CN113361577B - Category data determining method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to a category data determining method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring first business data of a target object; acquiring distance values between the first service data and preset characteristic data of each category; sorting the category characteristic data corresponding to each category characteristic data according to the distance value, and taking the category characteristic data with the smallest distance value as target category characteristic data; and determining the class data of the target class characteristic data as target class data of the target object. The target category data determined based on the category characteristic data in the method is more objective, and is beneficial to improving the accuracy of the determined category data.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a category data determining method and device, electronic equipment and a storage medium.
Background
At present, with the development of a live broadcast platform, live broadcast electronic commerce also develops rapidly, and more users adopt live broadcast to carry goods to increase sales volume. In practical applications, the live platform typically evaluates the merchant account and then feeds the evaluation back to the merchant. However, most merchant accounts are set to be high in evaluation by the existing evaluation method, so that the evaluation cannot be objectively fair.
Disclosure of Invention
The present disclosure provides a class data determining method and apparatus, an electronic device, and a storage medium, to solve the deficiencies of the related art.
According to a first aspect of embodiments of the present disclosure, there is provided a category data determination method, including:
acquiring first business data of a target object;
acquiring distance values between the first service data and preset characteristic data of each category; the class feature data is first business data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects;
sorting the category characteristic data corresponding to each category characteristic data according to the distance value, and taking the category characteristic data with the smallest distance value as target category characteristic data;
and determining the class data of the target class characteristic data as target class data of the target object.
Optionally, the method further comprises the step of acquiring preset characteristic data of each category, and specifically comprises the following steps:
acquiring object characteristics of a plurality of objects, wherein the object characteristics of each object comprise first service data of the object and second service data of the object, and the second service data refers to service data associated with the first service data of the object;
And clustering the plurality of objects according to the object characteristics of each object to obtain a plurality of clusters, and taking the first service data corresponding to the cluster center of each cluster as category characteristic data.
Optionally, the service data includes at least one of:
the business service parameters of the object in the business server, the business capability parameters of the object in the business server and the influence parameters of the object in the business server.
Optionally, after determining that the class data of the target class feature data is the target class data of the target object, the method further includes:
acquiring second service data of the target object, wherein the second service data refers to at least one service data associated with the first service data of the target object;
acquiring classification values of various service data;
and adjusting the target category data according to the classification values of various service data to obtain new target category data.
Optionally, obtaining classification values of various service data includes:
acquiring the value of each parameter in service data aiming at each service data;
and inputting the values of the parameters in the service data into a classification model corresponding to the service data, and calculating the classification value of the service data by the classification model according to the values of the parameters and the weights of the parameters.
Optionally, adjusting the target category data according to the classification value of the various service data includes:
acquiring a preset gear threshold corresponding to each service data;
and adjusting the target category data according to the classification values of various service data and the corresponding preset gear threshold values.
Optionally, adjusting the target category data according to the classification values of the various service data and the corresponding preset gear threshold values thereof includes:
comparing the classification value of each service data with a corresponding preset gear threshold value, wherein the corresponding preset gear threshold value of each service data comprises an upgrade gear threshold value and a degradation gear threshold value;
when the classification value of each service data exceeds the corresponding upgrading gear threshold value, adding the target class data;
when the classification value of at least one service data is lower than the corresponding degradation gear threshold value, the target class data is reduced;
and when the classification value of each service data exceeds the corresponding downgrade gear threshold value and is smaller than the corresponding upgrade gear threshold value, maintaining the target class data.
Optionally, acquiring a preset gear threshold corresponding to each service data includes:
acquiring object characteristics of a plurality of objects, wherein the object characteristics of each object refer to at least one service data associated with first service data of the object, and the values of the same service data of the plurality of objects form a corresponding service data value range;
And for any one of the service data, segmenting the service data value range into a plurality of sub-ranges, and taking the value of each segmentation point as a preset gear threshold of the service data.
According to a second aspect of the embodiments of the present disclosure, there is provided an object category data determination apparatus including:
a first data acquisition module configured to perform acquisition of first service data of a target object;
the distance value acquisition module is configured to acquire the distance value of the first service data and preset characteristic data of each category; the class feature data is first business data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects;
the target category obtaining module is configured to execute sorting according to the distance value corresponding to each category characteristic data, and the category characteristic data with the smallest distance value is used as target category characteristic data;
and the object category acquisition module is configured to determine the category data of the target category characteristic data as target category data of the target object.
