Intelligent sensing system based on system packaging and packaging method thereof

Abstract

The invention provides an intelligent sensing system based on system packaging and a packaging method of the intelligent sensing system, and relates to the technical field of semiconductor packaging. Determining intra-module combination characteristics of each function module according to each packaging concentration degree; determining function dependency between each function module and the sensing system according to the dependency relationship of each function module, and determining correlation characteristics between modules of the sensing system according to all the function dependency; carrying out fusion adjustment on the packaging strategies of all the functional modules based on the association features among the modules and the combination features in all the modules to obtain packaging fusion strategies of all the functional modules in the sensing system, and then carrying out automatic functional module packaging on the sensing system by using all the packaging fusion strategies. According to the invention, personalized fusion adjustment of the packaging strategy of the function module in the sensing system can be realized, so that the integration level of the function module of the sensing system can be improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor packaging technology, and more specifically, to an intelligent sensing system based on system packaging and a packaging method thereof. Background Art

[0002] With the rapid advancement of artificial intelligence technology, the perception system has been driven from traditional single perception to diversified and intelligent development. With the continuous innovation of sensor technology, the performance of various sensors (such as temperature, humidity, pressure, acceleration, sound, image, etc.) has been continuously improved, enabling the perception system to obtain environmental information more comprehensively. At the same time, the rise of AI technology, especially the widespread application of machine learning and deep learning, has enabled the perception system to not only collect data, but also analyze, judge and make decisions in real time, thereby realizing intelligent perception and adaptive control.

[0003] In the existing technology, the encapsulation of functional modules usually focuses on the independence and performance of a single module, while ignoring the collaborative work and interdependence between functional modules. Due to the lack of a comprehensive understanding of the complex relationships between modules, the design and encapsulation of each functional module often do not take into account the information transmission and collaboration requirements between modules, resulting in overly dispersed interfaces and transmission paths between functional modules, increasing the overall complexity of the system. In addition, standardized encapsulation solutions usually fail to optimize the functional requirements and interdependence of specific modules, resulting in space waste and redundant design, affecting the compactness and resource utilization of the system. Many encapsulation strategies lead to low integration between modules. Therefore, how to achieve personalized fusion adjustment of functional module encapsulation strategies in perception systems to improve the integration of functional modules in perception systems is a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides an intelligent perception system based on system encapsulation and its encapsulation method, which can realize personalized fusion adjustment of the functional module encapsulation strategy in the perception system, thereby improving the integration of the functional modules of the perception system.

[0005] In a first aspect, the present application provides a packaging method for an intelligent sensing system, the packaging method comprising the following steps:

[0006] Obtain each functional module in the perception system, and then extract the encapsulation tasks of each functional module;

[0007] The encapsulation concentration of the task logic in each functional module is determined by the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system. Then, the intra-module combination characteristics of each functional module in the perception system are determined according to the encapsulation concentration and the component combination mode of each functional module.

[0008] According to the functional module topology structure and dependency relationship between each functional module, the functional dependency between each functional module and the perception system is determined, and then the inter-module correlation characteristics of the perception system are determined by all the functional dependencies;

[0009] Based on the inter-module correlation features and the combined features within each module, the encapsulation strategies of each functional module in the perception system are fused and adjusted to obtain the encapsulation fusion strategies of each functional module in the perception system, and then all the encapsulation fusion strategies are used to automatically encapsulate the functional modules of the perception system.

[0010] In this embodiment, determining the encapsulation concentration of task logic in each functional module through the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system specifically includes:

[0011] For each functional module, the centralized features of the task logic in the functional module are extracted from the task logic associations between each encapsulated task;

[0012] Determine the logical impact factors of the packaging process on the functional modules by perceiving the initial packaging strategy of the system;

[0013] The encapsulation concentration of the task logic in the functional module is determined according to the logic impact factor and the concentration feature, and then the encapsulation concentration of the task logic in each functional module is obtained.

[0014] In this embodiment, determining the intra-module combination features of each functional module in the perception system based on the packaging concentration and the component combination of each functional module specifically includes:

[0015] For each functional module in the perception system, the signal interference in the functional module is determined by the combination of components in the functional module;

[0016] The intra-module combination characteristics of the functional modules are determined according to the signal interference and the packaging concentration of the functional modules, and then the intra-module combination characteristics of each functional module in the perception system are obtained.

