Object model adaptation method and device, readable storage medium, program product

Abstract

This application discloses a method and apparatus for adapting a device model, a readable storage medium, and a program product, applied to an Internet of Things (IoT) platform. The method includes: acquiring model information of a device model created for a target terminal to be integrated into a target application, and model information of multiple application integration models created for the target application. Different application integration models are created for different terminal categories. The model information includes attribute information, static information of function points, and dynamic information of function points. The method further involves matching the attribute information and static information of function points of the device model with those of the multiple application integration models to select a first function point from the function points included in the device model that is of the same type and matches the function point of the target application integration model. Finally, the method involves matching the dynamic information of the first function point with the dynamic information of the function points of the target application integration model to bind the first function point to the corresponding matching function point of the target application integration model, thereby adapting the device model of the target terminal to the target application.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a method and apparatus for adapting an object model, a readable storage medium, and a program product. Background Technology

[0002] With the continuous evolution of IoT technology, the scale of IoT applications is experiencing rapid growth. Application scenarios have gradually expanded from typical IoT areas such as smart homes, smart agriculture, smart cities, and industrial automation to multiple key industry sectors including transportation and energy, with increasingly broad coverage. Currently, the mainstream IoT application development model typically involves IoT platform vendors building a unified IoT platform, providing general platform service capabilities, and enabling unified access, protocol parsing, data collection, and remote control functions for various terminals. Simultaneously, it encapsulates various standardized data interfaces and reusable integrated components for the application layer. Application vendors then leverage the standardized capabilities provided by the IoT platform to connect with terminals, develop functions such as status monitoring and remote control, and quickly build IoT applications for specific scenarios.

[0003] As IoT application scenarios become increasingly diverse, their complexity and integration scale continue to grow. Individual applications often need to integrate and manage terminals from multiple different manufacturers. Although IoT platforms generally provide object model standards to standardize the data structure and format of terminals, due to differences in functional design, data type definition, and protocol support among different manufacturers, applications still face challenges in data compatibility and adaptability when connecting and integrating terminals.

[0004] Existing solutions utilize smart gateways for adaptation. Multiple projects with different adaptation systems are distilled into a single, IoT-based adaptation system. This system defines the business-side object model for each subsystem, designs the corresponding adapter's object model structure code for the subsystem, abstracts and maps terminals from different vendors within the same subsystem to functional points of the object model, and establishes a terminal identifier mapping template configuration between the adapter and third-party application platform systems. This enables adaptation to terminals from different vendors and with different standards, resolving the interfacing issues arising from numerous projects involving various vendors and terminals.

[0005] However, the aforementioned adaptation system is highly dependent on manual configuration, involving multiple manual configuration operations such as business subsystem object models, terminal object models, terminal information and mapping relationships. The process is cumbersome, inefficient, and prone to system data anomalies due to configuration errors, affecting system stability.

[0006] Improving the adaptation efficiency of terminal object models is a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this application is to provide a method and apparatus for adapting an object model, a readable storage medium, and a program product, in order to solve the problem of low adaptation efficiency of terminal object models.

[0008] To solve the above-mentioned technical problems, this specification is implemented as follows: Firstly, a method for adapting an object model is provided for application to an Internet of Things (IoT) platform. The method includes: Obtain the model information of the object model created corresponding to the target terminal to be integrated into the target application, and the model information of multiple application integration models created corresponding to the target application. Different application integration models are created for different terminal categories. The model information includes attribute information, static information of function points, and dynamic information of function points. The attribute information and static function point information of the object model are matched with the attribute information and static function point information of multiple application integration models to filter out at least one first function point from the function points included in the object model that is the same as and matches the function point type of the target application integration model. The target application integration model is any one of the multiple application integration models. The dynamic information of the function points of the first function point is matched with the dynamic information of the function points of the target application integration model to bind the at least one first function point to the corresponding function point of the target application integration model, so that the object model of the target terminal is adapted to the target application.

[0009] Optionally, at least one first functional point that is the same type as and matches the functional point of the target application integration model is selected from the functional points included in the object model, including: The attribute information of the object model is matched with the attribute information of the plurality of application integration models to obtain at least one matching first application integration model. The static information of the function points of the object model is matched with the static information of the function points of the at least one first application integration model to filter out at least one second function point from the function points included in the object model that is the same type as each function point of the target first application integration model. The target first application integration model is any one of the at least one first application integration models. Based on the similarity between the static information of the function points of the second function points and the static information of the function points of the third function points of the same type corresponding to the target first application integration model, at least one first function point that matches the target first application integration model is selected from the at least one second function point.

[0010] Optionally, the attribute information includes application scenarios and terminal categories. Obtain at least one matching first application integration model, including: If the application scenario and terminal category in the attribute information of the object model match the application scenario and terminal category in the attribute information of the target application integration model, then the target application integration model is determined to be the first matching application integration model.

[0011] Optionally, the static information of the function point includes the function point type, function point name, function point identifier, and function point description. From the functional points included in the object model, at least one second functional point of the same type as the functional points of the target first application integration model is selected, including: If the function point type of the target function point in the static information of the function points of the object model matches the function point type in the static information of the function points of the target first application integration model, then the target function point is determined to be the second function point. Selecting at least one first functional point from the at least one second functional point that matches the target first application integration model includes: Calculate the similarity between the function point name, function point identifier, and function point description in the static information of the function point of the target function point and the function point name, function point identifier, and function point description in the static information of the function point of the third function point corresponding to the target first application integration model. If the weighted similarity of the similarity between the function point name, the function point identifier, and the function point description of the target function point exceeds a preset similarity threshold, then the target function point is determined to be the first function point.

[0012] Optionally, binding the at least one first functional point to a corresponding functional point in the target application integration model includes: Based on the dynamic information of function points, the dynamic feature vectors of the first function point of each function point type corresponding to the object model and the dynamic feature vectors of the fourth function point of the same function point type in the target application integration model are obtained respectively. Calculate the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point included in each functional point combination; The first and fourth functional points with the highest similarity among the functional point combinations are used as the matching functional points and bound together.