Optionally, the system further comprises a category characteristic acquisition module, which is used for acquiring preset category characteristic data; the category characteristic acquisition module comprises:
An object feature acquisition sub-module configured to perform acquisition of object features of a plurality of objects, each object feature of the object including first service data of the object and second service data of the object, the second service data being service data associated with the first service data of the object;
the class feature acquisition sub-module is configured to execute clustering processing on a plurality of objects according to the object feature of each object to obtain a plurality of cluster clusters, and takes first service data corresponding to the cluster center of each cluster as class feature data.
Optionally, the service data includes at least one of:
transaction service parameters of the object in the service server, service capability parameters of the object in the service server and influence parameters of the object in the service server.
Optionally, the apparatus further comprises:
a second data acquisition module configured to perform acquisition of second service data of the target object, the second service data being at least one service data associated with the first service data of the target object;
a classification acquisition module configured to perform acquisition of classification values of various service data;
And the initial category adjustment module is configured to execute adjustment of the target category data according to the classification values of various service data to obtain new target category data.
Optionally, the classification acquisition module includes:
the value acquisition sub-module is configured to execute the acquisition of the value of each parameter in the service data aiming at each service data;
and the classification calculation sub-module is configured to input the value of each parameter in the service data into a classification model corresponding to the server data, and calculate the classification value of the service data according to the value of each parameter and the weight of each parameter by the classification model.
Optionally, the target category adjustment module includes:
the gear threshold value acquisition sub-module is configured to acquire a preset gear threshold value corresponding to each service data;
and the target category adjustment sub-module is configured to execute adjustment of the target category data according to the classification values of various service data and the corresponding preset gear threshold values.
Optionally, the target category adjustment submodule includes:
the classification comparison unit is configured to perform comparison of the classification value of each service data and the corresponding preset gear threshold value; the preset gear threshold value corresponding to each service data comprises an upgrade gear threshold value and a downgrade gear threshold value;
A target category adjustment unit configured to perform lifting of the target category data when the classification value of each service data exceeds the corresponding upgrade level threshold; when the classification value of at least one service data is lower than the corresponding degradation gear threshold value, the target class data is reduced; and when the classification value of each service data exceeds the corresponding upgrade gear threshold value and is smaller than the corresponding upgrade gear threshold value, the target class data is maintained.
Optionally, the target category adjustment module further includes a gear threshold obtaining sub-module, configured to obtain a preset gear threshold corresponding to each service data; the gear threshold value acquisition submodule comprises:
an object feature acquiring unit configured to perform acquiring object features of a plurality of objects, each object feature of the object referring to at least one service data associated with a first service of the object, values of the same service data of the plurality of objects forming a corresponding service data value range;
the gear threshold value acquisition unit is configured to execute the segmentation of any one of the service data into a plurality of sub-ranges, and takes the value of each segmentation point as a preset gear threshold value of the service data.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:
a processor;
a memory for storing a computer program executable by the processor;
wherein the processor is configured to execute the computer program in the memory to implement the method described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium, which when executed by a processor, enables the method described above.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor of an electronic device, implements the steps of the above method.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
in this embodiment, through preset category feature data, category data of category feature data with the smallest distance value from the first service data may be used as target category data of the target object; the class characteristic data is the first service data corresponding to the clustering center obtained after the object characteristics of the objects in the service server are clustered, so that the target class data determined based on the class characteristic data is more objective, and the accuracy of the determined class data is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart illustrating a class data determination method according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating the acquisition of category characteristic data, according to an exemplary embodiment.
FIG. 3 is a flowchart illustrating another category data determination method according to an exemplary embodiment.
Fig. 4 is a block diagram of a class data determining device, according to an example embodiment.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The embodiments described by way of example below are not representative of all embodiments consistent with the present disclosure.
To solve the above-described technical problems, the embodiments of the present disclosure provide a class data determining method, and fig. 1 is a flowchart of a class data determining method according to an exemplary embodiment, and is applied to an electronic device, which may be a mobile terminal, a server, or the like. Referring to fig. 1, a category data determining method includes steps 11 to 14.
In step 11, first business data of a target object is acquired.
In this embodiment, the electronic device may acquire first service data of the target object, where the first service data refers to service data corresponding to the target object. In one embodiment, the first service data refers to service data of the participant including the target object, and the service data is stored in the service server. Taking the service server as a server of the e-commerce service and the target object as a merchant account as an example, the first service data may include an order amount, such as an order amount stored by the merchant account at the service server, or the like. The first business data can be the order quantity of 3-20 months, and can be selected according to specific scenes.
It is understood that, in another embodiment, the first service data may refer to interaction data corresponding to the target object, such as an amount of fan of the merchant account, a collection amount or a browsing amount of a product associated with the merchant account, and the like, which may be selected according to a specific scenario.
In step 12, a distance value between the first service data and each preset category characteristic data is obtained.
