Data processing method and device, equipment, storage medium and program product

Abstract

The invention provides a data processing method and apparatus, a device, a storage medium and a program product. The data processing method comprises the steps of obtaining a newly added value of a target data item uploaded by a data acquisition end; obtaining the number of existing numerical values in the target interval; the value range of the target data item is divided into a plurality of intervals in advance, and the target interval is the interval in which the newly added value is located in the plurality of intervals; based on the attribute of the target data item in the target interval and the number of existing numerical values in the target interval, determining a contribution value of the newly added numerical value; and based on the contribution value of the newly added value, generating feedback information and sending the feedback information to the data acquisition end. By means of the characteristics of the data and the existing quantity of the numerical values in the interval, instant evaluation of the newly-added numerical values is achieved, the evaluation efficiency is high, the accuracy is high, and a reliable data basis is provided for follow-up applications such as data collection and purchase.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method, device, equipment, storage medium and program product. Background Art

[0002] With the continuous development of big data analysis and AI (Artificial Intelligence) technology, data value assessment has received more and more attention.

[0003] Data value assessment aims to determine the importance of data in the collected data set. In related technologies, one type of assessment method is to assess the value of data through the cost of data collection, the economic benefits it brings, etc. This type of indicator is difficult to obtain and usually relies on complex models for evaluation, with a large amount of calculation and high hardware requirements. Another type of assessment method relies on feedback from the application side, which is a delayed assessment method. For example, after removing a part of the data in the data set, the value of the data is determined based on the impact on the application stage before and after the removal. This method cannot be applied to scenarios of immediate evaluation, and the amount of calculation required is large.

[0004] Therefore, there is an urgent need to provide a low-cost and efficient real-time data evaluation solution. Summary of the invention

[0005] The present application provides a data processing method, apparatus, device, storage medium and program product, which realizes the determination of the contribution value of a numerical value by utilizing the distribution characteristics of data and existing numerical quantities in an interval, requires a small amount of calculation, can realize instant evaluation, and has high evaluation accuracy.

[0006] In a first aspect, the present application provides a data processing method, including: obtaining a newly added value of a target data item uploaded by a data acquisition terminal; obtaining the number of existing values ​​in a target interval; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval in the multiple intervals where the newly added value is located; based on the attributes of the target data item in the target interval and the number of existing values ​​in the target interval, determining the contribution value of the newly added value; based on the contribution value of the newly added value, generating feedback information and sending it to the data acquisition terminal.

[0007] Optionally, determining the contribution value of the newly added numerical value based on the attribute of the target data item in the target interval and the number of existing numerical values ​​in the target interval includes: obtaining a probability density distribution function of the target data item; determining the contribution value of the newly added numerical value based on an integral of the probability density distribution function in the target interval and the number of existing numerical values ​​in the target interval.

[0008] Optionally, determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval and the number of existing values ​​in the target interval includes: obtaining a target number of the target interval; the target number is the number of values ​​that need to be collected for the target data item in the target interval; determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number.

[0009] Optionally, the contribution value of the newly added value is determined based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number, including: substituting the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number into a preset relationship to obtain the contribution value of the newly added value.

[0010] Optionally, determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number includes: determining a preset value based on the number of existing values ​​in the target interval and the target number; wherein the preset value is negatively correlated with the number of existing values ​​in the target interval, and positively correlated with the target number; determining the contribution value of the newly added value as the product of the integral of the probability density distribution function in the target interval and the preset value.

[0011] Optionally, obtaining the target number in the target interval includes: obtaining a preset constant; and determining the target number as the product of a distribution probability of the target data item in the target interval and the preset constant.

[0012] Optionally, determining the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval includes: if the distribution of the target data item conforms to a uniform distribution, determining the contribution value of the newly added value based on the number of existing values ​​in the target interval and the target number of the target interval.

[0013] Optionally, the method also includes: if the distribution of the target data item does not conform to a uniform distribution, determining the target number of the target interval based on the distribution function of the target data item; determining the contribution value of the newly added value based on the integral of the probability density distribution function of the target data item in the target interval, the number of existing values ​​in the target interval, and the target number of the target interval.

[0014] Optionally, the number of existing values ​​in the target interval is the number of values ​​in the value set of the target data item that fall into the target interval, and the feedback information is generated based on the contribution value of the newly added value and sent to the data acquisition end, including: if the contribution value of the newly added value is greater than or equal to the preset contribution value, the newly added value is stored in the value set of the target data item, the number of existing values ​​in the target interval is updated, and data adoption information is generated; and the data adoption information is sent to the data acquisition end.

[0015] Optionally, obtaining the newly added values ​​of the target data items uploaded by the data acquisition end includes: obtaining user data uploaded by the user terminal; extracting the newly added values ​​of multiple target data items from the user data; generating feedback information based on the contribution value of the newly added values ​​and sending it to the data acquisition end includes: generating feedback information based on the contribution value of the newly added values ​​of each target data item and sending it to the user terminal.

[0016] Optionally, the contribution value of the newly added numerical value of each target data item is used to generate feedback information and send it to the user terminal, including: obtaining a weight coefficient of each target data item among the multiple target data items; based on the weight coefficient of each target data, calculating the weighted average of the contribution values ​​of each newly added numerical value extracted from the user data to obtain the contribution value of the user data; based on the contribution value of the user data, generating feedback information and sending it to the user terminal.

[0017] Optionally, there are multiple target data items; and the feedback information is generated based on the contribution value of the newly added numerical value and sent to the data collection end, including: for target data items whose contribution value of the newly added numerical value is higher than the first contribution value, generating a supplementary collection suggestion for the target data item and sending it to the data collection end; for target data items whose contribution value of the newly added numerical value is lower than the second contribution value, generating a stop collection suggestion for the target data item and sending it to the data collection end; wherein the second contribution value is lower than the first contribution value.

