Data processing method, computing device, storage medium and computer program product

Abstract

The present specification provides a data processing method, a computing device, a storage medium and a computer program product, the data processing method comprising: determining a target object, and determining a target audio unit set corresponding to the target object, the target audio unit set comprising at least two candidate audio units; obtaining object feedback information of the target object at the current moment, and obtaining a target audio parameter of the target object at the current moment according to reference audio information associated with the target audio unit set; and selecting a target audio unit from the at least two candidate audio units according to the object feedback information and the target audio parameter so as to play the target audio unit in the target object. The data processing method can be widely applied to the fields of virtual display processing software, digital culture product manufacturing software, digital culture creative software and the like in the digital creative technology.

Description

Technical Field

[0001] This manual relates to the fields of digital cultural and creative activities and computer technology, and in particular to data processing methods, computing devices, storage media and computer program products. Background Technology

[0002] In the current audio field, diverse audio playback is typically achieved through random or sequential playback. For example, audio players can be set to random or sequential playback, or background audio in virtual scenes can also be played in random or sequential order. However, this simple and fixed playback method is insufficient to meet the audio output needs of complex scenarios and cannot flexibly adapt to changes in scene and user requirements, leading to a decline in the user's audio playback experience. Therefore, an effective technical solution is urgently needed to address these issues. Summary of the Invention

[0003] In view of this, embodiments of this specification provide a data processing method. This specification also relates to a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the aforementioned problems existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, comprising: Identify the target object and determine the target audio unit set corresponding to the target object, wherein the target audio unit set includes at least two candidate audio units; Obtain the object feedback information of the target object at the current moment, and obtain the target audio parameters of the target object at the current moment based on the reference audio information associated with the target audio unit set; Based on the object feedback information and the target audio parameters, a target audio unit is selected from the at least two candidate audio units to play the target audio unit in the target object.

[0005] According to a second aspect of the embodiments of this specification, a data processing apparatus is provided, comprising: The determination module is configured to determine a target object and determine a set of target audio units corresponding to the target object, wherein the set of target audio units includes at least two candidate audio units; The acquisition module is configured to acquire the object feedback information of the target object at the current time, and acquire the target audio parameters of the target object at the current time based on the reference audio information associated with the target audio unit set; The selection module is configured to select a target audio unit from at least two candidate audio units based on the object feedback information and the target audio parameters, so as to play the target audio unit in the target object.

[0006] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0007] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores a computer program / instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0008] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0009] This specification provides a data processing method, comprising: determining a target object and determining a target audio unit set corresponding to the target object, wherein the target audio unit set includes at least two candidate audio units; obtaining object feedback information of the target object at the current time, and obtaining target audio parameters of the target object at the current time based on reference audio information associated with the target audio unit set; and selecting a target audio unit from the at least two candidate audio units based on the object feedback information and the target audio parameters, so as to play the target audio unit in the target object.

[0010] In the above method, after determining the target object, the set of target audio units corresponding to the target object can be determined, and the object feedback information of the target object at the current moment can be obtained. Based on the reference audio information associated with the set of target audio units, the target audio parameters of the target object at the current moment can be obtained. Based on the object feedback information and the target audio parameters, the target audio unit is selected from at least two candidate audio units, realizing the dynamic selection of the target audio unit. The target audio parameters of the target object at the current moment enable the selection of the target audio unit to meet the needs of scene changes. Combined with the object feedback information of the target object at the current moment, the selection of the target audio unit can meet the changes in user needs. The combination of target audio parameters and object feedback information realizes flexible adaptation to scene changes and changes in user needs, further ensuring the user's audio playback experience. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this specification; Figure 2 This is a schematic diagram of a random selection rule in a data processing method provided in one embodiment of this specification; Figure 3 This is a schematic diagram of the sorting and selection rules in a data processing method provided in one embodiment of this specification; Figure 4 This is a schematic diagram illustrating the rule for avoiding recent duplicate selections in a data processing method provided in one embodiment of this specification. Figure 5 This is a flowchart illustrating the dynamic adjustment of weight information in a data processing method provided in one embodiment of this specification. Figure 6 This is an architectural diagram of an audio unit control device provided in one embodiment of this specification; Figure 7 This is a schematic diagram of the structure of a data processing apparatus provided in one embodiment of this specification; Figure 8 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0012] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0013] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0014] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0015] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this manual are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0016] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0017] Proportional selection: A selection strategy where individuals with higher fitness have a greater probability of being selected. It's analogous to dividing a disk (roulette wheel) into several sector-shaped areas based on the fitness of each individual; the higher the fitness, the larger the area occupied, and the higher the probability of a random "pin toss" landing in that sector.

[0018] VR: Virtual Reality, a computer-generated virtual world in which users immerse themselves through specialized devices (such as VR headsets) and are isolated from the real environment.

[0019] AR: Augmented Reality, which overlays virtual information (such as images, text, and 3D models) onto the real world to achieve a combination of the virtual and the real.

[0020] This specification provides a data processing method, and also relates to a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0021] The data processing method provided in this specification can be used in digital cultural service software. By determining the target audio unit set corresponding to the target object, and by obtaining the target audio parameters of the target object at the current moment through the object feedback information of the target object at the current moment and the reference audio information associated with the target audio unit set, the target audio parameters of the target object at the current moment are obtained. Based on the object feedback information and the target audio parameters, the target audio unit is selected from at least two candidate audio units and played, thereby realizing real-time feedback and dynamic adjustment of the audio played on the target object, thus providing users with immersive digital cultural content services.

[0022] Figure 1 A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps: Step 102: Determine the target object and determine the target audio unit set corresponding to the target object, wherein the target audio unit set includes at least two candidate audio units.

[0023] Specifically, the data processing methods provided in the embodiments of this specification can be applied to the server of the target object.

[0024] Here, the target object can be understood as the object that needs to be played audio, and the target audio unit set can be understood as the set of audio units played in the target object. The candidate audio units included in the target audio unit set can have the same audio type.

