Sound box optimization method, system and device and storage medium

Abstract

The embodiment of the invention provides a sound box optimization method, system and device and a storage medium, and the method comprises the steps: determining at least one group of target test parameters based on the attribute information and / or use information of a sound box; performing displacement test on the sound box based on the at least one group of target test parameters, and determining a displacement data set of the sound box; and based on the displacement data set, determining optimization parameters of the sound box. The system comprises a determination module used for determining at least one group of target test parameters based on attribute information and / or use information of the sound box; the test module is used for carrying out displacement test on the sound box based on the at least one group of target test parameters and determining a displacement data set of the sound box; and the optimization module is used for determining optimization parameters of the sound box based on the displacement data set. The method can be realized through the loudspeaker box optimization device. The method can also be operated after a computer instruction stored in a computer readable storage medium is read.

Description

Technical Field

[0001] The present invention relates to the field of speakers, and in particular to a speaker optimization method, system, device and storage medium. Background Art

[0002] The speaker cavity will vibrate when it is working. When the cabinet is light and the audio played has more low-frequency content, the speaker may move significantly, which may cause the speaker to fall from a height or change its position, affecting the user experience.

[0003] Therefore, it is necessary to propose a speaker optimization method, system, device and storage medium, which can optimize the speaker in a targeted manner to reduce or even avoid speaker displacement. Summary of the invention

[0004] One or more embodiments of the present specification provide a method for optimizing a speaker. The method includes: determining at least one set of target test parameters based on attribute information and / or usage information of the speaker, the target test parameters including at least one of a test table material and a test audio; performing a displacement test on the speaker based on the at least one set of target test parameters to determine a displacement data set of the speaker; determining an optimization parameter of the speaker based on the displacement data set, the optimization parameter including at least one of an anti-slip coefficient increment and a counterweight increment.

[0005] One or more embodiments of the present specification provide a system for checking speaker displacement, the system comprising: a determination module for determining at least one set of target test parameters based on attribute information and / or usage information of the speaker, each set of target test parameters comprising at least one of a test table material and a test audio; a testing module for performing a displacement test on the speaker based on the at least one set of target test parameters to determine a displacement data set of the speaker; an optimization module for determining optimization parameters of the speaker based on the displacement data set, the optimization parameters comprising at least one of an anti-slip coefficient increment and a counterweight increment.

[0006] One or more embodiments of the present specification provide a device for checking speaker displacement, the device comprising at least one processor and at least one memory, the at least one memory being used to store computer instructions; the at least one processor being used to execute at least part of the computer instructions to implement the speaker optimization method as described above.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the speaker optimization method as described above.

[0008] Some embodiments of the present specification include at least the following beneficial effects: (1) a speaker is tested for displacement using multiple target test parameters, and then optimization parameters are determined based on process data during the displacement test and final displacement results, and the speaker is adjusted based on the optimization parameters. This can effectively evaluate the degree of influence of different audio signals on speaker displacement and improve the efficiency of optimizing the speaker; (2) based on process data and displacement results, design optimization is performed in a more targeted manner to reduce or even avoid speaker displacement, and the resonance design of the cabinet is accurately optimized, so that the speaker has good anti-slip performance in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0010] Figure 1 is a schematic diagram of an application scenario of a speaker optimization system according to some embodiments of this specification;

[0011] Figure 2 is an exemplary module diagram of a speaker optimization system according to some embodiments of this specification;

[0012] Figure 3 is an exemplary flow chart of a speaker optimization method according to some embodiments of this specification;

[0013] Figure 4 is an exemplary flow chart of updating a displacement data set and optimizing parameters according to some embodiments of this specification;

[0014] Figure 5 It is an exemplary schematic diagram of multiple rounds of similarity calculations shown in some embodiments of this specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0019] The speaker cavity will vibrate when it is working. The anti-skid design of the foot pad and other types can reduce or avoid the displacement of the speaker on the desktop due to vibration when it is working. Due to factors such as different materials of the cabinet, different numbers of speakers, different positions and directions of the speakers, different powers, and different desktop friction, the displacement of the speaker due to vibration will be different. Therefore, each type or style of speaker will have a special anti-skid design for the foot pad when it is designed. When each speaker is assembled, it needs to be specifically tested for displacement to optimize the anti-skid design.

[0020] The displacement test involves placing the speaker on a test table, letting the speaker play the test audio, and observing or measuring the actual displacement of the speaker after playing for a period of time. In conventional displacement tests, a test table with a relatively low friction coefficient (such as a glass table) is used, and then the speaker power is adjusted to the maximum, and the actual displacement of the speaker is observed or measured after playing for several hours. This approach can measure displacement, but the efficiency is relatively low, and only the final displacement result can be obtained. It is impossible to determine in which frequency range the speaker will produce a relatively large displacement. However, since the displacement test does not monitor the process data of the speaker displacement, it is impossible to accurately optimize the resonance design of the cabinet. Instead, it is necessary to repeatedly optimize, proof, assemble and test the anti-slip design of parts such as foot pads, which is relatively inefficient and also brings high verification costs.

[0021] In view of this, some embodiments of the present specification provide a speaker optimization method, system, device and storage medium, which can efficiently evaluate the influence of different audio signals on the speaker displacement, and accurately collect the process data of the speaker displacement change and the final displacement results, so as to perform anti-slip optimization more specifically based on the process data and displacement results, so as to reduce or even avoid the displacement of the speaker.

[0022] Figure 1It is a schematic diagram of an application scenario of a speaker optimization system shown in some embodiments of this specification.

[0023] like Figure 1 As shown, the application scenario 100 of the speaker optimization system may include a processor 110 , a memory 120 , a user terminal 130 , a network 140 , and a speaker 150 .

[0024] The speaker optimization system can analyze the displacement data of the speaker during use for the user (eg, a production technician of the speaker 150 ) by implementing the methods and / or processes disclosed in this specification, so as to optimize the anti-slip related designs.

[0025] The processor 110 may process data and / or information obtained from other components of the speaker optimization system or other external devices. The processor 110 may execute program instructions based on these data, information and / or processing results to perform one or more functions described in this specification. For example, the processor 110 may obtain attribute information and usage information of the speaker 150 through the network 140, and process the attribute information and usage information to determine at least one set of target test parameters; and, based on at least one set of target test parameters, perform a displacement test on the speaker 150 to determine a displacement data set; and determine the optimization parameters of the speaker 150 based on the displacement data set.

[0026] Processor 110 may include one or more sub-processing devices (eg, a single-core processing device or a multi-core multi-core processing device). As an example only, processor 120 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), a microprocessor, or any combination thereof.

[0027] The memory 120 may be used to store data, instructions, and / or any other information. For example, the memory 120 may store historical displacement data sets, attribute information, and usage information of the speaker. The memory 120 may include one or more storage components, each of which may be an independent device or part of other components in the application scenario 100 of the speaker optimization system.

[0028] The memory 120 may include a random access memory (RAM), a read-only memory (ROM), etc. or any combination thereof. In some embodiments, the memory 120 may be connected to the network 140 to communicate with one or more components in the application scenario 100 of the speaker optimization system. In some embodiments, the memory 120 may be part of the processor 110. The memory 120 may also be implemented on a cloud platform.

[0029] The user terminal 130 refers to one or more terminal devices or software used by the user. The user may refer to a production technician of the speaker 150. The user may use the user terminal 130 to select or switch optimization parameters. The user terminal 130 may be used by one or more users, and the one or more users may include users who directly use the service or other related users. Figure 1 As shown, the user terminal 130 may be a mobile device 130 - 1 , a tablet computer 130 - 2 , a laptop computer 130 - 3 , a desktop computer 130 - 4 , or any combination thereof, of other devices having input and / or output functions.

