CN-120802736-B - Weak current equipment intelligent control system and method based on Internet of things

CN120802736BCN 120802736 BCN120802736 BCN 120802736BCN-120802736-B

Abstract

The invention discloses an intelligent control system and method for weak current equipment based on the Internet of things, and relates to the technical field of weak current equipment control.A path health scoring function is constructed through topology modeling, path scoring, health feature analysis and supervised learning training, so that intelligent selection and switching of a main path are realized, and in the task execution process, the system can continuously monitor the state of nodes, identify abnormal nodes and perform dynamic path replacement, thereby ensuring control continuity; if the path switching fails, the fault root is automatically traced and the high-risk node is isolated, the linkage operation and maintenance platform intervenes to process, and the self-adaptability, self-healing capacity and engineering operation and maintenance efficiency of the weak current control system are obviously improved.

Inventors

ZHOU CHUNCHUN
GUO YAO
LI JUN
SUN XIN

Assignees

泰州市云联网络信息系统有限公司

Dates

Publication Date: 20260508
Application Date: 20250708

Claims (9)

1. The intelligent control method for the weak current equipment based on the Internet of things is characterized by comprising the following steps of: Step S100, setting weak current equipment as nodes, summarizing the physical connection relation and the control logic channel of each node, generating a topology structure diagram of a weak current control network, and generating a control path set corresponding to executing different task types in different functional areas through a history record; Step 200, calculating the path score of the history record by acquiring a state table of the node, obtaining a main control path according to the path score average value of each control path, simultaneously acquiring the history record of each control path, calculating the path stability index, the communication delay index and the node health index of the history record, constructing a path health feature set and a task execution result, performing fitting training, and generating a path health scoring function; Step 300, presetting a monitoring period, calculating health scores of each stage when executing a current task, judging whether to trigger a path switching judging process, if the path switching judging process is not triggered, continuing to collect, and if the path switching judging process is triggered, screening candidate control paths and synchronizing the candidate control paths meeting the conditions to a control center; step 400, determining a new main control path in a control center, activating a strategy copy of the new main control path, setting the duration of the initial switching period, judging whether the new main control path meets the preset consistency condition, if so, continuing to execute the new main control path, and if not, triggering a fault processing mechanism; step 500, marking an abnormal node to be confirmed in the path switching and task recovery process, and reminding maintenance personnel of processing if the abnormal node to be confirmed is a high-risk node by monitoring running state data of the abnormal node to be confirmed and drawing a fluctuation feature graph; the step S500 includes the steps of: step S501, in the process of path switching and task recovery, if detecting that a plurality of output consistency check fails, a control task repeatedly rolls back and path selection fails, the system acquires the association relationship between an abnormal trigger node and an upstream node and a downstream node based on path switching records, an abnormal log and a control path topological relationship, constructs a suspected fault node set, and marks the abnormal trigger node as an abnormal node to be confirmed; Step S502, monitoring an abnormal node to be confirmed, continuously collecting operation state data of the abnormal node to be confirmed, wherein the operation state data comprise communication delay, instruction response time, control output offset and upstream and downstream communication success rate, and drawing a fluctuation feature diagram of the operation state data in a preset judging period; Step S503, calculating the fluctuation slope of the running state data according to the fluctuation feature diagram of the running state data, presetting a fluctuation slope threshold, updating the abnormal node to be confirmed to be a high risk node when the fluctuation slope exceeds the fluctuation slope threshold, executing logic isolation operation, pushing the high risk node to an operation and maintenance management platform, and prompting maintenance personnel to intervene diagnosis and treatment.
2. The intelligent control method of weak current equipment based on the internet of things according to claim 1, wherein the step S100 comprises the following steps: Step S101, setting all weak current equipment deployed in a hospital building as nodes, and acquiring a physical connection relation and a control logic channel of each weak current equipment by analyzing a wiring drawing, a control system configuration table and a communication link log; Step S102, classifying devices with the same superior control nodes into the same functional group, constructing logic connection paths among the nodes, and generating a topology structure diagram of a weak current control network; step S103, collecting basic attributes of each node in the topological structure diagram, wherein the basic attributes comprise node types, unique identification information, communication parameters and control load types, writing the basic attributes into a state table of the node, and generating state tables of all the nodes; Step S104, in the control center, a history record of task scheduling is obtained, task types in the history record and functional areas corresponding to weak current equipment for executing scheduling are extracted, a corresponding relation of task paths is mapped, and a feasible control path set is generated by combining a current topological structure.
