Express data sensitivity calculation method, device, storage medium and equipment

Abstract

The present invention discloses a sensitivity calculation method, device, storage medium and equipment for express delivery data. The method includes: obtaining the current occurrence times of express delivery data under multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, and each express delivery data corresponds to a telephone number; integrating the current occurrence times according to the first dimension to obtain integrated data; performing KMeans clustering on the integrated data to obtain clustered data; sorting the centroid of each type of data in the clustered data according to the Euclidean clustering algorithm, and determining the corresponding sensitivity score value for each telephone number, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number. The present invention solves the technical problem of consuming a large amount of manpower and material resources and having low screening efficiency due to the prior art of conducting illegal express screening through express security inspection one by one.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, storage medium and equipment for calculating the sensitivity of express delivery data. Background Art

[0002] With the rapid development of informatization, the trend of big data computing has arrived. People's demand for online shopping has also increased dramatically, and there are many people who use express mail and other means to conceal their illegal and criminal activities. Therefore, how to quickly identify and obtain suspicious persons and express information has become an urgent problem to be solved. In the existing technology, illegal express delivery is mainly checked through express security inspection, but the above method requires a lot of manpower and material resources, and the screening efficiency is low.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, storage medium and equipment for calculating the sensitivity of express delivery data, so as to at least solve the technical problem that a large amount of manpower and material resources are consumed and the screening efficiency is low due to the existing technology of conducting illegal express screening by checking each express delivery one by one.

[0005] According to one aspect of an embodiment of the present invention, a method for calculating the sensitivity of express data is provided, comprising: obtaining the current occurrence times of the express data under multiple first dimensions, wherein the express data comprises: sending data and receiving data, and each of the express data corresponds to a telephone number; integrating the current occurrence times according to the first dimensions to obtain integrated data; performing KMeans clustering on the integrated data to obtain clustered data; sorting the centroid of each type of data in the clustered data according to the Euclidean clustering algorithm, and determining a corresponding sensitivity score value for each of the telephone numbers, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number.

[0006] Optionally, the above-mentioned obtaining of the current occurrence times of the express delivery data in multiple first dimensions includes: separately counting the first occurrence times of the above-mentioned sending data in each of the above-mentioned first dimensions, and the second occurrence times of the above-mentioned receiving data in each of the above-mentioned first dimensions; integrating the above-mentioned current occurrence times according to the above-mentioned first dimensions to obtain integrated data, including: integrating all the above-mentioned first occurrence times in each of the above-mentioned first dimensions, and integrating all the above-mentioned second occurrence times in each of the above-mentioned first dimensions to obtain the above-mentioned integrated data.

[0007] Optionally, the above-mentioned KMeans clustering processing is performed on the above-mentioned integrated data to obtain clustered data, including: performing data format processing on the above-mentioned integrated data according to a predetermined data range to obtain processed integrated data; performing dimensionality reduction processing on the above-mentioned processed integrated data using a dimensionality reduction algorithm to obtain dimensionality reduced data corresponding to the second dimension; standardizing the above-mentioned dimensionality reduced data to obtain target format data; performing the above-mentioned KMeans clustering processing on the above-mentioned target format data to obtain the above-mentioned clustered data.

[0008] Optionally, after sorting the centroid of each type of data in the above-mentioned clustered data according to the Euclidean clustering algorithm and determining the corresponding sensitivity score value for each of the above-mentioned telephone numbers, the above-mentioned method also includes: generating a sensitivity score table according to the corresponding sensitivity score value determined for each of the above-mentioned telephone numbers, wherein the above-mentioned sensitivity score table includes: a sender sensitivity data table and a recipient sensitivity data table; when receiving a sensitivity retrieval request, determining at least one of the above-mentioned telephone numbers carried in the above-mentioned sensitivity retrieval request; and retrieving the above-mentioned sensitivity score value corresponding to the at least one above-mentioned telephone number from the above-mentioned sensitivity score table.

[0009] Optionally, the above-mentioned sorting of the centroids of each category of data in the above-mentioned clustered data according to the Euclidean clustering algorithm to determine the corresponding sensitivity score value for each of the above-mentioned telephone numbers includes: sorting the centroids of each category of data in the above-mentioned clustered data according to the Euclidean clustering algorithm to obtain a first sorting result; performing secondary KMeans clustering processing on each category of data in the above-mentioned clustered data to obtain secondary clustered data; sorting the centroids of each category of data in the above-mentioned secondary clustering data according to the Euclidean clustering algorithm to obtain a second sorting result; determining the corresponding sensitivity score value for each of the above-mentioned telephone numbers according to the basic score determined based on the above-mentioned first sorting result and the above-mentioned second sorting result.

