75 results about "Noise optimization" patented technology

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

The invention relates to the technical field of vibration noise optimization, in particular to a vibration noise optimization method and system of an electric drive assembly. The method comprises the following steps of collecting multi-working-condition vibration noise of a transmission motor in an electric drive assembly and performing frequency domain conversion to generate vibration noise frequency domain data; on the basis of the data, carrying out gear meshing stress unbalance loading limit calculation, carrying out tooth surface pitting corrosion cycle ductility simulation, and identifying a bearing clearance expansion increment; optimizing the bearing structure according to the bearing clearance expansion incremental data to obtain bearing structure optimization data; performing tooth direction drum shape modification analysis according to the tooth surface pitting corrosion data to obtain modification data; and finally, carrying out anti-vibration strengthening design by combining bearing optimization and tooth direction modification data, generating structural anti-vibration strengthening design data, and sending the structural anti-vibration strengthening design data to a terminal so as to execute structural optimization and reduce the influence of vibration noise. According to the method, the vibration noise optimization technology is optimized, so that the vibration noise optimization technology is more perfect.

The invention relates to the technical field of reducer vibration noise optimization, and discloses a planet row coaxial reducer vibration noise optimization method, which comprises the steps of obtaining initial assembly parameters of a reducer, collecting load measurement signals of planet wheels of the reducer under an order frequency sweeping working condition, converting the load measurement signals into equivalent meshing loads based on a calibration curve, and calculating the equivalent meshing loads according to the equivalent meshing loads. Calculating a load imbalance coefficient according to the equivalent meshing load, and taking a peak value of the load imbalance coefficient as a first evaluation index; shell radial acceleration and sound pressure signals are synchronously collected under the order frequency sweeping working condition, the side-band ratio is calculated, and the peak value of the side-band ratio is taken as a second evaluation index; and performing diagnosis shunting based on the first evaluation index and the second evaluation index, determining an assembly parameter which is adjusted preferentially, executing iterative adjustment, and outputting a final assembly parameter after iteration is completed. According to the method, uniform load unbalance caused by assembly errors and side band amplification caused by geometric eccentricity can be effectively separated and solved, and vibration and noise of the speed reducer are reduced fundamentally.

The invention discloses an intelligent obstacle recognition method for an unmanned loader, and aims to solve the problems of low obstacle recognition precision, poor real-time performance and insufficient coordination of obstacle avoidance and operation in a complex operation scene. The method comprises the following steps: establishing a multi-source sensing system such as a laser radar and a camera, and realizing data time sequence synchronization, space unification and noise optimization through FPGA hardware; obstacle space and category features are extracted through double-branch parallel detection, and a modal weight fusion result is dynamically distributed in combination with a scene; an improved Hungary algorithm and a classification tracking model are adopted to realize obstacle ID stable association and trajectory tracking, and trajectory prediction is completed based on LSTM and Kalman filtering; and a dynamic avoidance area is constructed, and an obstacle avoidance path is generated through global-local bilevel programming and a safety potential field algorithm and is corrected in real time. The obstacle recognition robustness and real-time performance in a complex environment are improved, and safe and efficient operation of the unmanned loader is guaranteed.

The invention discloses an image scene conversion method and device, equipment and a storage medium. Firstly, an image to be processed and a conversion cue word used for indicating an image scene conversion direction and a specific attribute adjustment requirement can be obtained, the conversion cue word is input into a pre-trained scene noise optimizer, and structured guide noise accurately matched with the cue word is generated through noise optimization processing and used as target noise. Performing vector encoding on the to-be-processed image and the conversion prompt word through an image encoder and a text encoder to obtain an image feature vector and a text feature vector, and performing feature fusion on the image feature vector and the text feature vector by using a cross attention mechanism to generate a fusion vector; and finally, jointly inputting the fusion vector and the target noise into a pre-trained scene conversion model, and performing conditional decoding generation processing to obtain a scene conversion image corresponding to the to-be-processed image. According to the invention, disordered disturbance caused by noise injection is avoided, and drift of the image content in the conversion process is effectively inhibited.

