AI Task Readiness and Completion Detection and Feedback System Based on Stage Granularity

Abstract

This invention discloses an AI task readiness and completion detection and feedback system based on stage-level granularity, belonging to the field of artificial intelligence task management technology. The system includes: a status acquisition standardization module that collects and standardizes multi-dimensional status data at fixed intervals, with a built-in time-series readiness quantification algorithm; a topology readiness arbitration module that receives data and performs full-cycle readiness determination; a stage-level permission control module that unlocks or locks permissions based on the determination result and updates the status baseline; a multimodal interaction response module that renders the interface, triggers feedback, and responds to instructions; and a process configuration closed-loop optimization module that connects to each module, supporting process definition and configuration synchronization. This invention solves the problem of multi-source heterogeneous data fusion by building a full-process status acquisition and processing system, achieving accurate readiness detection; constructs a linkage mechanism to improve the timeliness of human-computer interaction; and relies on closed-loop optimization to achieve dynamic control and data traceability, improving the level of intelligence.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence task management technology, specifically to an AI task readiness and completion detection and feedback system based on stage-level granularity. Background Technology

[0002] With the rapid development of artificial intelligence technology, the execution process of AI tasks is becoming increasingly complex, often exhibiting multi-stage and multi-node topological structures. During execution, it frequently requires the coordinated cooperation of AI backend processing units, human operation terminals, and the underlying task scheduling subsystem. Close dependencies and flow relationships exist between each stage, and the coexistence of serial links and parallel branches has become the norm. In the practical execution of AI tasks, accurate detection and real-time feedback of the readiness and completion status of each stage are crucial to ensuring smooth task progress and improving overall execution efficiency. Simultaneously, AI task execution generates multi-source heterogeneous state data, and the timing requirements of different stages vary. The industry urgently needs to achieve fine-grained state control at the stage level, end-to-end state synchronization, and efficient human-machine collaborative interaction, necessitating a comprehensive detection and feedback system adapted to the execution management needs of complex AI tasks.

[0003] Traditional AI task status detection and feedback technologies often remain at the level of the overall task or simple monitoring of a single stage, lacking the ability to achieve refined, end-to-end control based on stage granularity. They cannot implement differentiated readiness determinations based on the different topological characteristics of serial link nodes and convergence nodes, and they lack systematic standardization processing for multi-source heterogeneous status data, which easily leads to a lack of data consistency and validity, thus affecting the accuracy of status determination. At the same time, traditional technologies have not established a linkage mechanism between readiness determination results and stage operation permissions, resulting in a disconnect between permission control and the actual stage status, which easily leads to problems such as misoperation or operation delays. Furthermore, the human-computer interaction is very limited, and the timeliness and comprehensiveness of status feedback are insufficient. They also lack the ability to standardize the archiving and traceability of end-to-end operational data, and the flexibility of algorithm and process configuration is poor, making it difficult to adapt to the personalized execution needs of different types of AI tasks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an AI task readiness and completion detection and feedback system based on stage-level granularity. This system consists of five modules: standardized state acquisition, topology readiness arbitration, stage-level permission control, multimodal interactive response, and closed-loop optimization of process configuration. These modules work together to form a complete detection and feedback system. Relying on proprietary quantization and arbitration algorithms, the system achieves accurate calculation of stage-level temporal readiness and differentiated readiness determination for topology nodes. It establishes a linkage control mechanism between readiness and operational permissions, and can also achieve state visualization and interactive response through multimodal methods. Simultaneously, it supports visualized configuration of process and algorithm parameters, as well as archiving and traceability of full-process operation data. Overall, it realizes refined state detection, dynamic permission management, and efficient human-computer interaction throughout the entire AI task process, adapting to the needs of AI tasks with different topologies and improving the intelligence and standardization of AI task execution and management.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an AI task readiness and completion detection and feedback system based on stage-level granularity, comprising: Status Acquisition Standardization Module: Connects to the AI ​​backend processing unit, manual operation terminal and task underlying scheduling subsystem, collects multi-dimensional status data of the entire process at fixed intervals and performs standardization processing on multi-source heterogeneous data, and has a built-in time-series readiness metric algorithm to calculate the time-series readiness of each process. Topology Readiness Arbitration Module: Receives standardized data, loads the task topology structure and preset arbitration rules, performs full-cycle readiness determination of serial link nodes and sink nodes, and has a built-in topology sink arbitration algorithm to calculate the readiness of link nodes and sink nodes. The task topology includes serial link nodes, parallel branch nodes, and a convergence node. The serial link nodes are connected sequentially in a unidirectional flow order, and each serial link node corresponds to a unique upstream dependent node and a unique downstream flow node. The parallel branch nodes are formed by splitting from the same upstream node, and multiple parallel branch nodes execute independently without interfering with each other. The convergence node serves as the common downstream node of multiple parallel branch nodes and summarizes the status of all parallel branch nodes. The preset arbitration rules include three types of configurable arbitration rules: serial link node arbitration rules and aggregation node arbitration rules. The serial link node arbitration rules stipulate that a serial link node can only enter the readiness determination when the timing readiness of the corresponding upstream dependent node meets the preset numerical threshold and is maintained for a preset period. The three types of configurable arbitration rules for aggregation nodes are full branch readiness rules, majority branch readiness rules, and specified branch readiness rules. The full branch readiness rule requires all upstream parallel branch nodes to complete the closed loop. The majority branch readiness rule requires the proportion of parallel branch nodes that have completed the closed loop to reach a preset ratio. The specified branch readiness rule requires all pre-set core parallel branch nodes to complete the closed loop. Link access control module: Receives the judgment result from the topology readiness arbitration module, performs unlocking or locking operations on the operation permissions of the task link according to the judgment result, updates the running status benchmark of the entire link and synchronizes it to the entire link, and has a built-in permission interaction linkage algorithm to update the status benchmark according to permission changes. Multimodal interaction response module: Receives the baseline of the entire operation status, renders a visual interactive interface based on the baseline and updates the status, triggers multimodal feedback, receives user operation instructions and calls the permission interaction linkage algorithm to respond to the instructions; The process configuration closed-loop optimization module interfaces with all preceding modules to visually define task process topology, step rules, arbitration rules and interaction configurations, archive full process operation data and create an index, and synchronize the configuration parameters of the time-series ready quantification algorithm, topology convergence arbitration algorithm and permission interaction linkage algorithm to the corresponding modules.

