Electronic device

Abstract

The application discloses an electronic device, which comprises a compressor, an exhaust pipeline connected with the compressor and used for conveying refrigerant discharged by the compressor, and a resonance cavity silencer connected with the exhaust pipeline and used for eliminating transmission sound generated by the compressor during operation. The electronic device comprises a controller configured to generate a plurality of initial structure parameters of the resonance cavity silencer, iteratively optimize each initial structure parameter by using a particle swarm algorithm to determine a final structure parameter of the resonance cavity silencer, determine a silencing difference value corresponding to the final structure parameter, and optimize the structure of the resonance cavity silencer according to the final structure parameter if the silencing difference value of the final structure parameter meets a structure optimization condition. The electronic device can accurately obtain the structure parameter of the resonance cavity silencer by using the particle swarm algorithm, and can effectively eliminate the transmission sound of the compressor.

Description

Technical Field

[0001] This invention relates to the field of electronic equipment technology, and in particular to an electronic device. Background Technology

[0002] In the existing technology, the structural parameters of the resonant cavity silencer are set manually based on experience. However, the structural parameters of the resonant cavity silencer cannot be accurately obtained by designing based solely on experience, thus failing to effectively eliminate the noise transmitted by the compressor. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, one object of this invention is to provide an electronic device that can accurately obtain the structural parameters of a resonant cavity silencer using a particle swarm optimization algorithm, thereby effectively eliminating the transmitted noise of the compressor.

[0004] To address the aforementioned problems, a first aspect of the present invention provides an electronic device for optimizing the structural parameters of a resonant cavity muffler in a compressor piping assembly of an air conditioner. The compressor piping assembly includes: a compressor; an exhaust pipe connected to the compressor for conveying refrigerant discharged by the compressor; and a resonant cavity muffler connected to the exhaust pipe for eliminating transmitted noise generated by the compressor during operation. The electronic device includes a controller configured to: generate multiple initial structural parameters for the resonant cavity muffler; iteratively optimize each initial structural parameter using a particle swarm optimization algorithm to determine the final structural parameters of the resonant cavity muffler; determine the muffler reduction difference corresponding to the final structural parameters; and if the muffler reduction difference of the final structural parameters satisfies the structural optimization conditions, optimize the structure of the resonant cavity muffler based on the final structural parameters.

[0005] According to the electronic device of the present invention, a particle swarm optimization algorithm is used to iteratively optimize each initial structural parameter to obtain the optimal structural parameter as the final structural parameter of the resonant cavity muffler. The noise reduction difference of the final structural parameter is used to determine whether the resonant cavity muffler designed with the final structural parameter can completely eliminate the transmitted noise of the compressor. If it can, the resonant cavity muffler is designed with the final structural parameter. Therefore, compared with the prior art where the structural parameters of the resonant cavity muffler are set manually based on experience, the present application uses a particle swarm optimization algorithm to automatically obtain the structural parameters of the resonant cavity muffler. This can accurately and efficiently calculate and optimize the optimal structural parameters of the resonant cavity muffler, ensuring that the maximum noise reduction of the resonant cavity muffler reliably covers and exceeds the peak value of the transmitted noise, thereby effectively eliminating the transmitted noise of the compressor.

[0006] In some embodiments, for iterative optimization of each initial structural parameter using a particle swarm optimization algorithm, the controller is specifically configured to: determine the iterative optimization speed for each initial structural parameter in each iteration; perform iterative optimization of each initial structural parameter according to the iterative optimization speed to obtain the initial structural parameters after each iteration; and determine the final structural parameters of the resonant cavity silencer according to the initial structural parameters after each iteration.

[0007] The above technical solution has the following advantages or beneficial effects: The final structural parameters of the resonant cavity muffler are determined based on the initial structural parameters after each iteration, allowing the best individual parameters to be used as the final structural parameters for the muffler. This facilitates the design of the muffler using the final structural parameters, effectively improving its noise reduction performance. Furthermore, the iterative optimization speed of each initial structural parameter is updated and optimized in each iteration, enabling each initial structural parameter to approach its optimal value more quickly.

[0008] In some embodiments, for determining the iterative optimization speed for each initial structural parameter, the controller is specifically configured to: determine the noise reduction difference for each initial structural parameter; determine the globally optimal structural parameter and the individually optimal structural parameter for each initial structural parameter in each iteration based on the noise reduction difference for each initial structural parameter; and in each iteration, determine the iterative optimization speed based on the individually optimal structural parameter for each initial structural parameter, the globally optimal structural parameter, and each initial structural parameter after the previous iteration.

[0009] The above technical solution has the following advantages or beneficial effects: In this application, the iterative optimization speed pair of the (t+1)th iteration is calculated by each initial structural parameter after the t-th iteration, the historical optimal structural parameter, and the global optimal structural parameter, so that the initial structural parameters can be iteratively optimized by the iterative optimization speed of the (t+1)th iteration, so that the initial structural parameters tend to the optimum under the action of the iterative optimization speed.

[0010] In some embodiments, the controller is specifically configured to: after determining that the compressor has transmitted sound, acquire the transmission frequency and peak sound pressure level of the compressor; determine the pure tone silencing amount of the resonant cavity silencer at the transmission frequency; and determine the noise reduction difference corresponding to the final structural parameter based on the peak sound pressure level and the pure tone silencing amount.

[0011] The above technical solution has the following advantages or beneficial effects: In this application, the silencing difference value corresponding to the final structural parameters is calculated based on the peak sound pressure level of the transmitted sound and the pure tone silencing amount, so that the silencing difference value can reflect whether the resonant cavity silencer designed by the final structural parameters can eliminate the compressor transmitted sound at the transmitted sound frequency.

