CN115619238A - Method for establishing inter-enterprise cooperation relationship for non-specific B2B platform - Google Patents
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Abstract
The invention provides a method for establishing an inter-enterprise cooperation relationship for a non-specific B2B platform, which is used for acquiring behavior data of an enterprise on the B2B platform and judging the business requirement and the business content of the enterprise; constructing a demand tree model of enterprise business behaviors according to business demands; constructing a supply tree model of enterprise business behaviors through business contents; constructing a business decision knowledge graph based on risk matching through a demand tree model and a supply tree model; calculating the service matching degree and risk coefficient among different enterprises through a service decision knowledge graph; and generating a recommendation list of enterprise cooperation according to the business matching degree and the risk coefficient.
Description
Technical Field
The invention relates to the technical field of B2B, in particular to a method for establishing inter-enterprise cooperation relationship for a non-specific B2B platform.
Background
At present, in an online process of an enterprise business relationship chain, a specific B2B platform mainly adopts two modes of invitation and request to help an enterprise and a partner establish a business relationship; for example:
the enterprise sends an invitation to a partner at a particular B2B platform, and the partner establishes a business relationship with the partner by activating the invitation link.
The enterprise sends a partnership establishment request to the platform specific enterprise on the specific B2B platform, and after the requested enterprise agrees, the establishment of a partnership can be performed.
However, the two modes have serious dependence on the online states of the partners involved in the business relationship, are not beneficial to the rapid and automatic establishment of the business relationship between the enterprise and the partners on a specific B2B platform, cannot realize the automatic identification of the business requirements of the enterprise, cannot automatically find the optimal cooperative enterprise for the enterprise, and the like.
Disclosure of Invention
The present invention provides a method for establishing inter-enterprise collaboration for a non-specific B2B platform, which is used to solve the above-mentioned background art.
A method for establishing inter-enterprise partnerships for a non-specific B2B platform, comprising:
acquiring behavior data of an enterprise on a B2B platform, and judging the business requirement and the business content of the enterprise;
constructing a demand tree model of enterprise business behaviors according to business demands;
constructing a supply tree model of enterprise business behaviors through business contents;
constructing a business decision knowledge graph based on risk matching through a demand tree model and a supply tree model;
calculating the service matching degree and risk coefficient among different enterprises through a service decision knowledge graph;
and generating a target recommendation list of enterprise cooperation according to the business matching degree and the risk coefficient.
In one embodiment of the invention: the method further comprises the following steps:
acquiring enterprise attribute information and enterprise service information of a B2B platform; wherein the content of the first and second substances,
the enterprise attribute information includes: enterprise name, enterprise industry, enterprise province, enterprise scale, enterprise credit;
the enterprise business information comprises: business range information, scientific and technological innovation information and enterprise output information;
establishing an enterprise classification management and control network based on the enterprise attribute information;
based on the enterprise business information, marking business interweaving information of different enterprises on an enterprise classification management and control network;
configuring a behavior trigger function for different business behaviors through business interweaving information;
when the enterprise end of the B2B platform is in an active state, acquiring the behavior data of the enterprise through a behavior trigger function, and binding the business behavior and the behavior data of the enterprise on an enterprise classification management and control network through the behavior data.
In one embodiment of the invention: the acquiring of the behavior data of the enterprise on the B2B platform and the judging of the business requirements and the business contents of the enterprise comprise:
acquiring the bound enterprise behavior data through an enterprise classification management and control network;
intention portrayal is carried out on enterprise business behaviors through behavior data;
constructing a behavior value model through the intention portrait, and determining target complaints and appeal values of different behavior characteristics;
determining corresponding appeal behaviors through target appeal, and calculating the relevance of different appeal behaviors;
according to the relevance, performing feature fusion on different appeal, determining the objective of the fused appeal, and using the objective as a service requirement;
and inputting the service requirements into a service network model database, determining similar service behaviors and acquiring corresponding service contents.
In one embodiment of the invention: the enterprise classification management and control network comprises the following construction steps:
step 1: creating a plurality of sandbox environments, and respectively implanting local enterprise statistical templates with different enterprise attributes into one sandbox environment;
step 2: carrying out weight assignment on the enterprise attributes;
and step 3: according to the weight assignment, carrying out digital quantization on the enterprise attributes, and determining the quantized value of different attributes of each enterprise;
and 4, step 4: enterprise element evaluation is carried out based on the quantized values of different attributes of each enterprise, and evaluation values and relevance are determined;
and 5: according to the evaluation value, carrying out enterprise coding on each sandbox environment;
step 6: establishing control plug-ins and association plug-ins of different enterprises according to the enterprise codes and the association;
and 7: and generating an enterprise classification management and control network through the management and control plug-in and the associated plug-in.
In one embodiment of the invention: the demand tree model comprises the following building steps:
acquiring a service requirement;
determining a first characteristic and a first characteristic behavior value corresponding to the behavior data of the enterprise according to the business requirements and a pre-trained tree model;
determining the child behavior characteristics of each child behavior data according to the tree structure of the tree model;
determining the main service requirement and the sub service requirement of an enterprise through behavior characteristics; wherein, the first and the second end of the pipe are connected with each other,
the secondary business requirement is to fulfill an execution requirement in the primary business requirement;
and generating a demand tree model according to the main service demand and the secondary service demand.
