Intelligent motor start-stop prediction control method and system for distributed control system

Abstract

The invention discloses an intelligent motor start-stop prediction control method and system for a decentralized control system, and relates to the technical field of start-stop control, and the method comprises the steps: determining an element number array based on system control elements for the decentralized control system; aiming at a target control scene, determining a series-parallel connection relation of the decentralized control system, performing initialization on a data interface at an acquisition side, performing packaging of an element number array and transmitting the element number array back to a controller; and the auxiliary controller executes a cascade control decision under serial packaging, determines a start-stop strategy according to verification deviation correction based on trusted start-stop and soft start-stop, and executes start-stop control in response to the motor set. According to the invention, the technical problems of untimely motor start-stop response, inaccurate control decision and insufficient short-time shutdown transition management in the decentralized control system in the prior art are solved, the intelligence and reliability of motor start-stop prediction and control in the decentralized control system are improved, and the reliability of the motor start-stop prediction and control in the decentralized control system is improved. And the technical effects of stable transition and start-stop optimization management of the motor under short-time shutdown are realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of start-stop control, in particular to a motor intelligent start-stop prediction control method and system for a decentralized control system. BACKGROUND

[0002] The decentralized control system is widely used in industrial automation, energy management and other fields, and the overall system is cooperatively controlled through independent operation of multiple nodes. In the decentralized control system, the motor serves as a key execution unit and undertakes important tasks such as equipment start-stop and load adjustment. However, due to the information synchronization delay between the control nodes, the overall system lacks a unified start-stop prediction mechanism, resulting in the problem of untimely motor start-stop response. At the same time, the motor start-stop control usually relies on single-point decision or local data, lacks comprehensive decision support for the system as a whole, and thus the control decision precision is insufficient. When the system needs to be operated for a short time, the conventional control method fails to fully consider the dynamic characteristics of the motor and the load change, lacks effective transition management strategy, and is prone to cause motor state fluctuation or abnormal energy consumption, thereby affecting the stability and efficiency of system operation. SUMMARY

[0003] The application provides a motor intelligent start-stop prediction control method and system for a decentralized control system, which is used to solve the technical problems of untimely motor start-stop response, inaccurate control decision and insufficient short-time shutdown transition management in the prior art in the decentralized control system.

[0004] In view of the above problems, the application provides a motor intelligent start-stop prediction control method and system for a decentralized control system.

[0005] In a first aspect, the application provides a motor intelligent start-stop prediction control method for a decentralized control system, which comprises:

[0006] For the decentralized control system, a factor matrix based on system control factors is determined, wherein the factor matrix is the numerical value of the real-time updated system control factors; for the target control scene, the series-parallel relationship of the decentralized control system is determined, the data interface on the collection side is initialized, the encapsulation of the factor matrix is performed and returned to the controller, wherein the data interface is an interface component for performing data encapsulation, and the controller is composed of a prediction node and a verification node; the controller is assisted to perform cascaded control decision under serial encapsulation, verification correction based on trusted start-stop and soft start-stop is determined, a start-stop strategy is determined, and start-stop control is performed in response to the motor group, wherein the differential unloading mode is introduced to perform motor transition management under short-time shutdown.

[0007] In a second aspect, the application provides a motor intelligent start-stop prediction control system for a decentralized control system, which comprises:

[0008] An element number array determination module is configured to determine an element number array based on system control elements for a distributed control system, wherein the element number array is a numerical value of a real-time updated system control element; an encapsulation module is configured to determine a series-parallel connection relationship of the distributed control system for a target control scenario, initialize a data interface on a collection side, perform encapsulation of the element number array and return to a controller, wherein the data interface is an interface component performing data encapsulation, and the controller is composed of a prediction node and a verification node; a start-stop control module is configured to assist the controller to perform a cascaded control decision under serial encapsulation, determine a start-stop strategy based on trusted start-stop and soft start-stop verification, and respond to motor group start-stop control by introducing a differential unloading mode to perform motor transition management under short-time shutdown.

[0009] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] The present application determines an element number array based on system control elements for a distributed control system, wherein the element number array is a numerical value of a real-time updated system control element; determines a series-parallel connection relationship of the distributed control system for a target control scenario, initializes a data interface on a collection side, performs encapsulation of the element number array and returns to a controller, wherein the data interface is an interface component performing data encapsulation, and the controller is composed of a prediction node and a verification node; assists the controller to perform a cascaded control decision under serial encapsulation, determines a start-stop strategy based on trusted start-stop and soft start-stop verification, and responds to motor group start-stop control by introducing a differential unloading mode to perform motor transition management under short-time shutdown. The present application solves the technical problems of existing technologies in the distributed control system, such as non-timely motor start-stop response, inaccurate control decision, and insufficient transition management under short-time shutdown. By constructing an element number array based on system control elements, series-parallel connection relationship modeling, serial encapsulation cascaded control decision, trusted start-stop verification, and differential unloading mode introduction, the present application achieves the technical effects of improving the intelligence and reliability of motor start-stop prediction and control in the distributed control system and realizing smooth motor transition and start-stop optimization management under short-time shutdown. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0012] Figure 1 A motor intelligent start-stop prediction control method flowchart for a distributed control system provided by the embodiments of the present application;

[0013] Figure 2 Figure 1 shows a schematic diagram of a motor intelligent start-stop prediction control system structure for a distributed control system according to an embodiment of the present application.