[0013] Optionally, the function point dynamic information includes dynamic features in multiple dimensions. The dynamic feature vectors of the first functional point corresponding to each functional point type of the object model and the dynamic feature vectors of the fourth functional point corresponding to the functional point type of the target application integration model are obtained respectively, including: Based on the dynamic features of the first functional point, which is a combination of target functional points, a dynamic feature vector of the first functional point is constructed. Based on the dynamic features of the fourth functional point in the combination of the target functional points, including the dynamic features of the multiple dimensions, a dynamic feature vector of the fourth functional point is constructed.

[0014] Optionally, the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point included in each functional point combination is calculated, including: Calculate the similarity between the dynamic features of each dimension in the dynamic feature vector of the first functional point of the target functional point combination and the dynamic features of the same dimension in the dynamic feature vector of the fourth functional point of the target functional point combination, and obtain the similarity of the dynamic features of each dimension of the target functional point combination. The average similarity of the dynamic features across the multiple dimensions is taken as the similarity of the target functional point combination.

[0015] In a second aspect, an adaptation device for an object model is provided, including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method described in the first aspect.

[0016] Thirdly, a readable storage medium is provided that stores a program or instructions which, when executed by a processor, implement the steps of the method described in the first aspect.

[0017] Fourthly, a computer program product is provided, comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform the steps of the method described in the first aspect.

[0018] In this embodiment, the IoT platform obtains model information of the object model created corresponding to the target terminal to be integrated into the target application, and model information of multiple application integration models created corresponding to the target application. Different terminal categories correspond to different application integration models. The model information includes attribute information, static function point information, and dynamic function point information. The attribute information and static function point information of the object model are matched with the attribute information and static function point information of the multiple application integration models to select at least one first function point from the function points included in the object model that is the same type as and matches the function point of the target application integration model. The target application integration model is any one of the multiple application integration models. The dynamic function point information of the first function point is matched with the dynamic function point information of the target application integration model to bind the at least one first function point to the function point corresponding to the target application integration model, so that the object model of the target terminal is adapted to the target application. Therefore, a multi-level matching strategy combining functional feature analysis and behavioral feature analysis is adopted. Functional feature matching analyzes and matches the functional semantic consistency between the application and the terminal object model at the static functional level, ensuring that the terminal meets the integration functional requirements of the application. Behavioral feature matching evaluates and analyzes the actual behavioral characteristics of the terminal object model at the dynamic operational level, ensuring that its operational characteristics meet the behavioral constraints of the application side. Through a two-dimensional re-matching mechanism of static and dynamic functional point information, the application's adaptation to the object model is automated and dynamic, and the terminal capabilities can be accurately evaluated, improving the accuracy and reliability of matching. This enhances the application's automated integration capability with multi-source terminals. The automated adaptation method simplifies the application adaptation process, improves the application adaptation flexibility, enhances the system's versatility and scalability, reduces development and maintenance costs, meets the needs of rapid integration and low-threshold development of intelligent applications on the IoT platform, and improves the adaptation efficiency of the object model. In addition, the application integration model, from the application perspective, is independent of specific terminals, achieving effective decoupling between the application and the terminal, and provides a standardized description of the terminal's capabilities and behavioral characteristics, ensuring the stability and consistency at the application level. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the object model adaptation method according to an embodiment of this application.

[0020] Figure 2 This is a flowchart illustrating the static feature matching steps of the model in an embodiment of this application.

[0021] Figure 3 This is a flowchart illustrating the model dynamic feature matching steps in an embodiment of this application.

[0022] Figure 4 This is a schematic diagram of the interface of the function point binding configuration table in an embodiment of this application.

[0023] Figure 5 This is a schematic diagram of the overall process of adapting a physical model according to a specific embodiment of this application.

[0024] Figure 6 This is a structural block diagram of an adapter for an object model according to an embodiment of this application.

[0025] Figure 7 This is a structural block diagram of an adapter for a physical model according to another embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. The drawing numbers in this application are only used to distinguish the various steps in the solution and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0027] To address the problems existing in the prior art, embodiments of this application provide a method for adapting an object model, applied to an Internet of Things (IoT) platform, such as... Figure 1 As shown, the method includes steps 102 to 106.

[0028] Step 102: Obtain the model information of the object model created corresponding to the target terminal to be integrated into the target application, and the model information of multiple application integration models created corresponding to the target application. Different application integration models are created for different terminal categories. The model information includes attribute information, static information of function points, and dynamic information of function points.

[0029] IoT platforms or IoT cloud platforms are general-purpose platforms that provide general capabilities for different application vendors to develop applications, such as smart building systems, smart communities, and other IoT applications.

[0030] Based on the functions implemented by each application, the same application, such as a smart building system, needs to integrate different types of terminals, including video equipment and sensor equipment. The same type of terminals can come from different terminal manufacturers.

[0031] Each terminal manufacturer can define and create corresponding object models for its terminal products. For example, based on the industry application scenario, terminal category, communication protocol, network connection method, and other attribute information of the terminal, the terminal manufacturer can create corresponding object models on the IoT platform to complete the standardized definition of terminal capabilities.

[0032] The static information parameters that the object model needs to define include function point types (e.g., attributes, events, services), function point names, function point identifiers, function point descriptions, data types, value ranges, units, read / write types, event types, input / output parameters, etc. Terminal manufacturers complete the creation, development, and debugging of terminals according to the object model definition, and connect the terminals to the IoT platform to achieve standardized deployment and production. The specific static information of function points may differ between similar terminals from different manufacturers.

[0033] Similarly, application vendors can define and create application integration models for their respective application products.

