Product data processing method and device, equipment, storage medium and program product

Abstract

The invention provides a product data processing method and device, equipment, a storage medium and a program product, which can be applied to the fields of big data and financial science and technology. The method comprises the steps of obtaining product experience statistical data of a target product, wherein the product experience statistical data comprises a (t-1)-th section data set collected in a (t-1)-th time period and t-th section data collected in a t-th time period; processing the (t-1) th section data set based on a fluctuation recognition algorithm to obtain a plurality of section data fluctuation ranges of the (t-1) th section data; determining the authenticity of the t-th cross-section data based on the cross-section data fluctuation range to obtain a target determination result, the t-th target cross-section data corresponding to the target determination result being data representing real fluctuation, and the target determination result being associated with a target determination value; processing the target judgment value based on a weighted fusion algorithm to obtain a tth fluctuation form value of the tth target section data; and determining a product optimization scheme of the target product according to the fluctuation form values of the T time periods.

Description

Technical Field

[0001] The present disclosure relates to the fields of big data and financial technology, and specifically to a product data processing method, a product data processing device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the rapid development of the experience economy, the evaluation of product experience levels has become quite common. In time series, product experience levels are constantly fluctuating. For example, before a product is updated, users have a good experience with the product. After the product is updated, users' experience with the product decreases. The product experience level fluctuates before and after the product is updated. There are many methods to verify whether the product experience level has abnormal fluctuations, but they all require sufficient product experience data over a longer time scale as a basis. In order to obtain more sufficient product experience data, a large amount of product experience data needs to be collected over a continuous period of time for the target product, resulting in a waste of resources. The system also processes a large amount of collected product experience data, which reduces operating efficiency. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a product data processing method, apparatus, electronic device, storage medium and program product.

[0004] According to a first aspect of the present disclosure, a product data processing method is provided, comprising:

[0005] Obtain product experience statistics for the target product, where the product experience statistics include the t-1th cross-sectional data set collected during the t-1th period and the tth cross-sectional data collected during the tth period, where t>1;

[0006] Processing the t-1 cross-sectional data set based on the fluctuation identification algorithm to obtain the cross-sectional data fluctuation ranges of multiple t-1 cross-sectional data;

[0007] Perform authenticity judgment on the t-th cross-sectional data based on the fluctuation range of the cross-sectional data to obtain a target judgment result. The t-th target cross-sectional data corresponding to the target judgment result is data representing the true fluctuation. The target judgment result is associated with the target judgment value.

[0008] Processing the target judgment value corresponding to the t-th target cross-section data based on the weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target cross-section data;

[0009] Determine the product optimization plan for the target product based on the fluctuation pattern values ​​in T time periods, where T ≥ t.

[0010] According to an embodiment of the present disclosure, the t-1th cross-sectional data set is processed based on a fluctuation identification algorithm to obtain the cross-sectional data fluctuation ranges of multiple t-1th cross-sectional data, including:

[0011] Determine M sampling data subsets from the t-1 cross-sectional data set, where the sampling data subsets include multiple cross-sectional data, and M>0;

[0012] For each subset of sampled data, the cross-sectional data is processed based on the experience level measurement algorithm to obtain the experience score data;

[0013] For each sample data subset, the standard deviation of the experience score data corresponding to each of the multiple cross-sectional data is calculated to obtain the standard deviation of the experience score of the sample data subset;

[0014] Based on M experience score data subsets and M experience score standard deviations, the fluctuation range of the cross-sectional data is determined.

[0015] According to an embodiment of the present disclosure, determining the cross-sectional data fluctuation range based on M experience score data subsets and M experience score standard deviations includes:

[0016] Determine the experience score mean based on all experience scores in the M experience score data subsets;

[0017] Based on the M experience score standard deviations, determine the mean of the standard deviations;

[0018] Subtract an integer multiple of the mean standard deviation from the mean experience score to obtain the lower limit of the cross-sectional data fluctuation range;

[0019] Add the mean of the experience score to an integer multiple of the mean of the standard deviation to obtain the upper limit of the fluctuation range of the cross-sectional data;

[0020] Determine the cross-sectional data fluctuation range based on the cross-sectional data fluctuation range lower limit and the cross-sectional data fluctuation range upper limit.

[0021] According to an embodiment of the present disclosure, authenticity determination is performed on the t-th cross-sectional data based on the fluctuation range of the cross-sectional data to obtain a target determination result, including:

[0022] Comparing the upper limit of the fluctuation range of the cross-sectional data or the lower limit of the fluctuation range of the cross-sectional data with the t-th cross-sectional data to obtain a comparison result; and

[0023] When the comparison result indicates that the t-th cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, or when the comparison result indicates that the t-th cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, a target determination result is obtained.

[0024] According to an embodiment of the present disclosure, the target determination value is determined by processing the target determination result based on a preset quantization rule;

[0025] The target determination value corresponding to the t-th target section data is processed based on the weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target section data, including:

[0026] The target judgment values ​​corresponding to the plurality of t-th target cross-sectional data are processed based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target cross-sectional data.

[0027] According to an embodiment of the present disclosure, the target determination value is determined based on the following operations:

[0028] When the t-th target cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, the target determination result is converted into a preset target positive value;

[0029] When the t-th target cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, the target determination result is converted into a preset target negative value.

