阅读说明：本技术 数据处理方法、装置、电子设备及存储介质 (Data processing method and device, electronic equipment and storage medium ) 是由 郭富祥 于 2021-06-09 设计创作，主要内容包括：本申请公开了一种数据处理方法、装置、电子设备及存储介质,涉及数据处理技术领域。该方法包括：根据待处理测量数据确定抖动特征数据,所述抖动特征数据用于验证滤波效果；根据所述待处理测量数据和所述抖动特征数据确定所述待处理测量数据在滤波过程中的权重参数；根据所述权重参数获取目标测量数据。本方法可以实现根据待处理测量数据的抖动特征灵活的调整待处理测量数据在滤波过程中的权重占比,进而有效提升滤波效果,从而提升数据测量的稳定性。(The application discloses a data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: determining jitter characteristic data according to the to-be-processed measurement data, wherein the jitter characteristic data is used for verifying the filtering effect; determining a weight parameter of the measured data to be processed in a filtering process according to the measured data to be processed and the jitter characteristic data; and acquiring target measurement data according to the weight parameters. The method can flexibly adjust the weight ratio of the to-be-processed measured data in the filtering process according to the jitter characteristics of the to-be-processed measured data, so that the filtering effect is effectively improved, and the stability of data measurement is improved.)

1. A method of data processing, the method comprising:

determining jitter characteristic data according to the to-be-processed measurement data, wherein the jitter characteristic data is used for verifying the filtering effect;

determining a weight parameter of the measured data to be processed in a filtering process according to the measured data to be processed and the jitter characteristic data;

and acquiring target measurement data according to the weight parameters.

2. The method of claim 1, wherein obtaining target measurement data according to the weight parameter comprises:

if the measurement times of the measurement data to be processed are equal to a time threshold value, acquiring current target measurement data based on a first output result, the measurement data to be processed and the weight parameter, wherein the first output result is obtained through sliding window filtering;

and if the measurement times of the measurement data to be processed are greater than a time threshold, acquiring current target measurement data based on a second output result, the measurement data to be processed and the weight parameter, wherein the second output result is the last target measurement data.

3. The method of claim 2, further comprising:

and if the measurement times of the measurement data to be processed are smaller than the time threshold value, taking the first output result as the current target measurement data.

4. The method of claim 1, wherein determining jitter characterizing data from the measurement data to be processed comprises:

and if the data volume of the measured data to be processed is equal to the data volume threshold, determining the jitter characteristic data according to the measured data to be processed.

5. The method of claim 1, wherein determining jitter characterizing data from the measurement data to be processed comprises:

and if the measurement times of the to-be-processed measurement data are larger than the time threshold, determining the jitter characteristic data according to the to-be-processed measurement data.

6. The method according to any of claims 1-5, wherein the measurement data to be processed comprises a plurality of measurement data, and wherein determining jitter characteristics data from the measurement data to be processed comprises:

determining a variance parameter based on the plurality of measurement data;

determining corresponding designated parameters based on the variance parameters, wherein the variance parameters and the designated parameters have a mapping relation;

determining jitter characterizing data based on the specified parameters.

7. The method according to claim 6, wherein the determining the weight parameter of the measurement data to be processed in the filtering process according to the measurement data to be processed and the jitter characteristic data comprises:

and determining a weight parameter of the to-be-processed measurement data in the filtering process according to the variance parameter and the jitter characteristic data.

8. A data processing apparatus, characterized in that the apparatus comprises:

the first data acquisition module is used for determining jitter characteristic data according to the to-be-processed measurement data, and the jitter characteristic data is used for verifying the filtering effect;

the data processing module is used for determining a weight parameter of the measured data to be processed in the filtering process according to the measured data to be processed and the jitter characteristic data;

and the second data acquisition module is used for acquiring target measurement data according to the weight parameters.

9. An electronic device comprising one or more processors and memory;

one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-7.