[0034] In the IoT cloud platform, an application-oriented application integration model is provided. This model describes the functional characteristics (features in static functional information) and behavioral characteristics (features in dynamic functional information, i.e., dynamic features) that the terminals to be integrated must possess from the perspective of capability integration, thus constructing an abstract model of application capabilities. Each application can define multiple application integration models simultaneously according to business needs to adapt to terminals in different industries (i.e., application scenarios) and different product categories. One application integration model is used to adapt to the object models of multiple terminals of the same type from different terminal manufacturers.

[0035] Application vendors define and create the application integration model required for their applications on the IoT platform. The parameters that need to be defined include the attribute information and function point configuration information of the application integration model (including static and dynamic function point information).

[0036] The attribute information is the basic information of the model, including the model name, industry application scenario, terminal category, model matching strategy, model update strategy, model description and tag information, etc. Each parameter of industry application scenario, terminal category and tag can be configured with multiple options at the same time. The model matching and update strategy can be set to automatic or manual confirmation. In automatic mode, the IoT platform will automatically complete the model matching. In manual mode, the system will complete the matching and then the user will confirm and complete the relevant configuration, ensuring the flexibility of matching.

[0037] The function point configuration information of the application integration model is used to define the capability characteristics of the terminal to be integrated by the application integration model. It does not depend on the specific physical terminal, but starts from the application perspective to build a corresponding set of function point feature information, including functional features and behavioral features, so as to realize the abstract description and structured definition of the functional and behavioral characteristics of the integrated terminal.

[0038] In the configuration of function point feature information, each function point needs to define a set of standardized parameter fields, including: function point name, function point type, function point identifier, data type, function point description, and function point behavior feature (or dynamic feature) type. Among them, the behavior feature category is used to identify the interactive behavior attributes of the function point, and needs to be selected from the platform's preset behavior feature library to achieve standardized classification of function point behavior characteristics.

[0039] The behavioral feature library is uniformly planned, constructed, and maintained by the platform. Based on industry application scenarios and terminal product classifications, and combined with the interaction characteristics of various terminal functions, the IoT platform establishes standardized behavioral feature libraries according to three main categories of functional points in the object model: attributes, services, and events. Each functional point's behavioral feature library consists of a set of configurable and scalable feature dimensions used to describe and define the dynamic behavioral characteristics of the functional point during actual operation.

[0040] Typically, an object model includes three categories of functional points: attributes, services, and events. In practical applications, IoT platforms can flexibly adjust and expand the categories of functional points in the object model and the dimensions of the corresponding behavioral characteristics according to business needs, in order to meet the modeling requirements of functional point behavioral characteristics in different scenarios.

[0041] Table 1 below provides examples of the definition of dynamic feature dimensions for the three functional categories: attributes, services, and events. Table 1

[0042] Based on the dynamic feature dimension definitions of the function point types in Table 1, the dynamic features of each function point type for specific industry application scenarios and terminal categories are defined. Table 2 below provides an example of the dynamic feature category definitions for environmental monitoring products in the smart park industry: Table 2

[0043] After creating an application integration model, application vendors can add target terminals to the target application through the terminal allocation or application functions provided by the IoT platform. The IoT platform supports batch allocation of terminal resources based on terminal identifiers, or authorization access for cross-entity terminals can be completed through the approval process of the terminal's affiliated unit. This mechanism binds application and terminal resources.

[0044] When a target terminal to be integrated into a target application is added to the target application, the IoT platform queries and loads the model information of the corresponding object model, including attribute information, static information of function points, and dynamic information of function points, based on the terminal identifier and product identifier of the target terminal.

[0045] Simultaneously, based on the target application's identifier, the system queries the application integration models already created under the current target application. As mentioned above, a target application can define and create multiple application integration models simultaneously according to business needs to adapt to terminals in different industries and product categories. Therefore, when multiple application integration models exist for a target application, these models can be traversed sequentially to filter out the set of application integration models that have not been bound and adapted to the object model of the target terminal. The system then retrieves and loads the attribute information, static information, and dynamic information of the relevant application integration models.

[0046] If an application integration model has been bound and adapted to the object model of the target terminal, it means that the application integration model has previously integrated the same model of product from the same terminal manufacturer as the target terminal, and the addition of the target terminal this time increases the number of products of that model. In this case, the object model of the target terminal can be directly adapted to the target application according to the historical adaptation method of the terminal object model.

[0047] If there is no application integration model that has been bound and adapted to the object model of the target terminal, it means that the target terminal is a product that is being integrated into the target application for the first time. Then, the adaptation of the object model of the target terminal and the target application will be performed according to the adaptation method in steps 104 to 106 below.

[0048] Step 104: Match the attribute information and function point static information of the object model with the attribute information and function point static information of multiple application integration models, so as to select at least one first function point from the function points included in the object model that is the same as and matches the function point type of the target application integration model. The target application integration model is any one of the multiple application integration models.

[0049] The matching process in this step involves analyzing the basic information and static information of each functional point of the application integration model created under the target application and the object model of the target terminal to achieve static feature matching between the functional points of the application integration model and the functional points of the object model. This matching process ensures, from a static functional perspective, that the object model of the target terminal can meet the functional integration requirements of the target application, and is a prerequisite for the dynamic feature matching in the subsequent step 106.

[0050] Based on the solution provided in the above embodiments, optionally, in step 104, selecting at least one first functional point from the functional points included in the object model that is of the same type and matches the functional point of the target application integration model includes: matching the attribute information of the object model with the attribute information of the plurality of application integration models to obtain at least one matching first application integration model; matching the static information of the functional points of the object model with the static information of the functional points of the at least one first application integration model to select at least one second functional point from the functional points included in the object model that is of the same type as each functional point of the target first application integration model, wherein the target first application integration model is any one of the at least one first application integration model; and selecting the at least one first functional point matching the target first application integration model from the at least one second functional point based on the similarity between the static information of the second functional point and the static information of the third functional point of the same type corresponding to the target first application integration model.

