A power dispatch service implementation method and device based on business semantics

Abstract

The application discloses a power dispatching service implementation method and device based on business semantics, and the method comprises the following steps: traversing a business process library, and respectively constructing a micro-service knowledge base and a business semantic knowledge base according to a micro-service identification algorithm and a business semantic knowledge extraction algorithm; acquiring a second business semantic model which does not match a first business semantic model in the business semantic knowledge base according to a business semantic model similarity algorithm; acquiring a first business process corresponding to the second business semantic model, and acquiring a first micro-service component which has a mapping relationship with the first business process; identifying a first micro-service node in the first micro-service component, and if IP0 information in the first micro-service node is different from preset IP0 information, then a second micro-service node is identified in the micro-service knowledge base by using a similarity algorithm to replace the first micro-service node, so as to generate a new power dispatching middle platform business process. The application fully utilizes domain knowledge and does not need to be repeatedly developed, and the development cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of power dispatching platform business service technology, and in particular to a method and apparatus for implementing power dispatching services based on business semantics. Background Technology

[0002] The development of smart grids and the upgrading of smart grid equipment have gradually increased the complexity of power dispatch services, leading to the construction of power dispatch services. Power dispatch business processes require a large amount of human and computing resources. Existing technologies mainly provide high-quality service construction methods for specific service needs. Under the premise of fixed business needs, they can continuously provide stable and high-quality services. However, when faced with complex and ever-changing business needs, the development cost of existing technologies is high, the reuse effect is poor, and repeated development is required, resulting in a decrease in the utilization efficiency of the domain knowledge contained in the power dispatch platform. Summary of the Invention

[0003] The purpose of this invention is to provide a method for implementing power dispatching services based on business semantics, so as to solve the problems of high cost and low efficiency of domain knowledge utilization caused by repeated development in the prior art.

[0004] To achieve the above objectives, the present invention provides a method for implementing power dispatching services based on business semantics, comprising:

[0005] Acquire multiple power dispatching business processes and establish a power dispatching business process library;

[0006] Traverse the business process library and construct a microservice knowledge base and a business semantic knowledge base based on the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively.

[0007] The power dispatching business requirements are used as the first business semantic model. Based on the business semantic model similarity algorithm, the business semantic knowledge base is traversed to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model.

[0008] Traverse the business process library to obtain the first business process corresponding to the second business semantic model, and obtain the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base.

[0009] The first microservice node in the first microservice component is identified. If the IP0 information in the first microservice node is different from the preset IP0 information, a similarity algorithm is used to identify a second microservice node in the microservice knowledge base for replacement, thereby generating a new power dispatching platform business process.

[0010] Preferably, a microservice knowledge base is constructed based on a microservice identification algorithm, including:

[0011] The multiple business processes include multiple microservice components, and the multiple microservice components include known microservice components and unknown microservice components;

[0012] The IP0 information of the known microservice components is used as a pattern feature to match in the business process library. The known microservice components are identified in the business processes that match successfully. For business processes that do not match successfully, the input and output interfaces of the business processes are abstracted and defined to form the IP0 information of unknown microservice components. The IP0 information of the unknown microservice components is then matched in the business process library. The unknown microservice components are identified in the business processes that match successfully.

[0013] The microservice knowledge base is constructed based on the identified known microservice components, the unknown microservice components, the mapping relationship of the known microservice components in the business process library, and the mapping relationship of the unknown microservice components in the business process library.

[0014] Preferably, the IP0 information in the first microservice node differs from the preset IP0 information, including:

[0015] The IPO information in the first microservice node and the preset IPO information include the input interface, function and output interface of the microservice component;

[0016] If at least one of the input interface, function, and output interface of the microservice component is different, then it is determined that the IPO information in the first microservice node is different from the preset IPO information.

[0017] Preferably, the IPO information is converted into a 0-1 vector, and the similarity of the IPO information to be compared is calculated using cosine distance;

[0018] If the similarity exceeds a preset threshold, the IPO information to be compared is determined to be the same; otherwise, they are different.

[0019] Preferably, after generating the new power dispatching platform business process, the following is also included:

[0020] The operational processes in the new power dispatching platform's business workflow are converted into business data, and this business data is returned to the data platform; wherein...

[0021] The operation process includes sequential operations, conditional operations, and loop operations. The business data includes a data model constructed by combining continuous functions transformed from the sequential operations, successor functions transformed from the conditional operations, and recursive functions transformed from the loop operations.

