Method and system for constructing middle-platform transaction platform based on data model driving

Abstract

The invention provides a method and a system for constructing a middle-platform transaction platform based on data model driving. The method comprises the following steps: acquiring business activity information of a target enterprise, constructing an enterprise framework and business activity data of the target enterprise, and constructing an enterprise data model of the target enterprise, determining a plurality of transaction stages of the transaction according to the to-be-transacted information of the target enterprise, analyzing transaction process information corresponding to each transaction stage by using the enterprise data model, and respectively identifying a transaction logic relationship included in each transaction process information, and constructing a transaction platform of the target enterprise by using the transaction logic relationship and the enterprise data model, transmitting real-time transaction data of the target enterprise and an object enterprise to the transaction platform, generating a real-time form of the transaction, and displaying the real-time form. The problems of overlapping of business systems, insufficient system functions and lack of partial new business systems in transaction activities can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of transaction service technology, and in particular to a method and system for constructing a central platform transaction platform driven by a data model. Background Art

[0002] There are two ways for enterprises to manage and maintain at present. One is: "chimney-style" construction of business systems, which realizes a unified service entrance by integrating front-end service pages, and then builds a unified data platform to realize data connection and basic analysis functions. However, this method separates the process and data between systems, and the data between systems can only be connected through interfaces and buses. The efficiency of cross-system collaboration is low, and the cost of connecting processes and data is high. The other is: SOA system architecture, which first meets the individual's needs for electronic data records, integrates presentation, logic and data storage in one system, and then meets the group-level data record and functional requirements for process support on the client, including presentation and part of the logic. The server is mainly responsible for logic and data storage, and then the server is responsible for the system's business logic and data storage, forming the prototype of the enterprise-level architecture, and finally the server begins to integrate and reconstruct, and the public functions / services of the original various application systems are shared from the system level to the enterprise level through ESB. However, the transaction business model of this method is similar, and each element transaction system is independently constructed, IT resources are repeatedly invested, and the common functions of each system are repeatedly constructed. The construction cycle is long, and it can only be demolished and rebuilt after the service expires.

[0003] Therefore, the present invention provides a method and system for constructing a centralized trading platform driven by a data model. Summary of the invention

[0004] The present invention provides a method and system for constructing a centralized trading platform based on data model drive, which utilizes data model drive means to construct an enterprise data model, and can uniformly manage and maintain the personalized business logic and processes of each trading business, and can effectively reduce the problems of overlapping business systems, insufficient system functions, and missing of some new business systems in trading activities.

[0005] The present invention provides a method for constructing a central platform trading platform based on data model drive, comprising: Step 1: Acquire the business activity information of the target enterprise, construct the enterprise framework and business activity data of the target enterprise, and construct the enterprise data model of the target enterprise; Step 2: Determine several transaction stages of this transaction according to the pending transaction information of the target enterprise, and use the enterprise data model to analyze the transaction process information corresponding to each transaction stage; Step 3: Identify the transaction logic relationship contained in each transaction process information respectively, and build the transaction platform of the target enterprise by using the transaction logic relationship and the enterprise data model; Step 4: The real-time transaction data between the target enterprise and the object enterprise is transmitted to the transaction platform, and a real-time form of the transaction is generated and displayed.

[0006] In one practicable manner, The step 1 comprises: Step 11: Collect the activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a number of activity features; Step 12: Match the corresponding structure point for each of the activity features respectively, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and use the structure adjustment information to synchronously modify the basic structure to generate the business framework of the target enterprise; Step 13: Screen the business activity information to obtain a number of enterprise activity data of the target business enterprise, input each enterprise activity data into the business framework for matching experiments, obtain the matching degree between each enterprise activity data and the business framework, and regard the enterprise activity data with a matching degree higher than the specified matching degree as business activity data; Step 14: Building an enterprise model framework of the target enterprise based on the enterprise framework, inputting each of the business activity data into the enterprise model framework respectively, and generating an enterprise data model of the target enterprise.

[0007] In one practicable manner, The step 14 comprises: Step 141: Decomposing the enterprise framework to generate a plurality of framework conditions, establishing corresponding virtual structures in the virtual space according to the framework conditions, and generating an enterprise model framework of the target enterprise; Step 142: Input each of the business activity data into the enterprise model framework respectively, and collect the data representation value of each of the business activity data in the enterprise model framework; Step 143: establishing the model credibility of the enterprise data model according to the discreteness between the data representation values, adjusting the model credibility to a specified credibility range, and obtaining a data adjustment value corresponding to each of the data representation values; Step 144: Use the data adjustment value to replace the corresponding data representation value to obtain the enterprise data model of the target enterprise.

