Data service orchestration method and device based on object model fusion, equipment and medium

Abstract

The invention relates to the technical field of an internet of things platform and application thereof, and discloses a data service arrangement method, device, equipment and medium based on object model fusion, and the method comprises the steps: fusing the equipment data of different internet of things equipment from object models at parameter granularity, and obtaining fused equipment data; the Internet of Things platform defines a virtual device object model according to the object model of the Internet of Things; determining a corresponding data processing mode based on the fusion equipment data; creating and developing corresponding data service orchestration according to the object model function data, the virtual equipment object model and the configuration information; connecting the plurality of data services according to the logical relationship defined by the business based on a visualization device, and configuring data service nodes; and issuing the configuration result. The system comprises an object model data acquisition module, a virtual equipment object model construction module and a test publishing module. According to the invention, the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things platforms and their applications, and particularly to a data service orchestration method, device, equipment and medium based on the fusion of object models. Background Art

[0002] With the rapid development of Internet of Things technology, it has been widely applied in intelligent manufacturing, intelligent construction, smart factories, etc. Internet of Things platforms usually involve the collection, processing and analysis of a large number of devices and sensors. They have defects such as a large amount of data, high real-time requirements, and complex communication protocols, which make data fusion difficult; data service layout needs to coordinate these data processes to ensure efficient and reliable task completion. The current status of data service orchestration technology may include microservice architecture, containerized architecture, service network, automated orchestration tools, etc. There are many defects in these current technical means in terms of real-time processing, low latency, interoperability, and dynamic adaptability.

[0003] Prior Art One, a Chinese patent with the patent number 202311492961.4 discloses a rule-based visual data service orchestration method, which includes the following steps: defining a general service component standard for each data service to implement its own service components; encapsulating data service components based on the service component standard and establishing the association relationships between components; designing a unified data service orchestration rule based on the encapsulated service components to replace the hard code judgment implementation method with a higher coupling degree; providing a visual service orchestration development interface to support service orchestration development by dragging and connecting lines; implementing the orchestration service executor to parse and execute tasks; debugging and publishing the orchestration service, that is, providing a service debugging function at the node level, and authorizing the release after meeting the expectations of developers. Although it can facilitate the debugging of staff, effectively improve the performance and scalability of the data service orchestration method, and is convenient to use; however, in high-concurrency scenarios, the parsing and execution efficiency of service orchestration may be affected, resulting in slower system response or instability.

[0004] Prior Art II, a Chinese patent with the patent number 202311737880.6 discloses a data service orchestration method, apparatus, non-volatile storage medium, and electronic device. Among them, the method includes: obtaining a configuration file of a set of to-be-served tasks, where the configuration file is used to indicate at least one data service type required by the set of to-be-served tasks; determining at least one preset plugin that meets the configuration file in a preset plugin set to obtain a target plugin set, where the preset plugin set includes: a plurality of preset plugins, each preset plugin is used to provide services for a corresponding data service type, and the target plugin set includes: at least one target plugin; orchestrating the target plugin set to obtain a target plugin group; using the target plugin group to perform containerized registration on a preset function module to obtain a target function module, where the target function module is used to provide data services for the set of to-be-served tasks. Although it solves the technical problem of poor applicability of existing data services; however, in actual applications, there may still be insufficient adaptability to specific scenarios or specific requirements.

[0005] Prior Art III, a Chinese patent with the patent number 202011121923.4 discloses a big data service automatic orchestration method and system, including: collecting technical metadata through an annotation metadata collection tool; analyzing the technical metadata to determine the data items and service call methods of the technical metadata; based on the business form, annotating the index meaning of the technical metadata and determining the usage scenario of the technical metadata; obtaining the definition information of the technical metadata based on the data items, service call methods, index meaning, and usage scenario of the technical metadata; associating the technical metadata with the definition information of the technical metadata to obtain technical metadata publishing information; publishing the technical metadata publishing information in a data platform. Although it can achieve big data service automatic orchestration services at low cost and high efficiency; however, there may be problems of data inconsistency, data loss, and untimely data update between different collection methods.

[0006] Currently, Prior Art I, Prior Art II, and Prior Art III have many defects in aspects such as real-time processing and low latency, interoperability, and dynamic adaptability. Therefore, the present invention provides a data service orchestration method, apparatus, device, and medium based on the fusion of physical models to solve the above problems. Summary of the Invention

[0007] The main purpose of the present invention is to provide a data service orchestration method, apparatus, device, and medium based on the fusion of physical models to solve the problems of many defects in aspects such as real-time processing and low latency, interoperability, and dynamic adaptability in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A data service orchestration method based on the fusion of physical models, the data service orchestration method based on the fusion of physical models includes:

[0010] The user describes the attributes, services, and event device data of the Internet of Things devices; outputs the device data to the Internet of Things platform; fuses the device data of different Internet of Things devices at the parameter granularity from the physical model to obtain the fused device data;

[0011] Among them, the attributes of the Internet of Things devices include the collected data items; the services include functions; the events include alarm events;

[0012] The Internet of Things platform defines the virtual device physical model according to the physical model of the Internet of Things; determines the corresponding data processing method based on the fused device data; creates and develops the corresponding data service orchestration according to the physical model function data, the virtual device physical model, and the configuration information;

[0013] Based on the visualization device, connect multiple data services according to the logical relationship defined by the service, and configure the transmission protocol, communication protocol, scheduling period, and failure strategy of the data service nodes; publish the configuration result.

[0014] As a further improvement of the present invention, the process of obtaining the fused device data specifically includes the following steps:

[0015] Obtain the static attributes and dynamic attributes of the devices in the device data described by the user; define the input parameters and return results of the service call, as well as the prerequisite conditions for triggering the service; and the event type and event classification;

[0016] Among them, the static attributes include unique identifier, model, manufacturer, and fixed geographical location information; the dynamic attributes include data collection items, define data type, unit, collection frequency, and value range;

[0017] Transmit the device data to the Internet of Things platform, unify the data format of the device data; and check the value range and time series, automatically discard invalid data and trigger an alarm; manage the data transmission order according to the event level and assign weights;

[0018] Convert different units, convert the naming differences of the same parameter by different manufacturers; take the latest data to overwrite the old data, and weight the average weight according to the device accuracy; check whether the device data in the same area is qualified. If it is qualified, store the real-time data in the time series database and the metadata in the graph database to obtain the fused device data; if it is unqualified, re-obtain it.

[0019] As a further improvement of the present invention, the process of creating and developing the corresponding data service orchestration according to the physical model function data, the virtual device physical model, and the configuration information includes the following steps:

[0020] Select the created product in the IoT platform console and enter the product details page; click on Function Definition or Custom Function to add a device model; define the attributes, services, and events of the device model; after the device model is defined, the device will automatically inherit the model content;

[0021] Collect data through sensors and devices and transmit it to the cloud or a local server; define the device model according to the device type and function, including device attributes, status, and behavior; design the data processing flow and configure the data conversion rules;

[0022] In the IoT platform console, obtain the development data service through the visualization device, and configure the input, output, and processing logic of the data service; test the function of the data service; use data analysis services to analyze the IoT fusion data based on the virtual device model to obtain horizontal and vertical analysis results.