10. A computer-readable storage medium, having program code stored therein, wherein the program code when executed by a processor performs the method of any of claims 1-7.

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a storage medium.

Background

An Ultra Wide Band (UWB) technique is a wireless carrier communication technique, which uses nanosecond-level non-sine wave narrow pulse transmission data, occupies a Wide spectrum range, and is widely used for measurement and calculation of Phase difference (PDoA) and the like. However, PDoA measured by the current mainstream UWB chip has significant jitter and poor filtering effect.

Disclosure of Invention

The present application provides a data processing method, an apparatus, an electronic device, and a storage medium to solve the above problems.

In a first aspect, an embodiment of the present application provides a data processing method, where the method includes: determining jitter characteristic data according to the to-be-processed measurement data, wherein the jitter characteristic data is used for verifying the filtering effect; determining a weight parameter of the measured data to be processed in a filtering process according to the measured data to be processed and the jitter characteristic data; and acquiring target measurement data according to the weight parameters.

In a second aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes: the first data acquisition module is used for determining jitter characteristic data according to the to-be-processed measurement data, and the jitter characteristic data is used for verifying the filtering effect; the data processing module is used for determining a weight parameter of the measured data to be processed in the filtering process according to the measured data to be processed and the jitter characteristic data; and the second data acquisition module is used for acquiring target measurement data according to the weight parameters.

In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more application programs being configured to perform the data processing method provided by the first aspect above.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a program code is stored in the computer-readable storage medium, and the program code may be called by a processor to execute the data processing method provided in the first aspect.

According to the data processing method, the data processing device, the electronic equipment and the storage medium, the jitter characteristic data are determined according to the to-be-processed measuring data and used for verifying the filtering effect, then the weight parameters of the to-be-processed measuring data in the filtering process are determined according to the to-be-processed measuring data and the jitter characteristic data, and then the target measuring data are obtained according to the weight parameters. The method can flexibly adjust the weight ratio of the to-be-processed measured data in the filtering process according to the jitter characteristics of the to-be-processed measured data, so that the filtering effect is effectively improved, and the stability of data measurement is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 shows a basic principle diagram of UWB goniometry in the related art.

Fig. 2 is a diagram showing an example of measurement results of each PDoA during a stationary-slightly moving-stationary process of the UWB device provided by the embodiment of the present application.

Fig. 3 is a diagram illustrating an exemplary effect of filtering measurement data by using a sliding window FIR filter according to an embodiment of the present application.

Fig. 4 shows a flowchart of a data processing method according to an embodiment of the present application.

Fig. 5 is a flowchart illustrating a data processing method according to another embodiment of the present application.

Fig. 6 shows a flowchart of a data processing method according to another embodiment of the present application.

Fig. 7 shows a logic diagram of an electronic device for performing data processing on measurement data to be processed according to an embodiment of the present application.

Fig. 8 is a graph showing the comparison between the filtering effect of the target measurement data proposed in the present embodiment and the effect after the conventional sliding window filtering process.

Fig. 9 shows a block diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 10 shows a block diagram of an electronic device according to an embodiment of the present application.

Fig. 11 illustrates a storage unit for storing or carrying program codes for implementing a data processing method according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

For the purpose of facilitating an understanding of the present application, the basic principles of UWB angle measurement in the related art will be described. As shown in fig. 1, the DUT characterizes a UWB device under test (e.g., a UWB tag), and the DUT transmits a UWB signal to a measurement device (which may be a mobile communication device such as a cell phone).

The measuring device (such as a mobile phone) is provided with two antennas antA and antB with a specific distance d. The measuring device can measure the phase of the UWB signal transmitted from the DUT received by antA and antB, thereby calculating the phase difference PDoA. The path difference p between the antenna distance antA and the antenna distance antB of the DUT can be calculated through the PDoA. From p and d, the angle of arrival θ (the azimuth angle of the DUT with respect to the measuring end) can be calculated by a (trigonometric) functional relationship.

