Intelligent distributed control system based on 5G Internet of Things

Abstract

The invention relates to the technical field of distributed control systems, in particular to an intelligent distributed control system based on the 5G Internet of Things. Comprising a central control unit used for establishing a cloud interaction platform and establishing a plurality of virtual machines according to parameters of to-be-controlled equipment; the data acquisition unit comprises a plurality of acquisition sub-modules; the acquisition sub-modules are arranged on each piece of to-be-controlled equipment and are used for acquiring operation data of each piece of to-be-controlled equipment; and the interaction unit comprises a plurality of interaction sub-modules and builds a cloud interaction platform based on the 5G technology. Each to-be-controlled device performs data interaction with the central control unit through the cloud, so that the labor cost and the risk of misoperation are reduced, a traditional thermal control wiring mode is abandoned, the overall building cost is reduced, and the operation state of each to-be-controlled device is remotely and wirelessly controlled by using a cloud platform. And meanwhile, by dynamically adjusting the interaction frequency of each piece of to-be-controlled equipment, the pressure of network resources is reduced, and the control efficiency of each piece of to-be-controlled equipment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of distributed control systems, and in particular to an intelligent distributed control system based on 5G Internet of Things. Background Art

[0002] At present, the traditional distributed control system (DCS) adopts a centralized structure, which is mainly composed of a central controller, input and output modules, communication network, etc. Each field device communicates with the central controller through analog signals or digital signals.

[0003] As thermal control systems become more and more advanced, the increase in measurement points makes cable layout and wiring work particularly important. Control wiring has always been the work with the largest workload and the most repetitive labor. Analog signal communication is slow, low in accuracy and easily interfered, and the wiring is complex. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides an intelligent distributed control system based on 5G Internet of Things, aiming to reduce the construction and operation costs of the distributed control system and improve overall production efficiency.

[0005] In some embodiments of the present application, a cloud interaction platform is built based on 5G technology. Each device to be controlled exchanges data with the central control unit through the cloud, reducing labor costs and the risk of operational errors, abandoning the traditional thermal control wiring mode, thereby reducing the overall construction cost, using the cloud platform to remotely and wirelessly control the operating status of each device to be controlled, and improving the overall operating efficiency.

[0006] In some embodiments of the present application, by establishing a virtual machine for each device to be controlled, a short-term prediction is made on the operating status of each device to be controlled, and the control parameters are dynamically corrected according to the prediction results. At the same time, by dynamically adjusting the interaction frequency of each device to be controlled, the pressure on network resources is reduced, and the control efficiency of each device to be controlled is improved, thereby improving the overall operating efficiency of the system.

[0007] In some embodiments of the present application, an intelligent distributed control system based on 5G Internet of Things is provided, including: The central control unit is used to establish a cloud interaction platform and create multiple virtual machines according to the parameters of the equipment to be controlled; A data acquisition unit, including a plurality of acquisition submodules; The acquisition submodule is arranged on each device to be controlled, and the acquisition submodule is used to collect the operation data of each device to be controlled; An interaction unit, including a plurality of interaction submodules; The interaction submodule is arranged on each device to be controlled, and the interaction submodule is used to perform data interaction with the cloud control platform; The central control unit comprises: The first processing module is used to establish a sequence A of devices to be controlled, A=(a1, a2...ai...an), where ai is the i-th device to be controlled; and n is the number of devices to be controlled.

[0008] In some embodiments of the present application, the central control unit further includes: The second processing module is used to establish a virtual machine for each device to be controlled, and establish a virtual machine sequence B, B=(b1, b2...bi...bn), where bi is the virtual machine of the i-th device to be controlled; n is the number of devices to be controlled; The second processing module is also used to establish a virtual machine-interaction submodule mapping table; A third processing module, used to set working parameters of each interaction submodule, and the third processing module is also used to set control strategies for each virtual machine; The fourth processing module is used to generate an operation deviation value of each virtual machine, and determine whether to modify the control strategy of each virtual machine according to all the operation deviation values.

[0009] In some embodiments of the present application, the second processing module is further used to: According to the sequence A of devices to be controlled, set ai as the target devices to be controlled in sequence; Obtain historical operating parameters of the target device to be controlled; Generate a simulation sub-model of the target device to be controlled according to historical operating parameters; Set multiple simulation cycles of the target device to be controlled; Establish a virtual machine of the target device to be controlled according to all simulation cycles and simulation sub-models; Set up the virtual machines of the devices to be controlled one by one.

[0010] In some embodiments of the present application, the third processing module is further used to: According to the virtual machine-interaction submodule mapping table, establish the interaction submodule sequence P, P = (p1, p2...pi...pn), where pi is the interaction submodule corresponding to the i-th virtual machine; n is the number of devices to be controlled; Sequentially set the i-th interaction submodule as the target interaction submodule; Set the virtual machine of the to-be-controlled device corresponding to the target interaction submodule as the to-be-controlled virtual machine; Generate the device evaluation value c of the target interaction submodule; According to the device evaluation value c, the interaction cycle duration t of the target interaction submodule is set; According to the duration of the interaction cycle, multiple interaction time nodes are set within a single simulation cycle of the virtual machine to be controlled; The cloud interaction platform obtains the real-time operation data of the to-be-controlled device corresponding to the target interaction submodule according to the interaction time node; Set the working parameters of each interactive sub-module in turn.

[0011] In some embodiments of the present application, when generating a device evaluation value c, it includes: c=e1*Q1*[ (µi*ji)]+e2*Q2*[ (βi*pi)]; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of equipment evaluation indicators; µi is the influencing factor of the i-th equipment evaluation indicator; ji is the reference value of the i-th equipment evaluation indicator in the equipment to be controlled corresponding to the target interaction submodule; is the number of model evaluation indicators; βi is the influencing factor of the i-th model evaluation indicator; pi is the reference value of the i-th model evaluation indicator in the virtual machine to be controlled.

[0012] In some embodiments of the present application, setting the interaction cycle duration t of the target interaction submodule includes: Preset a first device evaluation value interval (c1, c2), a second device evaluation value interval (c2, c3) and a third device evaluation value interval (c3, c4); If the device evaluation value c is within the preset first device evaluation value interval, the interaction cycle duration t is set to the preset first interaction cycle duration t1; If the device evaluation value c is within the preset second device evaluation value interval, the interaction cycle duration t is set to the preset second interaction cycle duration t2; If the device evaluation value c is within the preset third device evaluation value interval, the interaction cycle duration t is set to the preset third interaction cycle duration t3; and t1 <t2<t3。

[0013] In some embodiments of the present application, the third processing module is further used to: According to the virtual machine sequence B, the i-th virtual machine is sequentially set as the target virtual machine; Generate multiple control time nodes according to the simulation cycle length of the target virtual machine to be controlled; Obtaining a feedback data packet of the device to be controlled corresponding to the target virtual machine at the current control time node; Generate expected operating parameters of the target virtual machine in the current simulation cycle according to the feedback data packet; Generate the first-level control strategy of the target virtual machine in the current simulation cycle according to the expected operating parameters.

[0014] In some embodiments of the present application, the fourth processing module is further configured to: Set the i-th virtual machine as the virtual machine to be monitored in sequence according to the virtual machine sequence B; Obtain the monitoring data packet of the virtual machine to be monitored at the current interaction time node; Generate the operation deviation value f of the virtual machine to be monitored at the current interaction time node according to the monitoring data packet; Judge whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node according to the operation deviation value f.

[0015] In some embodiments of the present application, generating the operation deviation value f includes: f = e3 * Q3 * ci * (di - d'i) 2 + e4 * Q4 * U; Wherein, e3 is the third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; m1 is the number of operation indexes of the virtual machine to be monitored; ci is the influence factor of the i-th operation index of the virtual machine to be monitored; di is the real-time reference value of the i-th operation index of the virtual machine to be monitored at the current interaction time node; d'i is the expected reference value of the i-th operation index of the virtual machine to be monitored within the corresponding simulation cycle at the current interaction time node; U is the historical reference value.

[0016] In some embodiments of the present application, judging whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node includes: Preset the first operation deviation value threshold F1 and the second operation deviation value threshold F2, and F1 < F2; If f < F1, no correction instruction is generated at the current interaction time node; If F1 < f < F2, a first-level correction instruction is generated at the current interaction time node; If f > F2, a first-level warning instruction is generated at the current interaction time node.

[0017] Compared with the prior art, the beneficial effects of an intelligent distributed control system based on 5G Internet of Things in the embodiments of the present application are as follows: Build a cloud interaction platform based on 5G technology. Each device to be controlled conducts data interaction with the central control unit through the cloud, reducing the labor cost and the risk of operation errors, abandoning the traditional thermal control wiring mode, thereby reducing the overall construction cost, and using the cloud platform to remotely and wirelessly control the operation status of each device to be controlled, improving the overall operation efficiency.

[0018] By establishing a virtual machine for each device to be controlled, a short-term prediction of the operating status of each device to be controlled is made, and the control parameters are dynamically corrected according to the prediction results. At the same time, by dynamically adjusting the interaction frequency of each device to be controlled, the pressure on network resources is reduced, and the control efficiency of each device to be controlled is improved, thereby improving the overall operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural diagram of an intelligent distributed control system based on 5G Internet of Things in the preferred embodiment of the embodiment of the present application. DETAILED DESCRIPTION

[0020] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0021] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0024] like Figure 1 As shown, an intelligent distributed control system based on 5G Internet of Things in a preferred embodiment of the present application includes: The central control unit is used to establish a cloud interaction platform and create multiple virtual machines according to the parameters of the equipment to be controlled; A data acquisition unit, including a plurality of acquisition submodules; The acquisition submodule is arranged on each device to be controlled, and is used to collect the operation data of each device to be controlled; An interaction unit, including a plurality of interaction submodules; The interaction submodule is set on each device to be controlled, and the interaction submodule is used to interact with the cloud control platform for data; The central control unit includes: The first processing module is used to establish a sequence A of devices to be controlled, A=(a1, a2…ai…an), where ai is the i-th device to be controlled; n is the number of devices to be controlled; Specifically, the central control unit also includes: The second processing module is used to establish a virtual machine for each device to be controlled, and establish a virtual machine sequence B, B=(b1, b2...bi...bn), where bi is the virtual machine of the i-th device to be controlled; n is the number of devices to be controlled; The second processing module is also used to establish a virtual machine-interaction submodule mapping table; A third processing module is used to set working parameters of each interactive submodule, and the third processing module is also used to set control strategies for each virtual machine; The fourth processing module is used to generate an operation deviation value of each virtual machine, and determine whether to modify the control strategy of each virtual machine according to all the operation deviation values.

[0025] Specifically, the interaction sub-module is arranged on each device to be controlled, and the interaction sub-module is preferably a 5G communication device, which can obtain control instructions issued by the cloud interaction platform, and can also transmit the operating parameters of the device to be controlled to the cloud interaction platform, thereby realizing precise control of the operating status of each device to be controlled.

[0026] Specifically, the acquisition submodule is set in each device to be controlled. The acquisition submodule is preferably various sensors. The acquisition submodule is used to collect the operating parameters of each device to be controlled, and build a virtual machine of each device to be controlled on the cloud interactive platform through all the operating parameters.

[0027] Specifically, the second processing module is further used for: According to the sequence A of devices to be controlled, set ai as the target devices to be controlled in sequence; Obtain historical operating parameters of the target device to be controlled; Generate a simulation sub-model of the target device to be controlled according to historical operating parameters; Set multiple simulation cycles of the target device to be controlled; Establish a virtual machine of the target device to be controlled according to all simulation cycles and simulation sub-models; Set up the virtual machines of the devices to be controlled one by one.

[0028] Specifically, the operating parameters of the controlled device in the current simulation cycle are predicted and simulated through the simulation sub-model and historical operating parameters, and the control strategy in the current simulation cycle is set according to the prediction results.

[0029] Specifically, the duration of the simulation cycle of each virtual machine can be set according to the credibility of the corresponding simulation sub-model. The higher the credibility, the longer the corresponding simulation cycle, thereby avoiding frequent adjustment of the control parameters of the device to be controlled.

[0030] Specifically, by establishing a virtual machine for each device to be controlled, the operating status of each device to be controlled is simulated in real time on the cloud interactive platform, thereby improving the control efficiency of each device to be controlled and realizing remote monitoring and operation and maintenance of each device to be controlled.

[0031] It is understandable that in the above embodiments, a cloud interaction platform is built based on 5G technology. Each device to be controlled exchanges data with the central control unit through the cloud, reducing labor costs and the risk of operational errors, abandoning the traditional thermal control wiring mode, thereby reducing the overall construction cost, using the cloud platform to remotely and wirelessly control the operating status of each device to be controlled, and improving the overall operating efficiency.

[0032] In a preferred embodiment of the present application, the third processing module is also used for: According to the virtual machine-interaction submodule mapping table, establish the interaction submodule sequence P, P = (p1, p2...pi...pn), where pi is the interaction submodule corresponding to the i-th virtual machine; n is the number of devices to be controlled; Sequentially set the i-th interaction submodule as the target interaction submodule; Set the virtual machine of the to-be-controlled device corresponding to the target interaction submodule as the to-be-controlled virtual machine; Generate the device evaluation value c of the target interaction submodule; According to the device evaluation value c, the interaction cycle duration t of the target interaction submodule is set; According to the duration of the interaction cycle, multiple interaction time nodes are set within a single simulation cycle of the virtual machine to be controlled; The cloud interaction platform obtains the real-time operation data of the to-be-controlled device corresponding to the target interaction submodule according to the interaction time node; Set the working parameters of each interactive sub-module in turn.

[0033] Specifically, the virtual machine obtains the operating data of the device to be controlled according to the interaction time node, and the operating data refers to the actual operating parameters of the device to be controlled collected within the time interval between the previous interaction time node and the current interaction time node.

[0034] Specifically, a comprehensive analysis is performed based on the actual operating parameters sent back and the operating parameters in the predicted results to determine whether there are deviations and potential failures in the operating status of the equipment to be controlled, and the control parameters are corrected in time to improve the control efficiency of each equipment to be controlled.

[0035] Specifically, the interaction cycle duration refers to the time interval between two interaction time nodes, and the interaction time node refers to the time node when the interaction module on the device to be controlled returns the operating parameters of the device to be controlled.

[0036] Specifically, when generating the equipment evaluation value c, it includes: c=e1*Q1*[ (µi*ji)]+e2*Q2*[ (βi*pi)]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of equipment evaluation indicators; µi is the influencing factor of the i-th equipment evaluation indicator; ji is the reference value of the i-th equipment evaluation indicator in the equipment to be controlled corresponding to the target interaction submodule; is the number of model evaluation indicators; βi is the influencing factor of the i-th model evaluation indicator; pi is the reference value of the i-th model evaluation indicator in the virtual machine to be controlled.

[0037] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter is within the same value range.

[0038] Specifically, equipment evaluation indicators include, but are not limited to, equipment failure frequency, equipment operation stability, equipment control difficulty and other parameters.

[0039] Model evaluation indicators include, but are not limited to, the credibility of the simulation model, simulation cycle duration and other parameters. The simulation cycle refers to the longest time that the operating state of the device to be controlled can be predicted based on the current operating parameters.

[0040] Specifically, setting the interaction cycle duration t of the target interaction submodule includes: Preset a first device evaluation value interval (c1, c2), a second device evaluation value interval (c2, c3) and a third device evaluation value interval (c3, c4); If the device evaluation value c is within the preset first device evaluation value interval, the interaction cycle duration t is set to the preset first interaction cycle duration t1; If the device evaluation value c is within the preset second device evaluation value interval, the interaction cycle duration t is set to the preset second interaction cycle duration t2; If the device evaluation value c is within the preset third device evaluation value interval, the interaction cycle duration t is set to the preset third interaction cycle duration t3; and t1 <t2<t3。

[0041] Specifically, the larger the device evaluation value is, the smaller the possibility of operation fluctuations in the current device to be controlled is, and the corresponding interaction cycle duration is longer. By generating a device evaluation value for each device to be controlled, the data interaction frequency between each device to be controlled and the cloud interaction platform is dynamically adjusted, thereby reducing the overall pressure on network resources and ensuring the stability of data interaction.

[0042] It can be understood that in the above embodiments, by dynamically adjusting the interaction frequency of each device to be controlled, the pressure on network resources is reduced, and the control efficiency of each device to be controlled is improved, thereby improving the overall operation efficiency of the system.

[0043] In a preferred embodiment of the present application, the third processing module is also used for: According to the virtual machine sequence B, the i-th virtual machine is sequentially set as the target virtual machine; Generate multiple control time nodes according to the simulation cycle length of the target virtual machine to be controlled; Obtaining a feedback data packet of the device to be controlled corresponding to the target virtual machine at the current control time node; Generate expected operating parameters of the target virtual machine in the current simulation cycle according to the feedback data packet; Generate a first-level control strategy for the target virtual machine in the current simulation cycle based on the expected operating parameters.

[0044] Specifically, by obtaining the predicted results of the operating parameters of the device to be controlled in the current simulation cycle, the control parameters and start / stop status of the device to be controlled are dynamically adjusted to generate the corresponding first-level control strategy, and its specific adjustment logic can be set according to historical parameters.

[0045] Specifically, by establishing a virtual machine for each device to be controlled, a short-term prediction of the operating status of each device to be controlled is made, and the control parameters are dynamically modified according to the prediction results, thereby improving the control efficiency of each device to be controlled and improving the overall operation and maintenance efficiency of the system.

[0046] Specifically, the fourth processing module is also used for: According to the virtual machine sequence B, the i-th virtual machine is sequentially set as the virtual machine to be monitored; Obtaining a monitoring data packet of the virtual machine to be monitored at the current interaction time node; Generate a running deviation value f of the virtual machine to be monitored at the current interaction time node according to the monitoring data packet; Judge whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node according to the operation deviation value f.

[0047] Specifically, generating the operation deviation value f includes: f = e3 * Q3 * ci * (di - d'i) 2 + e4 * Q4 * U; Where, e3 is the third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; m1 is the number of operation indicators of the virtual machine to be monitored; ci is the influence factor of the i-th operation indicator of the virtual machine to be monitored; di is the real-time reference value of the i-th operation indicator of the virtual machine to be monitored at the current interaction time node; d'i is the expected reference value of the i-th operation indicator of the virtual machine to be monitored within the corresponding simulation period at the current interaction time node; U is the historical reference value.

[0048] Specifically, the historical reference value is set according to the sum of the operation deviation values of each interaction sub-node within the current simulation period. The larger the historical reference value, the greater the deviation value between the operation state of the device to be controlled and the expected value.

[0049] Specifically, all parameters in the model are normalized by the preset third fixed coefficient and fourth fixed coefficient, so that each parameter is within the same value range.

[0050] Specifically, the larger the operation deviation value, the lower the prediction accuracy of the simulation sub-model of the current device to be controlled, and the control strategy set for the prediction result cannot meet the efficient operation of the device to be controlled, and it needs to be corrected and adjusted in time.

[0051] Specifically, judging whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node includes: Preset the first operation deviation value threshold F1 and the second operation deviation value threshold F2, and F1 < F2; If f < F1, no correction instruction is generated at the current interaction time node; If F1 < f < F2, a first-level correction instruction is generated at the current interaction time node; If f > F2, a first-level warning instruction is generated at the current interaction time node.

[0052] Specifically, the first operation deviation value threshold and the second operation deviation value threshold can be set according to historical parameters.

[0053] Specifically, the first-level correction instruction refers to correcting the control strategy and simulation sub-model of the current device to be controlled, and the first-level warning instruction means that there may be potential failure risks in the current device to be controlled, and it needs to be repaired and processed in time.

[0054] According to the first concept of this application, a cloud interactive platform is built based on 5G technology. Each device to be controlled exchanges data with the central control unit through the cloud, reducing labor costs and the risk of operational errors, abandoning the traditional thermal control wiring mode, thereby reducing the overall construction cost, using the cloud platform to remotely and wirelessly control the operating status of each device to be controlled, and improving the overall operating efficiency.

[0055] According to the second concept of the present application, by establishing a virtual machine for each device to be controlled, a short-term prediction is made on the operating status of each device to be controlled, and the control parameters are dynamically corrected according to the prediction results. At the same time, by dynamically adjusting the interaction frequency of each device to be controlled, the pressure on network resources is reduced, and the control efficiency of each device to be controlled is improved, thereby improving the overall operating efficiency of the system.

[0056] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent distributed control system based on 5G Internet of Things, characterized in that: include: The central control unit is used to establish a cloud interaction platform and create multiple virtual machines according to the parameters of the equipment to be controlled; A data acquisition unit, including a plurality of acquisition submodules; The acquisition submodule is arranged on each device to be controlled, and the acquisition submodule is used to collect the operation data of each device to be controlled; An interaction unit, including a plurality of interaction submodules; The interaction submodule is arranged on each device to be controlled, and the interaction submodule is used to perform data interaction with the cloud control platform; The central control unit comprises: The first processing module is used to establish a sequence A of devices to be controlled, A=(a1, a2...ai...an), where ai is the i-th device to be controlled; and n is the number of devices to be controlled.

2. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 1, characterized in that: The central control unit also includes: The second processing module is used to establish a virtual machine for each device to be controlled, and establish a virtual machine sequence B, B=(b1, b2...bi...bn), where bi is the virtual machine of the i-th device to be controlled; n is the number of devices to be controlled; The second processing module is also used to establish a virtual machine-interaction submodule mapping table; A third processing module, used to set working parameters of each interaction submodule, and the third processing module is also used to set control strategies for each virtual machine; The fourth processing module is used to generate an operation deviation value of each virtual machine, and determine whether to modify the control strategy of each virtual machine according to all the operation deviation values.

3. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 2, characterized in that: The second processing module is also used for: According to the sequence A of devices to be controlled, set ai as the target devices to be controlled in sequence; Obtain historical operating parameters of the target device to be controlled; Generate a simulation sub-model of the target device to be controlled according to historical operating parameters; Set multiple simulation cycles of the target device to be controlled; Establish a virtual machine of the target device to be controlled according to all simulation cycles and simulation sub-models; Set up the virtual machines of the devices to be controlled one by one.

4. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 3, characterized in that: The third processing module is also used for: According to the virtual machine-interaction submodule mapping table, establish the interaction submodule sequence P, P = (p1, p2...pi...pn), where pi is the interaction submodule corresponding to the i-th virtual machine; n is the number of devices to be controlled; Sequentially set the i-th interaction submodule as the target interaction submodule; Set the virtual machine of the to-be-controlled device corresponding to the target interaction submodule as the to-be-controlled virtual machine; Generate the device evaluation value c of the target interaction submodule; According to the device evaluation value c, the interaction cycle duration t of the target interaction submodule is set; According to the duration of the interaction cycle, multiple interaction time nodes are set within a single simulation cycle of the virtual machine to be controlled; The cloud interaction platform obtains the real-time operation data of the to-be-controlled device corresponding to the target interaction submodule according to the interaction time node; Set the working parameters of each interactive sub-module in turn.

5. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 4, characterized in that: When generating the equipment evaluation value c, it includes: c=e1*Q1*[ (µi*ji)]+e2*Q2*[ (βi*pi)]; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of equipment evaluation indicators; µi is the influencing factor of the i-th equipment evaluation indicator; ji is the reference value of the i-th equipment evaluation indicator in the equipment to be controlled corresponding to the target interaction submodule; is the number of model evaluation indicators; βi is the influencing factor of the i-th model evaluation indicator; pi is the reference value of the i-th model evaluation indicator in the virtual machine to be controlled.

6. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 5, characterized in that: Set the interaction cycle duration t of the target interaction submodule, including: Preset a first device evaluation value interval (c1, c2), a second device evaluation value interval (c2, c3) and a third device evaluation value interval (c3, c4); If the device evaluation value c is within the preset first device evaluation value interval, the interaction cycle duration t is set to the preset first interaction cycle duration t1; When the device evaluation value c is within the preset second device evaluation value range, set the interaction cycle duration t to the preset second interaction cycle duration t2; When the device evaluation value c is within the preset third device evaluation value range, set the interaction cycle duration t to the preset third interaction cycle duration t3; and t1 < t2 < t3.

7. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 4, characterized in that: The third processing module is further configured to: Successively set the i-th virtual machine as the target virtual machine according to the virtual machine sequence B; Generate a plurality of control time nodes according to the simulation cycle duration of the target virtual machine to be controlled; Obtain the feedback data packet of the device to be controlled corresponding to the target virtual machine at the current control time node; Generate the expected operation parameters of the target virtual machine within the current simulation cycle according to the feedback data packet; Generate a first-level control strategy of the target virtual machine within the current simulation cycle according to the expected operation parameters.

8. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 7, characterized in that: The fourth processing module is further configured to: Successively set the i-th virtual machine as the virtual machine to be monitored according to the virtual machine sequence B; Obtain the monitoring data packet of the virtual machine to be monitored at the current interaction time node; Generate the operation deviation value f of the virtual machine to be monitored at the current interaction time node according to the monitoring data packet; Judge whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node according to the operation deviation value f.

9. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 8, characterized in that: Generating the operation deviation value f includes: f=e3*Q3*[ there*(di-d'i) 2 ]+e4*Q4*U; Where e3 is the third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; m1 is the number of operation indicators of the virtual machine to be monitored; ci is the influence factor of the i-th operation indicator of the virtual machine to be monitored; di is the real-time reference value of the i-th operation indicator of the virtual machine to be monitored at the current interaction time node; d'i is the expected reference value of the i-th operation indicator of the virtual machine to be monitored within the simulation cycle corresponding to the current interaction time node; U is the historical reference value.

10. The intelligent distributed control system based on 5G Internet of Things as claimed in claim 9, characterized in that: Judging whether to generate a correction instruction for the virtual machine to be monitored at the current interaction time node includes: Preset the first operation deviation value threshold F1 and the second operation deviation value threshold F2, and F1 < F2; If f < F1, no correction instruction is generated at the current interaction time node; If F1 < f < F2, a first-level correction instruction is generated at the current interaction time node; If f > F2, a first-level warning instruction is generated at the current interaction time node.

Images