[0014] Element number array determination module 11, packaging module 12, start-stop control module 13. DETAILED DESCRIPTION

[0015] The present application provides a motor intelligent start-stop prediction control method and system for a distributed control system, which aims to solve the technical problems of the prior art, such as the non-timely response of motor start-stop in a distributed control system, the non-accurate control decision, and the insufficient transition management of short-term shutdown. By constructing an element number array based on system control elements, modeling series-parallel connection relationship, making serial packaging cascade control decision, introducing a trusted start-stop verification and differential unloading mode, the technical effects of improving the intelligence and reliability of motor start-stop prediction and control in a distributed control system and realizing smooth transition and optimized management of motor start-stop under short-term shutdown are achieved.

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0017] It should be noted that any variation of the terms "comprise" and "have" is intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to the process, method, product or device.

[0018] Embodiment one, as shown in the present application provides a motor intelligent start-stop prediction control method for a distributed control system, which comprises: Figure 1

[0019] Step S100: For a distributed control system, an element number array based on system control elements is determined, wherein the element number array is the numerical value of the system control elements updated in real time.

[0020] In the embodiments of the present application, in a distributed control system, to support motor intelligent start-stop prediction control, first, a multi-channel data acquisition method is used to collect system control elements related to the motor in real time, including motor current, voltage, temperature, load change rate, start-stop state signal, and environmental auxiliary parameters, etc. During the collection process, a standard communication protocol is used for data synchronization to ensure that the data collected by each distributed node has a unified timestamp and format specification.​

[0021] After the data collection is completed, the actual measurement values of each control element are retained for processing. Among them, the motor current is recorded in amperes, the voltage is recorded in actual voltage value, the temperature is recorded in Celsius, and the load change rate is recorded in the load variation amplitude per unit time. By directly using the original physical quantity data, it is ensured that the start-stop control strategy is based on the basic data that accurately reflects the device operating state, and the correspondence between the control decision and the field working condition is improved.

[0022] Subsequently, according to the node number and element category of the distributed control system, a two-dimensional matrix organization method is used to arrange the control elements corresponding to each node according to the row and column rules to form an element matrix facing the distributed control system. Among them, the row represents the node number, and the column represents different control element types. Each matrix element corresponds to the real-time value of a specific node on a certain element.

[0023] To ensure the real-time of the element matrix, during the data collection and value change monitoring process, a refresh trigger mechanism is set. When the change amplitude of any control element exceeds the set threshold or reaches the predetermined time period, the corresponding matrix element is immediately updated locally, and the whole element matrix is updated in full according to the unified refresh period.

[0024] Step S200: For the target control scene, the series-parallel relationship of the distributed control system is determined, the data interface on the collection side is initialized, the packaging of the element matrix is performed and returned to the controller, wherein the data interface is an interface component for performing data packaging, and the controller is composed of a prediction node and a verification node.

[0025] In the embodiments of the present application, for the target control scene, first, the relationship of each motor unit and related control node in the distributed control system is combed through the control system topology identification method. According to the series or parallel characteristics of each node in physical connection and the logical sequence relationship in the control process, the series-parallel structure description of the system is established. This process is based on node association matrix and control flow order table analysis, through identifying the input-output dependence between nodes, load transmission path and synchronization action requirements, the specific series-parallel relationship of the distributed control system under the target control scene is determined.

[0026] After the series-parallel relationship modeling is completed, the data interface initialization stage on the collection side is entered. A standardized data transmission protocol such as OPC UA or Modbus TCP is used to configure the parameters of the collection side interface, and the address of the node data source, the element type identification, the data refresh period and the communication method are determined. During the interface initialization process, a basic data verification mechanism is set, such as connection confirmation and data format consistency verification based on handshake signaling, to ensure that the transmission link is stable and reliable when each distributed node transmits data, and to ensure data synchronization and consistency.

[0027] After the acquisition side data interface completes initialization, based on the control element data collected in real time, the element matrix encapsulation operation is performed. In the encapsulation process, first, one layer of encapsulation is performed according to the ownership relationship of each decentralized control system, that is, the node data belonging to the same control ownership range is packaged and aggregated to form an ownership encapsulation unit; then, two-layer serial encapsulation is performed based on the previously determined series-parallel relationship, that is, the ownership encapsulation units are sequentially arranged and control index marked in the order of series-parallel organization, to form the encapsulated element matrix with hierarchy and sequence. In the encapsulation process, each piece of data is attached with node number, element category and timestamp information, to ensure data traceability and integrity.

[0028] The encapsulated element matrix is returned to the controller through the acquisition side data interface, data packets are sent based on TCP / IP or other industrial Ethernet protocols during transmission, and error detection codes such as CRC check codes are embedded during transmission to improve fault tolerance during data transmission. After the controller receives the encapsulated data, the subsequent processing is performed by the prediction node and the verification node set internally, wherein the prediction node is used to predict the motor start-stop trend based on the encapsulated element matrix, and the verification node is responsible for checking and adjusting the prediction result.

[0029] Further, the method provided by the application embodiment further comprises:

[0030] The one-layer encapsulation is performed based on the ownership of each decentralized control system, and the two-layer serial encapsulation is performed based on the series-parallel relationship, to determine the encapsulated element matrix.

