Seat gesture sensing self-defined rapid adjustment interaction method

Abstract

The invention relates to the technical field of vehicle control and human-computer interaction technologies, and discloses a seat gesture sensing self-defined rapid adjustment interaction method. The method comprises the following steps: establishing an interaction baseline containing a space region and a time rule through initialization; then, gesture signals are collected, stability evaluation and lightweight recognition are carried out, and mistaken touch risk evaluation is completed synchronously; mapping according to an identification result to generate a function calling item containing a parameter draft; performing security constraint check and conflict arbitration, and outputting a final control parameter and an arbitration sequence; and finally, through integrated processing of instruction issuing, receipt collection and feedback learning, dynamically updating a user preference portrait and adjusting a threshold. According to the method, closed-loop self-adaptive control from gesture sensing to seat adjustment is realized, and the personalized level of interaction, the operation safety and the continuous optimization capability of the system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control and human-computer interaction technology, and in particular to a method for customized and rapid adjustment of seat gesture perception interaction. Background Technology

[0002] In existing technologies, virtual large-screen buttons are commonly used to replace traditional physical buttons. However, given the large amount of information displayed on the screen, adjusting the seat requires navigating multiple menu levels, which is inconvenient for users and may pose a potential safety risk. Furthermore, traditional seat adjustment mechanisms use physical switches, which are bulky and require multiple wiring harnesses to connect to the controller, increasing both material costs and weight. Moreover, current vehicle seat adjustments rely heavily on physical switches and multi-level menus, resulting in numerous hardware switches, complex wiring harnesses, lengthy operation processes, and dispersed adjustment entry points. While some models introduce gesture interaction, the sensing units are often located on the center console or roof, lacking a direct correspondence between gesture coordinates and seat actuator coordinates. The absence of alignment and synchronization strategies at the acquisition end leads to spatiotemporal misalignment in subsequent recognition and function mapping. Existing acquisition links often lack standardized de-jittering, slicing, and time-stamping processes in noisy scenarios, resulting in incomplete recording of sample boundaries and trajectory switching, insufficient stability quantification, and difficulty in obtaining traceable fragment-level and window-level evidence on the recognition side. Recognition algorithms often focus on action classification, and false trigger risk assessment and threshold management frequently employ fixed thresholds, making it difficult to revise the threshold sources based on real-time stability and scene disturbances. There is a lack of structured coupling between recognition results and stability metrics, failing to form a continuous decision chain from recognition pair to risk estimation to threshold revision. At the function mapping and parameter organization level, candidate functions, parameter boundaries, and calling order are often loosely associated with the recognition output. Mutual exclusion and interlocking rules are scattered across different modules, and conflict handling and security verification lack unified spatiotemporal index support, easily leading to resource contention and parameter out-of-bounds errors within the same time slice. At the execution level, control message encoding and acknowledgment verification lack consistent reference paths with the recognition, mapping, and verification sides. Feedback learning does not form a closed loop with threshold management, preference profiling, and interaction baselines, making it difficult to use historical execution results for rapid revision of subsequent recognition thresholds and parameter boundaries. In summary, existing technologies lack a mechanism for jointly processing lightweight recognition and accidental touch risk assessment recognition pairs with gesture acquisition and stability assessment outputs. They also lack a process for completing risk estimation and threshold revision and generating structured outputs under unified alignment index and time slice constraints. This makes it difficult to provide a consistent data thread and traceable reference for the mapping, verification, and distribution links in the rapid adjustment of seat gesture customization, thus forming the core technical problem addressed by this invention. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for customized and rapid adjustment of seat gesture perception interaction, comprising: Obtain gesture templates, adjustable item lists, and blind operation label layouts; perform interactive baseline initialization, alignment index generation preparation, synchronization configuration, coordinate alignment, storage, and publishing processing; and generate interactive baseline initialization and alignment index generation outputs containing alignment item numbers, spatial sub-region numbers, time slice rules, and module access paths. The process includes executing spatial sub-region sets and time slicing rules containing alignment indexes, gesture acquisition initiation, de-jittering, slicing, time stamping, stability calculation, and feature summary construction, generating gesture acquisition and stability evaluation output containing stability fields and feature summaries. Perform lightweight identification feature extraction, identification result calculation, confidence generation, accidental touch risk assessment, and threshold revision operations, including slice metadata and time stamps, to generate lightweight identification and accidental touch risk assessment output containing risk estimation elements and threshold revision items; The output of lightweight identification and accidental touch risk assessment includes alignment index and trigger order. It performs custom function mapping retrieval, function call item organization, parameter range generation, parameter draft conflict detection and sequence arrangement processing, and generates custom function mapping and parameter draft output containing function call table, parameter draft set and conflict candidate set. Perform operations including unified spatiotemporal reference keys, security constraint verification parameter construction, limit calculation, consistency verification, conflict arbitration and decision-making, and generate security constraint verification and conflict arbitration outputs including final control parameters and arbitration sequence list; The process integrates preference profile updates and threshold revisions, including alignment indexes and access paths, instruction encoding, execution, receipt collection, and feedback learning. It generates integrated update results containing preference profile update entries and threshold revision entries.

[0004] Furthermore, the process of generating an integrated update result of instruction issuance and feedback learning that includes preference profile update entries and threshold revision entries also includes: Obtain the output of security constraint verification and conflict arbitration, perform integrated encoding of instruction issuance and feedback learning that includes alignment index and access path, and obtain an integrated instruction set of instruction issuance and feedback learning that includes instruction sequence list and parameter image; The system performs instruction issuance and execution, and collects receipts to obtain an integrated feedback snapshot of instruction issuance and feedback learning, which includes arrival records, in-transit records, and exception records. The system updates preference profiles and revises thresholds, generating an integrated update result that combines instruction issuance and feedback learning, including preference profile update entries and threshold revision entries.

[0005] Furthermore, the process of obtaining an integrated instruction set for instruction issuance and feedback learning, which includes a list of instruction sequences and parameter images, also includes: Obtain the output of security constraint verification and conflict arbitration, parse the final control parameters, arbitration order list, allow labels, deny labels and continuation labels, and generate the alignment index and access path registered in the output according to the interaction baseline initialization and alignment index. Establish the correspondence between time slice and spatial sub-region number for each final control parameter, and generate an instruction orchestration skeleton consistent with the unified spatiotemporal reference key.

[0006] Furthermore, the process of generating an instruction orchestration skeleton consistent with the unified spatiotemporal reference key also includes: Under the order constraints of the arbitration sequence list, the allowed tags within the same time slice are merged at the channel level, a mapping table between the call channel and the executor is established, and each function call item is mirrored with the starting point, ending point, step granularity and change limit fields in its parameter draft, and merged with the corresponding unified spatiotemporal reference key, time slice sequence number and spatial sub-region number into an instruction unit.

[0007] Furthermore, for function call items marked as deferred, the target time slice given by the deferred label is inserted sequentially into the instruction arrangement skeleton, and the deferred source identifier and the original time slice sequence number are added to the instruction unit; for function call items marked as rejected, a placeholder instruction unit is generated, which only retains the function call item identifier and the reference path of the rejection description.

[0008] Furthermore, the process of issuing and executing instructions, collecting receipts, and obtaining an integrated feedback snapshot of instruction issuance and feedback learning that includes arrival records, in-transit records, and exception records also includes: According to the time slice order and call channel arrangement of the instruction sequence list, the instructions are issued one by one to the seat motor, backrest motor, lumbar support actuator, and ventilation and heating actuator, and the receipt information from the execution link is collected. According to the receipt verification rules, each pair of receipts is checked for consistency: if the actual value of position or angle is within the allowable deviation range of the value in the parameter mirror, it is recorded as an arrival record; if it has not arrived but the execution time is consistent with the change limit field, it is recorded as an in-transit record; if an interlock or timeout is triggered, it is recorded as an abnormal record.

[0009] Furthermore, the process of updating preference profiles, revising thresholds, and generating integrated update results that include both preference profile update entries and threshold revision entries also includes: Using the integrated feedback snapshot of instruction issuance and feedback learning as input, the system reads the parameter mirrors of arrival records and in-transit records and their corresponding acknowledgment values. Based on the time stamp, it reconstructs the target landing point and actual landing point of each function call item in this round of execution. It then compares these with the risk estimation elements and threshold revision items in the output of lightweight identification and accidental touch risk assessment. Function call items with stable actual landing points are registered as preferred candidates, while function call items with abnormal records or frequent delays are registered as boundary candidates.

[0010] Furthermore, within the preference candidate set, preferences are merged according to the same gesture name and the same spatial sub-region number, and common value starting points, common value ending points, and common step granularities within multiple rounds of windows are statistically analyzed to form preference segments; within the boundary candidate set, segments that have experienced interlocking, timeouts, or significant arrival deviations are extracted by combining anomaly descriptions and stability field references to form boundary segments.

[0011] Furthermore, based on preference fragments, the default values ​​and step granularity in the adjustable item list are fine-tuned to generate preference profile update items; based on boundary fragments, the threshold revision items in the output of lightweight recognition and accidental touch risk assessment are revised again. While keeping the original adaptive gating direction unchanged, the threshold is tightened or relaxed in combination with the consistency results of the receipt to form threshold revision items.