[0030] According to an embodiment of the present disclosure, a product optimization solution for a target product is determined based on the fluctuation pattern values ​​in T time periods, including:

[0031] Determining a first target value range corresponding to the Tth fluctuation pattern value in the product experience strategy table, wherein the product experience strategy table includes multiple value ranges, and the value ranges are associated with product optimization strategies;

[0032] Determine the product optimization strategy corresponding to the first target value range as the first recommended strategy;

[0033] Determine the standard deviation of fluctuations based on the fluctuation pattern values ​​of T periods;

[0034] Determining a second target value range corresponding to the fluctuation standard deviation in the product experience fluctuation strategy table, wherein the product experience fluctuation strategy table includes multiple value ranges, each value range corresponding to a product experience fluctuation strategy;

[0035] Determine the product experience fluctuation strategy corresponding to the second target value range as the second recommended strategy;

[0036] Determine the product optimization plan based on the first and second recommended strategies.

[0037] A second aspect of the present disclosure provides a product data processing device, comprising:

[0038] An acquisition module is used to acquire product experience statistics of a target product, where the product experience statistics include the t-1th cross-sectional data set collected during the t-1th period and the tth cross-sectional data collected during the tth period, where t>1;

[0039] A cross-sectional data fluctuation range obtaining module is used to process the t-1 cross-sectional data set based on a fluctuation identification algorithm to obtain the cross-sectional data fluctuation ranges of multiple t-1 cross-sectional data;

[0040] A target determination result obtaining module is used to perform authenticity determination on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result. The t-th target cross-sectional data corresponding to the target determination result is data representing the true fluctuation. The target determination result is associated with a target determination value.

[0041] a t-th fluctuation shape value obtaining module, for processing the target judgment value corresponding to the t-th target cross-section data based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target cross-section data;

[0042] The product optimization plan determination module is used to determine the product optimization plan of the target product according to the fluctuation shape values ​​of T time periods, where T≥t.

[0043] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0044] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0045] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0046] According to an embodiment of the present disclosure, by processing the t-1th section data set through a fluctuation identification algorithm, the section data fluctuation range of multiple t-1th section data can be obtained, and then, the authenticity of the tth section data is judged based on the section data fluctuation range, and the target judgment result can be obtained. The tth target section data corresponding to the target judgment result is the data representing the real fluctuation. The target judgment result is associated with the target judgment value. Based on the weighted fusion algorithm, the target judgment value corresponding to the tth target section data is processed to obtain the tth fluctuation shape value of the tth target section data. According to the fluctuation shape values ​​of T time periods, the product optimization plan of the target product can be determined, which is suitable for the situation where the current target product experience time series data is insufficient, avoids the waste of resources caused by collecting a large amount of product experience data in continuous time periods, and improves the operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0048] Figure 1 A diagram schematically illustrates an application scenario of a product data processing method according to an embodiment of the present disclosure;

[0049] Figure 2 The flowchart of the product data processing method according to the embodiment of the present disclosure is schematically shown;

[0050] Figure 3 Schematically shows a flow chart of a method for determining a fluctuation range of cross-sectional data according to an embodiment of the present disclosure;

[0051] Figure 4 A flowchart of a method for determining a target determination result according to an embodiment of the present disclosure is schematically shown;

[0052] Figure 5 The following schematically shows a structural block diagram of a product data processing device according to an embodiment of the present disclosure;

[0053] Figure 6 A block diagram of an electronic device suitable for implementing a product data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0054] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0055] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0056] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0057] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0058] With the rapid development of the experience economy, product experience evaluation and assessment have become widespread. Currently, identifying changes in experience levels is still at the stage of "increases mean improvement, decreases mean deterioration." This approach directly uses experience evaluation data as the basis for determining whether a product experience has truly changed, ignoring the fact that product experience is influenced not only by the product itself but also by factors such as the macroeconomic environment and social media reputation.

[0059] In a time series, product experience levels fluctuate constantly. Numerous methods exist for verifying whether these levels exhibit abnormal fluctuations, such as the Dickey-Fuller Test (DF), the Augmented Dickey-Fuller Test (ADF), and pure randomness tests. However, these methods all require sufficient product experience data as a foundation. In the field of product experience, however, high-quality, analyzable product experience data is scarce. Data measuring product user experience levels is often measured in monthly or quarterly units, making these classic techniques impractical for direct application. Furthermore, to obtain more sufficient product experience data, a large amount of continuous time-period product experience data must be collected for each product, resulting in a waste of resources. The system also processes this large amount of collected product experience data, reducing operational efficiency. In light of this, the present disclosure provides a product data processing method, device, equipment, storage medium, and product. The method includes: responding to a product data processing instruction, for each product type, obtaining product purchase data of a first preset number of reference users corresponding to the product type within a first preset time period, wherein the product purchase data represents purchase information of a second preset number of reference products by different reference users; performing array conversion processing on the product purchase data to obtain an initial integer array, wherein the initial integer array includes multiple array parameters, and the array parameters represent purchase information of the reference users on the reference products; obtaining user purchase tag information of the target user within a second preset time period; based on a preset similarity recommendation rule, determining at least one recommended product to be recommended to the target user from multiple reference products according to multiple initial integer arrays and user purchase tag information.

