A method for quickly building cross-scenario IoT applications based on the componentization capabilities of the AIOT platform

Abstract

The embodiment of the present application provides a method for quickly building a cross-scenario Internet of Things application based on the componentization capability of an AIOT middle platform, the method comprising: receiving a cross-scenario editing instruction sent by a user through a visual interface, matching the cross-scenario editing instruction with a preset scenario knowledge graph, determining the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, and receiving the Internet of Things device data sent by the edge layer; constructing a process for the pluggable components according to the scenario logic rules, determining the corresponding component association relationship and component interaction mode, and constructing a cloud service layer middle platform according to the component association relationship, component interaction mode, and key features; the present application can realize highly flexible and scalable cross-scenario Internet of Things application rapid construction and AIOT services.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things, and specifically to a method for quickly building cross-scenario Internet of Things applications based on the componentization capabilities of the AIOT middle platform. Background Art

[0002] In the modern AIoT (artificial intelligence and Internet of Things) architecture, the cloud-edge-end collaborative architecture has become the mainstream way to realize intelligent applications. This architecture combines the powerful data processing capabilities of cloud computing with the real-time data processing advantages of edge computing to support a wide range of application scenarios, from industrial automation to smart home systems. However, although the existing AIoT architecture provides certain flexibility and efficiency, there are still some significant shortcomings and deficiencies in the actual deployment and application process.

[0003] First, the demands for AIoT vary greatly across industries and application scenarios. For example, on a construction site, the intelligent monitoring system needs to be able to identify whether all personnel are wearing helmets, while monitoring heavy machinery and equipment such as tower cranes to prevent safety accidents caused by improper operation. Such applications not only require specific hardware equipment to adapt to harsh environmental conditions, but also require highly customized algorithms, such as collision detection and safety monitoring algorithms. In contrast, AIoT applications for community property management may focus more on crowd management and fire prevention, which requires the system to be able to effectively identify crowd gatherings and promptly detect fire hazards, such as smoke and flame detection. This differentiated demand makes it difficult for a single AIoT solution to be widely applicable, and customized technologies and services need to be developed for each specific scenario.

[0004] Secondly, the scalability of the system is another important issue. In the existing AIoT architecture, once the system is deployed, the access of new devices often requires updating and redeploying the service code. This process is not only time-consuming and labor-intensive, but each update may affect the stability and continuous online time of the system. With the rapid changes in business needs and the continuous emergence of new technologies, this low flexibility of the existing system is obviously unable to meet the needs of the market. For example, if it is necessary to add new sensors or smart devices to the deployed system, a lot of system testing and verification may be required to ensure the compatibility of the new device and the overall stability of the system.

[0005] Therefore, to overcome these shortcomings, future AIoT architectures need to be more flexible and scalable. This may include developing a more modular system design that allows for seamless integration of new devices and services without the need for extensive system reconfiguration or downtime for maintenance. In addition, the system's adaptability and responsiveness to new scenarios can be further improved by adopting more advanced machine learning and artificial intelligence algorithms. Such a system will not only be better able to meet the needs of specific industries, but will also be able to evolve and improve over time as technology advances and the market changes. Summary of the invention

[0006] In response to the problems in the prior art, the present application provides a method for quickly building cross-scenario IoT applications based on the componentization capabilities of the AIOT middle platform, which can achieve highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services.

[0007] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0008] In the first aspect, the present application provides a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform, which is applied to the cloud service layer in the artificial intelligence IoT. The method includes:

[0009] Receive cross-scenario editing instructions sent by the user through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library;

[0010] Establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0011] The component data source is analyzed through a matching scenario analysis algorithm to determine the corresponding key features, and the process of the pluggable components is constructed according to the scenario logic rules to determine the corresponding component association relationships and component interaction methods. The cloud service layer middle platform is constructed according to the component association relationships, the component interaction methods and the key features.

[0012] Furthermore, before matching the cross-scenario editing instruction with the preset scenario knowledge graph, the method further includes:

[0013] Determine the nodes in the scene knowledge graph according to the attribute information of the historical pluggable component, the historical scene analysis algorithm and the historical scene logic rule, and determine the edges in the scene knowledge graph according to the association between the historical pluggable component, the historical scene analysis algorithm and the historical scene logic rule;

[0014] The scene knowledge graph is constructed based on the nodes and the edges.

[0015] Furthermore, the matching pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithms in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library are determined according to the scenario requirements, the specific functions, and the preset scenario knowledge graph, including:

[0016] Determine a graph matching range in a preset scenario knowledge graph according to the scenario requirements and the specific function;

[0017] The similarity between the nodes and edges in the graph matching range is measured according to the similarity algorithm, and the matching pluggable components in the scene atomic component library, the matching scene analysis algorithm in the preset algorithm center, and the matching scene logic rules in the preset scene rule library are determined according to the nodes and edges with the greatest similarity.

[0018] Furthermore, the content parsing of the cross-scenario editing instruction to determine the corresponding scenario requirements and specific functions includes:

[0019] Performing semantic analysis on the cross-scenario editing instructions to determine corresponding scenario requirements;

[0020] Determine the specific function that best matches the scenario requirements based on the preset scenario function list.

[0021] Further, the receiving of IoT device data sent by the edge layer in the artificial intelligence IoT, configuring the IoT device data as a component data source of the pluggable component according to a set data configuration rule, wherein the IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface, includes:

[0022] Receiving IoT device data sent by an edge layer in an artificial intelligence IoT, wherein the IoT device data is sent by an intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface;

[0023] The IoT device data is standardized according to the configuration requirements of the pluggable component, and the IoT device data after the standardized processing is configured as a component data source of the pluggable component.

