Data processing method and device, computer equipment and storage medium

Abstract

The invention relates to a data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a simulation measurement data set and a current network measurement data set; determining a frequency domain difference value and a time domain difference value between the simulation measurement data set and the current network measurement data set, and determining a total difference value between the current network measurement data set and the simulation measurement data set according to each frequency domain difference value and each time domain difference value; and under the condition that the total difference value is greater than or equal to a preset total difference value threshold value, optimizing the simulation measurement data set to obtain a target simulation measurement data set. By adopting the method, the accuracy of the target simulation measurement data set can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and in particular to a data processing method and device, computer equipment, a storage medium and a computer program product. BACKGROUND

[0002] In the process of construction and optimization of a base station, in order to ensure that the network can meet the user experience requirements in various service scenarios, an operator usually evaluates the signal strength, interference condition and service performance of different positions in a to-be-measured area based on a large amount of network measurement data, so as to determine the layout and configuration parameters of the base station. However, in the actual measurement process, only a part of the network measurement data set of the to-be-measured area can be obtained. Therefore, it is necessary to generate a simulated measurement data set of the remaining part of the to-be-measured area by a data processing method, and to determine the base station layout and configuration parameters based on the simulated measurement data set and the network measurement data set.

[0003] The current data processing method calculates a simulation parameter vector containing base station transmission power, antenna model and terrain propagation model loss parameters by a path loss model in a simulation tool, to obtain a simulated measurement data set.

[0004] However, the current data processing method only calculates the simulated measurement data set based on the path loss model, and the simulation accuracy is not enough, resulting in low accuracy of the simulated measurement data set. There is an urgent need for a method for optimizing the simulated measurement data set. SUMMARY

[0005] Therefore, it is necessary to provide a data processing method, device, computer equipment, computer readable storage medium and computer program product in view of the above technical problems.

[0006] In a first aspect, the present application provides a data processing method, comprising:

[0007] obtaining a simulated measurement data set and a network measurement data set;

[0008] determining a frequency domain difference value and a time domain difference value between the simulated measurement data set and the network measurement data set, and determining a total difference value between the network measurement data set and the simulated measurement data set according to each frequency domain difference value and each time domain difference value;

[0009] in the case where the total difference value is greater than or equal to a preset total difference value threshold, optimizing the simulated measurement data set to obtain a target simulated measurement data set.

[0010] In one of the embodiments, the determination of the frequency domain difference value and the time domain difference value between the simulated measurement data set and the network measurement data set comprises:

[0011] determining, for each index, a real measurement data sequence corresponding to the simulation measurement data sequence in the real network measurement data set;

[0012] determining, according to a frequency domain difference algorithm, a frequency domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index;

[0013] determining, by a time domain coefficient algorithm and dynamic programming, a time domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index.

[0014] In one embodiment, the frequency domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index is determined according to the frequency domain difference algorithm, including:

[0015] performing discrete Fourier transform on the real measurement data sequence under the index to obtain a real frequency domain feature of the real measurement data sequence;

[0016] performing discrete Fourier transform on the simulation measurement data sequence under the index to obtain a simulation frequency domain feature of the simulation measurement data sequence;

[0017] performing data processing on the real frequency domain feature and the simulation frequency domain feature according to the frequency domain difference algorithm to obtain the frequency domain difference value.

[0018] In one embodiment, the time domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index is determined by the time domain coefficient algorithm and dynamic programming, including:

[0019] constructing a local distance matrix according to the simulation measurement data sequence and the real measurement data sequence;

[0020] determining a bending distance in the local distance matrix by dynamic programming; the bending distance represents the similarity between the real measurement data sequence and the simulation measurement data sequence after a moderate nonlinear transformation on a time axis;

[0021] performing data processing on the real measurement data sequence and the simulation measurement data sequence according to the time domain coefficient algorithm to obtain a time domain correlation value between the real measurement data sequence and the simulation measurement data sequence;

[0022] determining the time domain difference value of the index based on the time domain correlation value and the bending distance.

[0023] In one embodiment, the total difference value between the real network measurement data set and the simulation measurement data set is determined according to the frequency domain difference value and the time domain difference value, including:

[0024] determining a comprehensive difference value of each index according to the frequency domain difference value and the time domain difference value of the index;

[0025] weighting the comprehensive difference value of each index according to the weight of each index to obtain a total difference value between the real network measurement data set and the simulation measurement data set.

[0026] In one embodiment, in the case that the total difference value is greater than or equal to a preset total difference value threshold, the simulation measurement data set is optimized to obtain a target simulation measurement data set, which comprises:

[0027] updating a simulation parameter vector in the case that the total difference value is greater than or equal to a preset total difference value threshold;

[0028] generating an optimized simulation measurement data set by a simulation tool and the updated simulation parameter vector;

[0029] performing the step of determining the frequency domain difference value and the time domain difference value between the simulation measurement data set and the real network measurement data set until the total difference value is less than the total difference value threshold, and determining the simulation measurement data set as a target simulation measurement data set.

[0030] In a second aspect, the present application further provides a data processing device, which comprises:

[0031] an acquisition module configured to acquire a simulation measurement data set and a real network measurement data set;

[0032] a determination module configured to determine a frequency domain difference value and a time domain difference value between the simulation measurement data set and the real network measurement data set, and determine a total difference value between the real network measurement data set and the simulation measurement data set according to each frequency domain difference value and each time domain difference value;

[0033] an optimization module configured to optimize the simulation measurement data set to obtain a target simulation measurement data set in the case that the total difference value is greater than or equal to a preset total difference value threshold.

[0034] In a third aspect, the present application further provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0035] acquiring a simulation measurement data set and a real network measurement data set;

[0036] determining a frequency domain difference value and a time domain difference value between the simulation measurement data set and the real network measurement data set, and determining a total difference value between the real network measurement data set and the simulation measurement data set according to each frequency domain difference value and each time domain difference value;

[0037] In a case where the total difference value is greater than or equal to a preset total difference value threshold, the simulation measurement data set is optimized to obtain a target simulation measurement data set.