[0012] In some embodiments, for determining the pure tone silencing amount of the resonant cavity muffler at the transmission frequency, the controller is specifically configured to: determine the silencing center frequency of the resonant cavity muffler; and determine the pure tone silencing amount based on the silencing center frequency and the transmission frequency.

[0013] The above technical solution has the following advantages or beneficial effects: In this application, the pure tone silencing amount of the resonant cavity muffler at the transmission frequency is calculated by using the silencing center frequency and the transmission frequency. Thus, the pure tone silencing amount that the resonant cavity muffler needs to eliminate can be inferred by combining the silencing capacity of the resonant cavity muffler and the actual transmission sound level of the compressor. This can effectively guide the structural design of the resonant cavity muffler.

[0014] In some embodiments, the air conditioner includes an indoor unit, and for determining that the compressor is transmitting a sound, the controller is specifically configured to: acquire a noise time-domain signal of the indoor unit; determine actual roughness and actual sharpness based on the noise time-domain signal; and determine that the compressor is transmitting a sound based on the noise time-domain signal, the actual roughness, and / or the actual sharpness.

[0015] The above technical solution has the following advantages or beneficial effects: the compressor transmission sound is determined based on the actual roughness and actual sharpness, so as to combine the human ear's perception of different frequency bands to determine whether the compressor transmission sound occurs. Then, when selecting structural parameters for the compressor transmission sound frequency, the structural parameters of the resonant cavity silencer can be designed based on the transmission sound perceived by the human ear, so that the resonant cavity silencer can eliminate the transmission sound perceived by the human ear and improve the user experience.

[0016] In some embodiments, the compressor is determined to have a transmission sound based on a noise time-domain signal, the actual roughness, and / or the actual sharpness. The controller is specifically configured to: determine that the compressor has a transmission sound if the noise time-domain signal is determined to be a low-frequency signal and the actual roughness is determined to be higher than a preset roughness; and / or determine that the compressor has a transmission sound if the noise time-domain signal is determined to be a high-frequency signal and the actual sharpness is determined to be higher than a preset sharpness.

[0017] The above-mentioned technical solution has the following advantages or beneficial effects: This application determines whether the air conditioner indoor unit has compressor transmission sound by using the actual roughness of the noise time domain signal in the low-frequency band and the actual sharpness of the noise time domain signal in the high-frequency band. This allows for the determination of whether compressor transmission sound occurs by combining the human ear's perception of different frequency bands. Furthermore, when selecting structural parameters for compressor transmission sound frequency selection, the structural parameters of the resonant cavity silencer can be designed based on the transmission sound perceived by the human ear, so that the resonant cavity silencer can eliminate the transmission sound perceived by the human ear and improve the user experience.

[0018] In some embodiments, for determining the final structural parameters of the resonant cavity silencer based on the initial structural parameters after each iteration, the controller is specifically configured to: update the globally optimal structural parameters of each iteration based on the silencer difference corresponding to the initial structural parameters after each iteration in each iteration; until it is determined that the number of iterations has reached the maximum number of iterations, then the globally optimal structural parameters of the last iteration are used as the final structural parameters.

[0019] The above technical solution has the following advantages or beneficial effects: In this application, the final structural parameters are obtained by judging the number of iterations of the initial structural parameters, which can effectively improve the accuracy of obtaining the optimal structural parameters of the resonant cavity silencer. Moreover, the maximum number of iterations is used as the convergence criterion, which provides a clear termination boundary for the particle swarm algorithm and avoids the problem of infinite iteration or waste of computing resources caused by fuzzy convergence judgment.

[0020] In some embodiments, for determining that the noise reduction difference meets the structural optimization conditions, the controller is specifically configured to: determine that the noise reduction difference of the final structural parameters is lower than the target noise reduction threshold.

[0021] The above technical solution has the following advantages or beneficial effects: the final structural parameters are determined by the difference between the peak sound pressure level of the transmitted sound and the silencing amount of the pure tone corresponding to the transmitted sound frequency, so that the final structural parameters of the resonant cavity silencer are precisely matched with the transmitted sound frequency, thereby ensuring that the maximum silencing amount of the resonant cavity silencer reliably covers and exceeds the peak sound of the transmitted sound, which can effectively eliminate the compressor transmitted sound.

[0022] In some embodiments, for generating multiple initial structural parameters of the resonant cavity muffler, the controller is specifically configured to generate multiple initial structural parameters of the resonant cavity muffler while satisfying the structural boundary conditions of the resonant cavity muffler.

[0023] The above technical solution has the following advantages or beneficial effects: In this application, when generating multiple initial structural parameters of the resonant cavity muffler, the randomly generated initial structural parameters are restricted according to the structural boundary conditions so that the generated initial structural parameters can meet the conventional design and theoretical design of the resonant cavity muffler.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic diagram of a compressor piping assembly according to an embodiment of the present invention; Figure 2 This is a structural block diagram of an electronic device according to an embodiment of the present invention; Figure 3 This is a flowchart of the control process of a controller according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the sound reduction of a resonant cavity silencer according to an embodiment of the present invention; Figure 5 This is a flowchart of the control process of a controller according to another embodiment of the present invention; Figure 6 This is a schematic diagram of the Kf relationship curve of a resonant cavity silencer according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the relationship between the K value and structural parameters according to an embodiment of the present invention; Figure 8 This is a flowchart of the control process of a controller according to an embodiment of the present invention; Figure 9 This is a flowchart of the control process of a controller according to an embodiment of the present invention; Figure 10 This is a flowchart of the control process of a controller according to an embodiment of the present invention.

