal decision-making for processing paths under multi-objective constraints. Furthermore, the poor interaction between customer profiles and ticket systems prevents ticket processing personnel from obtaining accurate customer background information in real time, severely limiting the professionalism and timeliness of ticket processing. These problems significantly reduce operational efficiency and service quality, becoming urgent technical challenges that need to be addressed. Summary of the Invention

[0005] The purpose of this invention is to provide a system for dynamically constructing customer profiles and optimizing intelligent work order workflow, so as to solve the problems mentioned in the background art.

[0006] The technical solution of this invention includes: a multi-dimensional data fusion and perception module, used to access the full heterogeneous data of the business system in real time through change data capture technology, and to achieve comprehensive collection of customer behavior trajectories at business touchpoints by fully synchronizing historical stored data and capturing incremental log data at millisecond levels; a dynamic profile feature engineering engine, used to perform real-time stream computing processing on the collected multi-dimensional data, reduce the weight of historical features through a preset time decay function, and dynamically update the customer's semantic vector expression by combining the current behavioral trigger intensity, generating a real-time dynamic profile including dimensions of consumption tendency, credit rating, service sensitivity, and technology preference; a work order semantic intent parsing module, used to perform deep semantic analysis on newly generated work order text using natural language processing technology, extracting the core demands, urgency, related business domains, and potential emotional tags of the work order, and transforming the work order into a structured demand feature vector; and an intelligent workflow. The path decision center is used to construct an adaptability evaluation matrix for the current work order based on the real-time profile features output by the dynamic profile feature engineering engine and the demand feature vector output by the work order semantic intent parsing module. It then calculates the optimal workflow node and processing path using a multi-objective constraint solving algorithm, under constraints of human resource cost, expected response time, and service quality assessment. The real-time resource status monitoring unit monitors the current load, professional skill score, historical processing success rate, and immediate busy / idle status of each processing position in the service node, providing dynamic resource constraint parameters for workflow decisions. The adaptive closed-loop optimization executor issues execution instructions based on the optimal workflow path and captures processing feedback data from each node in real time during the work order workflow process. It analyzes the deviation between the processing results and the expected target, generates correction weights, and feeds them back to the dynamic profile feature engineering engine and the intelligent workflow path decision center, achieving the synergistic evolution of profile accuracy and workflow efficiency.

[0007] Furthermore, the multi-dimensional data fusion and perception module employs an asynchronous decoupling architecture based on a distributed message queue during data acquisition, uniformly encapsulating unstructured text, semi-structured logs, and structured relational database data from different sources. As one embodiment of the invention, this module incorporates data cleaning logic, filtering redundant information and invalid noise through regular expression matching and key field validation, ensuring that the data input to subsequent processes possesses a high degree of purity and consistency.

[0008] As one embodiment of the present invention, the dynamic profile feature engineering engine employs a feature update algorithm based on heat decay. Specifically, the engine assigns an initial weight to each profile tag and applies an exponential time decay function based on the frequency and time interval of the corresponding behavior. When a customer generates high-frequency business interactions within a short period, the system rapidly increases the weights of the relevant feature dimensions, while for outdated tags that have not interacted for a long time, the weights gradually converge towards 0 over time. Furthermore, the engine uses a multi-head attention mechanism to calculate the contribution of features of different dimensions to the current business scenario, thereby generating a comprehensive profile representation in the vector space that accurately represents the customer's instantaneous needs and long-term preferences.

[0009] Furthermore, the work order semantic intent parsing module constructs a semantic understanding model using a deep learning neural network. In one embodiment of the invention, this module first performs word segmentation and stop word removal on the original work order text, and then uses a word vector model to map the text to a high-dimensional semantic space. Further, this module extracts local key semantic features through a convolutional neural network and combines it with a bidirectional long short-term memory network to capture the logical connections within the context, thereby achieving accurate identification of the true intent behind the work order. In another embodiment of the invention, the parsing process also includes a quantitative score for the urgency of the work order. This score is calculated based on a weighted average of keyword strength, historical complaint records, and customer level, outputting a normalized value between 0 and 1.

[0010] In one embodiment of the present invention, the intelligent workflow decision center employs a graph theory-based optimal path search algorithm. This center abstracts all service nodes and expert seats within the enterprise as vertices in a topological graph, and the workflow relationships between nodes as weighted edges. Furthermore, the edge weights are jointly determined by the matching degree of the dynamic profile, the professional skill score of the processing seat, and the current queuing time. In another embodiment of the present invention, the decision center uses a heuristic search strategy to quickly locate the workflow sequence that maximizes the global utility function within a vast search space. This utility function comprehensively considers the estimated completion time of the work order, the probability of ensuring processing quality, and the overall load balancing of the system, ensuring that each work order is assigned to the most suitable processing entity.

[0011] Furthermore, the real-time resource status monitoring unit tracks the real-time workflow of each processing node through a heartbeat detection mechanism and a task state machine. As one embodiment of the invention, this unit not only monitors the physical online status of the workstations but also establishes a dynamic skill profile for each workstation through in-depth analysis of past processing records. The skill profile includes the average processing time for different types of business, customer satisfaction scores, and the success rate in solving complex problems. Furthermore, the unit periodically calculates the resource saturation of each node, and when the saturation of a node exceeds the 85% warning threshold, it automatically sends an expansion or diversion signal to the intelligent workflow decision center.

