CN112486964A - Target identification method and device - Google Patents
Target identification method and device Download PDFInfo
- Publication number
- CN112486964A CN112486964A CN202011351155.1A CN202011351155A CN112486964A CN 112486964 A CN112486964 A CN 112486964A CN 202011351155 A CN202011351155 A CN 202011351155A CN 112486964 A CN112486964 A CN 112486964A
- Authority
- CN
- China
- Prior art keywords
- target
- quasi
- information
- confirmed
- target information
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003032 molecular docking Methods 0.000 claims description 20
- 230000008685 targeting Effects 0.000 claims description 7
- 230000010354 integration Effects 0.000 claims description 3
- 210000001503 joint Anatomy 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 10
- 230000000694 effects Effects 0.000 description 8
- 238000011161 development Methods 0.000 description 7
- 230000018109 developmental process Effects 0.000 description 7
- 238000007726 management method Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000004148 unit process Methods 0.000 description 1
Images
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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- 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
-
- 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
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
Abstract
One or more embodiments of the present specification provide a target identification method and apparatus, including: acquiring target information to be confirmed, and judging the information integrity of the target information to be confirmed; if the information of the target information to be confirmed is complete, judging whether the information of the target to be confirmed is recorded in a target record table; if the target information to be confirmed is not recorded in the target record table, marking the target information to be confirmed as quasi-target information, and judging whether the quasi-target information is recorded in the quasi-target record table; if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information with a corresponding item in the quasi-target recording table; and outputting the integrated quasi-target recording table. One or more embodiments of the present disclosure may quickly and accurately mine potential targets meeting requirements from the information of the targets to be confirmed by quickly identifying and accurately classifying the targets to be confirmed.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of data management technologies, and in particular, to a target identification method and apparatus.
Background
With the rapid development of the IT technologies such as the internet, the internet of things, cloud computing and the like, various data volumes are larger and larger, and data information becomes more and more a serious challenge and a precious opportunity facing each industry. In a database of a large enterprise group, a large amount of customer information is accumulated in each channel. The quasi-clients are excavated, a large amount of developable client resources are provided for enterprises, the sales efficiency can be greatly improved, and the time cost is saved.
Therefore, how to rapidly and accurately mine the potential customers meeting the requirements from the customer information becomes a problem to be solved in the field.
Disclosure of Invention
In view of the above, an object of one or more embodiments of the present disclosure is to provide a target identification method and apparatus, so as to solve the problem of how to quickly and accurately determine a target in stored data information.
In view of the above, one or more embodiments of the present specification provide an object recognition method including:
acquiring target information to be confirmed, and judging the information integrity of the target information to be confirmed;
if the information of the target information to be confirmed is complete, judging whether the information of the target to be confirmed is recorded in a target record table;
if the target information to be confirmed is not recorded in the target record table, marking the target information to be confirmed as quasi-target information, and judging whether the quasi-target information is recorded in the quasi-target record table;
if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information with a corresponding item in the quasi-target recording table;
and outputting the integrated quasi-target recording table.
In some embodiments, the determining the information integrity of the target information to be confirmed includes:
all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged;
and if so, determining that the information of the target information to be confirmed is complete.
In some embodiments, the determining whether the target information to be confirmed is recorded in a target record table further includes:
if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table;
and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
In some embodiments, the determining whether the quasi-target information is recorded in a quasi-target recording table further includes:
and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
In some embodiments, the outputting the integrated quasi-target record table further comprises:
assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target;
acquiring an input preference threshold value of at least one docking person for each attribute item;
determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
Based on the same concept, one or more embodiments of the present specification further provide an object recognition apparatus including:
the acquisition module acquires target information to be confirmed and judges the information integrity of the target information to be confirmed;
the judging module is used for judging whether the target information to be confirmed is recorded in a target recording table or not if the target information to be confirmed is complete;
the marking module is used for marking the target information to be confirmed as quasi-target information if the target information to be confirmed is not recorded in the target recording table, and judging whether the quasi-target information is recorded in the quasi-target recording table or not;
the integration module integrates the quasi-target information and corresponding items in the quasi-target record table if the quasi-target information is recorded in the quasi-target record table;
and the output module is used for outputting the integrated quasi-target recording table.
