[go: up one dir, main page]

WO2019095768A1 - 用户信息筛选方法、服务器及计算机可读存储介质 - Google Patents

用户信息筛选方法、服务器及计算机可读存储介质 Download PDF

Info

Publication number
WO2019095768A1
WO2019095768A1 PCT/CN2018/102396 CN2018102396W WO2019095768A1 WO 2019095768 A1 WO2019095768 A1 WO 2019095768A1 CN 2018102396 W CN2018102396 W CN 2018102396W WO 2019095768 A1 WO2019095768 A1 WO 2019095768A1
Authority
WO
WIPO (PCT)
Prior art keywords
user information
probability
correct probability
correct
piece
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.)
Ceased
Application number
PCT/CN2018/102396
Other languages
English (en)
French (fr)
Inventor
徐国强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
OneConnect Smart Technology Co Ltd
Original Assignee
OneConnect Smart Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by OneConnect Smart Technology Co Ltd filed Critical OneConnect Smart Technology Co Ltd
Publication of WO2019095768A1 publication Critical patent/WO2019095768A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3346—Query execution using probabilistic model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification

Definitions

  • the present application relates to the field of data analysis and application technologies, and in particular, to a user information screening method, a server, and a computer readable storage medium.
  • the entered policy data has also exploded.
  • the original source is basically imported manually, so the error message will inevitably occur during the manual import process.
  • the user information data in the information database cannot be accurately classified according to the accuracy, and the user information data with high accuracy cannot be accurately selected.
  • the present application proposes a user information screening method, a server, and a computer readable storage medium to quickly and accurately filter comprehensive and accurate user information data from a complex information database.
  • the present application provides a server, where the server includes a memory, a processor, and a memory information filtering program executable on the processor, where the user information screening program is
  • the processor When the processor is executed, the following steps are performed: reading each piece of user information; determining a correct probability of each element in each piece of user information according to a preset determination rule; and calculating corresponding user information according to a correct probability of the element The correct probability is selected; the user information whose correct probability is greater than the preset probability threshold is selected for classification.
  • the present application further provides a user information screening method, where the method is applied to a server, the method includes: reading each piece of user information; and determining each piece of user information according to a preset determination rule. The correct probability of the element in the middle; the correct probability of the corresponding user information is calculated according to the correct probability of the element; and the user information whose correct probability is greater than the preset probability threshold is selected for the correctness classification.
  • the present application further provides a computer readable storage medium storing a user information screening program, the user information screening program being executable by at least one processor, so that The at least one processor performs the steps of the user information screening method as described above.
  • the user information screening method, the server and the computer readable storage medium proposed by the present application may first determine the correct probability of the elements constituting the user information, and then calculate the corresponding user by the correct probability of the element. The correctness level of the information, so that the comprehensive and accurate user information data is quickly and correctly selected from the complicated information database.
  • FIG. 1 is a schematic diagram of an optional hardware architecture of a server
  • FIG. 2 is a schematic diagram of a program module of a first embodiment of a user information screening program of the present application
  • FIG. 3 is a schematic diagram of a program module of a second embodiment of the user information screening program of the present application.
  • FIG. 4 is a schematic flowchart of a first embodiment of a method for screening user information according to the present application
  • FIG. 5 is a schematic flowchart of a second embodiment of a method for screening user information according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the server 1.
  • the server 1 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the server 1 may be a standalone server or a server cluster composed of multiple servers.
  • the server 1 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus.
  • the server 1 connects to the network through the network interface 13 (not shown in FIG. 1), and acquires or transmits all information including user information data.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as networks, Bluetooth, Wi-Fi, and call networks.
  • Figure 1 only shows the server 1 with the components 11-13, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), and a random access memory (RAM). , static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the server 1, such as a hard disk or memory of the server 1.
  • the memory 11 may also be an external storage device of the server 1, such as a plug-in hard disk equipped with the server 1, a smart memory card (SMC), and a secure digital (Secure Digital). , SD) card, flash card (Flash Card), etc.
  • the memory 11 can also include both the internal storage unit of the server 1 and its external storage device.
  • the memory 11 is generally used to store an operating system installed in the server 1 and various types of application software, such as program codes of the user information screening program 200. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is typically used to control the overall operation of the server 1, such as performing data interaction or communication related control and processing, and the like.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running the user information screening program 200 and the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the server 1 and other electronic devices.
  • the present application proposes a user information screening program 200.
  • FIG. 2 it is a program module diagram of the first embodiment of the user information screening program 200 of the present application.
  • the user information screening program 200 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the user information filtering operation of the embodiments of the present application may be implemented. .
  • the user information screening program 200 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 2, the user information screening program 200 can be divided into a reading module 201, a determining module 202, a computing module 203, and an output module 204. among them:
  • the reading module 201 is configured to read each piece of user information.
  • the user information screening program 200 may acquire user information data stored by the other electronic devices according to a user instruction; when the server 1 When the user information data is stored, the user information screening program 200 may directly acquire the user information data stored by the server 1.
  • the determining module 202 is configured to respectively determine a correct probability of an element in each piece of user information according to a preset determining rule.
  • each element may be compared with a determination rule of a correct probability corresponding to the element, thereby determining a correct probability of the element.
  • the user information in the entered policy data generally has a plurality of fields, including the name, the ID number, the mobile phone number, the mailbox, and the identification and encoding.
  • the name consists of the first name and the last name, the last name includes the hundred family names, and the name is composed of 1-6 Chinese characters; the ID number is composed of 18 digits, and the first 6 digits are the division code of the administrative district.
  • 7 to 14 digits are the date of birth code, 15th to 17th digits are sequential codes, and the checksum of all digits is a specific value;
  • the mobile phone number is composed of 11 digits, the first 3 digits of network identification number, 4
  • the -7 digit is the regional code;
  • the mailbox is composed of the username +@+mail server domain name, and the username is composed of letters, numbers and other common characters (such as underscores, addition and subtraction symbols), and the mail server domain name is through the Internet.
  • the network connection test can connect to the server domain name. Therefore, the determining module 202 can use the feature as a determination rule of the correct probability of the element, the ID card number, the mobile phone number, the mailbox, and the like in the user information.
  • the determining module 202 determines that the correct probability of the element is 1.
  • the determining module 202 determines that the correct probability of the element is a value less than one. For example, in the case of the name of the user information, the name does not exceed six Chinese characters, and the last name is included in the hundred names.
  • the correct probability of the name can be determined to be 90%; when the surname includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the name is composed of more than 6 Chinese characters, it can be determined
  • the correct rate of the name is 80%; when the name includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the above-mentioned one error condition occurs in both the first name and the first name, the correct probability of the name can be determined as the correctness of each of them
  • the ID number should be 18 digits, and the first 6 digits are the administrative division code, and the 7th to 14th digits are valid birth date codes.
  • the checksum of all digits is a specific one. value.
  • the correct probability of the ID card number is 40%; when the ID card number contains exactly 18, and the first 6 digits of the ID number are the administrative division code, the 7th Bits up to 14 are valid birth date codes, but the checksum of all digits is not a specific value, then the correct probability of determining the positive number is 80%; when the ID number contains non-numbers, it can be determined The correct probability of the ID number is 30%.
  • the mobile phone number is 11 digits
  • the current 3 digit network identification number, and 4-7 digits are area codes.
  • the correct probability of the mobile phone number can be determined to be 80%, when the mobile phone number is less than 11 digits If the composition, or the mobile number contains non-numeric, the correct probability of determining the mobile number is 30%.
  • the mailbox consists of the username +@+mail server domain name, and the username has the specified character format.
  • the determining module 203 compares the elements in each piece of user information acquired by the obtaining module 201 with the determining rule corresponding to the element, and can directly determine the correct probability of the element.
  • the ID number in the user information contains 18 digits, and the first 6 digits of the identity positive number are administrative division codes, and the 7th to 14th digits are valid birth date codes, but all digits are verified. And if it is not a specific value, the correct probability of the identity positive number is 80%.
  • the mobile phone number in the user information is composed of 11 digits, the first 3 digits of the network identification number, and the 4-7 digits are area codes, and the correct probability of the mobile phone number is 1.
  • the mailbox in the user information is composed of a username +@+mail server domain name, and the composition of the mailbox user name conforms to a preset rule, but the server domain name that cannot be connected through the Internet connection test is the mailbox
  • the correct probability is 50%. That is to say, in the read user information, the correct probability of the name is 80%, the correct probability of the identity card number is 80%, the correct probability of the mobile phone number is 1, and the correct probability of the mailbox is 50%.
  • the calculating module 203 is configured to calculate a correct probability of the corresponding user information according to the correct probability of the element.
  • the calculating module 203 respectively assigns a correct probability weight value to each element in the user information determined by the determining module 202, and then according to the correct probability of each element and the corresponding correct probability weight value. Calculate the correct probability of the user information.
  • the calculation module presets that the correct weight of the name is 0.3, the correct probability weight of the ID number is 0.3, the correct probability weight of the mobile phone number is 0.2, and the correct probability weight of the mailbox is 0.2.
  • the determining module 202 determines that the user information has a correct probability of 80%, the correct probability of the ID number is 80%, the correct probability of the mobile phone number is 1, and the correct probability of the mailbox is 50%.
  • the calculation module may calculate the correct probability of the corresponding user information according to the correct probability weight value of each element of the setting and integrate the correct probability of each element.
  • the output module 204 is configured to select user information whose correct probability is greater than a preset probability threshold to perform correctness classification.
  • the output module 204 presets at least one probability threshold; and then compares the correct probability of each piece of user information calculated by the calculating module 203 with the at least one probability threshold, thereby obtaining the accuracy of each piece of user information. level.
  • the output module 204 when the output module 204 is set with a probability threshold, the output module 204 directly outputs user information with a correct probability of user information greater than or equal to the probability threshold as correct user information.
  • the output module 204 when the output module 204 is configured with two probability thresholds or more than two probability thresholds, the output module 204 may respectively compare the correct probabilities of the user information with all the probability thresholds, thereby outputting The correctness level of a user's information. For example, when there are two probability thresholds, the output module 204 compares the correct probability of the user information calculated by the calculation module 203 with a preset first threshold and a second threshold, the first threshold being greater than the Second threshold.
  • the correctness of the user information is low; when the correct probability of the user information is less than the second threshold, it is determined that the accuracy of the user information is too low, and the user information is error information.
  • the accuracy level of the user information is then output in a form, document, graphic or other form.
  • the user information screening program 200 includes the decoding module 201, the determination module 202, the calculation module 203, and the output module 204 in the first embodiment, and includes a decomposition module 205, and a setting. Module 206.
  • the reading module 201, the determining module 202, the calculating module 203, and the output module 204 have the same functions as the corresponding program modules in the first embodiment of the user information screening program 200. I won't go into details here. Since the user information data that is sometimes entered does not decompose the user information into elements and save them to specific fields. Therefore, after the reading module 201 reads the user information, before the determining module 203 determines the correct probability of the elements in the user information, the decomposition module 205 and the setting module 206 are also required to perform processing.
  • the decomposition module 205 is configured to decompose user information into at least one element.
  • the decomposition module 205 first decomposes the user information into a name including a name, a mobile phone number, an ID card number, and the like according to content included in the user information, such as “name”, “mobile phone”, “identity”, “mailbox”, and the like. Elements such as mailboxes.
  • the decomposition module 205 can directly identify the characteristic content in the user information, and decompose the user information into elements according to the characteristic content. If the content of each user information needs to include the characteristic content, the content is decomposed as an element of the user information.
  • the setting module 206 is configured to set a composition format of each of the at least one element, and set a determination rule of a correct probability of each element according to a composition format of each element.
  • the setting module 206 may set a composition format of each element according to characteristics of the element, and then according to each element The format is composed, and the judgment rule of the correct probability of each element is set. For example, after the decomposition module 205 decomposes the user information into elements such as a name, an ID number, a mobile phone number, a mailbox, and the like, the setting module 206 first sets each element according to the characteristics of the name, the ID number, the mobile phone number, and the mailbox.
  • the composition format such as the name consists of the first name and the first name, the surname includes the hundred family names, and the name is composed of 1-6 Chinese characters; the ID number is composed of 18 digits, the first 6 is the administrative division code, the first 7 to 14 digits are the date of birth code, 15th to 17th digits are sequential codes, and the checksum of all digits is a specific value; the mobile phone number is composed of 11 digits, the first 3 digits of network identification number, 4 -7 digits are area codes, 8-11 digits are user numbers; mailboxes are composed of username +@+mail server domain name, and user names are composed of letters, numbers and other common characters (such as underscores, addition and subtraction symbols).
  • the mail server domain name is the server domain name that can be connected through the Internet connection test.
  • the setting module 206 also sets a judgment rule of the correct probability of the element according to the composition format of each element. For example, in the case of the name of the user information, the name does not exceed six Chinese characters, and the last name is included in the hundred names.
  • the surname is a Chinese character that is not in the family name
  • the correct probability of the name can be determined to be 90%
  • the surname includes non-Chinese characters the correct probability of the name can be determined to be 30%
  • the name is composed of more than 6 Chinese characters, it can be determined
  • the correct rate of the name is 80%
  • the correct probability of the name can be determined to be 30%
  • the correct probability of the name can be determined as the correctness of each of them
  • the product of the probabilities, such as the first name and the last name are not Chinese characters.
  • the ID number should be 18 digits, and the first 6 digits are the administrative division code, and the 7th to 14th digits are valid birth date codes.
  • the checksum of all digits is a specific one. value.
  • the correct probability of the ID card number is 40%; when the ID card number contains exactly 18, and the first 6 digits of the ID number are the administrative division code, the 7th Bits up to 14 are valid birth date codes, but the checksum of all digits is not a specific value, then the correct probability of determining the positive number is 80%; when the ID number contains non-numbers, it can be determined The correct probability of the ID number is 30%.
  • the mobile phone number is 11 digits, the current 3 digit network identification number, and 4-7 digits are area codes.
  • the correct probability of the mobile phone number can be determined to be 80%, when the mobile phone number is less than 11 digits If the composition, or the mobile number contains non-numeric, the correct probability of determining the mobile number is 30%.
  • the mailbox consists of the username +@+mail server domain name, and the username has the specified character format.
  • the present application also proposes a user information screening method.
  • FIG. 4 it is a schematic flowchart of the first embodiment of the user information screening method of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 4 may be changed according to different requirements, and some steps may be omitted.
  • step S500 each piece of user information is read.
  • the user information data stored by the other electronic devices may be read according to a user instruction; when the server 1 stores the user information data
  • the user information data stored by the server 1 can also be directly read.
  • Step S502 Determine, according to a preset determination rule, a correct probability of an element in each piece of user information.
  • each element may be compared with a determination rule of a correct probability corresponding to the element, thereby determining a correct probability of the element.
  • the user information in the entered policy data generally has a plurality of fields, including the name, the ID number, the mobile phone number, the mailbox, and the identification and encoding.
  • the name consists of the first name and the last name, the last name includes the hundred family names, and the name is composed of 1-6 Chinese characters; the ID number is composed of 18 digits, and the first 6 digits are the division code of the administrative district.
  • 7 to 14 digits are the date of birth code, 15th to 17th digits are sequential codes, and the checksum of all digits is a specific value;
  • the mobile phone number is composed of 11 digits, the first 3 digits of network identification number, 4
  • the -7 digit is the regional code;