[0018] In a second aspect, the present application provides a data processing device, including: a newly added value acquisition module, used to obtain a newly added value of a target data item uploaded by a data acquisition terminal; an interval existing quantity acquisition module, used to obtain the number of existing values ​​in a target interval; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval in the multiple intervals where the newly added value is located; a contribution value calculation module, used to determine the contribution value of the newly added value based on the attributes of the target data item in the target interval and the number of existing values ​​in the target interval; a feedback module, used to generate feedback information based on the contribution value of the newly added value and send it to the data acquisition terminal.

[0019] In a third aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the first aspect of the application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method provided in the first aspect of the present application is implemented.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the method provided in the first aspect of the present application when executed by a processor.

[0022] The data processing method, apparatus, device, storage medium and program product provided by the present application, after obtaining the newly added value of a certain required data item, i.e., the target data item, uploaded by the data acquisition terminal, utilizes the attributes of the target data item in the target interval, such as distribution function, probability density distribution function, usage frequency, data acquisition difficulty, etc., as well as the number of existing values ​​in the interval where the newly added value is located, to realize instant evaluation of the newly added value, determine the contribution value of the newly added value, such as a score, and generate a feedback message based on the evaluated contribution value and send it to the data acquisition terminal to indicate the subsequent collection behavior of the data acquisition terminal, or simply inform the data acquisition terminal of the contribution value of the value provided; the number of existing values ​​in the interval can characterize the rarity of the newly added value, and the attribute is data describing the characteristics of the target data item itself, and the newly added value is evaluated based on these two aspects of information, with high accuracy and small amount of calculation required, thereby improving the efficiency of data evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application;

[0025] Figure 2 A flowchart of a data processing method provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of a target data item partition provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of feedback information provided in an embodiment of the present application;

[0028] Figure 5A flowchart of another data processing method provided in an embodiment of the present application;

[0029] Figure 6 A schematic diagram of a user data interaction process provided in an embodiment of the present application;

[0030] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0031] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0033] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0034] With the continuous development of social networks, more and more individuals share their data through various platforms, such as basic user data, behavioral data, etc. For data collectors, when collecting the required data, they need to evaluate the data to determine whether to collect the corresponding data.

[0035] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application, such as Figure 1 As shown, a large number of data collection terminals, such as user terminals, collection terminals set up by the application system, etc., upload the collected data to the data evaluation device of the application system. The data evaluation device evaluates the received data to determine whether the data is required by the application system to complete the corresponding application task; at the same time, the evaluation results, such as evaluation passed, evaluation failed, etc., are fed back to the corresponding data collection terminal.

[0036] On the premise that the evaluation is passed and the data collection end authorizes the use of the data, the data that has passed the evaluation will be saved in the application system, such as the application device. After collecting enough data, the collected data will be processed and analyzed through the algorithm corresponding to the application task.

[0037] Figure 1 Take the data evaluation device and the application device as two separate devices as an example, that is, the data evaluation device is a separate device as an example. In other embodiments, the data evaluation device can be integrated in the application device, and is a chip or module in the application device.

[0038] Exemplarily, the application system can be a system for training a machine learning model, and the data collected by the data collection end is the source of training samples for the machine learning model. In order to obtain sufficient and comprehensive training samples, it is necessary to evaluate the value of the collected data and use high-value data as training samples.

[0039] Exemplarily, the application system may also be a statistical analysis system, for example, performing descriptive statistics, regression analysis, cluster analysis, correlation analysis, etc. on the collected data, thereby improving the accuracy of statistical analysis by collecting comprehensive and large amounts of data.

[0040] In related technologies, when evaluating data, data sets are often used as units, and there is a lack of single-data evaluation methods. For example, the value of the target data set is determined by removing the target data set from the overall data and observing the impact on the application side, such as the deviation of the training results. The evaluation results are delayed and can only be performed after all data has been collected, which cannot be applied to scenarios where real-time evaluation is required. At the same time, determining the impact on the application side requires a large amount of computing and is costly.

[0041] In order to realize instant evaluation of data and reduce evaluation costs, the present application provides a data processing method, which realizes instant evaluation of values ​​by utilizing the distribution characteristics of data items and the existing numerical quantities in the interval where the newly added values ​​are located. The distribution characteristics are known quantities, and the statistical method of the existing numerical quantities in the interval is simple. Therefore, the amount of calculation required to evaluate the values ​​is small, which reduces the evaluation cost and improves the evaluation efficiency. At the same time, the distribution attributes can characterize the density of data in different intervals, and the existing numerical quantities in the interval can characterize the rarity of the newly added values. The newly added values ​​are evaluated by using these two aspects of information, and the evaluation accuracy is high.

[0042] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0043] Figure 2 The following is a flow chart of a data processing method provided in an embodiment of the present application. The data processing method can be executed by a device with corresponding data processing capabilities, such as the aforementioned data evaluation device. Figure 2 As shown, the data processing method provided in this embodiment includes the following steps:

[0044] Step S201, obtaining a newly added value of a target data item uploaded by a data collection terminal.

[0045] The newly added value is the value of the target data item newly uploaded by the data collection terminal. The data collection terminal is any terminal that provides the newly added value, which can be a user terminal or a terminal for collection equipment deployed in the application system. The target data item can be data of any dimension, such as age, weight, occupation, user behavior data, etc.

[0046] The data acquisition end can upload the newly added values ​​of one or more target data items to the application system, such as uploading to the data evaluation device, through an interface, a network or other means.

[0047] Step S202, obtaining the number of existing values ​​in the target interval; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval in the multiple intervals where the newly added value is located.