[0031] In the application embodiment, first, for the system ownership relationship of each control node, the ownership grouping method is adopted, and according to the membership information of the node in physical deployment, control partition or functional grouping, the nodes and the corresponding control elements belonging to the same control ownership unit are one-layer encapsulated to form an ownership encapsulation unit, each ownership encapsulation unit internally contains all the nodes under the control ownership at the current time collecting real-time control element data, and an ownership identification code is attached for subsequent quick retrieval and management.

[0032] After completing the one-layer encapsulation based on the ownership relationship, further combining the previously identified system series-parallel relationship, two-layer serial encapsulation is performed, that is, according to the topology priority order and control logic dependence, the series-parallel organization mode is adopted to sequentially combine each ownership encapsulation unit, and the topology connection information between nodes is recorded during the encapsulation process to establish a logical association index, to ensure that the overall structure after encapsulation can accurately reflect the actual control relationship and execution order between nodes in the decentralized control system.

[0033] Finally, through the encapsulation of one layer and the encapsulation of two layers in series and parallel, a complete and structured element array after encapsulation is determined, which is organized as a two-dimensional array, wherein each row corresponds to a real-time control element set of an encapsulation unit, and each column corresponds to a system control element of different categories, and the overall array can be dynamically updated in real time.

[0034] Step S300: assisting the controller to perform cascade control decision under serial encapsulation, verifying and correcting based on trusted start-stop and soft start-stop, determining a start-stop strategy, and responding to the execution of the motor set to perform start-stop control, wherein the motor transition management under short-time shutdown is performed by introducing a differential unloading mode.

[0035] In the embodiments of the present application, first, based on the aforementioned element array after encapsulation, the controller performs cascade control decision under serial encapsulation after receiving data. Specifically, by analyzing the encapsulated data, the controller identifies the priority, start-stop sequence and control dependency of each decentralized control node, and then determines the interaction sequence between nodes through cascade control decision. This process is dynamically adjusted through the topological relationship between nodes to ensure that the start-stop control decision of the upstream node is executed first to achieve the coordination and efficient response of the overall system.

[0036] At the same time, the controller verifies and corrects based on the trusted start-stop verification mechanism and the soft start-stop strategy. Specifically, based on the start-stop decision given by the prediction node, the controller dynamically corrects potential abnormalities or misjudgments by introducing trusted start-stop verification to ensure the accuracy of the control strategy and the stability of the system. The soft start-stop verification relies on voltage ramp control or current limiting technology to slowly adjust the rate of motor start or stop, thereby avoiding excessive instantaneous impact and ensuring smooth transition of the motor system. On this basis, the controller finally determines the start-stop strategy and responds to the execution instruction of the motor set.

[0037] In the case of short-time shutdown, to ensure smooth transition of the motor set after shutdown, a differential unloading mode is introduced. This mode automatically adjusts the load unloading rate by analyzing the load change of the motor set before and after shutdown, thereby realizing motor transition management. In actual application, if the motor shutdown time is short, the differential unloading mode will intelligently adjust the unloading speed according to the real-time load condition to prevent damage or unstable transition of the motor due to sudden load changes.

[0038] Through the above series of steps, the controller accurately and dynamically adjusts the motor start-stop strategy in the decentralized control system to ensure efficient start-stop and smooth transition of the motor set, while avoiding system overload, damage and other problems.

[0039] Further, in the method provided by the embodiments of the present application, by introducing a differential unloading mode to perform motor transition management under short-time shutdown, the method further comprises:

[0040] Identify the initial start-stop strategy, if it is a shutdown control dimension, determine the shutdown time limit; introduce a preset shutdown time limit, if the shutdown time limit is greater than the preset shutdown time limit, take the motor shutdown as the control mode; if the shutdown time limit is less than or equal to the preset shutdown time limit, take the differential unloading as the control mode.

[0041] In the embodiments of the present application, first, the current initial start-stop strategy is identified through real-time data monitoring. When it is identified that the current start-stop strategy involves shutdown control, the determination process of the shutdown time limit is started based on the real-time running state of the motor. The shutdown time limit refers to the expected time interval between the completion of the current shutdown action of the motor and the start of the next start action.

[0042] To accurately estimate the shutdown time limit, the current working condition data, production task arrangement and historical operation records are comprehensively considered, and the method of task scheduling prediction and historical case analysis is used for calculation. Specifically, the historical scheduling data similar to the current running condition (such as the equipment to which the motor belongs, the operation process stage, and the load state) is searched, and the typical idle time of the motor from shutdown to restart under similar conditions is identified. For example, assuming that the current motor is used for a production line conveyor, the next shift start is usually required after 10 seconds after the current shift ends, and according to the historical records and real-time scheduling situation, the controller predicts the current shutdown time limit as 10 seconds.

[0043] After the current shutdown time limit is determined, it is compared with the preset shutdown time limit. The preset shutdown time limit is a standard time threshold preset in the system design and configuration stage according to the equipment energy efficiency management, protection strategy and production rhythm demand. If the actual calculated shutdown time limit is greater than the preset shutdown time limit, it means that the motor will remain in the shutdown state for a long time, and at this time the complete shutdown control strategy is selected to be executed. Specifically, by disconnecting the power, the motor is completely turned off to reduce energy consumption to the greatest extent and prevent invalid power consumption and heat accumulation during standby.

