CN109872065A - A kind of method of the preferred house ornamentation personnel of intelligence - Google Patents
A kind of method of the preferred house ornamentation personnel of intelligence Download PDFInfo
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Abstract
The invention discloses the methods of the preferred house ornamentation personnel of intelligence a kind of, are related to finishing business matching technique field, comprising the following steps: A, build house ornamentation database platform；B, output is fitted up personnel characteristics' vector and is recorded；C, owner's decoration requirements feature vector is obtained；D, matched finishing personnel ranking is fed back to by owner by similarity feature calculation method.The present invention is when owner has house ornamentation demand, it is no longer in face of unordered or simply dealt house ornamentation personal information, but after being calculated by the intelligent optimization algorithm of platform, provides and be suitble to each owner's more accurately to fit up personal information, so that the service quality and efficiency of platform be significantly increased；Using Nonlinear Multi perceptron, linear data and nonlinear data are uniformly processed, expanded convenient for more accurate processing rule；Definition is finely divided to the key element of finishing personnel, using cosine similarity principle, effectively improves the matching precision of owner Yu finishing personnel individual.
Description
Technical field
The present invention relates to finishing business matching technique field, the in particular to methods of a kind of preferred house ornamentation personnel of intelligence.
Background technique
Existing owner's decorating house usually sees decoration company's advertisement on network, or public with finishing by recommending
Department's communication carries out finishing cooperation and reaches, and this mode has information asymmetry, is easy to allow the damage of the rights and interests of owner, because
Quality for finishing personnel is very different, and the technique direction of decoration style and finishing is many and diverse, and owner can only be wanted by price one
Element evaluates finishing proficiency, hence it is evident that is unfavorable for the service that owner chooses suitable being customized of finishing personnel.
Meanwhile existing house ornamentation website or cell phone application, it is mostly simply to recommend the finishing in location using city as condition
Company, main with the user information for having finishing wish is collected, user is difficult to solve the problems, such as true finishing.Have individually to design
Case attracts user, also only simply enumerates, goes to take your time by user oneself, meanwhile, it can not ensure designer position
Room position is fitted up in the same area with owner.
Tables of data user_ is arranged in a kind of method for precisely matching owner in line decoration of patent document CN108133060A
Center, tables of data house, tables of data communities, tables of data housetypes, five big data of tables of data layouts
Library, can complete each subscriber identity information of typing, the position of owner's building and pictorial information, perfect O2O business model fitting up
The application of industry, which is built based on internet, with global, by the way that on-line system is integrated and big data advantage,
Faster more accurately can provide the service of good finishing for user, can each user of complete typing information, comprising: industry
Master, designer, decoration company, building materials quotient, upfitter, management every terms of information, improve from start to fit up to finishing complete it is whole
A process, and finishing service is precisely matched convenient for the platform.
But there is the matching that owner and finishing personnel are only carried out in such a way that big data is recommended in above-mentioned technical proposal,
The element of consideration does not have specific aim, and precision is also inadequate, when will lead to owner selection finishing personnel or finishing personnel selection owner
Matching inaccuracy.
Technical term is explained:
Owner: this context meaning is to need newly fit up, turn over newly-decorated user.
Finishing personnel: designer removes work, water power work, bricklayer's, carpenter, lacquerer etc..
Decoration style: modern concision style, pastoralism, post-modernism style, Chinese style style, New Chinese style, European Gu
Allusion quotation style, Mediterranean style, fashion is mashed up, Southeast Asia style, American Style, new classics style, Japanese style, rural style
Deng.
Popular colour system: romantic pink, warm orange, bright yellow, neutrality warm colour, dynamic green, ocean blue, mysterious purple
Color, neutral cool colour etc..
Intelligent optimization algorithm: a kind of heuristic value, including genetic algorithm, ant group algorithm, tabu search algorithm, mould
Quasi- annealing algorithm, particle swarm algorithm etc., what is solved is usually optimization problem.
