Industrial Internet of Things-oriented general inductance calculation and control integrated method and system

Abstract

The invention provides an industrial internet of things-oriented general inductance calculation and control integrated method and system. The method comprises the following steps: a controller dispatches sensors in a work area to collect environment data and production state data; receiving and aggregating the data acquired by each sensor to generate a control task; evaluating local and cloud resource states, and respectively calculating the total time delay of the control task processed in the local and the cloud; calculating the control cost based on the optimal control theory; generating an unloading decision of the control task according to the total time delay and the control cost; according to the unloading decision, the control task is distributed to the local or the cloud for processing, and a processing result is generated; and a control instruction is generated according to a processing result, and is issued to corresponding production equipment and sensors for adjustment, sensor parameter optimization and unloading decision making, so that a control closed loop is formed. According to the method provided by the invention, the resource utilization rate is improved, low time delay and high real-time performance are guaranteed, the system stability and reliability are enhanced, and the operation and maintenance complexity and cost are reduced.

Description

Technical Field

[0001] The present invention relates to the fields of industrial Internet of Things and data service technology, and in particular to an integrated method and system for synaesthesia, computing and control for the industrial Internet of Things. Background Art

[0002] The Industrial Internet of Things (IIoT), a core technology for the digital transformation of industrial manufacturing, achieves intelligent and automated production processes by deeply integrating sensors, controllers, communication networks, and computing resources. However, with the increasing complexity and diversity of industrial scenarios, traditional technical architectures face severe challenges in resource management, communication quality, and device collaboration.

[0003] Specifically, existing technologies are mainly divided into two categories: cloud computing-based industrial Internet of Things systems and integrated perception-transmission-control architectures.

[0004] Cloud computing-based Industrial IoT systems employ a "cloud-pipeline-edge-pipeline-terminal" architecture. The cloud is responsible for centralized data processing, providing the foundation for business processing. The pipeline leverages diverse remote communication technologies to ensure widespread and reliable device access. Edge devices handle real-time data collection, preprocessing, and upload, effectively shortening the data processing chain. Terminal devices accurately collect information from various devices, enriching data sources. This technical solution improves the efficiency of data collection and analysis, providing better support for business applications. However, this solution's reliance on centralized cloud processing can lead to network latency, making it difficult to meet the stringent low-latency requirements of the industrial sector, particularly in scenarios requiring real-time response. Furthermore, cloud computing resource orchestration is highly dependent on the network environment. In the event of poor network quality or interruptions, the system may not function properly, posing a potential threat to the continuity and stability of industrial production. Furthermore, while leveraging cloud resources, it neglects the resource utilization of the Industrial IoT devices themselves, failing to fully utilize the local computing and network resources of the devices.

[0005] For the integrated perception-transmission-control architecture, the system state is sensed based on the dynamically changing external disturbances of the industrial system, and state estimation is performed to support adaptive transmission for the heterogeneous quality of service (QoS) requirements of multiple businesses, thereby achieving optimal configuration of multi-domain resources. This technical solution can cope with complex network environments and changing operational requirements through precise control and scheduling, and can also optimize resource allocation to improve the overall efficiency and response speed of the system. However, this technical solution relies on perception and transmission. Network delays and bandwidth limitations may cause real-time data to be unable to be transmitted in a timely manner, thereby affecting the accuracy of control decisions. At the same time, the overall system lacks consideration of computing resources, which may affect the accuracy of perception and control, especially in extreme environments or when resources are limited. It may not meet the needs of efficient and precise control. Summary of the Invention

[0006] In view of this, an embodiment of the present invention provides an integrated method and system for sensing, computing and controlling in an industrial Internet of Things (IIoT) to eliminate or improve one or more defects in the prior art.

[0007] In one aspect, the present invention provides an integrated method for sensing, computing and controlling an industrial Internet of Things, the method comprising the following steps:

[0008] The sensors in the dispatching work area collect environmental data and production status data;

[0009] Receive and aggregate data collected by each sensor to generate a control task; evaluate local and cloud resource status and calculate the total processing delay of the control task locally and in the cloud respectively; construct a cost function based on optimal control theory to calculate the control cost; and generate an offloading decision for the control task based on the total delay and the control cost;

[0010] According to the offloading decision, the control task is assigned to a local or cloud for processing, and a processing result is generated;

[0011] A control instruction is generated according to the processing result and sent to the corresponding production equipment and sensors for adjustment, thereby optimizing the sensor parameters and the unloading decision.

[0012] In some embodiments of the present invention, sensors in a work area are scheduled to collect environmental data and production status data at a dynamic sampling rate, including:

[0013] The amount of environmental data and production status data collected by the sensor satisfies the following calculation formula:

[0014]

[0015] in, represents the amount of data collected by sensor k in work area n for control subtask i of control task m; α represents the sensor sampling rate; and t represents the sampling time.

[0016] In some embodiments of the present invention, calculating the total delay of locally processing the control task includes:

[0017] Receive and aggregate the data collected by each sensor to generate data aggregation delay, which is calculated as:

[0018]

[0019] Among them, max(·) means taking the maximum value; represents the sampling time of sensor k in work area n for control subtask i of control task m; Represents the wireless transmission delay from sensor to controller;

[0020] The control task is processed to generate a local computing delay, which is calculated as follows:

[0021]

[0022] in, represents the amount of data collected for control subtask i of control task m in work area n; Indicates the local calculation rate;

[0023] The data aggregation delay and the local calculation delay are added together to obtain the local total delay, which is calculated as follows:

[0024]

[0025] in, represents the data aggregation delay; Indicates the local calculation delay.

[0026] In some embodiments of the present invention, the step of calculating the wireless transmission delay from the sensor to the controller includes:

[0027] The data collected by each sensor is received through wireless transmission, resulting in wireless transmission path loss, which is calculated as follows:

[0028]

[0029] Among them, d n,m,k represents the transmission distance between the sensor k and the controller n that collects the data of the control task m; f n,m,k represents the signal transmission frequency; c represents the speed of light;

[0030] The signal receiving power is calculated based on the wireless transmission path loss, and the calculation formula is:

[0031]

[0032] in, Indicates the sensor transmission power; ε tra represents the transmission gain; ε rec Indicates receiving gain;

[0033] Based on the Shannon formula, the transmission rate from the sensor to the controller is calculated as follows:

[0034]

[0035] Among them, B n,m,k Indicates transmission bandwidth; represents the signal receiving power; N0 represents the Gaussian white noise power spectral density;

[0036] The wireless transmission delay from the sensor to the controller is calculated according to the transmission rate from the sensor to the controller, and the calculation formula is:

[0037]

[0038] in, Indicates the amount of data transmitted wirelessly; Indicates the transmission rate from the sensor to the controller.

[0039] In some embodiments of the present invention, calculating the total delay of processing the control task in the cloud includes:

[0040] Receive and aggregate the data collected by each sensor to generate data aggregation delay, which is calculated as:

[0041]

[0042] Among them, max(·) means taking the maximum value; represents the sampling time of sensor k in work area n for control subtask i of control task m; Represents the wireless transmission delay from sensor to controller;

[0043] The control task is transmitted to the cloud and processed by the cloud, resulting in a transmission delay from the controller to the cloud and a cloud computing delay;

[0044] The calculation formula for the transmission delay from the controller to the cloud is:

[0045]

[0046] The calculation formula for the cloud computing delay is:

[0047]

[0048] in, represents the amount of data collected for control subtask i of control task m in work area n; v n Indicates the transmission rate from the controller to the cloud; Indicates the cloud computing rate;

[0049] The data aggregation delay, the transmission delay from the controller to the cloud, and the cloud computing delay are added together to obtain the total cloud latency, which is calculated as follows:

[0050]

[0051] in, represents the data aggregation delay; represents the transmission delay from the controller to the cloud; Indicates the cloud computing latency.

[0052] In some embodiments of the present invention, the method further comprises:

[0053] A binary variable is introduced to represent whether the control task is processed locally or offloaded to the cloud for processing; the calculation formula for the processing delay generated by the control task being processed locally or offloaded to the cloud for processing is:

[0054]

[0055] in, represents the binary variable, when Indicates that the control task is processed locally. Indicates that the control task is offloaded to the cloud for processing; represents the amount of data collected for control subtask i of control task m in work area n; Indicates the local calculation rate; Indicates the cloud computing rate.

[0056] In some embodiments of the present invention, based on optimal control theory, a cost function is constructed to calculate the control cost, including:

[0057] The cost function satisfies the following formula:

[0058]

[0059] Where E[·] represents the mathematical expectation; T represents transpose; i Represents the system state vector after the i-th control; I n,m represents the total number of times controller n controls control task m; μ iRepresents the input vector of the i-th control; Δ represents the weight matrix of the system state vector ξ; Θ represents the weight matrix of the input vector μ.

[0060] On the other hand, the present invention also provides an integrated system of synaesthesia, computing and control for the industrial Internet of Things, the system comprising:

[0061] The perception layer is composed of sensors in multiple work areas, which are used to collect environmental data and production status data and transmit the data to the controller layer through wireless communication modules;

[0062] A controller layer, comprising a plurality of controllers, configured to implement the steps of any of the methods mentioned above;

[0063] The cloud layer includes a plurality of cloud servers, which are used to receive and process the control tasks offloaded by the controller layer and return the processing results to the controller layer;

[0064] Communication module, the perception layer and the controller layer are connected via wireless communication; the controller layer and the cloud layer are connected via wired communication;

[0065] A closed-loop control module is used to optimize the sensor parameters and the unloading decision according to the adjustment effect.

[0066] In some embodiments of the present invention, the controller layer also includes a resource orchestration module for integrating the sensor's perception resources, system communication resources, computing resources and intelligent resources of the controller and the cloud server through software-defined networking and virtualization technology to build a virtualized resource pool.

[0067] On the other hand, the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of any of the methods mentioned above when executed by a processor.

[0068] The present invention provides an integrated method and system for synergistic computing and control for the industrial Internet of Things, comprising: a controller dispatching sensors within a work area to collect environmental data and production status data; receiving and aggregating the data collected by each sensor to generate a control task; evaluating the resource status of the local and cloud resources, and calculating the total delay of the control task processing locally and in the cloud respectively; calculating the control cost based on optimal control theory; generating an offloading decision for the control task based on the total delay and control cost; assigning the control task to the local or cloud for processing based on the offloading decision, and generating a processing result; generating a control instruction based on the processing result, and sending it to the corresponding production equipment and sensors for adjustment, optimizing sensor parameters and offloading decisions, thereby forming a control closed loop. The method provided by the present invention realizes the reasonable scheduling and arrangement of diverse heterogeneous resources, making full use of the computing, storage, and communication resources of each device in the system. At the same time, the closed-loop control mechanism improves the consistency and adaptability of the system, can respond to user needs more quickly, and make timely adaptive adjustments to environmental changes in the work area, ensuring optimal control across local, regional, and global boundaries.

[0069] Furthermore, the integrated system of tele-sensing, computing and control for the Industrial Internet of Things provided by the present invention utilizes multi-mode communication to achieve interconnection and seamless connection between devices at various levels and within levels, and realizes ultra-high reliability and low-latency communication functions to ensure the real-time and stability of communication, thereby quickly and accurately responding to control instructions; it utilizes software-defined networking and network virtualization technologies to build a virtualized resource pool, supports the integration and orchestration of heterogeneous resources, and improves the production performance of the Industrial Internet of Things.

[0070] Additional advantages, objects, and features of the present invention will be set forth in part in the following description and will become apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the description and drawings.

[0071] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. In the drawings:

[0073] Figure 1 The figure is a schematic diagram of the steps of an integrated method of synergy, computing and control for the industrial Internet of Things in one embodiment of the present invention.

[0074] Figure 2This is a diagram of the architecture of an integrated system for industrial Internet of Things (IIoT) based on telepathy, computing, and control in one embodiment of the present invention.

[0075] Figure 3 This is a functional architecture diagram of the integrated sensing, computing and control system for the Industrial Internet of Things in one embodiment of the present invention.

[0076] Figure 4 The figure is a flow chart of an integrated method of synergy, computing and control for the industrial Internet of Things in one embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0078] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0079] It should be emphasized that the term "include / comprises" when used herein refers to the existence of features, elements, steps or components, but does not exclude the existence or addition of one or more other features, elements, steps or components.

[0080] It should also be noted that, unless otherwise specified, the term "connection" herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0081] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0082] It should be emphasized here that the step marks mentioned below do not limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0083] In order to solve the problems of poor real-time performance, poor stability, low resource utilization, slow response speed and insufficient computing resources in the existing industrial Internet of Things system, the present invention provides an integrated method of communication, computing and control for the industrial Internet of Things, wherein the integrated method of communication, computing and control refers to a processing method that integrates communication, perception, computing and control, such as Figure 1 As shown, the method includes the following steps S101 to S104:

[0084] Step S101: dispatching sensors in the work area to collect environmental data and production status data.

[0085] Step S102: Receive and aggregate the data collected by each sensor to generate a control task; evaluate the resource status of the local and cloud resources, and calculate the total delay of the control task processing locally and in the cloud respectively; based on optimal control theory, construct a cost function to calculate the control cost; and generate an offloading decision for the control task based on the total delay and control cost.

[0086] Step S103: Based on the offloading decision, the control task is assigned to the local or cloud for processing, and a processing result is generated.

[0087] Step S104: Generate control instructions based on the processing results and send them to the corresponding production equipment and sensors for adjustment, optimizing sensor parameters and unloading decisions.

[0088] Corresponding to the integrated synergy computing and control method for industrial Internet of Things, the present invention also provides an integrated synergy computing and control system for industrial Internet of Things, such as Figure 2 As shown, the system mainly includes a perception layer, a controller layer and a cloud layer, and is equipped with a communication module and a closed-loop control module.

[0089] The perception layer consists of multiple work areas. Because each work area performs different production tasks and operations, it is equipped with a variety of differentiated sensors, such as temperature sensors, humidity sensors, and light sensors. These sensors are responsible for collecting environmental information and production status within the work area and transmitting it to the controller layer for processing.

[0090] The controller layer consists of multiple controllers, each with a certain amount of computing resources and a deployed artificial intelligence (AI) model of a certain precision to process sensor data. However, given the limited local computing and storage resources of the controllers, the AI ​​models deployed on the controllers are compressed, which in turn reduces the accuracy of the model's reasoning and control task processing. The controllers are responsible for receiving and aggregating data collected by various sensors to generate control tasks; evaluating the status of local and cloud resources to make decisions about offloading control tasks; processing control tasks locally or offloading them to the cloud; and generating control instructions and sending them to the perception layer.

[0091] In some embodiments, the controller layer also includes a resource orchestration module for integrating the sensor's perception resources, the communication resources within the system, the computing resources and intelligent resources of the controller and cloud server through software-defined networking and virtualization technology to build a virtualized resource pool.

[0092] The cloud layer consists of multiple cloud servers. Compared to the processing power of the controller, cloud servers have more powerful computing and storage resources. Therefore, more accurate AI models can be deployed on cloud servers to handle more complex intelligent control tasks. The cloud servers are responsible for receiving and processing control tasks offloaded from the controller layer and returning the processing results to the controller layer.

[0093] To facilitate communication between layers, the system also features a communication module. Specifically, wireless communication connects the perception layer and the controller layer, minimizing geographical constraints on device deployment and accommodating the simultaneous transmission and access of numerous sensors, as well as the mobility requirements of some sensors. Wired communication connects the controller layer and the cloud layer, ensuring both the large data volumes required for control tasks and high-speed communication quality.

[0094] In order to realize the dynamic feedback optimization mechanism, the system also has a closed-loop control module to optimize sensor parameters and unloading decisions based on the adjustment effect.

[0095] In the above, from the perspective of physical deployment, the integrated system architecture of synergy computing and control for the Industrial Internet of Things was proposed, and the actual composition of hardware / software components was clarified. Figure 3 As shown in the figure, from the perspective of logical functions, an integrated functional architecture of synergy, computing and control for the Industrial Internet of Things is proposed, describing the responsibilities and collaborative relationships of each level.

[0096] The integrated functional architecture of Tongsen computing and control for the Industrial Internet of Things includes the infrastructure layer, computing and control layer, application and service layer, and orchestration and operation and maintenance layer. The various layers are interdependent and coordinated to promote the realization of a closed-loop system control mechanism.

[0097] Infrastructure layer (corresponding to the perception layer and communication module):

[0098] The infrastructure layer uses Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies to integrate the perception resources of sensors, the computing resources and intelligent resources of controllers and cloud servers, and the communication resources within the system to build a virtualized resource pool, that is, a multi-dimensional heterogeneous fusion pool of resources. This makes resource management more flexible, efficient, and scalable, and provides a basis for the precise and dynamic scheduling of resources within the system.

[0099] Computing and control layer (corresponding to the controller layer):

[0100] The computing and control layer integrates computing (data processing based on local AI models) and control (command generation) functions to achieve intelligent management of the Industrial Internet of Things. By processing sensor data and generating precise control commands, it ensures the continuity and reliability of the entire system, enables real-time decision-making and adaptive response, and improves the efficiency and stability of industrial processes. The control function is the core of the Industrial Internet of Things and is executed by the controller. It includes optimal control and full-process industrial optimization, implements feedback-triggered control mechanisms, facilitates dynamic adjustment of system manufacturing and operation, and ensures the system's adaptability and efficiency under changing conditions.

[0101] Service and application layer (corresponding to the cloud layer):

[0102] The service and application layer consists of two parts: service management and application support. Service management utilizes technologies such as the Service Function Chain (SFC) and multi-precision AI models to build an industrial service function chain, enabling full lifecycle management of services within the system. It integrates AI technology to drive industrial services, improving service scalability, adaptability, and efficiency, and supporting diverse industrial services and dynamic demands. As mentioned above, compared to controllers, cloud servers are more powerful computing units, possessing abundant resources and more complete, uncompressed AI models. Through parallel computing, they can more efficiently handle complex, data-intensive intelligent control tasks.

[0103] Orchestration and operation and maintenance layer (corresponding to the closed-loop control module and resource orchestration module):

[0104] Based on the deep integration of perception, communication, and computing, integrated intelligent multi-dimensional resource orchestration and intelligent operation are achieved, forming a closed-loop control. Resource orchestration integrates and manages heterogeneous, multi-dimensional resources through real-time status monitoring. When controller load is excessive, this layer enables collaborative computing between the controller and cloud servers, offloading compute on demand to prevent control errors from impacting system security. The orchestration layer plays a vital role in ensuring efficient resource scheduling and rapid response to emergencies, and is key to ensuring system reliability. Intelligent operations and maintenance, based on resource orchestration, leverages operations and maintenance technologies such as containers, AIOps, and serverless computing to achieve real-time system status awareness and data-driven operations and maintenance. The Industrial Internet of Things combines human-machine interaction with ultra-reliable and low-latency communication (URLLC) and high-performance intelligent computing to significantly improve production efficiency, optimize resource allocation, reduce production costs, and promote more intelligent and efficient industrial operations. Closed-loop control enables global control and end-to-end management of production lines and systems, while ensuring real-time correction of system deviations. It uses feedback principles to automate system behavior and refine operations, enhancing system robustness and ensuring stable and efficient industrial processes.

[0105] The integrated functional architecture of the synergistic computing and control system for the Industrial Internet of Things (IIoT) is closely interconnected across all layers. The application and service layer provides highly available industrial services and relies on the optimized scheduling and task coordination of the computing and control layer to achieve intelligent and automated functions. The computing and control layer, through functions such as feedback regulation and load balancing, leverages the perception, communication, computing, and intelligent resources provided by the infrastructure layer to ensure the smooth execution of computing tasks. The infrastructure layer builds a heterogeneous resource pool to support the tasks of the computing and control layer. The orchestration and operations layer plays a key role in this framework. Through functions such as resource scheduling and status monitoring, it coordinates and optimizes all layers to ensure overall system stability and efficient operation. These layers form a closed loop, from resource support to service implementation to system optimization. Their interdependence promotes the coordinated and efficient operation of the entire system.

[0106] Based on the above analysis and description of the integrated system architecture and functional architecture of synergy computing and control for the industrial Internet of Things, the integrated method of synergy computing and control for the industrial Internet of Things is further explained.

[0107] like Figure 4 The figure shows a flow chart of the integrated method of synaesthesia, computing and control for the industrial Internet of Things.

[0108] In step S101 , the controller schedules sensors in the work area to collect environmental data and production status data.

[0109] In some embodiments, the total amount of data collected by the sensor is calculated as shown in formula (1):

[0110]

[0111] in, It represents the amount of data collected by the sensor k dispatched by the controller n in the work area for the control subtask i of the control task m; α represents the sensor sampling rate; and t represents the sampling time.

[0112] In the present invention, n represents a controller, k represents a sensor, m represents a control task, and i represents a subtask of the control task m, which will not be described in detail in the following text.

[0113] In step S102, the controller receives and aggregates the data collected by each sensor to generate a control task. It then evaluates the resource status of the local and cloud resources, calculates the total latency of the control task processing locally and in the cloud, constructs a comprehensive cost function based on the total latency, and generates a decision to offload the control task by minimizing the comprehensive cost function.

[0114] In some embodiments, if the control task is processed locally, the total delay should include data aggregation delay and local calculation delay, where the data aggregation delay includes data collection delay and wireless transmission delay.

[0115] Data acquisition delay is the sampling time for the sensor to complete data acquisition.

[0116] Wireless transmission delay is the time it takes for the sensor to transmit the collected data to the local controller via the wireless network. Specifically:

[0117] Assuming that the wireless transmission path loss between the sensor and the controller only considers the free space path loss, the wireless transmission path loss calculation formula is shown in formula (2):

[0118]

[0119] Among them, d n,m,k Indicates the transmission distance between the sensor and the controller; f n,m,k represents the signal transmission frequency; c represents the speed of light.

[0120] The signal receiving power is calculated based on the wireless transmission path loss, as shown in formula (3):

[0121]

[0122] in, Indicates the sensor transmission power; ε tra represents the transmission gain; ε rec Indicates the receive gain.

[0123] Based on the Shannon formula, the transmission rate from the sensor to the controller is calculated as shown in formula (4):

[0124]

[0125] Among them, B n,m,k Indicates transmission bandwidth; represents the signal receiving power; N0 represents the power spectral density of Gaussian white noise.

[0126] Therefore, the calculation formula of wireless transmission delay satisfies formula (5):

[0127]

[0128] in, Indicates the amount of data transmitted wirelessly; Indicates the transmission rate from the sensor to the controller.

[0129] The controller receives the data collected by each sensor and aggregates the data. The calculation formula for the data aggregation delay is shown in formula (6):

[0130]

[0131] Among them, max(·) means taking the maximum value; Indicates the sensor sampling time; Indicates the wireless transmission delay from the sensor to the controller.

[0132] The local computing delay is the time it takes for the controller to process the received data (such as artificial intelligence model reasoning, control logic operations, etc.). The calculation formula for the local computing delay satisfies formula (7):

[0133]

[0134] in, Indicates the amount of data; Indicates the local calculation rate.

[0135] Therefore, if the control task is processed locally, the total delay satisfies formula (8):

[0136]

[0137] in, Indicates data aggregation delay; Indicates the local computing latency.

[0138] In some embodiments, if the control task is processed in the cloud, the total latency should include data aggregation latency, wired transmission latency, and cloud computing latency.

[0139] The calculation method of data aggregation delay can refer to formula (6).

[0140] Wired transmission latency is the time it takes for the controller to transmit the control task to the cloud. It should be noted that since the amount of data after task processing is relatively small, the transmission latency from the cloud server to the controller is negligible.

[0141] The calculation formula for the transmission delay from the controller to the cloud is shown in formula (9):

[0142]

[0143] in, Indicates the amount of data; v n Indicates the transmission rate from the controller to the cloud.

[0144] Cloud computing latency is the time it takes for a cloud server to process data (such as high-precision AI model analysis). The calculation formula for cloud computing latency satisfies formula (10):

[0145]

[0146] in, Indicates the amount of data; Indicates the cloud computing rate, and the cloud computing rate is greater than the local computing rate, that is,

[0147] Therefore, the control task is processed in the cloud, and the total delay satisfies formula (11):

[0148]

[0149] in, Indicates data aggregation delay; Indicates the transmission delay from the controller to the cloud; Indicates the cloud computing latency.

[0150] In some embodiments, a binary variable is introduced To indicate whether the control task is processed locally or offloaded to the cloud. Indicates that the control task is processed locally. Indicates that the control task is offloaded to the cloud for processing. Then the original local calculation delay formula (7) and the original cloud calculation delay formula (10) can be integrated and modified into formula (12):

[0151]

[0152] In formula (12), Part of it represents the local computing delay, The part represents the cloud computing delay. By making corresponding assignments, we can obtain the computing delay of the control task locally or in the cloud.

[0153] Similarly, the original controller-to-cloud transmission delay formula (9) can be modified to formula (13):

[0154]

[0155] That is, only when When the control task is offloaded to the cloud for processing, there is a transmission delay from the controller to the cloud.

[0156] Similarly, the original local calculation total delay formula (8) and the original cloud calculation total delay formula (11) can be integrated and modified into formula (14):

[0157]

[0158] In some embodiments, based on optimal control theory, a cost function is constructed to calculate the control cost, and the cost function is shown in formula (15):

[0159]

[0160] Where E[·] represents the mathematical expectation; T represents transpose; i Represents the system state vector after the i-th control; I n,m represents the total number of times controller n controls control task m; μ i Represents the input vector of the i-th control; Δ represents the weight matrix of the system state vector ξ; Θ represents the weight matrix of the input vector μ.

[0161] By evaluating the total delay and control cost J, it is determined whether the control task needs to be offloaded to the cloud and processed using more powerful computing resources and intelligent resources to minimize the total delay and control cost of completing the control operation.

[0162] In step S103, based on the offload decision generated in step S102, the control task is assigned to either local or cloud processing. If the task is processed locally, the controller directly processes the task and obtains the processing result. If the task is processed in the cloud, the controller offloads the task to the cloud, which processes the task and returns the processing result.

[0163] In step S104, control instructions are generated based on the processing results obtained by the controller or cloud. The control instructions are sent to the corresponding production equipment and sensors at the bottom layer, which make adjustments based on the control instructions, such as adjusting sensor parameters (sampling rate, transmission power, etc.) and offloading decisions, optimizing the equipment generation process, and forming a closed control loop.

[0164] Corresponding to the above method, the present invention also provides an electronic device, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described above.

[0165] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the art.

[0166] It should be understood by those skilled in the art that the various exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0167] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0168] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or replace features of other embodiments.

[0169] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations to the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for integrating synaesthesia, calculation and control for the industrial Internet of Things, characterized by: The method comprises the following steps: Dispatching sensors in the work area to collect data; the data includes environmental data and production status data; Receive and aggregate data collected by each sensor to generate a control task; evaluate local and cloud resource status and calculate the total processing delay of the control task locally and in the cloud respectively; construct a cost function based on optimal control theory to calculate the control cost; and generate an offloading decision for the control task based on the total delay and the control cost; According to the offloading decision, the control task is assigned to a local or cloud for processing, and the processing result is obtained; A control instruction is generated according to the processing result, and the control instruction is sent to the corresponding production equipment and sensors for adjustment, thereby optimizing the sensor parameters and the unloading decision.

2. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 1 is characterized in that: The amount of environmental data and production status data collected by the sensor satisfies the following calculation formula: in, It represents the amount of data of control subtask i of control task m collected by sensor k dispatched by controller n in the work area; α represents the sensor sampling rate; t represents the sampling time.

3. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 1 is characterized in that: Calculating the total delay of the control task in local processing includes: Receive and aggregate the data collected by each sensor to generate data aggregation delay, which is calculated as: Among them, max(·) means taking the maximum value; represents the sampling time of the control subtask i of the control task m by the controller n scheduling sensor k in the work area; Represents the wireless transmission delay from sensor to controller; The control task is processed to generate a local computing delay, which is calculated as follows: in, represents the amount of data collected for control subtask i of control task m in work area n; Indicates the local calculation rate; The data aggregation delay and the local calculation delay are added together to obtain the local total delay, which is calculated as follows: in, represents the data aggregation delay; Indicates the local calculation delay.

4. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 3 is characterized in that: The steps of calculating the wireless transmission delay from the sensor to the controller include: The data collected by each sensor is received through wireless transmission, resulting in wireless transmission path loss, which is calculated as follows: Among them, d n,m,k represents the transmission distance between the sensor k and the controller n that collects the data of the control task m; f n,m,k represents the signal transmission frequency; c represents the speed of light; The signal receiving power is calculated based on the wireless transmission path loss, and the calculation formula is: in, Indicates the sensor transmission power; ε tra represents the transmission gain; ε rec Indicates receiving gain; Based on the Shannon formula, the transmission rate from the sensor to the controller is calculated as follows: Among them, B n,m,k Indicates transmission bandwidth; represents the signal receiving power; N0 represents the Gaussian white noise power spectral density; The wireless transmission delay from the sensor to the controller is calculated according to the transmission rate from the sensor to the controller, and the calculation formula is: in, Indicates the amount of data transmitted wirelessly; Indicates the transmission rate from the sensor to the controller.

5. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 1 is characterized in that: Calculating the total latency of processing the control task in the cloud includes: Receive and aggregate the data collected by each sensor to generate data aggregation delay, which is calculated as: Among them, max(·) means taking the maximum value; represents the sampling time of sensor k in work area n for control subtask i of control task m; Represents the wireless transmission delay from sensor to controller; The control task is transmitted to the cloud and processed by the cloud, resulting in a transmission delay from the controller to the cloud and a cloud computing delay; The calculation formula for the transmission delay from the controller to the cloud is: The calculation formula for the cloud computing delay is: in, represents the amount of data collected for control subtask i of control task m in work area n; v n Indicates the transmission rate from the controller to the cloud; Indicates the cloud computing rate; The data aggregation delay, the transmission delay from the controller to the cloud, and the cloud computing delay are added together to obtain the total cloud latency, which is calculated as follows: in, represents the data aggregation delay; represents the transmission delay from the controller to the cloud; Indicates the cloud computing latency.

6. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 1 is characterized in that: The method further comprises: A binary variable is introduced to represent whether the control task is processed locally or offloaded to the cloud for processing; the calculation formula for the processing delay generated by the control task being processed locally or offloaded to the cloud for processing is: in, represents the binary variable, when Indicates that the control task is processed locally. Indicates that the control task is offloaded to the cloud for processing; represents the amount of data collected for control subtask i of control task m in work area n; Indicates the local calculation rate; Indicates the cloud computing rate.

7. The integrated method of synaesthesia, calculation and control for industrial Internet of Things according to claim 1, characterized in that: Based on optimal control theory, a cost function is constructed to calculate the control cost, including: The cost function satisfies the following formula: Where E[·] represents the mathematical expectation; T represents transpose; i Represents the system state vector after the i-th control; I n,m represents the total number of times controller n controls the control task m; μ i represents the input vector of the i-th control; Δ represents the weight matrix of the system state vector ξ; Θ represents the weight matrix of the input vector μ.

8. An integrated system of synaesthesia, calculation and control for industrial Internet of Things, characterized by: The system comprises: The perception layer is composed of sensors in multiple work areas, which are used to collect environmental data and production status data and transmit the data to the controller layer through wireless communication modules; A controller layer, comprising a plurality of controllers, configured to implement the steps of the method according to any one of claims 1 to 7; The cloud layer includes a plurality of cloud servers, which are used to receive and process the control tasks offloaded by the controller layer and return the processing results to the controller layer; Communication module, the perception layer and the controller layer are connected via wireless communication; the controller layer and the cloud layer are connected via wired communication; A closed-loop control module is used to optimize the sensor parameters and the unloading decision according to the adjustment effect.

9. The integrated system of synaesthesia, calculation and control for industrial Internet of Things according to claim 8, characterized in that: The controller layer also includes a resource orchestration module for integrating the sensor's perception resources, system communication resources, computing resources and intelligent resources of the controller and the cloud server through software-defined networking and virtualization technology to build a virtualized resource pool.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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