[0008] In one practicable manner, The step 2 comprises: Step 21: Obtain the pending transaction information of the target enterprise, establish the transaction purpose of the target enterprise according to the pending transaction information, obtain the current enterprise status of the target enterprise, and establish the predicted transaction process of the target enterprise completing the transaction in combination with the transaction purpose; Step 22: Decomposing the predicted transaction process into several transaction stages of the transaction, determining the transaction basis corresponding to each transaction stage according to the cross-relationship between different transaction stages, and generating a process tree of the transaction; Step 23: Identify several transaction branches included in the process tree, use the enterprise data model to perform data identification on each transaction branch, obtain transaction data corresponding to each transaction branch, and use the transaction data to determine data information corresponding to the transaction branch; Step 24: In the predicted transaction process, identify the data bits corresponding to each of the transaction data, locate the target data bits corresponding to each of the transaction bases, establish the exchange information corresponding to the transaction stage according to the target data bits, and establish corresponding transaction process information according to the data information and exchange information corresponding to each of the transaction stages.

[0009] In one practicable manner, The step 3 comprises: Step 31: simulating the transaction process of the transaction according to the transaction process information, obtaining the transaction conversion characteristics between different transaction process information, establishing a transaction logic diagram of the transaction, and determining the transaction logic relationships contained in different transaction processes in the transaction logic diagram; Step 32: Determine the transaction function of each transaction process information for the transaction according to the transaction logic relationship, generate a valid label corresponding to each transaction process information, divide the enterprise data model into a plurality of model layers according to the transaction stage, and respectively obtain the hierarchical function corresponding to each model layer; Step 33: Match the transaction function with the hierarchical function, set a corresponding valid label for the corresponding model layer according to the matching result, cluster the model layers with the same valid label, obtain a matching model layer set corresponding to each transaction process information, and determine the valid function corresponding to each model layer; Step 34: In the enterprise data model, the functional parameters corresponding to the effective functions of each model layer are expanded to obtain the perfect functions corresponding to each model layer, and the perfect functions are combined according to the transaction logic relationship to obtain the transaction platform of the target enterprise.

[0010] In one practicable manner, The step 4 comprises: Step 41: When the target enterprise conducts transactions with the object enterprise, corresponding real-time transaction data is collected, and the real-time transaction data is transmitted to the transaction platform for transaction supervision, so as to obtain the corresponding transaction details of the target enterprise at each supervision moment; Step 42: construct the transaction progress of the target enterprise and the real-time transaction characteristics of the target enterprise according to the transaction details, evaluate the real-time transaction characteristics, determine the real-time transaction risk of the target enterprise, mark the real-time transaction risk in the transaction progress, generate a real-time form of this transaction and display it.

[0011] In one practicable manner, Also includes: When the real-time transaction risk is higher than the standard risk at the corresponding monitoring moment, the corresponding abnormal level is identified and established at the corresponding monitoring moment, and is marked in the real-time form.

[0012] The present invention provides a data model-driven central platform trading platform construction system including: A data processing module, used to obtain business activity information of a target enterprise, construct an enterprise framework and business activity data of the target enterprise, and construct an enterprise data model of the target enterprise; A transaction pre-analysis module, used to determine several transaction stages of the transaction according to the pending transaction information of the target enterprise, and analyze the transaction process information corresponding to each transaction stage by using the enterprise data model; A platform building module, used to respectively identify the transaction logic relationship contained in each of the transaction process information, and build a transaction platform for the target enterprise using the transaction logic relationship and the enterprise data model; The transaction monitoring module is used to transmit the real-time transaction data between the target enterprise and the object enterprise to the transaction platform, generate a real-time form of this transaction and display it.

[0013] In one practicable manner, The data processing module comprises: A business decomposition unit, used to collect activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a plurality of activity features; A framework generation unit is used to match a corresponding structure point for each of the activity features, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and synchronously modify the basic structure using the structure adjustment information to generate the business framework of the target enterprise; A data matching unit, configured to perform data screening on the business activity information to obtain a plurality of enterprise activity data of the target business enterprise, respectively input each of the enterprise activity data into the business framework for a matching experiment, obtain a matching degree between each of the enterprise activity data and the business framework, and regard enterprise activity data with a matching degree higher than a specified matching degree as business activity data; The model building unit is used to build an enterprise model framework of the target enterprise according to the enterprise framework, input each of the business activity data into the enterprise model framework respectively, and generate an enterprise data model of the target enterprise.

[0014] In one practicable manner, The model building unit comprises: A preparation subunit is constructed to decompose the enterprise framework to generate a plurality of framework conditions, establish a corresponding virtual structure in the virtual space according to the framework conditions, and generate an enterprise model framework of the target enterprise; Building a training subunit, for inputting each of the business activity data into the enterprise model framework, respectively, and collecting the data performance value of each of the business activity data in the enterprise model framework; Building an adjustment subunit, used to establish the model credibility of the enterprise data model according to the discreteness between the data performance values, adjust the model credibility within a specified credibility range, and obtain a data adjustment value corresponding to each data performance value; The model generation subunit is used to replace the corresponding data representation value with the data adjustment value to obtain the enterprise data model of the target enterprise.