  • the mailbox is composed of the username +@+mail server domain name, and the username is composed of letters, numbers and other common characters (such as underscores, addition and subtraction symbols), and the mail server domain name is through the Internet.
  • the network connection test can connect to the server domain name. Therefore, the feature can be used as a judgment rule of the correct probability of the element such as the name, the ID number, the mobile phone number, and the mailbox in the user information.
  • the correct probability of the element when the element in the user information meets the judgment rule of the corresponding correct probability, it is determined that the correct probability of the element is 1.
  • the correct probability of the element is a value less than one. For example, in the case of the name of the user information, the name does not exceed six Chinese characters, and the last name is included in the hundred names.
  • the correct probability of the name can be determined to be 90%; when the surname includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the name is composed of more than 6 Chinese characters, it can be determined
  • the correct rate of the name is 80%; when the name includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the above-mentioned one error condition occurs in both the first name and the first name, the correct probability of the name can be determined as the correctness of each of them
  • the ID number should be 18 digits, and the first 6 digits are the administrative division code, and the 7th to 14th digits are valid birth date codes.
  • the checksum of all digits is a specific one. value.
  • the correct probability of the ID card number is 40%; when the ID card number contains exactly 18, and the first 6 digits of the ID number are the administrative division code, the 7th Bits up to 14 are valid birth date codes, but the checksum of all digits is not a specific value, then the correct probability of determining the positive number is 80%; when the ID number contains non-numbers, it can be determined The correct probability of the ID number is 30%.
  • the mobile phone number is 11 digits
  • the current 3 digit network identification number, and 4-7 digits are area codes.
  • the correct probability of the mobile phone number can be determined to be 80%, when the mobile phone number is less than 11 digits If the composition, or the mobile number contains non-numeric, the correct probability of determining the mobile number is 30%.
  • the mailbox consists of the username +@+mail server domain name, and the username has the specified character format.
  • Step S504 calculating a correct probability of the corresponding user information according to the correct probability of the element.
  • each element in the user information is respectively assigned a correct probability weight value, and then the correct probability of the user information is calculated according to the correct probability of each element and the corresponding correct probability weight value.
  • the specific calculation process is: multiplying the correct probability of each element by the corresponding correct probability weight value, and then adding, thereby obtaining the correct probability of the user information.
  • the correct probability weight of the name is 0.3
  • the correct probability weight of the ID number is 0.3
  • the correct probability weight of the mobile phone number is 0.2
  • the correct probability weight of the mailbox is 0.2.
  • the correct probability of the name is 80%
  • the correct probability of the identity card number is 80%
  • the correct probability of the mobile phone number is 1, and the correct probability of the mailbox is 50%.
  • Step S506 Select user information whose correct probability is greater than the preset probability threshold to perform correctness classification.
  • At least one probability threshold is preset; then the calculated correct probability of each piece of user information is compared with the at least one probability threshold, thereby obtaining a level of accuracy of each piece of user information.
  • a probability threshold when a probability threshold is set, user information whose correct probability of user information is greater than or equal to the probability threshold is directly output as correct user information.
  • the correct probability of the user information can be separately compared with all the probability thresholds, respectively, thereby outputting a level of accuracy of the user information. For example, when there are two probability thresholds, the correct probability of the user information is compared with a preset first threshold and a second threshold, the first threshold being greater than the second threshold.
  • the correctness of the user information is low; when the correct probability of the user information is less than the second threshold, it is determined that the accuracy of the user information is too low, and the user information is error information.
  • the accuracy level of the user information is then output in a form, document, graphic or other form.
  • the user information screening method provided in this embodiment may be configured to read each piece of user information, determine the correct probability of the element in each piece of user information according to a preset determination rule, and then determine the correct probability according to the element. Calculate the correct probability of the corresponding user information, and finally select the user information whose correct probability is greater than the preset probability threshold to classify the correctness. In this way, it is possible to quickly and correctly filter comprehensive and accurate user information data from a complex information database and provide a reference for accuracy.
  • FIG. 5 it is a schematic flowchart of a second embodiment of the user information screening method of the present application.
  • the steps S600-S606 of the user information screening method are similar to the steps S500-S506 of the first embodiment, except that the method further includes steps S608-S610.
  • steps S608-S610 are also required. among them:
  • Step S608 the user information is decomposed into at least one element.
  • the user information is first decomposed into elements including a name, a mobile phone number, an ID card number, a mailbox, and the like according to content included in the user information, such as "name”, “mobile phone”, “identity”, “mailbox” and the like.
  • content included in the user information such as "name”, "mobile phone”, “identity”, "mailbox” and the like.
  • the character recognition is a relatively common technical means, the characteristic content in the user information can be directly recognized, and the user information is decomposed into elements according to the characteristic content, and each When the user information includes the characteristic content, the content is decomposed as an element of the user information.
  • Step S610 setting a composition format of each of the at least one element, and setting a judgment rule of a correct probability of each element according to a composition format of each element.
  • a composition format of each element may be set according to characteristics of the element, and then each element is set according to a composition format of each element The rule of judgment of the correct probability.
  • the composition format of each element may be set according to the characteristics of the name, the ID number, the mobile phone number, and the mailbox, such as the name by the first name and the last name.
  • the surname includes a hundred family names, and the name is composed of 1-6 Chinese characters; the ID card number is composed of 18 digits, the first 6 digits are the administrative division code, and the 7th to 14th digits are the birth date code.
  • the 15th to 17th digits are sequential codes, and the checksum of all digits is a specific value;
  • the mobile phone number is composed of 11 digits, the first 3 digits of network identification number, 4-7 digits are area codes, 8-11
  • the digit is the user number;
  • the mailbox is composed of the username +@+mail server domain name, the username is composed of letters, numbers and other common characters (such as underscore, plus or minus symbols), and the mail server domain name is connected through the Internet. Test the domain name of the server to which you can connect.
  • the judgment rule of the correct probability of the element is set.
  • the name does not exceed six Chinese characters, and the last name is included in the hundred names.
  • the correct probability of the name can be determined to be 90%; when the surname includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the name is composed of more than 6 Chinese characters, it can be determined
  • the correct rate of the name is 80%; when the name includes non-Chinese characters, the correct probability of the name can be determined to be 30%; when the above-mentioned one error condition occurs in both the first name and the first name, the correct probability of the name can be determined as the correctness of each of them
  • the product of the probabilities, such as the first name and the last name are not Chinese characters.
  • the ID number should be 18 digits, and the first 6 digits are the administrative division code, and the 7th to 14th digits are valid birth date codes.
  • the checksum of all digits is a specific one. value.
  • the correct probability of the ID card number is 40%; when the ID card number contains exactly 18, and the first 6 digits of the ID number are the administrative division code, the 7th Bits up to 14 are valid birth date codes, but the checksum of all digits is not a specific value, then the correct probability of determining the positive number is 80%; when the ID number contains non-numbers, it can be determined The correct probability of the ID number is 30%.
  • the mobile phone number is 11 digits, the current 3 digit network identification number, and 4-7 digits are area codes.
  • the correct probability of the mobile phone number can be determined to be 80%, when the mobile phone number is less than 11 digits If the composition, or the mobile number contains non-numeric, the correct probability of determining the mobile number is 30%.
  • the mailbox consists of the username +@+mail server domain name, and the username has the specified character format.
  • the user information screening method provided in this embodiment may be configured to read each piece of user information, determine the correct probability of the element in each piece of user information according to a preset determination rule, and then determine the correct probability according to the element. Calculate the correct probability of the corresponding user information, and finally select the user information whose correct probability is greater than the preset probability threshold to classify the correctness. In this way, it is possible to intelligently, quickly and correctly filter out comprehensive and accurate user information data from a complex information database, and provide a reference for correctness.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computational Linguistics (AREA)
  • Information Transfer Between Computers (AREA)
  • Computer And Data Communications (AREA)

