Semantic control interface system connecting user behavior scheduling with language replacement engine

Abstract

This invention discloses a semantic control interface system connecting user behavior scheduling and a language replacement engine, comprising: a behavior signal receiving module for receiving behavior signals generated during user interaction; a control parameter mapping module connected to the behavior signal receiving module for converting behavior signals into language replacement control parameters; a state control module connected to the control parameter mapping module for controlling the system display state switching according to the language replacement control parameters; a priority arbitration module connected to the state control module for performing control priority determination when behavior scheduling requirements and content continuity requirements conflict; an interface output module connected to the priority arbitration module for encapsulating the language replacement control parameters into standardized semantic control instructions and outputting them to the language replacement engine; and a feedback write-back module connected to the language replacement engine and the control parameter mapping module for updating the language replacement control parameters according to subsequent user interaction results.

Description

Technical Field

[0001] This invention relates to the fields of human-computer interaction control, language content scheduling, intelligent learning systems, and artificial intelligence applications. Specifically, it relates to a semantic control interface system that connects a user behavior scheduling module and a language replacement engine, used to realize user behavior input parsing, replacement parameter generation, display status control, and feedback closed-loop scheduling. Background Technology

[0002] With the development of digital reading platforms, language learning systems, and generative artificial intelligence technologies, learning methods that gradually replace target language content with native language content are becoming an important direction in language training. These systems typically use vocabulary replacement, phrase replacement, sentence / paragraph replacement, or mixed language display to allow users to gradually engage with target language content while maintaining their existing comprehension abilities, thereby lowering the learning threshold and improving transfer efficiency.

[0003] In the existing technology, one type of system mainly focuses on collecting user behavior information, which can record data such as the user's scrolling speed, pause duration, review frequency, click behavior and page dwell time during the reading process. However, such systems usually only use the data for statistical analysis, recommendation ranking or interface optimization, and cannot further drive the language replacement process.

[0004] Another type of system focuses on language substitution execution capabilities. It can output target language content based on a preset vocabulary, fixed ratio rules, difficulty level, or static path. However, this type of system usually lacks the ability to respond to the user's real-time behavior status and cannot dynamically adjust the substitution strategy according to the user's current reading pace, cognitive load, and acceptance level.

[0005] Therefore, existing technologies generally suffer from the following problems: 1) The user behavior scheduling module and the language replacement engine are independent of each other and lack a unified connection structure; 2) Behavioral input cannot be converted into executable replacement control parameters; 3) The replacement process lacks a stable state switching mechanism; 4) Conflicts easily occur between changes in user behavior and content continuity; 5) The overall system structure is loose and it is difficult to form a closed-loop control system for continuous optimization; 6) Multi-source behavioral input lacks a unified protocol format and is difficult to be reused by different replacement engines.

[0006] Especially in scenarios involving long text reading, continuous learning, immersive training, and real-time interaction, the above problems can easily lead to a rigid replacement rhythm, interrupted user understanding, increased learning burden, and decreased training efficiency.

[0007] Therefore, it is necessary to propose a semantic control interface system that connects user behavior scheduling and language replacement engine to achieve real-time collaborative control between behavior input and language replacement. Summary of the Invention

[0008] The purpose of this invention is to provide a semantic control interface system that connects user behavior scheduling and language replacement engine. By establishing a unified control interface between the user behavior scheduling layer and the language replacement execution layer, user behavior signals are converted into standardized semantic control instructions through the semantic control interface system to drive the language replacement process, thereby realizing dynamic, continuous and individualized control of language transfer training.

[0009] To achieve the above objectives, this invention provides a semantic control interface system connecting user behavior scheduling and a language replacement engine, including a behavior signal receiving module, a control parameter mapping module, a state control module, a priority arbitration module, an interface output module, and a feedback write-back module. In some embodiments, the language replacement engine can execute the language generation or replacement process based on a limited expression space and path constraint mechanism to achieve coordinated control between the generation process and the execution scheduling.