[0015] The achievable beneficial effects of the above technical solution are as follows: in order to solve the defects of the existing technology, reduce the situation of process and data separation between systems, and improve the efficiency of system collaboration, firstly, the enterprise framework and business activity data of the target enterprise are constructed according to the business activity information of the target enterprise, so as to establish the enterprise data model of the target enterprise. The transaction process information corresponding to each transaction stage of this transaction can be determined through the enterprise data model, and then the transaction platform of the target enterprise is constructed according to the transaction logic relationship between the transaction process information and the enterprise data model. Finally, the transaction work of the target enterprise can be supervised through the platform, and the management personnel can analyze the risks of this transaction through the real-time form generated by the transaction platform. The transaction platform breaks the limitations of the traditional transaction platform and is a platform that can sort out and abstract the business process, define and componentize the common business modules, and uniformly manage and maintain the personalized business logic and process of each transaction business, so as to realize resource reuse and process integration by using the advanced nature of data model-driven technology, and deposit the core business common capabilities into the business middle platform, provide reusable business capabilities for the front-end system, and avoid duplication. Driven by data model-driven technology, the functions of the transaction platform are more complete and suitable for more enterprises.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a workflow of a method for constructing a central trading platform driven by a data model in an embodiment of the present invention; Figure 2 The present invention is a schematic diagram of the composition of a system for building a centralized trading platform driven by a data model in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] Example 1 This embodiment provides a method for constructing a central platform trading platform based on data model drive, such as Figure 1 As shown, including: Step 1: Acquire the business activity information of the target enterprise, construct the enterprise framework and business activity data of the target enterprise, and construct the enterprise data model of the target enterprise; Step 2: Determine several transaction stages of this transaction according to the pending transaction information of the target enterprise, and use the enterprise data model to analyze the transaction process information corresponding to each transaction stage; Step 3: Identify the transaction logic relationship contained in each transaction process information respectively, and build the transaction platform of the target enterprise by using the transaction logic relationship and the enterprise data model; Step 4: The real-time transaction data between the target enterprise and the object enterprise is transmitted to the transaction platform, and a real-time form of the transaction is generated and displayed.

[0021] In this example, the business activity information refers to the information generated by the target enterprise when performing different businesses; In this instance, the enterprise framework represents a general framework of work that can be performed by the target enterprise; In this example, the business activity data refers to the data generated by the target enterprise when performing the corresponding business; In this example, the enterprise data model represents a model that uses modeling technology to display data changes of the target enterprise; In this example, the pending transaction information refers to the preparation information about the upcoming transaction uploaded in advance before the transaction starts; In this example, a transaction corresponds to several transaction stages; In this example, each transaction stage may correspond to one or more transaction process information, and the transaction process information represents the complete information generated during the transaction process; In this example, the transaction logic relationship refers to the relationship before and after the transaction process contained in the transaction process information, as well as the relationship when multiple transaction processes are carried out simultaneously; In this example, the real-time form represents a supervision report generated during the supervision transaction process.

[0022] The working principle and beneficial effects of the above technical solution: In order to solve the defects of the existing technology, reduce the situation of process and data separation between systems, and improve the efficiency of system collaboration, firstly, the enterprise framework and business activity data of the target enterprise are constructed according to the business activity information of the target enterprise, so as to establish the enterprise data model of the target enterprise. The transaction process information corresponding to each transaction stage of this transaction can be determined through the enterprise data model, and then the transaction platform of the target enterprise is constructed according to the transaction logic relationship between the transaction process information and the enterprise data model. Finally, the transaction work of the target enterprise can be supervised through the platform, and the management personnel can analyze the risks of this transaction through the real-time form generated by the transaction platform. The transaction platform breaks the limitations of the traditional transaction platform and is a platform that can sort out and abstract the business process, define and componentize the common business modules, and uniformly manage and maintain the personalized business logic and process of each transaction business, so as to realize resource reuse and process integration by using the advanced nature of data model-driven technology, and deposit the core business common capabilities into the business middle platform, provide reusable business capabilities for the front-end system, and avoid duplication. Driven by data model-driven technology, the functions of the transaction platform are more complete and suitable for more enterprises.

[0023] Example 2 On the basis of Example 1, the method for constructing a central platform trading platform driven by a data model, step 1, comprises: Step 11: Collect the activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a number of activity features; Step 12: Match the corresponding structure point for each of the activity features respectively, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and use the structure adjustment information to synchronously modify the basic structure to generate the business framework of the target enterprise; Step 13: Screen the business activity information to obtain a number of enterprise activity data of the target business enterprise, input each enterprise activity data into the business framework for matching experiments, obtain the matching degree between each enterprise activity data and the business framework, and regard the enterprise activity data with a matching degree higher than the specified matching degree as business activity data; Step 14: Building an enterprise model framework of the target enterprise based on the enterprise framework, inputting each of the business activity data into the enterprise model framework respectively, and generating an enterprise data model of the target enterprise.