In a real device, the measured PDoA has significant jitter due to the effect of mutual coupling between the antennas (as shown in fig. 2, which shows the measurement result of each PDoA of the UWB device during the process of being stationary-slightly moving-stationary). In order to optimize this problem, a sliding window FIR filter may be used to filter the measurement data, however, the inventors have found through long-term research that, as shown in fig. 3, the sliding window FIR filter has the following problems: 1. if the filtering order is small (for example, the order 140-220 shown in fig. 3), although the group delay is low (the response speed is fast), the filtering effect is good; 2. if the filtering order is high (for example, the order 220-340 shown in fig. 3), although the filtering effect is good, the group delay is high (the response speed is slow).

Therefore, in order to solve the above problem, the inventor proposes a data processing method, an apparatus, an electronic device, and a storage medium, which are provided by the present application and can flexibly adjust the weight ratio of the to-be-processed measurement data in the filtering process according to the jitter characteristics of the to-be-processed measurement data, thereby effectively improving the filtering effect and thus improving the stability of data measurement.

Embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Referring to fig. 4, a flowchart of a data processing method according to an embodiment of the present application is shown, where the embodiment provides a data processing method applicable to an electronic device, and the method includes:

step S110: and determining jitter characteristic data according to the to-be-processed measurement data, wherein the jitter characteristic data is used for verifying the filtering effect.

In this embodiment, the measurement data to be processed may be understood as PDoA measurement values obtained by the electronic device, and the specific number of the measurement values may not be limited. Alternatively, if the buffer unit of the electronic device may buffer a fixed number of PDoA measurement values, the measurement data to be processed may be understood as the latest buffered fixed number of PDoA measurement values.

The jitter characteristic data can be used for verifying the filtering effect, that is, the jitter condition of the latest PDoA measurement data can be reflected. As a mode, the jitter characteristic data can be determined according to the measurement data to be processed, so that the jitter characteristic data can be determined according to the latest PDoA measurement value, and the filtering effect can be better prevented from being influenced by the jitter of the PDoA measurement data.

Step S120: and determining a weight parameter of the to-be-processed measured data in the filtering process according to the to-be-processed measured data and the jitter characteristic data.

After the jitter characteristic data is determined, the weight parameters of the to-be-processed measurement data in the filtering process can be reasonably adjusted according to the to-be-processed measurement data and the jitter characteristic data, so that the weight ratio of the to-be-processed measurement data in the filtering process can be adjusted in a targeted manner, and a measurement result with a better filtering effect can be obtained.

Step S130: and acquiring target measurement data according to the weight parameters.

Wherein, the target measurement data refers to current target measurement data. As a mode, if the measurement times of the measurement data to be processed are equal to the time threshold, the current target measurement data may be obtained based on the first output result, the measurement data to be processed, and the weight parameter of the measurement data to be processed in the filtering process, where the first output result is obtained through sliding window filtering.

As another mode, if the measurement times of the measurement data to be processed are greater than the time threshold, the current target measurement data may be obtained based on the second output result, the measurement data to be processed, and the weight parameter of the measurement data to be processed in the filtering process, where the second output result is the last target measurement data.

As another way, if the number of times of measurement of the measurement data to be processed is less than the number threshold, the first output result may be directly used as the current target measurement data, and at this time, the first output result is obtained by sliding window filtering, for example, the first output result may be a result of performing average filtering processing on the measurement data to be processed by a sliding window filtering unit of the electronic device. In this embodiment, the specific value of the number threshold may not be limited.

In the data processing method provided by this embodiment, the jitter characteristic data is determined according to the measurement data to be processed, the jitter characteristic data is used to verify the filtering effect, then the weight parameter of the measurement data to be processed in the filtering process is determined according to the measurement data to be processed and the jitter characteristic data, and then the target measurement data is obtained according to the weight parameter. The method can flexibly adjust the weight ratio of the to-be-processed measured data in the filtering process according to the jitter characteristics of the to-be-processed measured data, so that the filtering effect is effectively improved, and the stability of data measurement is improved.

