Data processing method and device, equipment and storage medium

Abstract

The invention relates to a data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring current channel state information (CSI) data generated by terminal equipment; wherein the current CSI data is generated by the terminal device through CSI measurement based on the indication information of the wireless access point; the current CSI data comprises a CSI sequence and a generation timestamp of the CSI sequence; determining an attenuation weight of the current CSI data according to a sending timestamp of the indication information sent by the wireless access point to the terminal equipment or a generation timestamp in the previous CSI data generated by the terminal equipment, and the current service scene and the generation timestamp in the current CSI data; and optimizing the CSI sequence by adopting the attenuation weight. By adopting the method, the accuracy of the sensing result can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data processing method, apparatus, device, and storage medium. Background Technology

[0002] With the continuous development of sensing technology, WiFi sensing technology has emerged, which can sense the scene information between the transmitter and receiver by analyzing the channel state information (CSI) between the wireless signal from the transmitter to the receiver.

[0003] However, due to the time delays in the processes of CSI request, execution, and data reporting, the accuracy of the sensing results may decrease due to these delays when using the aforementioned sensing technologies. Summary of the Invention

[0004] Therefore, it is necessary to provide a data processing method, apparatus, device, and storage medium to address the aforementioned technical problems, thereby improving the accuracy of perception results by optimizing CSI data.

[0005] In a first aspect, this application provides a data processing method, including:

[0006] Acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes the CSI sequence and the timestamp of the CSI sequence generation;

[0007] The attenuation weight of the current CSI data is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0008] The CSI sequence is optimized using attenuation weights.

[0009] In one embodiment, the attenuation weight of the current CSI data is determined based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data, including:

[0010] Determine the current attenuation factor based on the current business scenario;

[0011] The attenuation weight of the current CSI data is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current attenuation factor and the generation timestamp in the current CSI data.

[0012] In one embodiment, determining the current attenuation factor based on the current business scenario includes:

[0013] Based on the correspondence between business scenarios and attenuation factors, the basic attenuation factor is determined according to the current business scenario;

[0014] The basic attenuation factor is adjusted based on the current network load rate of the wireless access point to obtain the current attenuation factor.

[0015] In one embodiment, the attenuation weight of the current CSI data is determined based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current attenuation factor and the generation timestamp in the current CSI data, including:

[0016] The data latency is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device, or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data.

[0017] Based on the attenuation weight function, the attenuation weight of the current CSI data is determined according to the data delay and the current attenuation factor.

[0018] In one embodiment, determining the data delay based on the transmission timestamp of the wireless access point sending the indication information to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data, includes:

[0019] If the current CSI data is the first CSI data generated after the terminal device receives the indication information, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the transmission timestamp of the wireless access point sending the indication information to the terminal device.

[0020] If the current CSI data is not the first CSI data, the data latency is determined by the difference between the generation timestamp in the current CSI data and the generation timestamp in the previous CSI data generated by the terminal device.

[0021] In one embodiment, the CSI sequence is optimized using attenuation weights, including:

[0022] The elements in the CSI sequence are weighted using decay weights;

[0023] The elements in the weighted CSI sequence are normalized to obtain the optimized CSI sequence.

[0024] In one embodiment, when the method is applied to a wireless access point, the method further includes:

[0025] Target perception is performed based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point.

[0026] Secondly, this application also provides a data processing apparatus, comprising:

[0027] The data acquisition module is used to acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point to perform CSI measurements; the current CSI data includes the CSI sequence and the generation timestamp of the CSI sequence;

[0028] The weight determination module is used to determine the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0029] The data processing module is used to optimize the CSI sequence using decay weights.

[0030] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0031] Acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes the CSI sequence and the timestamp of the CSI sequence generation;

[0032] The attenuation weight of the current CSI data is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0033] The CSI sequence is optimized using attenuation weights.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0035] Acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes the CSI sequence and the timestamp of the CSI sequence generation;

[0036] The attenuation weight of the current CSI data is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0037] The CSI sequence is optimized using attenuation weights.