[0051] As mentioned above, an object model can include multiple different types of functional points, such as attributes, events, and services; the same type of functional point can include multiple different functional points, such as multiple functional points under an attribute; the same functional point can include multiple dimensions of static and dynamic features.

[0052] In step 104, it is necessary to select functional points from the functional points included in the object model of the target terminal that are the same type as and match the functional points of the application integration model of the target application, and these are called the first functional points.

[0053] The premise for determining whether the function point types are the same is whether the model attribute information matches. This can be determined by comparing the attribute information of the target terminal's object model with the attribute information of the target application's application integration model. Here, the application integration model being compared has not been bound to the object model.

[0054] Based on this matching, namely attribute information matching, at least one first application integration model of the target application can be selected, that is, the attribute information of the first application integration model is matched with the object model of the target terminal.

[0055] Specifically, the attribute information includes application scenarios and terminal categories, and obtaining at least one matching first application integration model includes: if the application scenario and terminal category in the attribute information of the object model both match the application scenario and terminal category in the attribute information of the target application integration model, then the target application integration model is determined to be the matching first application integration model.

[0056] The corresponding application integration model is extracted, including attribute information such as application scenario and terminal category. This information is then matched with the application scenario and terminal category of the object model. If a match is found, this application integration model is considered the first matching application integration model. If there are multiple corresponding application integration models, each model is iterated through sequentially. If a match is not found, the matching application integration model is skipped. In this way, one or more first matching application integration models can be identified.

[0057] If the application scenario and terminal category match, the static information of the function points in the target terminal's object model is further matched with the static information of the function points in the first application integration model. Function points with the same type as those in the first application integration model are selected from the function points included in the object model; these are called second function points. Besides matching function point types, it can also be determined whether the data type of the second function point selected from the object model matches the data type of the corresponding function point in the first application integration model, i.e., whether the data type conversion requirement is met. For example, if the data type of a second function point's name is numeric, it can be converted to and from data types including strings and numeric values, but not to and from boolean data types. If the data type conversion requirement is not met, the function point is removed from the second function point pool.

[0058] The premise for determining whether the function point types are the same is whether the static information of the function points of the models are matched. This can be determined by comparing the static information of the function points of the target terminal's object model with the static information of the function points of the first application integration model of the target application.

[0059] Specifically, the static information of the function points includes function point type, function point name, function point identifier, and function point description. Selecting at least one second function point from the function points included in the object model that has the same function point type as each function point of the target first application integration model includes: if the function point type of the target function point in the static information of the function points of the object model matches the function point type in the static information of the function points of the target first application integration model, then the target function point is determined to be the second function point.

[0060] Based on this matching, specifically the matching of function point types in the static information of function points, a set of second function points that are identical in type to each function point in the first application integration model can be selected from the function points of the object model. If multiple function point types are identical, then this set of second function points includes multiple second function points corresponding to the same function type, and each function point type may correspond to one or more matching second function points. If the first application integration model has three types, then the object model has second function points of the same type as each of the three types.

[0061] For example, if the first application integration model includes three functional point types: attribute, event, and service, then the second functional point set summarizes the second functional points of the corresponding object models for these three functional point types. A functional point type corresponding to an attribute may have one or more matching second functional points, a functional point type corresponding to an event may have one or more matching second functional points, and a functional point type corresponding to a service may have one or more matching second functional points. Furthermore, the second functional points in the set include functional points in the object model that share the same functional point type as any functional point in any first application integration model.

[0062] If none of the function point types of the object model match the function point types of a certain first application integration model, then skip that first application integration model. Repeat this process until all first application integration models have been traversed.

[0063] After obtaining the second functional point of the object model, a third functional point of the same type as the second functional point in the first application integration model can be determined. As mentioned above, each functional point type in the first application integration model has one or more corresponding matching second functional points. The third functional point refers to the functional points included in the target type in the first application integration model, and the second functional point refers to the functional points included in the target type in the object model.

[0064] For example, if the first application integration model's function point type a includes function point 1 and function point 2, then function point 1 and function point 2 belong to the third function point. If the object model's corresponding function point type a includes function point 3 and function point 4, then function point 3 and function point 4 belong to the second function point.

[0065] Then, based on the similarity between the static information of the function points of the second function point and the static information of the function points of the third function point, the function points that match the corresponding first application integration model are selected from the set of second function points and are called the first function points.

[0066] Continuing with the above example, this step requires calculating the similarity between the static information of function point 1 and function point 3 for the same function point type a, the similarity between the static information of function point 1 and function point 4, the similarity between the static information of function point 2 and function point 3, and the similarity between the static information of function point 2 and function point 4. Then, based on the calculated similarity, the first function point is selected from the second function point set.

[0067] Specifically, selecting at least one first functional point from the at least one second functional point that matches the target first application integration model includes: calculating the similarity between the function point name, function point identifier, and function point description in the static information of the function point of the target functional point and the function point name, function point identifier, and function point description in the static information of the function point of the third functional point corresponding to the target first application integration model; if the weighted similarity of the similarity of the function point name, the similarity of the function point identifier, and the similarity of the function point description corresponding to the target functional point exceeds a preset similarity threshold, then the target functional point is determined as a first functional point.

[0068] The function point names, function point identifiers, and function point descriptions of different dimensions in the static information of function points can be referred to as the static features of the model's function points. In this embodiment, by analyzing the semantic information of the static features of the second function points of the selected first application integration model and the object model of the target terminal, and using a similarity weighted calculation method based on multi-dimensional static features, the static feature matching of the function points of the first application integration model and the second function points of the object model can be achieved, thereby selecting the first function points of the object model.

[0069] This similarity process primarily involves analyzing and comparing the functional points of the application integration model and the object model from a semantic level of static functional features to establish a preliminary mapping relationship. The similarity analysis of static features is mainly based on the functional point name, functional point identifier, and functional point description, establishing a weighted similarity score. The calculation formula is as follows:

[0070] In this formula, The weighted similarity is the sum of the static features corresponding to the functional points. The similarity weight of the feature name. This represents the similarity value between the function point names. The similarity weights for function point identifiers, This represents the similarity value between function point identifiers. The similarity weights for the function point descriptions This is the similarity value for the function point descriptions.

