CN114529227A - Rural happy comprehensive service platform based on big data and deep learning - Google Patents
Rural happy comprehensive service platform based on big data and deep learning Download PDFInfo
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Abstract
The invention discloses a country happy comprehensive service platform based on big data and deep learning, which comprises a service data import module, a big data platform, a single accurate portrait application, an accurate single portrait, a server side service portrait and an accurate service push network, belongs to the technical field of artificial intelligence and big data, and particularly relates to a country happy comprehensive service platform based on big data and deep learning; the invention uses deep learning algorithm to push the latest policy to the potential target group and push the related target group which can handle the service to the service organization, thereby better serving village people, integrating platform data of each party by using the leading edge big data technology, completely portraying individuals or groups, integrating social resources, and realizing the accurate maintenance and management of individuals or groups by government affairs and third party service organization network members on the premise of meeting the individual demands and individual services of the groups.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence and big data, and particularly relates to a rural happy precise service platform based on big data and deep learning.
Background
Under the big background of the countryside joy strategy, the management problem of the countryside area is particularly important, but in many countryside laggard areas, vast farmers cannot know the policy of the farmers due to message blocking and many preferential policies, so that the preferential policies enjoyed by the farmers in the aspects of education, medical treatment, government affairs, social security and the like are unclear and unknown, and the implementation of the policy is delayed.
The crowd portrayal is a marketing phrase, is one of the growing interest of product users, can help people to explore the reasons behind product index numbers, and the main step of constructing the crowd portrayal is to analyze user characteristics such as age, hobby, education degree, living environment, economic income and the like.
At present, the problem of data islanding exists for government affairs and other third-party service organizations in rural areas, data separation exists among all departments, all-round accurate portrait of peasants cannot be performed, so that service objects are difficult to understand comprehensively, latest preferential policies issued by relevant departments often need to be applied actively by the peasants, in addition, crowd portrait is difficult to realize for communities, enterprises, villages and towns and the like in rural areas, and accurate maintenance and management is difficult; for the masses, the periodic and real-time business handling problems of the masses of farmers are time-consuming and labor-consuming due to the separation of system data of each mechanism.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems in the prior art, the invention provides a village happy comprehensive service platform based on big data and deep learning, which aims to integrate platform data of each party by using a leading-edge big data technology, completely figure individuals or groups, integrate social resources, realize accurate maintenance and management of the individuals or the groups by government affairs and third-party service organization network members on the premise of meeting individual demands and individual services of the groups, and simultaneously push the latest policies to potential target groups and related target groups capable of handling the services to service organizations by using a deep learning algorithm, thereby better serving village people.
(II) technical scheme
In order to achieve the purpose, the invention adopts the technical scheme that: the utility model provides a country happy comprehensive service platform based on big data and degree of depth study, includes that service data imports module, big data platform, monomer accurately portrays picture application, accurate monomer portrays, server side service portrays, accurate service propelling movement network and the sign indicating number application is swept to the multiangular color, service data imports module and big data platform link to each other, monomer accurately portrays picture application and accurate service propelling movement network are connected with big data platform respectively, server side service portrays picture and accurate monomer portrays and accurate service propelling movement network both way junction, accurate monomer portrays and is connected with big data platform, accurate service propelling movement network and monomer accurate portrays picture application are swept the sign indicating number application with the multiangular color and are connected.
Further, the service data import module comprises government affair data and third-party service data, the government affair data is imported manually by government affair staff, the third-party service data is imported manually by third-party service staff, the big data platform integrates the government affair data and third-party service organization data, the big data platform adopts big data analysis, data mining, data cleaning, principal component analysis and data aggregation algorithms to generate label data for peasant households, enterprises, communities and villages in the rural happy holding area, and stores the label data to the big data platform, and the principal component analysis utilizes the following correlation coefficient matrixes to select principal components:
in the above formula, the first and second carbon atoms are,r ij is the ith row and the jth column element, x in the correlation coefficient matrix ki And x kj For the normalized tag data, n is the dimension of each tag data, and m represents the total data number of the tag data; the big data platform shows the representation in the form of a label.
Furthermore, the service terminal service portrait comprises a bank service portrait, a community service portrait, a third party organization service portrait and a government service portrait, wherein the service terminal service portrait is obtained by accurately portraying various services provided by the service data import module, and is described in the form of label data; the big data platform further utilizes a machine learning algorithm to generate accurate single portrait from the label data, the accurate single portrait comprises a farmer portrait, an enterprise portrait, a village and town portrait and a community portrait, and the portrait is displayed in a label form after the big data platform integrates the data; the farmer portrait comprises farmer names, ages, household nationalities, professions, land existence, planting areas, types of planted crops, respective planting areas, family population number, low security, marriage and child and female number labels; the enterprise portrait comprises enterprise type, operation range and staff number labels; the village and town portrait comprises geographic positions, the total population number, male and female proportions, a population structure and a population GDP label; the community picture comprises a geographical position, the number of buildings and a cell name label.
Further, the accurate service push network comprises a client memory model, a server memory model, a dual-association model and a network forecast end, wherein the client memory model, the server memory model and the dual-association model are calculated in parallel, the respective output rights of the client memory model, the server memory model and the dual-association model are connected to the output end of the network, the essence of the client memory model and the server memory model is a generalized linear model and is responsible for the connection between the memory service requirement and the service provision, the essence of the dual-association model is a BP neural network and is responsible for recommending related services, the client memory model comprises a client input and a client output, and the client input is connected with the client output in a right manner; the server-side memory model comprises a server-side input and a server-side output, and the server-side input is connected with the server-side output in a right way; the double-thinking model comprises a deep layer input, dense discrete features, a middle hidden layer and a double-thinking model output end, the dense discrete features are connected with the middle hidden layer in a weight mode, and the middle hidden layer is connected with the double-thinking model output in a weight mode; and the client output, the server output and the duplex model output end are connected with the network prediction end right.
Further, the deep input of the double-association model comprises two categories of discrete features and continuous features, and the discrete features need to be subjected to Onehot coding firstly; performing discretization treatment on the continuous features by adopting box separation operation; processing the deep input by using an Embedding operation, so that extremely sparse vectors of the deep input are converted into low-dimensional dense vectors, the low-dimensional dense vectors are combined to become 1000-dimensional dense discrete features, the dense discrete features are spread forwards through weight vectors, enter an intermediate hidden layer and finally reach the output end of the double-association model, and the forward spreading calculation mode is as follows:
in the above formula, x is a dense discrete feature,w (0)andb (0)for the weight vector and offset between the dense discrete features and the intermediate hidden layer,w (1)andb (1)the weight vector and the offset inside the middle hidden layer,w (2)andb (2)for the weight vector and bias between the middle hidden layer to the output of the dual phantom, activation function ƒ is a Sigmoid activation function,α (3)values at the output of the dual phantom.
Further, the client input of the client memory model includes 3 cross features, and a vector formed by the 3 cross features input by the client is propagated forwards through the weight vector, and the calculation mode of the forward propagation is as follows:
in the above formula, x1An input vector that is input for the client,w 1andb 1weight vector and bias, activation function of client memory model respectivelyδThe function is activated for the Sigmoid and,y 1a value output for the client.
Further, a server input of the server memory model includes 3 cross features, and a vector formed by the 3 cross features input by the server is propagated forwards through a weight vector, and the specific calculation method is as follows:
in the above formula, x2For the input of the client, the client side inputs,w 2andb 2weight vector and bias, activation function for server memory modelδThe function is activated for the Sigmoid and,y 2is the value at the output end of the double-ideal model.
Further, the calculation results y1, y2 and alpha of the client output, the server output and the double-association model output are finally obtained(3)Performing polymerization calculation, and obtaining final output at a network prediction end, wherein the polymerization calculation method comprises the following steps:
in the above formula, the first and second carbon atoms are,w 1、w2and w3Respectively, the client output, the dual-association model output and the connection weight coefficient between the server output and the network prediction end, the prediction output of the network prediction end uses Relu activation function,Yis a binarized category label; the accurate service push network adopts strategies of off-line learning and on-line learning, the double-association model adopts a stochastic gradient descent algorithm for training, and the client memory model and the server memory model adopt FTRL and L1 regular direction joint training; an accurate service push module for pushing the service,the corresponding service data can be pushed to the peasant household and the third-party service mechanism by utilizing the government affair data and the service handling condition of the peasant household, on one hand, the corresponding service data is informed to the peasant household to provide related preferential service, on the other hand, the service that the peasant household can be handled is reminded of workers, and form filling work within a certain degree of authority range is provided, so that the peasant household and the third-party workers are facilitated while the peasant household is reminded of handling the service, and the workload of the workers is reduced.
Preferably, the multi-role code scanning application uses a single accurate portrait application accurate service push network to generate four types of single two-dimensional codes by using a data fusion algorithm, wherein the four types of single two-dimensional codes are divided into farmer codes, enterprise codes, village and town codes and community codes, and related identity verification and services are facilitated.
Furthermore, the multi-role code scanning application adopts a data security isolation technology to enable each organization grid member to only see data in the authority range of the organization grid member, and tourists can view public data in a grid area by scanning codes; the householder and the family members can view the joint defense situation of ten households, third-party service data and family information in the authority besides the visitor; the third-party service gridder can view the basic portrait data of the client in the authority range and other data which other organizations agree to share by scanning the code through the own server.
(III) advantageous effects
The invention relates to a countryside joyful comprehensive service platform based on big data and deep learning, aiming at reducing the comprehensive social service cost in rural areas, leading people in rural areas to enjoy convenient service brought by digital construction, analyzing and aggregating data from multiple channel sources by utilizing big data technology, and accurately portraying a single body in the form of a data label The service function is perfected, basic treatment is integrated, convenience channels are unblocked, and digital villages and intelligent communities which enable management to be more intelligent and service to be more convenient are constructed.
Drawings
FIG. 1 is a flow chart of a village happy comprehensive service platform based on big data and deep learning according to the present invention;
fig. 2 is a schematic diagram of a neural network connection of the precision service push network according to the present invention.
Wherein, 1, government affair data, 2, third party service data, 3, big data platform, 4, single accurate portrait application, 6, accurate service push network, 7, rural credit service portrait, 8, community service portrait, 9, third party organization service portrait, 10, government affair service portrait, 11, farmer portrait, 12, enterprise portrait, 13, community portrait, 14, village and town portrait, 15, accurate single portrait, 16, multi-angle color code scanning application, 17, farmer code, 18, enterprise code, 19, village code, 20, community code, 21, service terminal portrait, 22, service data import module, 23, deep layer input, 24, client input, 25, service terminal input, 26, dense discrete characteristic, 27, middle hidden layer, 28, dual-link model output, 29, network forecast terminal, 30, single two-dimensional code, 31, client memory model, 32. a server memory model 33, a double-association model 34, a client output 35 and a server output.
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Detailed Description
As shown in the figures 1 and 2, the invention utilizes a data import form to import government affair data 1 and third party service data 2, the large data platform 3 adopts data mining, data cleaning, principal component analysis and data aggregation algorithms to generate data labels for peasant households, enterprises, communities and villages and towns in a rural happy holding area, and stores the classified data labels to the large data platform 3, the large data platform 3 utilizes a machine learning algorithm to generate an accurate single portrait 15, and meanwhile, the large data platform 3 describes a rural credit agency service portrait 7, a community service portrait 8, a third party institution service portrait 9 and a government affair service portrait 10 in a service terminal service portrait 21 in the form of data labels.
Constructing a structure of the accurate service push network 6 and training the accurate service push network 6 aiming at specific services, wherein the forward propagation calculation mode of the duplex model 33 is as follows:
the calculation method of the forward propagation of the client memory model 31 is as follows:
the calculation method of the forward propagation of the server-side memory model 32 is as follows:
the calculation method for the forward propagation of the network forecast terminal 29 is as follows:
building a network model according to a forward propagation calculation mode of the duplex model 33, the client memory model 31, the server memory model 32 and the network forecast terminal 29; the method comprises the steps of training an accurate service push network 6 by using a Tensorflow deep learning framework, wherein the accurate service push network 6 adopts strategies of off-line learning and on-line learning, a dual-association model 33 adopts a stochastic gradient descent algorithm for training, and a client memory model 31 and a server memory model 32 adopt FTRL and L1 regular direction combined training.
The accurate service push network 6 can be put into use after the construction and training of the accurate service push network 6 are completed, and the accurate service push network 6 carries out service push of a specific target and reverse push meeting transacted services according to the accurate monomer portrait 15 and the server side service portrait 21.
As an embodiment of the present invention, the precise monomer image 15 is exemplified by a farmer image 11, the server service image 21 is exemplified by a rural credit agency service image 7, the deep input 23 is from the farmer image 11, the client input 24 and the server input 25 are from the cross feature composed of the farmer image 11 and the rural credit agency service image 7; the accurate service push network 6 pushes the relevant services of the rural credit society to the peasant households, and meanwhile, the accurate service push network 6 pushes the peasant households meeting the service handling conditions to the rural credit society.
As an embodiment of the present invention, the precise monomer representation 15 is exemplified by an enterprise representation 12, the server-side service representation 21 is exemplified by a government service representation 10, the deep input 23 is derived from the enterprise representation 11, the client input 24 and the server input 25 are derived from the intersection feature formed by transforming the enterprise representation 11 and the government service representation 7 by cross product transformation; the accurate service push network 6 pushes the relevant services of the government departments to the enterprises, and meanwhile, the accurate service push network 6 pushes the enterprises meeting the service handling conditions to the government departments.
For convenience of application of each organization, the multi-role code scanning application 16 generates 4 types of single two-dimensional codes 30 which comprise farmer codes 17, enterprise codes 18, village and town codes 19 and community codes 20 and facilitate related identity verification and services, the accurate service push network 6 continuously pushes related services to farmers, meanwhile, farmer portrait 11 is updated according to service handling conditions of the farmers, the big data platform 3 automatically updates portrait of the farmer 11 and server side service portrait 21 according to the service handling conditions pushed by the accurate service push network 6 and recommends new services according to government data 1, and meanwhile, the related farmer codes 17 are also correspondingly updated to facilitate related identity verification and services.
The countryside happiness precision service platform based on big data and deep learning of the invention adopts data safety isolation to ensure that each organization grid member can only see data in the authority range of the organization, tourists can view public data in grid areas by scanning codes, such as government affair grid managers, third-party service organization managers, appointment handling, business appointment handling related to the third-party service organizations, on-line job seeking and finding work, and epidemic prevention and control registration and homeland return reporting and preparing information are carried out; the farmer and family members can view the joint defense situation of ten households, third-party service data and family information of the farmer and family members besides the visitor; the third-party service gridder scans the code through the management end to view the client basic portrait data in the authority range of the third-party service gridder and other data which other organizations agree to share.
As an embodiment of the invention, bank staff apply for checking financial data and code scanning business conversion analysis of all customers after analysis and integration through a big data platform through own client, and each gridder collects updated basic data.
As an embodiment of the invention, a government affair gridder can check population distribution conditions and ten-family joint defense conditions through code scanning of a management terminal, can carry out card-punching and visiting management, and carries out basic data management on line; all new data are generated and then returned to the big data platform for iterative analysis, and the accurate monomer image 15 is updated in real time to form a closed loop for iterative update of data.
The specific working process of the invention is described above, and the steps are repeated when the device is used next time.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The present invention and its embodiments have been described above, and the description is not intended to be limiting, and the drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A rural happy comprehensive service platform based on big data and deep learning is characterized in that: including service data import module (22), big data platform (3), the accurate application of portrait of monomer (4), accurate monomer portrait (15), server side service portrait (21), accurate service propelling movement network (6) and the sign indicating number application (16) are swept to the multiangular color, service data import module (22) links to each other with big data platform (3), the accurate application of portrait of monomer (4) and accurate service propelling movement network (6) are connected with big data platform (3) respectively, server side service portrait (21) and accurate monomer portrait (15) and accurate service propelling movement network (6) both way junction, accurate monomer portrait (15) are connected with big data platform (3), accurate service propelling movement network (6) and accurate monomer portrait (4) are swept the sign indicating number application (16) with the multiangular color and are connected.
2. The rural happy integrated service platform based on big data and deep learning of claim 1, wherein: the service-side service image (21) comprises a rural credit agency service image (7), a community service image (8), a third-party institution service image (9) and a government service image (10), the service-side service image (21) is obtained by accurately imaging various services provided by a service data import module (22), and the service-side service image (21) is described in the form of label data; the big data platform (3) further utilizes a machine learning algorithm to generate a precise single portrait (15) from the label data, the precise single portrait (15) comprises a farmer portrait (11), an enterprise portrait (12), a community portrait (13) and a village and town portrait (14), and the portrait is displayed in a label form after the big data platform (3) integrates the data; the farmer portrait (11) comprises farmer names, ages, household nationalities, professions, whether land exists or not, planting areas, types of planted crops, respective planting areas, family population numbers, whether low insurance exists or not, whether marriage exists or not and labels of numbers of children and women; the enterprise image (12) comprises enterprise type, operation range and staff number labels; the community picture (13) comprises a geographical position, the number of buildings and a cell name label; the village and town images (14) comprise geographic positions, total population numbers, male and female proportions, population structures and population-average GDP labels.
3. The rural happy integrated service platform based on big data and deep learning of claim 2, wherein: the accurate service push network (6) comprises a client memory model (31), a server memory model (32), a double-association model (33) and a network prediction end (29), wherein the client memory model (31), the server memory model (32) and the double-association model (33) are calculated in parallel, the essence of the client memory model (31) and the server memory model (32) is a generalized linear model, the essence of the double-association model (33) is a BP neural network, the client memory model (31) comprises a client input (24) and a client output (34), and the client input (24) is connected with the client output (34) in a right way; the server memory model (32) comprises a server input (25) and a server output (35), and the server input (25) and the server output (35) are connected in right; the double-associative model (33) comprises a deep layer input (23), dense discrete features (26), an intermediate hidden layer (27) and a double-associative model output end (28), wherein the dense discrete features (26) are connected with the intermediate hidden layer (27) in a weight mode, and the intermediate hidden layer (27) is connected with the double-associative model output end (28) in a weight mode; the client output (34), the server output (35) and the double-association model output end (28) are connected with the network prediction end (29).