In some embodiments, the determining, by the obtaining module, the information integrity of the target information to be confirmed includes:
all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged;
and if so, determining that the information of the target information to be confirmed is complete.
In some embodiments, the determining module determines whether the target information to be confirmed is recorded in a target record table, and then further includes:
if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table;
and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
In some embodiments, the marking module determines whether the quasi-target information is recorded in a quasi-target recording table, and then further includes:
and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
In some embodiments, the output module outputs the integrated quasi-target record table, and then further includes:
assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target;
acquiring an input preference threshold value of at least one docking person for each attribute item;
determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
As can be seen from the above description, one or more embodiments of the present specification provide a target identification method and apparatus, including: acquiring target information to be confirmed, and judging the information integrity of the target information to be confirmed; if the information of the target information to be confirmed is complete, judging whether the information of the target to be confirmed is recorded in a target record table; if the target information to be confirmed is not recorded in the target record table, marking the target information to be confirmed as quasi-target information, and judging whether the quasi-target information is recorded in the quasi-target record table; if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information with a corresponding item in the quasi-target recording table; and outputting the integrated quasi-target recording table. One or more embodiments of the present disclosure may quickly and accurately mine potential targets meeting requirements from the information of the targets to be confirmed by quickly identifying and accurately classifying the targets to be confirmed.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
Fig. 1 is a schematic flow chart of a target identification method according to one or more embodiments of the present disclosure;
fig. 2 is a schematic structural diagram of an object recognition device according to one or more embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present specification more apparent, the present specification is further described in detail below with reference to the accompanying drawings in combination with specific embodiments.
It should be noted that technical terms or scientific terms used in the embodiments of the present specification should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that a element, article, or method step that precedes the word, and includes the element, article, or method step that follows the word, and equivalents thereof, does not exclude other elements, articles, or method steps. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As described in the background section, a prospective customer generally refers to a customer who has provided personal information and is collected by a server by participating in business activities, questionnaires, etc., although the product has not been purchased, and is in some way in the nature of a potential customer. In the prior art, a data warehouse is established to store all basic information of customers and customer 'activity' data, mining and correlation analysis are performed on the data to realize topic-oriented customer information extraction, and then the demand patterns and the profit values of the customers are classified to find out the most valuable and profit potential customer groups. The mining analysis is manually carried out, and is determined according to personal experience, and no systematic quasi-client extraction, merging and identification scheme exists.
In combination with the above practical situations, one or more embodiments of the present specification provide a target identification scheme, and through rapid identification and accurate classification of targets to be confirmed, potential targets meeting requirements can be quickly and accurately mined from the information of the targets to be confirmed.
Referring to fig. 1, a schematic flow chart of a target identification method according to an embodiment of the present disclosure is shown, and specifically includes the following steps:
This step is intended to acquire target information to be confirmed and judge the integrity thereof. The target information to be confirmed is target data information of a potential target acquired from other systems or channels, and may include basic attribute information of the target to be confirmed, such as name, gender, birth date, certificate type, certificate number, mobile phone number, and the like. And then, confirming the integrity of the target information to be confirmed, and determining whether the target information to be confirmed contains necessary information specified in the rule, for example, the name, sex, date of birth, certificate type and certificate number of the target to be confirmed are necessary in one rule, or only the name and certificate number of the target to be confirmed are necessary in the other rule, or only the name and mobile phone number of the target to be confirmed are necessary in the other rule, and the like.