[0010] Optionally, before obtaining the current number of occurrences of the express delivery data under multiple first dimensions, the above method also includes: taking the express delivery data table storing the above express delivery data as the data source, and extracting multiple of the above first dimensions; wherein the above first dimensions include: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus areas, sending and receiving express delivery at non-number-attributed locations, key focus items, and key focus groups.

[0011] According to another aspect of an embodiment of the present invention, a sensitivity calculation device for express data is also provided, including: a first acquisition module, used to obtain the current occurrence times of the express data under multiple first dimensions, wherein the express data includes: sending data and receiving data, and each of the express data corresponds to a telephone number; a second acquisition module, used to integrate the current occurrence times according to the first dimensions to obtain integrated data; a clustering module, used to perform KMeans clustering on the integrated data to obtain clustered data; a determination module, used to sort the centroid of each type of data in the clustered data according to the Euclidean clustering algorithm, and determine the corresponding sensitivity score value for each of the telephone numbers, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number.

[0012] Optionally, the above-mentioned clustering module includes: a first acquisition submodule, used to perform data format processing on the above-mentioned integrated data according to a predetermined data range to obtain processed integrated data; a second acquisition submodule, used to perform dimensionality reduction processing on the above-mentioned processed integrated data using a dimensionality reduction algorithm to obtain dimensionality reduced data corresponding to the second dimension; a third acquisition submodule, used to perform standardization processing on the above-mentioned dimensionality reduced data to obtain target format data; the first clustering submodule, used to perform the above-mentioned KMeans clustering processing on the above-mentioned target format data to obtain the above-mentioned clustered data.

[0013] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the above-mentioned express data sensitivity calculation methods.

[0014] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the above-mentioned methods for calculating the sensitivity of express delivery data.

[0015] In an embodiment of the present invention, a sensitivity calculation method of express data is adopted, by obtaining the current occurrence times of the express data under multiple first dimensions, wherein the express data includes: sending data and receiving data, and each express data corresponds to a telephone number; the current occurrence times are integrated according to the first dimension to obtain integrated data; the integrated data is clustered by KMeans to obtain clustered data; the centroid of each type of data in the clustered data is sorted according to the Euclidean clustering algorithm, and a corresponding sensitivity score value is determined for each telephone number, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number, so as to achieve the purpose of calculating the sensitivity score value of the express user according to the express data, and using this as a basis to quickly identify possible illegal express delivery, thereby achieving the technical effect of improving the efficiency of illegal express delivery screening and reducing labor costs, and further solving the technical problem of consuming a large amount of manpower and material resources and low screening efficiency due to the illegal express delivery screening in the form of express delivery security inspection one by one in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for calculating sensitivity of express delivery data according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of an optional sensitivity scoring result according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of an optional method for calculating sensitivity of express delivery data according to an embodiment of the present invention;

[0020] Figure 4 is a flow chart of an optional sensitivity score query according to an embodiment of the present invention;

[0021] Figure 5 is a flowchart of another optional method for calculating sensitivity of express delivery data according to an embodiment of the present invention;

[0022] Figure 6 is a flowchart of another optional method for calculating sensitivity of express delivery data according to an embodiment of the present invention;

[0023] Figure 7 It is a structural schematic diagram of a sensitivity calculation device for express delivery data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention are explained below:

[0027] PCA (principal components analysis): also known as principal component analysis, aims to transform multiple indicators into a few comprehensive indicators by using the idea of ​​dimensionality reduction.

[0028] Clustering: The process of dividing a collection of physical or abstract objects into multiple classes consisting of similar objects is called clustering. The clusters generated by clustering are a collection of data objects that are similar to objects in the same cluster and different from objects in other clusters. "Birds of a feather flock together." In natural sciences and social sciences, there are a large number of classification problems. Cluster analysis, also known as group analysis, is a statistical analysis method for studying (sample or indicator) classification problems. Cluster analysis originated from taxonomy, but clustering is not equal to classification. The difference between clustering and classification is that the classes required for clustering are unknown. Cluster analysis is very rich in content, including systematic clustering, ordered sample clustering, dynamic clustering, fuzzy clustering, graph clustering, cluster prediction, etc.