[0038] In a fourth aspect, the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0039] obtaining a simulation measurement data set and a live network measurement data set;

[0040] determining a frequency domain difference value and a time domain difference value between the simulation measurement data set and the live network measurement data set, and determining a total difference value between the simulation measurement data set and the live network measurement data set according to each of the frequency domain difference value and the time domain difference value;

[0041] In a case where the total difference value is greater than or equal to a preset total difference value threshold, the simulation measurement data set is optimized to obtain a target simulation measurement data set.

[0042] In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the following steps:

[0043] obtaining a simulation measurement data set and a live network measurement data set;

[0044] determining a frequency domain difference value and a time domain difference value between the simulation measurement data set and the live network measurement data set, and determining a total difference value between the simulation measurement data set and the live network measurement data set according to each of the frequency domain difference value and the time domain difference value;

[0045] In a case where the total difference value is greater than or equal to a preset total difference value threshold, the simulation measurement data set is optimized to obtain a target simulation measurement data set.

[0046] The aforementioned data processing method, apparatus, computer equipment, storage medium, and computer program product acquire simulated measurement datasets and live network measurement datasets. The simulated measurement datasets contain simulated measurement data sequences for each indicator; the live network measurement datasets contain actual measurement data sequences for each indicator. The method determines the frequency domain difference and time domain difference between each simulated measurement data sequence and each actual measurement data sequence. Based on the frequency domain difference and the time domain difference, it determines the total difference between the live network measurement data and the simulated measurement data. If the total difference is greater than or equal to a preset total difference threshold, the method optimizes the simulated measurement dataset and executes the steps of determining the frequency domain difference and time domain difference between each simulated measurement data sequence and each actual measurement data sequence until the total difference is less than the total difference threshold. This method determines the total difference between the live network measurement dataset and the simulated measurement dataset from both time and frequency domain perspectives. When the total difference is greater than or equal to the total difference threshold, the method optimizes the simulated measurement dataset, making it continuously approach the actual live network measurement dataset until the total difference is less than the total difference threshold. At this point, the difference between the target simulation measurement dataset and the existing network measurement dataset is small, which improves the accuracy of the target simulation measurement dataset and, consequently, the accuracy of the data processing method. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a data processing method in one embodiment;

[0048] Figure 2 This is a flowchart illustrating the process of determining the time-domain difference and the frequency-domain difference in one embodiment;

[0049] Figure 3 This is a flowchart illustrating the process of determining the frequency domain difference in one embodiment;

[0050] Figure 4 This is a flowchart illustrating the process of determining the time-domain difference in one embodiment;

[0051] Figure 5 This is a flowchart illustrating the process of determining the total difference in one embodiment;

[0052] Figure 6 This is a flowchart illustrating the process of determining a target simulation measurement dataset in one embodiment;

[0053] Figure 7 This is a schematic diagram of the architecture of a data processing method in an exemplary embodiment;

[0054] Figure 8 This is a structural block diagram of a data processing device in one embodiment;

[0055] Figure 9This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] 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.

[0057] In one embodiment, such as Figure 1 As shown, a data processing method is provided. This application embodiment uses the application of this method to a computer device as an example for illustration. This application embodiment does not limit the execution device for data processing, and includes the following steps 102 to 106:

[0058] Step 102: Obtain the simulated measurement dataset and the live network measurement dataset.

[0059] In implementation, the computer equipment is pre-configured with simulation tools. The computer equipment acquires simulation parameter vectors and uses the simulation tools to simulate these vectors, obtaining a simulated measurement dataset. Then, the computer equipment acquires the live network measurement dataset.

[0060] Specifically, the simulation parameter vector includes base station transmit power, antenna model, and terrain propagation model parameters. The simulation parameter vector... As shown in the following formula (1):

[0061] =( ,…)(1)

[0062] In the above formula (1), This refers to the base station's transmit power, typically measured in units of 1000 kJ / m². (decibels and milliwatts). The antenna model can also be represented as , indicating the horizontal angle and vertical angle Antenna gain (unit: dBi (unit of power gain)). Gain variations in different directions can be described using antenna patterns or gain coefficients. These are the parameters for the terrain propagation model, mainly referring to the various coefficients in the path loss formula, such as the path loss index. Standard deviation of shadow fading Scattering and fading characteristics, etc. Common models used in actual simulations include COST231 (an empirical model for predicting radio wave propagation path loss), Okumura-Hata (an empirical propagation model), or the 3GPP (Third Generation Partnership Project) standard model. The simulation parameter vector is pre-set in the computer device. The computer device obtains the simulation vector parameters based on the simulation vector model. Then, the computer device inputs the simulation vector parameters into the simulation tool, which generates a simulated measurement dataset. The computer device then obtains the live network measurement dataset. Both the simulated and live network measurement datasets contain measurement data sequences for each indicator. The simulated measurement dataset contains simulated measurement data sequences for each indicator, while the live network measurement dataset contains actual measurement data sequences for each indicator. In one example, the indicators are the reference signal received power indicator, the reference signal received quality indicator, and the signal-to-interference-plus-noise ratio indicator. That is, the simulated measurement dataset contains simulated measurement data sequences for the reference signal received power indicator, the reference signal received quality indicator, and the signal-to-interference-plus-noise ratio indicator. The live network measurement dataset contains actual measurement data sequences for the reference signal received power indicator, the reference signal received quality indicator, and the signal-to-interference-plus-noise ratio indicator.