The invention discloses a multi-source data fusion method and system based on data analysis, and relates to the technical field of electric digital data processing. The multi-source data fusion method based on data analysis comprises the following steps of performing fusion real-time analysis; performing dynamic fusion adaptive analysis; and interactive fusion validity analysis. According to the method, real-time analysis is carried out through real-time fusion data, whether sampling sensing frequency optimization is carried out or not is judged, then dynamic adaptability analysis is carried out according to sensing fusion data, whether sensing fusion parameter optimization is carried out or not is judged, finally interaction effectiveness analysis is carried out, and whether interaction fusion noise optimization is carried out or not is judged. Therefore, the reliability of the corresponding fire-fighting information dynamic fusion of the fire-fighting facilities in the transformer substation in the communication process of the internet of things is improved, and the problem that the reliability of the corresponding fire-fighting information dynamic fusion of the fire-fighting facilities in the transformer substation in the communication process of the internet of things is not high in the prior art is solved.

The invention relates to the technical field of voice recognition, in particular to an electric power self-service terminal voice intention recognition system based on background noise optimization. The system comprises a voice interaction noise optimization module, a scene voice transcription analysis module, a scene voice intention recognition module and a recognition confidence iterative optimization module, a voice interaction original data set during voice interaction between the power self-service terminal and a user can be collected, and noise optimization is carried out to generate a target voice interaction data set; performing voice transcription analysis based on the target voice interaction data set, and generating a text transcription result; performing keyword semantic matching calculation on the text transcription result and an intention recognition knowledge base to obtain an intention recognition result of the user; and calculating a comprehensive recognition confidence coefficient based on the intention recognition result, if the comprehensive recognition confidence coefficient is lower than a set threshold value, performing secondary noise optimization on the voice interaction original data set and re-executing the judgment step, and if not, outputting the intention recognition result of the user. According to the invention, the accuracy of terminal speech recognition and intention recognition can be improved.

The application relates to a noise optimization control method, device and equipment for rotor capture and a storage medium, and relates to the fan technical field, and particularly relates to a noise optimization control method, device and equipment for rotor capture and a storage medium, which comprises the following steps: obtaining a starting instruction issued by a fan; outputting first voltage data through voltage detection and comparison by using the starting instruction issued by the fan; determining the motion state of the fan at present by using the first voltage data; and obtaining the related results of noise optimization control of single-resistance sampling during rotor capture by using the determination result of the motion state of the fan at present and executing the corresponding motion mode. Through voltage detection and comparison, the first voltage data is outputted, the motion state of the fan at present is determined by using the first voltage data, and the corresponding motion mode is executed, so that single-resistance sampling realizes noiseless starting during rotor capture.

The present invention discloses a traffic classification method and system based on smooth noise data enhancement. The method comprises the following steps: a noise generation module generates unified normalized noise and converts the normalized noise into characteristic noise; a noise optimization module optimizes the noise shape through a gradient-based algorithm and optimizes the noise scale through an improved Bayesian optimizer; a data enhancement module generates optimized smooth noise based on the optimized noise shape and noise scale, and adds the optimized smooth noise to benign and malicious samples, respectively, to obtain enhanced new samples; an integrated learning module uses the enhanced samples as pseudo-malicious samples to train a traffic classifier, collects the results of traffic classifiers trained under multiple sets of noise scales, and combines the classification results using an improved Boosting algorithm to obtain the final classification result. The method effectively optimizes the classification boundary of the traffic classifier, improves the recall rate of the traffic classifier for unknown types of malicious traffic, and enhances the generalization of the classification results.

The invention relates to the technical field of vehicles, in particular to an electric vehicle noise optimization method and device, a vehicle, a storage medium and a product, and the method comprises the steps that an in-vehicle microphone is used for collecting in-vehicle noise data of the vehicle under the current working condition; extracting frequency spectrum characteristics of the in-vehicle noise data, removing random noise in the in-vehicle noise data according to the frequency spectrum characteristics, and determining target playing parameters of an in-vehicle loudspeaker according to the frequency spectrum characteristics of the in-vehicle noise data after the random noise is removed; and controlling an in-vehicle loudspeaker to play audio according to the target playing parameter to optimize the in-vehicle noise. Therefore, the problems that the effect is limited, the cost is too high or extra noise is introduced, and high-efficiency, low-cost and practical noise reduction is difficult to realize when the high-frequency noise of the motor of the electric vehicle is treated in the related technology are solved.

The invention relates to a combined type energy storage equipment noise reduction system and a noise optimization method. The combined type energy storage equipment noise reduction system comprises a noise reduction cover body arranged corresponding to a refrigeration assembly, and a noise reduction control module is further arranged in the noise reduction cover body; the noise reduction cover body comprises a perforated aluminum plate and a split type sound absorption plate, the perforated aluminum plate is used as a high-frequency noise reflecting layer, the split type sound absorption plate is used as a medium-frequency noise dissipation layer, the noise reduction cover body further comprises a bottom vibration isolation module, and the bottom vibration isolation module is used as a low-frequency vibration isolation layer for restraining low-frequency vibration transmission; and combining to form a physical filtering noise reduction structure for suppressing broadband noise through a gradient structure.

The invention relates to the technical field of communication, in particular to an ultra-wideband low-noise amplifier based on a coupling expansion technology. The ultra-wideband low-noise amplifier comprises a Cascode amplifying circuit, an input coupling inductor pair and an output coupling inductor pair. A drain stray capacitor Cd and a load capacitor CL in a circuit are rapidly charged through negative electromotive force generated by the mutual inductance effect, the charging time is remarkably shortened, the working bandwidth of the amplifier is greatly expanded on the premise that the noise performance is not sacrificed, meanwhile, the requirement for a multi-stage impedance matching network is simplified through the inherent broadband characteristic of the coupling structure, and the cost is reduced. According to the ultra-wideband power divider, ultra-wideband input and output matching is achieved, noise matching is optimized through the noise optimization capacitor Cex, the chip area and the number of peripheral elements are effectively reduced through the multiplexing inductor function, and finally the contradiction among bandwidth, noise, matching and area power consumption is cooperatively solved under the condition of high integration level.

The invention relates to an idling charging noise optimization method and device of a vehicle, electronic equipment and a medium, and the method comprises the steps: calculating the suspension loading force of the vehicle through measuring a related transfer function, the actual vibration acceleration of an indication point and the actual sound pressure of a target response point under the idling charging working condition of the vehicle by using an inverse matrix method; coupling the whole vehicle model and the acoustic cavity finite element model of the vehicle to obtain a whole vehicle interior noise vibration prediction simulation model; inputting the suspension loading force into the whole vehicle interior noise vibration prediction simulation model, and predicting the vehicle interior vibration noise under the idling charging excitation of the whole vehicle state of the vehicle; and identifying a problem characteristic frequency meeting a preset optimization condition from the in-vehicle vibration noise, and determining an idling charging noise optimization scheme of the vehicle based on the problem characteristic frequency so as to optimize the whole vehicle model by using the noise optimization scheme. Therefore, the technical problems of high time cost and high labor cost due to the fact that the real vehicle needs to be tested for multiple times to determine the optimization scheme according to the test data in the related technology are solved.

A method, apparatus, non-transitory computer readable medium, and system for generating synthetic image includes obtaining an input prompt describing a first element and a second element. In some cases, an image generation model generates an intermediate output based on the input prompt and optimizes the intermediate output based on an attention contrast loss to obtain an optimized intermediate output. For example, the optimized intermediate output represents the first element at a first location and the second element at a second location. The image generation model generates a synthetic image based on the optimized intermediate output. The synthetic image depicts the first element at the first location and the second element at the second location.

The application relates to the technical field of air conditioners, and discloses a silencer. The silencer provided by the application comprises a silencer body, a first pipe, a second pipe and an inner connecting pipe. The first pipe comprises a first folding pipe section; the second pipe is fixed at two ends of the silencer body with the first pipe; one end of the inner connecting pipe is inserted into the first pipe and penetrates into a silencing chamber, and the other end of the inner connecting pipe is used for connecting to a refrigerant circulating system; a positioning point is arranged at the middle part of the inner connecting pipe, and the positioning point is fixedly connected with the end part of the first pipe; the first folding pipe section can be expanded or contracted, and the first folding pipe section is used for adjusting the depth of the inner connecting pipe penetrating into the silencing chamber. The length of the inner connecting pipe in the silencing cavity is variable, the length of the silencing chamber and the length of the inserted inner connecting pipe are used for judging the frequency and the sound elimination amount which need to be eliminated by the system, so that the problem that the silencer needs to be frequently replaced in the process of optimizing and improving the air conditioner noise is solved. The application further discloses an air conditioner.

The invention provides a data set knowledge density evaluation method and device based on noise optimization type data set distillation, and relates to the technical field of data processing.The method comprises the steps that on the basis that data set distillation is carried out based on a diffusion model, DDPM denoising is carried out batch by batch, performing random noise optimization in each step of denoising of each batch based on distribution alignment loss, maximum occupancy loss and authenticity constraint loss, and iteratively obtaining a final synthetic sample set of the last step of denoising of each batch; obtaining a distillation data set according to the final synthesis sample set of the last step of all batches of de-noised samples; the test performance of the classifier trained by the distillation data set is the same as that of the classifier trained on the original data set; and determining the knowledge density of the original data set according to the total data number of the distillation data set and the total data number of the original data set. According to the invention, the accuracy of knowledge density evaluation of the data set is improved.

The invention discloses an initial noise optimization method of a new view angle synthesis model based on a diffusion model, belongs to the diffusion model, and can be used in the field of new view angle synthesis. According to the optimization method, firstly, a machine learning network is provided, and the network is composed of an encoder and a decoder; then training an encoder-decoder network according to initial noise-optimized noise pair acquired by a new view angle synthesis model based on a diffusion model; and finally, inserting the trained encoder-decoder network into the new visual angle synthesis model. The invention aims to change initial noise into optimized noise through network action, so that a model generation result is improved on the premise of not finely tuning a model.

This application discloses a focusing noise optimization method, apparatus, storage medium, and product, relating to the field of interferometric noise optimization technology. The focusing noise optimization method includes: acquiring an initial interferometric image containing multiple types of focusing noise and inputting it into a preset noise error correlation model to obtain the noise influence coefficients of each type of focusing noise. The noise error correlation model is obtained by training a model to be trained based on interferometric image samples, which include multiple types of focusing noise and actual focusing errors. Based on the noise influence coefficients, a corresponding denoising algorithm is used to remove the focusing noise of the appropriate type. This application enables the model to be trained to learn the relationship between various types of noise and focusing errors, thereby determining the influence coefficients of each type of focusing noise on the focusing errors. Based on the noise influence coefficients, the corresponding types of focusing noise are selectively removed to avoid affecting the wavefront curvature extraction accuracy, thereby improving the focusing accuracy of the wavefront curvature interferometry method.

This invention discloses a method for noise optimization in discrete spectrum nonlinear frequency division multiplexing (DSP) systems using geometric shaping, belonging to the field of communication technology. The method includes: considering ASE noise and establishing a channel model under noisy conditions; based on the channel model under noisy conditions, designing an olive-shaped 32QAM in the DSP system; and by optimizing the geometric distribution of constellation points, mapping the optical signal to the point distribution of the olive-shaped 32QAM, reducing the probability of high-power points and minimizing the impact of accumulated spontaneous emission noise (ASE) and phase noise, thereby improving the signal's noise immunity and system transmission stability.

The invention provides a camera / laser radar calibration method for galloping monitoring of a transmission conductor, and belongs to the technical field of crossing of multi-sensor fusion sensing and power system monitoring, and the method comprises the steps: reconstructing a dense point cloud through binocular stereo matching, and automatically segmenting a multi-plane structure existing in a near field and a far field; and normal vector clustering grouping is carried out on the laser point cloud to obtain near and far field multi-plane fragments, then cross-correlation of the image reconstruction multi-plane point cloud and the laser radar multi-plane point cloud is realized based on an SPFH descriptor constructed by local voxels, and finally, a camera / laser radar calibration algorithm based on active robust of a multi-plane structure is combined to estimate high-precision external parameters. According to the method, no calibration target is needed, a multi-distance and multi-angle plane structure in a scene is fully utilized, and full-environment and full-automatic calibration is achieved; and meanwhile, through multi-plane cross-correlation and active anti-noise optimization, the registration error is remarkably reduced, the external parameter precision is improved, and the robustness of image distortion and shielding noise in a complex environment is enhanced.

The invention belongs to the technical field of power equipment detection, and particularly relates to a power scene-oriented voiceprint sensing terminal, which comprises a receiving module, a filtering module, an analysis module and a judgment module, the receiving module is specially used for anti-interference acquisition and preprocessing of voiceprint signals of power equipment and provides high signal-to-noise ratio input for back-end analysis. The device specifically comprises a strong electromagnetic field anti-interference assembly and a wide-frequency-band noise optimization assembly. The strong electromagnetic field anti-interference component is used for resisting interference through a signal conditioning circuit, and comprises analog front-end filtering, particularly broadband noise suppression; high-precision ADC protection is further included, specifically, an isolation design is adopted, and a digital isolator is arranged; the system also comprises a digital signal processing anti-interference assembly which is specifically used for time-frequency combined noise reduction and deep learning noise reduction. According to the invention, the voiceprint emitted by the power equipment in the scene can be continuously and accurately detected, and an early warning signal is emitted in time when the power equipment is abnormal.

An artificial intelligence-based urban design noise optimization method and system includes: preprocessing building form and noise measurement data, constructing a basic data set after noise simulation verification; generating a three-dimensional urban model and a network of measurement points through a hierarchical parameterized framework, calculating the structure-borne noise level, and verifying it in real time in conjunction with planning constraints, providing a structured design space, basic noise data, and feasibility assurance; constructing a two-stage multilayer perceptron network to process the acoustic impact of local building clusters and global urban noise patterns, respectively, and achieving high-precision noise prediction through training; deploying a large language model as an optimization agent, and outputting the optimal solution through multiple rounds of iterative optimization and evaluation. Compared with traditional technologies, this invention effectively improves noise prediction accuracy. Parametric modeling provides a structured design space, real-time verification ensures feasibility, and the use of a large language model for optimization enables efficient iteration, significantly improving efficiency and reducing labor costs compared to traditional methods.

The invention relates to the technical field of power supply simulation, and discloses a voltage waveform simulation method and device of a power supply and computer equipment. When a first voltage waveform output by a power supply simulation link does not meet a preset condition of a specific low-frequency band, a preset condition of a third frequency band is used as a reference condition; according to the method, noise optimization is carried out according to different conditions of the first voltage waveforms of the specific low-frequency band, so that the different voltage waveforms of the first voltage waveforms under the specific low-frequency band meet the preset conditions, the power supply simulation link can normally converge even under the specific low-frequency band, and therefore, the power supply simulation efficiency is improved. According to the invention, the low-frequency noise of a specific low-frequency band can be accurately analyzed.

This invention relates to a fractional-number frequency division fully digital phase-locked loop (PLL) based on DTC and edge-time compensation algorithms, belonging to the field of radio frequency integrated circuit design. The overall circuit structure includes a time-to-digital converter (TDC), a digital loop filter (DLF), a digitally controlled oscillator (DCO), a programmable divider (MMD), a Sigma-delta modulator, and a digital time converter (DTC). The fractional-number frequency division function is implemented by the MMD, the Sigma-delta modulator, and the DTC. A time-domain compensation method is primarily used to design a quantization noise optimization circuit. By combining the DTC, low fractional-number spurious signals are achieved, and the fractional-number spurious signal output is then integrated into the PLL. Within the PLL, the Sigma-delta modulator output signal is integrated with the divided signal. The DTC then performs time-domain compensation based on the Sigma-delta modulator output signal and transmits it to the TDC for phase comparison with a reference signal to control the DCO's frequency output, thus achieving a high-performance fully digital PLL circuit with low fractional-number spurious signals.

The application discloses a control method and device of an air conditioner, the air conditioner, a storage medium and a computer program product, and relates to the technical field of air conditioners. The method comprises the following steps: acquiring current operation parameters of the air conditioner during air conditioner operation; determining regulation sound parameters of the air conditioner according to the operation parameters; identifying a use scenario in which a user is located; and generating regulation sound which is adapted to the regulation sound parameters according to a regulation sound output strategy corresponding to the use scenario, and controlling the air conditioner to output the regulation sound. According to the scheme, the adaptability of air conditioner noise optimization to dynamic working conditions is improved, and the matching degree of sound atmosphere demand in different scenarios is improved.

The invention discloses a noise optimization method of an electronic water pump and the electronic water pump applying the noise optimization method, and relates to the technical field of electromechanical equipment noise optimization, and the noise optimization method comprises the steps that the noise of the electronic water pump is divided into at least two noise frequency bands; under each noise frequency band, analyzing and determining a noise excitation source of the electronic water pump; and eliminating / inhibiting / isolating each noise excitation source of the electronic water pump, so as to optimize the noise of the electronic water pump under each noise frequency band. The invention mainly solves the problem of how to provide a vibration and noise optimization method based on spectral distribution for the electronic water pump. The effective optimization method is provided for essential causes (namely vibration / noise excitation sources under different noise spectrums) of vibration and noise of the electronic water pump, the optimization method has universality in the design stage and the manufacturing stage of the electronic water pump, and the mute performance of the electronic water pump is improved; and the user experience of a product applying the electronic water pump is improved.

The invention belongs to the technical field of servo rotary tables, and particularly relates to a low-noise servo rotary table and a design implementation method thereof. The combined lubricating structure design and the gear body resonance damping structure design are adopted, and the good popularization significance is achieved for the noise reduction design of a new transmission system. The combined lubricating structural design adopted by the device has an innovative structural design mode, the gear body resonance damping structural design is innovative, a numerical simulation noise reduction design method is combined, transmission system noise reduction design is carried out, the transmission design noise reduction advantage and effect are obvious, and it is proved through noise detection and corresponding ablation tests that the noise reduction effect is good. The noise optimization of the gear body resonance damping model design can reach-2dBA to-3dBA, and the noise optimization of the oil immersion lubrication damping model design can reach-4dBA to-5dBA. When the method is used for noise reduction design of the transmission system, the noise reduction effect is obvious, design parameters of a gear body resonance damping model and design parameters of an oil immersion lubrication damping model are corrected through multiple sets of sample test data, and noise reduction design theoretical numerical values and actually measured numerical values have more guiding significance.

This application provides an optimization method for an air conditioner chassis structure, relating to the field of air conditioning technology, for reducing vibration noise generated by the air conditioner during operation. The method includes: establishing a finite element model based on the configuration parameters of components mounted on the air conditioner chassis in the air conditioning system and the configuration parameters of the air conditioner chassis; performing vibration modal simulation calculations on the air conditioner chassis based on the finite element model to obtain simulation results; adjusting the target parameters in the results parameters of the air conditioner chassis when the simulation results do not meet the air conditioner noise optimization target, and re-executing the above model establishment steps and simulation calculation steps based on the adjusted target parameters; wherein, the target parameters include at least one of the chassis sheet metal thickness, chassis pressing quantity, or chassis counterweight; and outputting the configuration parameters of the components and the air conditioner chassis corresponding to the simulation results when the simulation results meet the air conditioner noise optimization target.

This invention relates to a design method for a spatially multiplexed off-axis bifocal multi-order diffractive lens based on blue noise optimization. The method involves determining the off-axis angle and off-axis focal length to generate an ideal off-axis hyperbolic phase distribution φ. L and φ R Calculate the local density of the initial random mask, iteratively optimize and generate a blue noise mask, and then apply φ... L and φ R Mapping to a blue noise mask yields an off-axis bifocal multiplexed phase. A phase library of multi-order diffraction structures is constructed. Phase discrete encoding is performed based on minimizing the phase residual, mapping the discrete phase to a height distribution of the multi-order diffraction structure, thus completing the lens construction. This invention optimizes traditional spatial multiplexing masks based on blue noise, weakening local clustering features in the mask distribution, thereby reducing the mask's spectral characteristics and achieving low background noise and high energy consistency for ideal bifocal phases. Multi-order diffraction structures are used for phase encoding to realize the generation of bifocal phase devices.

The embodiment of the invention provides an order noise control method and device and a medium, and relates to the technical field of vehicles, and the method comprises the steps: collecting motor / speed reducer parameters in a to-be-detected electric vehicle and whole vehicle NVH basic data, and carrying out the analysis to obtain each target order corresponding to the noise in the to-be-detected electric vehicle, and each transmission path corresponding to the noise in the to-be-tested electric vehicle is obtained through testing. And based on the target order and the transmission path, performing noise optimization on a motor and a vehicle body in the to-be-tested electric vehicle according to a set strategy. Testing current noise information in the to-be-tested electric vehicle after noise optimization under each working condition, determining whether the current noise information meets a set condition, and if yes, taking a set strategy as a target control strategy; and if not, updating the set strategy until the current noise information obtained again based on the updated strategy meets the set condition, and taking the updated strategy as the target control strategy. Therefore, reliable control of the order noise is realized.