[0029] Euclidean clustering: A clustering algorithm based on Euclidean distance metric. The nearest neighbor query algorithm based on KD-Tree is an important preprocessing method to accelerate the Euclidean clustering algorithm.

[0030] Example 1

[0031] According to an embodiment of the present invention, a method embodiment of calculating the sensitivity of express delivery data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1 is a flow chart of a method for calculating sensitivity of express delivery data according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0033] Step S102, obtaining the current occurrence times of express delivery data under multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, and each express delivery data corresponds to a telephone number;

[0034] Step S104, integrating the current occurrence times according to the first dimension to obtain integrated data;

[0035] Step S106, performing KMeans clustering processing on the above integrated data to obtain clustered data;

[0036] Step S108, sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm, and determining a corresponding sensitivity score value for each of the telephone numbers, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number.

[0037] In an embodiment of the present invention, a sensitivity calculation method of express data is adopted, by obtaining the current occurrence times of the express data under multiple first dimensions, wherein the express data includes: sending data and receiving data, and each express data corresponds to a telephone number; the current occurrence times are integrated according to the first dimension to obtain integrated data; the integrated data is clustered by KMeans to obtain clustered data; the centroid of each type of data in the clustered data is sorted according to the Euclidean clustering algorithm, and a corresponding sensitivity score value is determined for each telephone number, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number, so as to achieve the purpose of calculating the sensitivity score value of the express user according to the express data, and using this as a basis to quickly identify possible illegal express delivery, thereby achieving the technical effect of improving the efficiency of illegal express delivery screening and reducing labor costs, and further solving the technical problem of consuming a large amount of manpower and material resources and low screening efficiency due to the illegal express delivery screening in the form of express delivery security inspection one by one in the prior art.

[0038] Optionally, the express data table is used as the data source to extract the first dimension; the above-mentioned first dimension includes: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus areas, sending and receiving express delivery at non-number-attributed locations, key focus items, and key focus groups.

[0039] Optionally, the current occurrence times of the sending data and the receiving data in multiple first dimensions are counted respectively, that is, the first occurrence times of the sending data in each of the first dimensions and the second occurrence times of the receiving data in each of the first dimensions are counted respectively.

[0040] Optionally, the first occurrence times corresponding to all the sending data under each of the above-mentioned first dimensions are integrated and the second occurrence times corresponding to all the receiving data under each of the above-mentioned first dimensions are integrated based on the telephone number as a group to obtain the above-mentioned integrated data.

[0041] Optionally, the centroid is the distance from the cluster center to the origin; the sensitivity score includes at least one of the following: a sending sensitivity score, a receiving sensitivity score, Figure 2 The sensitivity score of Mr. Li is shown as follows: Figure 2 As shown, Li's sending sensitivity score and receiving sensitivity score are both 42.

[0042] Optionally, a higher sensitivity score value indicates that the user corresponding to the sensitivity score value has a greater possibility of illegal behavior.

[0043] As an optional embodiment, Figure 3 is a flowchart of an optional method for calculating the sensitivity of express delivery data according to an embodiment of the present invention. Figure 3 As shown, the above integrated data is subjected to KMeans clustering to obtain clustered data, including:

[0044] Step S302, performing data format processing on the above integrated data according to a predetermined data range to obtain processed integrated data;

[0045] Step S304, using a dimensionality reduction algorithm to perform dimensionality reduction processing on the processed integrated data to obtain dimensionality reduced data corresponding to the second dimension;

[0046] Step S306, performing standardization processing on the data after dimension reduction to obtain target format data;

[0047] Step S308, performing the KMeans clustering process on the target format data to obtain the clustered data.

[0048] Optionally, the above-mentioned predetermined data range may be but is not limited to the express delivery data of residents in the target area, such as the express delivery data of residents in XX city, the express delivery data of residents in XX province, the express delivery data of residents in XX autonomous region, and so on.

[0049] Optionally, the integrated data is processed in a data format according to a predetermined data range to obtain the processed integrated data as shown in Table 1.

[0050] Table 1

[0051]

[0052]

[0053] Optionally, the above dimensionality reduction algorithm may be, but is not limited to, the principal component analysis PCA algorithm in madlib. It should be noted that the above dimensionality reduction algorithm can use the least number of dimensions to represent the most meaning, and the variance calculated in low dimensions tends to be more stable. Therefore, using low-dimensional data is more convenient for calculation and can also represent the meaning of high-dimensional data.

