Target perception method and related apparatus

Abstract

The application discloses a target sensing method and related device. The method comprises: acquiring a first preset number of frame data in each window range in a window rolling manner; each frame data comprises a plurality of subcarriers; forming a two-dimensional array based on the first preset number of frame data in each window range; each row in the two-dimensional array corresponds to a frame data; determining a fluctuation data group corresponding to each two-dimensional array based on a plurality of subcarriers in a column direction of each two-dimensional array, thereby obtaining at least two fluctuation data groups; determining at least one target data group based on the at least two fluctuation data groups; wherein each target data group is calculated by adjacent fluctuation data groups; and judging whether a moving object exists in a region corresponding to the frame data according to the at least one target data group. Through the above method, the moving object can be accurately judged.

Description

Technical Field

[0001] This application relates to the field of motion sensing, and in particular to a target sensing method and related apparatus. Background Technology

[0002] Algorithms for environmental perception based on CSI (Channel State Information) amplitude signals primarily rely on statistical or similarity measures such as variance, range, or cosine similarity to detect object movement. The core idea of ​​these methods is to capture and compare the fluctuation characteristics of CSI signals under different environments: in a stationary state with no one around, the CSI amplitude typically remains relatively stable; however, when objects are moving, their reflection, scattering, and obstruction of the wireless signal cause significant fluctuations in the signal amplitude. However, in reality, amplitude is easily affected by interference, and the set judgment threshold can change due to environmental factors. Therefore, the algorithms based on fluctuation amplitude and judgment thresholds have certain limitations and suffer from a high false positive rate. Summary of the Invention

[0003] The main objective of this application is to provide a target perception method and related apparatus that can reduce the false judgment rate and improve perception performance.

[0004] Firstly, this application provides a target perception method. The method includes: acquiring a first preset number of frame data within each window range using a window scrolling method; each frame data includes several subcarriers; forming a two-dimensional array based on the first preset number of frame data within each window range; each row in the two-dimensional array corresponds to one frame data; determining a wave data group corresponding to each two-dimensional array based on several subcarriers in the column direction of each two-dimensional array, thereby obtaining at least two wave data groups; determining at least one target data group based on the at least two wave data groups; wherein each target data group is calculated from adjacent wave data groups; and determining whether a moving object exists in the region corresponding to the frame data based on the at least one target data group.

[0005] Specifically, determining the fluctuation data group corresponding to each two-dimensional array based on several subcarriers in the column direction of each two-dimensional array includes: obtaining the maximum and minimum values ​​of several subcarriers in each column of each two-dimensional array; determining the fluctuation value corresponding to each column based on the maximum and minimum values ​​in each column; and determining the fluctuation data group corresponding to each two-dimensional array based on the fluctuation value corresponding to each column.

[0006] The process of determining the fluctuation value corresponding to each column based on the maximum and minimum values ​​includes: taking the difference between the maximum and minimum values ​​corresponding to each column to obtain the difference value; and using the difference value as the fluctuation value.

[0007] The method of determining at least one target data group based on at least two fluctuation data groups includes: obtaining multiple pairs of adjacent fluctuation data groups from at least two fluctuation data groups; subtracting the element corresponding to the position in the previous fluctuation data group from the element in the latter fluctuation data group in each pair of adjacent fluctuation data groups to obtain the element difference; and constructing the target data group using several element differences to obtain at least one target data group.

[0008] The step of determining whether a moving object exists in the region corresponding to the frame data based on at least one target data group includes: calculating the variance of each target data group to obtain at least one first variance; and determining whether a moving object exists in the region corresponding to the frame data based on at least one first variance.

[0009] The step of determining whether a moving object exists in the region corresponding to the frame data based on at least one first variance includes: obtaining a second preset number of first variances; the preset number is greater than one; performing variance calculation on the second preset number of first variances to obtain a second variance; and determining whether a moving object exists in the region corresponding to the frame data based on the second variance.