[0071] During implementation, the weights of the three factors can be adjusted and optimized according to actual needs to ensure their significant impact on similarity. For single-string features such as function point names and function point identifiers, similarity can be calculated using algorithms such as edit distance. For long-text features such as function point descriptions, similarity can be calculated using algorithms such as cosine similarity.

[0072] Calculate the similarity of function point names pairwise between each second function point of the same type in the second function point set and each function point of the corresponding application integration model of the same function point type.

[0073] Continuing with the example of calculating the similarity between the static information of function point 1 and function point 3, we can calculate the similarity between the function point name of function point 1 and the function point name of function point 3 to obtain the similarity value between the function point names of function point 1 and function point 3 corresponding to function point type a. .

[0074] Similarly, the similarity values ​​of function point identifiers for function point 1 and function point 3 corresponding to function point type a can be obtained. Similarity value between the function point descriptions of function point 1 and function point 3 corresponding to function point type a

[0075] Using the above weighted similarity calculation formula, we can obtain the weighted similarity between the static features of function point 1 and function point 3 corresponding to function point type a.

[0076] Similarly, we can obtain the weighted similarity of the static features of function points 1 and 4 corresponding to function point type a, the weighted similarity of the static features of function points 2 and 3, and the weighted similarity of the static features of function points 2 and 4. Also, similarly, we can obtain the weighted similarity of the static features of the second and third function points of the same function point type for all other function point types in the second function point set (excluding function point type a).

[0077] By using the above method, each first application integration model is traversed one by one, and the weighted similarity between the third functional point of the first application integration model and the second functional point of the corresponding model is calculated sequentially according to the similarity calculation formula. If weighted similarity If the system's preset similarity threshold is reached, the corresponding object model's second functional point is designated as the first functional point and included in the static feature matching set. This process is repeated until all second functional points have been processed.

[0078] The static functional feature matching of the model adopts a multi-parameter comprehensive similarity evaluation method based on the functional point name, functional point identifier, and functional point description. Based on the different data types and structures of each parameter, different similarity calculation formulas are used. Through comprehensive analysis, the accuracy of functional feature matching is improved, while ensuring the consistency and alignment of the application and the terminal at the functional level.

[0079] Based on the matching results of step 104 above, a static feature matching set is generated. This set is a two-dimensional matrix. The rows of the matrix represent the functional points of the first application integration model, and the columns of the matrix represent the first functional points of the object model that match the static features of the functional points of the first application integration model.

[0080] In summary, the implementation process of static feature matching in step 104 is as follows: Figure 2 As shown, it includes the following steps: Step 202: When the target terminal is added to the target application, obtain and load the model information of the application integration model of the target application and the model information of the target terminal object model. Step 204: Construct an initial set of candidate function points based on model attributes (the application scenario, terminal classification, and function point type should match).

[0081] Step 206: Calculate and analyze the similarity of functional features (static features) of each functional point in the initial candidate functional point set; Step 208: Generate a set of model functional feature matching results (static feature matching set).

[0082] Step 106: Match the dynamic information of the function points of the first function point with the dynamic information of the function points of the target application integration model, so as to bind the at least one first function point with the corresponding function point of the target application integration model, so that the object model of the target terminal is adapted to the target application.

[0083] This step, based on the static matching of attribute information and static feature information in step 104, introduces dynamic matching based on behavior / dynamic features. That is, by establishing standard behavioral features of the three functional points of the application integration model, namely attributes, services and events, and extracting the behavioral features of the first functional point of the object model in actual operation, by comparing the matching degree of the behavioral features of the two, the functional points are deeply aligned at the dynamic behavior level, ensuring that the dynamic adaptability and actual usability of the object model are further improved while meeting the consistency of functional / static features.

[0084] Based on the solution provided in the above embodiments, optionally, in step 106, binding the at least one first functional point with the functional point corresponding to the target application integration model includes: obtaining the dynamic feature vectors of the first functional point of each functional point type corresponding to the object model and the dynamic feature vectors of the fourth functional point of the same functional point type in the target application integration model based on the dynamic information of the functional points; calculating the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point included in each functional point combination; and binding the first functional point and the fourth functional point with the maximum similarity corresponding to each functional point combination as the matching functional points.

[0085] By adapting each first application integration model to the object model of the target terminal, at least one first function point can be obtained in each function point type.

[0086] For the first functional point under each functional point type, it can be paired with each functional point of the same functional point type (i.e., the fourth functional point) corresponding to a first application integration model to obtain a functional point combination. The dynamic information of the first functional point of the object model and the dynamic information of the fourth functional point of the first application integration model in each functional point combination are obtained, along with the dynamic features of multiple dimensions included in the dynamic information of the functional points. Dynamic feature vectors corresponding to the first and fourth functional points included in each functional point combination are constructed, and the similarity between pairwise dynamic feature vectors is calculated.

[0087] Specifically, the dynamic information of the function points includes dynamic features in multiple dimensions. The dynamic feature vectors of the first function point corresponding to each function point type of the object model and the dynamic feature vector of the fourth function point corresponding to the function point type of the target application integration model are obtained respectively. This includes: constructing the dynamic feature vector of the first function point based on the dynamic features in multiple dimensions of the dynamic information of the first function point of the target function point combination; and constructing the dynamic feature vector of the fourth function point based on the dynamic features in multiple dimensions of the dynamic information of the fourth function point of the target function point combination.

[0088] The above embodiments describe the extraction of multidimensional dynamic features of function point dynamic information and the construction of vector sequences for calculating the pairwise similarity between the first function point and the fourth function point based on dynamic feature vectors.