[0024] Furthermore, before receiving the IoT device data sent by the edge layer in the artificial intelligence IoT, it also includes:

[0025] The data cleaning model built into the edge layer of the artificial intelligence Internet of Things is used to clean and filter the IoT device data sent by the smart terminal layer in the artificial intelligence Internet of Things;

[0026] The data analysis model built into the edge layer in the artificial intelligence Internet of Things is used to analyze the Internet of Things device data after data cleaning and filtering to obtain the Internet of Things device data after the data analysis.

[0027] In the second aspect, the present application provides a device for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform, including:

[0028] Build an element determination module, which is used to receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library;

[0029] Building a data configuration module, which is used to establish a communication connection with the edge layer in the artificial intelligence Internet of Things, receive the Internet of Things device data sent by the edge layer, convert the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configure it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0030] The middle platform construction module is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

[0031] In a third aspect, the present application provides a system for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform, wherein the system includes a cloud service layer, an edge layer, and an intelligent terminal layer in the artificial intelligence IoT, wherein the cloud service layer is connected to the edge layer, and the edge layer is connected to the intelligent terminal layer;

[0032] The cloud service layer includes:

[0033] Build an element determination module, which is used to receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library;

[0034] Building a data configuration module, which is used to establish a communication connection with the edge layer in the artificial intelligence Internet of Things, receive the Internet of Things device data sent by the edge layer, convert the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configure it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0035] The middle platform construction module is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

[0036] In a fourth aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform are implemented.

[0037] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform.

[0038] In a sixth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for quickly building cross-scenario IoT applications based on the componentization capabilities of the AIOT middle platform.

[0039] It can be seen from the above technical solution that the present application provides a method for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform. The method receives cross-scenario editing instructions sent by users through a visual interface, matches the cross-scenario editing instructions with the preset scenario knowledge graph, and determines the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; establishes a communication connection with the edge layer in the artificial intelligence IoT, and receives IoT device data sent by the edge layer; constructs the process of pluggable components according to the scenario logic rules, determines the corresponding component association relationship and component interaction method, and constructs a cloud service layer middle platform according to the component association relationship, component interaction method and key features, which can realize highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is one of the flow diagrams of a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application;

[0042] Figure 2 This is the second flow chart of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in the embodiment of the present application;

[0043] Figure 3 This is a flowchart of a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application;

[0044] Figure 4 This is a fourth flow chart of a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application;

[0045] Figure 5 This is a flowchart of a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application.

[0046] Figure 6 This is a flowchart of a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application.

[0047] Figure 7 A structural diagram of a device for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application;

[0048] Figure 8 A structural diagram of a system for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in an embodiment of the present application;

[0049] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0050] Reference numerals:

[0051] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0054] Considering the prior art, in the existing AIoT architecture, once the system is deployed, access to new devices often requires updating and redeploying service codes. This process is not only time-consuming and labor-intensive, but each update may also affect the stability and continuous online time of the system. With the rapid changes in business needs and the continuous emergence of new technologies, the low flexibility of the existing system is obviously unable to meet the needs of the market. This application provides a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform. The cross-scenario editing instructions sent by the user are received through a visual interface, and the cross-scenario editing instructions are matched with the preset scene knowledge graph to determine the pluggable components in the preset scene atomic component library, the scene analysis algorithm matched in the preset algorithm center, and the scene logic rules matched in the preset scene rule library; establish a communication connection with the edge layer in the artificial intelligence IoT, and receive the IoT device data sent by the edge layer; construct the process of the pluggable components according to the scene logic rules, determine the corresponding component association relationship and component interaction mode, and construct the cloud service layer middle platform according to the component association relationship, component interaction mode and key features, which can realize highly flexible and scalable cross-scenario IoT application rapid construction and AIOT service.

[0055] In order to achieve highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services, this application provides an embodiment of a cross-scenario IoT application rapid construction method based on the componentization capability of AIOT middle platform, which is applied to the cloud service layer in the artificial intelligence IoT. Figure 1 The method for quickly building cross-scenario IoT applications based on the componentization capabilities of the AIOT platform specifically includes the following contents:

[0056] Step S101: receiving a cross-scenario editing instruction sent by a user through a visual interface, matching the cross-scenario editing instruction with a preset scenario knowledge graph, and determining a pluggable component in a preset scenario atomic component library, a matching scenario analysis algorithm in a preset algorithm center, and a matching scenario logic rule in a preset scenario rule library;

[0057] Optionally, in today's technological environment, the processing of cross-scene editing instructions often involves a highly complex system architecture, especially in dynamic and changeable operation scenarios. Step S101 provides an in-depth solution to receive cross-scene editing instructions sent by users through a user-friendly visual interface. Users can send specific operation instructions through this interface, such as adjusting parameters, switching views, or modifying data sources. Once these instructions are received by the system, the next challenge is how to accurately parse and execute these instructions to ensure that user needs are met.

[0058] In order to process these instructions, the system first needs to parse the received information and convert it into a structured format that can be understood and processed by the machine. During this process, the system will use the preset scenario knowledge graph for matching. The scenario knowledge graph is a data structure that contains elements in different scenarios and the relationships between them, such as component dependencies, operation rules, etc. By matching with this graph, the system can quickly identify the specific scenarios mentioned in the user's instructions and the required operation components.

[0059] After confirming the user's instructions, the system then selects appropriate pluggable components from the preset scenario atomic component library. These components are pre-defined and can be dynamically combined or replaced as needed to adapt to different operational requirements and scenario changes. For example, if the user needs to add a new data visualization component to the data analysis application, the system can select a visualization tool that meets the requirements from the component library and integrate it into the current scenario.

[0060] At the same time, the system also needs to select a matching scenario analysis algorithm from the preset algorithm center. These algorithms are optimized and debugged in advance and can analyze and process data in specific scenarios, such as data mining, pattern recognition or predictive analysis. Selecting the right algorithm is crucial to ensure the accuracy and effectiveness of instruction execution.

