Method for Dynamically Managing Innovation Integration Elements of Technology-based Enterprises in Multiple Parks

Abstract

The present invention discloses a method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks, which relates to the technical field of data services and includes: establishing a structured meta-model and data model for enterprise innovation integral elements, and providing visual management of the meta-model and data model; establishing a model conversion relationship and a traceability connection between the innovation integral element data model and the display model, so that the display model can automatically adapt to the adjustment according to the changes after the innovation integral element data model is changed; realizing the declaration and implementation of the model and data access interfaces unchanged through defining extensible parameter objects and model conversion technologies; supporting the parallel operation of multiple models of enterprise innovation integral elements, and using clustering algorithms to implement multi-park domain labels. It solves the problems that when the enterprise innovation integral element data model changes, the page layout and elements need to be re-developed, and the service interface declaration and specific implementation also change accordingly, and realizes the dynamic management of enterprise innovation integral elements in multiple parks through visual configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of data services, and particularly to a method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks. Background Art

[0002] The Ministry of Science and Technology has launched a new technology-based policy tool of "Enterprise Innovation Scoring System". The implementation of the policy requires accurately identifying and effectively discovering technology-based enterprises with strong R & D capabilities and great growth potential, and being able to reflect the innovation and development degree of technology-based enterprises in a quantitative way, objectively reflecting the innovation and development gap between different enterprises through the level of scores. This requires good management of enterprise innovation integral elements. The core innovation integral elements led by the Ministry of Science and Technology include operating income, R & D ratio, proportion of scientific and technological personnel, etc. Different cities and high-tech parks also have their own different development directions and evaluation dimensions. Obviously, this new policy tool requires a flexible and elastic management method for enterprise innovation integral elements to help each city and each park freely define and manage innovation integral elements with park characteristics, facilitate precise policy implementation, and effectively guide the aggregation of resources such as technology, capital, talents, and public services to innovative enterprises.

[0003] In the prior art, the data model is basically defined by enumerating innovation integral elements in advance, which has technical problems that the innovation integral elements cannot be dynamically expanded, resulting in high costs for innovation ability evaluation and maintenance, and it is difficult to adjust the innovation integral elements after the definition is completed and with the in-depth application of the business. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks, establish a structured enterprise innovation integral element meta-model and data model, provide visual model and data management, dynamically horizontally expand innovation integral elements, support the parallel operation of multiple models of enterprise innovation integral elements in multiple parks. When the meta-model is dynamically adjusted, the corresponding page elements are dynamically updated, and the front-end display page automatically adapts with the adjustment of innovation integral elements, while keeping the service interface unchanged and the specific implementation of the service interface also without change, so as to enable data service personnel to realize the dynamic management of enterprise innovation integral elements by adjusting parameters or changing configurations, and solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following solution. A method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks, the method includes the following steps:

[0006] S1: Define the meta-model of enterprise innovation integral elements. Its attributes include classification attributes, basic attributes, and display attributes. The classification attributes include classification ID, classification name, classification description, classification order, whether to display, column layout style, and number of column in each row. The basic attributes include element ID, element name, element meaning, data type, and the classification to which it belongs. The display attributes include element sorting, whether to display, whether it is required, page controls, and verification rules;

[0007] S2: Establish the data model of enterprise innovation integral elements. The data model is a meta-model instance obtained according to the meta-model of innovation integral elements. Define and collect specific enterprise innovation integral elements based on the innovation capabilities of park enterprises, instantiate discrete innovation integral element items, and organize them into a structured data model, and provide a configuration management page for the data model of innovation integral elements;

[0008] S3: Establish a model conversion relationship and a traceability connection between the data model of innovation integral elements and the display model, so that the display model can automatically adapt to the changes after the innovation integral elements are changed. The display model includes page layout and page components. The model conversion relationship is to establish a mapping relationship between the data model and the display model. The traceability connection is to record the connection between the source and target elements during model conversion;

[0009] S4: Provide interface declarations and interface implementations for the access services of the meta-model and data model of innovation integral elements. The interface declarations use extensible data objects as parameters, and the interface implementations use model conversion and data mapping to complete data storage and data conversion, so that the corresponding access services remain unchanged after the meta-model and data model of innovation integral elements are changed;

[0010] S5: Implement parallel operation of multiple models of enterprise innovation integral elements in multiple parks based on the tenant mode. The tenant mode is to allocate independent memory spaces and independent databases for the data models and data storage of enterprise innovation integral elements in different parks, use data routing technology to establish access channels for different parks to achieve independent access to the memory and databases of different parks, and use clustering algorithms in machine learning for multi-park data to complete domain tagging and horizontal data comparison.