Furthermore, in some application scenarios, the determining the information integrity of the target information to be confirmed includes: all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged; and if so, determining that the information of the target information to be confirmed is complete. Wherein, the necessary items are necessary items in the preset rule, for example: names and the like, which are endowed with a specific value after being filled, and the specific assignment rule can be freely set according to specific application scenarios, for example, in the gender item, the gender male is 1, the gender female is 0 and the like, and in the name item, each word is subjected to binary or decimal code conversion for assignment according to the coding rule and the like. If at least one necessary item is absent in the target information to be confirmed, discarding the target information not in compliance.
This step is intended to determine whether the target information to be confirmed is already existing target information. The target record table is a record table for recording the confirmed or recorded targets. For example, in the process of determining a target client by a business, it is determined whether the target to be confirmed is already the target client of the business. The judgment mode can be confirmation through comparison of specific items, such as target ID and the like; the target information to be confirmed can be confirmed to be recorded in the target record table only if all information items of the target information to be confirmed are consistent with all information items of a certain target in the target record table.
In some application scenarios, when it is confirmed that the target information to be confirmed is recorded in the target recording table, it is further confirmed whether the target information to be confirmed is a determined client, that is, in a specific application scenario, after the enterprise determines that the target information to be confirmed is a target client, it needs to be further judged whether the target information to be confirmed is a real client which is developed from the target client to a real client which is really in service, if so, it is indicated that the target client has established a corresponding relationship with a certain enterprise marketer, and then, the data needs to be pertinently marked, so that the data cannot be distributed and developed any more; if not, the target client is not in real service, and the mark level client needs to be developed subsequently. That is, the determining whether the target information to be confirmed is recorded in the target record table further includes: if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table; and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
The step is to determine that the target information to be confirmed, which is not recorded in the target record table, is quasi-target information, and determine whether the target information is recorded in the quasi-target record table. The quasi-target recording table is a recording table for recording all quasi-target information. The judgment mode can be confirmation through comparison of specific items, such as target ID and the like; the quasi-target information may be determined by comparing all corresponding items, and the quasi-target information may be determined to be recorded in the quasi-target record table only if all information items of the quasi-target information are consistent with all information items of a certain target in the quasi-target record table.
In some application scenarios, if the current quasi-target information is not recorded in the quasi-target recording table, the quasi-target information can be used as a new quasi-target, and then the quasi-target information is inserted into the quasi-target recording table to supplement the quasi-target recording table. That is, the determining whether the quasi-target information is recorded in the quasi-target recording table further includes: and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
And 104, if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information and a corresponding item in the quasi-target recording table.
In this step, if the target item corresponding to the quasi-target information already exists, the corresponding item in the target record table is updated and integrated according to the quasi-target information. That is, in some application scenarios, after determining the corresponding item corresponding to the current targeting information according to the corresponding identification information such as the target ID, the information recorded in the target record table is updated and information is merged.
And 105, outputting the integrated quasi-target recording table.
The step aims to output the integrated quasi-target record table. For storing, presenting or reworking the quasi-target record table. According to different application scenarios and implementation requirements, the specific output mode of the quasi-target recording table can be flexibly selected.
For example, for an application scenario in which the method of the present embodiment is executed on a single device, the quasi-target recording list may be directly output in a display manner on a display unit (display, projector, etc.) of the current device, so that an operator of the current device can directly see the contents of the quasi-target recording list from the display unit.
For another example, for an application scenario executed by a system composed of multiple devices according to the method of this embodiment, the quasi-target record table may be sent to other preset devices serving as receivers in the system through any data communication manner (e.g., wired connection, NFC, bluetooth, wifi, cellular mobile network, etc.), so that the preset devices receiving the quasi-target record table may perform subsequent processing on the preset devices. Optionally, the preset device may be a preset server, and the server is generally arranged at a cloud end and used as a data processing and storage center, which can be used for storing and distributing a target record table; the receiver of the distribution is a terminal device, and the holders or operators of the terminal devices may be current users, related staff of a system providing data, target units corresponding to the integrated data, docking staff, and the like.