[0030] The network 140 includes any suitable network capable of facilitating information and / or data exchange of the speaker optimization system. In some embodiments, one or more components of the application scenario 100 of the speaker optimization system (e.g., the memory 120, the user terminal 130, and the speaker 150, etc.) can exchange information and / or data through the network 140.

[0031] The network 140 may be any one or more of a wired network or a wireless network. For example, the network 140 may include a cable network, an optical fiber network, a telecommunication network, a cable connection, or any combination thereof. The network connection between the various parts may be in one of the above-mentioned ways or in multiple ways.

[0032] The speaker 150 is a related device for converting audio signals into sound. The speaker 150 may include, but is not limited to, speaker devices used in shopping malls and homes, and products that can use voice playback functions (such as smart refrigerators, car speakers, etc.). In some embodiments, the speaker 150 may be connected to the network 140 to communicate with one or more components in the application scenario 100 of the speaker optimization system.

[0033] It should be noted that the above description of the application scenario 100 of the speaker optimization system is only for the convenience of description and does not limit this specification to the scope of the embodiments cited. It can be understood that for those skilled in the art, after understanding the principle of the speaker optimization system, it is possible to arbitrarily combine the various components without deviating from this principle. For example, the processor 110, the memory 120, the user terminal 130, and the network 140 can be integrated into the speaker 150 at the same time. For another example, the processor 110, the memory 120, and the network 140 can be integrated into the speaker 150 at the same time, and the user terminal 130 is an independent component. Such variations are within the scope of protection of this specification.

[0034] Figure 2 1 is a schematic diagram of a module of a system for optimizing speaker displacement according to some embodiments of this specification. Figure 2As shown, the system 200 for optimizing speaker displacement may include a determination module 210 , a testing module 220 and an optimization module 230 .

[0035] In some embodiments, the determination module 210 may be used to determine at least one set of target test parameters based on the attribute information and usage information of the speaker. Figure 3 and its related description.

[0036] In some embodiments, the testing module 220 may be configured to perform a displacement test on the speaker based on at least one set of target test parameters to determine a displacement data set of the speaker.

[0037] In some embodiments, the test module 220 can also determine the data sufficiency of the displacement data set; in response to the data sufficiency not meeting the sufficiency requirement, generate at least one set of supplementary test parameters based on at least one set of target test parameters; perform supplementary testing on the speaker based on at least one set of supplementary test parameters, and update the displacement data set.

[0038] In some embodiments, the test module 220 may also determine the test sequence based on at least one set of supplementary test parameters; perform supplementary tests on the speaker according to the test sequence based on at least one set of supplementary test parameters; add the corresponding supplementary displacement data to the displacement data set after the supplementary test is performed on a set of supplementary test parameters; determine the data sufficiency of the supplemented displacement data set; in response to the data sufficiency meeting the sufficiency requirement, stop the supplementary test and obtain the updated displacement data set. For more information about this embodiment, see Figure 3-Figure 5 Related description.

[0039] In some embodiments, the optimization module 230 may be configured to determine optimization parameters of the speaker based on the displacement data set.

[0040] In some embodiments, the optimization module 230 can also determine the parameters to be improved of the speaker based on the displacement data set; and determine the optimization parameters based on the parameters to be improved. Figure 3 and its related description.

[0041] It should be noted that the above description of the optimized speaker displacement system and its modules is only for convenience of description and cannot limit the present specification to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. In some embodiments, Figure 2The determination module 210, the test module 220 and the optimization module 230 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0042] Figure 3 is an exemplary flow chart of a speaker optimization method according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps.

[0043] Step 310 : Determine at least one set of target test parameters based on the attribute information and / or usage information of the speaker. Step 310 may be performed by the determination module 210 .

[0044] The attribute information refers to information related to the attributes of the speaker itself, for example, the model, weight, center of gravity, size, etc. of the speaker. In some embodiments, the determination module 210 may determine the attribute information based on the product specifications of the speaker.

[0045] Usage information refers to information related to the usage of the speaker, for example, usage information includes usage scenarios, placement locations, working parameters, etc.

[0046] The usage scenarios include conference rooms, homes, theaters, outdoor, etc. The usage scenarios of each speaker may include one or more. In some embodiments, the determination module 210 may determine the usage scenarios based on the product design information of the speaker. For example, when setting up the speaker, the designer may preset the usage scenarios of the speaker and save them in the product design information of the speaker.

[0047] The placement position refers to the position where the speaker is placed. For example, the placement position of a large speaker is generally the ground, and the placement position of a small speaker is generally the desktop, etc. In some embodiments, the determination module 210 can determine the placement position based on the weight and size of the speaker.

[0048] Working parameters refer to the parameters of the speaker when it plays audio, for example, rated power, frequency response, frequency range, etc.

[0049] Rated power refers to the maximum power that the speaker can continuously output without distorting the audio being played.

[0050] Frequency range refers to the range between the minimum effective playback frequency and the maximum effective playback frequency that a speaker can play back. Frequency response refers to the phenomenon that when an audio signal with a constant voltage output is connected to a speaker, the sound pressure generated by the speaker increases or decreases with the change of frequency, and the phase changes with the frequency. This relationship between sound pressure and frequency is called frequency response, and the unit is decibel (Db).

[0051] The frequency response can be represented by a frequency response curve, in which the ordinate represents the sound pressure value and the abscissa represents the frequency.

[0052] In some embodiments, the determination module 210 may determine the operating parameters of the speaker based on product design information of the speaker.

[0053] In some embodiments, the determination module 210 may also determine the attribute information, usage scenario, placement location, and working parameters of the speaker based on obtaining user input, etc. The embodiments of this specification do not specifically limit the method of obtaining the attribute information and usage information of the speaker, and operations familiar to those skilled in the art may be used.

[0054] The target test parameter refers to the parameter used when performing a displacement test on the speaker. In some embodiments, the target test parameter includes at least one of a test audio, a test table material, etc. For more information about the displacement test, please refer to the following.

[0055] The test audio refers to the audio played by the speaker during the displacement test. The test audio includes music audio, speech audio, film and television audio, etc. In some embodiments, the speaker optimization system can store multiple test audios in the memory in advance. When performing the displacement test, the determination module 210 can select the test audio from the memory based on the attribute information and / or usage information of the speaker.

[0056] In some embodiments, the determination module 210 can determine the correspondence between the attribute information or usage information and the test audio based on the historical data, and select the corresponding test audio from the memory based on the attribute information or usage information and the correspondence. For example, the historical data includes historical displacement data of multiple displacement tests on multiple speakers using multiple test audios. For each speaker, the determination module 210 can match the attribute information or usage information of the speaker with the test audio corresponding to the best historical displacement data (for example, the one with the least displacement). In the above manner, the correspondence between the attribute information or usage information and the test audio can be obtained.

[0057] In some embodiments, the determination module 210 may construct a first feature vector based on the attribute information and the usage information, perform vector matching in the audio vector library based on the first feature vector, determine a first reference vector that meets the first preset condition from the audio vector library as the first association vector; and determine the test audio of the speaker based on the first association vector. The first preset condition may refer to a preset condition for determining the first association vector. In some embodiments, the first preset condition may include that the vector distance is less than a distance threshold, that the vector distance is minimum, etc.

[0058] The first feature vector is a feature vector constructed based on the attribute information and the usage information. In some embodiments, the first feature vector can be obtained by extracting features from the attribute information and the usage information of the speaker.

[0059] The audio vector library refers to a database for storing, indexing and querying vectors. The audio vector library may include a plurality of first reference vectors and their corresponding reference audios, and the elements of the first reference vectors include attribute information and usage information of the speaker.

[0060] In some embodiments, the audio vector library can be pre-constructed by the speaker optimization system. In some embodiments, the speaker optimization system can construct an audio vector library based on the survey results of a questionnaire. The questionnaire distributed to the user may include the model, purpose, placement, etc. of the speaker. Among them, in the survey results, the purpose of the speaker includes playing music, playing film and television audio, etc., and the placement may include desktop, ground, etc. The speaker optimization system can determine the attribute information of the speaker model based on the speaker model in the survey results by querying the audio vector library; determine the usage information of the speaker model based on the placement location in the survey results; construct a first reference vector based on the attribute information and the usage information; and determine the reference audio based on the speaker purpose in the survey results.

[0061] For example, when the speaker is used to play music, the speaker optimization system can determine the popular songs on the music software as the reference audio corresponding to the first reference vector, or select multiple songs with a wide frequency range as the reference audio corresponding to the first reference vector. The speaker optimization system can construct an audio vector library based on multiple first reference vectors and their corresponding reference audio.

[0062] In some embodiments, the determination module 210 may determine the reference audio corresponding to the first associated vector with the smallest vector distance as the test audio of the speaker.

[0063] The test table material refers to the material where the speaker is placed during the displacement test. For example, the types of test table materials include but are not limited to glass, tiles, cement, wood, rubber, epoxy resin, etc.

[0064] In some embodiments, the determination module 210 may determine the material of the test table based on the placement position of the speaker. For example, the determination module 210 may determine the material of the test table based on the placement position by querying a preset material table.

[0065] The preset material table includes the correspondence between various placement positions and various test table materials. For example, when the placement position is the ground, the corresponding test table materials are cement floor, rubber floor, tile floor, wooden floor, epoxy floor, etc. The preset material table can be determined based on the material of the actual placement position of the speaker in the historical data.

[0066] In some embodiments, the determination module 210 may obtain a plurality of test audios and a plurality of test table materials, and arbitrarily combine the plurality of test audios and the plurality of test table materials to obtain a plurality of sets of target test parameters.

[0067] Step 320 , performing a displacement test on the speaker based on at least one set of target test parameters to determine a displacement data set of the speaker. Step 320 may be performed by the test module 220 .

[0068] In some embodiments, the displacement test includes: placing the speaker on a test table and playing test audio; recording the real-time position of the speaker at multiple preset time points during the displacement test, and then determining the displacement data corresponding to the target test parameters.

[0069] In some embodiments, the test duration of the displacement test can be preset by the system or manually.

[0070] The preset time point is a time point in the displacement test process. Multiple preset time points can be set at equal intervals or at non-equal intervals, and can be set according to actual needs.

[0071] It should be noted that in the displacement test, you only need to pay attention to the frequency of the test audio, so the playback volume can be directly adjusted to the highest level, or set according to actual needs.

[0072] The displacement data refers to data related to the position change of the speaker during the displacement test. In some embodiments, the displacement data may include the real-time position and displacement vector of the speaker at multiple preset time points during the displacement test.

[0073] The real-time position can be represented by coordinates. For example, a reference coordinate system can be set on the test table to determine the coordinate data corresponding to the real-time position of the speaker on the test table. As an example only, the initial position of the speaker can be used as the origin of the coordinate system, the direction of the front of the speaker can be used as the positive direction of the X-axis, and the left side of the speaker (i.e., the 90-degree clockwise direction of the front of the speaker) can be used as the positive direction of the Y-axis. The reference coordinate system can also be constructed in any other feasible way, which is not limited here.

[0074] The displacement vector refers to a vector pointing from the initial position of the speaker to the real-time position. In some embodiments, the test module 220 can calculate the displacement vector based on the coordinate data corresponding to the initial position and the real-time position.

[0075] In some embodiments, the displacement data corresponding to each set of target test parameters can be expressed in vector form, such as: [(A1, B1), (A2, B2),…, (An, Bn)], where A1, A2,…, An represent the real-time positions at time point 1, time point 2,…, and time point n, respectively, and B1, B2,…, Bn represent the displacement vectors at time point 1, time point 2,…, and time point n, respectively.

[0076] The displacement data set refers to a collection of displacement data corresponding to multiple groups of target test parameters.

[0077] In some embodiments, for each set of target test parameters in at least one set of target test parameters, the test module 220 can collect images of the speaker at multiple preset time points during the displacement test through an image acquisition device (e.g., a camera, etc.), determine the real-time position of the speaker at multiple preset time points through image recognition, and determine the displacement data based on the initial position and real-time position of the speaker. The test module 220 can perform at least one displacement test based on at least one set of target test parameters to obtain a displacement data set.

[0078] The embodiments of this specification do not specifically limit the method of acquiring the displacement data set, and operations well known to those skilled in the art may be used.

[0079] In some embodiments, the data in the displacement data set may not be sufficient. In this case, the speaker can be subjected to additional testing to update the displacement data set. For more information on additional testing, see Figure 4 Related instructions.

[0080] In some embodiments, the test module 220 can also synchronously record the sound frequencies corresponding to the test audio at multiple preset time points during the displacement test, and correspond the frequencies corresponding to the multiple preset time points to the real-time position. In some embodiments, during the displacement test or after the displacement test is completed, the test module 220 can construct a curve graph based on the real-time position and sound frequency at multiple preset time points. The horizontal axis of the curve graph is the real-time position, and the vertical axis is the corresponding frequency.

[0081] In some embodiments, the test module 220 may send the curve graph to the user terminal so as to directly display and output the curve graph on the display interface of the user terminal.

[0082] In some embodiments, the test module 220 can send the curve graph to the user in the form of a message. For example, it is sent to the user terminal through a prompt message. In some embodiments, the test module 220 can transfer the curve graph to a memory for storage. Among them, the memory can be a memory that comes with the speaker optimization system, or it can be a memory outside the system. For example, a CD, a hard disk, etc. In some embodiments, the curve graph can be passed to a specific interface, which includes but is not limited to a program interface, a data interface, a transmission interface, etc. In some embodiments, the curve graph can also be output in any manner known to those skilled in the art, without limitation here.

[0083] In some embodiments of the present specification, after the set test time is up, the speaker optimization system can automatically output a report (for example, a graph recording the correspondence between the real-time position and sound frequency of multiple preset time points), and the user can accurately see all the detailed data of the displacement test process and test results, so as to optimize the anti-slip design of the speaker more efficiently and accurately based on these data.

[0084] Step 330 , based on the displacement data set, determines the optimization parameters of the speaker. Step 330 may be performed by the optimization module 230 .

[0085] The optimization parameter refers to a parameter related to optimizing the speaker in order to reduce the displacement of the speaker. For example, the optimization parameter is a parameter related to optimizing the anti-slip design of the speaker.

[0086] In some embodiments, the optimization parameters may include the anti-slip coefficient of the anti-slip material, the weight of the speaker, etc.

[0087] Anti-skid materials refer to materials used for anti-skid purposes. In order to reduce or avoid displacement of the speaker on the desktop due to vibration when the speaker is working, anti-skid materials can be set at the bottom of the speaker. For example, anti-skid rubber pads can be set at the bottom of the speaker.

[0088] The anti-slip coefficient is used to measure the ability of anti-slip materials to prevent sliding. For example, the anti-slip coefficient can include the static friction coefficient or anti-slip value of the anti-slip material. The larger the static friction coefficient, the greater the friction between the two surfaces of the objects, and the more difficult it is for the two objects to move relative to each other. The higher the anti-slip value, the greater the resistance of the material surface.

[0089] In some embodiments, the optimization module 230 may determine the optimization parameters through the following steps S11 to S13.

[0090] S11. Determine the displacement range of the speaker based on the displacement data set, wherein the displacement range refers to the range of change of the speaker position with the initial position as a reference during the displacement test.

[0091] In some embodiments, the optimization module 230 may determine the displacement range based on the displacement vector at the last preset time point in the displacement data. For example, the displacement range may be a circular area with the initial position as the origin and the vector length of the displacement vector at the last preset time point as the radius. For another example, the displacement range may be a square area with the initial position as the geometric center and the vector length of the displacement vector at the last preset time point as the side length.

[0092] S12. Determine excessive displacement information based on the displacement range and the allowable displacement range.

[0093] The allowable displacement range refers to the maximum displacement range of the speaker allowed. The allowable displacement range can be determined based on the design requirements of the speaker. In some embodiments, the allowable displacement range can be a circular area with the initial position as the origin and a preset distance as the radius. The preset distance can be determined based on the design requirements of the speaker. For example, when the design requirement is that the displacement of the speaker during vibration does not exceed 2 cm, the preset distance can be determined to be 2 cm, that is, the allowable displacement range is a circular area with the initial position as the origin and a radius of 2 cm.

[0094] Excessive displacement information refers to the situation where the actual displacement range of the speaker exceeds the allowable displacement range. Excessive displacement information includes the direction in which the displacement range exceeds the allowable displacement range and the distance of the excess. For example, if the allowable displacement range is a square area of ​​2cm×2cm, the excessive displacement information can be that the front direction exceeds the allowable displacement range by 1cm, the left direction exceeds the allowable displacement range by 2cm, etc. Among them, the front direction is the positive direction of the X-axis of the reference coordinate system, the rear direction opposite to the front direction is the negative direction of the X-axis of the reference coordinate system, the left direction is the positive direction of the Y-axis of the reference coordinate system, and the right direction opposite to the left direction is the negative direction of the Y-axis of the reference coordinate system. For more information about the reference coordinate system, see step 320 and its related description.

[0095] S13. Determine optimization parameters based on the excessive displacement information.

[0096] In some embodiments, the optimization module 230 may determine the optimization parameters by querying a preset optimization table based on the over-displacement information.

[0097] In some embodiments, the preset optimization table may include a correspondence between a plurality of excessive displacement information and a plurality of optimization parameters. In some embodiments, the preset optimization table may be determined based on experimental data. For example, an experiment is conducted using a speaker whose actual displacement range exceeds the allowable displacement range. For a speaker having a certain excessive displacement information, during the experiment, the speaker is optimized using a plurality of optimization parameters. When the actual displacement range of the optimized speaker corresponding to a certain optimization parameter is within the allowable displacement range, the optimization parameter is corresponded one-to-one with the excessive displacement information of the speaker before optimization. Through multiple experiments, a preset optimization table may be constructed.

[0098] In some embodiments, the optimization module 230 may also determine parameters to be improved of the speaker based on the displacement data set; and determine optimization parameters based on the parameters to be improved.

[0099] The parameters to be improved refer to the parameters that need to be improved in order to reduce the displacement of the speaker. For example, the parameters to be improved may include the percentage of improvement of the anti-slip performance in multiple preset directions.

[0100] In some embodiments, the preset direction may include the front direction, the rear direction, the left direction, and the right direction of the speaker. For more information, see step 320 and its related description.

[0101] The anti-skid performance improvement percentage refers to the percentage of improvement of the optimized anti-skid performance compared to the initial anti-skid performance. The initial anti-skid performance refers to the anti-skid performance of the speaker before optimization.

[0102] The anti-slip performance is a parameter used to measure the risk of the speaker's displacement exceeding the allowable displacement range. The anti-slip performance can be expressed in numerical values, grades, etc. Taking the anti-slip performance expressed in numerical values ​​as an example, the greater the anti-slip performance, the smaller the risk of the speaker's displacement exceeding the allowable displacement range.

[0103] As an example, if the speaker is moved forward each time, the parameter to be improved may include the percentage by which the anti-slip performance of the front side of the speaker needs to be improved.

[0104] In some embodiments, the optimization module 230 may determine the optimization parameters based on the parameters to be improved through the following steps S21 to S25.

[0105] Step S21, determining a single displacement vector between two adjacent preset time points among a plurality of preset time points based on the real-time position information in the displacement data set.

[0106] A single displacement vector refers to a vector pointing from a real-time position at a previous preset time point to a real-time position at a next preset time point.

[0107] In some embodiments, the optimization module 230 may determine a single displacement vector between each two adjacent preset time points in the plurality of preset time points based on the coordinate data of the real-time position between each two adjacent preset time points in the plurality of preset time points. For example, the optimization module 230 may construct a vector based on the real-time position of the last preset time point and the real-time position of the next preset time point, and the vector direction is from the real-time position of the last preset time point to the real-time position of the next preset time point. By analogy, the optimization module 230 may obtain multiple single displacement vectors.

[0108] Step S22, decomposing the multiple single displacement vectors in multiple preset directions of the sound box respectively to obtain component vectors in multiple preset directions, and merging the component vectors in each preset direction to obtain splicing vectors in multiple preset directions.

[0109] The embodiments of this specification do not specifically limit the methods of vector decomposition and vector merging, and operations well known to those skilled in the art may be used.

[0110] Step S23, determining the ratio of the anti-slip performance of the speaker in multiple preset directions based on the splicing vectors in multiple preset directions.

[0111] In some embodiments, the anti-slip performance in multiple preset directions is negatively correlated with the modulus of the splicing vectors in the multiple preset directions. For example, if the moduli of the splicing vectors in the four directions of front, back, left, and right are 1.3, 1.5, 2, and 1.8 respectively, the ratio of the anti-slip performance in the four directions may be 1 / 1.3:1 / 1.5:1 / 2:1 / 1.8.

[0112] Step S24, determining the parameters to be improved based on the splicing vectors of multiple preset directions, the ratio of the anti-slip performance, and the allowable displacement range.

[0113] In some embodiments, the optimization module 230 may determine the splicing vector with the smallest modulus in multiple preset directions as the target splicing vector; in response to the modulus of the target splicing vector exceeding the unidirectional allowable displacement, the percentage by which the modulus of the target splicing vector exceeds the unidirectional allowable displacement is determined as the percentage of improvement in the anti-slip performance in the direction corresponding to the target splicing vector (i.e., the target direction). The unidirectional allowable displacement refers to the maximum displacement allowed by the speaker in a single preset direction.

[0114] For example, taking the preset directions including the front, back, left and right directions of the speaker as an example, the allowable displacement range is a square area of ​​2cm×2cm, and the unidirectional allowable displacement in each direction is 1cm. When the moduli of the splicing vectors in the front, back, left and right directions are 1.3, 1.5, 2, and 1.8 respectively, the splicing vector in the front direction can be determined as the target splicing vector, and the modulus of the target splicing vector can be determined to exceed the unidirectional allowable displacement by 30%, and the anti-slip performance in the front direction is improved by 30%.

[0115] In some embodiments, the optimization module 230 may determine the anti-skid performance improvement percentages of the remaining preset directions based on the anti-skid performance improvement percentage of the target direction and the preset rule. In some embodiments, the preset rule may be that the anti-skid performance of each preset direction is consistent. Accordingly, the optimization module 230 may determine the anti-skid performance improvement percentages of the remaining preset directions based on the anti-skid performance of the target direction, the anti-skid performance improvement percentage, and the anti-skid performance of the remaining preset directions.

[0116] For example, the optimization module 230 may determine the anti-skid performance improvement percentages in the remaining three directions based on the anti-skid performance improvement percentage in the front direction and the ratio of the anti-skid performance in the four directions. As an example only, the optimization module 230 may establish equation (1) based on the anti-skid performance in the four directions being equal after adjustment, and calculate the anti-skid performance improvement percentages in the remaining three directions.

[0117] Among them, p1, p2, and p3 are the improvement percentages of the anti-slip performance in the other three directions respectively.

[0118] In some embodiments, in response to the modulus of the target stitching vector not exceeding the unidirectional allowable displacement, the optimization module 230 may set the anti-skid performance improvement percentage in the target direction to 0, and calculate the anti-skid performance improvement percentages in the remaining three directions according to the aforementioned preset rules.

[0119] In some embodiments, the optimization module 230 may determine the anti-slip performance improvement percentages of each of the plurality of preset directions as parameters to be improved of the speaker.

[0120] Step S25, determining the optimized parameters of the speaker based on the parameters to be improved of the speaker.

[0121] In some embodiments, the optimization module 230 may construct a second feature vector based on the parameters to be improved and the attribute information of the speaker, perform vector matching in the optimization vector library based on the second feature vector, determine a second reference vector that meets the second preset condition from the optimization vector library as the second association vector; and determine the optimization parameters of the speaker based on the second association vector. The second preset condition may refer to a preset condition for determining the second association vector. In some embodiments, the second preset condition may include that the vector distance is less than a distance threshold, that the vector distance is minimum, etc.

[0122] The second feature vector is a feature vector constructed based on the parameters to be improved and the attribute information. In some embodiments, the second feature vector can be obtained by extracting features of the parameters to be improved and the attribute information of the speaker.

[0123] The optimization vector library refers to a database for storing, indexing and querying vectors. The optimization vector library includes multiple second reference vectors and corresponding reference optimization parameters, and the elements of the second reference vectors include parameters to be improved and attribute information of the speaker.

[0124] In some embodiments, the optimization vector library may be pre-built by the speaker optimization system. In some embodiments, the speaker optimization system may build the optimization vector library based on historical data or experimental data. For example, the speaker optimization system may determine the attribute information and parameters to be improved of the speaker whose displacement range is less than the allowable displacement range after optimizing the anti-slip performance of the speaker in the historical data or experimental data as the second reference vector, and determine the corresponding optimization parameters as the reference optimization parameters.

[0125] In some embodiments, the optimization module 230 may determine the reference optimization parameter corresponding to the second correlation vector as the optimization parameter of the speaker.

[0126] In some embodiments, the optimization module 230 may also determine at least one group of candidate optimization parameters based on the parameters to be improved; and determine the optimization parameters based on the optimization costs and optimization benefits of the candidate optimization parameters.

[0127] Candidate optimization parameters refer to parameters that may be determined as optimization parameters.

[0128] In some embodiments, the optimization module 230 may determine multiple groups of optimization parameters by vector matching. For example, the optimization module 230 may determine reference optimization parameters corresponding to multiple second associated vectors whose vector distance is less than a distance threshold in the optimization vector library and determine them as multiple candidate optimization parameters.

[0129] In some embodiments, the optimization module 230 may also determine at least one set of candidate optimization parameters based on the parameters to be improved and the anti-slip margin.

[0130] The anti-skid margin refers to the margin set for the anti-skid performance. For example, the anti-skid margin can be 10%.

[0131] In some embodiments, the optimization module 230 may determine the anti-skid margin based on the sufficiency of the displacement data set. The optimization module 230 may preset a correspondence between the sufficiency of the displacement data set and the anti-skid margin based on historical data; and determine the current anti-skid margin based on the correspondence and the sufficiency of the current displacement data set. For example, the correspondence between the sufficiency of the displacement data set and the anti-skid margin includes: the higher the sufficiency of the displacement data set, the more reliable the candidate optimization parameter, and the smaller the anti-skid margin.

[0132] For more information on the adequacy of the displacement dataset, see Figure 4 and its related description.

[0133] In some embodiments, the optimization module 230 can update the parameters to be improved based on the anti-slip margin, and determine multiple groups of candidate optimization parameters based on the updated parameters to be improved. For example, the optimization module 230 can calculate the updated parameters to be improved using the following formula (2). P n =P W ×(1+α) / (P o -1) (2)

[0134] Among them, P n is the percentage of anti-slip performance improvement after update in a preset direction, P W is the percentage of anti-skid performance improvement before updating in a preset direction, α is the anti-skid margin, P o The initial anti-slip performance in a preset direction.

[0135] In some embodiments of the present specification, the stability of the anti-skid performance can be improved and the anti-skid life can be increased by setting the anti-skid margin.

[0136] The optimization cost refers to the negative impact of adjusting the speaker using the candidate optimization parameters. For example, increasing the weight of the speaker to increase the anti-skid performance requires a significant increase in the weight of the speaker, and the corresponding optimization cost is relatively large; increasing the anti-skid performance by increasing the anti-skid coefficient, the weight of the speaker remains basically unchanged, and the corresponding optimization cost is relatively small. In some embodiments, the optimization cost can be represented by the weight of the speaker.

[0137] Optimization benefit refers to the positive impact of adjusting the speaker using the candidate optimization parameters. For example, increasing the weight of the speaker to increase the anti-skid performance can keep the speaker in good anti-skid performance for a long time, and the corresponding optimization benefit is higher; increasing the anti-skid coefficient to increase the anti-skid performance will cause the anti-skid material to wear faster, and the corresponding optimization benefit is lower; the rougher the anti-skid material, the faster it wears, and the corresponding optimization benefit is lower. In some embodiments, the optimization benefit is positively correlated with the anti-skid life.

[0138] The anti-skid life refers to the total playing time of the speaker before the anti-skid performance does not meet the requirements (that is, the displacement data exceeds the allowable displacement range).

[0139] In some embodiments, the optimization module 230 can determine the anti-skid life (ie, optimization benefit) based on the weight of the speaker and the anti-skid coefficient of the anti-skid material by querying a preset time table.

[0140] In some embodiments, the preset duration table includes a combination of the weight of the speaker, the anti-skid coefficient of the anti-skid material, and the corresponding relationship between the anti-skid life. In some embodiments, the preset duration table can be determined by experiments. For example, speakers of different weights and different anti-skid coefficients can be selected to continuously play the test audio, and the total playing time before the displacement range exceeds the allowable displacement range is determined as the anti-skid life.

[0141] In some embodiments, the optimization module 230 can obtain the optimization scores corresponding to multiple candidate optimization parameters through weighted sum calculation based on the optimization costs and optimization benefits of the candidate optimization parameters; and determine the candidate optimization parameter with the highest optimization score and meeting the preset requirements as the optimization parameter. The weights corresponding to the optimization costs and optimization benefits can be manually set.

[0142] In some embodiments, the preset requirements include that the weight of the speaker is less than a preset weight threshold and the anti-skid life is greater than a preset life threshold. The preset weight threshold can be determined by issuing a questionnaire to users. For example, the mode of the maximum speaker weight that users can accept can be determined as the preset weight threshold. The preset life threshold can be determined based on the product design information of the speaker.

[0143] Increasing the weight of the speaker can make the anti-skid effect more durable, but it will increase the weight of the speaker and reduce the convenience of the speaker (the speaker becomes heavier and it will be very laborious for the user to move it each time). Increasing the anti-skid coefficient (changing the anti-skid material or increasing the roughness) will not increase the weight, but the anti-skid performance of the anti-skid material will decrease after it is worn, shortening the anti-skid life. In some embodiments of this specification, by balancing the weight and anti-skid coefficient of the speaker, it is possible to take into account both the user experience and the anti-skid life, and the obtained optimization parameters are more reasonable.

[0144] In some embodiments of the present specification, by determining the parameters to be improved of the speaker and then determining the optimization parameters, the anti-slip performance of the speaker in all directions can be made consistent, so that the displacement range of the speaker in all directions is within the allowable displacement range.

[0145] In some embodiments of the present specification, a plurality of target test parameters are used to perform displacement tests on a speaker, and then optimization parameters are determined based on process data during the displacement test and final displacement results, and the speaker is adjusted based on the optimization parameters. This can efficiently evaluate the degree of influence of different audio signals on speaker displacement, improve the efficiency of optimizing the speaker, and more specifically perform design optimization to reduce or even avoid speaker displacement based on process data and displacement results, accurately optimize the resonance design of the cabinet, and ensure that the speaker has good anti-slip performance in different scenarios.

[0146] It should be noted that the above description of process 300 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0147] If the displacement data set obtained by performing displacement testing based on target test parameters is not sufficient, additional testing is required to update the displacement data set and determine the optimization parameters.

[0148] Figure 4 is an exemplary flow chart of updating a displacement data set and optimizing parameters according to some embodiments of the present specification.

[0149] Step 410, determining the data sufficiency of the displacement data set.

[0150] Data sufficiency refers to the sufficiency of displacement data in the displacement data set. When data sufficiency is low, data support is insufficient, which may result in large displacements in actual use after anti-slip optimization according to optimization parameters.

[0151] In some embodiments, the testing module 220 may determine the data sufficiency of the displacement data set in a variety of ways. In some embodiments, the testing module 220 may determine the data sufficiency of the displacement data set according to the following steps S31 to S35.

[0152] Step S31, constructing a plurality of curve graphs based on the displacement data set, and calculating the maximum displacement value corresponding to each sound frequency based on the plurality of curve graphs.

[0153] Each target test parameter corresponds to a displacement data, and a curve graph can be constructed based on the multiple displacement data in the displacement data set, thereby obtaining multiple curve graphs. For more information about the curve graph, see step 320 and its related description.

[0154] Since different test audio frequencies are used in different displacement test processes, the sound frequency corresponding to each preset time point may be different in different displacement test processes.

[0155] In some embodiments, for a certain sound frequency, the test module 220 can determine multiple actual positions with the same frequency from multiple curve graphs; based on the multiple actual positions and the initial positions, construct multiple displacement vectors respectively; determine the displacement vector with the largest modulus among the multiple displacement vectors, and determine its modulus length as the maximum displacement value corresponding to the sound frequency.

[0156] Through the above method, the testing module 220 can determine the maximum displacement values ​​corresponding to various sound frequencies.

[0157] Step S32, taking the sound frequency as the independent variable and the maximum displacement value corresponding to the sound frequency as the dependent variable, fitting the displacement functions corresponding to different test table materials, and calculating the first-order derivative function of the displacement function.

[0158] Step S33, calculating the terminal value of the first-order derivative function when the sound frequency takes the endpoint value of the audible frequency range.

[0159] The audible frequency range refers to the range of sound frequencies that the human ear can hear. The audible frequency range can be determined by the system or by human input.

[0160] The endpoint values ​​of the audible frequency range include the maximum value and the minimum value of the range, and the correspondingly determined terminal values ​​of the first-order derivative function include two.

[0161] Step S34, determining the sufficiency of the sub-data corresponding to the test table material according to the terminal value of the first-order derivative function.

[0162] The closer the absolute values ​​of the two end values ​​are to 0, the more sufficient the sub-data is. The closer the absolute values ​​of the two end values ​​are to 0, the more sufficient the displacement data is when the sound frequency is at the maximum and minimum.

[0163] For example, the sub-data sufficiency can be calculated by the following formula (3): k=-log[|m1|+|m2|] (3)

[0164] Among them, k is the sub-data sufficiency, and m1 and m2 are the two terminal values ​​of the first-order derivative function.

[0165] Step S35 , determining the data sufficiency of the displacement data set based on statistics of the sub-data sufficiency corresponding to the materials of the test table.

[0166] Statistical methods may include mean, weighted mean, mean-variance, etc.

[0167] In some embodiments, the test module 220 may also determine the data sufficiency of the displacement data set through a sufficiency model based on the displacement data set, the attribute information and the usage information of the speaker.

[0168] In some embodiments, the sufficiency model is a machine learning model. In some embodiments, the sufficiency model may include any one or combination of various feasible models such as a recurrent neural network (RNN) model, a deep neural network (DNN) model, etc.

[0169] In some embodiments, the input of the sufficiency model may include target test parameters, displacement data sets, attribute information of the speaker, and usage information. For more information about target test parameters, displacement data sets, attribute information of the speaker, and usage information of the speaker, see Figure 3 and related instructions.

[0170] In some embodiments, the output of the sufficiency model may include the data sufficiency of the displacement data set.

[0171] In some embodiments, the testing module 220 may obtain a plurality of labeled training samples to form a training sample set, and obtain a sufficiency model through training the training sample set.

[0172] In some embodiments, the training sample may include sample target test parameters of the sample speaker, a sample displacement data set, sample attribute information, and sample usage information. The label corresponding to the training sample may be the data sufficiency corresponding to the sample displacement data set.

[0173] In some embodiments, the training samples can be obtained based on historical data. For example, a speaker that has undergone a historical displacement test can be used as a sample speaker, and the historical target test parameters, historical displacement data sets, historical attribute information, and historical usage information of the speaker that has undergone a historical displacement test can be used as a training sample.

[0174] In some embodiments, the label can be determined by the system or by human annotation. For example, a technician can annotate the label according to the effect of setting the anti-skid optimization parameters for the speaker under the historical displacement data set. The effect of the anti-skid optimization parameters is obtained based on experimental data or user feedback data. If the anti-skid optimization parameters formulated according to the displacement data set are effective, the data sufficiency is higher; otherwise, the data sufficiency is lower.

[0175] In some embodiments, the test module 220 may input the training sample set into the initial sufficiency model to perform multiple rounds of iterations. Each round of iteration includes: selecting one or more training samples from the training sample set, inputting one or more training samples into the initial sufficiency model, and obtaining the model prediction output corresponding to the one or more training samples; substituting the model prediction output corresponding to the one or more training samples and the labels of the one or more training samples into the formula of the predefined loss function to calculate the value of the loss function; and updating the model parameters of the initial sufficiency model in reverse according to the value of the loss function. When the iteration end condition is met, the iteration ends and the trained sufficiency model is obtained. Among them, the iteration end condition may be the convergence of the loss function, the number of iterations reaching a threshold, etc.

[0176] In some embodiments of the present specification, the data sufficiency of a displacement data set is determined by a sufficiency model. The self-learning ability of a machine learning model can be used to find patterns from a large amount of historical data, obtain the relationship between target test parameters, displacement data sets, speaker attribute information and usage information, and data sufficiency, etc., thereby improving the accuracy and efficiency of determining data sufficiency.

[0177] Step 420 , in response to the data sufficiency not satisfying the sufficiency requirement, generating at least one set of supplemental test parameters based on at least one set of target test parameters.

[0178] The sufficiency requirement refers to a requirement that data sufficiency needs to meet. In some embodiments, the sufficiency requirement may include that the data sufficiency is greater than a sufficiency threshold.

[0179] In some embodiments, the sufficiency threshold may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs.

[0180] In some embodiments, the sufficiency threshold may be related to the rated power and response frequency of the speaker. In some embodiments, the sufficiency threshold may be positively correlated to the rated power and response frequency of the speaker. For example, the higher the rated power of the speaker, the larger the range of the response frequency, and the more displacement the speaker produces during playback. The sufficiency threshold needs to be increased to ensure that there is enough data to cover the displacement of the speaker during actual use. For more information about rated power and response frequency, please refer to Figure 3 Related instructions.

[0181] In some embodiments, in response to the data sufficiency of the displacement data set being not greater than the sufficiency threshold, the testing module 220 may determine that the data sufficiency of the displacement data set does not meet the sufficiency requirement.

[0182] Supplementary test parameters refer to test parameters for supplementary testing when data sufficiency is insufficient. The parameter types included in the supplementary test parameters are the same as those of the target test parameters, but the parameter values ​​are different. For example, the supplementary test parameters may include at least one of supplementary test table materials and supplementary test audio. The supplementary test table materials are the test table materials in the supplementary test parameters, and the supplementary test audio is the test audio in the supplementary test parameters. For more information about test table materials and test audio, see Figure 3 Related description.

[0183] In some embodiments, the test module 220 can determine the supplementary test parameters in a variety of ways. In some embodiments, the test module 220 can match multiple first associated vectors whose vector distance is less than a distance threshold from the audio vector library, and determine the reference audios corresponding to the multiple first associated vectors whose vector distance is less than the distance threshold as multiple supplementary test audios. The test module 220 can determine the supplementary test table material in the same way as determining the test table material. The test module 220 can arbitrarily combine multiple supplementary test audios with multiple supplementary test table materials to obtain multiple sets of supplementary test parameters.

[0184] In some embodiments, the test module 220 may also segment the test audio based on the sound frequency of the test audio in the target test parameters to obtain test sub-audios; and recombine the test sub-audios to obtain supplementary test audio of the supplementary test parameters.

[0185] In some embodiments, for the test audio in each set of target test parameters, the test module 220 starts from the starting point of the test audio and takes a continuous audio segment whose difference between the sound frequency and the sound frequency corresponding to the starting point is within a preset fluctuation range as a test sub-audio. After determining a test sub-audio, starting from the starting point of the remaining part of the test audio, the above operation is repeated to obtain the next test sub-audio until the entire test sub-audio is segmented.

[0186] In some embodiments, for the test audio in each set of target test parameters, the test module 220 can reassemble the multiple test sub-audios obtained by segmentation into a complete test audio in a different order from the current playback order as a supplementary test audio. Multiple supplementary test audios are obtained by multiple recombinations. The test module 220 can arbitrarily combine multiple supplementary test audios with multiple supplementary test table materials to obtain multiple sets of supplementary test parameters. In other words, for each set of target test parameters, multiple sets of supplementary test parameters can be determined accordingly.

[0187] In some embodiments of this specification, the test audio is segmented according to the sound frequency of the test audio in the target test parameter to obtain test sub-audio, and the test sub-audio is recombined to obtain the test audio of the supplementary test parameter, which is equivalent to that for each test audio of the target test parameter, multiple supplementary test parameters can be obtained. Through the above method, a large number of supplementary test parameters can be obtained, thereby ensuring the richness of the test data.

[0188] Step 430: Perform a supplementary test on the speaker based on at least one set of supplementary test parameters, and update the displacement data set.

[0189] The supplementary test refers to a displacement test that is performed in addition. In some embodiments, the test module 220 can perform a supplementary test on the speaker based on the supplementary test parameters in the same manner as performing the displacement test based on the target test parameters.

[0190] Supplementary displacement data may be obtained through the supplementary test. The testing module 220 may add the supplementary displacement data obtained through the supplementary test to the displacement data set to update the displacement data set.

[0191] In some embodiments, the test module 220 can determine the test order based on at least one set of supplementary test parameters; perform supplementary tests on the speakers according to the test order based on at least one set of supplementary test parameters; add the corresponding supplementary displacement data to the displacement data set after the supplementary test is performed with one set of supplementary test parameters; determine the data sufficiency of the supplemented displacement data set; in response to the data sufficiency meeting the sufficiency requirement, stop the supplementary test and obtain an updated displacement data set.

[0192] The test order refers to the order in which displacement tests are performed using different supplementary test parameters. In some embodiments, the test order can be determined in a variety of ways. For example, for each set of supplementary test parameters, the difference between the order of each test sub-audio in the supplementary test audio and the original order of each test sub-audio in the test audio is calculated; according to the degree of difference, the test order of different supplementary test parameters is determined. For example, the supplementary test parameter corresponding to the supplementary test audio with a higher degree of difference is ranked higher in the test order.

[0193] For multiple supplementary test audios with the same difference, the test module 220 can determine the test order of different supplementary test parameters according to the number of test sub-audios included. For example, the supplementary test parameter with more test sub-audios ranks higher in the test order.

[0194] In some embodiments, the test module 220 may determine the ratio of the number of test sub-audios whose order has changed to the total number of test sub-audios as the difference of the supplementary test audio. For example, a test audio includes 5 test sub-audios A, B, C, D, E, and the original order is ABCDE. The order of the test audio in the reorganized supplementary test parameters is BDCAE, and the order of the sub-audios ABD has changed, and the difference is 60%.

[0195] In some embodiments, the test module 220 may determine the test order of at least one set of supplementary test parameters through the following steps S41 to S43.

[0196] Step S41, determining a test similarity sequence between at least one set of supplementary test parameters and at least one set of target test parameters.

[0197] The test similarity refers to the similarity between the supplementary test parameters and the target test parameters. The test similarity sequence is a sequence consisting of the test similarities between the supplementary test parameters and at least one group of target test parameters. For each group of supplementary test parameters, a similarity test similarity sequence between the supplementary test parameters and the target test parameters can be determined accordingly.

[0198] In some embodiments, the method of calculating the test similarity includes but is not limited to similarity calculation based on Pearson correlation coefficient, similarity calculation based on Euclidean distance, and the like.

[0199] Step S42: determining a reference similarity of at least one supplementary test parameter based on a test similarity sequence corresponding to at least one set of supplementary test parameters.

[0200] The reference similarity refers to a test similarity selected from the test similarity sequence. In some embodiments, the test module 220 may determine the maximum value of multiple test similarities in the test similarity sequence as the reference similarity.

[0201] The lower the maximum value of the test similarity is, the lower the test similarity between the supplementary test parameters and all the target test parameters is.

[0202] Step S43: determining a test order of at least one set of supplementary test parameters based on the reference similarity of at least one set of supplementary test parameters.

[0203] In some embodiments, the test module 220 may sort the at least one group of supplementary test parameters in ascending order of reference similarity based on the reference similarity of the at least one group of supplementary test parameters to determine the test order of the at least one group of supplementary test parameters. That is, the lower the reference similarity, the higher the test order of the supplementary test parameters. For example, the lower the reference similarity, the higher the test order of the supplementary test parameters.

[0204] For more information on determining the test sequence, see Figure 5 and its related description.

[0205] The lower the reference similarity, the more likely it is to obtain displacement data with a large difference. In some embodiments of this specification, the test order of the supplementary test parameters is determined based on the order of the reference similarities of the supplementary test parameters, which can maximize the data sufficiency, and should be tested first to make the data sufficiency meet the sufficiency requirement as soon as possible.

[0206] In some embodiments, the testing module 220 may also determine the testing order through multiple rounds of similarity calculations.

[0207] like Figure 5As shown, in some embodiments, each round of similarity calculation in multiple rounds of similarity calculation includes the following steps S51-S53.

[0208] Step S51 : calculating a test similarity sequence 530 between each group of supplementary test parameters 510 in at least one group of supplementary test parameters 510 and each group of test parameters in the test parameter set 520 .

[0209] A test parameter set is a set consisting of multiple groups of test parameters for performing displacement test parameters. In some embodiments, the test parameter set may include at least one group of target test parameters. In some embodiments, the test parameter set may include at least one group of target test parameters and at least one group of supplementary test parameters.

[0210] Step S51 is similar to step S41, and for more details, please refer to the above related description.

[0211] Step S52 : determining a reference similarity 540 of at least one supplementary test parameter 510 based on the test similarity sequence 530 of at least one group of supplementary test parameters 510 .

[0212] Step S52 is the same as step S42. For more details, please refer to the above related description.

[0213] Step S53, adding the supplementary test parameter 510 with the lowest reference similarity 540 to the test parameter set 520, marking its test order, and entering the next round of similarity calculation.

[0214] In some embodiments, the test module 220 can add the supplementary test parameters with the lowest reference similarity in each round of similarity calculation to the test parameter set in sequence based on the order of multiple rounds of similarity calculation to obtain a test order. For example, in the first round of similarity calculation, the test order of the supplementary test parameter with the lowest reference similarity is the first, and in the second round of similarity calculation, the test order of the supplementary test parameter with the lowest reference similarity is the second, and so on, the test order of multiple groups of supplementary test parameters can be determined.

[0215] In some embodiments of the present specification, by calculating the test similarity sequence between the supplementary test parameters and the target test parameters and other supplementary test parameters in a determined test order, and then determining the reference similarity based on the test similarity sequence, and then determining the test order, it can be ensured that the similarity between multiple supplementary test parameters with relatively close test orders is low. When performing supplementary tests based on the test order, the resulting displacement data set is more complete and the test efficiency is higher.

[0216] Since the supplementary test can be stopped as long as the data sufficiency of the displacement data set is sufficient, it is not necessary to use each set of supplementary test parameters to perform the supplementary test, and different supplementary test parameters may contribute differently to the data sufficiency, so the test order will affect the speed of improving the data sufficiency in the displacement data set, thereby affecting the test efficiency. In some embodiments of the present specification, by determining the test order of multiple sets of supplementary test parameters for supplementary testing and performing supplementary tests in order, the duration of the supplementary test can be effectively shortened and the efficiency of the supplementary test can be improved.

[0217] Step 440 , determining optimization parameters based on the updated displacement data set.

[0218] In some embodiments, the optimization module 230 may determine the optimization parameters based on the updated displacement data set in the same manner as determining the optimization parameters based on the displacement data set. For example, the optimization module 230 may determine the optimization parameters based on the updated displacement data set through steps S11 to S13. For more information, see step 330 and its related description.

[0219] In some embodiments of the present specification, when the data sufficiency of the displacement data set is insufficient, a supplementary test is performed to update the displacement data set, so that the data in the updated displacement data set is sufficient and includes as many displacement conditions of the speaker as possible, so that the anti-slip design of the speaker can be better optimized based on the updated displacement data set.

[0220] One or more embodiments of the present specification provide a device for checking speaker displacement, the device comprising at least one processor and at least one memory, the at least one memory being used to store computer instructions; the at least one processor being used to execute at least part of the computer instructions to implement the speaker optimization method as described in any of the above embodiments.

[0221] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the speaker optimization method described in any of the above embodiments.

[0222] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0223] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0224] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0225] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0226] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0227] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0228] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for optimizing a speaker, characterized in that: The method comprises: Determine at least one set of target test parameters based on the property information and / or usage information of the speaker, the target test parameters including at least one of a test table material and a test audio; Performing a displacement test on the sound box based on the at least one set of target test parameters to determine a displacement data set of the sound box; Based on the displacement data set, an optimization parameter of the speaker is determined, wherein the optimization parameter includes at least one of an anti-slip coefficient increment and a counterweight increment.

2. The method according to claim 1, characterized in that The method further comprises: determining data sufficiency of the displacement data set; In response to the data sufficiency not satisfying a sufficiency requirement, generating at least one set of supplemental test parameters based on the at least one set of target test parameters; Performing a supplementary test on the sound box based on the at least one set of supplementary test parameters, and updating the displacement data set; The optimization parameters are determined based on the updated displacement data set.

3. The method according to claim 2, characterized in that The performing a supplementary test on the sound box based on the at least one set of supplementary test parameters and updating the displacement data set comprises: determining a test sequence based on the at least one set of supplemental test parameters; Based on the at least one set of supplementary test parameters, performing supplementary tests on the speaker in the test sequence; After a set of supplementary test parameters are subjected to supplementary testing, the corresponding supplementary displacement data is added to the displacement data set; determining the data sufficiency of the supplemented displacement data set; In response to the data sufficiency satisfying the sufficiency requirement, the supplementary test is stopped, and the updated displacement data set is obtained.

4. The method according to claim 1, characterized in that Determining the optimization parameters of the sound box based on the displacement data set includes: Determining parameters of the sound box to be improved based on the displacement data set; Based on the parameter to be improved, the optimization parameter is determined.

5. A speaker optimization system, characterized in that: The system comprises: A determination module, configured to determine at least one set of target test parameters based on the property information and / or usage information of the speaker, wherein the target test parameters include at least one of a test table material and a test audio; A testing module, performing a displacement test on the sound box based on the at least one set of target test parameters to determine a displacement data set of the sound box; The optimization module is used to determine the optimization parameters of the speaker based on the displacement data set, and the optimization parameters include at least one of the anti-slip coefficient increment and the counterweight increment.

6. The system according to claim 5, characterized in that The test module is also used to: determining data sufficiency of the displacement data set; In response to the data sufficiency not satisfying a sufficiency requirement, generating at least one set of supplemental test parameters based on the at least one set of target test parameters; Performing a supplementary test on the sound box based on the at least one set of supplementary test parameters, and updating the displacement data set; The optimization module is also used to: The optimization parameters are determined based on the updated displacement data set.

7. The system according to claim 5, characterized in that The test module is also used to: determining a test sequence based on the at least one set of supplemental test parameters; Based on the at least one set of supplementary test parameters, performing supplementary tests on the speaker in the test sequence; After a set of supplementary test parameters are subjected to supplementary testing, the corresponding supplementary displacement data is added to the displacement data set; determining the data sufficiency of the supplemented displacement data set; In response to the data sufficiency satisfying the sufficiency requirement, the supplementary test is stopped, and the updated displacement data set is obtained.

8. The system according to claim 5, characterized in that The optimization module is also used to: Determining parameters of the sound box to be improved based on the displacement data set; Based on the parameter to be improved, the optimization parameter is determined.

9. A sound box optimization device, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is configured to execute at least part of the computer instructions to implement the speaker optimization method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the speaker optimization method according to any one of claims 1 to 4.

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