[0010] Before determining the wave data group corresponding to each two-dimensional array based on several subcarriers in the column direction of each two-dimensional array, the method also includes: performing noise reduction processing on each two-dimensional array.

[0011] The denoising process includes a first denoising process, which includes: determining the corresponding target filtering element from each two-dimensional array; determining the target row based on the target filtering element; and deleting the frame data corresponding to the target row in each two-dimensional array.

[0012] The denoising process also includes a second denoising process, which includes performing two-dimensional filtering on the two-dimensional array after deleting the frame data corresponding to the target row in each two-dimensional array.

[0013] Secondly, this application provides an electronic device. The electronic device includes a memory and a processor, the memory for storing program data, and the computer program that can be executed by the processor to implement the method described in the first technical solution.

[0014] Thirdly, this application provides a computer-readable storage medium. This computer-readable storage medium stores a computer program that, when executed by a processor, can be used to implement the method described in the first technical solution.

[0015] Fourthly, this application provides a computer program product. This computer program product includes a computer program that, when executed by a processor, is used to implement the method described in the first technical solution.

[0016] Fifthly, this application provides an air conditioner. The air conditioner includes a memory and a processor, the memory storing a computer program that can be executed by the processor to implement the method described in the first technical solution.

[0017] The beneficial effects of this application are as follows: A first preset number of frame data is acquired within each window range using a window scrolling method; each frame data includes several subcarriers; a two-dimensional array is formed based on the first preset number of frame data within each window range; each row in the two-dimensional array corresponds to one frame data; based on several subcarriers in the column direction of each two-dimensional array, fluctuation data groups capable of characterizing the subcarrier fluctuations in the first preset number of frame data are determined, thereby obtaining at least two fluctuation data groups; at least one target data group is determined based on the at least two fluctuation data groups; wherein each target data group is calculated from adjacent fluctuation data groups; and the presence of a moving object in the region corresponding to the frame data is determined based on at least one target data group. Since the presence of a moving object in the region corresponding to the frame data is determined by judging the fluctuation situation or degree of fluctuation between subcarriers in two correlated two-dimensional arrays, the influence of environmental or interference signals on the subcarriers can be reduced, thus improving the accuracy of motion object perception. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart illustrating an embodiment of the target perception method provided in this application; Figure 2 yes Figure 1 A flowchart of an embodiment of step 13; Figure 3 yes Figure 1 A flowchart illustrating an embodiment of step 14; Figure 4 yes Figure 1 A flowchart of an embodiment of step 15; Figure 5 yes Figure 4 A flowchart illustrating an embodiment of step 152; Figure 6 This is a flowchart illustrating another embodiment of the target perception method provided in this application; Figure 7 This is a schematic diagram of the structure of an embodiment of the electronic device of this application; Figure 8This is a flowchart illustrating an embodiment of the computer-readable storage medium of this application; Figure 9 This is a schematic diagram of the structure of an embodiment of the computer program product of this application; Figure 10 This is a schematic diagram of the structure of an embodiment of the air conditioner of this application. Detailed Implementation

[0019] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0025] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0026] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0027] In typical CSI sensing algorithms, taking the method of calculating the amplitude range as an example, a set of subcarrier signals is selected, including multiple signals, each signal comprising multiple frames of subcarriers. These multiple signals are treated as multiple sensing data groups, each containing multiple sensing data acquired sequentially over time, corresponding to multiple frames of subcarriers. The differences between the maxima and minima of these sensing data are calculated according to the time series, resulting in a one-dimensional array containing multiple differences, the number of differences corresponding to the number of signals. As time progresses, new subcarriers are acquired, updating the original subcarrier signal group to obtain a new set of subcarrier signals. The same processing is then applied to this new set of subcarrier signals to obtain another one-dimensional array.

[0028] The absolute values ​​of the differences between two one-dimensional arrays at corresponding positions are taken to obtain multiple difference values. These multiple differences are then summed and averaged to obtain the perceived change. This perceived change is compared with a preset judgment threshold to determine whether there is motion in the current environment.

