Time filtering method and device, computer device, chip and medium
By dynamically adjusting the time filtering coefficient based on the current number of filtering iterations, the problem of poor filtering effect caused by fixed coefficients is solved, achieving better filtering effect and reducing complexity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING SPREADTRUM HI TECH COMM TECH CO LTD
- Filing Date
- 2026-03-03
- Publication Date
- 2026-06-05
AI Technical Summary
In existing time-based filtering methods, the fixed time-based filtering coefficients result in poor filtering performance.
Based on the current number of filters, determine the target reference data, and dynamically adjust the time filter coefficients based on this data to adapt to the needs of different number of filters.
It improves the time filtering effect, meets the filtering requirements of different application scenarios, and reduces the filtering complexity.
Smart Images
Figure CN122159829A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of communication technology, and in particular to a time filtering method, apparatus, computer equipment, chip, and medium. Background Technology
[0002] Time-based filtering methods are widely used in communication systems, control systems, and other communication systems. Current time-based filtering methods can be described as: The filtered sample value = the sample value before the previous filtering multiplied by the time-based filtering coefficient + the sample value before the current filtering multiplied by (1 - the time-based filtering coefficient). Therefore, the accurate selection of the time-based filtering coefficient is crucial to the filtering effect. Currently, fixed coefficients are typically used for time-based filtering, resulting in poor filtering performance. Summary of the Invention
[0003] Therefore, it is necessary to provide a time filtering method, apparatus, computer equipment, chip, and medium that can improve the filtering effect in order to address the above-mentioned technical problems.
[0004] In a first aspect, this application provides a time filtering method, comprising: acquiring the signal to be filtered corresponding to the current filtering iteration; determining the corresponding target reference data based on the current filtering iteration; determining the time filtering coefficients corresponding to the current filtering iteration based on the target reference data; and performing filtering processing on the signal to be filtered corresponding to the current filtering iteration based on the time filtering coefficients.
[0005] In one embodiment, determining the corresponding target reference data based on the current filtering count includes: determining the target reference data corresponding to the current filtering count based on the relationship between the current filtering count and the reference filtering count.
[0006] In one embodiment, determining the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number includes: in response to the current filtering number not being greater than the reference filtering number, determining the target reference data corresponding to the current filtering number based on a preset mapping relationship; wherein, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0007] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0008] In one embodiment, determining the target reference data corresponding to the current filtering count based on the relationship between the current filtering count and the reference filtering count includes: in response to the current filtering count being greater than the reference filtering count, using a first preset value as the target reference data corresponding to the current filtering count; wherein the first preset value is the largest preset reference data in the preset mapping relationship.
[0009] In one embodiment, determining the time filtering coefficient corresponding to the current filtering iteration based on target reference data includes: determining the time filtering coefficient corresponding to the current filtering iteration based on a first target value and a second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0010] Secondly, this application also provides a time filtering device, comprising: a signal acquisition module for acquiring the signal to be filtered corresponding to the current filtering count; a first determination module for determining the corresponding target reference data based on the current filtering count; a second determination module for determining the time filtering coefficients corresponding to the current filtering count based on the target reference data; and a filtering processing module for filtering the signal to be filtered corresponding to the current filtering count based on the time filtering coefficients.
[0011] 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 implement the steps of the time filtering method provided in the first aspect.
[0012] Fourthly, this application also provides a chip, including a processor and a communication interface, wherein the processor is configured to cause the chip to perform the steps of the time filtering method provided in the first aspect.
[0013] Fifthly, this application also provides a chip module, including a communication module, a power module, a storage module, and a chip, wherein: the power module is used to provide power to the chip module; the storage module is used to store data and instructions; the communication module is used for internal communication within the chip module, or for communication between the chip module and external devices; and the chip is used to execute the steps of the time filtering method provided in the first aspect above.
[0014] In a sixth aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the time filtering method provided in the first aspect.
[0015] In a seventh aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the time filtering method provided in the first aspect.