[0044] On the contrary, if the actual calculated shutdown time limit is less than or equal to the preset shutdown time limit, it is determined that the shutdown is a short-time shutdown situation, and at this time the differential unloading control mode is selected to be executed. In the differential unloading mode, the motor is not immediately completely powered off, but is gradually reduced in load or adjusted in running state, so that the motor maintains standby in a low-power mode, thereby realizing fast response to the next start command and avoiding mechanical impact and energy loss caused by frequent power-off and restart.

[0045] For example, in actual application, if the motor identifies that it is expected to be restarted after 10 minutes under the current working condition, and the preset shutdown time limit is 15 minutes, since the actual shutdown time limit is less than the preset value, the controller will automatically select the differential unloading mode to make the motor standby in a low load or heat preservation state, improve the response speed, and reduce energy consumption.

[0046] Further, the method provided by the application embodiment further comprises the following steps:

[0047] According to the prediction node, a first encapsulation layer is identified, the element number array is traversed to perform start-stop prediction of each distributed control system, a first-order start-stop strategy is determined, a second serial encapsulation layer is identified to determine the position relationship between the distributed control systems, the first-order start-stop strategy is constrained, and an initial start-stop strategy is determined; and the verification node is triggered to check and adjust the initial start-stop strategy.

[0048] In the application embodiment, the controller first performs start-stop prediction on the encapsulated element number array through the prediction node. To generate a first-order start-stop strategy, a start-stop prediction model based on historical data and real-time collected data is used. The model is trained by a supervised learning method, and a large number of historical running data sets are used as training samples. The training data set includes key factors such as the load, speed, temperature, and start-stop history record of the motor, and the corresponding start-stop decision result. By analyzing these data, the model can learn the influence law of different control elements on the start-stop decision.

[0049] In the training process, the controller first inputs the historical data into the model, and the model learns the relationship between the data through regression analysis or neural network algorithm. Each training sample is labeled with a real start-stop decision result, and the model adjusts the internal parameters by minimizing the error between the predicted value and the real value (such as using mean square error (MSE) as the loss function). Through multiple iterations of training, the model gradually optimizes its prediction ability, and finally can accurately predict the start-stop decision of the current control node according to real-time data.

[0050] After the model is trained, the prediction node traverses the real-time control elements of each distributed control system according to the data of the first encapsulation layer, and generates a first-order start-stop strategy through the trained start-stop prediction model. The strategy is a preliminary prediction based on local data, which considers the real-time load, speed, and other key control parameters of the node. However, the first-order start-stop strategy only depends on the local information of a single control unit, and does not consider the mutual dependence and priority of other nodes in the system.

[0051] Then the controller identifies the second serial packaging layer, which is a preliminary data structure generated based on the first packaging layer, and analyzes the bit order relationship between the control nodes. The bit order relationship reflects the dependency order and priority between the control nodes. For example, some nodes must wait for other nodes to complete the task before starting, or some nodes need to be executed in priority to avoid system conflicts. In the second layer, the controller analyzes the bit order relationship between the decentralized control systems, and constrains the first-order start-stop strategy according to the execution order of the control nodes, to ensure that the start-stop strategy is in the correct order and meets the global needs of the system. Through the optimization and constraint of this layer, the initial start-stop strategy is generated.

[0052] Finally, the controller triggers the verification node to check and adjust the generated initial start-stop strategy. The function of the verification node is to ensure the effectiveness and safety of the start-stop strategy and make necessary strategy modifications. The verification process relies on the comparison and analysis of real-time data and historical data, combined with changes in the external environment, to ensure that the preliminary strategy can adapt to actual operation requirements. During the verification process, the controller uses trusted start-stop verification and soft start-stop verification to confirm that the motor will not produce excessive impact during the start-stop process, and that parameters such as current and voltage will not exceed the safety threshold. In addition, according to the feedback of the verification node, adjust the soft control parameters such as voltage ramp or current limit to reduce the instantaneous impact during start-up and shutdown. Through this process, the final verified and adjusted start-stop strategy is generated.

[0053] Further, the method provided by the application embodiment further comprises:

[0054] The trusted start-stop verification includes hardware and software-based security and trusted verification, and permission-based operation security and trust; the soft start-stop verification is based on the regulation and control of start-stop instantaneous impact, and is processed according to the voltage ramp or current limit method.

[0055] In the application embodiment, the trusted start-stop verification is a key step to ensure the safety and stability of the motor start-stop process. First, the controller performs hardware and software-based security and trusted verification to ensure that the hardware and software of the motor meet the safety standards during the start-stop process. In terms of hardware, real-time monitoring of the motor driver, sensor and control unit is performed to ensure that these devices do not fail or degrade during start-stop operation. For software, it is verified whether the start-stop control algorithm is reliable and whether the transmission of control signals is error-free. This process is completed through an embedded safety monitoring system or a real-time operating system (RTOS) to ensure that the combined action of hardware and software can support safe start-stop control.

[0056] Then, the legality of the operation is further ensured by the permission-based operation security and trust verification. In this link, it is checked whether the permission of the execution start-stop operation meets the requirements, to ensure that only authorized devices or users can issue start-stop commands. This verification prevents unauthorized control signals from affecting the start-stop of the motor by checking the permission level of the operation, thereby effectively avoiding improper operation or external attacks.

