CN114817346A

CN114817346A - Service processing method and device, electronic equipment and computer readable medium

Info

Publication number: CN114817346A
Application number: CN202210323859.0A
Authority: CN
Inventors: 王佳佳; 王逸群
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-03-30
Filing date: 2022-03-30
Publication date: 2022-07-29

Abstract

The application discloses a service processing method, a service processing device, electronic equipment and a computer readable medium, which relate to the technical field of computers, and the method comprises the following steps: receiving a service processing request and acquiring a corresponding service identifier; acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features. Acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data; the bid inviting data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bid inviting data is received, and the target enterprises are determined according to the feedback information; outputting the target enterprise and bid data. The bidding data is publicized, and the bidding behavior is publicized and transparent.

Description

Service processing method and device, electronic equipment and computer readable medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing a service, an electronic device, and a computer-readable medium.

Background

In the process of implementing the present application, the inventor finds that at least the following technical problems to be solved exist in the prior art:

some existing bidding behaviors are unfair and opaque, the building market order is seriously disturbed, and great hidden dangers are brought to the engineering quality and the safety.

Disclosure of Invention

In view of this, embodiments of the present application provide a service processing method, an apparatus, an electronic device, and a computer readable medium, which can solve the technical problems that the existing bidding behavior is unfair and opaque, the building market order is seriously disturbed, and great hidden dangers are brought to the engineering quality and the safety.

In order to achieve the above object, according to an aspect of the embodiments of the present application, there is provided a service processing method, including:

receiving a service processing request and acquiring a corresponding service identifier;

acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features;

acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data;

the bidding data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bidding data is received, and then the target enterprises are determined according to the feedback information;

outputting the target enterprise and bid data.

Optionally, before pushing the bid data to the target enterprise, the method further comprises:

acquiring an execution state identifier of a bidding item corresponding to the bidding data;

in response to the execution state identification being non-empty, calling an early warning program to generate early warning information;

and sending the early warning information to a bid inviting supervision node so that the bid inviting supervision node intercepts an execution process corresponding to a bid inviting project based on the early warning information.

Optionally, acquiring corresponding enterprise data according to the service identifier includes:

acquiring service data corresponding to the service identifier;

and calculating the similarity between the business data and the enterprise data in the enterprise database, and further determining the enterprise data corresponding to the business identifier according to the similarity.

Optionally, calculating similarity of the business data and the enterprise data in the enterprise database includes:

extracting a business data entity in the business data, and extracting an enterprise data entity in an enterprise database;

and respectively calculating the similarity between the business data entity and each enterprise data entity.

Optionally, determining the target enterprise according to the feedback information includes:

performing semantic analysis on each feedback information to obtain a score corresponding to each feedback information;

and determining the enterprise corresponding to the feedback information corresponding to the score larger than the preset threshold value as a target enterprise.

Optionally, the service processing method further includes:

and associating and storing the target enterprise, the bidding data and the business identifier.

Optionally, after matching the bid data with the business image, the method further comprises:

and responding to the failure of matching of all the enterprise images and the bidding data, and ending the business processing process.

Optionally, outputting the target business and bidding data comprises:

and visually displaying the target enterprise and bid inviting data.

In addition, the present application also provides a service processing apparatus, including:

the receiving unit is configured to receive the service processing request and acquire a corresponding service identifier;

the enterprise portrait generation unit is configured to acquire corresponding enterprise data according to the business identifier, extract low-level features of the enterprise data, determine corresponding high-level features based on the low-level features, fuse the low-level features and the high-level features to generate fused features, and generate an enterprise portrait according to the fused features;

the candidate enterprise determining unit is configured to acquire bidding data corresponding to the business identifier, match the bidding data with the enterprise portrait, and determine an enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data;

the target enterprise determining unit is configured to push the bidding data to the candidate enterprises, receive feedback information of the candidate enterprises for the bidding data and further determine the target enterprises according to the feedback information;

an output unit configured to output the target enterprise and the bid data.

