CN110413654B - Method, device, computer equipment and storage medium for determining customer trusted contact information - Google Patents
Method, device, computer equipment and storage medium for determining customer trusted contact information Download PDFInfo
- Publication number
- CN110413654B CN110413654B CN201910534809.5A CN201910534809A CN110413654B CN 110413654 B CN110413654 B CN 110413654B CN 201910534809 A CN201910534809 A CN 201910534809A CN 110413654 B CN110413654 B CN 110413654B
- Authority
- CN
- China
- Prior art keywords
- contact
- credibility
- obtaining
- service
- scoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000010586 diagram Methods 0.000 claims abstract description 73
- 238000012795 verification Methods 0.000 claims description 12
- 230000000694 effects Effects 0.000 abstract description 3
- 238000000638 solvent extraction Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000010365 information processing Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Telephonic Communication Services (AREA)
Abstract
The invention relates to a method, a device, a computer device and a storage medium for determining trusted contact information of a client, wherein the method comprises the following steps: obtaining a time distribution diagram of contact information according to pre-stored contact information of a user; performing credibility scoring on service contacts of the pre-stored contact information of the user to obtain a credibility scoring result; obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval; optimizing the time distribution map according to the service contact classification level; obtaining an observation threshold; obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact with the maximum trusted degree. The effect of increasing the reliability comparison and reducing the misjudgment purely by a one-dimensional judgment mode under the condition of multiple contacts is achieved.
Description
Technical Field
The present invention relates to the field of information processing, and in particular, to a method, an apparatus, a computer device, and a storage medium for determining trusted contact information of a client.
Background
At present, an enterprise group contacts a client, and a plurality of contact scenes exist, which may relate to the acquisition of the client contact information, but the credibility of the client contact information acquired by different contact scenes is different, for example, the credibility of the client contact information is judged by simply updating dimensions according to time, namely, the more recent and more credible the update time, the probability of misjudgment is increased.
Disclosure of Invention
Based on this, it is necessary to provide a method, apparatus, computer device and storage medium for determining the customer trusted contact information, aiming at the problem that the probability of erroneous judgment is increased when the reliability of the customer trusted contact information is determined by simply updating the dimension in time.
A method of determining customer trusted contact information, the method comprising: obtaining a time distribution diagram of contact information according to pre-stored contact information of a user; performing credibility scoring on service contacts of the pre-stored contact information of the user to obtain a credibility scoring result; obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval; optimizing the time distribution map according to the service contact classification level; obtaining an observation threshold; obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact with the maximum trusted degree.
In one embodiment, the method further comprises: putting the pre-stored contact information of the user on a time axis; and obtaining a time distribution diagram of the pre-stored contact information of the user.
In one embodiment, the method further comprises: obtaining a first partitioning dimension, wherein the first partitioning dimension is the purpose of the service contact for collecting the contact information; obtaining a second scoring dimension, wherein the second scoring dimension is whether the service contact has verification measures to verify the reserved information of the client; obtaining a third scoring dimension, wherein the third scoring dimension is the historical accuracy of the contact information reserved by the service contact; assigning the first scoring dimension, the second scoring dimension, and the third scoring dimension to corresponding weight values; and obtaining the credibility scoring result according to the sum of the first scoring dimension, the second scoring dimension and the third scoring dimension given with the corresponding weight value.
In one embodiment, the method further comprises: obtaining the credibility grade value of each piece of contact information according to the service contact classification grade; and optimizing the time distribution map according to the credibility grade value.
In one embodiment, the time distribution diagram of the contact information includes a step-like pattern, a convex-like pattern, and a zigzag-like pattern, and if the time distribution diagram is in the step-like pattern, the obtaining, according to the observation threshold, the service contact with the maximum reliability from the optimized time distribution diagram includes: judging whether the time interval between two adjacent end points of the ladder-shaped graph is smaller than an observation threshold value or not; if the time interval between two adjacent end points of the ladder-shaped graph is smaller than the observation threshold value, comparing the credibility of the service contact corresponding to each ladder end point to obtain the service contact with the maximum credibility; and if the time interval between two adjacent endpoints of the step-shaped graph is greater than or equal to the observation threshold value, obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, if the time distribution diagram is in a convex shape, the obtaining, according to the observation threshold, the service contact with the highest reliability from the optimized time distribution diagram includes: judging whether the time interval between two adjacent endpoints of the convex graph is smaller than an observation threshold value or not; and if the time interval between the two endpoints of the convex pattern is smaller than an observation threshold value, comparing the credibility of the convex part of the convex pattern with the credibility of the rest part of the convex pattern to obtain the service contact with the maximum credibility. And if the time interval between the two endpoints of the convex graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, if the time distribution diagram is in a zigzag pattern, the obtaining, according to the observation threshold, the service contact with the highest reliability from the optimized time distribution diagram includes: judging whether the time interval between two adjacent endpoints of the zigzag graph is smaller than the observation threshold value; if the time interval between two endpoints of the serrated graph is smaller than an observation threshold, classifying the endpoints on the same horizontal line in the serrated graph into a group, and comparing the latest updating time and the reliability of each group of endpoints to obtain the service contact with the maximum reliability; and if the time interval between the two endpoints of the zigzag graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
An apparatus for determining trusted contact information for a client, the apparatus comprising: the time distribution diagram obtaining unit is used for obtaining a time distribution diagram of the contact information according to the pre-stored contact information of the user; the credibility scoring result obtaining unit is used for scoring credibility of the service contact points of the pre-stored contact information of the user to obtain a credibility scoring result; the service contact classification grade obtaining unit is used for obtaining the service contact classification grade according to the credibility scoring result and the credibility score interval; a time distribution map optimizing unit, configured to optimize the time distribution map according to the service contact classification level; an observation threshold value obtaining unit configured to obtain an observation threshold value; the service contact obtaining unit is used for obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and the trusted contact information obtaining unit is used for obtaining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact point with the maximum credibility.