Abstract

一种用户信息筛选方法、一种服务器及计算机可读存储介质，该方法包括：读取每一条用户信息（S500）；根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率（S502）；根据所述元素的正确概率计算对应的用户信息的正确概率（S504）；选择正确概率大于预设概率阈值的用户信息进行正确度分类（S506）。上述用户信息筛选方法、服务器及计算机可读存储介质能够快速、正确地从庞杂的信息数据库中筛选出全面而又精确的用户信息。

Description

用户信息筛选方法、服务器及计算机可读存储介质
本申请要求于2017年11月15日提交中国专利局、申请号为201711130640.4、发明名称为“用户信息筛选方法、服务器及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及数据分析及应用技术领域，尤其涉及一种用户信息筛选方法、服务器及计算机可读存储介质。
背景技术
随着保险行业的日益发展，录入的保单数据也成爆炸式增长。对于每个信息数据库中的保单数据，最初来源基本都是由人工导入进去的，因此，人工导入过程中就无可避免地会产生错误信息。现有技术中虽然有很多数据分析工具可以进行筛选，但都不能精准地对信息数据库中的用户信息数据按正确度来进行分类，也无法精确地筛选出正确度高的用户信息数据。
发明内容
有鉴于此，本申请提出一种用户信息筛选方法、服务器及计算机可读存储介质，以实现快速、正确地从庞杂的信息数据库中筛选出全面而又精确的用户信息数据。
首先，为实现上述目的，本申请提出一种服务器，所述服务器包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的用户信息筛选程序，所述用户信息筛选程序被所述处理器执行时实现如下步骤：读取每一条用户信息；根据预设的判断规则分别判断所述每一条用户信息中的元素的正 确概率；根据所述元素的正确概率计算对应的用户信息的正确概率；选择正确概率大于预设概率阈值的用户信息进行正确度分类。
此外，为实现上述目的，本申请还提供一种用户信息筛选方法，该方法应用于服务器，所述方法包括：读取每一条用户信息；根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；根据所述元素的正确概率计算对应的用户信息的正确概率；选择正确概率大于预设概率阈值的用户信息进行正确度分类。
进一步地，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质存储有用户信息筛选程序，所述用户信息筛选程序可被至少一个处理器执行，以使所述至少一个处理器执行如上述的用户信息筛选方法的步骤。
相较于现有技术，本申请所提出的用户信息筛选方法、服务器及计算机可读存储介质，可以先判断出构成用户信息的元素的正确概率，然后通过所述元素的正确概率计算对应的用户信息的正确度级别，从而快速、正确地从庞杂的信息数据库中筛选出全面而又精确的用户信息数据。
附图说明
图1是服务器一可选的硬件架构的示意图；
图2是本申请用户信息筛选程序第一实施例的程序模块示意图；
图3是本申请用户信息筛选程序第二实施例的程序模块示意图；
图4是本申请用户信息筛选方法第一实施例的流程示意图；
图5是本申请用户信息筛选方法第二实施例的流程示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
需要说明的是，在本申请中涉及“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。
参阅图1所示，是服务器1一可选的硬件架构的示意图。
所述服务器1可以是机架式服务器、刀片式服务器、塔式服务器或机柜式服务器等计算设备，该服务器1可以是独立的服务器，也可以是多个服务器所组成的服务器集群。本实施例中，所述服务器1可包括，但不仅限于，可通过系统总线相互通信连接存储器11、处理器12、网络接口13。
所述服务器1通过网络接口13连接网络(图1未标出)，获取或传递包括用户信息数据在内的所有资讯。所述网络可以是企业内部网(Intranet)、互联网(Internet)、全球移动通讯系统(Global System of Mobile communication，GSM)、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、4G网络、5G网络、蓝牙(Bluetooth)、Wi-Fi、通话网络等无线或有线网络。
需要指出的是，图1仅示出了具有组件11-13的服务器1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。
其中，所述存储器11至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、 随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器11可以是所述服务器1的内部存储单元，例如该服务器1的硬盘或内存。在另一些实施例中，所述存储器11也可以是所述服务器1的外部存储设备，例如该服务器1配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。当然，所述存储器11还可以既包括所述服务器1的内部存储单元也包括其外部存储设备。本实施例中，所述存储器11通常用于存储安装于所述服务器1的操作系统和各类应用软件，例如用户信息筛选程序200的程序代码等。此外，所述存储器11还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器12在一些实施例中可以是中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器12通常用于控制所述服务器1的总体操作，例如执行数据交互或者通信相关的控制和处理等。本实施例中，所述处理器12用于运行所述存储器11中存储的程序代码或者处理数据，例如运行所述的用户信息筛选程序200等。
所述网络接口13可包括无线网络接口或有线网络接口，该网络接口13通常用于在所述服务器1与其他电子设备之间建立通信连接。
至此，己经详细介绍了本申请各个实施例的应用环境和相关设备的硬件结构和功能。下面，将基于上述应用环境和相关设备，提出本申请的各个实施例。
首先，本申请提出一种用户信息筛选程序200。
参阅图2所示，是本申请用户信息筛选程序200第一实施例的程序模块图。
本实施例中，所述用户信息筛选程序200包括一系列的存储于存储器11上的计算机程序指令，当该计算机程序指令被处理器12执行时，可以实现本申请各实施例的用户信息筛选操作。在一些实施例中，基于该计算机程序指令 各部分所实现的特定的操作，用户信息筛选程序200可以被划分为一个或多个模块。例如，在图2中，所述用户信息筛选程序200可以被分割成读取模块201、判断模块202、计算模块203、输出模块204。其中：
所述读取模块201，用于读取每一条用户信息。
具体地，当所述服务器1通过有线或无线的方式与其他电子装置连接时，所述用户信息筛选程序200可以根据用户指令获取所述其他电子装置所存储的用户信息数据；当所述服务器1存储有用户信息数据时，所述用户信息筛选程序200也可以直接获取所述服务器1存储的用户信息数据。
所述判断模块202，用于根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率。
具体地，当所述用户信息包含有多个元素时，可以将每个元素与该元素对应的正确概率的判断规则进行比对，从而判断出该元素的正确概率。
在本实施例中，例如，录入的保单数据中的用户信息一般有很多个栏位，包括的姓名、身份证号码、手机号码、邮箱以及标识和编码。其中一般而言，姓名由姓和名组成，姓包括百家姓，而名则是由1-6个汉字构成；身份证号码则是由18位数字组成，前6位为行政区划分代码，第7位至14位为出生日期码，第15位至17位为顺序码，所有位数字的校验和为一个特定值；手机号码则是由11位数字构成，前3位网络识别号，4-7位是地区编码；邮箱则是由用户名+@+邮件服务器域名组成，用户名是由字母、数字和其他普通字符(例如下划线、加减符号)组成，邮件服务器域名则是通过英特网连接测试能够连接到的服务器域名。因此，所述判断模块202可以将该特征作为用户信息内姓名、身份证号码、手机号码、邮箱等元素的正确概率的判断规则。
具体的，在本实施例中，当用户信息中的元素符合对应的正确概率的判断规则时，所述判断模块202判定所述元素的正确概率为1。当用户信息中的元素不符合对应的正确概率的判断规则时，所述判断模块202判定所述元素的正确概率为小于1的值。例如：就用户信息中姓名这一元素而言，姓名不超过 6个汉字，姓包括在百家姓里。当姓是一个不在百家姓中汉字，可判定姓名的正确概率为90％；当姓包括非汉字，可判定姓名的正确概率为30％；当姓名是由超过6个的汉字构成，可判定姓名的正确率为80％；当名是包括非汉字，可判定姓名的正确概率为30％；当姓和名均出现上述一种错误情形，可判定姓名的正确概率为将它们各自导致的正确概率的乘积，例如姓和名均不为汉字，则根据前文可知所述姓名的正确概率为30％*30％＝9％。同理，对身份证号码而言，身份证号码应该是18位，且前6位为行政区划分代码，第7位至14位为有效的出生日期码，所有位数字的校验和为一个特定值。当身份证号码包含的数字不等于18个，可判定身份证号码的正确概率为40％；当身份证号码包含的数字刚好18个，且身份正号码的前6位为行政区划分代码，第7位至14位为有效的出生日期码，但所有位数字的校验和不为特定值时，则可判定身份正号码的正确概率为80％；当身份证号码中包含非数字，则可判定身份证号码的正确概率为30％。对手机号码而言，手机号码为11位数字，当前3位网络识别号，4-7位是地区编码。当手机号码是由大于11位数字构成，且当前3位网络识别号，4-7位是地区编码，则可判定所述手机号码的正确概率为80％，当手机号码是由小于11位数字构成，或者手机号码中包含有非数字，则判定手机号码的正确概率为30％。对邮箱而言，邮箱由用户名+@+邮件服务器域名组成，并且用户名有指定字符格式。当邮箱并不是由用户名+@+邮件服务器域名组成，则可判定邮箱的正确概率为30％；当邮箱的用户名除了由字母、数字和其他普通字符(例如下划线、加减符号)组成，还包括其他字符，则可判定邮箱的正确概率为40％；当邮箱的邮件服务器域名通过英特网连接测试不能够连接到服务器域名，则可判定邮箱的正确概率为50％；当邮箱的组成格式或用户名或邮件服务器域名均出现上述错误情形，则可判定邮箱的正确概率为将它们各自导致的正确概率的乘积，例如邮箱的组成格式和用户名出现错误，则根据前文可知，邮箱的正确概率为30％*40％＝12％。
因此，所述判断模块203将所述获取模块201获取的每一条用户信息中的 元素与该元素对应的判断规则相比对，就可以直接判断出所述元素的正确概率。
例如，当所述读取模块201读取的用户信息中的姓名中的姓是一个不在百家姓中汉字，姓名中的名是由2个的汉字构成，则该姓名的正确率为80％*1＝80％。所述用户信息中的身份证号码包含的数字为18个，且当身份正号码的前6位为行政区划分代码，第7位至14位为有效的出生日期码，但是所有位数字的校验和不为特定值，则该身份正号码的正确概率为80％。所述用户信息中的手机号码则是由11位数字构成，前3位网络识别号，4-7位是地区编码，则该手机号码正确概率为1。所述用户信息中的邮箱由用户名+@+邮件服务器域名组成，且邮箱的用户名的组成符合预设的规则，但是通过英特网连接测试不能够连接到的服务器域名，则该邮箱的正确概率为50％。也就是说，所读取的所述用户信息中，姓名正确概率为80％，身份证号码正确概率为80％，手机号码正确概率为1，邮箱正确概率为50％。
所述计算模块203，用于根据所述元素的正确概率计算对应的用户信息的正确概率。
具体地，所述计算模块203对所述判断模块202判断出的所述用户信息中的每个元素分别赋予正确概率权重值，再根据所述每个元素的正确概率以及对应的正确概率权重值计算所述用户信息的正确概率。
在本实施例中，所述计算模块预先设置姓名正确概率权重为0.3，身份证号码正确概率权重为0.3，手机号码正确概率权重为0.2，邮箱正确概率权重为0.2。当所述判断模块202判断出所述用户信息中，姓名正确概率为80％，身份证号码正确概率为80％，手机号码正确概率为1，邮箱正确概率为50％。则所述计算模块可以根据所述设置的每个元素的正确概率权重值，综合各个元素的正确概率计算对应的用户信息的正确概率。具体计算过程为：将每个元素的正确概率分别与对应的正确概率权重值相乘，然后再相加，因此，得到该用户信息的正确概率为：80％*0.3+80％*0.3+1*0.2+50％*0.2＝78％。
所述输出模块204，用于选择正确概率大于预设概率阈值的用户信息进行正确度分类。
具体地，所述输出模块204预先设置至少一个概率阈值；然后将所述计算模块203计算出的每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
在一实施例中，当所述输出模块204设置有一个概率阈值时，所述输出模块204直接将用户信息的正确概率大于或等于该概率阈值的用户信息作为正确的用户信息输出。
在另一实施例中，当所述输出模块204设置有两个概率阈值或者两个以上概率阈值时，所述输出模块204可以分别将用户信息的正确概率分别与所有的概率阈值比较，从而输出一个用户信息的正确度级别。例如：当有两个概率阈值时，所述输出模块204将所述计算模块203计算出的用户信息的正确概率与预设的第一阈值和第二阈值比较，所述第一阈值大于所述第二阈值。当所述用户信息的正确概率大于所述第一阈值，则判断所述用户信息正确度高；当所述用户信息的正确概率大于所述第二阈值并小于所述第一阈值，则判断所述用户信息的正确度较低；当所述用户信息的正确概率小于所述第二阈值，则判断所述用户信息的正确度太低，所述用户信息为错误信息。然后再将所述用户信息的正确度级别以表格、文档、图形或者其他形式输出。
参阅图3所示，是本申请用户信息筛选程序200第二实施例的程序模块图。本实施例中，所述的用户信息筛选程序200除了包括第一实施例中的所述读取模块201、判断模块202、计算模块203、输出模块204之外，还包括分解模块205，和设置模块206。
所述读取模块201、判断模块202、计算模块203以及所述输出模块204与所述用户信息筛选程序200第一实施例中的对应的程序模块功能相同。这里不再赘述。由于有时候录入的用户信息数据并未将用户信息分解成元素并分别保存到特定的栏位。因此，在所述读取模块201读取到用户信息之后，在所述 判断模块203判断用户信息中元素的正确概率之前，还需要分解模块205和设置模块206进行处理。
所述分解模块205用于将用户信息分解成至少一个元素。
具体地，所述分解模块205先根据用户信息所包含的内容，如“姓名”、“手机”“身份”“邮箱”等字眼，将该用户信息分解成包括姓名、手机号码、身份证号码、邮箱等元素。在本实施例中，由于文字识别是比较常用的技术手段，因此所述分解模块205可以直接识别用户信息中的具特征性的内容，而根据所述具特征性的内容将用户信息分解成元素，则需要将每条用户信息中均包括所述具特征性的内容时，才将该内容作为所述用户信息的元素分解出来。
所述设置模块206用于设置所述至少一个元素中的每个元素的组成格式，并根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
具体的，当所述分解模块205将所述用户信息分解成至少一个元素之后，所述设置模块206可以根据所述元素的特征，设置每个元素的组成格式，然后根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。例如，当所述分解模块205将用户信息分解为姓名、身份证号码、手机号码、邮箱等元素之后，所述设置模块206首先根据姓名、身份证号码、手机号码、邮箱的特征设置每个元素的组成格式，如姓名由姓和名组成，姓包括百家姓，而名则是由1-6个汉字构成；身份证号码则是由18位数字组成，前6位为行政区划分代码，第7位至14位为出生日期码，第15位至17位为顺序码，所有位数字的校验和为一个特定值；手机号码则是由11位数字构成，前3位网络识别号，4-7位是地区编码，8-11位是用户号码；邮箱则是由用户名+@+邮件服务器域名组成，用户名是由字母、数字和其他普通字符(例如下划线、加减符号)组成，邮件服务器域名则是通过英特网连接测试能够连接到的服务器域名。
所述设置模块206还会根据各个元素的组成格式，设置元素的正确概率的判断规则。例如：就用户信息中姓名这一元素而言，姓名不超过6个汉字，姓包括在百家姓里。当姓是一个不在百家姓中汉字，可判定姓名的正确概率为 90％；当姓包括非汉字，可判定姓名的正确概率为30％；当姓名是由超过6个的汉字构成，可判定姓名的正确率为80％；当名是包括非汉字，可判定姓名的正确概率为30％；当姓和名均出现上述一种错误情形，可判定姓名的正确概率为将它们各自导致的正确概率的乘积，例如姓和名均不为汉字，则根据前文可知所述姓名的正确概率为30％*30％＝9％。同理，对身份证号码而言，身份证号码应该是18位，且前6位为行政区划分代码，第7位至14位为有效的出生日期码，所有位数字的校验和为一个特定值。当身份证号码包含的数字不等于18个，可判定身份证号码的正确概率为40％；当身份证号码包含的数字刚好18个，且身份正号码的前6位为行政区划分代码，第7位至14位为有效的出生日期码，但所有位数字的校验和不为特定值时，则可判定身份正号码的正确概率为80％；当身份证号码中包含非数字，则可判定身份证号码的正确概率为30％。对手机号码而言，手机号码为11位数字，当前3位网络识别号，4-7位是地区编码。当手机号码是由大于11位数字构成，且当前3位网络识别号，4-7位是地区编码，则可判定所述手机号码的正确概率为80％，当手机号码是由小于11位数字构成，或者手机号码中包含有非数字，则判定手机号码的正确概率为30％。对邮箱而言，邮箱由用户名+@+邮件服务器域名组成，并且用户名有指定字符格式。当邮箱并不是由用户名+@+邮件服务器域名组成，则可判定邮箱的正确概率为30％；当邮箱的用户名除了由字母、数字和其他普通字符(例如下划线、加减符号)组成，还包括其他字符，则可判定邮箱的正确概率为40％；当邮箱的邮件服务器域名通过英特网连接测试不能够连接到服务器域名，则可判定邮箱的正确概率为50％；当邮箱的组成格式或用户名或邮件服务器域名均出现上述错误情形，则可判定邮箱的正确概率为将它们各自导致的正确概率的乘积，例如邮箱的组成格式和用户名出现错误，则根据前文可知，邮箱的正确概率为30％*40％＝12％。
此外，本申请还提出一种用户信息筛选方法。
参阅图4所示，是本申请用户信息筛选方法第一实施例的流程示意图。在 本实施例中，根据不同的需求，图4所示的流程图中的步骤的执行顺序可以改变，某些步骤可以省略。
步骤S500，读取每一条用户信息。
具体地，当所述服务器1通过有线或无线的方式与其他电子装置连接时，可以根据用户指令读取所述其他电子装置所存储的用户信息数据；当所述服务器1存储有用户信息数据时，也可以直接读取所述服务器1存储的用户信息数据。
步骤S502，根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率。
具体地，当所述用户信息包含有多个元素时，可以将每个元素与该元素对应的正确概率的判断规则进行比对，从而判断出该元素的正确概率。
在本实施例中，例如，录入的保单数据中的用户信息一般有很多个栏位，包括的姓名、身份证号码、手机号码、邮箱以及标识和编码。其中一般而言，姓名由姓和名组成，姓包括百家姓，而名则是由1-6个汉字构成；身份证号码则是由18位数字组成，前6位为行政区划分代码，第7位至14位为出生日期码，第15位至17位为顺序码，所有位数字的校验和为一个特定值；手机号码则是由11位数字构成，前3位网络识别号，4-7位是地区编码；邮箱则是由用户名+@+邮件服务器域名组成，用户名是由字母、数字和其他普通字符(例如下划线、加减符号)组成，邮件服务器域名则是通过英特网连接测试能够连接到的服务器域名。因此，可以将该特征作为用户信息内姓名、身份证号码、手机号码、邮箱等元素的正确概率的判断规则。
具体的，在本实施例中，当用户信息中的元素符合对应的正确概率的判断规则时，判定所述元素的正确概率为1。当用户信息中的元素不符合对应的正确概率的判断规则时，判定所述元素的正确概率为小于1的值。例如：就用户信息中姓名这一元素而言，姓名不超过6个汉字，姓包括在百家姓里。当姓是一个不在百家姓中汉字，可判定姓名的正确概率为90％；当姓包括非汉字， 可判定姓名的正确概率为30％；当姓名是由超过6个的汉字构成，可判定姓名的正确率为80％；当名是包括非汉字，可判定姓名的正确概率为30％；当姓和名均出现上述一种错误情形，可判定姓名的正确概率为将它们各自导致的正确概率的乘积，例如姓和名均不为汉字，则根据前文可知所述姓名的正确概率为30％*30％＝9％。同理，对身份证号码而言，身份证号码应该是18位，且前6位为行政区划分代码，第7位至14位为有效的出生日期码，所有位数字的校验和为一个特定值。当身份证号码包含的数字不等于18个，可判定身份证号码的正确概率为40％；当身份证号码包含的数字刚好18个，且身份正号码的前6位为行政区划分代码，第7位至14位为有效的出生日期码，但所有位数字的校验和不为特定值时，则可判定身份正号码的正确概率为80％；当身份证号码中包含非数字，则可判定身份证号码的正确概率为30％。对手机号码而言，手机号码为11位数字，当前3位网络识别号，4-7位是地区编码。当手机号码是由大于11位数字构成，且当前3位网络识别号，4-7位是地区编码，则可判定所述手机号码的正确概率为80％，当手机号码是由小于11位数字构成，或者手机号码中包含有非数字，则判定手机号码的正确概率为30％。对邮箱而言，邮箱由用户名+@+邮件服务器域名组成，并且用户名有指定字符格式。当邮箱并不是由用户名+@+邮件服务器域名组成，则可判定邮箱的正确概率为30％；当邮箱的用户名除了由字母、数字和其他普通字符(例如下划线、加减符号)组成，还包括其他字符，则可判定邮箱的正确概率为40％；当邮箱的邮件服务器域名通过英特网连接测试不能够连接到服务器域名，则可判定邮箱的正确概率为50％；当邮箱的组成格式或用户名或邮件服务器域名均出现上述错误情形，则可判定邮箱的正确概率为将它们各自导致的正确概率的乘积，例如邮箱的组成格式和用户名出现错误，则根据前文可知，邮箱的正确概率为30％*40％＝12％。因此，将读取的每一条用户信息中的元素与该元素对应的判断规则相比对，就可以直接判断出所述元素的正确概率。
步骤S504，根据所述元素的正确概率计算对应的用户信息的正确概率。
具体地，对所述用户信息中的每个元素分别赋予正确概率权重值，再根据所述每个元素的正确概率以及对应的正确概率权重值计算所述用户信息的正确概率。具体计算过程为：将每个元素的正确概率分别与对应的正确概率权重值相乘，然后再相加，从而得到该用户信息的正确概率。
例如，姓名正确概率权重为0.3，身份证号码正确概率权重为0.3，手机号码正确概率权重为0.2，邮箱正确概率权重为0.2。当所述用户信息中，姓名正确概率为80％，身份证号码正确概率为80％，手机号码正确概率为1，邮箱正确概率为50％。则该用户信息的正确概率为：80％*0.3+80％*0.3+1*0.2+50％*0.2＝78％。
步骤S506，选择正确概率大于预设概率阈值的用户信息进行正确度分类。
具体地，预先设置至少一个概率阈值；然后将计算出的每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
在一实施例中，当设置有一个概率阈值时，直接将用户信息的正确概率大于或等于该概率阈值的用户信息作为正确的用户信息输出。
在另一实施例中，当设置有两个概率阈值或者两个以上概率阈值时，可以分别将用户信息的正确概率分别与所有的概率阈值比较，从而输出一个用户信息的正确度级别。例如：当有两个概率阈值时，将所述用户信息的正确概率与预设的第一阈值和第二阈值比较，所述第一阈值大于所述第二阈值。当所述用户信息的正确概率大于所述第一阈值，则判断所述用户信息正确度高；当所述用户信息的正确概率大于所述第二阈值并小于所述第一阈值，则判断所述用户信息的正确度较低；当所述用户信息的正确概率小于所述第二阈值，则判断所述用户信息的正确度太低，所述用户信息为错误信息。然后再将所述用户信息的正确度级别以表格、文档、图形或者其他形式输出。
本实施例所提出的用户信息筛选方法，可以在读取每一条用户信息，根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；然后 再根据所述元素的正确概率计算对应的用户信息的正确概率，最后选择正确概率大于预设概率阈值的用户信息进行正确度分类。这样，可以实现快速、正确地从庞杂的信息数据库中筛选出全面而又精确的用户信息数据，并提供了正确度参考。
如图5所示，是本申请用户信息筛选方法的第二实施例的流程示意图。本实施例中，所述用户信息筛选方法的步骤S600-S606与第一实施例的步骤S500-S506相类似，区别在于该方法还包括步骤S608-S610。
由于有时候录入的用户信息数据并未将用户信息分解成元素并分别保存到特定的栏位。因此，在步骤600之后，步骤602之前，还需要有步骤S608-S610。其中：
步骤S608，将用户信息分解成至少一个元素。
具体地，先根据用户信息所包含的内容，如“姓名”、“手机”“身份”“邮箱”等字眼，将该用户信息分解成包括姓名、手机号码、身份证号码、邮箱等元素。在本实施例中，由于文字识别是比较常用的技术手段，因此可以直接识别用户信息中的具特征性的内容，而根据所述具特征性的内容将用户信息分解成元素，则需要将每条用户信息中均包括所述具特征性的内容时，才将该内容作为所述用户信息的元素分解出来。
步骤S610，设置所述至少一个元素中的每个元素的组成格式，并根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
具体地，当将所述用户信息分解成至少一个元素之后，可以根据所述元素的特征，设置每个元素的组成格式，然后根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
例如，当将用户信息分解为姓名、身份证号码、手机号码、邮箱等元素之后，可以根据姓名、身份证号码、手机号码、邮箱的特征设置每个元素的组成格式，如姓名由姓和名组成，姓包括百家姓，而名则是由1-6个汉字构成；身份证号码则是由18位数字组成，前6位为行政区划分代码，第7位至14位为出生日期码，第15位至17位为顺序码，所有位数字的校验和为一个特定值； 手机号码则是由11位数字构成，前3位网络识别号，4-7位是地区编码，8-11位是用户号码；邮箱则是由用户名+@+邮件服务器域名组成，用户名是由字母、数字和其他普通字符(例如下划线、加减符号)组成，邮件服务器域名则是通过英特网连接测试能够连接到的服务器域名。
然后再根据各个元素的组成格式，设置元素的正确概率的判断规则。例如：就用户信息中姓名这一元素而言，姓名不超过6个汉字，姓包括在百家姓里。当姓是一个不在百家姓中汉字，可判定姓名的正确概率为90％；当姓包括非汉字，可判定姓名的正确概率为30％；当姓名是由超过6个的汉字构成，可判定姓名的正确率为80％；当名是包括非汉字，可判定姓名的正确概率为30％；当姓和名均出现上述一种错误情形，可判定姓名的正确概率为将它们各自导致的正确概率的乘积，例如姓和名均不为汉字，则根据前文可知所述姓名的正确概率为30％*30％＝9％。同理，对身份证号码而言，身份证号码应该是18位，且前6位为行政区划分代码，第7位至14位为有效的出生日期码，所有位数字的校验和为一个特定值。当身份证号码包含的数字不等于18个，可判定身份证号码的正确概率为40％；当身份证号码包含的数字刚好18个，且身份正号码的前6位为行政区划分代码，第7位至14位为有效的出生日期码，但所有位数字的校验和不为特定值时，则可判定身份正号码的正确概率为80％；当身份证号码中包含非数字，则可判定身份证号码的正确概率为30％。对手机号码而言，手机号码为11位数字，当前3位网络识别号，4-7位是地区编码。当手机号码是由大于11位数字构成，且当前3位网络识别号，4-7位是地区编码，则可判定所述手机号码的正确概率为80％，当手机号码是由小于11位数字构成，或者手机号码中包含有非数字，则判定手机号码的正确概率为30％。对邮箱而言，邮箱由用户名+@+邮件服务器域名组成，并且用户名有指定字符格式。当邮箱并不是由用户名+@+邮件服务器域名组成，则可判定邮箱的正确概率为30％；当邮箱的用户名除了由字母、数字和其他普通字符(例如下划线、加减符号)组成，还包括其他字符，则可判定邮箱的正确概率为40％；当邮箱的邮 件服务器域名通过英特网连接测试不能够连接到服务器域名，则可判定邮箱的正确概率为50％；当邮箱的组成格式或用户名或邮件服务器域名均出现上述错误情形，则可判定邮箱的正确概率为将它们各自导致的正确概率的乘积，例如邮箱的组成格式和用户名出现错误，则根据前文可知，邮箱的正确概率为30％*40％＝12％。
本实施例所提出的用户信息筛选方法，可以在读取每一条用户信息，根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；然后再根据所述元素的正确概率计算对应的用户信息的正确概率，最后选择正确概率大于预设概率阈值的用户信息进行正确度分类。这样，可以实现智能、快速、正确地从庞杂的信息数据库中筛选出全面而又精确的用户信息数据，并提供了正确度参考。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种用户信息筛选方法，应用于服务器，其特征在于，所述方法包括步骤：
    读取每一条用户信息；
    根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；
    根据所述元素的正确概率计算对应的用户信息的正确概率；
    选择正确概率大于预设概率阈值的用户信息进行正确度分类。
  2. 如权利要求1所述的用户信息筛选方法，其特征在于，所述分别判断所述每一条用户信息中的元素的正确概率步骤之前，还包括步骤：
    将用户信息分解成至少一个元素；
    设置所述至少一个元素中的每个元素的组成格式，并根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
  3. 如权利要求1所述的用户信息筛选方法，其特征在于，该方法还包括步骤：
    对所述用户信息中的元素分别赋予正确概率权重值；
    根据每个元素的正确概率以及对应的正确概率权重值，计算所述用户信息的正确概率。
  4. 如权利要求1所述的用户信息筛选方法，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  5. 如权利要求2所述的用户信息筛选方法，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  6. 如权利要求3所述的用户信息筛选方法，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  7. 如权利要求1所述的用户信息筛选方法，其特征在于，所述元素包括用户姓名、身份证号码、手机号码、邮箱、标识、编码中的任意一种或多种。
  8. 一种服务器，其特征在于，所述服务器包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的用户信息筛选程序，所述用户信息筛选程序被所述处理器执行时实现如下步骤：
    读取每一条用户信息；
    根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；
    根据所述元素的正确概率计算对应的用户信息的正确概率；
    选择正确概率大于预设概率阈值的用户信息进行正确度分类。
  9. 如权利要求8所述的服务器，其特征在于，所述分别判断所述每一条用户信息中的元素的正确概率步骤之前，还包括步骤：
    将用户信息分解成至少一个元素；
    设置所述至少一个元素中的每个元素的组成格式，并根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
  10. 如权利要求8所述的服务器，其特征在于，所述用户信息筛选程序被所述处理器执行时还实现如下步骤：
    对所述用户信息中的元素分别赋予正确概率权重值；
    根据每个元素的正确概率以及对应的正确概率权重值，计算所述用户信息的正确概率。
  11. 如权利要求8所述的服务器，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  12. 如权利要求9所述的服务器，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  13. 如权利要求10所述的服务器，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  14. 如权利要求8所述的服务器，其特征在于，所述元素包括用户姓名、身份证号码、手机号码、邮箱、标识、编码中的任意一种或多种。
  15. 一种计算机可读存储介质，所述计算机可读存储介质存储有用户信息筛选程序，所述用户信息筛选程序可被至少一个处理器执行，以使所述至少一个处理器执行如下步骤：
    读取每一条用户信息；
    根据预设的判断规则分别判断所述每一条用户信息中的元素的正确概率；
    根据所述元素的正确概率计算对应的用户信息的正确概率；
    选择正确概率大于预设概率阈值的用户信息进行正确度分类。
  16. 如权利要求15所述的计算机可读存储介质，其特征在于，所述分别判断所述每一条用户信息中的元素的正确概率步骤之前，还包括步骤：
    将用户信息分解成至少一个元素；
    设置所述至少一个元素中的每个元素的组成格式，并根据所述每个元素的组成格式，设置所述每个元素的正确概率的判断规则。
  17. 如权利要求15所述的计算机可读存储介质，其特征在于，所述用户信息筛选程序被所述处理器执行时还实现如下步骤：
    对所述用户信息中的元素分别赋予正确概率权重值；
    根据每个元素的正确概率以及对应的正确概率权重值，计算所述用户信息的正确概率。
  18. 如权利要求15所述的计算机可读存储介质，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  19. 如权利要求16所述的计算机可读存储介质，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
  20. 如权利要求17所述的计算机可读存储介质，其特征在于，所述选择正确概率大于预设概率阈值的用户信息进行正确度分类的步骤还包括步骤：
    设置至少一个概率阈值；
    将每一条用户信息的正确概率与所述至少一个概率阈值比较，从而得到每一条用户信息的正确度级别。
PCT/CN2018/102396 2017-11-15 2018-08-27 用户信息筛选方法、服务器及计算机可读存储介质 Ceased WO2019095768A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201711130640.4 2017-11-15
CN201711130640.4A CN107977404B (zh) 2017-11-15 2017-11-15 用户信息筛选方法、服务器及计算机可读存储介质