3. The intelligent control method of weak current equipment based on the internet of things according to claim 2, wherein the step S200 comprises the following steps: step S201, acquiring a history record set of executing a certain task type in a certain functional area, extracting a control path adopted by executing the task type in the history record, classifying the history record of the same control path, collecting a state table of each node involved in the history record, extracting communication parameters in the state table, carrying out normalization processing, and calculating a path score according to the following formula: ; Wherein, A is expressed as a path score, B da is expressed as a normalized value of an a-th communication parameter in a D-th node, C a is expressed as a weight of the a-th communication parameter, D d is expressed as a weight of the D-th node, B is expressed as a total number of communication parameters, and e is expressed as a total number of nodes; Step S202, obtaining path scores corresponding to all histories in a certain control path, calculating to obtain a path score average value of the control path, sequencing the control paths adopted by executing a certain task type in a certain functional area according to the path score average value from high to low to obtain a candidate control path set, and setting a control path with the highest path score average value as a main control path; Step S203, acquiring a history of each control path, collecting task execution results, communication time delay and communication parameters of nodes of each history, counting the number of times of successfully executing the tasks as H1 and the total number of the histories as H2, calculating to obtain a path stability index as P1=H2, acquiring average communication time delay of all communication links, wherein the communication links are connections between every two nodes, and calculating to obtain a communication time delay index as Wherein t h is represented as the average communication delay of the h communication link, j is represented as the total number of communication links, the communication parameters of the nodes are normalized, and the health score of the nodes is calculated according to the following node health score formula: ; E is represented as the health score of the node, B f is represented as the normalized value of the f communication parameter, C f is represented as the weight of the f communication parameter, the health scores of all the nodes are summarized, and the average value of the health scores is calculated and set as the health index of the node; Step S204, carrying out normalization processing on the path stability index, the communication delay index and the node health index of the histories, summarizing, constructing and obtaining a path health feature set, taking the path health feature set of each histories as input, taking a corresponding task execution result as a label, constructing a supervision learning sample set, carrying out fitting training on the following path health scoring functions by a least square method, and training out the weight of the path health scoring functions, wherein the path health scoring functions are K=Q1X1+Q2X1+QP2+Q3X1, K is represented as the path health score, P3 is represented as the node health index, and Q1, Q2 and Q3 are respectively represented as the weight of the path stability index, the communication delay index and the node health index obtained by training.
4. The intelligent control method for weak current equipment based on the internet of things according to claim 3, wherein the step S300 comprises the following steps: step 301, presetting a monitoring period, acquiring a current execution task type and a functional area, determining a current main control path, collecting communication parameters of all nodes on the main control path according to the preset monitoring period, carrying out normalization processing, and calculating health scores of the nodes according to a node health score formula; Step S302, a health score threshold is preset, the health score of each node is compared with the health score threshold, if the health score of a certain node is lower than the health score threshold, the node is set as an unhealthy node, if any node in the main control path is an unhealthy node, the system triggers a path switching judging process, and if the unhealthy node does not exist in the main control path, the system continues to collect according to a preset monitoring period; Step S303, after the system triggers the path switching judging process, traversing the candidate control path set, obtaining the communication parameters of each node in the candidate control paths, calculating to obtain the health score of each node, eliminating the candidate control paths with unhealthy nodes, calculating the path stability index, the communication delay index and the node health index in the rest candidate control paths, inputting the path stability index, the communication delay index and the node health index into the path health scoring function, calculating the path health score of the rest candidate control paths, sorting the rest candidate control paths from high to low according to the path health score, and synchronizing the sorted rest candidate control path set to the control center.
