A method and system for conference acoustic simulation and automatic audio parameter transfer

Abstract

This invention discloses a method and system for conference acoustic simulation and automatic audio parameter migration, relating to the fields of intelligent conference systems and digital audio signal processing technology. The method includes acquiring a target acoustic parameter set defined in a device-independent acoustic description language, whereby the target acoustic parameter set is used to characterize the desired acoustic effect; identifying the device type identifier of the target audio processor; and querying a device feature knowledge base based on the device type identifier to obtain the corresponding internal audio processing chain logic model and parameter semantic mapping rules. This invention, by constructing a device feature knowledge base and a parameter semantic translation engine, accurately translates the device-independent abstract acoustic parameter set into a sequence of control instructions executable by the target audio processor. It fundamentally solves the semantic gap problem caused by differences in filter types, bandwidth algorithms, and parameter definitions on different brands of devices for the same acoustic intent, achieving lossless cross-platform migration of acoustic parameters.

Description

Technical Field

[0001] This invention relates to the fields of intelligent conference systems and digital audio signal processing technology, specifically to a conference acoustic simulation and automatic audio parameter transfer method and system. Background Technology

[0002] With the widespread application of digital audio processing technology in conference systems, numerous brands and models of digital audio processors have emerged in the market, such as Biamp, QSC, and Bosch. Although these processors have similar functions, their internal audio processing chain architecture, algorithm implementation, parameter definition, and control protocols are fundamentally heterogeneous. This heterogeneity leads to serious parameter semantic gaps: for example, for the acoustic intention of performing +3dB gain compensation at a Q value of 2 at 1kHz, the underlying control instructions required by different brands of DSPs are completely different in terms of filter type, bandwidth definition algorithm, parameter value range, and accuracy.

[0003] Furthermore, existing conference system debugging suffers from a disconnect between simulation and actual debugging. While some projects utilize acoustic simulation software (such as EASE and ODEON) to predict the sound field of the conference room before construction, the simulation results are only used for feasibility studies. The acoustic data obtained from the simulation (such as sound pressure distribution, reverberation time, and speech intelligibility) cannot be directly mapped to the actual control parameters of the conference audio processor. Simulation optimization suggestions (such as recommended equalization parameters to compensate for room modal resonance) cannot be automatically converted into specific control commands for the audio processor, making it difficult to apply simulation results. Simulation and actual system debugging become two independent processes, further exacerbating the problems of low debugging efficiency and difficulty in reusing experience.

[0004] Existing technologies, including automated debugging or AI parameter optimization solutions based on acoustic simulation, all default to operating on a single, known device platform. These technologies do not address, and cannot solve, the core engineering challenge of lossless parameter transfer across brands and models of equipment. Currently, optimization parameters generated by simulation or expert experience are highly dependent on specific devices and cannot be directly reused, resulting in low debugging efficiency and difficulty in knowledge accumulation, severely hindering the standardized deployment and large-scale application of conference systems.

[0005] Therefore, there is an urgent need for a solution that can understand and translate different device languages, enabling intelligent, accurate, and secure cross-platform migration of acoustic parameters. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for conference acoustic simulation and automatic audio parameter transfer, so as to overcome the shortcomings of the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for conference acoustic simulation and automatic audio parameter transfer, comprising:

[0008] Obtain a target acoustic parameter set defined in a device-independent acoustic description language, the target acoustic parameter set being used to characterize the desired acoustic effect;

[0009] Identify the device type identifier of the target audio processor;

[0010] Based on the device type identifier, query the device feature knowledge base to obtain the corresponding internal audio processing chain logic model and parameter semantic mapping rules;

[0011] Based on the internal audio processing chain logic model and parameter semantic mapping rules, the target acoustic parameter set is translated into a sequence of control instructions executable by the target audio processor through the parameter semantic translation engine.

[0012] The control command sequence is sent to the target audio processor through the corresponding communication protocol interface and the parameters are loaded.

[0013] In a preferred embodiment, the parameters defined by the device-independent acoustic description language include at least the acoustic effect type, center frequency, gain value, and quality factor Q value.

[0014] In a preferred embodiment, the internal audio processing chain logic model includes a topology sub-model, which is represented by a directed graph or metadata, and is used to define the type, order, and interconnection relationship of each processing module within the target audio processor.

[0015] In a preferred embodiment, the step of translating the target acoustic parameter set into a sequence of control commands using a parameter semantic translation engine specifically includes:

[0016] The acoustic effect requirements in the target acoustic parameter set are decomposed and mapped to the corresponding processing module nodes of the internal audio processing chain logic model.

[0017] Based on the parameter semantic mapping rules of the mapped module, unit conversion, numerical range limiting, and algorithm equivalence transformation are performed on device-independent parameter values;

[0018] Based on the instruction set syntax of the target audio processor, the converted parameter values ​​are generated into a sequence of control instructions that can be issued and executed.

[0019] In a preferred embodiment, the parameter semantic mapping rule defines the different underlying parameter identifiers, data structures, and numerical interpretation methods corresponding to the same acoustic effect parameter in different brands of audio processors;

[0020] The algorithm equivalent conversion includes: converting the acoustic parameter of equalizer bandwidth corresponding to the quality factor Q value according to a dedicated bandwidth algorithm or mapping table defined in the target device feature knowledge base, which is different from the standard Q value calculation model.

[0021] In a preferred embodiment, after the parameter loading is completed, the method further includes:

[0022] Initiate a closed loop of effect verification and adaptive correction: collect the output audio signal of the audio processor in a real conference scenario, extract at least one acoustic performance index, compare it with the expected index simulated based on the target acoustic parameter set, and if the deviation exceeds the dynamic threshold, generate parameter correction amount and trigger the adjustment of relevant mapping rules in the device feature knowledge base or the operating parameters of the audio processor.

[0023] In the effect verification and adaptive correction closed loop, the dynamic threshold is adaptively adjusted based on the current load of the audio processor, the ambient noise level, or historical deviation statistics.

[0024] In a preferred embodiment, the method further includes:

[0025] The target acoustic parameter set, which has been verified and confirmed to be stable through closed-loop effect testing, is associated and stored with the acoustic feature identifier of the meeting space to which it is applied, forming a reusable parameter template.

[0026] When configuring the system for a new meeting space, the acoustic feature identifier of the space is matched with the parameter template library, and the parameter template with the highest matching degree is recommended and called as the initial target acoustic parameter set.

