An intelligent control method and system for an electrical automation electrical cabinet based on an internet of things

Abstract

The application discloses an electrical automation electrical cabinet intelligent control method and system based on Internet of Things, which comprises the following steps: S1, when the network connection is normal, an electrical cabinet digital twin model is constructed in the central controller, which relates to the technical field of industrial automation and Internet of Things, and the electrical cabinet digital twin model is constructed based on the normal network; when the network is disconnected, an electrical cabinet expected state sequence is generated through the electrical cabinet digital twin model, a reconnect alignment trajectory library is pre-constructed according to a preset sampling period; after reconnection, a target reconnect alignment trajectory is matched, the target reconnect alignment trajectory is taken as a time-varying set value of a local controller in the edge electrical cabinet, real-time running data of the edge electrical cabinet is changed along with the target reconnect alignment trajectory through the local controller until the target alignment time is reached; and the system can reach the state consistency of the central controller and the edge electrical cabinet without impact in the network fluctuation scene.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and Internet of Things (IoT) technology, and in particular to an intelligent control method and system for electrical automation cabinets based on IoT. Background Technology

[0002] With the widespread adoption of IoT technology in industrial automation, distributed control systems based on cloud-edge collaboration have become mainstream. In such systems, a central controller connects to multiple edge nodes, such as smart electrical cabinets, via a network to collaboratively complete complex control tasks. A key challenge lies in network reliability, as network partitioning and outages are unavoidable in complex industrial environments.

[0003] Existing technologies typically employ two main approaches: 1. Master-Slave Forced Overlay: After network recovery, the central controller forcibly overwrites its state data onto all edge nodes. This method can generate huge step commands on physical devices when state differences are significant, leading to reconnection consistency shocks, manifested as transient current / voltage peaks, potentially damaging equipment or causing production interruptions. 2. Simple Command Replay: The central controller attempts to reissue all commands not executed during the network outage. This can easily cause command storms and may replay outdated commands or commands that conflict with locally executed actions at the edges, leading to system malfunctions. The problem with these two methods is that they treat the reconnection moment as an instantaneous switching point, attempting to immediately jump the system state from a divergence point to a consensus point, while ignoring the inertia of the physical system and the continuity requirements of the control system. Summary of the Invention

[0004] To address the technical problems existing in the background art, this invention proposes an intelligent control method and system for electrical automation cabinets based on the Internet of Things.

[0005] This invention proposes an intelligent control method for electrical automation cabinets based on the Internet of Things, comprising the following steps:

[0006] S1. When the network connection between the central controller and the edge electrical cabinet is normal, a digital twin model of the electrical cabinet is built in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment.

[0007] S2. When the network connection between the central controller and the edge electrical cabinet is interrupted, the predicted operating data of the electrical cabinet at each future moment output by the digital twin model of the electrical cabinet is used as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form a sequence of expected states of the electrical cabinet.

[0008] S3. During the network connection interruption between the central controller and the edge electrical cabinet, a series of consecutive hypothetical future reconnection times are acquired incrementally according to the preset sampling period, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library.

[0009] The preset sampling period ranges from 100 milliseconds to 10 seconds;

[0010] S4. When the network connection between the central controller and the edge electrical cabinet is restored, collect the real-time operating data of the edge electrical cabinet and transmit the real-time operating data to the central controller.

[0011] The real-time running data is matched with the reconnection alignment trajectory library to select a target reconnection alignment trajectory.

[0012] S5. The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet. The edge electrical cabinet receives the target reconnection alignment trajectory and uses it as the time-varying setpoint of the local controller. The local controller then uses the real-time operating data of the edge electrical cabinet to follow the changes in the target reconnection alignment trajectory until the target alignment time is reached, so as to achieve consistency with the state of the central controller.

[0013] Preferably, in S1, when the network connection between the central controller and the edge electrical cabinet is normal, a digital twin model of the electrical cabinet is constructed in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment, as follows:

[0014] When the central controller and the edge electrical cabinet network are connected normally, the static configuration parameters and historical operating data of the edge electrical cabinet are obtained, and the static configuration parameters and historical operating data are transmitted to the central controller.