[0025] For example, the target object can be a virtual scene, such as a game scene in a game. The target audio unit set can then be a set of background audio to be played within that game scene. Understandably, the target object can correspond to multiple candidate audio unit sets, each with a different audio type. The target audio unit set is any one of these candidate sets. For instance, if the game scene is a rainy day, the target audio unit set corresponding to that scene could be a thunder audio unit set. This thunder audio unit set could include at least two candidate thunder audio units of different types, such as a loud thunder audio unit or a booming thunder audio unit. Alternatively, the target audio unit set corresponding to the game scene could also be a weapon sound effect audio unit set or a noise reduction signal set; this specification does not limit this. Furthermore, the target object can also be an audio playback system, a noise reduction device, a VR / AR audio system, a scene-based sound effect output system, etc., and this specification does not limit this either.

[0026] Based on this, the target object for which audio playback is required can be identified, and the set of target audio units to be played within that target object can be determined.

[0027] In specific implementation, determining the set of target audio units corresponding to the target object includes: Obtain the reference audio information corresponding to the target object; From the candidate audio unit set, the candidate audio unit set associated with the reference audio information is determined as the target audio unit set corresponding to the target object, wherein the candidate audio unit set is an audio unit set created in advance for the target object, and the candidate audio unit set includes multiple candidate audio units.

[0028] The reference audio information can be understood as shared dynamic parameters pre-bound to the candidate audio unit set and the target object. The reference audio information can be associated with the audio type of the candidate audio unit set. For example, if the candidate audio unit set is a thunder audio unit set, the shared dynamic parameter bound to the candidate audio unit set can be a rainfall parameter.

[0029] Specifically, after identifying the target object, the shared dynamic parameters corresponding to that target object can be obtained. Then, from multiple candidate audio unit sets corresponding to the target object, the set of candidate audio units associated with these shared dynamic parameters can be determined as the target audio unit set corresponding to the target object. For example, taking the target object as a target game scene, after the game user controls the game character to enter the target game scene, the shared dynamic parameters corresponding to that target game scene can be obtained. Then, from multiple candidate audio unit sets corresponding to that target game scene, the set of candidate audio units associated with these shared dynamic parameters can be determined as the target audio unit set corresponding to the target game scene.

[0030] In practical applications, during the editing phase targeting a specific object, editors can use a visual editing interface displayed on the client to edit the set of candidate audio units for the target object, as well as the unit attribute information of each candidate audio unit within that set. For example, if the target object is a game scene, the editing phase could be the game development phase. If the target object is a noise reduction device, VR / AR audio system, or scene-based sound effects system, the editing phase could be the configuration phase, in which case the editors could be game developers, audio system engineers, etc.

[0031] This visual editing interface allows users to create candidate audio unit sets for a target object, add, delete, and adjust candidate audio units within these sets, bind shared dynamic parameters to the sets, configure weight calculation functions (linear, exponential, piecewise functions, etc.) for each candidate audio unit, and record attribute information such as basic weights, adaptive parameter ranges, and the number of audio samples for each candidate audio unit. Specifically, during the editing phase, multiple randomized audio units under the same parent level can be defined as a related unit group. This related unit group constitutes a candidate audio unit set, and the candidate audio units included in the set are the randomized audio units. Shared dynamic parameters (such as rainfall parameters, activity intensity parameters, and time period parameters) are bound to this related unit group, and a corresponding weight calculation function (positive correlation, negative correlation, or a custom curve) is configured for each candidate audio unit.

[0032] Furthermore, after configuring the candidate audio unit set during the configuration phase, the candidate audio unit set and the attribute information of each candidate audio unit can be stored in the target object's database based on a tree-structured hierarchical storage architecture. The parent node is the candidate audio unit set, and the child nodes are the candidate audio units within that set.

[0033] For example, taking the target game scene as an example, the candidate audio unit set can be a thunder audio unit set, and the shared dynamic parameter bound to the thunder audio unit set can be a rainfall parameter; the candidate audio unit set can be an activity sound effect audio unit set, and the shared dynamic parameter bound to the activity sound effect audio unit set can be an activity intensity parameter; the candidate audio unit set can be a battle sound effect audio unit set, and the shared dynamic parameter bound to the battle sound effect audio unit set can be a battle level parameter.

[0034] During the target object's execution phase, shared dynamic parameters of the current environment / scene (i.e., the target object) can be collected in real time based on the feedback acquisition module. For example, if the target game scene entered by the game user is a rainy game scene, the rainfall parameter of the rainy game scene at the current moment is collected as 80; if the target game scene entered by the game user is a battle scene, the activity intensity parameter of the battle scene at the current moment is collected as 10; if the target game scene entered by the game user is a late-night scene, the time period parameter of the late-night scene at the current moment is collected as 23:00. Then, based on the collected shared dynamic parameters, the target audio unit set bound to the shared dynamic parameters can be matched from multiple candidate audio unit sets, thereby automatically triggering the scheduling of the target audio unit set.

[0035] Understandably, a target object can correspond to multiple candidate audio unit sets. Taking a battle scene as an example, the activity intensity parameter and battle level parameter of the battle scene can be collected. Based on the activity intensity parameter, the target audio unit set can be determined as the activity sound effect audio unit set, and based on the battle level parameter, the target audio unit set can be determined as the battle sound effect audio unit set. For each target audio unit set, subsequent target audio units can be selected and played.

[0036] In summary, by manually configuring relevant parameters through a visual editing interface during the target object's editing phase, instead of using a script-based hard-coded weighting scheme, code maintenance costs can be reduced. During the target object's runtime phase, users can obtain a more immersive audio output experience that better meets their needs, enhancing the personalized audio playback experience and noise reduction effect.

[0037] Step 104: Obtain the object feedback information of the target object at the current moment, and obtain the target audio parameters of the target object at the current moment based on the reference audio information associated with the target audio unit set.