[0057] After the hardware and software verification is completed, soft start-stop verification is then performed, which aims to smooth the start-stop process of the motor, especially the transient impact that may occur during the control start-stop process. The soft start-stop verification adjusts the start and stop of the motor through voltage ramp control and current limiting techniques. The voltage ramp control technique can gradually increase the voltage of the motor to avoid sudden current impact. The current limiting technique limits the maximum current during the operation of the motor to prevent the motor from being damaged due to excessive current during the start-stop process. Through these methods, the voltage and current change during the start of the motor becomes more gentle, thereby reducing the transient impact in the motor and the control system, and ensuring the smooth operation of the motor.

[0058] Further, the method provided by the application embodiment further comprises:

[0059] The initial start-stop strategy is identified, the trusted start-stop verification and the soft start-stop verification are performed, the trusted response operation and the soft control parameter are added, and the first verification strategy is determined. The initial start-stop strategy is identified, the response time limit of the shutdown strategy part is determined, the shutdown strategy with a shutdown time limit less than or equal to the preset shutdown time limit is replaced by differential load control, and the strategy is reformed to determine the start-stop strategy.

[0060] In the application embodiment, first, the controller identifies the initial start-stop strategy through the start-stop strategy analysis method. Specifically, the start-stop instructions of each decentralized control node in the current strategy are analyzed, and the start-stop intention of each node under a specific working condition and the associated control parameters such as the load change rate, the current state of the motor, the start-stop timing requirement, etc. are extracted. Through this analysis step, a structured data of the start-stop action and node mapping is formed.

[0061] Then, based on the extracted start-stop action, the trusted start-stop verification method is executed, the real-time monitoring of the motor driver, sensor, communication link state and other hardware and software operation information is performed, and it is verified whether the start-stop action meets the safety requirements set by the system during the execution process. At the same time, in combination with the operation permission verification mechanism, it is confirmed whether the source of the start-stop instruction is authorized and legal. For example, if the start-stop instruction of a node comes from an unauthorized device or the driver is detected to have abnormal jitter, the node is marked as untrusted, and the strategy adjustment is prompted.

[0062] After completing the trustworthiness verification, the soft start-stop verification method is continued to be executed, the electrical characteristics of each start-stop action in the execution process are analyzed, and the voltage ramp control and current limiting means are used to smooth the adjustment of the motor start-stop process. For example, for the motor node with large load, the voltage rise time is set to 3 seconds and the current limiting is set to 80% of the rated current, so as to avoid excessive impact current during start-stop and ensure the stability of the motor and the control system.

[0063] After the above verification is completed, the controller applies the start-stop parameter addition method to add trusted response operations and soft control parameters based on the initial start-stop strategy. Among them, the trusted response operation ensures that each start-stop instruction execution can be confirmed in time by increasing the state feedback mechanism of the start-stop instruction execution; the soft control parameter includes the dynamic adjustment of the voltage change rate, the load removal rate and other flexible control details during start-stop, so as to further optimize the smoothness of the start-stop process and the system response ability. Thus, the first verification strategy based on the optimized initial start-stop strategy is formed.

[0064] After the first verification strategy is generated, the shutdown part in the initial start-stop strategy is continued to be identified, the shutdown response time limit identification method is used to extract nodes involving shutdown actions one by one, and the expected shutdown response time is calculated based on the real-time running data of the nodes (such as current load, speed, temperature, etc.) combined with historical working conditions. Then, the predicted shutdown time limit is compared with the preset shutdown time limit threshold. For example, if the estimated shutdown time of a motor node under the current conditions is 4 seconds, and the preset shutdown time limit is 5 seconds, it is determined that the shutdown action belongs to the short-time shutdown range.

[0065] For the action identified as short-time shutdown, the differential load control replacement method is applied to replace the original direct power-off shutdown strategy with differential unloading control. For example, in the above 4-second shutdown time limit scenario, at the shutdown operation node, such as the first second, differential unloading operation is performed, that is, the load driven by the motor is unloaded to maintain a low load or no load state. That is, in the short-time shutdown state, the motor does not perform shutdown operation and remains in the start state, but the driven load is unloaded and remains in standby state. At the fourth second after shutdown, that is, the node of re-starting, the controller starts the load lifting control, that is, within the 1 second, the load of the motor is gradually restored to more than 90% of the rated load through linear incremental or staged manner, to ensure that the motor load has flexible transition when reconnecting, avoiding mechanical impact or energy mutation problems caused by instantaneous loading.

[0066] After all the replacements and adjustments are completed, the controller uses a start-stop strategy reorganization method to reorganize the overall start-stop action sequence and timing relationship by combining the first verification strategy and the differential unloading adjustment result, corrects the conflicting instructions or unreasonable timing problems, and ensures that the dependency relationship and priority relationship between the distributed control nodes in the start-stop process are correct. Finally, a start-stop strategy with complete structure, reasonable sequence, and compliance with safety and flexible control requirements is formed.

[0067] Further, the method provided by the application embodiment further comprises:

[0068] A relay node is introduced, wherein the relay node is deployed between the motor set of the distributed control system and the controller; the start-stop strategy is acquired and transmitted to the relay node, strategy conversion based on the system protocol is performed, and the start-stop control of the motor set is executed by each distributed control system.