[0011] The above S1 defines the meta-data model of enterprise innovation elements, and the specific content is as follows:

[0012] Define the classification to which the innovation integral elements belong, including the classification name and classification description, and provide different display schemes for different classifications. Specifically, for each classification, define whether to display, the display order, the number of columns in each row of the classification, and the column layout style of each classification, such as the attribute key-value pair mode, data list mode, and file attachment mode;

[0013] Define the names and meaning descriptions of innovation integral elements, define a name that can be recognized by a computer for all innovation integral elements, and provide a meaning description that is convenient for users to understand;

[0014] Define the data types of innovation integral elements. All innovation integral elements need to define data types, such as numeric type, character type, date type, enumeration type, to facilitate the storage and display of innovation integral element instances;

[0015] Taking the element name as the traceability link, establish the mapping relationship between the basic meta-model and the display model, support binding the verification rules of the meta-model data to ensure the correctness of the data. On this basis, define the display rules of innovation integral elements to help users better understand and apply after the meta-model is instantiated. The provided display rule attributes include whether it is required, whether to display, display order, display control, and the legal verification rules bound to the innovation integral elements.

[0016] The S2 mentioned above establishes a structured enterprise innovation integral element data model, and the specific contents are as follows:

[0017] The innovation integral element data model is an instance of the meta-model. The expansion of the enterprise innovation integral element data model is reflected in the increase of the meta-model instance data. Conversely, the deletion or modification of the meta-model instance data is associated with the deletion or modification of the enterprise innovation integral element data model, realizing the dynamic horizontal expansion of enterprise innovation integral elements;

[0018] After the data model is defined, collect specific enterprise innovation integral elements according to the definition of the innovation ability of park enterprises. Each innovation integral element further instantiates the sorting attributes, whether it is required, whether to display, verification rules, the field type mapped to the database, and the corresponding visualization components when bound to the page presentation according to the display rules defined in the meta-model;

[0019] After the innovation integral elements are defined, provide a configuration management page for the innovation integral element data model.

[0020] The S3 mentioned above establishes a model conversion relationship and a traceability link between the innovation integral element data model and the display model. After the innovation integral elements change, the display model adjusts adaptively according to the changes. The specific contents are as follows:

[0021] The classified management of enterprise innovation integral elements defines the attributes of classification attributes when displayed on the page, and realizes the configurability of meta-model attributes through visual page management. Specifically, it includes: each classification decides to display or not display according to parameterized configuration; each classification defines the number of elements displayed in each row; each classification defines the display style, including the attribute key-value pair mode, the data list mode, and the file attachment mode; the display parameters of the classification are increased or adjusted according to the actual application, and the changes of the classification parameters automatically adjust the display of page components through model conversion, completing the adaptive management of classification page components;

[0022] When instantiating the data model of innovation integral elements, define the sorting order among elements, define the required attributes of each element, and inject or bind different verification rules for different elements; the configuration attributes of elements are increased or adjusted according to the actual application, generate the management page of innovation integral element data through the defined display rules, and the data content of operations is stored by establishing the mapping relationship between the basic meta-model and the display model. The changes of the innovation integral element data model automatically complete the adjustment of the corresponding page components of innovation integral elements through model conversion;

[0023] Realize the model conversion relationship and traceability connection between the data model and the display model. The traceability connection is based on the rule that the unique identifiers of elements are the same. The model conversion is that each innovation integral element in the data model is mapped to the display model, and there is a uniquely determined display control corresponding to it through the traceability connection, and vice versa. The uniquely determined display control refers to the control that binds the display attributes and model verification rules and has the same name identifier. The model conversion specifically includes:

[0024] The basic attributes of the innovation integral element are denoted as ;

[0025] The display attributes of the innovation integral element are denoted as ;

[0026] The classification of the innovation integral element is denoted as ;

[0027] The page component is denoted as , where represents the mapping from the set of basic attributes of innovation integral elements , the set of display attributes and the set of classifications to the set of page components , and can also be denoted as ;

[0028] For any , for any set , for the set The solution process is called elephant mapping, which completes the generation mapping from the innovation integral element data model to the display model; and for any set , for the set The solution process is called inverse elephant mapping, which completes the data storage inverse mapping from the innovation integral element display model to the data model.