[0038] The object feedback information can be understood as the feedback signal for the currently playing audio on the target object. This feedback information can include residual noise, scene parameters, and user actions related to the currently playing audio. For example, a user's action to skip the currently playing audio could be considered. The target audio parameters can be understood as the parameter values ​​corresponding to the collected reference audio information. For instance, if the reference audio information is rainfall parameters, then the target audio parameters are the rainfall parameter values. In other words, the target audio parameters are the values ​​of shared dynamic parameters.

[0039] Specifically, it can obtain the object feedback information of the target object at the current moment according to a preset time interval, and collect the target audio parameters of the target object at the current moment.

[0040] In practical applications, object feedback information and target audio parameters can be collected based on the target object's acquisition interface. For example, a microphone can be used to collect ambient noise at the current moment, sensors can be used to collect the device status information of the target object's operating equipment, scene parameter interfaces can be used to collect scene dynamic data, and user interaction interfaces can be used to collect user actions on the target object. A preset sampling frequency can be used to perform 16-bit quantization processing on the collected data, and a sliding window can be used to extract the feature values ​​of the collected data as object feedback information to ensure the real-time performance of the collected feedback signal. The preset sampling frequency can be 44.1kHz, and the sliding window size can be 10ms.

[0041] Step 106: Based on the object feedback information and the target audio parameters, select a target audio unit from the at least two candidate audio units to play the target audio unit in the target object.

[0042] Specifically, based on the object feedback information and target audio parameters collected at the current moment, a target audio unit can be selected from at least two candidate audio units so that the target audio unit can be played in the target object later.

[0043] In specific implementation, selecting the target audio unit from the at least two candidate audio units based on the object feedback information and the target audio parameters includes: Based on the object feedback information and the target audio parameters, calculate the weight information corresponding to each candidate audio unit; Based on the weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

[0044] Specifically, based on the object feedback information and the target audio parameters, the weight information corresponding to each candidate audio unit in the target audio unit set can be calculated, and the target audio unit can be selected from at least two candidate audio units based on the weight information corresponding to each candidate audio unit.

[0045] In one embodiment of this specification, the target audio unit can be selected from at least two candidate audio units based on the weight information corresponding to each candidate audio unit. In another embodiment of this specification, the target audio unit can be selected from at least two candidate audio units based on the weight information corresponding to each candidate audio unit, following a probability selection method where higher weight information corresponds to a higher selection probability.

[0046] In summary, by calculating weight information and determining the target audio unit based on object feedback information and target audio parameters, flexible adaptation to environmental changes can be achieved, further meeting the dynamic needs of user behavior and scene status, significantly improving robustness, and achieving good adaptive capabilities.

[0047] Further, the step of calculating the weight information corresponding to each candidate audio unit based on the object feedback information and the target audio parameters includes: Based on the feedback information from the object, determine the weight adjustment parameters; Based on the weight calculation function corresponding to each candidate audio unit, the target audio parameters and the weight adjustment parameters are calculated to obtain the weight information corresponding to each candidate audio unit.

[0048] The weight adjustment parameter can be understood as a correction factor used to adaptively adjust the accuracy of weight allocation. There is a one-to-one correspondence between candidate audio units and weight adjustment parameters, and one weight adjustment parameter can also correspond to multiple candidate audio units. The weight calculation function can be understood as a response function configured for the candidate audio units. The response function can include linear functions, exponential functions, and piecewise functions. This weight calculation function can be configured during the aforementioned editing stage, and the weight calculation function corresponding to each candidate audio unit can be customized according to different scenario requirements.

[0049] For example, the weight calculation function for the "thunderous" audio unit can be f(x) = x / 100, and the weight calculation function for the "muffled thunder" audio unit can be f(x) = (100-x) / 100. Here, x is the rainfall parameter, with a value ranging from 0 to 100.

[0050] Based on this, the weight adjustment parameters can be determined according to the object feedback information of the target object at the current moment. Based on the weight calculation function corresponding to each candidate audio unit, the target audio parameters and weight adjustment parameters are calculated to obtain the weight information corresponding to each candidate audio unit.

[0051] In practice, the weight adjustment parameters for each candidate audio unit can be determined based on the object feedback information for each candidate audio unit.

[0052] In practical applications, after collecting object feedback information, the collected feedback information (such as residual noise and scene parameters) can be filtered and normalized to remove outlier interference, thereby ensuring the stability of the collected object feedback information. Furthermore, when the object feedback information displays a deviation in output effect (such as insufficient noise attenuation or the user frequently skipping a certain audio segment), the weight adjustment parameters can be automatically corrected to optimize the weight allocation accuracy. The adjustment range of the weight adjustment parameters can be from 0.8 to 1.2.

[0053] In one optional embodiment of this specification, when calculating the weight information corresponding to each candidate audio unit, the basic weight corresponding to each candidate audio unit can be determined first. Then, based on the weight calculation function, the target audio parameters, the weight adjustment parameters, and the basic weights, the weight information corresponding to each candidate audio unit can be calculated. That is, the basic weights can be adjusted according to the weight calculation function, the target audio parameters, and the weight adjustment parameters to obtain the final weight information.

[0054] The formula for calculating the weight information adjustment is: w(n) = w0 × f(x) × α, where w0 is the basic weight, f(x) is the weight calculation function (x is a shared dynamic parameter), and α is the adaptive adjustment factor (i.e., the weight adjustment parameter), which can be dynamically corrected based on feedback signals. Furthermore, the weight adjustment response time can be less than or equal to 10ms, and the emergency scenario switching response time can be less than or equal to 5ms.

[0055] Furthermore, the deviation in the output effect of the object feedback information can be determined in the following ways: by comparing the object feedback information with a preset feedback threshold, it can be determined whether there is a deviation in the output effect. For example, in a noise reduction scenario, if the collected object feedback information is residual noise, and if the residual noise is determined to be greater than a preset noise threshold (e.g., the residual noise after noise reduction is greater than 40dB), it can be determined that the current noise attenuation is insufficient, resulting in a deviation in the output effect. In an audio playback scenario, if the user interaction interface detects that the user skips a certain type of audio more than twice within one minute, it can be determined that the adaptation of the candidate audio unit corresponding to that type of audio is poor, resulting in a deviation in the output effect. In a volume adaptation scenario, if the user behavior is determined to be continuously increasing the volume (i.e., three consecutive volume increase operations are detected), it can be determined that the volume reference of the currently playing candidate audio unit is insufficient, resulting in a deviation in the output effect.