[0010] The system includes: a behavior signal receiving module for receiving information such as swiping, pausing, rewinding, clicking, dwell time, voice commands, or visual gaze during user reading; a control parameter mapping module for converting the behavior information into replacement ratio, replacement level, context window, and rhythm control parameters; a state control module for controlling the system to switch between native language, mixed language, target language, and backtracking language; a priority arbitration module for performing priority determination when behavior control requirements conflict with content continuity requirements; an interface output module for sending standardized semantic control commands to the language replacement engine; and a feedback write-back module for correcting control parameters based on subsequent user interactions.

[0011] This invention uses a unified interface protocol to convert heterogeneous user behavior inputs such as swiping, pausing, clicking, rewinding, voice, or vision into a standardized set of semantic control instructions that can be called by the language replacement engine, thereby achieving cross-module collaborative control.

[0012] Compared with existing technologies, the present invention has at least the following advantages: 1) Establishing a unified connection structure between the behavior scheduling layer and the language replacement engine; 2) Mapping user behavior to executable replacement control parameters in real time; 3) Achieving smooth switching of language display modes through a state machine mechanism; 4) Achieving dynamic balance between behavior response and content continuity; 5) Constructing a continuously optimized closed-loop language migration system; 6) Supporting interface reuse and rapid access between different language replacement engines; 7) Applicable to various application scenarios such as reading and learning, real-time translation, listening training, speaking training, and multimodal language systems. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0014] Figure 1 This is a schematic diagram of the overall structure of the semantic control interface system for connecting the user behavior scheduling and language replacement engine according to an embodiment of the present invention.

[0015] Figure 2 This is a flowchart of behavioral signal reception and parsing according to an embodiment of the present invention;

[0016] Figure 3 This is a flowchart of the control parameter mapping according to an embodiment of the present invention;

[0017] Figure 4 This is a schematic diagram of the state control module switching according to an embodiment of the present invention;

[0018] Figure 5 This is a priority arbitration flowchart according to an embodiment of the present invention;

[0019] Figure 6 This is a flowchart of the feedback write-back and closed-loop optimization process according to an embodiment of the present invention;

[0020] Figure 7 This is a flowchart of a semantic control method for connecting a user behavior scheduling and language replacement engine according to an embodiment of the present invention;

[0021] Figure 8 This is a flowchart of another semantic control method for connecting user behavior scheduling and language replacement engine according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] like Figure 1 As shown, the system of this invention is positioned between the user behavior scheduling module and the language replacement engine, operating as an intermediate control layer. The overall data flow path is: user behavior input → control interface system → language replacement engine → user interface output → user feedback input. This structure allows user behavior to be converted into standardized semantic control commands by the semantic control interface system, influencing the language replacement process and forming a continuously optimizing closed-loop control mechanism.

[0025] 1) Behavioral signal receiving module

[0026] like Figure 2 As shown, the behavior signal receiving module continuously collects user interaction behaviors, including but not limited to: 1) page scrolling speed; 2) page scrolling direction; 3) reading pause duration; 4) number of times to revisit; 5) number of times to click to replace the area; 6) page dwell density; 7) quick skip behavior; 8) voice control commands; and 9) visual gaze information.

[0027] The system converts the above behavioral information into a time-series event stream: B(t) = {b1, b2, b3 ... bn}, where bi represents the i-th behavioral event.

[0028] 2) Control parameter mapping module

[0029] like Figure 3 As shown, the system maps the behavioral event stream to language substitution control parameters, which include: 1) substitution ratio R(t); 2) substitution level L; 3) context window W; 4) rhythm parameter T; 5) substitution intensity P; 6) backoff threshold Q.

[0030] The replacement ratio parameter is generated based on multiple behavioral parameters, including at least two of the following: page scrolling speed, reading pause duration, review frequency, and page dwell density. These parameters are calculated by combining them using a preset mapping rule.

[0031] The function f can be a linear mapping function, a piecewise function, a threshold function, a probabilistic model, or a machine learning prediction model.

[0032] When users read steadily and have a high level of acceptance, control parameters are generated to increase the replacement ratio of the target language; when users frequently pause, quickly skip, or rewind, control parameters are generated to reduce the replacement ratio or trigger a rewind strategy.

[0033] When real-time behavior data is insufficient, the system calls a preset parameter set to generate initial control instructions to execute the default replacement strategy.