[0054] Optionally, a dimensionality reduction algorithm (such as a PCA algorithm) is used to perform dimensionality reduction processing on the processed integrated data to obtain dimensionality reduced data corresponding to the second dimension, including:

[0055] S1, after processing, integrate the data (8 dimensions) for decorrelation processing, create the original dense matrix table (jqxx.jqxx_shpr_zz) and add data. The specific implementation code is as follows:

[0056] drop table if exists jqxx.jqxx_shpr_zz;

[0057] create table jqxx.jqxx_shpr_zz(id integer,row_vec DOUBLE PRECISION[]);

[0058] insert into jqxx.jqxx_shpr_zz values

[0059] (1,'{1,5,2,0,0,0,0,0}'),

[0060] (2,'{0,1,0,0,0,0,0,1}'),

[0061] (3,'{0,5,0,0,0,2,0,0}'),

[0062] (4,'{0,0,1,0,2,0,0,2}'),

[0063] (5,'{1,2,0,1,1,0,0,4}'),

[0064] (6,'{1,0,0,1,0,0,1,0}'),

[0065] (7,'{1,1,0,0,0,0,3,0}').

[0066] S2, call the PCA training function to train the added data, generate the feature vector matrix, obtain the training results, and output the training results shown in Table 2. The specific implementation code is as follows:

[0067] select madlib.pca_train(

[0068] 'jqxx.jqxx_shpr_zz',--Original table

[0069] --source-table

[0070] 'jqxx.result_table_shpr_zz',--output table

[0071] --output table

[0072] 'mobile',--Source table ID column

[0073] --row id of source table--number of principal components).

[0074] S3, calling the PCA projection function to project the above training results, and finally obtaining the reduced dimension data corresponding to the second dimension (3 dimensions). The reduced dimension data is shown in Table 3. The specific implementation code is as follows:

[0075] Select madlib.pca_project(

[0076] 'jqxx.jqxx_shpr_zz',

[0077] 'jqxx.result_table_shpr_zz',

[0078] 'jqxx.out_table_shpr_zz',

[0079] 'mobile',

[0080] 'jqxx.residual_table_shpr_zz',

[0081] 'jqxx.result_summary_table_shpr_zz').

[0082] Table 2

[0083]

[0084] Table 3

[0085] Row_id Row_vec 1 {3.29177676722938,-0.109192661697066,0.65027320246043} 2 {-0.833010395779005,0.0624998438474048,0.496073569262864} 3 {3.45713701219417,-0.0182366253911953,-0.280213936353739} 4 {-2.21222162912753,-1.16316894886941,1.30735249257714} 5 {-1.04652026547193,-2.75432751429412,-1.33946918701219} 6 {-1.67962629587755,1.54985327029896,0.118916372191323} 7 {-0.977535193166934,2.43257263610243,-0.952932513121798}

[0086] Optionally, the above standardization processing may be, but is not limited to, normalization processing, and normalization processing is performed on the above dimensionality-reduced data to obtain target format data as shown in Table 4.

[0087] Table 4

[0088] Row_id Row_vec 1 {0.970832636383322,0.5099644828125,0.248252194389862} 2 {0.243274648969286,0.543065660889226,0.306510608391326} 3 {1,0.527500204277838,0.599801052399269} 4 {0,0.306764834349678,0} 5 {0.205614327370166,0,1} 6 {0.0939427838218074,0.829817551869399,0.44900498191862} 7 {0.217782383171432,1,0.853961951093599}

[0089] Optionally, the kmeanspp function of madlib is used to perform the KMeans clustering process on the target format data to obtain the clustered data, wherein the target format data can be clustered into 5 categories but is not limited to.

[0090] Optionally, the above-mentioned KMeans clustering processing is performed on the above-mentioned target format data. For example, the above-mentioned target format data is clustered into 5 categories to obtain the above-mentioned clustered data, that is, the Kmeans clustering algorithm is used to select 5 cluster centers, and the data after dimension reduction is clustered. Each data is clustered to the cluster center closest to the above-mentioned 5 cluster centers, and the coordinate average of all points in each cluster is calculated, and this average value is used as the new cluster center. Repeat the iteration 20,000 times to finally obtain the above-mentioned clustered data. The specific implementation code is as follows:

[0091] Select madlib.kmeanspp(

[0092] 'jqxx.t_source_change_nor_cnee_zz',

[0093] --Source data table name 'row_vec',

[0094] --The column name containing the data point 5,

[0095] --Number of center points'madlib.squared_dist_norm2',

[0096] --Distance function 'madlib.avg',

[0097] --Aggregate function 20000,

[0098] --Iteration number 0.00000001--Stop iteration condition).

