Noise processing method, device, electronic apparatus, and storage medium

Abstract

To provide a noise processing method that is applied to an electronic apparatus and reduces noise pollution in a target workshop, a device, an electronic apparatus, and a storage medium.SOLUTION: A noise processing method includes: acquiring a first noise signal at a position of a target staff in a target workshop in a first period; predicting an overall control parameter of a plurality of signal transmitters in a second period which is a future period of the first period on the basis of the first noise signal to acquire a current parameter prediction result; determining one or more target transmitters that need to work in the second period from the plurality of signal transmitters on the basis of the current parameter prediction result and acquiring a parameter prediction value of each target transmitter in the second period; and controlling each target transmitter to transmit a noise interference signal in the second period according to a corresponding parameter prediction value to weaken a second noise signal at the position of the target staff in the target workshop in the second period.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and particularly to a noise processing method, apparatus, electronic device, and storage medium.

Background Art

[0002] In the industrial environment of the spinning process, the complexity and length of the process flow mean that it is necessary to install multiple production workplaces with different functions. Furthermore, a huge number of process facilities are installed in each production workplace, and significant noise pollution occurs when these process facilities operate.

Summary of the Invention

Problems to be Solved by the Invention

[0003] The present disclosure provides a noise processing method, apparatus, electronic device, and storage medium for solving or alleviating one or more technical problems in the prior art.

Means for Solving the Problems

[0004] In a first aspect, the present disclosure provides a noise processing method applied to an electronic device. The electronic device communicates with a plurality of signal transmitters arranged in a target workplace, and the arrangement manner of the plurality of signal transmitters in the target workplace is correlated with the real-time noise sound field in the target workplace. The method includes: acquiring a first noise signal of the location of a target staff member in a target workplace during a first period; predicting the overall control parameters of the plurality of signal transmitters during a second period, which is a future period of the first period, based on the first noise signal, and obtaining a current parameter prediction result; determining one or more target transmitters that need to operate during the second period from the plurality of signal transmitters based on the current parameter prediction result, and obtaining the parameter prediction values of each target transmitter during the second period; To weaken the second noise signal of the target staff's location in the target workplace during the second period, each target transmitter is controlled to transmit a noise interference signal during the second period according to the corresponding parameter prediction value, including.

[0005] The second aspect is that the present disclosure provides a noise processing device applied to an electronic device. The electronic device communicates with a plurality of signal transmitters arranged in a target workplace. The arrangement method of the plurality of signal transmitters in the target workplace has a corresponding relationship with the real-time noise sound field in the target workplace. The device includes A first acquisition unit for acquiring a first noise signal of the location of the target staff in the target workplace during the first period, Based on the first noise signal, predicting the overall control parameters of a plurality of signal transmitters during a second period, which is a future period of the first period, and acquiring the current parameter prediction result, a second acquisition unit, Based on the current parameter prediction result, determining one or more target transmitters that need to operate during the second period from a plurality of signal transmitters, and acquiring the parameter prediction value of each target transmitter during the second period, a third acquisition unit, To weaken the second noise signal of the location of the target staff in the target workplace during the second period, a transmission control unit for each target transmitter to transmit a noise interference signal during the second period according to the corresponding parameter prediction value, including.

[0006] The third aspect provides an electronic device, which Includes at least one processor, A memory communicatively connected to the at least one processor, including, Instructions executable by the at least one processor are stored in the memory. When the instructions are executed by the at least one processor, the at least one processor is caused to execute any method of the embodiments of the present disclosure.

[0007] A fourth aspect provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute any of the methods according to the embodiments of the present disclosure.

[0008] A fifth aspect provides a computer program product including a computer program that, when executed by a processor, executes any of the methods according to the embodiments of the present disclosure.

Advantages of the Invention

[0009] According to the invention of the present disclosure, after obtaining the first noise signal of the location of the target staff in the target workplace within the first period, based on the first noise signal, the overall control parameters of a plurality of signal transmitters within the second period (a future period of the first period) can be predicted, and the current parameter prediction result can be obtained. The current parameter prediction result is obtained by prediction based on the first noise signal, and is aimed at establishing a strong correlation with the second period, and is not obtained by directly analyzing the first noise signal. Therefore, based on the current parameter prediction result, one or more target transmitters that need to operate within the second period are determined from the plurality of signal transmitters, and after obtaining the parameter prediction values of each target transmitter within the second period, each target transmitter is controlled so that the noise interference signal transmitted within the second period according to the corresponding parameter prediction value can generate a good cancellation effect on the second noise signal of the location of the target staff in the target workplace within the second period, and the second noise signal of the location of the target staff in the target workplace within the second period can be weakened, thereby reducing the noise pollution in the target workplace.

Brief Description of the Drawings

[0010]

Figure 1

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Mode for Carrying Out the Invention

[0011] It should be understood that the content described in the summary section of the invention does not limit the key points or important features of the embodiments of the present disclosure, nor does it limit the scope of the present disclosure. Other features of the present disclosure will be easily understood from the following description.

[0012] In the drawings, unless otherwise specified, the same reference numerals in multiple drawings indicate the same or similar members or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings merely show some embodiments provided by the present disclosure and are not considered to limit the scope of the present disclosure.

[0013] Hereinafter, the present disclosure will be described in more detail with reference to the drawings. In the drawings, the same reference numerals indicate functionally identical or similar elements. Although various aspects of the embodiments are shown in the drawings, unless otherwise specified, the drawings are not necessarily drawn to scale.

[0014] In addition, for a better explanation of the present disclosure, a number of specific details are described in the following specific embodiments. Those skilled in the art should understand that the present disclosure can be implemented similarly without specific details. In some examples, well-known methods, means, elements, circuits, etc. by those skilled in the art are not described in detail so as to emphasize the gist of the present disclosure.

[0015] As described above, in the industrial environment of the spinning process, the complexity and length of the process flow mean that it is necessary to install a plurality of production workplaces with different functions. Furthermore, a huge number of process facilities are installed in each production workplace, and significant noise pollution occurs when these process facilities operate. For example, in a spinning workplace for producing winding package products, a huge number of spinning boxes, which are process facilities, are installed therein, and significant noise pollution occurs in the spinning workplace when these spinning boxes operate. Also, for example, in a winding workplace for winding winding package products, a huge number of winding machines, which are process facilities, are installed therein, and significant noise pollution occurs in the winding workplace when these winding machines operate.

[0016] To reduce noise pollution in the production workplace, the embodiments of the present disclosure provide a noise processing method, which is applied to an electronic device. The electronic device communicates with a plurality of signal transmitters arranged in the target workplace, and the arrangement mode of the plurality of signal transmitters in the target workplace is correlated with the real-time noise sound field in the target workplace. Here, the target workplace may be any one of a plurality of production workplaces with different functions related to the spinning process, and the signal transmitter may be a speaker.

[0017] In addition, in the embodiments of the present disclosure, the main types of winding package products may include at least one of partially oriented yarns (POY), fully drawn yarns (FDY), draw textured yarns (DTY) (or low stretch yarns, etc.). For example, the types of winding package products may specifically include polyester partially oriented yarns, polyester fully drawn yarns, polyester drawn yarns, polyester draw textured yarns, etc.

[0018] FIG. 1 is a schematic flowchart 1 of a noise processing method according to an embodiment of the present disclosure. Hereinafter, with reference to FIG. 1, the noise processing method according to the embodiment of the present disclosure will be described. Although a logical order is shown in the schematic flowchart, in some cases, the steps shown or described may be executed in other orders.

[0019] Step S101: Obtain a first noise signal of the location of the target staff in the target workplace within the first period.

[0020] Here, the first period may be the current period with a first predetermined time length as the time length. In one example, the first predetermined time length is determined according to the moving speed of the target staff. For example, the first predetermined time length may show a negative correlation with the moving speed of the target staff, that is, the faster the moving speed of the target staff, the shorter the first predetermined time length, and the slower the moving speed of the target staff, the longer the first predetermined time length.

