An adaptive resource coordination method and system based on a state network of a resource coordination model.

Abstract

This invention belongs to the field of cross-domain complex resource modeling technology. It discloses a state network adaptive resource coordination method and system based on a resource coordination model. Combining ontology and meta-modeling theory, it constructs a unified static model of cross-domain complex resources from a semantic perspective. It designs and implements a dynamic adaptive state network model based on the static resource coordination model to support the self-transformation and propagation of resource states in the network, achieving adaptive transformation and updating of the overall resource state. Finally, it constructs a model application programming interface for resource retrieval. Unlike the fixed state definitions in traditional collaborative modeling, this invention's dynamic adaptive state network modeling method based on a resource coordination model combines ontology modeling and meta-modeling to achieve unified management and collaborative modeling of various complex resources in CPHS (Content Management System). It constructs a dynamic coordination model based on traditional static resource modeling, realizing dynamic collaborative transformation of resource states and providing a resource foundation for resource scheduling.

Description

Technical Field

[0001] This invention belongs to the field of cross-domain complex resource modeling technology, and particularly relates to a state network adaptive resource coordination method and system based on a resource coordination model. Background Technology

[0002] Cyber-Physical-Human Systems (CPHS) integrate the physical, cyber, and human worlds based on real-time interaction. These worlds "communicate" with each other in space and time. Cyber-Physical Systems (CPS) are the foundation of CPHS. It is an intelligent system that uses computing and networking technologies to collaboratively integrate information networks and physical components. CPS connects the physical and virtual worlds and is primarily applied in fields such as smart manufacturing, smart healthcare, and smart cities. With the development of technologies such as human-computer interaction and human-computer collaboration, people, information systems, and physical devices are interconnected, exchanging information and jointly generating the ability to meet needs. The concept of CPHS has been proposed and has attracted widespread attention.

[0003] Unlike traditional computing systems, cyber-physical-human systems (CPHS) require the coordinated operation of various resources (including physical, virtual, human, and organizational resources) to effectively and rationally allocate these resources. For example, handling traffic accidents necessitates the coordinated allocation of resources such as traffic police, cameras, surveillance systems, and medical departments. To support the operation of resource allocation within a CPHS, a resource abstraction and coordination model is first needed to manage the various resources within the system. Unfortunately, modeling resources within a CPHS remains a challenging task. The main challenge stems from the complexity of the resources and the coordination issues between various resource entities. Specifically:

[0004] Complex Resources: The complexity of resources is mainly reflected in their discreteness, disorder, and diversity. Discreteness means that resources come from different domains and different worlds. Disorder is mainly reflected in the lack of unified standards for various resources. Diversity refers to the existence of various forms of resources (material resources, network resources, human and organizational resources, etc.). In cyber-physical systems, interactions between people, between people and objects, and between objects need to be tightly integrated in the physical, social, and virtual worlds. However, existing resource modeling and management research usually only focuses on relatively single resources, neglecting the unified representation of complex resources. Existing resource modeling and management methods pay less attention to environmental, social, complex, cross-network, and cross-domain factors, and lack consideration for the collaborative relationships between resources at the social and human levels.

[0005] Resource Coordination: Research on the dynamic collaborative relationships between complex resources is lacking. The difficulty of resource coordination and interaction across various fields and worlds limits the accuracy of subsequent resource scheduling research. Resources can exist in a single form or in aggregates, forming aggregated resources. This indicates that resources are not isolated; relationships exist between resources. Resources can interact, collaborate, and coordinate through these relationships to provide corresponding functions. When one resource changes, it affects other resources. Therefore, it is necessary to correlate and coordinate resources, maintain a unified standard, and dynamically coordinate their respective work. For example, if a hospital's emergency resources fail in an accident, then related subsequent resources will be affected and unable to function properly. This will impact the execution of the entire rescue process. However, existing research lacks comprehensive methods and frameworks to support the interaction and coordination of cyber-physical human systems.

[0006] Modeling is the process of representing a system as a set of abstract models of its essence. In traditional software modeling, static modeling, process modeling, and collaborative modeling are common methods. Static models, often called structural models, are used to fully or partially represent the structure of a system design. For example, UML (Unified Modeling Language) class diagrams are the most popular static models. However, complex resource relationships make static models insufficient to meet the needs of resource collaborative transformation. Dynamic Process Collaboration (DPC) was proposed to address the dynamic collaboration problem. DPC defines the dynamic message exchange capabilities of participating services. However, modules only truly begin to cooperate and understand each other until DPC runs, which introduces considerable uncertainty.

[0007] Therefore, CPHS systems integrate interconnected computational, physical, and human resources. CPHS typically contain complex resources that are multi-source, cross-domain, and heterogeneous. Furthermore, the resources within CPHS are dynamic. However, existing research on resource modeling and management primarily focuses on relatively single resources or specific domains, neglecting the modeling of complex cross-domain resources. There is also a lack of methods and frameworks to support the dynamic adaptation and collaborative updating of such resources. Moreover, the collaborative interaction of cross-domain and cross-world resources limits subsequent research on resource scheduling and related topics. Therefore, there is an urgent need to design a dynamic adaptive method for state networks.

[0008] Ontology-based resource modeling is currently a mainstream technology, but its limitations include being relatively domain-specific and lacking scalability; limited research considering social resources such as people and organizations; and weak expression of relationships between resources. In dynamic adaptive state network research, current techniques primarily rely on finite state machines and DEVS simulation systems. Research on state machines and state transitions mainly focuses on the transition from state A to state B, without considering the impact of state changes on other states. In DEVS research, state changes are primarily triggered by events, which are used to indirectly transmit messages and update information, without directly establishing explicit relationships between states.

[0009] In summary, many modeling studies have utilized advanced techniques such as ontology. Existing research increasingly focuses on cross-border resources in CPHS. However, these studies are relatively weak in describing the cooperative relationships between resources. Furthermore, the dynamic nature of resources presents challenges to resource updates. Currently, there is limited research on the collaborative propagation of resource states, lacking a dynamic and adaptive collaborative propagation mechanism. Therefore, there is an urgent need to comprehensively model the cooperative relationships between resources in CPHS from a static perspective, requiring the construction of a dynamic adaptive coordination mechanism for resource states to achieve coordinated propagation of resource states and improve the scalability of the model.

[0010] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0011] (1) Existing resource modeling and resource management methods only focus on specific domains and ignore the modeling of complex cross-domain resources, while paying less attention to environmental, social, complex, cross-network, and cross-domain factors.

[0012] (2) Existing technologies lack methods and frameworks to support dynamic adaptation and collaborative updating of complex cross-domain resources, and the collaborative interaction of cross-domain and cross-world resources limits subsequent research on resource scheduling and other related issues.

[0013] (3) In traditional software modeling, complex resource relationships make static models insufficient to meet the needs of resource collaboration and transformation. The modules do not truly begin to cooperate and understand each other until the DPC runs, which is uncertain. Summary of the Invention

[0014] To overcome the problems existing in related technologies, the present invention discloses an adaptive resource coordination method and system based on a resource coordination model, the technical solution of which is as follows:

[0015] This invention discloses an adaptive resource coordination method for state networks based on a resource coordination model. This method combines ontology and meta-modeling theory to construct a unified static model of complex cross-domain resources from a semantic perspective; designs and implements a dynamic adaptive state network model based on the static resource coordination model to support the self-transformation and propagation of resource states in the network, and realizes the adaptive transformation and updating of the overall resource state; and constructs a model application programming interface for resource retrieval.

[0016] In one embodiment, the state network dynamic adaptation method specifically includes the following steps:

[0017] Step 1: Combine ontology modeling and meta-modeling to construct a static resource collaboration model;

[0018] Step 2: Based on the static resource coordination model, construct the atomic resource state model;

[0019] Step 3: Link the atomic states to construct the entire dynamic adaptive state network model;

[0020] Step 4: Construct the application interface for the static resource collaboration model and the dynamic adaptive state network.

[0021] Another objective of this invention is to provide a state network adaptive resource coordination system based on a resource coordination model that implements the aforementioned state network adaptive resource coordination method based on a resource coordination model. The state network adaptive resource coordination system based on a resource coordination model includes:

[0022] The resource collaboration model building module is used to combine ontology modeling and meta-modeling to build a static resource collaboration model;

[0023] The resource status model construction module is used to construct atomic resource status models based on the static resource coordination model.

[0024] The network model building module is used to link atomic states and build the entire dynamic adaptive state network model.

[0025] The application interface building module is used to build application interfaces for static resource collaboration models and dynamic adaptive state networks.

[0026] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the state network adaptive resource coordination method based on the resource coordination model.

[0027] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the state network adaptive resource coordination method based on a resource coordination model.

[0028] Another object of the present invention is to provide an information data processing terminal, which is installed on an electronic device to provide a user input interface for implementing the state network-based adaptive resource coordination system.

[0029] Combining all the above technical solutions, the advantages and positive effects of this invention are as follows:

[0030] First, in view of the technical problems existing in the prior art and the difficulty of solving these problems, and closely combining the technical solution to be protected by this invention with the results and data during the research and development process, this paper analyzes in detail how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about after solving the problems, as described in detail below:

[0031] (1) This invention combines the advantages of static modeling and dynamic collaborative modeling methods to provide a unified model of complex resources from a static perspective. Simultaneously, it incorporates prior knowledge to construct a dynamic adaptive state network model from a dynamic perspective, fully describing the collaborative transformation process of resource states. Combining ontology modeling and meta-modeling, this invention designs a static resource collaborative modeling method, achieving a unified description and abstraction of various complex resources, including (physical resources, network resources, human and organizational resources, etc.). The functions that resources can provide are used as core attributes to define the relationships between resources. Furthermore, this invention emphasizes the heterogeneity of different resources, describing them from a semantic perspective by combining ontology modeling and meta-modeling. Meta-modeling is typically used to define static models. Meta-modeling is more abstract, focusing on structure and rules. Meanwhile, ontology is a knowledge representation method and conceptual abstraction. By constructing ontology, the sharing and understanding of information structures, and the reuse and analysis of domain knowledge can be achieved.

[0032] (2) This invention proposes a dynamic adaptive state network model based on a static resource collaboration model. It focuses on the relationships between resources and defines in detail the process of resource state collaboration. When resources change, the dynamics of the resource state propagate and transform within the state network. Then, it adaptively updates the state information of other resources in the network, achieving collaborative propagation of resource states. Unlike the fixed state definition in traditional collaborative modeling, this invention describes the resource state in more detail through state attributes, comprehensively describes resource changes through changes in state attributes, and finally designs a resource retrieval model application programming interface.

[0033] (3) Resource modeling technology based on ontology is currently the mainstream technology. However, the existing problems are that it is relatively limited to specific domains and has poor scalability; there is a lack of research considering social resources such as people and organizations; and the expression of the relationship between various resources is weak. In this invention, from the perspective of resource modeling, an ontology + meta-modeling approach is used to model from both abstract and instance levels. A more general framework is used to model and describe resources, fully describing and modeling the relationship between resources (impact effects, priorities, etc.) and focusing on the state of resources, thereby improving the scalability of the model.

[0034] (4) In the research of dynamic adaptive state networks, current technologies are mainly based on finite state machines and DEVS simulation systems. Research on state machines and state changes primarily focuses on the transition from state A to state B, without considering the impact of state changes on other states. In DEVS research, state changes are mainly triggered by events, which are used to indirectly transmit messages and update information, without directly establishing explicit relationships between states. This invention, however, directly models resource states and the relationships between them (already completed in resource modeling), providing a more detailed classification of resource state attributes, relationships, and state change rules. Knowledge is extracted and stored using a knowledge graph (using existing technology), and logical reasoning is performed based on the defined states, relationships, and rules to achieve adaptive dynamic updates of states. The innovation of this invention lies in addressing the current lack of direct adaptive transformation of resource states, combining the ideas of state machines and DEVS to directly model states and relationships.

[0035] Secondly, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0036] (1) This invention provides a Dynamic Adaptive State Network (DASN) model based on Static Resource Collaborative Modeling (SRCM). First, this invention combines ontology and meta-modeling theory to construct a unified static model of cross-domain complex resources from a semantic perspective. Second, it designs and implements a DASN model based on SRCM, which supports the self-transformation and propagation of resource states in the network, realizing the adaptive transformation and updating of the overall resource state. Finally, this invention proposes an application interface for resource retrieval.

[0037] (2) The dynamic adaptive state network modeling method based on the resource coordination model of the present invention realizes the unified management and collaborative modeling of various complex resources in CPHS. It constructs a dynamic coordination model on the basis of traditional static resource modeling, realizes the dynamic collaborative transformation of resource state, and provides a resource foundation for resource scheduling.

[0038] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0039] (1) The technical solution of the present invention fills the technical gap in the industry at home and abroad: Based on ontology and meta-modeling theory, the present invention constructs a resource collaboration model and a dynamic adaptive state network model, realizes the dynamic adaptive update of resource state under complex resources and dynamic conditions, proposes a general resource collaboration modeling and dynamic adaptive network modeling theory, and performs modeling and transfer in different fields through instantiation, fills the gap in the analysis of the collaborative relationship between resources and resource state at home and abroad, and improves the scalability of the model.

[0040] (2) Does the technical solution of this invention overcome technical bias? This invention defines a general resource collaboration model and a dynamic adaptive state network model, which can be extended to different fields. Different knowledge engineering techniques and newly developed collaborative algorithms can be incorporated into the model. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0042] Figure 1 This is a flowchart of the state network adaptive resource coordination method based on the resource coordination model provided in this embodiment of the invention;

[0043] Figure 2 This is a schematic diagram of the state network adaptive resource coordination method based on the resource coordination model provided in this embodiment of the invention.

[0044] Figure 3 This is a resource static modeling diagram based on ontology and meta-model provided in an embodiment of the present invention;

[0045] Figure 4 This is a structural diagram of the static resource collaboration model provided in the embodiments of the present invention;

[0046] Figure 5 This is a structural diagram of the basic atomic resource state model provided in the embodiments of the present invention;

[0047] Figure 6A This is a graph showing the variation of a single service resource in a regularized environment, as provided in an embodiment of the present invention.

[0048] Figure 6B This is a graph showing the variation of a single-service resource in a low-noise environment, as provided in an embodiment of the present invention.

[0049] Figure 6C This is a graph showing the variation of a single-service resource in a high-noise environment, as provided in an embodiment of the present invention.

[0050] Figure 7A This is a schematic diagram illustrating the number of resource status updates in a regular environment provided in this embodiment of the invention;

[0051] Figure 7B This is a schematic diagram of the resource state propagation (update) time in a regular environment provided by an embodiment of the present invention;

[0052] Figure 7C This is a schematic diagram of resource retrieval hit rate in a regular environment provided by an embodiment of the present invention;

[0053] Figure 7D This is a schematic diagram of the resource retrieval error rate in a regular environment provided by an embodiment of the present invention;

[0054] Figure 7E This is a schematic diagram of resource retrieval time in a regular environment provided by an embodiment of the present invention;

[0055] Figure 8A This is a schematic diagram illustrating the number of resource status updates in a low-noise environment provided in an embodiment of the present invention;

[0056] Figure 8B This is a schematic diagram of resource status propagation (update) time in a low-noise environment provided by an embodiment of the present invention;

[0057] Figure 8C This is a schematic diagram of resource retrieval hit rate in a weak noise environment provided by an embodiment of the present invention;

[0058] Figure 8D This is a schematic diagram of the resource retrieval error rate in a weak noise environment provided by an embodiment of the present invention;

[0059] Figure 8E This is a schematic diagram of resource retrieval time in a low-noise environment provided by an embodiment of the present invention;

[0060] Figure 9A This is a schematic diagram illustrating the number of resource status updates in a high-noise environment provided in an embodiment of the present invention;

[0061] Figure 9B This is a schematic diagram of resource status propagation (update) time in a high-noise environment provided by an embodiment of the present invention;

[0062] Figure 9C This is a schematic diagram of resource retrieval hit rate in a noisy environment provided by an embodiment of the present invention;

[0063] Figure 9D This is a schematic diagram of the resource retrieval error rate in a noisy environment provided by an embodiment of the present invention;

[0064] Figure 9E This is a schematic diagram of resource retrieval time in a noisy environment provided by an embodiment of the present invention. Detailed Implementation

[0065] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] I. Explanation of the Implementation Example:

[0067] like Figure 1 As shown in the figure, the state network adaptive resource coordination method based on the resource coordination model provided in this embodiment of the invention includes the following steps:

[0068] S101, combining ontology modeling and meta-modeling, constructs a static resource collaboration model;

[0069] S102, Based on the static resource coordination model, construct the atomic resource state model;

[0070] S103 links atomic states to construct the entire dynamic adaptive state network model.

[0071] S104 is the application interface for building a static resource collaboration model and a dynamic adaptive state network.

[0072] As a preferred embodiment, such as Figure 2 As shown in the figure, the application scenario of the state network adaptive resource coordination method based on the resource coordination model provided in this embodiment of the invention is smart campus service (smart city and smart factory are also applicable), and specifically includes the following steps:

[0073] Step S1: Combining ontology modeling and meta-modeling, design a static resource collaborative modeling method.

[0074] (1) Define the metamodel of the static resource collaboration model, which includes entity classes, relationship classes, and attribute classes. See the static resource collaboration modeling module for details.

[0075] (2) According to the definition of the static resource collaboration meta-model, taking smart campus as an example, the complex resources (physical resources: office buildings, laboratories, classrooms, etc.); network resources (campus information systems, open APIs such as weather, maps, etc.); organizational resources (departments, colleges); human resources (employees, teachers, etc.) existing in smart campus are instantiated and represented and stored in the form of knowledge graphs. This overcomes the heterogeneity between complex resources from a semantic perspective and improves the scalability of the model.

[0076] Instantiation of resources existing in the target object includes:

[0077] Step 1. Collect and label the resource dataset.

[0078] Collect and integrate relevant resource data within the field, then clean and organize it.

[0079] The processed data is then manually labeled with resources, states, and relational entities.

[0080] Step 2. Construct a resource entity recognition model.

[0081] The resource entity recognition model is obtained by training an LSTM+CRF model using the entity data labeled in the above resource dataset.

[0082] Step 3. Construct an entity relationship extraction model.

[0083] The entity and relation data annotated in the above resource dataset are used to train an extraction model based on the pre-trained BERT model.

[0084] Step 4. Use a knowledge graph for storage.

[0085] Knowledge is extracted from the dataset using a resource entity recognition model and an entity relationship extraction model.

[0086] The extracted knowledge is checked manually;

[0087] Knowledge is stored using neo4j.

[0088] Step S2: Based on the static resource coordination model, construct the atomic resource state model.

[0089] Define the structure and framework of the atomic state model, and create internal and external transition rules for the state model. This provides the basic state model foundation for subsequent adaptive state networks. See the dynamic adaptive state network module for details. The construction of the atomic resource state model includes: defining and modeling the states in the static resource coordination model, specifically including:

[0090] Step 1. Define the structure of the atomic state model.

[0091] Atom-RS =<X,Y,S,S0,δint,δext,R>

[0092] S(T+1)int = Fint(ST,Xint,R)

[0093] S(T+1)ext=Fext(ST,Xext,R)

[0094] S(T+1)={S(T+1)ext∩S(T+1)int|R}

[0095] Step 2. Define the transfer rules and classify them.

[0096] Rule =<rule name,category,input,output,precondition,calculation,priority>

[0097] output=Calculate(input,category)ifprecondition is satisfied

[0098] Step 3. Construct an atomic resource state model framework based on the above definitions.

[0099] Step S3: Link the atomic states together to construct the entire dynamic adaptive state network model.

[0100] (1) Link all atomic states to form a state network, and define the structure of the dynamic adaptive state network. See the dynamic adaptive state network module for details.

[0101] (2) Based on the definition of a dynamic adaptive state network, construct a state network for the state of smart campus service resources in the static resource collaboration model. See the dynamic adaptive state network module for details.

[0102] The specific structure of the dynamic adaptive state network includes the states of each atomic resource, a sensor, a controller, and a rule base.

[0103] DASN=(Xs,Ys,{Atom-RS},{Atom-RS-i},Zs,R)

[0104] {outputs}=Calculates({inputs,categories,priors})if{preconditions}aresatisfied

[0105] The state network construction for the target object service resources in the static resource collaboration model includes:

[0106] Step 1. Based on the target object's service resources and status, manually construct the rule base according to the definitions and classifications of the above rules;

[0107] Step 2. Construct internal and external transfer functions for atomic resource states based on the priority and effect defined in the rule base, combined with the resource state entities and related relationships in the static resource collaboration model;

[0108] Step 3. Link the states of atomic resources through the collaborative relationships in the static resource collaboration model to construct a dynamic adaptive state network.

[0109] Step S4: Construct the application interface of the static resource collaboration model and the dynamic adaptive state network: a resource retrieval method based on the static resource collaboration model and the dynamic adaptive state network.

[0110] (1) Update the resource and state information in the entire static resource coordination model and the dynamic adaptive state network according to the rules defined in the dynamic adaptive state network.

[0111] (SRCM,DASN)=update(SRCM,DASN,Rule)

[0112] (2) Decompose user needs into functional (basic) needs df and service needs ds.

[0113] (d f ,d s =Decompose(d u )

[0114] (3) According to the static resource collaboration model, the user's functional requirements are decomposed into atomic functional requirements {df1,df2,...,dfn}, and each atomic function is assigned service requirements {(df1,ds),(df2,ds),...(dfn,ds)} and added to the set Df.

[0115] {d f1 ,d f2 ,...,d fn}=DecomposeBySRCM(d f )

[0116] D f {(d f1 ,d s ),(d f2 ,d s ),...,(d fn ,d s )}=Add({d f1 ,d f2 ,...,d fn},d s )

[0117] (4) Prioritize the atomic functions in Df according to their service requirements to obtain a new Df set. Prioritize the requirements with higher priority.

[0118] D f_sort = Sort(D f )

[0119] (5) Traverse the Df set and find the resource r that satisfies each atomic function from the static resource coordination model. f _i.

[0120] r f _i = Retrieve(D f _i,SRCM)

[0121] (6) Determine whether resources are available through a dynamic adaptive state network.

[0122] s r _i = State_Review(r f _i,DASN)

[0123] (7) If s r If _i is available, then add the resource to the resource candidate set R. u middle.

[0124] (8) If s r If _i is unavailable, determine whether there are available and alternative resources in the static resource coordination model and state network. If so, add it to the resource candidate set R. u Otherwise, add it to the waiting sequence r for that resource. wait .

[0125] r f _i_replace=Retrieve_replace(r f _i,SRCM,DASN)

[0126] (9) Update the resource and state information in the entire static resource coordination model and the dynamic adaptive state network according to the rules defined in the dynamic adaptive state network.

[0127] (SRCM,DASN)=update(SRCM,DASN,Rule)

[0128] (10) After the traversal is complete, return the resource candidate set.

[0129] The state network dynamic adaptive system provided in this embodiment of the invention includes:

[0130] The resource collaboration model building module is used to combine ontology modeling and meta-modeling to build a static resource collaboration model;

[0131] The resource status model construction module is used to construct atomic resource status models based on the static resource coordination model.

[0132] The network model building module is used to link atomic states and build the entire dynamic adaptive state network model.

[0133] The application interface building module is used to build application interfaces for static resource collaboration models and dynamic adaptive state networks.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] The information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments.

[0137] II. Application Examples:

[0138] In CPHS (Content Collaborative Resource Management), there are various complex heterogeneous resources. If these resources (such as physical resources, network resources, human resources, and organizational resources) are considered as resource entities, the system will contain a large amount of heterogeneous resource data across networks, domains, and worlds. Simultaneously, these complex resources also exhibit corresponding interactions and synergies. Furthermore, the data contains a great deal of dynamic information. The system is required to quickly and accurately complete adaptive resource information transformation based on this information. It is necessary not only to maintain data accuracy but also to ensure the dynamic nature of the data. Therefore, this embodiment of the invention focuses on analyzing the transformation process of the static resource collaborative modeling method and the dynamic adaptive state network model based on the resource collaborative model.

[0139] The framework of the state network dynamic adaptive method provided in this embodiment of the invention is as follows: Figure 2As shown, this dynamic adaptive state network method mainly consists of two parts. First, it models various heterogeneous resources from a static perspective, fully describing the attributes of each resource. Simultaneously, it establishes collaborative relationships between resources based on the functions they provide. Second, building upon the static resource collaboration model, it constructs a dynamic adaptive state network model and a state propagation method to address the dynamic nature of resource states. This method adaptively propagates dynamic changes in resource states to related resource entities, completing the update of state information.

[0140] Taking smart campus services as an example, we first construct a collaborative model of static resources in the smart campus from a static perspective, fully defining the attributes of each resource and establishing collaborative relationships between them. Next, we construct a dynamic adaptive state network for the smart campus and define specific state transition rules.

[0141] When a user's request arises, the resource retrieval method retrieves and returns resources that meet that request based on the user's needs, the collaborative model of static resources in the smart campus, and the dynamic adaptive state network of the smart campus. This guides the user to use these resources. As the user uses these resources, they change. This changed data is transmitted back to the system for adaptive updates based on the state transition rules in the dynamic adaptive state network of the smart campus.

[0142] 1. Static resource collaborative modeling:

[0143] CPHS (Content Contains Harmony, Harmony, and Harmony) encompasses diverse resources from different sources, domains, and worlds. These resources possess varying structures, functions, and states, making traditional modeling methods challenging to meet the demands of such heterogeneous data. In recent years, the development of ontology and other semantic technologies, such as knowledge graphs (KG), has garnered significant attention. Ontology techniques can establish collaborative relationships between heterogeneous resources from a semantic perspective. Therefore, modeling these heterogeneous resources using ontology addresses resource diversity and heterogeneity, overcoming semantic interoperability issues. Simultaneously, it effectively establishes collaborative relationships between resources. Furthermore, the scalability of ontology models provides effective support for subsequent expansion of resource models.

[0144] Furthermore, this invention combines meta-modeling theory with ontology modeling to establish a more abstract meta-model for heterogeneous data and define the basic rules for static resource collaboration models. A meta-model is a model of models and is closely related to ontology. Both meta-models and ontology are used to describe and analyze relationships between concepts. Ontology focuses on semantic description, while meta-models focus on structure and rules. Therefore, this invention combines ontology and meta-model theory to achieve collaborative modeling of heterogeneous resources, improving abstraction and extensibility.

[0145] This invention introduces a resource static modeling method based on ontology and meta-model. Figure 3This illustrates the metamodel of the present invention, defined according to the MOF (Meta-Object Facility) standard. Entity classes include two types: atomic entities and aggregate entities. Aggregate entities are composed of atomic entities through aggregation relationships. Furthermore, entities can be generalized into more specific entities, including resource entities, functional entities, and state entities. Resource entities represent various types of resources. Functional entities represent the functions that a resource can provide.

[0146] A state entity refers to the state of a resource and the state of its functions. Relationships represent the collaborative relationships that exist between entities.

[0147] Relationships exist not only between entities, but also between entities and attributes. Finally, attribute classes represent the attributes contained in each entity or relationship.

[0148] Based on the above meta-model, this invention defines the framework of a static resource collaboration model, as shown in the equation:

[0149] SRCM = (RE, RF, RS, r)

[0150] RE=(resource name,resource description,resource properties)

[0151] RE

[0152] ∈{physical resources, network resources, organizational resources, human resources, mixed resourcesRF=(function name, function description, function properties)

[0153] RF∈{resourced function, business function, functional function}

[0154] RS=(state name,state value,state properties)

[0155] RS∈{resource state,resourcefunction state}

[0156] r=(relation name,relation value,relation priority,relation effect)

[0157] r∈{resource relation, function relation}

[0158] resource relation∈{include,realization}

[0159] function relation∈{dependency,generalization,composition,aggregation,replace,mutex}

[0160] properties=(temporal properties,spatial properties,scene properties)

[0161] In this invention, RE (Resource Entity) refers to various resource entities. Its definition includes name, description, and attributes, representing the name, specific description, and basic attributes of the resource entity, respectively. This invention also classifies REs into physical resources, network resources, human resources, organizational resources, and combinations thereof. RF (Resource Function) represents the function corresponding to a resource entity, composed of name, description, and attributes. It is divided into resource-type, business-type, and functional-type functions. Resource-type functions refer to the functions that the resource itself can provide, such as the function of fuel providing power. Business-type functions represent functions in business processes, such as information submission and information processing. Functional-type functions are often virtual functions, such as login and QR code scanning. RS (Resource State) represents the state of a resource. Since a resource may provide multiple functions, a single resource state cannot represent the true state of each function. Therefore, this invention splits RS into resource state and resource function state. The basic values ​​of resource state are defined as: available, unavailable, faulty, and offline. Furthermore, resource state is an important manifestation of resource changes. To enable the static resource coordination model to undergo rapid adaptive transformation, a dynamic adaptive state network model is constructed. The relevant strategies for state transitions are proposed. Details will be provided in the following section. R (relation) represents a relation, consisting of name, value, priority, and effect. Priority and effect are used for state transitions; see the section on dynamic adaptive state network modeling for more information.

[0162] In this model, the invention also classifies and abstracts relationships, defining them as resource relationships and functional relationships. The values ​​of resource relationships are inclusion and realization, while functional relationships include dependency, generalization, composition, aggregation, substitution, and mutual exclusion. As a general concept, attributes appear in resource entities, resource functions, and resource states. They are divided into time attributes, space attributes, and scenario attributes. Here, an attribute is an abstract concept. Its specific attribute value is determined based on different resources, functions, and states. For example, the meeting room as a resource entity has the following attributes in terms of time, space, and scenario: open from 8:00 to 17:00, located in Building A, Unit 001, 20 square meters, accommodating 20 people. The meeting function attribute provided by the meeting room is: meetings can be held from 9:00 to 11:00, located in Building A, Unit 001, accommodating 10 people. The corresponding state attribute of the meeting function is: currently occupied and unavailable from 9:00 to 11:00, available in Building A, Unit 001, currently accommodating 10 / 20 people.

[0163] To provide a detailed introduction to the static resource collaboration model, we will take smart campus services as an example and model the resources of smart campus services. Figure 3 This section shows a portion of the model structure. The online registration function is implemented by the registration API resource. The manual registration function is completed by combining the personnel function and the quota function. Resources and resource functions have a many-to-many relationship. Each function can be provided by one or more resources. For example, a personnel function includes the specific functions of three personnel, provided by three specific personnel resources. Substitution relationships exist between multiple resources. When one resource is occupied, another can perform the function. Simultaneously, a resource can provide multiple functions. For example, Person1 can simultaneously provide the staff function and the teaching function. Personnel functions have corresponding attributes, such as service time, number of people, and available resources. Status nodes have attributes such as whether time is available, whether location is available, and whether capacity is available. Through the resource case model defined above, the relevant resources, resource functions, resource states, relationships, and attributes are introduced. It fully describes the defined static resource collaboration model and provides a resource foundation for dynamic adaptive collaboration.

[0164] This invention primarily establishes static models for various resources in CPHS and constructs collaborative relationships between resource entities. To achieve dynamic adaptive transformation of resources, a thorough analysis of resource states and the transformation relationships between states is required.

[0165] 2. Dynamic Adaptive State Network:

[0166] In the aforementioned work, this invention constructs an SRCM. Furthermore, the resource coordination problem is also reflected in its dynamics. The dynamism of resources is mainly manifested in their state attributes, including the state transitions of the resources themselves and the state propagation between resources. This requires this invention to fully model the resource state transition process. Therefore, this invention proposes a DASN based on SRCM to describe the process of resource state transition and propagation, achieving dynamic adaptive updating of resource states.

[0167] To fully describe the dynamic and coordinated changes in resource states, this invention first performs a more complete modeling based on the resource state definition in SRCM, defining an atomic resource state model.

[0168] Atom-RS =<X,Y,S,S0,δint,δext,R>

[0169] Here, X represents the input event set. In the model, it mainly refers to the relationships between the current resource, resource function, and resource state. Y represents the output set of the current atomic resource state model. In the model, it represents the relationships indicated by the current resource, resource function, and resource state. S is the atomic resource state. S0 is the initial state of atomic resource state S, which is the initial value of each state entity in the SRCM. δ int This refers to the internal state transition function of the current atomic state, corresponding to the state transition of the resource itself. δ... ext Represents the external transition function, corresponding to the state transfer between resources in SRCM. R is the state transition rule.

[0170] The structure of the basic atomic resource state model is as follows: Figure 4 As shown, it mainly consists of two parts: a defined atomic state model and a perceptron and a controller. The perceptron is responsible for acquiring external environment information received by the resource model. The controller's role is rule-based reasoning; it processes the information received by the perceptron and the information in the input set X based on specific information in the rule base, realizing the internal and external transformations of the atomic states. Finally, the model publishes the processed output information to neighboring nodes through the relationships in the model, completing all state updates.

[0171] After constructing the atomic resource state model, this invention links these atomic states together to construct the entire DASN model.

[0172] DASN=(Xs,Ys,{Atom-RS},{Atom-RS-i},Zs,R)

[0173] Here, Xs represents the entire event input set of DASN, Ys represents the output set of the DASN model, including all relations related to resource states in all SRCMs. {Atom-RS} refers to the set of atomic resource state models, including all resource state nodes in the state network. {Atom-RS-i} is the set of all atomic state models in DASN related to the current i-th atomic state model. Zs represents the state transition relation from the current i-th atomic state model to the j-th atomic state model linked to it. R is the same as R in the atomic state model, representing the rule of state transition.

[0174] Figure 5 This is a simplified diagram of the DASN model. {Atomic states} comprise six atomic state models, from state 1 to state 6. Taking state 1 as an example, {state 1} includes state 2 and state 3 models. Z12 and Z13 represent the transition relationships between state 1 and the other two models. Similar to the atomic state model structure, when the DASN senses and receives environmental information and external inputs, the controller processes this data according to the rule base, and then completes the entire DASN state update through the transition relationships Z between the atomic state models.

[0175] To illustrate the state transition process in a dynamic adaptive state network in detail, a smart campus service is used as an example. Figure 3 As shown, when Person1 is unavailable, its staff and teaching functions will also become unavailable. When Room301 is under maintenance, Windows 1-3 are all disabled, and the corresponding local functions also fail. Since the staff and place functions are combined, the manual registration function will also be affected and malfunction.

[0176] Resource retrieval application interface:

[0177] In the aforementioned work, complex heterogeneous resources were first modeled from a static perspective. To handle the dynamic nature of resources, a DASN model was constructed from a dynamic perspective. This model can adaptively transform and propagate received data information within the network, achieving dynamic and adaptive updates to resource states. To verify the effectiveness of these two models, this invention constructs a model interface for resource retrieval. This interface is primarily used for resource retrieval.

[0178] In practical applications, the complexity of resources and the diversity of needs place higher demands on resource retrieval. First, resources are finite, with their own upper limits. Simultaneously, resources are dynamically changing, placing higher demands on the dynamism and accuracy of resource retrieval. Furthermore, user requirements are diverse, and these requirements may be concurrent, with multiple requests simultaneously seeking the same resource. In this situation, resource retrieval methods need to complete resource retrieval under conditions of numerous demands and limited resources to satisfy user needs. Therefore, this invention proposes a model application interface method for resource retrieval. The specific process of this method is described in the algorithm.

[0179] Input: User requirement set Ds = {D1, D2, ..., Dn}, where Di = (D{i_f}, D{i_s}), i = 1, 2, ..., n, SRCM, DASN, environmental data {EDs}.

[0180] Output: The required resource set R = (R1, R2, ..., Rn), where Ri is the resource set that satisfies the needs of user i, i = 1, 2, ..., m.

[0181] For ed in {EDS}:

[0182] Update the relevant resource and attribute data in SRCM according to ed;

[0183] Update the relevant status and attribute data in DASN according to ed;

[0184] State propagation is performed according to the DASN model definition;

[0185] For Di in{Ds}:

[0186] Decompose D{i_f} into a series of atomic functions {d{i_f}} and assign service requirements D{i_s}.

[0187] Add {d{i_f}} to the overall atomic function set Df

[0188] The atomic functions in Df are sorted according to the service requirements of each atomic function.

[0189] For d{i_f}in Df:

[0190] Retrieve the required resource Ri for d{i_f} from SRCM.

[0191] If Ri is available:

[0192] Add Ri to user i's resource collection {Ri}

[0193] ElseIfRi has an alternative resource Ri_a:

[0194] Add Ri_a to user i's resource set {Ri}

[0195] Else:

[0196] Add Ri to Ri's waiting queue. Add Ri to user i's resource set {Ri}.

[0197] State propagation is performed according to the DASN model definition;

[0198] Returns the resource set R.

[0199] In the algorithm, user needs D are divided into functional needs Df and service needs Ds. Functional needs Df represent the most basic functions that satisfy user needs, such as holding a meeting. Service needs Ds represent additional requirements that satisfy basic functions, such as time, space, or price requirements. In this invention, time requirements are the core of the resource retrieval function. First, the method decomposes the user's functional needs Df. The functional structure in SRCM decomposes the user's functional needs into a series of atomic functions, each of which cannot be further decomposed. The entire function is implemented by retrieving resources for each atomic function in the sequence. Second, the sequence of atomic functions for each need is sorted by time. Then, resource retrieval is performed for each atomic function. If the resource that satisfies the function is already occupied, it is determined whether there is an alternative resource. If there is no alternative resource, it is queued under that resource. After the available resources are arranged, the resource status is updated. The DASN sensor detects the status change, the controller completes the state propagation, and updates the DASN to the latest status. Finally, the resource list corresponding to each need is returned, completing the retrieval of multiple needs and restricted resources.

[0200] This invention also provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0201] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps described in the various method embodiments above.

[0202] This invention also provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, or switches.

[0203] This invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0204] This invention provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0205] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0206] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0207] III. Evidence of the relevant effects of the embodiments:

[0208] Simulation experiment: Using smart campus services as a case study to verify the defined SRCM and DASN.

[0209] Smart campus services utilize various resources within the campus to meet diverse needs. This improves resource utilization efficiency, optimizes campus service methods, and enhances the quality of campus services. It encompasses numerous application areas, including daily life, education, and security. This application involves a variety of complex campus resources (physical resources, network resources, organizational and human resources), such as classrooms, information systems, computer schools, and teachers. Simultaneously, these resources are subject to their own inherent constraints, and the impact of changing event demands is constantly evolving.

[0210] Therefore, in order to verify the performance of the model under such complex resource and dynamically changing environmental constraints, a simulation experiment was conducted, taking the application of new student enrollment in smart campus services as the research object.

[0211] Resources and demands in smart campus services are typically diverse, ranging from simple to complex. To fully simulate the real-world freshman enrollment service process in a smart campus, relevant resources and demands were simulated. The resources involved in smart campus freshman enrollment services are mainly related to enrollment, such as welcome resources, registration resources, physical examination resources, and accommodation resources. Some resources are shown in Table 1. Table 1 shows several examples of generated random demands. Random demands were generated based on different combinations of demands to simulate varied real-world situations, as shown in Table 2.

[0212] Table 1. Partial Smart Campus Service Resources

[0213]

[0214] Table 2 Examples of Random Demand

[0215]

[0216]

[0217] Because resources are constantly changing in real life, they may be affected by various disturbances, such as being occupied, destroyed, or withdrawn. To fully reflect the dynamic nature and complexity of smart campus resources, three dynamically changing simulation environments were established to simulate the real environment.

[0218] Regular environment:

[0219] In a regulated environment, smart campus resources will dynamically change according to the cyclical patterns of change in reality and their own lifecycles. For example, some resources will change based on commuting time. The changing patterns of resources in a regulated environment follow a periodic function, which can be used to describe the periodic changes of resource variables.

[0220] Low-noise environment:

[0221] A low-noise environment involves adding low-noise to a regular environment, causing a few resources to deviate from their original patterns and experience sudden changes, such as resource failure. This fully simulates unexpected events affecting smart campus resources in real-world scenarios, modifying the state of some resources by setting probabilities.

[0222] High-noise environment:

[0223] A noisy environment means that the state of most resources has deviated from its original rules and changed. The environment simulates the conditions of resources under extreme circumstances. A noisy environment can be simulated by setting a high probability threshold.

[0224] In the simulation experiment, the experimental environment of this invention was configured as follows: Windows 10 system, Intel-12700 (4.9GHz, 12 cores, 20 threads) CPU, 32GB RAM, and 1TB hard drive. Resource states were set to {Available, Occupied, Faulty, Offline}. Resource state attributes were set to {Availability, Available Time, Number of Services, Congestion Level, Bottleneck, Waiting Queue, Total Tasks}. State transition rules are shown in the table. Table 3 shows the random noise threshold parameters for different resource states in three environments. / indicates no noise impact. The resource retrieval process uses the above algorithm.

[0225] Table 3 Random noise threshold parameters for different resource states in three environments

[0226]

[0227] Table 4 State Transition Rules

[0228]

[0229] Figures 6A to 6C Taking a single resource as an example, we sampled its changes under different environments over a certain period of time. The time interval was one hour. Resource availability, service volume, and the number of waiting lists were selected as indicators. It can be seen that under regular environments, resource status changes periodically. In a low-noise environment, this periodicity is slightly disrupted. In a high-noise environment, due to the large amount of noise, the resource is only in a normal state for a small portion of the time.

[0230] The experiment uses the following evaluation criteria: number of resource status updates, status propagation time, resource hit rate, resource error rate, and resource retrieval time.

[0231] Status update count:

[0232] Calculate the number of state updates for DASN at each time point to represent the degree of change in state adaptation.

[0233] State propagation time:

[0234] Calculate the changes in SRCM and DASN at each time point from the beginning to the end.

[0235] Resource hit rate:

[0236] Resource hit rate refers to the ratio of retrieved resources to the required resources.

[0237] Resource error rate:

[0238] Resource error rate refers to the proportion of erroneous resources (those with incorrect status or that do not meet requirements) among the retrieved resources.

[0239] Resource retrieval time:

[0240] Calculate the time from demand generation to retrieval of the required resources.

[0241] Based on the simulation experiment settings described above, simulation experiments of DASN were completed under three different simulation environments. The results are as follows:

[0242] Regular environment:

[0243] Under regular conditions, Figures 7A-7B This displays the number of resource status updates and the state propagation time within a week. When the state of one resource changes, its state affects the state of other resources, resulting in state propagation. Because resource states change periodically in a normal environment, the number of status updates and the state propagation time vary systematically. When a demand arises, resource retrieval methods are used to retrieve resources to meet the user's needs. Figures 7C to 7E The resource retrieval results are displayed. It can be seen that the resource hit rate is high, and the changes also exhibit a regular pattern. This is because DASN's resource status is adaptively propagated. When the resource status changes periodically, it is updated through propagation. All resources retrieved by DASN are available, therefore DASN's error rate is zero. Resource retrieval time is in the millisecond range. Since DASN keeps the resource status up-to-date, there is no need to check the resource status again after retrieval, requiring no additional time.

[0244] Low-noise environment:

[0245] In a low-noise environment Figures 8A to 8E The results show that the number of state updates in DASN is significantly higher compared to the regular environment. This is because noise is introduced into the weakly noisy environment, causing the fluctuation range of resource state changes to be higher than that in the regular environment. Similarly, the state propagation time in DASN is also increased. In the resource retrieval results, the resource hit rate of DASN is slightly lower than that of the regular environment. However, the resource error rate remains at 0. The results fully demonstrate the necessity and effectiveness of adaptive resource state propagation. Regarding the resource retrieval time metric, the resource retrieval time of DASN is not significantly increased, and like the regular environment, no additional check time is required.

[0246] High-noise environment:

[0247] Due to the impact of resource state changes, DASN experiences a greater increase in resource state update frequency and state propagation time compared to the previous two environments, similar to the weak noise environment. The difference lies in the fact that resource changes are more pronounced in the strong noise environment. Therefore, DASN's resource state update frequency and state propagation time are both higher than in the weak noise environment. Compared to the weak noise environment, DASN's resource hit rate decreases again when facing user demands, due to the increased unavailable resources and decreased available resources caused by noise. Figures 9A to 9E As can be seen, the DASN error rate remains 0, and the resource retrieval time of DASN has not increased significantly and can be ignored.

[0248] The simulation experiments above simulated resource and state changes in a new student enrollment case under three different environments. By randomly generating user needs to retrieve smart campus resources, five indicators were calculated: resource state update frequency, state propagation time, resource hit rate, resource error rate, and resource retrieval time. The correlation between these five indicators and environmental changes was also calculated. The results show that, under the three different environments, the number of resource state updates and state propagation in DASN increases with increasing noise, indicating that the proposed DASN can effectively achieve adaptive resource state transitions and propagation. In the resource retrieval results, the resource hit rate of DASN decreases with increasing noise. This is because the introduction of noise makes resources less available. However, the resource error rate of DASN remains 0 in all three environments, indicating that DASN can effectively achieve adaptive state changes and maintain the update of all resource states. The resource retrieval time of DASN does not differ significantly under the three different environments, indicating that different environments do not affect the resource retrieval time.

[0249] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A state network adaptive resource coordination method based on a resource coordination model, characterized in that, The method combines ontology and meta-modeling theory, and constructs a unified static model of cross-domain complex resources from a semantic perspective; a dynamic adaptive state network model based on the static resource coordination model is designed and implemented, which is used to support the self-conversion and propagation of resource states in the network, and realize the adaptive conversion and update of the overall resource state; a model application program interface for resource retrieval is constructed; The state network dynamic adaptive method specifically includes the following steps: Step one, combining ontology modeling and meta-modeling, constructing a static resource coordination model; Step two, based on the static resource coordination model, constructing an atomic resource state model; Step three, link the atomic state to construct the whole dynamic adaptive state network model; Step four, construct the application interface of the static resource coordination model and the dynamic adaptive state network; In step one, the combination of ontology modeling and meta-modeling to construct a static resource coordination model includes: (1.1) Define the meta-model of the static resource coordination model, including entity class, relationship class and attribute class; (1.2) According to the definition of the static resource coordination meta-model, the complex resources, network resources, organizational resources and human resources existing in the target object are instantiated, and are represented and stored in the form of knowledge graph; In step two, the static resource coordination model is used to construct an atomic resource state model, which includes defining the structure and framework of the atomic state model, creating internal and external transition rules of the state model, and defining and modeling the state in the static resource coordination model; The definition and modeling of the state in the static resource coordination model specifically includes: (2.1) Define the structure of the atomic state model; (2.2) Define the transition rules and classify them; (2.3) Based on the definition, construct the framework of the atomic resource state model; In step three, the atomic state is linked to construct the whole dynamic adaptive state network model, which specifically includes: (3.1) Link all atomic states to form a state network, and define the structure of the dynamic adaptive state network; (3.2) According to the definition of the dynamic adaptive state network, construct the state network of the target object service resource state in the static resource coordination model.

2. The state network adaptive resource coordination method based on the resource coordination model according to claim 1, characterized in that, In step (1.2), the complex resources, network resources, organizational resources and human resources existing in the target object are instantiated, which specifically includes: S1, collect and label resource data set: collect and integrate related resource data in the field for cleaning and arrangement; for the processed data, the resource, state and relationship entities are manually labeled; S2, construct resource entity recognition model: use the entity data labeled in the resource data set to train the LSTM+CRF model based resource entity recognition model; S3, construct entity relationship extraction model: use the entity and relationship data labeled in the resource data set to train the extraction model based on the pre-trained model Bert; S4, store using knowledge graph: use the resource entity recognition model and entity relationship extraction model to extract knowledge from the data set; check the extracted knowledge manually, and store the knowledge in neo4j.

3. The state network adaptive resource coordination method based on the resource coordination model according to claim 1, characterized in that, In step (3.1), the dynamic adaptive state network structure set includes each atomic resource state, a sensor, a controller and a rule base; The state network of the target object service resource in the static resource coordination model is constructed as follows: (3.1.1) According to the definition and classification of the target object service resource and state, the rule base is constructed manually; (3.1.2) According to the priority and effect in the definition in the rule base, the internal and external transfer functions of the atomic resource state are constructed in combination with the resource state entity and the relevant relationship in the static resource coordination model; (3.1.3) The atomic resource states are linked through the coordination relationship in the static resource coordination model to construct a dynamic adaptive state network.

4. The state network adaptive resource coordination method based on the resource coordination model according to claim 1, characterized in that, In step four, the application interface of the static resource coordination model and the dynamic adaptive state network is constructed based on the static resource coordination model and the dynamic adaptive state network resource retrieval method, and specifically includes: (4.1) According to the rules defined in the dynamic adaptive state network, update the resource and state information in the entire static resource coordination model and dynamic adaptive state network; (SRCM, DASN) = update (SRCM, DASN, Rule); (4.2) decomposing the user requirements into functional requirements d f and service requirements d s ; (d f ,d s ) = Decompose(d u ); (4.3) According to the static resource coordination model, the functional requirements of the user are decomposed into atomic functional requirements {d f1 , f2 , fn} and service requirements {(d f1 , s ), (d f2 , s ),..., (d fn , s )} are given to each atomic function and added to the set D f . {d f1 ,d f2 ,...,d fn}=DecomposeBySRCM(d f ); D f {(d f1 ,d s ),(d f2 ,d s ),...,(d fn ,d s )}=Add({d f1 ,d f2 ,...,d fn},d s ); (4.4) Prioritizing atomic functions in D f according to service requirements, resulting in a new D f set; higher-priority requirements are satisfied first. D f _sort = Sort(D f ); (4.5) Traverse D f Set, from the static resource coordination model to find resources r that satisfy each atomic function f _i; r f _i= Retrieve(D f _i, SRCM); (4.6) Determine whether the resource is available through the dynamic adaptive state network; s r _i = State_Review(r f _i, DASN); (4.7) If s r _i is available, add the resource to the resource candidate set R u _i; (4.8) if s r _i is not available, determine in the static resource coordination model and state network whether there is an available alternative resource; if so, add the resource to the resource candidate set R u ; otherwise, add the resource to the resource waiting sequence r wait ; r f _i_replace = Retrieve_replace(r f _i, SRCM, DASN); (4.9) According to the rules defined in the dynamic adaptive state network, update the resource and state information in the entire static resource coordination model and dynamic adaptive state network; (SRCM, DASN) = update (SRCM, DASN, Rule); (4.10) After the traversal is completed, return the resource candidate set.

5. A resource coordination model based state network adaptive resource coordination system for implementing the resource coordination model based state network adaptive resource coordination method according to any one of claims 1-4, characterized in that, The state network adaptive resource coordination system based on the resource coordination model includes: A resource coordination model construction module for constructing a static resource coordination model in combination with ontology modeling and meta-modeling; A resource state model construction module for constructing an atomic resource state model based on the static resource coordination model; A network model construction module for linking the atomic states to construct an entire dynamic adaptive state network model; An application interface construction module for constructing an application interface of the static resource coordination model and the dynamic adaptive state network.

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