[0012] Furthermore, the updated preference profile entries are fed back to the preference profile entry that can be read by custom function mapping and parameter draft retrieval; the threshold revision entries are registered to the access entry for security constraint verification and conflict arbitration parameter construction; for preference segments that are stable across rounds, template micro-revision is triggered, the relevant entries are synchronized to the updatable fields of the interaction baseline initialization and alignment index generation output, and version identifiers and rollback identifiers are generated.

[0013] The key innovations of this invention include: (1) Construct a unified spatiotemporal reference key driven by alignment index and time slice, which runs through the stages of collection, identification, mapping, verification, distribution and learning; in the joint processing framework of lightweight identification and accidental touch risk assessment identification and gesture collection and stability assessment output, a structured product of risk estimation elements and threshold revision items is formed, which directly serves the function call table and parameter draft set; (2) A fragment organization strategy that combines de-shaking, slicing, time stamping and stability field is adopted to provide fragment-level and window-level evidence chains for subsequent identification and threshold revision; (3) In the stage of safety constraint verification and conflict arbitration, an arbitration order list derived from risk estimation elements and threshold revision items is introduced, and the final control parameters and labels such as rejection and extension are output, and correspond one-to-one with the acknowledgment verification rules; (4) Construct an integrated mechanism for instruction issuance and feedback learning, use feedback snapshots to drive preference profile updates and threshold revisions, and complete closed-loop maintenance of recognition thresholds, parameter boundaries and call priorities; (5) Complete the complex calculation process related to identification, risk estimation, threshold revision and arbitration on the end side, compress the cross-component signal link, and form a human-machine interaction and execution control collaborative architecture integrated with the seat body.

[0014] The following are its main beneficial effects: (1) The present invention constructs an interactive baseline initialization and alignment index, which runs through the end-side closed loop of gesture acquisition, stability evaluation, lightweight recognition, risk estimation and threshold revision, function mapping and parameter organization, safety constraint verification and conflict arbitration, instruction issuance and feedback learning; (2) Under the constraints of unified alignment index and time slice, the lightweight identification and accidental touch risk assessment identification pairs are jointly processed with the gesture collection and stability assessment output to generate a structured result containing identification records, confidence level, risk estimation elements and threshold revision items. This is directly connected to the function call table and parameter draft set to promote the formation of traceable final control parameters and arbitration order list for safety constraint verification and conflict arbitration. (3) During the receipt collection stage, a feedback snapshot is constructed to synchronously drive the update of the preference profile and the revision of the threshold, so that the identification threshold, parameter boundary and calling order remain consistent under the same spatiotemporal index; (4) The acquisition link completes de-jittering, slicing and time stamping, identifies the identification pairs related to the stability of the output link, the arbitration link completes threshold revision and conflict handling under the guidance of risk estimation elements, the execution link issues and archives the acknowledgment according to the arbitration order list, and the learning link feeds back preferences and thresholds to the interaction baseline, forming a continuous data mainline from identification to mapping, from verification to execution, and from execution to learning; (5) The complex calculation process of feature extraction and threshold revision is realized on the end side, reducing the timing misalignment caused by cross-module repetitive processing and multi-source threshold dispersion configuration, forming a unified parameter input and consistent control message structure for actuators such as seat position, backrest, lumbar support, ventilation and heating, improving link continuity and engineering feasibility. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a seat gesture perception-based custom quick adjustment interaction method provided in this application embodiment; Figure 2 A set of gesture control diagrams for seat adjustment provided in the embodiments of this application; Figure 3 A schematic diagram of a system hardware architecture provided for an embodiment of this application; Figure 4This is a schematic diagram of the overall process of adjusting a seat using gestures, provided as an embodiment of this application. Detailed Implementation

[0016] Example 1: Refer to Figure 1 This is a flowchart illustrating a seat gesture perception-based customized quick adjustment interaction method provided in an embodiment of the present invention. The process may include at least steps S100-S600: S100: Obtain gesture templates, adjustable item list and blind operation label layout; perform interactive baseline initialization, alignment index generation preparation, synchronization configuration, coordinate alignment, storage and publishing processing; generate interactive baseline initialization and alignment index generation output containing alignment item number, spatial sub-region number, time slice rules and module access path. S200: Execute the spatial sub-region set and time slicing rules containing the alignment index, perform gesture acquisition initiation, de-jittering, slicing, time stamping, stability calculation and feature summary construction processing, and generate gesture acquisition and stability evaluation output containing stability field and feature summary; S300: Perform lightweight identification feature extraction, identification result calculation, confidence generation, accidental touch risk assessment, and threshold revision operations, including slice metadata and time stamps, to generate lightweight identification and accidental touch risk assessment output containing risk estimation elements and threshold revision items. S400 performs lightweight identification and accidental touch risk assessment, outputting an alignment index and trigger order. It performs custom function mapping retrieval, function call item organization, parameter range generation, parameter draft conflict detection and sequence arrangement processing, and generates custom function mapping and parameter draft output containing a function call table, parameter draft set and conflict candidate set. S500 performs operations including unified spatiotemporal reference keys, security constraint verification parameter construction, limit calculation, consistency verification, conflict arbitration and decision-making, and generates security constraint verification and conflict arbitration outputs including final control parameters and arbitration sequence lists. S600: Perform integrated preference profile update and threshold revision processing, including alignment index and access path, instruction issuance encoding, instruction execution, receipt collection, and feedback learning, to generate integrated update results of instruction issuance and feedback learning, including preference profile update entries and threshold revision entries.

[0017] Step S100 includes at least steps S110-S130: S110: Obtain gesture templates, adjustable item list and blind operation label layout, perform interaction baseline initialization and alignment index generation preparation, and obtain interaction baseline data; The gesture templates, adjustable item list, and blind operation label layout are the fundamental data sources for constructing the interaction baseline. Specifically, the gesture templates include gesture definitions for system recognition (such as numerical descriptions of trajectories like forward swipes and clockwise rotations), gesture triggering conditions (such as pressure intensity and duration), and associated placement information with seat components (such as backrests and lumbar supports). The adjustable item list includes the names, physical or logical value ranges, adjustment step granularity, and call order placement information for each executable adjustment function of the seat (such as position adjustment, backrest adjustment, lumbar support adjustment, ventilation, and heating). The blind operation label layout specifically includes the physical coordinates of tactile labels (such as raised dots and recessed dots) set on the seat armrests or center console shell, their respective touch sensing ranges, and area boundary information based on the panel coordinate system. These three elements together provide a complete static configuration mapping relationship from gesture perception to function execution, providing a unified semantic foundation and spatial constraints for subsequent dynamic gesture acquisition, recognition alignment, function mapping, and safety arbitration.

[0018] Specifically, the inputs to this step are a gesture template, an adjustable item list, and a blind operation signage layout, and the output is interactive baseline data. First, the gesture template, adjustable item list, and blind operation signage layout are read from the storage medium, and their completeness is checked and format is standardized. The gesture template contains the gesture definition used for recognition, the gesture triggering conditions, and the associated placement information with the seat components. The adjustable item list contains the names, value ranges, and call order placement information for items such as position adjustment, backrest adjustment, lumbar support adjustment, and ventilation / heating. The blind operation signage layout contains the coordinates and reach range of the raised and recessed points on the outer panel. Further, reference surfaces and reference points are determined based on the seat structure diagram, and a panel coordinate system consistent with the blind operation signage layout is established. The hand movement descriptions in the gesture template are mapped to the panel coordinate system to ensure that subsequent alignment operations can be performed on the same coordinate basis. In essence, each item in the adjustable item list and the trigger pose in the gesture template are registered one by one to form an initial pairing list of gestures and items, and missing items are marked for later completion. Subsequently, boundary sampling and registration are performed on the blind operation sign layout to distinguish the reach areas of convex and concave points, and the spatial envelope of the reach areas is extracted to limit the position and range of the subsequent acquisition window. Further, unique identifiers are assigned to gesture template items, adjustable item items, and blind operation sign items according to a unified naming and numbering rule, and the source, time, and traceability information are recorded to form a traceable item registration table. Based on the above item registration table, a basic index set containing template index, item index, and sign index is constructed, and the mutual reference relationships among the three are recorded. To provide a directly usable data structure for entering the synchronization and alignment stage, without changing the semantics of the gesture template, adjustable item list, and blind operation sign layout, the item registration table and basic index set are encapsulated into interactive baseline data. The interactive baseline data explicitly includes panel coordinate system information, item unique identifier information, and item reference relationship information. The interactive baseline data, as the output of this step, is directly passed to the subsequent synchronization configuration and coordinate alignment steps, and is called as the starting basis in the gesture acquisition and stability evaluation startup of this invention, ensuring continuous consistency from the interactive baseline to the acquisition stage.