[0021] In addition, in the embodiments of the present disclosure, the target staff may be a worker entering the target workplace, and the first noise signal may be a real-time noise signal felt by the target staff within the first period. In one example, the first noise signal is collected by a first pickup worn by the target staff and transmitted to an electronic device. The first pickup may be an electromagnetic pickup, a piezoelectric pickup, an optical fiber pickup, a digital pickup, or the like.

[0022] Step S102: Based on the first noise signal, predict the overall control parameters of a plurality of signal transmitters within the second period, and obtain the current parameter prediction result.

[0023] The second period is a future period of the first period. For example, the second period may be a future period of the first period with a time length of a second predetermined time length. In the embodiments of the present disclosure, the second predetermined time length may be the same as the first predetermined time length or different from the first predetermined time length. In one example, the second predetermined time length is determined according to the moving speed of the target staff. For example, the second predetermined time length may show a negative correlation with the moving speed of the target staff, that is, the faster the moving speed of the target staff, the shorter the second predetermined time length, and the slower the moving speed of the target staff, the longer the second predetermined time length.

[0024] It should be noted that in the embodiments of the present disclosure, the current parameter prediction result is used to control the operating state of each signal transmitter within the second period.

[0025] Step S103: Based on the current parameter prediction result, determine one or more target transmitters that need to operate within the second period from the plurality of signal transmitters, and obtain the parameter prediction values of each target transmitter within the second period.

[0026] Since the current parameter prediction results are used to control the operating states of the respective signal transmitters during the second period, for each signal transmitter, based on the current parameter prediction results, it is possible to determine whether the signal transmitter is a target transmitter that needs to operate during the second period, and obtain the parameter prediction values of each target transmitter during the second period. Here, the parameter prediction values may include a frequency prediction value, a phase prediction value, an amplitude prediction value, and a direction prediction value.

[0027] Step S104: In order to weaken the second noise signal of the location of the target staff in the target workplace during the second period, control each target transmitter to transmit a noise interference signal during the second period according to the corresponding parameter prediction value.

[0028] Here, the second noise signal may be a real-time noise signal that can be felt by the target person during the second period.

[0029] After controlling each target transmitter to transmit a noise interference signal during the second period according to the corresponding parameter prediction value, the noise interference signal can generate a canceling effect on the second noise signal of the location of the target staff in the target workplace during the second period, thereby weakening the second noise signal of the location of the target staff in the target workplace during the second period.

[0030] According to the invention of the present disclosure, after obtaining the first noise signal of the location of the target staff in the target workplace within the first period, based on the first noise signal, the overall control parameters of a plurality of signal transmitters within the second period (a future period of the first period) can be predicted, and the current parameter prediction result can be obtained. The current parameter prediction result is obtained by prediction based on the first noise signal, and is intended to establish a strong correlation with the second period, and is not obtained by directly analyzing the first noise signal. Therefore, based on the current parameter prediction result, one or more target transmitters that need to start operating within the second period are determined from the plurality of signal transmitters, and after obtaining the predicted parameter values of each target transmitter within the second period, by controlling each target transmitter, the noise interference signal transmitted in the second period according to the corresponding predicted parameter value can produce a good cancellation effect on the second noise signal of the location of the target staff in the target workplace within the second period, thereby weakening the second noise signal of the location of the target staff in the target workplace within the second period, and thereby reducing the noise pollution in the target workplace.

[0031] Figure 2 is a schematic flowchart 2 of the noise processing method according to an embodiment of the present disclosure. Hereinafter, the noise processing method according to an embodiment of the present disclosure will be described with reference to Figure 2. Although a logical order is shown in the schematic flowchart, in some cases, the steps shown or described may be executed in other orders.

[0032] Step S201: Obtain the first noise signal of the location of the target staff in the target workplace within the first period.

[0033] The first period may be the current period with a time length of the first predetermined time length. In one example, the first predetermined time length is determined according to the moving speed of the target staff. For example, the first predetermined time length may show a negative correlation with the moving speed of the target staff, that is, the faster the moving speed of the target staff, the shorter the first predetermined time length, and the slower the moving speed of the target staff, the longer the first predetermined time length.

[0034] In addition, in the embodiments of the present disclosure, the target staff may be a worker entering the target workplace, and the first noise signal may be a real-time noise signal felt by the target staff within the first period. In one example, the first noise signal is collected by a first pickup worn by the target staff and transmitted to an electronic device. Here, the first pickup may be an electromagnetic pickup, a piezoelectric pickup, an optical fiber pickup, a digital pickup, or the like.

[0035] Step S202: Construct a first feature sequence based on the first noise signal and the historical parameter prediction result.

[0036] Here, the historical parameter prediction result is obtained by predicting the overall control parameters of a plurality of signal transmitters within the first period based on the zero-th noise signal of the location of the target staff in the target workplace within the zero-th period, and the zero-th period is the historical period of the first period. For example, the zero-th period may be the historical period of the first period with a time length of a third predetermined time length. In the embodiments of the present disclosure, the third predetermined time length may be the same as the first predetermined time length or different from the first predetermined time length. In one example, the third predetermined time length is determined according to the moving speed of the target staff. For example, the third predetermined time length may show a negative correlation with the moving speed of the target staff, that is, the faster the moving speed of the target staff, the shorter the third predetermined time length, and the slower the moving speed of the target staff, the longer the third predetermined time length.

[0037] In addition, in the embodiments of the present disclosure, the historical parameter prediction result is used to control the operating state of each signal transmitter within the first period. More specifically, for each signal transmitter, based on the historical parameter prediction result, the historical transmission parameter of the signal transmitter within the first period is obtained.

[0038] The historical transmission parameter may include a frequency value, a phase value, an amplitude value, and a direction value.

[0039] In an alternative embodiment, step S202 may include the following steps.

[0040] Step S202-1: Based on the first noise signal, obtain a sequence of noise signals arranged in chronological order.

[0041] In one example, the sequence of noise signals arranged in chronological order obtained based on the first noise signal may be represented as {X 11 , X 12 , ···, X 1n}. Here, X 11 represents the noise data of the location of the target staff in the target workplace at time T 11 within the first period, specifically including the frequency value F 11 , the phase value P 11 , the amplitude value A 11 and the direction value D 11 . X 12 represents the noise data of the location of the target staff in the target workplace at time T 12 within the first period, specifically including the frequency value F 12 , the phase value P 12 , the amplitude value A 12 and the direction value D 12 . Similarly hereinafter, X 1n represents the noise data of the location of the target staff in the target workplace at time T 1n within the first period, specifically including the frequency value F 1n , the phase value P 1n , the amplitude value A 1n and the direction value D 1n .

[0042] Step S202-2: Based on the historical parameter prediction result, obtain a sequence of known parameters arranged in chronological order.

[0043] In one example, the number of signal transmitters in the target workplace is K (K≥2 and K is an integer), and the known parameter sequence may include K known parameter subsequences that correspond one-to-one with the K signal transmitters. Moreover, the known parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters may be represented as {Qi 11 ,Qi 12 ,···,Qi 1n}, where 1≤i≤K and i is a positive integer.

[0044] Here, when i = 1, Q1 11 represents the predicted value of the historical parameters of the first signal transmitter among the K signal transmitters at time T 11 during the first period. Specifically, it may include the frequency prediction value F1 11 , the phase prediction value P1 11 , the amplitude prediction value A1 11 and the direction prediction value D1 11 . Q1 12 represents the predicted value of the historical parameters of the first signal transmitter among the K signal transmitters at time T 12 during the first period. Specifically, it may include the frequency prediction value F1 12 , the phase prediction value P1 12 , the amplitude prediction value A1 12 and the direction prediction value D1 12 . Similarly, hereinafter, Q1 1n represents the predicted value of the historical parameters of the first signal transmitter among the K signal transmitters at time T 1n during the first period. Specifically, it may include the frequency prediction value F1 1n , the phase prediction value P1 1n , the amplitude prediction value A1 1n and the direction prediction value D1 1n .

[0045] When the value of i is other values, for Qi 11 ,Qi 12 ,···,Qi 1n , it can be understood by referring to the relevant content above, and the detailed description is omitted here.

