A service scheduling and execution method and system for cross-application in a vehicle environment

Abstract

This application provides a cross-application service scheduling and execution method and system in a vehicle environment, relating to the field of intelligent vehicle system technology. It acquires known service-related data of the vehicle system, as well as environmental perception data and user voice information of the vehicle environment; parses the user voice information to obtain target demand information, intent association information, and voice feature information; forms scene adaptation parameters based on environmental perception data; generates multiple second instruction transmission signals based on target demand information; detects each target demand information based on service-related data, intent association information, voice feature information, and scene adaptation parameters, obtains detection results, and combines these with the second instruction transmission signals to schedule and execute multiple cross-application services in the vehicle environment, generating service scheduling and execution results. This achieves dynamic coordination and orderly scheduling of cross-application services, improving the response accuracy and execution security of the vehicle system in complex driving scenarios.

Description

Technical Field

[0001] This application relates to the field of intelligent vehicle system technology, and in particular to a service scheduling and execution method and system for cross-application in a vehicle environment. Background Technology

[0002] With the rapid development of intelligent vehicles, in-vehicle systems are increasingly integrating various application services such as navigation, entertainment, communication, and driver assistance. Users often trigger multiple cross-application operations simultaneously via voice commands while driving, such as navigating while playing music or making a phone call. These scenarios place high demands on the system's real-time performance, low interference, and environmental adaptability. The in-vehicle platform must be able to comprehensively perceive the current internal and external conditions of the vehicle and the user's intentions, efficiently coordinating the execution order and resource allocation of multiple services while ensuring driving safety.

[0003] Existing solutions typically employ a priority scheduling mechanism based on preset rules. This mechanism combines speech recognition results with basic vehicle status information to assign fixed priorities to different service requests and determine the execution order accordingly. However, this approach lacks the ability to jointly analyze the dynamic driving environment and the deep semantics of user speech, making it difficult to accurately determine potential conflicts and resource competition between services. When the vehicle is in complex road conditions or the user issues ambiguous, multi-intent voice commands, the fixed priority strategy can easily lead to delays in critical service responses or excessive consumption of system resources by non-critical services, thereby reducing overall interaction efficiency and driving safety. Summary of the Invention

[0004] The purpose of this application is to provide a service scheduling and execution method and system for cross-application in a vehicle environment, so as to solve the problem of service conflict and resource allocation imbalance caused by the inability of the fixed priority strategy to adapt to dynamic environment and multi-intent voice in the prior art.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a cross-application service scheduling and execution method in a vehicle environment, comprising:

[0006] Acquire known service-related data from in-vehicle systems, as well as environmental perception data and user voice information from the in-vehicle environment;

[0007] The user's voice information is analyzed using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services;

[0008] Based on the environmental perception data, scene adaptation parameters are formed, and based on the target requirement information, multiple first instruction transmission signals corresponding to each cross-application service are generated.

[0009] The first command transmission signal is optimized using an amplifier to obtain multiple second command transmission signals;

[0010] Based on the service-related data, intent association information, voice feature information, and scene adaptation parameters, the conflict coordination engine performs resource occupancy detection and execution order detection on each target requirement information to obtain the detection results.

[0011] Based on the detection results, a target service execution sequence adapted to the vehicle driving state is generated. Combined with the second instruction transmission signal, multiple cross-application services are scheduled and executed in the vehicle environment, and a service scheduling execution result is generated.

[0012] Optionally, based on the environmental perception data, scene adaptation parameters are formed, and based on the target requirement information, multiple first instruction transmission signals corresponding to each cross-application service are generated, including:

[0013] The environmental perception data is subjected to correlation analysis to generate information correlation features, and different driving scenario types are classified based on the information correlation features.

[0014] Based on the aforementioned information association characteristics, and the signal transmission requirements and triggering conditions for cross-application service execution, adaptation rules related to cross-application service execution are set for different driving scenario types.

[0015] Based on the parameter recognition specifications of the vehicle system, various adaptation rules are transformed into scene adaptation parameters that can be recognized by the vehicle system.

[0016] The target requirement information of each cross-application service is broken down into execution actions, operation objects, and operation parameters, and the execution actions, operation objects, and operation parameters corresponding to each target requirement information are converted into first instruction transmission signals that conform to the signal transmission specifications of the vehicle system.

[0017] Optionally, the first command transmission signal is optimized using an amplifier to obtain multiple second command transmission signals, including:

[0018] Based on the signal type of the first instruction transmission signal and the scenario adaptation parameters, determine the transmission characteristic parameters of each first instruction transmission signal;

[0019] Based on the signal transmission strength benchmark value and interference threshold under different driving scenario types in the scenario adaptation parameters, and combined with each transmission characteristic parameter, the initial gain parameter and initial filtering parameter of the amplifier are adjusted to generate an adaptation parameter set corresponding to each first command transmission signal.

[0020] According to each set of adaptation parameters, the corresponding first instruction transmission signal is calibrated in frequency band to obtain multiple preprocessed signals;

[0021] The preprocessed signals are optimized by the amplifier according to each set of adaptation parameters to obtain multiple optimized signals;

[0022] Based on the strength benchmark value and interference threshold, the signal strength and anti-interference capability of each optimized signal are verified, and the optimized signal that passes the verification is used as the second instruction transmission signal.

[0023] Optionally, based on the signal transmission strength reference value and interference threshold under different driving scenario types in the scenario adaptation parameters, and in conjunction with each transmission characteristic parameter, the initial gain parameter and initial filtering parameter of the amplifier are adjusted to generate an adaptation parameter set corresponding to each first command transmission signal, including:

[0024] Based on the signal transmission requirements of various driving scenarios, the parameter deviations of each transmission characteristic parameter from the strength benchmark value and interference threshold are analyzed, and a deviation dataset is generated.

[0025] Based on the deviation dataset, the initial gain parameters of the amplifier are adjusted in stages, and the initial filter parameters of the amplifier are adjusted for frequency band adaptation based on the numerical range of each interference threshold, so as to generate parameter adjustment records.

[0026] From the parameter adjustment records, a candidate parameter set is selected that matches the transmission characteristic parameters and deviation dataset of each first command transmission signal;

[0027] The candidate parameter sets corresponding to each first instruction transmission signal are verified to identify the parameter set that has no parameter conflicts, is adapted to the signal transmission requirements and corresponding transmission characteristic parameters of the corresponding driving scenario type, and is used as the adaptation parameter set.