[0056] Understandably, the adjustment range of the α factor can be fixed at 0.8-1.2. The direction of correction is strongly correlated with the type of deviation, and the magnitude of correction is positively correlated with the degree of deviation: When the effect is insufficient (e.g., insufficient noise attenuation, low audio adaptation to the scene), α > 1 (the higher the degree of deviation, the closer α is to 1.2). For example, when residual noise = 50dB (severely insufficient), α = 1.2; when residual noise = 45dB (slightly insufficient), α = 1.05. When the effect is excessive / incompatible (e.g., users frequently skip, excessive appearance of a certain type of audio), α < 1 (the higher the degree of deviation, the closer α is to 0.8). For example, when a user skips an audio unit 3 times in 1 minute (severely incompatible), α = 0.8; when a user skips an audio unit once in 1 minute (slightly incompatible), α = 0.95. When the object's feedback information is within the preset feedback threshold, α = 1, and the initial weight is maintained. By dynamically adjusting the weight adjustment parameters, the weights of candidate audio units are made to better match the actual output effect requirements, improving adaptability.

[0057] In summary, by determining the weight adjustment parameters based on object feedback information, the weight information can be adjusted according to changes in the current scene and user behavior, thereby enabling the selection of target audio units and achieving flexible adaptation to scene changes and user behavior.

[0058] In one embodiment of this specification, selecting a target audio unit from at least two candidate audio units based on the weight information corresponding to each candidate audio unit includes: Based on the weight information corresponding to each candidate audio unit, a set of candidate weight levels corresponding to each candidate audio unit is determined, wherein there are multiple sets of candidate weight levels, and the set of candidate weight levels includes candidate audio units whose weight information belongs to a preset weight range; Based on the preset weight ranges corresponding to multiple candidate weight level sets, a target weight level set is selected from the candidate weight level sets. Target audio units are selected from the target weight level set according to preset selection rules.

[0059] The candidate weight level set can be understood as a set of weight levels obtained by dividing multiple candidate audio units, and there is a one-to-one correspondence between the candidate weight level set and the preset weight interval.

[0060] Specifically, each candidate audio unit can be assigned to a corresponding candidate weight level set based on its weight information. Then, a target weight level set is selected from the candidate weight level sets based on a preset weight range corresponding to each set. In practice, a proportional selection method can be used to choose the target weight level set from multiple candidate weight level sets; the candidate weight level set with the higher preset weight range has a greater probability of being selected as the target weight level set.

[0061] In practical applications, the candidate weight level set can be understood as a priority pool. Candidate audio units in the same target audio unit set whose weight information belongs to the same preset weight range are classified into the same priority pool. Each priority pool corresponds to a priority. The higher the preset weight range corresponding to the priority pool, the higher the priority. Through the mapping of weight information to priority pool to selection probability, the adaptability (i.e., candidate audio units with higher weight information are given priority) and randomness (i.e., there is no fixed order within the same priority) are balanced.

[0062] Furthermore, the number of candidate weight level sets can be determined according to actual needs. For example, it can be divided into 3 candidate weight level sets (i.e., 3 priority pools): high weight pool: weight ≥ 60%, medium weight pool: 30% ≤ weight < 60%, and low weight pool: weight < 30%; or it can be subdivided into 10 candidate weight level sets, i.e., 10 priority pools, according to a weight range of 10%.

[0063] In summary, by dividing the priority pool according to the weight information, it is easier to perform random scheduling in each priority pool. The weight information can inversely constrain the selection probability of candidate audio units in the randomized priority pool, avoid the excessive occurrence of low-weight candidate audio units, and balance randomness and adaptability.

[0064] Further, the step of selecting target audio units from the target weight level set according to preset selection rules includes: From the set of target weight levels, candidate audio units are randomly selected as the target audio units; or Sort all candidate audio units included in the target weight level set to obtain sorted candidate audio units, and select the target audio unit from the sorted candidate audio units according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio units included in the target weight level set are filtered to obtain the filtered candidate audio units, and the target audio unit is selected from the filtered candidate audio units.

[0065] The historical playback information corresponding to the target object can be understood as the information of the audio units played by the target object within a historical time period. This historical playback information can be obtained from the target object's audio playback cache queue. Preset selection rules include random selection rules, sorted selection rules, and rules to avoid recent duplicate selections. The target weight level set can include one or more candidate audio units.

[0066] In one embodiment of this specification, any candidate audio unit can be randomly selected as the target audio unit from the target weight level set based on a random selection rule. In practical applications, a pseudo-random number generator can be used to randomly select from the target weight level set, with each candidate audio unit having an equal probability of being selected. See also Figure 2 , Figure 2 A schematic diagram of a random selection rule in a data processing method according to an embodiment of this specification is shown, such as... Figure 2 As shown, a target weight level set can be input into the pseudo-random number generator. The pseudo-random number generator selects target audio units from the target weight level set with equal probability and can output target audio units A, C, A, B... The output target audio units have no repetition restrictions.

[0067] In one embodiment of this specification, all candidate audio units included in the target weight level set can be sorted based on sorting selection rules to obtain sorted candidate audio units, and the target audio unit can be selected according to the sorting order. See also Figure 3 , Figure 3This diagram illustrates a sorting and selection rule in a data processing method according to an embodiment of this specification, such as... Figure 3 As shown, for the input target weight level set, all candidate audio units in the target weight level set can be sorted, and the target audio units can be selected according to the sorting order, and the target audio units A, B, C, D... can be output.