[0048] The number of existing values ​​in the target interval is the number of values ​​in the target interval among the values ​​of the target data items that have been collected or stored in the application system.

[0049] For each newly added value of the target data item, obtain the number of existing values ​​of the target data item in the interval where the newly added value is located.

[0050] For each target data item, the value range of the target data item can be divided into multiple intervals in advance, such as being evenly divided into multiple intervals, or can be divided based on experience. After obtaining the newly added value of the target data item, the interval where the newly added value is located is determined, and the number of existing values ​​in the interval where the newly added value is located is read, or the number of existing values ​​in the interval where the newly added value is located is counted.

[0051] In some embodiments, for target data items with continuous values, such as age and weight, their value range can be divided into multiple intervals by even division; for target data items with discrete values, such as occupation, each value of the target data item can be regarded as an interval, or multiple similar values ​​can be divided into one interval.

[0052] For example, if the target data item is age, and its value range is [18, 36], then the value range can be pre-divided into three intervals, namely [18, 24), [24, 30) and [30, 36]. If the newly added value of the uploaded age is 26, then its interval, i.e. the target interval, is [24, 30).

[0053] For example, Figure 3 A schematic diagram of a target data item partition provided in an embodiment of the present application, such as Figure 3 As shown in the figure, the distribution of the target data item conforms to the Gaussian distribution, and its value range is -4~4. When partitioning, its value range can be divided into 6 partitions, namely [-4, -2), [-2, -1), [-1, 0), [0, 1), [1, 2) and [2, 4]. Figure 3 Each dot in the figure represents the collected value of the target data item.

[0054] Step S203: determining the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval.

[0055] Among them, the properties of the target data item in the target interval may include distribution properties, usage preferences, usage frequency, data acquisition difficulty, etc. Distribution properties are used to describe the distribution probability or distribution density of the target data item in each interval, which can be obtained through the Cumulative Distribution Function (CDF) or the Probability Density Function (PDF). Usage preferences are used to describe the preferences of data users for the values ​​of the target data item in each interval.

[0056] The higher the frequency of use of the target interval, the more difficult it is to obtain data, and the higher the usage preference, the higher the contribution value of the new value in the target interval.

[0057] One or more attributes of each target data item in each interval may be pre-stored. When a new value of one of the target data items is received, the attribute of the target data item in the interval where the new value is located is read.

[0058] A calculation formula for the contribution value may be established in advance, such as F(k,f), where k represents the number of existing values ​​in the target interval, and f represents the attribute of the target data item in the target interval, such as the distribution attribute. For the newly added value of the target data item, the attribute of the target data item in the target interval and the number of existing values ​​in the target interval are substituted into the calculation formula to obtain the contribution value of the newly added value.

[0059] Taking the distribution attribute as an example, under the premise that the distribution attribute of the target interval is constant, the more existing values ​​in the target interval, the lower the rarity of the newly added values, and thus the lower the contribution value of the newly added values, that is, the contribution value is negatively correlated with the number of existing values ​​in the target interval; under the premise that the number of existing values ​​in the target interval is constant, the greater the distribution attribute of the target interval, the denser the data distribution in the target interval, and the more data that needs to be collected in the target interval, the greater the contribution value of the newly added values, that is, the contribution value is positively correlated with the distribution attribute of the target interval.

[0060] Step S204: Generate feedback information based on the contribution value of the newly added value and send it to the data acquisition terminal.

[0061] After obtaining the contribution value of the newly added value uploaded by the data collection end, a feedback message may be generated based on the contribution value, and the feedback message may be returned to the data collection end so that the data collection end can intuitively understand the value of the uploaded data.

[0062] In some embodiments, the feedback information may include the contribution value and collection prompt information, and the collection prompt information may be used to prompt the data collection end whether to continue to collect values ​​in the interval where the newly added value is located.

[0063] In other embodiments, the data demand level of the interval where the new value is located can be determined based on the contribution value of the new value, and feedback information can be generated based on the data demand level so that the data collection end can formulate a data collection plan based on the data demand level.

[0064] In the case where a data collection terminal uploads multiple newly added values, the multiple newly added values ​​may correspond to one or more target data items. After obtaining the contribution value of each newly added value, the contribution value of each newly added value may be displayed in a list in the feedback information, so that the data collection terminal can intuitively understand the importance of each type of value.

[0065] Exemplarily, the contribution value may range from 0 to 100. Figure 4 A schematic diagram of feedback information provided in an embodiment of the present application, such as Figure 4 As shown, the user terminal uploads the two data items of age (43) and occupation (doctor) to the application system through the web page interface (such as clicking the "Submit" button), and the application system transmits the two data items to the data evaluation device, which calculates the contribution values ​​of the two data items, namely "43" and "doctor", as 36 and 88 respectively. In the feedback information, in addition to displaying the contribution values ​​of the two data items, some additional information can also be added to describe the importance of the data uploaded by the user, such as adding a "super high" label at the contribution value of 88, and adding a "normal" label at the contribution value of 88, so that the user can understand the level of the data uploaded by the user in the overall level.

[0066] Taking the analysis of user behavior within an application as an example, when a user authorizes the application server to access his or her behavior data during the use of the application, he or she can also provide some attribute information of the user. This attribute information belongs to the aforementioned target data item, and it can be determined whether to collect the user's behavior data based on the contribution value of the target data item. For example, when the contribution value is higher than a certain value, feedback information can be generated to inform the user that his or her complete behavior data will be obtained. If the user agrees, the server will read his or her complete behavior data for a period of time from the user terminal.