A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the method of determining customer trusted contact information described above.
A storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method of determining customer trusted contact information described above.
The method, the device, the computer equipment and the storage medium for determining the customer trusted contact information are characterized in that the time distribution diagram of the contact information is obtained according to the customer contact information, the business contact points of the reserved contact information of the customer are subjected to credibility scoring, the credibility score of the business contact points is calculated through three dimensions, the grades of the business contact points are divided according to the credibility scoring result of the business contact points, the time distribution diagram is optimized according to the classification grades of the business contact points, after an observation threshold is set, the end point credibility of the diagram is compared in the optimized time distribution diagram, the contact way corresponding to the contact point with the largest credibility is selected, and a group with the largest credibility in multiple groups of contact information can be judged. Therefore, the method and the device can judge one group with the maximum credibility in the multiple groups of contact information by combining the rule of the change of the customer contact information on the time axis and the credibility of the contact point caused by the change, thereby increasing the comparison of the credibility and reducing the misjudgment effect of purely depending on a one-dimensional judgment mode under the condition of multiple contact points.
Drawings
FIG. 1 is a diagram of an implementation environment for a method of determining customer trusted contact information provided in one embodiment;
FIG. 2 is a block diagram of the internal architecture of a computer device in one embodiment;
FIG. 3 is a flow diagram of a method of determining customer trusted contact information in one embodiment;
FIG. 4 is a flow diagram of a time distribution diagram for obtaining contact information based on pre-stored contact information for a user, in one embodiment;
FIG. 5 is a flow chart of scoring the reliability of service contacts of the user pre-stored contact information to obtain a reliability scoring result in one embodiment;
FIG. 6 is a flow diagram of optimizing the time profile according to the business contact classification level in one embodiment;
FIG. 7 is a flow chart of obtaining a service contact with a maximum reliability from an optimized time profile according to the observation threshold in one embodiment;
FIG. 8 is a time distribution diagram of a client contact time distribution diagram in a ladder pattern in one embodiment;
FIG. 9 is a flow chart of obtaining a service contact with a maximum degree of confidence from an optimized time profile according to the observation threshold in one embodiment;
FIG. 10 is a time distribution diagram of a user contact time distribution diagram in a convex shape in one embodiment;
FIG. 11 is a flow chart of obtaining a service contact with a maximum degree of confidence from an optimized time profile according to the observation threshold in one embodiment;
FIG. 12 is a time distribution diagram of a user contact in a zigzag pattern in one embodiment;
FIG. 13 is a block diagram of an apparatus for determining trusted contact information for a client in one embodiment;
FIG. 14 is a block diagram showing the structure of a time distribution map obtaining unit in one embodiment;
FIG. 15 is a block diagram of the structure of a confidence score result obtaining unit in one embodiment;
FIG. 16 is a block diagram of the time profile optimization unit in one embodiment;
FIG. 17 is a block diagram of the service contact acquisition unit in one embodiment;
FIG. 18 is a block diagram of the service contact acquisition unit in one embodiment;
fig. 19 is a block diagram of the structure of the service contact obtaining unit in one embodiment.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms first, second, etc. as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element. For example, the script of the first determined client trusted contact information may be referred to as the script of the second determined client trusted contact information, and similarly, the script of the second determined client trusted contact information may be referred to as the script of the second determined client trusted contact information, without departing from the scope of the present application.
FIG. 1 is a diagram of an implementation environment for a method of determining customer trusted contact information provided in one embodiment, as shown in FIG. 1, in which a computer device 110 is included.
The computer device 110 is an intelligent electronic device, for example, a computer device such as a computer used by a technician, a software tool for determining the trusted contact information of the client is installed on the computer device 110, and when the reliability of the contact information of the client needs to be determined, the technician can obtain a time distribution diagram of the contact information on the computer device 110 according to the pre-stored contact information of the user; then, credibility scoring is carried out on the business contacts of the pre-stored contact information of the user, and a credibility scoring result is obtained; secondly, obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval; optimizing the time distribution map according to the service contact classification level; obtaining an observation threshold; obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and finally, determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact point with the maximum trusted degree.
It should be noted that, the terminal 120 and the computer device 110 may be, but not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like.
FIG. 2 is a schematic diagram of the internal structure of a computer device in one embodiment. As shown in fig. 2, the computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected by a system bus. The non-volatile storage medium of the computer device stores an operating system, a database, and computer readable instructions, where the database may store a control information sequence, and where the computer readable instructions, when executed by a processor, cause the processor to implement a method for determining trusted contact information of a client. The processor of the computer device is used to provide computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform a method of determining trusted contact information for a client. The network interface of the computer device is for communicating with a terminal connection. It will be appreciated by persons skilled in the art that the architecture shown in fig. 2 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
As shown in fig. 3, in one embodiment, a method for determining client trusted contact information is provided, and the method for determining client trusted contact information may be applied to the computer device 110, and specifically may include the following steps:
step 310: obtaining a time distribution diagram of the contact information according to the pre-stored contact information of the user;
in the embodiment of the application, all the contact information reserved by the client is put on a time axis to obtain the time distribution diagram of the contact information of the client.
FIG. 4 shows a flowchart of an implementation of step 310, i.e., the step of obtaining a time profile of contact information from a user pre-stored contact information, in one embodiment, which may include the steps of:
step 311, putting the pre-stored contact information of the user on a time axis;
step 312, a time profile of the user's pre-stored contact information is obtained.
