Data sequence comparative analysis method, apparatus, electronic device and storage medium

Abstract

The present application relates to a data sequence comparative analysis method, an apparatus, an electronic device and a storage medium. The data sequence comparative analysis method comprises: acquiring a first indicator data sequence and a second indicator data sequence about the same performance indicator; on the basis of the first indicator data sequence and the second indicator data sequence, generating a sequence to be classified, the sequence to be classified comprising information of the difference between the first indicator data sequence and the second indicator data sequence; and inputting the sequence to be classified into a preset comparative analysis model, such that the preset comparative analysis model classifies said sequence to obtain a comparative analysis result.

Description

Data sequence comparative analysis method, device, electronic device and storage medium

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202311290694.2 filed on September 28, 2023, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of computer technology, and in particular to a method, device, electronic device, and storage medium for comparing and analyzing data sequences. Background Art

[0004] Data analysis can be seen everywhere in all walks of life and in daily life, and comparative analysis is one of the most common and important contents in data analysis. The foundation of data analysis is statistics.

[0005] In statistics, there are several methods for comparing two numerical sequences to determine whether they are different or similar: difference judgment based on sequence distance measurement, similarity judgment based on sequence cosine similarity, similarity judgment based on the Pearson correlation coefficient or Spearman rank correlation coefficient of equal-length sequences, trend similarity judgment based on the first-order difference correlation coefficient of sequences, similarity judgment based on the ARIMA forecasting model parameters of sequences, similarity judgment of unequal-length sequences based on the dynamic time warping algorithm, statistical mean difference judgment of sequences based on the t-test (small sample) or Z-test (large sample) under the assumption of normal distribution, statistical standard deviation difference judgment of sequences based on the F-test under the assumption of normal distribution, statistical difference judgment of sequences based on the Wilcoxon rank sum test under the assumption of non-normal distribution, and so on.

[0006] Performance indicators in the mobile communications field are numerical sequences, and the corresponding comparative analysis methods are also based on the basic principles of statistics. However, due to the professionalism and particularity of its business, when analyzing specific performance indicators, the mathematical model and analysis method used should be determined according to the business background and data characteristics of the performance indicators.

[0007] Data analysis is based on statistics, and a wide range of statistical analysis software is available. These include comprehensive, powerful commercial software such as SAS, SPSS, Minitab, Statistica, S-Plus, GAUSS, and Eviews. There is also free, open-source software like the R language, specifically for statistical analysis, and Python with the Statsmodels / Scipy / Rpy2 statistical analysis libraries. Even the everyday office software Excel provides some basic statistical functions. Almost all statistical analysis software supports rich and diverse data visualization, enabling analysts to make intuitive comparative judgments through visual observation and operational experience.

[0008] In the communications field, performance indicator analysis is an important daily task for network operation and maintenance and performance optimization, and comparative analysis is the most common content. For example, the performance indicator differences between Network 1 and Network 2 can be compared. The performance indicator changes between today and yesterday can be compared. The performance indicator changes between today and the same day last week can be compared. The performance indicator changes between this week and last week can be compared. The performance indicator changes before and after enabling a certain function can be compared. The performance indicator changes before and after modifying a certain parameter can be compared. The performance indicator changes before and after upgrading can be compared, and so on.

[0009] Due to the complexity of communication networks, especially mobile communication networks, which include core networks, transmission networks, and access networks, they involve a variety of hardware equipment types and topological networking, software support functions and parameter configurations, application scenarios, user equipment, service types, perception evaluation, etc. There are many types and huge numbers of related performance indicators, such as status, access, disconnection, switching, load, rate, delay, packet error, packet loss, retransmission, energy consumption, efficiency, resource overhead, channel quality, scheduling performance, and proprietary indicators of various characteristics (such as characteristic indicators of voice services, characteristic indicators of high-speed mobility, characteristic indicators of space division multiplexing, etc.). Performance indicators have time by default (year / month / day / hour / minute / second / millisecond) The performance indicators of mobile communications are analyzed in terms of time and space (multiple time granularities, such as the entire network / subnet / base station / cell / antenna, etc.), plus the various random factors that affect the performance indicators of the wireless environment in the mobile communication network, such as the distribution and mobility of terminals, network load and changes, signal fading, interference noise, etc., as well as the analysis requirements of multiple granularities, such as summary analysis based on day granularity and real-time analysis based on millisecond granularity, macro analysis based on whole network granularity and micro analysis based on cell granularity, etc. Therefore, the performance indicator analysis of mobile communications is not only a large workload, but also a professional job with high technical level and ability requirements. It requires data analysts to have a deep understanding of mobile communication services and be proficient in statistical analysis methods.

[0010] However, comparative analysis of mobile communication performance indicators is currently a routine part of daily network operations and performance optimization. The workload is enormous, making it impossible to employ large numbers of professionals, nor is it feasible to recruit sufficient experts proficient in both business and statistics to handle the task. Instead, it is performed by ordinary business personnel using simple and easy-to-use statistical tools, such as Excel, to calculate a few basic statistics, such as the mean, standard deviation, difference in means and range of variation, and difference in standard deviation and range of variation, of the two performance indicator series to be compared. Combined with visual observation of the performance indicators displayed in visual charts, these personnel draw on their intuitive understanding of the business acquired through their daily work to make intuitive comparisons and judgments. Generally speaking, the more business analysis experience one has, the more accurate their intuitive judgments.

[0011] Therefore, the current challenges of comparative analysis of mobile communication performance indicators are: the workload is high, the technical difficulty is high, and it requires a large number of experts who are both business-savvy and statistically proficient, a requirement that is practically impossible to meet. Instead, ordinary business personnel are employed to make intuitive comparative judgments based on simple statistical models and data visualizations, combined with their business experience, to compensate for the shortcomings of statistical analysis. However, the accuracy of these judgments is dependent on the business experience and technical capabilities of the analysts and cannot be effectively guaranteed.

[0012] Summary of the Invention

[0013] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a data sequence comparison and analysis method, device, electronic device and storage medium.