Optionally, the service processing apparatus further includes an intercepting unit configured to:

Optionally, the enterprise representation generation unit is further configured to:

acquiring service data corresponding to the service identifier;

Optionally, the target enterprise determination unit is further configured to:

Optionally, the service processing apparatus further includes an association unit configured to:

Optionally, the candidate business determination unit is further configured to:

Optionally, the output unit is further configured to:

and visually displaying the target enterprise and bid inviting data.

In addition, the present application also provides a service processing electronic device, including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the business process method as described above.

In addition, the present application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements the service processing method as described above.

To achieve the above object, according to still another aspect of embodiments of the present application, there is provided a computer program product.

A computer program product according to an embodiment of the present application includes a computer program, and when the computer program is executed by a processor, the computer program implements a service processing method according to an embodiment of the present application.

One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of receiving a service processing request to obtain a corresponding service identifier; acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features; acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data; the bidding data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bidding data is received, and then the target enterprises are determined according to the feedback information; outputting the target enterprise and bid data. And automatically pushing bidding data for the enterprises meeting the bidding standards of the bidding enterprises by utilizing big data analysis and enterprise user portrait technology, and determining target enterprises according to enterprise feedback information. The bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. The order of the building market is maintained, and the engineering quality and safety are ensured.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a further understanding of the application and are not to be construed as limiting the application. Wherein:

fig. 1 is a schematic diagram of a main flow of a business processing method according to a first embodiment of the present application;

fig. 2 is a schematic diagram of a main flow of a service processing method according to a second embodiment of the present application;

fig. 3 is a schematic view of an application scenario of a service processing method according to a third embodiment of the present application;

fig. 4 is a schematic diagram of the main elements of a traffic processing apparatus according to an embodiment of the present application;

FIG. 5 is an exemplary system architecture diagram to which embodiments of the present application may be applied;

fig. 6 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application to assist in understanding, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness. According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.

Fig. 1 is a schematic diagram of a main flow of a service processing method according to a first embodiment of the present application, and as shown in fig. 1, the service processing method includes:

step S101, receiving a service processing request and acquiring a corresponding service identifier.

In this embodiment, an execution main body (for example, a server) of the service processing method may receive the service processing request through a wired connection or a wireless connection. Specifically, the service processing request may be a request for determining bidding data corresponding to a certain service and determining an enterprise interested in the bidding data from candidate bidding enterprises matched with the bidding data. After receiving the service processing request, the execution main body may obtain the service identifier carried in the request. The service identity is used to characterize which service is processed, for example, a: represents service a, D: representing service D.

For example, when the business is identified as a, for business a, the user wants to find bidding data B corresponding to the business a and determine a target business C interested in (i.e., having bidding intention) the bidding data B from the bidding businesses matching with the bidding data B.

And S102, acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features.

The executing agent may obtain enterprise data corresponding to the service identifier from a credit platform or a credit information platform of each province and city based on a big data information collection technology as shown in fig. 3.