[0034] 3) Status control module

[0035] like Figure 4 As shown, the system defines the following states:

[0036] S0: Native language voice; S1: Mixed language voice; S2: Target language voice; S3: Backtracking state. The state switching rules include: 1) The system initially enters S0; 2) When reading stability reaches the first threshold, it switches from S0 to S1; 3) When proficiency reaches the second threshold, it switches from S1 to S2; 4) When comprehension blockage, high-frequency rereading, or continuous pauses are detected, it switches from S1 or S2 to S3; 5) After backtracking is completed, it returns to S1 or S0.

[0037] In some implementations, state transitions can also be configured with minimum dwell time, rollback cooldown time, and cross-level jump limits to improve system stability. The system can also set a state transition hysteresis threshold to avoid frequent round-trip transitions near critical conditions.

[0038] 5) Priority Arbitration Module

[0039] like Figure 5 As shown, when behavior scheduling requirements conflict with content continuity requirements, the system performs a priority determination. For example: if a user's rapid swiping requests faster replacement, or if the current context stability condition or content continuity condition is not met, replacement scheduling is executed immediately.

[0040] At this point, the system prioritizes ensuring content continuity and implements delayed responses, reduced processing speed, or phased execution for behavior requests.

[0041] The priority rules may include: 1) content continuity priority; 2) user-initiated rollback priority; 3) system stability priority; 4) behavior acceleration requests secondary priority.

[0042] 6) Interface output module

[0043] The system encapsulates the final control results into standardized semantic control instructions and sends them to the language replacement engine, including: 1) replacement ratio update instructions; 2) replacement region selection instructions; 3) replacement level adjustment instructions; 4) state switching instructions; 5) rollback and recovery instructions; 6) animation rhythm instructions; and 7) buffer preloading instructions.

[0044] The language replacement engine performs word-level, phrase-level, sentence-level, or paragraph-level replacement operations accordingly.

[0045] The standardized semantic control instructions are used to define the scheduling basis and control boundaries of the language replacement engine, but do not limit the specific algorithms or implementation methods used inside the language replacement engine.

[0046] 7) Feedback Write-back Module

[0047] like Figure 6 As shown, the system generates feedback signals based on the user's subsequent behavior, including: 1) reading completion rate; 2) time spent in the replacement area; 3) number of times the user actively restores the original text; 4) skipping frequency; 5) continuous learning time; and 6) comprehension score input.

[0048] The system corrects the parameter model based on the feedback signal: θ(t+1)=θ(t)+Δθ, where θ represents the set of control parameters and Δθ represents the adjustment amount generated based on the feedback.

[0049] After continuous feedback and optimization, the system gradually develops a user-specific language migration control curve.

[0050] This invention can be applied to the following scenarios: 1) Foreign language reading learning system; 2) Bilingual content training system; 3) Real-time translation and reading system; 4) Multimodal language training platform.

[0051] The present invention also provides an electronic device, comprising: a processor; a memory; an input / output module; a display module; and a network communication module. The memory stores program instructions, and when the processor executes the program instructions, it implements the method steps of the aforementioned semantic control interface system.

[0052] This invention establishes a unified control interface between the user behavior scheduling layer and the language replacement engine to realize a behavior-driven language replacement scheduling mechanism. This enables the language migration process to have dynamic control capabilities, content continuity capabilities, and individualized optimization capabilities, and has good practical value and promotion prospects.

[0053] This application provides another semantic control method that connects user behavior scheduling and a language replacement engine, applied to the system described above. For example... Figure 7As shown, in this method, user behavior signals during the reading process are acquired and standardized to obtain a behavior data set. Based on the behavior data set, the behavior signals are converted into language replacement control parameters, which include at least replacement ratio parameters, replacement level parameters, context window parameters, rhythm control parameters, replacement intensity parameters, and fallback threshold parameters. Based on the language replacement control parameters, display state switching control is executed to obtain a target display state. Priority arbitration is performed between behavior scheduling requirements and content continuity requirements to obtain a target control strategy. Based on the target control strategy, standardized semantic control instructions are generated and output to the language replacement engine to control the language replacement execution process.

[0054] Specifically, such as Figure 8 As shown, the method includes:

[0055] Step S801: Obtain user behavior signals during the reading process.