5. The intelligent control method for weak current equipment based on the internet of things according to claim 4, wherein the step S400 comprises the following steps: Step S401, selecting a control path with highest path health score in a rest candidate control path set as a new main control path, calling a strategy template of a current task type through the control center, generating a control strategy copy according to a target control node in the new main control path, issuing the strategy copy to the target control node through a quick deployment mechanism, and marking the strategy copy as a state to be activated; Step S402, extracting task context information from a node executing a current task in an original main control path, wherein the task context information comprises task state variables, a sensor feedback state and last action output, writing the extracted task context information into a task context middleware for caching, and synchronously transmitting the task context information to a target control node deploying strategy copies in a new main control path through a control channel; Step S403, setting a strategy copy in the new main control path as an activated state, recovering task execution, presetting the time length of a switching initial stage, carrying out output consistency verification by comparing a control instruction output by the new main control path with the output of the original main control path in a corresponding state in the switching initial stage, if the output consistency verification meets a preset consistency condition, continuing to maintain task execution of the new main control path, and if the output consistency verification does not meet the preset consistency condition, triggering a fault processing mechanism; And S404, judging whether the original main control path is in an available state after triggering a fault processing mechanism, switching back to the original main control path if the original main control path is available, reactivating the original control strategy copy, recovering task execution, selecting a path with a path health score higher than a predetermined number of times as a new main control path based on the rest candidate control path set if the original main control path is unavailable, and repeatedly executing the steps of deployment of the control strategy copy, synchronization of the task context and activation of the strategy copy until task recovery is completed.
6. An intelligent control system of weak current equipment based on the Internet of things, which is used for realizing the intelligent control method of the weak current equipment based on the Internet of things according to any one of claims 1-5, is characterized by comprising a topology modeling module, a control path analysis module, a task monitoring module, a switching judgment module and an abnormal node identification module; The topology modeling module is used for setting weak current equipment as nodes, summarizing the physical connection relation and the control logic channel of each node, generating a topology structure diagram of a weak current control network, and generating a control path set corresponding to executing different task types in different functional areas through a history record; The control path analysis module is used for calculating the path score of the historical record by acquiring a state table of the node, obtaining a main control path according to the path score average value of each control path, simultaneously acquiring the historical record of each control path, calculating to obtain the path stability index, the communication delay index and the node health index of the historical record, constructing a path health feature set and a task execution result, and performing fitting training to generate a path health scoring function; the task monitoring module is used for presetting a monitoring period, calculating health scores of each stage when executing a current task, judging whether a path switching judging process is triggered, continuing to acquire if the path switching judging process is not triggered, screening candidate control paths if the path switching judging process is triggered, and synchronizing the candidate control paths meeting the conditions to a control center; The switching judging module is used for determining a new main control path in the control center, activating a strategy copy of the new main control path, setting the duration of the initial switching period, judging whether the new main control path meets the preset consistency condition, continuously executing the new main control path if the new main control path meets the preset consistency condition, and triggering a fault processing mechanism if the new main control path does not meet the preset consistency condition; the abnormal node identification module marks the abnormal node to be confirmed in the path switching and task recovery process, monitors the running state data marked as the abnormal node to be confirmed, draws a fluctuation feature diagram, determines whether the abnormal node to be confirmed is a high-risk node, and reminds maintenance personnel to process if the abnormal node to be confirmed is the high-risk node.