[0016] The aforementioned time filtering method, apparatus, computer equipment, chip, and medium determine the corresponding target reference data based on the current filtering iteration, and then determine the time filtering coefficients corresponding to the current filtering iteration based on the target reference data. It is evident that the time filtering coefficients determined in this embodiment adaptively change with the current filtering iteration, rather than remaining fixed, thereby improving the effectiveness of subsequent filtering of the signal to be filtered corresponding to the current filtering iteration using the time filtering coefficients. Furthermore, the time filtering coefficients corresponding to the target reference data can be pre-determined based on the application scenario, enabling subsequent filtering processing based on the time filtering coefficients to meet the filtering requirements of that application scenario. Attached Figure Description
[0017] 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.
[0018] Figure 1 This is a flowchart illustrating a time filtering method in one embodiment;
[0019] Figure 2 This is a flowchart illustrating the target reference data determination step in one embodiment;
[0020] Figure 3 This is a flowchart illustrating the target reference data determination step in one embodiment;
[0021] Figure 4 This is a flowchart illustrating the target reference data determination step in one embodiment;
[0022] Figure 5 This is a flowchart illustrating the steps for determining the time filter coefficients in one embodiment;
[0023] Figure 6 This is a structural block diagram of a time filtering device in one embodiment;
[0024] Figure 7 This is an internal structural diagram of a computer device in one embodiment;
[0025] Figure 8 This is an internal structure diagram of a chip module in one embodiment. Detailed Implementation
[0026] 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.
[0027] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.
[0028] In one exemplary embodiment, a time filtering method is provided. This method can be applied to a chip or chip module with data processing capabilities, as well as to a computer device with data processing capabilities, and of course, it can also be applied to other hardware platforms or software platforms.
[0029] See Figure 1 The time filtering method includes:
[0030] S110, obtain the signal to be filtered corresponding to the current number of filtering iterations.
[0031] Understandably, the time-based filtering method does not filter the first received signal, but starts filtering from the second received signal. Therefore, the current number of filtering iterations is greater than or equal to 2.
[0032] S120, determine the corresponding target reference data based on the current number of filtering iterations.
[0033] Among them, the target reference data is the preset reference data used to determine the time filter coefficients corresponding to the current number of filtering iterations.
[0034] Different preset filtering times may correspond to different preset reference data, so the target reference data is determined based on the current filtering times.
[0035] S130, Based on the target reference data, determine the time filtering coefficients corresponding to the current number of filtering iterations.
[0036] The method for determining the time filter coefficients corresponding to the current number of filters based on the target reference data can be selected according to the application scenario and is not limited here.
[0037] For example, in a scenario where the execution subject is a binary digital circuit, based on the binary number system and the target reference data, the time filtering coefficient corresponding to the current filtering count is determined. This allows the time filtering coefficient to be applied to the binary digital circuit, thereby enabling the binary digital circuit to perform filtering processing according to the time filtering coefficient.
[0038] Understandably, related technologies use a fixed time filtering coefficient. If the time filtering coefficient is too large, it is difficult to reach a steady state; if the time filtering coefficient is too small, the time to reach a steady state is too long. This results in poor filtering performance. In this embodiment, however, the target reference data adaptively changes with the current number of filtering iterations. Since the time filtering coefficient can be determined based on the target reference data, the adaptive change of the time filtering coefficient with the current number of filtering iterations improves the rationality of the time filtering coefficient and helps to improve the effect of subsequent filtering.
[0039] S140: Filter the signal to be filtered based on the time filtering coefficients for the current number of filtering iterations.
[0040] For example, the signal to be filtered corresponding to the current number of filtering iterations is filtered using the following formula:
[0041]
[0042] In the formula, This is the filtered signal corresponding to the current number of filtering iterations. These are the time filter coefficients corresponding to the current number of filters. This is the signal to be filtered corresponding to the previous filtering iteration before the current filtering iteration. This is the signal to be filtered corresponding to the current number of filtering iterations.
[0043] Of course, other methods can also be used to filter the signal to be filtered based on the time filtering coefficient, which is not limited here.