[0089] The static feature matching set generated in step 104 is used as the basis for dynamic information matching based on function points. The identifier of the first application integration model in the static feature matching set is identified and obtained, its model information is obtained and loaded, and the model information of the first function point of the object model in the static feature matching set is loaded as the data support for performing dynamic feature information matching in step 106.

[0090] For example, the dynamic feature vector of the fourth function point in the combination of function points is constructed as follows: , This represents the expected quantized value of the fourth function point on the nth dynamic feature dimension. Construct the dynamic feature vector of the first function point in each function point combination. , The actual quantified value of the first functional point of the object model in n dynamic feature dimensions can be obtained by extracting and calculating the corresponding dynamic feature data based on the historical statistical data of the first functional point of the object model or the real-time running monitoring results.

[0091] Specifically, calculating the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point in each functional point combination includes: calculating the similarity between the dynamic features of each dimension in the dynamic feature vector of the first functional point of the target functional point combination and the dynamic features of the same dimension in the dynamic feature vector of the fourth functional point of the target functional point combination, thereby obtaining the similarity of the dynamic features of each dimension of the target functional point combination; and determining the average of the similarities of the dynamic features of the multiple dimensions as the similarity of the target functional point combination.

[0092] This similarity calculation process mainly involves further quantifying and evaluating the dynamic characteristics of the functional points of the first application integration model and the first functional point of the object model during actual operation from the perspective of dynamic behavioral features. After completing the construction of the dynamic feature vector in the above steps, the dynamic feature similarity is calculated sequentially for each combination of functional points of the same functional point type in the static feature matching set (composed of the first functional point and the fourth functional point), resulting in the dynamic feature similarity matrix of the functional point combination corresponding to each functional point type.

[0093] The following describes the specific calculation process for dynamic feature similarity.

[0094] For any combination of function points (first function point, fourth function point), first calculate the difference in normalized eigenvalues ​​of the two function points across each dimension of the dynamic features. The calculation formula is shown below:

[0095] In the above formula, Let be the normalized difference between the first and fourth function points in a function point combination on the i-th dynamic feature dimension. The value range is [0,1]. Let be the value of the fourth function point on the i-th feature dimension. Let be the value of the first function point on the i-th feature dimension. It is a minimum value set to prevent division by zero.

[0096] The similarity value of each functional point combination in each dynamic feature dimension is calculated sequentially. In this embodiment, in order to improve the stability and generalization ability of dynamic feature matching in different application scenarios, a unified tolerance range can be introduced. This constraint constrains the acceptable error range for calculating similarity across all dynamic feature dimensions. The similarity is calculated using the following formula for the difference in dynamic features across each dimension:

[0097] In this formula, Let be the similarity of the function point combination in the i-th dynamic feature dimension. The minimum tolerance deviation threshold, Within this range, it can be considered to have no impact, and the similarity is 1. The maximum tolerance deviation threshold. Exceeding this range can be considered as complete dissimilarity, with a similarity score of 0. If the interval is specified, the score is calculated linearly. Following the formula above, the similarity of the function point combinations across each dynamic feature dimension is calculated sequentially.

[0098] The calculated similarities of the functional point combinations across each dynamic feature dimension are combined to obtain the final dynamic feature similarity of the corresponding functional point combinations. The calculation formula is as follows:

[0099] In this formula, The final dynamic feature similarity of a combination of functional points is defined, with a value range of [0,1]. The sum of similarities across n dynamic feature dimensions corresponding to a combination of functional points is used to calculate the final dynamic feature similarity. This formula is applied sequentially to calculate the dynamic feature similarity of functional point combinations in the static feature matching set, recording the similarity values ​​to obtain the final dynamic feature similarity matrix. In this matrix, rows represent the functional points of the first application ensemble model, columns represent the first functional points of the object model, and element values ​​are the final dynamic feature similarities of the corresponding functional point combinations.

[0100] A first functional point of the same type may be combined with multiple different fourth functional points. The final similarity calculated for each combination of functional points may vary, with some being higher than others. A higher similarity indicates a better match between the first and fourth functional points in the corresponding combination, and vice versa. Therefore, binding the first and fourth functional points with the highest similarity for each combination as the matched functional points can achieve optimal adaptation of the corresponding functional points between the object model and the application integration model.

[0101] Based on the dynamic feature similarity matrix obtained from the above steps, behavioral consistency evaluation and filtering are performed on the first functional point of the object model corresponding to each fourth functional point. Finally, a dynamic feature matching set with uniqueness and optimality is output. The specific implementation process is described below.

[0102] First, the dynamic feature similarity matrix is ​​traversed row by row, that is, each fourth function point is processed in turn to obtain the set of function point combinations corresponding to the first function point and the dynamic feature similarity of each function point combination.

[0103] In the set of function point combinations, they are sorted in descending order of dynamic feature similarity value. The first function point with the largest value is selected as the preferred matching object for the fourth function point, and a binding relationship is established.

[0104] Determine whether the first function point has been selected by other fourth function points of the same target application. If it is not occupied, assign it directly; if it has been assigned, compare the dynamic feature similarity values ​​of the two fourth function points, retain the one with the larger value, and release the binding relationship of the one with the lower value.

[0105] If the current fourth function point is not successfully bound, the next highest similarity value first function point is selected from the set of corresponding function point combinations, and the above judgment process is repeated until a match is successful or the list of first function points in the set of corresponding function point combinations has been completely traversed. The above steps are repeated until all fourth function points have completed the behavioral consistency assessment with their corresponding first function points.

[0106] The model's dynamic behavior feature matching is based on different functional point types such as attributes, events, and services of the object model. A corresponding standard library of behavior features is constructed, with each type of behavior feature consisting of multiple configurable and scalable feature dimensions. This allows for flexible and accurate description of the terminal's dynamic behavior during actual operation. The model's behavior feature matching extracts the behavior features of the terminal object model's functional points and performs similarity analysis with application standard behavior features. A similarity tolerance range is also introduced to ensure a high degree of consistency between the application and the terminal object model at the behavior level. This satisfies the application's restrictions on operational behavior features and effectively improves the accuracy and standardization of terminal adaptation.