[0029] The limitations of the above methods are that the judgment threshold is based on certain experience and environment, resulting in low universality, and the amplitude of the carrier signal is easily affected by external factors, which can easily influence the judgment result. Based on the above considerations, this application provides a target perception method. It can be implemented using the methods described in the following embodiments.

[0030] In some embodiments, such as Figure 1 As shown, Figure 1This is a flowchart illustrating the first embodiment of the target perception method provided in this application. It includes the following steps.

[0031] Step 11: Obtain the first preset number of frame data within each window range by scrolling the window.

[0032] Each frame of data includes several subcarriers. The first preset number is greater than one.

[0033] In some embodiments, the scrolling step of the window is one frame. For example, if the first preset number is 10, then the frame data corresponding to the first window range is frames 1-10, the frame data corresponding to the second window range is frames 2-11, the frame data corresponding to the third window range is frames 3-12, and the frame data corresponding to the fourth window range is frames 4-13.

[0034] In some embodiments, the window's scroll step size is two, three, or four frames. The specific step size can be set according to the actual situation. Generally, the window's scroll step size is less than or equal to a first preset number.

[0035] In some embodiments, each frame of data is acquired by a sensing device, and each frame of data includes several subcarriers acquired in chronological order.

[0036] Each time a sensing device acquires frame data, it can acquire several subcarriers. These subcarriers are obtained based on signals that are directly or reflected onto the sensing device.

[0037] Step 12: Form a two-dimensional array based on a first preset number of frame data within each window range.

[0038] Each row in the two-dimensional array corresponds to one frame of data.

[0039] In some embodiments, the two-dimensional array is arranged in rows and columns. Each row in the two-dimensional array represents a frame of data. The columns are arranged according to the order in which the frames of data were acquired. The earlier the frame of data appears in the column, the earlier it was acquired. It can be understood that the sensing device can continuously acquire frame data according to the corresponding sampling period, thereby forming a two-dimensional array.

[0040] In some embodiments, to ensure the accuracy of perception, the first preset number is typically an integer greater than three. For example, the first preset number can be 5, 10, 15, etc.

[0041] In some embodiments, it is assumed that there are multiple frames of data A1, A2, A3, A4, and A5.

[0042] A1: 45, 67, 89, 23, 12, 56, 78, 90, 34, 21.

[0043] A2: 11, 22, 33, 44, 55, 66, 77, 88, 99, 10.

[0044] A3: 12, 34, 56, 78, 90, 11, 22, 33, 44, 55.

[0045] A4: 67, 89, 12, 34, 56, 78, 90, 11, 22, 33.

[0046] A5: 44, 55, 66, 77, 88, 99, 10, 12, 34, 56.

[0047] Two-dimensional arrays can be formed by arranging rows and columns as shown above.

[0048] Step 13: Determine the fluctuation data group corresponding to each two-dimensional array based on several subcarriers in the column direction of each two-dimensional array, and then obtain at least two fluctuation data groups.

[0049] In some embodiments, see Figure 2 Step 13 can be the following process: Step 131: Obtain the maximum and minimum values ​​of several subcarriers in each column of each two-dimensional array.

[0050] In some embodiments, since the first preset quantity is greater than one, each column of the two-dimensional array has at least two subcarriers. Each subcarrier can be converted into amplitude form and used as an element in the two-dimensional array, thus obtaining the maximum and minimum values ​​among several subcarriers in each column of the two-dimensional array.

[0051] Step 132: Determine the fluctuation value corresponding to each column based on the maximum and minimum values ​​in each column.

[0052] In some embodiments, the difference between the maximum and minimum values ​​corresponding to each column is taken to obtain the difference value; the difference value is used as the fluctuation value.

[0053] Step 133: Determine the fluctuation data group corresponding to each two-dimensional array based on the fluctuation value corresponding to each column.