[0038] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0039] Acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes the CSI sequence and the timestamp of the CSI sequence generation;

[0040] The attenuation weight of the current CSI data is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0041] The CSI sequence is optimized using attenuation weights.

[0042] The aforementioned data processing method, apparatus, device, and storage medium, after acquiring the current CSI data generated by the terminal device through CSI measurement based on the indication information from the wireless access point, determines the attenuation weight of the current CSI data by using the transmission timestamp of the indication information sent by the wireless access point to the terminal device, the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current service scenario and the current CSI data. This attenuation weight is then used to optimize the CSI sequence. Compared to related technologies that directly parse the CSI data to obtain the channel's scenario information, the above method, by introducing attenuation weights determined based on the current service scenario, transmission timestamps, and CSI data generation timestamps, and using these attenuation weights to optimize the CSI sequence, effectively suppresses the impact of outdated CSI data on scene perception during the scene perception process, thereby ensuring the accuracy of the perception results. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a data processing method in one embodiment;

[0045] Figure 2 This is a schematic diagram of the process for determining the attenuation weight in one embodiment;

[0046] Figure 3 This is a schematic diagram of the process for determining the current attenuation factor in one embodiment;

[0047] Figure 4 This is a flowchart illustrating the process of determining the current attenuation factor in another embodiment;

[0048] Figure 5 This is a flowchart illustrating the optimization of the CSI sequence in one embodiment;

[0049] Figure 6 This is a flowchart illustrating the data processing method in another embodiment;

[0050] Figure 7 This is a structural block diagram of a data processing device in one embodiment;

[0051] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] With the continuous development of sensing technology, WiFi sensing technology has emerged, which can sense the scene information between the transmitter and receiver by analyzing the CSI data between the wireless signal and the receiver.

[0054] However, due to the time delays in the processes of CSI request, execution, and data reporting, the accuracy of the sensing results may decrease due to these delays when using the aforementioned sensing technologies.

[0055] Based on this, in an exemplary embodiment, a data processing method is provided. This method can be applied to terminal devices with powerful computing capabilities and sensing abilities, or it can also be applied to servers, or it can be implemented by a combination of a terminal device and a server. For example... Figure 1 As shown, the specific steps include:

[0056] S101, Obtain the Current Channel State Information (CSI) data generated by the terminal device.

[0057] The current CSI data refers to data that characterizes the channel environment at the current moment. Furthermore, the current CSI data is generated by the terminal device performing CSI measurements based on the indication information from the wireless access point. The indication information refers to relevant information instructing the terminal device to perform CSI measurements, and can be presented in the form of signaling, request frames, etc.

[0058] The current CSI data includes the CSI sequence and the generation timestamp of the CSI sequence. The CSI sequence is a mathematical description of the channel, which can be a complex matrix, also called a CSI matrix or CSI vector, and contains amplitude and phase information. The generation timestamp is the time when the CSI sequence was generated.

[0059] Optionally, the wireless access point can send indication information carrying measurement parameters to the terminal device based on a wireless communication protocol. These measurement parameters may include, but are not limited to, frequency band, number of subcarriers, sampling interval, and reference signal. After receiving the indication information from the wireless access point, the terminal device can perform CSI measurements on the channel based on the measurement parameters to generate a CSI sequence.

[0060] For example, a wireless access point (AP) can send a request frame (null data packet, NDP) or trigger frame carrying the frequency band, number of subcarriers, and sampling interval to a terminal device based on the WiFi protocol. Upon receiving the request frame, the terminal device can estimate the channel and generate a Channel Identity Matrix (CSI) based on the Zadoff-Chu sequence pilots. The number of rows and columns in the CSI matrix is ​​determined by the number of subcarriers and the sampling interval; the matrix elements in the CSI matrix represent the amplitude and phase of the subcarrier signals.