The enterprise representation is generated according to the enterprise data, and specifically, the abstract high-level features can be extracted from the enterprise data, and specifically, the semantics expressed by the enterprise data. For example, the high-level features of the image are what can be seen, for example, extracting the low-level features of a human face can extract the outline, nose, eyes and the like of the face, and then the high-level features are displayed as the human face. The extracting of the high-level features of the enterprise data may specifically be extracting low-level features of the enterprise data, for example, features of annual income of the enterprise, the number of people of the enterprise, types of projects contracted by the enterprise, the number of projects contracted by the enterprise, external evaluation on the enterprise, and the like, comprehensively analyzing according to the extracted low-level features to obtain the high-level features of the enterprise data, and specifically may be extracting high-level concepts from the low-level features to obtain the high-level features, for example, high-level features of the enterprise, such as risk prospects of undertaking bidding projects, quality expectations of undertaking bidding projects by the enterprise, and cost performance of the enterprise. After obtaining the high-level features of the enterprise data, the execution subject may fuse the bottom-level features and the high-level features, and the specific fusing method may include: (1) concat: and (3) series feature fusion, namely directly connecting the low-level features and the high-level features, and assuming that the dimensions of x and y of the two features of the low-level features and the high-level features are p and q, the dimension of z of the output fusion feature is p + q. (2) add: and combining vectors of the low-layer feature and the high-layer feature into a complex vector, and fusing z as x + iy for the low-layer feature x and the high-layer feature y, wherein i is an imaginary unit. The execution main body may call a TF-IDF algorithm (TF-IDF (Term-Inverse Document Frequency) is a commonly used weighting technique for information retrieval and data mining, TF is Term Frequency (Term Frequency) and IDF is Inverse text Frequency index (Inverse Document Frequency)) to determine a feature weight corresponding to the fusion feature, and then generate the enterprise portrait based on the fusion feature and the feature weight corresponding to each fusion feature.

The executive may also generate an enterprise representation based on high-level features of the enterprise data. Specifically, through high-performance data analysis with a machine learning algorithm as a core, service and guidance are provided for actual business, and further final expression of high-level feature data is achieved, and an enterprise image is formed. In order to improve the efficiency of feature fusion, the execution subject may also increase the semantics of the low-level features, for example, the high-level features may be fused into the low-level features; more spatial information is added to the higher layer features, for example, by embedding the resolution of the channel in the higher layer features.

Specifically, acquiring corresponding enterprise data according to the service identifier includes: acquiring service data corresponding to the service identifier; and calculating the similarity between the business data and the enterprise data in the enterprise database, and further determining the enterprise data corresponding to the business identifier according to the similarity.

The executing entity needs to acquire the enterprise data corresponding to the service identifier, that is, needs to acquire the enterprise data corresponding to the service identifier with higher correlation or higher similarity. The service data corresponding to the service identifier may include data related to a decoration service, such as a company name bearing decoration, historical decoration effect data, decoration quotation data, and the like. The service data may also include data related to the aldehyde removal service, specifically, aldehyde removal effect data, aldehyde removal price data, and the like.

After the execution main body obtains the service data, word embedding can be carried out on the service data so as to convert the service data into a vector form, and then a service data vector is obtained; the execution main body can also call an enterprise database, and then word embedding is carried out on each enterprise data in the enterprise database to generate each enterprise data vector; and calculating similarity (for example, cosine similarity) between the business data vector and each enterprise data vector, and determining enterprise data corresponding to the business identifier according to the similarity. For example, the similarity degrees are sorted according to a descending order, and the enterprise data corresponding to the similarity degree of the top N is determined as the enterprise data corresponding to the service identifier, where N may be any positive integer.

Specifically, calculating the similarity between the business data and the enterprise data in the enterprise database includes:

extracting a business data entity in the business data, and extracting an enterprise data entity in an enterprise database; and respectively calculating the similarity between the business data entity and each enterprise data entity.

The service data entity may include, for example, a subject, a predicate, an object, or a subject in the service data. For example, the service data entity may be words such as "decoration", "decoration effect", and "decoration quotation" in the service data, and the content corresponding to the service data entity is not limited in this application embodiment.

And S103, acquiring bidding data corresponding to the business identifier, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the fact that the enterprise portrait is successfully matched with the bidding data.

As shown in fig. 3, the executing entity may obtain bid data corresponding to the service identifier from a bid information network or a bid official network of each province and city, so as to ensure fairness and justness of the bid data to the bid enterprise. After the bid inviting data corresponding to the service identifier is obtained, similarity matching can be carried out on the bid inviting data and the enterprise image. Specifically, the bidding data is converted into bidding data vectors in a word embedding mode, each enterprise portrait is converted into enterprise portrait vectors in a word embedding mode, then the execution main body can calculate cosine similarity between the bidding data vectors and the enterprise portrait vectors, and enterprise portraits corresponding to the largest cosine similarity or enterprises corresponding to one or more enterprise portraits exceeding the cosine similarity of a preset threshold are determined as candidate enterprises. For example, the enterprise images corresponding to the cosine similarity exceeding the preset threshold are an enterprise image a, an enterprise image B, an enterprise image C and an enterprise image D, respectively, and the candidate enterprises include enterprise a, enterprise B, enterprise C and enterprise D.