[0068] In another embodiment of this specification, based on the rule of avoiding recent duplicate selection, all candidate audio units included in the target weight level set can be filtered according to the historical playback information corresponding to the target object. The most recently played candidate audio units in the target object are deleted, resulting in a filtered list of candidate audio units. The target audio unit is then selected from this list. In practical applications, a cache queue can be used to record the most recently played M audio units. During selection, the M audio units in this cache queue are excluded. M can be customized according to actual needs; for example, M could be 3. See also... Figure 4 , Figure 4 This diagram illustrates a rule for avoiding recent duplicate selections in a data processing method according to an embodiment of this specification. Figure 4 As shown, for the input target weight level set, the three most recent audio units can be cached. For all candidate audio units in the target weight level set, the three audio units are deleted. The target audio units are selected from the remaining candidate audio units, and the target audio units A, B, C, D, E, etc. can be output, so that the output target audio units have no recent repetition.

[0069] Furthermore, if it is determined that the weight information of a candidate audio unit in the target weight level set is less than a preset weight threshold, the candidate audio unit is removed from the target weight level set to avoid ineffective scheduling and thus improve operating efficiency. In practical applications, the preset weight threshold can be 5%.

[0070] In practical applications, selection probabilities can be initially allocated according to the priority pools (the overall probability of a high-priority pool being selected is much higher than that of a low-priority pool, determined by the total weight ratio of each priority pool in the target audio unit set). Once a priority pool is selected, random scheduling is performed among all candidate audio units within that pool according to preset selection rules to select the target audio unit for playback. For example, the high-weight pool (thunderous audio units) of the thunder audio unit set has a 70% selection probability, and thunderous samples are randomly selected from the priority pool according to the rule of avoiding recent repetition; the low-weight pool (muffled thunder audio units) has a 30% selection probability, and samples are selected from the priority pool according to random selection rules—ensuring a high proportion of thunderous samples when rainfall is heavy (adapting to the scenario) while avoiding repeated playback of thunderous samples (maintaining randomness).

[0071] Furthermore, the candidate audio unit includes at least one candidate audio sample; The step of selecting target audio units from the target weight level set according to preset selection rules includes: From at least one candidate audio sample included in the target weight level set, randomly select any candidate audio sample as the target audio sample; or Sort all candidate audio samples included in the target weight level set to obtain sorted candidate audio samples, and select target audio samples from the sorted candidate audio samples according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target weight level set are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples.

[0072] In this context, the candidate audio unit set can be understood as a large collection of sound effects of the same type. For example, the thunder audio unit set includes all audio units of thunder-related sound effects. The candidate audio units included in the candidate audio unit set can be understood as subdivided audio types. For example, the thunder audio unit set includes the booming thunder audio unit, the muffled thunder audio unit, etc. The candidate audio sample can be understood as a single playable sound effect segment or file in the candidate audio unit. For example, the booming thunder audio unit contains 10 booming thunder sound effects, and each booming thunder sound effect is a candidate audio sample. The muffled thunder audio unit contains 8 muffled thunder sound effects, and each muffled thunder sound effect is a candidate audio sample.

[0073] Based on this, when selecting a target audio unit from the target weight level set according to the preset selection rules, the target audio sample can be selected directly from all candidate audio samples included in the target weight level set according to the preset selection rules. The target audio sample can be selected according to the above-mentioned random selection rules, sorting selection rules, or rules to avoid recent duplicate selection.

[0074] It is understandable that the process of selecting target audio samples according to random selection rules, sorting selection rules, or rules to avoid recent duplicate selection is similar to the process of selecting target audio units according to random selection rules, sorting selection rules, or rules to avoid recent duplicate selection described above. Therefore, the embodiments in this specification will not be repeated.

[0075] In one embodiment of this specification, the target audio unit includes at least two candidate audio samples; After selecting the target audio unit from the at least two candidate audio units based on the object feedback information and the target audio parameters, the method further includes: At least two candidate audio samples included in the target audio unit are played in the target object according to a preset selection rule.

[0076] Specifically, based on the object's feedback information and the target audio parameters, after selecting the target audio unit with higher weight information from at least two candidate audio units, the target audio unit can be selected from at least two candidate audio units included in the target audio unit according to the preset selection rules and played.

[0077] In specific implementation, playing at least two candidate audio samples included in the target audio unit in the target object according to a preset selection rule includes: From the target audio unit, randomly select any candidate audio sample as the target audio sample and play it; or Sort all candidate audio samples included in the target audio unit to obtain sorted candidate audio samples, and play the sorted candidate audio samples in the sorted order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target audio unit are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples and played.

[0078] Specifically, when selecting target audio samples from target audio units according to preset selection rules, the selection of target audio samples can be based on the above-mentioned random selection rules, sorted selection rules, or rules to avoid recent duplicate selections.

[0079] It is understandable that the process of selecting target audio samples according to random selection rules, sorting selection rules, or rules to avoid recent duplicate selection is similar to the process of selecting target audio units according to random selection rules, sorting selection rules, or rules to avoid recent duplicate selection described above. Therefore, the embodiments in this specification will not be repeated.

[0080] Furthermore, after calculating the weight information corresponding to each candidate audio unit based on the object feedback information and the target audio parameters, the method further includes: The weight information corresponding to each candidate audio unit is normalized and corrected to obtain the corrected weight information corresponding to each candidate audio unit. The step of selecting a target audio unit from at least two candidate audio units based on the weight information corresponding to each candidate audio unit includes: Based on the corrected weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

[0081] Specifically, the sum of the weight information corresponding to each candidate audio unit can be calculated. Based on this sum, the weight information corresponding to each candidate audio unit is normalized and corrected so that the sum is 100%, thus obtaining the corrected weight information corresponding to each candidate audio unit. Subsequently, based on the corrected weight information corresponding to each candidate audio unit, the target audio unit can be selected from at least two candidate audio units using a proportional selection method. The higher the proportion of the weight information of a candidate audio unit, the greater the probability of it being selected as the target audio unit.

[0082] In practical applications, the normalization correction formula is: w_normalized(i) = w(i) / Σw(i), where w represents the weight information and i represents each candidate audio unit, ensuring that Σw_normalized(i) = 100%.

[0083] Based on this, an integrated and coordinated approach is achieved for randomized scheduling, dynamic weight adjustment, and normalized probability control of audio units at the same level.