[0027] In a preferred embodiment, the device feature knowledge base defines rules for mapping parameters in a device-independent acoustic description language to device-specific control parameters related to the physical implementation of the internal processing chain, based on the parameter semantic mapping rules stored for each device type identifier.

[0028] In a preferred embodiment, the device feature knowledge base can automatically correct or enrich the parameter semantic mapping rules based on the successful migration case data generated by the effect verification and adaptive correction closed loop.

[0029] The present invention also provides a conference acoustic simulation and automatic audio parameter transfer system, comprising:

[0030] The device feature knowledge base module is used to store the internal audio processing chain logic model and parameter semantic mapping rules of various audio processors, and supports dynamic updates based on feedback data;

[0031] The parameter semantic translation engine module, connected to the device feature knowledge base module, is used to translate the device-independent acoustic parameter set into a device-specific control command sequence based on the acquired logical model and mapping rules.

[0032] The protocol adaptation and migration execution module is connected to the parameter semantic translation engine module and is used to adapt the control command sequence to the communication protocol of the target device and complete the issuance and loading.

[0033] The operation monitoring and closed-loop correction module is used to collect the output signal of the audio processor, calculate the deviation of acoustic performance indicators, and generate correction instructions for the knowledge base or device parameters accordingly.

[0034] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0035] This invention constructs a device feature knowledge base and a parameter semantic translation engine to accurately translate device-independent abstract acoustic parameter sets into a sequence of control instructions executable by the target audio processor. This mechanism fundamentally solves the semantic barrier problem caused by differences in filter types, bandwidth algorithms, and parameter definitions on different brands of devices, achieving lossless cross-platform transfer of acoustic parameters.

[0036] This invention completely decouples acoustic effect optimization from device parameter execution. Optimization parameters generated by acoustic simulation software or expert experience can exist independently in a device-independent standardized format and be adapted to any target device through the migration system of this invention. This truly frees acoustic tuning knowledge from hardware constraints, enabling one-time optimization and deployment anywhere.

[0037] This invention combines virtual debugging parameters generated by conference acoustic simulation with automatic migration of audio processor parameters, achieving seamless integration from simulation design to real system deployment. This enables simulation results to directly guide on-site debugging, completely changing the current situation where simulation and debugging are separated, and providing an end-to-end intelligent solution for conference system engineering. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0039] Figure 1 This is a flowchart of the method of the present invention.

[0040] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This invention is particularly applicable to virtual debugging scenarios for conference systems integrating acoustic simulation software. After the acoustic simulation software completes the sound field design of the conference room and generates optimization suggestions, these suggestions can be encapsulated in a device-independent acoustic description language to form the target acoustic parameter set of this invention. Subsequently, through the parameter semantic translation engine and device feature knowledge base of this invention, the virtual debugging parameter set can be automatically and accurately migrated to any brand of actual audio processor, achieving a seamless connection from simulation design to real device deployment.

[0043] Example 1, please refer to Figure 1 As shown in the figure, the method for conference acoustic simulation and automatic audio parameter transfer described in this embodiment includes:

[0044] S1. Obtain a target acoustic parameter set defined in a device-independent acoustic description language, the target acoustic parameter set being used to characterize the desired acoustic effect;

[0045] S2. Identify the device type identifier of the target audio processor;

[0046] S3. Based on the device type identifier, query the device feature knowledge base to obtain the corresponding internal audio processing chain logic model and parameter semantic mapping rules;

[0047] S4. Based on the internal audio processing chain logic model and parameter semantic mapping rules, the target acoustic parameter set is translated into a sequence of control instructions executable by the target audio processor through the parameter semantic translation engine.

[0048] S5. The control command sequence is sent to the target audio processor through the corresponding communication protocol interface and the parameters are loaded.

[0049] As described in S1-S5 above, this invention solves the core engineering challenge of parameter migration across devices, enabling high-quality debugging experience to be accumulated, reused, and shared in the form of device-independent parameter templates. This fundamentally changes the current situation where conference system debugging is highly dependent on manual labor and experience is difficult to replicate, and powerfully promotes the industry's evolution towards standardization, platformization, and scaling.

[0050] In one specific implementation, step S1 specifically includes:

[0051] S11. Definition and structure of device-independent acoustic description language.

[0052] The device-independent acoustic description language is a structured data exchange format used to precisely characterize the acoustic performance expectations for a conference audio system. It only describes the desired effect, without specifying which device or instructions are required to achieve it. This language can be implemented using JSON, XML, or a custom binary format.

[0053] In a preferred embodiment, a basic acoustic processing unit (e.g., an equalizer band) described by this language includes the following key fields:

[0054] effect_type: Acoustic effect type. For example, PEQ (parametric equalizer), HShelf (high-pass shelving equalizer), Delay, Compressor, etc.

[0055] target: The target identifier. Specifies which physical or logical channel of the system the effect applies to, such as Main_SPK_L (main left speaker) or Mic_Array_Ch1 (microphone array channel 1).

[0056] params: A collection of effect parameters. This is a nested object whose contents are dynamically defined based on effect_type.

[0057] For PEQ type, the params object must include at least:

[0058] freq (center frequency): The unit is Hertz (Hz), and the value range is usually from 20Hz to 20kHz.

[0059] gain: The unit is decibel (dB), and the value range is, for example, -24dB to +12dB.

[0060] q (quality factor): dimensionless, used to define the bandwidth of the filter, with values ​​ranging from 0.1 to 10.

[0061] For the Delay type, the params object must include at least time (delay value), in milliseconds (ms).

[0062] Here is an example of a JSON format describing +3dB gain compensation for the main left speaker at 1kHz with a Q value of 2:

[0063] json{ : Parametric balancing, target: Main_SPK_L, effect parameter set: {freq: 1000, gain: 3.0, q: 2.0}}.

[0064] S12. Source and acquisition method of the target acoustic parameter set: The target acoustic parameter set can be acquired through one or more of the following methods and ultimately organized into a set defined by the description language:

[0065] S121. Exporting from Acoustic Simulation Software: During the conference room design phase, acoustic simulation software such as EASE and ODEON are used to perform simulation calculations based on the room's CAD model, material properties, and equipment layout. The optimization suggestions output by the simulation software (such as equalization parameters suggested to compensate for a certain modal resonance) can be converted into the description language format of this standard.

[0066] S122. Load from expert debugging template library: The system maintains a pre-set parameter template library. Templates are generated based on best practices for a large number of typical conference room scenarios (such as a 50-square-meter rectangular conference room, a 100-square-meter fan-shaped conference room, etc.) and stored in the device-independent language. Users can select a matching template as the initial target set according to the characteristics of the current conference room (area, volume, aspect ratio).