[0060] It should be noted that the product data processing method and product data processing device provided by the present disclosure can be used in the field of financial technology, such as banks and other financial institutions, and can also be used in any field other than the field of financial technology, such as user experience and big data. Therefore, the application field of the product data processing method and product data processing device provided by the present disclosure is not limited.

[0061] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0062] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.

[0063] Figure 1 The application scenario diagram of the product data processing method according to an embodiment of the present disclosure is schematically shown.

[0064] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0065] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0066] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0067] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0068] It should be noted that the product data processing method provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the product data processing device provided in the embodiments of the present disclosure can generally be set in the server 105. The product data processing method provided in the embodiments of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the product data processing device provided in the embodiments of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0069] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0070] Figure 2 The flowchart of the product data processing method according to the embodiment of the present disclosure is schematically shown.

[0071] like Figure 2 As shown, the product data processing method of this embodiment includes operations S210 to S250, and the product data processing method can be executed by an electronic device.

[0072] In operation S210 , product experience statistics for a target product are acquired.

[0073] In operation S220 , the t-1 th cross-sectional data set is processed based on a fluctuation identification algorithm to obtain a plurality of cross-sectional data fluctuation ranges of the t-1 th cross-sectional data.

[0074] In operation S230 , authenticity determination is performed on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result.

[0075] In operation S240 , the target determination value corresponding to the t th target cross-sectional data is processed based on a weighted fusion algorithm to obtain a t th fluctuation shape value of the t th target cross-sectional data.

[0076] In operation S250 , a product optimization solution for the target product is determined based on the fluctuation shape values ​​in T time periods.

[0077] According to embodiments of the present disclosure, target products may include physical products and services. Physical products may include food, beverages, clothing, etc., and service products may include financial services, travel services, educational services, etc. The embodiments of the present disclosure do not limit the types of target products. Product experience statistics are data obtained by surveying users based on product experience measurement methods. Product experience measurement methods may include questionnaires, card sorting, behavioral metrics, user testing, etc., but are not limited to these. The embodiments of the present disclosure do not limit the specific methods of product experience measurement.

[0078] According to an embodiment of the present disclosure, product experience statistical data may include a t-1th cross-sectional data set collected during the t-1th period and a tth cross-sectional data collected during the tth period, where t>1. During the t-1th period, a product experience measurement method may be used to collect user experience data on a target product to obtain the t-1th cross-sectional data. By collecting target product experience data from multiple users, the t-1th cross-sectional data set may be obtained. Similarly, the tth cross-sectional data collected during the tth period may be obtained.

[0079] According to an embodiment of the present disclosure, by processing the t-1th cross-sectional data set based on a fluctuation identification algorithm, a plurality of cross-sectional data fluctuation ranges for the t-1th cross-sectional data can be obtained. The obtained cross-sectional data fluctuation ranges can be used to reflect the product experience level during the t-1th period. The tth cross-sectional data can be compared with the cross-sectional data fluctuation ranges to determine whether the tth cross-sectional data has truly changed compared to the t-1th period. Based on the authenticity determination, a target determination result is obtained. The tth target cross-sectional data corresponding to the target determination result is data that represents the true fluctuation.

[0080] According to embodiments of the present disclosure, a target determination result is associated with a target determination value. The target determination value corresponding to the tth target cross-sectional data can be processed using a weighted fusion algorithm to obtain the tth fluctuation shape value of the tth target cross-sectional data. The tth fluctuation shape value is used to reflect the change in the experience level corresponding to the tth target cross-sectional data.

[0081] According to an embodiment of the present disclosure, a product optimization solution for a target product may be determined based on the fluctuation shape values ​​in T time periods, where T≥t.

[0082] According to an embodiment of the present disclosure, by processing the t-1th section data set through a fluctuation identification algorithm, the section data fluctuation range of multiple t-1th section data can be obtained, and then, the authenticity of the tth section data is judged based on the section data fluctuation range, and the target judgment result can be obtained. The tth target section data corresponding to the target judgment result is the data representing the real fluctuation. The target judgment result is associated with the target judgment value. Based on the weighted fusion algorithm, the target judgment value corresponding to the tth target section data is processed to obtain the tth fluctuation shape value of the tth target section data. According to the fluctuation shape values ​​of T time periods, the product optimization plan of the target product can be determined, which is suitable for the situation where the current target product experience time series data is insufficient, avoids the waste of resources caused by collecting a large amount of product experience data in continuous time periods, and improves the operation efficiency of the system.

[0083] Figure 3 A flowchart of a method for determining a fluctuation range of cross-sectional data according to an embodiment of the present disclosure is schematically shown.

[0084] like Figure 3 As shown, the method for determining the cross-sectional data fluctuation range of this embodiment includes operations S310 to S340.

[0085] In operation S310 , M sample data subsets are determined from the t-1 th cross-sectional data set, where the sample data subsets include a plurality of cross-sectional data.

[0086] In operation S320 , for each sample data subset, the cross-sectional data is processed based on the experience level measurement algorithm to obtain experience score data.

[0087] In operation S330 , for each sample data subset, a standard deviation calculation is performed on the experience score data corresponding to each of the plurality of cross-sectional data to obtain a standard deviation of the experience score of the sample data subset.

[0088] In operation S340 , a cross-sectional data fluctuation range is determined based on the M experience score data subsets and the M experience score standard deviations.