In this embodiment, preset various kinds of feature data may be stored in the electronic device, where the kind of feature data is first service data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects.
In this embodiment, the category characteristic data has the following characteristics: the distance between any two kinds of characteristic data exceeds a preset distance threshold, so that objects corresponding to different kinds of data are separated as much as possible; when the same category characteristic data of two objects has the same category, the similarity of the two objects does not exceed a preset similarity threshold. That is, the variability of the objects of different classes is as large as possible; while the similarity of objects of the same class is as large as possible. Taking the user evaluation dimension in the service data as an example, the value ranges of two categories can be obtained: class D (0, 20), class S (80, 100), such that any one of the objects of class S and any one of the objects of class D differ in user evaluation dimension by at least 60 minutes (minimum of class S minus maximum of class D equals), a distance sufficiently large; within the same category S or D, the scores of the user evaluation dimensions of any two objects differ by at most 20 minutes (the maximum value of category S minus the minimum value of category S0, or the maximum value of category D minus the minimum value of category D80), with a sufficiently large similarity.
In this embodiment, the electronic device may acquire preset feature data of each category, see fig. 2, including steps 21-22.
In step 21, the electronic device may acquire object features of a plurality of objects, each object feature of the objects including first service data of the object and second service data of the object; the first service data refers to service data corresponding to the object. The second service data refers to service data associated with the first service data of the object.
In step 22, the electronic device may perform clustering processing on the plurality of objects according to the object feature of each object to obtain a plurality of clusters, and use the first service data corresponding to the cluster center of each cluster as the category feature data. In the example, the category characteristic data meeting the characteristics can be obtained through clustering, so that the accuracy of the category data obtained later can be improved.
In this embodiment, the clustering method may include, but is not limited to, K-Means clustering, graph group detection, density-based clustering method DBSCAN, and the like, and may be selected according to a specific scene. Taking KMeans clustering as an example:
taking an account in which the object is live network red as an example, the object features include first service data and second service data. The first business data may include an order quantity, and the second business data refers to service data associated with the order quantity of the object. The service data may include at least one of: (1) service parameters, including: a. user evaluation dimensions, such as number of fan evaluations, number of positive evaluations, etc.; b. after-market service dimensions such as time taken by the merchant to reply to the user, after-market satisfaction, etc.; c. dispute service dimensions such as time taken by merchants to resolve disputes, dispute handling capacity, success rate, and the like; d. customer service dimensions, such as whether attitudes are friendly, whether timely response is possible, and the like; (2) Capability parameters such as live times and/or live durations; (3) Influence parameters such as vermicelli quantity and vermicelli growth rate.
In this embodiment, the electronic device may select a preset number of objects from the plurality of objects, and use each selected object as an initial center of one of the preset number of clusters. It is understood that the preset number may be selected according to a specific scenario, and in an example, the preset number has a value of 8, that is, in this example, 8 clusters are set, and then 8 kinds of feature data are obtained.
The electronic device may obtain a distance from each initial center for each of the plurality of objects, the distance may include one of: euclidean distance, manhattan distance, chebyshev distance, mahalanobis distance, angle cosine, etc., can be set according to a specific scene.
In one example, a Euclidean distance implementation is employed, as is object X= (X) 1 ，x 2 ，...，x n ) And an initial center y= (Y) 1 ，y 2 ，...，y n ) Are all a vector, distance dist ed (X, Y) is:
wherein X represents an object, X 1 、x 2 、……、x n The numbers of the object X in the 1 st to n th dimensions are respectively representedAccording to the above; y represents another object, Y 1 、y 2 、……、y n Data representing the object Y in the 1 st to n th dimensions respectively; n represents the number of dimensions, i.e., the number of categories, of the data of the object. It will be appreciated that the data of the object X in the 1 st to n th dimensions are identical to the first and second business data hereinbefore described, except that the 1 st to n th dimensions are descriptive of the data from a dimensional point of view, and the first and second business data are descriptive of the data from a business or service point of view of the target object.
For each object, the electronic device can sort the distances between the electronic device and each initial center, so that the initial center with the smallest distance to the object is obtained, and at the moment, each object can be divided into clusters with the initial center with the smallest distance to the object, so that the effect of classifying the objects is achieved.
The electronic device may obtain a new center for each cluster and obtain a distance of the new center from the initial center. For each cluster, the electronic device may calculate an average value of the traffic data of the objects within the cluster, with the average value being taken as the traffic data of the new center. Considering that each object comprises business data of a plurality of dimensions, when calculating an average value, the business data of the same dimension of the plurality of objects is averaged and then used as the data of the dimension of a new center. For example, taking the above objects X and Y as an example, then the 1 st dimension of the new center is data ofThe data of dimension 2 is +.>And so on until n dimensions of data are obtained. And, the electronic device may acquire the distance of the new center and the initial center of each cluster.