[0107] Based on the behavioral consistency assessment and filtering results in the above steps, the IoT platform generates the final dynamic feature matching set. This dynamic feature matching set is represented in the form of a two-dimensional matrix, where the rows of the matrix represent the fourth functional point in the corresponding first application integration model, and the columns represent the first functional point that is successfully bound to it and has functional and behavioral consistency.

[0108] In this embodiment, since static feature matching only performs static feature analysis at the functional description level, ignoring the dynamic behavioral characteristics of the terminal in actual operation, it can accurately determine to a certain extent whether the object model of the target terminal truly meets the functional requirements of the target application. When facing the same type of functional points, having corresponding capabilities in the static feature function definition is insufficient to avoid deviations between dynamic behavioral characteristics such as call frequency and response latency and the actual expectations of the application side, leading to inaccurate matching and abnormal operation of application-side business functions, affecting system stability and business continuity. To address this, by further considering the dynamic behavioral characteristics of the terminal in actual operation and combining dynamic feature matching of the model to perform dynamic feature analysis at the behavioral description level, deep alignment of functional points at the behavioral level is achieved, ensuring that the dynamic adaptability and actual usability of the model adaptation are further improved while meeting the consistency of functional characteristics.

[0109] In summary, the implementation process of dynamic feature matching in step 106 is as follows: Figure 3 As shown, it includes the following steps: Step 302: Obtain and load the functional feature matching set; Step 304: Extract and construct the model function point behavior feature vector (dynamic feature vector) in the function point combination. Step 306: Similarity calculation and analysis of model function point behavioral features (dynamic features); Step 308: Consistency assessment and filtering of model function point behavior; Step 310: Generate a set of model behavior feature matching results (dynamic feature matching set).

[0110] The IoT platform can establish a function point binding mapping relationship between the application integration model and the terminal device model based on the final function point matching results, and generate a corresponding function point binding configuration table. For example... Figure 4 As shown, this illustrates the mapping relationship between a specific application integration model and the corresponding functional points of the integrated terminal (Product A). Simultaneously, relevant data processing logic scripts are automatically generated to automatically convert and process data reported from the terminal into control commands, ensuring that the application can accurately and efficiently connect to terminal data.

[0111] Meanwhile, application vendors can view and manage the function point binding configuration table of the application integration model in the IoT platform's application management interface. They can manually edit and adjust the configuration to meet personalized needs. In response to dynamic changes in the object model's function points, the IoT platform will automatically trigger model relationship re-matching and calculation based on the application integration model's update strategy configuration. It will automatically update the binding configuration or push messages for manual confirmation, ensuring the continued validity of model binding relationships and the stability of system operation.

[0112] As the scale of integration expands, the number of models and mapping relationships increases dramatically. The above methods can reduce maintenance costs, facilitate readjustment of configurations after object model updates, and improve adaptation efficiency.

[0113] Figure 4 This application illustrates the overall process of adapting the object model in an embodiment, as follows: Figure 5 As shown, it includes the following steps: Step 402: Terminal manufacturers define the terminal object model and construct the terminal standard capability model (object model). Step 404: The application vendor defines the application integration model and constructs the application abstraction capability model (application integration model). Step 406: Integrate the application into the target terminal; Step 408, Model functional feature matching; Step 410: Matching of model function point behavioral features.

[0114] In this embodiment, the IoT platform obtains model information of the object model created corresponding to the target terminal to be integrated into the target application, and model information of multiple application integration models created corresponding to the target application. Different terminal categories correspond to different application integration models. The model information includes attribute information, static function point information, and dynamic function point information. The attribute information and static function point information of the object model are matched with the attribute information and static function point information of the multiple application integration models to select at least one first function point from the function points included in the object model that is the same type as and matches the function point of the target application integration model. The target application integration model is any one of the multiple application integration models. The dynamic function point information of the first function point is matched with the dynamic function point information of the target application integration model to bind the at least one first function point to the function point corresponding to the target application integration model, so that the object model of the target terminal is adapted to the target application. Therefore, a multi-level matching strategy combining functional feature analysis and behavioral feature analysis is adopted. Functional feature matching analyzes and matches the functional semantic consistency between the application and the terminal object model at the static functional level, ensuring that the terminal meets the integration functional requirements of the application. Behavioral feature matching evaluates and analyzes the actual behavioral characteristics of the terminal object model at the dynamic operational level, ensuring that its operational characteristics meet the behavioral constraints of the application side. Through a two-dimensional re-matching mechanism of static and dynamic functional point information, the application's adaptation to the object model is automated and dynamic, and the terminal capabilities can be accurately evaluated, improving the accuracy and reliability of matching. This enhances the application's automated integration capability with multi-source terminals. The automated adaptation method simplifies the application adaptation process, improves the application adaptation flexibility, enhances the system's versatility and scalability, reduces development and maintenance costs, meets the needs of rapid integration and low-threshold development of intelligent applications on the IoT platform, and improves the adaptation efficiency of the object model. In addition, the application integration model, from the application perspective, is independent of specific terminals, achieving effective decoupling between the application and the terminal, and provides a standardized description of the terminal's capabilities and behavioral characteristics, ensuring the stability and consistency at the application level.

[0115] Optionally, embodiments of this application also provide an adapter for an object model, applied to an Internet of Things (IoT) platform. The adapter includes modules such as... Figure 6 As shown.