For another example, for an application scenario executed on a system composed of multiple devices, the method of this embodiment may directly send the quasi-target recording table to a preset terminal device through any data communication manner, where the terminal device may be one or more of the foregoing paragraphs.
And then, the docking personnel can be allocated to each quasi-target in the target record table, the allocation mode can be screened according to various data of the alignment targets in a preference threshold interval set by the docking personnel, and the docking personnel most suitable for each quasi-target is selected. That is, in some application scenarios, the outputting the integrated quasi-target record table further includes: assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target; acquiring an input preference threshold value of at least one docking person for each attribute item; determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
In a specific application scenario, the information of the quasi-target and the preference information of the docking personnel are intelligently matched and pushed to a channel, and the matching rule is as follows: and each quasi-target is compared with the age, academic history, gender, child status, marital, occupation and native place of the butt joint personnel participating in the batch distribution, 1 point is added to each preference matching, the number of all the preferences is the score of the full score, the matching process with all the butt joint personnel is sequenced according to the scores of the high score and the low score, the quasi-target is finally determined to be distributed to the butt joint personnel with the highest score, meanwhile, the distribution capacity of the butt joint personnel is correspondingly reduced by 1, and when the distribution capacity reaches the upper limit, the quasi-target is not distributed to the butt joint personnel. For example, a docking person prefers docking conditions to be: the method comprises the steps of enabling persons aged 20-30 years, academic subjects and above, sex men, maid and native persons in specific regions to match each quasi-target with the preference of the dockee according to conditions, scoring the preferred items of the dockee, finally counting the score of each quasi-target for the dockee, and determining the dockee with the highest score to dock the quasi-target when judging which score is the best among all the dockees aiming at a single quasi-target.
In a specific application scenario, taking quasi-client identification in a large insurance enterprise as an example, the target identification scheme may include:
(1) a data acquisition unit; the unit processes the relational database of the data source into an HDFS file which can be directly imported by utilizing a big data Sqoop technology. The Sqoop has the functions of storage and increment introduction, wherein the increment introduction is judged according to the increment layer timestamp, and the increment running frequency can be adjusted according to the service requirement. The Sqoop is a source-opening tool, is mainly used for data transmission between Hadoop and a traditional database, and can lead data in a relational database (such as MySQL, Oracle, Postgres and the like) into an HDFS (Hadoop Distributed File System) of Hadoop and also can lead data of the HDFS into the relational database.
(2) Matching the "old" client unit; according to the requirement of quasi-client account opening management, according to the rule of MapReduce (MapReduce is a programming model and is used for parallel operation of large-scale data sets (more than 1 TB)) distribution calculation, client data extracted from data sources of a third-party system (administrative insurance, small painters, national and long-life E stores, activity platforms, group retention, financial risk falling, financial risk not falling, group list insured persons, activated cards, gifted risks, network marketing, running for 24, running for 700, E treasure self-testing, cloud assistants, broad-release joint cards, life and standby products, Xin singles and the like) are compared with enterprise stock client data, and the client data are compared according to the following four rules, and if corresponding records are matched, the client is an ' old ' client ' of an enterprise, and the ID of the ' old ' client is marked. Rule one is as follows: name + gender + date of birth + credential type + credential number; rule two: identification number + name; rule three: passport number + name; rule four: name + cell phone number.
(3) Matching the policy client unit; according to the quasi-client account opening management requirement, the client matched with the 'old' client number ID is screened for the policy owned by the client, and the client owning the 'non-virtual marketer order', namely the client who has been issued an order by the marketer, is the client of the marketer, is regarded as the policy client, and can not be distributed to develop.
(4) A matching quasi-client unit; according to the account opening management requirements of the quasi-clients, client data which are not matched as 'old' clients and client data which are not matched as policy clients are matched in an existing quasi-target record table by using a MapReduce or spark (computing engine) technology, matching rules are similar to the matching of the 'old' clients, and if corresponding records are matched, the client data are used as the existing quasi-clients and standard client ids; and if the corresponding record is not matched, generating and giving a unique quasi client id, and adding the data into the quasi target record table.
(5) The quasi-client transfers to the 'old' client unit; the unit is used for tracking and recording subsequent development success of a client or an 'old' client, comparing stock client data of a system with an enterprise client data table increment client data table by using a MapReduce or spark technology according to the account opening management requirement of the client, and matching the client data in the table and matching and inquiring policy data according to five elements (name, gender, birth date, certificate number and certificate type), wherein the matching rule of the five elements of the client is as follows: name + cell-phone number, name + certificate type + identification card number, name + certificate type + passport number, name + sex + birthday + certificate type + card number (non-identity card and passport) and so on, the matching rule of policy is: the name + mobile phone number, the name + certificate type + identity card number, the name + sex + birth date + certificate type + certificate piece number (non-identity card and passport), and the like, and the two are associated and matched, and the matched client is used as an enterprise 'old' client and marked with 'old' client number ID. That is, when the client increment table is tracked in real time, and when it is detected that the policy is established for the corresponding target in the quasi-target record table and is converted into the "old" client, all the information corresponding to the policy is transferred to the recording position of the "old" client (e.g., recorded in the target record table).
(6) An index processing unit; extracting all the record information of the clients, associating other label information corresponding to the record information, and merging the client information to generate a unique quasi-client id according to a merging rule by utilizing a big data MapReduce or spaker technology, wherein the merging rule is as follows: name + cell phone number, name + certificate number type + identification number, name + certificate type + passport number, name + certificate number type + gender + date of birth + certificate number (non-identification card and passport); and merging the record information and other label information of the client by taking the quasi-client id as a dimension, wherein a specific merging logic can be designed according to specific business logic, such as: the telephone numbers are merged based on the telephone numbers recorded in the last activity; the customer images provide various label characteristics of the customers for the front desk E store marketers, and marketing judgment orders of the marketers to the customers are improved.
According to the quasi-client portrait label information, a big data Sqoop technology is utilized to extract clients capable of being distributed and developed into a relational database, and the clients are provided for distribution and development of various subordinate enterprises, branch companies and the like, so that the problem of difficulty in acquiring client resources is solved, and the marketing and development rate of a butt staff team is improved.
(7) A quasi-client development result tracking unit; and associating stock quasi-client data of the system with an enterprise client policy data table incremental data table by using a MapReduce or spark technology, matching policy data in the table according to five elements (name, gender, birth date, certificate number and certificate type), taking specific policy data as policy data of the quasi-client, and accessing the policy data into a database for a front-end system to inquire.
(8) A docking personnel information maintenance unit; basic information and preference information of the butt joint personnel who are distributed are extracted from a database by utilizing a Sqoop technology, wherein the basic information can comprise: name, native place, ethnic group, state of employment, preference information contains: the age, the academic calendar, the occupation, the gender, the children and the marriage, whether the assignment identification, the organization, the job number of the docking personnel and the like are received, and the preference information comes from the initiative filling of the docking personnel.
And associating the incremental information of the enterprise docking personnel information table by using a MapReduce or spark technology, and recycling the quasi-clients distributed by the remote docking personnel.
(9) A quasi-client front-end development unit; the unit can be roughly divided into three roles: administrator, customer service personnel, channel personnel, the administrator carries out authority management, and customer service personnel filter some customers and establish distribution development activity and push for channel personnel, and concrete distribution activity can be divided into a segmentation distribution activity, two-segment distribution activity etc. and a segmentation distribution pushes customer information and butt joint personnel preference information intelligence matching for channel personnel, and the matching rule is as follows: each client compares the information such as age, academic history, sex, child status, marital, occupation, native place and the like with the butt joint personnel participating in the batch distribution, 1 score is added to each preference matching, the number of all the preferences is the score of the full score, the client is finally distributed to the butt joint personnel with the highest score according to the high-low score sequence in the matching process of all the butt joint personnel, meanwhile, the distribution capacity of the butt joint personnel is correspondingly reduced by 1, and when the distribution capacity reaches the upper limit, the client is not distributed to the butt joint personnel any more; the two-stage distribution does not carry out the association of quasi-clients and butt-joint personnel, and the matching rules are as follows: the two-stage type adopts an average mode to distribute customers; the channel personnel directly confirm the one-stage distribution and issue to the docking personnel, and the two-stage distribution channel personnel can upload proper docking personnel for auditing and then issue and develop.