[0022] The present invention also provides a power dispatching service implementation device based on business semantics, comprising:

[0023] The preprocessing module is used to acquire multiple power dispatching business processes and establish a power dispatching business process library;

[0024] The construction module is used to traverse the business process library and construct the microservice knowledge base and the business semantic knowledge base according to the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively.

[0025] The first acquisition module is used to take power dispatching business requirements as the first business semantic model, and according to the business semantic model similarity algorithm, traverse the business semantic knowledge base to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model.

[0026] The second acquisition module is used to traverse the business process library, acquire the first business process corresponding to the second business semantic model, and acquire the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base.

[0027] The update module is used to identify the first microservice node in the first microservice component. If the IP0 information in the first microservice node is different from the preset IP0 information, a similarity algorithm is used to identify the second microservice node in the microservice knowledge base for replacement, thereby generating a new power dispatching platform business process.

[0028] Preferably, the building module is further configured to:

[0029] The multiple business processes include multiple microservice components, and the multiple microservice components include known microservice components and unknown microservice components;

[0030] The IP0 information of the known microservice components is used as a pattern feature to match in the business process library. The known microservice components are identified in the business processes that match successfully. For business processes that do not match successfully, the input and output interfaces of the business processes are abstracted and defined to form the IP0 information of unknown microservice components. The IP0 information of the unknown microservice components is then matched in the business process library. The unknown microservice components are identified in the business processes that match successfully.

[0031] The microservice knowledge base is constructed based on the identified known microservice components, the unknown microservice components, the mapping relationship of the known microservice components in the business process library, and the mapping relationship of the unknown microservice components in the business process library.

[0032] Preferably, the update module is further configured to:

[0033] The IPO information in the first microservice node and the preset IPO information include the input interface, function and output interface of the microservice component;

[0034] If at least one of the input interface, function, and output interface of the microservice component is different, then it is determined that the IPO information in the first microservice node is different from the preset IPO information.

[0035] Preferably, it further includes an update submodule for:

[0036] The IPO information is converted into a 0-1 vector, and the similarity of the IPO information to be compared is calculated using cosine distance.

[0037] If the similarity exceeds a preset threshold, the IPO information to be compared is determined to be the same; otherwise, they are different.

[0038] Preferably, it further includes a transmission module for:

[0039] The operational processes in the new power dispatching platform's business workflow are converted into business data, and this business data is returned to the data platform; wherein...

[0040] The operation process includes sequential operations, conditional operations, and loop operations. The business data includes a data model constructed by combining continuous functions transformed from the sequential operations, successor functions transformed from the conditional operations, and recursive functions transformed from the loop operations.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] This invention constructs a microservice knowledge base and a business semantic knowledge base, traverses the business semantic knowledge base to obtain business processes that meet power requirements, then obtains microservice components that match the business processes that meet power requirements, and then identifies the relationship between the IPO information of the microservice nodes of the microservice components and the preset IPO information. A similarity algorithm is used to replace the identified second microservice knowledge, update the business processes, and generate new power dispatching platform business processes, thereby improving the utilization rate of domain knowledge and reducing development costs. Attached Figure Description

[0043] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1This is a flowchart illustrating a method for implementing power dispatching services based on business semantics according to a certain embodiment of the present invention.

[0045] Figure 2 This is a flowchart illustrating a method for implementing power dispatching services based on business semantics, provided in another embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the structure of a power dispatching service implementation device based on business semantics provided in a certain embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0049] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0050] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0051] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0052] Please see Figure 1 An embodiment of the present invention provides a method for implementing power dispatching services based on business semantics, comprising the following steps:

[0053] S101: Obtain multiple power dispatching business processes and establish a power dispatching business process library.

[0054] Specifically, a data-business extraction model is developed to extract data flow into business flow on the data model of the power dispatching platform. This aligns the data model and business model in the power dispatching platform with business representation methods, using a unified approach. Existing power dispatching business processes are acquired, and a collection of these processes is constructed as a power dispatching business process library.

[0055] S102: Traverse the business process library and construct a microservice knowledge base and a business semantic knowledge base according to the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively.

[0056] Specifically, microservices are identified and decomposed from existing business processes, and these microservices are used to represent the business processes. A microservice knowledge base is then built using the correspondence between business processes and microservices, as well as the identified microservices.

