CN109299153A - Active identification and robot system based on big data and deep learning - Google Patents

Active identification and robot system based on big data and deep learning Download PDF

Info

Publication number
CN109299153A
CN109299153A CN201811201409.4A CN201811201409A CN109299153A CN 109299153 A CN109299153 A CN 109299153A CN 201811201409 A CN201811201409 A CN 201811201409A CN 109299153 A CN109299153 A CN 109299153A
Authority
CN
China
Prior art keywords
data
standard
identification
single item
assert
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201811201409.4A
Other languages
Chinese (zh)
Other versions
CN109299153B (en
Inventor
朱定局
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.)
Daguo Innovation Intelligent Technology Dongguan Co ltd
Original Assignee
Daguo Innovation Intelligent Technology Dongguan 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 Daguo Innovation Intelligent Technology Dongguan Co ltd filed Critical Daguo Innovation Intelligent Technology Dongguan Co ltd
Priority to CN201811201409.4A priority Critical patent/CN109299153B/en
Publication of CN109299153A publication Critical patent/CN109299153A/en
Application granted granted Critical
Publication of CN109299153B publication Critical patent/CN109299153B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Active identification and robot system based on big data and deep learning, it include: the identification standard for obtaining pre-set categories, obtain the data for not applying for the object assert, the corresponding data of the identification standard are obtained from the data of the object, judgement ascertains whether to pass through, to identification pass through described in do not apply for that the object assert sends assert as a result, or sending information and inviting the object application identification for not applying assert.The above method and system improve popularity rate, the timeliness of identification, and be avoided that leakage is assert by actively assert technology based on big data and deep learning.

Description

Active identification and robot system based on big data and deep learning
Technical field
The present invention relates to information technology fields, more particularly to a kind of active identification side based on big data and deep learning Method and robot system.
Background technique
Realize process of the present invention in, inventor discovery at least there are the following problems in the prior art: under the prior art into It is all to assert (such as new high-tech enterprise is assert) by object (object includes enterprise etc.) application for not applying assert that row, which is assert, if right As not applying for assert, even when object has had reached the standard assert, it is also not possible to have an opportunity to pass through identification；Object with Under application will not be gone to assert in several situations: (1) object is unclear to the process or standard of identification, it is not known that how to apply recognizing Fixed: (2) object is subjective to underestimate oneself, it is believed that oneself does not reach the standard assert；(3) object does not know default class fundamentally The presence of other identification；(such as five years ago is assert in application to some objects after having reached some time after identification standard Just reach the standard of new high-tech enterprise's identification, but just applied for that new high-tech enterprise is assert after 5 years), also result in identification not in time.It can See, it is low to assert that there are popularity rates under the prior art, there is the disadvantages of leakage is assert, assert not in time.
Therefore, the existing technology needs to be improved and developed.
Summary of the invention
Based on this, it is necessary to it is for the defects in the prior art or insufficient, the master based on big data and deep learning is provided Dynamic identification and robot system, to solve, the popularity rate assert in the prior art is low, timeliness is poor, there is lacking for leakage identification Point.
In a first aspect, the embodiment of the present invention provides a kind of identification, which comprises
Identification standard obtaining step, for obtaining the identification standard of pre-set categories；
Object data obtaining step, for obtaining the data for not applying for the object assert；To do not apply assert object into The data of row data acquisition and identification, acquisition can come from third party, so that even if not applying assert that object will not be leaked Assert, to improve the popularity rate of identification.
The corresponding data acquisition step of standard, for obtaining the identification from the data for not applying for the object assert The corresponding data of standard；
Judgment step is assert, for judging whether the corresponding data of the identification standard meet the identification standard: being institute It states and does not apply for that the object identification assert passes through；It is no, it is described not apply for that the object identification assert does not pass through；
Active feedback step, the object for not applying described in passing through to identification assert send assert as a result, or hair Breath of delivering letters invites the object application identification for not applying assert.By the result of identification feed back to identification pass through described in do not apply The object of identification, so that not applying for that the object assert can be applied assert in time, to improve the timeliness of identification And popularity rate.
Preferably,
The object data obtaining step includes:
Data source obtaining step, for obtaining data source；
Object data retrieving step, for the number of the object for not applying assert to be retrieved and obtained from the data source According to；
The corresponding data acquisition step of the standard includes:
Data screening step, it is corresponding for filtering out the identification standard from the data for not applying for the object assert Data as the first data；
Data cleansing step, for extracting data corresponding with each single item standard from first data as described every Corresponding second data of one standard.
Preferably,
The data cleansing step includes:
Corresponding data source obtaining step, for obtaining the corresponding number of each second data in multiple second data According to source；
Confidence level obtaining step, for obtaining the confidence level of the corresponding data source of each second data；
Confidence level selecting step, it is highest for being chosen from the confidence level of the corresponding data source of each second data Confidence level, retains corresponding second data of highest confidence level described in the multiple second data, described in deletion Other described second data other than corresponding second data of highest confidence level described in multiple second data.
Preferably,
The identification judgment step includes:
Substandard obtaining step, for obtaining each single item standard and overall standard in the identification standard；
Corresponding data extraction step, for extracting each single item standard corresponding described second from first data Data；
The corresponding preset model obtaining step of each single item standard, for obtaining the corresponding default mould of each single item standard Type；
The corresponding third data generation step of each single item standard, for according to each single item standard corresponding described second Data and the corresponding preset model of each single item standard, are calculated the corresponding third data of each single item standard；
Each single item standard judgment step, for according to the corresponding third data of each standard and preset range, judgement It is described not apply for whether the object assert meets each single item standard；
The corresponding preset model obtaining step of overall standard, for obtaining the corresponding preset model of the overall standard；
Overall standard judgment step, for according to the corresponding third data of each single item standard, the overall standard pair The preset model and preset range answered, judgement is described not to apply for whether the object assert meets the overall standard；
Comprehensive descision step, for judging it is every described in the identification standard whether the object for not applying for identification meets One standard and the overall standard.
Preferably,
The corresponding preset model obtaining step of each single item standard includes:
The corresponding deep learning model initialization step of each single item standard, it is corresponding for initializing each single item standard Deep learning model is as the first deep learning model；
The corresponding historical data obtaining step of each single item standard, for obtaining each single item standard from history big data Second data and third data of the corresponding every an object assert；
Second deep learning model generation step, for by each single item standard it is corresponding carried out assert it is each Input data of second data of object as the first deep learning model, to the first deep learning model into The unsupervised training of row, obtained the first deep learning model is as the second deep learning model；
Third deep learning model generation step, for by each single item standard it is corresponding carried out assert it is each Input data and output of second data and the third data of object respectively as the second deep learning model Data carry out Training to the second deep learning model, and obtained the second deep learning model is as third Deep learning model；
The corresponding preset model setting steps of each single item standard, for using the third deep learning model as described every The corresponding preset model of one standard；
The corresponding preset model obtaining step of the overall standard includes:
The corresponding deep learning model initialization step of overall standard, for initializing the corresponding depth of the overall standard Learning model, the obtained deep learning model is as the 4th deep learning model；
The corresponding historical data obtaining step of overall standard, for having carried out identification described in the acquisition from history big data Every an object the identification standard in the corresponding third data of each single item standard set and the overall standard it is corresponding Third data；
5th deep learning model generation step, for having carried out each single item standard is corresponding in the identification standard Input data of the set of the third data for the every an object assert as the deep learning model, it is deep to the described 4th It spends learning model and carries out unsupervised training, obtained the 4th deep learning model is as the 5th deep learning model；
6th deep learning model generation step, for by it is described carried out assert every an object the identification mark The set of the corresponding third data of each single item standard and the corresponding third data of the overall standard are made respectively in standard For the input data and output data of the 5th deep learning model, supervision instruction has been carried out to the 5th deep learning model Practice, obtained the 5th deep learning model is as the 6th deep learning model；
The corresponding preset model setting steps of overall standard, for using the 6th deep learning model as the totality The corresponding preset model of standard.
Preferably,
The each single item standard judgment step includes:
The corresponding preset range obtaining step of each single item standard, for obtaining the corresponding default model of each single item standard It encloses；
The corresponding third data judgment step of each single item standard, for determining whether the object for not applying for identification meets The each single item standard；
The overall standard judgment step includes:
The corresponding third data generation step of overall standard, for according to the corresponding third number of each single item standard According to the preset model corresponding with the overall standard, the corresponding third data of the overall standard are calculated；
The corresponding preset range obtaining step of overall standard, for obtaining the corresponding preset range of the overall standard；
The corresponding third data judgment step of overall standard, for judging whether the object for not applying for identification meets institute State overall standard.
Second aspect, the embodiment of the present invention provide a kind of identification system, the system comprises:
Identification standard obtains module, for obtaining the identification standard of pre-set categories；
Object data obtains module, for obtaining the data for not applying for the object assert；
The corresponding data acquisition module of standard, for obtaining the identification from the data for not applying for the object assert The corresponding data of standard；
Judgment module is assert, for judging whether the corresponding data of the identification standard meet the identification standard: being institute It states and does not apply for that the object identification assert passes through；It is no, it is described not apply for that the object identification assert does not pass through；
Active feedback module, the object for not applying described in passing through to identification assert send assert as a result, or hair Breath of delivering letters invites the object application identification for not applying assert.
Preferably,
The object data obtains module
Data source obtains module, for obtaining data source；
Object data retrieving module, for the number of the object for not applying assert to be retrieved and obtained from the data source According to；
The corresponding data acquisition module of the standard includes:
Data screening module, it is corresponding for filtering out the identification standard from the data for not applying for the object assert Data as the first data；
Data cleansing module, for extracting data corresponding with each single item standard from first data as described every Corresponding second data of one standard.
Preferably,
The identification judgment module includes:
Substandard obtains module, for obtaining each single item standard and overall standard in the identification standard；
Corresponding data extraction module, for extracting each single item standard corresponding described second from first data Data；
The corresponding preset model of each single item standard obtains module, for obtaining the corresponding default mould of each single item standard Type；
The corresponding third data generation module of each single item standard, for according to each single item standard corresponding described second Data and the corresponding preset model of each single item standard, are calculated the corresponding third data of each single item standard；
Each single item standard judgment module, for according to the corresponding third data of each standard and preset range, judgement It is described not apply for whether the object assert meets each single item standard；
The corresponding preset model of overall standard obtains module, for obtaining the corresponding preset model of the overall standard；
Overall standard judgment module, for according to the corresponding third data of each single item standard, the overall standard pair The preset model and preset range answered, judgement is described not to apply for whether the object assert meets the overall standard；
Comprehensive judgment module, for judging it is every described in the identification standard whether the object for not applying for identification meets One standard and the overall standard.
The third aspect, the embodiment of the present invention provide a kind of robot system, are respectively configured in the robot just like second The described in any item identification systems of aspect.
The embodiment of the present invention has the advantage that includes: with beneficial effect
1, the embodiment of the present invention actively assert object, even if object is not applied assert, can also carry out active identification, can Discovery in time has reached the object of identification standard, and the object for having reached standard is notified to pass through identification or notice The object for reaching standard walks the process that application is assert, assert that the object of standard can be identified in time so that having reached, into And it can be improved the popularity rate and timeliness of identification.And it is (right by the object for not applying assert for being assert under the prior art all As including enterprise etc.) application identification (such as new high-tech enterprise is assert), if object is not applied assert, even when object has reached The standard assert is arrived, it is also not possible to have an opportunity to pass through identification；Some objects are when having reached very long after identification standard Between after just application assert, also result in identification not in time.
2, the embodiment of the present invention is assert in active and is also had the following advantages and beneficial effects:
(1) embodiment of the present invention can be used for the full-automatic identification to object during actively assert, or auxiliary is specially Family's evaluation carries out the semi-automatic identification of object, to improve the automation actively assert, intelligence and assert efficiency.
(2) it when can be related to a variety of data of object during pre-set categories identification, such as carry out new high-tech enterprise's identification, relates to And the financial data to Enterprise Object, intellectual property data, product data, secure data, qualitative data etc., these data are all Energy third party such as the administration for industry and commerce, Department of Intellectual Property, revenue department, public security department, quality testing department etc. obtains, but presets in reality Classification does not make full use of third-party data when assert to improve the confidence level of identification.The data source that the embodiment of the present invention utilizes It is more credible that big data including obtaining from third party compares the data provided as oneself.
(3) embodiment of the present invention carries out intellectual analysis based on big data combination identification standard to judge whether object can be recognized It is set to pre-set categories and carrys out auxiliary expert and evaluate, the workload of experts' evaluation can be reduced, improves the efficiency that expert assert.
(4) embodiment of the present invention is automatically generated for the default mould assert using deep learning technology based on history big data Type can be further improved the intelligence and accuracy of identification.
(5) embodiment of the present invention can filter out the data for meeting pre-set categories and assert standard by identification and system And object, it is referred to for evaluation expert, the speed of evaluation expert's evaluation can be improved in this way, reduce the work of evaluation expert's evaluation Amount.
(6) embodiment of the present invention can filter out the number for not meeting pre-set categories and assert standard by identification and system According to and object, for evaluation expert refer to, can make evaluation expert stringenter to ineligible data and object in this way Evaluation, to improve the accuracy rate of identification.
Active identification and robot system provided in an embodiment of the present invention based on big data and deep learning, packet It includes: obtaining the identification standard of pre-set categories, obtain the data for not applying for the object assert, institute is obtained from the data of the object State the corresponding data of identification standard, judgement ascertains whether to pass through, to identifications pass through described in do not apply for that the object assert is sent and recognize It is fixed as a result, or sending information and inviting the object application identification for not applying assert.The above method and system pass through based on big Data and deep learning actively assert technology, improve popularity rate, the timeliness of identification, and are avoided that leakage is assert.
Detailed description of the invention
Fig. 1 is the flow chart for the identification that the embodiment of the present invention 1 provides；
Fig. 2 is the flow chart for the identification judgment step that the embodiment of the present invention 4 provides；
Fig. 3 is the flow chart for the corresponding preset model obtaining step of each single item standard that the embodiment of the present invention 5 provides；
Fig. 4 is the flow chart for the corresponding preset model obtaining step of overall standard that the embodiment of the present invention 5 provides；
Fig. 5 is the functional block diagram for the identification system that the embodiment of the present invention 7 provides；
Fig. 6 is the functional block diagram for the identification judgment module that the embodiment of the present invention 10 provides；
Fig. 7 is that the corresponding preset model of each single item standard that the embodiment of the present invention 11 provides obtains the principle frame of module Figure；
Fig. 8 is that the corresponding preset model of overall standard that the embodiment of the present invention 11 provides obtains the functional block diagram of module.
Specific embodiment
Below with reference to embodiment of the present invention, technical solution in the embodiment of the present invention is described in detail.
(1) the various combinations that the method in various embodiments of the present invention includes the following steps:
Identification standard obtaining step S100: the identification standard of pre-set categories is obtained.
The identification standard of pre-set categories is exactly an object to be regarded as to the standard of pre-set categories object, such as one is looked forward to Industry regards as the standard of new high-tech enterprise.The pre-set categories such as new high-tech enterprise, for another example excellent student, for another example prominent teacher Etc..
For example, the Standard General that new high-tech enterprise is assert by country is as follows, the high-new enterprise of identification standard and country in each place The identification standard of industry can be similar: 1, must establish with limited laibility 1 year or more when enterprise's application is assert；2, enterprise by independent research, It the modes such as assigns, given, being merged, obtaining the intellectual property for technically playing its major product (service) core supporting function Ownership；3, to enterprise's major product (service) play core supporting function technology belong to " state key support it is high-new Technical field " as defined in range；4, enterprise is engaged in research and development and the scientific and technical personnel of the relevant technologies innovation activity account for enterprise current year worker The ratio of sum is not less than 10%；5, enterprise's nearly three fiscal years, (practical operational period discontented 3 years reality of pressing managed the time Calculate, similarly hereinafter) R&D expense total value account for the ratio of same period income from sales total value and meet following requirement: (1) nearest 1 year Enterprise of the income from sales less than 5,0,000,000 yuan (containing), ratio are not less than 5%；(2) a nearest annual sales revenue is at 5,0,000,000 yuan To the enterprise of 200,000,000 yuan (containing), ratio is not less than 4%；(3) enterprise of the nearest annual sales revenue at 200,000,000 yuan or more, ratio be not low Wherein in 3%., the R&D expense total value that enterprise occurs within Chinese territory accounts for the ratio of full-fledged research development cost total value not Lower than 60%；6, nearly 1 year new high-tech product (service) income accounts for the ratio of enterprise's same period total income not less than 60%；7, it looks forward to Industry Innovation Ability Evaluation should reach corresponding requirements；8, enterprise's application was assert in the previous year, and considerable safety, great quality thing do not occur Therefore or severe environments illegal activities.
Object data obtaining step S200: the data for not applying for the object assert are obtained.Do not apply for that the object assert includes Do not apply for the object assert, apply for the object assert.Actively assert that the key difference assert with tradition is that tradition is assert only The object for having applied for identification is assert, assert the object for not applying for assert without going, and non-Shen can be assert by actively assert The object that please be assert.The object such as enterprise, student, teacher etc..Such as need to assert whether enterprise is new and high technology enterprise Industry for another example needs to assert whether a student is excellent student, for another example needs to assert whether a teacher is prominent teacher etc..
Object data obtaining step S200 includes data source obtaining step S210, object data retrieving step S220.
Data source obtaining step S210: data source is obtained.The data source includes data, the third party's offer that object provides Data.The data that object provides refer to the data that the object provides.The data that third party provides include government department, industry association The object data of the departments such as meeting, Department of Intellectual Property storage.The presentation mode of data source includes data retrieval and acquisition interface.Pass through The interface can automatically retrieval and acquisition related data by computer program.Data source is generally online data source, passes through Internet can remotely obtain the data in online data source.The data source may include the multiple numbers for being distributed in different departments According to source.
Object data retrieving step S220: the data of the object are retrieved and obtained from data source.Because in data source It include the data and other data of many objects, if entirety obtains the network transmission for retrieving meeting overspending again Between, so needing first to retrieve the data of the object, then by the data acquisition of the object to locally.When there is multiple data sources When, the data of the object are retrieved from multiple data sources respectively, it is then locally downloading respectively.
The corresponding data acquisition step S300 of standard: the corresponding number of the identification standard is obtained from the data of the object According to.
The corresponding data acquisition step S300 of standard includes data screening step S310, data cleansing step S320.
Data screening step S310: according to the identification standard, the object pair is filtered out from the data of the object The first data answered.The data of the object include various data, and wherein different establish a capital of data assert standard with pre-set categories It is related, so needing to retrieve data related with pre-set categories identification standard from the data of the object, as described right As corresponding first data.It is multinomial that pre-set categories assert that standard has, and the corresponding data of all standard may be in different data sources In, such as financial data, in the data source that the administration for industry and commerce provides, intellectual property data provides in data source in Department of Intellectual Property, Quality detecting data is in the data source that quality testing department provides, and secure data is in the data source that public security department provides, so more preferably Mode is to obtain the default corresponding relationship in data source between data category and all standard, obtain from the data of the object The corresponding data of each data source are taken, are retrieved from the corresponding data of each data source corresponding with each data source The relevant data of standard.Such as the corresponding standard of data source that Intellectual Property Department provides is intellectual property standard, from knowledge Include the payment data of the intellectual property of this object in the data source that property right department provides, request for data, accept data, authorization Data, wherein the intellectual property of the data of " request for data accepts data, authorization data " these three classifications and pre-set categories identification Standard is related, then three data categories described in the data source of Intellectual Property Department's offer and the knowledge of pre-set categories identification produce Corresponding relationship is established between token standard, as the preset corresponding relationship.
Data cleansing step S320: corresponding second data of each single item standard are extracted from first data.By first Data corresponding with each single item standard are as the second data in data.Whether judge corresponding second data of each single item standard In the presence of: it is no, then the prompting message for lacking corresponding second data of each single item standard is sent to user；It is, then described in judgement Whether corresponding second data of each single item standard unique: it is no, then judge corresponding multiple second data of each single item standard it Between it is whether consistent: it is no, then retain highest second data of confidence level in the multiple second data, delete the multiple second number Other second data in.Corresponding second data of each single item standard extracted from first data are described first Data corresponding with each single item standard in data.
Highest second data of confidence level in the multiple second data of reservation described in data cleansing step S320, are deleted The step of other second data includes corresponding data source obtaining step S321, confidence level obtaining step in the multiple second data S322, confidence level selecting step S323.
Corresponding data source obtaining step S321: the corresponding data of each second data in the multiple second data are obtained Source.Because the first data are to obtain from data source, and the second data are extracted from the first data, it is possible to obtain To the corresponding data source of each second data.
Confidence level obtaining step S322: the confidence level of the corresponding data source of each second data is obtained.It is described credible Degree can be preset.Such as the confidence level of the data source of public security department is 100%, the confidence level of the data source of the administration for industry and commerce is 99%, the confidence level of the data source of Intellectual Property Department is 98%, and the confidence level for the data source that object itself provides is 80%.In advance The mode that the confidence level is first arranged includes being configured by expert to confidence level, further includes automatically generating the confidence level.
The step of automatically generating confidence level described in confidence level obtaining step S322 includes: by the confidence level of all data sources It is initialized as initial value, such as 50%.First data of each object are obtained from history big data and have cleaned to obtain The second data.The confidence level of the corresponding data source of the second data cleaned is increased into preset value, by described the The confidence level of data source corresponding with other consistent second data of the second data cleaned increases default in one data Value, such as 0.1%, it will be corresponding with other inconsistent second data of the second data cleaned in first data The confidence level of data source reduces preset value, such as 0.05%.When the confidence level of the corresponding data source of second data reaches When 100%, then preset value is not further added by；When the confidence level of the corresponding data source of second data is reduced to 0%, then no longer Build preset value.Increased this makes it possible to make the confidence level of different data sources whether correct according to the second data of history Subtract, to form the different confidence levels of different data sources.Second data cleaned refer to passing through manual type Or correct second data of other modes confirmation.As it can be seen that multiple pre- before the confidence level of unclear each data source If classification is assert, artificial mode is needed to carry out the cleaning of data, then can be analyzed according to these historical datas Obtain the confidence level of each data source.
Confidence level selecting step S323: it is chosen from the confidence level of the corresponding data source of each second data highest Confidence level.Retain corresponding second data of highest confidence level described in the multiple second data, deletes the multiple second number Other second data other than corresponding second data of the highest confidence level described in.This makes it possible in multiple second data phases Mutually when conflict, retain most believable data, and other conflicting data are deleted.
Assert judgment step S400: judging whether the corresponding data of the identification standard meet the identification standard: being, then The identification of the pre-set categories is the result is that pass through identification；No, then the identification of the pre-set categories is not the result is that pass through identification.
Assert that judgment step S400 includes substandard obtaining step S410, corresponding data extraction step S420, each single item mark Quasi- corresponding preset model obtaining step S430, each single item standard judgment step S440, the corresponding preset model of overall standard obtain Take step S450, overall standard judgment step S460, comprehensive descision step S470.
Substandard obtaining step S410: each single item standard and overall standard in the identification standard are obtained.
Corresponding data extraction step S420: corresponding second number of each single item standard is extracted from first data According to.Judge that corresponding second data of each single item standard whether there is: being then to jump to S430 and continue to execute；It is no, then by institute It states the corresponding third data of each single item standard and is set as empty, then branch to S450 and continue to execute.
The corresponding preset model obtaining step S430 of each single item standard: the corresponding default mould of each single item standard is obtained Type.The preset model includes formula or algorithm or deep learning model.
When the preset model is deep learning model, the corresponding preset model obtaining step S430 packet of each single item standard Include the corresponding deep learning model initialization step S431 of each single item standard, the corresponding historical data obtaining step of each single item standard S432, the second deep learning model generation step S433, third deep learning model generation step S434, each single item standard are corresponding Predetermined deep learning model setting steps S435.
The corresponding deep learning model initialization step S431 of each single item standard: it is corresponding to initialize each single item standard The input format of the deep learning model is set corresponding second data of each single item standard by deep learning model Format sets the output format of the deep learning model to the format of the corresponding third data of each single item standard, leads to The deep learning model for initializing and obtaining is crossed as the first deep learning model.
The corresponding historical data obtaining step S432 of each single item standard: each single item standard is obtained from history big data The second data and third data of the corresponding every an object assert.History big data refers to a large amount of historical data Or have accumulated the data of long period.The object assert was carried out, including having carried out assert the object passed through, carry out Assert but assert unsanctioned object.Wherein, third data can be the corresponding scoring of each single item standard or evaluation result Or the numerical value of other degree that can reflect each single item standard described in second data fit.
Second deep learning model generation step S433: by each single item standard it is corresponding carried out assert it is each Input data of second data of object as the first deep learning model carries out nothing to the first deep learning model Supervised training, the first deep learning model obtained by unsupervised training is as the second deep learning model.
Third deep learning model generation step S434: by each single item standard it is corresponding carried out assert it is each The second data and third data of object respectively as the second deep learning model input data and output data, to institute It states the second deep learning model and carries out Training.By the corresponding every an object assert of each single item standard The second data and third data respectively as the second deep learning model input data and output data, refer to by Second data of the corresponding every an object assert of each single item standard are as the second deep learning model Input data, using each single item standard it is corresponding carried out assert every an object third data as described in The output data of second deep learning model, the second deep learning model obtained by Training is as third depth Spend learning model.
The corresponding preset model setting steps S435 of each single item standard: using the third deep learning model as described every The corresponding preset model of one standard.
The corresponding third data generation step S440 of each single item standard: according to corresponding second data of each single item standard The corresponding third data of each single item standard are calculated in preset model corresponding with each single item standard.In computer It is upper to execute the corresponding preset model of each single item standard, using corresponding second data of each single item standard as described each The input of the corresponding preset model of item standard, the output being calculated is as the corresponding third data of each single item standard.It is excellent Selection of land, using corresponding second data of each single item standard as the corresponding third deep learning mould of each single item standard The input of type, the output for the third deep learning model being calculated is as the corresponding third number of each single item standard According to.Wherein, third data can be the corresponding scoring of each single item standard or evaluation result or other can reflect described the The numerical value of the degree of each single item standard described in two data fits.
Each single item standard judgment step S450: according to the corresponding third data of each standard and preset range, judgement It is described not apply for whether the object assert meets each single item standard.
Each single item standard judgment step S450 includes the corresponding preset range obtaining step S451 of each single item standard, each single item The corresponding third data judgment step S452 of standard.
The corresponding preset range obtaining step S451 of each single item standard: the corresponding default model of each single item standard is obtained It encloses.The corresponding preset range of different standards is different, has plenty of the standard of hardness, then has fixed range, and some standards are not It is the standard of hardness, then range is set as infinite to just infinite from bearing.If a standard is not the standard of hardness, this standard Corresponding result is all in the preset range.However, whether a standard is that rigid standard all can not apply recognizing to described Whether fixed object can be had an impact by assert, because can have an impact to final TOP SCORES, and TOP SCORES is corresponding Overall standard generally can all have a range, be greater than 80 points.
The corresponding third data judgment step S452 of each single item standard: judge the corresponding third data of each single item standard Whether it is empty:
It is then to judge whether the corresponding preset range of each single item standard is infinite to just infinite from bearing: is then to determine It is described not apply for that the object assert meets each single item standard；It is no, then determine that the object for not applying for identification does not meet institute State each single item standard；
It is no: then to judge the corresponding third data of each single item standard whether in the corresponding default model of each single item standard In enclosing: being then to determine that the object for not applying for identification meets each single item standard；No, then judgement is described does not apply assert Object do not meet each single item standard.
The corresponding preset model obtaining step S460 of overall standard: the corresponding preset model of the overall standard is obtained.Institute Stating preset model includes formula or algorithm or deep learning model.
When the preset model is deep learning model, the corresponding preset model obtaining step S460 of overall standard includes The corresponding deep learning model initialization step S461 of overall standard, the corresponding historical data obtaining step S462 of overall standard, 5th deep learning model generation step S463, the 6th deep learning model generation step S464, overall standard are corresponding default Model setting steps S465:
The corresponding deep learning model initialization step S461 of overall standard: the corresponding depth of the overall standard is initialized The input format of the deep learning model is set the corresponding third of each single item standard in the identification standard by learning model The output format of the deep learning model is set the corresponding third data of the overall standard by the format of the set of data Format, by initializing the obtained deep learning model as the 4th deep learning model.
The corresponding historical data obtaining step S462 of overall standard: identification had been carried out described in obtaining from history big data Every an object the identification standard in the corresponding third data of each single item standard set and the overall standard it is corresponding Third data.Wherein, the corresponding third data of each single item standard can be the corresponding scoring of each single item standard or evaluation knot Fruit or other can reflect the numerical value of the degree of each single item standard described in second data fit.The corresponding third of overall standard Data can be the corresponding scoring of the overall standard or evaluation result or other to can reflect each single item standard corresponding The numerical value of the degree of overall standard described in third data fit.
5th deep learning model generation step S463: it had carried out each single item standard is corresponding in the identification standard Input data of the set of the third data for the every an object assert as the deep learning model, to the 4th depth It practises model and carries out unsupervised training, the 4th deep learning model obtained by unsupervised training is as the 5th deep learning Model.
6th deep learning model generation step S464: by the identification mark of the every an object assert The set of the corresponding third data of each single item standard and the corresponding third data of the overall standard are respectively as described in standard The input data and output data of five deep learning models carry out Training to the 5th deep learning model.By institute State the set and the totality of the third data of the corresponding every an object assert of each single item standard in identification standard For the corresponding third data of standard respectively as the input data and output data of the 5th deep learning model, referring to will be every Input of the third data of the corresponding every an object assert of one standard as the 5th deep learning model Data, using the third data of the corresponding every an object assert of the overall standard as the 5th depth The output data of learning model, the 5th deep learning model obtained by Training is as the 6th deep learning mould Type.
The corresponding preset model setting steps S465 of overall standard: using the 6th deep learning model as the totality The corresponding preset model of standard.
Overall standard judgment step S470: according to the corresponding third data of each single item standard, the overall standard pair The preset model and preset range answered, judgement is described not to apply for whether the object assert meets the overall standard.
Overall standard judgment step S470 includes the corresponding third data generation step S471 of overall standard, overall standard pair The corresponding third data judgment step S473 of preset range obtaining step S472, overall standard answered.
The corresponding third data generation step S471 of overall standard: according to the corresponding third data of each single item standard and The corresponding preset model of the overall standard, is calculated the corresponding third data of the overall standard.It executes on computers The corresponding preset model of each single item standard, using the corresponding third data of each single item standard as the overall standard pair The input for the preset model answered, the output being calculated is as the corresponding third data of the overall standard.It preferably, will be described Input of the corresponding third data of each single item standard as the corresponding 6th deep learning model of the overall standard calculates The output of obtained the 6th deep learning model is as the corresponding third data of the overall standard.Wherein, each single item mark Quasi- corresponding third data can be the corresponding scoring of each single item standard or evaluation result or other can reflect described the The numerical value of the degree of each single item standard described in two data fits.The corresponding third data of overall standard can be the overall standard It is corresponding scoring evaluation result or other can reflect and totally marked described in the corresponding third data fit of each single item standard The numerical value of quasi- degree.
The corresponding preset range obtaining step S472 of overall standard: the corresponding preset range of the overall standard is obtained.Always The corresponding TOP SCORES of body standard generally can all have a range, be greater than 80 points.
The corresponding third data judgment step S473 of overall standard: whether judge the corresponding third data of the overall standard In the corresponding preset range of the overall standard: being that then judgement is described does not apply for that the object assert meets the overall standard； No, then judgement is described does not apply for that the object assert does not meet the overall standard.
Comprehensive descision step S480: judgement is described not to apply for whether the object assert meets each single item in the identification standard Standard and overall standard: being, then determine it is described do not apply for that the object assert belongs to pre-set categories, that is, it is assert the result is that it is described not The object that application is assert has passed through identification；It is no, then determine that the object for not applying for identification is not belonging to pre-set categories, that is, assert The result is that described do not apply the object assert not over identification.Meet each single item standard and overall standard in the identification standard It refers to meeting each single item standard and overall standard in the identification standard simultaneously.Not if there is a certain item standard or overall standard Meet, then judgement is described does not apply for that the object assert is not belonging to pre-set categories.
Active feedback step S500: whether the identification result for judging the pre-set categories is to pass through identification: being that will then pass through The result of identification is sent to the object for not applying assert or invites the application not applying for the object assert and being assert. It, then can be directly general without other processes when the identification of pre-set categories can be determined directly by the result actively assert The result of identification feeds back to the object for not applying assert；When the identification of pre-set categories needs additional process (such as object The application material affixed one's seal), then it needs to invite the application not applying for the object assert and being assert.
If the object for not applying assert and not over this identification, assert that explanation is described not over this The standard of identification has not been reached yet in the object that application is assert, it is not necessary to not apply recognizing described in the result notice by identification Fixed object will not thus bother institute also It is not necessary to invite the application not applying for the object assert and being assert The object for not applying for assert is stated, does not apply for that the object assert carries out the effect for actively assert service quietly to play to be described. During actively assert, the object for not applying for identification for not needing not apply assert pays any time and manpower and material resources Come participate in addition whole process described in do not apply assert object know nothing, only during actively assert identification lead to It just will receive notice or invitation in the case where crossing.
Above-mentioned steps can execute in the big data platforms such as Spark, to accelerate the speed of big data processing.
(2) the various combinations that the system in various embodiments of the present invention comprises the following modules:
Identification standard obtains module 100 and executes identification standard obtaining step S100.
It executes object data and obtains the execution of module 200 object data obtaining step S200.
It includes that data source obtains module 210, object data retrieving module 220 that object data, which obtains module 200,.
Data source obtains module 210 and executes data source obtaining step S210.
Object data retrieving module 220 executes object data retrieving step S220.
The corresponding data acquisition module 300 of standard executes the corresponding data acquisition step S300 of standard.
The corresponding data acquisition module 300 of standard includes data screening module 310, data cleansing module 320.
Data screening module 310 executes data screening step S310.
Data cleansing module 320 executes data cleansing step S320.
Data cleansing module 320 includes that corresponding data source obtains module 321, confidence level obtains module 322, confidence level is chosen Module 323.
Corresponding data source obtains module 321 and executes corresponding data source obtaining step S321.
Confidence level obtains module 322 and executes confidence level obtaining step S322.
Confidence level chooses module 323 and executes confidence level selecting step S323.
Assert that judgment module 400 executes and assert judgment step S400.
Assert that judgment module 400 includes that substandard obtains module 410, corresponding data extraction module 420, each single item standard pair The preset model answered obtains the corresponding preset model of module 430, each single item standard judgment module 440, overall standard and obtains module 450, overall standard judgment module 460, comprehensive judgment module 470.
Substandard obtains module 410 and executes substandard obtaining step S410.
Corresponding data extraction module 420 executes corresponding data extraction step S420.
The corresponding preset model of each single item standard obtains module 430 and executes the corresponding preset model acquisition step of each single item standard Rapid S430.
The corresponding preset model of each single item standard obtains module 430 including at the beginning of the corresponding deep learning model of each single item standard The corresponding historical data of beginningization module 431, each single item standard obtain module 432, the second deep learning model generation module 433, Third deep learning model generation module 434, the corresponding predetermined deep learning model setup module 435 of each single item standard.
The corresponding deep learning model initialization module 431 of each single item standard executes the corresponding deep learning of each single item standard Model initialization step S431.
The corresponding historical data of each single item standard obtains module 432 and executes the corresponding historical data acquisition step of each single item standard Rapid S432.
Second deep learning model generation module 433 executes the second deep learning model generation step S433.
Third deep learning model generation module 434 executes third deep learning model generation step S434.
The corresponding predetermined deep learning model setup module 435 of each single item standard executes the corresponding default mould of each single item standard Type setting steps S435.
The corresponding third data generation module 440 of each single item standard executes the corresponding third data of each single item standard and generates step Rapid S440.
Each single item standard judgment module 450 executes each single item standard judgment step S450.
Each single item standard judgment module 450 includes that the corresponding preset range of each single item standard obtains module 451, each single item mark Quasi- corresponding third data judgment module 452.
The corresponding preset range of each single item standard obtains module 451 and executes the corresponding preset range acquisition step of each single item standard Rapid S451.
The corresponding third data judgment module 452 of each single item standard executes the corresponding third data judgement step of each single item standard Rapid S452.
The corresponding preset model of overall standard obtains module 460 and executes the corresponding preset model obtaining step of overall standard S460。
It includes the corresponding deep learning model initialization of overall standard that the corresponding preset model of overall standard, which obtains module 460, It is deep that the corresponding historical data of module 461, overall standard obtains module 462, the 5th deep learning model generation module the 463, the 6th Spend learning model generation module 464, the corresponding preset model setup module 465 of overall standard:
The corresponding deep learning model initialization module 461 of overall standard executes the corresponding deep learning model of overall standard Initialization step S461.
The corresponding historical data of overall standard obtains module 462 and executes the corresponding historical data obtaining step of overall standard S462。
5th deep learning model generation module 463 executes the 5th deep learning model generation step S463.
6th deep learning model generation module 464 executes the 6th deep learning model generation step S464.
The corresponding preset model setup module 465 of overall standard executes the corresponding preset model setting steps of overall standard S465。
Overall standard judgment module 470 executes overall standard judgment step S470.
Overall standard judgment module 470 is corresponding including the corresponding third data generation module 471 of overall standard, overall standard Preset range obtain module 472, the corresponding third data judgment module 473 of overall standard.
The corresponding third data generation module 471 of overall standard executes the corresponding third data generation step of overall standard S471。
The corresponding preset range of overall standard obtains module 472 and executes the corresponding preset range obtaining step of overall standard S472。
The corresponding third data judgment module 473 of overall standard executes the corresponding third data judgment step of overall standard S473。
Comprehensive judgment module 480 executes comprehensive descision step S480.
Active feedback module 500 executes active feedback step S500.
Above-mentioned module can dispose in the big data platforms such as Spark, to accelerate the speed of big data processing.
(3) several embodiments of the invention
Embodiment 1 provides a kind of identification, and the identification includes identification standard obtaining step S100, object data The corresponding data acquisition step S300 of obtaining step S200, standard, assert judgment step S400, active feedback step S500, such as Shown in Fig. 1.
Embodiment 2 provides a kind of identification, each step including method described in embodiment 1；Wherein, object data obtains Taking step S200 includes data source obtaining step S210, object data retrieving S220, the corresponding data acquisition step S300 of standard Including data screening step S310, data cleansing step S320.
Embodiment 3 provides a kind of identification, each step including method described in embodiment 2；Wherein, data cleansing walks Rapid S320 includes corresponding data source obtaining step S321, confidence level obtaining step S322, confidence level selecting step S323.
Embodiment 4 provides a kind of identification, each step including method described in embodiment 2；Wherein, assert judgement step Rapid S400 includes that substandard obtaining step S410, corresponding data extraction step S420, the corresponding preset model of each single item standard obtain Take the corresponding third data generation step S440 of step S430, each single item standard, each single item standard judgment step S450, overall mark Quasi- corresponding preset model obtaining step S460, overall standard judgment step S470, comprehensive descision step S480, as shown in Figure 2.
Embodiment 5 provides a kind of identification, each step including method described in embodiment 4；Wherein, each single item standard Corresponding preset model obtaining step S430 includes the corresponding deep learning model initialization step S431 of each single item standard, each The corresponding historical data obtaining step S432 of item standard, the second deep learning model generation step S433, third deep learning mould The corresponding predetermined deep learning model setting steps S435 of type generation step S434, each single item standard, as shown in Figure 3；Overall mark Quasi- corresponding preset model obtaining step S460 includes the corresponding deep learning model initialization step S461 of overall standard, totality The corresponding historical data obtaining step S462 of standard, the 5th deep learning model generation step S463, the 6th deep learning model The corresponding preset model setting steps S465 of generation step S464, overall standard, as shown in Figure 4.
Embodiment 6 provides a kind of identification, each step including method described in embodiment 4；Wherein, each single item standard Judgment step S450 includes the corresponding preset range obtaining step S451 of each single item standard, the corresponding third data of each single item standard Judgment step S452；Overall standard judgment step S470 includes the corresponding third data generation step S471 of overall standard, totality The corresponding preset range obtaining step S472 of standard, the corresponding third data judgment step S473 of overall standard.
Embodiment 7 provides a kind of identification system, and the identification system includes that identification standard obtains module 100, object data It obtains module 200, the corresponding data acquisition module 300 of standard, assert judgment module 400, active feedback module 500, such as Fig. 5 institute Show.
Embodiment 8 provides a kind of identification system, each step including system described in embodiment 7；Wherein, object data obtains Modulus block 200 includes that data source obtains module 210, object data retrieving S220, and the corresponding data acquisition module 300 of standard includes Data screening module 310, data cleansing module 320.
Embodiment 9 provides a kind of identification system, each step including system described in embodiment 8；Wherein, data cleansing mould Block 320 includes that corresponding data source obtains module 321, confidence level obtains module 322, confidence level chooses module 323.
Embodiment 10 provides a kind of identification system, each step including system described in embodiment 8；Wherein, assert judgement Module 400 includes that substandard obtains the corresponding preset model acquisition of module 410, corresponding data extraction module 420, each single item standard The corresponding third data generation module 440 of module 430, each single item standard, each single item standard judgment module 450, overall standard pair The preset model answered obtains module 460, overall standard judgment module 470, comprehensive judgment module 480, as shown in Figure 6.
Embodiment 11 provides a kind of identification system, each step including system described in embodiment 10；Wherein, each single item mark Quasi- corresponding preset model obtain module 430 include the corresponding deep learning model initialization module 431 of each single item standard, it is each The corresponding historical data of item standard obtains module 432, the second deep learning model generation module 433, third deep learning model The corresponding predetermined deep learning model setup module 435 of generation module 434, each single item standard, as shown in Figure 7；Overall standard pair It includes the corresponding deep learning model initialization module 461 of overall standard, overall standard pair that the preset model answered, which obtains module 460, The historical data answered obtains module 462, the 5th deep learning model generation module 463, the 6th deep learning model generation module 464, the corresponding preset model setup module 465 of overall standard, as shown in Figure 8.
Embodiment 12 provides a kind of identification system, each step including system described in embodiment 10；Wherein, each single item mark Quasi- judgment module 450 includes that the corresponding preset range of each single item standard obtains module 451, the corresponding third data of each single item standard Judgment module 452；Overall standard judgment module 470 includes the corresponding third data generation module 471 of overall standard, overall standard Corresponding preset range obtains module 472, the corresponding third data judgment module 473 of overall standard.
Embodiment 13 provides a kind of robot system, is each configured in the robot such as embodiment 7 to embodiment 12 The identification system.
Method and system in the various embodiments described above can be in computer, server, Cloud Server, supercomputer, machine Device people, embedded device, electronic equipment etc. are upper to be executed and disposes.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.