The electronic device may compare the distance between the new center and the initial center with a preset distance threshold, and when the distance between the new center and the initial center is greater than the preset distance threshold, the electronic device may update the initial center to the new center and re-cluster the new center. And when the distance between the new center and the initial center is smaller than or equal to a preset distance threshold value, ending the clustering process, and acquiring the characteristic value data of the initial center of each cluster to obtain a preset number of category characteristic data.
In this embodiment, the electronic device may acquire each category feature data, and then acquire the distance value between the first service data and each category feature data, so as to obtain the distance value between the first service data and each category feature data, and the distance value acquiring manner may refer to the content of the foregoing embodiment, which is not described herein again.
In step 13, sorting is performed according to the distance value corresponding to each category characteristic data, and category characteristic data with the smallest distance value is used as target category characteristic data. Or, the distance value between the first service data and the target class characteristic data is the smallest.
In step 14, the class data of the target class feature data is determined as target class data of the target object.
In this embodiment, the electronic device may acquire category data corresponding to the target category feature data, and determine the category data as target category data of the target object.
So far, in this embodiment, through preset category feature data, category data of category feature data with the smallest distance value from the first service data may be used as target category data of the target object; the class characteristic data is the first service data corresponding to the clustering center obtained after the object characteristics of the objects in the service server are clustered, so that the target class data determined based on the class characteristic data is more objective, and the accuracy of the determined class data is improved.
The embodiment of the present disclosure also provides a type of data determining method, and fig. 3 is a flowchart of a type of data determining method according to an exemplary embodiment, and is applied to an electronic device, which may be a mobile terminal, a server, or the like. Referring to fig. 3, a category data determining method includes steps 31 to 37.
In step 31, first business data of a target object is acquired.
In this embodiment, the scheme of step 31 is the same as that of step 11, and specific reference may be made to the contents of step 11 and fig. 1, which are not described herein.
In step 32, a distance value between the first service data and each preset category characteristic data is obtained.
In this embodiment, the scheme of step 32 is the same as that of step 12, and the details of step 12 and fig. 1 can be seen, and will not be described here again.
In step 33, sorting is performed according to the distance value corresponding to each category characteristic data, and category characteristic data with the smallest distance value is used as target category characteristic data.
In this embodiment, the scheme of step 33 is the same as that of step 13, and specific reference may be made to the contents of step 13 and fig. 1, which are not described herein.
In step 34, the class data of the target class feature data is determined as target class data of the target object.
In this embodiment, the scheme of step 34 is the same as that of step 14, and the details of step 14 and fig. 1 can be seen, and will not be described here again.
In step 35, second business data of the target object is acquired.
In this embodiment, the electronic device may acquire the second service data of the target object. The second service data refers to at least one service data associated with the first service data of the target object, and the description of the second service data can be referred to as the content shown in fig. 2, which is not repeated herein.
It should be noted that, step 35 may be acquired before, during, or after the process of acquiring the target class data in step 34, and may be selected according to a specific scenario. The placement of step 35 after step 34 in this example is not limiting.
In step 36, for any one of the service data, classification values of the various service data are acquired according to classification models corresponding to the various service data.
In this embodiment, a classification model for any kind of service data may be stored in the electronic device, where the classification model includes parameters and weights of the parameters, such as polynomials, in the service data. For example, the service data is a transaction service parameter, and then includes a parameter that may include 4 dimensions: a. a user evaluation dimension; b. after-sales service dimension; c. dispute service dimensions; d. customer service dimension. The method comprises the steps of obtaining the values of all parameters after obtaining various service data, inputting the values of all the parameters into corresponding classification models, and calculating the classification values of the service data according to the values of all the parameters and the weights of all the parameters by the classification models. The classification value calculated by the classification model is more accurate than the manually set classification value.
Taking the classification model as a polynomial as an example, the values of the parameters can be weighted and summed to obtain classification values corresponding to various service data.
For example, the classification model is:
C＝a1*x1+a2*x2+a3*x3+a4*x4+a5；
wherein x1 to x4 represent each parameter in the service data, for example, x1 represents a user evaluation dimension; x2 represents an after-market service dimension; x3 represents a dispute services dimension; x4 represents the customer service dimension. a1 to a4 represent coefficients of the parameters x1 to x4, respectively, and a5 represents an adjustment coefficient. The values of a1 to a5 are respectively 0.5, 0.3, 0.2, 0.1 and 0.1, and the values of x1 to x4 are respectively 2, 10, 5 and 10, so that the classification value c=0.5×2+0.3×10+0.2×5+0.1×10+0.1=6.1.