Referring to fig. 5, a flowchart of a data processing method according to another embodiment of the present application is shown, where the embodiment provides a data processing method applicable to an electronic device, and the method includes:

step S210: and if the measurement times of the to-be-processed measurement data are larger than the time threshold, determining jitter characteristic data according to the to-be-processed measurement data, wherein the jitter characteristic data are used for verifying the filtering effect.

When the number of times of measurement of the measurement data to be processed is small, the jitter of the measured PDoA measurement data is not obvious, and if the jitter characteristic data is determined directly according to the measurement data to be processed, the verification of the filtering effect may be affected. In order to overcome this problem, in this embodiment, if the measurement times of the measurement data to be processed are greater than the time threshold, the jitter feature data is determined according to the measurement data to be processed, so that the jitter condition of the PDoA measurement data can be regularly analyzed under the condition that the PDoA measurement data are sufficient, and the filtering effect is effectively improved.

Step S220: and determining a weight parameter of the to-be-processed measured data in the filtering process according to the to-be-processed measured data and the jitter characteristic data.

Step S230: and acquiring target measurement data according to the weight parameters.

The data processing method provided by the embodiment can flexibly adjust the weight ratio of the to-be-processed measured data in the filtering process according to the jitter characteristics of the to-be-processed measured data, so that the filtering effect is effectively improved, and the stability of data measurement is improved.

Referring to fig. 6, a flowchart of a data processing method according to another embodiment of the present application is shown, where the embodiment provides a data processing method applicable to an electronic device, and the method includes:

step S310: a variance parameter is determined based on the plurality of measurement data.

In this embodiment, the measurement data to be processed may include a plurality of measurement data. For example, in a specific application scenario, as shown in fig. 7, the electronic device may include a PDoA measurement unit, a buffer unit, a sliding window filtering unit, a parameter calculation unit, and a second filtering calculation unit. When the electronic device (which may be understood as a UWB device) receives the UWB packet, the PDoA measurement value obtained by the PDoA measurement unit may be input to the buffer unit.

Optionally, if the buffer size of the buffer unit is N, the plurality of measurement data represents the latest N PDoA measurement values. When the data amount M cached by the cache unit is smaller than N, the cache unit transmits the M data to the sliding window filtering unit. When the data amount M cached by the cache unit is equal to N, the cache unit transmits the M data to the sliding window filtering unit and calculates the parameterThe unit transmits the latest PDoAk (k is assumed as the k-th data, k is more than or equal to N) to the second filtering calculation unit, and in this way, the variance parameter Var can be determined based on the N PDoA measurement data_k。

Step S320: and determining corresponding specified parameters based on the variance parameters, wherein the variance parameters and the specified parameters have a mapping relation.

Wherein the specified parameter may be a Q parameter. As one way, the variance parameter Var may be established by a mapping table or a mapping function_kAnd Q parameter so that it can be based on the variance parameter Var_kDetermining a corresponding Q parameter, in this embodiment, a mapping relationship between the variance parameter and the specified parameter may be a nonlinear relationship. For example, in a specific application scenario, a variance parameter Var may be established_kThe mapping relationship between the Q parameter and the corresponding Q parameter is as follows:

TABLE 1 variance parameter Var_kAnd the corresponding Q parameter

Vark	20	30	40	50	70	90	120	140
									Q	0.01	0.05	0.2	1	2	4	8	16

Wherein the variance parameter Var_kThe specific values of (2) and (Q) are merely examples and do not limit the specific values. Alternatively, for variance parameters Var that are not in the table_kThe corresponding Q parameter can be found by an interpolation (e.g., linear interpolation) algorithm.

Step S330: and determining jitter characteristic data based on the specified parameters, wherein the jitter characteristic data is used for verifying the filtering effect.

As one way, the present embodiment may determine the jitter characteristic data according to the following formula:

wherein the content of the first and second substances,characterizing jitter characterizing data, Q characterizing a specified parameter (i.e., the aforementioned Q parameter), P₀Characterizing a predetermined parameter, P_k-1Characterizing the last calculated second jitter parameter.

Step S340: and determining a weight parameter of the to-be-processed measurement data in the filtering process according to the variance parameter and the jitter characteristic data.

In this embodiment, the weight parameter of the measurement data to be processed in the filtering process may be determined according to the following formula:

wherein the content of the first and second substances,characterizing jitter characteristics data, Var_kThe variance parameter is characterized, and K is characterized as the weight parameter.

The current second jitter parameter P in the present embodiment_kThe formula can be used for calculation:

P_k＝(1-K)*P^- _k

wherein, P_kCharacterizing a current second jitter parameter, K characterizing a weight parameter,characterizing jitter characterizing feature data, P_kCan be used for the next (K + 1) filtering calculation.

Step S350: and acquiring target measurement data according to the weight parameters.

As one way, after the weight parameter is determined, the target measurement data may be acquired from the weight parameter according to the following calculation formula:

PDoA_AF_k＝PDoA_AF_k-1+K*(PDoA_k-PDoA_AF_k-1)

＝(1-K)PDoA_AF_k-1+K*PDoA_k

wherein, PDoA _ AF_k(PDoA after filter, K-th result) represents current target measurement data, K represents weight parameter, PDoA_kCharacterizing the measurement data to be processed (understood as the latest PDoA)_k)，PDoA_AF_k-1And characterizing the last target measurement data. As shown in fig. 7, PDoA _ AFk-1 is the output result of the sliding window filtering unit when k (representing the number of measurements) is equal to N (representing the number of times threshold), and when k is equal to N>And at N, PDoA _ AFk-1 is the output result of the second filtering unit.

In this embodiment, when the jitter trend of the PDoA measurement value changes, the variance parameter Var becomes larger, the corresponding Q parameter is also larger, and finally the calculated weight parameter K is larger. In the k times of filtering result output, the proportion of k-1 times of filtering results is small, the proportion of k times of PDoA measurement results is large, and the k times of filtering results lean to the value of PDoA _ k.

When the trend of the PDoA measurement value does not change due to jitter, the variance parameter Var is smaller, the corresponding Q parameter is also smaller, and the finally calculated weight parameter K is also smaller. In this way, in k times of filtering result output, the proportion of k-1 times of filtering results is large, the proportion of k-th time PDoA measurement result is small, and the difference between the k times of filtering results and the k-1 times of filtering results is small (the final result is that data jitter is small, and the filtering effect is good).

In a specific application scenario, please refer to fig. 8, which shows a comparison graph of the filtering effect of the target measurement data proposed in this embodiment and the effect after the conventional sliding window filtering process. As shown in fig. 8, the filtering effect of the target measurement data calculated by the data processing method according to the present embodiment is smaller than the jitter of the measurement data calculated by the sliding window FIR, so that the final measured azimuth angle of the UWB device of the electronic apparatus can be more stable, and the response can be fast, thereby the filtering effect is better.

According to the data processing method provided by the embodiment, the weight proportion of the to-be-processed measurement data in the filtering process can be flexibly adjusted according to the jitter characteristics of the to-be-processed measurement data, and the weight parameters can be dynamically adjusted according to the latest PDoA measurement data and the latest data jitter condition, so that the output result after filtering is dynamically adjusted, the filtering effect is effectively improved, and the stability of data measurement is improved.

Referring to fig. 9, which is a block diagram of a data processing apparatus according to an embodiment of the present disclosure, in this embodiment, a data processing apparatus 400 is provided, which can be operated in an electronic device, where the apparatus 400 includes: the first data acquisition module 410, the data processing module 420, and the second data acquisition module 430:

a first data obtaining module 410, configured to determine jitter characteristic data according to the measurement data to be processed, where the jitter characteristic data is used to verify a filtering effect.