[0063] In an exemplary embodiment, before installing base stations in the area to be measured, the administrator needs to measure the area to obtain a large amount of measurement data, thereby determining the layout and configuration parameters of the base stations based on the measurement data. However, due to terrain or the coverage signal of other base stations, only a portion of the existing network measurement dataset in the area to be measured can be obtained. The administrator then inputs the existing network measurement dataset into a computer device, allowing the computer device to acquire the dataset. The computer device then displays a simulation parameter vector template. This template includes the initial base station transmit power, the initial antenna model, and various initial terrain propagation models. The computer device obtains the base station transmit power input by the administrator based on the initial base station transmit power, and the antenna model input by the administrator based on the initial antenna model. The computer device determines the terrain propagation model from each initial terrain propagation model and obtains the terrain propagation model parameters input by the administrator for that model. Then, the computer device constructs a simulation parameter vector based on the base station transmit power, antenna model, and terrain propagation model parameters, and inputs this vector into a preset simulation tool to obtain a simulated measurement dataset. The set of indicators in the simulated measurement dataset and the existing network measurement dataset is: m∈{RSRP, RSRQ, SINR}. RSRP is the reference signal received power, RSRQ is the reference signal received quality, and SINR is the signal-to-interference-plus-noise ratio. The actual measurement data sequence of each indicator in the current network measurement dataset is shown in the following formula (2):

[0064] (2)

[0065] In the above formula (2), the real measurement data sequence of the index is the measurement time, i.e., when the real measurement data sequence is measured. is the measurement total period.

[0066] The simulation measurement data sequence of each index in the simulation measurement data set is shown in the following formula (3):

[0067] , t = 1, 2, …, T (3)

[0068] In the above formula (3), the simulation measurement data sequence of the index is the real measurement data sequence of the index is the measurement time, i.e., when the real measurement data sequence is measured. is the simulation parameter vector corresponding to the simulation measurement data sequence, i.e., the simulation measurement data sequence is generated by the simulation parameter vector . is the measurement total period.

[0069] In an optional embodiment, in order to facilitate alignment and comparison, it is necessary to determine that the real measurement data sequence and the simulation measurement data sequence are in the same time scale or at least have the same sampling rate. If the sampling rates of the two are different, interpolation or down-sampling processing can be performed on the real measurement data sequence or the simulation measurement data sequence to obtain consistent sampling sequence length.

[0070] Optionally, the simulation tool can be, but is not limited to, OMNeT++ (an object-oriented modular discrete event simulation environment) or Network Simulator 3 (a discrete event network simulator), and the embodiment of the present application does not limit the simulation tool.

[0071] In step 104, the frequency domain difference and the time domain difference between the simulation measurement data set and the real measurement data set are determined, and the total difference between the simulation measurement data set and the real measurement data set is determined according to the frequency domain differences and the time domain differences.

[0072] The simulation measurement data set contains simulation measurement data sequences of each index (measurement index). The real measurement data set contains real measurement data sequences of each index (measurement index).

[0073] In implementation, the computer device determines, for each index, the frequency domain difference and the time domain difference between the simulation measurement data sequence and the real measurement data sequence of the index. Then, the computer device determines the comprehensive difference of the index according to the frequency domain difference and the time domain difference of the index, and determines the total difference according to the comprehensive difference of each index.

[0074] ​​Specifically, the computer device determines, for each index, a real measurement data sequence and a simulation measurement data sequence under the index. Then, the computer device determines, according to a frequency domain difference algorithm, a frequency domain difference value between the real measurement data sequence and the simulation measurement data sequence under the index. Then, the computer device determines, based on a time domain coefficient algorithm and dynamic programming, a time domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index. Then, the computer device determines, according to the time domain difference value and the frequency domain difference value, a comprehensive difference value between the simulation measurement data sequence and the real measurement data sequence under the index. The computer device performs weighted processing on the comprehensive difference values of the indexes to obtain a total difference value between the real measurement data set and the simulation measurement data set.

[0075] In step 106, the simulation measurement data set is optimized to obtain a target simulation measurement data set when the total difference value is greater than or equal to a preset total difference value threshold.

[0076] In implementation, the computer device is preconfigured with the total difference value threshold. The computer device determines whether the total difference value is less than the total difference value threshold. When the total difference value is greater than or equal to the total difference value threshold, the computer device optimizes the simulation parameter vector and generates an optimized simulation measurement data set based on the optimized simulation parameter vector. Then, the computer device continues to perform step 104 described above until the total difference value is less than the total difference value threshold. The computer device determines the simulation measurement data set as the target simulation measurement data set.

[0077] In an optional embodiment, when the total difference value is less than the total difference value threshold, the computer device directly determines the simulation measurement data set as the target simulation measurement data set and determines the simulation parameter vector corresponding to the simulation measurement data set as a target simulation parameter vector. The management personnel determines the position and parameters of the base station according to the target simulation parameter vector, the target simulation measurement data set and the real measurement data set.

[0078] Optionally, the total difference value threshold is determined according to optimization requirements, and embodiments of the present application do not limit the total difference value threshold.

[0079] In the above data processing method, the total difference value between the real measurement data set and the simulation measurement data set is determined from the time domain and the frequency domain, and the simulation measurement data set is optimized when the total difference value is greater than or equal to the total difference value threshold, so that the simulation measurement data set continuously approaches the real real measurement data set until the total difference value is less than the total difference value threshold. At this time, the difference between the target simulation measurement data set and the real measurement data set is small, which improves the accuracy of the target simulation measurement data set and further improves the accuracy of the data processing method.

[0080] In an embodiment, as Figure 2As shown, the specific process of determining the frequency domain difference and the time domain difference between the simulation measurement data set and the real network measurement data set in step 104 includes steps 202 to 206. Among them:

[0081] In step 202, for each simulation measurement data sequence of each index in the simulation measurement data set, the corresponding real measurement data sequence of the simulation measurement data sequence is determined in the real network measurement data set.

[0082] Among them, the simulation measurement data set contains simulation measurement data sequences of each index, and the real network measurement data set contains real measurement data sequences of each index.

[0083] In implementation, the computer device determines the real measurement data sequence of each index in the simulation measurement data set for each simulation measurement data sequence of the index in the simulation measurement data set, and determines the real measurement data sequence as the corresponding real measurement data sequence of the simulation measurement data sequence.

[0084] In an exemplary embodiment, the simulation measurement data set contains simulation measurement data sequences of reference signal received power index, simulation measurement data sequences of reference signal received quality index, and simulation measurement data sequences of signal to interference plus noise ratio index. The computer device determines the real measurement data sequence of the reference signal received power index in the real measurement data set for the simulation measurement data sequence of the reference signal received power index, and determines the real measurement data sequence as the corresponding real measurement data sequence of the simulation measurement data sequence. The computer device determines the real measurement data sequence of the reference signal received quality index in the real measurement data set for the simulation measurement data sequence of the reference signal received quality index, and determines the real measurement data sequence as the corresponding real measurement data sequence of the simulation measurement data sequence. The computer device determines the real measurement data sequence of the signal to interference plus noise ratio index in the real measurement data set for the simulation measurement data sequence of the signal to interference plus noise ratio index, and determines the real measurement data sequence as the corresponding real measurement data sequence of the simulation measurement data sequence.

[0085] In step 204, the frequency domain difference between the simulation measurement data sequence and the real measurement data sequence under the index is determined according to the frequency domain difference algorithm.

[0086] In implementation, the computer device extracts features from the simulation measurement data sequence and the real measurement data sequence under the index to obtain simulation frequency domain features of the simulation measurement data sequence and real frequency domain features of the real measurement data sequence. The computer device performs data operation on the real frequency domain features and the simulation frequency domain features according to the frequency domain difference algorithm to obtain the frequency domain difference between the simulation measurement data sequence and the real measurement data sequence under the index.

[0087] Specifically, the computer device performs a discrete Fourier transform on the real measurement data sequence under the index to obtain a real frequency domain feature. Meanwhile, the computer device performs a discrete Fourier transform on the simulation measurement data sequence corresponding to the real measurement data sequence to obtain a simulation frequency domain feature. Then, the computer device performs data operation on the real frequency domain feature and the simulation frequency domain feature based on a frequency domain difference algorithm to obtain a frequency domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index.

[0088] In step 206, a time domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index is determined through a time domain coefficient algorithm and dynamic programming.

[0089] In implementation, the computer device performs data processing on the simulation measurement data sequence and the real measurement data sequence through dynamic programming to obtain a warping distance. Then, the computer device determines a time domain correlation value between the simulation measurement data sequence and the real measurement data sequence under the index according to the time domain coefficient algorithm. Thus, the computer device determines the time domain difference value between the simulation measurement data sequence and the real measurement data sequence under the index according to the warping distance and the time domain correlation value.

[0090] Specifically, the computer device constructs a local distance matrix according to the simulation measurement data sequence and the real measurement data sequence. Then, the computer device determines a warping distance in the local distance matrix through dynamic programming. Then, the computer device performs data operation on the simulation measurement data sequence and the real measurement data sequence according to the time domain coefficient algorithm to obtain a time domain correlation value. Then, the computer device fuses the time domain correlation value and the warping distance to obtain the time domain difference value between the simulation measurement data sequence and the real measurement data sequence.

[0091] In this embodiment, the frequency domain difference value and the time domain difference value are determined respectively, so that the simulation measurement data set is optimized according to the time domain difference value and the frequency domain difference value, and the optimized simulation measurement data set and the real measurement data set can achieve high consistency in overall energy distribution and subtle time sequence characteristics, thereby improving the data alignment efficiency and accuracy of the target simulation measurement data, and further improving the accuracy of the data processing method.

[0092] In one embodiment, as shown in FIG. 3, the specific processing process of step 204 includes steps 302 to 306. Wherein: Figure 3

[0093] In step 302, a discrete Fourier transform is performed on the real measurement data sequence under the index to obtain a real frequency domain feature of the real measurement data sequence.

[0094] In implementation, the computer device performs a discrete Fourier transform on the real measurement data sequence under the index to extract a real frequency domain feature of the real data sequence. ​

[0095] Specifically, the process of performing the discrete Fourier transform on the real measurement data sequence is shown in the following formula (4):

[0096] (4)

[0097] In the above formula (4), the real measurement data sequence is the real frequency domain feature of the real measurement data sequence. is a discrete frequency point, is the frequency index of the frequency point. is the total length of the real measurement data sequence, which is also the total measurement period. is the complex exponential function in the discrete Fourier transform.

[0098] In an exemplary embodiment, the computer device performs the discrete Fourier transform on the real measurement data sequence of the reference signal received power indicator. The computer device performs the discrete Fourier transform on the real measurement data sequence of the reference signal received power indicator according to the discrete Fourier algorithm, and obtains the real frequency domain feature under the reference signal received power indicator.

[0099] Step 304, performing the discrete Fourier transform on the simulated measurement data sequence of the indicator to obtain the simulated frequency domain feature of the simulated measurement data sequence.

[0100] In implementation, the computer device performs the discrete Fourier transform on the simulated measurement data sequence under the indicator, thereby extracting the simulated frequency domain feature of the simulated measurement data sequence.

[0101] Specifically, the process of performing the discrete Fourier transform on the simulated measurement data sequence is shown in the following formula (5):

[0102] (5)

[0103] In the above formula (5), the simulated measurement data sequence is the simulated frequency domain feature of the simulated measurement data sequence. is a discrete frequency point, is the frequency index of the frequency point. is the total length of the simulated measurement data sequence, which is also the total measurement period. is the complex exponential function in the discrete Fourier transform.

[0104] ​​​​In an example embodiment, the index is taken as the reference signal receiving power index. The computer device performs a discrete Fourier transform on the analog measurement data sequence of the reference signal receiving power index according to a discrete Fourier algorithm to obtain an analog frequency domain feature under the reference signal receiving power index.

[0105] In step 306, the real frequency domain feature and the analog frequency domain feature are processed according to a frequency domain difference algorithm to obtain a frequency domain difference value.

[0106] In implementation, the computer device determines a real amplitude spectrum of the real frequency domain feature and an analog amplitude spectrum of the analog frequency domain feature according to the frequency domain difference algorithm. Then, the computer device determines an amplitude spectrum difference between the real amplitude spectrum and the analog amplitude spectrum as the frequency domain difference value. The real amplitude spectrum is shown in the following formula (6) and the analog amplitude spectrum is shown in the following formula (7):

[0107] (6)

[0108] (7)

[0109] In the above formula (6), the real amplitude spectrum is the real frequency domain feature. the real part of the square of the real frequency domain feature, the imaginary part of the square of the real frequency domain feature. In the above formula (7), the analog amplitude spectrum is the analog frequency domain feature. the real part of the square of the analog frequency domain feature, the real part of the square of the analog frequency domain feature.