[0017] In this embodiment, determining the functional dependency between each functional module and the perception system based on the functional module topology and the dependency relationship between each functional module specifically includes:

[0018] For each functional module, the initial dependency value of the functional module on the perception system is determined through the dependency relationship of the functional module;

[0019] Extract the dependency influence of the functional module structure on the functional module from the functional module topology structure between each functional module;

[0020] The initial dependency value is modified according to the dependency influence amount to obtain the functional dependency between the functional module and the perception system, and then the functional dependency between each functional module and the perception system is obtained.

[0021] In this embodiment, determining the inter-module association characteristics of the perception system based on all functional dependencies specifically includes:

[0022] Construct a functional module association graph of the perception system through all functional dependencies;

[0023] Extracting the association strength value and inter-module synergy value of the perception system from the functional module association map;

[0024] The inter-module association characteristics of the perception system are determined according to the association strength value and the inter-module collaboration value.

[0025] In this embodiment, the encapsulation strategies of the functional modules in the perception system are integrated and adjusted based on the inter-module association features and the combined features within each module. The encapsulation integration strategies of the functional modules in the perception system are obtained, specifically including:

[0026] For each functional module in the perception system, obtain the encapsulation strategy of the functional module in the functional module;

[0027] Merging the inter-module association features and the intra-module combination features of the functional modules to obtain merged adjustment features of the functional modules;

[0028] The encapsulation strategy is adjusted through the merging adjustment feature to obtain the encapsulation fusion strategy of the functional modules, and then the encapsulation fusion strategy of each functional module in the perception system is obtained.

[0029] In this embodiment, using all encapsulation fusion strategies to automatically encapsulate the functional modules of the perception system specifically includes:

[0030] Each encapsulation fusion strategy is used as the encapsulation strategy of each functional module in the perception system to complete the automatic functional module encapsulation of the perception system.

[0031] In this embodiment, the functional module is an independent functional unit in the perception system.

[0032] In this embodiment, the initial packaging strategy is a basic strategy for packaging functional modules in the early stage of design.

[0033] In a second aspect, the present application provides an intelligent perception system based on system encapsulation, which is used to execute an encapsulation method of an intelligent perception system, wherein the perception system includes:

[0034] The acquisition module is used to obtain each functional module in the perception system and then extract the encapsulation tasks of each functional module;

[0035] The intra-module combination module is used to determine the encapsulation concentration of the task logic in each functional module through the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system, and then determine the intra-module combination characteristics of each functional module in the perception system based on each encapsulation concentration and the component combination mode of each functional module;

[0036] The inter-module association module is used to determine the functional dependencies between each functional module and the perception system based on the functional module topology structure and the dependency relationship between each functional module, and then determine the inter-module association characteristics of the perception system based on all the functional dependencies;

[0037] The fusion adjustment module is used to integrate and adjust the encapsulation strategies of each functional module in the perception system based on the correlation features between the modules and the combined features within each module, obtain the encapsulation fusion strategies of each functional module in the perception system, and then use all the encapsulation fusion strategies to automatically encapsulate the functional modules of the perception system.

[0038] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0039] Acquire each functional module in the perception system, and then extract the encapsulation task of each functional module; determine the encapsulation concentration of the task logic in each functional module through the task logic association between each encapsulation task and the initial encapsulation strategy of the perception system, and then determine the module combination characteristics of each functional module in the perception system according to each encapsulation concentration and the component combination method of each functional module; determine the functional dependency between each functional module and the perception system according to the functional module topology structure between each functional module and the dependency relationship of each functional module, and then determine the module-to-module association characteristics of the perception system from all functional dependencies; based on the inter-module association characteristics and the module-to-module combination characteristics, fuse and adjust the encapsulation strategy of each functional module in the perception system to obtain the encapsulation fusion strategy of each functional module in the perception system, and then use all the encapsulation fusion strategies to automatically encapsulate the functional modules of the perception system.