4. The village happy comprehensive service platform based on big data and deep learning of claim 3, characterized in that: the deep input (23) of the double-ideal model (33) comprises two major categories of discrete features and continuous features, and Onehot coding is firstly required for the discrete features; performing discretization treatment on the continuous features by adopting box separation operation; processing the deep input (23) by using an Embedding operation, so as to convert extremely sparse vectors of the deep input (23) into low-dimensional dense vectors, the low-dimensional dense vectors are combined to become 1000-dimensional dense discrete features (26), the dense discrete features (26) are propagated forwards into an intermediate hidden layer (27) through weight vectors and finally reach a double-associative model output end (28), and the forward propagation calculation mode is as follows:
in the above formula, x is a dense discrete feature (26),w (0)andb (0)for the weight vector and offset between the dense discrete features (26) and the intermediate hidden layer (27),w (1)andb (1)is the weight vector and the offset inside the middle hidden layer (27),w (2)andb (2)for the weight vector and bias between the middle hidden layer to the dual phantom output (28), the activation function ƒ is a Sigmoid activation function,α (3)is the value at the output (28) of the dual phantom.
5. The rural happy integrated service platform based on big data and deep learning of claim 4, wherein: the client input (24) of the client memory model (31) comprises 3 cross features, a vector formed by the 3 cross features of the client input (24) is transmitted forwards through a weight vector, and the calculation mode of the forward transmission is as follows:
in the above formula, x1An input vector for the client input (24),w 1andb 1a weight vector and a bias, respectively, activation function of the client memory model (31)δThe function is activated for the Sigmoid and,y 1the value of (34) is output for the client.
6. The village happy comprehensive service platform based on big data and deep learning of claim 5, characterized in that: the server input (25) of the server memory model (32) comprises 3 cross features, and a vector formed by the 3 cross features of the server input (25) is transmitted forwards through a weight vector, and the specific calculation mode is as follows:
in the above formula, x2For the client input (24),w 2andb 2respectively, a weight vector and a bias, activation function for the server memory model (32)δThe function is activated for the Sigmoid and,y 2the values at the outputs (28) of the dual phantom are used.
7. The rural happy integrated service platform based on big data and deep learning of claim 6, wherein: finally, calculating results y1, y2 and alpha of the client side output (34), the server side output (35) and the double-association model output end (28)(3)Performing aggregation calculation, and obtaining final output at a network prediction end (29), wherein the aggregation calculation method comprises the following steps:
in the above formula, the first and second carbon atoms are,w 1、w2and w3Respectively, the client output, the dual-association model output and the connection weight coefficient between the server output and the network prediction end, the prediction output of the network prediction end uses Relu activation function,Yis a binarized category label; the accurate service push network (6) adopts strategies of off-line learning and on-line learning, the dual-association model (33) is trained by adopting a stochastic gradient descent algorithm, and the client memory model (31) and the server memory model (32) are jointly trained by adopting FTRL and L1 regular direction.
8. The village happy comprehensive service platform based on big data and deep learning of claim 7, characterized in that: code application (16) is swept to multiple roles combines monomer accurate portrait application (4) and accurate service push network (6) to utilize data fusion algorithm to generate four types of monomer two-dimensional code (30), monomer two-dimensional code (30) include peasant household code (17), enterprise code (18), village and town code (19) and community code (20).
9. The village happy comprehensive service platform based on big data and deep learning of claim 8, characterized in that: the multi-role code scanning application (16) adopts data security isolation to ensure that a grid member can only see data in the authority range of the grid member, a tourist can only view public data in a grid area by scanning codes, and farmers and family members can view ten joint defense conditions and family information in the authority besides the tourist; the third-party service gridder can check recommended customer basic portrait data in the authority range through code scanning of the client of the third-party service gridder and other service data which are agreed to be shared by other organizations; the government affair gridder can check key population distribution conditions and ten-family joint defense conditions through code scanning of the management terminal, can carry out card-punching and visiting management and can carry out basic data management on line; all new data are generated and then returned to the big data platform (3) for iterative analysis, and the accurate monomer portrait (15) is updated in real time to form a closed loop for iterative updating of data.
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