[0057] In the established business process library, multiple business processes include multiple microservice components. These microservice components include known and unknown microservice components. The IP0 information of known microservice components is used as a pattern feature for matching in the business process library. Known microservice components are identified in business processes that match successfully. For business processes that do not match successfully, the input and output interfaces of the business processes are abstracted and defined to form the IP0 information of unknown microservice components. The IP0 information of unknown microservice components is then matched in the business process library. Unknown microservice components are identified in business processes that match successfully. A microservice knowledge base is constructed based on the identified known microservice components, unknown microservice components, the mapping relationship between known microservice components and the business process library, and the mapping relationship between unknown microservice components and the business process library.

[0058] Identifying and decomposing microservice components from existing business processes first requires identifying these components. This identification process consists of two parts: identifying known microservice components and identifying unknown microservice components. Identifying known microservice components refers to identifying components that already exist within the business process. These existing components can be custom-defined or obtained through subsequent decomposition. Microservice components are identified from the business process by using their IPO information (input interface, function, output interface) as pattern features. A sliding window is used to match these pattern features within the business process to identify the microservice components. Identifying unknown microservice components involves discovering potential microservice components when no corresponding microservice component exists in the business process. All known business processes are compared locally, and the least similar local processes that recur are extracted. The functions implemented by these local processes are abstracted and described, and the input and output interfaces are abstracted and defined based on their occurrence scenarios, forming the IPO information of the microservice component. This process is repeated until no less similar local processes recur. All previously extracted least similar local processes can then be defined as microservice components using their IPO information.

[0059] After identifying all known microservice components, the business process may still have unidentified parts. By comparing the remaining parts, we attempt to discover microservice components. The undiscovered microservice parts are treated as separate microservice components. After the business process has been identified as a microservice component, the business process is decomposed by extracting the identified microservice components.

[0060] By identifying and decomposing existing business processes, a large number of microservice components can be obtained. Each microservice component has a corresponding identification position in the existing business. Therefore, microservice components, business processes, and their corresponding relationships constitute microservice knowledge. By managing all microservice components and their corresponding relationships in a unified manner, a microservice knowledge base can be formed.

[0061] Extract business semantic models from existing business processes to achieve a logical representation of the business processes. Construct a business semantic knowledge base by using business descriptions that are independent of the implementation methods through business semantic representations.

[0062] Business semantics is an abstract representation of business processes; it's a business description language independent of specific hardware and methods. A business semantic model is the concrete representation of business semantics used to describe a particular business. In production, the same business operation may differ on different hardware devices. Therefore, for the same business requirements, different business processes need to be designed specifically for different hardware devices, because business processes involve the specific implementation of the business. Multiple business processes arising from this situation may differ in their specific implementation stages, but the functions, input interfaces, and output interfaces of the differing stages are consistent or similar. By functionalizing the production hardware devices and modeling their data and control interfaces, the differences caused by hardware variations can be unified at the logical level.

[0063] S103: Using power dispatching business requirements as the first business semantic model, and based on the business semantic model similarity algorithm, traversing the business semantic knowledge base to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model.

[0064] Specifically, based on the power dispatching business requirements, the semantics of the power dispatching business are extracted, the power dispatching business requirements are represented by the first business semantic model, and a similarity algorithm is used to match them in the business semantic knowledge base. If there is a complete match, the corresponding business process is called and applied; if there is no complete match model, the most similar business process is returned.

[0065] Matching algorithms can be calculated using Minkowski distance, cosine distance, and Pearson correlation coefficient. The IPO factors of the business semantic model to be matched are transformed into 0-1 vectors, and the similarity between the two vectors is calculated using the aforementioned matching algorithms. For example, given cosine distance v1 = (1, 1, 0, ..., 0) and v2 = (0, 1, 1, ..., 0), the cosine distance between the IPO factor feature vectors corresponding to the two business semantic models is... Set a similarity threshold; if the similarity exceeds the threshold, it is considered a match; otherwise, it is not a match.

[0066] S104: Traverse the business process library, obtain the first business process corresponding to the second business semantic model, and obtain the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base.

[0067] Specifically, there is a mapping relationship between business processes in the business process library and microservice components, and there is also a mapping relationship between business processes and business semantic models. By traversing the business process library, the first business process corresponding to the second business semantic model is obtained, and the first microservice component that has a mapping relationship with the first business process is obtained in the microservice knowledge base.

[0068] S105: Identify the first microservice node in the first microservice component. If the IPO information in the first microservice node is different from the preset IPO information, use a similarity algorithm to identify the second microservice node in the microservice knowledge base and replace it to generate a new power dispatching platform business process.