Publications (1)

Publication Number Publication Date
WO2019095768A1 true WO2019095768A1 (zh) 2019-05-23

Family

ID=62013519

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/102396 Ceased WO2019095768A1 (zh) 2017-11-15 2018-08-27 用户信息筛选方法、服务器及计算机可读存储介质

Country Status (2)

Country Link
CN (1) CN107977404B (zh)
WO (1) WO2019095768A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113378043A (zh) * 2021-06-03 2021-09-10 北京沃东天骏信息技术有限公司 用户筛选的方法和装置

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107977404B (zh) * 2017-11-15 2020-08-28 深圳壹账通智能科技有限公司 用户信息筛选方法、服务器及计算机可读存储介质
CN110705942A (zh) * 2019-10-10 2020-01-17 环旭电子股份有限公司 条码信息的筛选方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120166307A1 (en) * 2010-12-23 2012-06-28 Alibaba Group Holding Limited Determination of permissibility associated with e-commerce transactions
CN103500195A (zh) * 2013-09-18 2014-01-08 小米科技有限责任公司 分类器更新方法、装置、系统及设备
CN105825367A (zh) * 2016-03-16 2016-08-03 聚相投资管理（上海）有限公司 一种云端智能服务器及其在邮件分类中的应用
CN106650783A (zh) * 2015-10-30 2017-05-10 李静涛 用于移动终端数据分类、生成、匹配的方法、装置及系统
CN107977404A (zh) * 2017-11-15 2018-05-01 上海壹账通金融科技有限公司 用户信息筛选方法、服务器及计算机可读存储介质

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU2002952106A0 (en) * 2002-10-15 2002-10-31 Silverbrook Research Pty Ltd Methods and systems (npw008)
US20160051167A1 (en) * 2012-10-10 2016-02-25 Invensense, Inc. System and method for activity classification
CN103888254B (zh) * 2012-12-21 2017-05-31 阿里巴巴集团控股有限公司 一种网络验证信息的方法和装置
CN105589885B (zh) * 2014-10-24 2019-07-02 阿里巴巴集团控股有限公司 一种数据一致性校验的方法及系统
CN106326776A (zh) * 2015-07-02 2017-01-11 阿里巴巴集团控股有限公司 基于规则的数据对象验证方法、装置、系统及电子设备

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120166307A1 (en) * 2010-12-23 2012-06-28 Alibaba Group Holding Limited Determination of permissibility associated with e-commerce transactions
CN103500195A (zh) * 2013-09-18 2014-01-08 小米科技有限责任公司 分类器更新方法、装置、系统及设备
CN106650783A (zh) * 2015-10-30 2017-05-10 李静涛 用于移动终端数据分类、生成、匹配的方法、装置及系统
CN105825367A (zh) * 2016-03-16 2016-08-03 聚相投资管理（上海）有限公司 一种云端智能服务器及其在邮件分类中的应用
CN107977404A (zh) * 2017-11-15 2018-05-01 上海壹账通金融科技有限公司 用户信息筛选方法、服务器及计算机可读存储介质

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113378043A (zh) * 2021-06-03 2021-09-10 北京沃东天骏信息技术有限公司 用户筛选的方法和装置

Also Published As

Publication number Publication date
CN107977404A (zh) 2018-05-01
CN107977404B (zh) 2020-08-28

Similar Documents

Publication Publication Date Title
JP6594988B2 (ja) 住所テキストを処理する方法及び機器
CN108768929B (zh) 电子装置、征信反馈报文的解析方法及存储介质
WO2022156066A1 (zh) 文字识别方法、装置、电子设备及存储介质
US10645105B2 (en) Network attack detection method and device
CN111666971A (zh) 基于位置定位的事件识别方法、装置、设备及存储介质
CN111177129B (zh) 标签体系的构建方法、装置、设备及存储介质
CN113434674A (zh) 数据解析方法、装置、电子设备及可读存储介质
CN112036579A (zh) 多分类模型自学习在线更新方法、系统及装置
WO2019019636A1 (zh) 用户身份识别方法、电子装置及计算机可读存储介质
CN112579621B (zh) 数据展示方法、装置、电子设备及计算机存储介质
CN113254672A (zh) 异常账号的识别方法、系统、设备及可读存储介质
CN113434672B (zh) 文本类型智能识别方法、装置、设备及介质
WO2019095768A1 (zh) 用户信息筛选方法、服务器及计算机可读存储介质
CN111343162A (zh) 系统安全登录方法、装置、介质及电子设备
CN111046087A (zh) 一种数据处理方法、装置、设备及存储介质
CN110517154A (zh) 数据模型训练方法、系统及计算机设备
CN107944931A (zh) 种子用户拓展方法、电子设备及计算机可读存储介质
CN112199374A (zh) 针对数据缺失的数据特征挖掘方法及其相关设备
CN114900835A (zh) 恶意流量智能检测方法、装置及存储介质
US20160378817A1 (en) Systems and methods of identifying data variations
WO2019192101A1 (zh) 客户保障分析方法、电子装置及计算机可读存储介质
CN113824717B (zh) 一种配置检查方法及装置
CN113938408B (zh) 一种数据流量测试方法、装置、服务器及存储介质
CN110351330B (zh) 数据上传方法、装置、计算机设备及存储介质
CN110968584B (zh) 一种画像生成系统、方法、电子设备及可读存储介质

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 22/09/2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18879445

Country of ref document: EP

Kind code of ref document: A1