[0067] The data processing method provided in this embodiment, after obtaining the newly added value of a certain required data item, i.e., the target data item, uploaded by the data acquisition terminal, uses the attributes of the target data item in the target interval, such as distribution function, probability density distribution function, usage frequency, data acquisition difficulty, etc., and the number of existing values ​​in the interval where the newly added value is located, to achieve instant evaluation of the newly added value, determine the contribution value of the newly added value, such as a score, and generate a feedback message based on the evaluated contribution value and send it to the data acquisition terminal to indicate the subsequent collection behavior of the data acquisition terminal, or simply inform the data acquisition terminal of the contribution value of the value provided; the number of existing values ​​in the interval can characterize the rarity of the newly added value, and the attribute is data describing the characteristics of the target data item itself. The newly added value is evaluated based on these two aspects of information, with high accuracy and small amount of calculation required, thereby improving the efficiency of data evaluation.

[0068] Optionally, determining the contribution value of the newly added numerical value based on the attribute of the target data item in the target interval and the number of existing numerical values ​​in the target interval includes: obtaining a probability density distribution function of the target data item; determining the contribution value of the newly added numerical value based on an integral of the probability density distribution function in the target interval and the number of existing numerical values ​​in the target interval.

[0069] Among them, the integral of the probability density distribution function in the target interval is used to describe the probability that the value of the target data item falls within the target interval.

[0070] The probability density distribution function of each target data item can be pre-stored locally. When evaluating the newly added value of the target data item, the probability density distribution function of the target data item can be read locally, and the integral of the probability density distribution function in the interval where the newly added value is located is calculated, that is, the integral in the target interval; based on the integral and the number of existing values ​​in the target interval, the contribution value of the newly added value is calculated.

[0071] Exemplarily, a score, such as a first score, can be calculated based on the integral of the probability density distribution function in the target interval, and another score, such as a second score, can be calculated based on the number of existing values ​​in the target interval, and the contribution value of the newly added value can be determined by taking the weighted average of the first score and the second score.

[0072] The calculation formula for the contribution value can also be other functions about the integral of the probability density distribution function in the target interval and the number of existing values ​​in the target interval. It is only necessary to ensure that the contribution value is negatively correlated with the number of existing values ​​in the target interval and positively correlated with the integral of the probability density distribution function in the target interval.

[0073] The probability density distribution function can accurately describe the probability of data falling into different intervals. The probability density distribution function is used to calculate the probability value of new values ​​with high accuracy.

[0074] In addition to using probability density distribution functions, distribution attributes can also be used to describe the distribution law of target data items, such as uniform distribution, Gaussian distribution, Poisson distribution, etc.

[0075] For uniformly distributed target data items, since the distribution probability is the same everywhere, when the lengths of the divided intervals are the same, when calculating the contribution value of the newly added value, only the number of existing values ​​in the interval where the newly added value is located can be considered.

[0076] For target data items with non-uniform distribution, such as Gaussian distribution, when calculating the contribution value of the newly added value, it is necessary to consider the number of existing values ​​in the interval where the newly added value is located and the distribution properties of the target data item, specifically the distribution properties of the target data item in the target interval.

[0077] In some embodiments, when determining the contribution value of the newly added value, it is also necessary to consider the total amount of values ​​required in the interval where the newly added value is located, that is, the target number. That is, based on the distribution properties of the target data item in the target interval, as well as the number of existing values ​​in the target interval and the target number, the contribution value of the newly added value is determined. Among them, under the premise that other parameters remain unchanged, the larger the target number of the interval where the newly added value is located, the greater the contribution value of the newly added value, that is, the contribution value is positively correlated with the target attribute.

[0078] Optionally, determining the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval includes: if the distribution of the target data item conforms to a uniform distribution, determining the contribution value of the newly added value based on the number of existing values ​​in the target interval and the target number of the target interval.

[0079] Optionally, the method also includes: if the distribution of the target data item does not conform to a uniform distribution, determining the target number of the target interval based on the distribution function of the target data item; determining the contribution value of the newly added value based on the integral of the probability density distribution function of the target data item in the target interval, the number of existing values ​​in the target interval, and the target number of the target interval.

[0080] Whether the distribution of the target data item conforms to the uniform distribution can be determined based on the distribution law attribute of the target data item in the target interval. If the distribution law attribute is a value corresponding to the uniform distribution, the distribution of the target data item conforms to the uniform distribution, otherwise it does not conform.

[0081] For uniformly distributed target data items, when determining the contribution value of their newly added values, we can only consider the target number of the interval where the newly added values ​​are located, i.e., the target interval, and the number of existing values. For example, the contribution value of the newly added values ​​can be determined based on the difference or ratio between the two.

[0082] Exemplarily, for uniformly distributed target data items, the contribution value of the newly added data can be determined as: K*[N-(k+1)], where N is the target number of the target interval, k is the number of existing values ​​in the target interval, and K can be a constant or a variable related to N, such as the ratio of a constant to N.

[0083] For target data items with non-uniform distribution, the attributes of the target data items in the target interval also include the integral of the probability density distribution function of the target data item in the target interval and the target number in the target interval. When determining the contribution value of the newly added value, it is necessary to determine the contribution value of the newly added value based on the target number of the target interval and the number of existing values, as well as the integral of the probability density distribution function of the target data item in the target interval.

[0084] For a target data item with non-uniform distribution, the contribution value v of the newly added data can be expressed as: v=F(N,k,f(x)), where F() is a function and f(x) is the probability density function of the target data item.

[0085] Different contribution value calculation methods are provided for data with different distributions, which improves the adaptability and flexibility of contribution value calculation, simplifies the contribution value calculation of uniformly distributed data, further improves the efficiency of data evaluation, and reduces evaluation costs.

[0086] In some embodiments, the number of existing values ​​in each interval of the target data item can be updated as the value in the value set of the target data item changes, thereby avoiding repeated counting of the number of existing values ​​in the interval each time the contribution value of the newly added value is evaluated.