[0069] In the application embodiment, a relay node is introduced as a relay processing unit of the start-stop control instruction, the relay node is deployed between the motor set of the distributed control system and the controller, and a communication bridge between the controller and each distributed motor set is formed. The relay node is set to realize protocol adaptation and unified scheduling of the start-stop instruction and improve the flexibility and expansibility of system control.

[0070] In actual application, the generated start-stop strategy is directly transmitted to the relay node, a high-reliability communication link is used in the transmission process, and a data check mechanism (such as a CRC check code) is set to ensure that the strategy data reaches the relay node without error. After receiving the start-stop strategy, the relay node performs strategy conversion based on the system protocol, converts the start-stop strategy in a unified format into a data format and instruction structure required by the corresponding protocol according to the communication standards (such as Modbus, CANopen, or Profinet) adopted by different distributed control systems, and ensures that each motor set can correctly parse and execute the start-stop command.

[0071] After the strategy conversion is completed, the relay node executes strategy distribution according to the control logic defined in the start-stop strategy, distributes the adapted start-stop instruction to the corresponding distributed control node, and controls the motor set to execute according to the predetermined start-stop timing and priority. By introducing the relay node, the communication and protocol processing burden of the controller is reduced, and the accuracy and coordination of the start-stop action of each motor set in the distributed control system are ensured.

[0072] Further, the method provided by the application embodiment further comprises:

[0073] A dual-mode redundancy mechanism is introduced, and the controller and key system components are redundantly deployed; and start-stop response control of the motor set is performed according to dual-mode redundancy rotation under fault driving.

[0074] In the embodiments of the present application, in order to improve the reliability and fault recovery capability of the distributed control system in the motor set start-stop control process, a dual-mode redundancy mechanism is introduced. The dual-mode redundancy mechanism refers to, in the system design stage, for key control objects, including the controller body and key system components (such as power supply modules, communication interface units, actuator drive modules, etc.), synchronous redundancy deployment of the main unit and the standby unit is performed. The main unit is responsible for the normal processing and execution of the start-stop command, and the standby unit is in real-time synchronous updating state, maintains the same start-stop strategy, system state and control command as the main unit, and ensures the instant takeover capability.

[0075] In the system running process, through real-time monitoring of the health status of each key unit, a dual-mode redundancy rotation strategy under fault driving is applied. When it is detected that the main unit has failure signs or performance degradation, such as abnormal output voltage of the power supply module, controller command processing timeout, communication interface link interruption, actuator response delay, etc., based on the set fault discrimination rules, the redundancy rotation mechanism is quickly triggered. In the specific rotation process, the standby unit takes over the function of the main unit in a seamless switching manner, ensuring the continuous and effective issuance of the start-stop command and the accurate execution of the motor set start-stop action.

[0076] For example, when the main power supply module has unstable power supply during the motor start-stop process, and the output voltage drops to below 80% of the rated value, it is determined that the main power supply fails, and the standby power supply module immediately takes over the power supply task, ensuring the continuous and stable operation of the controller and the execution unit. For another example, if it is detected that the main controller has a response timeout exceeding the set threshold (such as 100 milliseconds) when processing the start-stop decision, the standby controller is immediately switched to the issuer of the start-stop command, ensuring that the motor set completes the start or stop action on time and does not affect the overall timing of the system.

[0077] In the entire fault-driven redundancy rotation process, through the dual-mode state synchronization and switching control mechanism, the control state of the standby unit and the main unit is synchronized in real time, ensuring that there is no interruption of command flow and no loss of start-stop action information during switching, and the switching process is completed within a millisecond-level time window, greatly improving the start-stop control robustness and fault resistance of the distributed control system in complex application scenarios.

[0078] Through the above introduction of the dual-mode redundancy mechanism and the execution of the dual-mode redundancy rotation under fault driving, the adverse effects of controller single-point failure, power supply abnormality or communication link failure on the motor start-stop process are resisted, and the continuity, safety and high reliability of the start-stop response of the distributed control system are ensured even under abnormal working conditions.

[0079] Further, the method provided by the embodiments of the present application further comprises:

[0080] The start-stop control tracking is performed, control response information is determined, and is stored to a temporary database; according to a preset period, the temporary database is called, a generalized control bias feature is mined, and the controller is updated and learned.

[0081] In the embodiment of the application, after the motor set performs the start-stop control, a start-stop control tracking mechanism is introduced to continuously track and collect data of effects after the execution of the start-stop instruction. Specifically, the controller monitors key response information of the motor set during the execution of the start-stop action in real time, including response time, actual load change, current change process, temperature rise condition and start-stop state confirmation result, and extracts to form control response information. These response information intuitively reflects consistency or deviation between actual execution result and preset start-stop requirement.

[0082] After the control response information extraction is completed, the control response information is recorded to a temporary database through a temporary data storage method. The temporary database adopts a high-performance storage structure, supports fast writing and periodic calling, ensures that the response data corresponding to each start-stop action can be saved in real time, and adds node identification, time stamp, start-stop instruction identification and other meta information to ensure traceability and integrity of data management.

[0083] According to a preset calling period, the start-stop response data accumulated in the temporary database is called in time to perform generalized control bias feature mining. In the mining process, a method based on data statistical analysis is applied, such as mean value offset detection, standard deviation analysis, trend offset analysis and the like, to identify the control bias phenomenon commonly existing from a large number of start-stop execution records. For example, the mining result can show that the start-stop response delay of a specific type of motor increases obviously under high load, or there is a common trend of abnormal fluctuation of stop current in a specific environmental temperature range.