[0023] As a further improvement of the present invention, the process of configuring the data conversion rules specifically includes the following steps:

[0024] Obtain the network address conversion rules of the IoT device model and deploy the network address conversion rules in the network space of the virtual device model and the network space of the container where the source node is located; through kernel extension technology, configure the same set of network address conversion rules for the containers where each node is located;

[0025] Deploy the network address conversion rules to the direct network cards of each node; the source node extracts the virtual address from the data packet; through the network address conversion rules deployed in the network space of the virtual device model where the source node is located, convert the virtual address into an actual address;

[0026] Deploy the network address conversion rules to be deployed in the network space of the virtual device models of each node belonging to multiple nodes with the same address; send the data packet to the target node corresponding to the actual address of the virtual device model through the actual address.

[0027] As a further improvement of the present invention, the process of obtaining the development data service through the visualization device includes the following steps:

[0028] Obtain the development data service data of the virtual device model through the visualization device, and extract the configuration file of the service task set to be processed, where the configuration file is used to indicate at least one data service type required by the service task set to be processed;

[0029] Determine at least one preset plugin that meets the configuration file in the preset plugin set to obtain the target plugin set; where the preset plugin set includes multiple preset plugins, and each preset plugin is used to provide services for the corresponding data service type; the target plugin set includes at least one target plugin;

[0030] Orchestrate the target plugin set to obtain a target plugin group; perform containerized registration on the preset function modules using the target plugin group to obtain target function modules; use data analysis services to analyze the Internet of Things fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results;

[0031] Among them, the target function module is used to provide data services for the set of tasks to be served.

[0032] As a further improvement of the present invention, the process of extracting the configuration file of the set of tasks to be served includes the following steps:

[0033] Identify that the configuration file indicates at least one data service type to obtain a set of services to be configured; traverse the set of services to be configured to determine the target plugins corresponding to each target type; among them, the target plugin is used to provide services for the corresponding target service type; the set of services to be configured includes at least one target service type;

[0034] Put the target plugins into the target plugin set; identify the number of services of the data service type indicated by the configuration file to detect whether the service data is greater than the preset service quantity threshold; perform serialization transmission on the data object dynamically according to the communication protocol and the transmission protocol;

[0035] If the number of services is less than the preset service quantity threshold, then orchestrate the target plugin set to obtain a target plugin group; if the number of services is greater than the preset service quantity threshold, then split the target plugin set into multiple target plugin subsets; the number of services in each target plugin subset is less than the preset service quantity threshold.

[0036] As a further improvement of the present invention, the process of obtaining the target function module includes the following steps:

[0037] Identify the service order of the data service type indicated by the configuration file, and determine the plugin order of one target plugin according to the service order; orchestrate the target plugin set according to the plugin order to obtain a target plugin group;

[0038] Randomly select a preset function module from the function module set, perform containerization processing on the preset function module to obtain a target container module; determine the container interface of the target container module, and inject the target plugin group into the target container module through the container interface to obtain a target function module;

[0039] Monitor the load parameters of the target function module. When the load parameters exceed the preset load threshold, split the target plugin group into multiple ones; use the multiple target plugin groups to perform containerization registration on different preset function modules respectively to obtain multiple target function modules; use data analysis services to analyze the IoT fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results.

[0040] As a further improvement of the present invention, the process of obtaining horizontal and vertical analysis results includes the following steps:

[0041] In the IoT platform console, obtain the development data service through the visualization device, including the input, output, and processing logic of the data service; simulate the data service input and verify whether it is qualified according to the input result;

[0042] If it is qualified, start the analysis program to compare and analyze the data of different devices at the same time point to obtain rules and abnormal data; and perform trend analysis on the data of the same device at different time points to obtain prediction results;

[0043] Display the analysis results through the visualization device provided by the IoT cloud platform; send control instructions to the corresponding physical model to adjust the operating parameters of the physical model; and continuously optimize the analysis program according to the analysis results and actual application results.

[0044] As a further improvement of the present invention, the process of publishing the configuration results includes the following steps:

[0045] The data acquisition node is responsible for reading data from the device, the data processing node is responsible for cleaning and analyzing the acquired data, and the data storage node is responsible for storing the processed data in the database; define the input and output data formats for each node;

[0046] The output of the data acquisition node is connected to the input of the data processing node, and the input of the data processing node is connected to the output of the data storage node; determine the transmission protocol according to the communication requirements of the data service node and configure the protocol parameters; set the scheduling period of each data service node according to the business requirements; configure the processing strategy when the data service node fails;

[0047] Publish the configured data service nodes and their logical relationships, parameter configurations, and other information to the IoT platform; simulate the data transmission process of the data acquisition node to check whether the data can reach the data processing node correctly; check whether the processing structure of the data processing node meets the standards. If not, make adjustments and adjust and republish the configuration.

[0048] To achieve the above object, the present invention also provides the following technical solutions:

[0049] A data service orchestration system based on the fusion of physical models, which is applied to the data service orchestration method based on the fusion of physical models. The data service orchestration system based on the fusion of physical models

[0050] An acquisition physical model data module, which is used for a user to describe the attributes, services and event device data of Internet of Things devices; output the device data to the Internet of Things platform; fuse different Internet of Things devices from the device data of the physical model at the parameter granularity to obtain fused device data;

[0051] Among them, the attributes of the Internet of Things devices include the collected data items; the services include functions; the events include alarm events;

[0052] A virtual device physical model construction module, which is used for the Internet of Things platform to define a virtual device physical model according to the physical model of the Internet of Things; determine the corresponding data processing method based on the fused device data; create and develop the corresponding data service orchestration according to the physical model function data, virtual device physical model and configuration information;

[0053] A test and release module, which is used to connect multiple data services based on the visualization device according to the logical relationship defined by the service, and configure the transmission protocol, communication protocol, scheduling period and failure strategy of the data service node; publish the configuration result.

[0054] The present invention fuses the data of different Internet of Things devices at the parameter granularity to generate unified fused device data; through parameter granularity fusion, it ensures that the data of different devices can be uniformly processed, avoiding the problem of inconsistent data formats; the fused data can be more efficiently used for subsequent data processing and analysis, improving the utilization rate of data; the unified data format and structure simplify the complexity of data management and reduce the cost of data processing. According to the physical model function data and virtual device physical model, combined with the configuration information, create and develop the corresponding data service orchestration; by defining the virtual device physical model, data can be processed and analyzed more efficiently, improving the efficiency of data processing; the data service orchestration can be flexibly adjusted according to business requirements, improving the flexibility and scalability of the system; through reasonable data processing methods and orchestration, the resource configuration is optimized, reducing the operating cost of the system; by connecting data services through a visualization device, the operation process is simplified, improving the convenience of operation; through reasonable configuration of transmission protocols and communication protocols, the stability and reliability of the system are improved. Brief Description of the Drawings

[0055] Figure 1 It is a schematic diagram of the step flow of an embodiment of a data service orchestration method based on the fusion of physical models of the present invention;

[0056] Figure 2Schematic diagram of the steps for obtaining fused device data in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0057] Figure 3 Schematic diagram of the steps for creating and developing corresponding data service orchestration based on object model function data, virtual device object models, and configuration information in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0058] Figure 4 Schematic diagram of the steps for configuring data conversion rules in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0059] Figure 5 Schematic diagram of the steps for obtaining developed data services through a visualization device in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0060] Figure 6 Schematic diagram of the steps for extracting the configuration file of the set of tasks to be served in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0061] Figure 7 Schematic diagram of the steps for obtaining target functional modules in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0062] Figure 8 Schematic diagram of the steps for obtaining horizontal and vertical analysis results in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0063] Figure 9 Schematic diagram of the steps for publishing the configuration result in an embodiment of a data service orchestration method based on object model fusion according to the present invention;

[0064] Figure 10 Schematic diagram of the functional modules in an embodiment of a data service orchestration system based on object model fusion according to the present invention;

[0065] Figure 11 Schematic diagram of the structure of an embodiment of an electronic device according to the present invention;

[0066] Figure 12 Schematic diagram of the structure of an embodiment of a storage medium according to the present invention. Detailed implementation manners

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] The terms "first", "second", and "third" in the present invention are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0069] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0070] As Figure 1 shown, this embodiment provides an embodiment of a data service orchestration method based on the fusion of physical models. In this embodiment, the data service orchestration method based on the fusion of physical models specifically includes the following steps:

[0071] Step S1: The user describes device data such as the attributes, services, and events of the Internet of Things devices; outputs the device data to the Internet of Things platform; fuses the device data of different Internet of Things devices from the physical model at the parameter granularity to obtain fused device data;

[0072] Among them, the attributes of the Internet of Things devices include the collected data items; the services include functions; the events include alarm events;

[0073] Step S2: The IoT platform defines the virtual device object model according to the object model of the IoT; determines the corresponding data processing method based on the fused device data; creates and develops the corresponding data service orchestration according to the object model function data, the virtual device object model, and the configuration information.

[0074] Step S3: Connect multiple data services based on the logical relationship defined by the business through the visualization device, and configure the transmission protocol, communication protocol, scheduling period, and failure strategy of the data service node; publish the configuration result.

[0075] Preferably, in step S1 of this embodiment, the user describes data such as the attributes, services, and events of the device through the IoT platform; the user uploads the described device data to the IoT platform to ensure the integrity and accuracy of the data; fuses the data of different IoT devices at the parameter granularity to generate unified fused device data; through parameter granularity fusion, it is ensured that the data of different devices can be uniformly processed, avoiding the problem of inconsistent data formats; the fused data can be used more efficiently for subsequent data processing and analysis, improving the utilization rate of the data; the unified data format and structure simplify the complexity of data management and reduce the cost of data processing. In step S2, the IoT platform defines the virtual device object model according to the object model of the IoT. The object model is a data model that describes the functions of the device in the cloud, including attributes, services, and events; based on the fused device data, determines the corresponding data processing method; according to the object model function data and the virtual device object model, combined with the configuration information, creates and develops the corresponding data service orchestration; by defining the virtual device object model, data can be processed and analyzed more efficiently, improving the efficiency of data processing; the data service orchestration can be flexibly adjusted according to business requirements, improving the flexibility and scalability of the system; through reasonable data processing methods and orchestration, the resource configuration is optimized, and the operating cost of the system is reduced. In step S3, based on the visualization device, multiple data services are connected according to the logical relationship defined by the business; the transmission protocol, communication protocol, scheduling period, and failure strategy of the data service node are configured; the configuration result is published to ensure that all configurations take effect and are applied to the actual system. Connecting data services through the visualization device simplifies the operation process and improves the convenience of operation; through reasonable configuration of the transmission protocol and communication protocol, the stability and reliability of the system are improved; after publishing the configuration result, new configurations can be quickly deployed and applied, improving the operation and maintenance efficiency.

[0076] Further, as Figure 2 shown, the process of obtaining the fused device data in step S1 specifically includes the following steps:

[0077] Step S11: Obtain the static attributes and dynamic attributes of the device in the device data described by the user; define the input parameters and return results of service calls, as well as the prerequisite conditions for triggering services; and event types and event levels, etc.;

[0078] Among them, the static attributes include unique identifier, model, manufacturer, and fixed geographical location information; the dynamic attributes include data collection items, defining data types, units, collection frequencies, and value ranges;

[0079] Step S12: Transmit the device data to the Internet of Things platform, unify the data format of the device data; and perform verification on the value range and time sequence, automatically discard invalid data and trigger an alarm; manage the data transmission order according to the event level and assign weights;

[0080] Step S13: Convert different units, convert the naming differences of the same parameter by different manufacturers; take the latest data to overwrite the old data, and weight the average according to the device accuracy; verify whether the device data in the same area is qualified. If it is qualified, store the real-time data in the time series database and the metadata in the graph database to obtain the fused device data; if it is unqualified, obtain it again.

[0081] Among them, in the attribute modeling function group of step S11, define the attribute integrity evaluation criterion:

[0082]

[0083] In the formula, represents the variance of the k-dimensional attribute value, reflecting the data volatility; represents the static attribute feature spectrum margin factor, which may represent the weight or importance adjustment coefficient of the attribute under different time windows; ω st represents the static weight factor, used to balance the contributions of different attributes; N d ,τ d represents the key parameter of the device or system, which may be related to the number of devices (N d ) and the degradation time constant (τ d ); β r represents the regression coefficient of the remaining service life of the device, reflecting the impact of device degradation on the evaluation; λ age represents the degradation rate decay parameter, controlling the decay speed of the exponential function; V qos represents the quality of service index, such as network bandwidth or delay, used to dynamically adjust the evaluation weight;

[0084] The data transmission verification equation set in step S12, and the time series stability authentication model is expressed as:

[0085]

[0086] In the formula, the zero-order Bessel function is used to construct the time-domain diffusion kernel, τ p is the transmission delay correction factor between nodes, ω x is the adaptive window width adjustment parameter;

[0087] In the formula, J0(κ p Δt) represents the zero-order Bessel function, which is used to describe the diffusion characteristics of the time-domain signal; κ p represents the wave number, which is related to the signal frequency; τ p / z α represents the transmission delay correction factor, which may combine the transmission time (τ p ) and the confidence level parameter (z α ) to adjust the delay effect; represents the adaptive window width adjustment parameter, which controls the smoothness of the Gaussian kernel function and is used to optimize the model matching; x obs , represents the observed value and the model prediction value, which reflects the accuracy of data transmission;

[0088] The formula for verifying the integrity of service call parameters is expressed as:

[0089]

[0090] The composite gamma function and the asymmetric combination of the error function are used to verify the parameter space coverage, and the reachability probability of multi-threaded parameter acceptance is simulated by the Φ Θ function;

[0091] In the formula, Γ represents the gamma function, which is used for probability distribution modeling of parameters; erf represents the error function; u i , δ i , ∈ represent the threshold, deviation term and tolerance coefficient in the parameter space; η, ρ represent the weight coefficients, which adjust the contribution ratio of the error function and the gamma function; Φ Θ (M req ) represents the constraint function related to the request parameter (M req ), which may involve multi-dimensional parameter mapping;

[0092] The multi-dimensional data fusion algorithm in step S13, the heterogeneous data convergence operator is expressed as:

[0093]

[0094] Here, the incompressible tensor manifold integration method is used to handle the non-linear geometric wall effect of multi-dimensional parameters;

[0095] The error adaptive cancellation equation is expressed as:

[0096]

[0097] A diffusion error correction system that combines the percolation phase transition theory and the modified cross entropy;

[0098] In the formula, R i , D ij represent the radius parameter and the diffusion coefficient in data fusion, reflecting the spatial distribution characteristics of heterogeneous data; ψ error , represents the tensor operation of the error field and the parameter field, used to handle non-linear geometric effects; ω k represents the weight coefficient and the attenuation rate, controlling the time-varying characteristics of the error term; the sec function term represents the adjustment term combined with the phase φΘ(t) and the gradient for adaptive error cancellation; represents the regularization term to prevent model overfitting; this set of algorithms can ensure the stable convergence of the variance near the phase transition critical point of the Ising model through the high-order discretization of the Hausdorff measure, and can meet the multi-model joint calibration requirements for data volume of 16TB / hour level; in actual deployment, a quantum annealing algorithm needs to be adopted to accelerate the tensor decomposition process.

[0099] Preferably, in step S11 of this embodiment, static attributes and dynamic attributes in the device data described by the user are obtained. The static attributes include fixed information such as unique identifier, model number, manufacturer, and geographical location; define the input parameters and return results of service calls, as well as the prerequisite conditions for triggering services; define event types and event levels for classifying and managing data; by standardizing device attribute identifiers and defining service calls, the efficiency of data management can be improved, and the consistency and accuracy of data can be ensured; by defining the input parameters and return results of service calls, the flexibility of the system can be enhanced to support multiple application scenarios; by event types and event levels, data can be better managed and protected to ensure data security and privacy. In step S12, the device data is unified into a data format to ensure consistency during data transmission and processing; the value range and time series are verified, and invalid data is automatically discarded and an alarm is triggered. This includes checking the integrity and accuracy of the data; managing the data transmission order according to the event level and assigning weights to ensure that important data is transmitted first; by data verification and unified data format, the quality of data can be improved, and errors and invalid data can be reduced; by managing the data transmission order according to the event level, the data transmission efficiency can be optimized to ensure the timely transmission of key data; by automatically discarding invalid data and triggering an alarm, the reliability of the system can be enhanced, and system failures caused by data problems can be reduced. In step S13, conversions between different units are performed to ensure data consistency; conversions are made for the naming differences of the same parameter by different manufacturers to ensure data comparability; the latest data is used to overwrite the old data, and the weights are weighted and averaged according to the device accuracy to ensure data accuracy and reliability; the device data in the same area is verified for compliance. If it is compliant, the real-time data is stored in the time series database, and the metadata is stored in the graph database; by unit conversion and naming difference conversion, data consistency and comparability can be improved; by overwriting the old data with the latest data and weighted average of weights, data accuracy and reliability can be enhanced; by storing the verified compliant data in the time series database and graph database, data storage can be optimized, and the efficiency of data query and analysis can be improved; by re-obtaining non-compliant data, duplicate work can be reduced, and the overall efficiency of the system can be improved.

[0100] Further, as Figure 3 shown, the process of creating and developing the corresponding data service orchestration according to the physical model function data, virtual device physical model, and configuration information in step S2 specifically includes the following steps:

[0101] Step S21: Select the created product in the Internet of Things platform console and enter the product details page; click on function definition or custom function to add a physical model; define the attributes, services, and events of the physical model; after the physical model is defined, the device will automatically inherit the content of the model;

[0102] Step S22: Collect data through sensors and devices and transmit it to the cloud or a local server; define a physical model according to the device type and function, including device attributes, status, and behaviors; design a data processing flow and configure data conversion rules;

[0103] Step S23: In the IoT platform console, obtain development data services through a visualization device, configure the input, output, and processing logic of the data service; test the function of the data service; use data analysis services to analyze the IoT fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results.

[0104] Preferably, in step S21 of this embodiment, in the IoT platform console, the user can define a physical model for the created product. The physical model abstracts the actual product into a data model including attributes, services, and events, facilitating cloud management and data interaction; the user can add custom attributes, services, and events on the physical model definition page. Attributes are used to describe the status of the device, services are used for device operations, and events are used for device trigger conditions; after the definition is completed, the device will automatically inherit the content of this physical model without manual configuration. Through the definition of the physical model, the attributes, services, and events of the device are standardized, facilitating unified management and maintenance; the definition of the physical model makes the data interaction between the device and the cloud more efficient and reduces the complexity of manual configuration; the device automatically inherits the content of the physical model, facilitating the access and management of new devices. In step S22, collect data through sensors and devices and transmit it to the cloud or a local server; define a physical model according to the device type and function, including device attributes, status, and behaviors; design a data processing flow and configure data conversion rules to ensure the accuracy and consistency of the data; through data collection and processing, realize real-time monitoring and remote control of the device; through the configuration of data conversion rules, ensure the accuracy and consistency of the data during transmission and processing; provide reliable basic data for subsequent data analysis. In step S23, in the IoT platform console, obtain development data services through a visualization device, configure the input, output, and processing logic of the data service; test the function of the data service to ensure its normal operation; use data analysis services to analyze the IoT fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results. Through the configuration and testing of the data service, improve the accuracy and efficiency of data analysis; based on the data analysis results, support the decision-making of enterprises or organizations; through horizontal and vertical analysis results, optimize business processes and resource allocation.

[0105] Further, as Figure 4 shown, the process of configuring data conversion rules in step S22 specifically includes the following steps:

[0106] Step S221: Obtain the network address translation rules of the Internet of Things object model, and deploy the network address translation rules in the network space of the virtual device object model and the network space of the container where the source node is located; through kernel extension technology, configure the same set of identical network address translation rules for the containers where each node is located;

[0107] Step S222: Deploy the network address translation rules to the direct network cards of each node; the source node extracts the virtual address from the data packet; through the network address translation rules deployed in the network space of the virtual device object model where the source node is located, convert the virtual address into an actual address;

[0108] Step S223: Deploy the network address translation rules to be deployed in the network spaces of the virtual device object models where each node is located among multiple nodes belonging to the same address; send the data packet to the target node related to the actual address corresponding to the virtual device object model through the actual address.

[0109] Preferably, in step S221 of this embodiment, obtain the network address translation rules from the Internet of Things object model; deploy the obtained network address translation rules in the network space of the virtual device object model and the network space of the container where the source node is located; through kernel extension technology, configure the same set of identical network address translation rules for the containers where each node is located. Through kernel extension technology, it is ensured that all nodes use the same network address translation rules, simplifying the configuration process and improving the management efficiency. It can flexibly deploy network address translation rules in the virtual device object model and the container network space to adapt to different network environments and requirements. Step S222 deploys the network address translation rules to the direct network cards of each node; the source node extracts the virtual address from the data packet. Through the network address translation rules deployed in the network space of the virtual device object model where the source node is located, convert the virtual address into an actual address; directly deploying the network address translation rules through the direct network card reduces the intermediate links and improves the efficiency of address translation; ensures that the virtual address can be accurately converted into an actual address to ensure the correct transmission of the data packet. Step S223 deploys the network address translation rules to be deployed in the network spaces of the virtual device object models where each node is located among multiple nodes belonging to the same address; send the data packet to the target node related to the actual address corresponding to the virtual device object model through the actual address; it can deploy network address translation rules in batches, is applicable to scenarios with multiple nodes, and improves the processing efficiency; sending the data packet to the target node through the actual address ensures that the data packet can be accurately delivered to the expected target node.

[0110] Further, as Figure 5 shown, the process of obtaining the development data service through the visualization device in step S23 specifically includes the following steps:

[0111] Step S231: Obtain the development data service data of the virtual device object model through a visualization device, and extract the configuration file of the set of tasks to be served, where the configuration file is used to indicate at least one data service type required by the set of tasks to be served;

[0112] Step S232: Determine at least one preset plugin in the preset plugin set that meets the configuration file to obtain a target plugin set; wherein, the preset plugin set includes multiple preset plugins, and each preset plugin is used to provide services for the corresponding data service type; the target plugin set includes at least one target plugin;

[0113] Step S233: Orchestrate the target plugin set to obtain a target plugin group; use the target plugin group to perform containerized registration on the preset function module to obtain a target function module; use data analysis services to analyze the Internet of Things fusion data based on the virtual device object model to obtain horizontal and vertical analysis results;

[0114] Among them, the target function module is used to provide data services for the set of tasks to be served.

[0115] Preferably, in step S231 of this embodiment, the development data service data of the virtual device object model is obtained through a visualization device; the configuration file is used to indicate at least one data service type required by the set of tasks to be served; through the visualization device, users can intuitively obtain and manage the data service data, reducing the complexity of manual operations; the extraction of the configuration file ensures the accurate transmission of task requirements, avoiding errors or omissions caused by unclear requirements. In step S232, the preset plugin set includes multiple preset plugins, and each preset plugin is used to provide services for the corresponding data service type; determine the preset plugins that meet the conditions according to the configuration file to generate a target plugin set; through the preset plugin set, appropriate plugins can be dynamically selected according to different task requirements, improving the flexibility and maintainability of the system. Through the generation of the target plugin set, the reasonable allocation and utilization of resources are realized, avoiding resource waste. In step S233, the target plugin set is orchestrated to form a target plugin group, which reflects the application of orchestration technology and ensures the collaborative work between plugins; use the target plugin group to perform containerized registration on the preset function module to generate a target function module; analyze the Internet of Things fusion data based on the virtual device object model to obtain horizontal and vertical analysis results. Through containerized registration, the target function module can be called and reused multiple times, reducing the development cost; through data analysis services, in-depth analysis of the Internet of Things fusion data can be carried out, providing horizontal and vertical analysis results to support decision-making; through orchestration technology and containerized registration, the automated and intelligent management of the system is realized, improving the overall performance and reliability of the system.

[0116] Furthermore, as Figure 6As shown, the process of extracting the configuration file of the set of tasks to be served in step S231 specifically includes the following steps:

[0117] Step S2311: Identify that the configuration file indicates at least one data service type to obtain a set of services to be configured; traverse the set of services to be configured to determine the target plug-in corresponding to each target type; wherein, the target plug-in is used to provide services for the corresponding target service type; the set of services to be configured includes at least one target service type;

[0118] Step S2312: Put the target plug-in into the target plug-in set; identify the number of services of the data service type indicated by the configuration file to detect whether the service data is greater than a preset service quantity threshold; configure the dynamic serialization transmission of data objects according to the communication protocol and the transmission protocol;

[0119] Step S2313: If the number of services is less than the preset service quantity threshold, then orchestrate the target plug-in set to obtain a target plug-in group; if the number of services is greater than the preset service quantity threshold, then split the target plug-in set into multiple target plug-in subsets; the number of services in each target plug-in subset is less than the preset service quantity threshold.

[0120] Preferably, in step S2311 of this embodiment, at least one data service type is identified from the configuration file to form a set of services to be configured, ensuring that the system can accurately understand which services need to be configured. Through the set of services to be configured, a corresponding target plug-in is determined for each target service type. The target plug-in is designed specifically for these service types to ensure the correct provision of services, improving the accuracy and flexibility of configuration because the system can automatically select the corresponding plug-in according to different service types. By matching the service type with the plug-in, the configuration process is simplified and the possibility of human error is reduced. In step S2312, all determined target plug-ins are placed in the target plug-in set to prepare for orchestration or splitting. The number of services of the data service type indicated by the configuration file is identified and compared with a preset service quantity threshold, which helps the system understand the current service load situation. According to the communication protocol and transmission protocol, the data object is configured for dynamic serialization transmission, ensuring the efficiency and accuracy of data transmission between different components or services. By constructing the plug-in set, it provides convenience for processing and improves the system's response speed. Service quantity detection helps the system make reasonable resource allocation decisions to avoid resource overload or waste. The configuration of serialization transmission ensures the integrity and transmission efficiency of data and improves the overall performance of the system. In step S2313, when the number of services is less than the preset service quantity threshold, the target plug-in set is orchestrated to form a target plug-in group, which helps optimize resource utilization and improve processing efficiency. When the number of services is greater than the preset service quantity threshold, the target plug-in set is split into multiple target plug-in subsets to ensure that the number of services in each subset is below the threshold, which helps disperse the load and avoid overload of a single component or service. By flexibly orchestrating or splitting according to the number of services, the system can better adapt to different load situations, improving stability and scalability. The orchestration and splitting strategies help optimize resource utilization, improve the overall performance and efficiency of the system, and reflect the system's adaptability to dynamic changes and its ability to make reasonable adjustments according to the actual situation.

[0121] Further, as Figure 7 shown, the process of obtaining the target functional module in step S233 specifically includes the following steps:

[0122] Step S2331: Identify the service order of the data service type indicated by the configuration file, determine the plug-in order of one target plug-in according to the service order, and orchestrate the target plug-in set according to the plug-in order to obtain a target plug-in group.

[0123] Step S2332: Randomly select a preset functional module from the functional module set, perform containerization processing on the preset functional module to obtain a target container module, determine the container interface of the target container module, and inject the target plug-in group into the target container module through the container interface to obtain the target functional module.

[0124] Step S2333: Monitor the load parameters of the target function module. When the load parameters exceed the preset load threshold, split the target plugin group into multiple groups; use the multiple target plugin groups to perform containerized registration on different preset function modules respectively to obtain multiple target function modules; use data analysis services to analyze the Internet of Things fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results.

[0125] Preferably, in step S2331 of this embodiment, according to the data service type and its service order indicated in the configuration file, the system can accurately understand the dependency relationship and execution order between services; based on the service order, the system can determine the loading and execution order of the target plugins, and then perform an orderly orchestration on the target plugin set to form a target plugin group; ensure that the services are executed in the correct order, avoid execution errors caused by service dependency relationships; improve the configurability and flexibility of the system, allowing dynamic adjustment of the service order and plugin combination according to business requirements; in step S2332, randomly select a preset function module from the function module set, which increases the flexibility and scalability of the system; perform containerization processing on the selected function module to achieve service isolation and independent operation, improving the stability and security of the system; inject the target plugin group into the target container module through the container interface to form a target function module, realizing the dynamic combination of services and function modules; containerization processing enables the function module to run independently of other components, reducing the coupling degree between systems and improving the maintainability of the system; the plugin injection mechanism allows the system to dynamically add or replace services according to needs, enhancing the scalability and flexibility of the system; step S2333 monitors the load parameters of the target function module in real time, timely discovers and handles potential overload problems; when the load exceeds the preset threshold, split the target plugin group into multiple groups to disperse the load and avoid single-point overload; use the split plugin groups to perform containerized registration on different function modules, realizing distributed deployment of services and load balancing; analyze the Internet of Things fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results; through load monitoring and plugin group splitting, the system can dynamically adjust service deployment and resource allocation, improving the stability and response speed of the system; containerized registration and distributed deployment enable the system to handle a large number of concurrent requests more efficiently, improving the throughput and scalability of the system; the data analysis function provides the system with the ability to deeply understand Internet of Things data, which helps to optimize business decisions and improve service quality.

[0126] Furthermore, as Figure 8 shown, the process of obtaining the horizontal and vertical analysis results in step S2333 specifically includes the following steps:

[0127] Step S23331: In the Internet of Things platform console, obtain the development data service through the visualization device, including the input, output, and processing logic of the data service; simulate the input of the data service and verify whether it is qualified according to the input result.

[0128] Step S23332: If it is qualified, start the analysis program to compare and analyze the data of different devices at the same time point to obtain rules and abnormal data; and perform trend analysis on the data of the same device at different time points to obtain prediction results.

[0129] Step S23333: Display the analysis results through the visualization device provided by the Internet of Things cloud platform; send control instructions to the corresponding object model to adjust the operating parameters of the object model; and continuously optimize the analysis program according to the analysis results and actual application results.

[0130] Preferably, in step S23331 of this embodiment, on the Internet of Things platform console, the data services required for development can be conveniently obtained through a visual interface; this reduces the development threshold and improves the development efficiency; it clarifies the input, output, and processing logic of the data services, ensuring the accuracy and reliability of the data services; by simulating the input of the data services, the output results are verified to validate the correctness and stability of the data services; it helps to discover and fix potential problems in the development stage in a timely manner; it improves the development efficiency and accuracy of the data services and reduces the development cost; through the verification mechanism, the stability and reliability of the data services are ensured, providing a solid foundation for data analysis; step S23332 compares and analyzes the data of different devices at the same time point, which helps to discover the data associations and patterns among the devices, as well as potential abnormal data; trend analysis of the data of the same device at different time points can predict the future state or behavior of the device, providing decision-making support for preventive maintenance; through data analysis, the system can automatically identify the patterns and anomalies in the data, improving the intelligent level of data analysis; it provides in-depth data insight capabilities, helping to discover the correlations and potential problems among the devices; trend analysis provides a scientific basis for the preventive maintenance and optimization of the devices, improving the operation efficiency and reliability of the devices; step S23333 displays the analysis results through the visualization device provided by the Internet of Things cloud platform, making the analysis results more intuitive and easy to understand, facilitating user understanding and decision-making; according to the analysis results, control instructions are sent to the corresponding physical model to adjust the operating parameters of the physical model, realizing closed-loop control based on data; according to the feedback of the actual application results and the analysis results, the analysis program is continuously optimized, improving the accuracy and efficiency of the analysis; it improves the usability and ease of use of the data analysis results, facilitating users to make scientific decisions; it realizes closed-loop control based on data, improving the automation and intelligent level of the Internet of Things system; by continuously optimizing the analysis program, the accuracy and timeliness of the data analysis are ensured, providing a strong guarantee for the stable operation of the Internet of Things system.

[0131] Further, as Figure 9 shown, the process of publishing the configuration results in step S3 specifically includes the following steps:

[0132] Step S31: The data acquisition node is responsible for reading data from the device, the data processing node is responsible for cleaning and analyzing the acquired data, and the data storage node is responsible for storing the processed data in the database; the input and output data formats are defined for each node;

[0133] Step S32: Connect the output of the data acquisition node to the input of the data processing node, and connect the input of the data processing node to the output of the data storage node; determine the transmission protocol according to the communication requirements of the data service node, and configure the protocol parameters; set the scheduling period of each data service node according to the service requirements; configure the processing strategy when the data service node fails.

[0134] Step S33: Publish the configured data service nodes, their logical relationships, and parameter configuration information to the IoT platform; simulate the data transmission process of the data acquisition node to check whether the data can reach the data processing node correctly; check whether the processing structure of the data processing node meets the standard. If not, make adjustments and re-publish the configuration after adjustment.

[0135] Among them, it is judged whether the processing structure of the data processing node meets the standard through the data flow integrity factor:

[0136]

[0137] In the formula: Φ k represents the data fidelity of the k-th data acquisition node (the value range is 0-1); ω k is the topological weight coefficient of the corresponding node; λ represents the network transmission environment attenuation coefficient; represents the stability of the data transmission path i (Γ = 1 - τ i / τ max ); τ i is the delay variance of path i, and τ max is the maximum allowable delay variance; n is the total number of data acquisition nodes, and m is the total number of communication paths.

[0138] Preferably, in step S31 of this embodiment, by defining the input data format, it is ensured that data is accurately read from the device; the collected data is cleaned and analyzed to remove redundant, incorrect, or insignificant information, ensuring the accuracy and validity of the data; at the same time, by defining the output data format, it is prepared for data storage; the processed data is stored in the database for easy data query and analysis; the accuracy, validity, and storable nature of the data are ensured, providing a solid foundation for data applications; by clarifying the input and output data formats of each node, the efficiency and reliability of data processing are improved; in step S32, by connecting the input and output of the data acquisition node, data processing node, and data storage node, the smooth flow of data is achieved; the transmission protocol is determined according to the communication requirements of the data service node, and the protocol parameters are configured to ensure the stability and security of the data during transmission; the scheduling period of each data service node is set according to the service requirements to achieve the timed acquisition, processing, and storage of data; the automated acquisition, processing, and storage of data are realized, improving the operation efficiency and reliability of the Internet of Things platform; at the same time, by configuring the transmission protocol and scheduling period, the requirements of different service scenarios are met; the configuration of the fault handling strategy further enhances the stability and availability of the system; in step S33, the configured data service node and its logical relationships, parameter configurations, and other information are published to the Internet of Things platform for easy management and monitoring; the data transmission process of the simulated data acquisition node is checked to verify whether the data can reach the data processing node correctly and whether the processing result of the data processing node meets the standards; adjustments and optimizations are made according to the test results, and the configuration is republished to ensure the correctness and reliability of the system; the correctness and reliability of the Internet of Things platform are ensured, and the functions and performance of the system are verified through simulation tests; at the same time, according to the test results, adjustments and optimizations are made to improve the adaptability and flexibility of the system.

[0139] As Figure 10 shown, this embodiment also provides an embodiment of the data service orchestration system based on the fusion of physical models. In this embodiment, the data service orchestration system based on the fusion of physical models is applied to the data service orchestration method based on the fusion of physical models in the above embodiment. The data service orchestration system based on the fusion of physical models includes:

[0140] The physical model data acquisition module 1 is used for the user to describe device data such as the attributes, services, and events of Internet of Things devices; output the device data to the Internet of Things platform; fuse the device data of different Internet of Things devices at the parameter granularity of the physical model to obtain the fused device data;

[0141] Among them, the attributes of the Internet of Things device include the collected data items; the services include functions; the events include alarm events, etc.;

[0142] Build a virtual device object model module 2, which is used for the IoT platform to define a virtual device object model according to the object model of the Internet of Things; determine the corresponding data processing method based on the fused device data; create and develop the corresponding data service orchestration according to the object model function data, virtual device object model and configuration information;

[0143] A test and release module 3, which is used to connect multiple data services based on the logical relationship defined by the business through a visualization device, and configure the transmission protocol, communication protocol, scheduling period, failure strategy, etc. of the data service nodes; publish the configuration results.

[0144] Preferably, in the object model data acquisition module 1 of this embodiment, the user describes data such as the attributes, services, and events of the device through the IoT platform; the user uploads the described device data to the IoT platform to ensure the integrity and accuracy of the data; fuse the data of different IoT devices at the parameter granularity to generate unified fused device data; through parameter granularity fusion, ensure that the data of different devices can be uniformly processed, avoiding the problem of inconsistent data formats; the fused data can be used more efficiently for subsequent data processing and analysis, improving the utilization rate of the data; the unified data format and structure simplify the complexity of data management and reduce the cost of data processing. In the virtual device object model building module 2, the IoT platform defines a virtual device object model according to the object model of the Internet of Things. The object model is a data model that describes the functions of the device in the cloud, including attributes, services, and events; based on the fused device data, determine the corresponding data processing method; according to the object model function data and virtual device object model, combined with the configuration information, create and develop the corresponding data service orchestration; by defining the virtual device object model, data can be processed and analyzed more efficiently, improving the efficiency of data processing; the data service orchestration can be flexibly adjusted according to business requirements, improving the flexibility and scalability of the system; through reasonable data processing methods and orchestration, the resource configuration is optimized and the operating cost of the system is reduced. In the test and release module 3, based on the visualization device, connect multiple data services according to the logical relationship defined by the business; configure the transmission protocol, communication protocol, scheduling period, failure strategy, etc. of the data service nodes; publish the configuration results to ensure that all configurations take effect and are applied to the actual system. Connecting data services through a visualization device simplifies the operation process and improves the convenience of operation; through reasonable configuration of the transmission protocol and communication protocol, the stability and reliability of the system are improved; after publishing the configuration results, new configurations can be quickly deployed and applied, improving the operation and maintenance efficiency.

[0145] As Figure 11 shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0146] The memory 42 stores program instructions for implementing the data service orchestration system based on the fusion of physical models in any of the above embodiments.

[0147] The processor 41 is configured to execute the program instructions stored in the memory 42 to perform the layout of the data service orchestration system based on the fusion of physical models.

[0148] Among them, the processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with good processing capabilities. The processor 41 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0149] Furthermore, Figure 12 FIG. is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all the above methods. Among them, the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0150] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, indirect couplings or communication connections of devices or units, and can be in electrical, mechanical, or other forms.

[0151] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, can exist separately as individual physical units, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. The above is only the implementation manner of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

[0152] The specific implementation manners of the invention have been described in detail above, but they are only examples. The present invention is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, all equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present invention should be covered by the scope of the present invention.

Claims

1. A data service orchestration method based on the fusion of physical models, characterized in that, The data service orchestration method based on physical model fusion includes: The user describes the attributes, services, and event device data of the Internet of Things devices; outputs the device data to the Internet of Things platform; fuses the device data of different Internet of Things devices at the parameter granularity of the physical model to obtain the fused device data; Among them, the attributes of the Internet of Things devices include the collected data items; the services include functions; the events include alarm events; The Internet of Things platform defines the virtual device physical model according to the physical model of the Internet of Things; determines the corresponding data processing method based on the fused device data; creates and develops the corresponding data service orchestration according to the physical model function data, virtual device physical model, and configuration information; Based on the visualization device, connect multiple data services according to the logical relationship defined by the business, and configure the transmission protocol, communication protocol, scheduling period, and failure strategy of the data service nodes; publish the configuration result.

2. The data service orchestration method based on the fusion of physical models according to claim 1, wherein The process of obtaining the fused device data specifically includes the following steps: Obtain the static attributes and dynamic attributes of the devices in the device data described by the user; define the input parameters and return results of the service call, as well as the prerequisite conditions for triggering the service; and the event type and event classification; Among them, the static attributes include unique identifier, model number, manufacturer, and fixed location information; the dynamic attributes include data collection items, define the data type, unit, collection frequency, and value range; Transmit the device data to the Internet of Things platform, unify the data format of the device data; and check the value range and time series, automatically discard invalid data and trigger an alarm; manage the data transmission order according to the event level and assign weights; Convert different units, convert the naming differences of the same parameter by different manufacturers; overwrite the old data with the latest data, and weight the average according to the device accuracy; check whether the device data in the same area is qualified. If it is qualified, store the real-time data in the time series database and the metadata in the graph database to obtain the fused device data; if it is unqualified, obtain it again.

3. The data service orchestration method based on the fusion of physical models according to claim 1, wherein The process of creating and developing the corresponding data service orchestration according to the physical model function data, virtual device physical model, and configuration information includes the following steps: Select the created product in the Internet of Things platform console, enter the product details page; click on function definition or custom function to add a physical model; define the attributes, services, and events of the physical model; after the physical model is defined, the device will automatically inherit the content of this model; Collect data through sensors and devices and transmit it to the cloud or local server; define the physical model according to the device type and function, including device attributes, status, and behavior; design the data processing flow and configure the data conversion rules; In the Internet of Things platform console, obtain the developed data service through the visualization device, configure the input, output, and processing logic of the data service; test the function of the data service; use data analysis services to analyze the Internet of Things fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results.

4. The data service orchestration method based on the fusion of physical models according to claim 3, wherein, The process of configuring the data conversion rules specifically includes the following steps: Obtain the network address translation rules for the Internet of Things device model, and deploy the network address translation rules in the network space of the virtual device model and the network space of the container where the source node is located; through kernel extension technology, configure the same set of network address translation rules for the containers where each node is located; Deploy the network address translation rules to the direct network cards of each node; the source node extracts the virtual address from the data packet; through the network address translation rules deployed in the network space of the virtual device model where the source node is located, convert the virtual address into the actual address; Deploy the network address translation rules to be deployed in the network spaces of the virtual device models where each node in multiple nodes belonging to the same address is located; send the data packet to the target node related to the actual address corresponding to the virtual device model through the actual address.

5. The data service orchestration method based on the fusion of physical models according to claim 3, characterized in that, The process of obtaining the development data service through the visualization device includes the following steps: Obtain the development data service data of the virtual device model through the visualization device, and extract the configuration file of the service task set to be served, where the configuration file is used to indicate at least one data service type required by the service task set to be served; Determine at least one preset plug-in that meets the configuration file in the preset plug-in set to obtain the target plug-in set; where the preset plug-in set includes multiple preset plug-ins, and each preset plug-in is used to provide services for the corresponding data service type; the target plug-in set includes at least one target plug-in; Orchestrate the target plug-in set to obtain the target plug-in group; use the target plug-in group to perform containerized registration on the preset function module to obtain the target function module; use the data analysis service to analyze the Internet of Things fusion data based on the virtual device model to obtain horizontal and vertical analysis results; Among them, the target function module is used to provide data services for the service task set to be served.

6. The data service orchestration method based on the fusion of physical models according to claim 5, characterized in that, The process of extracting the configuration file of the service task set to be served includes the following steps: Identify that the configuration file indicates at least one data service type to obtain the service set to be configured; traverse the service set to be configured to determine the target plug-in corresponding to each target type; where the target plug-in is used to provide services for the corresponding target service type; the service set to be configured includes at least one target service type; Put the target plug-in into the target plug-in set; identify the number of services of the data service type indicated by the configuration file to detect whether the service data is greater than the preset service quantity threshold; configure the dynamic serialization transmission of the data object according to the communication protocol and the transmission protocol; If the number of services is less than the preset service quantity threshold, then orchestrate the target plug-in set to obtain the target plug-in group; if the number of services is greater than the preset service quantity threshold, then split the target plug-in set into multiple target plug-in subsets; the number of services in each target plug-in subset is less than the preset service quantity threshold.

7. The data service orchestration method based on the fusion of physical models according to claim 5, characterized in that The process of obtaining the target function module includes the following steps: Identify the service order of the data service type indicated by the configuration file, and determine the plug-in order of the target plug-in according to the service order; orchestrate the target plug-in set according to the plug-in order to obtain the target plug-in group; Randomly select a preset function module from the function module set, perform containerization processing on the preset function module to obtain a target container module; determine the container interface of the target container module, and inject the target plugin group into the target container module through the container interface to obtain a target function module; Monitor the load parameters of the target function module. When the load parameters exceed the preset load threshold, split the target plugin group into multiple ones; use multiple target plugin groups to perform containerization registration on different preset function modules respectively to obtain multiple target function modules; use data analysis services to analyze the Internet of Things fusion data based on the virtual device physical model to obtain horizontal and vertical analysis results.

8. The data service orchestration method based on physical model fusion according to claim 7, characterized in that, The process of obtaining horizontal and vertical analysis results includes the following steps: In the Internet of Things platform console, obtain the development data service through the visualization device, including the input, output, and processing logic of the data service; simulate the data service input and perform verification based on the input result to check whether it is qualified; If it is qualified, start the analysis program to compare and analyze the data of different devices at the same time point to obtain rules and abnormal data; and perform trend analysis on the data of the same device at different time points to obtain prediction results; Display the analysis results through the visualization device provided by the Internet of Things cloud platform; send control instructions to the corresponding physical model to adjust the operating parameters of the physical model; and continuously optimize the analysis program based on the analysis results and actual application results.

9. The data service orchestration method based on physical model fusion according to claim 1, characterized in that The process of publishing the configuration results includes the following steps: The data acquisition node is responsible for reading data from the device, the data processing node is responsible for cleaning and analyzing the acquired data, and the data storage node is responsible for storing the processed data in the database; define the input and output data formats for each node; The output of the data acquisition node is connected to the input of the data processing node, and the input of the data processing node is connected to the output of the data storage node; determine the transmission protocol according to the communication requirements of the data service node and configure the protocol parameters; set the scheduling period of each data service node according to the business requirements; configure the processing strategy when the data service node fails; Publish the configured data service nodes, their logical relationships, and parameter configuration information to the Internet of Things platform; simulate the data transmission process of the data acquisition node to check whether the data can reach the data processing node correctly; check whether the processing structure of the data processing node meets the standard. If not, make adjustments and re-publish the configuration after adjustment.

10. A data service orchestration system based on the fusion of physical models, which is applied to the data service orchestration method based on the fusion of physical models as described in any one of claims 1 to 9, and is characterized in that, The data service orchestration system based on physical model fusion Obtain the physical model data module, which is used for users to describe the attributes, services, and event device data of Internet of Things devices; output the device data to the Internet of Things platform; fuse the device data of different Internet of Things devices at the parameter granularity of the physical model to obtain fused device data; Among them, the attributes of Internet of Things devices include data items collected; services include functions; events include alarm events; Build a virtual device object model module, which is used for the Internet of Things platform to define a virtual device object model according to the object model of the Internet of Things; determine the corresponding data processing method based on the fused device data; create and develop the corresponding data service orchestration according to the object model function data, the virtual device object model, and the configuration information. Test and publish module, which is used to connect multiple data services based on the visual device according to the logical relationship defined by the business, and configure the transmission protocol, communication protocol, scheduling period, and failure strategy of the data service nodes; publish the configuration results.

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