If it is assumed that client a leaves 5 different contact ways through 5 different contacts in 4, 8, 2015, 5, 12, and 3 of 2012, respectively, then corresponding 5 points D4 (4, 1, 2, 3 (5, 3, 2, 3, 12, 2, 0 (3, 5) can be derived; these five points may be plotted on a time distribution graph, wherein the time distribution graph of the customer contact of fig. 1 is obtained by representing the time change point as the horizontal axis and the different 5 contacts corresponding to the different 5 contacts by the vertical axis.
Step 320: performing credibility scoring on service contacts of pre-stored contact information of a user to obtain a credibility scoring result;
FIG. 5 shows a flowchart of an implementation of step 320, namely, the step of scoring the reliability of service contacts of pre-stored contact information of a user, to obtain a reliability scoring result, which may specifically include the following steps:
step 321, obtaining a first dividing dimension, wherein the first dividing dimension is the purpose of collecting contact information for service contacts;
step 322, obtaining a second scoring dimension, where the second scoring dimension is whether the service contact has a verification measure to verify the customer reservation information;
step 323, obtaining a third scoring dimension, wherein the third scoring dimension is the historical accuracy of the contact information reserved by the service contact;
step 324, assigning corresponding weight values to the first scoring dimension, the second scoring dimension, and the third scoring dimension;
and step 325, obtaining a credibility scoring result according to the sum of the first scoring dimension, the second scoring dimension and the third scoring dimension given the corresponding weight value.
In steps 321-325, credibility scoring is performed on service contacts corresponding to contact information reserved by a client, and the credibility score is as follows: c=a+u+b+s+c+r, where a, b, C are adjustable parameters, configurable as per 0.3,0.3,0.4. Parameters may also be modified as appropriate, and the application is not limited in this regard. The first classification dimension is mainly the purpose of the contact collection contact manner, namely the importance of providing services for users, and the purpose high-low classification comprises: for clients to receive critical information, higher than for profit identification classes, higher than for clients to receive non-critical information, higher than for non-pure information collection classes; the second tape dimension is whether the contact scene has verification measures to verify the reservation information of the client, and the reservation information high-low classification comprises: the verification of the short message verification code is higher than that of the short message verification code without verification; the third scoring dimension is the past accuracy of the information reserved by the contact, the third scoring dimension is obtained according to the historical accuracy, the historical accuracy is 60, the historical judgment has inaccurate recorded sub-60 score, and the accurate recorded sub-60 score.
Step 330: obtaining service contact classification grades according to the credibility scoring result and the credibility scoring interval;
in some embodiments, service contacts may be ranked according to a confidence score interval according to a confidence score result of the service contacts, and the service contacts may be classified into a low confidence class, a medium confidence class, a high confidence class, and the like. If the service contact points are divided into four grades according to the credibility score interval, the first grade: 100-90 minutes; second level: 90-70 minutes; third level: 70-60 minutes; fourth grade: 60 minutes or less.
Step 340: optimizing a time distribution diagram according to the classification level of the service contact;
FIG. 6 shows a flowchart of the implementation of step 340, i.e., the step of optimizing the time profile according to the business contact classification level, in one embodiment, which may include the steps of:
step 341, obtaining the credibility grade value of each contact information according to the service contact classification grade;
step 342, optimizing the time profile according to the confidence level value.
In steps 341-342, according to the result of the credibility classification level of the service contact, each piece of contact information is obtained to obtain different credibility level values according to the classification result of the service contact, and the time distribution diagram of the customer contact information in the first step is optimized.
Step 350: obtaining an observation threshold;
in the embodiment of the application, the observation threshold t is set according to the update characteristic of the contact information, and the observation threshold of the contact information can be set to be 1 year in general, or can be set to be other time periods according to different actual conditions, so that the application is not limited.
Step 360: obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold;
fig. 7 shows a flowchart of an implementation of the step of obtaining, in step 360, a service contact with the highest reliability from the optimized time distribution diagram according to the observation threshold, if the time distribution diagram is in a ladder shape, the time distribution diagram of the contact information includes a ladder shape, a convex shape, and a saw tooth shape, and may specifically include the following steps:
step 361a, judging whether the time interval between two adjacent end points of the step-shaped graph is smaller than an observation threshold value;
step 362a, comparing the reliability of the service contacts corresponding to each step endpoint if the time interval between two adjacent endpoints of the step graph is smaller than the observation threshold value, so as to obtain the service contact with the maximum reliability;
and step 363a, if the time interval between two adjacent endpoints of the step-shaped graph is greater than or equal to the observation threshold, obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
When the updating condition of the mobile phone is in a step-shaped graph on the time sequence. If the time interval of the two endpoints of the ladder-shaped graph is smaller than the observation threshold value, the credibility of the service contacts corresponding to the endpoints of each ladder is required to be compared, the contact with higher credibility is taken, and if the time interval of the two endpoints of the ladder-shaped graph is the same, the contact close to the tail end of the time sequence is taken; when the start-stop time interval of the ladder-shaped graph is smaller than the observation threshold value, the reliability of the service contacts at the two ends of the ladder needs to be judged and compared, and the contacts with higher reliability need to be taken. The time distribution diagram of the customer contact addresses of fig. 8 as listed in the first step is a ladder-like graph, which illustrates that the contact addresses reserved for the contacts contacted by the user at each time stage are different; at this time, the step end points D1 are taken to the left one by one from the end point D0 at the rightmost end of the step for comparison, firstly, whether the time interval T0-T1< T of D0 and D1 is established or not is checked, if so, the reliability grade values C0 and C1 of D0 and D1 are compared, if C0> C1, D0 is taken, and then the time interval T0-T2< T and the reliability grade C0> C2 are compared with the end point D2 at the next left; if C0< C1, D1 is selected, and the comparison of T1-T2< T and the reliability level C1> C2 is carried out with D2 until Tm-Tn > T, and the service contact with the maximum reliability is selected.