[0029] S4 provides an interface declaration and interface implementation for accessing services of the innovation integral element meta-model and data model. The interface declaration uses an extensible data object as a parameter, and the interface implementation uses model transformation and data mapping to complete data storage and data transformation, so as to ensure that the corresponding access services remain unchanged after the innovation integral element meta-model and data model are changed. The specific contents are as follows:

[0030] Provide a management page and data service for the enterprise innovation integral element meta-model, enabling data managers to manage the meta-model in a visual manner;

[0031] Provide a management page and data service for the enterprise innovation integral element data model, enabling business managers to maintain enterprise innovation integral elements in a visual manner;

[0032] Provide an operation service for instantiating model data, provide data services and access interface declarations externally, define interface parameters as extensible data objects, and internally complete the abstract encapsulation management of meta-model data and the conversion and storage of the database model at the implementation level. Use the elephant mapping when accessing and generating pages, and use the inverse elephant mapping when saving model data during page operations. When adding, reducing, or changing enterprise innovation integral elements, the interface declaration prototype and interface implementation remain unchanged, and it can adapt to the enterprise innovation integral element domain models in different parks and regions;

[0033] Provide an enterprise innovation data management page and business service. Through the design of the underlying meta-model, the mapping management between innovation integral data objects and integral element meta-model data instances is completed during interface implementation, enabling data operators to operate innovation integral data without perceiving the meta-model, just like operating ordinary data. All user interaction methods are the same as those of ordinary data models. The biggest difference between ordinary enterprise innovation integral element models and the innovation integral element models generated based on the present invention is that the present invention supports model adjustment and configuration at any time according to business needs without the intervention of programmers.

[0034] S5 realizes the parallel operation of multiple models of enterprise innovation integral elements based on the tenant mode. The specific contents are as follows:

[0035] Manage innovation integral elements in a hierarchical manner based on the tenant model. The hierarchical management of the tenant model divides managers into park managers, park superiors, and top superiors. Among them, park managers manage the data and models of their own parks, and park superiors or top superiors manage and view the innovation integral elements and innovation integral data of each subordinate park. The horizontal data comparison between peer parks is realized through the K-means clustering algorithm in machine learning;

[0036] The K-means clustering algorithm is an algorithm used in machine learning to achieve automatic domain classification for each park, including: 1) collecting enterprise data for domain identification; 2) counting the number of enterprises in different domains of the park; 3) creating initial cluster centers; 4) repeatedly traversing the distance from each park to the cluster center and finding the minimum distance; 5) continuously iterating steps 3 and 4 until the position of the cluster center no longer changes significantly or reaches the preset iteration frequency. Specifically as follows:

[0037] The collection of enterprise data provides domain identification for each enterprise, including electronic information, biology and new medicine, aerospace, new materials, high-tech services, new energy and energy conservation, resources and environment, advanced manufacturing and automation, and others;

[0038] The number of enterprises in each park domain is denoted as ;

[0039] The different park domain categories are denoted as indicating the category of point , and is used to represent the cluster center , and is used to represent the cluster center to which

[0040] belongs; The initial cluster center is generated using a random scheme, and the number of cluster centers is the same as the number of domains;

[0041] Traverse each point , and calculate the distance from point to the cluster center respectively, compare the sizes, and continuously mark and screen out the point with the smallest distance in the cluster;

[0042] Traverse all points in the same category respectively, calculate the geometric mean position of these points, and move the cluster center to this position. The cluster center during the traversal is denoted as: , and the system continuously solves the minimization of the sum of the squares of the distances between all points and their respective cluster centers during the machine learning process, denoted as: ;

[0043] The K-means algorithm needs to be iterated multiple times and optimized repeatedly. The cluster centers are randomly initialized each time until the positions of the cluster centers no longer change significantly or the preset number of iterations is reached.

[0044] Provide horizontal innovation integral element data comparison for enterprises in the same industry, provide horizontal data comparison of innovation integral elements for the same-level parks, and provide data training for different fields of the enterprise innovation integral element meta-model. The clustering K-means algorithm and the iterative learning process are also applicable to the classification and training of other innovation integral element data, constituting a complete innovation integral element training library.

[0045] Compared with the prior art, the method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks provided by the present invention has the following beneficial effects: establishing a set of scientific and standard innovation integral meta-model and data model for dynamically managing enterprise innovation integral elements; realizing visual management of the meta-model and data model, and the display model and components can perceive changes and adaptively adjust after the innovation integral element data model changes; being able to expand and change innovation integral elements at any time according to the needs of innovation integral business without the need for re-development and the intervention of programmers; ensuring the stability of the service interfaces provided by the enterprise innovation integral system externally and internally through the underlying model conversion technology; supporting the parallel operation of multiple models of innovation integral elements of enterprises in multiple parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The 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, but do not constitute a limitation to the present invention. In the drawings:

[0047] Figure 1 is a flowchart of the method for dynamically managing innovation integral elements of technology-based enterprises in multiple parks of the present invention;

[0048] Figure 2 is the management page of the enterprise innovation integral element meta-model in the embodiment of the present invention;

[0049] Figure 3 is the management page of the enterprise innovation integral element data model in the embodiment of the present invention;

[0050] Figure 4 is a storage example diagram of the enterprise innovation integral element data model in the embodiment of the present invention;

[0051] Figure 5 is a storage example diagram of the enterprise innovation integral element data in the embodiment of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0053] The objective of the embodiments of the present invention is to first define the meta-model of the innovation integral elements of technology-based enterprises, then establish a structured data model of enterprise innovation integral elements, next elaborate on how page components and data access interfaces can maintain automatic adaptation and interface stability after the change of the innovation integral element model, and finally explain the realization of parallel operation of multiple models of enterprise innovation integral elements based on the tenant mode.