[0067] S123. Inherit from existing project configuration: If the same conference room has historical debugging data before replacing the new brand audio processor, the final stable parameters of the old system (regardless of brand) can be converted into the cost standard description language format after a one-time manual or semi-automatic annotation, and used as the starting point for debugging the new system.

[0068] S124. Generated through interactive configuration tools: Users can directly add, modify, or delete acoustic processing units in the above format through a graphical interface to intuitively construct the desired acoustic processing link.

[0069] S13. Verification and preprocessing of the parameter set: Before proceeding to the subsequent migration process, the system performs basic logic and security verification on the acquired target acoustic parameter set.

[0070] S131. Format compliance check: Verify whether the data conforms to the syntax and schema definition of the description language.

[0071] S132, Parameter Range Verification: Check whether all parameter values ​​such as freq, gain, and q are within the acoustically reasonable physical range to prevent obviously erroneous data input.

[0072] S133. Conflict Detection: Preliminarily check whether there are contradictory instructions on the same processing target (e.g., setting two equal bands with huge gain at very close frequency points) and give a prompt.

[0073] S134. Serialization Packaging: The verified, discrete acoustic processing unit descriptions are grouped and sorted according to the processing target, and packaged into a complete target acoustic parameter set data package, along with metadata such as version and creation time, for subsequent steps to call.

[0074] In one specific implementation, step S2 includes:

[0075] S21. Multi-mode device discovery and identification acquisition: The system obtains the initial identification information of the target device through one or more combinations of the following methods:

[0076] S211. Automatic Network Protocol Discovery: When the target audio processor supports IP-based network management protocols (such as SNMP, HTTPAPI, or manufacturer-specific protocols), the system initiates a device discovery request within the local network segment. For example, the system broadcasts a specific UDP probe packet, and compatible devices will reply with a response message containing their brand, model, firmware version, and unique serial number.

[0077] For example, you might receive a response: Brand: Brand A; Model: Model T-XXX; HW-Rev: VI; Serial: 12345ABC.

[0078] S212. Communication Interface Reading: Through an established physical or logical communication connection (such as an RS-232 serial port, USB, or Ethernet TCP connection), the system sends a specific identity query command to the device (such as a standard SCPI command like *IDN?\n or a vendor-defined command, where "?" is a standard suffix character for SCPI query commands and is an integral part of the command). The string read back by the device is parsed into identification information.

[0079] S213. User Manual Selection and Specifying: When automatic selection is infeasible or the results are ambiguous, a human-computer interaction interface is provided. The interface presents a structured tree list of equipment models (brand -> series -> specific model) loaded from the equipment feature knowledge base, for engineering technicians to manually select. It also supports inputting more specific information such as the equipment serial number.

[0080] S214, Configuration File / Project File Import: Automatically parse the target processor model from existing conference room system design files (such as SystemDesigner project files of brand B, and exported metadata of configuration files of brand A).

[0081] S22. Standardization and encoding of identification information: To ensure that the identification can be queried unambiguously by the knowledge base, the original identification information obtained will be cleaned and converted into standardized internal device type identifiers.

[0082] S221. Information Extraction and Cleaning: Extract the core triplet from the acquired raw information: Brand (B), Model Series (M), and Hardware Version (H). Ignore secondary information that does not affect parameter migration (such as IP address). Standardize the brand and model names (e.g., unify Brand A, BrandA, and brand_a into Brand A).

[0083] S222, Generate standard identifiers: Encode the standardized information according to a predetermined format. A preferred encoding format is: Brand_ModelSeries_HWRev.

[0084] For example, the original response brand A model T-XXX (HardwareRev.VI) will be converted into the standard identifier: brand A_model T-XXX_VI. This identifier serves as the primary key for indexing in the device characteristics knowledge base.

[0085] S23. Identifier verification and knowledge base pre-verification: Before final identification confirmation, the system performs pre-verification to increase the robustness of subsequent processes.

[0086] S231. Knowledge Base Presence Query: The system uses a generated standard identifier to perform a quick query in the local or remote device feature knowledge base. If an entry corresponding to the identifier exists in the knowledge base, the identifier is considered valid and supported.

[0087] S232. Fuzzy matching and suggestions: If no completely matching entry is found, the system starts the fuzzy matching algorithm.

[0088] For example, if the search ignores the hardware version and finds brand A_model T-XXX_*, the user is prompted with: "The device has been identified as brand A model T-XXX, but no exact model for hardware version VI was found. Do you want to use the general T-XXX series template? Or please manually select the exact model."

[0089] S233. Initial capability assessment: Based on the summary information of the corresponding entry in the knowledge base, make a preliminary judgment on whether the device has the basic processing capabilities necessary to achieve the target acoustic parameter set (such as whether it supports a sufficient number of parametric equalizer bands), and provide a prompt.

[0090] S24. Confirmation and context association of the final identifier, specifically including:

[0091] S241. User Confirmation: The identified standardized device type identifier (e.g., Brand A_Model T-XXX_VI) and its user-friendly name (e.g., Brand A_Model T-XXX(Rev.VI)) are presented to the user for final confirmation. For results from automatic discovery, this step prevents misconfiguration due to unexpected devices in the network.

[0092] S242, Bind System Configuration Context: Logically bind the confirmed device type identifier to a specific audio channel or functional area of ​​the current conference room.

[0093] For example, the processor for the left channel of the main sound reinforcement zone is identified as 'Brand A_Model T-XXX_VI', with the serial number DSP_01. This ensures that in a multiprocessor system, the parameter set can be accurately migrated to the correct target device.

[0094] S243, Identification Information Encapsulation: The finally confirmed device type identifier and its context information are encapsulated into a structured target device descriptor object and passed to the subsequent S3 step as input for querying the knowledge base.

[0095] In one specific implementation, step S3 specifically includes:

[0096] S31. Architecture and data organization of the device feature knowledge base: The device feature knowledge base is a structured database, where each record uniquely corresponds to a specific brand and model of audio processor. Each record mainly consists of two core data blocks:

[0097] S311, Internal Audio Processing Chain Logic Model: This model, in a machine-readable form, depicts the fixed or configurable paths of the signal flow within the target processor.

[0098] A preferred implementation uses a directed graph structure for description:

[0099] S3111, Node: Represents a basic signal processing module, such as input gain high-pass filter (HPF), 31-segment graphic equalizer (GEQ), 8-band parametric equalizer (parametric equalizer), dynamic compressor (compressor), delay line (delay), matrix mixer (Mixer), and output attenuation.

[0100] S3112, Edge: Represents the signal flow relationship between nodes and has directionality. For example, the signal flow may follow a chain structure of input gain -> HPF -> GEQ -> parametric equalizer -> compressor -> delay -> output attenuation. For devices that support parallel or multiplexing processing, the model will describe its branching and merging relationships.

[0101] S3113, Node Attributes: Each node comes with a set of attributes that define its capability boundaries. For example, the attributes of a parametric equalizer node include: maximum number of bands: 8; frequency adjustment range: 20Hz-20kHz; gain adjustment range: -24dB~+12dB; Q value adjustment range: 0.1-10.

[0102] S3114, Parameter Semantic Mapping Rule Set: This is a set of rules that map abstract parameters in a device-independent acoustic description language to the specific control addresses and formats of the processing chain nodes mentioned above.

[0103] It typically takes the form of a rule table, containing the following fields:

[0104] Abstract parameter names: such as parametric equalization_Gain.

[0105] Target processing node: Points to the specific node ID in the logical model.

[0106] Device native parameter identifier: The address or name of the corresponding control parameter inside this node.

[0107] For example, in the protocol of brand A, it might be parametric equalization.1.Gain; in the system of brand B, it might be Eq.Band1.Gain; and in the underlying register mapping, it might be a hexadecimal address 0x1200.

[0108] S3115, Numerical Conversion Function / Mapping Table: Defines how to convert an abstract value to a device native value. For example, if the abstract layer gain unit is dB, while a device's native interface receives an integer scalar of 0-65535, the conversion function would be: Native Value = (Abstract Gain Value + 24) * (65535 / 36).

[0109] S32. Identifier-based precise query and matching process: The system uses the standard device type identifier obtained in step S2 as the query key and executes the following process:

[0110] S321, Exact Match Query: Retrieves records in the knowledge base that exactly match the identifier. This is the preferred and most efficient path.

[0111] S322, Version Tolerance and Inheritance Matching: If a perfect match is not found (e.g., the knowledge base has brand A_model T-XXX_VII, while the target identifier is _VI), the fault tolerance mechanism is activated. The system attempts to extract the hardware version number for querying to find the basic model for that series. During loading, the following log is recorded: The target device hardware version (VI) was not found precisely in the library; a general model of the same series (T-XXX) has been applied, and some advanced functions may be limited.

[0112] S323. Cloud Knowledge Base Synchronous Query: When a local knowledge base match fails, the system automatically queries the central cloud knowledge base via a secure connection. The cloud database can return a matching record or a response indicating that the model has been included and will be available in the next update. Simultaneously, a background download task can be triggered to incrementally update the model data of the new device to the local database.

[0113] S33. Model and rule parsing, validation, and instantiation: After successfully retrieving data records from a query, the system does not directly transmit the raw data, but performs in-depth parsing and preparation:

[0114] S331. Data Integrity Verification: Check whether the acquired records contain the logical model and necessary core mapping rules. Verify the compatibility of the data version number with the current translation engine.

[0115] S332. Logical Model Instantiation: The stored structured data, such as directed graphs, is reconstructed into a traversable object model in memory. This instantiated model enables the translation engine to dynamically traverse the processing chain; for example, when a balancer node needs to be inserted, its position on the chain can be accurately located.

[0116] S333, Mapping Rule Pre-compilation: To improve runtime efficiency, the numerical transformation functions in the rule set are pre-compiled from descriptive text (such as mathematical expression strings) into executable calculation functions or optimized lookup tables.

[0117] S334, Capability Alignment Check: Perform a quick alignment check between the instantiated logical model capabilities (e.g., maximum number of parametric equalization bands: 8) and the requirements of the target acoustic parameter set obtained in S1. If the parameter set requires 10 parametric equalization bands, but the model only supports 8, an explicit error report is immediately generated: The parametric equalizer capability (8 bands) of the target device brand A_model T-XXX_VI does not meet the parameter set requirement (10 bands), and the migration has been aborted.

[0118] S34. Query Result Encapsulation and Readiness: The internal audio processing chain logic model and parameter semantic mapping rule set, verified and instantiated, are encapsulated into a target device capability context package. This package is a complete runtime context containing the following information:

[0119] The device type identifier being referenced.

[0120] An instantiated, queryable processing graph object.

[0121] A pre-compiled, efficient set of parameter mapping rules.

[0122] The device has unique capability limitations and safety boundary parameters.

[0123] This context packet is output to the next stage as the sole authoritative basis for its translation operation, thereby ensuring that every control command generated precisely matches the internal architecture and control language of the target device.

[0124] In one specific implementation, step S4 specifically includes:

[0125] S41. Initialization of the translation engine and data loading.

[0126] S411, Engine Initialization: Loads and instantiates the parametric semantic translation engine. This engine includes a pre-built general translation logic library and supports a general conversion framework for handling various acoustic effect types (such as equalization, dynamics, and time delay).

[0127] S412, Context Binding: Load the target device capability context package (containing logical model instances and pre-compiled mapping rules) output in step S3 into the translation engine to establish a working context specific to this translation task.

[0128] S413. Input Data Loading: Load the standardized target acoustic parameter set data package generated in step S1 into the engine. The engine first verifies the version and format compatibility of the data package and parses it into an internally operable object tree.

[0129] S42. Decomposition and processing chain node mapping of acoustic effect requirements: The translation engine traverses the description of each acoustic processing unit in the target acoustic parameter set and performs deep mapping.

[0130] S421. Target Channel Location: Based on the target field (such as Main_SPK_L) in the processing unit description, determine the corresponding physical input / output port and the default signal processing path sub-graph bound to it in the device logic model.

[0131] S422, Effect Type Matching and Node Selection:

[0132] S4221. Based on the effect_type (e.g., parametric equalizer), the engine searches for compatible processing nodes with available capacity in the processing path subgraph defined by the logical model. For example, it searches for the first available parametric equalizer node group on the path.

[0133] S4222, Strategy Example: If the device has multiple parametric equalization node groups (such as parametric equalization group A before GEQ and parametric equalization group B after dynamic processing), the engine will prioritize parametric equalization group A, which is located before the dynamic processing node and closer to the front end of the signal chain, according to the preset sound quality priority strategy, to ensure the purity of the adjustment.