[0015] Historical operating data includes motor speed, equipment temperature, valve opening degree, and robotic arm position;

[0016] Correspondingly, the predicted operating data of the electrical cabinet includes the predicted motor speed, equipment temperature, valve opening degree, and robotic arm position;

[0017] In the central controller, a digital twin model of the electrical cabinet is constructed based on static configuration parameters and historical operating data through system identification technology. The system identification technology includes the least squares method or the state-space identification method. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment.

[0018] Real-time operating data of the edge electrical cabinet is collected and transmitted to the central controller. The real-time operating data is compared with the simulation output of the digital twin model of the electrical cabinet to obtain the output error of the digital twin model. With the goal of reducing the output error of the digital twin model, the internal parameters of the digital twin model are adjusted by parameter optimization method until the output error of the digital twin model is within a preset range per unit time, thereby completing the dynamic calibration of the digital twin model of the electrical cabinet.

[0019] Preferably, in S3, a reconnection alignment trajectory is generated for each assumed future reconnection time, and a reconnection alignment trajectory library is constructed as follows:

[0020] For each hypothetical future reconnection time, the expected state of the electrical cabinet at the corresponding hypothetical future reconnection time is obtained from the expected state sequence of the electrical cabinet, and used as the starting point of the trajectory; a fixed time after the hypothetical future reconnection time is used as the target alignment time; the expected state of the electrical cabinet at the target alignment time is obtained from the expected state sequence of the electrical cabinet, and used as the ending point of the trajectory; a trajectory planning algorithm is used to obtain a reconnection alignment trajectory that smoothly transitions from the starting point to the ending point of the trajectory.

[0021] As an explanation, the trajectory planning algorithm is used to calculate a smooth path that satisfies kinematic constraints from the starting point to the ending point of the trajectory, thereby forming a reconnected and aligned trajectory.

[0022] All the acquired reconnection alignment trajectories are stored to form a reconnection alignment trajectory library.

[0023] Preferably, in S4, the real-time running data is matched with the reconnection alignment trajectory library to select a target reconnection alignment trajectory, as follows:

[0024] In the reconnection and alignment trajectory library, obtain the degree of difference between the real-time running data and the trajectory starting point in each reconnection and alignment trajectory;

[0025] The reconnection alignment trajectory with the smallest difference from the real-time running data is selected as the target reconnection alignment trajectory.

[0026] Preferably, in S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained, as follows:

[0027] In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory;

[0028] The reconnection alignment trajectory with the smallest Euclidean distance is selected as the target reconnection alignment trajectory.

[0029] Preferably, in S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained, as follows:

[0030] In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory;

[0031] Reconnection alignment trajectories with Euclidean distance less than a preset threshold are selected as candidate reconnection alignment trajectories, forming a candidate trajectory set.

[0032] Obtain the acceleration curve corresponding to each candidate reconnected alignment trajectory in the candidate trajectory set; based on the acceleration curve corresponding to the candidate reconnected alignment trajectory, obtain the jerk corresponding to the acceleration curve; obtain the 2-norm of the jerk as the smoothness score of the corresponding candidate reconnected alignment trajectory;

[0033] The smoothness score corresponding to the candidate reconnected alignment trajectory is normalized with the Euclidean distance, and the comprehensive score of each candidate reconnected alignment trajectory is obtained by weighted summation according to the preset weight.

[0034] The candidate reconnection alignment trajectory with the highest overall score is selected as the target reconnection alignment trajectory.

[0035] Preferably, in S5, the local controller is a proportional-integral-derivative controller; using the target reconnection alignment trajectory as a time-varying setpoint means updating the setpoint input of the proportional-integral-derivative controller from a constant value to a signal that follows the change of the target reconnection alignment trajectory.

[0036] An intelligent control system for electrical automation cabinets based on the Internet of Things (IoT) includes:

[0037] Electrical cabinet digital twin model construction module: When the network connection is normal, an electrical cabinet digital twin model is built in the central controller. The electrical cabinet digital twin model is used to output the predicted operating data of the electrical cabinet at each future moment.