[0040] It can be seen that in this application, first, the combination characteristics within the module help to identify possible signal interference, power consumption problems and performance bottlenecks, thereby providing a personalized adjustment basis for the packaging strategy. Among them, the packaging concentration reflects the integration of components, which affects the functional execution efficiency and stability of the module. Optimizing the combination characteristics within the module can improve the packaging effect of the functional module, ensure efficient integration and stability, and thus improve the performance and reliability of the perception system; then, the correlation characteristics between modules reveal the collaborative work and dependency bottlenecks between modules. For highly dependent modules, the packaging can be optimized to reduce signal transmission delays and improve the collaborative efficiency between modules; for low-dependence modules, a looser packaging method can be selected to reduce system complexity and adjust the dependency relationships between modules. system, optimizing the coordination between functional modules, thereby improving the integration and overall performance of the perception system; finally, fusion regulation provides a customized regulation scheme for the packaging strategy of each functional module by combining the combination characteristics within the module and the correlation characteristics between modules. By optimizing the component layout within the module and enhancing the synergy between modules, fusion regulation can improve the compactness and collaboration efficiency of the modules, thereby achieving higher integration in the overall system architecture. Customized regulation ensures that the functional modules not only meet the independent performance requirements during the packaging process, but also effectively cooperate with the functional requirements of other modules, reducing redundancy and resource waste, thereby improving the module integration and the system's collaborative ability, making the entire perception system more efficient, stable and reliable when performing tasks.

[0041] To sum up, the technical solution adopted in this application can realize personalized fusion adjustment of the functional module packaging strategy in the perception system, thereby improving the integration of the functional modules of the perception system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0043] Figure 1 is a flow chart of the packaging method of the intelligent perception system provided by this application;

[0044] Figure 2 is an exemplary flow chart for determining intra-module combination features of each functional module in a perception system according to the present application;

[0045] Figure 3 This is a module structure diagram of the intelligent perception system based on system encapsulation provided by this application. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the examples and accompanying drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0047] Example 1: In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG, this figure is an exemplary flow chart of a packaging method of an intelligent sensing system according to this embodiment of the present application, and the packaging method includes the following steps:

[0048] In step S1, each functional module in the perception system is obtained, and then the encapsulation tasks of each functional module are extracted.

[0049] It should be noted that in this application, the functional module refers to an independent functional unit in the perception system, which is responsible for performing specific functions or tasks and collaborating with other functional modules to achieve the overall goal of the perception system. The encapsulation task refers to the various design, processing, integration and optimization work carried out in the encapsulation process to achieve the performance goals required by the functional module; in specific implementation, the various functional modules in the perception system can be obtained from the system specification of the perception system, thereby extracting the encapsulation tasks of each functional module, where each functional module has multiple encapsulation tasks. In this application, one encapsulation task is used for processing. In actual use, other encapsulation tasks in each functional module are processed in the same way.

[0050] In step S2, the encapsulation concentration of the task logic in each functional module is determined by the task logic association between each encapsulation task and the initial encapsulation strategy of the perception system, and then the intra-module combination characteristics of each functional module in the perception system are determined according to each encapsulation concentration and the component combination method of each functional module.

[0051] In this embodiment, the encapsulation concentration of the task logic in each functional module is determined by the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system, which can be specifically implemented by the following steps:

[0052] For each functional module, the centralized features of the task logic in the functional module are extracted from the task logic associations between each encapsulated task;

[0053] Determine the logical impact factors of the packaging process on the functional modules by perceiving the initial packaging strategy of the system;

[0054] The encapsulation concentration of the task logic in the functional module is determined according to the logic impact factor and the concentration feature, and then the encapsulation concentration of the task logic in each functional module is obtained.

[0055] It should be noted that, in this application, packaging concentration refers to the degree of integration of components within the functional module; concentration characteristics refer to the layout characteristics and concentration of interactions of components within the functional module; the logic impact factor refers to the degree of influence of design decisions during the packaging process on the task logic and performance of the functional module; the task logic association refers to the logical dependency relationship between tasks within the functional module; the initial packaging strategy refers to the basic strategy for packaging the functional module in the early stage of design.