[0084] Based on the generalized control bias feature obtained by mining, a controller parameter updating method is used to adaptively adjust the start-stop control parameters in the controller. The adjustment content includes but is not limited to response time threshold of start-stop action, load change rate compensation parameter, current slope control parameter and the like. For example, when it is identified that the motor start response is generally slow in a low temperature environment, the current rise tolerance in the start stage is increased or the start determination window is appropriately prolonged to improve the matching degree and stability of the subsequent start-stop process.

[0085] Finally, through the above series of steps of start-stop control tracking, response information collection, temporary data storage, bias feature mining and controller parameter updating, the controller is updated and learned based on the actual start-stop execution result, and the adaptive optimization of the start-stop control strategy is realized.

[0086] In the embodiment of the application, as described above, the embodiment of the application at least has the following technical effects:

[0087] The application is directed to a distributed control system, determining an element array based on system control elements, wherein the element array is the numerical value of the real-time updated system control elements; for a target control scene, determining the series-parallel relationship of the distributed control system, initializing the data interface on the collection side, performing the encapsulation of the element array and returning to the controller, wherein the data interface is an interface component for performing data encapsulation, and the controller is composed of a prediction node and a verification node; assisting the controller to execute the cascade control decision under serial encapsulation, and determining the start-stop strategy based on the verification correction of trusted start-stop and soft start-stop, and responding to the start-stop control of the motor group, wherein the motor transition management under short-time shutdown is performed by introducing the differential unloading mode. The application solves the technical problems of the prior art in the distributed control system, such as the non-timely response of motor start-stop, the inaccurate control decision, and the insufficient transition management under short-time shutdown. By constructing the element array based on the system control elements, the series-parallel relationship modeling, the serial encapsulation cascade control decision, the trusted start-stop verification, and the differential unloading mode introduction, the technical effects of improving the intelligence and reliability of the motor start-stop prediction and control in the distributed control system and realizing the smooth transition and start-stop optimization management of the motor under short-time shutdown are achieved.

[0088] Embodiment two, based on the same inventive concept as the motor intelligent start-stop prediction control method for the distributed control system in the foregoing embodiments, as shown in the figure, the application provides a motor intelligent start-stop prediction control system for a distributed control system, and the system and method embodiments in the application embodiment are based on the same inventive concept. Wherein, the system comprises: Figure 2

[0089] The element array determination module 11 is configured to determine an element array based on system control elements for a distributed control system, wherein the element array is the numerical value of the real-time updated system control elements; the encapsulation module 12 is configured to determine the series-parallel relationship of the distributed control system for a target control scene, initialize the data interface on the collection side, perform the encapsulation of the element array and return to the controller, wherein the data interface is an interface component for performing data encapsulation, and the controller is composed of a prediction node and a verification node; the start-stop control module 13 is configured to assist the controller to execute the cascade control decision under serial encapsulation, and determine the start-stop strategy based on the verification correction of trusted start-stop and soft start-stop, and respond to the start-stop control of the motor group, wherein the motor transition management under short-time shutdown is performed by introducing the differential unloading mode.

[0090] Further, the system is also configured to realize the following functions:

[0091] The element array is encapsulated based on the ownership of each distributed control system, and the element array after encapsulation is determined based on the two-layer serial encapsulation of the series-parallel relationship.

[0092] ​Further, the system is also used to realize the following functions:

[0093] The initial start-stop strategy is identified, and if it is a shutdown control dimension, a shutdown time limit is determined; a preset shutdown time limit is introduced, and if the shutdown time limit is greater than the preset shutdown time limit, the motor shutdown is used as the control mode; if the shutdown time limit is less than or equal to the preset shutdown time limit, the differential unloading is used as the control mode.

[0094] Further, the system is also used to realize the following functions:

[0095] According to the prediction node, the first encapsulation layer is identified, the start-stop prediction of each distributed control system is performed by traversing the element number array, and the first-order start-stop strategy is determined; the second serial encapsulation layer is identified, the position relationship between the distributed control systems is determined, the first-order start-stop strategy is constrained, and the initial start-stop strategy is determined; the verification node is triggered, and the initial start-stop strategy is checked and adjusted.

[0096] Further, the system is also used to realize the following functions:

[0097] The trusted start-stop verification includes software and hardware-based security trusted verification, and operation security trusted based on permission; the soft start-stop verification is a regulation and control based on start-stop instantaneous impact, and is processed according to the voltage slope or current limiting method.

[0098] Further, the system is also used to realize the following functions:

[0099] The initial start-stop strategy is identified, the trusted start-stop verification and the soft start-stop verification are performed, the trusted response operation and the soft control parameter are added, and the first verification strategy is determined; the initial start-stop strategy is identified, the response time limit of the shutdown strategy part is determined, the shutdown strategy with a shutdown time limit less than or equal to the preset shutdown time limit is replaced by the differential load control, and the strategy is reformed to determine the start-stop strategy.

[0100] Further, the system is also used to realize the following functions:

[0101] The transfer node is introduced, wherein the transfer node is deployed between the motor group of the distributed control system and the controller; the start-stop strategy is acquired and transmitted to the transfer node, the strategy conversion based on the system protocol is performed, and the start-stop control of the motor group is distributed to each distributed control system.