[0061] In addition, the system will refer to the scenario logic rules in the preset scenario rule library to ensure that all operations comply with the established business logic and compliance requirements. This step is the key to ensuring that system operations are not only technically feasible but also business-reasonable.

[0062] Through this series of complex matching and selection processes, the system can accurately respond to users' cross-scenario editing instructions, achieving highly customized operations and dynamic scene management. The implementation of this technical solution significantly improves the flexibility of the system and the user experience, allowing users to interact with the system more intuitively and efficiently, while also ensuring the accuracy of operations and compliance with business rules. In terms of operational efficiency and system reliability, such technical implementation provides users with great convenience, especially in application scenarios that require frequent adjustments and highly personalized configurations.

[0063] Step S102: establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0064] Optionally, in modern Internet of Things (IoT) applications, especially in scenarios involving artificial intelligence (AI), how to effectively manage and process the huge data streams coming from smart devices is a key challenge. Step S102 describes this process in detail, starting from establishing a communication connection with the edge layer, to receiving and converting IoT device data, and finally configuring it as a data source for pluggable components. The entire step reflects the high integration and automated processing capabilities of the technology.

[0065] IoT devices such as sensors and smart meters are usually deployed in dispersed geographical locations, and they continuously collect and send various types of data. In the AI ​​IoT architecture, the edge layer plays a vital role. It not only reduces the data transmission pressure on the central server, but also performs preliminary data processing and analysis, thereby speeding up the response speed and reducing bandwidth usage. The first step of step S102 is to establish a communication connection with this edge layer to ensure smooth data transmission.

[0066] Once a stable communication connection is established, the edge layer starts sending IoT device data to the central system. This data is usually collected from the smart terminal layer through a preset unified service interface in various formats, including but not limited to temperature readings, energy consumption data, operating status, etc. Since different devices may use different data formats, the received data needs to be formatted to meet the needs of downstream processing and analysis. This conversion process involves parsing the original data structure and converting it into the standard format required by pluggable components. For example, the original data may be an unstructured text log, while the converted data may be in structured JSON or XML format, which is critical for subsequent data processing and analysis.

[0067] The converted data is then further processed according to the set data configuration rules to be configured as a data source for specific pluggable components. These rules define how data is stored, indexed, and retrieved to support efficient data query and operation. The design of data configuration rules takes into account the real-time needs of data and long-term storage strategies, ensuring that data can be accessed quickly and supports the analysis of historical data.

[0068] In terms of technical effects, step S102 greatly optimizes the data processing process and improves the efficiency and accuracy of data processing. By delegating data processing tasks to the edge layer, the system can respond to changes in device status more quickly, while reducing the load on the central data center and the demand for network bandwidth. The unified conversion and standardized configuration of data formats enable data from different devices and platforms to be seamlessly integrated and analyzed, improving the availability and value of data.

[0069] In addition, this highly automated data processing method reduces the need for human intervention, reduces the risk of operational errors, and improves the overall reliability and security of the system. In practical applications, this means that companies can more effectively use the data generated by their IoT devices to support more complex decision-making processes, thereby gaining a competitive advantage in the market. In short, step S102 not only improves the efficiency and accuracy of data processing, but also provides strong technical support for the management and use of IoT devices, and promotes the development of intelligent applications.

[0070] Step S103: Perform data analysis on the component data source through a matching scenario analysis algorithm to determine the corresponding key features, build a process for the pluggable components according to the scenario logic rules, determine the corresponding component association relationships and component interaction methods, and obtain a cloud service layer middle platform based on the component association relationships, the component interaction methods and the key features.

[0071] Optionally, the technical principles and working processes involved in step S103 are the core parts of modern IoT and AI systems, which build an efficient and intelligent cloud service layer platform based on data collected from IoT devices. The main task of this link is to parse data through advanced data analysis algorithms, build processes that adapt to specific application scenarios, and achieve effective collaboration between components.

[0072] After receiving and formatting the data from IoT devices, the system needs to conduct in-depth analysis of the data to extract key features. This process is done through pre-selected scenario analysis algorithms. These algorithms may include machine learning models, statistical analysis methods, or deep learning frameworks, which are designed to identify patterns, trends, and anomalies from large amounts of raw data. For example, for a temperature monitoring system, key features may include daily cycle changes in temperature, abnormally high or low temperature events, etc.

[0073] After determining these key characteristics, the system further operates according to preset scenario logic rules. Scenario logic rules define how various data and events should affect the behavior of the system, including how data triggers specific processes or actions. These rules ensure that the system's response closely corresponds to business logic and operational requirements, allowing the system to take the right action at the right time.

[0074] Next, a critical step in step S103 is to build a process for pluggable components. At this stage, the system uses scenario logic rules to determine the associations between different components and how they interact. This means that the system will dynamically combine and configure the components based on the data analysis results to optimize specific tasks. For example, in a smart manufacturing scenario, data from a sensor may trigger an inspection process for a quality control component, which may further affect the adjustment of the production line.

[0075] Through such an approach, the system can flexibly adjust the workflow according to real-time data and business rules, optimize resource allocation, and improve efficiency. Finally, using these settings and key features, the system built a cloud service layer middle platform, which is a highly integrated service platform that supports the rapid development, deployment, and management of various applications and services. The middle platform not only encapsulates the complexity of business logic and data processing, but also provides an easy-to-use interface, making it easier for end users and developers to access services and data.

[0076] In terms of technical effects, step S103 significantly improves the intelligence level and operational efficiency of the entire system through intelligent analysis and refined management of data. The identification of key features enables the system to predict and respond to various situations more accurately, while the efficient associations and interactions between components ensure the smoothness and efficiency of the process. In addition, the construction of the cloud service layer middle platform greatly simplifies the development of new services and the maintenance of old services, reduces operating costs, and improves the scalability and flexibility of the system. These technical advantages not only enhance the competitiveness of enterprises, but also bring users a more reliable and efficient service experience.