[0019] S120. Initialize the interactive baseline from the interactive baseline data, generate the synchronization configuration from the alignment index, and align the coordinates to obtain the alignment index and synchronization strategy: The input for this step is the interactive baseline data, and the output is the alignment index and synchronization strategy. Specifically, based on the panel coordinate system information and label index in the interactive baseline data, the layout of blind operation labels and the installation geometry of the sensing devices are established. The overlap between the reach area of ​​each blind operation label and the field of view of the sensing devices is calculated to determine the spatial sub-regions that can be used for acquisition. Further, according to the unique identifier information of the entries, the key action segments in the gesture template are mapped to the spatial sub-regions to form initial alignment entries. To ensure consistency in acquisition time, the acquisition start and end reference times and time stamp rules are assigned to each alignment entry based on the entry reference relationship information, forming time slice rules bound to the spatial sub-regions. To provide a unified entry point for subsequent serialized acquisition and inference, an alignment index is established. The alignment index consists of three parts: the alignment entry number, the spatial sub-region number, and the time slice rules. The unique identifier of the entry is used as the index key to ensure that the specific spatial sub-region and time slice can be directly located according to the index key during the acquisition stage. Intuitively, by combining the alignment index, the trigger order of the adjustable item list recorded in the interactive baseline data is organized, arranging multiple items that may be associated with the same gesture into a defined query order for subsequent mapping and arbitration calls. Furthermore, a synchronization strategy is established around the acquisition and processing flow. This strategy includes the working rhythm of the sensing devices, the stepping rules of the time stamps, and the caching rules of the data slices. It also specifies the initial source of the sampling density level and trigger threshold values ​​in different gesture time periods, ensuring that the acquisition timing and spatial alignment operate under unified constraints. To support subsequent gesture acquisition and stability evaluation jitter reduction and consistency between slices and time stamps, this step encapsulates the alignment index and synchronization strategy using a fixed structure, clearly defining the one-to-one correspondence between index keys, spatial sub-regions, time slices, and trigger order. The alignment index and synchronization strategy, as the output of this step, undergo correctness verification before proceeding to the next step and are prepared for storage and release. Simultaneously, the alignment index and synchronization strategy will be used to initiate gesture acquisition and stability evaluation, serving as acquisition input parameters to achieve seamless transition from coordinate alignment to acquisition control.

[0020] S130, Perform interactive baseline initialization, alignment index generation, storage and publication on the alignment index and synchronization strategy, and generate interactive baseline initialization and alignment index generation output; The input for this step is the alignment index and synchronization strategy, and the output is the interactive baseline initialization and alignment index generation output. Specifically, firstly, the alignment index and synchronization strategy are version registered and consistency checked. The correspondence between the index key, spatial sub-region, time slice, and trigger order, along with the panel coordinate system information, are written to non-volatile storage to ensure that they can still be retrieved after a system reset. Further, a delivery interface is established, encapsulating the alignment index and synchronization strategy into a data structure that can be directly parsed by the acquisition control module and the identification and evaluation module. Field names, field order, and parsing order are clearly defined to avoid ambiguity in cross-module calls. To ensure reliable delivery during runtime, this step completes three types of releases in a predetermined order during the release phase: First, it releases the availability status and version markers of the alignment index and synchronization strategy to the acquisition control module, triggering the start of gesture acquisition and stability assessment; second, it releases the query entry point for the alignment index to the recognition and evaluation module, used to obtain time slice and spatial sub-region constraint information in lightweight recognition and accidental touch risk assessment feature extraction; third, it releases the read entry point for the trigger order to the mapping arbitration module, used to retrieve and sort function candidates by the alignment index in custom function mapping and parameter draft retrieval. Understandably, to ensure traceability throughout the entire process from storage to release, this step records the write time, version marker, and call scope in the delivery interface and provides a rollback entry point to restore the alignment index and synchronization strategy to a previous version when necessary. Furthermore, it generates the outputs for interactive baseline initialization and alignment index generation storage and release, explicitly including the access path for the alignment index and synchronization strategy that the acquisition control module can directly read, the set of index keys that the recognition and evaluation module can query, and the trigger order table that the mapping arbitration module can call. This output is first acquired by the acquisition control module during system runtime. It is used to initiate gesture acquisition and stability evaluation, as well as subsequent de-jittering, slicing, and time stamping. After acquisition, the recognition and evaluation module uses the aforementioned access path to call the index key and time slice information for feature extraction. The mapping and arbitration module then uses this information to perform custom function mapping and parameter draft retrieval, thus achieving sequential connection from the interaction baseline to acquisition, recognition, and mapping. To ensure consistency with subsequent security constraint verification, conflict arbitration, and integrated instruction issuance and feedback learning, this step synchronously registers the version tag and access path to the system's policy management unit during the release process. This allows security constraint verification and conflict arbitration to reuse the same aligned index triggering order when reading the custom function mapping and parameter draft output, and enables integrated instruction issuance and feedback learning to reference the same time slice rules and spatial sub-region definitions when generating feedback snapshots, achieving end-side consistency from initialization to closed-loop learning.In summary, through the continuous operation of interactive baseline initialization and alignment index generation preparation, interactive baseline initialization and alignment index generation synchronization configuration and coordinate alignment, and interactive baseline initialization and alignment index generation storage and publication, a basic parameter set and access path are formed that can be directly referenced by modules such as acquisition control, identification and evaluation, mapping arbitration, and subsequent security verification and instruction issuance. This ensures that the interactive baseline data, alignment index and synchronization strategy remain in a unified, traceable and callable state within the system, completing the technical closed loop from initialization to multi-module collaboration.

[0021] Step S200 includes at least steps S210-S230: S210. Obtain the output of interactive baseline initialization and alignment index generation, start gesture acquisition and stability evaluation, and obtain gesture acquisition and stability evaluation input; This step takes the interactive baseline initialization and alignment index generation output as input, parses the alignment index and synchronization strategy, determines the spatial sub-region set and time slicing rules according to the alignment index, and establishes a one-to-one mapping between the spatial sub-region set and time slicing rules and the acquisition channels, forming a channel mapping table. Specifically, based on the timing constraints of the synchronization strategy, the channel state, buffer queue, and time stamp entry are initialized, a set of startup parameters is generated, and the startup order and preemption relationship of each channel are registered, so that each channel enters the ready state according to the time slicing rules. Further, combined with the triggering order in the alignment index, a sampling step and de-jitter reference value are set for each spatial sub-region, and the overlap range of adjacent spatial sub-regions is registered to maintain continuous recording of boundary behavior in subsequent slicing. Subsequently, a startup command is issued, and acquisition is started sequentially according to the channel mapping table. After receiving the first batch of data, the acquisition end sends back a startup receipt; when the receipt matches the time stamp entry, the acquisition time base is established. Based on this, the channel mapping table, spatial sub-region set, time slicing rules, and de-jitter reference values ​​are summarized to form the input for gesture acquisition and stability evaluation. This serves as the sole entry point for subsequent de-jittering, slicing, and time stamping. The version tag, access path, and interaction baseline initialization and alignment index generation output are kept consistent to support source verification for subsequent lightweight recognition and accidental touch risk assessment feature extraction. The output of this step serves as the input for gesture acquisition and stability evaluation, directly passed to the next step. Simultaneously, the reference relationship is recorded in the system registration table, enabling subsequent custom function mapping and parameter draft retrieval to perform candidate positioning and sequential arrangement based on the same alignment index.

[0022] S220. Perform gesture acquisition and stability assessment de-jittering, slicing and time stamping on the gesture acquisition and stability assessment input to obtain the gesture acquisition and stability assessment time sequence segment; This step takes gesture acquisition and stability evaluation as input, completes the jitter removal, slicing, and time stamping of the acquired data, and outputs a time sequence segment for gesture acquisition and stability evaluation. Specifically, firstly, based on the channel mapping table, channel-level jitter removal is performed on the data of each channel. Adjacent samples of the same channel are continuously read, and short-term jumps are identified with reference to the jitter removal reference value. Samples with short-term jumps are suppressed and filled with adjacent stable samples to ensure that the channel-level sample sequence meets the stability requirements of subsequent slicing. Further, the jitter-removed sample sequence is divided into preset time windows according to the time slicing rules, and spatial sub-region matching is performed within each time window. When a sample is located within the overlapping registration range of two spatial sub-regions, the sample is assigned to the priority sub-region according to the triggering order in the alignment index, and the boundary marker is recorded at the window boundary to identify the switching point in downstream processing. Subsequently, while slicing, time stamps are added to each time window and the samples within that window according to the time stamp entry point. The time stamps cover the acquisition start time, window number, and relative time interval within the window, and are merged with the channel number and spatial sub-region number to form a stereo index. The stereo index and the decluttered samples together constitute the slice metadata. After the slice metadata is formed, a consistency check is performed, removing segments with missing time stamps or inconsistent spatial sub-regions. Adjacent segments with continuous slice metadata are merged into standardized segments. Standardized segments retain boundary markers and stereo indexes, and include decluttering reference values ​​for parameter reuse in subsequent stability calculations. Finally, standardized segments are organized with window order as the outer index and channel number and spatial sub-region number as the inner index, forming the gesture acquisition and stability evaluation time-series segments, which serve as the sole input for the next step. Simultaneously, the reference relationship between the gesture acquisition and stability evaluation inputs and the gesture acquisition and stability evaluation time-series segments is recorded in the system registration table, ensuring that subsequent stability calculations and feature summary construction can trace the corresponding time windows and spatial sub-regions by index.