[0087] Optionally, the number of existing values ​​in the target interval is the number of values ​​in the value set of the target data item that fall into the target interval, and the feedback information is generated based on the contribution value of the newly added value and sent to the data acquisition end, including: if the contribution value of the newly added value is greater than or equal to the preset contribution value, the newly added value is stored in the value set of the target data item, the number of existing values ​​in the target interval is updated, and data adoption information is generated; and the data adoption information is sent to the data acquisition end.

[0088] The preset contribution value may be a default value or a configurable parameter, and may be configured based on the requirements of the application task for the numerical value of each interval.

[0089] Taking the contribution value range of 0-100 as an example, illustratively, the preset contribution value can be 60, 50, 35, 10 or other values.

[0090] For each newly added value of each target data item, after determining the contribution value of the newly added value, it is also necessary to determine whether to adopt the newly added value, or the newly added value and other data associated with it, such as user behavior data, based on its contribution value. Specifically, when the contribution value of the newly added value is greater than or equal to the preset contribution value, the newly added value is added to the data set of the target data item, so that a value is added to the interval where the newly added value is located, that is, the target interval, and the number of existing values ​​in the target interval is increased by 1.

[0091] The data adoption information is used to inform the data collection end that the uploaded new value, which may also include other data associated with the new value, has been adopted by the application system.

[0092] If the contribution value of the newly added value is lower than the preset contribution value, there is no need to add the newly added data to the value set of the target data item.

[0093] By adding new data with higher contribution values ​​to the value set, the value set can store high-quality values. At the same time, it avoids storing too many values ​​in the data set, which would cause excessive memory usage. This achieves efficient data collection and provides a high-quality data foundation for subsequent applications.

[0094] Optionally, there are multiple target data items; and the feedback information is generated based on the contribution value of the newly added numerical value and sent to the data collection end, including: for target data items whose contribution value of the newly added numerical value is higher than the first contribution value, generating a supplementary collection suggestion for the target data item and sending it to the data collection end; for target data items whose contribution value of the newly added numerical value is lower than the second contribution value, generating a stop collection suggestion for the target data item and sending it to the data collection end; wherein the second contribution value is lower than the first contribution value.

[0095] The first contribution value and the second contribution value may be default values ​​or configurable parameters.

[0096] Taking the contribution value range of 0-100 as an example, illustratively, the first contribution value may be 50 and the second contribution value may be 10.

[0097] In the data collection stage, in order to carry out subsequent statistical analysis, model training and other application tasks, it is often necessary to collect data in multiple dimensions, and each dimension of data is a target data item. The data collection end can upload multiple new values ​​of multiple target data items to the data evaluation device at one time, and the data evaluation device will evaluate the contribution value of each new value.

[0098] After obtaining the contribution value of each newly uploaded value, if the contribution value of the newly added value is high, that is, higher than the first contribution value, it indicates that the target data item corresponding to the newly added value has insufficient value collection in the interval where the newly added value is located, that is, the target interval. Feedback of supplementary collection suggestions can be used to prompt the data collection end to increase the collection of values ​​of the target data item in the interval where the newly added value is located, so that sufficient values ​​are collected in the target interval for subsequent application tasks, ensuring the smooth progress of the application tasks. Conversely, if the contribution value of the newly added value is too low, that is, lower than the smaller second contribution value, it indicates that the target data item corresponding to the newly added value has sufficient value collection in the interval where the newly added value is located. Feedback of stop collection suggestions can be used to prompt the data collection end to stop collecting values ​​of the target data item in the interval where the newly added value is located to avoid wasting resources.

[0099] Figure 5 A flow chart of another data processing method provided in an embodiment of the present application. Figure 2 Based on the embodiment shown, step S201 and step S203 are further refined. This embodiment is described by taking multiple target data items as an example. Figure 5 As shown, the data processing method provided in this embodiment may specifically include the following steps:

[0100] Step S501: acquiring user data uploaded by a user terminal.

[0101] The user terminal is a terminal held by a user, and an application provided by the application system may be installed in the user terminal.

[0102] Exemplarily, the user terminal may be a mobile phone, a laptop computer, a tablet computer, a wearable device, etc.

[0103] The user can upload the user data by opening the relevant webpage through the user terminal, and can also upload the user data through the relevant interface of the application program installed in the user terminal.

[0104] User data may include multiple attributes of the user (or basic data), such as gender, age, occupation, income, etc. It may also include user behavior data, such as data generated when the user uses an application.

[0105] Exemplarily, the user's text data may include page dwell time, purchase records, search records, etc.

[0106] Step S502: extracting newly added values ​​of multiple target data items from the user data.

[0107] Different application tasks in the application system need to collect different sets of target data items. For the current application task, the newly added values ​​of each target data item that needs to be collected by the application task can be extracted from the uploaded user data.

[0108] For example, when an enterprise needs to understand the basic characteristics of a user group, it mainly focuses on age and income. The values ​​of the two target data items, age and income, can be extracted from the user data as new values.

[0109] For example, when a hospital is counting the characteristics of a patient group for a certain disease, it mainly focuses on four data items: age, weight, gender, and sleep time. The values ​​of these four target data items, age, weight, gender, and sleep time, can be extracted from the user data as new values.

[0110] Step S503 : for each target data item in the plurality of target data items, obtain a probability density distribution function of the target data item, and obtain a target quantity in a target interval.

[0111] The target quantity is the number of values ​​that need to be collected for the target data item in the target interval.

[0112] Optionally, obtaining a target number in the target interval includes: obtaining a preset constant; and determining the target number as the product of a distribution probability of the target data item in the target interval and the preset constant.

[0113] The preset constant is a preset constant, which may be a fixed value, such as 1000, or a configurable parameter, for example, which may be determined based on the total amount of required values ​​of the target data item.

[0114] By utilizing the distribution probability of the target data item in the interval, the target quantity in the interval is determined, thereby improving the accuracy of determining the target quantity. Through the accurate target data volume, the accuracy of determining the contribution value of the new numerical value is improved, thereby providing a reliable reference basis for data collection and avoiding waste of resources.

[0115] Step S504: Determine the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number.

[0116] The contribution of the newly added value is positively correlated with the target number and the integral of the probability density distribution function in the target interval, and negatively correlated with the number of existing values ​​in the target interval. The contribution of the newly added value can be calculated using any function that meets the above premise.

[0117] Optionally, the contribution value of the newly added value is determined based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number, including: substituting the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number into a preset relationship to obtain the contribution value of the newly added value.

[0118] The preset relationship can be stored locally in advance. After obtaining the required parameters, including the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number in the target interval, the various parameters are substituted into the preset relationship to obtain the contribution value of the newly added value.

[0119] For example, the preset relationship may be v = V × e -(k+1) / N , or v = a× V + b×(Nk-1). Among them, v is the contribution value of the newly added value, e is a natural constant, V is the product of the integral of the probability density distribution function in the target interval and a constant, and a and b are both known coefficients.

[0120] The contribution value is calculated using a preset relationship, which has extremely low logical complexity and small amount of calculation, thus reducing the cost of contribution value calculation and improving calculation efficiency.

[0121] Optionally, determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval, and the target number includes:

[0122] Determine a preset value based on the number of existing values ​​in the target interval and the target number; wherein the preset value is negatively correlated with the number of existing values ​​in the target interval and positively correlated with the target number; determine the contribution value of the newly added value as the product of the integral of the probability density distribution function in the target interval and the preset value.

[0123] Exemplarily, the preset value may be a function of the difference between the target number and the number of existing values ​​in the target interval.

[0124] Exemplarily, the preset value may be: c×e -(k+1) / N , where c is a known coefficient.

[0125] The contribution value of the newly added value is obtained by calculating the preset value and the product of the preset value and the integral of the probability density distribution function in the target interval. The logic complexity is low and the calculation amount is small, which reduces the cost of contribution value calculation and improves calculation efficiency.

[0126] Step S505: Generate feedback information based on the contribution value of the newly added value of each target data item and send it to the user terminal.

[0127] After obtaining the contribution value of each newly added value extracted from the user data uploaded by the user, the purchase price of the user data can be determined based on the contribution value of each newly added value, and then based on the purchase price of the user data, transaction information of the user data can be generated, and the transaction information can be sent to the user terminal. When the user agrees to the transaction, the user data is purchased at the purchase price to complete the transaction of the user data, and the newly added values ​​extracted from the purchased user data are saved in the value set of the corresponding target data item, and the number of existing values ​​in the corresponding target interval is updated.

[0128] When the contribution value of user data is low, such as lower than or equal to a lower threshold, data non-adoption information can be generated to inform the user that the user data provided by the user is not adopted. When the contribution value of user data is high, such as higher than the lower threshold, data adoption information can be generated to inform the user that the user data provided by the user is adopted.

[0129] Optionally, based on the contribution value of the newly added numerical value of each of the target data items, feedback information is generated and sent to the user terminal, including: obtaining a weight coefficient of each of the multiple target data items; based on the weight coefficient of each of the target data, calculating the weighted average of the contribution values ​​of each of the newly added numerical values ​​extracted from the user data to obtain the contribution value of the user data; based on the contribution value of the user data, feedback information is generated and sent to the user terminal.

[0130] A corresponding weight coefficient may be assigned to each target data item in advance, and then the weight coefficient may be used to weight the newly added values ​​of multiple target data items extracted from the user data provided by the same user terminal to obtain the contribution value of the user data. Furthermore, feedback information may be generated based on the contribution value of the user data and the contribution value of each newly added data in the user data, so that the user can understand the overall value of the provided data and the value of a single data.

[0131] Specifically, the purchase price of the user data can be determined based on the contribution value of the user data, and then based on the purchase price of the user data, transaction information of the user data can be generated, and the transaction information can be sent to the user terminal. When the user agrees to the transaction, the user data is purchased at the purchase price to complete the transaction of the user data, and the newly added values ​​extracted from the purchased user data are saved in the value set of the corresponding target data item, and the number of existing values ​​in the corresponding target interval is updated.

[0132] Taking the model training scenario as an example, a large number of user terminals upload their own user data to the collection terminal of the model training sample. The collection terminal needs to filter the required data and process it into training samples. Assuming that the training samples of the model training involve three target data items: age, weight and income, the values ​​of the three target data items can be extracted from the user data respectively, and their contribution values ​​can be determined.

[0133] For example, Figure 6 A schematic diagram of a user data interaction process provided in an embodiment of the present application, such as Figure 6 As shown, the model training system includes a training sample collection end and a model training end. A large number of user terminals package their own user data and upload them to the training sample collection end. Figure 6 Take three user terminals as an example, namely user terminal 61 to user terminal 63. The specific content of the user data uploaded by each user terminal is as follows: Figure 6 Model training requires data from two dimensions: age and income. That is, age and income are used as target data items. There are three age partitions, namely 18-25, 25-35, and above 35. There are five income partitions, namely below 5000, 5000-8000, 8000-10000, 10000-20000, and above 20000. The target number of each partition is ( Figure 6 The column corresponding to "Target" in the table represents the number of targets) and the number of existing values ​​( Figure 6 The number of columns of data corresponding to "already" in the table indicates the number of existing values. Figure 6As shown. By calculating the contribution value, it is determined that the contribution values ​​of age and income in user terminal 61 are 80 and 20 respectively, the contribution values ​​of age and income in user terminal 62 are 50 and 60 respectively, and the contribution values ​​of age and income in user terminal 63 are 70 and 60 respectively. Assuming that the weight of age is 0.3 and the weight of income is 0.7, the contribution values ​​of user data provided by user terminal 61 to user terminal 63 are 38, 57 and 63 respectively. Among them, the contribution value of user data provided by user terminal 61 is too low and is not adopted, then the user terminal 61 can be replied "Sorry, the data you provided is not adopted"; the contribution value of user data provided by user terminal 62 and user terminal 63 is high and is adopted, then a purchase price can be generated based on the contribution value of the user data, and a transaction request can be returned, such as transaction request 1 and transaction request 2. When the user agrees to the transaction, the user data is purchased at the determined purchase price, and a new training sample is added based on the purchased user data. After the training sample collection is completed, the model training end performs model training based on the collected training samples.

[0134] In this embodiment, the newly added values ​​of multiple target data items are extracted at one time through the user data uploaded by the user, and the contribution values ​​of the newly added values ​​are calculated in batches, thereby improving the parallelism of the contribution value calculation; at the same time, when calculating the newly added values, the target number of the target interval, that is, the total demand number, and the existing number, that is, the number of existing values, and the probability that the value falls within the interval are introduced to comprehensively evaluate the contribution value of the value, thereby further improving the accuracy of the value evaluation and limiting the total amount of value collection, thereby avoiding the waste of resources caused by collecting too many values.

[0135] Corresponding to the data processing method provided in the aforementioned embodiment, the embodiment of the present application also provides a data processing device, including: a newly added value acquisition module, used to obtain the newly added value of the target data item uploaded by the data acquisition end; an interval existing quantity acquisition module, used to obtain the number of existing values ​​in the target interval; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval in the multiple intervals where the newly added value is located; a contribution value calculation module, used to determine the contribution value of the newly added value based on the attributes of the target data item in the target interval and the number of existing values ​​in the target interval; a feedback module, used to generate feedback information based on the contribution value of the newly added value and send it to the data acquisition end.

[0136] Optionally, the contribution value calculation module includes: a function acquisition unit, used to obtain the probability density distribution function of the target data item; a contribution value calculation unit, used to determine the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval and the number of existing values ​​in the target interval.

[0137] Optionally, the contribution value calculation unit includes: a target quantity acquisition subunit, used to obtain the target quantity of the target interval; the target quantity is the number of values ​​that need to be collected for the target data item in the target interval; a contribution value calculation subunit, used to determine the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target quantity.

[0138] Optionally, the contribution value calculation subunit is specifically used to: substitute the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number into a preset relationship to obtain the contribution value of the newly added value.

[0139] Optionally, a contribution value calculation subunit is specifically used to: determine a preset value based on the number of existing values ​​in the target interval and the target number; wherein the preset value is negatively correlated with the number of existing values ​​in the target interval and positively correlated with the target number; determine the contribution value of the newly added value as the product of the integral of the probability density distribution function in the target interval and the preset value.

[0140] Optionally, the target quantity acquisition subunit is specifically used to: obtain a preset constant; and determine the target quantity as the product of the distribution probability of the target data item in the target interval and the preset constant.

[0141] Optionally, the contribution value calculation module is specifically used to: if the distribution of the target data item conforms to a uniform distribution, determine the contribution value of the newly added value based on the number of existing values ​​in the target interval and the target number of the target interval.

[0142] Optionally, the contribution value calculation module is also used to: if the distribution of the target data item does not conform to a uniform distribution, determine the target number of the target interval based on the distribution function of the target data item; determine the contribution value of the newly added value based on the integral of the probability density distribution function of the target data item in the target interval, the number of existing values ​​in the target interval, and the target number of the target interval.

[0143] Optionally, the number of existing numerical values ​​in the target interval is the number of numerical values ​​in the numerical value set of the target data item that fall into the target interval; the feedback module is specifically used to: if the contribution value of the newly added numerical value is greater than or equal to the preset contribution value, then the newly added numerical value is stored in the numerical value set of the target data item, the number of existing numerical values ​​in the target interval is updated, and data adoption information is generated; and the data adoption information is sent to the data acquisition end.

[0144] Optionally, the newly added value acquisition module includes: a user data acquisition unit, used to acquire user data uploaded by a user terminal; and a newly added value extraction unit, used to extract newly added values ​​of multiple target data items from the user data.

[0145] Correspondingly, the feedback module is specifically used to: generate feedback information based on the contribution value of the newly added value of each target data item and send it to the user terminal.

[0146] Optionally, the feedback module is specifically used to: obtain the weight coefficient of each of the multiple target data items; based on the weight coefficient of each target data, calculate the weighted average of the contribution values ​​of each newly added numerical value extracted from the user data to obtain the contribution value of the user data; based on the contribution value of the user data, generate feedback information and send it to the user terminal.

[0147] Optionally, there are multiple target data items; the feedback module is specifically used to: for target data items whose contribution values ​​of newly added values ​​are higher than the first contribution value, generate a suggestion for supplementary collection of the target data items and send it to the data collection end; for target data items whose contribution values ​​of newly added values ​​are lower than the second contribution value, generate a suggestion for stopping collection of the target data items and send it to the data collection end; wherein the second contribution value is lower than the first contribution value.

[0148] The data processing device provided in the embodiment of the present application can be used to execute the technical solution of the data processing method provided in any of the above embodiments of the present application. The implementation principle and technical effect are similar, and this embodiment will not be repeated here.

[0149] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device of this embodiment may include: at least one processor 701; and a memory 702 communicatively connected to the at least one processor; wherein the memory 702 stores instructions executable by the at least one processor 701, and the instructions are executed by the at least one processor 701 so that the electronic device executes a method as in any of the above embodiments.