[0089] According to an embodiment of the present disclosure, M rounds of sampling can be performed from the t-1th cross-sectional data set to obtain M sampling data subsets, which can include N cross-sectional data of the t-1th time period, where M>0, N>0.

[0090] According to embodiments of the present disclosure, cross-sectional data can be processed using an experience level measurement algorithm to obtain corresponding experience scores. Experience level measurement algorithms may include, but are not limited to, Net Promoter Score (NPS), Customer Satisfaction (CSAT), User Experience Questionnaire (UES), and the like. Embodiments of the present disclosure do not limit experience level measurement algorithms. Applying the experience level measurement algorithm to M sample data subsets can calculate M*N experience score data, and simultaneously obtain the corresponding M experience score data subsets.

[0091] According to an embodiment of the present disclosure, each sampled data subset may include N experience score data. By calculating the standard deviation of the N experience score data, the experience score standard deviation corresponding to the sampled data subset can be obtained. For M sampled data subsets, M experience score standard deviations can be obtained.

[0092] According to an embodiment of the present disclosure, the cross-sectional data fluctuation range may be determined based on M experience score data subsets and M experience score standard deviations.

[0093] According to the embodiments of the present disclosure, by determining the cross-sectional data fluctuation range of the t-1 cross-sectional data, it is checked whether there is a real change in the t cross-sectional data compared with the previous period based on the obtained cross-sectional data fluctuation range, and by sampling the t-1 cross-sectional data set, the accuracy of fluctuation identification is improved, the operating efficiency is improved, and the degree of discreteness of the experience score can be measured by calculating the standard deviation, so that the finally generated cross-sectional data fluctuation range is more reliable.

[0094] According to an embodiment of the present disclosure, the cross-sectional data fluctuation range is determined based on M experience score data subsets and M experience score standard deviations, including: determining the experience score mean based on all the experience scores in the M experience score data subsets; determining the standard deviation mean based on the M experience score standard deviations; subtracting an integer multiple of the standard deviation mean from the experience score mean to obtain the lower limit of the cross-sectional data fluctuation range; adding an integer multiple of the standard deviation mean to the experience score mean to obtain the upper limit of the cross-sectional data fluctuation range; determining the cross-sectional data fluctuation range based on the lower limit and the upper limit of the cross-sectional data fluctuation range.

[0095] According to an embodiment of the present disclosure, the mean of the experience score can be determined based on all the experience scores in the M experience score data subsets. Based on the M experience score standard deviations, the mean of the standard deviations can be determined. Based on the properties of the normal distribution standard deviation, it can be seen that 68% of the data will fall within the range of the mean plus or minus one standard deviation, 95% of the data will fall within the range of the mean plus or minus two standard deviations, and 99.7% of the data will fall within the range of the mean plus or minus three standard deviations. The larger the integer multiple, the more data there is in this interval. Therefore, the lower limit of the cross-sectional data fluctuation range can be obtained by subtracting the integer multiple of the mean of the standard deviation from the mean of the experience score, and the upper limit of the cross-sectional data fluctuation range can be obtained by adding the integer multiple of the mean of the standard deviation to the mean of the experience score. The embodiment of the present disclosure does not limit the value of the integer multiple, and it can be set according to actual needs. According to the lower limit and the upper limit of the cross-sectional data fluctuation range, the cross-sectional data fluctuation range can be determined.

[0096] According to an embodiment of the present disclosure, the standard deviation distribution of the normal distribution can be used to quickly determine the fluctuation range of the cross-sectional data according to the mean of the standard deviation and the mean of the experience score.

[0097] According to an embodiment of the present disclosure, the authenticity of the t-th cross-sectional data is judged based on the cross-sectional data fluctuation range to obtain a target judgment result, including: comparing the upper limit of the cross-sectional data fluctuation range or the lower limit of the cross-sectional data fluctuation range with the t-th cross-sectional data to obtain a comparison result; when the comparison result indicates that the t-th cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, or the comparison result indicates that the t-th cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, the target judgment result is obtained.

[0098] According to an embodiment of the present disclosure, the upper limit of the cross-sectional data fluctuation range or the lower limit of the cross-sectional data fluctuation range is compared with the experience score corresponding to the t-th cross-sectional data to obtain a comparison result. If the comparison result indicates that the t-th cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, or if the comparison result indicates that the t-th cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, a target determination result can be obtained.

[0099] Figure 4 The flowchart of the target determination result determination method according to the embodiment of the present disclosure is schematically shown.

[0100] like Figure 4As shown, the cross-sectional data fluctuation range of the t-1 period can be determined first (S410), and then it can be determined whether the t-1 period is an initial period (S420). The initial period can mean that product experience statistical data cannot be obtained in the period before this period. If the t-1 period is the initial period, the cross-sectional data fluctuation range of the t-1 period can be used as the identification standard (S430), and it can be determined whether the experience score corresponding to the t-th cross-sectional data is outside the cross-sectional data fluctuation range of the t-1 period (S450). If the t-1 period is not the initial period, the cross-sectional data fluctuation ranges obtained in the t-1, t-2, and t-3 periods can be averaged as the identification standard (S440). For example, the cross-sectional data fluctuation range of the t-1 period is [2, 5], the cross-sectional data fluctuation range of the t-2 period is [4, 7], and the cross-sectional data fluctuation range of the t-3 period is [3, 6]. The cross-sectional data fluctuation ranges obtained in the t-1, t-2, and t-3 periods can be averaged to obtain a cross-sectional data fluctuation range of [3, 6]. If the experience score corresponding to the t-th cross-sectional data is outside the fluctuation range of the cross-sectional data in the t-1 period, the target judgment value S460 can be obtained. For example, if the experience score corresponding to the t-th cross-sectional data is 8 points and the fluctuation range of the cross-sectional data in the t-1 period is [3, 6], it can be seen that 8 is greater than 6, that is, the experience score corresponding to the t-th cross-sectional data is outside the fluctuation range of the cross-sectional data in the t-1 period.