[0116] The application integration mode management module 1002 manages the application integration model, including its basic attributes, feature function point configuration, and model function point binding mapping configuration. It also monitors the triggering of events related to model feature analysis and initiates subsequent feature matching processes accordingly. The function feature similarity calculation module 1004 analyzes and calculates the static attributes of the model's function features, generating a set of function feature matching points as input for subsequent behavioral feature matching. The behavior feature library management module 1006 manages and maintains standard behavioral feature information for various industry scenarios and terminal categories, providing data support for behavior feature extraction and behavior feature similarity calculation. The data analysis and statistics module 1008 analyzes and statistically analyzes the model information of real-time and historical object models. The behavior feature extraction module 1010 calculates and extracts the corresponding behavioral features of the object model's function points based on the behavioral feature category definitions of the application integration model's function points. The behavior feature similarity calculation module 1012 analyzes and calculates the dynamic behavioral features of function points, generating a set of behavioral feature function point matching points, i.e., the final model matching result. The data logic script generation module 1014 is used to automatically generate data logic conversion scripts between the functional points of the application integration model and the functional points of the object model, realizing automatic matching and conversion processing of business logic. The model update notification and synchronization module 1016 is used to listen for structural or attribute change events of the object model, triggering the re-matching process of the corresponding model features or sending the corresponding model notification.

[0117] The adaptor for the object model provided in the embodiments of this specification can achieve... Figures 1 to 4 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0118] Optionally, such as Figure 7 As shown, this application embodiment also provides an adaptation device 2000 for an object model, including a processor 2400 and a memory 2200. The memory 2200 stores a program or instructions that can be executed on the processor 2400. When the program or instructions are executed by the processor 2400, they implement the various steps of the above-described object model adaptation method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0119] This application also provides a readable storage medium storing a program or instructions. When executed by a processor, the program or instructions implement the various processes of any of the above-described adaptation method embodiments for the object model, achieving the same technical effect. To avoid repetition, these will not be described again here. The readable storage medium includes computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to enable a computer to execute various processes of any of the above-described object model adaptation method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0121] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0123] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for adapting a physical model, characterized in that, Applied to an Internet of Things (IoT) platform, the method includes: Obtain the model information of the object model created corresponding to the target terminal to be integrated into the target application, and the model information of multiple application integration models created corresponding to the target application. Different application integration models are created for different terminal categories. The model information includes attribute information, static information of function points, and dynamic information of function points. The attribute information and static function point information of the object model are matched with the attribute information and static function point information of multiple application integration models to filter out at least one first function point from the function points included in the object model that is the same as and matches the function point type of the target application integration model. The target application integration model is any one of the multiple application integration models. The dynamic information of the function points of the first function point is matched with the dynamic information of the function points of the target application integration model to bind the at least one first function point to the corresponding function point of the target application integration model, so that the object model of the target terminal is adapted to the target application.

2. The method according to claim 1, characterized in that, From the functional points included in the object model, at least one first functional point of the same type and matching with the functional point of the target application integration model is selected, including: The attribute information of the object model is matched with the attribute information of the plurality of application integration models to obtain at least one matching first application integration model. The static information of the function points of the object model is matched with the static information of the function points of the at least one first application integration model to filter out at least one second function point from the function points included in the object model that is the same type as each function point of the target first application integration model. The target first application integration model is any one of the at least one first application integration models. Based on the similarity between the static information of the function points of the second function points and the static information of the function points of the third function points of the same type corresponding to the target first application integration model, at least one first function point that matches the target first application integration model is selected from the at least one second function point.

3. The method according to claim 2, characterized in that, The attribute information includes the application scenario and the terminal category. Obtain at least one matching first application integration model, including: If the application scenario and terminal category in the attribute information of the object model match the application scenario and terminal category in the attribute information of the target application integration model, then the target application integration model is determined to be the first matching application integration model.

4. The method according to claim 2, characterized in that, The static information of the function point includes the function point type, function point name, function point identifier, and function point description. From the functional points included in the object model, at least one second functional point of the same type as the functional points of the target first application integration model is selected, including: If the function point type of the target function point in the static information of the function points of the object model matches the function point type in the static information of the function points of the target first application integration model, then the target function point is determined to be the second function point. Selecting at least one first functional point from the at least one second functional point that matches the target first application integration model includes: Calculate the similarity between the function point name, function point identifier, and function point description in the static information of the function point of the target function point and the function point name, function point identifier, and function point description in the static information of the function point of the third function point corresponding to the target first application integration model. If the weighted similarity of the similarity between the function point name, the function point identifier, and the function point description of the target function point exceeds a preset similarity threshold, then the target function point is determined to be the first function point.

5. The method according to claim 1, characterized in that, Binding the at least one first functional point to the corresponding functional point of the target application integration model includes: Based on the dynamic information of function points, the dynamic feature vectors of the first function point of each function point type of the object model and the dynamic feature vectors of the fourth function point of the same function point type of the target application integration model are obtained respectively. Calculate the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point included in each functional point combination; The first and fourth functional points with the highest similarity among the functional point combinations are used as the matching functional points and bound together.

6. The method according to claim 5, characterized in that, Function point dynamic information includes dynamic features in multiple dimensions. The dynamic feature vectors of the first functional point corresponding to each functional point type of the object model and the dynamic feature vectors of the fourth functional point corresponding to the functional point type of the target application integration model are obtained respectively, including: Based on the dynamic features of the first functional point, which is a combination of target functional points, a dynamic feature vector of the first functional point is constructed. Based on the dynamic features of the fourth functional point in the combination of the target functional points, including the dynamic features of the multiple dimensions, a dynamic feature vector of the fourth functional point is constructed.

7. The method according to claim 6, characterized in that, Calculate the similarity between the dynamic feature vector of a first functional point and the dynamic feature vector of a fourth functional point included in each functional point combination, including: Calculate the similarity between the dynamic features of each dimension in the dynamic feature vector of the first functional point of the target functional point combination and the dynamic features of the same dimension in the dynamic feature vector of the fourth functional point of the target functional point combination, and obtain the similarity of the dynamic features of each dimension of the target functional point combination. The average similarity of the dynamic features across the multiple dimensions is taken as the similarity of the target functional point combination.

8. An adapter for a physical model, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the method as described in any one of claims 1-7.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform the steps of the method as described in any one of claims 1-7.

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