[0054] In some embodiments, since the two-dimensional array has multiple columns, multiple fluctuation values ​​can be obtained through the above method. Based on this, the fluctuation values ​​can be filled into the one-dimensional array in column order to obtain a fluctuation data set.

[0055] For example, a two-dimensional array is as follows: 45, 67, 89, 23, 12, 56, 78, 90, 34, 21.

[0056] 11, 22, 33, 44, 55, 66, 77, 88, 99, 10.

[0057] 12, 34, 56, 78, 90, 11, 22, 33, 44, 55.

[0058] 67, 89, 12, 34, 56, 78, 90, 11, 22, 33.

[0059] 44, 55, 66, 77, 88, 99, 10, 12, 34, 56.

[0060] The data in the first column (subcarrier) is processed to obtain a fluctuation value of 56 (67-11). The data in the second column is processed to obtain a fluctuation value of 67 (89-22). The remaining columns of data are processed in the same way. The final fluctuation data set is 56, 67, 77, 55, 78, 88, 80, 79, 77, 46.

[0061] Using the above window scrolling method, a first preset number of frame data within each window range can be obtained as a fluctuating data group, and thus a fluctuating data group can be obtained every time the window is scrolled, thereby obtaining at least two fluctuating data groups.

[0062] Step 14: Identify at least one target data set based on at least two fluctuation data sets.

[0063] Each target data set is calculated from the adjacent fluctuation data sets.

[0064] For example, if there are two fluctuating data sets, a target data set can be obtained by subtracting the previous fluctuating data set from the latter. The specific subtraction method involves subtracting elements at the same positions. The result of the subtraction can be taken directly, or the absolute value of the subtraction can be taken.

[0065] For example, if there are three sets of fluctuation data, the first set of fluctuation data can be obtained by subtracting the first set of fluctuation data from the second set of fluctuation data. The second set of fluctuation data can be obtained by subtracting the second set of fluctuation data from the third set of fluctuation data.

[0066] In some embodiments, see Figure 3 Step 14 can be the following process: Step 141: Obtain multiple pairs of adjacent fluctuation data sets from at least two fluctuation data sets.

[0067] In some embodiments, the following example illustrates the use of at least two fluctuation data sets: Z1, Z2, Z3, Z4, and Z5. Z1 and Z2 are a pair of adjacent fluctuation data sets. Z2 and Z3 are a pair of adjacent fluctuation data sets. Z3 and Z4 are a pair of adjacent fluctuation data sets. Z4 and Z5 are a pair of adjacent fluctuation data sets.

[0068] Step 142: Subtract the element corresponding to the position in the previous fluctuation data group from the element in the latter fluctuation data group in each pair of adjacent fluctuation data groups to obtain the element difference.

[0069] For example, subtract the corresponding element in Z1 from the element in Z2 to get the element difference. Subtract the corresponding element in Z2 from the element in Z3 to get the element difference. Subtract the corresponding element in Z3 from the element in Z4 to get the element difference. Subtract the corresponding element in Z4 from the element in Z5 to get the element difference.

[0070] For example, Z2 is a1, a2, a3, a4, a5. Z1 is b1, b2, b3, b4, b5. Then we can use a1-b1=c1, a2-b2=c2, a3-b3=c3, a4-b4=c4, a5-b5=c5. In some embodiments, we can use |a1-b1|=c1, |a2-b2|=c2, |a3-b3|=c3, |a4-b4|=c4, |a5-b5|=c5.

[0071] Step 143: Construct a target data set using the differences between several elements, thereby obtaining at least one target data set.

[0072] In some embodiments, a target data group is constructed based on c1, c2, c3, c4, and c5 as described above. For example, the target data group is c1, c2, c3, c4, and c5.

[0073] Based on this, multiple target data sets can be obtained when there are multiple pairs of adjacent fluctuation data sets.

[0074] Step 15: Determine whether there is a moving object in the region corresponding to the frame data based on at least one target data group.