[0056] The user's behavioral signals during the reading process are acquired through a behavioral signal receiving module. These behavioral signals include at least one of the following: page scrolling speed, page scrolling direction, reading pause duration, number of revisits, click frequency, page dwell density, quick skipping behavior, voice control commands, visual gaze position, and visual gaze duration. Considering the differences in acquisition methods and data structures among different types of behavioral signals, this embodiment performs unified encoding processing on all types of behavioral signals, transforming heterogeneous inputs into a structurally consistent set of behavioral data.

[0057] Specifically, firstly, continuous signals are quantified, for example, the page scrolling speed is expressed as the scrolling distance per unit time, and the page dwell density is expressed as the dwell time distribution within a unit area; discrete signals are frequency-statistically analyzed, for example, the number of replays, click frequency, and quick skip behavior are converted into statistical values ​​within a unit time window; voice control commands are semantically parsed and converted into preset control identifiers; and visual gaze information is spatially mapped, with the visual gaze position expressed as page coordinates and the visual gaze duration expressed as the dwell time at the corresponding coordinates.

[0058] After completing the above processing, all kinds of behavioral signals are uniformly constructed into a standardized behavioral data set. The behavioral data set is used to uniformly represent page scrolling speed, page scrolling direction, reading pause duration, number of reviews, click frequency, page dwell density, intensity of fast skip behavior, voice control commands, and visual gaze information.

[0059] Furthermore, to ensure the consistency of data under different time sampling conditions, the behavioral data set is subjected to time window normalization processing to transform behavioral data with different sampling frequencies and different numerical scales to a unified time scale, thereby providing regular and consistent input data for subsequent control parameter calculation.

[0060] In some implementations, abnormal behaviors can also be filtered. For example, when the sliding speed suddenly exceeds a preset threshold or the visual gaze time deviates abnormally, the data is marked as an outlier and removed or downweighted to improve data reliability.

[0061] In this embodiment, behavioral signals during the user's reading process are acquired through a behavioral signal receiving module. Various heterogeneous behavioral signals with different acquisition methods and data structures are uniformly encoded. Continuous behavioral signals are numerically represented, discrete behavioral signals are frequency-counted within a time window, voice control commands are semantically parsed and converted into preset control identifiers, and visual gaze information is mapped to page space coordinates and represented by duration. The encoded behavioral signals are then integrated into a standardized behavioral data set with a unified structure. This standardized behavioral data set is then normalized to a time window, ensuring that behavioral data with different sampling frequencies and types are represented on a unified time scale, providing a consistent input for subsequent control parameter calculations. Simultaneously, abnormal behavioral data that abruptly exceeds a preset threshold or deviates abnormally in value are marked, removed, or filtered with reduced weight to improve the overall reliability of the behavioral data.

[0062] Step S802: Convert the behavioral signal into language substitution control parameters.

[0063] After acquiring and standardizing the behavioral data set, the behavioral signals are converted into language replacement control parameters to drive the subsequent language replacement process. In this embodiment, the language replacement control parameters include at least one of the following: a replacement ratio parameter. Replace hierarchical parameters Context window parameters Rhythm control parameters Replace strength parameters and the backoff threshold parameter Among them, regarding the replacement ratio parameter The system generates the replacement ratio parameter using a mapping method based on multi-behavioral signal fusion. In one embodiment, the system generates the replacement ratio parameter based on the combination relationship between multiple behavioral parameters, and the generation method includes at least one of linear mapping, piecewise mapping, threshold mapping, probability mapping, or prediction model mapping. Through the above mapping methods, the dynamic generation of the replacement ratio parameter is achieved.

[0064] In this embodiment, based on the standardized behavioral data set, at least two behavioral parameters are selected as input variables; according to a preset mapping rule, the at least two behavioral parameters are combined and calculated to generate a replacement ratio parameter; wherein, the mapping rule includes at least one of the following: linear mapping rule, segmented mapping rule, threshold mapping rule, probability mapping rule, or prediction model mapping rule; based on at least one or more behavioral parameters in the behavioral data set, a replacement level parameter, a context window parameter, a rhythm control parameter, a replacement intensity parameter, and a backoff threshold parameter are generated respectively; the replacement ratio parameter, replacement level parameter, context window parameter, rhythm control parameter, replacement intensity parameter, and backoff threshold parameter are combined to obtain a language replacement control parameter set. For example, based on at least two behavioral parameters in the behavioral data set, a replacement ratio parameter is generated through a preset mapping relationship; a replacement level parameter is generated based on the click frequency and replay count in the behavioral data set; a context window parameter is generated based on the page dwell density in the behavioral data set; a rhythm control parameter is generated based on the page swiping speed in the behavioral data set; a replacement intensity parameter is generated based on the click frequency and visual gaze duration in the behavioral data set; and a back threshold parameter is generated based on the replay count and quick skip behavior in the behavioral data set.