[0023] S230. Perform gesture acquisition and stability assessment stability calculation and feature summary construction on the gesture acquisition and stability assessment time segment, and generate gesture acquisition and stability assessment output; This step takes gesture acquisition and stability assessment time-series segments as input, completes stability calculation and feature summary construction, and outputs gesture acquisition and stability assessment results. Specifically, firstly, based on the slice metadata, each gesture acquisition and stability assessment time-series segment is scanned at the segment level. The time-series index within the segment is reconstructed using window number and time stamp, confirming continuous records within the same channel and the same spatial sub-region. Combining de-jitter reference values ​​and boundary markers, the continuity, dwell consistency, and switching integrity of samples within the segment are judged item by item, and the intermediate judgment results are bound to the time-series index to ensure that the original segment location can be reused during aggregation. Further, the samples within the segment are merged according to the time stamp order to generate segment-level stability labels, and the start and end positions and switching intervals of the segments with boundary markers are extracted to form a switching summary. Subsequently, multi-channel segments under the same window number are merged according to spatial sub-region numbers to form a window-level set. Within the window-level set, boundary pairs introduced by overlapping registrations are supplemented, and the switching summaries of each boundary pair are archived. The archived content is cross-referenced with the spatial sub-region number and channel number, enabling unified retrieval of cross-channel behaviors within the same window. Based on the window-level set, a sequence-level structure covering multiple windows is generated. Segments with the same spatial sub-region number and continuous segment-level stability labels in adjacent windows are serialized. The serialization process retains the switching summary and boundary identifier of each window to identify the trajectory's dwell and switching segments during feature summary construction. After serialization, the stability summary of the window group is calculated according to the sequence-level structure to form a stability field, which is associated with the segment-level stability labels by reference, providing multi-granularity reading entry points. Subsequently, based on the information required by the identification and arbitration sides, key information characterizing trajectory and attitude changes is extracted from the sequence-level structure to construct a feature summary. This feature summary includes at least the index of the window-level set, segment-level stability annotations, references to switching summaries and boundary markers, and is consistent with the time stamp and spatial sub-region numbering. The aforementioned stability fields and feature summaries are encapsulated in a unified structure as the output for gesture acquisition and stability evaluation. The output of this step, after its formation, serves as input for lightweight identification and accidental touch risk assessment feature extraction, establishing the input basis for identification pairs and confidence levels. Simultaneously, the stability fields referenced in this step are used by safety constraint verification and conflict arbitration parameter construction to support limit calculation and consistency verification. Furthermore, it maintains consistency with the interactive baseline initialization and alignment index generation outputs in terms of index structure, time stamp, and spatial sub-region numbering, enabling custom function mapping and parameter draft retrieval to perform candidate positioning and sequential arrangement based on the same alignment index. In summary, the aforementioned three steps form a continuous link from startup parameters to standardized fragments and then to stability fields and feature summaries, enabling the data collected to be delivered in a structured, fragmented, and referable form, providing a verifiable spatiotemporal index and stability basis for subsequent identification, mapping, verification, and unified reference for instruction issuance.

[0024] Step S300 includes at least steps S310-S330: S310. Obtain the gesture acquisition and stability assessment output, perform lightweight recognition and accidental touch risk assessment feature extraction, and obtain the lightweight recognition and accidental touch risk assessment input. This step takes the gesture acquisition and stability assessment output as input, and parses the slice metadata, time stamps, window sequence numbers, channel numbers, spatial sub-region numbers, and stability fields to output lightweight recognition and accidental touch risk assessment inputs. Specifically, firstly, under the constraint of alignment index, the segment order within each window sequence number is reconstructed based on the time stamps. Then, using the channel number and spatial sub-region number as search conditions, the start and end positions and boundary markers of the segments are located from the slice metadata, forming a directly traversable set of segment references. Further, for each set of segment references, the stability field is read, and continuous records and switching records are sorted separately. Three types of elements related to hand trajectory are extracted: displacement change description, dwell state description, and switching transition description, maintaining a one-to-one correspondence with the time stamps so that subsequent calculations can be processed under a unified time base. Subsequently, the gesture templates and trigger sequences are retrieved, and the fragment reference set is arranged according to the trigger sequence. An index correspondence between fragments and template entries is established, completing feature aggregation. The displacement change description, dwell state description, and transition description, along with the template entry number, time stamp, and spatial sub-region number, are encapsulated together as feature units for lightweight recognition and accidental touch risk assessment input. These feature units are structured with the window number as the outer index and the channel number as the inner index. To ensure traceability with previous stages, this step retains the fragment references and stability field references attached to the feature units as is, and registers the index key of the source interactive baseline initialization and alignment index generation output in the feature unit header, so that subsequent recognition result calculations and risk estimations can be directly traced back to specific time windows and spatial sub-regions. After the above processing, the resulting lightweight recognition and accidental touch risk assessment input includes a set of feature units screened by the alignment index, a correspondence established with gesture template entries, and reference fields that can be directly called downstream. These serve as the sole input for the next step. At the same time, the system registration table records the reference relationship between the output of this step and subsequent steps to ensure that the custom function mapping and parameter draft retrieval can be candidate-located and arranged in order according to the same alignment index.

[0025] S320. Calculate the recognition results and generate confidence scores from the inputs of lightweight recognition and accidental touch risk assessment to obtain lightweight recognition and accidental touch risk assessment recognition pairs. This step takes lightweight recognition and accidental touch risk assessment as inputs, calculates recognition results and generates confidence scores for the feature unit set, and outputs lightweight recognition and accidental touch risk assessment recognition pairs. Specifically, firstly, based on the correspondence between fragments and template entries in the feature units, the process is traversed in order of channel number according to the time sequence of the window number. Within the same time window, feature units with different spatial sub-region numbers are compared in parallel, maintaining the continuity of time stamps when crossing windows to ensure that the time order of the recognition pairs is consistent with the acquisition time base. Understandably, during the comparison process, the dwell state description and trigger order are used for initial screening to select candidates from the feature unit set that match the dwell requirements of the gesture template entries. Then, displacement change descriptions and switching transition descriptions are introduced into the refinement process. Candidates are segmented and verified based on the start and end positions of the fragments and boundary markers to eliminate fragments with discontinuous cross-boundary conditions and abnormal time intervals, resulting in a set of matching fragments that satisfy the constraints of the template entries. Furthermore, for each matching segment, a confidence element is generated by referencing the corresponding stability field. This confidence element maintains a one-to-one correspondence with the time stamp, spatial sub-region number, and template entry number, and is summarized using the window sequence number as the outer index to form a window-level confidence list. Subsequently, the verified matching segments are combined with their corresponding template entries to form recognition entries. Recognition entries consist of the gesture name, segment location information, and time stamp. When multiple recognition entries exist within the same window, they are arranged in order of triggering, and in cross-window cases, they are linked by time stamps to establish a continuous chain of recognition entries. After the recognition entries are generated, the window-level confidence list is invoked to calculate the confidence score for each recognition entry, and these scores are merged with the recognition entries to form recognition records. All recognition records are grouped and aggregated according to the window sequence number, channel number, and spatial sub-region number, forming lightweight recognition and accidental touch risk assessment recognition pairs. To facilitate downstream risk estimation and threshold revision, this step retains both the fragment reference of the source feature unit and the stability field reference in the output identification pair, allowing risk estimation to directly retrieve the original slice metadata and stability field. The lightweight identification and accidental touch risk assessment identification pair generated after the above processing serves as the direct input for the next step, and the identification record and the reference relationship of the corresponding window-level confidence list are registered in the system registration table for synchronous use in subsequent safety constraint verification and conflict arbitration parameter construction.