[0101] According to the embodiments of the present disclosure, by comparing the tth target cross-sectional data with the upper and lower limits of the cross-sectional data fluctuation range, abnormal data can be accurately identified, thereby improving the recognition rate of data authenticity, changing the single evaluation standard of "increase means better, decrease means worse". By identifying abnormal data, further processing of abnormal data can be reduced, resources and costs can be saved, and the stability and consistency of user experience can be more accurately evaluated and monitored.

[0102] According to an embodiment of the present disclosure, the target judgment value is determined based on the following operations: when the tth target cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, the target judgment result can be converted into a preset target positive value; when the tth target cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, the target judgment result can be converted into a preset target negative value.

[0103] According to an embodiment of the present disclosure, for example, when the cross-sectional data fluctuation range is [55, 75], and the t-th target cross-sectional data is 85, the t-th target cross-sectional data 85 is greater than the upper limit of the cross-sectional data fluctuation range 75, and the target determination result can be converted to a preset target positive value, such as 1. When the t-th target cross-sectional data is 35, the t-th target cross-sectional data 35 is less than the lower limit of the cross-sectional data fluctuation range 55, and the target determination result can be converted to a preset target negative value, such as -1. When the t-th target cross-sectional data is 65, the t-th target cross-sectional data 65 is within the cross-sectional data fluctuation range, and the target determination result can be converted to 0.

[0104] According to an embodiment of the present disclosure, by converting a target determination result into a preset target positive value or a preset target negative value, the safety and reliability of the system are enhanced.

[0105] According to an embodiment of the present disclosure, the target judgment value is determined by processing the target judgment result based on a preset quantization rule; the target judgment value corresponding to the t-th target section data is processed based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target section data, including: processing the target judgment value corresponding to each of multiple t-th target section data based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target section data.

[0106] According to an embodiment of the present disclosure, the target judgment values ​​corresponding to the plurality of t-th target section data can be processed based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target section data, as shown in formula (1).

[0107] (1)

[0108] Among them, S represents the t-th fluctuation shape value, C1, C2, C3, C4, and C5 are the target judgment values ​​of the t-th time period obtained in chronological order, C1 is the target judgment value corresponding to the t-th target section data farthest from the present, and C5 is the target judgment value corresponding to the t-th target section data closest to the present.

[0109] For example, there are 5 t-th target section data. In chronological order, the target judgment values ​​corresponding to the 5 t-th target section data are 1, 1, -1, 1, and -1 respectively. The weighted fusion algorithm can be used to process the target judgment values ​​corresponding to the multiple t-th target section data, that is, 0.05*1+0.1*1+0.15*-1+0.3*1+0.4*-1, so that the t-th fluctuation shape value of the t-th target section data is -0.1.

[0110] According to another embodiment of the present disclosure, the fluctuation shape values ​​corresponding to each of the T time periods may be processed based on a weighted fusion algorithm to obtain the Tth fluctuation shape value, as shown in formula (2).

[0111] (2)

[0112] in, Indicates the fluctuation shape value of the T period, 、 、 、 、

[0113] is the fluctuation shape value corresponding to different time periods, It is the fluctuation shape value corresponding to the T-5 period, which is the furthest from now. It is the fluctuation pattern value corresponding to the most recent T-1 period.

[0114] According to the embodiments of the present disclosure, by processing the target judgment value through a weighted fusion algorithm, the deviation of a single data source can be reduced, the robustness of the overall data can be improved, and the fluctuation shape value can be made more stable and reliable.

[0115] According to an embodiment of the present disclosure, a product optimization plan for a target product is determined based on the fluctuation shape values ​​of T time periods, including: determining a first target value range corresponding to the Tth fluctuation shape value in a product experience strategy table, wherein the product experience strategy table includes multiple value ranges, and the value ranges are associated with the product optimization strategy; determining the product optimization strategy corresponding to the first target value range as a first recommended strategy; determining the fluctuation standard deviation based on the fluctuation shape values ​​of T time periods; determining a second target value range corresponding to the fluctuation standard deviation in the product experience fluctuation strategy table, wherein the product experience fluctuation strategy table includes multiple value ranges, and each value range corresponds to a product experience fluctuation strategy; determining the product experience fluctuation strategy corresponding to the second target value range as a second recommended strategy; and determining a product optimization plan based on the first recommended strategy and the second recommended strategy.

[0116] According to an embodiment of the present disclosure, the product experience strategy table may include multiple value ranges, each value range is associated with a product optimization strategy, and the product experience fluctuation strategy table may include multiple value ranges, each value range corresponds to a product experience fluctuation strategy.