[0046] Step S202-3: Splice the known parameter sequence to the noise signal sequence to obtain a first initial sequence.

[0047] Step S202-4: Obtain a first additional feature based on the real-time task information of the target workplace.

[0048] Here, the real-time task information may include the product type, product specifications, etc. of the winding package product processed at the target workplace. Also, in the embodiments of the present disclosure, based on the real-time task information, the operating parameters used when the process equipment at the target workplace operates can be obtained. When the target workplace is a spinning workplace, the process equipment at the target workplace is a spinning box, and the operating parameters used when the spinning box operates may include the specification parameters of the spinneret, spinning speed, etc. When the target workplace is a winding workplace, the process equipment at the target workplace is a winder, and the operating parameters used when the winder operates may include the brand of the winder, specification parameters, winding speed, number of winding heads, etc.

[0049] Based on this, in one example, the real-time task information of the target workplace and the first workplace identifier can be used as the first additional feature together, or the operating parameters used when the process equipment at the target workplace operates and the first workplace identifier of the target workplace can be used as the first additional feature together. Here, the first workplace identifier of the target workplace can be used to determine the processing task type, workplace location, etc. of the target workplace, and the processing task type can include the production of winding package products, the winding of winding package products, etc.

[0050] Step S202-5: Construct a first feature sequence based on the first initial sequence and the first additional feature.

[0051] Continuing with the above example, the noise signal sequence is {X 11 ,X 12 ,···,X 1n} may be represented as, the number of signal transmitters in the target factory is K (K≥2 and is an integer), the known parameter sequence may include K known parameter subsequences that correspond one-to-one with the K signal transmitters, and the known parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters is {Qi 11 , Qi 12 , ···, Qi 1n}, where 1≤i≤K and i is a positive integer. When the first additional feature includes the real-time task information of the target workplace and the first workshop identifier, the first feature sequence constructed based on the first initial sequence obtained by stitching the known parameter sequence to the noise signal sequence and the first additional feature obtained based on the real-time task information of the target workplace may include feature data as shown in Table 1.

[0052]

Table 1

[0053] Here, Y 11 represents the real-time task information of the target workplace at time T 11 within the first period. Specifically, at time T 11 within the first period, it may include the product type Ty 11 of the winding package product processed in the target workplace, the product specification Sp 11 , etc. Y 12 represents the real-time task information of the target workplace at the time within the first period. Specifically, at time T 12 within the first period, it may include the product type Ty 12 of the winding package product processed in the target workplace, the product specification Sp1 12 , etc. Similarly hereinafter, Y 1n represents the real-time task information of the target workplace at time T 1n within the first period. Specifically, at time T 1n within the first period, it may include the product type Ty 1n, Product Specification Sp 1n etc. may be included, and Wid indicates the first workplace sign of the target workplace.

[0054] Thus, in the embodiments of the present disclosure, in addition to including the first initial sequence obtained by splicing the known parameter sequence into the noise signal sequence, the first feature sequence can also include the first additional features obtained based on the real-time task information of the target workplace. Therefore, the integrity of the feature data included in the first feature sequence can be guaranteed, thereby enhancing the reliability of the current parameter prediction result.

[0055] Step S203: Construct a second feature sequence based on the historical parameter prediction result and the future parameter sequence.

[0056] Among them, for the prediction result of the historical parameter, it can be understood with reference to the relevant content, and detailed description is omitted here.

[0057] In addition, in the embodiments of the present disclosure, the future parameter sequence is an input sequence corresponding to the current parameter prediction result, and the sequence length of the future parameter sequence may be the same as or different from the sequence length of the known parameter sequence.

[0058] Continuing with the above example, the number of signal transmitters in the target workplace is K (K ≥ 2 and is an integer), and the future parameter sequence may similarly include K future parameter subsequences that correspond one-to-one with the K signal transmitters. Among them, the future parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters may be represented as {Zi 21 , Zi 22 , ···, Zi 2n}, where 1 ≤ i ≤ K and i is a positive integer.

[0059] Here, when i = 1, Z1 21 is the time T within the second period21 represents a data element corresponding to the predicted parameter value of the first signal transmitter among the K signal transmitters, specifically, the predicted frequency value F1 21 the data element corresponding to, the predicted phase value P1 21 the data element corresponding to, the predicted amplitude value A1 21 the data element corresponding to and the predicted direction value D1 21 may include the data element corresponding to, and the values of these four may be any set values, for example, 0, and also, Z1 22 is the time T within the second period 22 represents a data element corresponding to the predicted parameter value of the first signal transmitter among the K signal transmitters, specifically, the predicted frequency value F1 22 the data element corresponding to, the predicted phase value P1 22 the data element corresponding to, the predicted amplitude value A1 22 the data element corresponding to and the predicted direction value D1 22 may include the data element corresponding to, and the values of these four may be any set values, for example, 0, and similarly hereinafter, Z1 2n is the time T within the second period 2n represents a data element corresponding to the predicted parameter value of the first signal transmitter among the K signal transmitters, specifically, the predicted frequency value F1 2n the data element corresponding to, the predicted phase value P1 2n the data element corresponding to, the predicted amplitude value A1 2n the data element corresponding to and the predicted direction value D1 2n may include the data element corresponding to.

[0060] When the value of i is other values, Zi 21 , Zi 22 , ···, Zi 2n can be understood by referring to the relevant content above, and detailed description is omitted here.

[0061] In an alternative embodiment that can be selected, step S203 may include the following steps.

[0062] Step S203-1: Based on the predicted results of the historical parameters, obtain a known parameter sequence arranged in chronological order.

[0063] Here, for the known parameter sequence, it can be understood by referring to the foregoing related content, and detailed description is omitted here.

[0064] Step S203-2: Splice the future parameter sequence into the known parameter sequence to obtain a second initial sequence.

[0065] Step S203-3: Based on the real-time task information of the target workplace, obtain a second additional feature.

[0066] As described above, the real-time task information may include product types, product specifications, etc. of the winding package products processed in the target workplace. Also, in the embodiments of the present disclosure, based on the real-time task information, the operating parameters used when the process equipment in the target workplace operates can be obtained. When the target workplace is a spinning workplace, the process equipment in the target workplace is a spinning box, and the operating parameters used when the spinning box operates may include specification parameters of the spinneret, spinning speed, etc. in the spinning box. Also, when the target workplace is a winding workplace, the process equipment in the target workplace is a winder, and the operating parameters used when the winder operates may include the brand, specification parameters, winding speed, number of winding heads, etc. of the winder.

[0067] Based on this, in one example, both the real-time task information of the target workplace and the first workplace identifier may be used as the second additional feature, or both the operating parameters used when the process equipment in the target workplace operates and the first workplace identifier of the target workplace may be used as the second additional feature. Here, the first workplace identifier of the target workplace can be used to determine the processing task type, workplace location, etc. of the target workplace, and the processing task type can include the production of winding package products, the winding of winding package products, etc.

[0068] Step S203-4: Construct a second feature sequence based on the second initial sequence and the second additional feature.

[0069] Continuing with the above example, the number of signal transmitters in the target workplace is K (K ≥ 2 and K is an integer), and the known parameter sequence may include K known parameter subsequences that correspond one-to-one with the K signal transmitters. Moreover, the known parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters may be represented as {Qi 11 , Qi 12 , ···, Qi 1n}, and the future parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters may be represented as {Zi 21 , Zi 22 , ···, Zi 2n}, where 1 ≤ i ≤ K and i is a positive integer. When the second additional feature includes the real-time task information of the target workplace and the first workplace identifier, the second feature sequence constructed based on the second initial sequence obtained by stitching the future parameter sequence to the known parameter sequence and the second additional feature obtained based on the real-time task information of the target workplace may include feature data as shown in Table 2.

[0070]

Table 2

[0071] Here, Y 11 represents the real-time task information of the target workplace at time T 11 during the first period. Specifically, it may include the product type Ty 11 of the winding package product processed at the target workplace at time T 11 during the first period, the product specification Sp 11 , etc. Moreover, Y 12 represents the real-time task information of the target workplace at time T 12shows the real-time task information of the target workplace, specifically the time T within the first period 12 shows the product type Ty of the wound package product processed at the target workplace at 12 , product specification Sp1 12 etc. may be included, and similarly hereinafter, Y 1n is the real-time task information of the target workplace at the time T within the first period 1n shows the real-time task information of the target workplace, specifically the time T within the first period 1n shows the product type Ty of the wound package product processed at the target workplace at 1n , product specification Sp 1n etc. may be included, Y 21 is the time T within the second period 21 shows the real-time task information of the target workplace at the time T within the second period, specifically the time T within the second period 21 shows the product type TY of the wound package product processed at the target workplace at 21 , product specification Sp 21 etc. may be included, Y 22 is the time T within the second period 22 shows the real-time task information of the target workplace at the time T within the second period, specifically the time T within the second period 22 shows the product type Ty of the wound package product processed at the target workplace at 22 , product specification Sp1 22 etc. may be included, and similarly hereinafter, Y 2n is the time T within the second period 2n shows the real-time task information of the target workplace at the time T within the second period, specifically the time T within the second period 2n shows the product type Ty of the wound package product processed at the target workplace at 2n , product specification Sp 2n etc. may be included, and Wid indicates the first workplace identifier of the target workplace.

[0072] Thus, in the embodiments of the present disclosure, in addition to the second feature sequence including the second initial sequence obtained by splicing the unknown parameter sequence to the known parameter sequence, the second additional feature obtained based on the real-time task information of the target workplace may also be included, and the integrity of the feature data included in the second feature sequence can be guaranteed, thereby improving the reliability of the current parameter prediction result.

[0073] Step S204: Using the target time series model, predict the overall control parameters of a plurality of signal transmitters within the second period based on the first feature sequence and the second feature sequence, and obtain the current parameter prediction result.

[0074] Here, the target time series model may be a trained time series model, for example, an Informer model, an Autoregressive Integrated Moving Average Model, etc.

[0075] After obtaining the first feature sequence and the second feature sequence, input the first feature sequence and the second feature sequence into the target time series model, obtain the feature processing result output by the target time series model, and based on the feature processing result, the current parameter prediction result can be obtained. As described above, the second feature sequence may be obtained based on the second initial sequence and the second additional feature, and the second initial sequence may also be obtained by splicing the future parameter sequence to the known parameter sequence. Therefore, after obtaining the feature processing result, the output sequence corresponding to the future parameter sequence in the feature processing result can be used as the current parameter prediction result.

[0076] Since the second feature sequence is constructed based on the historical parameter prediction results and the future parameter sequence, in the process of using the target time series model to predict the overall control parameters of a plurality of signal transmitters within the second period and obtain the current parameter prediction results based on the first feature sequence and the second feature sequence, the historical parameter prediction results included in the second feature sequence play a positive guiding role in the feature processing of the target time series model, thereby further improving the reliability of the current parameter prediction results.

[0077] Furthermore, in the embodiments of the present disclosure, the target time series model may have a model structure as shown in FIG. 3, that is, the target time series model may include an encoder and a decoder. Based on this, in an optional embodiment, step S204 may include the following steps.

[0078] Step S204-1: Input the first feature sequence into the encoder, and process the first feature sequence by using a feature processing layer including a first self-attention module and a distillation module in the encoder to obtain a first feature mapping result.

[0079] Here, the encoder may include a plurality of feature encoding structures connected in series, and each feature encoding structure may include a first self-attention module and a distillation module connected in series.

[0080] Also, in the embodiments of the present disclosure, the first self-attention module is used to implement self-attention calculation on the input features by using a ProbSparse subsampling self-attention mechanism to obtain intermediate features, and the intermediate features are input into a distillation module belonging to the same feature encoding structure as the first self-attention module, and the distillation module performs distillation processing on the intermediate features, thereby reducing the complexity of the output features and using the distilled intermediate features as the output features of the feature encoding structure.

[0081] In an embodiment of the present disclosure, it is understood that the output feature of the last feature encoding structure among a plurality of serially connected feature encoding structures is the first feature mapping result.

[0082] Step S204-2: Input the second feature sequence into the second self-attention module in the encoder, use the second self-attention module to process the second feature sequence, and obtain a second feature mapping result.

[0083] Here, the second self-attention module is used to obtain the second feature mapping result by realizing self-attention calculation on the second feature sequence by using the ProbSparse subsampling self-attention mechanism and the masking mechanism.

[0084] Step S204-3: Input the first feature mapping result and the second feature mapping result into the cross-attention module in the encoder, use the cross-attention module to process the first feature mapping result and the second feature mapping result, and obtain a feature processing result.

[0085] The feature processing result includes the current parameter prediction result obtained by predicting the overall control parameters of a plurality of signal transmitters within the second period.

[0086] Continuing with the foregoing example, a second feature sequence constructed based on a second initial sequence obtained by splicing a future parameter sequence into a known parameter sequence and a second additional feature obtained according to the real-time task information of the target workplace may include feature data as shown in Table 2. In this case, an output sequence corresponding to the future parameter sequence among the feature processing results is predicted as the overall control parameter of a plurality of signal transmitters within a second period, and a current parameter prediction result is obtained. For example, the current parameter prediction result may include K standby parameter subsequences that correspond one-to-one with K signal transmitters, and the standby parameter subsequence corresponding to the i-th signal transmitter among the K signal transmitters may be represented as {Z'i 21 , Z'i 22 , ···, Z'i 2n}, where 1 ≤ i ≤ K and i is a positive integer.

[0087] Here, when i = 1, Z'1 21 represents the parameter prediction value of the first signal transmitter among the K signal transmitters at time T 21 within the second period. Specifically, it may include a frequency prediction value F'1 21 , a phase prediction value P'1 21 , an amplitude prediction value A'1 21 and a direction prediction value D'1 21 . Z'1 22 represents the parameter prediction value of the first signal transmitter among the K signal transmitters at time T 22 within the second period. Specifically, it may include a frequency prediction value F'1 22 , a phase prediction value P'1 22 , an amplitude prediction value A'1 22 and a direction prediction value D'1 22 . Similarly hereinafter, Z'1 2n represents the parameter prediction value of the first signal transmitter among the K signal transmitters at time T 2n within the second period. Specifically, it may include a frequency prediction value F'1 2n , a phase prediction value P'1 2n , an amplitude prediction value A'1 2n and a direction prediction value D'12n may include.

[0088] That is, the current parametric prediction result may include feature data as shown in Table 3.

[0089]

Table 3

[0090] Based on the above model structure of the target time series model, the current parameter prediction result can be quickly obtained, and the reliability of the current parameter prediction result can be further improved.

[0091] Step S205: Based on the current parameter prediction result, determine one or more target transmitters that need to start operations within the second period from a plurality of signal transmitters, and obtain the parameter prediction values of each target transmitter within the second period.

[0092] Since the current parameter prediction result can be used to control the operating state of each signal transmitter within the second period, for each signal transmitter, based on the current parameter prediction result, it can be determined whether the signal transmitter is a target transmitter that needs to operate during the second period, and the parameter prediction values of each target transmitter within the second period can be obtained. Here, the parameter prediction values can include frequency prediction values, phase prediction values, amplitude prediction values, and direction prediction values.

[0093] In one example, for each signal transmitter, if the frequency prediction value among the parameter prediction values of the signal transmitter at any time within the second period is 0, the signal transmitter can be set as an idle-capable transmitter that does not need to start operations at that time within the second period, while if not, the signal transmitter can be set as a target transmitter that needs to start operations at that time within the second period. For example, for the first signal transmitter among K signal transmitters, the frequency prediction value F'1 included in the parameter prediction value of the first signal transmitter at time T within the second period 21 in the second period21 When it is 0, the first signal transmitter is an idle-capable transmitter that does not need to start an operation at time T within the second period. On the other hand, for example, the predicted frequency value F'1 included in the parameter prediction value of the first signal transmitter at time T within the second period 21 When it is not necessary to start an operation at time T within the second period, while for example, the predicted frequency value F'1 included in the parameter prediction value of the first signal transmitter at time T within the second period 22 If the predicted frequency value F'1 included in the parameter prediction value of the first signal transmitter at time T 21 is not 0, the first signal transmitter can be a target transmitter that needs to start an operation at time T within the second period, and moreover, at time T within the second period 22 The parameter prediction value of the first signal transmitter at time T within the second period can be used as the predicted frequency value F'1 22 The parameter prediction value of the first signal transmitter at time T includes the predicted frequency value F'1 22 the predicted phase value P'1 22 the predicted amplitude value A'1 22 and the predicted direction value D'1 22 and includes.

[0094] Step S206: In order to weaken the second noise signal of the location of the target staff in the target workplace within the second period, each target transmitter is controlled to transmit a noise interference signal within the second period according to the corresponding parameter prediction value.

[0095] Here, the second noise signal may be a real-time noise signal that can be felt by the target person within the second period.

[0096] When each target transmitter is controlled to transmit a noise interference signal within the second period according to the corresponding parameter prediction value, the noise interference signal can generate a canceling effect on the second noise signal of the location of the target staff in the target workplace within the second period, thereby weakening the second noise signal of the location of the target staff in the target workplace within the second period.

[0097] In addition, in the embodiments of the present disclosure, after obtaining the first noise signal of the location of the target staff in the target workplace within the first period by executing step S201, when the signal intensity of the first noise signal is lower than the predetermined intensity threshold, steps S201 to S206 are executed. On the other hand, when the signal intensity of the first noise signal is equal to or higher than the predetermined intensity threshold, the historical parameter prediction result can be directly used as the current parameter prediction result.

[0098] In addition, in the embodiments of the present disclosure, before executing the noise processing method, the target time series model can be obtained by training the time series model as follows.

[0099] Obtain the third noise signal of the location of the target staff in the designated workplace within the third period. Here, the designated workplace is any one of a plurality of production workplaces with different functions related to the spinning process, and the target staff is the worker entering the target workplace.

[0100] Using the time series model, based on the third noise signal, predict the overall control parameters of a plurality of signal transmitters arranged in the target workplace within the fourth period, and obtain the prediction result of the current training parameters. Here, the fourth period is the future period of the third period.

[0101] Based on the current training parameter prediction result, determine one or more preselected transmitters that need to operate within the fourth period from the plurality of signal transmitters, and obtain the training parameter prediction values of each preselected transmitter within the fourth training period.

[0102] Control each preselected transmitter to transmit a noise interference signal within the fourth period according to the corresponding training parameter prediction value.

[0103] Optimize the model parameters of the time series model based on the difference between the second noise signal and the ideal noise signal of the target staff's location in the designated workplace during the fourth period. Here, the ideal noise signal can be set according to the application needs.

[0104] Regarding the above content, it can be understood by referring to the relevant content, and detailed description is omitted here.

[0105] Note that in the embodiments of the present disclosure, when the difference between the second noise signal and the ideal noise signal meets the predetermined difference requirement, the latest time series model may be used as the target time series module.

[0106] In some selectable embodiments, the noise processing method may include the following before executing step 102 or step 202.

[0107] Remove the abnormal signal in the first noise signal and obtain a new first noise signal.

[0108] Here, the abnormal signal may be other noise signals other than the noise signals generated during the operation of the process equipment in the target workplace. For example, it may be a voice signal generated by an operator shouting loudly in the target workplace or a noise signal generated by an operator touching the process equipment.

[0109] Based on this, when executing step S102, based on the new first noise signal, the overall control parameters of the plurality of signal transmitters during the second period can be predicted, and the current parameter prediction result can be obtained. When executing step 202, by constructing the first feature sequence based on the new first noise signal and the historical parameter prediction result, the subsequent steps in the noise processing method can be executed based on the first feature sequence.

[0110] Since the new first noise signal is obtained by removing the abnormal signal in the first noise signal, when performing subsequent steps in the noise processing method based on the new first noise signal, the interference of the abnormal signal can be avoided, thereby improving the reliability of the current parameter prediction result.

[0111] In one example, "obtaining a new first noise signal by removing the abnormal signal in the first noise signal" means

[0112] performing wavelet packet transform on the first noise signal to obtain a plurality of initial wavelet packet coefficients,

[0113] performing threshold processing on each initial wavelet packet in the plurality of initial wavelet packet coefficients to obtain a plurality of target wavelet packet coefficients that correspond one-to-one to the plurality of initial wavelet packet coefficients,

[0114] and performing inverse wavelet packet transform on the plurality of target wavelet packet coefficients to obtain a new first noise signal.

[0115] Referring to FIG. 4, in one specific example, by using a wavelet packet kernel constrained convolution network, wavelet packet transform can be performed on the first noise signal to obtain a plurality of initial wavelet packet coefficients. When performing wavelet packet transform on the first noise signal, a preset multi-channel wavelet regularization term can be used as the conversion constraint condition of the filter in the wavelet packet kernel constrained convolution network to decompose the first noise signal and obtain a plurality of initial wavelet packet coefficients. Here, the multi-channel wavelet regularization term can be expressed as follows.

Equation

[0116] Here, c represents the channel of the filter h in the wavelet packet kernel constrained convolutional network, C represents the number of channels of the filter h in the wavelet packet kernel constrained convolutional network, and R wave represents a single-channel wavelet normalization term, p represents the number of filters h, and u represents the degree of the filter h.

[0117] After obtaining a plurality of initial wavelet packet coefficients, by using an activation network, threshold processing is performed on each initial wavelet packet in the plurality of initial wavelet packet coefficients, and a plurality of target wavelet packet coefficients corresponding one-to-one to the plurality of initial wavelet packet coefficients can be obtained. For each initial wavelet packet coefficient, threshold processing is performed on the initial wavelet packet coefficient by a preset soft shrinkage function in the activation network, and a target wavelet packet coefficient corresponding to the initial wavelet packet coefficient can be obtained. Here, the soft shrinkage function can be expressed as follows: [Number]

[0118] Here, λ represents a learnable parameter, sigmoid(λ) represents processing λ using the sigmoid function, σ represents a standard deviation operation, and x represents an initial wavelet packet coefficient that needs to perform threshold processing.

[0119] After obtaining a plurality of target wavelet packet coefficients corresponding one-to-one to the plurality of initial wavelet packet coefficients, an inverse wavelet packet transform is performed on the plurality of target wavelet packet coefficients by using an inverse wavelet packet kernel constrained convolutional network, and a new first noise signal can be obtained by performing the reconstruction of the noise signal.

[0120] Thus, in the embodiments of the present disclosure, the improved wavelet packet noise removal technology can be used to remove abnormal signals in the first noise signal and obtain a new first noise signal. Since the improved wavelet packet noise removal technology has an excellent removal effect on abnormal signals, the interference of abnormal signals can be avoided to the greatest extent, thereby further improving the reliability of the current parameter prediction result.

[0121] Furthermore, in the embodiments of the present disclosure, a series-connected wavelet packet core-constrained convolution network, activation network, and inverse wavelet packet core-constrained convolution network are used as one abnormal signal processing network, and a plurality of abnormal signal processing networks are connected in series to obtain an abnormal signal processing model. Further, the abnormal signal in the first noise signal is removed by the abnormal signal processing model, and a new first noise signal is obtained, thereby further improving the reliability of the current parameter prediction result.

[0122] As described above, in the embodiments of the present disclosure, the arrangement method of a plurality of signal transmitters in the target workplace is correlated with the real-time noise sound field in the target workplace. Based on this, in an optional embodiment, the noise processing method is to, before executing step S101 or step S201,

[0123] select a plurality of strong noise points with the maximum noise in the target workplace based on the real-time noise sound field in the target workplace;

[0124] select a plurality of target points corresponding one-to-one to the plurality of strong noise points at the top of the target workplace;

[0125] and further include adjusting the arrangement method of the plurality of signal transmitters in the target workplace so that the plurality of signal transmitters are arranged at the plurality of target points in a one-to-one correspondence with the plurality of target points.

[0126] Here, the real-time noise sound field in the target workplace can be obtained by means of indoor acoustic simulation.

[0127] In one example, first, by modeling the target workplace according to the workplace characteristics of the target workplace with acoustic simulation software, a workplace model of the target workplace can be obtained.

[0128] Here, the acoustic simulation software may be COMSOL Multiphysics simulation software, the workplace characteristics may include building characteristics and equipment characteristics of process equipment, the building characteristics may include building structure, building size, building materials used, etc., and the equipment characteristics may include the number of installed process equipment, layout method, and equipment structure, equipment size, manufacturing materials used, etc. of the process equipment. Based on this, in this example, the workplace model may include a building model and an equipment model of the process equipment. Furthermore, in this example, according to the building characteristics of the target workplace, building modeling is performed to obtain a building model of the target workplace, equipment modeling is performed according to the equipment characteristics of the process equipment in the target workplace to obtain an equipment model of the process equipment in the target workplace, and an acoustic simulator can be added to each equipment model in the workplace model of the target workplace with the acoustic simulation software.

[0129] After that, based on the real-time task information of the target workplace, first simulation parameters are obtained, the acoustic simulators added to each equipment model in the workplace model of the target workplace are controlled with the acoustic simulation software, a noise simulation signal is transmitted according to the first simulation parameters, and furthermore, the real-time noise sound field in the target workplace is obtained.

[0130] Here, the real-time task information may include the product type, product specifications, etc. of the winding package products processed at the target workplace, and based on the real-time task information, it is possible to obtain the operation parameters used when the process equipment at the target workplace operates. Based on this, in this example, based on the real-time task information of the target workplace, the operation parameters used when the process equipment at the target workplace operates are obtained, any process equipment at the target workplace is selected as the test equipment, and based on the operation parameters, the startup operation of the test equipment is controlled, and at the same time, the real-time noise signal generated when the test equipment operates is collected using the second pickup installed near the test equipment, the real-time noise signal is analyzed, the first simulation parameters are obtained, and the acoustic simulator is controlled to transmit a noise simulation signal that is the same as the real-time noise signal (for example, the frequency value, phase value, amplitude value, and direction value are all the same). When the process equipment is a spinning box, the operation parameters used when the process equipment operates may include the specification parameters of the die head in the spinning box, the spinning speed, etc. When the process equipment is a winder, the operation parameters used when the process equipment operates may include the brand of the winder, the specification parameters, the winding speed, the number of winding heads, etc. The second pickup may be an electromagnetic pickup, a piezoelectric pickup, an optical fiber pickup, a digital pickup, etc.

[0131] After obtaining the real-time noise sound field in the target workplace, based on the real-time noise sound field in the target workplace, a plurality of strong noise points with the maximum noise in the target workplace are selected, and further, a plurality of target points corresponding one-to-one to the plurality of strong noise points at the top of the target workplace are selected, and the arrangement method of the plurality of signal transmitters in the target workplace can be adjusted so that the plurality of signal transmitters are arranged at the plurality of target points in a one-to-one correspondence.

[0132] In one example, an attachment frame for a signal transmitter is installed at the top of the target workplace. The attachment frame includes a plurality of long rails fixed to the top of the target workplace, and a plurality of movable rails for attaching the signal transmitter are installed between two adjacent long rails. Here, the movable rail can move on two long rails corresponding to the movable rail in response to a first movement control command, and the signal transmitter can move on the movable rail corresponding to the signal transmitter in response to a second movement control command. Based on this, after selecting a plurality of target points that correspond one-to-one with a plurality of strong noise points at the top of the target workplace, by moving the movable rail and / or the signal transmitter attached to the movable rail, a plurality of signal transmitters can be arranged at the plurality of target points in a one-to-one correspondence with the plurality of target points.

[0133] Referring to FIG. 5, exemplarily, based on the real-time noise sound field in the target workplace 501, eight strong noise points with the maximum noise are selected from the target workplace 501. The eight strong noise points are strong noise point A1, strong noise point A2, strong noise point A3, strong noise point A4, strong noise point A5, strong noise point A6, strong noise point A7, and strong noise point A8, respectively. Then, eight target points that correspond one-to-one with the plurality of strong noise points can be selected from the top 501-1 of the target workplace 501. The eight target points are target point A1', target point A2', target point A3', target point A4', target point A5', target point A6', target point A7', and target point A8', respectively.

[0134] Referring further to FIG. 6, after selecting eight target points corresponding one-to-one to eight strong noise points at the top of the target workplace, the movable rail 601 is moved, and the signal transmitter 602 attached to the movable rail 601 is moved, so that the signal transmitter 602 can be arranged at the target point A1'. In a similar manner, the signal transmitter 604 can be arranged at the target point A2', the signal transmitter 606 can be arranged at the target point A3', the signal transmitter 608 can be arranged at the target point A4', the signal transmitter 610 can be arranged at the target point A5', the signal transmitter 612 can be arranged at the target point A6', the signal transmitter 614 can be arranged at the target point A7', and the signal transmitter 616 can be arranged at the target point A8'.

[0135] In the embodiments of the present disclosure, since the arrangement method of a plurality of signal transmitters in the target workplace is automatically adjusted, the degree of automation of the noise processing method can be improved. On the other hand, since a plurality of target points correspond one-to-one to a plurality of strong noise points where the noise in the target workplace is the largest, after arranging a plurality of signal transmitters corresponding one-to-one to the plurality of target points at the plurality of target points, the noise interference signals transmitted by each target transmitter within the second period according to the predicted parameter values corresponding thereto can generate a better cancellation effect on the second noise signal of the location of the target staff in the target workplace within the second period.

[0136] In order to better implement the above noise processing method, the embodiments of the present disclosure further provide a noise processing device, which can be applied to an electronic device. The electronic device communicates with a plurality of signal transmitters arranged in the target workplace, and the arrangement method of the plurality of signal transmitters in the target workplace has a corresponding relationship with the real-time noise sound field in the target workplace.

[0137] Hereinafter, with reference to the configuration schematic diagram shown in FIG. 7, the noise processing device according to the embodiments of the present disclosure will be described.

[0138] A noise processing device,

[0139] A first acquisition unit 701 for acquiring a first noise signal of the location of target staff in a target workplace during a first period;

[0140] A second acquisition unit 702 for predicting overall control parameters of a plurality of signal transmitters during a second period which is a future period of the first period based on the first noise signal and obtaining a current parameter prediction result;

[0141] Based on the current parameter prediction result, a third acquisition unit 703 for determining one or more target transmitters that need to start operations during the second period from the plurality of signal transmitters and obtaining predicted parameter values of each target transmitter during the second period, and

[0142] A transmission control unit 704 for controlling each target transmitter to transmit a noise interference signal during a second period which is a future period of the first period according to the predicted parameter value corresponding to the target transmitter in order to weaken a second noise signal of the location of target staff in the target workplace during the second period. A noise processing apparatus comprising the above.

[0143] In an optional embodiment, the second acquisition unit 702 is used to perform the following.

[0144] Construct a first feature sequence based on the first noise signal and a historical parameter prediction result obtained by predicting the overall control parameters of a plurality of signal transmitters during the first period according to the first noise signal and a zero-th noise signal of the location of target staff in a target workplace during a zero-th period which is a historical period of the first period.

[0145] Construct a second feature sequence based on the historical parameter prediction result and a future parameter sequence which is an input sequence corresponding to the current parameter prediction result.

[0146] Using a target time series model, predict the overall control parameters of a plurality of signal transmitters during the second period based on the first feature sequence and the second feature sequence, and obtain a current parameter prediction result.

[0147] In an alternative embodiment, the second acquisition unit 702 is used to perform the following operations.

[0148] Based on the first noise signal, obtain a noise signal sequence arranged in time series.

[0149] Based on the historical parameter prediction results, obtain a known parameter sequence arranged in chronological order.

[0150] By splicing the known parameter sequence into the noise signal sequence, obtain a first initial sequence.

[0151] Based on the real-time task information of the target workplace, obtain a first additional feature.

[0152] Construct a first feature sequence based on the first initial sequence and the first additional feature.

[0153] In an alternative embodiment, the second acquisition unit 702 is used to perform the following operations.

[0154] Based on the historical parameter prediction results, obtain a known parameter sequence arranged in chronological order.

[0155] By splicing the future parameter sequence into the known parameter sequence, obtain a second initial sequence.

[0156] Based on the real-time task information of the target workplace, obtain a second additional feature.

[0157] Construct a second feature sequence based on the second initial sequence and the second additional feature.

[0158] In an alternative embodiment, the target time series model includes an encoder and a decoder, and the second acquisition unit 702 is used to perform the following operations.

[0159] Input the first feature sequence into the encoder, and process the first feature sequence by using a feature processing layer including a first self-attention module and a distillation module in the encoder to obtain a first feature mapping result.

[0160] Input the second feature sequence into the second self-attention module in the encoder, and process the second feature sequence by using the second self-attention module to obtain a second feature mapping result.

[0161] Input the first feature mapping result and the second feature mapping result into the cross-attention module in the encoder, and process the first feature mapping result and the second feature mapping result by using the cross-attention module to obtain a feature processing result including the current parameter prediction result obtained by predicting the overall control parameters of a plurality of signal transmitters within the second period.

[0162] In an alternative embodiment, the noise processing device further includes a noise removal unit.

[0163] The noise removal unit is used to remove abnormal signals in the first noise signal to obtain a new first noise signal.

[0164] Also, obtaining the prediction result of the current parameters of a plurality of signal transmitters within the second period based on the first noise signal means

[0165] including predicting the overall control parameters of a plurality of signal transmitters within the second period based on the new first noise signal to obtain the current parameter prediction result.

[0166] In an alternative embodiment, the noise removal unit is used to perform the following.

[0167] Perform wavelet packet transform on the first noise signal to obtain a plurality of initial wavelet packet coefficients.

[0168] Perform threshold processing on each initial wavelet packet among the plurality of initial wavelet packet coefficients to obtain a plurality of target wavelet packet coefficients that correspond one-to-one with the plurality of initial wavelet packet coefficients.

[0169] Perform inverse wavelet packet transform on the plurality of target wavelet packet coefficients to obtain a new first noise signal.

[0170] In an alternative embodiment that can be selected, the noise processing device further includes a transmission head placement unit for performing the following.

[0171] Based on the real-time noise sound field in the target workplace, select a plurality of strong noise points where the noise is the maximum in the target workplace.

[0172] Select a plurality of target points corresponding one-to-one with the plurality of strong noise points at the top of the target workplace.

[0173] Adjust the placement method of the plurality of signal transmitters in the target workplace so that the plurality of signal transmitters are arranged at the plurality of target points in a one-to-one correspondence.

[0174] For the specific functions and exemplary descriptions of each module and sub-module of the device according to the embodiments of the present disclosure, reference can be made to the relationship descriptions of the corresponding steps in the embodiments related to the above method, and detailed descriptions are omitted here.

[0175] The acquisition, storage, and application of the personal information of the user according to the technical solution of the present disclosure comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0176] FIG. 8 is a block diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 8, the electronic device includes a memory 801 and a processor 802. The memory 801 stores a computer program executable by the processor 802. The number of the memory 801 and the processor 802 may be one or more. The memory 801 may store one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device is caused to execute the method according to the method embodiment. The electronic device may further include a communication interface 803 for communicating with an external device and performing interactive transmission of data.

[0177] If the memory 801, the processor 802, and the communication interface 803 are separate, the memory 801, the processor 802, and the communication interface 803 are connected to each other via a bus and can communicate with each other. The bus may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For convenience, in FIG. 8, only one thick line is shown, but it does not mean that there is only one bus or one type of bus.

[0178] Optionally, when specifically implemented, if the memory 801, the processor 802, and the communication interface 803 are integrated on one chip, the memory 801, the processor 802, and the communication interface 803 can communicate with each other via an internal interface.

[0179] The processor may be a Central Processing Unit (CPU), or may be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components, etc. It should be understood that general-purpose processors may be microprocessors or any ordinary processors, etc. Note that the processor may also be a processor capable of corresponding to the Advanced RISC Machine (ARM) architecture.

[0180] Further, optionally, the memory may include a read-only memory and a random access memory, or may include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. The non-volatile memory may include a ROM (Read-Only Memory), a programmable ROM (PROM), an EPROM (Erasable PROM), an EEPROM (Electrically EPROM), or a flash memory. The volatile memory may include a random access memory (RAM) used as an external cache. The above description is merely illustrative and not restrictive. Many forms of RAM can be used. For example, a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchlink dynamic random access memory (SLDRAM), and a direct RAM bus random access memory (DR RAM) may be used.

[0181] In the above embodiments, all or part thereof may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part thereof can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer loads and executes the computer instructions, all or part of the flow or functions described in the embodiments of the present disclosure are generated. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, Bluetooth®, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer, or may be a data storage device such as a server or data center that includes one or more available media integrated. The available media may be a magnetic medium (e.g., floppy (registered trademark) disk, hard disk, or magnetic tape), an optical medium (e.g., Digital Versatile Disc (DVD)), a semiconductor medium (e.g., Solid State Disk (SSD)), etc. It should be noted that the computer-readable storage medium according to the present disclosure may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0182] A person skilled in the art can understand that all or part of the steps for implementing the above embodiments may be completed by hardware, or may be completed by instructing the relevant hardware by a program. The program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like.

[0183] In the description of the embodiments of the present disclosure, the descriptions of the terms "one embodiment", "some embodiments", "exemplification", "specific exemplification", or "some exemplifications" mean that the specific features, structures, materials, or characteristics described in relation to the embodiment or exemplification are included in at least one embodiment or exemplification of the present disclosure. In addition, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or exemplifications in an appropriate manner. Also, unless they are mutually contradictory, a person skilled in the art can combine the different embodiments or exemplifications described in this specification and the features in different embodiments or exemplifications.

[0184] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means "or". For example, "A / B" can represent "A" or "B". The "and / or" in this specification is only a relational description of the related objects and means that there are three possible relationships. For example, "A and / or B" can indicate three situations: "A" exists alone, "A" and "B" exist simultaneously, and "B" exists alone.

[0185] In the description of the embodiments of the present disclosure, the terms "first" and "second" are for description only and should not be understood as indicating or implying relative importance or implying the number of the indicated components. Thus, the features limited by "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "a plurality" is two or more.

[0186] The above are only exemplary embodiments of the present disclosure and do not limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made in accordance with the spirit and principles of the present disclosure should all be included within the scope of the claims of the present disclosure.

Claims

1. A noise processing method applied to an electronic device, wherein the electronic device communicates with a plurality of signal transmitters arranged in a target workplace, and the arrangement method of the plurality of signal transmitters in the target workplace is correlated with the real-time noise sound field in the target workplace, the noise processing method includes acquiring a first noise signal of the location of a target staff member in the target workplace during a first period; predicting the overall control parameters of the plurality of signal transmitters during a second period, which is a future period of the first period, based on the first noise signal, and obtaining a current parameter prediction result; determining one or more target transmitters that need to operate during the second period from the plurality of signal transmitters based on the current parameter prediction result, and obtaining parameter prediction values of each target transmitter during the second period; controlling each target transmitter to transmit a noise interference signal during the second period according to the corresponding parameter prediction value in order to weaken a second noise signal of the location of the target staff member in the target workplace during the second period; Predicting the overall control parameters of the plurality of signal transmitters during the second period based on the first noise signal and obtaining the current parameter prediction result includes constructing a first feature sequence based on the first noise signal and a historical parameter prediction result obtained by predicting the overall control parameters of the plurality of signal transmitters during the first period according to a zero-th noise signal of the location of the target staff member in the target workplace during a zero-th period, which is a historical period of the first period; constructing a second feature sequence based on the historical parameter prediction result and a future parameter sequence, which is an input sequence corresponding to the current parameter prediction result; using a target time series model to predict the overall control parameters of the plurality of signal transmitters during the second period based on the first feature sequence and the second feature sequence, and obtaining the current parameter prediction result; A noise processing method.

2. Constructing a first feature sequence based on the first noise signal and a historical parameter prediction result includes acquiring a noise signal sequence arranged in chronological order based on the first noise signal; Obtaining a known parameter sequence arranged in chronological order based on the historical parameter prediction result; Splicing the known parameter sequence into the noise signal sequence to obtain a first initial sequence; Obtaining a first additional feature based on the real-time task information of the target workplace; Constructing the first feature sequence based on the first initial sequence and the first additional feature, including: The noise processing method according to claim 1.

3. Constructing a second feature sequence based on the historical parameter prediction result and the future parameter sequence includes: Obtaining a known parameter sequence arranged in chronological order based on the historical parameter prediction result; Splicing an unknown parameter sequence into the known parameter sequence to obtain a second initial sequence; Obtaining a second additional feature based on the real-time task information of the target workplace; Constructing the second feature sequence based on the second initial sequence and the second additional feature, including: The noise processing method according to claim 1.

4. The target time series model includes an encoder and a decoder. Using the target time series model to predict the overall control parameters of the plurality of signal transmitters within the second period based on the first feature sequence and the second feature sequence, and obtaining the current parameter prediction result includes: Inputting the first feature sequence into the encoder and processing the first feature sequence using a feature processing layer including a first self-attention module and a distillation module in the encoder to obtain a first feature mapping result; Inputting the second feature sequence into a second self-attention module in the encoder and processing the second feature sequence using the second self-attention module to obtain a second feature mapping result; Input the first feature mapping result and the second feature mapping result into the cross-attention module in the encoder, use the cross-attention module to process the first feature mapping result and the second feature mapping result, and obtain a feature processing result including the current parameter prediction result obtained by predicting the overall control parameters of the plurality of signal transmitters during the second period. The noise processing method according to any one of claims 1 to 3.

5. The noise processing method includes: further removing abnormal signals in the first noise signal to obtain a new first noise signal; Based on the first noise signal, obtaining the current parameter prediction result of the plurality of signal transmitters during the second period includes: predicting the overall control parameters of the plurality of signal transmitters during the second period based on the new first noise signal, and obtaining the current parameter prediction result. The noise processing method according to claim 1.

6. Removing abnormal signals in the first noise signal to obtain a new first noise signal includes: performing wavelet packet transform on the first noise signal to obtain a plurality of initial wavelet packet coefficients; performing threshold processing on each initial wavelet packet in the plurality of initial wavelet packet coefficients to obtain a plurality of target wavelet packet coefficients corresponding one-to-one to the plurality of initial wavelet packet coefficients; performing inverse wavelet packet transform on the plurality of target wavelet packet coefficients to obtain the new first noise signal. including The noise processing method according to claim 5.

7. The noise processing method includes: selecting a plurality of strong noise points with the maximum noise in the target workplace based on the real-time noise sound field in the target workplace; selecting a plurality of target points corresponding one-to-one to the plurality of strong noise points at the top of the target workplace; adjusting the arrangement method of the plurality of signal transmitters in the target workplace so that the plurality of signal transmitters are arranged at the plurality of target points in a one-to-one correspondence. including The noise processing method according to claim 1.

8. A noise processing device applied to an electronic device, The electronic device communicates with a plurality of signal transmitters arranged in the target workplace, and the arrangement method of the plurality of signal transmitters in the target workplace correlates with the real-time noise sound field in the target workplace. The noise processing device A first acquisition unit for acquiring a first noise signal of the location of the target staff in the target workplace within the first period; Based on the first noise signal, predict the overall control parameters of the plurality of signal transmitters within a second period, which is the future period of the first period, and obtain a current parameter prediction result. A second acquisition unit; Based on the current parameter prediction result, determine one or more target transmitters that need to operate within the second period from the plurality of signal transmitters, and obtain parameter prediction values of each of the target transmitters within the second period. A third acquisition unit; In order to weaken the second noise signal of the location of the target staff in the target workplace within the second period, a transmission control unit for controlling each of the target transmitters to transmit a noise interference signal within the second period according to the corresponding parameter prediction value. The second acquisition unit Based on the first noise signal and the historical parameter prediction result obtained by predicting the overall control parameters of the plurality of signal transmitters within the first period according to the zero-th noise signal of the location of the target staff in the target workplace within the zero-th period, which is the historical period of the first period, construct a first feature sequence. Based on the historical parameter prediction result and the future parameter sequence, which is an input sequence corresponding to the current parameter prediction result, construct a second feature sequence. Using a target time series model, based on the first feature sequence and the second feature sequence, predict the overall control parameters of the plurality of signal transmitters within the second period, and obtain the current parameter prediction result. Noise processing device.

9. The second acquisition unit Based on the first noise signal, obtain a noise signal sequence arranged in chronological order. Based on the historical parameter prediction result, obtain a known parameter sequence arranged in chronological order. Splice the known parameter sequence into the noise signal sequence to obtain a first initial sequence. Based on the real-time task information of the target workplace, obtain the first additional feature, Based on the first initial sequence and the first additional feature, used to construct the first feature sequence, The noise processing device according to claim 8.

10. The second acquisition unit, Based on the historical parameter prediction result, obtain a known parameter sequence arranged in chronological order, Splice the future parameter sequence into the known parameter sequence to obtain a second initial sequence, Based on the real-time task information of the target workplace, obtain a second additional feature, Based on the second initial sequence and the second additional feature, used to construct the second feature sequence, The noise processing device according to claim 8.

11. The target time series model includes an encoder and a decoder The second acquisition unit, Input the first feature sequence into the encoder, and use a feature processing layer including a first self-attention module and a distillation module in the encoder to process the first feature sequence to obtain a first feature mapping result, Input the second feature sequence into the second self-attention module in the encoder, and use the second self-attention module to process the second feature sequence to obtain a second feature mapping result, Input the first feature mapping result and the second feature mapping result into the cross-attention module in the encoder, and use the cross-attention module to process the first feature mapping result and the second feature mapping result, and obtain a feature processing result including the current parameter prediction result of the plurality of signal transmitters within the second period, which is used to predict the overall control parameters of the plurality of signal transmitters within the second period, The noise processing device according to any one of claims 8 to 10.

12. The noise processing device, Further includes a noise removal unit that removes abnormal signals in the first noise signal and obtains a new first noise signal, Based on the first noise signal, obtaining the current parameter prediction result of the plurality of signal transmitters within the second period, Based on the new first noise signal, predicting the overall control parameters of the plurality of signal transmitters within the second period and obtaining the current parameter prediction result. The noise processing device according to claim 8.

13. The noise removal unit performs wavelet packet transform on the first noise signal to obtain a plurality of initial wavelet packet coefficients, performs threshold processing on each initial wavelet packet among the plurality of initial wavelet packet coefficients to obtain a plurality of target wavelet packet coefficients corresponding one-to-one to the plurality of initial wavelet packet coefficients, performs inverse wavelet packet transform on the plurality of target wavelet packet coefficients and is used to obtain the new first noise signal. The noise processing device according to claim 12.

14. The noise processing device further includes a transmission head placement unit, wherein the transmission head placement unit selects a plurality of strong noise points with the maximum noise in the target workplace based on the real-time noise sound field in the target workplace, selects a plurality of target points corresponding one-to-one to the plurality of strong noise points at the top of the target workplace, and is used to adjust the placement method of the plurality of signal transmitters in the target workplace so that the plurality of signal transmitters are arranged at the plurality of target points corresponding one-to-one to the plurality of target points. The noise processing device according to claim 8.

15. including at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is caused to execute the method according to any one of claims 1 to 3. An electronic device.

16. A non-transitory computer-readable storage medium storing instructions for causing a computer to execute the method according to any one of claims 1 to 3.

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