[0069] Specifically, the IPO information in the first microservice node and the preset IPO information include the input interface, function, and output interface of the microservice component. If at least one of the input interface, function, and output interface of the microservice component is different, then the IPO information in the first microservice node is determined to be different from the preset IPO information. The IPO information is converted into a 0-1 vector, and the similarity of the IPO information to be compared is calculated using cosine distance. If the similarity exceeds a preset threshold, the IPO information to be compared is determined to be the same; otherwise, they are different.

[0070] In the reuse of power dispatching business processes, it is usually not necessary to rebuild services entirely. Instead, new service functions are implemented through partial modifications. Microservice node identification methods are used to identify the parts of the business process that need to be replaced or modified. Accurately locating the microservice nodes within the business process is crucial for enabling partial modifications based on business requirements. Business semantic models and microservice composition are two logically layered technologies. Business semantic models are more abstract, closer to human language, while microservice composition is more concrete, implementing business details at the software level and including some hardware-level business details. Existing power business processes can serve as middleware, establishing the connection between the business semantic model and microservice composition. This involves extracting functional descriptions from the power business processes, constructing the business semantic model, and associating the power business processes with functions and hardware to achieve microservice composition.

[0071] Since there is no perfect matching model, the most similar business process is returned (i.e., the similarity exceeds the threshold), indicating that there are functional nodes in the business process that do not conform to the business semantic description. This is achieved by identifying nodes with different IPO factors.

[0072] Please see Figure 2Based on the power dispatching platform's business process set, a microservice knowledge base and a business semantic knowledge base are constructed respectively. This allows for the identification of microservice nodes, and the updating of differing microservice nodes according to power dispatching business requirements, generating flexible power dispatching services. The power dispatching data platform stores business information in the form of a data model; therefore, the symbolic business information needs to be represented digitally before it can be returned to the data platform. The underlying layer of the business process is data flow. The business operation process is transformed into the process of business data change and the function definition of business data change. Sequential operations, conditional operations, and loop operations are respectively transformed into continuous functions, successor functions, and recursive functions. Data models are constructed through function combinations and returned to the data platform. Specifically, the operation process in the new power dispatching platform's business process is transformed into business data, and the business data is returned to the data platform. The operation process includes sequential operations, conditional operations, and loop operations, and the business data includes a data model constructed by combining continuous functions transformed from sequential operations, successor functions transformed from conditional operations, and recursive functions transformed from loop operations.

[0073] This invention constructs a microservice knowledge base and a business semantic knowledge base, uses the business semantic knowledge base to obtain matching business processes, and then finds corresponding microservice components in the matching business processes. In this way, it identifies and updates and replaces mismatched microservice nodes, thereby improving the domain knowledge coverage and reducing development costs.

[0074] Please see Figure 3 Another embodiment of the present invention provides a power dispatching service implementation device based on business semantics, comprising:

[0075] The preprocessing module 11 is used to acquire multiple power dispatching business processes and establish a power dispatching business process library.

[0076] The construction module 12 is used to traverse the business process library and construct a microservice knowledge base and a business semantic knowledge base according to the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively.

[0077] The first acquisition module 13 is used to take the power dispatching business requirements as the first business semantic model, and according to the business semantic model similarity algorithm, traverse the business semantic knowledge base to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model.

[0078] The second acquisition module 14 is used to traverse the business process library, acquire the first business process corresponding to the second business semantic model, and acquire the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base.

[0079] The update module 15 is used to identify the first microservice node in the first microservice component. If the IP0 information in the first microservice node is different from the preset IP0 information, a similarity algorithm is used to identify the second microservice node in the microservice knowledge base for replacement, thereby generating a new power dispatching platform business process.

[0080] Specific limitations regarding the implementation device for business semantics-based power dispatching services can be found in the limitations of the implementation method for business semantics-based power dispatching services described above, and will not be repeated here. Each module in the aforementioned business semantics-based power dispatching service implementation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0081] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for implementing power dispatching services based on business semantics, characterized in that, include: Acquire multiple power dispatching business processes and establish a power dispatching business process library; Traverse the business process library, and construct a microservice knowledge base and a business semantic knowledge base based on the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively; wherein, constructing the microservice knowledge base based on the microservice identification algorithm includes: The multiple business processes include multiple microservice components, and the multiple microservice components include known microservice components and unknown microservice components; The IP0 information of the known microservice components is used as a pattern feature to match in the business process library. The known microservice components are identified in the business processes that match successfully. For business processes that do not match successfully, the input and output interfaces of the business processes are abstracted and defined to form the IP0 information of unknown microservice components. The IP0 information of the unknown microservice components is then matched in the business process library. The unknown microservice components are identified in the business processes that match successfully. The microservice knowledge base is constructed based on the identified known microservice components, the unknown microservice components, the mapping relationship of the known microservice components in the business process library, and the mapping relationship of the unknown microservice components in the business process library. The power dispatching business requirements are used as the first business semantic model. Based on the business semantic model similarity algorithm, the business semantic knowledge base is traversed to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model. Traverse the business process library to obtain the first business process corresponding to the second business semantic model, and obtain the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base. The first microservice node in the first microservice component is identified. If the IP0 information in the first microservice node is different from the preset IP0 information, a similarity algorithm is used to identify a second microservice node in the microservice knowledge base for replacement, thereby generating a new power dispatching platform business process.