Fig. 9 shows a flowchart of an implementation of the step of obtaining, in step 360, the service contact with the highest reliability from the optimized time distribution map according to the observation threshold, if the time distribution map is in a convex shape, and may specifically include the following steps:
step 361b, judging whether the time interval between two adjacent endpoints of the convex graph is smaller than an observation threshold value;
step 362b, if the time interval between two endpoints of the convex pattern is smaller than the observation threshold, comparing the credibility of the convex portion and the rest portion of the convex pattern to obtain the service contact with the maximum credibility;
and step 363b, if the time interval between the two endpoints of the convex pattern is greater than or equal to the observation threshold, obtaining the service contact near the tail end of the time sequence as the service contact with the highest credibility.
If the time interval between two adjacent endpoints of the convex pattern is smaller than the observation threshold, the credibility of the convex portion and the rest portion needs to be compared. FIG. 10, which is a time distribution plot of customer contact addresses in a convex shape, illustrates that the service contacts contacted earlier or later in time are reserved for the same addresses, and that the reserved addresses for earlier and later periods are different from the addresses for the intermediate period; at this time, similarly, starting from the rightmost end point D0, comparing whether the time interval T0-T1< T between the D0 and the convex end point D1 is true or not, if true, comparing the credibility of the D0 and the D1, and taking the higher credibility grade C value; if not, directly taking the contact way corresponding to the D0 endpoint.
FIG. 11 is a flowchart showing an implementation of the step of obtaining the service contact with the highest reliability from the optimized time distribution diagram in step 360 according to the observation threshold if the time distribution diagram is in a zigzag pattern, which may specifically include the following steps:
step 361c, judging whether the time interval between two adjacent endpoints of the zigzag pattern is smaller than an observation threshold;
step 362c, if the time interval between two endpoints of the serrated graph is smaller than the observation threshold, classifying the endpoints on the same horizontal line in the serrated graph into a group, and comparing the latest update time and reliability of each group of endpoints to obtain the service contact with the maximum reliability;
and step 363c, if the time interval between the two endpoints of the zigzag pattern is greater than or equal to the observation threshold, obtaining the service contact near the tail end of the time sequence as the service contact with the highest credibility.
Referring to a processing mode that a time distribution diagram is a step-shaped graph, firstly, if the time interval between two endpoints of the sawtooth-shaped graph is smaller than an observation threshold value, grouping the endpoints on the same horizontal line in the sawtooth-shaped graph into one group, and comparing the latest updating time and credibility of the endpoints of the groups. FIG. 12, which is a time distribution graph of customer contact addresses in a zigzag shape, illustrates that different contact addresses reserved for different periods have the same contact addresses as before; at this time, the endpoints on the same horizontal line are connected, the rightmost endpoint on each horizontal line is taken for comparison one by one, the rightmost endpoint in all endpoints is also started from the rightmost T0 during comparison, when T0-T1 is less than T, the reliability grade C value is compared, and then the next point with higher reliability grade C value is taken for comparison; and selecting the service contact with the maximum reliability until Tm-Tn is more than or equal to t.
Step 370: and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact point with the maximum trusted degree.
In the embodiment of the application, the time distribution diagram of the user contact information is compared with the graph to obtain the time distribution diagram which is met by the user, the service contact with the maximum credibility is calculated from the time distribution diagram which is met by the user, and the service contact is credible contact information. The method combines the rule of the change of the customer contact information on the time axis with the credibility of the contact point of the change to judge the group with the largest credibility in the multiple groups of contact information, thereby increasing the comparison of the credibility and reducing the misjudgment effect purely depending on the one-dimensional judgment mode under the condition of multiple contact points.
As shown in fig. 13, in one embodiment, an apparatus for determining customer trusted contact information is provided, and the apparatus for determining customer trusted contact information may be integrated into the computer device 110, and may specifically include a time profile obtaining unit 410, a confidence score obtaining unit 420, a service contact classification level obtaining unit 430, a time profile optimizing unit 440, an observation threshold obtaining unit 450, a service contact obtaining unit 460, and a trusted contact information obtaining unit 470.
A time profile obtaining unit 410, configured to obtain a time profile of contact information according to pre-stored contact information of a user;
a reliability scoring result obtaining unit 420, configured to perform reliability scoring on service contacts of the pre-stored contact information of the user, to obtain a reliability scoring result;
a service contact classification level obtaining unit 430, configured to obtain the service contact classification level according to the reliability scoring result and the reliability score interval;
a time profile optimizing unit 440, configured to optimize the time profile according to the service contact classification level;
an observation threshold value obtaining unit 450 for obtaining an observation threshold value;
a service contact obtaining unit 460, configured to obtain, according to the observation threshold, a service contact with the maximum reliability from the optimized time distribution diagram;
and the trusted contact information obtaining unit 470 is configured to obtain trusted contact information of the client from the pre-stored contact information of the user according to the service contact point with the maximum reliability.
As shown in fig. 14, in one embodiment, the time profile obtaining unit 410 includes a customer reservation contact information module 411, a time profile obtaining module 412:
A customer reservation contact information module 411, configured to put the pre-stored contact information of the user on a time axis;
a time distribution map obtaining module 412, configured to obtain a time distribution map of the pre-stored contact information of the user.
As shown in fig. 15, in one embodiment, the reliability scoring result obtaining unit 420 includes a first scoring dimension obtaining module 421, a second scoring dimension obtaining module 422, a third scoring dimension obtaining module 423, a weight value assigning module 424, and a reliability scoring result obtaining module 425:
a first dimension obtaining module 421, configured to obtain a first dimension, where the first dimension is a purpose of the service contact to collect the contact information;
a second scoring dimension obtaining module 422, configured to obtain a second scoring dimension, where the second scoring dimension is whether the service contact has a verification measure to verify the customer reservation information;
a third scoring dimension obtaining module 423, configured to obtain a third scoring dimension, where the third scoring dimension is the historical accuracy of the contact information reserved by the service contact;
a weight value assignment module 424, configured to assign the first scoring dimension, the second scoring dimension, and the third scoring dimension corresponding weight values;
A confidence score result obtaining module 425, configured to obtain the confidence score result according to a sum of the first scoring dimension, the second scoring dimension, and the third scoring dimension given the corresponding weight value.
As shown in fig. 16, in one embodiment, the time profile optimization unit 440 includes a confidence level value obtaining module 441 and a time profile optimization module 442:
the credibility grade value obtaining module 441 is configured to obtain a credibility grade value of each piece of contact information according to the service contact classification grade;
the time distribution map optimizing module 442 is configured to optimize the time distribution map according to the confidence level value.
As shown in fig. 17, in one embodiment, the time profile of the contact information includes a step-like pattern, a convex-like pattern, and a saw-tooth-like pattern, where if the time profile is a step-like pattern, the service contact obtaining unit 460 includes a time interval judging module 461a, a first service contact obtaining module 462a, and a second service contact obtaining module 463a:
a time interval judging module 461a, configured to judge whether a time interval between two adjacent end points of the step-like pattern is smaller than an observation threshold;
A first service contact obtaining module 462a, configured to compare the reliability of the service contact corresponding to each step endpoint if the time interval between two adjacent endpoints of the step-like graph is less than the observation threshold, and obtain the service contact with the maximum reliability;
and a second service contact obtaining module 463a, configured to obtain, when the time interval between two adjacent endpoints of the step-like pattern is greater than or equal to the observation threshold, a service contact near the tail end of the time sequence as the service contact with the highest reliability.
As shown in fig. 18, in one embodiment, if the time profile is in a convex shape, the service contact obtaining unit 460 includes a time interval judging unit 461b, a first service contact obtaining module 462b, and a second service contact obtaining module 463b:
a time interval judging module 461b, configured to judge whether a time interval between two adjacent endpoints of the convex graph is less than an observation threshold;
and the first service contact obtaining module 462b is configured to compare the credibility of the convex portion of the convex pattern with the rest of the convex portion to obtain the service contact with the maximum credibility if the time interval between the two endpoints of the convex pattern is smaller than the observation threshold.
And a second service contact obtaining module 463b, configured to obtain, as the service contact with the highest reliability, a service contact near the tail end of the time sequence if the time interval between the two endpoints of the convex pattern is greater than or equal to the observation threshold.
As shown in fig. 19, in one embodiment, if the time profile is in a zigzag pattern, the service contact obtaining unit 460 includes a time interval judging unit 461c, a first service contact obtaining module 462c, and a second service contact obtaining module 463c:
a time interval judging unit 461c, configured to judge whether a time interval between two adjacent endpoints of the zigzag pattern is smaller than the observation threshold;
a first service contact obtaining module 462c, configured to, if a time interval between two endpoints of the zigzag pattern is smaller than an observation threshold, group endpoints on a same horizontal line in the zigzag pattern, and compare a latest update time and a reliability of each group of endpoints to obtain a service contact with the maximum reliability;
and a second service contact obtaining module 463c, configured to obtain, as the service contact with the highest reliability, a service contact near the tail end of the time sequence if the time interval between the two endpoints of the zigzag pattern is greater than or equal to the observation threshold.
In one embodiment, a computer device is presented, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: obtaining a time distribution diagram of contact information according to pre-stored contact information of a user; performing credibility scoring on service contacts of the pre-stored contact information of the user to obtain a credibility scoring result; obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval; optimizing the time distribution map according to the service contact classification level; obtaining an observation threshold; obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact with the maximum trusted degree.
In one embodiment, the steps performed by the processor further include: putting the pre-stored contact information of the user on a time axis; and obtaining a time distribution diagram of the pre-stored contact information of the user.
In one embodiment, the steps performed by the processor further include: obtaining a first partitioning dimension, wherein the first partitioning dimension is the purpose of the service contact for collecting the contact information; obtaining a second scoring dimension, wherein the second scoring dimension is whether the service contact has verification measures to verify the reserved information of the client; obtaining a third scoring dimension, wherein the third scoring dimension is the historical accuracy of the contact information reserved by the service contact; assigning the first scoring dimension, the second scoring dimension, and the third scoring dimension to corresponding weight values; and obtaining the credibility scoring result according to the sum of the first scoring dimension, the second scoring dimension and the third scoring dimension given with the corresponding weight value.
In one embodiment, the steps performed by the processor further include: obtaining the credibility grade value of each piece of contact information according to the service contact classification grade; and optimizing the time distribution map according to the credibility grade value.
In one embodiment, the time distribution diagram of the contact information includes a step-like pattern, a convex-like pattern, and a saw-tooth-like pattern, wherein if the time distribution diagram is in the step-like pattern, the steps executed by the processor include: judging whether the time interval between two adjacent end points of the ladder-shaped graph is smaller than an observation threshold value or not; if the time interval between two adjacent end points of the ladder-shaped graph is smaller than the observation threshold value, comparing the credibility of the service contact corresponding to each ladder end point to obtain the service contact with the maximum credibility; and if the time interval between two adjacent endpoints of the step-shaped graph is greater than or equal to the observation threshold value, obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, if the time profile is in a convex shape, the steps performed by the processor include: judging whether the time interval between two adjacent endpoints of the convex graph is smaller than an observation threshold value or not; and if the time interval between the two endpoints of the convex pattern is smaller than an observation threshold value, comparing the credibility of the convex part of the convex pattern with the credibility of the rest part of the convex pattern to obtain the service contact with the maximum credibility. And if the time interval between the two endpoints of the convex graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, the steps performed by the processor include: judging whether the time interval between two adjacent endpoints of the zigzag graph is smaller than the observation threshold value; if the time interval between two endpoints of the serrated graph is smaller than an observation threshold, classifying the endpoints on the same horizontal line in the serrated graph into a group, and comparing the latest updating time and the reliability of each group of endpoints to obtain the service contact with the maximum reliability; and if the time interval between the two endpoints of the zigzag graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: obtaining a time distribution diagram of contact information according to pre-stored contact information of a user; performing credibility scoring on service contacts of the pre-stored contact information of the user to obtain a credibility scoring result; obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval; optimizing the time distribution map according to the service contact classification level; obtaining an observation threshold; obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold; and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact with the maximum trusted degree.
In one embodiment, the steps performed by the processor further include: putting the pre-stored contact information of the user on a time axis; and obtaining a time distribution diagram of the pre-stored contact information of the user.
In one embodiment, the steps performed by the processor further include: obtaining a first partitioning dimension, wherein the first partitioning dimension is the purpose of the service contact for collecting the contact information; obtaining a second scoring dimension, wherein the second scoring dimension is whether the service contact has verification measures to verify the reserved information of the client; obtaining a third scoring dimension, wherein the third scoring dimension is the historical accuracy of the contact information reserved by the service contact; assigning the first scoring dimension, the second scoring dimension, and the third scoring dimension to corresponding weight values; and obtaining the credibility scoring result according to the sum of the first scoring dimension, the second scoring dimension and the third scoring dimension given with the corresponding weight value.
In one embodiment, the steps performed by the processor further include: obtaining the credibility grade value of each piece of contact information according to the service contact classification grade; and optimizing the time distribution map according to the credibility grade value.
In one embodiment, the time distribution diagram of the contact information includes a step-like pattern, a convex-like pattern, and a saw-tooth-like pattern, wherein if the time distribution diagram is in the step-like pattern, the steps executed by the processor include: judging whether the time interval between two adjacent end points of the ladder-shaped graph is smaller than an observation threshold value or not; if the time interval between two adjacent end points of the ladder-shaped graph is smaller than the observation threshold value, comparing the credibility of the service contact corresponding to each ladder end point to obtain the service contact with the maximum credibility; and if the time interval between two adjacent endpoints of the step-shaped graph is greater than or equal to the observation threshold value, obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, if the time profile is in a convex shape, the steps performed by the processor include: judging whether the time interval between two adjacent endpoints of the convex graph is smaller than an observation threshold value or not; and if the time interval between the two endpoints of the convex pattern is smaller than an observation threshold value, comparing the credibility of the convex part of the convex pattern with the credibility of the rest part of the convex pattern to obtain the service contact with the maximum credibility. And if the time interval between the two endpoints of the convex graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
In one embodiment, the steps performed by the processor include: judging whether the time interval between two adjacent endpoints of the zigzag graph is smaller than the observation threshold value; if the time interval between two endpoints of the serrated graph is smaller than an observation threshold, classifying the endpoints on the same horizontal line in the serrated graph into a group, and comparing the latest updating time and the reliability of each group of endpoints to obtain the service contact with the maximum reliability; and if the time interval between the two endpoints of the zigzag graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored in a computer-readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method of determining trusted contact information for a client, the method comprising:
according to the pre-stored contact information of the user, drawing a time change point of the contact information on a horizontal axis of a time axis, and drawing a contact generated based on a contact mode corresponding to the time change point on a vertical axis of the time axis to form a time distribution diagram of the pre-stored contact information of the user;
Obtaining a plurality of scoring dimensions, giving corresponding weight values to each scoring dimension, and performing credibility scoring on service contacts of pre-stored contact information of the user according to the scoring dimensions and the weight values to obtain credibility scoring results; the scoring dimension comprises a first scoring dimension, a second scoring dimension and a third scoring dimension, wherein the first scoring dimension is the purpose of the service contact to collect the contact information, the second scoring dimension is whether the service contact has verification measures to verify the reserved information of the client, and the third scoring dimension is the historical accuracy of the reserved contact information of the service contact;
obtaining the service contact classification level according to the credibility scoring result and the credibility scoring interval;
obtaining the credibility grade value of each piece of contact information according to the service contact classification grade; optimizing the time distribution map according to the credibility grade value;
obtaining an observation threshold;
obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold;
and determining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact with the maximum trusted degree.
2. The method of claim 1, wherein scoring the reliability of the business contact of the pre-stored contact information of the user according to the scoring dimension and the weight value to obtain a reliability scoring result comprises:
and obtaining the credibility scoring result according to the sum of the first scoring dimension, the second scoring dimension and the third scoring dimension given with the corresponding weight value.