Artificial neural network: the research hotspot that artificial intelligence field rises since being the 1980s.It is from information
Reason angle is abstracted human brain neuroid, establishes certain naive model, different nets is formed by different connection types
Network.Neural network or neural network are also often directly referred to as in engineering and academia.Neural network is a kind of operational model, by
Composition is coupled to each other between a large amount of node or neuron.A kind of each specific output function of node on behalf, is referred to as motivated
Function activation function.Connection between every two node all represents a weighting for passing through the connection signal
Value, referred to as weight, this is equivalent to the memory of artificial neural network.The output of network is then according to the connection type of network, weighted value
With the difference of excitation function and it is different.And network itself is approached certain algorithm of nature or function, it can also
It can be the expression to a kind of logic strategy.
Data model: data characteristics is abstracted.Data Data is the symbol record for describing things, and model M odel is reality
The world is abstracted.Data model describes static nature, dynamic behaviour and the constraint condition of system from abstraction hierarchy, is data
The information of library system indicates to provide an abstract frame with operation.Three parts are had in described in data model: data
Structure, data manipulation and data constraint.
Data modeling: to the abstract tissue of real world Various types of data, range, the group of data that database need to administer are determined
Form etc. is knitted until being converted to the database of reality.Physics mould is converted by the conceptual model abstracted after network analysis
After type, the process of relationship between database entity and each entity is established in tools such as visio or erwin, entity is usually table.
Entity-Relationship Model (abbreviation E-R model): ER model, full name are entity relationship model, entity relationship model or reality
Body link model figure ERD (Entity-relationship Diagram) is invented by the Chinese descendant in America mountain computer scientist Chen Pin,
It is data model or ideograph used in the high level description of conceptual data model.It provide not by any DBMS constrain towards
The expression of user is widely used as the tool of data modeling in database design.
Vector space model (or phrase vector model): it is one and is applied to information filtering, information extraction is indexed and commented
Estimate the algebraic model of correlation.(VSM:Vector Space Model)
Rule-based classifier: data are divided by the regular collection of one group of if ... then ....The premise or condition of rule
It is the expression formula of attribute conjunction.The conclusion of rule be one just or negative classification.
Multilayer perceptron (MLP:Multi-Layer Perceptron): i.e. multilayer perceptron is before one kind to structure
Artificial neural network, one group of input vector of mapping to one group of output vector.MLP can be seen as a digraph, by multiple
Node layer composition, each layer are connected to next layer entirely.In addition to input node, each node is one with nonlinear activation letter
Several neurons (or processing unit).It is a kind of be referred to as back-propagation algorithm supervised learning method be often used to train MLP.
MLP is the popularization of perceptron, overcomes the shortcomings that perceptron cannot achieve to the identification of linearly inseparable data.
Vector space cosine similarity (Cosine Similarity): with two vectorial angle cosines in vector space
It is worth as the size for measuring two inter-individual differences.Cosine value closer to 1, indicate that angle closer to 0 degree, that is, two to
Amount is more similar, this is just cosine similarity.
Summary of the invention
It is an object of the invention to: the method for preferred house ornamentation personnel of intelligence a kind of is provided, solves existing internet
Finishing service platform is intended only as a decoration company substantially and fits up the intermediate media of owner, does not refine to owner and finishing
Accurate matching process between personnel, the matching scheme not also being suitble to.
The technical solution adopted by the invention is as follows:
A kind of method of the preferred house ornamentation personnel of intelligence, comprising the following steps:
A, it according to the information of practical house ornamentation, is modeled using entity relationship, builds house ornamentation database platform, the house ornamentation data
Information in the platform of library includes finishing personnel's classification chart, and decoration style table fits up colour system table, fits up personnel's basic data table, dress
Repair personnel's other information table, owner individual's basic data table, owner's decoration requirements table, owner's browsing information table；
B, the information of personnel, including finishing personnel's classification chart, decoration style table, finishing colour system table, dress will be fitted up involved in A
Repair personnel's basic data table, finishing personnel's other information table, unbalanced input multilayer perceptron and export finishing personnel characteristics to
Amount, and it is recorded into finishing personal information library；
C, owner, which issues, needs the required list of the personnel of fitting up, after the required list unbalanced input multilayer perceptron, write-in
Owner's decoration requirements table obtains owner's decoration requirements feature vector；
D, based on owner's decoration requirements feature vector, owner's decoration requirements are matched by similarity feature calculation method
Feature vector and finishing personnel characteristics' vector obtain and according to the degree of correlation to finishing personnel's ranking, and the degree of correlation is highest to come head
Position, the degree of correlation it is minimum come last bit, and matched finishing personnel ranking is fed back into owner.