[0117] According to an embodiment of the present disclosure, for example, the Tth fluctuation form value is 0.8, and the product experience measurement table is shown in Table 1. In the product experience strategy table, it can be determined that the first target value range corresponding to the Tth fluctuation form value of 0.8 is (0.5, 1). The product optimization strategy corresponding to the first target value range (0.5, 1), namely, the current experience is good and it is recommended to actively expand the product market share, can be determined as the first recommended strategy.

[0118] According to embodiments of the present disclosure, a fluctuation standard deviation can be determined based on the fluctuation shape values ​​for T time periods. The present disclosure does not impose any restrictions on the value of T. For example, based on the fluctuation shape values ​​for five time periods, a fluctuation standard deviation of 1.5 can be determined. The product experience fluctuation strategy table is shown in Table 2. The product experience fluctuation strategy table can include multiple value ranges. For example, if the fluctuation standard deviation is greater than 10, the standard deviation for the recent T period is high; if the fluctuation standard deviation is less than 3, the standard deviation for the recent T period is low; and if the fluctuation standard deviation is greater than or equal to 3 and less than or equal to 10, the standard deviation for the recent T period is medium. Therefore, if the fluctuation standard deviation is less than 3, the corresponding product experience fluctuation strategy (small change, attention recommended) is determined as the second recommended strategy. The first recommended strategy, "Current experience is good, actively expand product market share recommended," and the second recommended strategy, "small change, attention recommended," can be entered into a blank document to generate a product optimization plan. Note that if the Tth fluctuation shape value and the corresponding fluctuation standard deviation do not show significant fluctuation, the conclusion and recommendation from the previous significant fluctuation can be retained.

[0119] Table 1 Product experience measurement table

[0120] Serial number S value range Recommended strategies 1 0.5,1 The current experience is good, and it is recommended to actively expand the product market share 2 0.25,0.5 The current experience is acceptable. We recommend that you continue to improve product quality and expand product market share. 3 -0.25,0.25 The current product condition is average, it is recommended to find points to improve product quality 4 -0.25,-0.5 The current product condition is poor. It is recommended to analyze the reasons for product quality and consider product iteration. 5 -1,-0.5 The current product condition is poor. We recommend conducting an in-depth analysis of quality and market performance, or considering exiting the market.

[0121] Table 2 Product experience fluctuation strategy table

[0122] Serial number Standard deviation of S value in the last 5 periods Recommended strategies 1 high The fluctuation is large, so it is recommended to pay close attention 2 middle There is a certain degree of fluctuation, it is recommended to continue to pay attention 3 Low The change is small, please pay attention

[0123] According to the embodiments of the present disclosure, subsequent product development strategies and optimization suggestions can be given based on the measured fluctuation shape values ​​and fluctuation standard deviations of the target product.

[0124] Figure 5 The structural block diagram of the product data processing device according to an embodiment of the present disclosure is schematically shown.

[0125] like Figure 5 As shown, the product data processing device 500 of this embodiment includes a first acquisition module 510 , a conversion module 520 , a second acquisition module 530 , and a determination module 540 .

[0126] An acquisition module 510 is configured to acquire product experience statistics related to a target product, where the product experience statistics include a t-1th cross-sectional data set collected during a t-1th period and a tth cross-sectional data collected during a tth period, where t>1;

[0127] The cross-sectional data fluctuation range obtaining module 520 is configured to process the t-1 cross-sectional data set based on a fluctuation identification algorithm to obtain cross-sectional data fluctuation ranges of a plurality of t-1 cross-sectional data;

[0128] The target determination result obtaining module 530 is used to perform authenticity determination on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result. The t-th target cross-sectional data corresponding to the target determination result is data representing the true fluctuation. The target determination result is associated with a target determination value.

[0129] The t-th fluctuation shape value obtaining module 540 is used to process the target determination value corresponding to the t-th target cross-sectional data based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target cross-sectional data;

[0130] The product optimization solution determination module 550 is used to determine a product optimization solution for a target product based on the fluctuation pattern values ​​in T time periods, where T≥t.

[0131] According to an embodiment of the present disclosure, by processing the t-1th section data set through a fluctuation identification algorithm, the section data fluctuation ranges of multiple t-1th section data can be obtained, and then, the authenticity of the tth section data is judged based on the section data fluctuation range, and the target judgment result can be obtained. The tth target section data corresponding to the target judgment result is the data representing the real fluctuation. The target judgment result is associated with the target judgment value. The target judgment value corresponding to the tth target section data is processed based on the weighted fusion algorithm to obtain the tth fluctuation shape value of the tth target section data. According to the fluctuation shape values ​​of T time periods, the product optimization plan of the target product can be determined, which is suitable for the situation where the current target product experience time series data is insufficient, avoids the waste of resources caused by collecting a large amount of product experience data in continuous time periods, and improves the operation efficiency of the system.

[0132] According to an embodiment of the present disclosure, the cross-sectional data fluctuation range obtaining module 520 includes: a sampling data subset determining unit, an experience score data obtaining unit, and a cross-sectional data fluctuation range determining unit.

[0133] The sampling data subset determination unit is used to determine M sampling data subsets from the t-1th cross-sectional data set, where the sampling data subsets include multiple cross-sectional data, and M>0.

[0134] The experience score data obtaining unit is used to process the cross-sectional data based on the experience level measurement algorithm for each sampled data subset to obtain experience score data.