[0026] S330. The lightweight recognition and accidental touch risk assessment are combined with the gesture acquisition and stability assessment output to perform lightweight recognition, accidental touch risk assessment risk estimation and threshold revision, and generate lightweight recognition and accidental touch risk assessment output. This step takes the lightweight recognition and accidental touch risk assessment pair and the gesture acquisition and stability assessment output as input, and performs risk estimation and threshold revision around the recognition record and stability field, outputting the lightweight recognition and accidental touch risk assessment output. Specifically, firstly, the corresponding stability field reference and slice metadata are located according to the window sequence number, channel number, and spatial sub-region number in the recognition record, restoring the temporal position and segment continuity state of each recognition record at the time of acquisition, and reconstructing the dwell and switching trajectory of the recognition record by combining boundary markers. Further, under the constraint of the dwell and switching trajectory, the consistency of the time stamp sequence of each recognition record is checked, segments that do not match the alignment index trigger order are identified and marked, and the jitter reference value is retrieved to confirm the short-term jump situation within the segment. Segments with short-term jumps and incomplete segments that cross boundaries are registered as high-risk candidates. Subsequently, based on the segment-level annotations of the confidence and stability fields of the identified records, risk estimation elements are calculated for each identified record. These risk estimation elements are consistent with the window sequence number and spatial sub-region number. For high-risk candidates, boundary markers and segment start and end positions are added to form a traceable high-risk description. After completing the risk estimation, a sorting list is established for multiple identified records within the same time window, based on the triggering order of the alignment index. The trigger threshold parameters in the sorting list are adjusted according to the risk estimation elements. The threshold parameters corresponding to identified records with low stability and frequent short-term fluctuations are increased, while the threshold parameters corresponding to identified records with high stability and good continuity are decreased, forming threshold revision entries. When identified records are continuous across windows, the threshold revision entries of adjacent windows are smoothly merged according to time stamps to maintain the continuity of threshold parameters for the same trajectory in adjacent windows. To support the parallel invocation of subsequent custom function mapping, parameter draft retrieval, security constraint verification, and conflict arbitration, this step encapsulates the identification records, confidence levels, risk estimation elements, and threshold revision items into a lightweight identification and accidental touch risk assessment output. The output header registers the source's interactive baseline initialization and alignment index generation output index key, as well as the window sequence range of the gesture acquisition and stability assessment output, clearly defining the downstream searchable access path. After formation, the lightweight identification and accidental touch risk assessment output is used by the custom function mapping and parameter draft retrieval to directly read the identification records and confidence levels for candidate retrieval and function call item organization. Simultaneously, the security constraint verification and conflict arbitration directly read the risk estimation elements and threshold revision items for limit calculation, consistency verification, arbitration, and decision-making. Furthermore, the above outputs are referenced in the registration table and in the integrated instruction set generation and feedback snapshot establishment of subsequent instruction issuance and feedback learning, enabling feedback learning to trace back to the threshold revision items and risk estimation elements of this step, completing the connection from identification to execution and learning.In summary, through the continuous operation of feature extraction, recognition result calculation and confidence generation, as well as risk estimation and threshold revision, a set of recognition pairs, confidence, risk estimation elements and threshold revision entries are formed that are consistent with the alignment index and the collection time base and can be synchronously called by the mapping and verification stages. This supports the accurate connection and closed-loop execution of subsequent function mapping, security verification and instruction issuance.

[0027] Step S400 includes at least steps S410-S430: S410: Obtain the output of lightweight recognition and accidental touch risk assessment, perform custom function mapping and parameter draft retrieval, and obtain custom function mapping and parameter draft candidates; This step takes the output of lightweight recognition and accidental touch risk assessment as input, calls the interaction baseline initialization and alignment index to generate the alignment index and trigger order in the output, and reads the gesture templates and adjustable item list registered in the interaction baseline data according to the access path, outputting a custom function mapping and parameter draft candidates. Specifically, firstly, according to the window sequence number, channel number, and spatial sub-region number in the recognition record, the corresponding time slice and spatial sub-region are located by referring to the alignment index, so that each recognition record has a retrieval entry consistent with the interaction baseline; then, according to the gesture name and template item number in the recognition record, the function set corresponding to the gesture is retrieved from the gesture template. The function set is capped by the adjustable item list, and the candidate functions within the same time slice are arranged sequentially according to the trigger order. Furthermore, to ensure linkage with risk estimation elements and threshold revision items, this step registers the confidence level and risk estimation elements attached to each recognition record, marks high-risk items as constraint retrieval, and constraint retrieval only returns functions marked as low sensitivity in the adjustable item list, while low-risk items perform a full retrieval and return all candidate functions. Understandably, after completing the function set retrieval, for each candidate function, the name, calling channel, value boundary, and step granularity from the adjustable item list are read. Preliminary annotations are generated by combining the timestamps and spatial sub-region numbers of the identification records. These preliminary annotations, along with the source index and stability field references of the identification records, are then encapsulated to form the original unit of the custom function mapping and parameter draft candidate. To support subsequent processing, this step groups the original units using the window sequence number as the outer index and the channel number and spatial sub-region number as the inner index, along with trigger order and time slice identifiers, forming a directly readable candidate set. Once formed, the candidate set maintains a one-to-one correspondence with the identification records, serving as the sole input for the next step. Simultaneously, the references between the candidate set and the identification records, alignment indexes, and gesture templates are recorded in the system registration table, ensuring that the index key of the interaction baseline and the item number of the adjustable item list can be traced back during subsequent function call item processing and parameter range generation.

[0028] S420. From the custom function mapping and parameter draft candidates, perform custom function mapping, parameter draft function call items organization, and parameter range generation to obtain custom function mapping and parameter draft entries; This step takes custom function mappings and parameter draft candidates as input, and outputs custom function mappings and parameter draft entries based on the adjustable item list, threshold revision items, and risk estimation elements. Specifically, firstly, under the constraints of window sequence number and trigger order, the candidate set within the same time slice is deduplicated and merged. Duplicate functions with the same name are merged into a single function call item according to the call channel, and the reference source of the function call item is summarized and registered according to the identification record for consistency verification in the subsequent security constraint stage. Furthermore, for each function call item, the value boundaries and step granularity are read from the adjustable item list, and combined with the threshold adjustment information given for the corresponding identification record in the threshold revision entry, the revision result of the parameter boundary is formed. When the risk estimation factor indicates that the identification record has short-term jumps and incomplete cross-boundary conditions, this step introduces a gradual change constraint when revising the parameter boundary, sets a maximum cumulative step value for parameter changes within a unit time slice, and registers it as a parameter change restriction field. When the risk estimation factor indicates that the identification record has high stability, this step retains the original step granularity in the adjustable item list when revising the parameter boundary, and synchronously records the relaxation information in the threshold revision entry to the call priority label. Understandably, after revising the parameter boundaries, this step generates a parameter draft for each function call item. The parameter draft includes the starting point, ending point, step granularity, change limit field, and the referenced threshold revision source, and is consistent with the function call item's calling channel, time slice, and spatial sub-region number. For scenarios with multiple function call items within the same time slice, this step organizes them sequentially based on the triggering order and whether the calling channel is shared, providing a calling order label. This label indicates the processing order during subsequent arbitration. After the above function call items and parameter drafts are organized, they are merged and encapsulated into custom function mapping and parameter draft entries. These custom function mapping and parameter draft entries use the window sequence number, channel number, and spatial sub-region number as index keys, recording the reference path of the identification record and the reference path of the stability field in the entry header to ensure that the next step can perform conflict detection and sequential arrangement accordingly. Simultaneously, the custom function mapping and parameter draft entries establish a reference relationship with the custom function mapping and parameter draft candidates in the registration table, serving as the sole input for the next step.

[0029] S430. Perform custom function mapping, parameter draft conflict detection, and sequential arrangement on the custom function mapping and parameter draft entries to generate custom function mapping and parameter draft output. This step takes custom function maps and parameter draft entries as input, combines alignment indexes with the list of adjustable items and trigger order to complete the conflict detection and sequencing of custom function maps and parameter drafts, and outputs the custom function maps and parameter drafts. Specifically, it first traverses the entries along the window sequence dimension. For multiple function calls within the same time slice, a call resource view is established. The call resource view uses call channels, executor occupancy, and time slices as basic units, and loads the mutual exclusion and interlocking relationships registered in the list of adjustable items to form a conflict rule set. After the conflict rule set is established, each function call in the entries is compared one by one. If there is a case of overlapping call channels and mutual exclusion of executors, a mutual exclusion conflict bar is generated. If there is a case of incompatibility between the starting point or ending point of the value and the interlocking relationship, an interlocking conflict bar is generated, and the corresponding parameter draft reference is added to the conflict bar for backtracking. Furthermore, regarding the identified conflicts, this step groups the conflict entries according to the order of invocation and the order of triggering: For mutually exclusive conflicts within the same time slice, the function call items with the earlier triggering order and higher stability fields are retained first, and the time slices of other call items are postponed to the next available time slice, with the reason for postponement and the original time slice number recorded in the postponement annotation; For interlocking conflicts, the starting point or ending point of the value is adjusted within the range allowed by the parameter draft according to the interlock release conditions in the adjustable item list, so that it meets the interlock release conditions. If it cannot be adjusted, the function call item is marked as an item to be arbitrated, and its parameter draft is retained for the final decision in the subsequent security constraint verification and conflict arbitration process. Understandably, after conflict resolution, this step reorders the extended and adjusted function call items, following the order of time stamps and the parallel capability of the call channels, while maintaining continuous window numbers. To ensure continuity with subsequent steps, this step also generates a function call table, indexed by time slices and by call channels. Cells record function names, parameter drafts, sequence labels, and extension labels. Correspondingly, a parameter draft set is generated, summarizing the starting point, ending point, step granularity, and change restriction fields of all function call items, pointing to their corresponding function call table locations. Furthermore, this step compiles conflict candidate sets of items that cannot be resolved in this stage. These sets record references to mutually exclusive or interlocked conflict clauses, the index keys of the involved function call items, and corresponding parameter draft references, for direct reading by subsequent security constraint checks and conflict arbitration.The aforementioned function call table, parameter draft set, and conflict candidate set, after encapsulation, together constitute the custom function mapping and parameter draft output. Its header registers the alignment index key, window sequence range, and identification record reference path. References are established in the system registration table and the construction of security constraint verification and conflict arbitration parameters. This allows the next stage to directly call the function call table for limit calculation and consistency verification under the constraints of the same time slice and spatial sub-region numbering, and to perform arbitration and decision-making on the conflict candidate set. In summary, through the continuous operation of custom function mapping and parameter draft retrieval, custom function mapping and parameter draft function call item organization and parameter range generation, and custom function mapping and parameter draft conflict detection and sequential arrangement, a function call table, parameter draft set, and conflict candidate set are formed that are consistent with the alignment index, traceable to the identification records, and directly invoked by security verification and command issuance. This achieves structured delivery from identification output to function mapping and parameter organization, providing a coherent data thread and verifiable reference relationships for subsequent limit calculation, arbitration, and execution.

[0030] Step 500 includes at least steps S510-S530: S510: Obtain the output of custom function mapping and parameter draft, as well as the output of gesture acquisition and stability evaluation; perform safety constraint verification and conflict arbitration parameter construction to obtain safety constraint verification and conflict arbitration input. This step uses the custom function mapping and parameter draft output, along with the gesture acquisition and stability evaluation output, as inputs to construct the safety constraint verification and conflict arbitration inputs. Specifically, it first reads the function call table, parameter draft set, and conflict candidate set from the custom function mapping and parameter draft output, and establishes a traversal sequence according to the time slices and call channels contained in the function call table. Then, it generates the alignment index in the output based on the interaction baseline initialization and alignment index, mapping the time slices and spatial sub-region numbers of the traversal sequence to a unified spatiotemporal reference key, forming a retrieval entry consistent with the previous steps. Further, for each function call item in the traversal sequence, it extracts the value start point, value end point, step granularity, and change limit fields from the parameter draft set, and merges them with the sequential labeling and continuation labeling in the function call table to obtain the item to be verified. For function call items marked as items to be arbitrated, it adds mutual exclusion conflict bars or interlocking conflict bar references from the conflict candidate set for differentiated processing in subsequent steps. Understandably, to enable constraint calculation and consistency verification to revise boundaries based on the perceived state of the field, this step simultaneously acquires the stability field from the gesture acquisition and stability assessment outputs, and establishes a one-to-one correspondence between it and the items to be verified using a unified spatiotemporal reference key. Simultaneously, to ensure consistency with the adaptive gating on the recognition side, this step reserves an access point for the outputs of lightweight recognition and accidental touch risk assessment during the parameter construction phase, allowing direct reading of risk estimation elements and threshold revision items during subsequent arbitration and decision-making. After completing the above aggregation, the items to be verified are grouped according to time slices, forming safety constraint calculation units oriented towards a single time slice. Each safety constraint calculation unit includes function call items, parameter draft references, conflict references, stability field references, and unified spatiotemporal reference keys, and registers the source index key and window sequence range at the unit header to ensure that subsequent steps can perform traceable verification by comparing with previous outputs. After the above processing, the input for safety constraint verification and conflict arbitration is generated, and the reference relationship between this input and the output of custom function mapping and parameter draft, gesture acquisition and stability assessment is registered in the policy management unit. This allows the next step to directly read the required fields by pressing the unified spatiotemporal reference key, maintaining a smooth connection from mapping organization to constraint construction.

[0031] S520. Perform security constraint verification, conflict arbitration limit calculation, and consistency verification from the input of security constraint verification and conflict arbitration to obtain the security constraint verification and conflict arbitration revision items. This step takes the safety constraint verification and conflict arbitration inputs as inputs, and completes the limit calculation and consistency verification for each safety constraint calculation unit one by one, outputting the safety constraint verification and conflict arbitration revision entries. Specifically, firstly, the set of function call items within the current time slice is located based on the unified spatiotemporal reference key. A resource view is established for the function call items of shared call channels or shared executors, and the travel boundaries, speed limits, timeouts, mutual exclusion combinations, and interlocking conditions recorded in the adjustable item list are loaded to form the basic constraint set. Then, the value start point, value end point, step granularity, and change limit fields in the entries to be verified are compared with the basic constraint set item by item. For value end points that exceed the travel boundary, a boundary revision record is generated; for step granularity that exceeds the speed limit, a step revision record is generated; for calls with cumulative steps exceeding the change limit field within the same time slice, a cumulative revision record is generated. The above revision records are then updated synchronously with the sequential or sequential annotations of the function call items. Furthermore, to reflect the impact of on-site stability on the limits, this step references the stability field, marking function calls with lower stability as the tightening group and function calls with higher stability as the relaxing group. The tightening group adds a reserved buffer within the travel boundary and reduces the available range of step granularity, while the relaxing group retains the original step granularity and removes unnecessary cumulative restrictions without exceeding the basic constraint set. All these adjustments generate corresponding limit revision records and are bound to a unified spatiotemporal reference key. Understandably, after completing the limit revision, a consistency check is performed, verifying each resource view within the same time slice. If function calls in the shared call channel still have overlapping execution cycles, a follow-up annotation is added to subsequent calls based on the sequence annotation, and a follow-up revision record is generated. If interlocking conditions are not met, the starting or ending point of the value is fine-tuned within the range allowed by the parameter draft, and an interlocking revision record is generated. In cases where these issues cannot be resolved through revision in this step, a consistency notification record is generated and the system remains in an arbitration pending state. Finally, the boundary revision records, step revision records, cumulative revision records, sequential revision records, interlocked revision records, and consistency notification records are merged and packaged into a security constraint verification and conflict arbitration revision entry. The entry header retains function call item references, parameter draft references, stability field references, and unified spatiotemporal reference keys as the sole input for the next step of arbitration and decision-making. At the same time, the generation time and source range of the revision entry are registered in the policy management unit to ensure a coherent call from limit calculation to arbitration decision-making.

[0032] S530. Combine the safety constraint verification and conflict arbitration revision items with lightweight identification and accidental trigger risk assessment output to perform safety constraint verification, conflict arbitration and decision-making, and generate safety constraint verification and conflict arbitration output. This step takes the safety constraint verification and conflict arbitration revision items and the output of lightweight identification and accidental trigger risk assessment as input, completes arbitration and decision-making, and outputs the safety constraint verification and conflict arbitration output. Specifically, firstly, the revision items are merged according to the unified spatiotemporal reference key, and the risk estimation elements and threshold revision items of the lightweight identification and accidental trigger risk assessment output are read in the merged unit to establish the correspondence between limit revision and risk gating. After the correspondence is established, for function calls that have resource competition within the same time slice, they are evaluated according to the combination order of sequential labeling, risk estimation elements and stability fields: when the risk estimation element is high and the revision item indicates cumulative limit or unresolved interlock, the function call item is labeled as rejected; when the risk estimation element is medium and the revision item only contains the continuation revision record, the function call item is retained as allowed and the continuation label is retained; when the risk estimation element is low and the revision item only involves boundary revision record or step revision record, the function call item is labeled as allowed and the revised parameters are adopted. Furthermore, for situations where multiple allow tags exist within a merged unit and share a call channel, this step sorts the allow tags according to the threshold adjustment information provided by the threshold revision entries, from strictest to broadest. Function call items ranked higher are retained in the current time slice, while those ranked lower are carried over to subsequent time slices with the extension label. For function call items marked with a rejection label, a rejection explanation is generated, recording the triggered risk estimation elements and the reference path of relevant revision records for retrospective analysis in subsequent feedback learning. Understandably, after arbitration, this step assembles the deployable parameter set, merging the function call items marked with allow tags with their corresponding revised value start point, value end point, step granularity, and change limit fields to generate the final control parameters, arranged sequentially by time slice. Simultaneously, an arbitration order list is generated, showing the correspondence between allow tags, rejection tags, and extension labels, and linking the function call table location with the parameter draft set location to ensure a one-to-one correspondence between subsequent coding and execution. To maintain consistency in references within the system, this step registers the source index key, window sequence range, unified spatiotemporal reference key set, and access path in the header of the security constraint verification and conflict arbitration output. References are also established in the policy management unit and the integrated instruction issuance and feedback learning, enabling subsequent direct access to the final control parameters and arbitration sequence list for coding and issuance. After the receipt arrives, the allow label, deny label, and continuation label are mapped back to the previous identification record and function call table, realizing the connection from mapping organization, limit calculation to arbitration decision and execution chain.In summary, through the continuous operation of parameter construction, limit calculation and consistency verification, as well as arbitration and decision-making, this step, under the constraints of unified spatiotemporal references and alignment indexes, forms a safety constraint verification and conflict arbitration output that can be directly invoked by integrated instruction issuance and feedback learning. The output includes final control parameters, allow labels, deny labels, and arbitration sequence lists, and maintains a traceable association with the function call table, parameter draft set, stability field, risk estimation elements, and threshold revision entries, providing a clear input basis and stable reference path for subsequent coding, issuance, and receipt collection.

[0033] Step S600 includes at least steps S610-S630: S610: Obtain the output of security constraint verification and conflict arbitration, perform integrated coding of instruction issuance and feedback learning, and obtain an integrated instruction set of instruction issuance and feedback learning. This step takes the safety constraint verification and conflict arbitration output as input, parses the final control parameters, arbitration sequence list, allow labels, deny labels, and continuation labels, and generates the alignment index and access path registered in the output according to the interaction baseline initialization and alignment index. It establishes a correspondence between time slices and spatial sub-region numbers for each final control parameter, generating an instruction orchestration skeleton consistent with the unified spatiotemporal reference key. Specifically, firstly, under the order constraints of the arbitration sequence list, allow labels within the same time slice are merged at the channel level, establishing a mapping table between call channels and executors. Each function call item is mirrored with its parameter draft's starting point, ending point, step granularity, and change restriction fields, and then merged with the corresponding unified spatiotemporal reference key, time slice number, and spatial sub-region number into an instruction unit. For function calls marked as "deferred," the target time slice given by the deferred label is sequentially inserted into the instruction orchestration skeleton, and the deferred source identifier and original time slice sequence number are appended to the instruction unit. For function calls marked as "rejected," a placeholder instruction unit is generated, retaining only the function call item identifier and the reference path of the rejection description, serving as a necessary entry point for subsequent receipt verification and learning-side comparison. Subsequently, based on the execution-side message format, without changing the existing field semantics, necessary security identifiers, version identifiers, and sequence numbers are added to each instruction unit, and a one-to-one correspondence between instruction units and parameter images is established, forming a list of instruction sequences that can be directly issued. At the same time, receipt verification rules are generated, binding the allowed status code range corresponding to the label, the allowable deviation of position and attitude, the allowable deviation of temperature and time, and the response thresholds of timeout and interlock with a unified spatiotemporal reference key for verification and judgment in the subsequent receipt collection stage. Understandably, to maintain data consistency with previous steps, this step registers the source alignment index key, window number range, and access path at the header of the instruction sequence list, and retains the reference paths of the rejection label and continuation annotation as is. After the above encoding and assembly, an integrated instruction set for instruction issuance and feedback learning is output. This instruction set includes an instruction sequence list, parameter mirroring, receipt verification rules, and reference relationship descriptions. It also registers bidirectional references between the policy management unit and the security constraint verification and conflict arbitration outputs, enabling the next step to directly execute and collect receipts based on the unified spatiotemporal reference key.

[0034] S620: From the integrated instruction set for instruction issuance and feedback learning, perform instruction issuance, integrated instruction execution for feedback learning, and receipt collection to obtain an integrated feedback snapshot of instruction issuance and feedback learning. This step takes the integrated instruction set for instruction issuance and feedback learning as input. Following the time slice order and call channel arrangement of the instruction sequence list, it issues instructions one by one to the seat motor, backrest motor, lumbar support actuator, and ventilation / heating actuator, collecting acknowledgment information from the execution chain and outputting an integrated feedback snapshot of instruction issuance and feedback learning. Specifically, firstly, a channel group is opened within the current time slice based on the unified spatiotemporal reference key. Instruction units corresponding to the allowed tags are sent to the corresponding call channels according to the order in the arbitration sequence list. For instruction units with delayed tags, pre-fetching entries are registered before the target time slice is triggered. Placeholder instruction units with rejected tags are not issued, but their reference paths are retained for statistical purposes. After each allowed tag is issued, acknowledgment listening is immediately initiated. Listening entries are generated according to acknowledgment verification rules, including fields such as position feedback, angle feedback, temperature feedback, execution time feedback, and interlock status feedback, and are paired according to the instruction unit's sequence number and the unified spatiotemporal reference key. When multiple channels are concurrent within the same time slice, this step maintains a receipt aggregation buffer within the channel group. Receipts with asynchronous arrival times are queued according to their time stamps and aggregation is completed before the window closes. Further, a consistency check is performed on each paired receipt according to the receipt verification rules: if the actual value of the position or angle is within the allowable deviation range of the value in the parameter mirror, it is registered as an arrival record; if it has not arrived but the execution time matches the change limit field, it is registered as an in-transit record and monitoring continues in adjacent time slices; if an interlock or timeout is triggered, it is registered as an exception record with a corresponding interlock or timeout description. For the instruction unit corresponding to the delay label, the issuance and receipt are completed according to the above process after arriving at the target time slice; for the function call item corresponding to the rejection label, a rejection mirror is generated in the feedback snapshot, recording the rejection description reference path and the index of the relevant sensing item within that time slice. To ensure direct reuse on the learning and mapping sides in subsequent steps, this step aggregates all arrival records, in-transit records, abnormal records, and rejection images into time-slice snapshots, and then aggregates them into spatiotemporal snapshots using a unified spatiotemporal reference key. Each snapshot node is then linked with the source alignment index key, the identification record reference path, and the stability field reference from the gesture acquisition and stability evaluation output. Through the above acquisition, pairing, verification, and aggregation, a feedback snapshot is formed, containing instruction unit acknowledgments, execution link status, abnormal descriptions, and source references. The correlation between the feedback snapshot and the instruction set is registered in the policy management unit, serving as the sole input for the next step: preference profile update and threshold revision.

[0035] S630. Perform instruction issuance, feedback learning integrated preference profile update, and threshold revision on the integrated instruction issuance and feedback learning snapshot, and generate integrated instruction issuance and feedback learning update results. This step takes the integrated instruction issuance and feedback learning snapshot as input, and focuses on the user's adjustment landing point and stable performance of the execution chain in each time slice to complete the preference profile update and threshold revision, outputting the integrated instruction issuance and feedback learning update results. Specifically, firstly, using the spatiotemporal snapshot as the outer organizational unit, the parameter mirrors of arrival records and in-transit records and their corresponding receipt values ​​are read. Based on the time stamp, the target landing point and actual landing point of each function call item in this round of execution are restored, and compared with the risk estimation elements and threshold revision items in the output of lightweight recognition and accidental touch risk assessment. Function call items with stable actual landing points are registered as preference candidates, and function call items with abnormal records or frequent delays are registered as boundary candidates. Subsequently, within the preference candidate set, they are merged according to the same gesture name and the same spatial sub-region number, and the common value starting point, common value ending point and common step granularity in multiple round windows are counted to form preference segments; within the boundary candidate set, combined with the abnormal description and stability field reference, segments with interlocking, timeout or large arrival deviation are extracted to form boundary segments, and their corresponding recognition record reference paths and function call table positions are retained. Furthermore, based on preference fragments, the default values ​​and step granularity in the adjustable item list are fine-tuned to generate preference profile update entries, and the time slice range and unified spatiotemporal reference key set of the source are recorded in the entries. Based on boundary fragments, the threshold revision entries in the output of lightweight recognition and accidental touch risk assessment are revised again. While maintaining the original adaptive gating direction, the threshold is tightened or relaxed in combination with the consistency results of the receipt, forming threshold revision entries. To ensure a closed loop between the preceding and following steps, this step feeds back the preference profile update entries to the preference profile entry that can be read by the custom function mapping and parameter draft retrieval, so that the subsequent function call item organization and parameter range generation can directly reference the latest preferences. At the same time, the threshold revision entries are registered to the access entry of security constraint verification and conflict arbitration parameter construction, so that the subsequent limit calculation and consistency verification can read the new threshold baseline under the same alignment index. For preference fragments that are stable across rounds, this step triggers template micro-revision in the policy management unit, synchronizes the relevant entries to the updatable fields of the interactive baseline initialization and alignment index generation output, and generates version identifiers and rollback identifiers to ensure that it can be restored by version at any time. Finally, the updated preference profile entries, threshold revision entries, template micro-revision records, and corresponding source citation paths are integrated into a unified update result of instruction issuance and feedback learning. The sequence number range, alignment index key, and unified spatiotemporal citation key set are registered in the header as a unified input baseline for the next round of gesture collection and stability assessment, lightweight recognition and accidental touch risk assessment, custom function mapping and parameter drafting, as well as security constraint verification and conflict arbitration.In summary, through the continuous operation of encoding, distribution and receipt collection, as well as preference profile updates and threshold revisions, this step, under the same constraints as the alignment index, forms an updated result that can be mapped, verified, and synchronously referenced in the execution chain, achieving a closed-loop connection from final control parameters to feedback learning and then to baseline revision.

[0036] Figure 2 A set of gesture control diagrams for seat adjustment provided in this application embodiment is shown in the figure. The system presets a variety of intuitive gesture templates to realize blind operation control of various seat adjustment functions. Specifically, it includes but is not limited to: gesture 1 (slide forward) is mapped to the function of adjusting the entire seat forward; gesture 2 (slide backward) is mapped to the function of adjusting the entire seat backward; gesture 3 (slide upward) is mapped to the function of adjusting the seat height upward; gesture 4 (slide downward) is mapped to the function of adjusting the seat height downward; gesture 5 (touch and rotate clockwise) is mapped to the function of adjusting the seat backrest forward; gesture 6 (touch and rotate counterclockwise) is mapped to the function of adjusting the seat backrest backward. These predefined gesture templates, together with adjustable items (seat position, height, backrest angle, etc.), constitute the basis for interaction baseline initialization (as described in S110). The gesture operation shown in the figure intuitively demonstrates the specific application example of custom function mapping (as described in S400), that is, each specific gesture trajectory is accurately mapped to a specific seat control command. This diagram clearly illustrates how the present invention recognizes user gestures within the layout of blind operation markings, ultimately achieving safe, accurate, and personalized seat control, effectively improving the ease of use and reliability of blind operation.

[0037] Figure 3A schematic diagram of a system hardware architecture provided for an embodiment of this application is shown in the figure. This architecture specifically illustrates the physical basis for implementing the above-described method flow (S100-S600). Its core lies in an integrated control module (module 2) that integrates user gesture recognition and control functions. The user performs operations by executing specific gestures on the gesture sensing module (module 1, which is integrated in module 2). This module is responsible for sensing the raw signals of the user's gestures. The main control unit MCU (module 3) inside the integrated control module acts as the processing core and executes the key process described in this patent: it collects the sensing signals from module 2, corresponding to the gesture acquisition and stability evaluation (S200) and lightweight recognition and accidental touch risk evaluation (S300) processes, and completes the gesture recognition and safety analysis; then, based on the results of the custom function mapping (S400) and safety constraint verification and conflict arbitration (S500), it generates control commands. Subsequently, the MCU (module 3) sends this command to the drive module (module 4), and finally the seat motor (module 5) executes the specific adjustment action. This process corresponds to the integrated processing of command issuance and feedback learning (S600). This architecture clarifies the data flow and control flow from gesture perception to instruction execution, and clearly reveals the collaborative relationship between the software algorithm and the hardware carrier of this invention, that is, each step of the method is ultimately implemented by the integrated hardware system.

[0038] Figure 4 This diagram illustrates the overall process of seat gesture adjustment, as provided in this application embodiment. It shows the complete interactive loop of a user controlling the seat via gestures from a high-level logic perspective. As shown, the process begins with the user's adjustment intention, activating the gesture recognition system described in this invention via a sensor (corresponding to S100 interaction baseline initialization and S200 gesture acquisition and stability assessment). The system continuously detects and recognizes gestures (corresponding to S300 lightweight recognition and accidental touch risk assessment). After determining a valid gesture type, the seat performs the corresponding precise adjustment (this "adjustment execution" step specifically covers S400 custom function mapping, S500 safety constraint verification and conflict arbitration, and S610-S620 instruction issuance and execution processes). The system further determines whether the gesture is continuous to decide whether the loop continues (this judgment logic reflects the consideration of operational continuity and the basis of dynamic threshold adjustment in the S630 feedback learning integrated processing). If the gesture ends, the current adjustment loop terminates. This diagram clearly shows that this invention does not achieve a one-time isolated recognition, but rather a closed-loop control system that continuously senses, makes intelligent decisions, executes safely, and can adaptively optimize.

Claims

1. A method for customized and rapid adjustment of seat interaction based on gesture perception, characterized in that, include: Obtain gesture templates, adjustable item lists, and blind operation label layouts; perform interactive baseline initialization, alignment index generation preparation, synchronization configuration, coordinate alignment, storage, and publishing processing; and generate interactive baseline initialization and alignment index generation outputs containing alignment item numbers, spatial sub-region numbers, time slice rules, and module access paths. The process includes executing spatial sub-region sets and time slicing rules containing alignment indexes, gesture acquisition initiation, de-jittering, slicing, time stamping, stability calculation, and feature summary construction, generating gesture acquisition and stability evaluation output containing stability fields and feature summaries. Perform lightweight identification feature extraction, identification result calculation, confidence generation, accidental touch risk assessment, and threshold revision operations, including slice metadata and time stamps, to generate lightweight identification and accidental touch risk assessment output containing risk estimation elements and threshold revision items; The output of lightweight identification and accidental touch risk assessment includes alignment index and trigger order. It performs custom function mapping retrieval, function call item organization, parameter range generation, parameter draft conflict detection and sequence arrangement processing, and generates custom function mapping and parameter draft output containing function call table, parameter draft set and conflict candidate set. Perform operations including unified spatiotemporal reference keys, security constraint verification parameter construction, limit calculation, consistency verification, conflict arbitration and decision-making, and generate security constraint verification and conflict arbitration outputs including final control parameters and arbitration sequence list; The process integrates preference profile updates and threshold revisions, including alignment indexes and access paths, instruction encoding, execution, receipt collection, and feedback learning. It generates integrated update results containing preference profile update entries and threshold revision entries.

2. The method according to claim 1, characterized in that, Gesture templates, adjustable item lists, and blind operation signage layouts include: The gesture template includes gesture definitions for system recognition, gesture triggering conditions, and associated placement information with seat components; the adjustable item list includes the name of each adjustable function of the seat, its physical or logical value range, adjustment step granularity, and call order placement information when performing multi-function mapping; the blind operation label layout includes the physical layout coordinates of the tactile labels set on the seat armrests or center console shell, their respective touch sensing range, and area boundary information based on the panel coordinate system.

3. The method according to claim 1, characterized in that, The process of generating integrated update results for instruction issuance and feedback learning, which includes preference profile update entries and threshold revision entries, also includes: Obtain the output of security constraint verification and conflict arbitration, perform integrated encoding of instruction issuance and feedback learning that includes alignment index and access path, and obtain an integrated instruction set of instruction issuance and feedback learning that includes instruction sequence list and parameter image; The system performs instruction issuance and execution, and collects receipts to obtain an integrated feedback snapshot of instruction issuance and feedback learning, which includes arrival records, in-transit records, and exception records. The system updates preference profiles and revises thresholds, generating an integrated update result that combines instruction issuance and feedback learning, including preference profile update entries and threshold revision entries.

4. The method according to claim 3, characterized in that, The process of obtaining an integrated instruction set for instruction issuance and feedback learning, which includes a list of instruction sequences and parameter images, also includes: Obtain the output of security constraint verification and conflict arbitration, parse the final control parameters, arbitration order list, allow labels, deny labels and continuation labels, and generate the alignment index and access path registered in the output according to the interaction baseline initialization and alignment index. Establish the correspondence between time slice and spatial sub-region number for each final control parameter, and generate an instruction orchestration skeleton consistent with the unified spatiotemporal reference key.

5. The method according to claim 4, characterized in that, The process of generating an instruction orchestration skeleton consistent with the unified spatiotemporal reference key also includes: Under the order constraints of the arbitration sequence list, the allowed tags within the same time slice are merged at the channel level, a mapping table between the call channel and the executor is established, and each function call item is mirrored with the starting point, ending point, step granularity and change limit fields in its parameter draft, and merged with the corresponding unified spatiotemporal reference key, time slice sequence number and spatial sub-region number into an instruction unit.

6. The method according to claim 5, characterized in that, For function call items marked as deferred, the target time slice given by the deferred label is inserted sequentially into the instruction arrangement skeleton, and the deferred source identifier and the original time slice sequence number are appended to the instruction unit; for function call items marked as rejected, a placeholder instruction unit is generated, which only retains the reference path of the function call item identifier and the rejection description.

7. The method according to claim 3, characterized in that, The process of issuing and executing instructions, collecting receipts, and obtaining an integrated feedback snapshot of instruction issuance and feedback learning that includes arrival records, transit records, and exception records also includes: According to the time slice order and call channel arrangement of the instruction sequence list, the instructions are issued one by one to the seat motor, backrest motor, lumbar support actuator, and ventilation and heating actuator, and the receipt information from the execution link is collected. According to the receipt verification rules, each pair of receipts is checked for consistency: if the actual value of position or angle is within the allowable deviation range of the value in the parameter mirror, it is recorded as an arrival record; if it has not arrived but the execution time is consistent with the change limit field, it is recorded as an in-transit record; if an interlock or timeout is triggered, it is recorded as an abnormal record.

8. The method according to claim 1, characterized in that, The process of updating preference profiles, revising thresholds, and generating integrated update results that include preference profile update entries and threshold revision entries, as well as the integrated instruction issuance and feedback learning, also includes: Using the integrated feedback snapshot of instruction issuance and feedback learning as input, the system reads the parameter mirrors of arrival records and in-transit records and their corresponding acknowledgment values. Based on the time stamp, it reconstructs the target landing point and actual landing point of each function call item in this round of execution. It then compares these with the risk estimation elements and threshold revision items in the output of lightweight identification and accidental touch risk assessment. Function call items with stable actual landing points are registered as preferred candidates, while function call items with abnormal records or frequent delays are registered as boundary candidates.

9. The method according to claim 8, characterized in that, Within the preference candidate set, preferences are merged according to the same gesture name and the same spatial sub-region number. Common starting points, common ending points, and common step granularities within multiple rounds of windows are statistically analyzed to form preference segments. Within the boundary candidate set, segments that have experienced interlocking, timeouts, or significant arrival deviations are extracted by combining anomaly descriptions and stability field references to form boundary segments.

10. The method according to claim 9, characterized in that, include: Based on preference fragments, the default values ​​and step granularity in the adjustable item list are fine-tuned to generate preference profile update items; based on boundary fragments, the threshold revision items in the output of lightweight recognition and accidental touch risk assessment are revised again. While keeping the original adaptive gating direction unchanged, the threshold is tightened or relaxed in combination with the consistency results of the receipt to form threshold revision items.

Images