[0038] Electrical cabinet expected state sequence generation module when network connection is interrupted: When the network connection is interrupted, the predicted operating data of the electrical cabinet at each future moment is used as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form the expected state sequence of the electrical cabinet.

[0039] Reconnection alignment trajectory generation and trajectory library construction module during network interruption: During network connection interruption, a series of consecutive hypothetical future reconnection times are incrementally acquired according to a preset sampling period, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library.

[0040] Real-time data acquisition and target reconnection alignment trajectory matching module when network connection is restored: When network connection is restored, the module collects real-time operating data of the edge electrical cabinet and matches the real-time operating data with the reconnection alignment trajectory library to select a target reconnection alignment trajectory;

[0041] Target reconnection alignment trajectory alignment module: The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet; the target reconnection alignment trajectory is used as the time-varying setpoint of the local controller, and the local controller makes the real-time operating data of the edge electrical cabinet follow the changes of the target reconnection alignment trajectory until the target alignment time is reached.

[0042] The intelligent control method and system for electrical automation cabinets based on the Internet of Things proposed in this invention have the following beneficial technical effects:

[0043] 1. This application constructs a digital twin model of the electrical cabinet based on the static configuration parameters and historical operating data of the edge electrical cabinet when the network is normal. When the network is interrupted, the expected state sequence of the electrical cabinet is generated through the digital twin model of the electrical cabinet, and a reconnection alignment trajectory library is pre-constructed according to a preset sampling period. After reconnection, the target reconnection alignment trajectory is matched and used as the time-varying setpoint of the local controller in the edge electrical cabinet. The local controller makes the real-time operating data of the edge electrical cabinet follow the changes of the target reconnection alignment trajectory until the target alignment time is reached, so as to achieve consistency with the state of the central controller. This achieves the system's state consistency between the central controller and the edge electrical cabinet without impact under network fluctuation scenarios, effectively alleviating the technical problem of existing technologies that treat the reconnection moment as an instantaneous switching point, causing the system state to jump immediately from a divergence point to a consistency point, while ignoring the inertia of the physical system and the continuity requirements of the control system.

[0044] 2. When the network is normal, this application constructs a digital twin model of the electrical cabinet by combining the static configuration parameters and historical operating data of the edge electrical cabinet using the least squares method or state-space identification method. Then, by comparing the output of the digital twin model of the electrical cabinet with the real-time operating data, the error of the digital twin model of the electrical cabinet is controlled within a preset range. Since the digital twin model of the electrical cabinet can output the predicted operating data of the electrical cabinet at every future moment, it provides a reliable benchmark for the expected state of the electrical cabinet in the case of network interruption. This alleviates the problem in the prior art that when the network is interrupted, the central controller lacks accurate reference and cannot determine the reasonable operating target of the electrical cabinet, resulting in the system having no benchmark operation during the interruption and excessive state deviation after reconnection.

[0045] 3. After a network interruption, the predicted operating data of the electrical cabinet output by the digital twin model is first sorted by time to form the expected state sequence of the electrical cabinet. Then, according to a preset sampling period, a series of continuous hypothetical future reconnection times are incrementally acquired, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library. Since the alignment trajectories under different reconnection scenarios are stored in advance, there is no need to start calculating from scratch after the network is restored. The target reconnection alignment trajectory is directly matched from the reconnection alignment trajectory library, which greatly shortens the trajectory generation time. This alleviates the problem of long alignment delay and the system being in an inconsistent state for a long time when trajectory calculation is started only after the network is restored in the existing technology, and significantly improves the recovery efficiency of the system after reconnection. Attached Figure Description

[0046] Figure 1 This is a flowchart of an intelligent control method for electrical cabinets based on the Internet of Things (IoT) according to the present invention.

[0047] Figure 2 This is a schematic diagram of an intelligent control system for electrical cabinets based on the Internet of Things (IoT) according to the present invention. Detailed Implementation

[0048] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0049] like Figure 1 The method for intelligent control of electrical cabinets based on the Internet of Things (IoT) includes the following steps:

[0050] S1. When the network connection between the central controller and the edge electrical cabinet is normal, a digital twin model of the electrical cabinet is built in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment.