[0084] In summary, normalization ensures that the total weight of the target audio unit set remains constant, avoids probability conflicts, and balances the flexibility and rationality of audio combination.

[0085] See Figure 5 , Figure 5 A flowchart illustrating the dynamic adjustment of weight information in a data processing method according to one embodiment of this specification is shown, specifically including the following steps.

[0086] Step 502: Real-time parameter acquisition.

[0087] Specifically, feedback signals can be collected in real time.

[0088] Step 504: Signal preprocessing.

[0089] Specifically, the collected feedback signals can be filtered, anomaly removed, and standardized.

[0090] Step 506: Calculate weight information.

[0091] Specifically, the initial weights can be calculated by multiplying the base weights and the scenario coefficients.

[0092] Step 508: Dynamic weight correction.

[0093] Specifically, the initial weights can be dynamically corrected based on the response function and weight adjustment parameters to obtain weight information.

[0094] Step 510: Adaptive adjustment.

[0095] Step 512: Abnormal weight filtering.

[0096] Specifically, candidate audio units with a weight information of less than 5% can be removed.

[0097] Step 514: Weight normalization.

[0098] Specifically, the weight information of multiple candidate audio units can be normalized so that the sum of the weight information is 100%.

[0099] Step 516: Roulette probability selection.

[0100] Specifically, based on the weight information of multiple candidate audio units, the target audio unit is selected by a roulette probability (i.e., proportional selection method).

[0101] Step 518: Identify the target audio unit and play it.

[0102] Specifically, when playing the target audio unit, step 502 can be executed to collect parameters in real time, thereby achieving audio playback effect feedback.

[0103] The data processing method provided in the embodiments of this specification can be applied to the scene-based thunder ratio control in virtual scenes. The target audio unit set is configured as follows: parent unit (thunder audio unit set), child units include booming thunder audio units (including 10 booming thunder sound effects, i.e., audio samples) and muffled thunder audio units (including 8 muffled thunder sound effects, i.e., audio samples). The bound shared dynamic parameter is the rainfall parameter, with a value range of 0 to 100. The weight calculation function is configured as follows: the weight information w1 corresponding to the booming thunder audio unit = rainfall parameter × 0.8, the weight information w2 corresponding to the muffled thunder audio unit = (100 - rainfall parameter) × 0.8, the total weight ratio after normalization is 80%, and 20% is reserved for other thunder types. During operation, when the rainfall parameter is 70, the weight of the thunder audio unit is normalized to account for 70%, and the weight of the muffled thunder audio unit is normalized to account for 30%. When thunder is triggered, the thunder audio unit or the muffled thunder audio unit can be selected according to this probability and the internal audio samples can be played randomly. The greater the rainfall, the higher the probability of playing the thunder audio unit, and the scene simulation is significantly improved.

[0104] The data processing method provided in the embodiments of this specification can also be applied to the control of the proportion of activity intensity sound effects. The target audio unit set is configured as the parent unit (the set of activity sound effect audio units), and the child units include high-frequency sound effect units, mid-frequency sound effect units, and low-frequency sound effect units. The bound shared dynamic parameter is the activity intensity parameter, with a value range of 0 to 10. The weight calculation function can be configured, for example, as follows: the weight information w1 of the low-frequency sound effect unit is 10 / (activity intensity + 1) × 0.5, the weight information w2 of the high-frequency sound effect unit is 10 / 20 / 8, and the weight information w3 of the mid-frequency sound effect unit is 10 / 20 / 5. During the operation phase, when the activity intensity parameter is 1, it indicates that the activity is low-intensity, and the weight of the low-frequency sound effect unit accounts for 60%. When the activity intensity parameter is 10, it indicates that the activity is high-intensity, and the total weight of the high-frequency and mid-frequency sound effect units increases to 85%, which matches the change in scene atmosphere.

[0105] The data processing method provided in the embodiments of this specification can also be applied to active noise cancellation signal control. The target audio unit set is configured as the parent unit (noise-cancelled signal audio unit set), and the child units include low-frequency noise cancellation units, mid-frequency noise cancellation units, and high-frequency noise cancellation units. The bound shared dynamic parameter is the noise intensity parameter, with a value range of 0 to 100. The weight calculation function can be configured, for example, as follows: weight information w1 of the low-frequency noise cancellation unit = noise intensity × 0.01 × 0.7, weight information w2 of the mid-frequency noise cancellation unit = noise intensity × 0.01 × 0.2, and weight information w3 of the high-frequency noise cancellation unit = (100 - noise intensity) × 0.01 × 0.1. During the operation phase, the higher the noise intensity, for example, when the noise intensity is 80, the weight ratio of the low-frequency noise cancellation unit is 70%, specifically suppressing the main noise. When the noise intensity is low, for example, when the noise intensity is 30, the weight ratio of the high-frequency noise cancellation unit is increased to ensure audio clarity.

[0106] The data processing method provided in the embodiments of this specification can also be applied to personalized audio playback control. The target audio unit set is configured as the parent unit (audio style unit set), and the child units may include pop style audio units, rock style audio units, and classical style audio units. The bound shared dynamic parameter is the user preference value, which can be generated based on the user's playback history and user skip audio behavior, and the value range is 0 to 100. The weight calculation function can be configured, for example, as follows: the weight information w1 of the pop style audio unit = user preference value × 0.01 × 0.5, the weight information w2 of the rock style audio unit = (user preference value - 50) × 0.02 × 0.3 (effective when the user preference value ≥ 50), and the weight information w3 of the classical style audio unit = (100 - user preference value) × 0.01 × 0.2. During the operation phase, the higher the user preference value, for example, when the user preference value is 80, the playback probability of the pop style audio unit is 50% and the playback probability of the rock style audio unit is 30%. When the user preference value is low, for example, when the user preference value is 30, the playback probability of the classical style audio unit is 20% and the playback probability of the pop style audio unit is 30%. It can be understood that this playback probability is the weight ratio, which can match user preferences and ensure the diversity of audio playback.