[0065] Step S803: Based on the language replacement control parameters, execute the display state switching control to obtain the target display state;

[0066] The state control module defines at least one of the following states: S0 is the native language state, used for displaying at a low replacement ratio; S1 is the mixed state, used for displaying at a medium replacement ratio; S2 is the target language state, used for displaying at a high replacement ratio; and S3 is the fallback state, used to perform protection or recovery display when the user's comprehension ability declines.

[0067] To replace the scaling parameter As the primary driving variable, and in conjunction with the backoff threshold parameter A state determination is performed. In one implementation, the system performs state control based on a preset state switching threshold. When the replacement ratio parameter or user proficiency parameter reaches a first threshold, the system switches from S0 to S1; when the parameter reaches a second threshold, the system switches from S1 to S2; when a decline in user comprehension or performance indicators is detected (such as an increase in the number of replays or enhanced skipping behavior) and the conditions are met... When the recovery conditions are met, the system switches from S1 or S2 to S3; when the recovery conditions are met, the system returns from S3 to S1 or S2.

[0068] In actual operation, to avoid frequent state switching near the critical threshold, this embodiment further adopts multiple stability control mechanisms: one of which is the minimum dwell time mechanism, that is, the system must meet certain conditions after entering a certain state. Only then can the next state transition be executed; the second is the rollback cooldown mechanism, that is, after entering S3 from S1 or S2, during the cooldown period. The internal limit triggers a rollback again; the third is the cross-level jump limit mechanism, which restricts the state to switch only between adjacent levels, avoiding a direct jump from S0 to S2 or a direct rollback from S2 to S0; the fourth is the state switching hysteresis threshold mechanism, which sets different judgment conditions for state upgrades and state rollbacks, thereby forming a hysteresis interval to suppress frequent back-and-forth switching of states near the critical conditions.

[0069] It should be noted that the state control results generated by the semantic control interface system are used as the scheduling basis for the language replacement engine, and do not limit the specific layout, empty area allocation or text presentation structure in the display interface.

[0070]

[0071]

[0072] Through the synergistic effect of the above mechanisms, while ensuring responsiveness to changes in user behavior, frequent state oscillations caused by threshold fluctuations are effectively suppressed. Ultimately, a stable target display state is obtained, and this target display state serves as the basis for generating subsequent semantic control instructions.

[0073] In this embodiment, display state switching control is performed based on the language replacement control parameter set. The current system state is determined and switched between multiple states, including native language state, mixed state, target language state, and fallback state. State determination is performed according to the replacement ratio parameter and the fallback threshold parameter. When a preset threshold condition is met, a state upgrade or fallback is executed. When a decline in user comprehension is detected, the system switches from the mixed state or target language state to the fallback state. When the recovery condition is met, the system returns from the fallback state to the mixed state or target language state. During the state switching control, at least one stabilization control mechanism is employed to suppress frequent state switching near critical conditions. This stabilization control mechanism includes at least one of the following: minimum dwell time mechanism, fallback cooldown time mechanism, cross-level jump restriction mechanism, and state switching hysteresis threshold mechanism. The minimum dwell time mechanism is used to limit the current state from undergoing state switching before reaching a preset dwell time after the system enters the current state, so as to obtain a stable state maintenance result; the rollback cooldown time mechanism is used to limit the triggering of rollback operation again within a preset cooldown time after the system switches from a mixed state or target state to a rollback state, so as to obtain a controlled rollback result; the cross-level jump restriction mechanism is used to restrict the system state to switch only between adjacent levels, so as to obtain a restricted state transition result; the state switching hysteresis threshold mechanism is used to set different judgment conditions for state upgrade and state rollback, so that the system performs state switching under different judgment conditions, so as to obtain a state switching result with hysteresis characteristics.