[0024] In this example, the basic enterprise data represents the data presented by the target enterprise when it does not perform any business; In this example, the activity characteristics represent the activities performed by the target enterprise when executing its business activities; In this example, a structure point represents a structural position in the base structure, which is used to match the active features; In this example, the matching result represents the matching relationship between the activity feature and the structural point; In this example, the mapping result represents the result of mapping the activity features into the base structure; In this example, the structural adjustment information represents information generated when the corresponding structural points are adjusted using the activity features; In this example, the specified matching degree is: 75%.

[0025] The working principle and beneficial effects of the above technical solution are as follows: In order to reduce the waste caused by repeated investment of resources, the activity characteristics of the target enterprise are constructed according to the business activity information of the enterprise and the basic structure, and then the corresponding structural points are matched for each activity characteristic respectively. The basic structure is further adjusted by mapping to generate the business framework of the target enterprise, and then the enterprise activity data in the business activity information is extracted, and then the enterprise activity data is input into the business framework for matching experiments. The business activity data of the target enterprise is determined according to the experimental results, and finally the enterprise framework is used to build the enterprise model framework, and the enterprise data model is generated in combination with the business activity data. In this way, the platform can be built in a simulation way, reducing the waste of physical resources.

[0026] Example 3 On the basis of Example 2, the method for constructing a central platform trading platform driven by a data model, the step 14 includes: Step 141: Decomposing the enterprise framework to generate a plurality of framework conditions, establishing corresponding virtual structures in the virtual space according to the framework conditions, and generating an enterprise model framework of the target enterprise; Step 142: Input each of the business activity data into the enterprise model framework respectively, and collect the data representation value of each of the business activity data in the enterprise model framework; Step 143: establishing the model credibility of the enterprise data model according to the discreteness between the data representation values, adjusting the model credibility to a specified credibility range, and obtaining a data adjustment value corresponding to each of the data representation values; Step 144: Use the data adjustment value to replace the corresponding data representation value to obtain the enterprise data model of the target enterprise.

[0027] In this instance, the framework condition represents the specification of a framework position in the enterprise framework; In this instance, the virtual structure represents the enterprise framework established in the virtual space; In this example, the data representation value represents the representation value of the business activity data in the enterprise model framework; In this example, the specified confidence range indicates the confidence level that meets the requirements of model building, which is 75% and above.

[0028] The working principle and beneficial effects of the above technical solution are as follows: by utilizing the structure of the enterprise framework to establish framework conditions, a corresponding virtual structure is built in the virtual space to generate an enterprise model framework, and then the business activity data is input into the enterprise model framework for model building, and the data expression value of each business activity data in the enterprise model framework is obtained. The enterprise data model is further adjusted according to the discreteness between the data expression values, thereby improving the accuracy and practicality of the enterprise data model and improving the supervision efficiency of the trading platform.

[0029] Example 4 On the basis of Example 1, the method for constructing a central platform trading platform driven by a data model, the step 2 includes: Step 21: Obtain the pending transaction information of the target enterprise, establish the transaction purpose of the target enterprise according to the pending transaction information, obtain the current enterprise status of the target enterprise, and establish the predicted transaction process of the target enterprise completing the transaction in combination with the transaction purpose; Step 22: Decomposing the predicted transaction process into several transaction stages of the transaction, determining the transaction basis corresponding to each transaction stage according to the cross-relationship between different transaction stages, and generating a process tree of the transaction; Step 23: Identify several transaction branches included in the process tree, use the enterprise data model to perform data identification on each transaction branch, obtain transaction data corresponding to each transaction branch, and use the transaction data to determine data information corresponding to the transaction branch; Step 24: In the predicted transaction process, identify the data bits corresponding to each of the transaction data, locate the target data bits corresponding to each of the transaction bases, establish the exchange information corresponding to the transaction stage according to the target data bits, and establish corresponding transaction process information according to the data information and exchange information corresponding to each of the transaction stages.

[0030] In this example, the predicted transaction process refers to a transaction process generated by performing transaction prediction based on the information to be traded and the current state of the enterprise; In this example, transaction basis means the basic preparation work for executing a transaction phase; In this example, the process tree represents the cross-relationships between the different transaction stages of this transaction; In this example, the data bit represents the location where the transaction data is generated during the predicted transaction process; In this example, exchange information refers to information generated during a transaction phase; In this example, the data information represents a summary of transaction data generated by each transaction branch; In this example, a transaction branch represents a tree branch in a process tree.

[0031] The working principle and beneficial effects of the above technical solution are as follows: by processing the pending transaction information of the target enterprise and combining it with the current enterprise status of the target enterprise to establish the predicted transaction process for the target enterprise to complete the transaction, and then decomposing it to obtain several transaction stages, and further determining the transaction basis of the transaction stage based on the cross-relationship between different transaction stages to establish a process tree, and by identifying the transaction branches contained in the process tree and the transaction data of each transaction branch combined with the exchange information generated at each transaction stage in the transaction process to construct the transaction process information of each transaction stage, in this way, the transaction process information of each transaction stage can be summarized to lay the foundation for building a transaction platform.

[0032] Example 5 On the basis of Example 1, the method for constructing a central platform trading platform driven by a data model, the step 3 includes: Step 31: simulating the transaction process of the transaction according to the transaction process information, obtaining the transaction conversion characteristics between different transaction process information, establishing a transaction logic diagram of the transaction, and determining the transaction logic relationships contained in different transaction processes in the transaction logic diagram; Step 32: Determine the transaction function of each transaction process information for the transaction according to the transaction logic relationship, generate a valid label corresponding to each transaction process information, divide the enterprise data model into a plurality of model layers according to the transaction stage, and respectively obtain the hierarchical function corresponding to each model layer; Step 33: Match the transaction function with the hierarchical function, set a corresponding valid label for the corresponding model layer according to the matching result, cluster the model layers with the same valid label, obtain a matching model layer set corresponding to each transaction process information, and determine the valid function corresponding to each model layer; Step 34: In the enterprise data model, the functional parameters corresponding to the effective functions of each model layer are expanded to obtain the perfect functions corresponding to each model layer, and the perfect functions are combined according to the transaction logic relationship to obtain the transaction platform of the target enterprise.

[0033] In this example, the transaction transition feature represents the feature generated when switching between different transaction processes; In this example, the transaction logic diagram represents the summary of various logics in this transaction process; In this example, the transaction function indicates the function that needs to be performed in this transaction; In this example, the valid tag represents an identification tag used to distinguish different transaction process information; In this instance, the hierarchical function represents the functions possessed by a model layer; In this example, the purpose of expanding the function parameter data is to improve the effective functions and strengthen the effective functions in the model layer.

[0034] The working principle and beneficial effects of the above technical solution: In order to build a trading platform suitable for the target enterprise, the transaction process of this transaction is simulated by using the transaction process information, and the transaction conversion characteristics between different transaction process information are determined, so as to establish a transaction logic diagram, and then the transaction logic relationship contained in different transaction processes is determined in the transaction logic diagram, so as to determine several transaction functions of this transaction, and then establish effective labels for the transaction process information, and analyze the hierarchical functions of the model layer, and pass the effective labels by matching the transaction functions and the hierarchical functions, and then expand the effective functions of the model layer, and use the perfect functions generated after the expansion to establish the trading platform of the target enterprise. In this way, a trading platform suitable for the target enterprise can be built, which can not only improve the supervision of the target enterprise, but also make the construction cost of the trading platform itself low, and can also be suitable for different enterprises through slight adjustments.

[0035] Example 6 On the basis of Example 1, the method for constructing a central platform trading platform driven by a data model, the step 4 includes: Step 41: When the target enterprise conducts transactions with the object enterprise, corresponding real-time transaction data is collected, and the real-time transaction data is transmitted to the transaction platform for transaction supervision, so as to obtain the corresponding transaction details of the target enterprise at each supervision moment; Step 42: construct the transaction progress of the target enterprise and the real-time transaction characteristics of the target enterprise according to the transaction details, evaluate the real-time transaction characteristics, determine the real-time transaction risk of the target enterprise, mark the real-time transaction risk in the transaction progress, generate a real-time form of this transaction and display it.

[0036] The working principle and beneficial effects of the above technical solution are as follows: when the target enterprise conducts transactions with the target enterprise, the real-time transaction data generated will be transmitted to the transaction platform for supervision, and the transaction details corresponding to the target enterprise at different supervision times will be determined, so as to build the transaction progress, determine the real-time transaction characteristics of the target enterprise, and finally generate a real-time transaction form for reference by managers, thus realizing synchronous supervision.

[0037] Example 7 On the basis of Example 6, the method for constructing a central platform trading platform based on data model driving further includes: When the real-time transaction risk is higher than the standard risk at the corresponding monitoring moment, the corresponding abnormal level is identified and established at the corresponding monitoring moment, and is marked in the real-time form.

[0038] In this example, the standard risk is 30%.

[0039] The working principle and beneficial effects of the above technical solution: When the real-time transaction risk is too high, its abnormal level is determined and marked in the real-time form for reference by managers.

[0040] Example 8 This embodiment provides a system for constructing a central trading platform based on data model drive, such as Figure 2 As shown, including: A data processing module, used to obtain business activity information of a target enterprise, construct an enterprise framework and business activity data of the target enterprise, and construct an enterprise data model of the target enterprise; A transaction pre-analysis module, used to determine several transaction stages of the transaction according to the pending transaction information of the target enterprise, and analyze the transaction process information corresponding to each transaction stage by using the enterprise data model; A platform building module, used to respectively identify the transaction logic relationship contained in each of the transaction process information, and build a transaction platform for the target enterprise using the transaction logic relationship and the enterprise data model; The transaction monitoring module transmits the real-time transaction data between the target enterprise and the object enterprise to the transaction platform, generates a real-time form of the transaction and displays it.

[0041] In this example, the business activity information refers to the information generated by the target enterprise when performing different businesses; In this instance, the enterprise framework represents a general framework of work that can be performed by the target enterprise; In this example, the business activity data refers to the data generated by the target enterprise when performing the corresponding business; In this example, the enterprise data model represents a model that uses modeling technology to display data changes of the target enterprise; In this example, the pending transaction information refers to the preparation information about the upcoming transaction uploaded in advance before the transaction starts; In this example, a transaction corresponds to several transaction stages; In this example, each transaction stage may correspond to one or more transaction process information, and the transaction process information represents the complete information generated during the transaction process; In this example, the transaction logic relationship refers to the relationship before and after the transaction process contained in the transaction process information, as well as the relationship when multiple transaction processes are carried out simultaneously; In this example, the real-time form represents a supervision report generated during the supervision transaction process.

[0042] The working principle and beneficial effects of the above technical solution: In order to solve the defects of the existing technology, reduce the situation of process and data separation between systems, and improve the efficiency of system collaboration, firstly, the enterprise framework and business activity data of the target enterprise are constructed according to the business activity information of the target enterprise, so as to establish the enterprise data model of the target enterprise. The transaction process information corresponding to each transaction stage of this transaction can be determined through the enterprise data model, and then the transaction platform of the target enterprise is constructed according to the transaction logic relationship between the transaction process information and the enterprise data model. Finally, the transaction work of the target enterprise can be supervised through the platform, and the management personnel can analyze the risks of this transaction through the real-time form generated by the transaction platform. The transaction platform breaks the limitations of the traditional transaction platform and is a platform that can sort out and abstract the business process, define and componentize the common business modules, and uniformly manage and maintain the personalized business logic and process of each transaction business, so as to realize resource reuse and process integration by using the advanced nature of data model-driven technology, and deposit the core business common capabilities into the business middle platform, provide reusable business capabilities for the front-end system, and avoid duplication. Driven by data model-driven technology, the functions of the transaction platform are more complete and suitable for more enterprises.

[0043] Example 9 On the basis of Example 8, the data model-driven central platform trading platform construction system, the data processing module includes: A business decomposition unit, used to collect activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a plurality of activity features; A framework generation unit is used to match a corresponding structure point for each of the activity features, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and synchronously modify the basic structure using the structure adjustment information to generate the business framework of the target enterprise; A data matching unit, configured to perform data screening on the business activity information to obtain a plurality of enterprise activity data of the target business enterprise, respectively input each of the enterprise activity data into the business framework for a matching experiment, obtain a matching degree between each of the enterprise activity data and the business framework, and regard enterprise activity data with a matching degree higher than a specified matching degree as business activity data; The model building unit is used to build an enterprise model framework of the target enterprise according to the enterprise framework, input each of the business activity data into the enterprise model framework respectively, and generate an enterprise data model of the target enterprise.

[0044] In this example, the basic enterprise data represents the data presented by the target enterprise when it does not perform any business; In this example, the activity characteristics represent the activities performed by the target enterprise when executing its business activities; In this example, a structure point represents a structural position in the base structure, which is used to match the active features; In this example, the matching result represents the matching relationship between the activity feature and the structural point; In this example, the mapping result represents the result of mapping the activity features into the basic structure; In this example, the structural adjustment information represents information generated when the corresponding structural points are adjusted using the activity features; In this example, the specified matching degree is: 75%.

[0045] The working principle and beneficial effects of the above technical solution are as follows: In order to reduce the waste caused by repeated investment of resources, the activity characteristics of the target enterprise are constructed according to the business activity information of the enterprise and the basic structure, and then the corresponding structural points are matched for each activity characteristic respectively. The basic structure is further adjusted by mapping to generate the business framework of the target enterprise, and then the enterprise activity data in the business activity information is extracted, and then the enterprise activity data is input into the business framework for matching experiments. The business activity data of the target enterprise is determined according to the experimental results, and finally the enterprise framework is used to build the enterprise model framework, and the enterprise data model is generated in combination with the business activity data. In this way, the platform can be built in a simulation way, reducing the waste of physical resources.