[0006] Furthermore, in the state acquisition standardization module, the fixed period is configured within the range of 10ms to 500ms according to the scenario type of the target AI task. The multi-dimensional state data of the entire process includes the completion status data of upstream related processes, the AI ​​processing progress data of this process, the manual operation execution data, and the readiness status data of parallel branch processes. The standardization processing of multi-source heterogeneous data includes unified conversion of multi-source data formats, removal of invalid and abnormal data, completion of missing data, and time sequence alignment of multi-source data. The converted standardized data has a unique process ID, timestamp, and data source identifier.

[0007] Furthermore, in the state acquisition standardization module, the mathematical expression for the timing readiness metric algorithm is:

[0008] in, Let be the standardized value of the upstream dependency completion degree of the i-th step at time t. Let be the standardized value of the A1 processing completion degree of the i-th step at time t. Let be the standardized value of the completion rate of manual confirmation for the i-th step at time t. Let be the standard deviation of the readiness of the i-th stage within the T consecutive acquisition cycles prior to time t. This is a time-series stability weighting coefficient, determined by calibration based on the time-series sensitivity of the AI ​​task and historical operational data, with a value range of 0.05-0.8. Let be the timing readiness of the i-th parallel branch. The timing readiness quantification algorithm calculates the timing readiness of each branch in the following steps: First, obtain the standardized values ​​of upstream dependency completion, AI processing completion, and manual confirmation completion after standardization. Multiply these three values ​​to obtain the basic readiness value. Then, set the continuous acquisition period window length and calculate the standard deviation of the basic readiness value within the window to characterize the timing fluctuation of the branch's readiness. Next, set the timing stability weight coefficient, multiply the standard deviation by the weight coefficient, and take the negative exponent to obtain the timing stability penalty factor. Finally, multiply the basic readiness value by the timing stability penalty factor to obtain the timing readiness of each branch at the corresponding time. The timing readiness value is limited to the range of 0 to 1.

[0009] Furthermore, the topology readiness arbitration module performs full-cycle readiness determination for serial link nodes and aggregation nodes, including loading the target task topology structure map, establishing the topology dependency matrix for the entire process, periodically verifying the time-series readiness of upstream associated links of serial link nodes, centrally verifying the readiness of parallel branches of aggregation nodes, outputting the binary determination result of node readiness or in readiness, and outputting data on the location and cause analysis of in readiness links.

[0010] Furthermore, in the topology readiness arbitration module, the mathematical expression for the topology convergence arbitration algorithm is:

[0011] in, Let be the complete set of all parallel branch stages corresponding to the j-th convergence node. Let be the set of required branches corresponding to the j-th convergence node, and , Let be the weight coefficient of the i-th branch under the j-th aggregation node. This coefficient is assigned based on the importance of the branch to the aggregation node's determination, combined with business rules and historical anomaly data, and its value ranges from 0.1 to 1.0. Let be the timing readiness of the i-th parallel branch. Let j be the arbitration readiness of the j-th node; the calculation steps of the topology convergence arbitration algorithm for calculating the readiness of link nodes and convergence nodes are as follows: when calculating the readiness of a link node, first obtain the timing readiness of the upstream associated links corresponding to the link node, verify whether the timing readiness of the upstream associated links meets the preset judgment conditions, and directly use the timing readiness of the upstream associated links that meet the conditions as the readiness of the link node; when calculating the readiness of a convergence node, first clarify the complete set of all parallel branch links and the set of mandatory branches corresponding to the convergence node, and for each parallel branch The process involves configuring corresponding weight coefficients for each parallel branch; then multiplying the timing readiness of each parallel branch by its corresponding weight coefficient and summing the results, dividing by the sum of the weight coefficients of all parallel branch branches to obtain the overall average readiness of the parallel branch branches; next, extracting the timing readiness of all branches in the set of mandatory branches and taking the minimum value as the mandatory branch readiness constraint value; finally, multiplying the overall average readiness of the parallel branch branches by the mandatory branch readiness constraint value to obtain the arbitration readiness of the convergence node, with the arbitration readiness value limited to the range of 0 to 1.