[0044] The aforementioned time-based filtering method determines the corresponding target reference data based on the current filtering iteration, and then determines the time-based filtering coefficients corresponding to the current filtering iteration based on the target reference data. It is evident that the time-based filtering coefficients determined in this embodiment adaptively change with the current filtering iteration, rather than remaining fixed, thus improving the rationality of the time-based filtering coefficients and consequently enhancing the effectiveness of subsequent filtering of the signal to be filtered corresponding to the current filtering iteration using the time-based filtering coefficients. Furthermore, the time-based filtering coefficients corresponding to the target reference data can be pre-determined based on the application scenario, ensuring that subsequent filtering processing based on the time-based filtering coefficients meets the filtering requirements of that application scenario.
[0045] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this optional embodiment, the target reference data determination step in S120 is refined.
[0046] See Figure 2 The steps for determining the target reference data include:
[0047] S210, determine the target reference data corresponding to the current filtering count based on the relationship between the current filtering count and the reference filtering count.
[0048] The number of reference filters can be set as needed and is not limited here.
[0049] Among them, the magnitude relationship includes the current filtering count not being greater than the reference filtering count and the current filtering count being greater than the reference filtering count.
[0050] In this embodiment, when the current filtering count is no greater than the reference filtering count, the method for determining the target reference data can be set as needed. When the current filtering count is greater than the reference filtering count, the method for determining the target reference data can also be set as needed. Different methods are used to determine the target reference data in different situations to meet different filtering requirements.
[0051] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this optional embodiment, the target reference data determination step in S210 is refined.
[0052] See Figure 3 The detailed steps for determining target reference data include:
[0053] S310, in response to the current filtering count not being greater than the reference filtering count, determine the target reference data corresponding to the current filtering count according to the preset mapping relationship.
[0054] Among them, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering times within a preset number of times and the corresponding preset reference data. The reference filtering times are determined based on the maximum preset reference data in the preset mapping relationship.
[0055] In one optional implementation, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable.
[0056] For example, an increasing function takes the following specific form:
[0057]
[0058] In the formula, k is the preset number of filtering iterations, f(k) is the preset reference data, and f(k) is a positive integer.
[0059] As can be seen, in the above formula, the increasing function is the floor function. Of course, the increasing function can take other forms, which are not limited here.
[0060] Based on the floor function described above, the number of reference filters can be the square of the largest preset reference data in the preset mapping relationship. For example, if the largest preset reference data is the maximum value M in f(k), the number of reference filters is the square of M.
[0061] In the above implementation, the preset mapping relationship is an increasing function. This increasing function uses a preset number of filtering iterations within a preset range as the independent variable and preset reference data as the dependent variable. This ensures the discrete constraint of the target reference data, thereby guaranteeing that the subsequently calculated time filtering coefficients conform to the discrete constraint principle, meaning that the time filtering coefficients can only take a limited number of values. Since the signal to be filtered gradually exhibits memorylessness as the number of filtering iterations increases—for example, regardless of the value of the time filtering coefficients, after a dozen or so filtering processes, the effect of historical signals from distant past filtering iterations on the signal to be filtered at the current filtering iteration approaches zero—this phenomenon is considered, and the time filtering coefficients only need to take a limited number of values, which can reduce the filtering complexity.
[0062] In another optional implementation, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset filtering times within a preset number of times.
[0063] That is, the preset parameter table stores preset reference data corresponding to multiple preset filtering times within a preset number of times. In this way, if the current filtering time is not greater than the reference filtering time, the target reference data corresponding to the current filtering time can be found in the preset parameter table, which is simple and fast.
[0064] For example, the set formed by each preset filtering number in the preset parameter table is: {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}. The preset reference data corresponding to preset filtering count 1 is 1; the preset reference data corresponding to preset filtering count 2 is 1; the preset reference data corresponding to preset filtering count 1 is 1; the preset reference data corresponding to preset filtering count 2 is 2; the preset reference data corresponding to preset filtering count 3 is 2; the preset reference data corresponding to preset filtering count 4 is 3; the preset reference data corresponding to preset filtering count 5 is 3; the preset reference data corresponding to preset filtering count 6 is 3; the preset reference data corresponding to preset filtering count 7 is 4; the preset reference data corresponding to preset filtering count 8 is 4; the preset reference data corresponding to preset filtering count 9 is 4; the preset reference data corresponding to preset filtering count 10 is 5; the preset reference data corresponding to preset filtering count 11 is 5; the preset reference data corresponding to preset filtering count 12 is 6; the preset reference data corresponding to preset filtering count 13 is 6; the preset reference data corresponding to preset filtering count 14 is 7; and the preset reference data corresponding to preset filtering count 15 is 7.