[0135] The experience score standard deviation obtaining unit is used to calculate the standard deviation of the experience score data corresponding to each of the plurality of cross-sectional data for each sampled data subset, so as to obtain the experience score standard deviation of the sampled data subset.

[0136] The cross-sectional data fluctuation range determining unit is configured to determine the cross-sectional data fluctuation range based on the M experience score data subsets and the M experience score standard deviations.

[0137] According to an embodiment of the present disclosure, the cross-sectional data fluctuation range determination unit includes: an experience score mean determination subunit, a standard deviation mean determination subunit, a cross-sectional data fluctuation range lower limit acquisition subunit, a cross-sectional data fluctuation range upper limit acquisition subunit and a cross-sectional data fluctuation range determination subunit.

[0138] The experience score mean determination subunit is configured to determine the experience score mean based on all the experience scores in the M experience score data subsets.

[0139] The standard deviation mean determination subunit is configured to determine the standard deviation mean based on the M experience score standard deviations.

[0140] The lower limit of the fluctuation range of cross-sectional data is obtained by subtracting an integer multiple of the mean of standard deviation from the mean of experience score to obtain the lower limit of the fluctuation range of cross-sectional data.

[0141] The upper limit of the fluctuation range of the cross-sectional data is obtained by a subunit, which is used to add an integer multiple of the mean of the standard deviation to the mean of the experience score to obtain the upper limit of the fluctuation range of the cross-sectional data.

[0142] The section data fluctuation range determination subunit is used to determine the section data fluctuation range according to the section data fluctuation range lower limit and the section data fluctuation range upper limit.

[0143] According to an embodiment of the present disclosure, the target determination result module 530 includes: a comparison result obtaining unit and a target determination result obtaining unit.

[0144] The comparison result obtaining unit is used to compare the upper limit of the fluctuation range of the cross-sectional data or the lower limit of the fluctuation range of the cross-sectional data with the t-th cross-sectional data to obtain a comparison result.

[0145] The target determination result obtaining unit is used to obtain the target determination result when the comparison result indicates that the t-th section data is greater than the upper limit of the section data fluctuation range, or when the comparison result indicates that the t-th section data is less than the lower limit of the section data fluctuation range.

[0146] According to an embodiment of the present disclosure, the target determination value is determined by processing the target determination result based on a preset quantization rule.

[0147] According to an embodiment of the present disclosure, the t-th fluctuation form value obtaining module 540 includes: a t-th fluctuation form value unit.

[0148] The tth fluctuation shape value unit is used to process the target judgment values ​​corresponding to the plurality of tth target section data based on a weighted fusion algorithm to obtain the tth fluctuation shape value of the tth target section data.

[0149] According to an embodiment of the present disclosure, the t-th fluctuation form value obtaining module 540 includes: a preset target positive value conversion unit and a preset target negative value conversion unit.

[0150] The preset target positive value conversion unit is used to convert the target judgment result into a preset target positive value when the t-th target section data is greater than the upper limit of the section data fluctuation range.

[0151] The preset target negative value conversion unit is used to convert the target judgment result into a preset target negative value when the t-th target section data is less than the lower limit of the section data fluctuation range.

[0152] According to an embodiment of the present disclosure, the product optimization solution determination module 550 includes: a first target value range unit and a first suggestion strategy unit.

[0153] The first target value range unit is used to determine the first target value range corresponding to the Tth fluctuation shape value in the product experience strategy table, wherein the product experience strategy table includes multiple value ranges, and the value ranges are associated with the product optimization strategy.

[0154] The first suggested strategy unit is configured to determine a product optimization strategy corresponding to a first target value range as a first suggested strategy.

[0155] The fluctuation standard deviation determining unit is used to determine the fluctuation standard deviation according to the fluctuation shape values ​​of T time periods.

[0156] The second target value range determination unit is used to determine the second target value range corresponding to the fluctuation standard deviation in the product experience fluctuation strategy table, wherein the product experience fluctuation strategy table includes multiple value ranges, and each value range corresponds to a product experience fluctuation strategy.

[0157] The second recommended strategy determining unit is configured to determine the product experience fluctuation strategy corresponding to the second target value range as the second recommended strategy.

[0158] The product optimization solution determination unit is used to determine the product optimization solution according to the first recommended strategy and the second recommended strategy.

[0159] According to embodiments of the present disclosure, any multiple modules among the first acquisition module 510, conversion module 520, second acquisition module 530, and determination module 540 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the first acquisition module 510, conversion module 520, second acquisition module 530, and determination module 540 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of software, hardware, and firmware, or a suitable combination of any of these. Alternatively, at least one of the first acquisition module 510 , the conversion module 520 , the second acquisition module 530 , and the determination module 540 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0160] Figure 6 A block diagram of an electronic device suitable for implementing a product data processing method according to an embodiment of the present disclosure is schematically shown.

[0161] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0162] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0163] According to an embodiment of the present disclosure, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0164] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0165] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0166] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the product data processing method provided by the embodiments of the present disclosure.