[0026] Figure label: Compressor piping assembly 10; Electronic equipment 20; 1. Exhaust pipe; 2. Resonance chamber muffler; 3. Controller; Detailed Implementation The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0027] To address the aforementioned problems, a first aspect of the present invention provides an electronic device for optimizing the structural parameters of a resonant cavity muffler in the compressor piping assembly of an air conditioner. This electronic device can accurately obtain the structural parameters of the resonant cavity muffler using a particle swarm optimization algorithm, thereby effectively eliminating the transmitted noise from the compressor.

[0028] In an embodiment, such as Figure 1 As shown, the compressor piping assembly 10 includes: a compressor (not shown), an exhaust pipe 1, and a resonant cavity silencer 2. The exhaust pipe is connected to the compressor and is used to transport the refrigerant discharged by the compressor; the resonant cavity silencer is connected to the exhaust pipe and is used to eliminate the transmitted noise generated by the compressor during operation. The resonant cavity silencer consists of an opening (neck) in the exhaust pipe wall and a sealed cavity outside the pipe wall.

[0029] In an embodiment, such as Figure 2 As shown, the electronic device 10 includes a controller 3, such as Figure 3 As shown, the controller is configured to perform the following steps.

[0030] Step S1: Generate multiple initial structural parameters for the resonant cavity silencer.

[0031] The initial structural parameters can be understood as those generated in the initial stage of structural design for the resonant cavity muffler. These initial structural parameters include the resonant cavity volume, neck length, and cross-sectional area of ​​the muffler. The resonant cavity muffler can be a side-supported type.

[0032] Specifically, multiple initial structural parameters for the resonant cavity silencer are randomly generated.

[0033] Step S2: The particle swarm optimization algorithm is used to iteratively optimize each initial structural parameter in order to determine the final structural parameters of the resonant cavity silencer.

[0034] Among them, the Particle Swarm Optimization (PSO) algorithm is an optimization algorithm based on swarm intelligence. Individual components continuously adjust their positions within the group by sharing information to find the optimal solution. PSO is a method that ignores the exploration space and iteratively updates the search for the optimal solution or the nearest best solution. The final structural parameters are the design parameters for the resonant cavity silencer that maximizes the noise reduction of the compressor.

[0035] Specifically, a particle swarm optimization (PSO) algorithm is used to iteratively optimize each randomly generated initial structural parameter of the resonant cavity muffler. Multiple initial structural parameters form a particle swarm, meaning each initial structural parameter learns from the others and continuously adjusts and optimizes itself to obtain the optimal structural parameter as the final structural parameter of the resonant cavity muffler. In other words, each initial structural parameter changes during iterative updates to approach the optimal structural parameter, which is then used as the final structural parameter of the resonant cavity muffler. The resonant cavity muffler designed with the final structural parameter exhibits the best sound attenuation effect. Therefore, this application uses the PSO algorithm to iteratively optimize each initial structural parameter, achieving automatic adjustment and iterative acquisition of the optimal structural parameter. Furthermore, as a highly efficient optimization algorithm, the PSO algorithm can quickly converge to obtain the final structural parameter.

[0036] Step S3: Determine the noise reduction difference corresponding to the final structural parameters.

[0037] The silencing difference can be understood as a quantification of the silencing capability of a resonant cavity silencer at a certain frequency when designing the final structural parameters. The pulsating noise generated by the air conditioner compressor during operation is one of the main noise sources affecting user comfort, and this type of noise often causes user dissatisfaction and complaints.

[0038] Specifically, after iteratively optimizing each initial structural parameter using the particle swarm optimization algorithm, the noise reduction difference corresponding to the final structural parameter is calculated to determine whether the final structural parameter can eliminate the compressor-transmitted noise and its noise reduction capability.

[0039] Step S4: If the noise reduction difference of the final structural parameters meets the structural optimization conditions, optimize the structure of the resonant cavity muffler according to the final structural parameters.

[0040] Among them, the structural optimization condition is the judgment condition that the resonant cavity silencer designed in the controller to identify the final structural parameters can eliminate the sound transmitted by the compressor.

[0041] Specifically, if the noise reduction difference of the final structural parameters meets the structural optimization conditions, it means that the final structural parameters can completely eliminate the compressor's transmitted noise. Therefore, optimizing the structure of the resonant cavity silencer based on the final structural parameters can effectively eliminate the compressor's transmitted noise. For example, ... Figure 4As shown, the resonant cavity silencer of this application exhibits significant advantages in the field of narrowband noise control. Its resonant frequency can accurately match the target noise peak frequency, achieving selective noise reduction. Compared with ordinary silencers under the same volume constraint, it significantly improves the noise reduction at characteristic frequencies. Furthermore, if the noise reduction difference of the final structural parameters does not meet the structural optimization conditions, it indicates that the final structural parameters cannot eliminate the transmitted noise of the compressor. In this case, the particle swarm optimization algorithm is used again to iteratively optimize each initial structural parameter.

[0042] Therefore, compared with the existing technology where the structural parameters of the resonant cavity muffler are set manually based on experience, this application uses the particle swarm optimization algorithm to dynamically adjust the structural parameters of the resonant cavity muffler. This can quickly and efficiently calculate and optimize the optimal structural parameters of the resonant cavity muffler, and the accuracy of obtaining the structural parameters of the resonant cavity muffler is very high. This ensures that the maximum noise reduction of the resonant cavity muffler reliably covers and exceeds the peak value of the transmitted sound, thereby effectively eliminating the compressor transmitted sound and avoiding the problem of insufficient noise reduction of the resonant cavity muffler.