[0028] Optionally, the detection results include resource conflict detection results and sequence detection results;

[0029] Based on the service-related data, intent association information, voice feature information, and scene adaptation parameters, the conflict coordination engine performs resource occupancy detection and execution order detection on each target requirement information to obtain detection results, including:

[0030] Based on the resource configuration information of the vehicle system and the target requirement information, determine the types of hardware resources and the amount of software resources required to execute each target requirement information, so as to form a resource requirement list;

[0031] Based on the service-related data, analyze the occupancy status of each hardware resource type in the vehicle system, the remaining availability of each software resource, and combine the historical time consumption data of each resource executing cross-application services to generate a resource status dataset.

[0032] Extract resource scheduling constraints corresponding to different driving scenario types from the scenario adaptation parameters;

[0033] Based on the intent association information, the execution association relationship between each target requirement information is analyzed, and based on the voice feature information, the expression features corresponding to each target requirement information are determined;

[0034] Based on the resource demand list and resource status dataset, the conflict coordination engine performs resource occupancy analysis on the target demand information and generates resource conflict detection results.

[0035] Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression characteristics, the conflict coordination engine performs conflict resolution processing on all target requirement information to generate sequence detection results.

[0036] Optionally, based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression features, a conflict coordination engine performs conflict resolution processing on all target requirement information to generate sequence detection results, including:

[0037] Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression characteristics, the conflict coordination engine determines the conflict resolution priority of each target requirement information.

[0038] Based on the resource conflict detection results, identify conflict information and corresponding related demand information where resource occupation conflicts exist, and confirm the resource conflict type and resource impact range of the conflict information and related demand information;

[0039] Based on the execution association and the conflict resolution priority of each target requirement information, the conflict information and associated requirement information are resolved to obtain conflict-free information.

[0040] All conflict-free information is verified, and the verified conflict-free information is integrated according to the execution association to form a sequential detection result.

[0041] Optionally, based on the detection results, a target service execution sequence adapted to the vehicle's driving state is generated. Combined with the second instruction transmission signal, multiple cross-application services are scheduled and executed in the vehicle environment, generating service scheduling and execution results, including:

[0042] Based on the detection results and the service execution constraints corresponding to different driving scenario types in the scenario adaptation parameters, an initial service execution sequence is generated.

[0043] The second instruction transmission signal corresponding to each target requirement information is bound to the corresponding service execution node in the initial service execution sequence to form a target service execution sequence;

[0044] According to the target service execution sequence, an execution trigger instruction is sent to the corresponding functional module of the vehicle system to trigger the startup of the first cross-application service corresponding to the first service execution node. After confirming that the first cross-application service has completed the preset operation according to the target service execution sequence and the second instruction transmission signal, the startup of the cross-application service corresponding to the next service execution node is triggered based on the target service execution sequence. This continues until all cross-application services corresponding to all service execution nodes have completed the preset operation. Then, the execution feedback information of each cross-application service is integrated to form a service scheduling execution result.

[0045] Secondly, this application provides a cross-application service scheduling and execution system in a vehicle environment, including:

[0046] The acquisition module is used to acquire known service-related data of the in-vehicle system, as well as environmental perception data of the in-vehicle environment and user voice information;

[0047] The parsing module is used to parse the user's voice information using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services;

[0048] The generation module is used to form scene adaptation parameters based on the environmental perception data and generate multiple first instruction transmission signals corresponding to each cross-application service based on the target requirement information.

[0049] An optimization module is used to optimize the first command transmission signal using an amplifier to obtain multiple second command transmission signals;

[0050] The detection module is used to perform resource occupancy detection and execution order detection on each target requirement information based on the service-related data, intent association information, voice feature information and scene adaptation parameters, and obtain the detection results through the conflict coordination engine.

[0051] The execution module is used to generate a target service execution sequence adapted to the vehicle driving state based on the detection results, and, in conjunction with the second instruction transmission signal, to schedule and execute multiple cross-application services in the vehicle environment, and generate service scheduling and execution results.

[0052] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the steps of the cross-application service scheduling and execution method in a vehicle environment as described in the first aspect above.

[0053] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements the steps of a cross-application service scheduling execution method in a vehicle environment as described in the first aspect above.

[0054] The cross-application service scheduling and execution method provided in this application has the following beneficial effects: By acquiring service performance data and multi-dimensional environmental and voice information of the vehicle system, it is possible to accurately analyze the user's cross-application service needs and generate scenario adaptation parameters based on dynamic driving status to guide the generation of command signals; then, the initial command signal is optimized using an amplifier to improve the transmission reliability of the signal in complex vehicle environments; furthermore, by combining service history performance, voice semantic features and scenario context, a conflict coordination mechanism is used to intelligently evaluate the resource consumption and execution timing of each service request, and finally, a service execution sequence that highly matches the current driving status is formed and the scheduling execution is completed, thereby enhancing the system's coordination ability and response adaptability to concurrent requests with multiple intentions while ensuring driving safety.

[0055] Furthermore, this application dynamically configures the amplifier's gain and filtering parameters based on the command signal type and the current driving scenario to perform frequency band calibration and optimization on the initial command signal. It also introduces a signal strength and anti-interference verification mechanism based on scenario characteristics to ensure that the optimized command signal has good transmission stability and environmental robustness. This overcomes the problem of service mis-triggering or response failure caused by signal distortion or interference under complex driving conditions in traditional fixed strategies, enabling cross-application scheduling commands to be transmitted and executed more reliably, thereby supporting the system to maintain stable and orderly service collaboration capabilities in highly dynamic and multi-task scenarios. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating a cross-application service scheduling and execution method in a vehicle environment, provided as an embodiment of this application;

[0058] Figure 2 This application provides a schematic diagram illustrating a specific implementation of a cross-application service scheduling and execution method in a vehicle environment.

[0059] Figure 3This is a schematic diagram of the structure of a cross-application service scheduling and execution system in a vehicle environment, provided as an embodiment of this application. Detailed Implementation

[0060] To address the problem that existing scheduling mechanisms, which rely on fixed priorities, are ill-suited for complex driving conditions and concurrent scenarios with multiple intent voice commands, this application provides a cross-application service scheduling and execution method in an in-vehicle environment. The core idea of ​​this method is to construct a collaborative scheduling framework that integrates vehicle dynamic environment perception, service historical performance data, and deep semantic understanding of user voice. By extracting multi-source information such as vehicle speed, vibration, environmental and voice features in real time, scenario adaptation parameters are generated, and command signals for cross-application services are dynamically generated and optimized accordingly. Combined with a joint detection mechanism for resource occupancy and execution order, a service execution sequence matching the current driving state is formed, thereby achieving orderly, coordinated, and reliable scheduling of multiple service requests while ensuring driving safety.