3. The method of claim 1, wherein the time profile of the contact information includes a stepped pattern, a convex pattern, and a zigzag pattern, and wherein if the time profile is a stepped pattern, the obtaining, according to the observation threshold, the service contact with the greatest reliability from the optimized time profile includes:
judging whether the time interval between two adjacent end points of the ladder-shaped graph is smaller than an observation threshold value or not;
if the time interval between two adjacent end points of the ladder-shaped graph is smaller than the observation threshold value, comparing the credibility of the service contact corresponding to each ladder end point to obtain the service contact with the maximum credibility;
and if the time interval between two adjacent endpoints of the step-shaped graph is greater than or equal to the observation threshold value, obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
4. A method according to claim 3, wherein, if the time profile is in a convex shape, the obtaining the service contact with the highest reliability from the optimized time profile according to the observation threshold value comprises:
judging whether the time interval between two adjacent endpoints of the convex shape graph is smaller than an observation threshold value or not;
if the time interval between the two endpoints of the convex pattern is smaller than an observation threshold value, comparing the credibility of the convex part of the convex pattern with the credibility of the rest part of the convex pattern to obtain a service contact with the largest credibility;
and if the time interval between the two endpoints of the convex shape graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
5. A method according to claim 3, wherein, if the time profile is in a zigzag pattern, the obtaining the service contact with the highest reliability from the optimized time profile according to the observation threshold value comprises:
judging whether the time interval between two adjacent endpoints of the zigzag graph is smaller than the observation threshold value;
if the time interval between two endpoints of the serrated graph is smaller than an observation threshold, classifying the endpoints on the same horizontal line in the serrated graph into a group, and comparing the latest updating time and the reliability of each group of endpoints to obtain the service contact with the maximum reliability;
And if the time interval between the two endpoints of the zigzag graph is greater than or equal to an observation threshold value, obtaining a service contact close to the tail end of the time sequence as the service contact with the maximum credibility.
6. An apparatus for determining trusted contact information for a client, the apparatus comprising:
the time distribution diagram obtaining unit is used for drawing a time change point of the contact information on the horizontal axis of the time axis according to the pre-stored contact information of the user, drawing a contact point generated based on the contact information corresponding to the time change point on the vertical axis of the time axis, and forming a time distribution diagram of the pre-stored contact information of the user;
the credibility scoring result obtaining unit is used for obtaining a plurality of scoring dimensions, endowing each scoring dimension with a corresponding weight value, and carrying out credibility scoring on service contacts of the pre-stored contact information of the user according to the scoring dimensions and the weight values to obtain a credibility scoring result; the scoring dimension comprises a first scoring dimension, a second scoring dimension and a third scoring dimension, wherein the first scoring dimension is the purpose of the service contact to collect the contact information, the second scoring dimension is whether the service contact has verification measures to verify the reserved information of the client, and the third scoring dimension is the historical accuracy of the reserved contact information of the service contact;
The service contact classification grade obtaining unit is used for obtaining the service contact classification grade according to the credibility scoring result and the credibility score interval;
the time distribution map optimizing unit is used for obtaining the credibility grade value of each piece of contact information according to the service contact classification grade; optimizing the time distribution map according to the credibility grade value;
an observation threshold value obtaining unit configured to obtain an observation threshold value;
the service contact obtaining unit is used for obtaining the service contact with the maximum credibility from the optimized time distribution diagram according to the observation threshold;
and the trusted contact information obtaining unit is used for obtaining the trusted contact information of the client from the pre-stored contact information of the user according to the service contact point with the maximum credibility.
7. The apparatus of claim 6, wherein the time profile of the contact information comprises a staircase pattern, a convex pattern, a saw tooth pattern, and wherein the service contact obtaining unit comprises a time interval judging module, a first service contact obtaining module, and a second service contact obtaining module if the time profile is a staircase pattern:
the time interval judging module is used for judging whether the time interval between two adjacent end points of the ladder-shaped graph is smaller than an observation threshold value or not;
The first service contact obtaining module is used for comparing the service contact credibility corresponding to each step endpoint to obtain the service contact with the maximum credibility if the time interval between the two adjacent endpoints of the step graph is smaller than an observation threshold;
and the second service contact obtaining module is used for obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility if the time interval between the two adjacent endpoints of the ladder-shaped graph is more than or equal to the observation threshold value.
8. The apparatus of claim 7, wherein the service contact obtaining unit includes a time interval judging unit if the time profile is in a convex shape, a first service contact obtaining module, and a second service contact obtaining module:
the time interval judging module is used for judging whether the time interval between two adjacent endpoints of the convex shape graph is smaller than an observation threshold value or not;
the first service contact obtaining module is used for comparing the credibility of the convex part of the convex shape graph with the credibility of the rest part of the convex shape graph to obtain the service contact with the maximum credibility if the time interval between the two endpoints of the convex shape graph is smaller than an observation threshold value;
and the second service contact obtaining module is used for obtaining the service contact close to the tail end of the time sequence as the service contact with the maximum credibility if the time interval between the two endpoints of the convex shape graph is more than or equal to the observation threshold value.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the method of determining customer trusted contact information as claimed in any one of claims 1 to 5.