[0012] In one embodiment of the present invention, the adaptive closed-loop optimization executor establishes a reinforcement learning-based feedback mechanism. After the work order is processed and closed, the executor automatically collects key performance indicators during the processing, including but not limited to the actual number of transfers, the dwell time at each stage, and the final customer's evaluation score. Further, the executor compares the actual performance data with the predicted indicators at the time of decision-making and calculates the loss function value. In another embodiment of the present invention, using the gradient descent algorithm, the system backpropagates the error generated by the loss function to the profile building engine and decision center, fine-tuning the calculation logic of feature weights and the heuristic parameters of path search. Through this continuous iterative learning, the system can autonomously identify changes in the business environment, achieving automated optimization of the profile model and transfer rules, giving it a strong self-evolutionary capability.

[0013] Furthermore, the system also includes a global anomaly intervention module. This module is used to monitor timeout risks and logical deadlocks in the work order process in real time. When it is detected that the dwell time of a work order in a specific stage exceeds 1.2 times the preset threshold, the module will immediately trigger an early warning and forcibly take over the flow control, elevating it to a high-priority processing queue or directly assigning it to a manual supervision position to ensure the absolute reliability of the service chain.

[0014] As one embodiment of the present invention, during the customer profile construction process, the system dynamically adjusts the active dimensions of the profile according to the switching of business scenarios. For example, in a marketing consulting scenario, the focus is on the customer's purchasing power and brand loyalty characteristics; while in a complaint and appeal scenario, it automatically switches to focusing on the customer's negative sentiment index and historical complaint frequency characteristics. This dimension switching is implemented by a context-aware scenario recognition operator, ensuring the relevance and usability of the profile data in different application environments.

[0015] Furthermore, the intelligent work order automatic workflow optimization system interacts with the enterprise's core business system through an integrated middleware platform. As one embodiment of the invention, this platform provides a standardized application programming interface, supporting data retrieval and command issuance via multiple protocols. When processing sensitive customer data, the system incorporates a privacy computing unit, using differential privacy technology to ensure the accuracy of the profile while de-identifying sensitive fields involving personal privacy, thus ensuring the security of data flow.

[0016] As one embodiment of the present invention, a collaborative game model is also introduced into the flow optimization logic. This model treats different functional departments as game participants and, by balancing the interest functions and cost functions of each party, solves for the Nash equilibrium solution that minimizes the overall operating cost of the enterprise and optimizes the customer experience. This design effectively solves the problem of shirking responsibility in cross-departmental collaboration, elevating the originally rigid rule-based flow to an intelligent flow based on maximizing global benefits.

[0017] Furthermore, to cope with the impact of large-scale concurrent work orders, the system adopts a microservice cluster and elastic computing resource pool in its underlying architecture. As one implementation of this invention, both the dynamic profile feature engineering engine and the intelligent workflow decision center support horizontal scaling. When the system load surges instantaneously, the automated operation and maintenance scripts will, according to a preset load balancing strategy, complete the expansion of computing resources within 30 seconds, ensuring low-latency response of the work order processing logic.

[0018] As one embodiment of the present invention, the automatic workflow of work orders also incorporates geolocation information services. For work orders requiring offline cooperation, the system calculates the optimal physical delivery or service coverage radius based on the geographic tags in the customer's dynamic profile and the real-time latitude and longitude coordinates of offline service outlets. Furthermore, by combining real-time traffic condition prediction data, the system incorporates a geolocation attenuation factor into the weight matrix of the workflow path, achieving deep coupling between online intelligent workflow and precise offline services.

[0019] Furthermore, the adaptive closed-loop optimization executor also possesses a pattern recognition capability. Through cluster analysis of a large number of historical successful cases, the executor can identify the optimal processing path for specific types of customers and specific intent work orders. As one embodiment of the invention, these optimal paths will be fixed as templates in a knowledge base. In subsequent path decisions, the decision center will prioritize referring to these high-performance templates. Only when the current work order characteristics deviate significantly from the templates will the path calculation logic based on global search be initiated, thereby greatly improving the system's decision-making efficiency.

[0020] As one embodiment of the present invention, the dynamic profile feature engineering engine also integrates a feature drift detector. This detector continuously monitors the changing trends of customer behavior distribution. When it detects a statistically significant drift in the feature distribution of a large-scale customer group, the system automatically triggers a process of redefining the profile dimensions. This mechanism ensures that the system can keenly capture the long-term impact of macro-market environment or corporate business strategy adjustments on customer behavior, avoiding decision-making biases caused by model expiration.

[0021] Furthermore, the system's interactive interface layer uses visualization to show operations personnel the real-time dynamic flow trajectory of each work order and the underlying profiling logic. As one implementation of this invention, the system allows operations personnel to manually intervene and modify the flow rules under special circumstances. The data from this manual intervention is also input as feedback samples into the adaptive closed-loop optimization executor, serving as an important source for the system to learn from human experience and continuously improve its automation strategies.