[0046] Example 10 On the basis of Example 9, the data model-driven central platform trading platform construction system, the model construction unit includes: A preparation subunit is constructed to decompose the enterprise framework to generate a plurality of framework conditions, establish a corresponding virtual structure in the virtual space according to the framework conditions, and generate an enterprise model framework of the target enterprise; Building a training subunit, for inputting each of the business activity data into the enterprise model framework, respectively, and collecting the data performance value of each of the business activity data in the enterprise model framework; Building an adjustment subunit, used to establish the model credibility of the enterprise data model according to the discreteness between the data performance values, adjust the model credibility within a specified credibility range, and obtain a data adjustment value corresponding to each data performance value; The model generation subunit is used to replace the corresponding data representation value with the data adjustment value to obtain the enterprise data model of the target enterprise.

[0047] In this instance, the framework condition represents the specification of a framework position in the enterprise framework; In this example, the virtual structure represents the enterprise framework established in the virtual space; In this example, the data representation value represents the representation value of the business activity data in the enterprise model framework; In this example, the specified confidence range indicates the confidence level that meets the requirements of model building, which is 75% and above.

[0048] The working principle and beneficial effects of the above technical solution are as follows: by utilizing the structure of the enterprise framework to establish framework conditions, a corresponding virtual structure is built in the virtual space to generate an enterprise model framework, and then the business activity data is input into the enterprise model framework for model building, and the data expression value of each business activity data in the enterprise model framework is obtained. The enterprise data model is further adjusted according to the discreteness between the data expression values, thereby improving the accuracy and practicality of the enterprise data model and improving the supervision efficiency of the trading platform.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these 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 these modifications and variations.

Claims

1. A method for constructing a central trading platform based on data model drive, characterized in that: include: Step 1: Acquire the business activity information of the target enterprise, construct the enterprise framework and business activity data of the target enterprise, and construct the enterprise data model of the target enterprise; Step 2: Determine several transaction stages of this transaction according to the pending transaction information of the target enterprise, and use the enterprise data model to analyze the transaction process information corresponding to each transaction stage; Step 3: Identify the transaction logic relationship contained in each transaction process information respectively, and build the transaction platform of the target enterprise by using the transaction logic relationship and the enterprise data model; Step 4: The real-time transaction data between the target enterprise and the object enterprise is transmitted to the transaction platform, and a real-time form of the transaction is generated and displayed.

2. A method for constructing a central trading platform based on data model drive according to claim 1, characterized in that: The step 1 comprises: Step 11: Collect the activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a number of activity features; Step 12: Match the corresponding structure point for each of the activity features respectively, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and use the structure adjustment information to synchronously modify the basic structure to generate the business framework of the target enterprise; Step 13: Screen the business activity information to obtain a number of enterprise activity data of the target business enterprise, input each enterprise activity data into the business framework for matching experiments, obtain the matching degree between each enterprise activity data and the business framework, and regard the enterprise activity data with a matching degree higher than the specified matching degree as business activity data; Step 14: Building an enterprise model framework of the target enterprise based on the enterprise framework, inputting each of the business activity data into the enterprise model framework respectively, and generating an enterprise data model of the target enterprise.

3. A method for constructing a central trading platform based on data model drive as claimed in claim 2, characterized in that: The step 14 comprises: Step 141: Decomposing the enterprise framework to generate a plurality of framework conditions, establishing corresponding virtual structures in the virtual space according to the framework conditions, and generating an enterprise model framework of the target enterprise; Step 142: Input each of the business activity data into the enterprise model framework respectively, and collect the data representation value of each of the business activity data in the enterprise model framework; Step 143: establishing the model credibility of the enterprise data model according to the discreteness between the data representation values, adjusting the model credibility to a specified credibility range, and obtaining a data adjustment value corresponding to each of the data representation values; Step 144: Use the data adjustment value to replace the corresponding data representation value to obtain the enterprise data model of the target enterprise.

4. A method for constructing a central trading platform based on data model drive as claimed in claim 1, characterized in that: The step 2 comprises: Step 21: Obtain the pending transaction information of the target enterprise, establish the transaction purpose of the target enterprise according to the pending transaction information, obtain the current enterprise status of the target enterprise, and establish the predicted transaction process of the target enterprise completing the transaction in combination with the transaction purpose; Step 22: Decomposing the predicted transaction process into several transaction stages of the transaction, determining the transaction basis corresponding to each transaction stage according to the cross-relationship between different transaction stages, and generating a process tree of the transaction; Step 23: Identify several transaction branches included in the process tree, use the enterprise data model to perform data identification on each transaction branch, obtain transaction data corresponding to each transaction branch, and use the transaction data to determine data information corresponding to the transaction branch; Step 24: In the predicted transaction process, identify the data bits corresponding to each of the transaction data, locate the target data bits corresponding to each of the transaction bases, establish the exchange information corresponding to the transaction stage according to the target data bits, and establish corresponding transaction process information according to the data information and exchange information corresponding to each of the transaction stages.