[0167] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 601 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0168] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0169] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0170] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0171] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0173] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A product data processing method, characterized in that: include: Obtain product experience statistics for the target product, where the product experience statistics include a t-1th cross-sectional data set collected during a t-1th period and a tth cross-sectional data collected during a tth period, where t>1; Processing the t-1th cross-sectional data set based on a fluctuation identification algorithm to obtain cross-sectional data fluctuation ranges of a plurality of t-1th cross-sectional data; Performing authenticity determination on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result, wherein the t-th target cross-sectional data corresponding to the target determination result is data representing the true fluctuation, and the target determination result is associated with a target determination value; Processing the target determination value corresponding to the t-th target cross-sectional data based on a weighted fusion algorithm to obtain a t-th fluctuation shape value of the t-th target cross-sectional data; A product optimization solution for the target product is determined based on the fluctuation shape values ​​in T time periods, where T≥t.

2. The method according to claim 1, characterized in that The step of processing the t-1th cross-sectional data set based on the fluctuation identification algorithm to obtain the cross-sectional data fluctuation ranges of multiple t-1th cross-sectional data includes: Determine M sampling data subsets from the t-1th cross-sectional data set, wherein the sampling data subsets include a plurality of cross-sectional data, and M>0; For each of the sampled data subsets, processing the cross-sectional data based on the experience level measurement algorithm to obtain experience score data; For each of the sampled data subsets, calculating the standard deviation of the experience score data corresponding to each of the plurality of cross-sectional data to obtain the standard deviation of the experience score of the sampled data subset; The cross-sectional data fluctuation range is determined based on the M experience score data subsets and the M experience score standard deviations.

3. The method according to claim 2, characterized in that The determining the cross-sectional data fluctuation range based on the M experience score data subsets and the M experience score standard deviations includes: Determine an experience score mean based on all experience scores in the M experience score data subsets; Determining a mean of the standard deviations based on the M standard deviations of the experience scores; Subtract an integer multiple of the mean of the standard deviation from the mean of the experience score to obtain the lower limit of the cross-sectional data fluctuation range; Add the mean of the experience scores to an integer multiple of the mean of the standard deviations to obtain the upper limit of the fluctuation range of the cross-sectional data; The cross-sectional data fluctuation range is determined according to the cross-sectional data fluctuation range lower limit and the cross-sectional data fluctuation range upper limit.

4. The method according to claim 3, characterized in that The performing authenticity determination on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result includes: Comparing the upper limit of the fluctuation range of the cross-sectional data or the lower limit of the fluctuation range of the cross-sectional data with the t-th cross-sectional data to obtain a comparison result; and The target determination result is obtained when the comparison result indicates that the t-th cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, or when the comparison result indicates that the t-th cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range.

5. The method according to claim 4, characterized in that The target determination value is determined by processing the target determination result based on a preset quantization rule; The step of processing the target determination value corresponding to the t-th target cross-sectional data based on a weighted fusion algorithm to obtain the t-th fluctuation shape value of the t-th target cross-sectional data includes: The target judgment values ​​corresponding to each of the plurality of t-th target cross-sectional data are processed based on a weighted fusion algorithm to obtain a t-th fluctuation shape value of the t-th target cross-sectional data.

6. The method according to claim 5, characterized in that The target determination value is determined based on the following operations: In the case where the t-th target cross-sectional data is greater than the upper limit of the cross-sectional data fluctuation range, converting the target determination result into a preset target positive value; In a case where the t-th target cross-sectional data is less than the lower limit of the cross-sectional data fluctuation range, the target determination result is converted into a preset target negative value.

7. The method according to claim 1, characterized in that Determining a product optimization plan for the target product based on the fluctuation pattern values ​​in T time periods includes: Determining a first target value range corresponding to the Tth fluctuation pattern value in a product experience strategy table, wherein the product experience strategy table includes multiple value ranges, and the value ranges are associated with product optimization strategies; Determining the product optimization strategy corresponding to the first target value range as a first recommended strategy; Determine the standard deviation of fluctuations based on the fluctuation shape values ​​of the T time periods; Determining a second target value range corresponding to the fluctuation standard deviation in a product experience fluctuation strategy table, wherein the product experience fluctuation strategy table includes multiple value ranges, each value range corresponding to a product experience fluctuation strategy; Determining the product experience fluctuation strategy corresponding to the second target value range as the second recommended strategy; Determine a product optimization plan based on the first recommended strategy and the second recommended strategy.

8. A product data processing device, characterized in that: include: An acquisition module is used to acquire product experience statistics of a target product, wherein the product experience statistics include a t-1th cross-sectional data set collected during a t-1th period and a tth cross-sectional data collected during a tth period, where t>1; a cross-sectional data fluctuation range obtaining module, configured to process the t-1th cross-sectional data set based on a fluctuation identification algorithm to obtain cross-sectional data fluctuation ranges of a plurality of t-1th cross-sectional data; a target determination result obtaining module, configured to perform authenticity determination on the t-th cross-sectional data based on the cross-sectional data fluctuation range to obtain a target determination result, wherein the t-th target cross-sectional data corresponding to the target determination result is data representing the true fluctuation, and the target determination result is associated with a target determination value; a t-th fluctuation shape value obtaining module, configured to process the target determination value corresponding to the t-th target cross-sectional data based on a weighted fusion algorithm to obtain a t-th fluctuation shape value of the t-th target cross-sectional data; The product optimization solution determination module is used to determine the product optimization solution of the target product according to the fluctuation shape values ​​of T time periods, where T≥t.

9. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

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

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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