[0012] Furthermore, in the aforementioned link permission control module, the unlocking or locking operation of task link operation permissions is performed based on the judgment result. This includes loading the target task link permission configuration list, establishing the mapping relationship between link ID, permission type, and readiness judgment condition, binary judgment of permission status, hierarchical opening of corresponding link operation permissions, generation of permission change logs, locking of corresponding link operation permissions, interception of operation requests, and generation of operation interception logs. The updating of the entire link operation status benchmark and synchronizing it to the entire link includes updating the entire link operation status benchmark, synchronizing the updated status benchmark to the multimodal interaction response module, and reverse synchronizing the updated status benchmark to the status acquisition standardization module and the topology readiness arbitration module.

[0013] Furthermore, in the aforementioned access control module, the mathematical expression for the access control interaction algorithm is:

[0014] in, This is a sign function; it outputs 1 when the input value is greater than 0, and -1 when the input value is less than or equal to 0. Let j be the arbitration readiness level of the j-th node. The basic threshold for unlocking permissions is determined by considering the operational error tolerance and business compliance requirements of the task phase, and its value ranges from 0.6 to 0.95. The interaction sensitivity coefficient is determined based on the response time requirements of human-computer interaction and user operating habits, and its value ranges from 0.1 to 0.5. Let represent the permission and interaction trigger coefficient of the j-th task node at time t. The permission interaction linkage algorithm is used to update the state baseline according to permission changes. The calculation steps are as follows: First, obtain the arbitration readiness of the aggregation node, set the basic threshold for permission unlocking and the interaction sensitivity coefficient, multiply the basic threshold by the difference between 1 and the interaction sensitivity coefficient to obtain the permission judgment threshold; then, subtract the arbitration readiness from the permission judgment threshold, and use the sign function to judge the difference to obtain the permission status judgment result; next, multiply the interaction sensitivity coefficient by the arbitration readiness and add 1 to obtain the interaction feedback intensity coefficient; then, multiply the permission status judgment result by the interaction feedback intensity coefficient to obtain the permission and interaction trigger coefficient; finally, update the permission status of each link according to the changes in the value of the permission and interaction trigger coefficient, and synchronously integrate the permission status change information into the overall operation status baseline to complete the update of the status baseline.

[0015] Furthermore, the multimodal interactive response module renders and updates the visual interactive interface based on the state baseline. This includes adaptive layout rendering of the interactive interface, construction of the task flow topology map, configuration of three interactive indicator units corresponding to each task stage, and synchronous updating of hierarchical light indicator status. Specifically, the configuration of three interactive indicator units corresponding to each task stage includes: a stage readiness status indicator unit, an operation permission status indicator unit, and a task completion progress indicator unit. Each indicator unit is implemented using independent visual controls, including status icons, text labels, or progress bar components. The synchronous updating of hierarchical light indicator status specifically involves dividing the stage readiness value range into at least three levels of status indicators: a red warning light indicator for readiness below 0.3, a yellow reminder light indicator for readiness between 0.3 and 0.8, and a higher readiness level for readiness above 0.8. The corresponding green ready light indicator updates in real time, and each light indicator and corresponding interactive indicator unit are linked to update, synchronizing the changes in the status baseline of the process. The triggering of multimodal feedback receives user operation instructions and calls the permission interaction linkage algorithm to respond to the instructions, including interface highlighting triggering, pop-up prompting triggering, voice broadcasting triggering, vibration feedback triggering, real-time reception of user operation instructions, instruction validity verification, synchronization of verified instructions to the corresponding module, and refresh of the interactive interface status.

[0016] Furthermore, the process configuration closed-loop optimization module visually defines the task process topology, link rules, arbitration rules, and interaction configurations, and synchronizes the configuration parameters of the time-series ready quantification algorithm, topology convergence arbitration algorithm, and permission interaction linkage algorithm to the corresponding modules. It includes providing a drag-and-drop visual configuration interface, supporting the addition, removal, and adjustment of task links, supporting the construction of topology structures, supporting the definition of link dependencies, supporting the setting of arbitration rules, supporting the configuration of interactive feedback methods, supporting the visual configuration of all parameters of the three types of algorithms, saving standardized process templates, generating task execution rule files, and synchronizing task execution rule files to the corresponding modules.

[0017] Furthermore, the process configuration closed-loop optimization module archives and indexes the entire process operation data, including receiving the full-cycle operation data, assigning a unique identifier ID to each task stage, binding the full lifecycle operation data of the corresponding stage to the unique identifier ID, establishing a multi-dimensional structured index system, supporting full-link data retrieval, and supporting traceability report generation; the full-cycle operation data includes the full-cycle operation data output by the status acquisition standardization module, the topology readiness arbitration module, the stage permission control module, and the multimodal interaction response module.