[0099] As an optional embodiment, after sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm and determining the corresponding sensitivity score value for each of the telephone numbers, the method further includes:

[0100] Step S402, generating a sensitivity score table according to the sensitivity score value corresponding to each of the above telephone numbers, wherein the above sensitivity score table includes: a sender sensitivity data table and a receiver sensitivity data table;

[0101] Step S404, upon receiving the sensitivity search request, determining at least one of the telephone numbers carried in the sensitivity search request;

[0102] Step S406: retrieve the sensitivity score value corresponding to at least one of the telephone numbers from the sensitivity score table.

[0103] Optionally, a sensitivity score table as shown in Table 5 is generated by determining the corresponding sensitivity score value according to each of the above telephone numbers. After the sensitivity score table is calculated, the telephone number is input for sensitivity scoring and detailed search, so that sensitive behavior can be analyzed and judged intuitively and quickly.

[0104] Table 5

[0105] telephone number Rating value 18998765222 44 18998764352 81 18998765201 44 18998764210 61 18998764153 43 18998763451 21 18998764523 61

[0106] As an optional embodiment, Figure 4 is a flowchart of an optional sensitivity score query according to an embodiment of the present invention. Figure 4 As shown, the process includes: inputting a phone number, parsing request parameters after receiving the phone number; encapsulating the parsed request parameters into SQL query statements, querying the outgoing data sensitivity score and details, and the receiving data sensitivity score and details respectively, encapsulating the query results, and returning them in JSON format to obtain return results; parsing the above return results, and displaying the outgoing data sensitivity score and details, and the receiving data sensitivity score and details corresponding to the phone number in the return results on the console.

[0107] As an optional embodiment, Figure 5 is a flowchart of another optional method for calculating the sensitivity of express delivery data according to an embodiment of the present invention. Figure 5 As shown, the centroids of each type of data in the clustered data are sorted according to the Euclidean clustering algorithm, and the corresponding sensitivity score value is determined for each of the above telephone numbers, including:

[0108] Step S502, sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm to obtain a first sorting result;

[0109] Step S504, performing secondary KMeans clustering processing on each type of data in the above clustered data to obtain secondary clustered data;

[0110] Step S506, sorting the centroids of each type of data in the above secondary clustered data according to the Euclidean clustering algorithm to obtain a second sorting result;

[0111] Step S508: determining the corresponding sensitivity score value for each of the telephone numbers according to the basic score determined based on the first sorting result and the second sorting result.

[0112] Optionally, the centroids of each type of data in the clustered data (i.e., the distance from each cluster center to the origin) are sorted according to the Euclidean clustering algorithm to obtain a first sorting result, and the basic score is determined based on the first sorting result. For example, the five centroids in the clustered data are sorted to obtain the first sorting result, and five basic scores of 0, 20, 40, 60, and 80 are set based on the first sorting result, which correspond one-to-one to the five sorted centroids respectively.

[0113] Optionally, performing secondary KMeans clustering processing on each category of data in the above clustered data to obtain secondary clustered data, including: normalizing the above clustered data, performing secondary clustering processing on each category of data after normalization, for example, clustering each category of data after normalization into 20 categories, and finally obtaining 100 categories of data, which is the above secondary clustered data.

[0114] Optionally, the centroid of each type of data in the above-mentioned secondary clustering data (i.e., the data of the above-mentioned 100 types) (i.e., the distance from each cluster center to the origin) is sorted according to the Euclidean clustering algorithm to obtain a second sorting result, and based on the above-mentioned second sorting result and the above-mentioned 5 basic scores of 0, 20, 40, 60, 80, a corresponding sensitivity score value is determined for each telephone number, wherein the value range of the above-mentioned sensitivity score value is 0-100.

[0115] As an optional embodiment, Figure 6 is a flowchart of another optional method for calculating the sensitivity of express delivery data according to an embodiment of the present invention. Figure 6As shown, the express data table is used as the data source, the current occurrence times of the sending data and the receiving data in multiple dimensions are obtained, the above current occurrence times are integrated according to the dimensions to obtain integrated data, and the above integrated data are formatted to obtain processed integrated data, wherein the above processed integrated data is data of 8 dimensions; the above processed integrated data is subjected to dimensionality reduction processing by the PCA algorithm, and the above processed integrated data is reduced from 8 dimensions to 3 dimensions to obtain reduced dimension data; the above reduced dimension data is standardized to obtain target format data; the above target format data is subjected to the above KMe ans clustering processing, re-clustering the above target format data into 5 classes to obtain clustered data; clustering processing is performed on the above clustered data again, each of the above classes is further clustered into 20 classes, and the above clustered data is re-clustered to 100 classes to obtain secondary clustered data; sorting the centroid of each class of data in the above secondary clustered data according to the Euclidean clustering algorithm, and determining the corresponding sensitivity score value for each of the above telephone numbers according to the sorting result; generating a sensitivity score table based on multiple sensitivity score values, wherein the sensitivity score table is used to record the sensitivity score value and details corresponding to each telephone number.

[0116] The embodiments of the present invention can at least achieve the following technical effects: for the calculated sensitivity data, the user can directly search for the telephone number, and the sensitivity coefficient is intuitively given for the user's reference; it can increase the efficiency of case handlers' information query on sensitive persons, and at the same time support the provision of the dimensional range required for reference in the investigation of different types of cases, and has a wider range of applications.

[0117] As an optional embodiment, before obtaining the current occurrence counts of the express delivery data under the multiple first dimensions, the method further includes:

[0118] Step S602, using the express data table storing the express data as the data source, extracting multiple first dimensions; wherein the first dimensions include: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus areas, receiving and sending express at non-number-attributed locations, key focus items, and key focus groups.

[0119] Optionally, the above-mentioned name ambiguity may include but is not limited to: Mr., Ms., classmate, manager, brother, elder brother, sister, and elder sister.

[0120] Optionally, the address blurring may include, but is not limited to, information such as intersections, supermarkets, shopping malls, squares, libraries, hotels (excluding Room XX, Room XX), or intercepting part of the detailed address, such as an address length of less than 5 digits.

[0121] Optionally, the above-mentioned frequent name changes may include but are not limited to: the number of times the sender / receiver name is frequently changed in the sender / receiver data is greater than a predetermined frequency (such as 2 times).

[0122] Optionally, the above-mentioned frequent address changes may include but are not limited to: the number of times the sending and receiving addresses are frequently changed in the sending and receiving data is greater than a predetermined frequency (such as 2 times).

[0123] Optionally, the above-mentioned key focus areas can be understood as express delivery data involving key focus areas in different periods.

[0124] Optionally, the above-mentioned sending and receiving of express delivery at a non-number-attributed location can be understood as the registration location of the receiving or sending phone number is not at the receiving or sending location.

[0125] Optionally, the above-mentioned key items of concern may include but are not limited to: special topics on investigating counterfeit liquor, items such as wine bottles, wine caps, labels, etc.

[0126] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0127] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, 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, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0128] Example 2

[0129] In this embodiment, a sensitivity calculation device for express delivery data is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the terms "module" and "device" can be a combination of software and / or hardware that implements predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0130] According to an embodiment of the present invention, there is also provided an apparatus embodiment for implementing the above-mentioned method for calculating the sensitivity of express delivery data. Figure 7 is a schematic diagram of a sensitivity calculation device for express delivery data according to an embodiment of the present invention. Figure 7 As shown, the above-mentioned express data sensitivity calculation device includes: a first acquisition module 700, a second acquisition module 702, a clustering module 704, and a determination module 706, wherein:

[0131] The first acquisition module 700 is used to acquire the current occurrence times of the express delivery data under multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, and each express delivery data corresponds to a telephone number;

[0132] The second acquisition module 702 is used to integrate the current occurrence times according to the first dimension to obtain integrated data;

[0133] The clustering module 704 is used to perform KMeans clustering on the integrated data to obtain clustered data;

[0134] The determination module 706 is used to sort the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm, and determine a corresponding sensitivity score value for each of the telephone numbers, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number.

[0135] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0136] It should be noted that the first acquisition module 700, the second acquisition module 702, the clustering module 704, and the determination module 706 correspond to steps S102 to S108 in Example 1, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the modules as part of the device can be run in a computer terminal.

[0137] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in Example 1, which will not be repeated here.