[0061] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] The core of this application is to provide a cross-application service scheduling and execution method in a vehicle environment, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0063] Step 101: Obtain known service-related data of the in-vehicle system, as well as environmental perception data of the in-vehicle environment and user voice information.

[0064] In this step, service-related data refers to various service operation data generated by the vehicle system during the historical execution of cross-application services, including service response data and service latency data.

[0065] Environmental perception data refers to vehicle-related environmental data collected by various onboard sensors, including driving speed information, vehicle vibration information, and environmental information.

[0066] User voice information refers to the voice command data issued by the user through the in-vehicle voice interaction device.

[0067] In this embodiment, the system first retrieves all known service-related data of the vehicle system from the historical data storage module of the vehicle system. At the same time, the vehicle speed sensor, body vibration sensor, and environmental sensor are activated to collect the vehicle's driving speed information, body vibration information, and environmental information in real time. The three types of data are integrated to obtain the environmental perception data of the vehicle environment. Then, the vehicle voice acquisition device is activated to collect the voice command data issued by the user in real time to obtain the user's voice information.

[0068] Step 102: Analyze the user's voice information using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services.

[0069] In this step, the target requirement information for cross-application services refers to the service requirements across multiple functional modules that the user expects the in-vehicle system to execute, obtained from parsing the user's voice information.

[0070] Intent association information refers to the logical execution association between target requirement information of various cross-application services obtained based on the parsing of user voice information.

[0071] Speech feature information refers to the acoustic attribute feature data of speech itself extracted based on user speech information.

[0072] In this embodiment, the aggregated and stored user voice information is first preprocessed by sequentially performing noise reduction and frame segmentation to remove environmental noise from the voice information, resulting in a preprocessed voice signal. Then, the preprocessed voice signal is divided into multiple voice frames, and speech recognition technology is used to semantically parse these frames to extract the target demand information of multiple cross-application services that the user expects to execute. Next, the logical relationships between the target demand information of each cross-application service are analyzed to obtain the intent association information corresponding to each target demand information. Finally, acoustic attribute features such as pitch, speech rate, and voiceprint are extracted from these voice frames to obtain the final voice feature information.

[0073] Step 103: Based on the environmental perception data, form scene adaptation parameters, and based on the target requirement information, generate multiple first instruction transmission signals corresponding to each cross-application service.

[0074] In this step, the scenario adaptation parameters refer to the parameters that the vehicle system can recognize, which are obtained after correlation analysis and rule transformation based on environmental perception data. These parameters are used to adapt cross-application service execution and signal transmission for different driving scenario types.

[0075] Cross-application services refer to services in an in-vehicle system that are executed collaboratively across multiple functional modules. They correspond to the target requirement information parsed from the user's voice information and rely on the cooperation of multiple modules to complete.

[0076] The first instruction transmission signal refers to the signal that converts the execution action, operation object, and operation parameters after breaking down the target requirement information into a signal that conforms to the vehicle system signal transmission specification, and is used to trigger the vehicle system to execute the corresponding cross-application service.

[0077] In this embodiment of the application, step 103 specifically includes the following steps:

[0078] Step 301: Perform correlation analysis on the environmental perception data to generate information correlation features, and classify different driving scenario types based on the information correlation features.

[0079] In this step, the information association feature refers to the feature obtained after performing association analysis on various types of data in the environmental perception data. There can be multiple features, which can form a feature set that can reflect the relationship between driving speed information, vehicle vibration information, and environmental information.

[0080] Driving scenario type refers to the category of vehicle driving scenarios classified based on the differences in feature values ​​of information association features.

[0081] In this embodiment, firstly, all data features of driving speed information, vehicle vibration information, and environmental information in the environmental perception data are extracted, and correlation analysis is performed on the three types of data features to explore the inherent correlation patterns between different data. Finally, various correlation patterns are integrated to generate information correlation features. Then, the classification criteria for driving scenario types are preset, and the vehicle driving scenario is divided into different driving scenario types according to the feature value differences of the information correlation features.

[0082] Step 302: Based on the information association characteristics and the signal transmission requirements and triggering conditions for cross-application service execution, set adaptation rules related to cross-application service execution for different driving scenario types.

[0083] In this step, the signal transmission requirements refer to the requirements for signal strength, anti-interference capability, transmission rate, etc., that the instruction transmission needs to meet during the execution of cross-application services. This embodiment does not limit the specific form of these requirements.

[0084] The triggering condition refers to the prerequisite for starting the cross-application service execution and the corresponding instruction signal transmission. This embodiment does not limit the specific content of the condition, and it can be set according to the actual situation.

[0085] Adaptation rules refer to rules set for different driving scenario types. These rules are formulated based on information association characteristics and are used to coordinate the execution requirements and signal transmission strategies across application services to ensure that they match the current scenario.

[0086] In this embodiment, the information association features corresponding to each driving scenario type are first analyzed to clarify the characteristics of the vehicle environment under different scenarios. Then, combined with the signal transmission requirements and service triggering conditions when cross-application services are executed, corresponding signal transmission adaptation rules and service execution triggering adaptation rules are set for each type of driving scenario. This ensures that all adaptation rules match the information association features of the corresponding driving scenario type, thereby guaranteeing the reasonable execution of cross-application services in the corresponding scenarios.

[0087] Step 303: Based on the parameter recognition specifications of the vehicle system, convert various adaptation rules into scene adaptation parameters that the vehicle system can recognize.

[0088] In this step, the parameter recognition specification of the vehicle system refers to the unified specification requirements for parameter formats, encoding methods, data types, etc. that the vehicle system can directly recognize and parse. This embodiment does not limit the specific content of the specification.

[0089] In this embodiment, the parameter recognition specification of the vehicle system is first retrieved, and then the various adaptation rules set for different driving scenario types are converted and encoded one by one according to the specification. The textual rule content is converted into parameter form that the vehicle system can recognize. Finally, all the converted parameters are integrated to generate scenario adaptation parameters.

[0090] Step 304: Decompose the target requirement information of each cross-application service into execution actions, operation objects, and operation parameters, and convert the execution actions, operation objects, and operation parameters corresponding to each target requirement information into first instruction transmission signals that conform to the signal transmission specifications of the vehicle system.

[0091] In this step, the action to be performed refers to the specific operation that needs to be completed when executing across application services.