[0061] Furthermore, the terminal device can be configured with a local clock, so that when generating the CSI matrix, the terminal device can read the time data of the local clock as the generation timestamp of the CSI matrix.

[0062] For example, when the data processing method provided in this application embodiment is applied to a terminal device, the terminal device can directly obtain CSI data.

[0063] When the data processing method provided in this application embodiment is applied to a server, the terminal device can encapsulate the CSI matrix and the generation timestamp of the CSI matrix into a User Datagram Protocol (UDP) packet and send it back to the server. Thus, the server can obtain the CSI data generated by the terminal device.

[0064] S102, determine the attenuation weight of the current CSI data based on the transmission timestamp of the instruction information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0065] The transmission timestamp refers to the moment when the wireless access point sends the indication information. The current service scenario refers to the service scenario in which the CSI measurement is currently performed, such as action recognition, gesture control, environmental monitoring, or presence awareness. The attenuation weight is the weight value used when attenuating the CSI data.

[0066] It is understandable that the latency issue of CSI data is essentially a problem of asynchrony in time-series data. According to the time-varying channel model in information theory, the change of channel state over time can be modeled as a first-order Markov process, meaning the current channel state is only related to the previous moment. Based on this, interference from outdated data can be effectively suppressed by assigning attenuation weights to data with different latency levels.

[0067] In one optional implementation, the timestamp of the wireless access point sending the indication information to the terminal device or the generation timestamp of the previous CSI data generated by the terminal device can be used as a reference timestamp, based on the current CSI data's position during data transmission. For example, if the current CSI data is the first CSI data generated by the terminal device after receiving the indication information, the timestamp of the wireless access point sending the indication information to the terminal device is used as the reference timestamp; if the current CSI data is not the first CSI data, the generation timestamp of the previous CSI data generated by the terminal device is used as the reference timestamp.

[0068] Furthermore, the latency information between the reference timestamp and the generation timestamp in the current CSI data, as well as the current business scenario, can be used as index information to query the correspondence between candidate latency information, candidate business scenarios, and candidate attenuation weights, thereby determining the attenuation weight of the current CSI data.

[0069] In another alternative implementation, the reference timestamp, the current business scenario, and the generation timestamp in the current CSI data can be simultaneously input into a trained first weight determination model. The first weight determination model then outputs the decay weight of the current CSI data based on the reference timestamp, the current business scenario, and the generation timestamp in the current CSI data.

[0070] S103 uses attenuation weights to optimize the CSI sequence.

[0071] Optionally, after determining the attenuation weights, each element in the CSI sequence can be weighted using these attenuation weights to obtain optimized CSI data. Then, based on the optimized CSI data at each time point, scenario information in the current business scenario can be analyzed.

[0072] In the above data processing method, after obtaining the current CSI data generated by the terminal device through CSI measurement based on the indication information of the wireless access point, the attenuation weight of the current CSI data is determined by considering the transmission timestamp of the indication information sent by the wireless access point to the terminal device, the generation timestamp of the previous CSI data generated by the terminal device, the current service scenario, and the generation timestamp of the current CSI data. This attenuation weight is then used to optimize the CSI sequence. Compared to related technologies that directly parse the CSI data to obtain the channel's scenario information, the above method, by introducing attenuation weights determined based on the current service scenario, transmission timestamps, and generation timestamps of the CSI data, and using these attenuation weights to optimize the CSI sequence, effectively suppresses the impact of outdated CSI data on scene perception during the scene perception process, thereby ensuring the accuracy of the perception results.

[0073] Based on the above embodiments, this application provides an optional method for determining the attenuation weight, such as... Figure 2 As shown, the specific steps include:

[0074] S201, Determine the current attenuation factor based on the current business scenario.