Present invention is mainly applied to internet+family's assembling platforms, and the platform is according to intelligent optimization algorithm, to finishing personnel's difference
Data modeling is carried out, by the requirement of different types of data model, has included the basic letter of a large amount of finishing personal information and owner
Breath is no longer in face of unordered or simply dealt house ornamentation personal information, but by the intelligence of platform when owner has house ornamentation demand
After energy optimization algorithm calculates, provides and be suitble to each owner's more accurately to fit up personal information, to be significantly increased
The service quality and efficiency of platform.
For Nonlinear Multi perceptron, linear data can either be handled, nonlinear data is also capable of handling, that is, uses
Rule-based classifier, the technology which classifies to record using one group of if ... then ... rule, model
Rule indicates that wherein R is referred to as rule set, and ri is classifying rules, and issue noted above is all with R=(r1 ∨ r2 ∨ ... ∨ rk)
In the covering of rule set.Such as geographical location problem, use global position system GPS, come determine finishing personnel and owner it
Between longitude and latitude；Decoration style, finishing colour system, decoration quality are more in rule set.
Further, the B, be related to finishing personnel information include gender, geographical location, operating characteristic, style and features,
Complete workload, job case feature, service features, the last operating characteristic.The key element that personnel are fitted up in the B is defeated
Enter, the main influence factor according to decoration quality, effect and cost, geographical location considers finishing personnel to the expense at scene
With, and with owner is more convenient links up face-to-face；Operating characteristic refers mainly to the job specification of finishing personnel, such as designer, removes
Work, water power work, bricklayer's, carpenter, lacquerer etc.；Style and features refer to decoration style and popular colour system；Workload is completed to refer to originally flat
The order volume completed in platform reacts the operating rate and ability to work of finishing personnel；Job case feature directly reacts finishing
The working effect of personnel；Service features refer to the feedback opinion of owner；The last operating characteristic reaction finishing personnel's is current
Working condition；Influence of the personnel to style and features is fitted up in gender key reaction, is more suitable for which kind of style and features be engaged in.
Further, the B, geographical location, gender, operating characteristic, the last operating characteristic are independent characteristic, service
Feature, completion workload, job case feature, style and features are associated with gender, operating characteristic.Distinguish independent data and incidence number
According to being conducive to restore associated data in the processing of Nonlinear Multi perceptron and carry out the adjusting of weighted value, convenient for improving matching
Precision.
Further, completion workload is normalized, it is special to geographical location, gender, operating characteristic, style
Sign, service features, the last operating characteristic are normalized again after being converted to linear data using vector space model.
Wherein, completing workload is linear data, geographical location, gender, operating characteristic, style and features, service features, the last time
Operating characteristic is nonlinear data, to linear data, is normalized, and convenient for comparing, while being able to maintain its accuracy；
Linear data are converted using vector space model vsm to nonlinear data.
Further, the C, owner issue need the personnel of fitting up required list include geographical location, finishing room features,
Fit up room environment feature, tendency decoration style, required finishing personnel characteristics, style search characteristics.
Further, the D, processing method of the platform for owner are as follows:
D11 verifies the registration user identity of owner；
D12, owner input the requirement of finishing；
D13 submits the decoration requirements information of owner, carries out similarity query；
D14 returns to the finishing personal information of recommendation.
Further, the D, processing method of the platform for finishing personnel are as follows:
D21 verifies the registration user identity of finishing personnel；
D22 submits finishing personal information table；
D23, return are submitted successfully and are waited auditing result, write information in finishing personnel's raw information table.
Further, the D, processing method of the platform to administrator are as follows:
D31, the management identity of authentic administrator；
D32 checks and audits finishing personnel；
D33, database processing simultaneously update as a result, for the information input by finishing personnel；
D34, display operation successful information.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
1, a kind of method of the preferred house ornamentation personnel of intelligence of the present invention is no longer in face of unordered when owner has house ornamentation demand
Or simply dealt house ornamentation personal information, but after being calculated by the intelligent optimization algorithm of platform, provide and be suitble to each industry
Main more accurately fits up personal information, so that the service quality and efficiency of platform be significantly increased.
2, a kind of method of the preferred house ornamentation personnel of intelligence of the present invention, using Nonlinear Multi perceptron, to linear data and
Nonlinear data is uniformly processed, and is expanded convenient for more accurate processing rule.
3, a kind of method of the preferred house ornamentation personnel of intelligence of the present invention is finely divided definition to the key element of finishing personnel,
Using cosine similarity principle, the matching precision of owner Yu finishing personnel individual are effectively improved.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is the structural schematic diagram of an embodiment of the present invention；
Fig. 2 be an embodiment of the present invention platform for owner processing method timing diagram；
Fig. 3 be an embodiment of the present invention platform for fit up personnel processing method timing diagram；
Fig. 4 is the timing diagram of the platform to the processing method of administrator of an embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention, i.e., described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is logical
The component for the embodiment of the present invention being often described and illustrated herein in the accompanying drawings can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art
Member's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
It should be noted that the relational terms of term first and second or the like are used merely to an entity or operation
It is distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation, there are any this
Actual relationship or sequence.Moreover, term includes, includes or its any other variant is intended to the packet of nonexcludability
Contain, so that the process, method, article or equipment for including a series of elements not only includes those elements, but also including
Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device.
The element limited in the absence of more restrictions, including one by sentence ..., it is not excluded that including the element
There is also other identical elements in process, method, article or equipment.
It elaborates below with reference to Fig. 1, Fig. 2 to Fig. 4 to the present invention.
A kind of method of the preferred house ornamentation personnel of intelligence of the present invention, comprising the following steps:
A, it according to the information of practical house ornamentation, is modeled using entity relationship, builds house ornamentation database platform, the house ornamentation data
Information in the platform of library includes finishing personnel's classification chart, and decoration style table fits up colour system table, fits up personnel's basic data table, dress
Repair personnel's other information table, owner individual's basic data table, owner's decoration requirements table, owner's browsing information table；
B, the information of personnel, including finishing personnel's classification chart, decoration style table, finishing colour system table, dress will be fitted up involved in A
Repair personnel's basic data table, finishing personnel's other information table, unbalanced input multilayer perceptron and export finishing personnel characteristics to
Amount, and it is recorded into finishing personal information library；
C, owner, which issues, needs the required list of the personnel of fitting up, after the required list unbalanced input multilayer perceptron, write-in
Owner's decoration requirements table obtains owner's decoration requirements feature vector；
D, based on owner's decoration requirements feature vector, owner's decoration requirements are matched by similarity feature calculation method
Feature vector and finishing personnel characteristics' vector obtain and according to the degree of correlation to finishing personnel's ranking, and the degree of correlation is highest to come head
Position, the degree of correlation it is minimum come last bit, and matched finishing personnel ranking is fed back into owner.Present invention is mainly applied to interconnect
Net+family's assembling platform, which carries out data modeling according to intelligent optimization algorithm, to finishing personnel respectively, by different types of data
The requirement of model, the essential information for having included a large amount of finishing personal information and owner are no longer when owner has house ornamentation demand
In face of unordered or simply dealt house ornamentation personal information, but after being calculated by the intelligent optimization algorithm of platform, provide suitable
Each owner's more accurately fits up personal information, so that the service quality and efficiency of platform be significantly increased.
For Nonlinear Multi perceptron, linear data can either be handled, nonlinear data is also capable of handling, that is, uses
Rule-based classifier, the technology which classifies to record using one group of if ... then ... rule, model
Rule indicates that wherein R is referred to as rule set, and ri is classifying rules, and issue noted above is all with R=(r1 ∨ r2 ∨ ... ∨ rk)
In the covering of rule set.Such as geographical location problem, use global position system GPS, come determine finishing personnel and owner it
Between longitude and latitude；Decoration style, finishing colour system, decoration quality are more in rule set.
Specifically, the finishing personal information of collection is disposably inputted to corresponding Nonlinear Multi perceptron, through handling
Afterwards, finishing personnel characteristics' N-dimensional vector is exported.
For Nonlinear Multi perceptron, training process is as follows:
The table for needing first to establish input layer, hidden layer and output layer resettles the connection between hidden layer and node, even
The content of input is connect as the mark for indicating the hiding node layer of difference that different inputs generate, then circulation, which is established, hides node layer
Between connection, then establish the 0th layer of connection between hidden layer and input layer, weight is defaulted as 0.1, (n-1)th layer, i.e., finally
One layer, the connection between hidden layer and output layer, weight is defaulted as 0.2, and these information are stored in table.Produce hidden layer institute
After having node, network can be begun setting up, utilizes the information saved in database, it is established that including all present weight values
Corresponding network inside.The setupnetwork method of Searchnet class defines multiple instance variables, comprising: input content
Node layer and classification difference are hidden in list, and the numerical value of each node exports, and the weighted value between node is obtained from database.
Next network is established, and calculates output.For each layer of node in network, have from upper one layer of input value, Σ w*
X, i.e. weight and the sum from upper one layer input product, the as sum in program；Input is by being the layer section after activation primitive
The output of point uses atanh function tanh (x) as activation primitive in program.
Back propagation is recycled to adjust weight, backpropagation allows error along network backpropagation, is with ω ij
Example, using j layers of output for input derivative as a regulatory factor, then along network backpropagation, in the mistake of propagation
Weight can weight the error after adjusting in journey, and be multiplied with the output of i node layer and learning rate, be exactly the regulated quantity of ω ij.
Back propagation is classical modified weight algorithm,
N is learning rate, tanh (y)=1-y*y approximation arctan function derivative, as the regulatory factor of error, y 0
When, tanh is maximum, and when y is 1, tanh is minimum because, in training, can specify the output target an of output node be 1 (or
Close to 1), in this way, if the output of output node close to 1, tanh (output) very little, the correction amount of weight with regard to small, conversely,
The correction amount of weight is with regard to big, so it can be used as regulatory factor.
When output result is relatively closer to target value relative to initial output result.It can be revised weight
Update database.
For B, the method fitted up personnel characteristics' vector and be recorded into finishing personal information library is as follows: passing through traversal
Each data in feature vector, by recording for Max and Min, and by Max-Min as radix, i.e. Min
=0, Max=1 carry out the normalized of data, and rule is as follows:
For C, the relevant information of the owner can once be inputed to corresponding multilayer perceptron by platform, after processing, defeated
Owner's decoration requirements feature out, this is the N-dimensional vector with finishing personnel characteristics' identical dimensional.
For D, 4) according to owner's decoration requirements feature, after normalized, and data in finishing personal information library, into
Row vector space cosine similarity calculates, and carries out descending arrangement, k before taking.
Here Ai, Bi respectively represent owner's decoration requirements feature normalization vector A and finishing personnel characteristics normalize to
Measure each component of B.
Cosine value indicates that angle closer to 0 degree, that is, two vectors are more similar closer to 1.
By the above method, definition is finely divided to the key element of finishing personnel, using cosine similarity principle, effectively
It improves owner and fits up the matching precision of personnel's individual.
As a preferred embodiment, the B, the information for being related to finishing personnel includes gender, geographical location, work
Feature, style and features complete workload, job case feature, service features, the last operating characteristic.People is fitted up in the B
The key element input of member, the main influence factor according to decoration quality, effect and cost, geographical location considers finishing people
Member is more convenient to link up face-to-face to live expense, and with owner；Operating characteristic refers mainly to the job specification of finishing personnel, such as
Designer removes work, water power work, bricklayer's, carpenter, lacquerer etc.；Style and features refer to decoration style and popular colour system；Complete workload
Refer to the order volume completed in this platform, reacts the operating rate and ability to work of finishing personnel；Job case feature is straight
The reversed working effect that should fit up personnel；Service features refer to the feedback opinion of owner；The last operating characteristic reaction finishing people
The current working condition of member；Influence of the personnel to style and features is fitted up in gender key reaction, is more suitable for which kind of style spy be engaged in
Sign.The B, geographical location, gender, operating characteristic, the last operating characteristic are independent characteristic, and service features complete work
Amount, job case feature, style and features are associated with gender, operating characteristic.Independent data and associated data are distinguished, is conducive to non-
When the processing of linear multi-layer perceptron, restores associated data and carry out the adjusting of weighted value, convenient for improving matched precision.To completion
Workload is normalized, to geographical location, gender, operating characteristic, style and features, service features, the last work
Feature is normalized again after being converted to linear data using vector space model.Wherein, it is linear for completing workload
Data, geographical location, gender, operating characteristic, style and features, service features, the last operating characteristic are nonlinear data, right
Linear data, are normalized, and convenient for comparing, while being able to maintain its accuracy；To nonlinear data, using space
Vector model vsm converts linear data.
For the B, the coding of men and women is respectively 0,1 in gender；Geographical location: it is indicated respectively by longitude and latitude；Work is special
Sign is by four coded representations: designer 0000, dismounting work are 0001, water power work is 0010, bricklayer's 0011, Mu Gongwei
0100, lacquerer is 0101 etc.；Style and features use 2 vector space models, respectively indicate decoration style and popular colour system.Dress
Style vector space model is repaired modern concision style, pastoralism, post-modernism style, Chinese style style, New Chinese style, European
Classicism, Mediterranean style, fashion is mashed up, Southeast Asia style, American Style, new classics style, Japanese style, rural style
Deng be used as 13 dimension space vector models, when designer is good at a certain style, representing the value of the dimension of the style is 1, is otherwise 0；
Popular colour system similarly romantic pink, warm orange, bright yellow, neutral warm colour, dynamic green, ocean blue, mysterious purple,
Neutral cool colour etc. is used as 8 dimension space vector models.The style and features of certain designer can be indicated in this way: [[1,0,0,0,0,0,
0,0,0,0,0,0,0,1],[0,1,0,0,0,0,1,0]]；Completing workload is exactly to complete order volume；Job case feature according to
Whether finishing personnel have a case displaying, and promising 1, it is not 0；If service features are more according to the good evaluation of the evaluation that owner feeds back
Then it is 1 in bad evaluation, is otherwise 0；Whether the last operating characteristic is completed according to last time order, completes in order
In process, otherwise it is 0 that being not finished, which is 1,.The wherein rule of data: r1: address location；R2: gender；R3: operating characteristic；r4:
Style and features；R5: service features；R6: the last operating characteristic, rule set are { r1Vr2Vr3Vr4Vr6Vr5 }.
The C, owner, which issues, needs the required list for the personnel of fitting up to include geographical location, finishing room features, fit up room
Environmental characteristic, tendency decoration style, required finishing personnel characteristics, style search characteristics.
As shown in Fig. 2, the D, processing method of the platform for owner are as follows:
D11 verifies the registration user identity of owner；
D12, owner input the requirement of finishing；
D13 submits the decoration requirements information of owner, carries out similarity query；
D14 returns to the finishing personal information of recommendation.
As shown in figure 3, the D, processing method of the platform for finishing personnel are as follows:
D21 verifies the registration user identity of finishing personnel；
D22 submits finishing personal information table；
D23, return are submitted successfully and are waited auditing result, write information in finishing personnel's raw information table.
As shown in figure 4, the D, processing method of the platform to administrator are as follows:
D31, the management identity of authentic administrator；
D32 checks and audits finishing personnel；
D33, database processing simultaneously update as a result, for the information input by finishing personnel；
D34, display operation successful information.
The above, only the preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto, it is any
Those skilled in the art within the technical scope disclosed by the invention, can without the variation that creative work is expected or
Replacement, should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be limited with claims
Subject to fixed protection scope.
Claims (8)
1. a kind of method of the preferred house ornamentation personnel of intelligence, it is characterised in that: the following steps are included:
A, it according to the information of practical house ornamentation, is modeled using entity relationship, builds house ornamentation database platform, the house ornamentation database is flat
Information in platform includes finishing personnel's classification chart, and decoration style table fits up colour system table, fits up personnel's basic data table, fits up people
Member's other information table, owner individual's basic data table, owner's decoration requirements table, owner browse information table；
B, the information of personnel, including finishing personnel's classification chart, decoration style table, finishing colour system table, finishing people will be fitted up involved in A
Member's basic data table, finishing personnel's other information table, unbalanced input multilayer perceptron simultaneously export finishing personnel characteristics' vector, and
It is recorded into finishing personal information library；
C, owner, which issues, needs the required list of the personnel of fitting up, and after the required list unbalanced input multilayer perceptron, owner is written
Decoration requirements table obtains owner's decoration requirements feature vector；
D, based on owner's decoration requirements feature vector, owner's decoration requirements feature is matched by similarity feature calculation method
Vector and finishing personnel characteristics' vector obtain and according to the degree of correlation to finishing personnel's ranking, and the degree of correlation is highest to rank the first, phase
Guan Du it is minimum come last bit, and matched finishing personnel ranking is fed back into owner.
2. the method for the preferred house ornamentation personnel of intelligence according to claim 1 a kind of, it is characterised in that: the B is related to fitting up
The information of personnel includes gender, geographical location, operating characteristic, style and features, completes workload, job case feature, service spy
Sign, the last operating characteristic.
3. the method for the preferred house ornamentation personnel of intelligence according to claim 2 a kind of, it is characterised in that: the B, geographical position
Set, gender, operating characteristic, the last operating characteristic are independent characteristic, service features, complete workload, job case feature,
Style and features are associated with gender, operating characteristic.
4. the method for the preferred house ornamentation personnel of intelligence according to claim 3 a kind of, it is characterised in that: to complete workload into
Row normalized uses geographical location, gender, operating characteristic, style and features, service features, the last operating characteristic
Vector space model is normalized again after being converted to linear data.
5. the method for the preferred house ornamentation personnel of intelligence according to claim 1 to 4 a kind of, it is characterised in that: the C, industry
Main issue needs the required list for the personnel of fitting up to include geographical location, finishing room features, finishing room environment feature, be inclined to finishing
Style, required finishing personnel characteristics, style search characteristics.
6. the method for the preferred house ornamentation personnel of intelligence according to claim 5 a kind of, it is characterised in that: the D, platform for
The processing method of owner are as follows:
D11 verifies the registration user identity of owner；
D12, owner input the requirement of finishing；
D13 submits the decoration requirements information of owner, carries out similarity query；
D14 returns to the finishing personal information of recommendation.
7. the method for -4 and 6 any preferred house ornamentation personnel of a kind of intelligence according to claim 1, it is characterised in that: described
D, processing method of the platform for finishing personnel are as follows:
D21 verifies the registration user identity of finishing personnel；
D22 submits finishing personal information table；
D23, return are submitted successfully and are waited auditing result, write information in finishing personnel's raw information table.
8. the method for the preferred house ornamentation personnel of intelligence according to claim 7 a kind of, it is characterised in that: the D, platform is to pipe
The processing method of reason person are as follows:
D31, the management identity of authentic administrator；
D32 checks and audits finishing personnel；
D33, database processing simultaneously update as a result, for the information input by finishing personnel；
D34, display operation successful information.
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