In one embodiment of the invention: the supply tree model comprises the following construction steps:
determining a behavior path for meeting the main service requirement according to the requirement tree model;
determining the supply requirement corresponding to each demand point through the behavior path;
determining a primary supply enterprise and a secondary supply enterprise which meet the supply requirement through the supply requirement;
a supply tree model is generated by the primary and secondary supply enterprises.
In one embodiment of the invention: the business decision knowledge graph based on risk matching is constructed through a demand tree model and a supply tree model, and comprises the following steps:
extracting supply branches and demand branches in a demand tree model and a supply tree model, matching the supply branches and the demand branches from a database to obtain the corresponding incidence relation of each supply branch and each demand branch, and establishing a feature model according to the incidence relation;
separating feature networks belonging to the same relevance from the feature model according to the relevance relation;
calling a standard network corresponding to the feature network from the database according to the relevance, screening out the feature network of which the difference value with the corresponding standard network is smaller than a preset value, and taking the item represented by the corresponding mark set of the screened feature network as an analysis item;
acquiring key information of an enterprise corresponding to the required branch according to the enterprise information of the supply branch, analyzing risk problems according to the key information to obtain project analysis values, and summing the project analysis values of all analysis projects to obtain a matching risk value of input information;
and constructing business decision knowledge maps between different demand tree models and supply tree models by matching the risk values.
In one embodiment of the invention: the calculating of the business matching degree and the risk coefficient among different enterprises through the business decision knowledge graph comprises the following steps:
determining map nodes between enterprise demand services and supply enterprises through a service decision knowledge map;
determining business association attributes and business risk factors between demand enterprises and supply enterprises through map nodes;
calculating the matching degree between the demand enterprises and the supply enterprises through the business association attributes;
and calculating the risk coefficient between the demand enterprise and the supply enterprise through the business risk factors.
In one embodiment of the invention: the generating of the target recommendation list of enterprise cooperation according to the service matching degree and the risk coefficient comprises the following steps:
acquiring the business matching degree and risk coefficient of different enterprises in the state to be matched;
performing feature extraction on the business matching degrees and risk coefficients of different enterprises by using a business matching model, and determining and extracting the similarity between feature vectors of different enterprises;
determining the enterprises in the same matching group according to the similarity, and determining a recommendation candidate list of supply enterprises from the matching enterprise set according to the matching enterprise set of the enterprises in the same matching group;
constructing a ranking model to extract the characteristics of the demand enterprises and the supply enterprises in the matching group by using the risk coefficients, and predicting the preference of different demand enterprises in the recommendation candidate list to the supply enterprises;
and generating a target recommendation list of enterprise cooperation through the preference.
In one embodiment of the invention: the method further comprises the following steps:
acquiring a target recommendation list, and determining enterprise data of supply enterprises on the target recommendation list;
determining enterprise characteristic data of the enterprise to be supplied and scored according to the enterprise data;
according to the enterprise characteristic data, carrying out industry classification on the enterprises to be supplied and scored so as to obtain industry classification results of the enterprises to be supplied and scored;
determining an enterprise cooperation scoring model corresponding to the enterprise to be provided and scored in enterprise cooperation scoring models of a plurality of industry classifications according to industry classification results of the enterprise to be provided and scored;
and performing enterprise cooperation scoring on the enterprise to be scored according to the enterprise characteristic data through the enterprise cooperation scoring model so as to obtain an enterprise cooperation scoring result of the enterprise to be scored.
The invention has the beneficial effects that: the method and the system can ensure that any enterprise has the possibility of cooperation without being limited by the relation of region, space-time and competition, generate the recommendation list with scores, label all risk factors and ensure that the enterprise evaluates. Compared with the B2B platform in the prior art, the prior art may also perform matching of corresponding enterprises according to enterprise requirements, but compared with the prior art, the method has the advantages that the comprehensive enterprise cooperation matching is performed, the requirement tree model of each individual business of each enterprise is customized, the enterprise analysis is automatically performed through the behavior information of the enterprise, then the customized supply tree model is constructed for the enterprise, each enterprise has one requirement tree model according to the business of the enterprise, all business requirements of the enterprise are determined, and one supply tree model is provided to determine all businesses which can be involved by the enterprise. And then according to the supply tree model, each enterprise also has a supply tree model customized individually, so that the recommendation of enterprise cooperation is realized, and in the enterprise cooperation recommendation process, risk analysis can be performed on the enterprises which can be supplied according to the relevance and the risk, so that the most appropriate and safe cooperative enterprise is recommended, and the firmest and most stable cooperative relationship is generated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
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. In the drawings:
FIG. 1 is a schematic diagram of a method for establishing inter-enterprise collaboration for a non-specific B2B platform in accordance with an embodiment of the present invention;
fig. 2 is a flowchart illustrating the construction and enterprise binding of an enterprise classification management and control network according to an embodiment of the present invention;
FIG. 3 is a flowchart of enterprise recommendation scoring in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
The invention provides a method for establishing an inter-enterprise cooperative relationship for a non-specific B2B platform, which comprises the following steps:
the non-specific B2B platform is a common platform for public businesses and businesses to conduct business transactions, and is not specific to certain businesses or classes of businesses.
Acquiring behavior data of an enterprise on a B2B platform, and judging the business requirement and the business content of the enterprise;
the enterprise behavior data comprises advertisement data of the enterprise, business cooperation information of the enterprise and business of the enterprise
Constructing a demand tree model of enterprise business behaviors according to business demands;
constructing a supply tree model of enterprise business behaviors through business contents;
constructing a business decision knowledge graph based on risk matching through a demand tree model and a supply tree model;
calculating the service matching degree and risk coefficient among different enterprises through a service decision knowledge graph;
and generating a target recommendation list of enterprise cooperation according to the business matching degree and the risk coefficient.
The principle of the technical scheme is as follows:
the invention is suitable for a method for establishing a cooperative relationship among unrelated enterprises, a B2B platform can be constructed, and registered enterprises on the platform can implement various business behaviors, including the release of business requirements, the disclosure of business contents and the judgment of business requests. In the invention, the risk matching is carried out by automatically constructing the demand tree and the supply tree of different enterprises, so that a recommendation list is generated, and different enterprises are recommended to cooperate. The demand tree model determines commodity information or service information of emergency demands of registered enterprises according to business behaviors of the enterprises, such as: buying office components, enterprise financing cooperation, legal services and other businesses needed by the enterprises. The supply tree model is a separate supply tree model generated for each enterprise aiming at a demand tree model, self business scales of different enterprises, business fields of business services, enterprise regional distribution and the like, each enterprise has a self supply tree model, and the supply tree model has a self supply tree model capable of carrying out all business businesses. Meanwhile, the supply tree model also comprises a customized supply tree model constructed according to the self requirement of each enterprise, and the supply tree model formed by supply companies of a complete system can be generated according to the self business requirement of the enterprise. The risk matching business decision knowledge graph can analyze risk factors between business requirements and business supply among different enterprises from multiple angles, multiple levels and multiple dimensions, such as: the business decision knowledge graph may show that when collaborating with a provisioning enterprise, there are risks affecting business performance, the reputation of the provisioning enterprise, whether the enterprise size of the provisioning enterprise is sufficient to perform the full provisioning needs, and so on. And finally, generating a recommendation list through the service matching degree and the risk coefficient, so that the user can clearly know the advantages and disadvantages of cooperation with each enterprise according to the recommendation list, and then performing cooperation enterprise selection.
The beneficial effects of the above technical scheme are that: the method has the advantages that the method is not limited to regions, time-space and competitive relations, the possibility of cooperation exists among any enterprises, the generated recommendation list has scores, and all risk factors can be labeled to enable the enterprises to evaluate. Compared with the B2B platform in the prior art, the prior art may also perform matching of corresponding enterprises according to enterprise requirements, but compared with the prior art, the method has the advantages that the comprehensive enterprise cooperation matching is performed, the requirement tree model of each individual business of each enterprise is customized, the enterprise analysis is automatically performed through the behavior information of the enterprise, then the customized supply tree model is constructed for the enterprise, each enterprise has one requirement tree model according to the business of the enterprise, all business requirements of the enterprise are determined, and one supply tree model is provided to determine all businesses which can be involved by the enterprise. And then, according to the supply tree model, each enterprise can also customize the supply tree model so as to realize the recommendation of enterprise cooperation, and in the process of enterprise cooperation recommendation, risk analysis can be carried out on the enterprises which can be supplied according to the relevance and the risk, so that the most suitable and safe cooperative enterprise is recommended, and the firmest and most stable cooperative relationship is generated.
In one embodiment of the invention: the method further comprises the following steps:
acquiring enterprise attribute information and enterprise service information of a B2B platform; wherein the content of the first and second substances,
the enterprise attribute information includes: enterprise name, enterprise industry, enterprise province, enterprise scale, enterprise credit;
the enterprise business information comprises: business range information, scientific and technological innovation information and enterprise output information;
establishing an enterprise classification management and control network based on the enterprise attribute information;
based on the enterprise business information, marking business interweaving information of different enterprises on an enterprise classification management and control network;
configuring a behavior trigger function for different business behaviors through business interweaving information;
when the enterprise end of the B2B platform is in an active state, acquiring the behavior data of the enterprise through a behavior trigger function, and binding the business behavior and the behavior data of the enterprise on an enterprise classification management and control network through the behavior data.
The principle of the technical scheme is as follows:
after enterprise attribute information and business information of an enterprise are registered in the enterprise, the attribute and business information of the enterprise are automatically searched through big data, enterprise analysis is carried out according to different business behaviors of the enterprise on a B2B platform, and the enterprise attribute information and the enterprise business information of the enterprise are determined. The enterprise classification management and control network performs classification management according to regions, industry types and scales of enterprises. The business interweaving information is business behaviors of cooperation relations and business behaviors of competition relations of different enterprises on business, and a set behavior trigger function can trigger corresponding cooperation services when any business behaviors exist on a B2B platform of the enterprises. The enterprise business behavior and behavior data binding is the business behavior carried out by the enterprise; for example: the enterprise producing the robot can bind and store the behavior data of all enterprises such as robot customization, robot export, robot intelligent data updating, robot cooperation research and development and the like of the enterprise, so that risk assessment and business matching can be conveniently carried out when the enterprise has business requirements.
The invention has the beneficial effects that:
the enterprise information management and control method based on the enterprise classification can count all information of the enterprise, divide different enterprises, further construct a classification management and control network of the enterprise, set trigger functions of different businesses after the enterprise classification management and control, timely and clearly determine demand information of different enterprises, then recommend proper enterprises to cooperate, and bind any behaviors of the enterprise with the classification management and control network. The classification management and control network can perform rapid enterprise calibration in the enterprise cooperation process, because the classification management and control network is classified and controlled, the speed is higher and the analysis is more comprehensive in the aspect of enterprise information calling and matching analysis, and meanwhile, because the classification management and control network is similar to the business behaviors of the same type of enterprises, the sensitivity of the system to business behavior recognition can be continuously enhanced through the deep neural network.
In one embodiment of the invention: the acquiring of the behavior data of the enterprise on the B2B platform and the judging of the business requirements and the business contents of the enterprise comprise:
acquiring the bound enterprise behavior data through an enterprise classification management and control network;
intention portrayal is carried out on enterprise business behaviors through behavior data; constructing a behavior value model through the intention portrait, and determining target complaints and appeal values of different behavior characteristics;
determining corresponding appeal behaviors through target appeal, and calculating the relevance of different appeal behaviors;
according to the relevance, performing feature fusion on different appeal, determining the objective of the fused appeal, and using the objective as a service requirement;
and inputting the service requirements into a service network model database, determining similar service behaviors and acquiring corresponding service contents.
The principle of the technical scheme is as follows:
in determining business demand data and business content for an enterprise, the business content includes business content and demand business content, i.e., business services that can be served when the enterprise is provisioned. The required service content is the whole service behavior corresponding to the service requirement, for example: when financing is required, the business content is the reason of financing, the development vision and future planning of financing, and the specific matters and financing scale requirements of financing. The intention portrayal is portrayal of corresponding behavior data of enterprise requirements, and the data comprises specific requirement items, such as required services, service standards, service regions, service payment methods and the like. A behavior value model, which represents a weight evaluation model of the value generated according to the requirements and purposes achieved by the business needs after the business demand determination is made, for example: in the production process, functions, service life, shapes, materials, power, application regions and the like of a batch of robots are target demands, and the demand value is the weight of each demand in the whole business behavior. The objective of the fused appeal is to fuse all service sub-items of the whole service, namely target appeal, and generate different standard detail meeting standards in a service transaction contract corresponding to the complete service requirement. The service network model database stores a large amount of service behavior data, and can determine the whole content of the service, namely the whole process of service implementation according to the service requirement.
The beneficial effects of the above technical scheme are that:
the invention determines whether the business requirement of the enterprise needs products or services when the enterprise implements different behaviors by carrying out classification management and control on the enterprise and analyzing the value of target complaints and complaints of the enterprise, further determines the corresponding business content and carries out better recommendation of cooperative enterprises, and the technical scheme can carry out rigorous analysis on various requirements of the enterprise in the process of enterprise cooperation, thereby determining the complete business process and preventing the situation that the requirements which need to reach the standard are artificially forgotten when the business cooperation is carried out, such as: painting of robots cannot contain chemical substances harmful to human bodies, but enterprises often ignore these requirements to reach standards.
In one embodiment of the invention: the enterprise classification management and control network comprises the following construction steps:
step 1: creating a plurality of sandbox environments, and respectively implanting local enterprise statistical templates with different enterprise attributes into one sandbox environment;
step 2: carrying out weight assignment on the enterprise attributes;
and 3, step 3: according to the weight assignment, carrying out digital quantization on the enterprise attributes, and determining the quantized value of different attributes of each enterprise;
and 4, step 4: based on the quantized values of different attributes of each enterprise, enterprise element evaluation is carried out, and evaluation values and relevance are determined;
and 5: according to the evaluation value, carrying out enterprise coding on each sandbox environment;
step 6: establishing control plug-ins and association plug-ins of different enterprises according to the enterprise codes and the association;
and 7: and generating an enterprise classification management and control network through the management and control plug-in and the associated plug-in.
The principle of the technical scheme is as follows:
in the prior art, different system platforms are all used for public management of enterprises, and management data of the enterprises are disclosed in the platforms, so that business losses, such as business secret leakage, are caused. Therefore, when the enterprise classification management and control network is carried out, a sandbox environment is established for the confidentiality of enterprise information, each enterprise forms a management and control space, each service of the enterprise is quantized, after data are quantized, the service information of the enterprise cannot be disclosed, and the theoretical calculation of cooperation and recommendation among the enterprises is more convenient to carry out. The evaluation value is the evaluation of business elements of the enterprises, and can also determine the association and similarity of the associated enterprises among different enterprises. However, the sandbox environment is equivalent to a virtual machine, and in order to more conveniently perform cooperation deployment among different enterprises, the management and control plug-in and the association plug-in are arranged to perform management control and association analysis on the enterprises.
The beneficial effects of the above technical scheme are that: according to the invention, through the sandbox environment, the safety of enterprise information can be ensured, the enterprise information leakage is prevented, and independent control can be performed on cooperative enterprises through the control plug-in and the associated plug-in, so that the enterprises can cooperate efficiently.
In one embodiment of the invention: the demand tree model comprises the following building steps:
acquiring a service requirement;
determining a first characteristic and a first characteristic behavior value corresponding to the behavior data of the enterprise according to the business requirement and a pre-trained tree model;
determining the child behavior characteristics of each child behavior data according to the tree structure of the tree model;
determining the main service requirement and the secondary service requirement of an enterprise through the behavior characteristics; wherein the content of the first and second substances,
the secondary business requirements are the execution requirements in the achievement of the main business requirements;
and generating a demand tree model according to the main service demand and the secondary service demand.
In one embodiment of the invention: the supply tree model comprises the following construction steps:
determining a behavior path for meeting the main service requirement according to the requirement tree model;
determining the supply requirement corresponding to each demand point through the behavior path;
determining a main supply enterprise and a secondary supply enterprise which reach the supply requirement through the supply requirement;
a supply tree model is generated by the primary and secondary supply enterprises.
The principle of the technical scheme is as follows:
the supply tree model is built based on the enterprise demand tree model, and the supply tree model can provide more supply options through the enterprise demand tree, so that an enterprise has more options when performing a business. After the business requirements are determined, the first characteristic and the first behavior value of the behavior data are the requirement target corresponding to the behavior data, namely the first characteristic, and other requirements which need to reach the standard in the process of realizing the first characteristic are the sub-behavior data and the sub-behavior characteristics, and also correspond to the main business requirements and the sub-business requirements, and then a requirement tree model based on the main business requirements and the sub-business requirements is formed.
The beneficial effects of the above technical scheme are that:
the invention can determine the business requirement data which needs to be reached when an enterprise carries out different business behaviors by constructing the requirement tree, divides the business requirements according to primary and secondary, generates the requirement tree model which can be matched one by one according to the requirements, and carries out multilevel detailed matching after the business behaviors of the enterprise are deconstructed, thereby enhancing the accuracy of enterprise sequencing when the enterprise cooperates, and also being more accurate business matching.
In one embodiment of the invention:
the supply tree model comprises the following construction steps:
determining a behavior path for meeting the main service requirement according to the requirement tree model;
determining the supply requirement corresponding to each demand point through the behavior path;
determining a primary supply enterprise and a secondary supply enterprise which meet the supply requirement through the supply requirement;
a supply tree model is generated by the primary and secondary supply enterprises.
The principle of the technical scheme is as follows:
after the requirement tree model is built, a supply tree model corresponding to each primary and secondary service requirement needs to be determined, and in the process, the action path is a step to be performed and a secondary service requirement to be executed in the process of achieving the primary service requirement, namely, a full-flow model of the primary service requirement is achieved. And then the supply requirement required by each business requirement is determined point to point, the corresponding supply enterprises are determined through the supply requirement, and in the supply enterprises, the enterprise which can meet the final purpose and most of business requirements is the main supply enterprise, and other enterprises, for example: in the process of producing the robot, the enterprise of a manufacturing factory is a main supply enterprise, and the part on the robot body and the enterprise in the supply chain are secondary supply enterprises.
The beneficial effects of the above technical scheme are that:
the invention can carry out point-to-point and single-to-single full-flow supply analysis matching on the enterprise according to the demand tree model, and customize the supply tree model of the enterprise individually.
In one embodiment of the invention: the business decision knowledge graph based on risk matching is constructed through a demand tree model and a supply tree model, and the business decision knowledge graph based on risk matching comprises the following steps:
extracting supply branches and demand branches in a demand tree model and a supply tree model, matching the supply branches and the demand branches from a database to obtain the corresponding incidence relation of each supply branch and each demand branch, and establishing a feature model according to the incidence relation;
separating feature networks belonging to the same relevance from the feature models according to the relevance relation;
calling a standard network corresponding to the feature network from the database according to the relevance, screening out the feature network of which the difference value with the corresponding standard network is smaller than a preset value, and taking the item represented by the corresponding mark set of the screened feature network as an analysis item;
acquiring key information of enterprises corresponding to the required branches according to the enterprise information of the supply branches, analyzing risk problems according to the key information to obtain item analysis values, and summing the item analysis values of all analysis items to obtain a matching risk value of input information;
and constructing business decision knowledge maps between different demand tree models and supply tree models by matching the risk values.
The principle of the technical scheme is as follows: the business decision knowledge graph is a decision model for analyzing and marking risks possibly existing in the cooperation process according to the cooperative business behaviors of supply enterprises and demand enterprises, and the incidence relation represents the supply risk degree and the efficiency of meeting business demands. The established characteristic model is a model of the correlation strength characteristics and also comprises a strong correlation that a supply enterprise can meet all business requirements of a demand enterprise. The feature network and the standard network are used for analyzing the associated enterprises, analyzing the risks possibly generated by the enterprise cooperation, and calculating the risks, namely the item analysis value, each sub-requirement is a sub-item, and the sub-requirements also comprise the risk requirements of the enterprises, for example, the enterprises can not have civil litigation. And then, the risks among enterprises are judged through the demand tree model and the supply tree model, and a business decision knowledge graph formed according to the risks is generated.
The invention has the beneficial effects that:
the business decision knowledge graph can highlight the project risks in cooperation among different enterprises, perform differentiated feature calculation and prevent low relevance and high risk among the enterprises.
In one embodiment of the invention: the calculating of the business matching degree and the risk coefficient among different enterprises through the business decision knowledge graph comprises the following steps:
determining map nodes between enterprise demand services and supply enterprises through a service decision knowledge map;
determining business association attributes and business risk factors between demand enterprises and supply enterprises through map nodes;
calculating the matching degree between the demand enterprises and the supply enterprises through the business association attributes;
and calculating the risk coefficient between the demand enterprise and the supply enterprise through the business risk factors.