[0014] In a first aspect, the present application provides a comparative analysis method for data sequences, including: obtaining a first indicator data sequence and a second indicator data sequence regarding the same performance indicator; generating a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, wherein the sequence to be classified contains difference information between the first indicator data sequence and the second indicator data sequence; inputting the sequence to be classified into a preset comparative analysis model so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

[0015] In the second aspect, the present application provides a comparative analysis device for data sequences, comprising: an acquisition module for acquiring a first indicator data sequence and a second indicator data sequence regarding the same performance indicator; a determination module for generating a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, wherein the sequence to be classified contains difference information between the first indicator data sequence and the second indicator data sequence; a classification module for inputting the sequence to be classified into a preset comparative analysis model so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

[0016] In a third aspect, the present application provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement any of the data sequence comparison and analysis methods described in the first aspect when executing the programs stored in the memory.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, on which a program for a comparative analysis method of data sequences is stored. When the program for a comparative analysis method of data sequences is executed by a processor, the steps of any of the comparative analysis methods of data sequences described in the first aspect are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] FIG1 is a flow chart of a comparative analysis method for a data sequence provided in an embodiment of the present application;

[0021] FIG2 is a flow chart of step S102 in FIG1 ;

[0022] FIG3 is a flow chart of a method for training a preset comparative analysis model provided in an embodiment of the present application;

[0023] FIG4 is a data visualization diagram of sequence 1 corresponding to the number of network users, a performance indicator provided by an embodiment of the present application;

[0024] FIG5 is a data visualization diagram of sequence 2 corresponding to the number of network users, a performance indicator provided by an embodiment of the present application;

[0025] FIG6 is a data visualization diagram of sequence 3 corresponding to the number of network users, a performance indicator provided by an embodiment of the present application;

[0026] FIG7 is a data visualization diagram of sequence 1 corresponding to a performance indicator - network traffic provided by an embodiment of the present application;

[0027] FIG8 is a data visualization diagram of sequence 2 corresponding to a performance indicator - network traffic provided by an embodiment of the present application;

[0028] FIG9 is a data visualization diagram of sequence 3 corresponding to a performance indicator - network traffic provided by an embodiment of the present application;

[0029] FIG10 is a data visualization diagram of sequence 1 corresponding to a performance indicator - network connection rate provided in an embodiment of the present application;

[0030] FIG11 is a data visualization diagram of sequence 2 corresponding to a performance indicator - network connection rate provided in an embodiment of the present application;

[0031] FIG12 is a data visualization diagram of sequence 3 corresponding to a performance indicator - network connection rate provided in an embodiment of the present application;

[0032] FIG13 is a schematic diagram of an exemplary expert judgment result provided in an embodiment of the present application;

[0033] FIG14 is a schematic diagram of an analysis description in an exemplary analysis result provided in an embodiment of the present application;

[0034] FIG15 is a structural diagram of a data sequence comparison and analysis device provided in an embodiment of the present application;

[0035] FIG16 is a structural diagram of a data sequence comparison and analysis system provided in an embodiment of the present application;

[0036] FIG17 is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] The current challenges of comparative analysis of mobile communication performance indicators are: the workload is high, the technical difficulty is high, and the need for a large number of experts who are both business-savvy and statistically savvy is virtually impossible. Instead, a large number of ordinary business personnel, based on simple statistical models and visually visualized data, combined with their business experience, compensate for the shortcomings of statistical analysis and make intuitive comparative judgments. However, the accuracy of these judgments is related to the business experience and technical capabilities of the analysts and cannot be effectively guaranteed. To address the aforementioned challenges of comparative analysis of mobile communication performance indicators, inspired by the intuitive judgments widely made by ordinary business personnel based on visual comparisons combined with business experience, and drawing on the highly successful and widely used deep learning models based on deep neural networks in current artificial intelligence, the present invention provides a data sequence comparative analysis method, device, electronic device, and storage medium. These methods integrate two data comparative analysis methods, one based on statistical analysis models and the other based on deep learning models, leveraging the respective advantages of statistical analysis and business analysis to achieve intelligent, automated, and professional comparative analysis of mobile communication performance indicators. This method reduces the need for significant analytical manpower while ensuring the accuracy of the comparative analysis.

[0039] The data sequence comparative analysis method, device, electronic device, and storage medium provided in this application can be applied, on the one hand, to data analysis primarily in the mobile communications industry, particularly in the comparative analysis of mobile communications performance indicators (numerical sequence data), supporting business personnel in related industries such as network operation and maintenance and performance optimization to quickly and efficiently complete the large-scale daily comparative analysis of performance indicators. Furthermore, it can also be applied to the comparison of one-dimensional business data sequences in other industries, particularly in industries with a strong correlation with business knowledge and experience.

[0040] In one embodiment of the present application, a comparative analysis method for a data sequence is provided. As shown in FIG1 , the method may include the following steps:

[0041] Step S101: Acquire a first indicator data sequence and a second indicator data sequence regarding the same performance indicator.

[0042] In the embodiment of the present application, the performance indicator may refer to a performance indicator in the field of mobile communications. For example, the performance indicator may be the number of network users, network traffic volume, network connection rate, network disconnection rate, etc.

[0043] The first indicator data sequence and the second indicator data sequence may be collected under different conditions or before and after the same event node. For example, the first indicator data sequence and the second indicator data sequence may be indicator data sequences of the same performance indicator of different networks, or indicator data sequences of the same performance indicator of different dates, or indicator data sequences of the same performance indicator before and after a certain function is enabled, or indicator data sequences of the same performance indicator before and after a certain parameter is modified, or indicator data sequences of the same performance indicator before and after a version is upgraded, and so on.

[0044] Step S102: Generate a sequence to be classified based on the first indicator data sequence and the second indicator data sequence.

[0045] In the embodiment of the present application, the sequence to be classified includes difference information between the first indicator data sequence and the second indicator data sequence.

[0046] In order to be able to use the preset comparative analysis model to compare the views of two one-dimensional data sequences, a sequence to be classified can be generated based on the first indicator data sequence and the second indicator data sequence, and only the difference information between the first indicator data sequence and the second indicator data sequence is retained. The difference information is the information in the two indicator data sequences that affects the comparative analysis results.

[0047] The present application combines two sequences into one sequence, thus transforming the comparison problem of the two sequences into a classification problem of one sequence, thus achieving the transformation of the problem. In this way, the combined sequence can be classified by using a one-dimensional convolutional neural network model, thereby achieving comparative judgment of the two sequences.

[0048] Step S103 : inputting the sequence to be classified into a preset comparative analysis model, so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

[0049] In the embodiments of the present application, the preset comparative analysis model can adopt a deep learning model, with a deep convolutional neural network being preferred. This model leverages the advantages of convolutional neural networks in image feature recognition, extraction, and representation to better simulate the way business personnel make comparative judgments based on data visualizations, more in line with the way business personnel make intuitive judgments based on data views combined with business experience. Because the sequence being processed is a one-dimensional sequence, a convolutional neural network based on one-dimensional convolution is used.

[0050] In this step, the one-dimensional sequence to be classified is input into a preset comparative analysis model. The preset comparative analysis model can classify the differences between the two indicator data sequences and obtain comparative analysis results.

[0051] The method provided in the embodiment of the present application generates a sequence to be classified containing difference information between the first indicator data sequence and the second indicator data sequence corresponding to the performance indicator, and inputs the sequence to be classified into a preset comparative analysis model so that the preset comparative analysis model classifies the differences between the two indicator data sequences to obtain comparative analysis results, thereby realizing intelligent, automated and professional comparative analysis of mobile communication performance indicators. It can replace a large number of business personnel to complete a large amount of daily data comparative analysis work, while ensuring the efficiency and effectiveness of data analysis, saving a large amount of business human resource expenses.

[0052] In another embodiment of the present application, as shown in FIG2 , step S102 generates a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, including:

[0053] Step S201 : preprocessing the first indicator data sequence and the second indicator data sequence respectively to obtain a first equal-length sequence and a second equal-length sequence.

[0054] In this step, in order to facilitate the subsequent subtraction difference processing, the first indicator data sequence and the second indicator data sequence need to be preprocessed first. The preprocessing process includes: outlier processing, time alignment, length alignment and other preprocessing steps, turning them into equal-length sequences of length N.

[0055] In one embodiment of the present application, step S201 preprocesses the first indicator data sequence and the second indicator data sequence respectively to obtain a first equal-length sequence and a second equal-length sequence, including: 1. Correcting the outliers in the first indicator data sequence and the second indicator data sequence respectively to obtain a first corrected sequence and a second corrected sequence; 2. Time-aligning the first corrected sequence and the second corrected sequence respectively to obtain a first aligned sequence and a second aligned sequence; 3. Length-aligning the first aligned sequence and the second aligned sequence to obtain the first equal-length sequence and the second equal-length sequence respectively.

[0056] For example, two indicator data sequences Seq1 to be compared 和 After the first three preprocessing steps of outlier processing, time alignment, and length alignment, Seq2 becomes two equal-length sequences of length N.

[0057] Step S202 : performing subtraction and difference processing on the first equal-length sequence and the second equal-length sequence to obtain the sequence to be classified.

[0058] After obtaining the first and second equal-length sequences, they need to be merged. Based on the objective task of "comparing the differences between two sequences with the same performance indicators", the sequences are processed by subtraction and difference. By subtraction and difference, information irrelevant to the comparison is eliminated, or in other words, information that does not affect the comparison is removed, thus achieving sequence simplification and slimming.

[0059] In one embodiment of the present application, performing subtraction and difference processing on the first equal-length sequence and the second equal-length sequence to obtain the sequence to be classified includes:

[0060] The first indicator data sequence and the second indicator data sequence are input into a preset subtraction difference formula, and the sequence to be classified is calculated using the preset subtraction difference formula.

[0061] In the embodiment of the present application, the preset subtraction difference formula is:

[0062] Exemplarily, two equal-length sequences Seq1(n) and Seq2(n) of length N are input into a preset subtraction difference formula to obtain a new sequence Sep3.

[0063] For example, since the indicator data series of performance indicators are often time series, the influence of time factors on the series can be eliminated by using the subtraction difference of the preset subtraction difference formula.

[0064] In addition, by combining the two sequences into one, the comparison problem of the two sequences can be transformed into a classification problem of one sequence, thus realizing the transformation of the problem. In this way, the merged sequence can be classified by using a deep neural network model to realize the comparative judgment of the two sequences.

[0065] The preprocessing process described above, based on the target task (comparing the differences between two series of the same performance metric), performs a subtraction between the two series. This eliminates information irrelevant to the comparison, or in other words, removes information that does not affect the comparison, streamlining the series. This allows for smaller models, lower costs, and faster training. Since mobile communication performance metrics are generally time series, this preprocessing also eliminates the influence of time on the services represented by the sequences, enabling convolutional neural networks (CNNs) to process these time series.

[0066] In another embodiment of the present application, as shown in FIG3 , the training method of the preset comparative analysis model includes:

[0067] Step S301: Acquire first training data corresponding to a plurality of performance indicators and first data labels corresponding thereto.

[0068] To accelerate the training of deep neural networks, the data subsystem can pre-collect and organize a large amount of reliable basic training data. This includes performance indicators that clearly maintain consistency during stable network operation, randomly splitting a stable performance indicator into two segments with no significant differences, performance indicators that clearly vary during network operation, performance indicators and comparative analysis results that have been previously compared and analyzed by business personnel and have stood the test of time, and performance indicators that clearly maintain consistency or significantly vary, generated through artificial construction, noise addition, or simulated data. This training data can be easily batch-labeled for label comparison and judgment, eliminating the need for manual labeling of each individual data point.

[0069] In one embodiment of the present application, step S301 obtains first training data corresponding to multiple performance indicators and first data labels corresponding thereto, including: 1. Obtaining a fifth indicator data sequence and a sixth indicator data sequence corresponding to multiple performance indicators; 2. Batch labeling the fifth indicator data sequence and the sixth indicator data sequence corresponding to multiple performance indicators to obtain the first data label corresponding to each performance indicator; 3. Combining the fifth indicator data sequence and the sixth indicator data sequence corresponding to each performance indicator to obtain the first training data corresponding to the performance indicator; 4. Obtaining the first training data and the first data label corresponding to each performance indicator.

[0070] In the embodiment of the present application, the first training data corresponding to each performance indicator is a one-dimensional data sequence obtained by performing the preprocessing and subtraction difference processing on the original training data as described in the above embodiment.

[0071] The present application provides several examples of the process of generating the first training data and the first data label:

[0072] Example 1: Compare two series of the performance indicator "number of network users".

[0073] Sequence 1 and Sequence 2 are data from two consecutive weeks. The length of each sequence is 7*24=168, which means there are 7 days in a week and 24 hours in a day. There is 1 statistical sample data every hour. The comparison result is "no difference".

[0074] Seq1={40.43,37.07,34.81,30.64,29.34,29.76,38.29,51.77,59.12,57.53,59.01,63.04,62.03,59.44,57.9,60.84,61.43,65.46,65.24,64.77,61.34,57.47,51.14,44.1,40.9,35.01,33.01,32.69,31.3,32.98,41.16,54.03,57.79,59.62,59.12,60.24,57.68,55.29,56.9,60.31,63.39,68.59,67.54,69.11,65.12,60.5,54.97,46.74,40.62,35.35,33.39,30.82,29.52,31.25,37.92,50.76,54.62,55.91,59.06,59.71,60.39,58.16,58.68,62.1,61,66.66,65.02,65.74,60.8,58.03,55.22,48.68,40.44,35.05,33.11,30.26,28.94,31.84,38.99,53.02,59.98,56.3,56.38,59.09,57.64,54.81,57.93,60.28,62.72,72.03,72.18,70.67,63.03,58.9,55.05,45.59,38.67,35.08,31.99,30.91,31.24,32.7,41.07,56.46,58.93,56.53,57.83,60.67,61.49,59.16,60.21,64.56,60.62,69.41,72.32,72.67,69.29,67.04,59.3,50.04,43.23,38.43,33.9,30.83,29.89,31.35,40.47,50.33,57.28,57.85,58.24,59.86,59.53,58.95,58.97,60.18,60.72,64.53,62.1,65.47,62.1,58.18,55.11,48.9,42.59,36.07,34.03,31.65,29.07,30.83,38.02,47.09,54.08,57.64,61.14,61.6,61.72,62.49,61.21,63.83,65.04,67.94,67.02,67.85,61.89,59.35,54.7,48.2}

[0075] Seq2={38.67,41.51,31.54,32.81,25.46,30.18,34.72,50.71,60.91,56.55, 56.13,60.11,57.44,60.63,58.43,62.56,61.01,66.89,63.34,65.5,62.39,54.24,55.28,47.39,37.02,33.2,33.82,35.02,35.28,29.37,44.7,57.28,56.88,55.63,61.12,62.16,62.38,54.59,60.17,59.76,68.37,70.94,70.79,68.1,70.09,61.32,58.69,44.75,42.43, 33.81,36.81,33.73,27.27,28,33.39,55.5,55.13,53.02,57.62,57.41,60.25,57.36,60.81,60.21,58.22,66.81,67.18,68.35,62.36,57.79,59.75,46.7,38.12,36.57,37.13,33.4,30.66,28.33,36.74,54.64,60.22,51.66,54.13,63.94,57.82,54.9,59.78,60.98,58. 53,73.08,73.33,68.52,59.17,61.49,55.77,41.56,36.65,39.05,33.23,35.3,34,28.74,36.9,59.67,61.93,59.16,56.02,65.22,60.1,63.19,57.1,67.6,59.38,69.91,67.98,72.79,68.06,67.53,56.9,49.31,42.88,40.91,30.72,34.89,28,26.45,44.35,54.68,53.95 ,55.95,57.57,62.65,64.32,62.53,63.71,61.81,63.68,60.9,60.09,68.41,59.99,53.51,52.84,49.22,38.4,36.41,37.46,34.34,26.4,33.95,35.09,45.61,53.91,52.73,58.3,61.74,58.42,67.16,62.81,64.86,63.68,71.74,62.32,66.13,63.48,56.9,54.81,44.88}

[0076] Sequence 1 and Sequence 2 already meet the requirements of no outliers, time alignment, and equal length, so Sequence 3 can be calculated using the preset subtraction difference formula. Sequence 3, as training data, has a label of 0, indicating no difference.

[0077] Seq3={0.04,-0.06,-0.13,-0.14,0.13,0.14,-0.03,0.04,-0.05,-0.08,0.01,0.05,-0.04,0.01,-0.02,0.06,-0.05,-0.05,0,-0.0 4,0.06,0.01,-0.02,0.05,-0.09,-0.06,-0.05,-0.14,-0.09,-0.15,0.07,-0.01,-0.05,-0.08,-0.01,-0.05,0.06,-0.01,0.04,-0. 07,-0.04,0.01,0,-0.06,-0.06,0.01,-0.04,-0.04,0.02,0.07,0.07,0.05,-0.02,-0.03,0.02,0,-0.06,-0.09,0.01,-0.08,-0.08,-0.05,0.05,0.07,-0.02,0.02,0.03,0.03,-0.03,0,-0.07,-0.02,0.1,0.03,-0.07,0.14,0.05,-0.16,0.09,-0.01,0.07,-0.08,-0. 02,0.02,-0.05,0.02,0.08,0.05,-0.01,-0.02,0,-0.03,-0.03,0.01,-0.01,0.02,0.12,0.04,-0.05,-0.04,0.02,-0.05,0.06,-0.05,0.07,0.07,0.02,0.07,0.04,0,-0.01,0.03,0.02,0.01,-0.02,0.04,0.02,0.06,0,-0.08,0.07,-0.09,0.07,0,0.11,-0.01,-0.1 2,-0.08,-0.04,-0.06,-0.08,0.07,-0.07,-0.05,-0.03,0.02,-0.02,-0.01,0.01,0.03,0.07,-0.07,0.06,0.08,0.06,0.07,-0.08,0.02,-0.18,-0.03,-0.01,0.1,0.05,-0.04,0.06,-0.01,-0.06,0.04,-0.04,0.07,0.04,0.07,0.03,0.07,-0.05,-0.03,0.04,0.01}

[0078] The data visualization diagrams of sequence 1, sequence 2, and sequence 3 corresponding to the performance indicator - number of network users are shown in Figures 4, 5, and 6.

[0079] Example 2: Comparing two series of the performance indicator "network traffic".

[0080] Sequence 1 and Sequence 2 are data from two consecutive weeks. The length of each sequence is 7*24=168, and the comparison result is "different".

[0081] Seq1={0.22,0.24,0.2,0.17,0.28,0.29,0.8,1.09,2,2.8,3.34,3.42,2.15,2.69,2.63,2.59,2.55,2.93,2.38,2.23,1.5 4,1.38,0.81,0.44,0.35,0.16,0.27,0.22,0.13,0.27,0.73,1.42,2.39,3.09,2.76,3.27,2.44,1.95,2.33,2.85,3.25,2 .82,2.41,2.69,2.54,1.51,1.52,1.15,0.82,0.22,0.17,0.11,0.16,0.33,0.63,1.25,2.16,2.96,3.16,3.05,2.64,1.96,2.63,2.72,2.93,3.3,3.6,3.15,2.58,1.71,1.35,0.69,0.55,0.23,0.45,0.15,0.15,0.53,0.88,1.37,2.65,2.72,2.72, 2.86,1.99,1.69,2.52,2.49,2.64,3.6,2.99,2.52,1.88,1.87,1.56,0.6,0.35,0.21,0.19,0.25,0.4,0.42,0.68,1.4,1.98,2.92,2.79,2.57,2.11,1.8,2.26,3.16,2.86,3.22,2.82,2.26,2.13,1.56,1.04,0.59,0.37,0.39,0.31,0.2,0.21,0. 35,0.64,1.25,2.1,2.8,2.95,3.04,2.29,2.25,2.42,2.48,2.28,2.3,2.27,2.67,2.53,1.43,1.5,0.91,0.35,0.24,0.17,0.21,0.19,0.39,0.82,1.16,2.2,2.56,2.56,3.01,2.07,2.18,2.05,2.24,2.7,2.26,2.01,2.36,2.11,1.52,1.21,0.53}

[0082] Seq2={0.22,0.26,0.2,0.18,0.29,0.3,0.83,1.13,2.08,2.89,3.44,3.63,2.29,2.87,2.69,2.8,2.58,3.13,2.4,2.4,1 .62,1.46,0.84,0.45,0.36,0.18,0.28,0.22,0.13,0.28,0.79,1.52,2.62,3.12,2.87,3.49,2.45,2.02,2.43,3.03,3.49 ,3.1,2.42,2.78,2.66,1.64,1.66,1.18,0.85,0.24,0.18,0.12,0.17,0.37,0.65,1.3,2.25,3.2,3.32,3.16,2.69,2.07,2.78,2.74,3.17,3.33,3.78,3.46,2.67,1.88,1.43,0.72,0.58,0.24,0.49,0.16,0.15,0.56,0.94,1.42,2.8,2.75,2.84 ,2.95,2.15,1.77,2.7,2.63,2.89,3.7,3.04,2.53,1.9,1.97,1.57,0.6,0.37,0.22,0.21,0.27,0.41,0.42,0.69,1.5,2,2.94,2.98,2.6,2.2,1.8,2.38,3.43,3.13,3.45,2.95,2.44,2.21,1.63,1.08,0.59,0.37,0.41,0.32,0.21,0.21,0.37, 0.7,1.29,2.24,3.03,3.15,3.11,2.33,2.27,2.46,2.68,2.34,2.44,2.46,2.89,2.62,1.44,1.59,0.95,0.36,0.24,0.18,0.21,0.19,0.42,0.9,1.26,2.42,2.8,2.77,3.15,2.14,2.39,2.1,2.26,2.88,2.46,2.16,2.47,2.18,1.59,1.32,0.55}

[0083] Sequence 1 and Sequence 2 already meet the requirements of no outliers, time alignment, and equal length, so Sequence 3 can be calculated using the preset subtraction difference formula. Sequence 3, as training data, has a label of 1, indicating that there is a difference.

[0084] Seq3={0,0.06,0.02,0.05,0.01,0.04,0.04,0.03,0.04,0.03,0.03,0.06,0.07,0.06,0.02,0.08,0.01,0.06,0.01,0.08 ,0.06,0.06,0.04,0.02,0.01,0.09,0.04,0.01,0.03,0.04,0.08,0.07,0.09,0.01,0.04,0.06,0,0.04,0.04,0.06,0.07 ,0.09,0,0.03,0.05,0.09,0.09,0.02,0.04,0.08,0.01,0.02,0.06,0.09,0.02,0.04,0.04,0.08,0.05,0.04,0.02,0.06,0.06,0.01,0.08,0.01,0.05,0.09,0.04,0.09,0.06,0.05,0.05,0.03,0.09,0.05,0.02,0.05,0.07,0.03,0.06,0.01,0. 04,0.03,0.08,0.05,0.07,0.05,0.09,0.03,0.02,0,0.01,0.05,0.01,0.01,0.05,0.03,0.06,0.08,0.02,0,0.01,0.06,0.01,0.01,0.07,0.01,0.04,0,0.05,0.08,0.09,0.07,0.04,0.08,0.04,0.05,0.04,0,0.02,0.05,0.06,0.09,0.01,0.0 5,0.09,0.03,0.07,0.08,0.07,0.02,0.02,0.01,0.02,0.08,0.03,0.06,0.08,0.08,0.03,0,0.06,0.04,0.03,0.02,0.07,0,0.03,0.09,0.1,0.08,0.09,0.09,0.08,0.05,0.04,0.09,0.02,0.01,0.07,0.08,0.07,0.05,0.04,0.05,0.09,0.04}

[0085] The data visualizations of sequence 1, sequence 2, and sequence 3 corresponding to the performance indicator - number of network users are shown in Figures 7, 8, and 9.

[0086] Example 3: Comparing two series of the performance indicator "network connection rate".

[0087] Sequence 1 and Sequence 2 are data from two consecutive weeks. The length of Sequence 1 and Sequence 2 is 6*24=144, but Sequence 1 is from Tuesday to Sunday, while Sequence 2 is from Monday to Saturday. The comparison result is "no difference".

[0088] Seq1={98.97%,98.98%,98.95%,98.98%,98.98%,98.96%,98.94%,98.93%,98.92%,98.93%,98.86%,98.91%,98.89%,98.92%,98.92%,98.93%,98.88%,98.90%,98.92%,98.93%,98.91%,98.93%,98.92%,98.94%,98.95%,98.94%,98.98%,98.98%,98.98%,98.95%,98.94%,98.92%,98.91%,98.88%,98.90%,98.92%,98.94%,98.88%,98.92%,98.92%,98.87%,98.89%,98.90%,98.91%,98.90%,98.94%,98.91%,98.94%,98.91%,98.97%,98.97%,98.96%,98.99%,98.94%,98.96%,98.94%,98.92%,98.88%,98.92%,98.89%,98.87%,98.89%,98.91%,98.92%,98.91%,98.89%,98.91%,98.91%,98.89%,98.93%,98.91%,98.97%,98.96%,98.95%,98.96%,98.97%,98.94%,98.93%,98.95%,98.93%,98.92%,98.88%,98.92%,98.90%,98.91%,98.90%,98.94%,98.93%,98.91%,98.80%,98.91%,98.88%,98.92%,98.87%,98.93%,98.89%,98.96%,98.96%,98.97%,98.94%,98.91%,98.95%,98.94%,98.89%,98.90%,98.89%,98.92%,98.91%,98.89%,98.92%,98.96%,98.91%,98.93%,98.93%,98.96%,98.92%,98.94%,98.94%,98.63%,98.90%,98.97%,98.89%,98.87%,98.98%,98.97%,98.96%,98.94%,98.91%,98.89%,98.90%,98.95%,98.86%,98.85%,98.89%,98.88%,98.90%,98.89%,98.83%,98.88%,98.91%,98.94%,98.92％,98.85％,98.93％}.

[0089] Seq2={98.95%,98.96%,98.97%,99.02%,98.96%,98.95%,98.94%,98.93%,98.90%, 98.92%,98.86%,98.87%,98.86%,98.93%,98.88%,98.96%,98.86%,98.92%,98.93%,98.95%,98.88%,98.97%,98.88%,98.93%,98.98. 95%,98.99%,99.02%,99.00%,98.90%,98.94%,98.96%,98.88%,98.86%,98.94%,98.88%,98.93%,98.87%,98.95%,98.90%,98.89%,98.93%, 98.94%,98.93%,98.93%,98.97%,98.93%,98.94%,98.92%,98.97%,99.00%,99.00%,99.04%,98.98%,99.00%,98.95%,98.89%,98.91%,98.9 3%,98.91%,98.84%,98.87%,98.94%,98.93%,98.95%,98.88%,98.89%,98.93%,98.87%,98.93%,98.92%,98.92%,98.91%,98.93%,98.94%,9 8.97%,98.89%,98.89%,98.96%,98.91%,98.95%,98.87%,98.95%,98.85%,98.89%,98.91%,98.89%,98.94%,98.94%,98.77%,98.93%,98.84 %,98.89%,98.86%,98.94%,98.91%,98.99%,99.00%,98.94%,98.96%,98.93%,98.99%,98.93%,98.86%,98.86%,98.84%,98.92%,98.91%,98 .92%,98.89%,98.99%,98.94%,98.91%,98.91%,99.00%,98.89%,98.89%,98.96%,98.87%,98.86%,98.98%,98.87%,98.89%,98.99%,98.93% ,99.00%,98.93%,98.87%,98.89%,98.93%,98.92%,98.87%,98.85%,98.89%,98.87%,98.92%,98.91%,98.81%,98.92%,98.89%,98.94%,98.92%, 98.85%, 98.95%}.

[0090] Sequences 1 and 2 meet the requirements of no outliers and equal length, but their time periods are not completely aligned. Since the comparison result is "no difference," the process follows the requirements. Sequence 3 is calculated using the pre-set subtraction difference formula by intercepting the overlapping aligned data of Sequences 1 and 2 from Tuesday to Saturday. The label of Sequence 3, used as training data, is 0, indicating no difference.

[0091] Seq3={0.03%,-0.01%,0.04%,-0.04%,0.00%,-0.05%,0.00%,0.02%,-0.04%,0.02%,0.04%,0.00%,0.02%,0.01%,0.0 3%, -0.02%, 0.05%, 0.00%, -0.01%, -0.01%, -0.03%, 0.05%, -0.01%, 0.03%, -0.04%, -0.01%, -0.02%, 0.01%, 0.02%, -0. 01%,-0.02%,-0.01%,0.03%,0.03%,0.04%,-0.04%,0.05%,0.03%,-0.03%,0.02%,0.00%,0.04%,-0.02%,-0.03%,-0.01%,0.02%,-0.02%,-0.02%,0.03%,0.02%,-0.03%,0.00%,-0.02%,-0.04%,0.05%,0.05%,0.03%,0.04%,0.00%,0.01% ,-0.04%,0.04%,0.01%,-0.01%,0.02%,0.04%,0.01%,-0.05%,0.03%,0.01%,-0.04%,0.03%,-0.03%,0.03%,-0.01%,-0.04%,-0.02%,0.05%,0.01%,-0.04%,0.02%,0.03%,-0.03%,-0.02%,0.01%,0.00%,-0.03%,-0.04%,0.01%,-0.04%, 0.05%,-0.01%,-0.01%,0.00%,0.01%,0.02%,0.04%,0.04%,-0.01%,-0.03%,-0.01%,0.01%,0.05%,-0.02%,-0.04%,-0.03%,0.04%,-0.01%,0.00%,-0.03%,0.00%,-0.02%,0.01%,-0.03%,0.01%,-0.01%,0.04%,0.04%,-0.01%,-0.04%}

[0092] The data visualizations of sequence 1, sequence 2, and sequence 3 corresponding to the performance indicator - number of network users are shown in Figures 10, 11, and 12.

[0093] Example 4: Compare two series of the performance indicator "network drop rate".

[0094] Sequence 1 and Sequence 2 contain data from two consecutive weeks. The length of Sequence 1 is 7*24=168, but the length of Sequence 2 is only 1*12=12, which means it only contains half a day's worth of data. Since Sequence 2 has significantly fewer samples, the comparison result is "unable to determine." We need to wait until Sequence 2 has more data over a longer period of time before we can make a valid comparison.

[0095] Step S302: Perform supervised training on the initial model using the first training data and the first data label to obtain a pre-trained model;

[0096] The first training data and first data labels collected and organized in advance are used to perform supervised pre-training on the deep neural network to obtain an integrated pre-training model.

[0097] Step S303: Obtain second training data corresponding to a plurality of performance indicators and second data labels corresponding thereto.

[0098] In an embodiment of the present application, the second training data is more complex and more difficult to classify than the first training data, so further judgment by experts is required. Using more complex second training data can increase the robustness of the model, and the second data label is determined based on the expert judgment results.

[0099] The human expert group is responsible for receiving and completing the data analysis tasks arranged by the system and providing feedback on the analysis results. The expert analysis results include two parts: one is the expert judgment result, such as no difference or difference, or a more detailed classification judgment; the other is the analysis description, which is used to indicate the analysis method, prompt analysis associations, etc.

[0100] The results of the comparative analysis of two series of a certain performance indicator include, in addition to the expert judgment results, an analysis explanation. The analysis explanation is an important part of the analysis result and, together with the judgment results, constitutes the final analysis result. Table 1 shows an example of an analysis result:

[0101] Table 1. Standardized format for comparative analysis results of a single performance indicator series

[0102] The expert judgment results of the comparative analysis can be simple binary, determining whether a difference exists, or more complex multi-classification, such as distinguishing the magnitude of the difference, such as no difference, slight difference, or significant difference. More detailed multi-classification, for example, determining whether sequence 2 is significantly different from sequence 1, or whether the trend is increasing or decreasing. For cases where a judgment cannot be made, an "unable to judge" result can be added. The expert judgment results are shown in Figure 13.

[0103] The analysis description in the analysis results can be referred to as shown in Figure 14. Analysis instructions can be given, that is, what analysis method to use for analysis, and explanation prompts can also be given, such as considering factors related to business rules, paying attention to related performance indicators, and processing prompts for abnormal data with random fluctuations.

[0104] In one embodiment of the present application, step S303 obtains second training data corresponding to multiple performance indicators and second data labels corresponding thereto, including: 1. Obtaining third indicator data sequences and fourth indicator data sequences corresponding to multiple performance indicators; 2. For each of the performance indicators, obtaining analysis results based on the third indicator data sequence and the fourth indicator data sequence, the analysis results including expert judgment results and analysis description data; 3. Combining the third indicator data sequence, the fourth indicator data sequence and the analysis description data corresponding to each of the performance indicators to obtain the second training data corresponding to the performance indicator; 4. Determining the expert judgment result corresponding to each of the performance indicators as the second data label corresponding to the performance indicator; 5. Obtaining second training data and second data labels corresponding to each performance indicator.

[0105] Step S304: Use the second training data and the second data label to train the pre-training model to obtain the preset comparative analysis model.

[0106] The pre-training model is further trained with the second training data and the second data label to obtain a preset comparative analysis model.

[0107] The embodiment of the present application trains the model in two stages, using less complex first training data and first data labels in the pre-training stage, and using more complex second training data and second data labels obtained based on expert judgment results in the further training stage. This can increase the richness of the training data and improve the robustness and accuracy of the final preset comparative analysis model.

[0108] In another embodiment of the present application, as shown in Figure 15, a comparative analysis device for data sequences is further provided, including: an acquisition module 11, used to acquire a first indicator data sequence and a second indicator data sequence about the same performance indicator; a determination module 12, used to generate a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, wherein the sequence to be classified contains difference information between the first indicator data sequence and the second indicator data sequence; a classification module 13, used to input the sequence to be classified into a preset comparative analysis model, so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

[0109] In another embodiment of the present application, a comparative analysis system for data sequences is also provided. As shown in FIG16 , the comparative analysis system for data sequences includes three subsystems: a control subsystem, a data subsystem, and an analysis subsystem, as well as a human expert group and a large language model similar to ChatGPT (optional) that externally supports the system. The data subsystem includes a data sample library and three modules: a network data collection module, a simulation data generation module, and a training data summary module; the analysis subsystem includes an analysis method library and three modules: a data processing module, a data comparison module based on a statistical analysis method, and a data comparison module based on a deep neural network. The data processing module includes five submodules: an indicator classification submodule, a task scheduling submodule, a result processing submodule, an analysis method submodule, and a training data submodule.

[0110] The main control module of the control subsystem is responsible for the operation of the entire system process, and sends the performance indicators to be compared and analyzed to the analysis subsystem for analysis by notifying the data subsystem.

[0111] The data subsystem contains performance indicators collected from mobile communication networks by the "Network Data Collection Module," performance indicators generated based on simulation algorithms by the "Simulation Data Generation Module," and performance indicators with analysis results collected by the "Training Data Aggregation Module" and returned to the data subsystem after analysis by the analysis subsystem. Performance indicators with comparative analysis results are training data that can be used to train deep neural networks, where the comparative analysis results serve as labels for the training data. The "Network Data Collection Module" and "Simulation Data Generation Module" can also output performance indicators that can be batch-labeled with comparative analysis results and used as training data.

[0112] Performance indicators sent from the data subsystem to the analysis subsystem first enter the data processing module, where they are classified by the indicator classification submodule. The task scheduling submodule then assigns them to one or more of the following: an external expert group, a statistical analysis-based data comparison module, or a deep neural network-based data comparison module for analysis. The statistical analysis-based data comparison module relies on the guidance of the analysis method library in the data processing module to determine which statistical method to use for data analysis. The deep neural network-based data comparison module requires training with a large amount of training data. Therefore, until the analysis method library is fully developed and the deep learning model based on the deep neural network is fully trained, analysis tasks are primarily assigned to external expert groups.

[0113] The indicator classification submodule classifies performance indicators based on their business categories as described above. In addition, different performance indicators have different importance, i.e., weights, in business evaluation. This submodule is used to count and control the proportion of various performance indicators in the training data, ensuring the generalization performance of the training while taking into account the balance consistent with the indicator weights.

[0114] The task scheduling submodule, based on the control subsystem's instructions, assigns performance indicators sent by the data subsystem for analysis to an external expert group or to two internal data comparison modules. Each performance indicator comparison analysis task can be assigned to multiple experts, each of whom can analyze multiple indicators. Each expert can perform multiple comparative analyses on each indicator and provide feedback on the results of these analyses.

[0115] The result processing submodule mainly performs two functions. One is to summarize and process the results returned by the performance indicator analysis task. If a single indicator returns multiple analysis results, a voting mechanism is used to determine the final judgment result. The other is to decompose and process the results of the external expert analysis feedback (including the judgment results and analysis instructions). The judgment results and the corresponding two compared performance indicator sequences are sent to the training data submodule. After preprocessing, the training data is obtained and then sent to the data comparison module based on the deep neural network for supervised training of the deep network. The analysis instructions and corresponding performance indicators are then sent to the analysis method submodule for clustering and classification of the analysis instructions, or sent to an external large language model. After understanding, refining, summarizing, and summarizing by the large language model, the results are fed back to the analysis method submodule. The relevant analysis methods or processing tips that have been determined for each performance indicator are finally incorporated into the analysis method library.

[0116] The analysis methods submodule refines and summarizes prior business knowledge and expert analysis of each performance indicator, gradually establishing, supplementing, and refining the corresponding analysis methods and processing tips for each performance indicator. It also determines the analysis methods that can be used for each indicator (statistical comparison and / or visual comparison, which uses deep neural network-based data comparison), which business factors need attention, which related indicators need attention, and so on. These performance indicator analysis operation suggestions and opinions are ultimately integrated into the analysis method library to serve as guidance or reference during analysis.

[0117] The training data submodule is responsible for generating training data from the comparison data and analysis results input by the result processing submodule, and sending it to the data comparison module based on the deep neural network.

[0118] In another embodiment of the present application, an electronic device is provided, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement the data sequence comparison and analysis method described in any of the aforementioned method embodiments when executing the program stored in the memory.

[0119] In the electronic device provided by the embodiment of the present application, the processor generates a sequence to be classified containing difference information between the first indicator data sequence and the second indicator data sequence corresponding to the performance indicator by executing a program stored in the memory, and inputs the sequence to be classified into a preset comparative analysis model so that the preset comparative analysis model classifies the differences between the two indicator data sequences to obtain comparative analysis results, thereby realizing intelligent, automated, and professional comparative analysis of mobile communication performance indicators. It can replace a large number of business personnel to complete a large amount of daily data comparative analysis work, while ensuring the efficiency and effectiveness of data analysis, saving a large amount of business human resource expenses.

[0120] The communication bus 1140 mentioned in the electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, FIG17 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0121] The communication interface 1120 is used for communication between the electronic device and other devices.

[0122] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage. In one embodiment, the memory may also be at least one storage device located away from the processor.

[0123] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0124] In another embodiment of the present application, a computer-readable storage medium is provided, on which a program of a comparative analysis method for data sequences is stored. When the program of the comparative analysis method for data sequences is executed by a processor, the steps of the comparative analysis method for data sequences described in any of the aforementioned method embodiments are implemented.

[0125] In this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0126] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A comparative analysis method for data series, wherein: The method comprises: Acquire a first indicator data series and a second indicator data series about the same performance indicator; generating a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, wherein the sequence to be classified includes difference information between the first indicator data sequence and the second indicator data sequence; The sequence to be classified is input into a preset comparative analysis model, so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

2. The comparative analysis method of data sequence according to claim 1, wherein: The generating a sequence to be classified based on the first indicator data sequence and the second indicator data sequence comprises: Preprocessing the first indicator data sequence and the second indicator data sequence respectively to obtain a first equal-length sequence and a second equal-length sequence; The first equal-length sequence and the second equal-length sequence are subjected to subtraction and difference processing to obtain the sequence to be classified.

3. The comparative analysis method of data sequence according to claim 2, wherein: The preprocessing of the first indicator data sequence and the second indicator data sequence to obtain a first equal-length sequence and a second equal-length sequence respectively includes: Correcting the abnormal values ​​in the first indicator data sequence and the second indicator data sequence respectively to obtain a first corrected sequence and a second corrected sequence; Time-aligning the first corrected sequence and the second corrected sequence respectively to obtain a first aligned sequence and a second aligned sequence; The first aligned sequence and the second aligned sequence are length-aligned to obtain the first equal-length sequence and the second equal-length sequence, respectively.

4. The comparative analysis method of data sequence according to claim 2, wherein: The step of performing subtraction difference processing on the first equal-length sequence and the second equal-length sequence to obtain the sequence to be classified includes: The first indicator data sequence and the second indicator data sequence are input into a preset subtraction difference formula, and the sequence to be classified is calculated by the preset subtraction difference formula.

5. The comparative analysis method of data sequence according to claim 1, wherein: The training method of the preset comparative analysis model includes: Obtaining first training data corresponding to a plurality of performance indicators and first data labels corresponding thereto; Performing supervised training on the initial model using the first training data and the first data label to obtain a pre-trained model; Acquire second training data corresponding to a plurality of performance indicators and second data labels corresponding thereto, wherein the second data labels are determined according to expert judgment results; The pre-trained model is trained using the second training data and the second data label to obtain the preset comparative analysis model.

6. The comparative analysis method of data sequence according to claim 5, wherein: The obtaining of second training data corresponding to the plurality of performance indicators and second data labels corresponding thereto includes: Obtaining a third indicator data sequence and a fourth indicator data sequence corresponding to a plurality of the performance indicators; For each of the performance indicators, obtaining analysis results based on the third indicator data sequence and the fourth indicator data sequence, wherein the analysis results include expert judgment results and analysis explanation data; Combine the third indicator data sequence, the fourth indicator data sequence and the analysis description data corresponding to each performance indicator to obtain second training data corresponding to the performance indicator; Determine the expert judgment result corresponding to each performance indicator as a second data label corresponding to the performance indicator; The second training data and the second data label corresponding to each performance indicator are obtained.

7. The comparative analysis method of data sequence according to claim 5, wherein: The obtaining of first training data corresponding to the plurality of performance indicators and first data labels corresponding thereto includes: Acquire a fifth indicator data sequence and a sixth indicator data sequence corresponding to a plurality of the performance indicators; Batch labeling the fifth indicator data series and the sixth indicator data series corresponding to the plurality of performance indicators to obtain a first data label corresponding to each performance indicator; Combine the fifth indicator data sequence and the sixth indicator data sequence corresponding to each of the performance indicators to obtain first training data corresponding to the performance indicator; The first training data and the first data label corresponding to each performance indicator are obtained.

8. A comparative analysis device for data sequences, wherein: The device comprises: An acquisition module, used to acquire a first indicator data sequence and a second indicator data sequence for the same performance indicator; A determination module, configured to generate a sequence to be classified based on the first indicator data sequence and the second indicator data sequence, wherein the sequence to be classified includes difference information between the first indicator data sequence and the second indicator data sequence; The classification module is used to input the sequence to be classified into a preset comparative analysis model so that the preset comparative analysis model classifies the sequence to be classified to obtain a comparative analysis result.

9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein: The processor, the communication interface, and the memory communicate with each other via a communication bus; The memory is used to store computer programs; The processor is used to implement the data sequence comparison and analysis method described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores a program of a comparative analysis method for data sequences, and when the program of the comparative analysis method for data sequences is executed by a processor, the steps of the comparative analysis method for data sequences described in any one of claims 1 to 7 are implemented.

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