10. A storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the method of determining customer trusted contact information as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910534809.5A CN110413654B (en) | 2019-06-20 | 2019-06-20 | Method, device, computer equipment and storage medium for determining customer trusted contact information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910534809.5A CN110413654B (en) | 2019-06-20 | 2019-06-20 | Method, device, computer equipment and storage medium for determining customer trusted contact information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110413654A CN110413654A (en) | 2019-11-05 |
| CN110413654B true CN110413654B (en) | 2023-11-21 |
Family
ID=68359461
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910534809.5A Active CN110413654B (en) | 2019-06-20 | 2019-06-20 | Method, device, computer equipment and storage medium for determining customer trusted contact information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110413654B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001203794A (en) * | 2000-01-21 | 2001-07-27 | Sharp Corp | Telephone number deciding method, telephone number deciding/originating method and medium recording the same |
| KR20050056113A (en) * | 2004-07-05 | 2005-06-14 | (주) 후이즈홀딩스 | An method and system for creating and updating contact information based on confidence index |
| CN103226949A (en) * | 2011-09-30 | 2013-07-31 | 苹果公司 | Using context information to facilitate processing of commands in a virtual assistant |
| CN104333530A (en) * | 2013-07-22 | 2015-02-04 | 深圳市腾讯计算机系统有限公司 | Information credibility verifying method and apparatus |
| CN106294398A (en) * | 2015-05-21 | 2017-01-04 | 富士通株式会社 | Information processor and information processing method |
| CN106570699A (en) * | 2015-10-08 | 2017-04-19 | 平安科技（深圳）有限公司 | Client contact information excavation method and server |
| CN106604281A (en) * | 2015-10-20 | 2017-04-26 | 阿里巴巴集团控股有限公司 | Method and equipment for determining stability of mobile phone number |
| CN106713676A (en) * | 2017-01-23 | 2017-05-24 | 北京奇虎科技有限公司 | Communication number identification control method and device, and communication terminal |
| CN107908702A (en) * | 2017-11-06 | 2018-04-13 | 深圳市牛鼎丰科技有限公司 | Contact method lookup method, device, computer equipment and storage medium |
| CN108564238A (en) * | 2017-12-28 | 2018-09-21 | 百度在线网络技术（北京）有限公司 | Data assessment method and apparatus, server, storage medium |
| CN109801156A (en) * | 2018-12-18 | 2019-05-24 | 深圳市佰仟金融服务有限公司 | A kind of lending risk management and control method and lending risk management and control equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10255602B2 (en) * | 2017-09-01 | 2019-04-09 | Operr Technologies, Inc. | Location-based verification for predicting user trustworthiness |
-
2019
- 2019-06-20 CN CN201910534809.5A patent/CN110413654B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001203794A (en) * | 2000-01-21 | 2001-07-27 | Sharp Corp | Telephone number deciding method, telephone number deciding/originating method and medium recording the same |
| KR20050056113A (en) * | 2004-07-05 | 2005-06-14 | (주) 후이즈홀딩스 | An method and system for creating and updating contact information based on confidence index |
| CN103226949A (en) * | 2011-09-30 | 2013-07-31 | 苹果公司 | Using context information to facilitate processing of commands in a virtual assistant |
| CN104333530A (en) * | 2013-07-22 | 2015-02-04 | 深圳市腾讯计算机系统有限公司 | Information credibility verifying method and apparatus |
| CN106294398A (en) * | 2015-05-21 | 2017-01-04 | 富士通株式会社 | Information processor and information processing method |
| CN106570699A (en) * | 2015-10-08 | 2017-04-19 | 平安科技（深圳）有限公司 | Client contact information excavation method and server |
| CN106604281A (en) * | 2015-10-20 | 2017-04-26 | 阿里巴巴集团控股有限公司 | Method and equipment for determining stability of mobile phone number |
| CN106713676A (en) * | 2017-01-23 | 2017-05-24 | 北京奇虎科技有限公司 | Communication number identification control method and device, and communication terminal |
| CN107908702A (en) * | 2017-11-06 | 2018-04-13 | 深圳市牛鼎丰科技有限公司 | Contact method lookup method, device, computer equipment and storage medium |
| CN108564238A (en) * | 2017-12-28 | 2018-09-21 | 百度在线网络技术（北京）有限公司 | Data assessment method and apparatus, server, storage medium |
| CN109801156A (en) * | 2018-12-18 | 2019-05-24 | 深圳市佰仟金融服务有限公司 | A kind of lending risk management and control method and lending risk management and control equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110413654A (en) | 2019-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106874199B (en) | Test case processing method and device | |
| CN112950119A (en) | Method, device, equipment and storage medium for splitting instant logistics order | |
| US20150227530A1 (en) | Address Book Ranking Method and Apparatus | |
| CN109408640B (en) | Log classification method and device and storage medium | |
| CN110413654B (en) | Method, device, computer equipment and storage medium for determining customer trusted contact information | |
| CN110909129A (en) | Abnormal complaint event identification method and device | |
| CN111783107B (en) | Multi-source trusted data access method, device and equipment | |
| CN110489416B (en) | Information storage method based on data processing and related equipment | |
| CN110690987B (en) | Account information management method, device and equipment | |
| CN112182579A (en) | Process list generation method and device and abnormal process detection method and device | |
| CN114443246B (en) | Intelligent scheduling method, device, equipment and computer readable storage medium | |
| JP6574310B2 (en) | Evaluation information matching method, apparatus and server | |
| JP6756378B2 (en) | Anomaly detection methods, systems and programs | |
| CN113468886B (en) | Work order processing method and device and computer equipment | |
| CN115550178A (en) | Intelligent gateway control method and system | |
| US20190279041A1 (en) | Information processing apparatus | |
| CN115657971B (en) | Cloud storage allocation method and system for enterprise digital service and cloud server | |
| CN111399754A (en) | Storage space release method and device and distributed system | |
| CN118193503B (en) | Hierarchical management system for server center data | |
| KR20200036173A (en) | Method and apparatus for recommending user grouping and groupwise preferred machine learning analysis algorithms | |
| CN115187153B (en) | Data processing method and system applied to business risk tracing | |
| CN115099922B (en) | Financial data query method, system, readable storage medium and computer equipment | |
| CN113723522B (en) | Abnormal user identification method and device, electronic equipment and storage medium | |
| JP2019164687A (en) | Information processing device | |
| JP7103392B2 (en) | Anomaly detection methods, systems and programs |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |