CN109191015A - New high-tech enterprise's identification and robot system based on big data and deep learning - Google Patents
New high-tech enterprise's identification and robot system based on big data and deep learning Download PDFInfo
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Abstract
New high-tech enterprise's identification and robot system based on big data and deep learning, it include: the identification standard for obtaining new high-tech enterprise, obtain the data of enterprise to be assert, the corresponding data of the identification standard are obtained from the data of the enterprise, judge whether the corresponding data of the identification standard meet the identification standard.The above method and system assert technology by the new high-tech enterprise based on big data and deep learning, so that the identification of new high-tech enterprise more automate, is intelligent, improve objectivity, confidence level, accuracy, efficiency that new high-tech enterprise is assert.
Description
Technical field
The present invention relates to information technology fields, recognize more particularly to a kind of new high-tech enterprise based on big data and deep learning
Determine 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 determine that an enterprise could regard as new high-tech enterprise by experts' evaluation that row new high-tech enterprise, which is assert,.In a first aspect,
The foundation of experts' evaluation is the declaration data and data that enterprise provides, although enterprise is required to provide true data when declaring
And data, or even require enterprise to provide authenticity commitment letter, but actually can not objectively guarantee the true of its data and data
Reality inevitably has a small number of enterprises that can provide false data or be exaggerated to certain data, makes so that evaluation expert can be misled
The judgement of mistake, and then obtain the identification result of mistake.Second aspect, because the enterprise that application new high-tech enterprise is assert is very
It is more, and evaluation expert's limited amount, often an expert will evaluate the identification application of a enterprises up to a hundred within one month time,
And evaluation expert be only invited to it is part-time evaluate, can only be spent a little bit of time after routine work to be evaluated, because
For being pressed for time for evaluation, cause evaluation course very rough, evaluation expert only sweeps and mistake many data and data, makes
The result that must be assert lacks accuracy.The third aspect, although many evaluation experts are domain experts, the evaluation to new high-tech enterprise
Rule is not to be very familiar with, although having evaluation rule documents in review system, actually expert, which reads through, is difficult whole
Remember and use, or even the expert having is busy, no time goes to see evaluation document, causes when experts' evaluation to be actually all
Or whether at last part is gone to judge a new high-tech enterprise of an enterprise according to the standard of oneself, so that review standards of designing and government
Defined identification standard is inconsistent, causes to assert that the subjectivity height of result, objectivity are poor, identification result is inaccurate.
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 height based on big data and deep learning is provided
New spectra identification and robot system, to solve the objectivity that new high-tech enterprise is assert in the prior art, confidence level, accuracy
The shortcomings that insufficient and low efficiency.
In a first aspect, the embodiment of the present invention provides a kind of new high-tech enterprise's identification, which comprises
Identification standard obtaining step, for obtaining the identification standard of new high-tech enterprise；
Business data obtaining step, for obtaining the data of enterprise to be assert；The data of acquisition include third party's data,
Data are more objective and feasible, it is possible to improve the objectivity and confidence level of identification；If data are not objective, identification also would not
Accurately, because data are more objective, and then accuracy is also just improved；
The corresponding data acquisition step of standard, for obtaining the identification standard from the data of the enterprise to be assert
Corresponding data；
Judgment step is assert, for judging whether the corresponding data of the identification standard meet the identification standard.According to
Standard is assert automatically, and efficiency can be improved.
Preferably,
The business data obtaining step includes:
Data source obtaining step, for obtaining data source；
Business data searching step, for the data of the enterprise to be 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 of the enterprise to be assert
According to 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 preset model include deep learning model；
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
Whether the enterprise to be 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, institute
Stating preset model includes deep learning model；
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, judge whether the enterprise to be assert meets the overall standard；
Comprehensive descision step, for judging whether the enterprise to be assert meets each single item mark described in the identification standard
The quasi- and described overall 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 it is described whether the enterprise to be assert 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 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 it is described total whether the enterprise to be assert meets
Body standard.
Second aspect, the embodiment of the present invention provide a kind of new high-tech enterprise's identification system, the system comprises:
Identification standard obtains module, for obtaining the identification standard of new high-tech enterprise；
Business data obtains module, for obtaining the data of enterprise to be assert；
The corresponding data acquisition module of standard, for obtaining the identification standard from the data of the enterprise to be assert
Corresponding data；
Judgment module is assert, for judging whether the corresponding data of the identification standard meet the identification standard.
Preferably,
The business data obtains module
Data source obtains module, for obtaining data source；
Business data retrieval module, for the data of the enterprise to be 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 of the enterprise to be assert
According to 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
Whether the enterprise to be 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, judge whether the enterprise to be assert meets the overall standard；
Comprehensive judgment module, for judging whether the enterprise to be assert meets each single item mark described in the identification standard
The quasi- and described overall standard.
Preferably,
The each single item standard judgment module includes:
The corresponding preset range of each single item standard obtains module, for obtaining the corresponding default model of each single item standard
It encloses；
The corresponding third data judgment module of each single item standard, for determining it is described whether the enterprise to be assert meets
Each single item standard；
The overall standard judgment module includes:
The corresponding third data generation module 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 of overall standard obtains module, for obtaining the corresponding preset range of the overall standard；
The corresponding third data judgment module of overall standard, for judging it is described total whether the enterprise to be assert meets
Body 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
System is assert by the described in any item new high-tech enterprises of aspect.
The embodiment of the present invention has the advantage that includes: with beneficial effect
1, the embodiment of the present invention, which can be used for evaluating the full-automatic identification of new high-tech enterprise or auxiliary expert, carries out high-new enterprise
The semi-automatic identification of industry, to improve identification automation, intelligent and identification efficiency.
2, new high-tech enterprise can be related to the financial data, intellectual property data, product data, safety of enterprise during assert
Data, qualitative data etc., these data can be from third party such as the administration for industry and commerce, Department of Intellectual Property, revenue department, the Ministry of Public Security
Door, quality testing department etc. obtain, but do not make full use of third-party data to improve identification when new high-tech enterprise's identification in reality
Confidence level.The data source that the embodiment of the present invention utilizes includes the big data obtained from third party, the data provided than enterprise oneself
It is more credible.
3, the embodiment of the present invention carries out intellectual analysis based on big data combination identification standard to judge whether enterprise belongs to height
New spectra carrys out auxiliary expert and evaluates, and can reduce the workload of experts' evaluation, improves the efficiency of experts' evaluation.
4, the 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, the embodiment of the present invention can filter out the data for meeting new high-tech enterprise and assert condition by identification and system
And enterprise, 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, the embodiment of the present invention can filter out the number for not meeting new high-tech enterprise and assert condition by identification and system
According to and enterprise, for evaluation expert refer to, can make evaluation expert stringenter to ineligible data and enterprise in this way
Evaluation, to improve the accuracy rate of identification.
7, the embodiment of the present invention can assert the data of condition according to new high-tech enterprise, carry out to the indices of enterprise automatic
Scoring, and COMPREHENSIVE CALCULATING goes out the total score of enterprise, refers to for evaluation expert, to further increase the efficiency of experts' evaluation.
New high-tech enterprise's identification and robot system provided in an embodiment of the present invention based on big data and deep learning,
Include: the identification standard for obtaining new high-tech enterprise, obtain the data of enterprise to be assert, from the data of the enterprise described in acquisition
The corresponding data of identification standard, judge whether the corresponding data of the identification standard meet the identification standard.The above method and
System assert technology by new high-tech enterprise based on big data and deep learning so that the identification of new high-tech enterprise more automate,
Intelligence improves objectivity, the confidence level, accuracy, efficiency of new high-tech enterprise's identification.
Detailed description of the invention
Fig. 1 is the flow chart for new high-tech enterprise's 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 that system is assert by the new high-tech enterprise 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 new high-tech enterprise is obtained.
The identification standard of new high-tech enterprise is exactly that an enterprise is regarded as to the standard of new high-tech enterprise.New high-tech enterprise is also referred to as high-new
Technology enterprise.Country is as follows to the identification Standard General of new high-tech enterprise, the identification standard in each place and national new high-tech enterprise
Identification standard can be similar: 1, must establish with limited laibility 1 year or more when enterprise's application is assert；2, enterprise by independent research, by
It the modes such as allows, given, being merged, obtaining the intellectual property for technically playing its major product (service) core supporting function
Ownership；3, the technology for playing core supporting function to enterprise's major product (service) belongs to " the high-new skill that state key is supported
Art field " as defined in range；4, it is total to account for enterprise current year worker by the scientific and technical personnel that enterprise is engaged in research and development and the relevant technologies innovation activity
Several ratios is not less than 10%；5, enterprise's nearly three fiscal years (were managed based on the time as practical for practical operational period discontented 3 years
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) sell within nearest 1 year
The enterprise taken in less than 5,0,000,000 yuan (containing) is sold, ratio is not less than 5%；(2) a nearest annual sales revenue is at 5,0,000,000 yuan to 2
The enterprise of hundred million yuan (containing), ratio are not less than 4%；(3) enterprise of the nearest annual sales revenue at 200,000,000 yuan or more, ratio are not less than
3%. wherein, and the ratio that the R&D expense total value that enterprise occurs within Chinese territory accounts for full-fledged research development cost total value is not low
In 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, enterprise
Innovation Ability Evaluation should reach corresponding requirements；8, enterprise's application was assert in the previous year, and considerable safety, great quality accident do not occur
Or severe environments illegal activities.
Business data obtaining step S200: the data of enterprise to be assert are obtained.
Business data obtaining step S200 includes data source obtaining step S210, business data searching step S220.
Data source obtaining step S210: data source is obtained.The data source includes data, the third party's offer that enterprise provides
Data.The data that enterprise provides refer to the data that the enterprise provides.The data that third party provides include government department, industry association
The business 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.
Business data searching step S220: the data of the enterprise are retrieved and obtained from data source.Because in data source
It include the data and other data of many enterprises, if entirety obtains the network transmission for retrieving meeting overspending again
Between, so needing first to retrieve the data of the enterprise, then by the data acquisition of the enterprise to locally.When there is multiple data sources
When, the data of the enterprise 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 enterprise
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 enterprise pair is filtered out from the data of the enterprise
The first data answered.The data of the enterprise include various data, and wherein different establish a capital of data assert standard with new high-tech enterprise
It is related, so needing to retrieve data related with new high-tech enterprise's identification standard from the data of the enterprise, as the enterprise
Corresponding first data of industry.Multinomial, including multinomial standard that new high-tech enterprise assert that standard has, the corresponding data of all standard may be
In different data sources, such as financial data, in the data source that the administration for industry and commerce provides, intellectual property data is in Department of Intellectual Property
It provides in data source, quality detecting data is in the data source that quality testing department provides, data source that secure data is provided in public security department
In, so more preferably mode is, the default corresponding relationship in data source between data category and all standard is obtained, from the enterprise
The corresponding data of each data source are obtained in the data of industry, retrieved from the corresponding data of each data source with it is described every
The relevant data of the corresponding standard of one data source.Such as the corresponding standard of data source that Intellectual Property Department provides is intellectual property
Standard, from the data source that Intellectual Property Department provides include the payment data of intellectual property of this enterprise, request for data, by
Data, authorization data are managed, wherein the data of " request for data accepts data, authorization data " these three classifications and new high-tech enterprise are recognized
Fixed intellectual property standard is related, then three data categories and new high-tech enterprise described in the data source of Intellectual Property Department's offer
Corresponding relationship is established between the intellectual property standard of identification, 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 enterprise 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 enterprise 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 the multiple height before the confidence level of unclear each data source
When new spectra 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
Judge that the enterprise to be assert belongs to new high-tech enterprise；It is no, then judge that the enterprise to be assert is not belonging to new high-tech enterprise.
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, the corresponding third data generation step S440 of each single item standard, each single item standard
The corresponding preset model obtaining step S460 of judgment step S450, overall standard, overall standard judgment step S470, comprehensive descision
Step S480.
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 each enterprise assert.History big data refers to a large amount of historical data
Or have accumulated the data of long period.The enterprise assert was carried out, including having carried out assert the enterprise passed through, carry out
Assert but assert unsanctioned enterprise.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, degree can be a percentage
Than, such as 0% to 100%, 0% indicates not meeting completely, and 100% indicates to comply fully with.
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 enterprise 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 enterprise 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 each enterprise 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 each enterprise 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 each enterprise 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
Whether the enterprise to be 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 be to described wait assert
Whether enterprise can be had an impact by assert, because can be had an impact to final TOP SCORES, and the corresponding totality of TOP SCORES
Standard General 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 (the corresponding third data of each single item standard are empty situation), then judges that each single item standard is corresponding
Whether preset range is infinite to just infinite from bearing: being then to determine that the enterprise to be assert meets each single item standard；It is no,
Then determine that the enterprise to be assert does not meet each single item standard；
No (the corresponding third data of each single item standard are not empty situation): then judge that each single item standard is corresponding
Third data whether in the corresponding preset range of each single item standard: be then to determine that the enterprise to be assert meets institute
State each single item standard；It is no, then determine that the enterprise to be assert does 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
Each enterprise 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 each enterprise 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 each enterprise 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 each enterprise 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 each enterprise assert of one standard as the 5th deep learning model
Data, using the third data of the corresponding each enterprise 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, judge whether the enterprise to be 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 then to judge that the enterprise to be assert meets the overall standard；It is no, then
Judge that the enterprise to be assert does not meet the overall standard.
Comprehensive descision step S480: judge the enterprise to be assert whether meet in the identification standard each single item standard and
Overall standard: being, then determines that the enterprise to be assert belongs to new high-tech enterprise, that is, assert the result is that the enterprise to be assert passes through
New high-tech enterprise identification；It is no, then determine that the enterprise to be assert is not belonging to new high-tech enterprise, that is, it is assert the result is that described wait assert
Enterprise is assert not over new high-tech enterprise.Meet each single item standard and overall standard in the identification standard to refer to meeting simultaneously
Each single item standard and overall standard in the identification standard.If there is a certain item standard or overall standard are not met, then institute is determined
It states enterprise to be assert and is not belonging to new high-tech enterprise.
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 business data and obtains the execution of module 200 business data obtaining step S200.
It includes that data source obtains module 210, business data retrieval module 220 that business data, which obtains module 200,.
Data source obtains module 210 and executes data source obtaining step S210.
Business data retrieval module 220 executes business data searching 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 module 430, the corresponding third data generation module S440 of each single item standard, each single item standard and judges mould
The corresponding preset model of block 450, overall standard obtains module 460, overall standard judgment module 470, comprehensive judgment module 480.
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 S440 of each single item standard executes the corresponding third data of each single item standard and generates
Step 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.
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 new high-tech enterprise's identification, and new high-tech enterprise's identification includes that identification standard obtains
Step S100, the corresponding data acquisition step S300 of business data obtaining step S200, standard, identification judgment step S400, such as
Shown in Fig. 1.
Embodiment 2 provides a kind of new high-tech enterprise's identification, each step including method described in embodiment 1；Wherein, it looks forward to
Industry data acquisition step S200 includes data source obtaining step S210, business data retrieval S220, the corresponding data acquisition of standard
Step S300 includes data screening step S310, data cleansing step S320.
Embodiment 3 provides a kind of new high-tech enterprise's identification, each step including method described in embodiment 2；Wherein, number
It include corresponding data source obtaining step S321, confidence level obtaining step S322, confidence level selecting step according to cleaning step S320
S323。
Embodiment 4 provides a kind of new high-tech enterprise's identification, each step including method described in embodiment 2；Wherein, recognize
Determining judgment step S400 includes that substandard obtaining step S410, corresponding data extraction step S420, each single item standard are corresponding pre-
If the corresponding third data generation step S440 of model obtaining step S430, each single item standard, each single item standard judgment step
The corresponding preset model obtaining step S460 of S450, overall standard, overall standard judgment step S470, comprehensive descision step
S480, as shown in Figure 2.
Embodiment 5 provides a kind of new high-tech enterprise's identification, each step including method described in embodiment 4；Wherein, often
The corresponding preset model obtaining step S430 of one standard includes the corresponding deep learning model initialization step of each single item standard
The corresponding historical data obtaining step S432 of S431, each single item standard, the second deep learning model generation step S433, third are deep
Learning model generation step S434, the corresponding predetermined deep learning model setting steps S435 of each single item standard are spent, such as Fig. 3 institute
Show；The corresponding preset model obtaining step S450 of overall standard includes the corresponding deep learning model initialization step of overall standard
The corresponding historical data obtaining step S452 of S451, overall standard, the 5th deep learning model generation step S453, the 6th depth
The corresponding preset model setting steps S455 of learning model generation step S454, overall standard, as shown in Figure 4.
Embodiment 6 provides a kind of new high-tech enterprise's identification, each step including method described in embodiment 4；Wherein, often
One standard judgment step S450 includes that the corresponding preset range obtaining step S451 of each single item standard, each single item standard are corresponding
Third data judgment step S452；Overall standard judgment step S470 includes the corresponding third data generation step of overall standard
The corresponding preset range obtaining step S472 of S471, overall standard, the corresponding third data judgment step S473 of overall standard.
Embodiment 7 provides a kind of new high-tech enterprise's identification system, and the new high-tech enterprise assert that system includes that identification standard obtains
Module 100, business data obtain module 200, the corresponding data acquisition module 300 of standard, assert judgment module 400, such as Fig. 5 institute
Show.
Embodiment 8 provides a kind of new high-tech enterprise's identification system, each step including system described in embodiment 7；Wherein, it looks forward to
Industry data acquisition module 200 includes that data source obtains module 210, business data retrieves S220, the corresponding data acquisition mould of standard
Block 300 includes data screening module 310, data cleansing module 320.
Embodiment 9 provides a kind of new high-tech enterprise's identification system, each step including system described in embodiment 8；Wherein, number
It include that corresponding data source obtains module 321, confidence level obtains module 322, confidence level chooses module 323 according to cleaning module 320.
Embodiment 10 provides a kind of new high-tech enterprise's identification system, each step including system described in embodiment 8；Wherein,
Assert that judgment module 400 is corresponding default including substandard acquisition module 410, corresponding data extraction module 420, each single item standard
Model obtains module 430, the corresponding third data generation module S440 of each single item standard, each single item standard judgment module 450, total
The corresponding preset model of body standard obtains module 460, overall standard judgment module 470, comprehensive judgment module 480, such as Fig. 6 institute
Show.
Embodiment 11 provides a kind of new high-tech enterprise's identification system, each step including system described in embodiment 10；Wherein,
It includes the corresponding deep learning model initialization module of each single item standard that the corresponding preset model of each single item standard, which obtains module 430,
431, the corresponding historical data of each single item standard obtains module 432, the second deep learning model generation module 433, third depth
The corresponding predetermined deep learning model setup module 435 of learning model generation module 434, each single item standard, as shown in Figure 7；Always
The corresponding preset model of body standard obtain module 450 include the corresponding deep learning model initialization module 451 of overall standard, it is total
The corresponding historical data of body standard obtains module 452, the 5th deep learning model generation module 453, the 6th deep learning model
The corresponding preset model setup module 455 of generation module 454, overall standard, as shown in Figure 8.
Embodiment 12 provides a kind of new high-tech enterprise's identification system, each step including system described in embodiment 10；Wherein,
Each single item standard judgment module 450 is corresponding including the corresponding preset range acquisition module 451 of each single item standard, each single item standard
Third data judgment module 452；Overall standard judgment module 470 include the corresponding third data generation module 471 of overall standard,
The corresponding preset range of overall standard 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
System is assert by the new high-tech enterprise.
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 new high-tech enterprise's identification, which is characterized in that the described method includes:
Identification standard obtaining step, for obtaining the identification standard of new high-tech enterprise；
Business data obtaining step, for obtaining the data of enterprise to be assert；
The corresponding data acquisition step of standard, it is corresponding for obtaining the identification standard from the data of the enterprise to be assert
Data；
Judgment step is assert, for judging whether the corresponding data of the identification standard meet the identification standard.
2. new high-tech enterprise's identification according to claim 1, which is characterized in that
The business data obtaining step includes:
Data source obtaining step, for obtaining data source；
Business data searching step, for the data of the enterprise to be assert to be retrieved and obtained from the data source；
The corresponding data acquisition step of the standard includes:
Data screening step is made for filtering out the corresponding data of the identification standard from the data of the enterprise to be assert
For 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. new high-tech enterprise's 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. new high-tech enterprise's 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, institute
Stating preset model includes deep learning model；
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
Whether enterprise to be assert meets each single item standard；
The corresponding preset model obtaining step of overall standard, it is described pre- for obtaining the corresponding preset model of the overall standard
If model includes deep learning model；
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, judge whether the enterprise to be assert meets the overall standard；
Comprehensive descision step, for judge the enterprise to be assert whether meet each single item standard described in the identification standard and
The overall standard.
5. new high-tech enterprise's 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 each whether the enterprise to be assert meets
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 whether the enterprise to be assert meets the overall mark
It is quasi-.
6. system is assert by a kind of new high-tech enterprise, which is characterized in that the system comprises:
Identification standard obtains module, for obtaining the identification standard of new high-tech enterprise；
Business data obtains module, for obtaining the data of enterprise to be assert；
The corresponding data acquisition module of standard, it is corresponding for obtaining the identification standard from the data of the enterprise to be assert
Data；
Judgment module is assert, for judging whether the corresponding data of the identification standard meet the identification standard.
7. system is assert by new high-tech enterprise according to claim 6, which is characterized in that
The business data obtains module
Data source obtains module, for obtaining data source；
Business data retrieval module, for the data of the enterprise to be assert to be retrieved and obtained from the data source；
The corresponding data acquisition module of the standard includes:
Data screening module is made for filtering out the corresponding data of the identification standard from the data of the enterprise to be assert
For 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.
8. system is assert by new high-tech enterprise according to claim 7, 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
Whether enterprise to be 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, judge whether the enterprise to be assert meets the overall standard；
Comprehensive judgment module, for judge the enterprise to be assert whether meet each single item standard described in the identification standard and
The overall standard.
9. system is assert by new high-tech enterprise according to claim 8, which is characterized in that
The each single item standard judgment module includes:
The corresponding preset range of each single item standard obtains module, for obtaining the corresponding preset range of each single item standard；
The corresponding third data judgment module of each single item standard, for determining it is described each whether the enterprise to be assert meets
Item standard；
The overall standard judgment module includes:
The corresponding third data generation module 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 of overall standard obtains module, for obtaining the corresponding preset range of the overall standard；
The corresponding third data judgment module of overall standard, for judging whether the enterprise to be assert meets the overall mark
It is quasi-.
10. a kind of robot system, which is characterized in that be respectively configured in the robot just like any one of claim 6-9 institute
System is assert by the new high-tech enterprise stated.
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