[0018] Compared with existing technologies, this AI task readiness and completion detection and feedback system based on stage-specific granularity has the following beneficial effects: I. This invention establishes a standardized system for collecting and processing the entire process status at the stage-by-stage granularity. It connects to multiple data sources to achieve unified processing of multi-dimensional status data across all stages, effectively solving the problem of fusion of heterogeneous multi-source data. Combining the characteristics of the task topology, it designs targeted readiness arbitration rules to implement differentiated full-cycle readiness judgments for serial links and convergence nodes. Through quantitative algorithms, it achieves accurate calculation of the temporal readiness of each stage. Relying on the topology convergence arbitration algorithm, it completes the comprehensive judgment of the readiness of links and convergence nodes, and outputs the location and cause analysis data of incomplete stages. This enables refined and comprehensive detection of the readiness and completion of the entire AI task process, avoiding the limitations of single-dimensional detection. It makes the status judgment of the task process conform to the actual execution logic, greatly improving the accuracy and relevance of status detection.

[0019] Second, this invention establishes a linkage mechanism between readiness determination results and process permission control. Based on the node readiness status, it dynamically unlocks and locks operation permissions, synchronously updates the benchmark of the entire process's operating status, and completes full-link synchronization, forming a two-way linkage control of permissions and status. Combined with a multimodal interactive response mechanism, it achieves visualized display and multi-form feedback of task status, while supporting real-time response and processing of user operation commands, improving the timeliness and convenience of human-computer interaction. Relying on the process configuration closed-loop optimization module, it achieves visualized configuration of task processes and algorithm parameters, completes the archiving of full-process operation data and the establishment of multi-dimensional indexes, realizing dynamic control, personalized configuration, and full lifecycle data traceability of AI task processes. This allows the system to adapt to different AI task execution needs, improving task execution efficiency and the level of intelligent process management.

[0020] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0022] Figure 1 This is a block diagram of the overall architecture of an AI task readiness and completion detection and feedback system based on stage-level granularity. Figure 2 A schematic diagram illustrating the entire process of visual configuration and closed-loop optimization for AI task workflows; Figure 3 This diagram illustrates the data interaction and linkage relationships between various modules of the detection feedback system. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0024] Example 1: AI-powered task scenarios for automated processing of customer service work orders.

[0025] This embodiment is applied to an AI task scenario involving automated processing of enterprise intelligent customer service work orders. The core of this AI task is to automate the entire process of user request work orders from access to final dispatch and execution. The task topology includes serial link nodes, parallel branch nodes, and a convergence node. The serial link nodes are, in sequence, the work order access stage, the text parsing stage, the work order classification stage, the intelligent dispatch stage, the manual review stage, and the work order execution follow-up stage. The text parsing stage is divided into three parallel branch nodes: the user intent recognition stage, the user sentiment analysis stage, and the request keyword extraction stage. The common downstream of these three parallel branch nodes is the work order classification stage, which is the convergence node. The remaining stages are unidirectional serial link nodes. The execution process of the AI ​​task readiness and completion detection feedback system based on stage granularity of this invention in this scenario is as follows: Figure 1 As shown: The status acquisition standardization module connects to the intelligent customer service AI backend processing unit, customer service manual operation terminal, and work order scheduling subsystem. Based on the scenario type of intelligent customer service work order processing, a fixed acquisition period of 200ms is configured. This period is used to collect multi-dimensional status data across all stages, specifically including completion data of upstream related stages, AI processing progress data for this stage, customer service manual operation execution data, and readiness data of parallel branch stages. Subsequently, standardization processing is performed on the collected multi-source heterogeneous data, completing unified format conversion, invalid and abnormal data removal, missing data completion, and multi-source data time-series alignment. The converted standardized data is assigned a unique stage ID, timestamp, and data source identifier. Then, the built-in time-series readiness metric algorithm calculates the time-series readiness of all stages, including work order access, text parsing, and user intent recognition, at each acquisition moment. The mathematical expression of the time-series readiness metric algorithm is:

[0026] in, Let be the standardized value of the upstream dependency completion degree of the i-th step at time t. Let be the standardized value of the A1 processing completion degree of the i-th step at time t. Let be the standardized value of the completion rate of manual confirmation for the i-th step at time t. Let be the standard deviation of the readiness of the i-th stage within the T consecutive acquisition cycles prior to time t. This is a time-series stability weighting coefficient, determined by calibration based on the time-series sensitivity of the AI ​​task and historical operational data, with a value range of 0.05-0.8. Let be the timing readiness of the i-th parallel branch.

[0027] The topology readiness arbitration module receives standardized data output from the status acquisition standardization module, loads the task topology structure and preset arbitration rules for this intelligent customer service work order processing, where serial link nodes execute their corresponding serial link node arbitration rules, and the aggregation node (i.e., the work order classification stage) is configured with full-branch readiness rules. Subsequently, this module performs a full-cycle readiness determination for both serial link nodes and the aggregation node, completing the loading of the target task topology structure map, the establishment of the topology dependency matrix for the entire process, the cycle-by-cycle verification of the temporal readiness of upstream related stages of serial link nodes, and the centralized verification of the parallel branch readiness of the aggregation node. It outputs a binary determination result of node readiness or in readiness, and simultaneously outputs the location and cause analysis data for in readiness stages. During the determination process, the built-in topology aggregation arbitration algorithm calculates the readiness of all link nodes and the aggregation node. The mathematical expression of the topology aggregation arbitration algorithm is:

[0028] in, Let be the complete set of all parallel branch stages corresponding to the j-th convergence node. Let be the set of required branches corresponding to the j-th convergence node, and , Let be the weight coefficient of the i-th branch under the j-th aggregation node. This coefficient is assigned based on the importance of the branch to the aggregation node's determination, combined with business rules and historical anomaly data, and its value ranges from 0.1 to 1.0. Let be the timing readiness of the i-th parallel branch. Let be the arbitration readiness of the j-th node.

[0029] The process access control module receives the judgment result from the topology readiness arbitration module. Based on this result, it performs unlocking or locking operations on the task process operation permissions. Specifically, it loads the target task process permission configuration list for intelligent customer service work orders, establishes a mapping relationship between process ID, permission type, and readiness judgment conditions, completes a binary judgment of permission status, grants tiered access to processes that meet the readiness conditions, locks the operation permissions of incomplete processes and intercepts their operation requests, and generates permission change logs and operation interception logs. This module also updates the overall process operation status baseline, synchronizes the updated status baseline to the multimodal interaction response module, and simultaneously synchronizes it in reverse to the status acquisition standardization module and the topology readiness arbitration module. During the synchronization process, the built-in permission interaction linkage algorithm updates the status baseline according to permission changes. The mathematical expression of the permission interaction linkage algorithm is:

[0030] in, This is a sign function; it outputs 1 when the input value is greater than 0, and -1 when the input value is less than or equal to 0. Let j be the arbitration readiness level of the j-th node. The basic threshold for unlocking permissions is determined by considering the operational error tolerance and business compliance requirements of the task phase, and its value ranges from 0.6 to 0.95. The interaction sensitivity coefficient is determined based on the response time requirements of human-computer interaction and user operating habits, and its value ranges from 0.1 to 0.5. This represents the permissions and interaction trigger coefficients of the j-th task node at time t.

[0031] The multimodal interactive response module receives a synchronized full-process operational status benchmark from the permission control module. Based on this benchmark, a visual interactive interface is rendered and its status is updated in real time. This completes the adaptive layout rendering of the interactive interface and the construction of the work order processing task flow topology. Three interactive indicator units are configured for each work order processing stage: a ready status indicator unit, an operation permission status indicator unit, and a task completion progress indicator unit. Each indicator unit is implemented using independent visual controls, including status icons, text labels, or progress bar components. Hierarchical lighting indicators are divided into three levels based on the readyness value range of the work order processing stage: a red warning light for a readyness below 0.3, a yellow alert light for a readyness between 0.3 and 0.8, and a warning light for a readyness above 0.8. The green ready indicator light corresponds to the status of the work order processing stage. Each indicator light and corresponding interactive indicator unit are updated in tandem, and the status of the work order processing stage is updated in real time. The tiered indicator lights are updated synchronously to show the stage status. At the same time, multimodal feedback is triggered according to the stage readiness and completion status, including interface highlighting, pop-up prompts, voice broadcasts, etc. The module also receives operation instructions from customer service personnel in real time, verifies the validity of the instructions, synchronizes the verified instructions to the corresponding modules, calls the permission interaction linkage algorithm to respond to the instructions, and refreshes the interactive interface status.

[0032] The process configuration closed-loop optimization module interfaces with the status acquisition standardization module, topology readiness arbitration module, link permission control module, and multimodal interaction response module, providing customer service system administrators with a drag-and-drop visual configuration interface. It supports adding, removing, and adjusting work order processing task links, building topology structures, and defining link dependencies. It also supports setting arbitration rules, configuring interactive feedback methods, and providing full-parameter visual configuration for time-series readiness quantification algorithms, topology convergence arbitration algorithms, and permission interaction linkage algorithms. The configured work order processing flow can be saved as a standardized process template, generating task execution rule files and synchronizing them to the corresponding modules. This module also receives all operational data from each module throughout the entire process, assigns a unique identifier ID to each work order processing link, binds the corresponding link's full-lifecycle operational data to the unique identifier ID, and establishes a multi-dimensional structured index system. This supports full-link data retrieval and traceability report generation. Furthermore, based on the rationality of the algorithm configuration parameters analyzed from the archived operational data, the optimized parameters are synchronized to the corresponding module's algorithm, achieving closed-loop optimization of the AI ​​task processing flow.

[0033] In summary, the detection feedback system of this invention, in the intelligent customer service work order processing scenario, achieves refined control over the entire work order processing process through the collaboration of various modules, such as... Figure 2As shown, the status acquisition standardization module completes efficient acquisition and standardized processing of multi-source data, and achieves accurate quantification of process readiness based on the time-series readiness metric algorithm; the topology readiness arbitration module completes node determination based on preset rules, and scientifically calculates node readiness through the topology convergence arbitration algorithm; the process permission control module realizes intelligent management of permissions, and updates the status benchmark with the help of permission interaction linkage algorithm; the multimodal interaction response module creates an intuitive human-computer interaction interface; and the process configuration closed-loop optimization module realizes flexible configuration and continuous optimization of the process, comprehensively improving the automation and intelligence level of customer service work order processing, and ensuring the efficiency and accuracy of work order flow.

[0034] Example 2: AI-powered task scenarios for intelligent image quality inspection of industrial products.

[0035] This embodiment is applied to an AI task scenario for intelligent image quality inspection of industrial products in a manufacturing production line. This AI task automates image detection and quality inspection result determination of the appearance, size, and material of finished products. The task topology includes serial link nodes, parallel branch nodes, and a convergence node. The serial link nodes sequentially consist of a product image acquisition stage, an image preprocessing stage, a quality inspection result determination stage, a result manual review stage, a non-conforming product labeling stage, and a quality inspection report generation stage. The image preprocessing stage is divided into three parallel branch nodes: an appearance defect detection stage, a dimensional accuracy detection stage, and a material defect detection stage. The common downstream of these three parallel branch nodes is the quality inspection result determination stage, which is the convergence node. The remaining stages are unidirectional serial link nodes. The execution process of the AI ​​task readiness and completion detection feedback system based on stage granularity of this invention in this scenario is as follows: Figure 3 As shown: The status acquisition standardization module interfaces with the industrial quality inspection AI backend processing unit, production line manual operation terminals, and the underlying scheduling subsystem for quality inspection tasks. Based on the high-speed detection scenario of industrial product image quality inspection, a fixed acquisition cycle of 100ms is configured. This cycle collects multi-dimensional status data across all stages, specifically including completion data of upstream related stages, AI image detection and processing progress data for this stage, operation execution data of production line quality inspectors, and readiness data of parallel branch stages. Subsequently, standardization processing is performed on the collected multi-source heterogeneous data, completing unified format conversion, removal of invalid and abnormal data, completion of missing data, and temporal alignment of multi-source data. The standardized data is assigned a unique stage ID, timestamp, and data source identifier. Then, the built-in temporal readiness metric algorithm calculates the temporal readiness of all stages, including product image acquisition, image preprocessing, and appearance defect detection, at each acquisition moment. The mathematical expression of the temporal readiness metric algorithm is:

[0036] in, Let be the standardized value of the upstream dependency completion degree of the i-th step at time t. Let be the standardized value of the A1 processing completion degree of the i-th step at time t. Let be the standardized value of the completion rate of manual confirmation for the i-th step at time t. Let be the standard deviation of the readiness of the i-th stage within the T consecutive acquisition cycles prior to time t. This is a time-series stability weighting coefficient, determined by calibration based on the time-series sensitivity of the AI ​​task and historical operational data, with a value range of 0.05-0.8. Let be the timing readiness of the i-th parallel branch.

[0037] The topology readiness arbitration module receives standardized data output from the status acquisition standardization module, loads the task topology structure and preset arbitration rules for this intelligent image quality inspection of industrial products, where serial link nodes execute their corresponding serial link node arbitration rules, and the convergence node, i.e., the quality inspection result judgment stage, is configured with majority branch readiness rules, with the proportion of parallel branch nodes completing the closed loop preset to 80%. Subsequently, this module performs full-cycle readiness judgment for serial link nodes and convergence nodes, completing the loading of the target task topology structure map, the establishment of the topology dependency matrix for the entire process, the cycle-by-cycle verification of the temporal readiness of upstream related links of serial link nodes, and the centralized verification of the parallel branch readiness of convergence nodes. It outputs a binary judgment result of node readiness or in readiness, and simultaneously outputs the location and cause analysis data of in readiness links. During the judgment process, the built-in topology convergence arbitration algorithm calculates the readiness of all link nodes and convergence nodes. The mathematical expression of the topology convergence arbitration algorithm is:

[0038] in, Let be the complete set of all parallel branch stages corresponding to the j-th convergence node. Let be the set of required branches corresponding to the j-th convergence node, and , Let be the weight coefficient of the i-th branch under the j-th aggregation node. This coefficient is assigned based on the importance of the branch to the aggregation node's determination, combined with business rules and historical anomaly data, and its value ranges from 0.1 to 1.0. Let be the timing readiness of the i-th parallel branch. Let be the arbitration readiness of the j-th node.

[0039] The process access control module receives the judgment result from the topology readiness arbitration module. Based on this result, it performs unlocking or locking operations on the task process operation permissions. Specifically, it loads the target task process permission configuration list for industrial product image quality inspection, establishes a mapping relationship between process ID, permission type, and readiness judgment conditions, completes a binary judgment of permission status, grants tiered access to processes that meet the readiness conditions, locks the operation permissions of incomplete processes and intercepts their operation requests, and generates permission change logs and operation interception logs. This module also updates the overall process operation status baseline, synchronizes the updated status baseline to the multimodal interaction response module, and simultaneously synchronizes it in reverse to the status acquisition standardization module and the topology readiness arbitration module. During the synchronization process, the built-in permission interaction linkage algorithm updates the status baseline according to permission changes. The mathematical expression of the permission interaction linkage algorithm is:

[0040] in, This is a sign function; it outputs 1 when the input value is greater than 0, and -1 when the input value is less than or equal to 0. Let j be the arbitration readiness level of the j-th node. The basic threshold for unlocking permissions is determined by considering the operational error tolerance and business compliance requirements of the task phase, and its value ranges from 0.6 to 0.95. The interaction sensitivity coefficient is determined based on the response time requirements of human-computer interaction and user operating habits, and its value ranges from 0.1 to 0.5. This represents the permissions and interaction trigger coefficients of the j-th task node at time t.

[0041] The multimodal interactive response module receives the full-process operational status benchmark synchronized by the permission control module. Based on this benchmark, it renders a visual interactive interface and updates the status in real time. This completes the adaptive layout rendering of the interactive interface and the construction of the image quality inspection task flow topology. Each quality inspection stage is configured with three interactive indicator units: a quality inspection stage readiness status indicator unit, an operation permission status indicator unit, and a task completion progress indicator unit. Each indicator unit is implemented using independent visual controls, including status icons, text labels, or progress bar components. Hierarchical lighting indicators are divided into three levels based on the readiness value range of the quality inspection stage: a red warning light for readiness below 0.3, a yellow alert light for readiness between 0.3 and 0.8, and a red alert light for readiness above 0.8. The green ready indicator light corresponds to the status of each indicator light and the corresponding interactive indicator unit. The system updates in real time to reflect changes in the status benchmark of the quality inspection process, and updates the graded indicator lights to show the status of each process. At the same time, it triggers multimodal feedback based on the readiness and completion status of each process, including highlighting the interface of abnormal processes, pop-up prompts, voice broadcasts from the production line, and vibration feedback from the quality inspection terminal. The module also receives operation instructions from production line quality inspectors in real time, verifies the validity of the instructions, synchronizes the verified instructions to the corresponding modules, calls the permission interaction linkage algorithm to respond to the instructions, and refreshes the status of the interactive interface.

[0042] The process configuration closed-loop optimization module interfaces with the status acquisition standardization module, topology readiness arbitration module, link permission control module, and multimodal interaction response module, providing production line technicians with a drag-and-drop visual configuration interface. It supports adding, removing, and adjusting image quality inspection task links, building topology structures, and defining link dependencies. It also supports setting arbitration rules, configuring interactive feedback methods, and providing full-parameter visual configuration for timing readiness quantification algorithms, topology convergence arbitration algorithms, and permission interaction linkage algorithms. The configured image quality inspection process can be saved as a standardized process template, generating task execution rule files and synchronizing them to the corresponding modules. This module also receives all operational data from each module throughout the entire lifecycle, assigns a unique identifier ID to each image quality inspection link, binds the corresponding link's full lifecycle operational data to the unique identifier ID, and establishes a multi-dimensional structured index system. This supports full-link quality inspection data retrieval and quality inspection process traceability report generation. Furthermore, based on archived operational data analysis algorithms, it configures parameters and the matching degree of the quality inspection process, synchronizing the optimized parameters to the corresponding module's algorithm, thus achieving closed-loop optimization of the intelligent image quality inspection AI task processing flow for industrial products.

[0043] In summary, the detection feedback system of this invention is highly adapted to the high-speed production requirements of intelligent image quality inspection of industrial products, with each module performing its specific function and working together efficiently. The status acquisition standardization module completes the acquisition and standardization of quality inspection data in a short cycle, and realizes real-time quantification of process status through a time-series readiness metric algorithm; the topology readiness arbitration module completes node determination by combining majority branch readiness rules, and relies on the topology convergence arbitration algorithm to calculate node readiness in line with the production line; the process permission control module realizes standardized control of permissions in the quality inspection process, and uses permission interaction linkage algorithm to ensure the real-time performance of the status benchmark; the multimodal interaction response module adapts to industrial scenarios to realize multi-form feedback; and the process configuration closed-loop optimization module realizes flexible configuration of the quality inspection process and continuous algorithm optimization, effectively improving the efficiency and accuracy of production line quality inspection and reducing production line losses caused by quality inspection problems.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An AI task readiness and completion detection and feedback system based on stage-specific granularity, characterized in that, The system includes: Status Acquisition Standardization Module: Connects to the AI ​​backend processing unit, manual operation terminal and task underlying scheduling subsystem, collects multi-dimensional status data of the entire process at fixed intervals and performs standardization processing on multi-source heterogeneous data, and has a built-in time-series readiness metric algorithm to calculate the time-series readiness of each process. Topology Readiness Arbitration Module: Receives standardized data, loads the task topology structure and preset arbitration rules, performs full-cycle readiness determination of serial link nodes and sink nodes, and has a built-in topology sink arbitration algorithm to calculate the readiness of link nodes and sink nodes. Link access control module: Receives the judgment result from the topology readiness arbitration module, performs unlocking or locking operations on the operation permissions of the task link according to the judgment result, updates the running status benchmark of the entire link and synchronizes it to the entire link, and has a built-in permission interaction linkage algorithm to update the status benchmark according to permission changes. Multimodal interaction response module: Receives the baseline of the entire operation status, renders a visual interactive interface based on the baseline and updates the status, triggers multimodal feedback, receives user operation instructions and calls the permission interaction linkage algorithm to respond to the instructions; The process configuration closed-loop optimization module interfaces with all preceding modules to visually define task process topology, step rules, arbitration rules and interaction configurations, archive full process operation data and create an index, and synchronize the configuration parameters of the time-series ready quantification algorithm, topology convergence arbitration algorithm and permission interaction linkage algorithm to the corresponding modules.

2. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, In the state acquisition standardization module, the fixed period is configured within the range of 10ms to 500ms according to the scenario type of the target AI task. The multi-dimensional state data of the entire process includes the completion status data of upstream related processes, the AI ​​processing progress data of this process, the manual operation execution data, and the readiness status data of parallel branch processes. The standardization processing of multi-source heterogeneous data includes unified conversion of multi-source data formats, removal of invalid and abnormal data, completion of missing data, and time sequence alignment of multi-source data. The converted standardized data has a unique process ID, timestamp, and data source identifier.

3. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, In the state acquisition standardization module, the mathematical expression of the timing readiness metric algorithm is: ,in, Let be the standardized value of the upstream dependency completion degree of the i-th step at time t. Let be the standardized value of the A1 processing completion degree of the i-th step at time t. Let be the standardized value of the completion rate of manual confirmation for the i-th step at time t. Let be the standard deviation of the readiness of the i-th stage within the T consecutive acquisition cycles prior to time t. For time series stability weighting coefficients, Let be the timing readiness of the i-th parallel branch.

4. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, The topology readiness arbitration module performs full-cycle readiness determination for serial link nodes and aggregation nodes, including loading the target task topology structure map, establishing the topology dependency matrix for the entire process, periodically verifying the time-series readiness of upstream related links of serial link nodes, centrally verifying the readiness of parallel branches of aggregation nodes, outputting the binary determination result of node readiness or not readiness, and outputting data on the location and cause analysis of not readiness links.

5. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, In the topology readiness arbitration module, the mathematical expression of the topology convergence arbitration algorithm is: ,in, Let be the complete set of all parallel branch stages corresponding to the j-th convergence node. Let be the set of required branches corresponding to the j-th convergence node, and , Let be the weight coefficient of the i-th branch under the j-th aggregation node. Let be the timing readiness of the i-th parallel branch. Let be the arbitration readiness of the j-th node.

6. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, In the aforementioned link permission control module, the unlocking or locking operation of task link operation permissions is performed according to the judgment result. This includes loading the target task link permission configuration list, establishing the mapping relationship between link ID, permission type, and readiness judgment condition, binary judgment of permission status, hierarchical opening of corresponding link operation permissions, generation of permission change logs, locking of corresponding link operation permissions, interception of operation requests, and generation of operation interception logs. The updating of the entire link operation status benchmark and synchronizing it to the entire link includes updating the entire link operation status benchmark, synchronizing the updated status benchmark to the multimodal interaction response module, and reverse synchronizing the updated status benchmark to the status acquisition standardization module and the topology readiness arbitration module.

7. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity as described in claim 1, characterized in that, In the aforementioned access control module, the mathematical expression for the access interaction linkage algorithm is: ,in, This is a sign function; it outputs 1 when the input value is greater than 0, and -1 when the input value is less than or equal to 0. Let j be the arbitration readiness level of the j-th node. The basic threshold for unlocking permissions is determined by considering the operational error tolerance and business compliance requirements of the task phase, and its value ranges from 0.6 to 0.

95. This is the interaction sensitivity coefficient. This represents the permissions and interaction trigger coefficients of the j-th task node at time t.

8. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity according to claim 1, characterized in that, The multimodal interaction response module renders and updates the state of the visual interactive interface based on the state baseline, including adaptive layout rendering of the interactive interface, construction of the task flow topology map carrier, configuration of three interactive indicator units corresponding to each task stage, and synchronous update of the hierarchical light indicator status; the triggering of multimodal feedback receives user operation instructions and calls the permission interaction linkage algorithm to respond to the instructions, including interface highlighting triggering, pop-up prompting triggering, voice broadcasting triggering, vibration feedback triggering, real-time reception of user operation instructions, instruction validity verification, synchronization of verified instructions to the corresponding module, and refresh of the interactive interface status.

9. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity according to claim 1, characterized in that, The process configuration closed-loop optimization module visualizes and defines the task process topology, step rules, arbitration rules, and interaction configurations. It synchronizes the configuration parameters of the time-readiness quantification algorithm, topology convergence arbitration algorithm, and permission interaction linkage algorithm to the corresponding modules. It includes a drag-and-drop visual configuration interface, support for adding, removing, and adjusting task steps, support for building topology structures, support for defining step dependencies, support for setting arbitration rules, support for configuring interactive feedback methods, support for visual configuration of all parameters of the three types of algorithms, saving standardized process templates, generating task execution rule files, and synchronizing task execution rule files to the corresponding modules.

10. The AI ​​task readiness and completion detection and feedback system based on stage-level granularity according to claim 1, characterized in that, The process configuration closed-loop optimization module archives and indexes the entire process operation data, including receiving all operation data throughout the entire lifecycle, assigning a unique identifier ID to each task link, binding the operation data of the corresponding link throughout its entire lifecycle with the unique identifier ID, establishing a multi-dimensional structured index system, supporting full-link data retrieval, and supporting traceability report generation. The full operational data includes the full-cycle operational data output by the status acquisition standardization module, the topology readiness arbitration module, the process permission control module, and the multimodal interaction response module.

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