The object identification method provided by applying one or more embodiments of the present specification includes: acquiring target information to be confirmed, and judging the information integrity of the target information to be confirmed; if the information of the target information to be confirmed is complete, judging whether the information of the target to be confirmed is recorded in a target record table; if the target information to be confirmed is not recorded in the target record table, marking the target information to be confirmed as quasi-target information, and judging whether the quasi-target information is recorded in the quasi-target record table; if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information with a corresponding item in the quasi-target recording table; and outputting the integrated quasi-target recording table. One or more embodiments of the present disclosure may quickly and accurately mine potential targets meeting requirements from the information of the targets to be confirmed by quickly identifying and accurately classifying the targets to be confirmed.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, one or more embodiments of the present specification further provide an object recognition apparatus, as shown in fig. 2, including:
the acquiring module 201 acquires target information to be confirmed and judges the information integrity of the target information to be confirmed;
a determining module 202, configured to determine whether the target information to be confirmed is recorded in a target record table if the target information to be confirmed is complete;
a marking module 203, configured to mark the target information to be confirmed as quasi-target information if the target information to be confirmed is not recorded in the target record table, and determine whether the quasi-target information is recorded in the quasi-target record table;
an integration module 204, configured to integrate the quasi-target information with a corresponding item in the quasi-target record table if the quasi-target information is recorded in the quasi-target record table;
the output module 205 outputs the integrated quasi-target record table.
As an optional embodiment, the step of determining, by the obtaining module 201, the information integrity of the target information to be confirmed includes:
all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged;
and if so, determining that the information of the target information to be confirmed is complete.
As an optional embodiment, the determining module 202 determines whether the target information to be confirmed is recorded in a target record table, and then further includes:
if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table;
and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
As an optional embodiment, the marking module 203 determines whether the quasi-target information is recorded in a quasi-target recording table, and then further includes:
and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
As an optional embodiment, the output module 205 outputs the integrated quasi-target record table, and then further includes:
assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target;
acquiring an input preference threshold value of at least one docking person for each attribute item;
determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The device of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Further, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method of object recognition, comprising:
acquiring target information to be confirmed, and judging the information integrity of the target information to be confirmed;
if the information of the target information to be confirmed is complete, judging whether the information of the target to be confirmed is recorded in a target record table;
if the target information to be confirmed is not recorded in the target record table, marking the target information to be confirmed as quasi-target information, and judging whether the quasi-target information is recorded in the quasi-target record table;
if the quasi-target information is recorded in the quasi-target recording table, integrating the quasi-target information with a corresponding item in the quasi-target recording table;
and outputting the integrated quasi-target recording table.
2. The method according to claim 1, wherein the determining the information integrity of the target information to be confirmed comprises:
all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged;
and if so, determining that the information of the target information to be confirmed is complete.
3. The method of claim 1, wherein the determining whether the target information to be confirmed is recorded in a target record table further comprises:
if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table;
and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
4. The method of claim 1, wherein said determining whether the quasi-target information is recorded in a quasi-target recording table further comprises:
and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
5. The method of claim 1, wherein outputting the integrated table of quasi-target records further comprises:
assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target;
acquiring an input preference threshold value of at least one docking person for each attribute item;
determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
6. An object recognition device, comprising:
the acquisition module acquires target information to be confirmed and judges the information integrity of the target information to be confirmed;
the judging module is used for judging whether the target information to be confirmed is recorded in a target recording table or not if the target information to be confirmed is complete;
the marking module is used for marking the target information to be confirmed as quasi-target information if the target information to be confirmed is not recorded in the target recording table, and judging whether the quasi-target information is recorded in the quasi-target recording table or not;
the integration module integrates the quasi-target information and corresponding items in the quasi-target record table if the quasi-target information is recorded in the quasi-target record table;
and the output module is used for outputting the integrated quasi-target recording table.
7. The method according to claim 6, wherein the determining, by the obtaining module, the information integrity of the target information to be confirmed includes:
all necessary items in the target information to be confirmed are obtained, and whether the necessary items are assigned or not is judged;
and if so, determining that the information of the target information to be confirmed is complete.
8. The method according to claim 6, wherein the determining module determines whether the target information to be confirmed is recorded in a target record table, and then further comprises:
if the target information to be confirmed is recorded in the target recording table, determining whether the target information to be confirmed is recorded in a determination information table;
and if the quasi-target information is not recorded in the determination information table, marking the target information to be confirmed as the quasi-target information, and executing the judgment of whether the quasi-target information is recorded in the quasi-target recording table.
9. The method of claim 6, wherein the marking module determines whether the quasi-target information is recorded in a quasi-target recording table, and then further comprising:
and if the quasi-target information is not recorded in the quasi-target recording table, recording the quasi-target information into the quasi-target recording table.
10. The method of claim 6, wherein the output module outputs the integrated quasi-target record table, and thereafter further comprises:
assigning values to each attribute item of each quasi-target in the quasi-target record table to generate at least one attribute score of the quasi-target;
acquiring an input preference threshold value of at least one docking person for each attribute item;
determining, for each of the targeting objects, the dockee for which the attribute score is least different from the corresponding preference threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011351155.1A CN112486964B (en) | 2020-11-26 | 2020-11-26 | Target identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011351155.1A CN112486964B (en) | 2020-11-26 | 2020-11-26 | Target identification method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112486964A true CN112486964A (en) | 2021-03-12 |
| CN112486964B CN112486964B (en) | 2024-04-26 |
Family
ID=74935247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011351155.1A Active CN112486964B (en) | 2020-11-26 | 2020-11-26 | Target identification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112486964B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130238351A1 (en) * | 2012-03-12 | 2013-09-12 | Icon Clinical Research Limited | Clinical data management system |
| WO2014049322A1 (en) * | 2012-09-28 | 2014-04-03 | Brainstorm Mobile Solutions Limited | Method of processing data for marketing and advertising purposes |
| US20140244351A1 (en) * | 2013-02-28 | 2014-08-28 | Single Funnel Pty Ltd | Customer relationship management (crm) systems |
| CN106326438A (en) * | 2016-08-26 | 2017-01-11 | 南威软件股份有限公司 | Personnel information correlating method |
| CN107545332A (en) * | 2016-06-23 | 2018-01-05 | 平安科技（深圳）有限公司 | Prospect's information combined analysis method and server |
| CN108510239A (en) * | 2018-03-13 | 2018-09-07 | 平安普惠企业管理有限公司 | Employee information management method, device, computer equipment and storage medium |
| CN108694657A (en) * | 2018-07-13 | 2018-10-23 | 平安科技（深圳）有限公司 | Client's identification device, method and computer readable storage medium |
| CN109064342A (en) * | 2018-07-20 | 2018-12-21 | 阳光保险集团股份有限公司 | Client identity recognition methods and device |
| CN109087140A (en) * | 2018-08-07 | 2018-12-25 | 广州航海学院 | A kind of closed loop target client's recognition methods based on spark big data |
| CN110119951A (en) * | 2019-04-15 | 2019-08-13 | 中国平安人寿保险股份有限公司 | Record single method, apparatus, computer equipment and storage medium |
| CN111311326A (en) * | 2020-02-18 | 2020-06-19 | 平安科技（深圳）有限公司 | User behavior real-time multidimensional analysis method and device and storage medium |
-
2020
- 2020-11-26 CN CN202011351155.1A patent/CN112486964B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130238351A1 (en) * | 2012-03-12 | 2013-09-12 | Icon Clinical Research Limited | Clinical data management system |
| WO2014049322A1 (en) * | 2012-09-28 | 2014-04-03 | Brainstorm Mobile Solutions Limited | Method of processing data for marketing and advertising purposes |
| US20140244351A1 (en) * | 2013-02-28 | 2014-08-28 | Single Funnel Pty Ltd | Customer relationship management (crm) systems |
| CN107545332A (en) * | 2016-06-23 | 2018-01-05 | 平安科技（深圳）有限公司 | Prospect's information combined analysis method and server |
| CN106326438A (en) * | 2016-08-26 | 2017-01-11 | 南威软件股份有限公司 | Personnel information correlating method |
| CN108510239A (en) * | 2018-03-13 | 2018-09-07 | 平安普惠企业管理有限公司 | Employee information management method, device, computer equipment and storage medium |
| CN108694657A (en) * | 2018-07-13 | 2018-10-23 | 平安科技（深圳）有限公司 | Client's identification device, method and computer readable storage medium |
| CN109064342A (en) * | 2018-07-20 | 2018-12-21 | 阳光保险集团股份有限公司 | Client identity recognition methods and device |
| CN109087140A (en) * | 2018-08-07 | 2018-12-25 | 广州航海学院 | A kind of closed loop target client's recognition methods based on spark big data |
| CN110119951A (en) * | 2019-04-15 | 2019-08-13 | 中国平安人寿保险股份有限公司 | Record single method, apparatus, computer equipment and storage medium |
| CN111311326A (en) * | 2020-02-18 | 2020-06-19 | 平安科技（深圳）有限公司 | User behavior real-time multidimensional analysis method and device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 陈玉山;: "数据挖掘在客户关系管理中的应用", 宁波职业技术学院学报, no. 02, pages 51 - 53 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112486964B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20230377032A1 (en) | System and method for processing transaction records for users | |
| CN103620601B (en) | Joining tables in a mapreduce procedure | |
| US10853634B2 (en) | Methods and systems for updating a database based on object recognition | |
| CN107832291B (en) | Man-machine cooperation customer service method, electronic device and storage medium | |
| CN107918618B (en) | Data processing method and device | |
| US10977677B2 (en) | Contact importer | |
| CN105787781A (en) | Book collection sharing system based on internet | |
| CN112506925A (en) | Data retrieval system and method based on block chain | |
| CN105589911A (en) | Customer value assessment method and system | |
| CN106326243A (en) | Data processing method and apparatus | |
| CN112417274A (en) | Message pushing method and device, electronic equipment and storage medium | |
| CN109544266A (en) | Product data method for pushing, device and computer readable storage medium | |
| CN111324786A (en) | Method and device for processing consultation problem information | |
| CN112116103A (en) | Method, device and system for evaluating personal qualification based on federal learning and storage medium | |
| CN105630995A (en) | Method and system for querying credit | |
| US20210158253A1 (en) | System for executing automatic resource transfers using predictive electronic data analysis | |
| CN110084467B (en) | Mobile label verification method, mobile label verification device, computer equipment and storage medium | |
| CN112486964A (en) | Target identification method and device | |
| CN110019237B (en) | System and method for analyzing criminal whereabouts based on map | |
| CN108268545B (en) | Method and device for establishing hierarchical user label library | |
| CN112116483B (en) | Policy data processing method, query method, storage medium and electronic device | |
| US11941153B2 (en) | De-identification method for big data | |
| CN112750043A (en) | Business data pushing method and device and server | |
| CN107545332A (en) | Prospect's information combined analysis method and server | |
| US20150348209A1 (en) | Method and system for linking forensic data with purchase behavior |
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 |