[0150] Optionally, the memory 702 may be independent or integrated with the processor 701 .

[0151] When the memory 702 is independently provided, the device further includes a bus for connecting the memory 702 and the processor 701 .

[0152] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the aforementioned embodiments and will not be described in detail here.

[0153] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the method provided in any of the aforementioned embodiments can be implemented.

[0154] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method provided in any of the above embodiments when executed by a processor.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0156] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application.

[0157] It should be understood that the above-mentioned processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The memory may include high-speed memory, and may also include non-volatile memory, such as at least one disk memory, and can also be a USB flash drive, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0158] The storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The storage medium may be any available medium that can be accessed by a general or special purpose computer.

[0159] An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in a dedicated integrated circuit. Of course, the processor and the storage medium can also exist as discrete components in the electronic device or the control device of the vehicle sentry mode.

[0160] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0161] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods provided in each embodiment of the present application.

[0163] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0164] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: include: Get the newly added value of the target data item uploaded by the data collection end; The number of existing values ​​in the target interval is obtained; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval in which the newly added value is located among the multiple intervals; Determine the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval; Based on the contribution value of the newly added value, feedback information is generated and sent to the data acquisition end.

2. The method according to claim 1, characterized in that The determining the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval includes: Obtaining a probability density distribution function of the target data item; Based on the integral of the probability density distribution function in the target interval and the number of existing values ​​in the target interval, the contribution value of the newly added value is determined.

3. The method according to claim 2, characterized in that The determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval and the number of existing values ​​in the target interval includes: Obtaining a target number for the target interval; the target number is the number of values ​​of the target data item that needs to be collected in the target interval; Based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number, the contribution value of the newly added value is determined.

4. The method according to claim 3, characterized in that: The determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number, comprises: The integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number are substituted into a preset relationship to obtain the contribution value of the newly added value.

5. The method according to claim 3, characterized in that: The determining the contribution value of the newly added value based on the integral of the probability density distribution function in the target interval, the number of existing values ​​in the target interval and the target number, comprises: Determine a preset value according to the number of existing values ​​in the target interval and the target number; wherein the preset value is negatively correlated with the number of existing values ​​in the target interval and positively correlated with the target number; The contribution value of the newly added value is determined as the product of the integral of the probability density distribution function in the target interval and the preset value.

6. The method according to claim 3, characterized in that The obtaining of the target number of the target interval includes: Get preset constants; The target quantity is determined as the product of the distribution probability of the target data item in the target interval and the preset constant.

7. The method according to claim 1, characterized in that The determining the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval includes: If the distribution of the target data item conforms to a uniform distribution, the contribution value of the newly added value is determined based on the number of existing values ​​in the target interval and the target number of the target interval.

8. The method according to claim 7, characterized in that The method further comprises: If the distribution of the target data item does not conform to a uniform distribution, determining a target number of the target interval based on a distribution function of the target data item; The contribution value of the newly added value is determined based on the integral of the probability density distribution function of the target data item in the target interval, the number of existing values ​​in the target interval, and the target number of the target interval.

9. The method according to any one of claims 1 to 8, characterized in that: The number of existing values ​​in the target interval is the number of values ​​of the target data item that fall into the target interval, and the generating of feedback information based on the contribution value of the newly added value and sending it to the data acquisition end includes: If the contribution value of the newly added value is greater than or equal to the preset contribution value, the newly added value is stored in the value set of the target data item, the number of existing values ​​in the target interval is updated, and data adoption information is generated; The data adoption information is sent to the data acquisition terminal.

10. The method according to any one of claims 1 to 8, characterized in that: The step of obtaining the newly added value of the target data item uploaded by the data collection terminal includes: Obtain user data uploaded by the user terminal; Extracting newly added values ​​of multiple target data items from the user data; The step of generating feedback information based on the contribution value of the newly added value and sending the feedback information to the data acquisition end includes: Based on the contribution value of the newly added value of each of the target data items, feedback information is generated and sent to the user terminal.

11. The method according to claim 10, characterized in that The step of generating feedback information based on the contribution value of the newly added value of each target data item and sending the feedback information to the user terminal includes: Obtaining a weight coefficient of each of the target data items in the multiple target data items; Calculating a weighted average of the contribution values ​​of each of the newly added values ​​extracted from the user data based on the weight coefficient of each of the target data to obtain the contribution value of the user data; Based on the contribution value of the user data, feedback information is generated and sent to the user terminal.

12. The method according to any one of claims 1 to 8, characterized in that: The target data item is multiple; the contribution value based on the newly added value generates feedback information and sends it to the data acquisition end, including: For a target data item whose contribution value of the newly added value is higher than the first contribution value, generating a supplementary collection suggestion for the target data item and sending the suggestion to the data collection terminal; For a target data item whose contribution value of the newly added value is lower than the second contribution value, generating a suggestion for stopping collection of the target data item and sending the suggestion to the data collection terminal; The second contribution value is lower than the first contribution value.

13. A data processing device, characterized in that: include: A new value acquisition module is added to obtain the new value of the target data item uploaded by the data collection end; An interval existing quantity acquisition module is used to obtain the number of existing values ​​in a target interval; the value range of the target data item is pre-divided into multiple intervals, and the target interval is the interval where the newly added value is located in the multiple intervals; A contribution value calculation module, used to determine the contribution value of the newly added value based on the attribute of the target data item in the target interval and the number of existing values ​​in the target interval; A feedback module is used to generate feedback information based on the contribution value of the newly added value and send it to the data acquisition end.

14. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 12 is implemented.

16. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 12 when being executed by a processor.

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