In step 37, the target class data is adjusted according to the classification values of the various service data to obtain new target class data.
In this embodiment, a preset gear threshold is stored in the electronic device. The preset gear threshold may be obtained by the electronic device obtaining object characteristics of a plurality of objects, where the object characteristics of each object refer to at least one service data associated with the first service data, where values of one service data of the plurality of objects form a corresponding service data value range, for example, 4 objects, where values of one service data of the plurality of objects are 1,2, 25, and 100, and then the corresponding service data value range is [1,100]. For any of the service data, the electronic device may segment the service value range into a plurality of sub-ranges, for example, a segment point is 25, so as to obtain a sub-range [1, 25], (25, 100]. Then, the value of a part of the segment points may be selected as a preset gear threshold of the service data, for example, 25.
In one embodiment, the electronic device may segment the service value range using a segmentation point model, wherein the segmentation point model may include at least one of: the number of the segmentation points can be set according to specific scenes, such as a CART regression tree segmentation model, an XGBoost segmentation model, a quantile segmentation model and the like.
Taking a quantile segmentation mode as an example, acquiring a preset number of sub-ranges includes:
for any service data value range, the electronic device may use the service data value range as an initial value range. The electronic equipment can acquire the optimal quantile in the initial value range; wherein the optimal quantile satisfies: taking the optimal fractional number as a segmentation point, segmenting an initial value range into two sub-ranges, finding a representative value in each sub-range, summing the squares of the difference values of each representative value and each data in the sub-range, and obtaining a sum value corresponding to the two sub-ranges, such as a first sum value corresponding to the first sub-range and a second sum value corresponding to the second sub-range; the sum of the first sum and the second sum is minimal.
For example, an initial value range XX is selected, the jth dimension of the service data XXj is a segmentation variable, the XXj comprises a plurality of objects Xj, the value s is a fractional number, and a first sub-range R is obtained 1 And a second subrange R 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the first sub-range R 1 R is shown as the following 1 (j, s) = { x|x+.ltoreq.s }, i.e. a first subrange R 1 The value of the service data of each object Xj in the system is smaller than or equal to s; a second sub-range R 2 R is shown as the following 2 (j,s)＝{x|x>s }, i.e. the second subrange R 2 The service data of each object Xj in the system has a value larger than s. When j and s are fixed, a representative value c1 in the first sub-range and a representative value c2 in the second sub-range can be obtained, the two representative values being satisfied toThe following formula:
in the above formula, i1 and i2 represent the ordinal numbers of objects in the first sub-range and the second sub-range, x i1 、x i2 Respectively representing the ith object in the first sub-range R1 and the ith object 2 in the second sub-range R2, y i1 、y i2 Respectively represent the object x i1 、x i2 Is provided.
It should be noted that, the foregoing describes a process of obtaining a quantile, and since each Xj includes a plurality of object samples, it needs to be performed multiple times to obtain the optimal quantile (j, s) of each service data value range, where (j, s) represents the optimal quantile s of the jth service data value range.
The electronic device may update the initial value range sequentially to two sub-ranges corresponding to the initial value range. That is, after the initial value range is divided into two, the value of the initial value range may be sequentially updated to the value of the sub-range, that is, the segmentation is continued for 2 sub-regions. Then, the electronic device may continue to perform the step of obtaining the optimal quantile in the initial value range until the number of sub-ranges obtained by cutting each value range is equal to the preset number. Therefore, the classification point model can be used for objectively obtaining the class gear threshold value, and the subsequent objective obtaining of the class of the target object is facilitated.
It should be noted that, the number of the obtained sub-ranges is an even number in the above-mentioned fractional division method. In practical application, the number of the sub-ranges can be adjusted according to specific scenes, so that the number of the sub-ranges is changed into odd number. In one example, the preset number is 5, such as SABCD for a total of 5 gear thresholds.
After determining the preset gear threshold, a part of the preset gear threshold can be selected from the preset gear threshold to serve as a category gear threshold (such as a B gear threshold) in combination with the service scene, so that an upgrade gear threshold and a downgrade gear threshold are obtained.
After determining the class gear threshold, service data of the object in a specified time period (such as within 5 months) can be obtained, and the first variance is calculated by using the service data; adjusting the service data of the specified time period according to the upgrading gear threshold, namely updating the category data in the service data; then, a second variance is calculated based on the adjusted traffic data. If the difference between the first variance and the second variance is within the variance threshold, it is reasonable to indicate that the upgrade shift threshold is not updated. The variance threshold range may take a value of-5% to +5%. If the difference of the variances exceeds the variance threshold range, the upgrade threshold is unreasonably set, and the upgrade threshold may need to be adjusted. For example, the optimal score in the above embodiment is adjusted, the preset gear threshold is updated, and the category gear threshold is reselected. Repeating the steps until the difference value of the variances is within the variance threshold range, and obtaining a final upgrading gear threshold.