[0110] In an example embodiment, the index is taken as the reference signal receiving power index. The computer device performs data processing on the real frequency domain feature of the reference signal receiving power index and the analog frequency domain feature of the reference signal receiving power index according to the frequency domain difference algorithm to obtain a real amplitude spectrum of the reference signal receiving power index and an analog amplitude spectrum of the reference signal receiving power index. Then, the computer device determines an amplitude spectrum difference between the real amplitude spectrum and the analog amplitude spectrum as the frequency domain difference value of the reference signal receiving power index.

[0111] In an optional embodiment, the computer device determines a two-norm difference between the real amplitude difference and the analog amplitude difference as the frequency domain difference value of the index. The smaller the frequency domain difference value is, the closer the real measurement data sequence and the analog measurement data sequence are in the frequency domain.

[0112] ​​In this embodiment, by determining the frequency domain difference between the real measurement data sequence and the simulation measurement data sequence under each index, the difference between the real measurement data sequence and the simulation measurement data sequence in the frequency domain is determined, which facilitates subsequent determination of the total difference.

[0113] In one embodiment, as shown in FIG. 4, the specific processing procedure of step 206 includes steps 402 to 408. Among them: Figure 4

[0114] Step 402, constructing a local distance matrix according to the simulation measurement data sequence and the real measurement data sequence.

[0115] Among them, the simulation measurement data sequence contains simulation measurement data, and the real measurement data sequence contains real measurement data.

[0116] In implementation, the computer device determines the distance between each simulation measurement data and each real measurement data, and constructs a local distance matrix according to each distance.

[0117] Specifically, the computer device determines the local distance between each simulation measurement data and each real measurement data. For example, the local distance D(i, j). Among them, D(i, j) represents the local distance between and ). Among them, represents the i th real measurement data in the real measurement data sequence, represents the j th simulation measurement data in the simulation measurement data sequence. Then, the computer device constructs a local distance matrix D according to each local distance.

[0118] Step 404, determining the bending distance in the local distance matrix by dynamic programming.

[0119] Among them, the bending distance represents the similarity between the real measurement data sequence and the simulation measurement data sequence after the time axis is moderately nonlinearly transformed.

[0120] In implementation, the computer device finds a path with the minimum accumulated distance in the local distance matrix by dynamic programming, and determines the accumulated distance of the path as the bending distance. The smaller the bending distance is, the more the two sequences can match after the "time axis bending".

[0121] ​Specifically, in a network measurement scenario, the real measurement data sequence and the simulated measurement data sequence as time series data tend to have local time offset or sampling incomplete synchronization and other problems, and simply doing point-to-point mean squared error (MSE, Mean Squared Error) comparison is not flexible enough. Therefore, in order to better capture the "shape" or "trend" consistency, the present application adopts dynamic time warping (DTW, Dynamic Time Warping) to evaluate the gap between the simulation data and the real network data. The computer device determines a path with the minimum accumulated distance from the top left corner to the bottom right corner of the local distance matrix through dynamic programming, and determines the accumulated distance of the path as the warping distance. The warping distance is shown in the following formula (8):

[0122] (8)

[0123] In the above formula (8), is the real measurement data sequence, is the simulated measurement data sequence. is the dynamic time warping function, is the warping distance. is the index, indicates the simulation vector parameter. The smaller the value of the warping distance, the higher the similarity between the real measurement data sequence and the simulated measurement data sequence after moderate nonlinear transformation on the time axis; the larger the value of the warping distance, the lower the similarity between the real measurement data sequence and the simulated measurement data sequence after moderate nonlinear transformation on the time axis.

[0124] In an exemplary embodiment, taking the reference signal received power index corresponding to the simulated measurement data sequence as an example. The computer device constructs a local distance matrix according to the real measurement data sequence and the simulated measurement data sequence under the reference signal received power index, and determines the warping distance between the simulated measurement data sequence and the real measurement data sequence under the reference signal received power index in the local distance matrix through dynamic programming.

[0125] Step 406, according to the time domain coefficient algorithm, the real measurement data sequence and the simulated measurement data sequence are processed to obtain the time domain correlation value between the real measurement data sequence and the simulated measurement data sequence.

[0126] In implementation, the time domain coefficient algorithm is provided in the computer device. The computer device performs data operation on the real measurement data sequence and the simulated measurement data sequence according to the time series coefficient algorithm to obtain the time domain correlation value between the real measurement data sequence and the simulated measurement data sequence. The time domain coefficient algorithm is shown in the following formula (9):

[0127] (9)

[0128] wherein in the above formula (9), is a time domain correlation value, is real measurement data at time t in the real measurement data sequence. is a real measurement data mean of the real measurement data sequence, the real measurement data mean . is simulation measurement data at time t in the simulation measurement data sequence. is a simulation measurement data mean of the simulation measurement data sequence, the simulation measurement data mean . If the value is close to 1, it indicates that the rising and falling trend synchronism between the real test data sequence and the simulation measurement data sequence is very good, i.e., the correlation between the two sequences is very good. If the value is close to 0, it indicates that the rising and falling trend synchronism between the real test data sequence and the simulation measurement data sequence is relatively poor, i.e., the correlation between the two sequences is relatively poor.

[0129] In an exemplary embodiment, taking the reference signal received power index corresponding to the real measurement data sequence as an example, the computer device performs data operation on the real measurement data sequence and the simulation measurement data sequence of the reference signal received power index according to the time domain coefficient algorithm, to obtain the time domain correlation value between the real test data sequence and the simulation measurement data sequence.

[0130] Step 408, determining the time domain difference value of the index based on the time domain correlation value and the bending distance.

[0131] In implementation, the computer device performs data operation on the time domain correlation value and the bending distance according to the time domain difference value algorithm, to obtain the time domain difference value of the index. The time domain difference value algorithm is shown in the following formula (10):

[0132] (10)

[0133] wherein in the above formula (10), is a time domain difference value, is a bending distance, is a time domain correlation value. is a weight of the bending distance, is a weight of the time domain correlation value. is used to measure the importance of the bending distance; is used to measure the weight of the time domain correlation value; when is larger, is smaller, which is beneficial to reduce . ​​​is an index, is expressed as a simulation vector parameter.

[0134] In an example embodiment, taking the index of reference signal received power as an example, the computer device performs data operation on the time domain correlation value and the bending distance of the index of reference signal received power according to the time domain difference algorithm, to obtain the time domain difference of the index of reference signal received power.

[0135] Optionally, the weight of the bending distance and the weight of the time domain correlation value are set according to data processing requirements, and the embodiment of the present application does not limit the weight of the bending distance and the weight of the time domain correlation.

[0136] In the embodiment, by determining the time domain difference between the real measurement data sequence and the simulation measurement data sequence under each index, the difference between the real measurement data sequence and the simulation measurement data sequence in the time domain is determined, which facilitates subsequent determination of the total difference.

[0137] In an example embodiment, as shown in Figure 5 the specific processing process of step 104 for determining the total difference between the real measurement data set and the simulation measurement data set according to the frequency domain differences and the time domain differences includes steps 502 to 504. Wherein:

[0138] Step 502, according to the frequency domain difference and the time domain difference of each index, determine the comprehensive difference of the index.

[0139] Wherein, the comprehensive difference of the index represents the difference between the real measurement data sequence and the simulation measurement data sequence in the index. The greater the comprehensive difference of the index, the greater the difference between the real measurement data sequence and the simulation measurement data sequence in the index.

[0140] In implementation, the computer device performs weighted processing on the frequency domain difference and the time domain difference of each index according to the comprehensive difference algorithm, to obtain the comprehensive difference of the index. Wherein, the comprehensive difference algorithm is shown in the following formula (11):

[0141] (11)

[0142] Wherein, in the above formula (11), is the comprehensive difference, is the weight of the time domain difference, is the time domain difference, is the weight of the frequency domain difference, is the frequency domain difference. is an index, is expressed as a simulation vector parameter.

[0143] In an example embodiment, the indicators are respectively a reference signal received power indicator, a reference signal received quality indicator and a signal to interference plus noise ratio indicator. The computer device performs weighted processing on the time domain difference value and the frequency domain difference value of the reference signal received power indicator according to the comprehensive difference algorithm to obtain a comprehensive difference value of the reference signal received power indicator. Meanwhile, the computer device performs weighted processing on the reference signal received quality indicator according to the comprehensive difference algorithm to obtain a comprehensive difference value of the reference signal received quality indicator. The computer device performs weighted processing on the time domain difference value and the frequency domain difference value of the signal to interference plus noise ratio indicator according to the comprehensive difference algorithm to obtain a comprehensive difference value of the signal to interference plus noise ratio indicator.

[0144] Optionally, the weights of the time domain difference value and the frequency domain difference value are determined according to data processing requirements, and the example embodiments of the present application do not limit the time domain difference value and the frequency domain difference value.

[0145] Step 504, weighted processing is performed on the comprehensive difference values of the indicators according to the weights of the indicators to obtain a total difference value between the live network measurement data set and the simulation measurement data set.

[0146] In implementation, the computer device performs weighted processing on the comprehensive difference values of the indicators according to the total difference function and the weights of the indicators to obtain a total difference value between the live network measurement data set and the simulation measurement data set. The total difference function is shown in the following formula (12):

[0147] (12)

[0148] In the above formula (12), is the total difference value, is the indicator set, is an indicator in the indicator set, is the comprehensive difference value of the indicator , and is the weight of the indicator . Optionally, {RSRP, RSRQ, SINR}.

[0149] In an exemplary embodiment, the metrics are a reference signal received power metric, a reference signal received quality metric, and a signal-to-interference-plus-noise ratio (SNR) metric. The computer device performs weighted processing on the comprehensive difference of the reference signal received power metric according to its weight, obtaining a weighted comprehensive difference of the reference signal received power metric. Simultaneously, the computer device performs weighted processing on the comprehensive difference of the reference signal received quality metric according to its weight, obtaining a weighted comprehensive difference of the reference signal received quality metric, and also performs weighted processing on the comprehensive difference of the signal-to-interference-plus-noise ratio metric according to its weight, obtaining a weighted comprehensive difference of the signal-to-interference-plus-noise ratio metric.

[0150] Optionally, the weights of each indicator can be adjusted appropriately according to the business scenario and accuracy requirements. For example, the weight of the reference signal received power indicator is 0.5, the weight of the reference signal received quality indicator is 0.3, and the weight of the signal-to-interference-plus-noise ratio indicator is 0.2. This application does not limit the weights of each indicator in its embodiments.

[0151] In this embodiment, the total difference is obtained by weighting the time-domain and frequency-domain differences of each indicator, thus comprehensively evaluating network coverage, interference, and performance levels. Compared to simple single-indicator or single-evaluation methods, this data processing method exhibits higher robustness in complex environments.

[0152] In one embodiment, such as Figure 6 As shown, the specific processing procedure of step 106 includes steps 602 to 606. Wherein:

[0153] Step 602: If the total difference is greater than or equal to the preset total difference threshold, update the simulation parameter vector.

[0154] During implementation, if the total difference is greater than or equal to the preset total difference threshold, the computer device updates the simulation parameter vector.

[0155] Specifically, when the total difference is greater than or equal to a preset total difference threshold, the computer device adds the simulation parameter vector to the simulation parameter vector template and displays the simulation parameter vector template. Then, the computer device updates the simulation parameter vector based on the simulation parameter vector template to obtain the updated simulation parameter vector.

[0156] In an exemplary embodiment, the computer device displays a simulation vector parameter template added with a simulation parameter vector. The simulation vector parameter template contains base station transmission power, antenna model, and terrain propagation model. The computer device obtains the base station transmission power input by the administrator, and updates the base station transmission power based on the input base station transmission power to obtain a new transmission power. The computer device obtains the antenna model input by the administrator, and updates the antenna model based on the input antenna model to obtain a new antenna model. Then, the computer device obtains the terrain propagation model and terrain propagation model parameters input by the user, and updates the terrain propagation model and terrain propagation model parameters based on the input terrain propagation model and terrain propagation model parameters to obtain a new terrain propagation model and terrain propagation model parameters. Then, the computer device constructs an updated simulation parameter vector based on the new base station transmission power, the new antenna model, the new terrain propagation model, and the terrain propagation model parameters.

[0157] In an optional embodiment, the computer device updates the simulation parameter vector by a specific optimization algorithm to obtain an updated simulation parameter vector. The specific optimization algorithm may be, for example, gradient descent, genetic algorithm, particle swarm optimization, or Bayesian optimization.

[0158] In step 604, the simulation tool and the updated simulation parameter vector are used to generate an optimized simulation measurement data set.

[0159] In implementation, the computer device inputs the updated simulation parameter vector into the simulation tool, and generates the optimized simulation measurement data set by the simulation tool.

[0160] In step 606, the steps of determining the frequency domain difference and the time domain difference between the simulation measurement data set and the network measurement data set are performed until the total difference is less than a total difference threshold, and the simulation measurement data set is determined as a target simulation measurement data set.

[0161] In implementation, the computer device performs the steps of determining the frequency domain difference and the time domain difference between the simulation measurement data set and the network measurement data set until the total difference is less than a preset total difference threshold, and the computer device determines the simulation measurement data set as a target simulation measurement data, and determines the simulation parameter vector corresponding to the target simulation measurement data as a target simulation parameter vector. The determination of the frequency domain difference and the time domain difference between the simulation measurement data set and the network measurement data set is the step 104 described above, and the specific processing process of the step 104 has been described in detail in the above embodiment, which will not be repeated here.

[0162] In this embodiment, when the total difference is greater than or equal to a total difference threshold, the simulated measurement dataset is optimized so that it continuously approaches the actual live network measurement dataset until the total difference is less than the total difference threshold. At this point, the difference between the target simulated measurement dataset and the live network measurement dataset is small, improving the accuracy of the target simulated measurement dataset and thus improving the accuracy of the data processing method.

[0163] In one exemplary embodiment, Figure 7 This is a schematic diagram of the architecture of a data processing method in an exemplary embodiment. For example... Figure 7 As shown, the computer device acquires a simulated measurement dataset and a live network measurement dataset. The simulated measurement dataset contains simulated measurement data sequences for the RSRP, RSRQ, and SINR indices. The live network measurement dataset contains actual measurement data sequences for the RSRP, RSRQ, and SINR indices. The computer device extracts time-series and frequency-domain features from both datasets. Then, the computer device compares the time-series features of the simulated and live network measurement datasets, and also compares their frequency-domain features to obtain a total difference. The computer device then determines whether the total difference is less than a preset threshold. If the total difference is greater than or equal to the preset threshold, the computer device optimizes the simulated measurement dataset and extracts both time-series and frequency-domain features until the total difference is less than the threshold. The computer device then designates the simulated measurement dataset as the target simulated measurement dataset.

[0164] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0165] Based on the same inventive concept, the embodiments of the present application also provide a data processing apparatus for implementing the data processing method described above. The implementation scheme of the problem provided by the apparatus is similar to the implementation scheme described in the above method, so the specific limitations in one or more data processing apparatus embodiments provided below can refer to the limitations of the data processing method described above, which will not be repeated here.

[0166] In one exemplary embodiment, as shown in Figure 8 A data processing apparatus 800 is provided, comprising an acquisition module 801, a determination module 802 and an optimization module 803, wherein:

[0167] The acquisition module 801 is configured to acquire the simulation measurement data set and the live measurement data set.

[0168] The determination module 802 is configured to determine the frequency domain difference and the time domain difference between the simulation measurement data set and the live measurement data set, and determine the total difference between the simulation measurement data set and the live measurement data set according to the frequency domain differences and the time domain differences.

[0169] The optimization module 803 is configured to optimize the simulation measurement data set to obtain a target simulation measurement data set when the total difference is greater than or equal to a preset total difference threshold.

[0170] In an exemplary embodiment, the determination module 802 comprises a first determination sub-module and a second determination sub-module. The first determination sub-module comprises:

[0171] The third determination sub-module is configured to determine, for each simulation measurement data sequence of each index in the simulation measurement data set, a real measurement data sequence corresponding to the simulation measurement data sequence in the live measurement data set.

[0172] The fourth determination sub-module is configured to determine the frequency domain difference between the simulation measurement data sequence and the real measurement data sequence under the index according to a frequency domain difference algorithm.

[0173] The fifth determination sub-module is configured to determine the time domain difference between the simulation measurement data sequence and the real measurement data sequence under the index by a time domain coefficient algorithm and dynamic programming.

[0174] In an exemplary embodiment, the fourth determination sub-module comprises:

[0175] The first transform sub-module is configured to perform discrete Fourier transform on the real measurement data sequence under the index to obtain a real frequency domain feature of the real measurement data sequence.

[0176] The second transform sub-module is configured to perform discrete Fourier transform on the simulation measurement data sequence of the index to obtain a simulation frequency domain feature of the simulation measurement data sequence.

[0177] The first processing submodule is configured to perform data processing on the real frequency domain feature and the simulated frequency domain feature according to a frequency domain difference algorithm, to obtain a frequency domain difference value.

[0178] In an example embodiment, the fifth determining submodule includes:

[0179] The first constructing submodule is configured to construct a local distance matrix according to the simulated measurement data sequence and the real measurement data sequence.

[0180] The sixth determining submodule is configured to determine a bending distance in the local distance matrix through dynamic programming. The bending distance represents the similarity between the real measurement data sequence and the simulated measurement data sequence after a moderate nonlinear transformation on a time axis.

[0181] The second processing submodule is configured to perform data processing on the real measurement data sequence and the simulated measurement data sequence according to a time domain coefficient algorithm, to obtain a time domain correlation value between the real measurement data sequence and the simulated measurement data sequence.

[0182] The seventh determining submodule is configured to determine a time domain difference value of the index based on the time domain correlation value and the bending distance.

[0183] In an example embodiment, the second determining submodule includes:

[0184] The eighth determining submodule is configured to determine a comprehensive difference value of the index according to the frequency domain difference value and the time domain difference value of each index.

[0185] The third processing submodule is configured to perform weighted processing on the comprehensive difference values of the indexes according to the weights of the indexes, to obtain a total difference value between the real measurement data set and the simulated measurement data set.

[0186] In an example embodiment, the optimization module 803 includes:

[0187] The updating submodule is configured to update the simulation parameter vector in a case where the total difference value is greater than or equal to a preset total difference value threshold.

[0188] The generating submodule is configured to generate an optimized simulated measurement data set through the simulation tool and the updated simulation parameter vector.

[0189] The executing submodule is configured to execute the steps of determining the frequency domain difference value and the time domain difference value between the simulated measurement data set and the real measurement data set until the total difference value is less than the total difference value threshold, and determine the simulated measurement data set as a target simulated measurement data set.

[0190] Each of the modules in the data processing apparatus can be implemented by software, hardware and combinations thereof in whole or in part. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0191] In an exemplary embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 9 The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals, and the wireless communication can be achieved through WIFI, mobile cellular network, Near Field Communication (NFC) or other technologies. The computer program is executed by the processor to implement a data processing method. The display unit of the computer device is configured to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0192] Those skilled in the art can understand that Figure 9 The structure shown in the above embodiments is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0193] In an embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0194] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps of the above method embodiments.

[0195] In an embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps of the above method embodiments.

[0196] A person of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the computer program can include the processes of the above method embodiments. In each embodiment provided in the present application, any reference to a memory, a database or other medium can include at least one of a non-volatile memory and a volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc. The database involved in each embodiment provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in each embodiment provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., and is not limited thereto.

[0197] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of these technical features is deemed to be within the scope of the present application.

[0198] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Obtain the simulated measurement dataset and the live network measurement dataset; Determine the frequency domain difference and time domain difference between the simulated measurement dataset and the live network measurement dataset, and determine the total difference between the live network measurement dataset and the simulated measurement dataset based on each of the frequency domain differences and each of the time domain differences; If the total difference is greater than or equal to a preset total difference threshold, the simulated measurement dataset is optimized to obtain the target simulated measurement dataset.

2. The method according to claim 1, characterized in that, Determining the frequency domain difference and time domain difference between the simulated measurement dataset and the live network measurement dataset includes: For each indicator in the simulated measurement dataset, the corresponding real measurement data sequence is determined in the live network measurement dataset. The frequency domain difference between the simulated measurement data sequence and the real measurement data sequence under the specified index is determined based on the frequency domain difference algorithm. The time-domain difference between the simulated measurement data sequence and the real measurement data sequence under the specified index is determined by using a time-domain coefficient algorithm and dynamic programming.

3. The method according to claim 2, characterized in that, The step of determining the frequency domain difference between the simulated measurement data sequence and the real measurement data sequence under the specified index, based on the frequency domain difference algorithm, includes: Perform a discrete Fourier transform on the actual measurement data sequence under the aforementioned index to obtain the true frequency domain characteristics of the actual measurement data sequence; Perform a discrete Fourier transform on the simulated measurement data sequence of the index to obtain the simulated frequency domain characteristics of the simulated measurement data sequence; According to the frequency domain difference algorithm, the real frequency domain features and the simulated frequency domain features are processed to obtain the frequency domain difference.

4. The method according to claim 2, characterized in that, The step of determining the time-domain difference between the simulated measurement data sequence and the actual measurement data sequence under the specified index using a time-domain coefficient algorithm and dynamic programming includes: A local distance matrix is ​​constructed based on the simulated measurement data sequence and the real measurement data sequence; The bending distance is determined in the local distance matrix by dynamic programming; the bending distance characterizes the similarity between the real measurement data sequence and the simulated measurement data sequence after a moderately nonlinear transformation along the time axis. According to the time-domain coefficient algorithm, the real measurement data sequence and the simulated measurement data sequence are processed to obtain the time-domain correlation value between the real measurement data sequence and the simulated measurement data sequence; The time-domain difference of the index is determined based on the time-domain correlation value and the bending distance.

5. The method according to claim 1, characterized in that, The step of determining the total difference between the live network measurement dataset and the simulated measurement dataset based on each of the frequency domain differences and each of the time domain differences includes: The comprehensive difference of the indicators is determined based on the frequency domain difference and time domain difference of each indicator; Based on the weights of each indicator, the comprehensive difference between each indicator is weighted to obtain the total difference between the live network measurement dataset and the simulated measurement dataset.

6. The method according to claim 1, characterized in that, The step of optimizing the simulated measurement dataset to obtain a target simulated measurement dataset when the total difference is greater than or equal to a preset total difference threshold includes: If the total difference is greater than or equal to a preset total difference threshold, update the simulation parameter vector; An optimized simulation measurement dataset is generated using simulation tools and updated simulation parameter vectors; Perform the steps of determining the frequency domain difference and time domain difference between the simulated measurement dataset and the live network measurement dataset until the total difference is less than the total difference threshold, and then determine the simulated measurement dataset as the target simulated measurement dataset.

7. A data processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire simulated measurement datasets and live network measurement datasets; The determination module is used to determine the frequency domain difference and time domain difference between the simulated measurement dataset and the live network measurement dataset, and to determine the total difference between the live network measurement dataset and the simulated measurement dataset based on each of the frequency domain differences and each of the time domain differences; An optimization module is used to optimize the simulated measurement dataset to obtain a target simulated measurement dataset when the total difference is greater than or equal to a preset total difference threshold.

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 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.

10. A computer program product, comprising a computer program, 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.

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