Specifically, after matching the bidding data with the business imagery, the method further comprises:

When the execution main body determines that all enterprise figures corresponding to the acquired enterprise data do not meet the requirement of the bidding data, namely all enterprise figures are not matched with the bidding data, the execution main body can end the business processing process based on the business processing request.

And step S104, pushing bidding data to the candidate enterprises, receiving feedback information of the candidate enterprises for the bidding data, and determining the target enterprises according to the feedback information.

After determining the candidate enterprises, the executive body can send the bidding data to each candidate enterprise to discover which candidate enterprises have bidding intentions on the bidding data, and determine the candidate enterprises with the bidding intentions as target enterprises. And further, a subsequent bidding process can be performed on the target enterprise, for example, more detailed bidding data is continuously sent to the determined target enterprise or the bidding responsible person of the target enterprise is contacted by telephone to perform further bidding intention determination, so as to determine whether the target enterprises have strong intention to participate in the bidding and how strong the intention strength of participation in the bidding is, thereby further narrowing the range of the finally obtained target enterprises, so as to find the most suitable target enterprise to cooperate with the bidding project on the premise of ensuring better bidding conditions, and achieve the goal of mutual benefits and mutual benefits. The number of the finally obtained target enterprises may be one or more, and the number of the target enterprises is not specifically limited in the embodiment of the present application.

Specifically, determining the target enterprise according to the feedback information includes:

performing semantic analysis on each feedback information to obtain a score corresponding to each feedback information; and determining the enterprise corresponding to the feedback information corresponding to the score larger than the preset threshold value as a target enterprise.

The execution main body can perform emotion semantic analysis on the feedback information by adopting a big data analysis and mining technology so as to obtain the interest degree data of each candidate enterprise represented by the feedback information on the bid inviting data; the level of interest data may include, for example, "consider", "may", "agree", "accept", "thank you for, currently there is no intent to do so", and the like. After obtaining the interest degree data analyzed from the feedback information of each candidate enterprise, the executing subject may input the interest degree data into a pre-trained scoring model to output a score corresponding to each interest degree data, and then the executing subject may determine the target enterprise according to each output score. Specifically, the executing body may find the maximum score from the output scores, and determine the candidate enterprise corresponding to the feedback information corresponding to the data of the degree of interest corresponding to the maximum score as the target enterprise. For example, the maximum score is 60, the corresponding data of the interest degree is "accept", the corresponding feedback information is "hello", i receives the construction work of the bidding project corresponding to the bidding data ", the corresponding candidate enterprise is a candidate enterprise B, and the candidate enterprise B is determined as the final target enterprise. The executing body may further find out a score larger than a preset threshold (for example, 40) from the scores of the data, and determine, as the target business (a plurality of businesses may be provided), each candidate business corresponding to each feedback information corresponding to each data of the degree of interest corresponding to the score larger than the preset threshold.

And step S105, outputting the target enterprise and bid data.

After the execution main body determines the target enterprise, the determined target enterprise and the bid inviting data can be output according to the business processing requirement, so that the user can check the target enterprise and the bid inviting data at a user side sending the business processing request.

Specifically, outputting target business and bidding data includes: and visually displaying the target enterprise and bid inviting data.

Specifically, the method further comprises: and associating and storing the target enterprise, the bidding data and the service identification so as to be called by subsequent services at any time.

In the embodiment, a corresponding service identifier is acquired by receiving a service processing request; acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features; acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data; the bidding data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bidding data is received, and then the target enterprises are determined according to the feedback information; outputting the target enterprise and bid data. And automatically pushing bidding data for the enterprises meeting the bidding standards of the bidding enterprises by utilizing big data analysis and enterprise user portrait technology, and determining target enterprises according to enterprise feedback information. The bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. The order of the building market is maintained, and the engineering quality and safety are ensured.

Fig. 2 is a schematic main flow diagram of a service processing method according to a second embodiment of the present application, and as shown in fig. 2, the service processing method includes:

step S201, receiving a service processing request, and acquiring a corresponding service identifier.

The number of service identities may be one or more.

Step S202, acquiring corresponding enterprise data according to the business identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features.

The enterprise data collection in the embodiment of the application refers to various types of structured, semi-structured (or weakly structured) and unstructured massive enterprise data obtained in the ways of RFID radio frequency data, sensor data, social network interaction data, mobile internet data and the like.

After the execution subject obtains the enterprise data corresponding to the enterprise identifier, the enterprise data can be preprocessed based on a big data preprocessing technology. The method can specifically complete operations such as resolution, extraction, cleaning and the like of the acquired enterprise data. After the preprocessing of the acquired enterprise data is executed, the execution main body can perform big data storage and management, specifically, the preprocessed enterprise data is stored by a memory, a corresponding database is established, and management and calling are performed, so that the problems of complex structured, semi-structured and unstructured big data management and processing are solved.

And step S203, acquiring bidding data corresponding to the business identifier, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the fact that the enterprise portrait is successfully matched with the bidding data.

The bidding data is collected from a bidding data network and a bidding official network of each province city, the bidding data is obtained after big data storage, calculation and analysis, then the bidding data is matched with the generated enterprise portrait, if the bidding data meets the bidding standard, the bidding data is pushed to each candidate enterprise meeting the bidding standard through channels such as public numbers, short messages, mails and the like, and if the bidding data does not meet the bidding standard, the bidding data is not pushed.

In step S204, the execution state identifier of the bid item corresponding to the bid data is acquired.

Before pushing the bidding data to the target enterprise, the execution subject needs to check whether the bidding project corresponding to the bidding data is not allowed to start the construction, and specifically, the execution state identification of the bidding project corresponding to the bidding data can be obtained. The execution state identification is used for representing information such as whether construction of a bidding project is started or not, construction time and construction progress. The construction state identifier may be empty or non-empty.

And step S205, responding to the execution state identification as non-null, and calling an early warning program to generate early warning information.

When the execution state identifier is not empty, for example, the execution state identifier may be 1-2-50, which means that the bidding project has started to be constructed, the construction time is 2 days ago, the construction progress is 50%, the execution state identifier may also be 1-0-0, which means that the bidding project has started to be constructed, and the construction time is now, the construction progress is 0%. When the execution main body detects that the execution state identification is not empty, the fact that the construction project has illegal operation can be determined, and an early warning program is called to generate early warning information according to the execution state identification. Illustratively, the warning information generated according to the execution state identifier 1-2-50 is: "the tender mark project XX starts working before formal tender mark, specifically starts construction 2 days ago, the construction progress is 50%, please check the standing horse and prevent the continuation of construction of the tender mark project XX".

And step S206, sending the early warning information to the tender monitoring node so that the tender monitoring node intercepts an execution process corresponding to the tender item based on the early warning information.

After the execution main body generates the early warning information based on the execution state identification, the early warning information can be sent to the bidding and supervising node (namely, the node responsible for handling the illegal project construction has the authority of suspending the illegal project and the punishment authority), so that the bidding and supervising node intercepts the execution process of the illegal project construction after verifying the authenticity of the early warning information and punishs if necessary.

And step S207, pushing bidding data to the candidate enterprises, receiving feedback information of the candidate enterprises for the bidding data, and determining the target enterprises according to the feedback information.

And step S208, outputting the target enterprise and bid data.

When the number of the service identifications corresponding to the service processing request is multiple, it indicates that the corresponding bidding data and the target enterprises matched with the bidding data need to be determined for multiple services at the same time, and the final output bidding data and the corresponding target enterprises are also multiple. For example, bidding data 1-target business 1, bidding data 2-target business 2, bidding data 3-target business 3 are ultimately output.

According to the embodiment of the application, big data analysis and enterprise user portrait technology are utilized, bidding data are automatically pushed for enterprises meeting bidding standards of the bidding enterprises, and target enterprises are determined according to enterprise feedback information. The bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. The order of the building market is maintained, and the engineering quality and safety are ensured.

Fig. 3 is a schematic view of an application scenario of a service processing method according to a third embodiment of the present application. The business processing method of the embodiment of the application is applied to determining the corresponding bidding data and the scene of the bidding enterprises matched with the bidding data for the business concerned by the user. As shown in fig. 3, during the lifecycle of big data, data collection is in the first segment. In the embodiment of the application, the acquisition of enterprise data originates from a credit platform and each province and city integrity information platform, and the storage and management of PB magnitude (PB is a unit of data storage capacity, which is equal to 50 bytes of 2) data are realized by combining technologies such as column storage or row-column mixed storage and coarse-grained index and the like and an MPP (massive Parallel processing) architecture efficient distributed computing mode. According to different data characteristics and calculation characteristics of big data, low-level characteristics are extracted from diversified big data calculation problems and requirements, various high-level abstractions (abstrations) or models (models) are established based on the low-level characteristics, and selection of appropriate calculation technologies and system tools in actual big data processing application is further facilitated. Through high-performance data analysis with a machine learning algorithm as a core, service and guidance are provided for actual business, further, final appearance of data is achieved, fusion characteristics are obtained, and further enterprise portrait is formed based on the fusion characteristics.

The bidding data of the embodiment of the application is collected from a bidding data network and a bidding official network of each province and city, the bidding data is obtained after big data storage, calculation and analysis, then the analyzed enterprise portrait is matched, if the bidding data meets the bidding standard, the bidding data is pushed to candidate enterprises meeting the bidding standard through channels such as public numbers, short messages, mails and the like, and if the bidding data does not meet the bidding standard, the bidding data is not sent to the candidate enterprises. And then, performing big data analysis on the feedback information of the candidate enterprises to finally obtain target enterprises which are intentionally involved in bidding and matched with the bidding data, and then enabling the execution main body to visually display the bidding data and the corresponding target enterprises.

According to the embodiment of the application, the big data analysis and enterprise user portrait technology are utilized, the bidding data are automatically pushed for the enterprise meeting the bidding standard of the bidding enterprise, so that the bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. The order of the building market is maintained, and the engineering quality and safety are ensured.

Big data technology is a technology for rapidly obtaining valuable information from various types of data. The big data processing key technology comprises the following steps: big data collection, big data preprocessing, big data storage and management, big data analysis and mining, big data presentation and application (big data retrieval, big data visualization, big data application, big data security, etc.). The big data technology is combined with the enterprise image technology, so that a pair of 'eyes' can be added to bidding, potential risk factors are identified, and the method is helpful for strengthening bidding supervision.

Fig. 4 is a schematic diagram of main units of a service processing apparatus according to an embodiment of the present application. As shown in fig. 4, the business processing apparatus 400 includes a receiving unit 401, an enterprise representation generating unit 402, a candidate enterprise determining unit 403, a target enterprise determining unit 404, and an output unit 405.

The receiving unit 401 is configured to receive a service processing request, and obtain a corresponding service identifier.

An enterprise representation generating unit 402 configured to obtain corresponding enterprise data according to the service identifier, extract low-level features of the enterprise data, determine corresponding high-level features based on the low-level features, fuse the low-level features and the high-level features to generate fused features, and generate an enterprise representation according to the fused features.