[0077] From the above description, it can be seen that the method for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform provided in the embodiment of the present application can receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; establish a communication connection with the edge layer in the artificial intelligence Internet of Things, and receive IoT device data sent by the edge layer; construct a process for pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction method, and obtain a cloud service layer middle platform based on the component association relationship, component interaction method and key features, which can realize highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services.

[0078] In one embodiment of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform of the present application, see Figure 2 , and can also include the following:

[0079] Step S201: determining nodes in the scene knowledge graph according to the attribute information of the historical pluggable component, the historical scene analysis algorithm, and the historical scene logic rule, and determining edges in the scene knowledge graph according to the association between the historical pluggable component, the historical scene analysis algorithm, and the historical scene logic rule;

[0080] Step S202: construct the scene knowledge graph based on the nodes and the edges.

[0081] Optionally, in steps S201 and S202, the technical principle and working process mainly involve building a scenario knowledge graph, which is a complex system for characterizing and analyzing the relationships between various components, algorithms and rules. This graph not only helps the system understand and execute user needs, but also improves the system's adaptive ability and intelligence level.

[0082] The construction of the scenario knowledge graph first relies on the analysis of historical data, including the attribute information of historical pluggable components, historical scenario analysis algorithms, and historical scenario logic rules. This information includes the functions, performance parameters, application scenarios, and usage frequency of each component. The scenario analysis algorithm may include the type, applicability, efficiency, and accuracy of the algorithm, while the scenario logic rules cover the scope of application, trigger conditions, and execution operations of the rules. These detailed attribute information are the basis for building knowledge graph nodes. Each attribute can be defined as a node, and the detailed definition of the node depends on its role and importance in the business process.

[0083] Next, step S201 also involves determining the edges in the knowledge graph based on the correlation in these historical information. Edges represent the relationship between different nodes, such as dependencies, collaborative relationships, or mutually exclusive relationships. For example, a specific scenario analysis algorithm may require a specific type of data input, and this requirement relationship will be represented as an edge in the knowledge graph. By analyzing historical usage patterns and effect feedback, the system can identify which components are often used together, which algorithms are more effective for certain types of tasks, and which rules are more applicable in specific situations. Such association analysis not only enhances the information richness of the graph, but also makes the graph more accurately reflect the complex relationships in the real world.

[0084] In step S202, the system constructs a scene knowledge graph based on these defined nodes and edges. This process usually involves complex data structures and algorithms to ensure the accuracy and usability of the graph. The constructed knowledge graph is a powerful tool that can support system decision-making, optimize problem-solving processes, predict system behavior, and even automatically adjust strategies to adapt to new operating environments.

[0085] In terms of technical effects, the use of scenario knowledge graphs has greatly improved the intelligence and automation level of the system. First, it enables the system to automatically optimize operations based on accumulated experience and predict and solve potential problems through historical data analysis. Secondly, the dynamic update and expansion capabilities of the knowledge graph ensure that the system can continue to evolve and adapt to new business needs and technological changes. In addition, through the graph, the system can manage and utilize resources more effectively, for example, by identifying commonly used components and algorithm combinations to optimize resource allocation, reduce redundant operations, and improve efficiency. Ultimately, this approach not only enhances the reliability and performance of the system, but also improves the user's operating experience by providing faster and more accurate services to meet the specific needs of users.

[0086] In one embodiment of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform of the present application, see Figure 3 , and can also include the following:

[0087] Step S301: determining a graph matching range in a preset scene knowledge graph according to the scene requirements and the specific function;

[0088] Step S302: Measure the similarity between nodes and edges in the graph matching range according to the similarity algorithm, and determine the matching pluggable components in the scene atomic component library, the matching scene analysis algorithm in the preset algorithm center, and the matching scene logic rules in the preset scene rule library according to the nodes and edges with the greatest similarity.

[0089] Optionally, steps S301 and S302 constitute a highly specialized knowledge graph matching process, which aims to select components, algorithms and logical rules that match the requirements and functions of specific scenarios from the preset knowledge graph. This process involves complex data analysis and intelligent algorithms to ensure that the system can provide the most appropriate resource combination for each specific scenario.

[0090] In step S301, the system first needs to clearly understand the scenario requirements and the specific functions required. This usually involves interaction with users to collect detailed requirements descriptions, such as performance indicators, operating environment, user expected goals, etc. Based on these requirements, the system administrator or automation tool will define the matching scope of the knowledge graph, that is, determine which part of the information in the graph is relevant to the current needs. This step is critical because it determines the direction and efficiency of the subsequent search and matching process. For example, if the scenario requirement is about an automated system for temperature control, the matching scope may focus on the part of the graph that contains temperature sensors, related control algorithms, and execution rules.

[0091] Step S302 further uses a similarity algorithm to measure the similarity between nodes and edges within the graph matching range. These algorithms may include traditional statistical similarity calculations, machine learning-based pattern recognition techniques, or deep learning methods, which can analyze and compare the attribute information of nodes and the relationship characteristics of edges. Through the calculated similarity scores, the system can identify the components, algorithms, and rules that best match the current needs. This process not only considers the consistency of technical parameters, but also takes into account factors such as historical performance data, user feedback, and frequency of use to ensure the effectiveness and reliability of the recommended solutions in practical applications.

[0092] For example, the system may find that a certain temperature sensor performs well in similar applications due to its high accuracy and fast response time, while a certain temperature control algorithm is frequently used due to its optimized energy consumption management and fault prediction capabilities. In addition, a set of rules may be selected for its efficiency in handling abnormal situations. This data and algorithm-based matching process greatly enhances the system's adaptability and intelligence level.

[0093] In terms of technical effects, this knowledge graph-based matching method has significantly improved the flexibility and efficiency of the system. First, by accurately matching user needs with system resources, the system's operating efficiency and resource utilization can be greatly improved, reducing resource waste. Secondly, this method supports highly customized service provision, enabling the system to provide more personalized solutions for different users, thereby improving user satisfaction and the market competitiveness of the system. In addition, the scalability and maintainability of the system have also been enhanced, because the use of knowledge graphs enables the system to quickly adapt to new technologies and market changes while maintaining the stability and reliability of core functions.

[0094] In short, through intelligent graph matching and resource optimization, the system can not only provide more efficient and accurate services, but also continuously learn and adapt to new operating environments and user needs while maintaining high flexibility and scalability. This technology has broad application prospects and is of great significance to improving the overall performance and user experience of intelligent systems.

[0095] In one embodiment of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform of the present application, see Figure 4 , and can also include the following:

[0096] Step S401: semantically analyze the cross-scenario editing instruction to determine the corresponding scenario requirements;

[0097] Step S402: Determine the specific function that best matches the scenario requirement according to the preset scenario function list.

[0098] Optionally, in modern intelligent systems, the processing of cross-scenario editing instructions is a key technology that allows the system to understand and respond to specific instructions from different application scenarios. The implementation of this technology involves two main steps: semantic parsing and function matching, which together ensure that the system can accurately and efficiently recognize and execute user instructions.

[0099] The first task of step S401 is to perform semantic analysis on the cross-scene editing instructions. Semantic analysis is a core technology in the field of natural language processing (NLP). It analyzes the structure and semantic content of user instructions and converts them into a data format that the system can understand and process. Specifically, this step involves multiple subtasks such as vocabulary analysis, syntactic parsing, and semantic understanding. For example, if a smart home system receives the instruction "reduce the brightness of the living room light", the system will first decompose the keywords in the instruction such as "reduce", "living room", "light" and "brightness", and then determine through syntactic parsing that "reduce" is the action and the brightness of the "living room light" is the object being operated. Then, in the semantic understanding stage, the system needs to recognize that this operation means that the settings of the lighting equipment need to be adjusted, rather than other possible meanings.

[0100] After completing the semantic parsing, step S402 involves determining the specific function that best matches the scene requirements based on the preset scene function list. This step is function-oriented and requires the system to be able to filter out functions that match the parsed requirements from its database. The system's preset scene function list is a database containing a variety of function options that cover various operations that the system may need to perform, such as temperature control, lighting control, security monitoring, etc. Taking the aforementioned smart home system as an example, the system will compare the "adjust lighting" option in the function list and confirm that this function can meet the requirement of "reducing the brightness of the living room lights". In this process, the system may also consider the user's preference settings, historical operation data, and environmental information to ensure maximum adaptability and personalization of function selection.

[0101] The implementation effect of this technology is extremely significant. First, through precise semantic parsing and function matching, the system can provide fast and accurate responses, greatly improving the user experience. Users do not need complex operations or multiple modification instructions, the system can directly understand and execute. Secondly, this technology supports a high degree of system flexibility and scalability. With the continuous expansion and optimization of the preset function list, the system can adapt to a wider range of application scenarios and complex user needs. In addition, by continuously learning user preferences and operating habits, the intelligent system can further optimize the function matching algorithm and achieve more personalized services.

[0102] As a specific application example, consider an office environment that integrates multiple smart devices. Employees can trigger the system to automatically adjust the conference room lighting, projection equipment, and air conditioning through simple voice commands, such as "prepare the video conference room." Here, the system needs to parse the command and select the appropriate lighting adjustment, equipment startup, and temperature control functions from the function list to meet the specific setting requirements of the conference room. Through this technology, the office environment becomes more intelligent and efficient, greatly improving work efficiency and employee satisfaction.

[0103] In one embodiment of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform of the present application, see Figure 5 , and can also include the following:

[0104] Step S501: receiving IoT device data sent by the edge layer in the artificial intelligence IoT, wherein the IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface;

[0105] Step S502: standardize the IoT device data according to the configuration requirements of the pluggable component, and configure the IoT device data after the standardization as the component data source of the pluggable component.

[0106] Optionally, in the practice of artificial intelligence Internet of Things (AIoT), efficient management of data flow is crucial. This process starts with data collection at the intelligent terminal layer and involves the reception, processing and final application of data. Among them, steps S501 and S502 constitute the core of this process, ensuring that data can be effectively received and processed to adapt to different application scenarios and needs.

[0107] In step S501, the main task of the system is to receive the IoT device data sent by the edge layer. At this stage, the data mainly comes from the intelligent terminal layer, which includes various sensors and actuators, such as temperature sensors, cameras, smart bulbs, etc. These devices collect and send data through a preset unified service interface. The design of this interface standardizes the data communication method between different devices, so that the data flow can be smoothly moved to the edge layer in the Internet of Things. The main function of the edge layer is to serve as a data transfer station. It not only reduces the data transmission demand on the central server, but also can perform preliminary data processing, which can reduce the load on the central server and reduce the response time.

[0108] Next, in step S502, the data received from the intelligent terminal layer needs to be standardized. Standardization is the key to ensuring that data can be seamlessly connected between different system components. In this process, the original data will be converted into a format that meets the basic requirements of all pluggable components. For example, the original data may include data formats unique to various sensors, and the standardized data will be converted into JSON or XML formats, which are widely supported and easy to process across platforms. After the standardization is completed, the data will be configured as the data source of the pluggable component for further data analysis and application use.

[0109] This series of technical processing not only improves the efficiency of data processing, but also enhances the flexibility and scalability of the system. Through standardized data interfaces and formats, new equipment and services can be more easily integrated into existing systems. In addition, the use of the edge layer greatly reduces the dependence on central data processing facilities, which is especially important in applications with limited bandwidth or latency sensitivity. For example, in a large factory, it is crucial to monitor and adjust the operating status of the production line in real time. By deploying sensors throughout the factory to collect data and performing preliminary processing at the edge layer, it is possible to quickly respond to various production needs and emergencies, thereby optimizing production efficiency and reducing maintenance costs.

[0110] Overall, the implementation of steps S501 and S502 not only improves the efficiency of data processing, but also provides a solid foundation for the reliability and real-time performance of the AIoT system. This efficient and standardized data processing process is the key to realizing a variety of modern technology applications such as smart cities, smart homes, and industrial automation.

[0111] In one embodiment of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform of the present application, see Figure 6 , and can also include the following:

[0112] Step S601: Performing data cleaning and filtering on IoT device data sent by the smart terminal layer in the AI ​​IoT through a data cleaning model built into the edge layer in the AI ​​IoT;

[0113] Step S602: Perform data analysis on the IoT device data after data cleaning and filtering through a data analysis model built into the edge layer in the artificial intelligence IoT to obtain IoT device data after data analysis.

[0114] Optionally, in step S601, the system first needs to clean and filter the IoT device data sent from the smart terminal layer through the data cleaning model built into the edge layer. The smart terminal layer mainly includes various sensors and devices, such as temperature sensors, video cameras, environmental monitors, etc., which continuously generate a large amount of data. However, these data may contain erroneous, duplicate or incomplete information, which may affect the quality and accuracy of data analysis. The task of the data cleaning model is to identify and remove these inaccurate or irrelevant data parts, such as correcting obvious erroneous readings, deleting duplicate records or filling missing values. For example, in a smart farm management system, data from a soil moisture sensor may show abnormally high readings due to equipment failure. The data cleaning model will identify these anomalies and correct or mark them based on historical data to prevent these erroneous data from affecting subsequent analysis and decision-making.

[0115] Next, in step S602, the data analysis model built into the edge layer will conduct an in-depth analysis of the cleaned and filtered data. This step is the core function of the AIoT system, which involves the use of statistical methods, machine learning algorithms, or deep learning techniques to parse data to extract meaningful patterns and trends. These analysis results can be used in a variety of applications, such as predictive maintenance, resource optimization, behavioral analysis, etc. For example, in an intelligent transportation system, by analyzing traffic flow data from various places, the model can predict congestion at specific times and locations, and adjust the working mode of traffic lights accordingly to optimize traffic flow and reduce congestion.

[0116] The implementation of these two steps significantly improves the availability and usefulness of data, allowing AIoT systems to respond more accurately to environmental changes and user needs. Data cleaning ensures the quality of the basic data for analysis, while efficient data analysis provides a scientific basis for decision-making, which is essential for systems that implement automated control and intelligent decision-making. By processing these steps at the edge layer, the need for data transmission to the central server can also be significantly reduced, thereby reducing latency and improving the responsiveness and reliability of the system. For example, in the field of intelligent manufacturing, real-time monitoring and analysis of production line operation data through edge computing models can instantly adjust production parameters, optimize production efficiency and reduce downtime.

[0117] In short, steps S601 and S602 not only improve the efficiency and quality of the data processing process, but also enhance the real-time and intelligent level of the AIoT system, making its application in many fields more extensive and effective. This data-driven intelligent system is gradually changing many fields such as industrial automation, urban management, and environmental monitoring, showing great development potential and application value.

[0118] In order to achieve highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services, the present application provides an embodiment of a cross-scenario IoT application rapid construction device based on AIOT middle platform componentization capability for realizing all or part of the content of the cross-scenario IoT application rapid construction method based on AIOT middle platform componentization capability, see Figure 7 The cross-scenario IoT application rapid construction device based on the componentization capability of the AIOT platform specifically includes the following contents:

[0119] Build an element determination module 10, which is used to receive a cross-scenario editing instruction sent by a user through a visual interface, match the cross-scenario editing instruction with a preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library;

[0120] Building a data configuration module 20, which is used to establish a communication connection with the edge layer in the artificial intelligence Internet of Things, receive the Internet of Things device data sent by the edge layer, convert the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configure it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0121] The middle platform building module 30 is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

[0122] From the above description, it can be seen that the cross-scenario IoT application rapid construction device based on the componentization capability of the AIOT middle platform provided in the embodiment of the present application can receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; establish a communication connection with the edge layer in the artificial intelligence IoT, and receive IoT device data sent by the edge layer; construct a process for pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction method, and obtain a cloud service layer middle platform based on the component association relationship, component interaction method and key features, which can realize highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services.

[0123] In order to achieve highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services, the present application provides a system embodiment of a cross-scenario IoT application rapid construction device based on AIOT middle platform componentization capability for implementing all or part of the content of the cross-scenario IoT application rapid construction method based on AIOT middle platform componentization capability, see Figure 8 , the system includes a cloud service layer, an edge layer and an intelligent terminal layer in the artificial intelligence Internet of Things, the cloud service layer is connected to the edge layer, and the edge layer is connected to the intelligent terminal layer;

[0124] The cloud service layer includes:

[0125] Build an element determination module 10, which is used to receive a cross-scenario editing instruction sent by a user through a visual interface, match the cross-scenario editing instruction with a preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library;

[0126] Building a data configuration module 20, which is used to establish a communication connection with the edge layer in the artificial intelligence Internet of Things, receive the Internet of Things device data sent by the edge layer, convert the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configure it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0127] The middle platform building module 30 is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

[0128] From the hardware level, in order to achieve highly flexible and scalable cross-scenario IoT application rapid construction and AIOT services, the present application provides an embodiment of an electronic device for implementing all or part of the content of the cross-scenario IoT application rapid construction method based on the componentization capability of the AIOT middle platform, and the electronic device specifically includes the following content:

[0129] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the device for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform and related devices such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the method for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform and the embodiment of the device for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0130] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0131] In practical applications, part of the method for quickly building cross-scenario IoT applications based on the componentization capabilities of the AIOT middle platform can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0132] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0133] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0134] In one embodiment, a method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT platform can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0135] Step S101: receiving a cross-scenario editing instruction sent by a user through a visual interface, matching the cross-scenario editing instruction with a preset scenario knowledge graph, and determining a pluggable component in a preset scenario atomic component library, a matching scenario analysis algorithm in a preset algorithm center, and a matching scenario logic rule in a preset scenario rule library;

[0136] Step S102: establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0137] Step S103: Perform data analysis on the component data source through a matching scenario analysis algorithm to determine the corresponding key features, build a process for the pluggable components according to the scenario logic rules, determine the corresponding component association relationships and component interaction methods, and obtain a cloud service layer middle platform based on the component association relationships, the component interaction methods and the key features.

[0138] From the above description, it can be seen that the electronic device provided in the embodiment of the present application receives cross-scenario editing instructions sent by the user through a visual interface, matches the cross-scenario editing instructions with the preset scenario knowledge graph, determines the pluggable components in the preset scenario atomic component library, the matching scene analysis algorithm in the preset algorithm center, and the matching scene logic rules in the preset scenario rule library; establishes a communication connection with the edge layer in the artificial intelligence Internet of Things, and receives the Internet of Things device data sent by the edge layer; constructs the process of pluggable components according to the scene logic rules, determines the corresponding component association relationship and component interaction method, and obtains a cloud service layer middle platform based on the component association relationship, component interaction method and key features, which can realize highly flexible and scalable cross-scenario Internet of Things application rapid construction and AIOT services.

[0139] In another embodiment, the device for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform can be configured separately from the central processing unit 9100. For example, the device for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform can be configured as a chip connected to the central processing unit 9100, and the function of the method for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform can be realized through the control of the central processing unit.

[0140] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0141] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0142] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0143] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0144] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0145] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0146] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0147] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0148] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform, where the execution subject in the above embodiment is a server or a client. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all the steps in the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform, where the execution subject in the above embodiment is a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0149] Step S101: receiving a cross-scenario editing instruction sent by a user through a visual interface, matching the cross-scenario editing instruction with a preset scenario knowledge graph, and determining a pluggable component in a preset scenario atomic component library, a matching scenario analysis algorithm in a preset algorithm center, and a matching scenario logic rule in a preset scenario rule library;

[0150] Step S102: establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0151] Step S103: Perform data analysis on the component data source through a matching scenario analysis algorithm to determine the corresponding key features, build a process for the pluggable components according to the scenario logic rules, determine the corresponding component association relationships and component interaction methods, and obtain a cloud service layer middle platform based on the component association relationships, the component interaction methods and the key features.

[0152] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application receives cross-scenario editing instructions sent by the user through a visual interface, matches the cross-scenario editing instructions with the preset scenario knowledge graph, determines the pluggable components in the preset scenario atomic component library, the matching scene analysis algorithm in the preset algorithm center, and the matching scene logic rules in the preset scenario rule library; establishes a communication connection with the edge layer in the artificial intelligence Internet of Things, and receives the Internet of Things device data sent by the edge layer; constructs the process of pluggable components according to the scene logic rules, determines the corresponding component association relationship and component interaction method, and obtains the cloud service layer middle platform according to the component association relationship, component interaction method and key features, which can realize highly flexible and scalable cross-scenario Internet of Things application rapid construction and AIOT services.

[0153] The embodiments of the present application also provide a computer program product that can implement all the steps in the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform in the above embodiment, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the method for quickly building a cross-scenario IoT application based on the componentization capability of the AIOT middle platform are implemented. For example, the computer program / instruction implements the following steps:

[0154] Step S101: receiving a cross-scenario editing instruction sent by a user through a visual interface, matching the cross-scenario editing instruction with a preset scenario knowledge graph, and determining a pluggable component in a preset scenario atomic component library, a matching scenario analysis algorithm in a preset algorithm center, and a matching scenario logic rule in a preset scenario rule library;

[0155] Step S102: establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, wherein the Internet of Things device data is sent by the intelligent terminal layer in the artificial intelligence Internet of Things through a preset unified service interface;

[0156] Step S103: Perform data analysis on the component data source through a matching scenario analysis algorithm to determine the corresponding key features, build a process for the pluggable components according to the scenario logic rules, determine the corresponding component association relationships and component interaction methods, and obtain a cloud service layer middle platform based on the component association relationships, the component interaction methods and the key features.

[0157] From the above description, it can be seen that the computer program product provided in the embodiment of the present application receives cross-scenario editing instructions sent by the user through a visual interface, matches the cross-scenario editing instructions with the preset scenario knowledge graph, determines the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; establishes a communication connection with the edge layer in the artificial intelligence Internet of Things, and receives the Internet of Things device data sent by the edge layer; constructs the process of pluggable components according to the scenario logic rules, determines the corresponding component association relationship and component interaction mode, and obtains a cloud service layer middle platform based on the component association relationship, component interaction mode and key features, which can realize highly flexible and scalable cross-scenario Internet of Things application rapid construction and AIOT services.

[0158] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for quickly building cross-scenario IoT applications based on the componentization capability of AIOT middleware, characterized in that: Applied to the cloud service layer in the artificial intelligence Internet of Things, the method includes: Receive cross-scenario editing instructions sent by the user through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; Wherein, the determining of the pluggable components in the preset scenario atomic component library includes: dynamically combining the pluggable components or replacing the pluggable components, the scenario analysis algorithm is an algorithm for analyzing and processing data in a specific scenario, and the analysis and processing includes data mining, pattern recognition or predictive analysis; Establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, the Internet of Things data including: temperature reading, energy consumption data, and operating status, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configuring it as a component data source of the pluggable component according to the set data configuration rules, the set data configuration rules including: storage rules, indexing rules, and retrieval rules; The IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface, and the data format conversion of the IoT device data includes: parsing the original data structure, the original data structure is an unstructured data format, and the data after data conversion is a structured data format; The component data source is analyzed through a matching scenario analysis algorithm to determine the corresponding key features, and the process of the pluggable components is constructed according to the scenario logic rules to determine the corresponding component association relationships and component interaction methods. The cloud service layer middle platform is constructed according to the component association relationships, the component interaction methods and the key features.

2. The method for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform according to claim 1 is characterized in that: Before matching the cross-scenario editing instruction with the preset scenario knowledge graph, the method includes: Determine the nodes in the scene knowledge graph according to the attribute information of the historical pluggable components, the historical scene analysis algorithm and the historical scene logic rules, and determine the edges in the scene knowledge graph according to the association between the historical pluggable components, the historical scene analysis algorithm and the historical scene logic rules; The scene knowledge graph is constructed based on the nodes and the edges.

3. The method for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform according to claim 1 is characterized in that: The matching of the cross-scenario editing instruction with the preset scenario knowledge graph to determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library includes: Determine a graph matching range in a preset scene knowledge graph according to the scene requirements and specific functions in the cross-scene editing instruction; The similarity between the nodes and edges in the graph matching range is measured according to the similarity algorithm, and the matching pluggable components in the scene atomic component library, the matching scene analysis algorithm in the preset algorithm center, and the matching scene logic rules in the preset scene rule library are determined according to the nodes and edges with the greatest similarity.

4. According to claim 1, the method for quickly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform is characterized in that: The cross-scenario editing instructions are analyzed for content to determine corresponding scenario requirements and specific functions, including: Performing semantic analysis on the cross-scenario editing instructions to determine corresponding scenario requirements; Determine the specific function that best matches the scenario requirements based on the preset scenario function list.

5. The method for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform according to claim 1 is characterized in that: The receiving the IoT device data sent by the edge layer, and configuring the IoT device data as a component data source of the pluggable component according to a set data configuration rule, wherein the IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface, including: Receiving IoT device data sent by an edge layer in an artificial intelligence IoT, wherein the IoT device data is sent by an intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface; The IoT device data is standardized according to the configuration requirements of the pluggable component, and the IoT device data after the standardized processing is configured as a component data source of the pluggable component.

6. The method for rapidly building cross-scenario IoT applications based on the componentization capability of the AIOT middle platform according to claim 1 is characterized in that: Before receiving the IoT device data sent by the edge layer, the method further includes: The data cleaning model built into the edge layer of the artificial intelligence Internet of Things is used to clean and filter the IoT device data sent by the smart terminal layer in the artificial intelligence Internet of Things; The data analysis model built into the edge layer in the artificial intelligence Internet of Things is used to analyze the Internet of Things device data after data cleaning and filtering to obtain the Internet of Things device data after the data analysis.

7. A device for quickly building cross-scenario IoT applications based on the componentization capability of AIOT middle platform, characterized in that: The device comprises: Build an element determination module, which is used to receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; Wherein, the determining of the pluggable components in the preset scenario atomic component library includes: dynamically combining the pluggable components or replacing the pluggable components, the scenario analysis algorithm is an algorithm for analyzing and processing data in a specific scenario, and the analysis and processing includes data mining, pattern recognition or predictive analysis; Building a data configuration module, which is used to establish a communication connection with the edge layer in the artificial intelligence Internet of Things, receive the Internet of Things device data sent by the edge layer, the Internet of Things data includes: temperature readings, energy consumption data, and operating status, convert the data format of the Internet of Things device data according to the data format requirements of the pluggable component, and configure it as a component data source of the pluggable component according to the set data configuration rules, the set data configuration rules include: storage rules, indexing rules, and retrieval rules; The IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface; the data format conversion of the IoT device data includes: parsing the original data structure, the original data structure is an unstructured data format, and the data after data conversion is a structured data format; The middle platform construction module is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

8. A cross-scenario IoT application rapid construction system based on the componentization capability of AIOT middle platform, characterized by: The system includes a cloud service layer, an edge layer and an intelligent terminal layer in an artificial intelligence Internet of Things, wherein the cloud service layer is connected to the edge layer, and the edge layer is connected to the intelligent terminal layer; The cloud service layer includes: Build an element determination module, which is used to receive cross-scenario editing instructions sent by users through a visual interface, match the cross-scenario editing instructions with the preset scenario knowledge graph, and determine the pluggable components in the preset scenario atomic component library, the matching scenario analysis algorithm in the preset algorithm center, and the matching scenario logic rules in the preset scenario rule library; Among them, the determination of the pluggable components in the preset scenario atomic component library includes: dynamically combining the pluggable components or replacing the pluggable components, the scenario analysis algorithm is an algorithm for analyzing and processing data in a specific scenario, and the analysis and processing includes data mining, pattern recognition or predictive analysis; building a data configuration module for establishing a communication connection with the edge layer in the artificial intelligence Internet of Things, receiving the Internet of Things device data sent by the edge layer, the Internet of Things data includes: temperature readings, energy consumption data, operating status, converting the data format of the Internet of Things device data according to the data format requirements of the pluggable components, and configuring it as a component data source of the pluggable components according to the set data configuration rules, the set data configuration rules include: storage rules, indexing rules and retrieval rules; The IoT device data is sent by the intelligent terminal layer in the artificial intelligence IoT through a preset unified service interface; the data format conversion of the IoT device data includes: parsing the original data structure, the original data structure is an unstructured data format, and the data after data conversion is a structured data format; The middle platform construction module is used to perform data analysis on the component data source through a matching scenario analysis algorithm, determine the corresponding key features, build the process of the pluggable components according to the scenario logic rules, determine the corresponding component association relationship and component interaction mode, and obtain the cloud service layer middle platform based on the component association relationship, the component interaction mode and the key features.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for quickly building cross-scenario Internet of Things applications based on the componentization capabilities of the AIOT middle platform as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quickly building a cross-scenario Internet of Things application based on the componentization capability of the AIOT middle platform as described in any one of claims 1 to 6 are implemented.

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