The principle of the technical scheme is as follows:
the invention can decide the knowledge graph according to the service. Determining business risk factors and business association attributes between a supply enterprise and a demand enterprise, wherein each graph node represents a business risk existing between enterprises ready for cooperation, and the business association attributes can judge the degree of the supply enterprise capable of meeting the business requirements of the supply enterprise and judge whether the supply enterprise is the optimal cooperation enterprise on business behaviors of the demand enterprise. The risk coefficient is a risk prediction value which is possibly existed in the cooperation through calculating the risk between the demand enterprise and the supply enterprise.
The beneficial effects of the above technical scheme are that:
the method and the system can judge the matching degree of the existing risk coefficient and the business when the enterprises cooperate, thereby judging the adhesiveness between the enterprises in a multi-dimensional way.
In one embodiment of the invention: generating a target recommendation list of enterprise cooperation according to the service matching degree and the risk coefficient, wherein the target recommendation list comprises the following steps:
acquiring the service matching degree and risk coefficient of different enterprises in a state to be matched;
performing feature extraction on the business matching degrees and risk coefficients of different enterprises by using a business matching model, and determining and extracting the similarity between feature vectors of different enterprises;
determining the enterprises in the same matching group according to the similarity, and determining a recommendation candidate list of supply enterprises from the matching enterprise set according to the matching enterprise set of the enterprises in the same matching group;
utilizing the risk coefficients, constructing a sequencing model to extract the characteristics of the demand enterprises and the supply enterprises in the matching group, and predicting the preference of different demand enterprises in the recommendation candidate list to the supply enterprises;
and generating a target recommendation list of enterprise cooperation through the preference degrees.
The principle of the technical scheme is as follows:
in the process of generating the recommendation list capable of enterprise cooperation, feature transformation is carried out on the matching degree and the risk coefficient among enterprises, then the similarity of feature vectors among different enterprises is calculated, in the process of carrying out matching enterprise set recommendation, a matching group of the enterprises is generated through the fusion feature of the risk service matching degree, the supply enterprises are recommended according to the matching group, a recommendation candidate list is generated, and then the enterprises capable of cooperation are ranked through a ranking model and enterprise preference. The business matching model represents a two-tier deep extraction model between enterprises. The first layer comprises extracting correlation characteristics through service correlation; and the second layer extracts the risk characteristics of the business risk factors.
The beneficial effects of the above technical scheme are that:
in the process, the recommendation of the preference degree of the target candidate recommendation list can be carried out according to the feature vector integrating the enterprise risk coefficient and the business matching degree, compared with the prior art that optimal recommendation is directly carried out, the recommendation method can provide more choices, can analyze the enterprise preference, and recommends the target enterprise according to the preference.
In one embodiment of the invention: the method further comprises the following steps:
acquiring a target recommendation list, and determining enterprise data of the supply enterprises on the target recommendation list;
determining enterprise characteristic data of the enterprise to be supplied and scored according to the enterprise data;
according to the enterprise characteristic data, carrying out industry classification on the enterprises to be supplied and scored so as to obtain industry classification results of the enterprises to be supplied and scored;
determining an enterprise cooperation scoring model corresponding to the scoring enterprise to be supplied in enterprise cooperation scoring models of a plurality of industry classifications according to an industry classification result of the scoring enterprise to be supplied;
and carrying out enterprise cooperation scoring on the enterprise to be scored according to the enterprise characteristic data through the enterprise cooperation scoring model so as to obtain an enterprise cooperation scoring result of the enterprise to be scored.
The principle of the technical scheme is as follows: the enterprise can be scored according to the target recommendation list, in the process, enterprise characteristic data are collected, the enterprise characteristic data comprise data such as the scale of the enterprise, risk items of the enterprise, the business credibility of the enterprise and the like, then the enterprise cooperative scoring model is converted into a single scoring model for cooperatively scoring a single enterprise, the model is formed based on a deep neural network, and through training of different enterprise data, any enterprise can be singly scored, and the scoring is used as a cooperative reference of the recommendation list.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for establishing inter-enterprise partnerships for a non-specific B2B platform, comprising:
acquiring behavior data of an enterprise on a B2B platform, and judging the business requirements and the business content of the enterprise;
constructing a demand tree model of enterprise business behaviors according to business demands;
constructing a supply tree model of enterprise business behaviors through business contents;
constructing a business decision knowledge graph based on risk matching through a demand tree model and a supply tree model;
calculating the service matching degree and risk coefficient among different enterprises through a service decision knowledge graph;
and generating a target recommendation list of enterprise cooperation according to the business matching degree and the risk coefficient.
2. The method of establishing inter-enterprise partnership relationships for a non-specific B2B platform according to claim 1, the method further comprising:
acquiring enterprise attribute information and enterprise service information of a B2B platform; wherein, the first and the second end of the pipe are connected with each other,
the enterprise attribute information includes: enterprise name, enterprise industry, enterprise province, enterprise scale, enterprise credit;
the enterprise business information comprises: business range information, scientific and technological innovation information and enterprise output information;
establishing an enterprise classification management and control network based on the enterprise attribute information;
based on the enterprise business information, marking business interweaving information of different enterprises on an enterprise classification management and control network;
configuring a behavior trigger function for different business behaviors through business interweaving information;
when the enterprise end of the B2B platform is in an active state, acquiring the behavior data of the enterprise through a behavior trigger function, and binding the business behavior and the behavior data of the enterprise on an enterprise classification management and control network through the behavior data.
3. The method of claim 2, wherein the obtaining behavior data of the enterprise on the B2B platform and determining the business requirements and the business contents of the enterprise comprises:
acquiring behavior data of the bound enterprise through an enterprise classification management and control network;
intention portrayal is carried out on enterprise business behaviors through behavior data;
constructing a behavior value model through the intention portrait, and determining target complaints and appeal values of different behavior characteristics;
determining corresponding appeal behaviors through target appeal, and calculating the relevance of different appeal behaviors;
according to the relevance, performing feature fusion on different appeal, determining the objective of the fused appeal, and using the objective as a service requirement;
and inputting the service requirements into a preset service network model database, determining similar service behaviors, and acquiring corresponding service contents.
4. The method of claim 2, wherein the enterprise taxonomy management and control network comprises the following steps:
step 1: creating a plurality of sandbox environments, and respectively implanting local enterprise statistical templates with different enterprise attributes into one sandbox environment;
and 2, step: carrying out weight assignment on the enterprise attributes;
and 3, step 3: according to the weight assignment, carrying out digital quantization on the enterprise attributes, and determining the quantized value of different attributes of each enterprise;
and 4, step 4: enterprise element evaluation is carried out based on the quantized values of different attributes of each enterprise, and evaluation values and relevance are determined;
and 5: according to the evaluation value, carrying out enterprise coding on each sandbox environment;
and 6: establishing control plug-ins and association plug-ins of different enterprises according to the enterprise codes and the association;
and 7: and generating an enterprise classification management and control network through the management and control plug-in and the associated plug-in.
5. The method of claim 2, wherein the demand tree model comprises the following building steps:
acquiring a service requirement;
determining a first characteristic and a first characteristic behavior value corresponding to the behavior data of the enterprise according to the business requirements and a pre-trained tree model;
determining the child behavior characteristics of each child behavior data according to the tree structure of the tree model;
determining the main service requirement and the sub service requirement of an enterprise through behavior characteristics; wherein, the first and the second end of the pipe are connected with each other,
the secondary business requirements are the execution requirements in the achievement of the main business requirements;
and generating a demand tree model according to the main service demand and the secondary service demand.
6. The method of claim 2, wherein the supply tree model comprises the steps of:
determining a behavior path for meeting the main service requirement according to the requirement tree model;
determining the supply requirement corresponding to each demand point through the behavior path;
determining a primary supply enterprise and a secondary supply enterprise which meet the supply requirement through the supply requirement;
a supply tree model is generated by the primary and secondary supply enterprises.
7. The method of claim 1, wherein the building of the risk matching-based business decision knowledge graph through the demand tree model and the supply tree model comprises:
extracting supply branches and demand branches in a demand tree model and a supply tree model, matching the supply branches and the demand branches in a database to obtain the corresponding incidence relation of each supply branch and each demand branch, and establishing a characteristic model according to the incidence relation;
separating feature networks belonging to the same relevance from the feature model according to the relevance relation;
calling a standard network corresponding to the feature network from the database according to the relevance, screening out the feature network of which the difference value with the corresponding standard network is smaller than a preset value, and taking the items represented by the corresponding mark set of the screened feature network as analysis items;
acquiring key information of enterprises corresponding to the required branches according to the enterprise information of the supply branches, analyzing risk problems according to the key information, determining project analysis values, summing the project analysis values of all analysis projects, and generating a matched risk value;
and constructing a business decision knowledge graph between different demand tree models and supply tree models by matching the risk values.
8. The method of claim 1, wherein the calculating the business matching degree and risk factor between different enterprises through the business decision knowledge graph comprises:
determining map nodes between enterprise demand services and supply enterprises through a service decision knowledge map;
determining business association attributes and business risk factors between demand enterprises and supply enterprises through map nodes;
calculating the matching degree between the demand enterprises and the supply enterprises through the business association attributes;
and calculating the risk coefficient between the demand enterprise and the supply enterprise through the business risk factors.
9. The method of claim 1, wherein the generating of the target recommendation list of the enterprise cooperation according to the business matching degree and the risk factor comprises:
acquiring the business matching degree and risk coefficient of different enterprises in the state to be matched;
performing feature extraction on the business matching degrees and risk coefficients of different enterprises by using a business matching model, and determining and extracting the similarity between feature vectors of different enterprises;
determining the enterprises in the same matching group according to the similarity, and determining a recommendation candidate list of supply enterprises from the matching enterprise set according to the matching enterprise set of the enterprises in the same matching group;
constructing a ranking model to extract the characteristics of the demand enterprises and the supply enterprises in the matching group by using the risk coefficients, and predicting the preference of different demand enterprises in the recommendation candidate list to the supply enterprises;
and generating a target recommendation list of enterprise cooperation through the preference degrees.
10. The method of claim 1, wherein the method further comprises:
acquiring a target recommendation list, and determining enterprise data of the supply enterprises on the target recommendation list;
determining enterprise characteristic data of the enterprise to be supplied and scored according to the enterprise data;
according to the enterprise characteristic data, carrying out industry classification on the enterprises to be supplied and scored so as to obtain industry classification results of the enterprises to be supplied and scored;
determining an enterprise cooperation scoring model corresponding to the scoring enterprise to be supplied in enterprise cooperation scoring models of a plurality of industry classifications according to an industry classification result of the scoring enterprise to be supplied;
and performing enterprise cooperation scoring on the enterprise to be scored according to the enterprise characteristic data through the enterprise cooperation scoring model so as to obtain an enterprise cooperation scoring result of the enterprise to be scored.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013175512A1 (en) * | 2012-05-25 | 2013-11-28 | Vissapragada Seetaramaiah | General enterprise modeling system and methodology for constructing enterprise applications |
| US20180053255A1 (en) * | 2016-08-19 | 2018-02-22 | Noonum | System and Method for end to end investment and portfolio management using machine driven analysis of the market against qualifying factors |
| CN110458697A (en) * | 2019-08-19 | 2019-11-15 | 北京百度网讯科技有限公司 | Method and apparatus for assessing risk |
| CN110889556A (en) * | 2019-11-28 | 2020-03-17 | 福建亿榕信息技术有限公司 | Enterprise operation risk prediction method and system |
| CN111581990A (en) * | 2020-05-14 | 2020-08-25 | 中国银行股份有限公司 | Cross-border transaction matching method and device |
| CN111597433A (en) * | 2020-04-10 | 2020-08-28 | 北京百度网讯科技有限公司 | Resource searching method and device and electronic equipment |
| CN111861268A (en) * | 2020-07-31 | 2020-10-30 | 平安金融管理学院(中国·深圳) | Candidate recommending method and device, electronic equipment and storage medium |
| CN112132401A (en) * | 2020-08-28 | 2020-12-25 | 上海明寰科技有限公司 | Enterprise rating system based on artificial intelligence algorithm |
| CN112308672A (en) * | 2020-11-03 | 2021-02-02 | 上海智能制造功能平台有限公司 | Intelligent manufacturing industry supply and demand matching method based on knowledge graph |
| CN112434811A (en) * | 2019-08-26 | 2021-03-02 | 华为技术有限公司 | Knowledge graph construction method and device, computing equipment and storage medium |
| CN113836310A (en) * | 2021-08-04 | 2021-12-24 | 欧冶工业品股份有限公司 | Knowledge graph driven industrial product supply chain management method and system |
| CN114140036A (en) * | 2022-02-08 | 2022-03-04 | 北京融信数联科技有限公司 | Enterprise portrait method, system and readable storage medium based on data analysis |
| CN114529038A (en) * | 2021-12-31 | 2022-05-24 | 上海阿法析地数据科技有限公司 | Intelligent matching business recruitment strategy system and method based on enterprise demands |
| CN115115257A (en) * | 2022-07-15 | 2022-09-27 | 广东粤财金融云科技股份有限公司 | Enterprise risk early warning method and system based on relational graph |
-
2022
- 2022-12-20 CN CN202211635872.6A patent/CN115619238B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013175512A1 (en) * | 2012-05-25 | 2013-11-28 | Vissapragada Seetaramaiah | General enterprise modeling system and methodology for constructing enterprise applications |
| US20180053255A1 (en) * | 2016-08-19 | 2018-02-22 | Noonum | System and Method for end to end investment and portfolio management using machine driven analysis of the market against qualifying factors |
| CN110458697A (en) * | 2019-08-19 | 2019-11-15 | 北京百度网讯科技有限公司 | Method and apparatus for assessing risk |
| CN112434811A (en) * | 2019-08-26 | 2021-03-02 | 华为技术有限公司 | Knowledge graph construction method and device, computing equipment and storage medium |
| CN110889556A (en) * | 2019-11-28 | 2020-03-17 | 福建亿榕信息技术有限公司 | Enterprise operation risk prediction method and system |
| CN111597433A (en) * | 2020-04-10 | 2020-08-28 | 北京百度网讯科技有限公司 | Resource searching method and device and electronic equipment |
| CN111581990A (en) * | 2020-05-14 | 2020-08-25 | 中国银行股份有限公司 | Cross-border transaction matching method and device |
| CN111861268A (en) * | 2020-07-31 | 2020-10-30 | 平安金融管理学院(中国·深圳) | Candidate recommending method and device, electronic equipment and storage medium |
| CN112132401A (en) * | 2020-08-28 | 2020-12-25 | 上海明寰科技有限公司 | Enterprise rating system based on artificial intelligence algorithm |
| CN112308672A (en) * | 2020-11-03 | 2021-02-02 | 上海智能制造功能平台有限公司 | Intelligent manufacturing industry supply and demand matching method based on knowledge graph |
| CN113836310A (en) * | 2021-08-04 | 2021-12-24 | 欧冶工业品股份有限公司 | Knowledge graph driven industrial product supply chain management method and system |
| CN114529038A (en) * | 2021-12-31 | 2022-05-24 | 上海阿法析地数据科技有限公司 | Intelligent matching business recruitment strategy system and method based on enterprise demands |
| CN114140036A (en) * | 2022-02-08 | 2022-03-04 | 北京融信数联科技有限公司 | Enterprise portrait method, system and readable storage medium based on data analysis |
| CN115115257A (en) * | 2022-07-15 | 2022-09-27 | 广东粤财金融云科技股份有限公司 | Enterprise risk early warning method and system based on relational graph |
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|---|---|
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