2. The method for implementing power dispatching services based on business semantics according to claim 1, characterized in that, The IP0 information in the first microservice node differs from the preset IP0 information, including: The IPO information in the first microservice node and the preset IPO information include the input interface, function and output interface of the microservice component; If at least one of the input interface, function, and output interface of the microservice component is different, then it is determined that the IPO information in the first microservice node is different from the preset IPO information.

3. The method for implementing power dispatching services based on business semantics according to claim 2, characterized in that, The IPO information is converted into a 0-1 vector, and the similarity of the IPO information to be compared is calculated using cosine distance. If the similarity exceeds a preset threshold, the IPO information to be compared is determined to be the same; otherwise, they are different.

4. The method for implementing power dispatching services based on business semantics according to claim 1, characterized in that, After generating the new power dispatching platform business process, it also includes: The operational processes in the new power dispatching platform's business workflow are converted into business data, and this business data is returned to the data platform; wherein... The operation process includes sequential operations, conditional operations, and loop operations. The business data includes a data model constructed by combining continuous functions transformed from the sequential operations, successor functions transformed from the conditional operations, and recursive functions transformed from the loop operations.

5. A power dispatching service implementation device based on business semantics, characterized in that, include: The preprocessing module is used to acquire multiple power dispatching business processes and establish a power dispatching business process library; wherein, the multiple business processes include multiple microservice components, and the multiple microservice components include known microservice components and unknown microservice components; The IP0 information of the known microservice components is used as a pattern feature to match in the business process library. The known microservice components are identified in the business processes that match successfully. For business processes that do not match successfully, the input and output interfaces of the business processes are abstracted and defined to form the IP0 information of unknown microservice components. The IP0 information of the unknown microservice components is then matched in the business process library. The unknown microservice components are identified in the business processes that match successfully. A microservice knowledge base is constructed based on the identified known microservice components, the unknown microservice components, the mapping relationship of the known microservice components in the business process library, and the mapping relationship of the unknown microservice components in the business process library. The construction module is used to traverse the business process library and construct the microservice knowledge base and the business semantic knowledge base according to the microservice identification algorithm and the business semantic knowledge extraction algorithm, respectively. The first acquisition module is used to take power dispatching business requirements as the first business semantic model, and according to the business semantic model similarity algorithm, traverse the business semantic knowledge base to obtain a second business semantic model in the business semantic knowledge base that does not match the first business semantic model. The second acquisition module is used to traverse the business process library, acquire the first business process corresponding to the second business semantic model, and acquire the first microservice component that has a mapping relationship with the first business process in the microservice knowledge base. The update module is used to identify the first microservice node in the first microservice component. If the IP0 information in the first microservice node is different from the preset IP0 information, a similarity algorithm is used to identify the second microservice node in the microservice knowledge base for replacement, thereby generating a new power dispatching platform business process.

6. The power dispatching service implementation device based on business semantics according to claim 5, characterized in that, The update module is also used for: The IPO information in the first microservice node and the preset IPO information include the input interface, function and output interface of the microservice component; If at least one of the input interface, function, and output interface of the microservice component is different, then it is determined that the IPO information in the first microservice node is different from the preset IPO information.

7. The power dispatching service implementation device based on business semantics according to claim 6, characterized in that, It also includes an update submodule, used for: The IPO information is converted into a 0-1 vector, and the similarity of the IPO information to be compared is calculated using cosine distance. If the similarity exceeds a preset threshold, the IPO information to be compared is determined to be the same; otherwise, they are different.

8. The power dispatching service implementation device based on business semantics according to claim 5, characterized in that, It also includes a transmission module for: The operational processes in the new power dispatching platform's business workflow are converted into business data, and this business data is returned to the data platform; wherein... The operation process includes sequential operations, conditional operations, and loop operations. The business data includes a data model constructed by combining continuous functions transformed from the sequential operations, successor functions transformed from the conditional operations, and recursive functions transformed from the loop operations.

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