[0065] Based on the aforementioned preset parameter table, the reference filtering count can be the preset filtering count corresponding to the largest preset reference data in the preset mapping relationship. For example, the largest preset reference data is the maximum value Q in f(k), and the preset filtering count corresponding to the largest preset reference data is the largest preset filtering count K in the preset parameter table. Therefore, the reference filtering count is K.
[0066] Similarly, in the above implementation, the preset parameter table stores a limited number of correspondences. This ensures the discrete constraint of the target reference data, thereby guaranteeing that the subsequently calculated time filter coefficients conform to the discrete constraint principle, meaning the time filter coefficients can only take a limited number of values. Since the signal to be filtered gradually exhibits memorylessness as the number of filtering iterations increases, considering this phenomenon, using only a limited number of values for the time filter coefficients reduces the filtering complexity.
[0067] Of course, the preset mapping relationship can take other specific forms, which are not limited here.
[0068] In this embodiment, when the current filtering iteration is no greater than the reference filtering iteration, the target reference data corresponding to the current filtering iteration can be quickly determined according to the preset mapping relationship. Furthermore, since the preset mapping relationship characterizes the mapping relationship between different preset filtering iterations and corresponding preset reference data within a preset range, the preset mapping relationship represents a finite number of mapping relationships. Considering that the signal to be filtered will gradually exhibit memorylessness, a finite number of mapping relationships can reduce the filtering complexity.
[0069] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this optional embodiment, the target reference data determination step in S210 is refined.
[0070] See Figure 4 The detailed steps for determining target reference data include:
[0071] S410, in response to the current filtering count being greater than the reference filtering count, the first preset value is used as the target reference data corresponding to the current filtering count.
[0072] The first preset value is the largest preset reference data in the preset mapping relationship.
[0073] That is, when the current number of filters is greater than the reference number of filters, the target reference data corresponding to the current number of filters is a fixed value.
[0074] For example, when the preset mapping relationship is the floor function mentioned above, and when the current filtering count is greater than the reference filtering count (i.e., the square of M), the target reference data corresponding to the current filtering count is M, where M is the maximum value in f(k).
[0075] For example, when the preset mapping relationship is the preset parameter table mentioned above, and when the current filtering count is greater than the reference filtering count (i.e., M), the target reference data corresponding to the current filtering count is the largest preset reference data Q in the preset parameter table.
[0076] In this embodiment, when the current number of filtering iterations is greater than the reference number of filtering iterations, the largest preset reference data in the preset mapping relationship is used as the target reference data corresponding to the current number of filtering iterations. This is because when the current number of filtering iterations is large, the signal to be filtered will gradually exhibit a memoryless phenomenon. Therefore, it is simple and convenient to directly use a fixed value as the target reference data.
[0077] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this optional embodiment, the step of determining the time filter coefficients in S130 is refined.
[0078] See Figure 5 The steps for determining the refined time filter coefficients include:
[0079] S510, determine the time filtering coefficient corresponding to the current number of filtering iterations based on the first target value and the second target value.
[0080] The first target value is determined based on the number system used in the time filtering process.
[0081] For example, the time filtering method is performed by a binary digital circuit, so the number system used in the time filtering process is binary, and therefore the first target value is 2.
[0082] The second target value is determined based on the target reference data.
[0083] For example, the second target value can be a negative value of the target reference data. Of course, the second target value can also be other values determined based on the target reference data, which are not limited here.
[0084] For example, the time filter coefficient corresponding to the current number of filtering iterations can be expressed as:
[0085]
[0086] It is evident that a time filter coefficient that is a power of 2 is easy to implement in binary digital circuits.