[0092] The target of the operation refers to the specific objects such as the functional modules and service content of the vehicle system to which the action is performed.

[0093] Operational parameters refer to various parameter indicators required during the execution of an action, used to limit the scope, degree, and method of the operation.

[0094] In this embodiment, firstly, according to the service execution logic of the vehicle system, the target requirement information corresponding to each cross-application service is decomposed into specific execution actions, corresponding operation objects, and operation parameters required to perform the action; then, the signal transmission specifications of the vehicle system are retrieved, and the decomposed execution actions, operation objects, and operation parameters are encoded and converted into formats according to the specifications, so that each part of the content meets the signal transmission requirements of the vehicle system; finally, the converted signals are integrated to generate the first instruction transmission signal corresponding to each cross-application service.

[0095] The embodiments of this application achieve deep adaptation between driving scenarios and cross-application service execution and signal transmission, providing core adaptation basis and basic instruction signals for subsequent optimization of first instruction transmission signals and resource scheduling of cross-application services, ensuring the scenario-specificity and system compatibility of in-vehicle cross-application service scheduling.

[0096] Step 104: Optimize the first command transmission signal using an amplifier to obtain multiple second command transmission signals.

[0097] In this step, the amplifier refers to the device in the vehicle system used to adjust the strength of the command transmission signal and filter interference signals to optimize signal quality.

[0098] The second command transmission signal refers to the high-quality signal obtained after the first command transmission signal has undergone frequency band calibration, amplifier optimization, and dual verification.

[0099] In the embodiments of this application, such as Figure 2 As shown, step 104 specifically includes the following steps:

[0100] Step 401: Determine the transmission characteristic parameters of each first instruction transmission signal based on the signal type of the first instruction transmission signal and the scenario adaptation parameters.

[0101] In this step, the signal type refers to the signal category corresponding to the first instruction transmission signal, such as control signals, data signals, etc., and the transmission requirements for different types of signals are different.

[0102] Transmission characteristic parameters refer to parameters that reflect the transmission performance of the first instruction transmission signal. These parameters include signal bandwidth, transmission rate, signal amplitude, etc.

[0103] In this embodiment, each first instruction transmission signal is first classified to determine its corresponding signal type; then, the signal transmission requirements for the corresponding driving scenario type are retrieved from the scenario adaptation parameters, and the transmission characteristic parameters corresponding to each first instruction transmission signal are determined in combination with the inherent attributes of the signal type to ensure that the parameters match the signal type and scenario requirements.

[0104] Step 402: Based on the signal transmission strength benchmark value and interference threshold under different driving scenario types in the scenario adaptation parameters, and combined with each transmission characteristic parameter, adjust the initial gain parameter and initial filtering parameter of the amplifier to generate an adaptation parameter set corresponding to each first command transmission signal.

[0105] In this step, the strength benchmark value refers to the standard strength value that the signal transmission needs to reach under the corresponding driving scenario type, which is preset in the scenario adaptation parameters.

[0106] The interference threshold refers to the maximum allowable interference signal strength value in the corresponding driving scenario type, which is preset in the scenario adaptation parameters. Exceeding this value will affect signal transmission.

[0107] Initial gain parameters and initial filter parameters refer to the amplification factor of the signal strength before the amplifier is adjusted and the frequency band parameters used for filtering interference signals, respectively.

[0108] The adaptation parameter set refers to the set of amplifier gain parameters and filter parameters that, after adjustment, match the first command transmission signal and scenario requirements.

[0109] In this embodiment of the application, step 402 specifically includes the following steps:

[0110] Step 411: Based on the signal transmission requirements of each driving scenario type, analyze the parameter deviations of each transmission characteristic parameter from the strength benchmark value and interference threshold, and generate a deviation dataset.

[0111] In this step, parameter deviation refers to the difference between the actual value of each transmission characteristic parameter and the strength reference value, and the difference between the signal anti-interference capability corresponding to the transmission characteristic parameter and the interference threshold, reflecting the gap between the transmission characteristics and the scenario standard requirements.

[0112] A deviation dataset refers to a dataset formed by integrating the parameter deviations corresponding to all transmission characteristic parameters.

[0113] In this embodiment, firstly, according to the signal transmission requirements corresponding to each driving scenario type, the intensity benchmark value and interference threshold of the corresponding scenario are retrieved; then, the difference between the actual value and the intensity benchmark value of each transmission characteristic parameter is calculated one by one, and the difference between the signal anti-interference capability and the interference threshold corresponding to the transmission characteristic parameter is calculated at the same time. All the calculated parameter deviations are grouped and organized according to the first instruction transmission signal to generate a deviation dataset.

[0114] Step 412: Based on the deviation dataset, adjust the initial gain parameters of the amplifier in different increments, and simultaneously adjust the initial filter parameters of the amplifier according to the frequency band adaptation range of each interference threshold, so as to generate a parameter adjustment record.

[0115] In this step, the parameter adjustment record refers to the document that records the amplifier's initial parameters, adjustment level, adjustment range, adjusted parameters, and the corresponding first command transmission signal.

[0116] In this embodiment, the deviation dataset is first divided into multiple adjustment levels, each corresponding to a fixed gain parameter adjustment range. Then, the initial gain parameter of the amplifier is adjusted according to the corresponding level. At the same time, the interference signal frequency band to be filtered is determined by combining the interference threshold value range of each driving scenario. The initial filtering parameter of the amplifier is adjusted to adapt to the frequency band. The complete information of each parameter adjustment is recorded one by one to generate a parameter adjustment record.

[0117] Step 413: Select a candidate parameter set from the parameter adjustment record that matches the transmission characteristic parameters and deviation dataset of each first command transmission signal.

[0118] In this step, the candidate parameter set refers to the amplifier parameter combination selected from the parameter adjustment record that matches the transmission characteristic parameters and deviation dataset of the first command transmission signal.

[0119] In this embodiment of the application, the parameter adjustment records are traversed, and parameter combinations that are adapted to the transmission characteristic parameters of each first instruction transmission signal and whose adjustment magnitude matches the deviation dataset are selected. The selected parameter combinations are used as the candidate parameter set corresponding to the first instruction transmission signal.

[0120] Step 414: Verify the candidate parameter set corresponding to each first instruction transmission signal to identify the parameter set that has no parameter conflicts, is adapted to the signal transmission requirements and corresponding transmission characteristic parameters of the corresponding driving scenario type, and serves as the adaptation parameter set.