5. A method for constructing a central trading platform based on data model drive as claimed in claim 1, characterized in that: The step 3 comprises: Step 31: simulating the transaction process of the transaction according to the transaction process information, obtaining the transaction conversion characteristics between different transaction process information, establishing a transaction logic diagram of the transaction, and determining the transaction logic relationships contained in different transaction processes in the transaction logic diagram; Step 32: Determine the transaction function of each transaction process information for the transaction according to the transaction logic relationship, generate a valid label corresponding to each transaction process information, divide the enterprise data model into a plurality of model layers according to the transaction stage, and respectively obtain the hierarchical function corresponding to each model layer; Step 33: Match the transaction function with the hierarchical function, set a corresponding valid label for the corresponding model layer according to the matching result, cluster the model layers with the same valid label, obtain a matching model layer set corresponding to each transaction process information, and determine the valid function corresponding to each model layer; Step 34: In the enterprise data model, the functional parameters corresponding to the effective functions of each model layer are expanded to obtain the perfect functions corresponding to each model layer, and the perfect functions are combined according to the transaction logic relationship to obtain the transaction platform of the target enterprise.

6. A method for constructing a central trading platform based on data model drive as claimed in claim 1, characterized in that: The step 4 comprises: Step 41: When the target enterprise conducts transactions with the object enterprise, corresponding real-time transaction data is collected, and the real-time transaction data is transmitted to the transaction platform for transaction supervision, so as to obtain the corresponding transaction details of the target enterprise at each supervision moment; Step 42: construct the transaction progress of the target enterprise and the real-time transaction characteristics of the target enterprise according to the transaction details, evaluate the real-time transaction characteristics, determine the real-time transaction risk of the target enterprise, mark the real-time transaction risk in the transaction progress, generate a real-time form of this transaction and display it.

7. A method for constructing a central trading platform based on data model drive as claimed in claim 6, characterized in that: Also includes: When the real-time transaction risk is higher than the standard risk at the corresponding monitoring moment, the corresponding abnormal level is identified and established at the corresponding monitoring moment, and is marked in the real-time form.

8. A data model-driven central trading platform construction system, characterized in that: include: A data processing module, used to obtain business activity information of a target enterprise, construct an enterprise framework and business activity data of the target enterprise, and construct an enterprise data model of the target enterprise; A transaction pre-analysis module, used to determine several transaction stages of the transaction according to the pending transaction information of the target enterprise, and analyze the transaction process information corresponding to each transaction stage by using the enterprise data model; A platform building module, used to respectively identify the transaction logic relationship contained in each of the transaction process information, and build a transaction platform for the target enterprise using the transaction logic relationship and the enterprise data model; The transaction monitoring module transmits the real-time transaction data between the target enterprise and the object enterprise to the transaction platform, generates a real-time form of the transaction and displays it.

9. A data model-driven central platform trading platform construction system as claimed in claim 8, characterized in that: The data processing module comprises: A business decomposition unit, used to collect activity data generated by the target enterprise when executing each business activity, establish corresponding business activity information, determine the basic structure of the target enterprise according to the basic enterprise data of the target enterprise, and decompose the business activity information according to the basic structure to obtain a plurality of activity features; A framework generation unit is used to match a corresponding structure point for each of the activity features, map the activity features to the corresponding structure points of the basic structure according to the matching results, determine the structure adjustment information corresponding to each of the structure points according to the mapping results, and synchronously modify the basic structure using the structure adjustment information to generate the business framework of the target enterprise; A data matching unit, configured to perform data screening on the business activity information to obtain a plurality of enterprise activity data of the target business enterprise, respectively input each of the enterprise activity data into the business framework for a matching experiment, obtain a matching degree between each of the enterprise activity data and the business framework, and regard enterprise activity data with a matching degree higher than a specified matching degree as business activity data; The model building unit is used to build an enterprise model framework of the target enterprise according to the enterprise framework, input each of the business activity data into the enterprise model framework respectively, and generate an enterprise data model of the target enterprise.

10. A data model-driven central platform trading platform construction system as claimed in claim 9, characterized in that: The model building unit comprises: A preparation subunit is constructed to decompose the enterprise framework to generate a plurality of framework conditions, establish a corresponding virtual structure in the virtual space according to the framework conditions, and generate an enterprise model framework of the target enterprise; Building a training subunit, for inputting each of the business activity data into the enterprise model framework, respectively, and collecting the data performance value of each of the business activity data in the enterprise model framework; Building an adjustment subunit, used to establish the model credibility of the enterprise data model according to the discreteness between the data performance values, adjust the model credibility within a specified credibility range, and obtain a data adjustment value corresponding to each data performance value; The model generation subunit is used to replace the corresponding data representation value with the data adjustment value to obtain the enterprise data model of the target enterprise.

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