[0022] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention achieves a leap from static, lagging customer descriptions to real-time, dynamic semantic vector representations by constructing a multi-dimensional data fusion perception module and a dynamic profiling feature engineering engine. Compared to traditional batch-processing-based offline profiling technology, this solution utilizes stream computing and time decay functions to capture instantaneous changes in customer behavior at the millisecond level, ensuring a strong timeliness match between profiling data and actual business needs, and providing real-time, in-depth decision-making basis for the accurate flow of work orders.

[0023] This invention introduces deep learning-based semantic intent parsing of work orders and graph theory-based multi-objective workflow path search technology, completely changing the rigid mode of traditional work order systems that rely on fixed logic and manual scheduling. The system can calculate the globally optimal workflow path within milliseconds based on the complexity and urgency of the work order, as well as the real-time load and skill matching of processing nodes. This highly adaptive workflow mechanism significantly reduces the average turnaround time of work orders and greatly improves resource utilization and service response speed.

[0024] This invention innovatively designs an adaptive closed-loop optimization executor, constructing a complete closed loop from execution to feedback and then to learning and evolution. The system continuously analyzes the deviation between actual performance and predicted targets through reinforcement learning algorithms, automatically adjusting profile weights and decision parameters. This self-evolving capability enables the system to become increasingly intelligent as business data accumulates. It can not only handle complex existing multi-business scenarios but also effectively predict and adapt to future business changes, significantly reducing the system's manual maintenance costs and ensuring the continuous growth of enterprise service efficiency.

[0025] This invention constructs a multi-layered reliability assurance system through a real-time resource status monitoring unit and a global anomaly intervention module. The system not only enables dynamic assignment based on load balancing but also intervenes quickly when bottlenecks or logical anomalies occur, ensuring that every work order runs on a safe and controlled track. Combined with privacy computing and a resilient architecture design, this invention improves business efficiency while ensuring data security and high system availability, providing large enterprises with a highly professional, secure, and scalable digital operations foundation. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall technical solution architecture proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the dynamic portrait feature engineering engine in this invention; Figure 3 This is a logical flowchart of the work order semantic intent parsing and demand feature extraction in this invention; Figure 4 This is a logical flowchart of the intelligent flow path decision-making and resource constraint matching in this invention; Figure 5 This is a schematic diagram of the multi-level interaction relationship and data flow of the adaptive closed-loop optimization actuator based on reinforcement learning in this invention. Detailed Implementation

[0027] Example 1 Please refer to the attached document. Figure 1 This invention proposes a dynamic customer profile construction and intelligent work order automatic flow optimization system. Its core architecture aims to build an intelligent closed loop with self-evolutionary capabilities by deeply integrating data across the entire business chain. The system uses a multi-dimensional data fusion and perception module as the underlying data access support, transforming previously isolated business system data flows into high-value digital assets. During the entire system operation, the multi-dimensional data fusion and perception module plays the role of data source governance, achieving non-intrusive collection of heterogeneous data sources within the enterprise through change data capture technology. Specifically, this module can not only fully synchronize all static data historically stored in relational databases, but more importantly, it can perform millisecond-level capture and parsing of incremental log data. This millisecond-level response capability ensures that all subsequent profile construction and decision-making logic are based on the current real business state, rather than delayed offline snapshots.

[0028] When the multi-dimensional data fusion and perception module performs data acquisition tasks, it internally employs an asynchronous decoupled architecture based on a distributed message queue. This architecture effectively isolates the acquisition pressure from the operational pressure of the business system. Unstructured text, semi-structured logs, and structured relational database data from different dimensions are uniformly encapsulated into standardized data frames before entering the system. The module incorporates complex data cleaning logic, which, through preset regular expression matching rules and key field validation algorithms, can accurately identify and filter out redundant duplicate information, incomplete fields, and invalid system noise. This data preprocessing mechanism ensures that the data input into subsequent processes has a high degree of purity and logical consistency, laying a physical foundation for the accuracy of dynamic profiling.

[0029] Combined with appendix Figure 2 The dynamic profile feature engineering engine is the core algorithm of this system, responsible for transforming the raw behavioral trajectories output by the multi-dimensional data fusion perception module into semantic vectors reflecting deep customer characteristics. This engine employs a real-time stream computing-based processing framework, enabling instantaneous feature extraction for each incoming behavioral record. To address the natural shift in customer preferences over time, the engine introduces a feature update algorithm based on heat decay. In its implementation, the engine assigns an initial weight to each profile tag and dynamically monitors the frequency and time interval of the behavior corresponding to that tag.

[0030] To accurately quantify this time decay effect, this embodiment employs the following exponential time decay function to dynamically adjust the feature weights:

[0031] In this formula, Represents the current moment The feature weight values, This represents the feature at the previous time step. The residual weights, The preset time decay coefficient typically ranges from 0.01 to 0.1. In this embodiment, it is set to 0.05, depending on the sensitivity of the specific business dimension. This represents the trigger strength of the action occurring at the current moment, while This represents the correlation coefficient between the behavior and a specific profile dimension. Using this formula, when a customer generates frequent business interactions within a short period, the system will quickly increase the weight of the relevant feature dimensions; while for outdated tags that haven't been interacted with for a long time, their weights will gradually converge towards 0 over time.

[0032] The dynamic profile feature engineering engine further utilizes a multi-head attention mechanism to calculate the contribution of features across different dimensions to the current business scenario. In vector space, the system generates a comprehensive profile representation that accurately reflects a customer's instantaneous needs and long-term preferences, encompassing four core dimensions: consumption tendency, credit rating, service sensitivity, and technology preference. This dynamically generated semantic vector representation, compared to traditional static labels, possesses stronger predictive power and higher discriminative power. Furthermore, the engine integrates a feature drift detector to continuously monitor trends in customer behavior distribution. When a statistically significant shift in the feature distribution of a large customer group is detected, the system automatically triggers a redefinition process for the profile dimensions, ensuring that the system can keenly capture the long-term impact of the macro-market environment on customer behavior.

[0033] Combined with appendix Figure 3 When the system receives a newly generated work order, the work order semantic intent parsing module is immediately activated. This module utilizes a semantic understanding model built with a deep learning neural network to perform in-depth analysis of the work order text. First, the module performs word segmentation and stop word removal on the original work order text, transforming it into a clean sequence of words. Then, using a pre-trained word vector model, these words are mapped to a high-dimensional semantic space. The core structure of the module consists of a convolutional neural network and a bidirectional long short-term memory network. The convolutional neural network is responsible for extracting local key semantic features in the work order, such as specific product names or sensitive action words; while the bidirectional long short-term memory network is used to capture long-range logical connections in the text context, identifying transitions, emphasis, and emotional fluctuations in the customer's expression.

[0034] The work order semantic intent parsing module not only extracts the core demands and related business domains of the work order, but also generates potential emotion tags specifically for customer sentiment. The parsing process includes a quantitative scoring of the work order's urgency. This scoring logic is not a simple keyword matching, but rather a comprehensive weighted average based on keyword strength, the customer's historical complaint records in the system, and the customer's level in the dynamic profile. Finally, the module outputs a normalized value between 0 and 1 as the urgency score, which, along with a structured demand feature vector, is sent to the decision-making layer. This deep semantic parsing capability ensures that the system can see beyond the surface of the text and identify the customer's true intent.

[0035] Please refer to the attached document. Figure 4The intelligent workflow decision center serves as the system's command center, responsible for finding the optimal work order workflow solution under complex constraints. This center abstracts all service nodes and expert seats within the enterprise as vertices in a topology graph, and the workflow relationships between nodes as weighted edges. Based on real-time profile features output by the dynamic profile feature engineering engine and the demand feature vector output by the work order semantic intent parsing module, the decision center constructs an adaptability evaluation matrix for the current work order. To find a balance point among massive workflow combinations, the decision center employs a multi-objective constraint solving algorithm.

[0036] To measure the global utility of different flow paths, this embodiment introduces the following utility function formula: in, Representative path The overall utility score. The skill matching score, representing the current processing node and the work order requirements, is calculated by the cosine similarity between the profile features and the node's skill profile. The maximum processing time limit stipulated by the business regulations. This represents the expected response time for this path. This represents the current load saturation of the processing node. , , Assign weights to each objective based on the service sensitivity dimension; =0.4, =0.3, =0.3, and their sum is equal to 1.

[0037] The decision center uses a heuristic search strategy to quickly locate the global utility function within a vast search space. Maximizing the workflow sequence. This center not only focuses on the matching degree of individual points but also comprehensively considers constraints such as human resource costs, expected response time, and service quality assessment. This graph theory-based search model completely changes the rigid state of traditional systems that rely on fixed logic, enabling personalized scheduling for each work order.

[0038] While decisions are being executed, the real-time resource status monitoring unit tracks the workflow of each processing node in real time through a heartbeat detection mechanism and a task state machine. This unit not only monitors the physical online status of workstations but also builds a dynamic skill profile for each workstation through in-depth analysis of past processing records. The skill profile includes the average time for handling different types of business, customer satisfaction scores, and the success rate in solving complex problems. The monitoring unit periodically calculates the resource saturation of each node. Once the saturation of a node exceeds the 85% warning threshold, it automatically sends an expansion or diversion signal to the intelligent workflow decision center, prompting the decision center to dynamically adjust path weights.

[0039] Combined with appendix Figure 5 An adaptive closed-loop optimization executor forms the system's evolutionary loop. After a work order is issued and executed according to the optimal workflow path, the executor begins to capture processing feedback data from each node in real time. After the work order is processed and closed, the executor automatically collects key performance indicators, such as the actual number of workflows, the dwell time at each stage, and the final customer evaluation. The executor compares these actual performance data with the predicted indicators used in the decision-making process to calculate the deviation. Utilizing the error backpropagation mechanism in reinforcement learning algorithms, the system transforms this deviation into correction weights and feeds them back to the dynamic profile feature engineering engine and the intelligent workflow path decision center. Through this continuous iterative learning, the system can autonomously identify changes in the business environment and achieve the co-evolution of profile accuracy and workflow efficiency.

[0040] This system also includes a global anomaly intervention module as the last line of defense for service reliability. This module operates independently of the main transfer logic, monitoring timeout risks and logical deadlocks in the work order flow in real time. When it detects that the dwell time of a work order at a specific stage exceeds 1.2 times the preset threshold, this module will immediately trigger an alert and forcibly take over control of the flow. At this time, the anomaly intervention module will elevate the work order to the highest priority processing queue, or, according to the preset security degradation policy, directly assign it to an experienced human supervisor to ensure the absolute reliability of the service chain under extreme circumstances.

[0041] Furthermore, during the customer profile construction process, the system dynamically adjusts the profile's activity dimensions based on changes in business scenarios. This dimension switching is achieved by a context-aware scenario recognition operator. For example, in a marketing consulting scenario, the operator automatically increases the weight of customer purchasing power and brand loyalty characteristics; while in a complaint and appeal scenario, it automatically switches to focusing on monitoring the customer's negative sentiment index and historical complaint frequency. This design ensures the relevance and usability of the profile data in different application environments.

[0042] Example 2 Building upon Example 1, this example details the system's resilience mechanisms and data security strategies when handling large-scale concurrent work order surges. Addressing the complex microservice architectures of modern enterprises, the intelligent work order auto-flow optimization system deeply interacts with the enterprise's core business systems through an integrated middleware platform. This platform provides standardized application programming interfaces, supports data retrieval and command issuance via multiple communication protocols, ensuring seamless integration into the existing information ecosystem.

[0043] To cope with surges in traffic, the system employs a microservice cluster and an elastic computing resource pool at its core. Both the dynamic profiling feature engineering engine and the intelligent workflow decision center are designed as stateless services that support horizontal scaling. When the monitoring unit detects a sudden surge in global system load, automated operation and maintenance scripts rapidly expand computing resources within 30 seconds. This elastic scaling capability ensures that even under high concurrency, the system's work order semantic parsing latency and path calculation latency remain at the millisecond level, preventing work order backlogs caused by computing bottlenecks.

[0044] When processing sensitive customer data, the system incorporates a privacy-preserving computation unit. This unit employs differential privacy technology to perform in-depth analysis of the profile data while anonymizing privacy fields such as personal identification and contact information. During the transmission of profile features to the decision-making center, only vectorized values ​​reflecting group characteristics and business preferences are retained, without disclosing the original underlying data. This design maximizes data security protection obligations while ensuring the accuracy of decision-making, meeting stringent cybersecurity standards.

[0045] The workflow optimization logic also incorporates a collaborative game theory model. This model treats different functional departments within the enterprise as game participants. Since different departments have different profit motives and cost considerations when processing work orders, the collaborative game theory model defines the profit functions of each party to find the Nash equilibrium solution that minimizes the overall operating costs of the enterprise and optimizes the customer experience. This design concept solves the common problem of shirking responsibility in cross-departmental collaboration from a management perspective, elevating rigid administrative rules to an intelligent game outcome based on maximizing global interests.

[0046] Furthermore, the automated workflow of work orders is deeply integrated with geolocation information services. For work orders requiring offline physical cooperation, the system extracts geotags from the customer's dynamic profile and combines this with the real-time latitude and longitude coordinates of offline service points to calculate the optimal physical coverage radius. The system considers not only straight-line distance but also real-time traffic forecast data. The geolocation attenuation factor is included as an important dynamic parameter in the weight matrix of the workflow path, thus achieving a deep coupling between intelligent online workflow and precise offline services. This design significantly improves the scheduling efficiency for services involving on-site surveys and in-home repairs.

[0047] Example 3 Building upon Examples 1 and 2, this example focuses on how the system achieves a significant improvement in decision-making efficiency through pattern recognition and the consolidation of human experience. The adaptive closed-loop optimization executor not only possesses reinforcement learning capabilities but also integrates an advanced pattern recognition algorithm. By clustering millions of historically generated successful cases, the executor can automatically identify the optimal processing path for specific types of customers and specific intent work orders.

[0048] These identified optimal processing paths will be automatically stored as high-value knowledge templates in the system's built-in expert knowledge base. In subsequent work order routing decisions, the intelligent routing decision center will first use a fast matching algorithm to check if the current work order's feature vector is highly similar to the optimal path templates in the knowledge base. Once a match with a similarity exceeding 95% is found, the decision center will prioritize referencing and reusing these high-performance templates, thus skipping the complex global path search process. Only when the current work order's features deviate significantly from existing templates, or when business rules undergo major adjustments, will the system restart the global path calculation logic based on graph theory and multi-objective constraints. This strategy greatly improves the system's average decision-making speed, especially when handling routine, high-frequency business work orders.

[0049] Furthermore, the system's interactive interface layer provides a highly visualized monitoring view. Operations managers can intuitively observe the real-time dynamic flow of each work order through the graphical interface, as well as the supporting logic and utility function score behind each decision. The system also includes a manual intervention interface, allowing operations personnel to manually intervene and modify the flow rules of specific work orders in extremely special business scenarios or sudden public relations crises.

[0050] It is worth noting that this human intervention is not simply manual operation. All data from these interventions is used as high-quality feedback samples and fed into the adaptive closed-loop optimization executor in real time. The executor analyzes the logic of the human intervention and the resulting performance, using this data as a crucial source for the system to learn from human experience and continuously improve its automated decision-making strategies. Through this human-machine collaborative learning model, the system can continuously absorb the domain knowledge of human experts, enabling its automated strategies to increasingly approach and even surpass the decision-making level of experienced schedulers.

[0051] In the continuous operation of the dynamic profile feature engineering engine, the feature drift detector acts as a sentinel. Through periodic scanning of the statistical distribution of features across various dimensions, it can detect changes in customer behavior patterns caused by seasonal factors, marketing promotions, or competitor strategy adjustments. Once a deviation in the statistical distribution exceeding three standard deviations is detected, the system automatically alerts the administrator and recommends initiating a model retraining process. This proactive risk control mechanism effectively avoids decision failures due to model expiration, ensuring the system maintains a high level of intelligent performance throughout long-term operation.

[0052] In summary, the customer profiling dynamic construction and intelligent work order automatic workflow optimization system constructed in this invention achieves real-time access to all heterogeneous data through a multi-dimensional data fusion and perception module. It utilizes a dynamic profiling feature engineering engine and a work order semantic intent parsing module to achieve a deep understanding of customers and their needs. Combined with an intelligent workflow decision center and a real-time resource status monitoring unit, it achieves globally optimal resource allocation. Finally, through the continuous evolution of the adaptive closed-loop optimization executor and the collaborative cooperation between various functional modules, it provides enterprises with a highly reliable automated operation foundation that is perceptive, analytical, and evolving. This systematic innovation not only significantly improves the workflow efficiency and resource utilization of work orders but also fundamentally enhances the enterprise's responsiveness to complex business environments and improves customer satisfaction.

[0053] In its implementation, the data access layer of the multi-dimensional data fusion and perception module adopts a highly available cluster deployment mode. To ensure the integrity of change data capture, the acquisition unit records the log sequence number of each transaction, ensuring accurate continuation from the breakpoint after network jitter or service restart, without losing any critical business touchpoint information. During the data cleaning phase, the system employs a multi-level filtering architecture. The first layer is physical validation based on data type, eliminating invalid packets with incorrect formats; the second layer is validity validation based on business logic, identifying logically illogical garbage data by comparing it with metadata from the main database.

[0054] The dynamic profile feature engineering engine employs differentiated update frequencies for features of different dimensions when generating semantic vectors. For example, for relatively stable long-term features like credit rating, the engine uses a longer time decay period and a lower sampling frequency; while for instantaneous features like service sensitivity and current sentiment, it uses an extremely short decay period and the highest level of real-time streaming computation. This hierarchical processing strategy ensures the sensitivity of important features while effectively conserving system computational resources. After generating the vector representation, the engine normalizes it to ensure comparability of features of different dimensions in subsequent adaptation evaluation matrix calculations.

[0055] The work order semantic intent parsing module incorporates an active learning mechanism during the training of the deep learning model. The system periodically filters work order samples with low model confidence and pushes them to a professional human review team for annotation. The manually annotated samples are then re-fed into the neural network for fine-tuning. Through this continuous small-sample learning, the model can quickly master newly emerging industry terms, internet slang, and evolving customer expression habits, maintaining extremely high accuracy in semantic understanding.

[0056] When performing multi-objective constraint solving, the intelligent workflow decision center also considers the synergistic effect of nodes. In some complex work order processing scenarios, experts from multiple nodes may need to collaborate. By analyzing historical collaboration data, the decision center can identify node combinations that work well together and have high collaboration efficiency, and assign higher weights to these combinations during the path search process. This workflow logic based on team effectiveness transcends the simple idea of ​​single-point optimization, achieving a leap towards organizational collaboration optimization.

[0057] The adaptive closed-loop optimization executor employs a learning rate decay strategy during backpropagation optimization. In the initial stages of system operation, a high learning rate is set to quickly explore business patterns, enabling rapid convergence of profile weights and decision parameters. As the system stabilizes, the learning rate is gradually reduced to prevent drastic model fluctuations caused by isolated outliers. The executor also periodically generates system performance reports, quantifying the economic benefits and efficiency improvements brought by the system by comparing various indicators of automated workflows with traditional manual scheduling, providing data support for enterprise management decisions.

[0058] In the overall system lifecycle management, the global anomaly intervention module also undertakes stress testing. By simulating extreme concurrent request and node failure scenarios, the intervention module can assist technical personnel in assessing the system's capacity limits and recovery time. This approach of combining monitoring and testing enhances the system's resilience to unpredictable risks. The integrated middleware platform, through load balancing technology, evenly distributes massive data requests to various backend processing units, avoiding system paralysis caused by single points of failure.

[0059] This system employs a hot-and-cold storage strategy for its underlying data storage. Currently active user profile data and features of pending work orders are stored in a high-performance in-memory database to support sub-millisecond read / write access. Conversely, closed historical work order data and outdated feature snapshots are periodically migrated to a low-cost, high-capacity distributed file system for subsequent long-term statistical analysis and model training. This storage architecture achieves an excellent balance between performance and cost.

[0060] When implementing differential privacy protection, the privacy computing unit dynamically adjusts the injected noise level. When processing low-risk routine business, the noise level is reduced to retain more feature details; however, when processing high-risk business involving financial assets, healthcare, etc., the noise level is significantly increased to ensure that even if the data is subjected to malicious attacks, attackers cannot deduce specific personal information through statistical analysis. This security mechanism is linked to the business risk level, achieving a dynamic balance between security and efficiency.

[0061] In terms of system deployment, this system supports multiple deployment modes, including private cloud, public cloud, and hybrid cloud. Containerized encapsulation technology enables the rapid migration and deployment of various system modules across different physical environments. The adaptive closed-loop optimization executor, while running in the cloud, can fully utilize the elastic computing resources of the cloud platform to execute large-scale parallel reinforcement learning training tasks. Meanwhile, the multi-dimensional data fusion and perception module deployed on the enterprise's local edge nodes ensures low-latency data access and physical isolation of sensitive source data.

[0062] In the intelligent work order automatic workflow optimization system, a key technical detail is the real-time consistency of path decisions. Since the load data acquired by the resource status real-time monitoring unit is dynamically changing, to prevent a large number of work orders from flooding a previously idle single node at the same time, causing that node to become instantly overloaded, the intelligent workflow path decision center introduces an assignment strategy with a probability distribution. This strategy assigns work orders to the top three candidate nodes according to their comprehensive utility scores, rather than assigning all to the first-ranked node. This probability-based load balancing algorithm effectively smooths out system traffic spikes, ensuring the stable operation of the entire service network.

[0063] When solidifying optimal paths, pattern recognition algorithms consider the timeliness of these paths. The system re-evaluates the optimal paths in its database periodically. If a path's recent processing performance declines, or if related nodes fail due to internal organizational restructuring, the system automatically discards the template or marks it as pending update. This dynamic knowledge base maintenance mechanism ensures that the system's decisions always reference the best-performing processing model in the current environment.

[0064] To address the challenges of large enterprises operating across geographical regions, this system supports a multi-tiered workflow system. At the group headquarters level, the system performs global resource monitoring and macro-level cross-branch workflow scheduling; within each branch, independent subsystem instances run to handle routine business within their respective regions. The systems at headquarters and branches exchange feature vectors through an encrypted security gateway. When a region experiences an extreme surge in business leading to complete resource exhaustion, the headquarters decision-making center automatically initiates cross-regional collaboration logic, distributing some of the pressure to resource-redundant remote centers, achieving mutual assistance and balance of service capabilities across the entire group.

[0065] Finally, it is important to emphasize that all numerical values, thresholds, and weight parameters mentioned in this system can be flexibly configured according to the needs of actual business scenarios. For example, for the high-end luxury goods industry, which emphasizes customer loyalty, the weight of the service sensitivity dimension in the dynamic profile can be increased; while for municipal service hotlines, which emphasize processing efficiency, the weight coefficient of the expected response time target in the intelligent workflow decision center can be increased. This highly parameterized design gives the system strong versatility and cross-industry adaptability.

[0066] During data acquisition, the multi-dimensional data fusion and perception module also integrates a heartbeat survival detection subunit. This subunit is responsible for performing health checks on all downstream consumption links. If processing delays accumulate in subsequent feature engines or parsing modules, the acquisition unit will automatically activate a backpressure mechanism, slowing down the data retrieval frequency or temporarily storing incremental logs in a high-performance local cache. This self-protection mechanism effectively prevents system crashes caused by sudden traffic surges.

[0067] In the intelligent workflow decision-making center, the multi-objective constraint-solving algorithm not only focuses on the current static optimal solution but also possesses a certain degree of predictability. By analyzing the inflow trend of work orders over the past hour, the decision-making center can predict the distribution of resource pressure in the near future. When selecting paths, the system deliberately avoids nodes that are about to reach their load peak, even if these nodes are currently relatively idle. This prediction-based scheduling strategy further enhances the system's robustness in highly dynamic business environments.

[0068] In the adaptive closed-loop optimization executor, the reinforcement learning reward function is designed to be multi-dimensionally weighted. In addition to considering processing time, number of iterations, and satisfaction scores, a resource balancing term is specifically introduced. If the path allocation at the decision center leads to an increase in the standard deviation of the load on all nodes in the system, the reward function will provide negative feedback, prompting the model to favor achieving global load balancing in subsequent decisions. This design avoids employee fatigue and turnover risks caused by overworking the most capable, reflecting a humanistic management consideration integrated into the system's resource scheduling.

[0069] Finally, all logical processing steps of the system run on a highly secure server cluster with redundant backups. Every change to core configurations and every instance of manual intervention is recorded in an immutable system audit log, ensuring that every decision is traceable and meeting the stringent requirements of enterprise compliance management. This comprehensive coverage, from bottom-level data collection to high-level decision-making, from automated operation to anomaly intervention, and from pattern solidification to continuous evolution, constitutes the complete technical implementation landscape of this invention.