[0121] In this step, parameter conflict refers to a situation where the gain parameters and filter parameters in the candidate parameter set influence each other and cannot simultaneously meet the signal optimization requirements.

[0122] In this embodiment, each candidate parameter set is first verified. Specifically, the gain parameters and filtering parameters within the set are checked for parameter conflicts, and parameter combinations with conflicts are eliminated. Then, it is verified whether the remaining parameter combinations are suitable for the signal transmission requirements of the corresponding driving scenario type and match the transmission characteristic parameters of the first command transmission signal. Finally, the parameter combinations that pass the verification are determined as the adapted parameter set.

[0123] Step 403: According to each set of adaptation parameters, perform frequency band calibration on the corresponding first instruction transmission signal to obtain multiple preprocessed signals.

[0124] In this step, the preprocessed signal refers to the signal obtained after the first instruction transmission signal has been frequency-calibrated.

[0125] In this embodiment, for each first instruction transmission signal, its corresponding adaptation parameter set is first called, and the filtering parameter requirements in these adaptation parameter sets are extracted; then, the transmission frequency band of the first instruction transmission signal is adjusted by the signal processing module to correct the frequency band offset, so that the signal frequency band accurately matches the filtering parameter requirements, and a preprocessed signal is obtained.

[0126] Step 404: Optimize the corresponding preprocessed signals by using amplifiers according to each set of adaptation parameters to obtain multiple optimized signals.

[0127] In this step, the optimized signal refers to the preprocessed signal that has been optimized by the amplifier and has reached the required strength, with interference signals effectively filtered out.

[0128] In this embodiment, each preprocessed signal is input into an amplifier. The amplifier calls the corresponding set of adaptation parameters and amplifies the intensity of the preprocessed signal to a range that meets the intensity reference value according to the gain parameter. At the same time, it filters out interference signals that exceed the interference threshold according to the filtering parameter, and finally outputs an optimized signal.

[0129] Step 405: Based on the strength benchmark value and interference threshold, perform signal strength verification and anti-interference capability verification on each optimized signal, and use the optimized signal that passes the verification as the second instruction transmission signal.

[0130] In this embodiment, the optimized signals are first verified one by one. Specifically, the strength of the optimized signal is first checked to see if it meets the strength benchmark value requirement. Then, the interference signal environment of the corresponding scenario is simulated to test whether the anti-interference capability of the optimized signal meets the interference threshold requirement. The optimized signal that passes both verifications is determined as the second instruction transmission signal, and the one that fails is returned to be re-optimized.

[0131] The embodiments of this application realize scenario-based precise optimization of the first instruction transmission signal, improve signal strength and anti-interference capability, provide high-quality instruction support for the stable scheduling and execution of subsequent cross-application services, and avoid service execution delays or failures due to signal problems.

[0132] Step 105: Based on the service-related data, intent association information, voice feature information, and scene adaptation parameters, the conflict coordination engine performs resource occupancy detection and execution order detection on each target requirement information to obtain the detection results.

[0133] In this step, the detection result refers to the set of results obtained after resource occupancy detection and execution order detection, which includes resource conflict detection results and order detection results.

[0134] In this embodiment of the application, step 105 specifically includes the following steps:

[0135] Step 501: Based on the resource configuration information of the vehicle system and the target requirement information, determine the types of hardware resources and the amount of software resources required to execute each target requirement information, so as to form a resource requirement list.

[0136] In this step, resource configuration information refers to basic information such as the types and quantities of hardware resources in the vehicle system, as well as the capacity and functional distribution of software resources.

[0137] Hardware resource type refers to the category of in-vehicle hardware devices required to execute target requirements, such as navigation modules, audio modules, etc.

[0138] Software resource consumption refers to the software resource capacity and computing power required to execute the target information.

[0139] The resource requirements list refers to a list formed by integrating the hardware resource types and software resource usage corresponding to various target requirements.

[0140] In this embodiment, the resource configuration information of the vehicle system is first retrieved to clarify the available hardware resource types and software resource capacity; then, for each target requirement information, the hardware resource type required to execute the requirement is analyzed and determined in conjunction with its corresponding cross-application service content, and the software resource consumption required is calculated. The above two types of information are organized according to the target requirement information to form a resource requirement list.

[0141] Step 502: Based on the service-related data, analyze the occupancy status of each hardware resource type and the remaining availability of each software resource in the vehicle system, and combine the historical time consumption data of each resource executing cross-application services to generate a resource status dataset.

[0142] In this step, hardware resource occupancy status refers to whether each type of hardware resource is currently occupied, the degree of occupancy, and the duration of occupancy.

[0143] The remaining available software resources refer to the remaining allocated capacity and computing power of software resources after deducting the occupied portion.

[0144] Historical time consumption data refers to the time required for various resources to execute cross-application services in the past, which is used to help determine the rationality of resource consumption.

[0145] A resource status dataset refers to a dataset formed by integrating hardware resource occupancy status, remaining available software resources, and historical latency data.

[0146] In this embodiment, historical occupancy records of hardware resources and consumption records of software resources are extracted from service-related data to analyze the current occupancy status of each hardware resource type and calculate the remaining available quantity of each software resource. At the same time, historical time consumption data of each resource executing cross-application services is extracted, and the three types of data are grouped and organized according to resource type to generate a resource status dataset.

[0147] Step 503: Extract resource scheduling constraints corresponding to different driving scenario types from the scenario adaptation parameters.

[0148] In this step, resource scheduling constraints refer to the resource allocation restriction rules preset in the scenario adaptation parameters and formulated for different driving scenario types. These constraints include resource allocation priority, maximum single resource occupancy time, and upper limit of parallel resource allocation.

[0149] In this embodiment, the scenario adaptation parameters are categorized and broken down according to different driving scenario types to determine the adaptation rules related to resource scheduling for each scenario type. These rules are then used as initial resource scheduling constraint rules. The rule content for different scenarios is compared, duplicates are removed to avoid constraint conflicts, and explanations of the rule's scope of application and execution boundaries are added to ensure the rules are clear and implementable, resulting in processed rules. Finally, the processed rules are organized and integrated according to driving scenario types to form a set of resource scheduling constraints.

[0150] Step 504: Based on the intent association information, analyze the execution association relationship between each target requirement information, and based on the voice feature information, determine the expression features corresponding to each target requirement information.

[0151] In this step, the execution relationship refers to the inherent logical execution relationship between the target requirement information, which includes three types: sequential execution, parallel execution, and mutually exclusive execution.

[0152] Expression features refer to features determined based on speech feature information that reflect the degree of importance a user attaches to the target information. These features include pitch, speech rate, etc.