[0054] Refer to Figure 1 , the method for dynamically managing the innovation integral elements of technology-based enterprises in multiple parks is as follows:

[0055] S1: Define the meta-model of enterprise innovation integral elements, whose attributes include classification attributes, basic attributes, and display attributes. The classification attributes include classification ID, classification name, classification description, classification order, whether to display, column layout style, and number of columnar columns. The basic attributes include element ID, element name, element meaning, data type, and the classification to which it belongs. The display attributes include element sorting, whether to display, whether it is required, page controls, and verification rules;

[0056] S2: Establish a data model of enterprise innovation integral elements. The data model is a meta-model instance obtained based on the meta-model of innovation integral elements. Define and collect specific enterprise innovation integral elements according to the innovation capabilities of park enterprises, instantiate the discrete innovation integral element items, and organize them into a structured data model, and provide a configuration management page for the innovation integral element data model;

[0057] S3: Establish a model conversion relationship and a traceability connection between the innovation integral element data model and the display model to achieve automatic adaptation and adjustment of the display model according to changes after the innovation integral element changes. The display model includes page layout and page components. The model conversion relationship is to establish a mapping relationship between the data model and the display model. The traceability connection is to record the connection between the source and target elements during model conversion;

[0058] S4: Provide interface declarations and interface implementations for accessing services of the innovation integral element meta-model and data model. The interface declarations use extensible data objects as parameters, and the interface implementations use model conversion and data mapping to complete data storage and data conversion, so as to ensure that the corresponding access services remain unchanged after the change of the innovation integral element meta-model and data model;

[0059] S5: Implement parallel operation of multiple models for enterprise innovation integral elements based on the tenant mode. The tenant mode allocates independent memory spaces and independent databases for the data models and data storage of enterprise innovation integral elements in different parks. Data routing technology is used to establish access channels for different parks to achieve independent access to the memory and databases of different parks. The clustering algorithm is used in machine learning for multi-park data to complete domain tagging and horizontal data comparison.

[0060] In a specific embodiment of the present invention, the S1 for defining the enterprise innovation element metadata model is as follows:

[0061] Step 1: Determine the relational database mysql to complete the data storage of the present invention example;

[0062] Step 2: Define a classification table to which the innovation integral elements belong. The core attribute fields include classification id, classification name title, classification description remark, whether the classification is displayed is_show, display order sort, the number of columns per row for display view_columns, and the column style view_type. The column style corresponds to a dropdown list or a sub-table. The classification content includes the attribute key-value pair mode, data list mode, and file attachment mode; in addition to the above core fields, classification attributes are added or adjusted according to specific business.

[0063] Step 3: Define the column name of the ID of the classification to which the innovation integral element table belongs as menu_id, store it using the long type, and associate it with the classification table defined in Step 2;

[0064] Step 4: Define the column name of the element name of the innovation integral element table as name, store the innovation integral element identifier recognizable by the computer, and store it using the varchar type;

[0065] Step 5: Define the column name of the element meaning of the innovation integral element table as remark, store the meaning description for the user to understand conveniently, and store it using the varchar type;

[0066] Step 6: Define the column name of the data type of the innovation integral element table as data_type, store it using the varchar type. The data type supports numeric, character, date, and enumeration types. The data type is not limited to those listed above and can be extended as needed;

[0067] Step 7: Define the column name of the element sorting of the innovation integral element table as sort, store it using the int type;

[0068] Step 8: Define the column name of whether to display of the innovation integral element table as is_show, store it using the char type;

[0069] Step 9: Define the column name indicating whether it is required in the innovation integral element table as is_required, and store it using the char type;

[0070] Step 10: Define the column name indicating the data type in the innovation integral element table as view_type, and store it using the varchar type;

[0071] Step 11: Define the column name indicating the verification rule in the innovation integral element table as view_validate, and store it using the varchar type. The verification rule script or rule code can be directly stored according to the implementation situation;

[0072] For the definition and description of the relevant fields in the innovation integral element table, see Figure 2 , in addition to the above core fields, the element attributes can be added or adjusted according to the specific business.