[0056] In specific implementation, first, the concentrated features of the task logic in each functional module can be obtained from the system description of the perception system, so that the set of all concentrated features can be used as the task logic association between each encapsulated task. For each functional module, the concentrated features of the task logic in the functional module are extracted from the task logic association between each encapsulated task; then, the perception quantization training algorithm (for example: QAT algorithm) can be used to quantify the logical influence of the initial encapsulation strategy in the perception system, so that the quantization result is used as the logical influence factor of the encapsulation process on the functional module; finally, the product of the logical influence factor and the concentrated feature is used as the encapsulation concentration of the task logic in the functional module. The encapsulation concentration of the task logic in the functional module can be obtained in the above way.

[0057] Preferably, in this embodiment, reference Figure 2 As shown in FIG, this figure is an exemplary flow chart for determining the intra-module combination characteristics of each functional module in the perception system according to an embodiment of the present application. In this embodiment, the intra-module combination characteristics of each functional module in the perception system are determined based on the packaging concentration and the component combination mode of each functional module, which can be specifically implemented by the following steps:

[0058] First, in step S21, for each functional module in the perception system, the signal interference in the functional module is determined by the component combination of the functional module;

[0059] Then, in step S22, the intra-module combination features of the functional modules are determined according to the signal interference and the packaging concentration of the functional modules, thereby obtaining the intra-module combination features of each functional module in the perception system;

[0060] It should be noted that, in the present application, the intra-module combination feature represents the packaging feature of the component combination within the functional module; in specific implementation, first, the component combination method of each functional module can be obtained from the system description of the perception system, and the signal interference quantification of the component combination method in the functional module can be performed using a joint multi-domain feature extraction algorithm based on a convolutional neural network (for example: JMDFE algorithm), so that the result of the signal interference quantification can be used as the signal interference in the functional module, and the signal interference represents the degree of interference suffered by the signal in the functional module; then, a functional module with a higher packaging concentration usually means that multiple components are integrated in a smaller space, which increases the risk of interaction and interference between signals. On the contrary, a functional module with a lower packaging concentration may adopt a more dispersed component layout to reduce the occurrence of interference. The ratio of the packaging concentration of the functional module to the signal interference can be used as the intra-module combination feature of the functional module. The intra-module combination feature of each functional module in the perception system can be obtained in the above manner.

[0061] In step S3, the functional dependencies between each functional module and the perception system are determined based on the functional module topology structure and the dependency relationship between each functional module, and then the inter-module association characteristics of the perception system are determined by all the functional dependencies.

[0062] In this embodiment, the following steps may be used to determine the functional dependencies between each functional module and the perception system based on the functional module topology structure and the dependency relationships between each functional module:

[0063] For each functional module, the initial dependency value of the functional module on the perception system is determined through the dependency relationship of the functional module;

[0064] Extract the dependency influence of the functional module structure on the functional module from the functional module topology structure between each functional module;

[0065] The initial dependency value is modified according to the dependency influence amount to obtain the functional dependency between the functional module and the perception system, and then the functional dependency between each functional module and the perception system is obtained.

[0066] It should be noted that in this application, functional dependency represents the degree of mutual dependence between the functional module and the perception system; the initial dependency value reflects the degree of dependence of the functional module on other functional modules or the overall function of the system; and the dependency influence value represents the degree of influence of the module structure on the dependency value of the functional module.

[0067] In the specific implementation, first of all, for each functional module, each functional module may rely on the data input, processing results or signals of other functional modules when performing perception tasks. The quantitative perception algorithm framework can be used to quantify the role of the functional module in the perception system and the degree of dependence of the task on the perception system. For example, the video acquisition functional module may rely on the data processing results of the image processing functional module, or the environmental perception functional module may rely on the real-time data of the temperature and humidity detection functional module. The quantitative value of the degree of dependence can be used as the initial dependence value of the functional module on the perception system; then, the Bayesian network algorithm can be used to analyze the physical and logical connection methods between functional modules, that is, the dependency paths, data flows and communication methods between functional modules, so as to quantify the position of the functional module in the topological structure and its dependence on other functional modules. The dependency influence degree of the block can be used as the quantitative value of the dependency influence degree as the dependency influence quantity of the functional module. Among them, the functional module topology structure can be presented in the form of network diagram, dependency matrix, etc., revealing the direct and indirect dependencies between functional modules. For some functional modules, their functions may directly affect the normal operation of other functional modules, while the influence of some functional modules is more indirect. By quantifying the position of each functional module in the topological structure and its dependency degree on other functional modules, the dependency influence quantity of each functional module can be extracted; finally, the dependency influence quantity is used as the correction value of the initial dependency value, that is, the sum of the dependency influence quantity and the initial dependency value can be used as the functional dependency between the functional module and the perception system. The functional dependency between each functional module and the perception system can be obtained in the above way.