[0153] In this embodiment, the intent association information is first parsed, the execution association relationship between each target requirement information is analyzed, and it is determined whether they are sequential, parallel or mutually exclusive execution relationships, so as to ensure that the execution of the requirement matches the user's potential intent; then, the tone, speech rate and other attributes in the speech feature information are extracted, and combined with the user's voice interaction habits, these attributes are transformed into expressive features that reflect the importance of the requirement, and bound to the corresponding target requirement information.

[0154] Step 505: Based on the resource requirement list and resource status dataset, perform resource occupancy analysis on each target requirement information through the conflict coordination engine to generate resource conflict detection results;

[0155] In this step, resource occupancy analysis refers to comparing the resource requirements of each target demand with the current resource status to determine whether there are multiple demands competing for the same resource.

[0156] Resource conflict detection results refer to the result data that records the target demand information, conflicting resource types, conflict degree, and conflict scope when resource occupation conflicts exist.

[0157] In this embodiment, the resource requirement list and resource status dataset are input into the conflict coordination engine. The engine compares the resource requirement of each target requirement with the current status of the corresponding resource to determine whether there are multiple target requirements competing for the same hardware or software resource. The engine then marks the requirement information involved in the conflict, the type of conflicting resource, and the scope of the conflict. Finally, these marked information are integrated to form a resource conflict detection result.

[0158] Step 506: Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression features, the conflict coordination engine performs conflict resolution processing on all target requirement information to generate sequence detection results.

[0159] In this step, the sequential detection result refers to the reasonable execution order and resource allocation scheme of each target requirement information determined after conflict resolution.

[0160] In this embodiment of the application, step 506 specifically includes the following steps:

[0161] Step 511: Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression characteristics, determine the conflict resolution priority of each target requirement information through the conflict coordination engine.

[0162] In this step, conflict resolution priority refers to the priority of target demand information determined by the conflict coordination engine for sorting and processing resource conflicts. The priority is determined based on a comprehensive assessment of resource scheduling constraints, expression characteristics, and execution relationships, with higher priority demands receiving resource allocation first.

[0163] In this embodiment of the application, the conflict resolution priority of each target requirement information is determined by a conflict coordination engine. The specific process includes: first, determining the basic priority according to resource scheduling constraints; then, adjusting the priority according to expression characteristics, such as increasing the priority of requirements with high user importance; and finally, modifying the priority based on execution association relationships, such as sorting mutually exclusive requirements according to association logic, and finally determining the conflict resolution priority of each target requirement information.

[0164] Step 512: Based on the resource conflict detection results, identify the conflict information and corresponding related demand information where resource occupation conflicts exist, and confirm the resource conflict type and resource impact range of the conflict information and related demand information.

[0165] In this step, conflict information refers to the target demand information marked in the resource conflict detection results that indicates a resource occupation conflict.

[0166] Related demand information refers to other target demand information that competes with conflicting information for the same resource.

[0167] Resource conflict type refers to the specific manifestation of the conflict, which includes hardware resource conflict, software resource conflict, and mixed hardware and software conflict.

[0168] The scope of resource impact refers to the number of resources involved in the conflict, the amount of related demand information, and the degree of impact on the overall service scheduling.

[0169] In this embodiment, firstly, based on the resource conflict detection results, conflict information and corresponding related demand information with resource occupation conflicts are screened out, and the resource involved in the conflict is determined one by one to be hardware, software or mixed type, so as to clarify the type of resource conflict; at the same time, the number of resources involved in the conflict and the number of related demand information are counted, the potential impact of the conflict on the overall service scheduling is analyzed, and the scope of resource impact is determined.

[0170] Step 513: Based on the execution association and the conflict resolution priority of each target requirement information, resolve the conflict information and associated requirement information to obtain conflict-free information.

[0171] In this step, conflict-free information refers to the set of target requirement information that has been processed to eliminate resource occupation conflicts, conform to execution relationships, and be sorted by priority.

[0172] In this embodiment, resource allocation is adjusted for conflict information and related requirement information in descending order of conflict resolution priority. Specifically, high-priority requirements are allocated the required resources first, while low-priority requirements are adjusted for execution timing or replaced with resources based on remaining resources. At the same time, the execution relationship is combined to ensure that the adjusted execution order of requirements is logical, eliminate all resource occupation conflicts, and obtain conflict-free information.

[0173] Step 514: Verify all conflict-free information and integrate the verified conflict-free information according to the execution association to form a sequential detection result.

[0174] In this embodiment, conflict-free information is first verified one by one. The specific verification steps include: checking whether there are any hidden resource conflicts that have not been eliminated, whether the resource scheduling constraints of the corresponding driving scenario are met, and whether the execution relationship is consistent. Then, information that fails the verification is removed and readjusted. The conflict-free information that passes the verification is sorted out according to the execution relationship and the execution order is integrated to form the sequence detection result.

[0175] This application's embodiments solve the problem of execution chaos caused by resource contention in in-vehicle cross-application services, ensuring the orderliness of service scheduling and the rationality of resource utilization, and providing a reliable basis for the subsequent generation of target service execution sequences.

[0176] Step 106: Based on the detection results, generate a target service execution sequence adapted to the vehicle driving state, and combine it with the second instruction transmission signal to schedule and execute multiple cross-application services in the vehicle environment, generating service scheduling and execution results.

[0177] In this step, the vehicle driving status refers to the real-time operating status of the vehicle determined based on environmental perception data. This vehicle driving status includes a comprehensive status such as driving speed, vehicle vibration, and external environment.

[0178] The target service execution sequence refers to the sequence formed after scenario adaptation and instruction binding, which clarifies the execution order of each cross-application service and the corresponding instructions, and is used to guide the orderly scheduling of services.

[0179] The service scheduling execution result refers to the final result formed by integrating the feedback information of each service execution after all cross-application services have been completed, reflecting the overall situation of service scheduling execution.

[0180] In this embodiment of the application, step 106 specifically includes the following steps:

[0181] Step 601: Generate an initial service execution sequence based on the detection results and the service execution constraints corresponding to different driving scenario types in the scenario adaptation parameters.

[0182] In this step, service execution constraints refer to the preset restrictions on service execution under the corresponding driving scenario type in the scenario adaptation parameters. These service execution constraints include the upper limit of service execution time, the upper limit of the number of parallel services, etc.

[0183] The initial service execution sequence refers to a preliminary sequence generated based on the sequential detection results in the detection results and combined with service execution constraints, which clarifies the order of service execution.