[0073] In a specific embodiment of the present invention, in S2, a structured enterprise innovation integral element data model is established, and the specific steps are as follows:

[0074] Step 1: Determine that the present invention example is implemented using the JAVA language;

[0075] Step 2: Define the classification of the innovation integral elements in the present invention example. For example, Figure 4 In the storage example shown, define a classification "Industrial and Commercial Information" with an identification ID of 10000, and define a classification "Growth and Operation Index" with an identification ID of 100101. The enterprise innovation integral elements of the present invention example can be respectively classified into the corresponding categories. For example, Figure 4 The establishment date belongs to the industrial and commercial information with a classification ID of 10000. Another example is Figure 4 The net asset profit rate belongs to the growth and operation index with a classification ID of 100101;

[0076] Step 3: Collect the innovation integral elements of the enterprise. As Figure 4 shown, there are establishment date, whether it is a high-tech enterprise, high-tech product income, operating income, proportion of personnel with postgraduate degrees or above, tax reduction amount for additional deduction of R & D expenses, net asset profit rate, operating income growth rate. Figure 4 The innovation integral elements listed as the data model storage example are only for the purpose of explaining the invention example and are not limited to these elements. In fact, the present invention can horizontally expand any innovation integral elements according to the demand level;

[0077] Step 4: As Figure 4 shown, further instantiate the specific sorting attributes, whether it is required, whether it is displayed, verification rules, field types mapped to the database, and the corresponding visualization components bound when presenting on the page according to the display rules defined in the meta-model. For example, Figure 4The operating income in it. In this record, the sort value is 2 and the menu_id value is 100101, indicating that the innovation integral element of operating income ranks second among all elements in the growth and operation indicators with the classification ID of 100101. The is_required value of 1 in this record indicates that this element is a required item. The view_type value of WANYUAN_INPUT in this record means that it will be bound to the ten-thousand-yuan input control WANYUAN_INPUT when displayed on the page. The data_type value of decimal in this record indicates that the type stored in the data instance is the numerical decimal type;

[0078] Step 5: After defining the enterprise innovation integral element meta-model and the innovation integral element model instance, further define a data table to store the specific data of each innovation integral element. As Figure 5 shown, the names of each field in this data table correspond one by one to the values of the attribute name defined in the meta-model in Step 4. For example, for the operating income mentioned in Step 4, its name value in the meta-model is idx009, so the field name in this data table should be set to idx009. In this way, the operating income will be stored in the idx009 field of the data table in the future. Through the definition, transformation, and field matching mapping in Steps 4 and 5, all the innovation integral element data can be stored as needed and freely expanded.

[0079] In a specific embodiment of the present invention, S3 establishes a model conversion relationship and a traceability connection between the innovation integral element data model and the display model, so that the display model can automatically adapt and adjust according to the changes after the innovation integral elements are changed. The specific steps are as follows:

[0080] Step 1: Define the classification management of enterprise innovation integral elements. Different classifications determine whether to display or turn off the display and the display order according to parameterized configuration; different classifications define the number of elements displayed in each row; different classifications define the display styles, including the attribute key-value pair mode, the data list mode, and the file attachment mode; the display parameters of the classification are also increased or adjusted according to the actual application. The classification parameterization scheme managed by the meta-model can complete the adaptive management of the classification page components. For example, define a classification name as "Technological Innovation Indicators", and its is_show = true, sort = 4. As Figure 3 shown, "Technological Innovation Indicators" is displayed at the 4th position in the classification menu list on the right side of this figure. The attribute is_show is also used to determine whether to use a certain type of innovation integral element during model debugging. When this attribute is set to false, the display is turned off and it is not displayed in the classification menu. At the same time, all the innovation integral elements under this classification are not only not displayed but also do not participate in the input and output of the model and data;

[0081] Step 2: Figure 3 It is a management page for instantiating the data model of the present invention. Drag each innovation integral element on the page to complete the sorting and positioning of the element. In the small menu on the right side of the innovation integral element, complete the setting of required or cancellation of required and bind different verification rules for the innovation integral element; click [Add Field] to add a new innovation integral element; select the delete button on the right side of a row with the mouse to delete the corresponding innovation integral element; click the edit icon on the right side of each classification to set the classification attributes and modify the name; click [Add Classification] below the right classification list to achieve classification addition;

[0082] Step 3: Implement the model conversion relationship and traceability connection between the data model and the display model. The traceability connection is based on the rule that the unique identifiers of the elements are the same. The model conversion is that each innovation integral element in the data model is mapped to the display model, and through the traceability connection, there is a unique display control corresponding to it. The unique display control refers to the control with the same name identifier, and the control is bound with the display attributes and model verification rules of the innovation integral element with the same name. The specific content of the model conversion includes:

[0083] The basic attributes of the innovation integral element are denoted as ;

[0084] The display attributes of the innovation integral element are denoted as ;

[0085] The classification of the innovation integral element is denoted as ;

[0086] The page component is denoted as , where represents the mapping from the set of basic attributes of the innovation integral element , the set of display attributes and the set of classifications to the set of page components , and can also be denoted as ;

[0087] For any , for any set , the solution process for the set is called the image mapping, which completes the generation mapping from the innovation integral element data model to the display model; and for any set , for the set the solution process is called the inverse image mapping, which completes the data storage inverse mapping from the innovation integral element display model to the data model.