[0107] Furthermore, corresponding to the above method embodiments, this specification also provides an audio unit control device, see [link to documentation]. Figure 6 , Figure 6 An architectural diagram of an audio unit control device according to one embodiment of this specification is shown. Figure 6As shown, the audio unit control device may include an audio unit management module, a randomized scheduling module, a dynamic weight decision module, a feedback acquisition module, and a probabilistic playback decision module. The audio unit management module is used to classify and store audio units at the same level, manage their status (activation / disabling) and attributes (duration, volume baseline, suitable scene, and audio unit set), and supports dynamic addition and removal of units and hierarchical association configuration. The randomized scheduling module supports three basic modes: random selection rule, sorted selection rule, and avoid recent repetition rule. It can configure a repetition avoidance threshold (e.g., the original unit can only be repeated after playing N new units) to ensure diverse random outputs. The dynamic weight decision module, based on real-time feedback signals (environmental noise, user interaction behavior, scene dynamic parameters, etc.), uses an optimized adaptive algorithm and temporal modeling logic to adjust the weight coefficients of audio units at the same level in real time, supporting custom configuration of linear, curvilinear, and other types of weight response functions. The feedback acquisition module acquires feedback signals in real time (sampling frequency 44.1kHz) through built-in sensors, user interaction interfaces, or scene parameter interfaces, including residual noise, user operations (volume adjustment, unit skipping), and scene dynamic parameters (rainfall, activity intensity, time period, etc.). The probabilistic playback decision module performs weight normalization on the associated unit group (i.e., the set of candidate audio units), constraining the total weight to a fixed value (e.g., 100%), and selects the target audio unit based on probability according to the dynamic weight, triggering the playback of the audio sample. All modules work together to achieve full-process control.

[0108] In summary, the above method, after determining the target object, can determine the set of target audio units corresponding to the target object, obtain the object feedback information of the target object at the current moment, and obtain the target audio parameters of the target object at the current moment based on the reference audio information associated with the target audio unit set. Based on the object feedback information and the target audio parameters, the target audio unit is selected from at least two candidate audio units, realizing the dynamic selection of the target audio unit. The target audio parameters of the target object at the current moment enable the selection of the target audio unit to meet the needs of scene changes. Combining the object feedback information of the target object at the current moment enables the selection of the target audio unit to meet the changes in user needs. Combining the target audio parameters and object feedback information achieves flexible adaptation to scene changes and changes in user needs, further ensuring the user's audio playback experience.

[0109] Corresponding to the above method embodiments, this specification also provides data processing apparatus embodiments. Figure 7 A schematic diagram of the structure of a data processing apparatus provided in one embodiment of this specification is shown. For example... Figure 7 As shown, the device includes: The determination module 702 is configured to determine a target object and determine a set of target audio units corresponding to the target object, wherein the set of target audio units includes at least two candidate audio units; The acquisition module 704 is configured to acquire the object feedback information of the target object at the current time, and acquire the target audio parameters of the target object at the current time based on the reference audio information associated with the target audio unit set; Selection module 706 is configured to select a target audio unit from the at least two candidate audio units based on the object feedback information and the target audio parameters, so as to play the target audio unit in the target object.

[0110] In an optional embodiment, the selection module 706 is further configured to: Based on the object feedback information and the target audio parameters, calculate the weight information corresponding to each candidate audio unit; Based on the weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

[0111] In an optional embodiment, the selection module 706 is further configured to: Based on the weight information corresponding to each candidate audio unit, a set of candidate weight levels corresponding to each candidate audio unit is determined, wherein there are multiple sets of candidate weight levels, and the set of candidate weight levels includes candidate audio units whose weight information belongs to a preset weight range; Based on the preset weight ranges corresponding to multiple candidate weight level sets, a target weight level set is selected from the candidate weight level sets. Target audio units are selected from the target weight level set according to preset selection rules.

[0112] In an optional embodiment, the selection module 706 is further configured to: From the set of target weight levels, candidate audio units are randomly selected as the target audio units; or Sort all candidate audio units included in the target weight level set to obtain sorted candidate audio units, and select the target audio unit from the sorted candidate audio units according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio units included in the target weight level set are filtered to obtain the filtered candidate audio units, and the target audio unit is selected from the filtered candidate audio units.

[0113] In one optional embodiment, the candidate audio unit includes at least one candidate audio sample; The selection module 706 is further configured as follows: From at least one candidate audio sample included in the target weight level set, randomly select any candidate audio sample as the target audio sample; or Sort all candidate audio samples included in the target weight level set to obtain sorted candidate audio samples, and select target audio samples from the sorted candidate audio samples according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target weight level set are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples.

[0114] In one optional embodiment, the target audio unit includes at least two candidate audio samples; The selection module 706 is further configured as follows: At least two candidate audio samples included in the target audio unit are played in the target object according to a preset selection rule.

[0115] In an optional embodiment, the selection module 706 is further configured to: From the target audio unit, randomly select any candidate audio sample as the target audio sample and play it; or Sort all candidate audio samples included in the target audio unit to obtain sorted candidate audio samples, and play the sorted candidate audio samples in the sorted order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target audio unit are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples and played.

[0116] In an optional embodiment, the selection module 706 is further configured to: Based on the feedback information from the object, determine the weight adjustment parameters; Based on the weight calculation function corresponding to each candidate audio unit, the target audio parameters and the weight adjustment parameters are calculated to obtain the weight information corresponding to each candidate audio unit.

[0117] In an optional embodiment, the selection module 706 is further configured to: The weight information corresponding to each candidate audio unit is normalized and corrected to obtain the corrected weight information corresponding to each candidate audio unit. The step of selecting a target audio unit from at least two candidate audio units based on the weight information corresponding to each candidate audio unit includes: Based on the corrected weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

[0118] In an optional embodiment, the determining module 702 is further configured to: Obtain the reference audio information corresponding to the target object; From the candidate audio unit set, the candidate audio unit set associated with the reference audio information is determined as the target audio unit set corresponding to the target object, wherein the candidate audio unit set is an audio unit set created in advance for the target object, and the candidate audio unit set includes multiple candidate audio units.