[0184] In this embodiment, service execution constraints corresponding to the driving scenario type are first extracted based on the scenario adaptation parameters. Then, the execution order in the sequence detection results is adjusted according to the constraint conditions, and the arrangement that does not meet the constraints is eliminated to generate an initial service execution sequence with a clear service execution order, so as to ensure that the sequence is adapted to the current vehicle driving state.

[0185] Step 602: Bind the second instruction transmission signal corresponding to each target requirement information to the corresponding service execution node in the initial service execution sequence to form a target service execution sequence.

[0186] In this step, the service execution node refers to the node corresponding to each cross-application service in the initial service execution sequence. Each node corresponds to a target requirement information and the associated cross-application service.

[0187] The target service execution sequence refers to the complete sequence that, after binding the second instruction transmission signal to the service execution node, clearly defines the service execution order and includes the execution instructions.

[0188] In this embodiment, the second instruction transmission signal is first associated and matched with the corresponding target requirement information. Then, the target requirement information corresponding to each service execution node in the initial service execution sequence is located. The matched second instruction transmission signal is bound to the corresponding service execution node one by one, so that each node contains both execution order information and instructions that can trigger service execution, and is integrated to form a target service execution sequence.

[0189] Step 603: According to the target service execution sequence, send an execution trigger instruction to the corresponding functional module of the vehicle system to trigger the startup of the first cross-application service corresponding to the first service execution node. After confirming that the first cross-application service has completed the preset operation according to the target service execution sequence and the second instruction transmission signal, trigger the startup of the cross-application service corresponding to the next service execution node based on the target service execution sequence. Continue until all cross-application services corresponding to all service execution nodes have completed the preset operation. Then, integrate the execution feedback information of each cross-application service to form a service scheduling execution result.

[0190] In this step, the execution trigger instruction refers to the instruction generated based on the second instruction transmission signal in the target service execution sequence, which is used to start the cross-application service.

[0191] The first cross-application service refers to the cross-application service corresponding to the service execution node that is first in the target service execution sequence.

[0192] Execution feedback information refers to the information returned after each cross-application service is completed, such as the execution status and whether the result is qualified.

[0193] In this embodiment, firstly, following the order of the target service execution sequence, the second instruction transmission signal bound to the first service execution node is extracted, a corresponding execution trigger instruction is generated and sent to the corresponding in-vehicle functional module, triggering the start of the first cross-application service. The execution process of this service is monitored in real time. After confirming that it has completed the preset operation according to the target service execution sequence requirements and the second instruction transmission signal, the instruction of the next service execution node is extracted and the corresponding cross-application service is triggered. This process is repeated until all services are completed. Finally, the execution feedback information of each cross-application service is collected and integrated into the service scheduling execution result, completing the scheduling and execution of the entire in-vehicle cross-application service.

[0194] This application embodiment realizes the scenario-based and orderly scheduling of in-vehicle cross-application services, which not only ensures that the service execution matches the current driving status and user needs, but also ensures the quality of service execution through successive confirmation and feedback integration, and finally outputs complete scheduling results, providing data support for the optimization of in-vehicle system services and improving the entire cross-application service scheduling link.

[0195] Figure 3 This is a schematic diagram illustrating a specific implementation of a cross-application service scheduling and execution system in a vehicle environment, as provided in this application embodiment. (Refer to...) Figure 3 The system may include:

[0196] The acquisition module 31 is used to acquire known service-related data of the vehicle system, as well as environmental perception data of the vehicle environment and user voice information;

[0197] The parsing module 32 is used to parse the user's voice information using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services;

[0198] The generation module 33 is used to form scene adaptation parameters based on the environmental perception data and generate multiple first instruction transmission signals corresponding to each cross-application service based on the target requirement information.

[0199] Optimization module 34 is used to optimize the first instruction transmission signal using an amplifier to obtain multiple second instruction transmission signals;

[0200] The detection module 35 is used to perform resource occupancy detection and execution order detection on each target requirement information based on the service-related data, intent association information, voice feature information and scene adaptation parameters, and obtain the detection results through the conflict coordination engine.

[0201] The execution module 36 is used to generate a target service execution sequence adapted to the vehicle driving state based on the detection results, and, in conjunction with the second instruction transmission signal, to schedule and execute multiple cross-application services in the vehicle environment, and generate service scheduling and execution results.

[0202] This application provides an embodiment of a cross-application service scheduling and execution system in a vehicle environment to implement the aforementioned cross-application service scheduling and execution method in a vehicle environment. Therefore, the specific implementation of the cross-application service scheduling and execution system in a vehicle environment can be found in the embodiment section of the cross-application service scheduling and execution method in a vehicle environment described above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0203] This application also provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the steps of the cross-application service scheduling and execution method in a vehicle environment as described above.

[0204] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, implements the steps of the cross-application service scheduling execution method in a vehicle environment as described above.

[0205] In one exemplary embodiment, the computer storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0206] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the cross-application service scheduling execution method in a vehicle environment.

[0207] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0208] The above provides a detailed description of a cross-application service scheduling and execution method and system in a vehicle environment provided by this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A cross-application service scheduling and execution method in a vehicle environment, characterized in that, include: Acquire known service-related data from in-vehicle systems, as well as environmental perception data and user voice information from the in-vehicle environment; The user's voice information is analyzed using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services; Based on the environmental perception data, scene adaptation parameters are formed, and based on the target requirement information, multiple first instruction transmission signals corresponding to each cross-application service are generated. The first command transmission signal is optimized using an amplifier to obtain multiple second command transmission signals; Based on the service-related data, intent association information, voice feature information, and scene adaptation parameters, the conflict coordination engine performs resource occupancy detection and execution order detection on each target requirement information to obtain the detection results. Based on the detection results, a target service execution sequence adapted to the vehicle driving state is generated. Combined with the second instruction transmission signal, multiple cross-application services are scheduled and executed in the vehicle environment, and a service scheduling execution result is generated. The detection results include resource conflict detection results and sequence detection results; Based on the service-related data, intent association information, voice feature information, and scene adaptation parameters, the conflict coordination engine performs resource occupancy detection and execution order detection on each target requirement information to obtain detection results, including: Based on the resource configuration information of the vehicle system and the target requirement information, determine the types of hardware resources and the amount of software resources required to execute each target requirement information, so as to form a resource requirement list; Based on the service-related data, analyze the occupancy status of each hardware resource type in the vehicle system, the remaining availability of each software resource, and combine the historical time consumption data of each resource executing cross-application services to generate a resource status dataset. Extract resource scheduling constraints corresponding to different driving scenario types from the scenario adaptation parameters; Based on the intent association information, the execution association relationship between each target requirement information is analyzed, and based on the voice feature information, the expression features corresponding to each target requirement information are determined; Based on the resource demand list and resource status dataset, the conflict coordination engine performs resource occupancy analysis on the target demand information and generates resource conflict detection results. Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression features, the conflict coordination engine performs conflict resolution processing on all target requirement information to generate sequence detection results. Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression features, the conflict coordination engine performs conflict resolution processing on all target requirement information to generate sequence detection results, including: Based on the resource conflict detection results, resource scheduling constraints, execution relationships, and expression characteristics, the conflict coordination engine determines the conflict resolution priority of each target requirement information. Based on the resource conflict detection results, identify conflict information and corresponding related demand information where resource occupation conflicts exist, and confirm the resource conflict type and resource impact range of the conflict information and related demand information; Based on the execution association and the conflict resolution priority of each target requirement information, the conflict information and associated requirement information are resolved to obtain conflict-free information. All conflict-free information is verified, and the verified conflict-free information is integrated according to the execution association to form a sequential detection result.