[0075] In some embodiments, when there is a target data group, the number of elements in the target data group that meet the preset conditions is obtained, and the presence of moving objects in the region corresponding to the frame data is determined based on the number of elements.

[0076] In some embodiments, the preset condition is greater than a preset threshold. In some embodiments, the preset condition is less than or equal to a preset threshold.

[0077] In some embodiments, the number of elements in the target data group whose element values ​​are greater than a first preset threshold is obtained.

[0078] In some embodiments, the number of elements in the target data group whose element values ​​are less than or equal to a first preset threshold is obtained.

[0079] In some embodiments, when the number of elements is the number of elements in the target data group whose element values ​​are greater than a first preset threshold, if the number of elements is greater than a second preset threshold, or the proportion of the number of elements in the target data group is greater than a preset proportion, then it is considered that there are many data points with large fluctuations in the frame data, and it is determined that there are moving objects in the region corresponding to the frame data. If the number of elements is less than or equal to the second preset threshold, or the proportion of the number of elements in the target data group is less than or equal to a preset proportion, then it is considered that there are few data points with large fluctuations in the frame data, and it is determined that there are no moving objects in the region corresponding to the frame data.

[0080] In some embodiments, when the number of elements is the number of elements in the target data group whose values ​​are less than or equal to a first preset threshold, if the number of elements is greater than a second preset threshold, or the proportion of the number of elements in the target data group is greater than a preset proportion, then it is considered that there are few data points with large fluctuations in the frame data, and it is determined that there are no moving objects in the region corresponding to the frame data. If the number of elements is less than or equal to the second preset threshold, or the proportion of the number of elements in the target data group is less than or equal to a preset proportion, then it is considered that there are many data points with large fluctuations in the frame data, and it is determined that there are moving objects in the region corresponding to the frame data.

[0081] In some embodiments, see Figure 4 Step 15 can be the following process: Step 151: Calculate the variance for each target data set to obtain at least one first variance.

[0082] Since each target data set has multiple elements, the variance of each element can be calculated to obtain the corresponding first variance. Because there is at least one target data set, at least one first variance can be obtained.

[0083] Step 152: Determine whether there is a moving object in the region corresponding to the frame data based on at least one first variance.

[0084] In some embodiments, if multiple first variances exist, these variances can be recalculated to obtain a fluctuation reference value. The presence of a moving object in the region corresponding to the frame data is then determined based on the fluctuation reference value. For example, if the fluctuation reference value is greater than or equal to a preset reference threshold, it is determined that a moving object exists in the region corresponding to the frame data. Conversely, if the fluctuation reference value is less than the preset reference threshold, it is determined that no moving object exists in the region corresponding to the frame data.

[0085] In some embodiments, see Figure 5 Step 152 can be the following process: Step 1521: Obtain the second preset number of first variances.

[0086] In some embodiments, the second preset quantity is greater than one.

[0087] In some embodiments, since frame data is continuously acquired, target data groups are also continuously generated, and the first variance is also continuously generated. Therefore, several first variances can be acquired by scrolling the window. For example, the first variances can be stored as a fixed-length array. For instance, if the array can store 10 first variances, then the second preset quantity is 10. Similarly, if the array can store 15 first variances, then the second preset quantity is 15.

[0088] Step 1522: Calculate the variance of the second preset number of first variances to obtain the second variance.

[0089] Step 1523: Determine whether there is a moving object in the region corresponding to the frame data based on the second variance.

[0090] In some embodiments, if the second variance is greater than or equal to a preset reference threshold, it is determined that a moving object exists in the region corresponding to the frame data. Conversely, if the second variance is less than the preset reference threshold, it is determined that no moving object exists in the region corresponding to the frame data.

[0091] In this embodiment, it is considered that when there are no moving objects in the sensing environment, the fluctuation or change amplitude of the acquired subcarriers is similar. However, when there are moving objects in the sensing environment, the fluctuation of the acquired subcarriers is not necessarily consistent. Moving objects move over time and may only affect a portion of the frame data (subcarriers). Therefore, if there is a large fluctuation in the fluctuation, it is determined that there is a moving object; if the fluctuation is not significant, it is determined that there is no moving object. The judgment based on fluctuation is not affected by environmental or interference signals and remains at a constant level.

[0092] In this embodiment, a first preset number of frame data is acquired within each window range using a window scrolling method. Each frame data includes several subcarriers. A two-dimensional array is formed based on the first preset number of frame data within each window range. Each row in the two-dimensional array corresponds to one frame data. Based on several subcarriers in the column direction of each two-dimensional array, fluctuation data groups that can characterize the subcarrier fluctuations in the first preset number of frame data are determined, thereby obtaining at least two fluctuation data groups. At least one target data group is determined based on the at least two fluctuation data groups. Each target data group is calculated from adjacent fluctuation data groups. Based on at least one target data group, it is determined whether there is a moving object in the area corresponding to the frame data. Since the presence of a moving object in the area corresponding to the frame data is determined by judging the fluctuation situation or fluctuation degree between subcarriers in two correlated two-dimensional arrays, the influence of environmental or interference signals on the subcarriers can be reduced, thereby improving the accuracy of motion object perception.

[0093] See Figure 6 , Figure 6 This is a flowchart illustrating the first embodiment of the target perception method provided in this application. It includes the following steps.

[0094] Step 61: Obtain a first preset number of frame data within each window range by scrolling the window.

[0095] Step 62: Form a two-dimensional array based on a first preset number of frame data within each window range.

[0096] Each row in the two-dimensional array corresponds to one frame of data.

[0097] Step 63: Denoise each two-dimensional array.

[0098] To ensure the accuracy of the sensing results, the two-dimensional array is denoised before processing and sensing, removing abnormal and noisy data.

[0099] In some embodiments, the denoising process includes a first denoising process. Step 63 may be the following process: Step 631: Determine the corresponding target filtering element from each two-dimensional array.

[0100] In some embodiments, the standard deviation of several subcarriers in each column of the first two-dimensional array is calculated. Subcarriers in each column that are greater than the standard deviation of that column are selected as target filtering elements.

[0101] Step 632: Determine the target row based on the target filter elements.

[0102] Step 633: Delete the frame data corresponding to the target row in each two-dimensional array.

[0103] Since each row in the two-dimensional array represents a frame of data, after determining the target row based on the target filtering element, the frame data corresponding to the target row in the first two-dimensional array is directly deleted. That is, the frame data of the row containing the target filtering element is deleted.

[0104] For example, the following two-dimensional array is used as an example for illustration: A1: 45, 67, 89, 23, 12, 56, 78, 90, 34, 21.

[0105] A2: 11, 22, 33, 44, 55, 66, 77, 88, 99, 10.

[0106] A3: 12, 34, 56, 78, 90, 11, 22, 33, 44, 55.

[0107] A4: 67, 89, 12, 34, 56, 78, 90, 11, 22, 33.

[0108] A5: 44, 55, 66, 77, 88, 99, 10, 12, 34, 56.

[0109] Here, A1, A2, A3, A4, and A5 represent the sequence numbers of the frame data. The standard deviation for each column of subcarriers is calculated. Subcarriers in the corresponding column that are greater than the standard deviation are selected (target selection elements). This yields 89 in the second column and 99 in the sixth column as target selection elements. Therefore, before processing and sensing the two-dimensional array, the frame data corresponding to A4 (89) and A5 (99) are deleted.

[0110] In some embodiments, the denoising process further includes a second denoising process. The above method also includes performing two-dimensional filtering on the two-dimensional array after deleting the frame data corresponding to the target row in each two-dimensional array.

[0111] Two-dimensional filtering can include median filtering, mean filtering, Hampelle filtering, Wiener filtering, and so on. For example, filtering a two-dimensional array by row and column separately can reduce noise in the final data used for judgment.

[0112] Step 64: Determine the fluctuation data group corresponding to each two-dimensional array based on several subcarriers in the column direction of each two-dimensional array, thereby obtaining at least two fluctuation data groups.

[0113] Step 65: Identify at least one target data set based on at least two fluctuation data sets.

[0114] Each target data set is calculated from the adjacent fluctuation data sets.

[0115] Step 66: Determine whether there is a moving object in the region corresponding to the frame data based on at least one target data group.

[0116] In some embodiments, steps 64 to 66 have the same or similar technical solutions as other embodiments of this application.

[0117] In this embodiment, in addition to the effects of the above embodiments, denoising the two-dimensional array can effectively eliminate abnormal subcarriers and reduce subcarrier noise.

[0118] See Figure 7 , Figure 7 This is a schematic diagram of the structure of an embodiment of the electronic device of this application.

[0119] The electronic device includes a processor 110 and a memory 120.

[0120] Processor 110 controls the operation of electronic devices. Processor 110 may also be referred to as a CPU (Central Processing Unit). Processor 110 may be an integrated circuit chip with signal sequence processing capabilities. Processor 110 may also be a general-purpose processor, a digital signal sequence processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0121] The memory 120 stores the instructions and computer programs required for the processor 110 to operate.

[0122] The processor 110 is used to execute instructions to implement the method provided by any of the embodiments and possible combinations thereof in the above embodiments of the target perception method of this application.

[0123] See Figure 8 , Figure 8 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application.

[0124] One embodiment of the readable storage medium of this application includes a memory 210 storing a computer program that, when executed, implements the method provided by any of the embodiments and possible combinations thereof in the above embodiments of the target perception method of this application.

[0125] The memory 210 may include a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or other media that can store program instructions. Alternatively, it may be a server that stores the program instructions, which can send the stored program instructions to other devices for execution or execute the stored program instructions itself.

[0126] See Figure 9 , Figure 9 This is a schematic diagram of the structure of an embodiment of the computer program product of this application.

[0127] The computer program product 310 of this application includes a computer program that, when executed, implements the method provided by any of the embodiments and possible combinations thereof in the above embodiments of the target perception method of this application.

[0128] Computer program product 310 may include media capable of storing program instructions, such as USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, or a server storing the program instructions. The server may send the stored program instructions to other devices for execution, or it may execute the stored program instructions itself.

[0129] See Figure 10 , Figure 10 This is a schematic diagram of the structure of an embodiment of the air conditioner of this application.

[0130] The air conditioner includes a processor 410 and a memory 420.

[0131] Processor 410 controls the operation of electronic devices. Processor 410 can also be referred to as a CPU (Central Processing Unit). Processor 410 may be an integrated circuit chip with signal sequence processing capabilities. Processor 410 can also be a general-purpose processor, a digital signal sequence processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0132] The memory 420 stores the instructions and computer programs required for the processor 410 to operate.

[0133] The processor 410 is used to execute instructions to implement the method provided by any of the embodiments and possible combinations thereof in the above embodiments of the target perception method of this application.

[0134] When the air conditioner detects the presence of a moving target object based on the aforementioned target perception method, it can perform corresponding control operations based on the detection result, such as power on / off, power control, etc.

[0135] In summary, a first preset number of frame data is acquired within each window using a window scrolling method. Each frame data includes several subcarriers. A two-dimensional array is formed based on the first preset number of frame data within each window. Each row in the two-dimensional array corresponds to one frame data. Based on several subcarriers in the column direction of each two-dimensional array, fluctuation data groups that can characterize the subcarrier fluctuations in the first preset number of frame data are determined, thereby obtaining at least two fluctuation data groups. At least one target data group is determined based on the at least two fluctuation data groups. Each target data group is calculated from adjacent fluctuation data groups. Based on at least one target data group, it is determined whether there is a moving object in the area corresponding to the frame data. Since the presence of a moving object in the area corresponding to the frame data is determined by judging the fluctuation situation or fluctuation degree between subcarriers in two correlated two-dimensional arrays, the influence of environmental or interference signals on the subcarriers can be reduced, thus improving the accuracy of motion object perception.