[0119] In the aforementioned device, after determining the target object, the set of target audio units corresponding to the target object can be determined, and the object feedback information of the target object at the current moment can be obtained. Based on the reference audio information associated with the set of target audio units, the target audio parameters of the target object at the current moment can be obtained. Based on the object feedback information and the target audio parameters, the target audio unit is selected from at least two candidate audio units, realizing the dynamic selection of the target audio unit. The target audio parameters of the target object at the current moment enable the selection of the target audio unit to meet the needs of scene changes. Combined with the object feedback information of the target object at the current moment, the selection of the target audio unit can meet the changes in user needs. The combination of target audio parameters and object feedback information realizes flexible adaptation to scene changes and changes in user needs, further ensuring the user's audio playback experience.

[0120] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0121] Figure 8 A structural block diagram of a computing device 800 according to an embodiment of this specification is shown. The components of the computing device 800 include, but are not limited to, a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and a database 850 is used to store data.

[0122] The computing device 800 also includes an access device 840, which enables the computing device 800 to communicate via one or more networks 860. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 840 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0123] In one embodiment of this specification, the above-described components of the computing device 800 and Figure 8 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 8 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0124] The computing device 800 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 800 can also be a mobile or stationary server.

[0125] The processor 820 is used to execute the following computer program / instructions, which, when executed by the processor, implement the steps of the above data processing method.

[0126] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0127] An embodiment of this specification also provides a computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0128] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0129] An embodiment of this specification also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0130] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the data processing method described above.

[0131] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0132] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0133] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this specification is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this specification. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this specification.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. These embodiments have been selected and specifically described in this specification to better explain the principles and practical applications of this specification, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, characterized in that, include: Identify the target object and determine the target audio unit set corresponding to the target object, wherein the target audio unit set includes at least two candidate audio units; Obtain the object feedback information of the target object at the current moment, and obtain the target audio parameters of the target object at the current moment based on the reference audio information associated with the target audio unit set; Based on the object feedback information and the target audio parameters, a target audio unit is selected from the at least two candidate audio units to play the target audio unit in the target object.

2. The method according to claim 1, characterized in that, The step of selecting a target audio unit from at least two candidate audio units based on the object feedback information and the target audio parameters includes: Based on the object feedback information and the target audio parameters, calculate the weight information corresponding to each candidate audio unit; Based on the weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

3. The method according to claim 2, characterized in that, The step of selecting a target audio unit from at least two candidate audio units based on the weight information corresponding to each candidate audio unit includes: Based on the weight information corresponding to each candidate audio unit, a set of candidate weight levels corresponding to each candidate audio unit is determined, wherein there are multiple sets of candidate weight levels, and the set of candidate weight levels includes candidate audio units whose weight information belongs to a preset weight range; Based on the preset weight ranges corresponding to multiple candidate weight level sets, a target weight level set is selected from the candidate weight level sets. Target audio units are selected from the target weight level set according to preset selection rules.

4. The method according to claim 3, characterized in that, The step of selecting target audio units from the target weight level set according to preset selection rules includes: From the set of target weight levels, candidate audio units are randomly selected as the target audio units; or Sort all candidate audio units included in the target weight level set to obtain sorted candidate audio units, and select the target audio unit from the sorted candidate audio units according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio units included in the target weight level set are filtered to obtain the filtered candidate audio units, and the target audio unit is selected from the filtered candidate audio units.

5. The method according to claim 4, characterized in that, The candidate audio unit includes at least one candidate audio sample; The step of selecting target audio units from the target weight level set according to preset selection rules includes: From at least one candidate audio sample included in the target weight level set, randomly select any candidate audio sample as the target audio sample; or Sort all candidate audio samples included in the target weight level set to obtain sorted candidate audio samples, and select target audio samples from the sorted candidate audio samples according to the sorting order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target weight level set are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples.

6. The method according to claim 1 or 2, characterized in that, The target audio unit includes at least two candidate audio samples; After selecting the target audio unit from the at least two candidate audio units based on the object feedback information and the target audio parameters, the method further includes: At least two candidate audio samples included in the target audio unit are played in the target object according to a preset selection rule.

7. The method according to claim 6, characterized in that, Playing at least two candidate audio samples included in the target audio unit in the target object according to a preset selection rule includes: From the target audio unit, randomly select any candidate audio sample as the target audio sample and play it; or Sort all candidate audio samples included in the target audio unit to obtain sorted candidate audio samples, and play the sorted candidate audio samples in the sorted order; or Based on the historical playback information corresponding to the target object, all candidate audio samples included in the target audio unit are filtered to obtain filtered candidate audio samples, and the target audio sample is selected from the filtered candidate audio samples and played.

8. The method according to claim 2, characterized in that, The step of calculating the weight information corresponding to each candidate audio unit based on the object feedback information and the target audio parameters includes: Based on the feedback information from the object, determine the weight adjustment parameters; Based on the weight calculation function corresponding to each candidate audio unit, the target audio parameters and the weight adjustment parameters are calculated to obtain the weight information corresponding to each candidate audio unit.

9. The method according to claim 2, characterized in that, After calculating the weight information corresponding to each candidate audio unit based on the object feedback information and the target audio parameters, the method further includes: The weight information corresponding to each candidate audio unit is normalized and corrected to obtain the corrected weight information corresponding to each candidate audio unit. The step of selecting a target audio unit from at least two candidate audio units based on the weight information corresponding to each candidate audio unit includes: Based on the corrected weight information corresponding to each candidate audio unit, a target audio unit is selected from the at least two candidate audio units.

10. The method according to any one of claims 1-9, characterized in that, The determination of the target audio unit set corresponding to the target object includes: Obtain the reference audio information corresponding to the target object; From the candidate audio unit set, the candidate audio unit set associated with the reference audio information is determined as the target audio unit set corresponding to the target object, wherein the candidate audio unit set is an audio unit set created in advance for the target object, and the candidate audio unit set includes multiple candidate audio units.

11. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 10.

12. A computer-readable storage medium storing a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 10.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 10.

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