[0043] According to the electronic device of the present invention, a particle swarm optimization algorithm is used to iteratively optimize each initial structural parameter to obtain the optimal structural parameter as the final structural parameter of the resonant cavity muffler. The noise reduction difference of the final structural parameter is used to determine whether the resonant cavity muffler designed with the final structural parameter can completely eliminate the transmitted noise of the compressor. If it can, the resonant cavity muffler is designed with the final structural parameter. Therefore, compared with the prior art where the structural parameters of the resonant cavity muffler are set manually based on experience, the present application uses a particle swarm optimization algorithm to automatically obtain the structural parameters of the resonant cavity muffler. This can accurately and efficiently calculate and optimize the optimal structural parameters of the resonant cavity muffler, ensuring that the maximum noise reduction of the resonant cavity muffler reliably covers and exceeds the peak value of the transmitted noise, thereby effectively eliminating the transmitted noise of the compressor.

[0044] In some embodiments, for iterative optimization of each initial structure parameter using a particle swarm optimization algorithm, the controller is specifically configured as follows: Step S5: In each iteration, determine the iterative optimization speed for each initial structural parameter.

[0045] The iterative optimization speed can be understood as the direction and speed at which each initial structural parameter moves towards the optimal solution in each iteration within the search space. The iterative optimization speed gets closer to the optimal solution as the number of iterations increases.

[0046] Specifically, the iteration optimization speed of each initial structural parameter is updated and optimized in each iteration so that each initial structural parameter can approach the optimum more quickly.

[0047] Step S6: Iteratively optimize each initial structural parameter according to the iteration optimization speed to obtain the initial structural parameters after each iteration.

[0048] The initial structural parameters after each iteration are calculated using the following formula: Formula (1) is xi(t+1)=xi(t)+vi(t+1) Where xi(t) is the i-th initial structure parameter of the t-th iteration, vi(t+1) is the iteration optimization speed of the (t+1)-th iteration, and xi(t+1) is the (i+1)-th initial structure parameter of the t-th iteration.

[0049] For example, in the (i+1)th iteration, the i-th initial structural parameter xi(t) of the t-th iteration and the iteration optimization speed vi(t+1) of the (t+1)-th iteration are obtained. The i-th initial structural parameter xi(t) of the t-th iteration and the iteration optimization speed vi(t+1) of the (t+1)-th iteration are substituted into formula (1) to calculate the i-th initial structural parameter after the (i+1)-th iteration. By analogy, the initial structural parameter after each iteration is obtained.

[0050] Step S7: Determine the final structural parameters of the resonant cavity silencer based on the initial structural parameters after each iteration.

[0051] Specifically, the final structural parameters of the resonant cavity muffler are determined based on the initial structural parameters after each iteration. That is, each initial structural parameter is iteratively optimized multiple times according to the iteration optimization speed to obtain the initial structural parameters after each iteration. Then, the best individual is selected from the initial structural parameters after each iteration as the final structural parameters of the resonant cavity muffler. The best individual is the one with the best sound-passing silencing effect in the resonant cavity muffler. Therefore, this application uses the best individual as the final structural parameters of the resonant cavity muffler, so that the silencing effect of the resonant cavity muffler can be effectively improved when designing the muffler using the final structural parameters.

[0052] In some embodiments, the controller is specifically configured to determine the iterative optimization rate for each initial structural parameter as follows: First, determine the noise reduction difference for each initial structural parameter.

[0053] Among them, the noise reduction difference can be understood as the noise reduction capability of the resonant cavity muffler at a certain frequency when the muffler is designed with initial structural parameters.

[0054] Secondly, the global optimal structural parameter and the historical optimal structural parameter for each initial structural parameter are determined based on the noise reduction difference of each initial structural parameter in each iteration. Specifically, after each iteration for each initial structural parameter, the silencing difference for each initial structural parameter is calculated. The initial structural parameter with the smallest silencing difference from all initial structural parameters across all iterations is selected as the globally optimal structural parameter. Furthermore, after each iteration, the historical optimal structural parameter for each initial structural parameter after multiple iterations is recorded. For example, with three initial structural parameters, after four iterations, the initial structural parameter after four iterations is obtained. For instance, after four iterations of the first initial structural parameter, the initial structural parameter after four iterations is obtained. The initial structural parameter with the smallest silencing difference in the four iterations is compared with the initial structural parameter with the smallest silencing difference in the previous three iterations. If the initial structural parameter with the smallest silencing difference in the four iterations is smaller, it is selected as the historically optimal structural parameter after four iterations. Furthermore, when updating the globally optimal structural parameters for each iteration, among the individual optima of all structural parameters in the current iteration, the particle with the best noise reduction difference A is found. If this noise reduction difference is better than the current globally optimal noise reduction difference, the globally optimal noise reduction difference is updated, and this particle is taken as the globally optimal structural parameter.

[0055] Finally, in each iteration, the iteration optimization speed is determined based on the individual optimal structure parameter, the global optimal structure parameter, and each initial structure parameter after the previous iteration. The iteration optimization speed can be understood as the direction and speed at which the initial structure parameters move towards the optimal state in the search space, considering the particle position, individual optimality, and swarm optimality in each iteration. The iteration optimization speed is calculated using the following formula: vi(t+1)=ω vi(t)+c1 r1 (pBesti xi(t))+c2 r2 (gBest Formula (2) for xi(t) Where ω is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers in the range of 0 to 1, vi(t) is the iteration optimization speed of the t-th iteration, xi(t) is each initial structure parameter after the t-th iteration, pBesti is the historical best structure parameter of the t-th iteration, and gBest is the global best structure parameter. For example, c1=c2≈2, and r1 and r2 are random numbers in the range of 0 to 1. The purpose is to increase the randomness of the generated structure parameters and increase the range of generated structure parameters.

[0056] Specifically, for the (t+1)th iteration, each initial structural parameter xi(t) after the tth iteration, the historical optimal structural parameter pBesti and the global optimal structural parameter gBest after the tth iteration are obtained. Each initial structural parameter xi(t) after the tth iteration, the historical optimal structural parameter pBesti and the global optimal structural parameter gBest after the tth iteration are substituted into formula (2) to calculate the iteration optimization speed of the (t+1)th iteration. Thus, in this application, the iteration optimization speed of the (t+1)th iteration is calculated using each initial structural parameter, the historical optimal structural parameter and the global optimal structural parameter after the tth iteration, so that the initial structural parameters can be iteratively optimized using the iteration optimization speed of the (t+1)th iteration, so that the initial structural parameters tend to the optimum under the action of the iteration optimization speed.

[0057] It should be noted that the iteration optimization speed of each iteration will be optimized as the historical optimal structure parameters and the global optimal structure parameters are continuously updated.

[0058] In this embodiment, the iteration optimization speed of each iteration needs to be limited. If the iteration optimization speed exceeds the set maximum speed vmax, the iteration optimization speed is limited to [-vmax, vmax].

[0059] In some embodiments, the controller is specifically configured to determine the noise reduction difference corresponding to the final structural parameters as follows: First, after determining that the compressor is transmitting sound, the transmission frequency and peak sound pressure level of the compressor are obtained. The transmission frequency is dynamically changing.

[0060] Among them, the peak sound pressure level of the transmitted sound is the size of the transmitted sound heard by the human ear, and is used to quantify the sound transmitted by the human ear.

[0061] Specifically, after determining that the compressor is transmitting sound, the transmission frequency and peak sound pressure level of the compressor are obtained from the noise file of the indoor unit.

[0062] Secondly, determine the pure tone silencing amount corresponding to the transmission frequency of the resonant cavity silencer.

[0063] Finally, the noise reduction difference corresponding to the final structural parameters is determined based on the peak sound pressure level of the transmitted sound and the pure tone noise reduction. The noise reduction difference is... It is obtained by calculation using the following formula: Formula (3) in, To transmit peak sound pressure level, V is the pure tone noise reduction, S is the volume of the resonant cavity, L is the cross-sectional area of ​​the neck tube of the resonant cavity silencer, and L is the length of the neck tube.

[0064] Specifically, the peak sound pressure level of the transmitted sound will be... Pure tone silencing Substituting into formula (3), the noise reduction difference A corresponding to the final structural parameters is calculated, with the noise reduction difference A serving as the objective function. Thus, in this application, the noise reduction difference corresponding to the final structural parameters is calculated based on the peak sound pressure level of the transmitted sound and the pure tone noise reduction, so that the noise reduction difference can reflect whether the resonant cavity silencer designed by the final structural parameters can eliminate the compressor transmitted sound at the transmitted sound frequency.

[0065] In some embodiments, for determining the pure-tone noise reduction of the resonant cavity muffler at the transmitted frequency, the controller is specifically configured to: determine the noise reduction center frequency of the resonant cavity muffler, wherein the frequency at which the resonant cavity muffler theoretically has the best noise reduction effect and the largest noise reduction is the noise reduction center frequency of the resonant cavity muffler. It is obtained by calculation using the following formula: Formula (4) Where c represents the speed of sound, K=G / V, V is the volume of the resonant cavity, G represents the conductivity, and G is related to the cross-sectional area S and length L of the neck tube of the resonant cavity silencer.

[0066] Specifically, the Kf relationship curve of the resonant cavity silencer pre-stored in the electronic device, such as... Figure 6 As shown, the K value is obtained by querying the Kf relationship curve based on the compressor's transmission frequency. That is, the K value is found by determining the compressor's silencing frequency. Based on this, K is substituted into formula (4) to calculate the silencing center frequency of the resonant cavity silencer. .

[0067] Furthermore, it should be noted that K is related to the cavity volume V, the neck length L, and the cross-sectional area S. The relationships between K and V, and between L and S are as follows: Figure 7 As shown.

[0068] The pure-tone noise reduction is determined based on the silencing center frequency and the transmission frequency. The pure-tone noise reduction, specifically, refers to the noise reduction at the transmission frequency, and is the ability of the resonant cavity silencer to attenuate the transmission frequency. It is a fundamental indicator for evaluating the noise reduction effect of the resonant cavity silencer at different frequencies. Pure-tone noise reduction. It is obtained by calculation using the following formula: Formula (5) in, Pure tone silencing, The silencing center frequency of the resonant cavity silencer is... f To transmit audio rate, Z The structural coefficient of the resonant cavity silencer. Z = / 2S is related to G, V, and the cross-sectional area through which the airflow passes.

[0069] Based on this, the silencing center frequency of the resonant cavity silencer is... , transmit audio rate f and structural coefficient Z Substitute into formula (5) to calculate the pure tone silencing amount. Therefore, this application calculates the pure-tone noise reduction of the resonant cavity muffler at the corresponding transmission frequency by using the silencing center frequency and the transmission frequency. This allows for the estimation of the required pure-tone noise reduction by combining the muffler's noise reduction capability with the actual transmission noise level of the compressor, effectively guiding the structural design of the muffler. Since the noise reduction performance of the resonant cavity muffler highly depends on the precise matching of structural parameters with the dynamic transmission frequency of the noise, this application can accurately identify the transmission frequency in real time and quickly and efficiently calculate and optimize the optimal structural parameters of the resonant cavity muffler. This ensures that its maximum noise reduction reliably covers and exceeds the target transmission noise peak, thereby effectively eliminating compressor transmission noise and avoiding the problem of insufficient noise reduction by the resonant cavity muffler.

[0070] In some embodiments, the air conditioner includes an indoor unit, and for determining that a transmission sound is occurring in the compressor, the controller is specifically configured to: acquire a noise time-domain signal of the indoor unit; determine actual roughness and actual sharpness based on the noise time-domain signal; and determine that a transmission sound is occurring in the compressor based on the noise time-domain signal, actual roughness, and / or actual sharpness.

[0071] Among them, actual roughness and actual sharpness are used to quantify the human ear's perception of noise time-domain signals. Actual sharpness reflects the amount of high-frequency components in a sound; a sharper sound is more piercing and more likely to cause discomfort. Actual roughness reflects the amount of fast amplitude modulation (20-300 Hz) components in a sound; a rougher sound is more likely to cause discomfort.

[0072] Specifically, the noise time-domain signal of the indoor unit is collected by a device that collects time-domain signals. The collection range of this device includes all frequency points within the operating range of the outdoor compressor. The noise time-domain signal of the indoor unit is then stored as a noise file (time-domain file) and sent to the controller. The noise file is a spectrum file. The controller obtains the noise time-domain signal and calculates the actual roughness and actual sharpness based on the noise time-domain signal. Then, it combines the magnitude of the actual roughness and / or actual sharpness to determine whether the human ear can detect the compressor transmission sound. Thus, in this application, the presence of compressor transmission sound is determined based on the actual roughness and / or actual sharpness, i.e., whether the transmission sound is qualified and the abnormal frequency is accurately located. This allows for the determination of whether compressor transmission sound occurs based on the human ear's perception of different frequency bands. Furthermore, when compressor transmission sound occurs, a particle swarm optimization algorithm is used to calculate structural parameters. Moreover, when filtering structural parameters based on the compressor transmission sound frequency, the structural parameters of the resonant cavity silencer can be designed based on the transmission sound perceived by the human ear, so that the resonant cavity silencer can eliminate the transmission sound perceived by the human ear and improve the user experience. Furthermore, using actual roughness and actual sharpness to determine the transmission sound of the compressor can improve the accuracy of compressor transmission sound identification.

[0073] In some embodiments, the compressor is determined to have transmitted noise based on the actual roughness or the actual sharpness. Specifically, the controller is configured to: determine that the compressor has transmitted noise if the noise time-domain signal is determined to be a low-frequency signal and the actual roughness is determined to be higher than a preset roughness; and / or determine that the compressor has transmitted noise if the noise time-domain signal is determined to be a high-frequency signal and the actual sharpness is determined to be higher than a preset sharpness.

[0074] Here, preset roughness and preset sharpness can be understood as the thresholds at which the human ear can detect the transmitted sound from the compressor, as determined by experiments. Preset roughness can be 1 asper, and preset sharpness can be 1.5 acum.

[0075] Specifically, because the human ear is more sensitive to the roughness of low-frequency noise and more sensitive to the sharpness of high-frequency noise, the compressor transmission sound is analyzed in two segments: low frequency and high frequency. In the low-frequency segment, the actual roughness of the noise time-domain signal is used to determine whether the compressor transmission sound is present in the air conditioner's indoor unit. Similarly, in the high-frequency segment, the actual sharpness of the noise time-domain signal is used to determine whether the compressor transmission sound is present in the air conditioner's indoor unit. That is, if the noise time-domain signal is determined to be a low-frequency signal (for example, if the frequency of the noise time-domain signal is lower than a preset frequency value, where the preset frequency value can be 300Hz), and the actual roughness is determined to be higher than the preset roughness, then the compressor transmission sound is confirmed; and / or, if the noise time-domain signal is determined to be a high-frequency signal... For example, if the frequency of the noise time-domain signal is determined to be greater than a preset frequency value, the noise time-domain signal is a high-frequency signal, and the actual sharpness is determined to be higher than the preset sharpness, then it is determined that the compressor is transmitting sound. Therefore, this application determines whether the air conditioner indoor unit is transmitting sound by using the actual roughness of the noise time-domain signal in the low-frequency band and the actual sharpness of the noise time-domain signal in the high-frequency band. This allows for the determination of whether the compressor is transmitting sound by combining the human ear's perception of different frequency bands. Furthermore, when selecting structural parameters for the compressor transmitting sound frequency, the structural parameters of the resonant cavity silencer can be designed based on the transmitted sound perceived by the human ear, so that the resonant cavity silencer can eliminate the transmitted sound perceived by the human ear and improve the user experience.

[0076] The following is for reference. Figure 8 The control process of the controller in an embodiment of the present invention will be described in detail.

[0077] Step S8: Collect the noise time-domain signal of the indoor unit.

[0078] Step S9: Perform signal processing on the noise time-domain signal to obtain the actual sharpness and actual roughness.

[0079] Step S10: Calculate the structural parameters of the resonant cavity silencer based on the transmitted audio frequency.

[0080] In some embodiments, the final structural parameters of the resonant cavity silencer are determined based on the initial structural parameters after each iteration, such as... Figure 9 As shown, the controller is specifically configured to execute steps S11-S12.

[0081] Step S11: In each iteration, update the global optimal structural parameters for each iteration based on the noise reduction difference corresponding to the initial structural parameters after each iteration.

[0082] Specifically, in each iteration, the globally optimal structural parameter is determined based on the noise reduction difference corresponding to the initial structural parameters after each iteration. That is, in n iterations of all initial structural parameters, the initial structural parameter with the smallest noise reduction difference is selected as the globally optimal structural parameter from all the initial structural parameters after each iteration. For example, if the number of iterations is 5, after iterating 5 times on all initial structural parameters, the initial structural parameter with the smallest noise reduction difference is selected as the globally optimal structural parameter from all the initial structural parameters obtained in these 5 iterations.

[0083] Step S12 continues until the maximum number of iterations is reached, then the globally optimal structure parameters of the last iteration are taken as the final structure parameters.

[0084] The maximum number of iterations is used to balance the quality of the optimal particle and the solution efficiency. The maximum number of iterations can be set according to the number of iterations to obtain the quality of the optimal solution based on the initial structure parameters. It can be understood that the larger the maximum number of iterations, the better the global optimal structure parameters.

[0085] Specifically, when iterating and optimizing each initial structural parameter, the number of iterations for the initial structural parameter is obtained. If the number of iterations reaches the maximum number of iterations, it indicates that the globally optimal structural parameter corresponding to the maximum number of iterations has reached the optimal solution. The globally optimal structural parameter from the last iteration is then used as the final structural parameter. Therefore, this application obtains the final structural parameter by determining the number of iterations for the initial structural parameter, which effectively improves the accuracy of obtaining the optimal structural parameter of the resonant cavity muffler. Furthermore, using the maximum number of iterations as the convergence criterion provides a clear termination boundary for the particle swarm optimization algorithm, avoiding infinite iterations or wasted computational resources due to ambiguity in convergence determination.

[0086] In some embodiments, for determining that the noise reduction difference meets the structural optimization conditions, the controller is specifically configured to: determine that the noise reduction difference of the final structural parameters is lower than the target noise reduction threshold.

[0087] The target noise reduction threshold is used to identify the critical value at which a resonant cavity muffler designed with the final result parameters can eliminate the compressor-transmitted sound at the compressor's transmitted frequency. The target noise reduction threshold is zero.

[0088] Specifically, if the silencing difference of the final structural parameters is determined to be lower than the target silencing threshold, that is, if the silencing difference between the peak sound pressure level of the transmitted sound and the corresponding pure tone silencing amount at the transmitted sound frequency is lower than the target silencing threshold, then the pure tone silencing amount of the resonant cavity muffler at the transmitted sound frequency is greater than the peak sound pressure level of the transmitted sound. In other words, the maximum silencing amount of the resonant cavity muffler is greater than the sound transmitted from the compressor to the human ear. This indicates that the maximum silencing amount of the resonant cavity muffler reliably covers and exceeds the peak sound level of the transmitted sound, thereby effectively eliminating the compressor transmitted sound. Therefore, in this application, the final structural parameters are determined by the silencing difference between the peak sound pressure level of the transmitted sound and the corresponding pure tone silencing amount at the transmitted sound frequency, so that the final structural parameters of the resonant cavity muffler are precisely matched with the transmitted sound frequency, thereby ensuring that the maximum silencing amount of the resonant cavity muffler reliably covers and exceeds the peak sound level of the transmitted sound, effectively eliminating the compressor transmitted sound.

[0089] In some embodiments, for generating multiple initial structural parameters of the resonant cavity muffler, the controller is specifically configured to generate multiple initial structural parameters of the resonant cavity muffler while satisfying the structural boundary conditions of the resonant cavity muffler.

[0090] Among them, the structural boundary conditions can be understood as the conventional and theoretical design constraints when designing a resonant cavity muffler. The structural boundary conditions can be geometric boundary conditions, which can be the limiting values ​​of the cavity volume V (i.e., the cavity volume of the resonant cavity), the neck length L, and the cross-sectional area S of the resonant cavity muffler. For example, V is in the range of [9.4, 1005], S is in the range of [3.14, 314], and L is in the range of [1, 50].

[0091] Specifically, under the condition that the structural boundary conditions of the resonant cavity muffler are satisfied, multiple initial structural parameters of the resonant cavity muffler are randomly generated. For example, they can be represented as xi (Vi, Si, Li). That is, multiple initial structural parameters of the resonant cavity muffler all satisfy the structural boundary conditions. For example, the initial structural parameters include the cavity volume V, the neck length L, and the cross-sectional area S. Then, the cavity volume V of the generated resonant cavity muffler is in the range of 9.4-1005, the neck length L is in the range of 1-50, and the cross-sectional area S is in the range of 3.14-314. Therefore, in this application, when generating multiple initial structural parameters of the resonant cavity muffler, the randomly generated initial structural parameters are restricted according to the structural boundary conditions so that the generated initial structural parameters can meet the conventional design and theoretical design of the resonant cavity muffler.

[0092] In this embodiment, if an initial structural parameter exceeds the structural boundary conditions in each iteration, the initial structural parameter is truncated according to a preset strategy, that is, the initial structural parameter is deleted.

[0093] In this embodiment, particle swarm parameters are set when initializing the particle swarm. These parameters include the number of initial structure parameters, the maximum number of iterations, the inertia weight, and the learning factor.

[0094] The following is for reference. Figure 10 The specific details of the control process of the controller in an embodiment of the present invention are described.

[0095] Step S13: Collect the noise time-domain signal of the indoor unit.

[0096] Step S14: Divide the noise time-domain signal into two parts with a frequency f=300Hz as the line.

[0097] Step S15: Determine whether the frequency f of the noise time domain signal is lower than 300Hz. For example, it can be expressed as determining f≤300Hz. If yes, proceed to step S16; otherwise, proceed to step S17.

[0098] Step S16: Calculate the actual roughness R of the noise time-domain signal, and then proceed to step S18.

[0099] Step S17: Calculate the actual sharpness S of the noise time-domain signal, and then proceed to step S18.

[0100] Step S18: Call the transmission sound determination condition, which is used to determine whether the compressor produces a transmission sound.

[0101] Step S19: Determine whether the actual roughness R of the noise time-domain signal is greater than the preset roughness, or determine whether the actual sharpness S of the noise time-domain signal is greater than the preset sharpness. For example, determine whether R > 1 or S > 1.5. If yes, proceed to step S20; otherwise, proceed to step S26.

[0102] Step S20: Locate the transmitted audio frequency and the transmitted peak sound pressure level.

[0103] Step S21: Read K based on the Kf curve.

[0104] Step S22: Optimize each initial structural parameter using a particle swarm optimization algorithm to determine the final structural parameters of the resonant cavity silencer.

[0105] Step S23: Calculate the noise reduction difference A of the final structural parameters based on K.

[0106] Step S24: Determine whether the noise reduction difference A of the final structural parameters is lower than the target noise reduction threshold. For example, it can be expressed as determining that A≤0. If yes, proceed to step S25; otherwise, proceed to step S22.

[0107] Step S25: Output the final structural parameters.

[0108] Step S26, End.

[0109] In summary, this application determines whether the compressor transmits noise by using the actual roughness R and actual sharpness S of the noise time-domain signal. A particle swarm optimization algorithm is used to iteratively optimize each initial structural parameter to obtain the optimal structural parameters as the final structural parameters of the resonant cavity muffler. Finally, the noise reduction difference of the final structural parameters is used to determine whether the resonant cavity muffler designed with the final structural parameters can completely eliminate the compressor's transmitted noise. If so, the resonant cavity muffler is designed using the final structural parameters. This allows for the rapid and efficient calculation and optimization of the optimal structural parameters of the resonant cavity muffler, ensuring that the maximum noise reduction of the resonant cavity muffler reliably covers and exceeds the peak value of the transmitted noise, thereby effectively eliminating the compressor's transmitted noise.

[0110] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0111] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An electronic device, characterized in that, Used to optimize the structural parameters of the resonant cavity silencer in the compressor piping assembly of an air conditioner; The compressor piping assembly includes: compressor; An exhaust pipe, which is connected to the compressor, is used to transport the refrigerant discharged by the compressor; A resonant cavity silencer, which is connected to the exhaust pipe, is used to eliminate the transmitted noise generated by the compressor during operation; The electronic device includes a controller, the controller being configured to: Generate multiple initial structural parameters for the resonant cavity silencer; The particle swarm optimization algorithm is used to iteratively optimize each initial structural parameter in order to determine the final structural parameters of the resonant cavity silencer. Determine the noise reduction difference corresponding to the final structural parameters; If the noise reduction difference of the final structural parameters is determined to meet the structural optimization conditions, the structure of the resonant cavity muffler is optimized based on the final structural parameters.

2. The electronic device according to claim 1, characterized in that, For iterative optimization of each initial structure parameter using the particle swarm optimization algorithm, the controller is specifically configured as follows: In each iteration, determine the iterative optimization rate for each initial structural parameter; Each initial structural parameter is iteratively optimized according to the iterative optimization speed to obtain the initial structural parameters after each iteration. The final structural parameters of the resonant cavity silencer are determined based on the initial structural parameters after each iteration.

3. The electronic device according to claim 2, characterized in that, To determine the iterative optimization rate for each initial structural parameter, the controller is specifically configured as follows: Determine the noise reduction difference for each initial structural parameter; The global optimal structural parameter and the individual optimal structural parameter for each initial structural parameter are determined based on the noise reduction difference of each initial structural parameter in each iteration. In each iteration, the iterative optimization speed is determined based on the individual optimal structural parameter of each initial structural parameter, the global optimal structural parameter, and each initial structural parameter after the previous iteration.

4. The electronic device according to claim 1, characterized in that, To determine the noise reduction difference corresponding to the final structural parameters, the controller is specifically configured as follows: After determining that the compressor is transmitting sound, the frequency of the transmitting sound and the peak sound pressure level of the transmitting sound are obtained; Determine the pure tone silencing amount of the resonant cavity silencer at the transmitted audio frequency; The silencing difference corresponding to the final structural parameters is determined based on the peak sound pressure level of the transmitted sound and the pure tone silencing amount.

5. The electronic device according to claim 4, characterized in that, To determine the amount of pure tone silencer produced by the resonant cavity silencer at the transmitted frequency, the controller is specifically configured as follows: Determine the silencing center frequency of the resonant cavity silencer; The pure tone silencing amount is determined based on the silencing center frequency and the transmission frequency.

6. The electronic device according to claim 4, characterized in that, The air conditioner includes an indoor unit, and the controller is specifically configured to detect the compressor transmitting a sound as follows: Obtain the noise time-domain signal of the indoor unit; The actual roughness and actual sharpness are determined based on the noise time-domain signal; The transmission sound of the compressor is determined based on the noise time-domain signal, the actual roughness, and / or the actual sharpness.

7. The electronic device according to claim 6, characterized in that, Based on the noise time-domain signal, the actual roughness, and / or the actual sharpness, the controller is specifically configured to determine that the compressor exhibits a transmitted sound, and the controller is specifically configured to: If the noise time-domain signal is determined to be a low-frequency signal, and the actual roughness is determined to be higher than the preset roughness, then it is determined that the compressor is transmitting noise. And / or, if it is determined that the noise time-domain signal is a high-frequency signal, and it is determined that the actual sharpness is higher than the preset sharpness, then it is determined that the compressor has transmitted sound.

8. The electronic device according to claim 2, characterized in that, The controller is specifically configured to determine the final structural parameters of the resonant cavity silencer based on the initial structural parameters after each iteration: In each iteration, the global optimal structural parameters for each iteration are updated based on the noise reduction difference corresponding to the initial structural parameters after each iteration. The process continues until the maximum number of iterations is reached, at which point the globally optimal structural parameters from the last iteration are taken as the final structural parameters.

9. The electronic device according to claim 1, characterized in that, To determine whether the noise reduction difference satisfies the structural optimization conditions, the controller is specifically configured as follows: The noise reduction difference of the final structural parameters is determined to be lower than the target noise reduction threshold.

10. The electronic device according to claim 1, characterized in that, For generating multiple initial structural parameters regarding the resonant cavity silencer, the controller is specifically configured as follows: Under the condition that the structural boundary conditions of the resonant cavity muffler are satisfied, multiple initial structural parameters of the resonant cavity muffler are generated.

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