[0087] In this embodiment, a second target value is determined based on target reference data, and a first target value is determined based on the target reference data. Then, the time filtering coefficient corresponding to the current number of filtering iterations is determined based on the first target value and the second target value, so as to implement the time filtering method in the hardware structure of the corresponding number system.
[0088] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this embodiment, the target reference data is determined using any of the following methods:
[0089] (1) When k is less than or equal to the square of M, When k is greater than the square of M, f(k) = M. M is the maximum value of f(k).
[0090] (2) When k is less than or equal to K, LUT() is a preset parameter table; when k is greater than K, f(k) = Q, where Q is the largest preset reference data in the preset parameter table.
[0091] After determining the target reference data based on any of the above methods, the time filter coefficients corresponding to the current number of filters are determined using the following formula:
[0092]
[0093] In this embodiment, the time filtering coefficient gradually decreases as the number of filtering iterations k increases. When k increases to a certain level, the time filtering coefficient remains unchanged, which can ensure that the filtering converges as soon as possible and achieve a better filtering effect.
[0094] It should be understood that although the steps in the flowcharts of the above embodiments 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 above embodiments 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 in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.
[0095] Based on the same inventive concept, this application also provides a time filtering device for implementing the time filtering method described above. This device can be applied to or integrated into a chip or chip module, for example. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more time filtering device embodiments provided below can be found in the limitations of the time filtering method described above, and will not be repeated here.
[0096] In one exemplary embodiment, a time filtering device is provided. See also Figure 6 The time filtering device includes:
[0097] Signal acquisition module 610 is used to acquire the signal to be filtered corresponding to the current number of filtering iterations;
[0098] The first determining module 620 is used to determine the corresponding target reference data based on the current number of filtering iterations;
[0099] The second determining module 630 is used to determine the time filtering coefficients corresponding to the current number of filtering iterations based on the target reference data.
[0100] The filtering module 640 is used to filter the signal to be filtered corresponding to the current filtering iteration based on the time filtering coefficient.
[0101] In one embodiment, the first determining module includes: a first determining unit, configured to determine the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number.
[0102] In one embodiment, the first determining unit is specifically used to: in response to the current filtering number not being greater than the reference filtering number, determine the target reference data corresponding to the current filtering number according to a preset mapping relationship; wherein, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0103] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0104] In one embodiment, the first determining unit is specifically used to: in response to the current filtering count being greater than the reference filtering count, use a first preset value as the target reference data corresponding to the current filtering count; wherein, the first preset value is the largest preset reference data in the preset mapping relationship.
[0105] In one embodiment, the second determining module is specifically used to: determine the time filtering coefficient corresponding to the current number of filtering iterations based on the first target value and the second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0106] Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all of their modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all of their modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.
[0107] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As 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 time-filtering method.
[0108] Those skilled in the art will understand that Figure 7 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.
[0109] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring the signal to be filtered corresponding to the current filtering count; determining the corresponding target reference data based on the current filtering count; determining the time filtering coefficient corresponding to the current filtering count based on the target reference data; and filtering the signal to be filtered corresponding to the current filtering count based on the time filtering coefficient.
[0110] In one embodiment, the step of "determining the corresponding target reference data based on the current filtering count" implemented by the processor when executing the computer program includes: determining the target reference data corresponding to the current filtering count based on the magnitude relationship between the current filtering count and the reference filtering count.
[0111] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the size relationship between the current filtering number and the reference filtering number" implemented by the processor when executing the computer program includes: in response to the current filtering number not being greater than the reference filtering number, determining the target reference data corresponding to the current filtering number based on a preset mapping relationship; wherein, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0112] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0113] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number" implemented by the processor when executing the computer program includes: in response to the current filtering number being greater than the reference filtering number, using a first preset value as the target reference data corresponding to the current filtering number; wherein the first preset value is the largest preset reference data in the preset mapping relationship.
[0114] In one embodiment, the step of "determining the time filtering coefficient corresponding to the current filtering count based on the target reference data" implemented by the processor when executing the computer program includes: determining the time filtering coefficient corresponding to the current filtering count based on a first target value and a second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0115] Based on the same inventive concept, this application also provides a chip, including a processor and a communication interface; the communication interface is used to receive or send data; the processor is configured to cause the chip to perform the following steps: acquiring the signal to be filtered corresponding to the current filtering count; determining the corresponding target reference data based on the current filtering count; determining the time filtering coefficient corresponding to the current filtering count based on the target reference data; and filtering the signal to be filtered corresponding to the current filtering count based on the time filtering coefficient.