2. The method according to claim 1, characterized in that, Based on the environmental perception data, scene adaptation parameters are formed. Based on the target requirement information, multiple first instruction transmission signals corresponding to each cross-application service are generated, including: The environmental perception data is subjected to correlation analysis to generate information correlation features, and different driving scenario types are classified based on the information correlation features. Based on the aforementioned information association characteristics, and the signal transmission requirements and triggering conditions for cross-application service execution, adaptation rules related to cross-application service execution are set for different driving scenario types. Based on the parameter recognition specifications of the vehicle system, various adaptation rules are transformed into scene adaptation parameters that can be recognized by the vehicle system. The target requirement information of each cross-application service is broken down into execution actions, operation objects, and operation parameters, and the execution actions, operation objects, and operation parameters corresponding to each target requirement information are converted into first instruction transmission signals that conform to the signal transmission specifications of the vehicle system.

3. The method according to claim 1, characterized in that, The first command transmission signal is optimized using an amplifier to obtain multiple second command transmission signals, including: Based on the signal type of the first instruction transmission signal and the scenario adaptation parameters, determine the transmission characteristic parameters of each first instruction transmission signal; Based on the signal transmission strength benchmark value and interference threshold under different driving scenario types in the scenario adaptation parameters, and combined with each transmission characteristic parameter, the initial gain parameter and initial filtering parameter of the amplifier are adjusted to generate an adaptation parameter set corresponding to each first command transmission signal. According to each set of adaptation parameters, the corresponding first instruction transmission signal is calibrated in frequency band to obtain multiple preprocessed signals; The preprocessed signals are optimized by the amplifier according to each set of adaptation parameters to obtain multiple optimized signals; Based on the strength benchmark value and interference threshold, the signal strength and anti-interference capability of each optimized signal are verified, and the optimized signal that passes the verification is used as the second instruction transmission signal.

4. The method according to claim 3, characterized in that, Based on the signal transmission strength benchmark and interference threshold under different driving scenario types in the scenario adaptation parameters, and combined with various transmission characteristic parameters, the initial gain parameters and initial filtering parameters of the amplifier are adjusted to generate an adaptation parameter set corresponding to each first command transmission signal, including: Based on the signal transmission requirements of various driving scenarios, the parameter deviations of each transmission characteristic parameter from the strength benchmark value and interference threshold are analyzed, and a deviation dataset is generated. Based on the deviation dataset, the initial gain parameters of the amplifier are adjusted in stages, and the initial filter parameters of the amplifier are adjusted for frequency band adaptation based on the numerical range of each interference threshold, so as to generate parameter adjustment records. From the parameter adjustment records, a candidate parameter set is selected that matches the transmission characteristic parameters and deviation dataset of each first command transmission signal; The candidate parameter sets corresponding to each first instruction transmission signal are verified to identify the parameter set that has no parameter conflicts, is adapted to the signal transmission requirements and corresponding transmission characteristic parameters of the corresponding driving scenario type, and is used as the adaptation parameter set.

5. The method according to claim 1, characterized in that, Based on the detection results, a target service execution sequence adapted to the vehicle's driving state is generated. Combined with the second instruction transmission signal, multiple cross-application services are scheduled and executed in the vehicle environment, generating service scheduling and execution results, including: Based on the detection results and the service execution constraints corresponding to different driving scenario types in the scenario adaptation parameters, an initial service execution sequence is generated. The second instruction transmission signal corresponding to each target requirement information is bound to the corresponding service execution node in the initial service execution sequence to form a target service execution sequence; According to the target service execution sequence, an execution trigger instruction is sent to the corresponding functional module of the vehicle system to trigger the startup of the first cross-application service corresponding to the first service execution node. After confirming that the first cross-application service has completed the preset operation according to the target service execution sequence and the second instruction transmission signal, the startup of the cross-application service corresponding to the next service execution node is triggered based on the target service execution sequence. This continues until all cross-application services corresponding to all service execution nodes have completed the preset operation. Then, the execution feedback information of each cross-application service is integrated to form a service scheduling execution result.

6. A cross-application service scheduling and execution system in a vehicle environment, used for the cross-application service scheduling and execution method in a vehicle environment as described in any one of claims 1 to 5, characterized in that, include: The acquisition module is used to acquire known service-related data of the in-vehicle system, as well as environmental perception data of the in-vehicle environment and user voice information; The parsing module is used to parse the user's voice information using speech recognition technology to obtain target demand information, intent association information, and voice feature information across multiple application services; The generation module is used to form scene adaptation parameters based on the environmental perception data and generate multiple first instruction transmission signals corresponding to each cross-application service based on the target requirement information. An optimization module is used to optimize the first command transmission signal using an amplifier to obtain multiple second command transmission signals; The detection module is used to perform resource occupancy detection and execution order detection on each target requirement information based on the service-related data, intent association information, voice feature information and scene adaptation parameters, and obtain the detection results through the conflict coordination engine. The execution module is used to generate a target service execution sequence adapted to the vehicle driving state based on the detection results, and, in conjunction with the second instruction transmission signal, to schedule and execute multiple cross-application services in the vehicle environment, and generate service scheduling and execution results.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a cross-application service scheduling and execution method in a vehicle environment as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a cross-application service scheduling and execution method in a vehicle environment as described in any one of claims 1 to 5.

Images