[0088] In a specific embodiment of the present invention, the S4 provides an interface declaration and an interface implementation for accessing services of the innovation integral element meta-model and the data model. The interface declaration uses an extensible data object as a parameter, and the interface implementation uses model transformation and data mapping to complete data storage and data conversion, so as to ensure that the corresponding access services remain unchanged after the innovation integral element meta-model and the data model are changed. The specific steps are as follows:

[0089] Step 1: Provide a management page and data services for the enterprise innovation integral element meta-model shown in Figure 2 The data manager configures and manages the enterprise innovation integral element meta-model. On the management page, configuration parameters of the meta-model attributes can be added, modified or deleted as needed, as well as the display controls corresponding to each meta-model attribute after instantiation, such as text boxes, drop-down boxes, etc. If it is a drop-down box, the data dictionary corresponding to the drop-down options also needs to be set. After the definition of the meta-model is completed, the system will provide services for adding, deleting, modifying and querying the enterprise innovation integral element meta-model and a mapping service from the display control data to JAVA data;

[0090] Step 2: Provide a management page and data services for the instances of the enterprise innovation integral element meta-model. Figure 3 is the management page of the enterprise innovation integral element data model. Figure 3 The unique identifier of the display control in Figure 4 is consistent with the name field in the Figure 4 table. The business manager maintains the enterprise innovation integral elements in a visual way, and provides the management ability of the enterprise innovation integral element meta-model instances for the model storage instances shown in Figure 3 , including the type management of innovation integral elements, the management page of innovation integral element configuration, the corresponding display controls, data dictionaries, and the storage services of relevant model instances, until the mapping, transformation and operation services from the management page elements shown in Figure 4 to the model storage instances shown in

[0091] Step 3: Provide an innovation integral element data management page and data services for the innovation integral element data shown in Figure 5 . The system will dynamically generate an operation page for the data instances according to the content of the meta-model instances stored in Figure 4 . On the page, the innovation integral elements will be displayed in the corresponding categories in sequence according to the data results saved in the previous steps. Each innovation integral element is bound to the page according to the designed controls, and data verification is performed according to the set verification rules when submitting. Through the design of the underlying meta-model, the mapping management between the innovation integral data object and the integral element meta-model data instance is completed during the interface implementation, so that the data operator does not need to perceive the meta-model and can operate the innovation integral data just like operating ordinary data. All user interaction methods are the same as those of ordinary data models;

[0092] Step 4: Provide an operation service interface for instantiating model data, and externally provide data services and access interfaces to express service interfaces similar to ordinary innovation integral element models. The interface parameter definitions use relatively simple extensible data objects, and at the same time support lightweight data exchange objects JSON and extensible language XML as interface parameter construction interfaces; when converting parameter objects to Java objects, use Java reflection technology to dynamically generate corresponding attributes and attribute set and get methods. In this way, the interface declaration and the interface parameter object have good flexibility and good scalability. Based on the implementation method of the present invention, when adding, reducing, or changing enterprise innovation integral elements, the interface declaration prototype remains unchanged;

[0093] Step 5: Next, at the internal implementation level, use the unique identifier of the innovation integral element as the traceability connection. When accessing and generating pages, use the forward mapping, and when saving model data after page operations, use the inverse mapping. The meta-model data abstraction encapsulation management and database model conversion and storage are completed. Map the collected extensible data objects to the meta-model instance table by name. The stored data format is as Figure 5 shown. Based on the implementation method of the present invention, when adding, reducing, or changing enterprise innovation integral elements, the interface implementation content remains unchanged, and it can adapt to the enterprise innovation integral element data models of different parks and different regions.