[0074] Step S804: When there is a conflict between behavior scheduling requirements and content continuity requirements, multi-stage priority arbitration control is executed.

[0075] After completing the state transition control and obtaining the current system state, the system further processes the conflict between user behavior requests and system constraints to determine the final control strategy. Specifically, the system receives user-input behavior requests, categorizes and parses these requests, and generates behavior scheduling requirements. These requirements include at least one of the following: acceleration requests to increase the replacement ratio or advance the language replacement progress, upgrade requests to improve the display status level, and skip operations to skip the current content. Simultaneously, based on the current system state, the language replacement control parameter set, and the current text context information, the system obtains system constraints, including content continuity protection constraints, context stability constraints, and read integrity constraints. The system then performs constraint judgment processing on these constraints to obtain constraint evaluation results, where the constraint evaluation results characterize whether the current context allows the execution of the behavior scheduling requirements.

[0076] Based on this, the system performs correlation analysis between the behavior scheduling requirements and the constraint evaluation results to identify whether there are any conflicts. When there is no conflict between the behavior scheduling requirements and the system constraints, the system directly generates a corresponding control strategy based on the behavior scheduling requirements. When a conflict exists, the system further performs priority arbitration processing. Specifically, the system sorts and compares the behavior scheduling requirements and the system constraints according to preset priority rules. The priority rules include at least one of the following: user-initiated rollback requests or prompts for recovery take priority; content continuity takes priority; explicit user parameter settings take priority over automatic system inference; system stability takes priority; and behavior acceleration requests take a secondary priority. Based on the priority rules, conflicting items are filtered, suppressed, or delayed to generate a set of candidate control strategies.

[0077] Furthermore, the system makes a decision on the set of candidate control strategies to determine a target control strategy, wherein the target control strategy includes at least one of the following: applying an upgrade strategy, i.e., performing a state upgrade or increasing the replacement ratio; delaying an upgrade strategy, i.e., suspending the execution of the upgrade request and maintaining the current replacement progress; maintaining a state, i.e., keeping the current system state unchanged; and executing a rollback strategy, i.e., generating control parameters for reducing the replacement ratio or triggering a rollback strategy or switching to a rollback state. In some embodiments, the system can also perform consistency verification of the target control strategy based on the current system state and historical control results to avoid frequent switching or strategy oscillations.

[0078] Finally, based on the target control strategy, the system adjusts the set of language replacement control parameters and updates the current system state to obtain the arbitrated control result. The arbitrated control result is then used as the input for generating subsequent semantic control instructions, thereby completing the coordinated control between behavior scheduling requirements and content continuity requirements.

[0079] In this embodiment, user-input behavior requests are parsed and processed to generate behavior scheduling requirements, which include at least one of acceleration requests, upgrade requests, and skip operations. Based on the current system state, the language replacement control parameter set, and the current context information, system constraints are obtained and evaluated to obtain constraint assessment results. These constraints include at least one of content continuity protection, context stability, and read integrity. Conflict identification is performed between the behavior scheduling requirements and the constraint assessment results. When a conflict occurs, priority is determined according to preset priority rules, which include at least one of the following: user-initiated rollback requests or prompts for recovery take priority; content continuity takes priority; explicit user parameter settings take priority over automatic system inference; system stability takes priority; and behavior acceleration requests have secondary priority. Based on the priority determination results, the behavior scheduling requirements are filtered, suppressed, or delayed to generate candidate control strategies. A target control strategy is determined from the candidate control strategies, which includes at least one of application upgrade, delayed upgrade, maintaining the current state, or performing a rollback. The language replacement control parameter set and the current system state are adjusted based on the target control strategy.

[0080] Step S805: Generate standardized semantic control instructions based on the target control strategy.

[0081] After completing priority arbitration and determining the target control strategy, the system generates standardized semantic control instructions based on the target control strategy to achieve unified control and decoupled invocation of the language replacement engine. Specifically, the system first performs structured parsing of the target control strategy, mapping the control intent involved in the target control strategy to corresponding control type identifiers; then, based on the control type identifiers, it matches the corresponding instruction generation rules from a preset instruction template set, and combines the current system state and the language replacement control parameter set to perform parameter filling processing on the instruction generation rules to generate candidate semantic control instructions.