Claims (10)

1. a kind of identification, which is characterized in that the described method includes:
Identification standard obtaining step, for obtaining the identification standard of pre-set categories；
Object data obtaining step, for obtaining the data for not applying for the object assert；
The corresponding data acquisition step of standard, for obtaining the identification standard from the data for not applying for the object assert Corresponding data；
Assert judgment step, for judging whether the corresponding data of the identification standard meet the identification standard: be, it is described not The object identification that application is assert passes through；It is no, it is described not apply for that the object identification assert does not pass through；
Active feedback step, the object for not applying described in passing through to identification assert send assert as a result, or sending letter Breath invites the object application identification for not applying assert.
2. identification according to claim 1, which is characterized in that
The object data obtaining step includes:
Data source obtaining step, for obtaining data source；
Object data retrieving step, for the data of the object for not applying assert to be retrieved and obtained from the data source；
The corresponding data acquisition step of the standard includes:
Data screening step, for filtering out the corresponding number of the identification standard from the data for not applying for the object assert According to as the first data；
Data cleansing step, for extracting data corresponding with each single item standard from first data as each single item Corresponding second data of standard.
3. identification according to claim 2, which is characterized in that
The data cleansing step includes:
Corresponding data source obtaining step, for obtaining the corresponding data of each second data in multiple second data Source；
Confidence level obtaining step, for obtaining the confidence level of the corresponding data source of each second data；
Confidence level selecting step, it is highest credible for being chosen from the confidence level of the corresponding data source of each second data Degree retains corresponding second data of highest confidence level described in the multiple second data, deletes the multiple Other described second data other than corresponding second data of highest confidence level described in second data.
4. identification according to claim 2, which is characterized in that
The identification judgment step includes:
Substandard obtaining step, for obtaining each single item standard and overall standard in the identification standard；
Corresponding data extraction step, for extracting corresponding second number of each single item standard from first data According to；
The corresponding preset model obtaining step of each single item standard, for obtaining the corresponding preset model of each single item standard；
The corresponding third data generation step of each single item standard, for according to corresponding second data of each single item standard The corresponding third data of each single item standard are calculated in the preset model corresponding with each single item standard；
Each single item standard judgment step, for according to the corresponding third data of each standard and preset range, described in judgement Do not apply for whether the object assert meets each single item standard；
The corresponding preset model obtaining step of overall standard, for obtaining the corresponding preset model of the overall standard；
Overall standard judgment step, for corresponding according to the corresponding third data of each single item standard, the overall standard Preset model and preset range, judgement is described not to apply for whether the object assert meets the overall standard；
Comprehensive descision step, for judging whether the object for not applying for identification meets each single item described in the identification standard Standard and the overall standard.
5. identification according to claim 4, which is characterized in that
The corresponding preset model obtaining step of each single item standard includes:
The corresponding deep learning model initialization step of each single item standard, for initializing the corresponding depth of each single item standard Learning model is as the first deep learning model；
The corresponding historical data obtaining step of each single item standard, it is corresponding for obtaining each single item standard from history big data Carried out assert every an object second data and third data；
Second deep learning model generation step, for by each single item standard it is corresponding carried out assert every an object Input data of second data as the first deep learning model, nothing is carried out to the first deep learning model Supervised training, obtained the first deep learning model is as the second deep learning model；
Third deep learning model generation step, for by each single item standard it is corresponding carried out assert every an object Second data and the third data respectively as the second deep learning model input data and output data, Training is carried out to the second deep learning model, obtained the second deep learning model is as third depth Practise model；
The corresponding preset model setting steps of each single item standard, for using the third deep learning model as each single item The corresponding preset model of standard；
The corresponding preset model obtaining step of the overall standard includes:
The corresponding deep learning model initialization step of overall standard, for initializing the corresponding deep learning of the overall standard Model, the obtained deep learning model is as the 4th deep learning model；
The corresponding historical data obtaining step of overall standard, it is every for having carried out assert described in the acquisition from history big data The set and the corresponding third of the overall standard of the corresponding third data of each single item standard in the identification standard of an object Data；
5th deep learning model generation step, for having carried out identification for each single item standard is corresponding in the identification standard Every an object the third data input data of the set as the deep learning model, to the 4th depth It practises model and carries out unsupervised training, obtained the 4th deep learning model is as the 5th deep learning model；
6th deep learning model generation step, for that described will carry out in the identification standard of every an object of identification The set and the corresponding third data of the overall standard of the corresponding third data of each single item standard are respectively as institute The input data and output data for stating the 5th deep learning model carry out Training to the 5th deep learning model, Obtained the 5th deep learning model is as the 6th deep learning model；
The corresponding preset model setting steps of overall standard, for using the 6th deep learning model as the overall standard Corresponding preset model.
6. identification according to claim 4, which is characterized in that
The each single item standard judgment step includes:
The corresponding preset range obtaining step of each single item standard, for obtaining the corresponding preset range of each single item standard；
The corresponding third data judgment step of each single item standard, for determining it is described whether the object for not applying for identification meets Each single item standard；
The overall standard judgment step includes:
The corresponding third data generation step of overall standard, for according to the corresponding third data of each single item standard and The corresponding preset model of the overall standard, is calculated the corresponding third data of the overall standard；
The corresponding preset range obtaining step of overall standard, for obtaining the corresponding preset range of the overall standard；
The corresponding third data judgment step of overall standard, for judging it is described total whether the object for not applying for identification meets Body standard.
7. a kind of identification system, which is characterized in that the system comprises:
Identification standard obtains module, for obtaining the identification standard of pre-set categories；
Object data obtains module, for obtaining the data for not applying for the object assert；
The corresponding data acquisition module of standard, for obtaining the identification standard from the data for not applying for the object assert Corresponding data；
Assert judgment module, for judging whether the corresponding data of the identification standard meet the identification standard: be, it is described not The object identification that application is assert passes through；It is no, it is described not apply for that the object identification assert does not pass through；
Active feedback module, the object for not applying described in passing through to identification assert send assert as a result, or sending letter Breath invites the object application identification for not applying assert.
8. identification system according to claim 7, which is characterized in that
The object data obtains module
Data source obtains module, for obtaining data source；
Object data retrieving module, for the data of the object for not applying assert to be retrieved and obtained from the data source；
The corresponding data acquisition module of the standard includes:
Data screening module, for filtering out the corresponding number of the identification standard from the data for not applying for the object assert According to as the first data；
Data cleansing module, for extracting data corresponding with each single item standard from first data as each single item Corresponding second data of standard.
9. identification system according to claim 8, which is characterized in that
The identification judgment module includes:
Substandard obtains module, for obtaining each single item standard and overall standard in the identification standard；
Corresponding data extraction module, for extracting corresponding second number of each single item standard from first data According to；
The corresponding preset model of each single item standard obtains module, for obtaining the corresponding preset model of each single item standard；
The corresponding third data generation module of each single item standard, for according to corresponding second data of each single item standard The corresponding third data of each single item standard are calculated in the preset model corresponding with each single item standard；
Each single item standard judgment module, for according to the corresponding third data of each standard and preset range, described in judgement Do not apply for whether the object assert meets each single item standard；
The corresponding preset model of overall standard obtains module, for obtaining the corresponding preset model of the overall standard；
Overall standard judgment module, for corresponding according to the corresponding third data of each single item standard, the overall standard Preset model and preset range, judgement is described not to apply for whether the object assert meets the overall standard；
Comprehensive judgment module, for judging whether the object for not applying for identification meets each single item described in the identification standard Standard and the overall standard.
10. a kind of robot system, which is characterized in that be respectively configured in the robot just like any one of claim 7-9 institute The identification system stated.
CN201811201409.4A 2018-10-17 2018-10-17 Active identification method based on big data and deep learning and robot system Active CN109299153B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811201409.4A CN109299153B (en) 2018-10-17 2018-10-17 Active identification method based on big data and deep learning and robot system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811201409.4A CN109299153B (en) 2018-10-17 2018-10-17 Active identification method based on big data and deep learning and robot system

Publications (2)

Publication Number Publication Date
CN109299153A true CN109299153A (en) 2019-02-01
CN109299153B CN109299153B (en) 2021-12-03

Family

ID=65162735

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811201409.4A Active CN109299153B (en) 2018-10-17 2018-10-17 Active identification method based on big data and deep learning and robot system

Country Status (1)

Country Link
CN (1) CN109299153B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030212621A1 (en) * 2002-05-10 2003-11-13 Portfolio Aid Inc. System and method for evaluating securities and portfolios thereof
CN106934589A (en) * 2017-01-18 2017-07-07 上海爱韦讯信息技术有限公司 A kind of examination result automatic processing device and method
CN107563630A (en) * 2017-08-25 2018-01-09 前海梧桐（深圳）数据有限公司 Enterprise's methods of marking and its system based on various dimensions
CN107977789A (en) * 2017-12-05 2018-05-01 国网河南省电力公司南阳供电公司 Based on the audit work method under big data information
CN108182389A (en) * 2017-12-14 2018-06-19 华南师范大学 User data processing method, robot system based on big data and deep learning
CN108364067A (en) * 2018-01-05 2018-08-03 华南师范大学 Deep learning method and robot system based on data segmentation
CN108595546A (en) * 2018-04-09 2018-09-28 武汉科技大学 Based on semi-supervised across media characteristic study search method
CN108629479A (en) * 2018-03-22 2018-10-09 安徽华普生产力促进中心有限公司 The self-evaluation system of Enterprise Application new high-tech enterprise project

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030212621A1 (en) * 2002-05-10 2003-11-13 Portfolio Aid Inc. System and method for evaluating securities and portfolios thereof
CN106934589A (en) * 2017-01-18 2017-07-07 上海爱韦讯信息技术有限公司 A kind of examination result automatic processing device and method
CN107563630A (en) * 2017-08-25 2018-01-09 前海梧桐（深圳）数据有限公司 Enterprise's methods of marking and its system based on various dimensions
CN107977789A (en) * 2017-12-05 2018-05-01 国网河南省电力公司南阳供电公司 Based on the audit work method under big data information
CN108182389A (en) * 2017-12-14 2018-06-19 华南师范大学 User data processing method, robot system based on big data and deep learning
CN108364067A (en) * 2018-01-05 2018-08-03 华南师范大学 Deep learning method and robot system based on data segmentation
CN108629479A (en) * 2018-03-22 2018-10-09 安徽华普生产力促进中心有限公司 The self-evaluation system of Enterprise Application new high-tech enterprise project
CN108595546A (en) * 2018-04-09 2018-09-28 武汉科技大学 Based on semi-supervised across media characteristic study search method

Also Published As

Publication number Publication date
CN109299153B (en) 2021-12-03

Similar Documents

Publication Publication Date Title
US7720777B2 (en) Method, device, and program product to monitor the social health of a persistent virtual environment
US7716149B2 (en) Method, device, and program product for a social dashboard associated with a persistent virtual environment
CN109697233A (en) A kind of knowledge mapping system building method
CN112153426B (en) Content account management method and device, computer equipment and storage medium
CN102262534B (en) System and method for developing computer online games based on Internet browser/server (B/S) structure
CN110417721A (en) Safety risk estimating method, device, equipment and computer readable storage medium
CN109740155A (en) A kind of customer service system artificial intelligence quality inspection rule self concludes the method and system of model
CN109345133A (en) Reviewing method and robot system based on big data and deep learning
CN111917878B (en) Message processing method, device, equipment and storage medium
CN110196711B (en) Plug-in type work order assistant decision-making method and system based on artificial intelligence
CN110090446A (en) Method and apparatus for handling the report information in game
CN106202507A (en) Power line repair drill simulation system and method
CN109947462A (en) A kind of decision support method and device that the change of software-oriented code is integrated
CN107577748A (en) Building trade information acquisition system and its method based on big data
CN109033220A (en) Automatically selecting method, system, equipment and the storage medium of labeled data
CN111510783A (en) Method, device, electronic equipment and storage medium for determining video exposure
CN109446229A (en) Identification and robot system based on big data and deep learning
CN115563259A (en) Multi-mode question and answer data acquisition method and device
CN109003211B (en) Emergency drilling system and method
CN110660287A (en) Simulation examination system for distribution network uninterrupted operation
CN109299153A (en) Active identification and robot system based on big data and deep learning
CN109191015A (en) New high-tech enterprise's identification and robot system based on big data and deep learning
CN110245862A (en) Electric system project management system
CN109300025A (en) Auditing method and robot system based on big data and deep learning
CN110059117A (en) A kind of analysis and processing method and device of big data of imparting knowledge to students

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant