Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, completeSite preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hairEmbodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative effortsExample, shall fall within the protection scope of the present invention.
Loan checking method provided in an embodiment of the present invention based on membership grade evaluation, can be applicable to the application such as Fig. 1In environment, wherein client (computer equipment) is communicated by network with server-side.Client asks member's rankingIt asks and is sent to server-side, server-side obtains the first preset rules, it is scored according to the first preset rules user basic information,Obtain basic score；If user base is scored above default scoring threshold value, sends information collection and request to client；Obtain visitorThe voice data and video data that family end returns；Additional scoring is obtained according to voice data and video data；Finally according to basisScoring and additional scoring obtain the membership grade of user.And then after getting loan audit request, asked according to loan auditUser identifier in asking obtains corresponding membership grade；The corresponding pending nuclear information of user identifier is obtained according to membership grade；RootMsu message is treated according to the second preset rules to score, and obtains user's scoring；It is finally scored to obtain loan audit according to userInformation.Wherein, client (computer equipment) can be, but not limited to various personal computers, laptop, smart phone, put downPlate computer and portable wearable device.Server-side can use the server of the either multiple server compositions of independent serverCluster is realized.
In one embodiment, as shown in Fig. 2, a kind of loan checking method based on membership grade evaluation is provided, with the partyMethod is illustrated for applying the server-side in Fig. 1, is included the following steps:
S10: membership grade evaluation request is obtained, membership grade evaluation request includes user identifier and user basic information.
Wherein, membership grade evaluation request is that the evaluation for the member subscription proposed to existing customer that client is initiated is askedIt asks.Specifically, user inputs corresponding instruction or information by client to trigger membership grade evaluation request or userAt the scene or sales counter is filed an application, and is requested by contact staff by client sponsor member ranking.Client is by the meetingMember's ranking request is sent to server-side, and server-side gets membership grade evaluation request.User identifier refers to can be onlyOne identifies the information of user.In one embodiment, user identifier can be certificate number, user name or user number.UserNumber refer to the identiflication number for the distribution of a platform, application program or system user, the corresponding unique member number of a user.User basic information includes the information relevant with user that the needs that may relate to during membership grade evaluation are audited.It is optionalGround, user basic information include the identity information of user, assets information, income information, reference information, loan information, hand-held setAt least one of standby information, social account information, third-party platform consumption information or loan application behavioural information etc..
S20: the first preset rules are obtained, is scored according to the first preset rules user basic information, obtains basisScoring.
Wherein, the first preset rules are a pre-set standards of grading, which can be according to realityThe needs of loan product are configured, and can also be counted to obtain according to historical data, specifically can according to actual needs andSetting, details are not described herein.After the configuration for being previously-completed the first preset rules, which is stored in serviceIn end.It is scored according to the first preset rules user basic information and is scored to get to user base.It is alternatively possible to beEach single item user basic information sets first preset rules, to obtain more accurate user base scoring.
It illustratively, can be identity information if user basic information includes the identity information and assets information of userThe first different preset rules are respectively set with assets information.For example, if identity information includes gender, occupation, age and bodySituation.It then can be respectively that different genders, occupation, age bracket and physical condition preset different score values, further according to thisThe corresponding identity information of user identifier come obtain corresponding score value and be overlapped to get arrive identity information score value.And forAssets information can then preset different assets sections, and the different corresponding score values in assets section is also different, then basisThe assets information of the user identifier finds corresponding assets section, that is, gets the corresponding score value of assets information.Finally by identityThe corresponding score value of information and assets information are directly or indirectly added one score value and are scored to get to user base.Ground connection, which is added, among it can be presented as in advance be that differently different weights is arranged in user basic information, and each user is basicThe score value of information is overlapped again later multiplied by corresponding weight.
S30: it if basic score is more than default scoring threshold value, sends information collection and requests to client.
After basic score is calculated, the basic score and default scoring threshold value are compared, if the basis is commentedPoint be more than default scoring threshold value, then illustrate that basic score has passed through preliminary audit survey, can with the judgement of further progress membership grade,Therefore it issues information collection to request to client, to prompt client to carry out corresponding information collection, be requested by information collectionTo obtain further information.Preferably, information collection request includes information collection content.The information collection content, which refers to, to be neededThe information further to acquire to client, optionally, the information collection content can be a Duan Yuyin, by client to clientThis section of voice is played, and acquires the voice data of client.It is to be appreciated that the information collection content can be for user's baseThe lower information of score value carries out supplemental information acquisition in this information.For example, being acquired to the supplement of identity information, to income informationIt further determines that or further determining that reference information.
In a specific embodiment, it if user base scoring is not above default scoring threshold value, issues registration and losesThe prompt information lost.It is to be appreciated that illustrating the essential information of user not if basic score is not above default scoring threshold valueIt is up to standard, issue the prompt information of registration failure.
S40: voice data and video data that client returns are obtained.
Client acquires the voice data and video data of user by voice capture device and video capture device respectively.Particularly, video data major embodiment be the facial parts of user in answer to a question video data.
S50: additional scoring is obtained according to voice data and video data.
In this step, the additional scoring of user is obtained by the voice data of acquisition and video data.It specifically, can be withA speech assessment is obtained according to voice data and video scoring is obtained according to video data, then the two scores are addedOr it is weighted and is added to get the additional scoring of user is arrived.
Specifically, voice data can be carried out to the conversion of text data by speech recognition algorithm.And then according to this turnText data after change to carry out speech assessment for the voice data.The voice data can be beaten using specific peoplePoint, obtain speech assessment.Table is corresponded to it is possible to further preset a speech score, by presetting corresponding keyWord and score value, then the algorithm of string matching is used to carry out string matching for the text data after conversion, after conversionText data and speech score correspond to the matching degree of table to obtain speech assessment.And video scoring can carry out video dataSub-frame processing, and micro- expression information after each framing or motor unit information are obtained to obtain.Optionally, if passing through micro- tableFeelings information obtains, then sets different correspondence score values in advance for each micro- expression information, further according to occurring in the video dataMicro- expression information obtain corresponding score value, and be added, obtain micro- expression scoring.It is to be appreciated that when micro- expression is believedWhen breath is tranquil or happy, corresponding score value is higher, when micro- expression information is nervous or anxiety, corresponding score value compared withIt is low.After respectively obtaining speech assessment and the scoring of micro- expression, the two is weighted addition and is commented to get to the additional of userPoint.
S60: the membership grade of user identifier is obtained according to basic score and additional scoring.
Specifically, first basic score is directly or indirectly added with additional scoring and is commented to get the target to userPoint.Wherein, it is added to score and add to score based on being presented as in advance indirectly and different weights is set, by basic scoreIt scores respectively multiplied by being overlapped again after corresponding weight to get the target for arriving user with additional scoring.Obtaining the userAfter the target scoring of mark, scored according to the target to inquire the membership grade that the user identifier can obtain.Optionally, in advanceDifferent score value sections is first set, and the corresponding membership grade in different score value sections is different.It is to be appreciated that point that numerical value is biggerIt is higher to be worth the corresponding membership grade in section.Therefore, inquire which target scoring belongs to after the target scoring for obtaining userScore value section, then the corresponding membership grade in score value section is got to get the membership grade of the user identifier is arrived.
S70: loan audit request is obtained, loan audit request includes user identifier.
Wherein, what the loan application proposed to existing customer that loan audit request is initiated for client was audited asksIt asks.Specifically, user inputs corresponding instruction or information by client to trigger loan audit request or user existingField or sales counter are filed an application, and are passed through client by contact staff and are initiated loan audit request.Client asks loan auditIt asks and is sent to server-side, server-side gets loan audit request.Loan audit request includes user identifier.
S80: corresponding membership grade is obtained according to user identifier.
Specifically, the corresponding membership grade of the user is obtained according to user identifier.By the database in query service end,Obtain the corresponding membership grade of the user identifier.
Further, if inquiring membership grade corresponding less than the user identifier in the database, member can be issuedRanking request, prompts user to register.
S90: the corresponding pending nuclear information of user identifier is obtained according to membership grade.
In this step, the corresponding pending nuclear information of the user identifier is obtained by membership grade.In this motion, noThe pending nuclear information audited with user's needs of membership grade is different.It is to be appreciated that membership grade is higher, need to examineThe pending nuclear information of core is fewer, and membership grade is lower, and the pending nuclear information for needing to audit is more.It specifically, can be preparatoryDifferent pending nuclear informations is set for different membership grades.In this step, so that it may which the user is obtained according to membership gradeIdentify corresponding pending nuclear information.Optionally, pending information includes the identity information, assets information, income information, sign of userLetter information, loan information, handheld device information, social account information, third-party platform consumption information or loan application behavior letterAt least one of breath etc..