[0138] The above-mentioned sensitivity calculation device for express delivery data may also include a processor and a memory. The above-mentioned first acquisition module 700, second acquisition module 702, clustering module 704, determination module 706, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0139] In an optional embodiment, the clustering module 704 includes: a first acquisition submodule, which is used to perform data format processing on the integrated data according to a predetermined data range to obtain processed integrated data; a second acquisition submodule, which is used to perform dimensionality reduction processing on the processed integrated data using a dimensionality reduction algorithm to obtain dimensionality reduced data corresponding to the second dimension; a third acquisition submodule, which is used to perform standardization processing on the dimensionality reduced data to obtain target format data; and a first clustering submodule, which is used to perform the KMeans clustering processing on the target format data to obtain the clustered data.

[0140] The processor includes a kernel, which retrieves the corresponding program unit from the memory. The kernel may be one or more. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory includes at least one memory chip.

[0141] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned methods for calculating the sensitivity of express data.

[0142] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0143] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: obtaining the current occurrence times of express delivery data in multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, and each of the express delivery data corresponds to a telephone number; integrating the current occurrence times according to the first dimension to obtain integrated data; performing KMeans clustering on the integrated data to obtain clustered data; sorting the centroid of each type of data in the clustered data according to the Euclidean clustering algorithm, and determining a corresponding sensitivity score value for each of the telephone numbers, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number.

[0144] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: respectively counting the first occurrence number of the above-mentioned outgoing data in each of the above-mentioned first dimensions, and the second occurrence number of the above-mentioned received data in each of the above-mentioned first dimensions.

[0145] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: integrate all the above-mentioned first occurrence times under each of the above-mentioned first dimensions, and integrate all the above-mentioned second occurrence times under each of the above-mentioned first dimensions to obtain the above-mentioned integrated data.

[0146] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: perform data format processing on the above-mentioned integrated data according to a predetermined data range to obtain processed integrated data; use a dimensionality reduction algorithm to perform dimensionality reduction processing on the above-mentioned processed integrated data to obtain dimensionality reduced data corresponding to the second dimension; perform standardization processing on the above-mentioned dimensionality reduced data to obtain target format data; perform the above-mentioned KMeans clustering processing on the above-mentioned target format data to obtain the above-mentioned clustered data.

[0147] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: generate a sensitivity scoring table based on the corresponding sensitivity scoring value of each of the above-mentioned telephone numbers, wherein the above-mentioned sensitivity scoring table includes: a sender sensitivity data table and a recipient sensitivity data table; when a sensitivity retrieval request is received, determine at least one of the above-mentioned telephone numbers carried in the above-mentioned sensitivity retrieval request; and retrieve the above-mentioned sensitivity scoring value corresponding to the at least one above-mentioned telephone number from the above-mentioned sensitivity scoring table.

[0148] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: sorting the centroids of each type of data in the above clustered data according to the Euclidean clustering algorithm to obtain a first sorting result; performing secondary KMeans clustering processing on each type of data in the above clustered data to obtain secondary clustered data; sorting the centroids of each type of data in the above secondary clustered data according to the Euclidean clustering algorithm to obtain a second sorting result; determining the corresponding sensitivity score value for each of the above telephone numbers according to the basic score determined based on the above first sorting result and the above second sorting result.

[0149] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: using the express data table storing the above-mentioned express data as the data source, extracting multiple of the above-mentioned first dimensions; wherein the above-mentioned first dimensions include: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus areas, sending and receiving express delivery at non-number-attributed locations, key focus items, and key focus groups.

[0150] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any one of the above-mentioned methods for calculating the sensitivity of express delivery data when the program is run.

[0151] According to an embodiment of the present application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is suitable for executing a program that initializes any of the above-mentioned steps of the method for calculating the sensitivity of express delivery data.

[0152] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: obtaining the current number of occurrences of express data in multiple first dimensions, wherein the above-mentioned express data includes: sending data and receiving data, and each of the above-mentioned express data corresponds to a telephone number; integrating the above-mentioned current number of occurrences according to the above-mentioned first dimensions to obtain integrated data; performing KMeans clustering processing on the above-mentioned integrated data to obtain clustered data; sorting the centroid of each type of data in the above-mentioned clustered data according to the Euclidean clustering algorithm, and determining a corresponding sensitivity score value for each of the above-mentioned telephone numbers, wherein the above-mentioned sensitivity score value is used to indicate the sensitivity coefficient of the user of the above-mentioned telephone number.

[0153] According to an embodiment of the present application, an embodiment of an electronic device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the above-mentioned methods for calculating the sensitivity of express delivery data.

[0154] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0155] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0158] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a non-volatile storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating the sensitivity of express delivery data, characterized in that: include: Obtain the current occurrence times of express delivery data under multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, each express delivery data corresponds to a telephone number, and the first dimension includes at least one of the following: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus area, sending and receiving express delivery in a non-number-attributed area, key focus items, and key focus people; Integrate the current number of occurrences according to the first dimension to obtain integrated data; Performing KMeans clustering processing on the integrated data to obtain clustered data; The centroids of each type of data in the clustered data are sorted according to the Euclidean clustering algorithm, and a corresponding sensitivity score value is determined for each telephone number, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number, and the centroid is the distance from each cluster center to the origin.

2. The method according to claim 1, characterized in that: The obtaining of the current occurrence times of the express delivery data in the multiple first dimensions comprises: respectively counting the first occurrence times of the sending data in each of the first dimensions and the second occurrence times of the receiving data in each of the first dimensions; The current occurrence times are integrated according to the first dimension to obtain integrated data, including: All the first occurrence times under each of the first dimensions are integrated, and all the second occurrence times under each of the first dimensions are integrated to obtain the integrated data.

3. The method according to claim 1, characterized in that The KMeans clustering process is performed on the integrated data to obtain clustered data, including: Performing data format processing on the integrated data according to a predetermined data range to obtain processed integrated data; Performing dimensionality reduction processing on the processed integrated data using a dimensionality reduction algorithm to obtain dimensionality reduced data corresponding to the second dimension; Performing standardization processing on the data after dimension reduction to obtain target format data; The KMeans clustering process is performed on the target format data to obtain the clustered data.

4. The method according to claim 1, characterized in that: After sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm and determining a corresponding sensitivity score value for each of the telephone numbers, the method further includes: Determining the corresponding sensitivity score value according to each of the telephone numbers to generate a sensitivity score table, wherein the sensitivity score table includes: a sender sensitivity data table and a receiver sensitivity data table; Upon receiving a sensitivity search request, determining at least one of the telephone numbers carried in the sensitivity search request; The sensitivity score value corresponding to at least one of the telephone numbers is retrieved from the sensitivity score table.

5. The method according to claim 1, characterized in that The step of sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm and determining a corresponding sensitivity score value for each telephone number includes: Sorting the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm to obtain a first sorting result; Performing secondary KMeans clustering processing on each type of data in the clustered data to obtain secondary clustered data; Sorting the centroids of each type of data in the secondary clustered data according to the Euclidean clustering algorithm to obtain a second sorting result; The corresponding sensitivity score value is determined for each of the telephone numbers according to the basic score determined based on the first sorting result and the second sorting result.

6. The method according to any one of claims 1 to 5, characterized in that Before obtaining the current occurrence counts of the express delivery data under the multiple first dimensions, the method further includes: The express data table storing the express data is used as a data source to extract a plurality of the first dimensions.

7. A sensitivity calculation device for express delivery data, characterized in that: include: A first acquisition module is used to obtain the current occurrence times of express delivery data under multiple first dimensions, wherein the express delivery data includes: sending data and receiving data, each of the express delivery data corresponds to a telephone number, and the first dimension includes at least one of the following: ambiguous name, ambiguous address, frequently changed name, frequently changed address, key focus area, sending and receiving express delivery in a non-number-attributed area, key focus items, and key focus people; A second acquisition module is used to integrate the current occurrence times according to the first dimension to obtain integrated data; A clustering module, used for performing KMeans clustering processing on the integrated data to obtain clustered data; A determination module is used to sort the centroids of each type of data in the clustered data according to the Euclidean clustering algorithm, and determine a corresponding sensitivity score value for each telephone number, wherein the sensitivity score value is used to indicate the sensitivity coefficient of the user of the telephone number, and the centroid is the distance from each cluster center to the origin.

8. The device according to claim 7, characterized in that The clustering module comprises: A first acquisition submodule is used to perform data format processing on the integrated data according to a predetermined data range to obtain processed integrated data; A second acquisition submodule is used to perform dimensionality reduction processing on the processed integrated data using a dimensionality reduction algorithm to obtain dimensionality reduced data corresponding to a second dimension; A third acquisition submodule is used to perform standardization processing on the data after dimension reduction to obtain target format data; The first clustering submodule is used to perform the KMeans clustering process on the target format data to obtain the clustered data.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the sensitivity calculation method for express delivery data as described in any one of claims 1 to 6.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the sensitivity calculation method for express delivery data as described in any one of claims 1 to 6.

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