[0116] In one embodiment, the processor is configured to cause the chip to perform the step of "determining the corresponding target reference data based on the current filtering count", which includes: determining the target reference data corresponding to the current filtering count based on the magnitude relationship between the current filtering count and the reference filtering count.
[0117] In one embodiment, the processor is configured to cause the chip to perform the step of "determining the target reference data corresponding to the current filtering number based on the magnitude relationship between the current filtering number and the reference filtering number", which includes: in response to the current filtering number not being greater than the reference filtering number, determining the target reference data corresponding to the current filtering number according to a preset mapping relationship; wherein the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0118] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0119] In one embodiment, the processor is configured to cause the chip to perform the step of "determining the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number", which includes: in response to the current filtering number being greater than the reference filtering number, using a first preset value as the target reference data corresponding to the current filtering number; wherein the first preset value is the largest preset reference data in the preset mapping relationship.
[0120] In one embodiment, the processor is configured to cause the chip to perform the step "determine the time filtering coefficients corresponding to the current filtering count based on target reference data", which includes: determining the time filtering coefficients corresponding to the current filtering count based on a first target value and a second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0121] It is understood that the chip involved in the embodiments of this application may be a field-programmable gate array (FPGA), may be an application-specific integrated circuit (ASIC), may be a system on chip (SoC), may be a central processor unit (CPU), may be a network processor (NP), may be a digital signal processor (DSP), may be a microcontroller unit (MCU), may be a programmable logic device (PLD), or other integrated chips, etc.
[0122] Based on the same inventive concept, this application also provides a chip module, such as... Figure 8 As shown, the chip module includes a communication module, a power module, a storage module, and a chip. Specifically: the power module provides power to the chip module; the storage module stores data and instructions; the communication module enables internal communication within the chip module or communication between the chip module and external devices; and the chip corresponds to the chip in the aforementioned chip embodiment. The implementation of this chip module can be found in the relevant content of the aforementioned chip embodiment, and will not be repeated here.
[0123] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it performs the following steps: acquiring the signal to be filtered corresponding to the current filtering count; determining the corresponding target reference data based on the current filtering count; determining the time filtering coefficient corresponding to the current filtering count based on the target reference data; and performing filtering processing on the signal to be filtered corresponding to the current filtering count based on the time filtering coefficient.
[0124] In one embodiment, the step of "determining the corresponding target reference data based on the current filtering count" implemented when the computer program is executed by the processor includes: determining the target reference data corresponding to the current filtering count based on the magnitude relationship between the current filtering count and the reference filtering count.
[0125] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the magnitude relationship between the current filtering number and the reference filtering number" implemented when the computer program is executed by the processor includes: in response to the current filtering number not being greater than the reference filtering number, determining the target reference data corresponding to the current filtering number based on a preset mapping relationship; wherein, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0126] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0127] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number" implemented when the computer program is executed by the processor includes: in response to the current filtering number being greater than the reference filtering number, using a first preset value as the target reference data corresponding to the current filtering number; wherein the first preset value is the largest preset reference data in the preset mapping relationship.
[0128] In one embodiment, the step of "determining the time filtering coefficient corresponding to the current filtering count based on the target reference data" implemented when the computer program is executed by the processor includes: determining the time filtering coefficient corresponding to the current filtering count based on a first target value and a second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0129] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: acquiring the signal to be filtered corresponding to the current filtering count; determining the corresponding target reference data based on the current filtering count; determining the time filtering coefficient corresponding to the current filtering count based on the target reference data; and performing filtering processing on the signal to be filtered corresponding to the current filtering count based on the time filtering coefficient.
[0130] In one embodiment, the step of "determining the corresponding target reference data based on the current filtering count" implemented when the computer program is executed by the processor includes: determining the target reference data corresponding to the current filtering count based on the magnitude relationship between the current filtering count and the reference filtering count.
[0131] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the magnitude relationship between the current filtering number and the reference filtering number" implemented when the computer program is executed by the processor includes: in response to the current filtering number not being greater than the reference filtering number, determining the target reference data corresponding to the current filtering number based on a preset mapping relationship; wherein, the preset mapping relationship is used to characterize the mapping relationship between different preset filtering numbers and corresponding preset reference data within a preset number range, and the reference filtering number is determined based on the largest preset reference data in the preset mapping relationship.
[0132] In one embodiment, the preset mapping relationship is an increasing function with a preset number of filters within a preset range as the independent variable and preset reference data as the dependent variable; or, the preset mapping relationship is a preset parameter table used to store preset reference data corresponding to multiple preset number of filters within a preset range.
[0133] In one embodiment, the step of "determining the target reference data corresponding to the current filtering number based on the relationship between the current filtering number and the reference filtering number" implemented when the computer program is executed by the processor includes: in response to the current filtering number being greater than the reference filtering number, using a first preset value as the target reference data corresponding to the current filtering number; wherein the first preset value is the largest preset reference data in the preset mapping relationship.
[0134] In one embodiment, the step of "determining the time filtering coefficient corresponding to the current filtering count based on the target reference data" implemented when the computer program is executed by the processor includes: determining the time filtering coefficient corresponding to the current filtering count based on a first target value and a second target value; wherein the first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
[0135] 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.
[0136] 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.
[0137] 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 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 time-based filtering method, characterized in that, include: Obtain the signal to be filtered corresponding to the current number of filtering iterations; Based on the current number of filtering iterations, determine the corresponding target reference data; Based on the target reference data, determine the time filtering coefficient corresponding to the current number of filtering iterations; The signal to be filtered, corresponding to the current number of filtering iterations, is filtered according to the time filtering coefficients.
2. The method according to claim 1, characterized in that, The step of determining the corresponding target reference data based on the current number of filtering iterations includes: Based on the relationship between the current number of filters and the reference number of filters, the target reference data corresponding to the current number of filters is determined.
3. The method according to claim 2, characterized in that, The step of determining the target reference data corresponding to the current filtering count based on the relationship between the current filtering count and the reference filtering count includes: In response to the fact that the current filtering count is not greater than the reference filtering count, the target reference data corresponding to the current filtering count is determined according to a preset mapping relationship; The preset mapping relationship is used to characterize the mapping relationship between different preset filtering times within a preset number of times and the corresponding preset reference data. The reference filtering times are determined based on the maximum preset reference data in the preset mapping relationship.
4. The method according to claim 3, characterized in that, The preset mapping relationship is an increasing function with a preset number of filters within the preset number range as the independent variable and preset reference data as the dependent variable; Alternatively, the preset mapping relationship can be a preset parameter table used to store preset reference data corresponding to multiple preset filtering times within the preset number range.
5. The method according to claim 3, characterized in that, The step of determining the target reference data corresponding to the current filtering count based on the relationship between the current filtering count and the reference filtering count includes: In response to the current filtering count being greater than the reference filtering count, a first preset value is used as the target reference data corresponding to the current filtering count; Wherein, the first preset value is the largest preset reference data in the preset mapping relationship.
6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the time filtering coefficients corresponding to the current filtering iteration based on the target reference data includes: Based on the first target value and the second target value, determine the time filtering coefficient corresponding to the current number of filtering iterations; The first target value is determined based on the number system used in the time filtering process, and the second target value is determined based on the target reference data.
7. A time filtering device, characterized in that, include: The signal acquisition module is used to acquire the signal to be filtered corresponding to the current number of filtering iterations; The first determining module is used to determine the corresponding target reference data based on the current number of filtering iterations; The second determining module is used to determine the time filtering coefficient corresponding to the current filtering count based on the target reference data; The filtering module is used to filter the signal to be filtered corresponding to the current filtering count according to the time filtering coefficient.
8. 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 6.
9. A chip, characterized in that, The device includes a processor and a communication interface, wherein the processor is configured to cause the chip to perform the steps of the method described in any one of claims 1 to 6.
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 6.