[0094] In a specific embodiment of the present invention, S5 realizes the parallel operation of multiple models of enterprise innovation integral elements based on the tenant mode. The specific steps are as follows:

[0095] Step 1: Support the tenant mode to incorporate the model data of multiple park enterprises. The system can accommodate the data and models of multiple parks at the same time, provide independent park identifiers and memory spaces for each park, and independently build databases for the data of each park. Use the data routing technology of java to direct to the corresponding data and memory areas according to the park identifier to complete the independent access of the data;

[0096] Step 2: The system uses the collected enterprise data training as the input data for model training to better identify the innovation fields and development needs of the park or street. The system completes machine learning through the clustering analysis algorithm K-means, and labels the park or street with domain labels for horizontal data observation of different parks or streets. The K-means clustering algorithm is an algorithm used in machine learning to achieve the automatic classification of each park, including 1) collecting enterprise data for domain identification, 2) counting the number of enterprises in different domains of the park, 3) creating an initial clustering center, 4) repeatedly traversing the distance from each park to the clustering center and finding the minimum distance, 5) continuously iterating steps 3 and 4 until the position of the clustering center no longer changes significantly or reaches the preset iteration frequency, specifically as follows:

[0097] Collect the enterprise data, and provide a domain identifier for each enterprise, including electronic information, biology and new medicine, aerospace, new materials, high-tech services, new energy and energy conservation, resources and environment, advanced manufacturing and automation, and others;

[0098] The number of enterprises in each park domain is denoted as ;

[0099] The different park domain categories are denoted as indicating the category of point , and using to represent the clustering center , and using to represent the clustering center to which it belongs;

[0100] The initial clustering center is generated using a random scheme , and the number of clustering centers is the same as the number of domains;

[0101] Traverse each point , and calculate the distances from the point to the clustering centers respectively, compare the magnitudes, and continuously mark and screen out the points with the smallest distance to the clustering center where they are located;

[0102] Traverse all the points in the same category respectively, calculate the geometric mean position of these points, and move the clustering center to this position. The clustering centers during the traversal process are denoted as: , and the system continuously solves the minimization of the sum of the squares of the distances between all points and their respective clustering centers during the machine learning process, denoted as: ;

[0103] The K-means algorithm needs to be iterated multiple times and optimized repeatedly. Each time it is iterated, the clustering centers are randomly initialized until the positions of the clustering centers no longer change significantly or the preset iteration frequency is reached;

[0104] Step 3: Support the model data training for different domains of the enterprise innovation integral element meta-model. The clustering K-means algorithm and the iterative learning process are also applicable to the classification and training of other innovation integral element data in the park or street, constituting a complete innovation integral element training library;

[0105] Step 4: Provide the simultaneous operation of different enterprise models of innovation integral elements in the same stage and the same industry, provide bar chart and radar chart modes, support the horizontal comparison of enterprises in the same industry, and find the horizontal advantages and disadvantages of enterprise data.

Claims

1. A method for dynamically managing innovation points of technology-based enterprises in multiple parks, characterized in that: The method comprises: S1: Define the enterprise innovation score element meta-model, whose attributes include classification attributes, basic attributes and display attributes. The classification attributes include classification ID, classification name, classification description, classification order, whether to display, column style, and number of columns. The basic attributes include element ID, element name, element meaning, data type, and category. The display attributes include element sorting, whether to display, whether required, page controls, and validation rules. S2: Establishing an enterprise innovation score factor data model, which is a meta-model instance obtained based on the innovation score factor meta-model, defining and collecting specific enterprise innovation score factors based on the innovation capabilities of the park enterprises, instantiating discrete innovation score factor items, organizing them into a structured data model, and providing a configurable management page for the innovation score factor data model; S3: Establishing a model conversion relationship and a traceability connection between the innovation credit element data model and the display model, so that the display model automatically adapts to the change after the innovation credit element is changed. The display model includes a page layout and page components. The model conversion relationship is to establish a mapping relationship between the data model and the display model. The traceability connection is to record the connection between the source and target elements during model conversion. S4: Provide interface declaration and interface implementation for access services of innovation credit element metamodel and data model. The interface declaration uses extensible data objects as parameters. The interface implementation uses model conversion and data mapping to complete data storage and data conversion, so as to ensure that the corresponding access services remain unchanged after the innovation credit element metamodel and data model are changed. S5: Based on the tenant model, multiple models of enterprise innovation score elements in multiple parks can be operated in parallel. The tenant model allocates independent memory space and independent database for enterprise innovation score element data models and data storage in different parks, uses data routing technology to establish access channels for different parks to achieve independent access to memory and databases in different parks, and uses clustering algorithm machine learning to complete domain labels and horizontal data comparisons for multi-park data.

2. The method for dynamically managing innovation points of technology-based enterprises in multiple parks according to claim 1 is characterized in that Establish a structured enterprise innovation score element metamodel and data model to achieve visual management of the metamodel and data model. The visual management is to achieve metamodel attributes, metamodel data types, and mapping binding relationships between metamodel attributes and page elements through page management. It supports the definition of validation rules for binding metamodel data. The validation rules will be executed when the data is submitted to ensure the legitimacy of the data. The visual management also includes generating a visual innovation score element data management page through the defined metamodel.

3. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 2 is characterized in that: The method also includes: the data model of the innovation points element is an instance of the metamodel, the expansion of the data model of the enterprise innovation points element is reflected in the increase of the metamodel instance data, and conversely, the deletion or modification of the metamodel instance data is associated with the deletion or modification of the enterprise innovation points element data model, thereby realizing the dynamic horizontal expansion of the enterprise innovation points element.

4. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 1 is characterized in that: The method further includes: realizing a model conversion relationship and a traceability connection between a data model and a display model, wherein the traceability connection is based on the same unique identifier of the elements, wherein the model conversion is to map each innovation score element in the data model to the display model, and to connect a unique display control corresponding thereto through a traceability connection, wherein the unique display control refers to a control with the same name identifier, and the control is bound to display properties and model verification rules of the innovation score element with the same name, and the specific contents of the model conversion include: The basic attributes of the innovation score elements are recorded as ; The display attribute of the innovation score element is recorded as ; The classification of the innovation points elements is recorded as follows: ; The page components are denoted as ,in Represents the basic attribute set of innovation integral elements , Display attribute collection and classification collection To the page component collection The mapping can also be written as ; For any , for any set , for the set The solving process is called image mapping, which completes the mapping from the innovative integral element data model to the display model; , for the set The solution process is called inverse image mapping, which completes the inverse mapping of data storage from the innovation integral element display model to the data model.

5. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 4 is characterized in that: The method also includes: providing a service interface of the enterprise innovation integral element metamodel and the data model to realize model conversion using image mapping and inverse image mapping, the service interface declaration of the enterprise innovation integral element metamodel and the data model uses extensible data objects as data carriers for displaying the model in parameter definition, the extensible data objects include lightweight data exchange objects JSON and extensible language XML, when the enterprise innovation integral element changes, the service interface declaration of the metamodel and the data model is kept stable by changing the data object attributes.

6. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 4 is characterized in that: The method also includes: using the image mapping technology to achieve page layout and control generation and automatic adaptive adjustment when generating a page, using the inverse image mapping technology to save the model and data after the operation is completed, and storing the collected extensible data objects in the data model instance table by name mapping, and when the enterprise innovation score elements change, the interface implementation keeps the content unchanged and adapts to the enterprise innovation score element data models in different parks and regions.

7. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 1 is characterized in that: Innovation score elements are managed hierarchically based on the tenant model. The tenant model hierarchical management divides managers into park managers, park superior managers and superior superior managers. Park managers manage the data and models of their own park, and park superior managers or superior superior managers manage and view the innovation score elements and innovation score element data of each park under their jurisdiction. Machine learning is completed through the cluster analysis algorithm K-means to realize horizontal data comparison between parks of the same level.

8. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 7 is characterized in that: The cluster analysis algorithm K-means is an algorithm used in machine learning to complete the automatic field classification of each park, including 1) collecting enterprise data for field identification, 2) counting the number of enterprises in different fields of the park, 3) creating an initial cluster center, 4) repeatedly traversing the distance from each park to the cluster center and finding the minimum distance, 5) continuously iterating steps 3 and 4 until the position of the cluster center no longer changes significantly or reaches a preset iteration frequency, as follows: The aforementioned collection of enterprise data provides a field identification for each enterprise, including electronic information, biology and new medicine, aerospace, new materials, high-tech services, new energy and energy conservation, resources and environment, advanced manufacturing and automation; The number of enterprises in each park area is recorded as ; The different park field categories are recorded as Indicate point The category, use Represents the cluster center ,use express The cluster center to which it belongs; The initial cluster center Use random scheme to generate , the number of cluster centers and the number of territories are consistent; Traverse each point , calculate the points To cluster center Distances are compared, and the points with the smallest distance in the cluster are continuously marked and screened out; Traverse all points of the same class separately, calculate the geometric mean position of these points, and move the cluster center to this position. The cluster center during the traversal process is recorded as: ,In the process of machine learning, the system continuously seeks to minimize the sum of the squares of the distances between all points and their cluster centers, which can be expressed as: ; The cluster analysis algorithm K-means requires multiple iterations and repeated tuning, and the cluster center is randomly initialized in each iteration until the position of the cluster center no longer changes significantly or reaches a preset iteration frequency.

9. The method for dynamically managing innovation score elements of technology-based enterprises in multiple parks according to claim 8 is characterized in that: The method also includes: providing horizontal data comparison of innovation score elements of enterprises in the same industry, providing horizontal data comparison of innovation score elements of parks at the same level, and providing data training in different fields of enterprise innovation score element meta-model. The clustering analysis algorithm K-means and iterative learning process are also applicable to the classification and training of other innovation score element data, forming a complete innovation score element training library.

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