[0134] S4223, Node Status Marking: After a successful match, the engine marks the node as occupied and records it as the execution host node of the abstract processing unit. At the same time, it retrieves the detailed attributes (such as precision and range) and exclusive mapping rules corresponding to the node from the context package.

[0135] S43. Algorithmic equivalent transformation of device-independent parameter values: For each mapped processing unit, the engine performs transformation on each value in its effect parameter set by calling the pre-compiled mapping rules obtained in S3.

[0136] S431, Basic Linear Transformation: Apply a linear transformation function to parameters that are consistent in units and require only scaling and offsetting.

[0137] For example (gain conversion): Abstract parameter gain: 3.0dB, the native gain parameter range of the target device node is -12800 to +1200 (one-hundredth of a dB unit). Application rule: Native value = (Abstract gain value * 100). Conversion result: 300.

[0138] S432, Nonlinear Algorithm Conversion (Key Innovation): Perform dedicated calculations for parameters with different algorithms.

[0139] For example (equalizer Q-value / bandwidth conversion): Abstract parameter q: 2.0 (standard Q-value definition). The target device (such as a specific DSP model) uses octaves as the bandwidth parameter, and its conversion formula is not industry standard. The engine calls the device-specific conversion rules, which may be a mathematical function or a lookup table.

[0140] For example, functions:

[0141] octave=1.0 / (q*log2((freq+sqrt(freq^2+(freq / q)^2)) / (freq-sqrt(freq^2+(freq / q)^2)))).

[0142] Substituting freq=1000 and q=2.0 into the calculation, we get octave≈0.29.

[0143] Example of lookup: A mapping table of [(q: 0.5, oct: 2.0), (q: 1.0, oct: 0.9), (q: 2.0, oct: 0.29), ...] is pre-stored in the device feature library. The corresponding value is obtained by interpolation.

[0144] S433, Range Limiting and Rounding: The converted native value is constrained within the range of [minimum, maximum] defined by the target node attribute, and rounded according to the precision required by the device (e.g., rounded to the nearest integer). If limiting occurs, a warning log is recorded.

[0145] S44. Generation of device-specific control command sequence: The engine assembles the converted device-related parameter values ​​into the final control command according to the target device's communication protocol syntax.

[0146] S441, Instruction Template Rendering: Each device node / parameter is associated with an instruction template in the mapping rules. The template defines the binary or text structure of the instruction, the command word, parameter placeholders, and checksum positions.

[0147] S442, Text Protocol Example (based on TCP): The template might be SET<node path><parameter name><value>\r\n. The engine fills in the value 300, generating: SET / Main_SPK_L / parameter balancing / 1 / Gain300\r\n.

[0148] S443, Binary Protocol Example: The template defines the instruction header (e.g., 0xAA55), the destination register address (obtained from the mapping rules, e.g., 0x1200), the data length, and the encoding format (e.g., big-endian 16-bit integer). The engine converts the value 300 to 0x012C and fills it into the template, generating a binary instruction frame: {0xAA55, 0x1200, 0x0002, 0x012C, 0xXXXX (CRC)}.

[0149] S444, Instruction Sorting and Batch Processing: The engine sorts all generated instructions according to the signal flow order in the logical model, ensuring that the configuration order matches the signal flow order and avoiding transient state conflicts. Subsequently, multiple instructions are packaged into an instruction sequence or transaction to improve issuance efficiency. For devices supporting atomic operations, a composite instruction can be generated that ensures all parameters take effect simultaneously.

[0150] S445. Generate a migration report: While generating the instruction sequence, the engine simultaneously generates a readable migration report, which details how each abstract parameter is mapped, transformed, and used to generate specific instructions, for debugging and auditing purposes.

[0151] S45. Output Delivery: The translation engine encapsulates the final generated, directly deployable control command sequence and optional migration report into a translation result package and delivers it to the protocol adaptation and migration execution module in step S5.

[0152] In one specific implementation, step S5 includes:

[0153] S51, Communication Session Establishment and Protocol Adaptation.

[0154] S511, Interface and Protocol Adapter Selection: The system automatically selects or loads the corresponding communication protocol adapter based on the target device's type identifier and capability context.

[0155] S512, Network Protocol: For devices that support network management (such as those based on Dante, AES67 or vendor-specific TCP / IP protocols), the adapter initializes the Ethernet Socket connection and sets the target IP address and port (e.g., 192.168.1.100:23 for Telnet, or :80 for HTTP API).

[0156] S513, Serial Protocol: For devices using RS-232 / 422 / 485, the adapter is configured with the specified serial port number, baud rate (e.g., 115200), data bits, stop bits, and parity bits.

[0157] S514, Proprietary Bus Protocol: For devices connected via a specific control bus (such as CobraNet, AVB, DALI), the adapter calls the corresponding driver library to perform bus access initialization.

[0158] S515, Session Establishment and Authentication: Initiate a connection request and perform the necessary session handshake and authentication. For example, after establishing a TCP connection, it may be necessary to send a login command (LOGINadminpassword123\r\n) and wait for a LOGINSUCCESSFUL response to ensure configuration permissions are granted.

[0159] S516. Connection Health Check: Before the official release, send a simple status query command (such as *IDN?\n or GET / api / v1 / device / status) to verify the bidirectional stability of the communication link and the device's responsiveness.

[0160] S52. Secure issuance and timing control of instruction sequences: The issuance process is not a simple batch transmission, but is controlled by a sophisticated state machine.

[0161] S521, Blocking and Flow Control: Long sequences of instructions are divided into appropriately sized blocks (e.g., every 10 instructions per transaction block) to avoid network buffer overflows or device timeouts. After each block is sent, an acknowledgment response from the device (such as OK\r\n or a specific success status code) is awaited.

[0162] S522, Ordered Delivery: Commands are sent strictly in the order generated by the translation engine. For commands with dependencies (such as commands where a processing module must be enabled before its parameters can be set), the order is crucial. The system maintains an internal command dependency graph to ensure topological order.

[0163] S523, Reliable transmission with retry mechanism:

[0164] S5231 Success Confirmation: For each key instruction sent, the adapter waits for a predetermined time (e.g., 200ms) for a clear and successful response from the device.

[0165] S5232, Retry on Failure: If no response is received within a timeout period or an error response is received (such as ERROR: PARAM_OUT_OF_RANGE), the adapter will automatically retry the command (up to 3 times).

[0166] S5233, Critical Instruction Verification: For core parameters (such as the feedback suppressor threshold), a write-after-read verification method is used: After issuing the setting instruction, immediately send a read instruction for that parameter, and compare the read value with the expected value. If they do not match, record a serious error and may abort the process.

[0167] S5234, Atomic Loading and Silent Mode: Some high-end processors support atomic commit or configuration preset functions. The system utilizes this feature to first write all instructions to a temporary or inactive preset area. After all instructions are verified to be correct, a final ACTIVATEPRESET1 or APPLYCHANGES command is sent, causing all new parameters to take effect simultaneously within the same audio processing cycle inside the device, achieving silent switching without clicking or transition distortion.

[0168] S53. Status monitoring and exception handling during the loading process: The system implements comprehensive monitoring throughout the entire distribution process.

[0169] S531, Device Status Monitoring: Continuously listen to the status information actively reported by the device (via asynchronous messages or periodic queries), and monitor key indicators such as CPU_LOADTEMPERATURECLIPPING. If the CPU load exceeds 85% or a clipping alarm occurs, the system can pause the issuance of subsequent commands and issue an early warning.

[0170] S532, Exception interrupt handling:

[0171] S5321, Communication Interruption: If the connection is unexpectedly lost, the system suspends all dispatched tasks and attempts to reconnect automatically. After a successful reconnection, based on the recorded progress, it determines whether to continue from the point of interruption or roll back the entire process and start again.

[0172] S5322, Device Error: If the device returns an error that cannot be resolved by retrying (such as ERROR: MEMORY_FULL), the system immediately stops sending, records the error code and context to the log, and pushes a clear fault alarm to the user interface, indicating the possible cause (such as the target device's DSP resources have been exhausted and cannot accommodate all equalization parameters).

[0173] S5323, Safety Timer: Set a global safety timer (e.g., 5 minutes) for the entire loading process. If the process is not completed within the timeout period, the process will be forcibly terminated to prevent the system from getting stuck in an infinite wait due to unknown errors.

[0174] S54. Loading Completion Confirmation and Post-Processing: After all instruction sequences have been confirmed and successfully verified, the system performs the final loading completion procedures:

[0175] S541, Device Operation Mode Switching: Send a command to switch the device from a possible configuration mode back to the normal operation mode.

[0176] S542. Generate Loading Report: Summarize all key information for this loading process, including: load start / end time, target device identifier, total number of commands issued, success / failure / retry statistics, any warnings that occurred, and the final overall status (complete success or partial success, but with warnings). This report is linked to the migration report in S4 to form a complete audit trail.

[0177] S543. Update system configuration status: In the local management system or cloud platform, mark the configuration status of the target audio processor as updated and record the version hash value of the new configuration.

[0178] S544, Trigger subsequent processes: Send an event notification that new parameters have been loaded to the system's internal operation monitoring and closed-loop correction module, and start effect verification and adaptive correction closed loop.

[0179] Example 2: Virtual debugging and automatic migration application integrated into conference acoustic simulation software.

[0180] This embodiment integrates the method of the present invention into a conference acoustic simulation software to realize the entire process from simulation modeling to automatic migration of equipment parameters.

[0181] First, users obtain the geometric parameters of the meeting room (such as length, width, height, and shape), the acoustic parameters of the decorative materials (such as sound absorption coefficient), the microphone arrangement parameters (such as model, location, and directivity), and the speaker arrangement parameters (such as model, location, and coverage angle) through the meeting modeling module of the software, and then establish a three-dimensional geometric model of the meeting room.

[0182] Secondly, the software constructs a conference acoustic simulation model based on this model, and uses the ray tracing method or virtual source method to simulate the propagation process of direct sound, early reflection sound and reverberation sound, and obtains acoustic response data of each listening point (such as the audience seats, chairman's seat), including frequency response, impulse response, reverberation time T60, speech transmission index STI, etc.

[0183] Then, the software's built-in virtual debugging parameter solving module automatically calculates the virtual debugging parameters of the conference system based on the aforementioned acoustic response data. For example:

[0184] For the peaks and valleys in the frequency response, calculate the compensation parameters (center frequency, gain, Q value) of the parametric equalizer.

[0185] Calculate the speaker delay compensation parameters based on the direct sound arrival time difference from each speaker to the audience area;

[0186] Based on the acoustic feedback path between the microphone and the speaker, identify the frequency points prone to howling and generate the center frequency parameters of the feedback suppressor;

[0187] By combining the microphone signal level and background noise, the automatic mixer's activation threshold and gain allocation parameters are automatically calculated.

[0188] The reference path delay parameters for the echo canceller are generated based on the path of the speaker signal leaking to the microphone.

[0189] The aforementioned virtual debugging parameters are automatically encapsulated by the software into a target acoustic parameter set in a device-independent acoustic description language format. For example, for a certain equalization compensation requirement, the following JSON format description is generated: {effect_type: PEQ, target: Main_SPK_L, params: {freq: 125, gain: -3.5, q: 1.8}};

[0190] Subsequently, when deploying the audio processor on-site, the user uses the software to identify the target device type (e.g., through IP discovery or manual selection), queries the built-in device characteristic knowledge base, and obtains the corresponding device's internal audio processing chain logic model and parameter semantic mapping rules. The parameter semantic translation engine automatically converts the aforementioned target acoustic parameter set into a sequence of control commands executable by the target device and sends them to the device via Ethernet to complete parameter loading.

[0191] Ultimately, during the actual meeting operation, the software continuously collects the audio processor's output signal and compares it with the simulated expected indicators. If a deviation is found (such as the difference between the actual reverberation time and the simulated value exceeding a threshold), the parameter correction process is automatically triggered, generating correction values ​​and re-migrating to achieve closed-loop optimization. The corrected stable parameters are associated with and stored with the acoustic feature identifiers of the meeting space, forming a reusable parameter template for rapid deployment in similar meeting rooms in the future.

[0192] This embodiment demonstrates that the method can be perfectly integrated into the workflow of conference acoustic simulation software, enabling one-click parameter migration from virtual design to real equipment, and significantly improving the automation level and engineering efficiency of conference system debugging.

[0193] Example 3, please refer to Figure 2 As shown in this embodiment, a conference acoustic simulation and automatic audio parameter transfer system includes:

[0194] The device feature knowledge base module is used to store the internal audio processing chain logic model and parameter semantic mapping rules of various audio processors, and supports dynamic updates based on feedback data;

[0195] The device feature knowledge base module is essentially a structured, scalable database management system, not simply a parameter list. It comprises the following sub-components:

[0196] Logical Model and Rule Database: This database persistently stores the internal audio processing chain logical models and parameter semantic mapping rules for all supported audio processors. Each record uses the device type identifier as the primary key. Data is stored in JSON, XML, or binary serialization formats for easy querying and updating. The logical models stored in the database employ directed graph description languages ​​(such as GraphML) or custom metadata formats, explicitly recording the types, identifiers, configurable parameter sets, and topological connections of the device's internal processing units (such as input gain, filters, equalizers, dynamic processors, matrices, and outputs) within the signal flow.

[0197] Model Building and Rule Extraction Toolset: This is an offline support toolset for the initial construction and expansion of the knowledge base. It allows engineers to extract the processing chain information and parameter control addresses of the target device by parsing the official configuration software data files and SDK documents of the device manufacturer or by reverse engineering the device communication protocol, and to model the data according to the system-defined format to generate records that can be imported into the database.

[0198] Knowledge base management service: Provides standard external query interfaces (such as RESTful APIs or function libraries). When the parameter semantic translation engine module initiates a query, this service retrieves and returns the corresponding model and rule data package based on the device type identifier. Furthermore, it is responsible for managing the dynamic updates of the knowledge base.

[0199] Online Update: Receives verified successful mapping cases or rule optimization suggestions from the operation monitoring and closed-loop correction module. After administrator review or automatic threshold judgment, it incrementally updates the mapping rules of specific devices, such as optimizing the conversion accuracy from Q value to octave.

[0200] Offline synchronization: Regularly pull new device models or update patches for existing models from the central knowledge base in the cloud to ensure that the coverage and accuracy of the local knowledge base continue to evolve.

[0201] The parameter semantic translation engine module, connected to the device feature knowledge base module, is used to translate the device-independent acoustic parameter set into a device-specific control command sequence based on the acquired logical model and mapping rules.

[0202] The parametric semantic translation engine module is responsible for executing the core semantic transformation logic. It is a standalone software service or library that internally implements a multi-stage translation pipeline:

[0203] Initialization and Context Loading Unit: Receives the target acoustic parameter set and target device type identifier from the upstream process. Then, it calls the interface of the device feature knowledge base module to load the corresponding logical model and mapping rules, instantiating a device context specific to this translation task in memory.

[0204] Syntax parsing and requirement decomposition unit: Parses device-independent acoustic description languages ​​(such as parameter sets in JSON format) and transforms them into structured objects representing the internal structure. Simultaneously, based on acoustic effect types (such as equalization and dynamics) and preset strategy rules, it decomposes complex acoustic requirements into atomic operation requests for individual processing units.

[0205] Topology Mapping and Resource Allocation Unit: This unit, in conjunction with the loaded device context, maps atomic operation requests to specific processing nodes within the device's logical model. For example, a request to apply notch filtering at 800Hz will be mapped to a specific band of the first available parametric equalizer (PEE) module in the target device's signal chain. This process must consider device resource limitations (such as the total number of parametric equalizer bands) and best audio quality practices (such as equalization prior to dynamic processing).

[0206] Semantic Conversion and Algorithm Calculation Unit: This is the core calculation unit of the module. It performs precise calculations on abstract parameters based on the conversion functions defined in the mapping rules. For example, it converts the standard Q value of 2 to a bandwidth value of 0.29 octaves recognized by the device using a device-specific algorithm (which may be a formula or a lookup table). It also performs unit conversion (dB to internal gain value) and range limiting.

[0207] The instruction synthesis and output unit: This unit takes the converted, device-related parameter values, and fills them into predefined instruction templates according to the target device instruction set syntax rules, generating the final, deployable control instruction sequence. This unit is also responsible for sorting and grouping the instructions to optimize deployment efficiency and generating a translation report containing detailed mapping logs.

[0208] The protocol adaptation and migration execution module is connected to the parameter semantic translation engine module and is used to adapt the control command sequence to the communication protocol of the target device and complete the issuance and loading.

[0209] The protocol adaptation and migration execution module is responsible for reliable and secure interaction with heterogeneous hardware in the physical world. It employs a layered and plug-in design:

[0210] Protocol Adaptation Layer: This layer contains a series of drivers or adapter plugins for different communication protocols (such as TCP / IP (HTTP / HTTPS, Telnet), RS-232 / 485, vendor-specific binary protocols (e.g., DSP control protocol of brand A, audio processor network API of brand B), and industry standard protocols (e.g., AVB, OCA)). The system automatically loads the corresponding adapter based on the target device type.

[0211] Session Management and Security Control Layer: Responsible for establishing, maintaining, and releasing communication sessions with the target audio processor. This includes connection handshake, authentication (such as username / password), heartbeat maintenance of the communication link, and timeout reconnection. This layer implements security policies, such as pre-validating sent parameters to prevent the transmission of dangerous values ​​that could cause device overload or damage.

[0212] Instruction Execution and State Machine Layer: This is the core control logic of the module. It receives a sequence of control instructions from the translation engine and executes them following a rigorous state machine process.

[0213] Preprocessing and grouping: Group the instruction sequence according to transaction logic.

[0214] Reliable delivery: Commands are sent in sequence, and a request-acknowledgment-timeout retry mechanism is implemented for each critical command. For protocols that do not support acknowledgment, a write-after-read verification strategy can be adopted.

[0215] Atomic loading control: For devices that support configuration presets or transaction commits, this layer first writes all parameters to a temporary area and then sends an activation command to make all changes take effect simultaneously, achieving seamless switching.

[0216] Anomaly monitoring and rollback: Monitor device return codes and network status throughout the process. If a fatal error is detected (such as parameters exceeding limits or device being busy), the process is immediately paused, and an automatic rollback to the previous stable state can be attempted according to the policy.

[0217] Execution report generator: Records the entire process of issuing commands and generates a detailed execution report that includes timestamps, the sending and response status of each command, and an overall success / failure conclusion.

[0218] The operation monitoring and closed-loop correction module is used to collect the output signal of the audio processor, calculate the deviation of acoustic performance indicators, and generate correction instructions for the knowledge base or device parameters accordingly.

[0219] The operation monitoring and closed-loop correction module operates independently of the configuration process and continues to function during the system's daily operation.

[0220] Signal Acquisition and Preprocessing Unit: This unit acquires the output signal of the audio processor via a diagnostic microphone connected to the conference system or through a direct splitter, recording audio segments at a specific sampling rate (e.g., 48kHz) and period (e.g., every 10 minutes, or triggered when a major speech is detected). The recorded signals are preprocessed, including noise reduction and Active Voice Detection (VAD), to filter out valid speech segments for analysis.

[0221] Acoustic Feature Extraction and Performance Calculation Unit: This unit analyzes the preprocessed audio signal to extract key quantifiable acoustic performance indicators. These indicators correspond to set optimization objectives, such as:

[0222] Speech Proficiency Index: Estimates STI or STI values ​​in real time by calculating the Modulation Transfer Function (MTF).

[0223] Frequency response uniformity: The standard deviation of the sound pressure level at each frequency point is calculated based on the test signal response collected at multiple representative listening points.

[0224] Feedback margin: Monitor the gain margin of each microphone channel in the main frequency band and identify the critical frequency at which feedback is about to occur.

[0225] Comparison Analysis and Deviation Decision Unit: This unit compares the calculated actual acoustic performance indicators with the target indicators expected by the method simulation or template, generating a quantified deviation. This unit contains a decision engine, which includes a set of rule-based or lightweight machine learning models for determining:

[0226] Is the deviation significant? For example, the measured STI is more than 0.05 lower than the target value, or the margin at a certain frequency point is consistently less than 3dB.

[0227] Possible causes of the deviation: is it due to inaccurate current device mapping rules (knowledge base problem), or is it due to environmental changes (such as full seating) causing the original parameters to no longer be optimal (operational parameter problem)?

[0228] Correction instruction generation and feedback unit: Based on the decision results, it generates specific correction actions.

[0229] Fine-tuning of equipment operating parameters: For example, generating a device-independent parameter adjustment command to increase the gain of a certain parametric equalization band by 0.5dB and submitting it to the system to trigger a transfer process again.

[0230] Optimization suggestions for knowledge base mapping rules: For example, if it is found that the Q-value conversion formula of a certain device consistently leads to narrow bandwidth in actual applications, a rule correction suggestion is generated (such as adjusting a certain coefficient in the formula from 1.0 to 1.05), and submitted to the device feature knowledge base module for review and learning.

[0231] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for conference acoustic simulation and automatic audio parameter transfer, characterized in that, include: Obtain a target acoustic parameter set defined in a device-independent acoustic description language, the target acoustic parameter set being used to characterize the desired acoustic effect; Identify the device type identifier of the target audio processor; Based on the device type identifier, query the device feature knowledge base to obtain the corresponding internal audio processing chain logic model and parameter semantic mapping rules; Based on the internal audio processing chain logic model and parameter semantic mapping rules, the target acoustic parameter set is translated into a sequence of control instructions executable by the target audio processor through the parameter semantic translation engine. The control command sequence is sent to the target audio processor through the corresponding communication protocol interface and the parameters are loaded.

2. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 1, characterized in that: The parameters defined by the device-independent acoustic description language include at least the acoustic effect type, center frequency, gain value, and quality factor Q value.

3. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 1, characterized in that: The internal audio processing chain logical model includes a topology sub-model, which is represented by a directed graph or metadata and is used to define the type, order, and interconnection relationship of each processing module within the target audio processor.

4. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 2, characterized in that: The process of translating the target acoustic parameter set into a sequence of control commands using a parameter semantic translation engine specifically includes: The acoustic effect requirements in the target acoustic parameter set are decomposed and mapped to the corresponding processing module nodes of the internal audio processing chain logic model. Based on the parameter semantic mapping rules of the mapped module, unit conversion, numerical range limiting, and algorithm equivalence transformation are performed on device-independent parameter values; Based on the instruction set syntax of the target audio processor, the converted parameter values ​​are generated into a sequence of control instructions that can be issued and executed.

5. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 4, characterized in that: The parameter semantic mapping rule defines the different underlying parameter identifiers, data structures, and numerical interpretation methods corresponding to the same acoustic effect parameter in different brands of audio processors; The algorithm equivalent conversion includes: converting the acoustic parameter of equalizer bandwidth corresponding to the quality factor Q value according to a dedicated bandwidth algorithm or mapping table defined in the target device feature knowledge base, which is different from the standard Q value calculation model.

6. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 1, characterized in that: After the parameter loading is completed, the following is also included: Initiate a closed loop of effect verification and adaptive correction: collect the output audio signal of the audio processor in a real conference scenario, extract at least one acoustic performance index, compare it with the expected index simulated based on the target acoustic parameter set, and if the deviation exceeds the dynamic threshold, generate parameter correction amount and trigger the adjustment of relevant mapping rules in the device feature knowledge base or the operating parameters of the audio processor. In the effect verification and adaptive correction closed loop, the dynamic threshold is adaptively adjusted based on the current load of the audio processor, the ambient noise level, or historical deviation statistics.

7. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 6, characterized in that: The method further includes: The target acoustic parameter set, which has been verified and confirmed to be stable through closed-loop effect testing, is associated and stored with the acoustic feature identifier of the meeting space to which it is applied, forming a reusable parameter template. When configuring the system for a new meeting space, the acoustic feature identifier of the space is matched with the parameter template library, and the parameter template with the highest matching degree is recommended and called as the initial target acoustic parameter set.

8. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 7, characterized in that: In the device feature knowledge base, the parameter semantic mapping rules stored for each device type identifier define rules for mapping parameters in the device-independent acoustic description language to control parameters specific to that device and related to the physical implementation of the internal processing chain.

9. The method for conference acoustic simulation and automatic audio parameter transfer according to claim 8, characterized in that: The device feature knowledge base can automatically correct or enrich the parameter semantic mapping rules based on the successful migration case data generated by the effect verification and adaptive correction closed loop.

10. A conference acoustic simulation and automatic audio parameter transfer system, used to implement the conference acoustic simulation and automatic audio parameter transfer method according to any one of claims 1-9, characterized in that, include: The device feature knowledge base module is used to store the internal audio processing chain logic model and parameter semantic mapping rules of various audio processors, and supports dynamic updates based on feedback data; The parameter semantic translation engine module, connected to the device feature knowledge base module, is used to translate the device-independent acoustic parameter set into a device-specific control command sequence based on the acquired logical model and mapping rules. The protocol adaptation and migration execution module is connected to the parameter semantic translation engine module and is used to adapt the control command sequence to the communication protocol of the target device and complete the issuance and loading. The operation monitoring and closed-loop correction module is used to collect the output signal of the audio processor, calculate the deviation of acoustic performance indicators, and generate correction instructions for the knowledge base or device parameters accordingly.

Images