[0082] The standardized semantic control instructions include at least one of the following: replacement ratio update instruction, replacement area selection instruction, replacement level adjustment instruction, state switching instruction, rollback and recovery instruction, animation rhythm instruction, and buffer preloading instruction; wherein, the replacement ratio update instruction is used to update the proportion of target language content during language replacement, the replacement area selection instruction is used to specify the text area range to be replaced, the replacement level adjustment instruction is used to adjust the granularity level of replacement, the state switching instruction is used to control the switching of the system between different display states, the rollback and recovery instruction is used to restore the original content or reduce the replacement intensity when the user's comprehension ability declines, the animation rhythm instruction is used to control the display rhythm during the replacement process, and the buffer preloading instruction is used to preload the content to be replaced to improve response efficiency.

[0083] Furthermore, to ensure compatibility between different language replacement engines, the system performs field-based encapsulation of the candidate semantic control instructions to generate standardized semantic control instructions in a unified format. These standardized semantic control instructions include fields representing the replacement ratio, replacement level, context window, status identifier, priority identifier, and rollback strategy. Specifically, the replacement ratio field describes the current replacement intensity level, the replacement level field describes the replacement granularity, the context window field limits the replacement context, the status identifier field identifies the current system state, the priority identifier field reflects the control priority in the arbitration result, and the rollback strategy field indicates the rollback conditions or rollback method.

[0084] After the field encapsulation is completed, the system performs a consistency check on the standardized semantic control instructions to ensure that the semantic control instructions are consistent with the current system state and the language replacement control parameter set, and adjusts or corrects conflicting fields to obtain the final semantic control instructions. Subsequently, the final semantic control instructions are sent as output data to the language replacement engine to drive the language replacement engine to perform the corresponding replacement operation.

[0085] Step S806: Update the language replacement control parameters based on the results of subsequent user interactions.

[0086] After the semantic control command is sent and drives the language replacement engine to perform the corresponding replacement operation, the system further obtains the user's subsequent interaction results through the feedback write-back module, and updates the language replacement control parameters based on the interaction results, thereby forming a closed-loop optimization process. Specifically, the feedback write-back module obtains user feedback information during the language replacement execution process, which includes at least one of the following: reading completion rate, duration of stay in the replacement area, number of times the original text is actively restored, skip frequency, continuous learning duration, and user comprehension score; and performs structured processing on the feedback information, uniformly converting different types of feedback signals into a set of feedback data that can be used for evaluation.

[0087] After constructing the feedback data set, the system evaluates the effectiveness of the current language replacement control parameter set based on this data set. This evaluation characterizes the adaptation of the current replacement strategy to user reading behavior and comprehension levels. Specifically, when the reading completion rate and continuous learning time increase, and the number of times users actively restore the original text and the skipping frequency decrease, the current control strategy is considered to have a high degree of adaptation. Conversely, when the user's comprehension score decreases or the number of times they actively restore the original text increases, the current control strategy is considered to have a mismatch risk. Based on this, the system adjusts the language replacement control parameter set according to the evaluation results. This includes adjusting the replacement ratio parameter, adjusting the replacement level parameter, expanding or shrinking the context window parameter, and dynamically correcting the fallback threshold parameter, thereby generating an updated control parameter set.

[0088] Furthermore, in some implementations, the system can also incorporate historical feedback data to constrain the update process, preventing drastic parameter fluctuations caused by short-term abnormal behavior. For example, by performing cumulative analysis or trend judgment on multiple feedback results, the update process of the language replacement control parameters can be kept smooth. Finally, the system uses the updated set of control parameters as input data for subsequent behavior signal conversion processing, enabling the new control parameters to participate in the next round of language replacement control, thereby forming a closed-loop control chain of behavior input, control decision, execution, and feedback update.

[0089] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A semantic control interface system connecting a user behavior scheduling and a language replacement engine, characterized in that, include: The behavior signal receiving module is used to receive behavior signals generated during user interaction. A control parameter mapping module, connected to the behavior signal receiving module, is used to convert the behavior signal into language replacement control parameters; A status control module, connected to the control parameter mapping module, is used to control the system display status switching according to the language replacement control parameters. The priority arbitration module, connected to the state control module, is used to perform control priority determination when there is a conflict between behavior scheduling requirements and content continuity requirements. An interface output module, connected to the priority arbitration module, is used to encapsulate the language replacement control parameters into standardized semantic control instructions and output them to the language replacement engine. The feedback write-back module is connected to the language replacement engine and the control parameter mapping module, and is used to update the language replacement control parameters according to the user's subsequent interaction results. Its characteristics also include: The language replacement engine does not directly receive original user behavior signals; its replacement scheduling process related to user behavior is only allowed to be controlled according to the standardized semantic control instructions output by the semantic control interface system. The semantic control interface system constitutes a unified control path between the user behavior scheduling module and the language replacement engine. After the language replacement control parameters are generated into standardized semantic control instructions by the interface output module, they serve as the scheduling basis for controlling the language replacement engine to perform replacement ratio adjustment, replacement level adjustment, state switching, rollback recovery, or rhythm control. The semantic control interface system constitutes the sole control path between user behavior scheduling and language replacement execution. The semantic control interface system controls the language replacement engine through standardized semantic control instructions at the execution layer, without relying on modifications or interventions to the internal generation process of the language replacement engine.

2. The system according to claim 1, characterized in that, The behavioral signals include at least one of the following: page scrolling speed; page scrolling direction; reading pause duration; number of reviews; click frequency; page dwell density; quick skip behavior; voice control commands; visual gaze position; visual gaze duration.

3. The system according to claim 1, characterized in that, The language replacement control parameters include at least one of the following: replacement ratio parameter; replacement level parameter; context window parameter; rhythm control parameter; replacement intensity parameter; backoff threshold parameter; wherein the replacement ratio parameter is generated based on at least two parameters in the behavior signal through a preset mapping rule, and the mapping rule includes at least one of linear mapping rule, segmented mapping rule, threshold mapping rule, probability mapping rule or prediction model mapping rule.

4. The system according to claim 1, characterized in that, The state control module defines at least one of the following states: S0: native language state; S1: Mixed state; S2: Target voice; S3: Back off state; and state switching control is performed between the states according to a preset threshold.

5. The system according to claim 4, characterized in that, The state control module also includes at least one of the following mechanisms: minimum dwell time mechanism; rollback cooldown time mechanism; cross-level jump restriction mechanism; state switching hysteresis threshold mechanism; to suppress frequent switching of system state near critical conditions.

6. The system according to claim 1, characterized in that, When the behavior scheduling requirements conflict with the content continuity requirements, the priority arbitration module shall execute control according to at least one of the following priorities: user-defined parameter settings take precedence over automatic system inference; User-initiated rollback requests or prompts for recovery are given priority; content continuity is given priority; system stability is given priority; requests for behavior acceleration are given secondary priority.

7. The system according to claim 1, characterized in that, The standardized semantic control instructions include at least one of the following instructions: replacement ratio update instruction; replacement region selection instruction; replacement level adjustment instruction; state switching instruction; rollback and recovery instruction; animation rhythm instruction; buffer preloading instruction; wherein, the standardized semantic control instructions include fields for characterizing instruction type, replacement ratio, replacement level, context window, state identifier, priority identifier, execution timing, or rollback strategy.

8. The system according to claim 1, characterized in that, The feedback write-back module updates the language replacement control parameters based on at least one of the following feedback information: reading completion rate; duration of stay in the replacement area; number of times the original text is actively restored; skipping frequency; continuous learning duration; and user comprehension score.

9. A semantic control method connecting user behavior scheduling and a language substitution engine, characterized in that, include: Receive behavioral signals generated during user interaction; The behavioral signals are converted into language replacement control parameters; The system switches between native language mode, mixed language mode, target language mode, and fallback mode according to the language replacement control parameters. When there is a conflict between behavior scheduling requirements and content continuity requirements, priority arbitration is performed; The language replacement engine outputs standardized semantic control instructions, so that the replacement scheduling process related to user behavior in the language replacement engine is controlled according to the standardized semantic control instructions; The language replacement control parameters are updated based on subsequent user interactions.

10. An electronic device, characterized in that, include: processor; Memory; The memory stores a computer program, which, when executed by the processor, causes the electronic device to perform the method of claim 9.

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