[0068] In this embodiment, determining the inter-module association characteristics of the perception system based on all functional dependencies can be achieved by using the following steps:

[0069] Construct a functional module association graph of the perception system through all functional dependencies;

[0070] Extracting the association strength value and inter-module synergy value of the perception system from the functional module association map;

[0071] The inter-module association characteristics of the perception system are determined according to the association strength value and the inter-module collaboration value.

[0072] It should be noted that the inter-module association feature reflects the degree of dependency and coordination between different functional modules. In the specific implementation, first, a network diagram is initialized, and each functional module is used as a node of the network diagram. The functional dependency between the functional module and the perception system is used as an edge connecting the nodes. Each edge can be weighted according to the frequency and intensity of data flow, task dependency or information interaction between the functional modules, reflecting the closeness and mutual dependence between the functional modules. The updated network diagram can be used as the functional module association map of the perception system. The functional module association map is a network diagram that intuitively displays the functional relationship between the functional modules in the perception system. Then, the spectral clustering algorithm finds the natural relationship between the functional modules by performing eigenvalue decomposition on the Laplace matrix of the association map. Clustering patterns help identify the association strength and collaborative working relationship between modules. Therefore, the graph clustering algorithm can be used to extract the association strength value and inter-module collaboration value of the perception system from the functional module association graph. Among them, the association strength value reflects the strength of the functional dependency between modules. Modules with strong dependencies contribute more to the system, and usually have higher association strength values. The inter-module collaboration value reflects the degree to which multiple modules work together to realize the system function. Modules with higher collaboration values ​​usually undertake important joint tasks in the perception system. The functions of multiple functional modules may work together at the same time, and there is more information exchange and task sharing between functional modules. Finally, the combination of association strength values ​​and inter-module collaboration values ​​can be used as the inter-module association feature of the perception system.

[0073] In step S4, the encapsulation strategies of each functional module in the perception system are fused and adjusted based on the inter-module association features and the combined features within each module to obtain the encapsulation fusion strategies of each functional module in the perception system, and then all the encapsulation fusion strategies are used to automatically encapsulate the functional modules of the perception system.

[0074] In this embodiment, based on the inter-module association features and the combined features within each module, the encapsulation strategies of each functional module in the perception system are integrated and adjusted. The encapsulation integration strategies of each functional module in the perception system can be obtained by the following steps:

[0075] For each functional module in the perception system, obtain the encapsulation strategy of the functional module in the functional module;

[0076] Merging the inter-module association features and the intra-module combination features of the functional modules to obtain merged adjustment features of the functional modules;

[0077] The encapsulation strategy is adjusted through the merging adjustment feature to obtain the encapsulation fusion strategy of the functional modules, and then the encapsulation fusion strategy of each functional module in the perception system is obtained.

[0078] It should be noted that, in the present application, the encapsulation fusion strategy represents the optimized encapsulation design parameters of the functional module; in specific implementation, first, for each functional module in the perception system, the encapsulation strategy of the functional module in the functional module can be obtained from the system specification of the perception system; then, the feature merging algorithm (for example: principal component analysis algorithm) can be used to merge the inter-module correlation features and the intra-module combination features of the functional module, and the result of the feature merging can be used as the merged adjustment feature of the functional module, and the merged adjustment feature is a feature used to optimize and adjust the encapsulation strategy; finally, a multi-objective optimization model based on the particle swarm algorithm is initialized, and the merged adjustment feature is used as the optimization variable in the multi-objective optimization model, and the encapsulation strategy is used as the optimization target in the multi-objective optimization model, and the multi-objective optimization model is used to optimize and adjust the encapsulation strategy, so that the optimized and adjusted encapsulation strategy can be used as the encapsulation fusion strategy of the functional module. The encapsulation fusion strategy of each functional module in the perception system can be obtained in the above manner.

[0079] In this embodiment, the following steps can be used to automatically encapsulate the functional modules of the perception system using all encapsulation fusion strategies:

[0080] Each encapsulation fusion strategy is used as the encapsulation strategy of each functional module in the perception system to complete the automatic functional module encapsulation of the perception system.

[0081] It can be seen that in this application, first, the combination characteristics within the module help to identify possible signal interference, power consumption problems and performance bottlenecks, thereby providing a personalized adjustment basis for the packaging strategy. Among them, the packaging concentration reflects the integration of components, which affects the functional execution efficiency and stability of the module. Optimizing the combination characteristics within the module can improve the packaging effect of the functional module, ensure efficient integration and stability, and thus improve the performance and reliability of the perception system; then, the correlation characteristics between modules reveal the collaborative work and dependency bottlenecks between modules. For highly dependent modules, the packaging can be optimized to reduce signal transmission delays and improve the collaborative efficiency between modules; for low-dependence modules, a looser packaging method can be selected to reduce system complexity and adjust the dependency relationships between modules. system, optimizing the coordination between functional modules, thereby improving the integration and overall performance of the perception system; finally, fusion regulation provides a customized regulation scheme for the packaging strategy of each functional module by combining the combination characteristics within the module and the correlation characteristics between modules. By optimizing the component layout within the module and enhancing the synergy between modules, fusion regulation can improve the compactness and collaboration efficiency of the modules, thereby achieving higher integration in the overall system architecture. Customized regulation ensures that the functional modules not only meet the independent performance requirements during the packaging process, but also effectively cooperate with the functional requirements of other modules, reducing redundancy and resource waste, thereby improving the module integration and the system's collaborative ability, making the entire perception system more efficient, stable and reliable when performing tasks.

[0082] To sum up, the technical solution adopted in this application can realize personalized fusion adjustment of the functional module packaging strategy in the perception system, thereby improving the integration of the functional modules of the perception system.

[0083] In the second embodiment, the present application provides an intelligent sensing system based on system encapsulation, referring to Figure 3 As shown, this figure is a schematic diagram of a perception system according to this embodiment of the present application, and the perception system includes:

[0084] An acquisition module 100 is used to acquire each functional module in the perception system and extract the encapsulation tasks of each functional module;

[0085] The intra-module combination module 200 is used to determine the encapsulation concentration of the task logic in each functional module based on the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system, and then determine the intra-module combination characteristics of each functional module in the perception system based on each encapsulation concentration and the component combination mode of each functional module;

[0086] The inter-module association module 300 is used to determine the functional dependencies between each functional module and the perception system based on the functional module topology structure and the dependency relationship between each functional module, and then determine the inter-module association characteristics of the perception system based on all the functional dependencies;

[0087] The fusion adjustment module 400 is used to integrate and adjust the encapsulation strategies of each functional module in the perception system based on the correlation characteristics between the modules and the combined characteristics within each module, obtain the encapsulation fusion strategies of each functional module in the perception system, and then use all the encapsulation fusion strategies to automatically encapsulate the functional modules of the perception system.

[0088] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A packaging method for an intelligent sensing system, characterized in that: The packaging method comprises the following steps: Obtain each functional module in the perception system, and then extract the encapsulation tasks of each functional module; The encapsulation concentration of the task logic in each functional module is determined by the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system. Then, the intra-module combination characteristics of each functional module in the perception system are determined according to the encapsulation concentration and the component combination mode of each functional module. According to the functional module topology structure and dependency relationship between each functional module, the functional dependency between each functional module and the perception system is determined, and then the inter-module correlation characteristics of the perception system are determined by all the functional dependencies; Based on the inter-module correlation features and the combined features within each module, the encapsulation strategies of each functional module in the perception system are fused and adjusted to obtain the encapsulation fusion strategies of each functional module in the perception system, and then all the encapsulation fusion strategies are used to automatically encapsulate the functional modules of the perception system.

2. The packaging method of an intelligent sensing system according to claim 1, wherein: The encapsulation concentration of task logic in each functional module is determined by the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system, specifically including: For each functional module, the centralized features of the task logic in the functional module are extracted from the task logic associations between each encapsulated task; Determine the logical impact factors of the packaging process on the functional modules by perceiving the initial packaging strategy of the system; The encapsulation concentration of the task logic in the functional module is determined according to the logic impact factor and the concentration feature, and then the encapsulation concentration of the task logic in each functional module is obtained.

3. The packaging method of an intelligent sensing system according to claim 1, wherein: The intra-module combination characteristics of each functional module in the perception system are determined based on the concentration of each package and the component combination of each functional module, including: For each functional module in the perception system, the signal interference in the functional module is determined by the combination of components in the functional module; The intra-module combination characteristics of the functional modules are determined according to the signal interference and the packaging concentration of the functional modules, and then the intra-module combination characteristics of each functional module in the perception system are obtained.

4. The packaging method of an intelligent sensing system according to claim 1, wherein: The functional dependencies between each functional module and the perception system are determined based on the functional module topology and the dependency relationships between each functional module. Specifically, the functional dependencies between each functional module and the perception system include: For each functional module, the initial dependency value of the functional module on the perception system is determined through the dependency relationship of the functional module; Extract the dependency influence of the functional module structure on the functional module from the functional module topology structure between each functional module; The initial dependency value is modified according to the dependency influence amount to obtain the functional dependency between the functional module and the perception system, and then the functional dependency between each functional module and the perception system is obtained.

5. The packaging method of an intelligent sensing system according to claim 1, wherein: The inter-module correlation characteristics of the perception system determined by all functional dependencies include: Construct a functional module association graph of the perception system through all functional dependencies; Extracting the association strength value and inter-module synergy value of the perception system from the functional module association map; The inter-module association characteristics of the perception system are determined according to the association strength value and the inter-module collaboration value.

6. The packaging method of an intelligent sensing system according to claim 1, wherein: Based on the inter-module correlation features and the combined features within each module, the encapsulation strategies of each functional module in the perception system are integrated and adjusted, and the encapsulation fusion strategies of each functional module in the perception system are obtained, which specifically include: For each functional module in the perception system, obtain the encapsulation strategy of the functional module in the functional module; Merging the inter-module association features and the intra-module combination features of the functional modules to obtain merged adjustment features of the functional modules; The encapsulation strategy is adjusted through the merging adjustment feature to obtain the encapsulation fusion strategy of the functional modules, and then the encapsulation fusion strategy of each functional module in the perception system is obtained.

7. The packaging method of an intelligent sensing system according to claim 1, wherein: Use all packaging fusion strategies to automatically package the functional modules of the perception system, including: Each encapsulation fusion strategy is used as the encapsulation strategy of each functional module in the perception system to complete the automatic functional module encapsulation of the perception system.

8. The packaging method of an intelligent sensing system according to claim 1, wherein: The functional module is an independent functional unit in the perception system.

9. The packaging method of an intelligent sensing system according to claim 1, wherein: The initial packaging strategy is the basic strategy for packaging functional modules in the early stage of design.

10. An intelligent perception system based on system encapsulation, used to execute the encapsulation method of an intelligent perception system according to any one of claims 1 to 9, characterized in that: The perception system includes: The acquisition module is used to obtain each functional module in the perception system and then extract the encapsulation tasks of each functional module; The intra-module combination module is used to determine the encapsulation concentration of the task logic in each functional module through the task logic association between each encapsulated task and the initial encapsulation strategy of the perception system, and then determine the intra-module combination characteristics of each functional module in the perception system based on each encapsulation concentration and the component combination mode of each functional module; The inter-module association module is used to determine the functional dependencies between each functional module and the perception system based on the functional module topology structure and the dependency relationship between each functional module, and then determine the inter-module association characteristics of the perception system based on all the functional dependencies; The fusion adjustment module is used to integrate and adjust the encapsulation strategies of each functional module in the perception system based on the correlation features between the modules and the combined features within each module, obtain the encapsulation fusion strategies of each functional module in the perception system, and then use all the encapsulation fusion strategies to automatically encapsulate the functional modules of the perception system.

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