As one mode, the first data obtaining module 410 may be specifically configured to determine jitter characteristic data according to the to-be-processed measurement data if a data amount of the to-be-processed measurement data is equal to a data amount threshold; or the method can be used for determining the jitter characteristic data according to the to-be-processed measurement data if the measurement times of the to-be-processed measurement data are larger than a time threshold.

In this embodiment, the measurement data to be processed includes a plurality of measurement data. Optionally, the first data obtaining module 410 may be further specifically configured to determine a variance parameter based on the plurality of measurement data; determining corresponding designated parameters based on the variance parameters, wherein the variance parameters and the designated parameters have a mapping relation; determining jitter characterizing data based on the specified parameters.

And the data processing module 420 is configured to determine a weight parameter of the measurement data to be processed in the filtering process according to the measurement data to be processed and the jitter characteristic data.

As one way, the data processing module 420 may be specifically configured to determine a weight parameter of the measurement data to be processed in the filtering process according to the variance parameter and the jitter characteristic data.

And a second data obtaining module 430, configured to obtain target measurement data according to the weight parameter.

As a manner, the second data obtaining module 430 may be specifically configured to, if the measurement times of the measurement data to be processed are equal to a time threshold, obtain current target measurement data based on a first output result, the measurement data to be processed, and the weight parameter, where the first output result is obtained through sliding window filtering; and if the measurement times of the measurement data to be processed are greater than a time threshold, acquiring current target measurement data based on a second output result, the measurement data to be processed and the weight parameter, wherein the second output result is the last target measurement data.

Optionally, the second data obtaining module 430 may be further configured to take the first output result as current target measurement data if the measurement times of the measurement data to be processed are smaller than a time threshold.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, the coupling between the modules may be electrical, mechanical or other type of coupling.

In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.

Referring to fig. 10, based on the data processing method and apparatus, an electronic device 100 capable of executing the data processing method is further provided in the embodiment of the present application. The electronic device 100 includes a memory 102 and one or more processors 104 (only one shown) coupled to each other, the memory 102 and the processors 104 being communicatively coupled to each other. The memory 102 stores therein a program that can execute the contents of the foregoing embodiments, and the processor 104 can execute the program stored in the memory 102.

The processor 104 may include one or more processing cores, among other things. The processor 104 interfaces with various components throughout the electronic device 100 using various interfaces and circuitry to perform various functions of the electronic device 100 and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 102 and invoking data stored in the memory 102. Alternatively, the processor 104 may be implemented in hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 104 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 104, but may be implemented by a communication chip.

The Memory 102 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 102 may be used to store instructions, programs, code sets, or instruction sets. The memory 102 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the foregoing embodiments, and the like. The data storage area may also store data created by the electronic device 100 during use (e.g., phone book, audio-video data, chat log data), and the like.

Referring to fig. 11, a block diagram of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable medium 500 has stored therein a program code that can be called by a processor to execute the method described in the above-described method embodiments.

The computer-readable storage medium 500 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 500 includes a non-volatile computer-readable storage medium. The computer readable storage medium 500 has storage space for program code 510 for performing any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. The program code 510 may be compressed, for example, in a suitable form.

In summary, according to the data processing method, the data processing device, the electronic device, and the storage medium provided by the embodiments of the present application, the jitter characteristic data is determined according to the to-be-processed measurement data, the jitter characteristic data is used for verifying the filtering effect, then the weight parameter of the to-be-processed measurement data in the filtering process is determined according to the to-be-processed measurement data and the jitter characteristic data, and then the target measurement data is obtained according to the weight parameter. The method can flexibly adjust the weight ratio of the to-be-processed measured data in the filtering process according to the jitter characteristics of the to-be-processed measured data, so that the filtering effect is effectively improved, and the stability of data measurement is improved.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.