S100: the second preset rules are obtained, msu message is treated according to the second preset rules and scores, obtain user and commentPoint.
Wherein, the second preset rules are a pre-set standards of grading, which can be according to realityThe needs of loan product are configured, and can also be counted to obtain according to historical data, specifically can be according to practical Xu ErsheFixed, details are not described herein.After the configuration for being previously-completed the second preset rules, which is stored in server-sideIn.Pending nuclear information is scored by second preset rules and is scored to get to user.It is alternatively possible to be each single itemOne the second preset rules of pending information setting, to obtain more accurate user's scoring.The step specifically can and stepS12 is identical, and details are not described herein.
S110: it is scored to obtain loan msu message according to user.
Wherein, loan msu message is a feedback information to loan audit request, which embodiesThe auditing result of loan audit request.Loan msu message includes passing through and not passing through.Specifically, according to user's scoring comeObtain different loan msu messages.Corresponding score value section is set for different loan msu messages in advance, judgement should laterWhich section user base scoring falls within to get to corresponding msu message.
Illustratively, using the standards of grading of hundred-mark system, for by with do not pass through respectively arranged score value section are as follows: [85,100] and [0,85).At this point, corresponding loan msu message is to pass through if user's scoring is 90；If user's scoring is 75,Corresponding loan msu message is not pass through.
In this embodiment, after getting member registration request, according to the first preset rules to user's baseThis information scores, and obtains user base scoring；If user base is scored above default scoring threshold value, information collection is sentIt requests to client；And obtain the voice data and video data of the user of client return；According to voice data and video countsAccording to the additional scoring for obtaining user；The membership grade of user is obtained according to basic score and additional scoring.Basic by userInformation determines that user can become the membership grade that member further determines user by voice data and video data later,The accuracy and validity of membership grade distribution are further ensured while guaranteeing user's member registration efficiency.And then it is obtainingAfter getting loan audit request, corresponding membership grade is obtained according to the user identifier in loan audit request；According to memberGrade obtains the corresponding pending nuclear information of user identifier；Msu message is treated according to the second preset rules to score, and is usedFamily scoring；It is finally scored to obtain loan msu message according to user.By identifying corresponding different membership grades to different userDifferent user basic informations is obtained, to complete the audit of loan, preferably improves the efficiency of loan audit.
In one embodiment, as shown in figure 3, obtaining additional scoring according to voice data and video data, specifically include asLower step:
S51: micro- expression information of user is obtained in video data.
Wherein, micro- expression information refers to micro- expression that correspondence image is embodied.It in this step, can be by presetting oneTime interval carrys out micro- expression information of timing acquisition video data septum reset part.Specifically, it can be set in video dataAt predetermined time intervals obtain video data septum reset part micro- expression information.Illustratively, the predetermined time be 3s, 5s,8s or 10s.
Specifically, the acquisition of corresponding facial image, and the people that will acquire are carried out to video data according to the predetermined timeFace image is input in the micro- Expression Recognition model pre-set and is identified, obtains micro- expression information.Specifically, micro- tableFeelings information can be tranquil, happy, nervous or anxiety etc..
S52: additional scoring is obtained according to voice data and micro- expression information.
In this step, the additional scoring of user is obtained according to voice data and micro- expression information.It is alternatively possible to respectivelyIt obtains a speech assessment according to voice data and micro- expression is obtained according to micro- expression information and score, then the two scores are carried outIt is added or weighting summation is to get the additional scoring for arriving user.
Specifically, voice data can be carried out to the conversion of text data by speech recognition algorithm.And then according to this turnText data after change to carry out speech assessment for the voice data.The voice data can be beaten using specific peoplePoint, obtain speech assessment.Table is corresponded to it is possible to further preset a speech score, by presetting corresponding keyWord and score value, then the algorithm of string matching is used to carry out string matching for the text data after conversion, after conversionText data and speech score correspond to the matching degree of table to obtain speech assessment.And micro- expression scoring can be believed by micro- expressionBreath sets different correspondence score values, further according to the micro- table occurred in the video data to obtain for each micro- expression information in advanceFeelings information obtains corresponding score value, and is added, and micro- expression scoring is obtained.It is to be appreciated that when micro- expression information is flatWhen quiet or happy, corresponding score value is higher, and when micro- expression information is nervous or anxiety, corresponding score value is lower.DividingAfter not obtaining speech assessment and the scoring of micro- expression, the two is weighted and is added to get the additional scoring of user is arrived.
In this embodiment, micro- expression information of user is obtained in video data, and according to voice data and micro- tableThe additional scoring of feelings acquisition of information user ensure that the accuracy that the additional scoring of user obtains.
In one embodiment, it as shown in figure 4, obtaining micro- expression information of user in video data, specifically includes as followsStep:
S511: carrying out sub-frame processing to video data according to the first preset interval, obtain N facial image to be identified,In, N is positive integer.
Wherein, which can be thought as a time value or represent the numerical value of frame number.According to settingThe first preset interval to video data carry out sub-frame processing, obtain N facial image to be identified, wherein N is positive integer.
Optionally, server-side can obtain facial image to be identified by way of screenshotss from video data.Specifically,Can realize the process for obtaining facial image to be identified by OpenCV, OpenCV provide a simple and easy-to-use frame withExtract the picture frame in video file.Illustratively, the operation that video reads and writees is carried out using VideoCapture class.Corresponding video data is shown using cap=cv2.VideoCapture () function in VideoCapture class first, then is led toCap.read () function in VideoCapture class is crossed by preset frame per second reading video data, cap.read () functionThere are two return values: ret and frame.Wherein, ret is Boolean, True is returned to if reading frame is correctly, if shouldVideo data has read ending, its return value is just False, it can passes through the return value of cap.read () functionIt is finished to judge whether the video data reads.Frame is exactly current truncated picture, can be a three-dimensional matrice.It canTo understand ground, client directly can also carry out intercept operation to video data, obtain wait know after getting video dataOthers' face image, i.e., the above-mentioned process that facial image to be identified is obtained from video data can also be realized by client.VisitorFamily end sends server-side for facial image to be identified again, and server-side directly gets facial image to be identified from client.
S512: N facial image to be identified being input in micro- Expression Recognition model and is identified, is obtained each to be identifiedMicro- expression information of facial image.
Micro- Expression Recognition model is the identification model for judging face mood in input picture, micro- Expression Recognition modelIt may determine that probability value of the face corresponding to preset a variety of moods in input picture, if the probability value of certain mood is more than to correspond toPreset threshold, then obtaining the corresponding mood of the input picture is Emotion identification result.It, can be with for example, in the present embodimentMood in micro- Expression Recognition model is set as tranquil, happy, nervous and 4 kinds of anxiety.Specifically, difference can be acquired in advanceThe great amount of samples image for representing this 4 kinds of moods is labeled, and is formed sample graph image set, is then selected corresponding neural network modelOr classifier is trained, and finally obtains micro- Expression Recognition model.
In this step, N obtained facial image to be identified is input in micro- Expression Recognition model and is identified, i.e.,Obtain micro- expression information of each facial image to be identified.
In the present embodiment, sub-frame processing is carried out to video data by preset interval, obtains N face figure to be identifiedPicture, and N facial image to be identified is input in micro- Expression Recognition model and is identified, obtain each facial image to be identifiedMicro- expression information.It ensure that the accuracy that micro- expression information of video data obtains with this.
In one embodiment, voice data includes M sub- voice data sections, wherein M is positive integer.
Wherein, sub- voice data section is the message segment distinguished according to different problems or information point.Such as: voice dataIn include user to the supplement acquisition of identity information, to income information further determine that or it is to reference information furtherVoice data, then can be divided into 3 sub- voice data sections by the voice data such as determining.Each sub- voice data section represents differenceInformation point.Further, it can also further be segmented according to difference problem further in each information point,It is not specifically limited herein.Specifically, can when acquiring voice data according to each asked questions or guidance voice comeThe division of sub- voice data section is carried out to collected voice data.
In the present embodiment, it as shown in figure 5, obtaining micro- expression information of user in video data, specifically includes as followsStep:
S511 ': it is segmented according to the when ordered pair video data of M sub- voice data sections, obtains M sub-video dataSection.
It is to be appreciated that voice data and video data acquire simultaneously, therefore the period of the two is corresponding.CauseThis, first gets in M sub- voice data sections each sub- voice data section corresponding period, further according to the period to video countsAccording to being segmented, M sub- video-data fragments are obtained.It is to be appreciated that sub- voice data section each at this time all has corresponding sonVideo-data fragment.
S512 ': sub-frame processing is carried out to each sub-video data section according to the second preset interval, obtains each sub-video numberAccording to K facial image to be identified of section, wherein K is positive integer.
Wherein, which can be thought as a time value or represent the numerical value of frame number.According to settingThe second preset interval to video data carry out sub-frame processing, obtain K facial image to be identified of each sub-video data section,Wherein, K is positive integer.The specific sub-frame processing mode can be similar with step S511, and which is not described herein again.
S513 ': K facial image to be identified of each sub-video data section is input in micro- Expression Recognition model and is carried outIdentification, obtains micro- expression information of each sub-video data section.
In this step, K facial image to be identified of obtained each sub-video data section micro- expression is input to knowIt is identified in other model to get micro- expression information of each sub-video data section is arrived.Specific micro- Expression Recognition model can be withIdentical with step S512, which is not described herein again.
In this embodiment, first video data is segmented according to the period of M sub- voice data sections, obtains MSub-video data section.Sub-frame processing is carried out to each sub-video data section according to the second preset interval, obtains each sub-video numberAccording to K facial image to be identified of section, wherein K is positive integer；Finally by K face to be identified of each sub-video data sectionImage is input in micro- Expression Recognition model and is identified, obtains micro- expression information of each sub-video data section.By voice numberIt is associated according to video data, guarantees the accuracy that subsequent score value calculates.
In one embodiment, it as shown in fig. 6, obtaining additional scoring according to voice data and micro- expression information, specifically includesFollowing steps:
S521: the speech score of each sub- voice data section is obtained.
Specifically, each sub- voice data section is carried out to the conversion of text data by speech recognition algorithm.And then basisText data after the conversion to carry out speech assessment for each sub- voice data section, obtains speech score.It is alternatively possible to adoptIt is given a mark with specific people to the voice data, obtains speech score.It is possible to further preset a speech score pairTable is answered, by presetting corresponding keyword and score value, then uses the algorithm of string matching for the text data after conversionCarry out string matching, according to after conversion text data and speech score correspond to the matching degree of table and obtain each sub- voiceThe speech score of data segment.
S522: the power of corresponding each sub- voice data section is calculated according to micro- expression information of each sub-video data sectionValue.
Each sub-video data section all includes micro- expression information, it is possible to understand that ground, micro- expression information are at least one.By counting the quantity of different micro- expression informations in each sub-video data section, corresponding each sub- voice data section is setWeight.Wherein, corresponding sub- voice data section refers to the correspondence for the period embodied in step S511 '.It specifically, can be according to everyOne sub-video data Duan Zhongwei expression information is nervous or anxiety ratio corresponding weight is arranged, and micro- expression information is tightIt opens or the ratio of anxiety is higher, the weight is lower, and micro- expression information is that tranquil or happy ratio is higher, the weightIt is higher.It further, is more than certain threshold value when a sub-video data Duan Zhongwei expression information is nervous or anxiety ratio,It is 0 that corresponding weight, which is then arranged,.Optionally, which is 75%, 80% or 85%.
S523: according to the additional scoring of the speech score of each sub- voice data section and weight computing user.
After the speech score and weight for obtaining each sub- voice data section, the additional scoring of user is calculated.Specifically,The additional scoring of user can be calculated by the following formula:
Wherein, S is the additional scoring of user, AiFor the speech score of the i-th cross-talk voice data section, QiFor the i-th cross-talk voiceThe corresponding weight of data segment, n are the quantity of sub- voice data section.
In this embodiment, the speech score for first obtaining each sub- voice data section, further according to each sub-video dataMicro- expression information of section calculates the weight of corresponding each sub- voice data section, finally according to the voice of each sub- voice data sectionThe additional scoring of score value and weight computing user.By the way that sub- voice data section and corresponding sub-video data section are associated,And the setting of weight is carried out to speech score using corresponding micro- expression information, the additional scoring for further ensuring user calculatesAccuracy.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each processExecution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limitIt is fixed.
In one embodiment, a kind of loan audit device based on membership grade evaluation is provided, should be commented based on membership gradeLoan checking method based on membership grade evaluation in fixed loan audit device and above-described embodiment corresponds.Such as Fig. 7 instituteShow, should include that membership grade evaluates request module 10, basic score obtains based on the loan audit device that membership grade is evaluatedModulus block 20, information collection request sending module 30, data acquisition module 40, additional scoring acquisition module 50, membership grade are trueCover half block 60, loan audit request module 70, membership grade obtain module 80, pending data obtaining module 90, userScoring obtains module 100 and loan msu message determining module 110.Detailed description are as follows for each functional module:
Membership grade evaluates request module 10, for obtaining membership grade evaluation request, membership grade evaluation requestIncluding user identifier and user basic information；
Basic score obtains module 20, for obtaining the first preset rules, is believed substantially according to the first preset rules userBreath scores, and obtains basic score；
Information collection request sending module 30 sends information collection if being more than default scoring threshold value for basic scoreIt requests to client；
Data acquisition module 40, for obtaining the voice data and video data of client return；
Additional scoring obtains module 50, for obtaining additional scoring according to voice data and video data；
Membership grade determining module 60, for obtaining the membership grade of user identifier according to basic score and additional scoring；
Loan audit request module 70, for obtaining loan audit request, loan audit request includes user identifier；
Membership grade obtains module 80, for obtaining corresponding membership grade according to user identifier；
Pending data obtaining module 90, for obtaining the corresponding pending nuclear information of user identifier according to membership grade；
User, which scores, obtains module 100, for obtaining the second preset rules, treats msu message according to the second preset rulesIt scores, obtains user's scoring；
Loan msu message determining module 110 obtains loan msu message for scoring according to user.
Preferably, as shown in figure 8, it includes micro- expression information acquiring unit 51 and additional scoring that additional scoring, which obtains module 50,Acquiring unit 52.
Micro- expression information acquiring unit 51, for obtaining micro- expression information of user in video data；
Additional scoring acquiring unit 52, for obtaining additional scoring according to voice data and micro- expression information.
Preferably, micro- expression information acquiring unit 51 is used to carry out at framing video data according to the first preset intervalReason, obtains N facial image to be identified, wherein N is positive integer；N facial image to be identified is input to micro- Expression Recognition mouldIt is identified in type, obtains micro- expression information of each facial image to be identified.
Preferably, voice data includes M sub- voice data sections, wherein M is positive integer.Micro- expression information acquiring unit51, for being segmented according to the when ordered pair video data of M sub- voice data sections, obtain M sub- video-data fragments；According toTwo preset intervals carry out sub-frame processing to each sub-video data section, obtain K face to be identified of each sub-video data sectionImage, wherein K is positive integer；K facial image to be identified of each sub-video data section is input to micro- Expression Recognition modelIn identified, obtain micro- expression information of each sub-video data section.
Preferably, the speech score that scoring acquiring unit 52 is used to obtain each sub- voice data section is added；According to eachMicro- expression information of sub-video data section calculates the weight of corresponding each sub- voice data section；According to each sub- voice data sectionSpeech score and weight computing user additional scoring.
Specific restriction about the loan audit device evaluated based on membership grade may refer to above for member etc.The restriction of grade assessment method, details are not described herein.Modules in the above-mentioned loan audit device based on membership grade evaluationIt can be realized fully or partially through software, hardware and combinations thereof.Above-mentioned each module can be embedded in the form of hardware or independently ofIn processor in computer equipment, it can also be stored in a software form in the memory in computer equipment, in order to locateIt manages device and calls the corresponding operation of the above modules of execution.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junctionComposition can be as shown in Figure 9.The computer equipment include by system bus connect processor, memory, network interface andDatabase.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipmentInclude non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and dataLibrary.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculatingThe database of machine equipment is used to store the data arrived used in the above-mentioned loan checking method based on membership grade evaluation.The meterThe network interface for calculating machine equipment is used to communicate with external terminal by network connection.When the computer program is executed by processorTo realize a kind of loan checking method based on membership grade evaluation.
In one embodiment, a kind of computer equipment is provided, including memory, processor and storage are on a memoryAnd the computer program that can be run on a processor, processor are realized in above-described embodiment when executing computer program based on memberThe step of loan checking method of ranking.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculatedMachine program realizes the step of loan checking method based on membership grade evaluation in above-described embodiment when being executed by processor.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be withRelevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computerIn read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,To any reference of memory, storage, database or other media used in each embodiment provided herein,Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may includeRandom access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancingType SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each functionCan unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by differentFunctional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completingThe all or part of function of description.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned realityApplying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned eachTechnical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modifiedOr replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should allIt is included within protection scope of the present invention.