And a candidate enterprise determining unit 403 configured to obtain bidding data corresponding to the service identifier, match the bidding data with the enterprise portrait, and determine, as a candidate enterprise, an enterprise corresponding to the successfully matched enterprise portrait in response to the existence of the enterprise portrait successfully matched with the bidding data.

And the target enterprise determining unit 404 is configured to push the bidding data to the candidate enterprise, receive feedback information of the candidate enterprise on the bidding data, and determine the target enterprise according to the feedback information.

An output unit 405 configured to output the target business and the bid data.

In some embodiments, the apparatus further comprises an intercepting unit, not shown in fig. 4, configured to: acquiring an execution state identifier of a bidding item corresponding to the bidding data; in response to the execution state identification being non-empty, calling an early warning program to generate early warning information; and sending the early warning information to a bid inviting supervision node so that the bid inviting supervision node intercepts an execution process corresponding to a bid inviting project based on the early warning information.

In some embodiments, enterprise representation generation unit 402 is further configured to: acquiring service data corresponding to the service identifier; and calculating the similarity between the business data and the enterprise data in the enterprise database, and further determining the enterprise data corresponding to the business identifier according to the similarity.

In some embodiments, enterprise representation generation unit 402 is further configured to: extracting a business data entity in the business data, and extracting an enterprise data entity in an enterprise database; and respectively calculating the similarity between the business data entity and each enterprise data entity.

In some embodiments, the target enterprise determination unit 404 is further configured to: performing semantic analysis on each feedback information to obtain a score corresponding to each feedback information; and determining the enterprise corresponding to the feedback information corresponding to the score larger than the preset threshold value as a target enterprise.

In some embodiments, the traffic processing apparatus further comprises an association unit, not shown in fig. 4, configured to: and associating and storing the target enterprise, the bidding data and the business identifier.

In some embodiments, the candidate business determination unit 403 is further configured to: and responding to the failure of matching of all the enterprise images and the bidding data, and ending the business processing process.

In some embodiments, the output unit 405 is further configured to: and visually displaying the target enterprise and bid inviting data.

It should be noted that, in the present application, the service processing method and the service processing apparatus have corresponding relation in the specific implementation content, so the repeated content is not described again.

Fig. 5 shows an exemplary system architecture 500 to which the service processing method or the service processing apparatus according to the embodiment of the present application may be applied.

As shown in fig. 5, the system architecture 500 may include

terminal devices

501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the

terminal devices

501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The

terminal devices

501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

501, 502, 503 may be various electronic devices having a business process screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 505 may be a server providing various services, such as a background management server (for example only) providing support for business processing requests submitted by users using the

terminal devices

501, 502, 503. The background management server can receive the service processing request and acquire a corresponding service identifier; acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features; acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data; the bidding data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bidding data is received, and then the target enterprises are determined according to the feedback information; outputting the target enterprise and bid data. And automatically pushing bidding data for the enterprises meeting the bidding standards of the bidding enterprises by utilizing big data analysis and enterprise user portrait technology, and determining target enterprises according to enterprise feedback information. The bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. The order of the building market is maintained, and the engineering quality and safety are ensured.

It should be noted that the service processing method provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the service processing apparatus is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a terminal device of an embodiment of the present application. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a signal processing section such as a Cathode Ray Tube (CRT), a liquid crystal credit authorization inquiry processor (LCD), and the like, and a speaker and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, according to embodiments disclosed herein, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a receiving unit, an enterprise sketch generating unit, a candidate enterprise determining unit, a target enterprise determining unit, and an output unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs, and when the one or more programs are executed by one of the devices, the device receives a service processing request and acquires a corresponding service identifier; acquiring corresponding enterprise data according to the service identification, extracting low-level features of the enterprise data, further determining corresponding high-level features based on the low-level features, fusing the low-level features and the high-level features to generate fused features, and further generating an enterprise portrait according to the fused features; acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait successfully matched with the bidding data; the bidding data is pushed to the candidate enterprises, feedback information of the candidate enterprises for the bidding data is received, and then the target enterprises are determined according to the feedback information; outputting the target enterprise and bid data.