It can be appreciated that the updating manner of the degraded shift threshold is the same as that of the upgrade shift threshold, and will not be described herein.
In this embodiment, after the electronic device obtains the preset gear threshold, the classification value of each service data of the target object and the corresponding preset gear threshold may be compared. For example, when the classification value corresponding to each service data exceeds the upgrade level threshold, the electronic device may promote the target class data of the target object at this time, for example, may promote to class a when the target class data is class B. For another example, when the classification value corresponding to each service data exceeds the degradation gear threshold and is smaller than the promotion gear threshold, the target class data is kept at this time, and if the target class data is the class B, the class B is kept unchanged. For another example, when one of the classification values corresponding to the various service data of the object is lower than the degradation gear threshold, the target class data may be reduced, e.g., the target class data may be degraded to class C when the target class data is class B.
In the embodiment, the target class data is adjusted by combining the service classification, so that the service classification of the object is matched with the class of the object, the object can be guided to improve the class of the object, and the accuracy, objectivity and fairness of the class of the object are improved.
In this embodiment, a preset correspondence between category data and virtual resources may be further set in the electronic device, and after determining the target category data of each object, the corresponding virtual resources may be allocated to the object according to the correspondence. According to the embodiment, the object can be more objectively and accurately informed of the object category, and the accuracy, objectivity and fairness of the object category are improved.
Fig. 4 is a block diagram of a type of data determining apparatus according to an exemplary embodiment, applied to an electronic device, which may be a mobile terminal, a server, or the like. Referring to fig. 4, a category data determining apparatus includes:
a first data acquisition module 41 configured to perform acquisition of first service data of a target object;
a distance value obtaining module 42 configured to obtain a distance value between the first service data and each preset category characteristic data; the class feature data is first business data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects;
the target category obtaining module is configured to execute sorting according to the distance value corresponding to each category characteristic data, and the category characteristic data with the smallest distance value is used as target category characteristic data;
And the object category acquisition module is configured to determine the category data of the target category characteristic data as target category data of the target object.
Optionally, the system further comprises a category characteristic acquisition module, which is used for acquiring preset category characteristic data; the category characteristic acquisition module comprises:
an object feature acquisition sub-module configured to perform acquisition of object features of a plurality of objects, each object feature of an object including first service data of the object and second service data of the object; the second service data refers to service data associated with the first service data of the object;
the class feature acquisition sub-module is configured to execute clustering processing on a plurality of objects according to the object feature of each object to obtain a plurality of cluster clusters, and takes first service data corresponding to the cluster center of each cluster as class feature data.
Optionally, the service data includes at least one of:
transaction service parameters of the object in the service server, service capability parameters of the object in the service server and influence parameters of the object in the service server.
Optionally, the apparatus further comprises:
A second data acquisition module configured to perform acquisition of second service data of the target object, the second service data being at least one service data associated with the first service data of the target object;
a classification acquisition module configured to perform acquisition of classification values of various service data;
and the target category adjustment module is configured to execute adjustment of the target category data according to the classification values of various service data to obtain new target category data.
Optionally, the classification acquisition module includes:
the value acquisition sub-module is configured to execute the acquisition of the value of each parameter in the service data aiming at each service data;
and the classification calculation sub-module is configured to input the value of each parameter in the service data into a classification model corresponding to the server data, and calculate the classification value of the service data according to the value of each parameter and the weight of each parameter by the classification model.
Optionally, the target category adjustment module includes:
the gear threshold value acquisition sub-module is configured to acquire a preset gear threshold value corresponding to each service data;
and the target category adjustment sub-module is configured to execute adjustment of the target category data according to the classification values of various service data and the corresponding preset gear threshold values.
Optionally, the target category adjustment submodule includes:
the classification comparison unit is configured to perform comparison of the classification value of each service data and the corresponding preset gear threshold value; the preset gear threshold value corresponding to each service data comprises an upgrade gear threshold value and a downgrade gear threshold value;
a target category adjustment unit configured to perform lifting of the target category data when the classification value of each service data exceeds the corresponding upgrade level threshold; when the classification value of at least one service data is lower than the corresponding degradation gear threshold value, the target class data is reduced; and when the classification value of each service data exceeds the corresponding upgrade gear threshold value and is smaller than the corresponding upgrade gear threshold value, the target class data is maintained.