[0051] In an optional embodiment, in S1, when the network connection between the central controller and the edge electrical cabinet is normal, a digital twin model of the electrical cabinet is constructed in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment, as follows:

[0052] When the central controller and the edge electrical cabinet network are connected normally, the static configuration parameters and historical operating data of the edge electrical cabinet are obtained, and the static configuration parameters and historical operating data are transmitted to the central controller.

[0053] Historical operating data includes motor speed, equipment temperature, valve opening degree, and robotic arm position;

[0054] Correspondingly, the predicted operating data of the electrical cabinet includes the predicted motor speed, equipment temperature, valve opening degree, and robotic arm position;

[0055] In the central controller, a digital twin model of the electrical cabinet is constructed based on static configuration parameters and historical operating data through system identification technology. The system identification technology includes the least squares method or the state-space identification method. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment.

[0056] Real-time operating data of the edge electrical cabinet is collected and transmitted to the central controller. The real-time operating data is compared with the simulation output of the digital twin model of the electrical cabinet to obtain the output error of the digital twin model. With the goal of reducing the output error of the digital twin model, the internal parameters of the digital twin model are adjusted by parameter optimization method until the output error of the digital twin model is within a preset range within a unit of time, thereby completing the dynamic calibration of the digital twin model of the electrical cabinet.

[0057] S2. When the network connection between the central controller and the edge electrical cabinet is interrupted, the predicted operating data of the electrical cabinet at each future moment output by the digital twin model of the electrical cabinet is used as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form a sequence of expected states of the electrical cabinet.

[0058] When the network is normal, this application constructs a digital twin model of the electrical cabinet by combining the static configuration parameters and historical operating data of the edge electrical cabinet using the least squares method or state-space identification method. Then, by comparing the output of the digital twin model with the real-time operating data, the error of the digital twin model is controlled within a preset range. Since the digital twin model of the electrical cabinet can output the predicted operating data of the electrical cabinet at every future moment, it provides a reliable benchmark for the expected state of the electrical cabinet in network interruption scenarios. This alleviates the problem in the prior art that when the network is interrupted, the central controller lacks accurate reference data and cannot determine the reasonable operating target of the electrical cabinet, resulting in no benchmark operation during the interruption and excessive state deviation after reconnection.

[0059] S3. During the network connection interruption between the central controller and the edge electrical cabinet, a series of consecutive hypothetical future reconnection times are acquired incrementally according to the preset sampling period, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library.

[0060] The preset sampling period ranges from 100 milliseconds to 10 seconds;

[0061] In an optional embodiment, in S3, a reconnection alignment trajectory is generated for each assumed future reconnection time, and a reconnection alignment trajectory library is constructed as follows:

[0062] For each hypothetical future reconnection time, the expected state of the electrical cabinet at the corresponding hypothetical future reconnection time is obtained from the expected state sequence of the electrical cabinet, and used as the starting point of the trajectory; a fixed time after the hypothetical future reconnection time is used as the target alignment time; the expected state of the electrical cabinet at the target alignment time is obtained from the expected state sequence of the electrical cabinet, and used as the ending point of the trajectory; a trajectory planning algorithm is used to obtain a reconnection alignment trajectory that smoothly transitions from the starting point to the ending point of the trajectory.

[0063] As an explanation, the trajectory planning algorithm is used to calculate a smooth path that satisfies kinematic constraints from the starting point to the ending point of the trajectory, thereby forming a reconnected and aligned trajectory.

[0064] All the acquired reconnection alignment trajectories are stored to form a reconnection alignment trajectory library.

[0065] After a network outage, the predicted operating data of the electrical cabinet output by the digital twin model is first sorted by time to form a sequence of expected states of the electrical cabinet. Then, according to a preset sampling period, a series of consecutive hypothetical future reconnection times are incrementally acquired, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library. Since the alignment trajectories under different reconnection scenarios are stored in advance, there is no need to start calculating from scratch after the network is restored. The target reconnection alignment trajectory is directly matched from the reconnection alignment trajectory library, which greatly shortens the trajectory generation time. This alleviates the problem of long alignment delays and the system being in an inconsistent state for a long time when trajectory calculation is started only after the network is restored in the existing technology, and significantly improves the recovery efficiency of the system after reconnection.

[0066] S4. When the network connection between the central controller and the edge electrical cabinet is restored, collect the real-time operating data of the edge electrical cabinet and transmit the real-time operating data to the central controller.

[0067] The real-time running data is matched with the reconnection alignment trajectory library to select a target reconnection alignment trajectory.

[0068] In an optional embodiment, in S4, the real-time running data is matched against the reconnection alignment trajectory library to select a target reconnection alignment trajectory, as follows:

[0069] In the reconnection and alignment trajectory library, obtain the degree of difference between the real-time running data and the trajectory starting point in each reconnection and alignment trajectory;

[0070] Select the reconnection alignment trajectory with the smallest difference from the real-time running data as the target reconnection alignment trajectory;

[0071] In an optional embodiment, in S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained as follows:

[0072] In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory;

[0073] The reconnection alignment trajectory with the smallest Euclidean distance is selected as the target reconnection alignment trajectory.

[0074] In an optional embodiment, in S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained as follows:

[0075] In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory;

[0076] Reconnection alignment trajectories with Euclidean distance less than a preset threshold are selected as candidate reconnection alignment trajectories, forming a candidate trajectory set.

[0077] Obtain the acceleration curve corresponding to each candidate reconnected alignment trajectory in the candidate trajectory set; based on the acceleration curve corresponding to the candidate reconnected alignment trajectory, obtain the jerk corresponding to the acceleration curve; obtain the 2-norm of the jerk as the smoothness score of the corresponding candidate reconnected alignment trajectory;

[0078] The smoothness score corresponding to the candidate reconnected alignment trajectory is normalized with the Euclidean distance, and the comprehensive score of each candidate reconnected alignment trajectory is obtained by weighted summation according to the preset weight.

[0079] Select the candidate reconnection alignment trajectory with the highest comprehensive score as the target reconnection alignment trajectory;

[0080] In an optional embodiment, after normalization, the preset weights for both the smoothness score and the Euclidean distance are 0.5.

[0081] S5. The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet. The edge electrical cabinet receives the target reconnection alignment trajectory and uses it as the time-varying setpoint of the local controller. The local controller then uses the real-time operating data of the edge electrical cabinet to follow the target reconnection alignment trajectory until the target alignment time is reached, thus achieving consistency with the state of the central controller.

[0082] In an optional embodiment, in S5, the local controller is a proportional-integral-derivative controller; using the target reconnection alignment trajectory as a time-varying setpoint means updating the setpoint input of the proportional-integral-derivative controller from a constant value to a signal that follows the change of the target reconnection alignment trajectory.

[0083] This application can be applied to distributed systems that include a central controller and multiple edge electrical cabinets.

[0084] This application constructs a digital twin model of the electrical cabinet based on the static configuration parameters and historical operating data of the edge electrical cabinet when the network is normal. When the network is interrupted, the expected state sequence of the electrical cabinet is generated through the digital twin model of the electrical cabinet, and a reconnection alignment trajectory library is pre-constructed according to a preset sampling period. After reconnection, the target reconnection alignment trajectory is matched and used as the time-varying setpoint of the local controller in the edge electrical cabinet. The local controller makes the real-time operating data of the edge electrical cabinet follow the changes of the target reconnection alignment trajectory until the target alignment time is reached, so as to achieve consistency with the state of the central controller. This achieves the system's state consistency between the central controller and the edge electrical cabinet without impact under network fluctuation scenarios, effectively alleviating the technical problem of existing technologies that treat the reconnection moment as an instantaneous switching point, causing the system state to jump immediately from a divergence point to a consistency point, while ignoring the inertia of the physical system and the continuity requirements of the control system.

[0085] like Figure 2 The illustrated IoT-based intelligent control system for electrical automation cabinets includes:

[0086] Electrical cabinet digital twin model construction module: When the network connection is normal, an electrical cabinet digital twin model is built in the central controller. The electrical cabinet digital twin model is used to output the predicted operating data of the electrical cabinet at each future moment.

[0087] Electrical cabinet expected state sequence generation module when network connection is interrupted: When the network connection is interrupted, the predicted operating data of the electrical cabinet at each future moment is used as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form the expected state sequence of the electrical cabinet.

[0088] Reconnection alignment trajectory generation and trajectory library construction module during network interruption: During network connection interruption, a series of consecutive hypothetical future reconnection times are incrementally acquired according to a preset sampling period, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library.

[0089] Real-time data acquisition and target reconnection alignment trajectory matching module when network connection is restored: When network connection is restored, the module collects real-time operating data of the edge electrical cabinet and matches the real-time operating data with the reconnection alignment trajectory library to select a target reconnection alignment trajectory;

[0090] Target reconnection alignment trajectory alignment module: The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet; the target reconnection alignment trajectory is used as the time-varying setpoint of the local controller, and the local controller makes the real-time operating data of the edge electrical cabinet follow the changes of the target reconnection alignment trajectory until the target alignment time is reached.

[0091] For clarification, "acquisition" in this application refers to obtaining the required content or data using existing technical means.

[0092] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0093] In the embodiments provided by this invention, it should be understood that the disclosed system or method can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0094] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0096] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0097] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent control of electrical cabinets in electrical automation based on the Internet of Things, characterized in that, Includes the following steps: S1. When the network connection is normal, construct a digital twin model of the electrical cabinet in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment. S2. When the network connection is interrupted, the predicted operating data of the electrical cabinet at each future moment is taken as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form a sequence of expected states of the electrical cabinet. S3. During the network connection interruption, according to the preset sampling period, a series of consecutive hypothetical future reconnection times are obtained incrementally, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library. S4. When the network connection is restored, collect the real-time operating data of the edge electrical cabinet, match the real-time operating data with the reconnection alignment trajectory library, and select a target reconnection alignment trajectory. S5. The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet; the target reconnection alignment trajectory is used as the time-varying setpoint of the local controller, and the real-time operating data of the edge electrical cabinet follows the target reconnection alignment trajectory until the target alignment time is reached.

2. The intelligent control method for electrical automation cabinets based on the Internet of Things according to claim 1, characterized in that, In S1, when the network connection between the central controller and the edge electrical cabinet is normal, a digital twin model of the electrical cabinet is built in the central controller. The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at each future moment, as follows: When the central controller and the edge electrical cabinet network are connected normally, the static configuration parameters and historical operating data of the edge electrical cabinet are obtained, and the static configuration parameters and historical operating data are transmitted to the central controller. In the central controller, a digital twin model of the electrical cabinet is constructed based on static configuration parameters and historical operating data through system identification technology; system identification technology includes least squares method or state-space identification method; The digital twin model of the electrical cabinet is used to output the predicted operating data of the electrical cabinet at every future moment; Real-time operating data of the edge electrical cabinet is collected and transmitted to the central controller. The real-time operating data is compared with the simulation output of the digital twin model of the electrical cabinet to obtain the output error of the digital twin model. With the goal of reducing the output error of the digital twin model, the internal parameters of the digital twin model are adjusted by parameter optimization method until the output error of the digital twin model is within the preset range per unit time.

3. The intelligent control method for electrical automation cabinets based on the Internet of Things according to claim 1, characterized in that, In S3, a reconnection alignment trajectory is generated for each hypothetical future reconnection time, and a reconnection alignment trajectory library is constructed as follows: For each hypothetical future reconnection time, the expected state of the electrical cabinet at the corresponding hypothetical future reconnection time is obtained from the expected state sequence of the electrical cabinet, and used as the starting point of the trajectory; a fixed time after the hypothetical future reconnection time is used as the target alignment time; the expected state of the electrical cabinet at the target alignment time is obtained from the expected state sequence of the electrical cabinet, and used as the ending point of the trajectory; a trajectory planning algorithm is used to obtain a reconnection alignment trajectory that smoothly transitions from the starting point to the ending point of the trajectory. All the acquired reconnection alignment trajectories are stored to form a reconnection alignment trajectory library.

4. The intelligent control method for electrical automation cabinets based on the Internet of Things according to claim 1, characterized in that, In S4, real-time running data is matched with the reconnection alignment trajectory library to select a target reconnection alignment trajectory, as follows: In the reconnection and alignment trajectory library, obtain the degree of difference between the real-time running data and the trajectory starting point in each reconnection and alignment trajectory; The reconnection alignment trajectory with the smallest difference from the real-time running data is selected as the target reconnection alignment trajectory.

5. The intelligent control method for electrical automation cabinets based on the Internet of Things according to claim 4, characterized in that, In S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained as follows: In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory; The reconnection alignment trajectory with the smallest Euclidean distance is selected as the target reconnection alignment trajectory.

6. The intelligent control method for electrical automation cabinets based on the Internet of Things according to claim 4, characterized in that, In S4, the difference between the real-time running data and the starting point of each reconnected alignment trajectory is obtained as follows: In the reconnected alignment trajectory library, obtain the Euclidean distance between the real-time running data and the starting point of each reconnected alignment trajectory; Reconnection alignment trajectories with Euclidean distance less than a preset threshold are selected as candidate reconnection alignment trajectories, forming a candidate trajectory set. Obtain the acceleration curve corresponding to each candidate reconnected aligned trajectory in the candidate trajectory set; Based on the acceleration curves corresponding to the candidate reconnection alignment trajectories, obtain the jerk corresponding to the acceleration curves; obtain the 2-norm of the jerk as the smoothness score of the corresponding candidate reconnection alignment trajectory; The smoothness score corresponding to the candidate reconnected alignment trajectory is normalized with the Euclidean distance, and the comprehensive score of each candidate reconnected alignment trajectory is obtained by weighted summation according to the preset weight. The candidate reconnection alignment trajectory with the highest overall score is selected as the target reconnection alignment trajectory.

7. The intelligent control method for electrical cabinets based on the Internet of Things according to claim 1, characterized in that, In S5, the local controller is a proportional-integral-derivative controller; using the target reconnection alignment trajectory as a time-varying setpoint means updating the setpoint input of the proportional-integral-derivative controller from a constant value to a signal that follows the changes in the target reconnection alignment trajectory.

8. An intelligent control system for electrical automation cabinets based on the Internet of Things (IoT), used with the intelligent control method for electrical automation cabinets based on the IoT as described in any one of claims 1 to 7, characterized in that, include: Electrical cabinet digital twin model construction module: When the network connection is normal, an electrical cabinet digital twin model is built in the central controller. The electrical cabinet digital twin model is used to output the predicted operating data of the electrical cabinet at each future moment. Electrical cabinet expected state sequence generation module when network connection is interrupted: When the network connection is interrupted, the predicted operating data of the electrical cabinet at each future moment is used as the expected state of the electrical cabinet at each future moment, and all the expected states of the electrical cabinet are sorted in chronological order to form the expected state sequence of the electrical cabinet. Reconnection alignment trajectory generation and trajectory library construction module during network interruption: During network connection interruption, a series of consecutive hypothetical future reconnection times are incrementally acquired according to a preset sampling period, and a reconnection alignment trajectory is generated for each hypothetical future reconnection time to build a reconnection alignment trajectory library. Real-time data acquisition and target reconnection alignment trajectory matching module when network connection is restored: When network connection is restored, the module collects real-time operating data of the edge electrical cabinet and matches the real-time operating data with the reconnection alignment trajectory library to select a target reconnection alignment trajectory; Target reconnection alignment trajectory alignment module: The central controller sends the target reconnection alignment trajectory to the edge electrical cabinet; The target reconnection alignment trajectory is used as the time-varying setpoint of the local controller. The real-time operating data of the edge electrical cabinet is then changed by the local controller to follow the target reconnection alignment trajectory until the target alignment time is reached.

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