The computer program product of the present application includes a computer program, and the computer program realizes the service processing method in the embodiment of the present application when being executed by a processor.

According to the technical scheme of the embodiment of the application, by utilizing big data analysis and enterprise user portrait technology, bidding data is automatically pushed for enterprises meeting bidding standards of the bidding enterprises, and target enterprises are determined according to enterprise feedback information. The bidding data is publicized, and the bidding behavior is publicized and transparent. The enterprise for tendering and bidding is enabled to be matched to the maximum extent, and the supervision in the tendering and bidding process is further enhanced. Maintain the order of the building market and ensure the quality and the safety of the engineering.

The above-described embodiments should not be construed as limiting the scope of the present application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing a service, comprising:

acquiring bidding data corresponding to the business identification, matching the bidding data with the enterprise portrait, and determining the enterprise corresponding to the enterprise portrait successfully matched as a candidate enterprise in response to the enterprise portrait being successfully matched with the bidding data;

pushing the bidding data to the candidate enterprises, receiving feedback information of the candidate enterprises aiming at the bidding data, and determining target enterprises according to the feedback information;

and outputting the target enterprise and the bidding data.

2. The method of claim 1, wherein prior to said pushing the bid data to the target business, the method further comprises:

responding to the execution state identification as non-null, calling an early warning program to generate early warning information;

and sending the early warning information to a bid inviting supervision node so that the bid inviting supervision node intercepts an execution process corresponding to the bid inviting project based on the early warning information.

3. The method of claim 1, wherein the obtaining corresponding enterprise data according to the service identifier comprises:

acquiring service data corresponding to the service identifier;

and calculating the similarity between the business data and the enterprise data in an enterprise database, and further determining the enterprise data corresponding to the business identifier according to the similarity.

4. The method of claim 3, wherein calculating the similarity between the business data and the enterprise data in an enterprise database comprises:

extracting a business data entity in the business data, and extracting an enterprise data entity in the enterprise database;

5. The method of claim 1, wherein determining the target business based on the feedback information comprises:

6. The method of claim 1, further comprising:

and associating and storing the target enterprise, the bidding data and the service identifier.

7. The method of claim 1, wherein after said matching the tender data to the enterprise representation, the method further comprises:

and responding to the failure of matching of all the enterprise portraits and the bidding data, and ending the business processing process.

8. The method of claim 1, wherein the outputting the target business and the bid data comprises:

and visually displaying the target enterprise and the bidding data.

9. A traffic processing apparatus, comprising:

an enterprise portrait generation unit configured to obtain corresponding enterprise data according to the service identifier, extract low-level features of the enterprise data, determine corresponding high-level features based on the low-level features, fuse the low-level features and the high-level features to generate fused features, and generate an enterprise portrait according to the fused features;

the candidate enterprise determining unit is configured to acquire bidding data corresponding to the business identifier, match the bidding data with the enterprise portrait, and determine an enterprise corresponding to the successfully matched enterprise portrait as a candidate enterprise in response to the fact that the enterprise portrait is successfully matched with the bidding data;

the target enterprise determining unit is configured to push the bidding data to the candidate enterprise, receive feedback information of the candidate enterprise for the bidding data, and determine a target enterprise according to the feedback information;

an output unit configured to output the target enterprise and the bid data.

10. The apparatus of claim 9, further comprising an interception unit configured to:

11. The apparatus of claim 9, wherein the enterprise representation generation unit is further configured to:

acquiring service data corresponding to the service identifier;

12. The apparatus of claim 11, wherein the enterprise representation generation unit is further configured to:

13. The apparatus of claim 9, wherein the target enterprise determination unit is further configured to:

14. A transaction processing electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.

15. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1-8.

16. A computer program product comprising a computer program, characterized in that the computer program realizes the method according to any of claims 1-8 when executed by a processor.