[0136] Furthermore, denoising the two-dimensional array can effectively eliminate abnormal subcarriers and reduce subcarrier noise.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0139] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated units in the other embodiments described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A target perception method, characterized in that, The method includes: The first preset number of frame data within each window is acquired by scrolling the window; each frame data includes several subcarriers. A two-dimensional array is formed based on a first preset number of frame data within each window range; each row in the two-dimensional array corresponds to one frame data. Based on several subcarriers in the column direction of each two-dimensional array, the fluctuation data group corresponding to each two-dimensional array is determined, thereby obtaining at least two fluctuation data groups; At least one target data set is determined based on at least two of the said fluctuation data sets; wherein each of the target data sets is calculated from the adjacent fluctuation data sets; Determine whether there is a moving object in the region corresponding to the frame data based on at least one of the target data groups.

2. The target perception method according to claim 1, characterized in that, The step of determining the fluctuation data group corresponding to each two-dimensional array based on a plurality of subcarriers in the column direction of each two-dimensional array includes: Obtain the maximum and minimum values ​​of several subcarriers in each column of each of the two-dimensional arrays; The fluctuation value corresponding to each column is determined based on the maximum and minimum values ​​in each column; The fluctuation data group corresponding to each two-dimensional array is determined based on the fluctuation value corresponding to each column.

3. The target perception method according to claim 2, characterized in that, The determination of the fluctuation value corresponding to each column based on the maximum and minimum values ​​includes: The difference is obtained by subtracting the maximum and minimum values ​​corresponding to each column; The difference is taken as the fluctuation value.

4. The target perception method according to claim 1, characterized in that, The determination of at least one target data set based on at least two of the fluctuation data sets includes: From at least two of the said fluctuation data groups, obtain multiple pairs of adjacent fluctuation data groups; The element difference is obtained by subtracting the element corresponding to the position in the previous fluctuation data group from the element in the later fluctuation data group in each pair of adjacent fluctuation data groups. The target data set is constructed using the differences between several elements, thereby obtaining at least one target data set.

5. The target perception method according to claim 1, characterized in that, The step of determining whether a moving object exists in the region corresponding to the frame data based on at least one of the target data groups includes: For each target data set, the variance is calculated to obtain at least one first variance; The presence of a moving object in the region corresponding to the frame data is determined based on at least one first variance.

6. The target perception method according to claim 5, characterized in that, The step of determining whether there is a moving object in the region corresponding to the frame data based on at least one first variance includes: Obtain a second preset number of first variances; the preset number is greater than one. The variance of the second preset number of first variances is calculated to obtain the second variance; The presence of moving objects in the region corresponding to the frame data is determined based on the second variance.

7. The target perception method according to claim 1, characterized in that, Before determining the wave data group corresponding to each two-dimensional array based on a plurality of subcarriers in the column direction of each two-dimensional array, the method further includes: Denoising is performed on each of the two-dimensional arrays.

8. The target perception method according to claim 7, characterized in that, The noise reduction process includes a first noise reduction process, which includes: Determine the corresponding target filtering element from each of the two-dimensional arrays; The target row is determined based on the target filtering elements; Delete the frame data corresponding to the target row in each of the two-dimensional arrays.

9. The target perception method according to claim 8, characterized in that, The noise reduction process further includes a second noise reduction process, which includes: Two-dimensional filtering is performed on the two-dimensional array after deleting the frame data corresponding to the target row in each of the two-dimensional arrays.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory being used to store a computer program that can be executed by the processor to implement the method as described in any one of claims 1-9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-9.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, is used to implement the method as described in any one of claims 1-9.

13. An air conditioner, characterized in that, The air conditioner includes a memory and a processor, the memory being used to store a computer program that can be executed by the processor to implement the method as described in any one of claims 1-9.

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