Claims

1. A customer profile dynamic construction and intelligent work order automatic flow optimization system, characterized in that, include: The multi-dimensional data fusion and perception module is used to access the full heterogeneous data of the business system in real time through change data capture technology, to fully synchronize historical stored data and to capture incremental log data at the millisecond level, so as to realize the collection of customer behavior trajectory at various business touchpoints. The dynamic profile feature engineering engine is used to perform real-time stream computing processing on the multi-dimensional data, reduce the weight of historical features based on the time decay mechanism, and dynamically update the semantic vector expression of customers in combination with the current behavior trigger intensity to generate a real-time dynamic profile containing dimensions of consumption tendency, credit rating, service sensitivity and technology preference. The work order semantic intent parsing module is used to perform deep semantic analysis on newly generated work order text using natural language processing technology, extract the core demands, urgency, related business domains and potential emotional tags of the work order, and transform the work order into a structured demand feature vector. The intelligent flow path decision center is used to construct an adaptability evaluation matrix based on the real-time dynamic profile and the demand feature vector, and to calculate the optimal flow node and processing path under the constraints of human resource cost, response time expectation and service quality evaluation through a multi-objective constraint solving algorithm. The real-time resource status monitoring unit is used to monitor the current load, professional skill score, historical processing success rate and busy / idle status of each service node's processing seat in real time, and to provide dynamic resource constraint parameters for the intelligent flow path decision center. An adaptive closed-loop optimization executor is used to issue execution instructions according to the optimal workflow path, capture the processing feedback data of each node during the work order workflow, analyze the deviation between the actual processing results and the expected target, generate correction weights, and feed them back to the dynamic profile feature engineering engine and the intelligent workflow path decision center to achieve the synergistic evolution of profile accuracy and workflow efficiency.

2. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The multidimensional data fusion sensing module includes: The heterogeneous data unified encapsulation unit is used to asynchronously decouple and standardize the encapsulation of data from unstructured text, semi-structured logs and structured relational databases through a distributed message queue; The data cleaning and filtering unit is used to filter redundant information and invalid noise based on regular expression matching and key field validation logic, ensuring the purity and consistency of the output data.

3. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The dynamic portrait feature engineering engine includes: The heat decay weight update unit is used to set the initial weight for each portrait tag and dynamically adjust the feature weights based on the frequency and time interval of the behavior using a time decay function. The multi-head attention feature fusion unit is used to calculate the contribution of different profile dimensions to the current business scenario and generate a semantic vector that comprehensively represents the customer's instantaneous needs and long-term preferences.

4. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The work order semantic intent parsing module includes: The text preprocessing unit is used to perform word segmentation and stop word removal on the original work order text; The high-dimensional semantic mapping unit is used to map the preprocessed word sequence to a high-dimensional semantic space through a word vector model. The deep semantic parsing unit is used to combine convolutional neural networks and bidirectional long short-term memory networks to extract key local semantic features and capture contextual logical relationships in order to identify the true intent of the work order.

5. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 4, characterized in that, The work order semantic intent parsing module also includes: The urgency level quantification scoring unit is used to calculate the urgency level of a work order based on keyword strength, historical complaint records, and customer level, and output a normalized urgency level value.

6. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The intelligent circulation path decision center includes: The service topology modeling unit is used to abstract service nodes and expert seats as vertices in a graph structure, and to abstract flow relationships as weighted edges. The multi-objective utility evaluation unit is used to calculate edge weights based on dynamic profile matching degree, node skill score and queuing waiting time, and solve the flow path that maximizes global utility through a heuristic search strategy.

7. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The real-time resource status monitoring unit includes: The seat status tracking subunit is used to track the workflow of each processing seat in real time through heartbeat detection and task state machine; The dynamic skills profile construction subunit is used to generate skills profiles based on historical processing records, including average processing time, satisfaction score, and success rate in solving complex problems. The resource saturation early warning subunit is used to periodically calculate the resource saturation of nodes and send a diversion or expansion signal to the intelligent flow path decision center when the saturation exceeds a preset threshold.

8. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, The adaptive closed-loop optimization actuator includes: The performance indicator collection unit is used to automatically collect the actual number of processes, the dwell time at each stage, and customer evaluation scores after the work order is closed. The deviation analysis and feedback unit is used to compare actual performance with predicted indicators, calculate the loss function, generate correction weights through the error backpropagation mechanism, and feed them back to the profile construction and path decision module.

9. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, Also includes: The global anomaly intervention module is used to monitor timeout risks and logical dead loops in the work order process in real time, and to forcibly take over the process control when the dwell time exceeds a preset threshold multiple, and to elevate the work order to a high-priority queue or assign it to a manual supervision position.

10. The customer profile dynamic construction and intelligent work order automatic flow optimization system according to claim 1, characterized in that, Also includes: The scene-aware profile modulation module is used to identify the current business scenario based on context and dynamically adjust the weight of active dimensions in the customer profile, so that the profile focuses on purchasing power and loyalty in the marketing consultation scenario, and on negative sentiment index and historical complaint frequency in the complaint and appeal scenario.

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