[0102] Further, the system is also used to realize the following functions:

[0103] The dual-mode redundancy mechanism is introduced, and the controller and the key system components are redundantly deployed; according to the dual-mode redundancy rotation under the fault driving, the start-stop response control of the motor group is performed.

[0104] Further, the system is also used to realize the following functions:

[0105] The start-stop control tracking is performed, the control response information is determined, and is stored to a temporary database; according to a preset period, the temporary database is called, the generalization control bias feature is mined, and the controller is updated and learned.

[0106] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0107] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0108] The present application and the drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalent technology, the present application intends to include these modifications and changes.

Claims

1. A method for intelligent start-stop predictive control of a motor oriented to a decentralized control system, characterized by, The method comprises: For a distributed control system, determining an element matrix based on system control elements, wherein the element matrix is a real-time updated numerical value of the system control elements; For a target control scenario, determining a series-parallel relationship of the distributed control system, initializing a data interface on the collection side, performing encapsulation of the element matrix and returning to a controller, wherein the data interface is an interface component for performing data encapsulation, and the controller is composed of a prediction node and a verification node; Auxiliary the controller executes the cascade control decision under the serial encapsulation, and the verification correction based on the trusted start-stop and soft start-stop is determined to determine the start-stop strategy, and the motor group executes the start-stop control in response to the motor group.

2. The motor intelligent start-stop predictive control method for a decentralized control system of claim 1, wherein, Based on the ownership of each distributed control system, one layer of encapsulation is performed, and two layers of serial encapsulation are performed based on the series-parallel relationship to determine the encapsulated element matrix.

3. The motor intelligent start-stop predictive control method for a decentralized control system of claim 1, wherein, By introducing a differential unloading mode, the motor transition management under short-time shutdown is performed, including: Identify the initial start-stop strategy, if it is a shutdown control dimension, determine the shutdown time limit; If the shutdown time limit is greater than the preset shutdown time limit, the motor shutdown is used as the control mode; If the shutdown time limit is less than or equal to the preset shutdown time limit, the differential unloading is used as the control mode.

4. The decentralized control system oriented motor intelligent start-stop predictive control method of claim 3, wherein, Performing the cascade control decision under the serial encapsulation, and the verification correction based on the trusted start-stop and soft start-stop is determined to determine the start-stop strategy, including: According to the prediction node, by identifying the first encapsulation layer, the start-stop prediction of each distributed control system is performed by traversing the element matrix to determine the first-order start-stop strategy; By identifying the second serial encapsulation layer, the position relationship between the distributed control systems is determined, the first-order start-stop strategy is constrained, and the initial start-stop strategy is determined; Trigger the verification node to check and adjust the initial start-stop strategy.

5. The motor intelligent start-stop predictive control method for a decentralized control system of claim 4, wherein, The trusted start-stop verification includes security and trusted verification based on software and hardware, and operation security and trust based on permissions; The soft start-stop verification is a regulation and control based on start-stop instantaneous impact, which is processed according to the voltage slope or current limiting mode.

6. The decentralized control system oriented motor intelligent start-stop predictive control method of claim 5, wherein, Triggering the verification node to check and adjust the initial start-stop strategy, including: Identify the initial start-stop strategy, perform trusted start-stop verification and soft start-stop verification, add trusted response operation and soft control parameters, and determine the first verification strategy; Identify the initial start-stop strategy, determine the response time limit of the shutdown strategy part, replace the shutdown strategy with a shutdown time limit less than or equal to the preset shutdown time limit with a differential load control, and perform strategy reorganization to determine the start-stop strategy.

7. The decentralized control system oriented motor intelligent start-stop predictive control method of claim 1, wherein, In response to the motor group executing the start-stop control, including: Introducing a transfer node, wherein the transfer node is deployed between the motor group of the distributed control system and the controller; Obtain the start-stop strategy and transmit it to the transfer node, perform strategy conversion based on the system protocol, and distribute it to each distributed control system to execute the start-stop control of the motor group.

8. The decentralized control system oriented motor intelligent start-stop predictive control method of claim 1, wherein, The method further comprises: Introducing a dual-mode redundancy mechanism, redundantly deploying the controller and key system components; According to the dual-mode redundancy rotation under fault driving, the start-stop response control of the motor group is performed.

9. The motor intelligent soft start predictive control method for decentralized control system oriented as claimed in claim 1, wherein, After executing the start-stop control, including: The start-stop control tracking is performed, control response information is determined, and is stored in a temporary database; According to a preset period, the temporary database is called, generalized control bias features are mined, and the controller is updated and learned.

10. A motor intelligent start-stop predictive control system for a decentralized control system, characterized by, The system comprises: An element matrix determination module is configured to determine an element matrix based on system control elements for a distributed control system, wherein the element matrix is a numerical value of a real-time updated system control element; A packaging module is configured to determine a series-parallel connection relationship of the distributed control system for a target control scene, initialize a data interface on a collection side, perform packaging of the element matrix, and return to a controller, wherein the data interface is an interface component for performing data packaging, and the controller is composed of a prediction node and a verification node; A start-stop control module is configured to assist the controller to perform a cascaded control decision under serial packaging, perform verification correction based on trusted start-stop and soft start-stop, determine a start-stop strategy, and respond to motor group start-stop control, wherein a differential unloading mode is introduced to perform motor transition management under short-time shutdown.

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