Optionally, the target category adjustment module further includes a gear threshold obtaining sub-module, configured to obtain a preset gear threshold corresponding to each service data; the gear threshold value acquisition submodule comprises:
an object feature acquiring unit configured to perform acquiring object features of a plurality of objects, each object feature of the object referring to at least one service data associated with a first service of the object, values of the same service data of the plurality of objects forming a corresponding service data value range;
The gear threshold value acquisition unit is configured to execute the segmentation of any one of the service data into a plurality of sub-ranges, and takes the value of each segmentation point as a preset gear threshold value of the service data.
It can be understood that the apparatus provided in the embodiments of the present disclosure corresponds to the method shown in fig. 1 or fig. 3, and specific content may refer to content of each embodiment of the method, which is not described herein again.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment. Referring to fig. 5, an electronic device 500 may include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, a multimedia component 508, an audio component 510, an input/output (I/O) interface 512, a sensor component 514, a communication component 516, and an image acquisition component 518.
The processing component 502 generally controls overall operation of the electronic device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 502 can include one or more processors 520 to execute instructions. Further, the processing component 502 can include one or more modules that facilitate interactions between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.
The memory 504 is configured to store various types of data to support operations at the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 504 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 506 provides power to the various components of the electronic device 500. The power components 506 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 500.
The multimedia component 508 includes a screen that provides an output interface between the electronic device 500 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front-facing camera and/or a rear-facing camera. When the electronic device 500 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 510 is configured to output and/or input audio signals. For example, the audio component 510 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 further comprises a speaker for outputting audio signals.
The I/O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 514 includes one or more sensors for providing status assessment of various aspects of the electronic device 500. For example, the sensor assembly 514 may detect an on/off state of the electronic device 500, a relative positioning of components such as a display and keypad of the electronic device 500, a change in position of the electronic device 500 or a component of the electronic device 500, the presence or absence of a user's contact with the electronic device 500, an orientation or acceleration/deceleration of the electronic device 500, and a change in temperature of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 516 is configured to facilitate communication between the electronic device 500 and other devices, either wired or wireless. The electronic device 500 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an embodiment of the present disclosure, electronic device 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the steps of the above-described methods.
In an embodiment of the present disclosure, a computer readable storage medium is also provided, such as memory 504 comprising a computer program product, which is capable of executing the steps of the above-described method by processor 520 of electronic device 500.
In an embodiment of the present disclosure, there is also provided a computer program product which, when executed by a processor of an electronic device, enables the electronic device to perform the steps of the above-described method.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus/server/storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and references to the parts of the description of the method embodiments are only required.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the embodiments described above that follow, in general, the principles of the disclosure and include such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (17)
1. A category data determination method, comprising:
acquiring first business data of a target object;
acquiring distance values between the first service data and preset characteristic data of each category; the class feature data is first business data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects;
Sorting the category characteristic data corresponding to each category characteristic data according to the distance value, and taking the category characteristic data with the smallest distance value as target category characteristic data;
determining the class data of the target class characteristic data as target class data of the target object;
acquiring second service data of the target object, wherein the second service data refers to at least one service data associated with the first service data of the target object;
acquiring classification values of various service data; and adjusting the target category data according to the classification values of various service data to obtain new target category data.
2. The method according to claim 1, further comprising the step of acquiring preset feature data of each category, specifically comprising:
acquiring object characteristics of a plurality of objects, wherein the object characteristics of each object comprise first service data of the object and second service data of the object, and the second service data refers to service data associated with the first service data of the object;
and clustering the plurality of objects according to the object characteristics of each object to obtain a plurality of clusters, and taking the first service data corresponding to the cluster center of each cluster as category characteristic data.
3. The method of claim 2, wherein the service data comprises at least one of:
the business service parameters of the object in the business server, the business capability parameters of the object in the business server and the influence parameters of the object in the business server.
4. The method of claim 1, wherein obtaining classification values for various service data comprises:
acquiring the value of each parameter in service data aiming at each service data;
and inputting the values of the parameters in the service data into a classification model corresponding to the service data, and calculating the classification value of the service data by the classification model according to the values of the parameters and the weights of the parameters.
5. The method of claim 1, wherein adjusting the target class data according to classification values of various service data comprises:
acquiring a preset gear threshold corresponding to each service data;
and adjusting the target category data according to the classification values of various service data and the corresponding preset gear threshold values.
6. The method of claim 5, wherein adjusting the target class data according to the classification values of the various service data and their corresponding preset gear thresholds comprises:
Comparing the classification value of each service data with a corresponding preset gear threshold value, wherein the corresponding preset gear threshold value of each service data comprises an upgrade gear threshold value and a degradation gear threshold value;
when the classification value of each service data exceeds the corresponding upgrading gear threshold value, adding the target class data;
when the classification value of at least one service data is lower than the corresponding degradation gear threshold value, the target class data is reduced;
and when the classification value of each service data exceeds the corresponding downgrade gear threshold value and is smaller than the corresponding upgrade gear threshold value, maintaining the target class data.
7. The method of claim 5, wherein obtaining a preset gear threshold corresponding to each service data comprises:
acquiring object characteristics of a plurality of objects, wherein the object characteristics of each object refer to at least one service data associated with first service data of the object, and the values of the same service data of the plurality of objects form a corresponding service data value range;
and for any one of the service data, segmenting the service data value range into a plurality of sub-ranges, and taking the value of each segmentation point as a preset gear threshold of the service data.
8. An object class data determining apparatus, comprising:
a first data acquisition module configured to perform acquisition of first service data of a target object;
the distance value acquisition module is configured to acquire the distance value of the first service data and preset characteristic data of each category; the class feature data is first business data corresponding to a clustering center, and the clustering center is a center object obtained by clustering object features of one or more objects;
the target category obtaining module is configured to execute sorting according to the distance value corresponding to each category characteristic data, and the category characteristic data with the smallest distance value is used as target category characteristic data;
an object class acquisition module configured to perform determination of class data of the target class feature data as target class data of the target object;
a second data acquisition module configured to perform acquisition of second service data of the target object, the second service data being at least one service data associated with the first service data of the target object;
a classification acquisition module configured to perform acquisition of classification values of various service data;
And the initial category adjustment module is configured to execute adjustment of the target category data according to the classification values of various service data to obtain new target category data.
9. The device according to claim 8, further comprising a category feature acquisition module configured to acquire preset respective category feature data; the category characteristic acquisition module comprises:
an object feature acquisition sub-module configured to perform acquisition of object features of a plurality of objects, each object feature of the object including first service data of the object and second service data of the object, the second service data being service data associated with the first service data of the object;
the class feature acquisition sub-module is configured to execute clustering processing on a plurality of objects according to the object feature of each object to obtain a plurality of cluster clusters, and takes first service data corresponding to the cluster center of each cluster as class feature data.
10. The apparatus of claim 9, wherein the service data comprises at least one of:
transaction service parameters of the object in the service server, service capability parameters of the object in the service server and influence parameters of the object in the service server.
11. The apparatus of claim 8, wherein the classification acquisition module comprises:
the value acquisition sub-module is configured to execute the acquisition of the value of each parameter in the service data aiming at each service data;
and the classification calculation sub-module is configured to input the value of each parameter in the service data into a classification model corresponding to the service data, and calculate the classification value of the service data according to the value of each parameter and the weight of each parameter by the classification model.
12. The apparatus of claim 8, wherein the target class adjustment module comprises:
the gear threshold value acquisition sub-module is configured to acquire a preset gear threshold value corresponding to each service data;
and the target category adjustment sub-module is configured to execute adjustment of the target category data according to the classification values of various service data and the corresponding preset gear threshold values.
13. The apparatus of claim 12, wherein the target category adjustment submodule comprises:
the classification comparison unit is configured to perform comparison of the classification value of each service data and the corresponding preset gear threshold value; the preset gear threshold value corresponding to each service data comprises an upgrade gear threshold value and a downgrade gear threshold value;
A target category adjustment unit configured to perform lifting of the target category data when the classification value of each service data exceeds the corresponding upgrade level threshold; when the classification value of at least one service data is lower than the corresponding degradation gear threshold value, the target class data is reduced; and when the classification value of each service data exceeds the corresponding upgrade gear threshold value and is smaller than the corresponding upgrade gear threshold value, the target class data is maintained.
14. The apparatus of claim 12, wherein the target class adjustment module further comprises a gear threshold acquisition sub-module configured to acquire a preset gear threshold corresponding to each service data; the gear threshold value acquisition submodule comprises:
an object feature acquiring unit configured to perform acquiring object features of a plurality of objects, each object feature of the object referring to at least one service data associated with a first service of the object, values of the same service data of the plurality of objects forming a corresponding service data value range;
the gear threshold value acquisition unit is configured to execute the segmentation of any one of the service data into a plurality of sub-ranges, and takes the value of each segmentation point as a preset gear threshold value of the service data.
15. An electronic device, comprising:
a processor;
a memory for storing a computer program executable by the processor;
wherein the processor is configured to execute the computer program in the memory to implement the method of any of claims 1-7.
16. A computer readable storage medium, characterized in that an executable computer program in the storage medium, when executed by a processor of an electronic device, enables the electronic device to implement the method of any one of claims 1-7.
17. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor of an electronic device, implements the method of any one of claims 1-7.
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