[0075] The current attenuation factor is the attenuation factor that is related to the current business scenario. Furthermore, a larger attenuation factor indicates a lower demand for real-time data in the corresponding business scenario. For example, in scenarios with high real-time requirements (such as action recognition and gesture control), a smaller attenuation factor (such as 30ms) is set to retain less historical data; in scenarios with high latency tolerance (such as environmental monitoring and presence awareness), a larger attenuation factor (such as 100ms) is set to retain more historical data.

[0076] Optionally, based on the real-time data requirements of business scenarios, a correspondence between candidate business scenarios and candidate decay factors can be pre-built. Based on this, the current business scenario can be directly used as index information to query the correspondence between candidate business scenarios and candidate decay factors to obtain the current decay factor.

[0077] S202, determine the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current attenuation factor and the generation timestamp in the current CSI data.

[0078] In one optional implementation, the transmission timestamp of the wireless access point sending the instruction information to the terminal device or the generation timestamp of the previous CSI data generated by the terminal device can be used as a reference timestamp, based on the current CSI data's position during data transmission. Then, the current attenuation factor can be processed based on the delay information between the reference timestamp and the generation timestamp in the current CSI data to obtain the attenuation weight of the current CSI data.

[0079] In another alternative implementation, the reference timestamp, the current decay factor, and the generation timestamp in the current CSI data can be directly input into the trained second weight determination model. The second weight determination model then outputs the decay weight of the current CSI data based on the reference timestamp, the current decay factor, and the generation timestamp in the current CSI data.

[0080] In this embodiment, by selecting the current attenuation factor based on the current business scenario and combining the current attenuation factor to determine the attenuation weight of the current CSI data, it is possible to ensure that the subsequent processing of the CSI sequence is adapted to the actual business scenario, thereby improving the rationality of CSI data processing.

[0081] Based on the above embodiments, this application provides an optional method for determining the current attenuation factor, such as... Figure 3 As shown, the specific steps include:

[0082] S301, based on the correspondence between business scenarios and attenuation factors, determines the basic attenuation factor according to the current business scenario.

[0083] The so-called basic attenuation factor is the basic attenuation factor under the current business scenario.

[0084] Optionally, based on the real-time data requirements of business scenarios, a correspondence between candidate business scenarios and candidate basic attenuation factors can be pre-constructed. Based on this, the current business scenario can be directly used as index information to query the correspondence between candidate business scenarios and candidate basic attenuation factors to obtain the basic attenuation factor.

[0085] For example, if the current business scenario is determined to be action recognition, the basic attenuation factor is determined to be 30ms; if the current business scenario is determined to be environmental monitoring, the basic attenuation factor is determined to be 100ms.

[0086] S302 adjusts the basic attenuation factor based on the current network load rate of the wireless access point to obtain the current attenuation factor.

[0087] The so-called current network load rate is used to measure the usage of network resources at the current moment.

[0088] Optionally, after determining the current network load rate based on the bandwidth usage and CPU usage in the wireless access point, the current network load rate can be used to determine the factor adjustment weight; then, the factor adjustment weight can be used to weight the basic attenuation factor to obtain the current attenuation factor.

[0089] For example, the formula σ = σ0 × (1 + β × load) can be used to adjust the basic attenuation factor σ0 based on the current network load rate (load) and the load rate adjustment coefficient β, thus obtaining the current attenuation factor σ. The load rate adjustment coefficient β can be determined based on the correlation between network load and sensing accuracy in historical sensing data.

[0090] In this embodiment, by adjusting the basic attenuation factor related to the current business scenario based on the current network load rate, the current attenuation factor can be obtained, which can ensure the adaptability of the current attenuation factor to the network load situation, thereby ensuring the rationality of subsequent CSI data processing.

[0091] Based on the above embodiments, this application provides another optional method for determining the current attenuation factor, such as... Figure 4 As shown, the specific steps include:

[0092] S401, determine the data delay based on the transmission timestamp of the instruction information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data.

[0093] The so-called data latency refers to the data delay of the current CSI data.

[0094] Optionally, the timestamp of the wireless access point sending the instruction information to the terminal device or the generation timestamp of the previous CSI data generated by the terminal device can be used as a reference timestamp, based on the current position of the CSI data during data transmission. Then, the data delay is determined based on the difference between the reference timestamp and the generation timestamp in the current CSI data.

[0095] For example, if the current CSI data is the first CSI data generated after the terminal device receives the indication information, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the transmission timestamp of the wireless access point sending the indication information to the terminal device; if the current CSI data is not the first CSI data, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the generation timestamp in the previous CSI data generated by the terminal device.

[0096] It is understandable that, if the current CSI data is the first CSI data generated after the terminal device receives the indication information, there is no adjacent previous CSI data. Therefore, the difference between the generation timestamp of the current CSI data and the sending timestamp of the indication information can be used as the data delay.

[0097] If the current CSI data is not the first CSI data, the channel situation at the current moment is only related to the previous CSI data. Therefore, the difference between the generation timestamp of the current CSI data and the generation timestamp of the previous CSI data can be used as the data delay.

[0098] S402 determines the attenuation weight of the current CSI data based on the attenuation weight function, according to the data delay and the current attenuation factor.

[0099] The attenuation weighting function is the specific function used to calculate the attenuation weights. It's understandable that the time-varying characteristics of a channel can be simulated using an exponential function; therefore, an attenuation weighting function can be constructed based on an exponential function.

[0100] Optionally, the data latency and the current attenuation factor can be substituted into a preset attenuation weight function to obtain the attenuation weight of the current CSI data.

[0101] For example, the decay weight function can be set to By combining the current attenuation factor σ and the data delay Substitute into the formula Then, the decay weight w(t) at the current time can be obtained.

[0102] Furthermore, based on this, a decay weight lookup table can be constructed. Table 1 is the decay weight lookup table.

[0103] Table 1 Attenuation Weight Lookup Table

[0104]

[0105] In this embodiment of the application, the attenuation weight of the current CSI data is determined based on the attenuation weight function, data delay, and current attenuation factor, which ensures the accuracy of the attenuation weight calculation.

[0106] Based on the above embodiments, this application provides an optional method for optimizing CSI sequences, such as... Figure 5 As shown, the specific steps include:

[0107] S501 uses attenuation weights to weight the elements in the CSI sequence.

[0108] Optionally, attenuation weights can be used to weight each element in the CSI sequence individually. For example, when the CSI sequence is presented in matrix form, attenuation weights can be used to weight the matrix elements in the CSI matrix. It is worth noting that this weighting only applies to the amplitude values ​​of the matrix elements, while the phase values ​​are directly retained.

[0109] S502, normalize the elements in the weighted CSI sequence to obtain the optimized CSI sequence.

[0110] Optionally, to ensure the ease of feature extraction from the CSI sequence in the future, the elements in the weighted CSI sequence can be normalized according to the differences between the elements in the weighted CSI sequence to obtain an optimized CSI sequence.

[0111] For example, the amplitudes of matrix elements in the CSI matrix can be normalized based on the mean and standard deviation of the amplitudes of each matrix element in each CSI matrix; the phases of each matrix element in the CSI matrix can be mapped from the range of [-π, π] to [0, 2π] or further normalized to the interval of [0, 1], thereby solving the phase entanglement problem.

[0112] In this embodiment of the application, by normalizing the elements in the weighted CSI sequence, it can be ensured that the optimized CSI sequence is more suitable for subsequent feature extraction processing, thereby ensuring the accuracy of scene perception.

[0113] Based on the above embodiments, this application provides an optional method for target perception, specifically, target perception is performed based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point.

[0114] The so-called standard CSI sequence is the CSI sequence collected under interference-free conditions.

[0115] Optionally, a standard CSI sequence can be pre-collected within the sensing area under interference-free conditions and stored in the wireless access point. Based on this, a sensing algorithm can be used to compare and identify the optimized CSI sequence with the standard CSI sequence corresponding to the wireless access point, thereby obtaining the sensing information within the sensing area at the current moment. Alternatively, the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point can be simultaneously input into a trained scene recognition model, which will then output the sensing information within the sensing area based on the optimized CSI sequence and the standard CSI sequence.

[0116] Furthermore, the perceived information at the current moment can be fused with the perceived information at historical moments to obtain a coherent picture of scene changes within the perceived area.

[0117] It is worth noting that, in order to ensure the reliability of the sensed information, if the data delay is greater than the delay threshold, the processing of the CSI sequence can be abandoned directly. For example, the delay threshold can be set to 100ms, that is, if the data delay is greater than 100ms, the CSI sequence can be marked as outdated data and not processed.

[0118] In this embodiment of the application, by performing target perception based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point, the reliability of the perception processing can be guaranteed, thereby improving the accuracy of the perception results.

[0119] Figure 6 This is a flowchart illustrating data processing between devices in another embodiment. Based on the above embodiments, this embodiment provides an optional example of executing a data processing method between a server and a terminal device. (Combined with...) Figure 6 The specific implementation process is as follows:

[0120] S601, the server sends instruction information to the terminal device.

[0121] S602, the terminal device performs CSI measurement according to the instruction information and generates the current CSI data.

[0122] S603: Based on the correspondence between service scenarios and attenuation factors, the terminal device determines the basic attenuation factor according to the current service scenario, and adjusts the basic attenuation factor according to the current network load rate of the wireless access point to obtain the current attenuation factor.

[0123] S604, the terminal device determines the data delay based on the transmission timestamp of the instruction information sent by the wireless access point to the terminal device, or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data.

[0124] Optionally, if the current CSI data is the first CSI data generated after the terminal device receives the indication information, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the transmission timestamp of the wireless access point sending the indication information to the terminal device; if the current CSI data is not the first CSI data, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the generation timestamp in the previous CSI data generated by the terminal device.

[0125] S605, the terminal device determines the attenuation weight of the current CSI data based on the attenuation weight function, according to the data delay and the current attenuation factor.

[0126] S606, the terminal device uses attenuation weights to weight the elements of the CSI sequence in the current CSI data, and then normalizes the elements in the weighted CSI sequence to obtain the optimized CSI sequence.

[0127] S607, the terminal device sends the optimized CSI sequence back to the server.

[0128] S608: The server performs target perception based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point.

[0129] The specific processes of S601-S608 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be repeated here.

[0130] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0131] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data processing apparatus embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0132] In one exemplary embodiment, such as Figure 7 As shown, a data processing device 1 is provided, including: a data acquisition module 10, a weight determination module 20, and a weight determination module 20, wherein:

[0133] The data acquisition module 10 is used to acquire the current channel state information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point to perform CSI measurement; the current CSI data includes the CSI sequence and the generation timestamp of the CSI sequence;

[0134] The weight determination module 20 is used to determine the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the generation timestamp in the current service scenario and the current CSI data.

[0135] The data processing module 30 is used to optimize the CSI sequence by applying decay weights.

[0136] In one exemplary embodiment, the weight determination module 20 includes:

[0137] The factor determination unit is used to determine the current attenuation factor based on the current business scenario.

[0138] The weight determination unit is used to determine the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current attenuation factor and the generation timestamp in the current CSI data.

[0139] In one exemplary embodiment, the factor determination unit is specifically used for:

[0140] Based on the correspondence between business scenarios and attenuation factors, the basic attenuation factor is determined according to the current business scenario; the basic attenuation factor is adjusted according to the current network load rate of the wireless access point to obtain the current attenuation factor.

[0141] In an exemplary embodiment, the weight determination unit is specifically used for:

[0142] The data latency is determined based on the transmission timestamp of the instruction information sent from the wireless access point to the terminal device, or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data; the attenuation weight of the current CSI data is determined based on the attenuation weight function, according to the data latency and the current attenuation factor.

[0143] In one exemplary embodiment, the weight determination unit is further configured to:

[0144] If the current CSI data is the first CSI data generated after the terminal device receives the indication information, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the transmission timestamp of the wireless access point sending the indication information to the terminal device. If the current CSI data is not the first CSI data, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the generation timestamp in the previous CSI data generated by the terminal device.

[0145] In one exemplary embodiment, the data processing module 30 is specifically used for:

[0146] The elements in the CSI sequence are weighted using decay weights; the elements in the weighted CSI sequence are then normalized to obtain the optimized CSI sequence.

[0147] In an exemplary embodiment, the data processing device 1 further includes a sensing module, wherein the sensing module is specifically used for:

[0148] Target perception is performed based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point.

[0149] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0150] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a data processing method.

[0151] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0153] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0154] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0155] It should be noted that the data involved in this application (including but not limited to CSI data) are all data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Acquire Current Channel State Information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes a CSI sequence and a timestamp of the generation of the CSI sequence; Based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current service scenario and the generation timestamp in the current CSI data, the attenuation weight of the current CSI data is determined. The CSI sequence is optimized using the aforementioned attenuation weights.

2. The method according to claim 1, characterized in that, The step of determining the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, and the current service scenario and the generation timestamp in the current CSI data, includes: Determine the current attenuation factor based on the current business scenario; The attenuation weight of the current CSI data is determined based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current attenuation factor and the generation timestamp in the current CSI data.

3. The method according to claim 2, characterized in that, The step of determining the current attenuation factor based on the current business scenario includes: Based on the correspondence between business scenarios and attenuation factors, the basic attenuation factor is determined according to the current business scenario; The basic attenuation factor is adjusted based on the current network load rate of the wireless access point to obtain the current attenuation factor.

4. The method according to claim 2, characterized in that, The step of determining the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent from the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, the current attenuation factor, and the generation timestamp in the current CSI data includes: The data delay is determined based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device, or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data. Based on the attenuation weight function, the attenuation weight of the current CSI data is determined according to the data delay and the current attenuation factor.

5. The method according to claim 4, characterized in that, The step of determining the data delay based on the transmission timestamp of the indication information sent from the wireless access point to the terminal device, or the generation timestamp in the previous CSI data generated by the terminal device, and the generation timestamp in the current CSI data, includes: When the current CSI data is the first CSI data generated after the terminal device receives the indication information, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the transmission timestamp of the wireless access point sending the indication information to the terminal device. If the current CSI data is not the first CSI data, the data delay is determined based on the difference between the generation timestamp in the current CSI data and the generation timestamp in the previous CSI data generated by the terminal device.

6. The method according to any one of claims 1-5, characterized in that, The optimization of the CSI sequence using the attenuation weight includes: The elements in the CSI sequence are weighted using the aforementioned attenuation weights; The elements in the weighted CSI sequence are normalized to obtain the optimized CSI sequence.

7. The method according to any one of claims 1-5, characterized in that, When the method is applied to the wireless access point, the method further includes: Target perception is performed based on the optimized CSI sequence and the standard CSI sequence corresponding to the wireless access point.

8. A data processing apparatus, characterized in that, The device includes: The data acquisition module is used to acquire Current Channel State Information (CSI) data generated by the terminal device; wherein, the current CSI data is generated by the terminal device based on the indication information of the wireless access point during CSI measurement; the current CSI data includes a CSI sequence and a timestamp of the generation of the CSI sequence; The weight determination module is used to determine the attenuation weight of the current CSI data based on the transmission timestamp of the indication information sent by the wireless access point to the terminal device or the generation timestamp in the previous CSI data generated by the terminal device, as well as the current service scenario and the generation timestamp in the current CSI data. The data processing module is used to optimize the CSI sequence using the attenuation weight.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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