7. The weak current equipment intelligent control system based on the internet of things according to claim 6, wherein the topology modeling module comprises a topology structure unit and a control path aggregation unit: Setting all weak current devices deployed in a hospital building as nodes, acquiring physical connection relation and control logic channels of each weak current device by analyzing a wiring drawing, a control system configuration table and a communication link log, classifying devices with the same upper control node into the same functional group, constructing logic connection paths among all the nodes, and generating a topology structure diagram of a weak current control network; The control path aggregation unit is used for collecting basic attributes of each node in the topological structure diagram, wherein the basic attributes comprise node types, unique identification information, communication parameters and control load types, writing the basic attributes into a state table of the node to generate state tables of all the nodes, acquiring a task scheduling history in a control center, extracting the task types in the history and functional areas corresponding to weak current equipment for scheduling, mapping a task path corresponding relation, and generating a feasible control path aggregation by combining the current topological structure.
8. The intelligent control system of weak current equipment based on the internet of things according to claim 6, wherein the task monitoring module comprises a path switching judging unit and a remaining candidate control path determining unit: the path switching judging unit is used for presetting a monitoring period, acquiring a current execution task type and a function area, determining a current main control path, collecting communication parameters of all nodes on the main control path according to the preset monitoring period, carrying out normalization processing, calculating health scores of the nodes according to a node health score formula, presetting a health score threshold, comparing the health score of each node with the health score threshold, setting a certain node as an unhealthy node if the health score of the node is lower than the health score threshold, triggering a path switching judging process by a system if any node in the main control path is an unhealthy node, and continuously collecting according to the preset monitoring period if the unhealthy node is not in the main control path; And the residual candidate control path determining unit is used for traversing the candidate control path set after the system triggers the path switching judging process, obtaining the communication parameters of each node in the candidate control paths, calculating to obtain the health score of each node, eliminating the candidate control paths with unhealthy nodes, calculating the path stability index, the communication delay index and the node health index in the residual candidate control paths, inputting the path stability index, the communication delay index and the node health index into the path health scoring function, calculating the path health score of the residual candidate control paths, sequencing the residual candidate control paths from high to low according to the path health score, and synchronizing the sequenced residual candidate control path set to the control center.
9. The internet of things-based weak current device intelligent control system of claim 6, wherein the abnormal node identification module comprises a marking abnormal node units to be confirmed and a high risk node unit: The system acquires association relations between abnormal trigger nodes and upstream and downstream nodes based on path switching records, abnormal logs and control path topological relations, constructs a suspected fault node set and marks the abnormal trigger nodes as abnormal nodes to be confirmed if detecting that a plurality of output consistency check fails, the control tasks roll back repeatedly and the path selection fails in the path switching and task recovery processes; The high risk node unit is used for monitoring the abnormal node to be confirmed, continuously collecting the operation state data of the abnormal node to be confirmed, wherein the operation state data comprises communication delay, instruction response time, control output offset and upstream and downstream communication success rate, drawing a fluctuation characteristic diagram of the operation state data in a preset judging period, calculating the fluctuation slope of the operation state data according to the fluctuation characteristic diagram of the operation state data, presetting a fluctuation slope threshold, updating the abnormal node to be confirmed as the high risk node when the fluctuation slope exceeds the fluctuation slope threshold, executing logic isolation operation, pushing the high risk node to an operation and maintenance management platform, and prompting maintenance personnel to intervene diagnosis and treatment.

Description

Weak current equipment intelligent control system and method based on Internet of things Technical Field The invention relates to the technical field of weak current equipment control, in particular to an intelligent weak current equipment control system and method based on the Internet of things. Background In the existing control system of the internet of things, the operation of weak current equipment depends on a fixed communication topology and a control link structure, once nodes in the system are in fault or communication interruption, the whole control path is directly interrupted, the normal operation of key equipment is seriously influenced, especially in high-reliability scenes such as hospitals, the continuous operation of the weak current equipment such as illumination, security and emergency broadcasting is vital, the traditional system lacks a real-time sensing and rapid recovery mechanism after the fault of the node state, and the vulnerability of the control link is obvious; The current weak current control architecture mostly adopts a unidirectional control path or a main-standby switching mechanism, has the problems of response lag, unstable switching, manual intervention in a reconstruction process, and the like, has insufficient overall robustness and automatic recovery capability of the system, and part of the system supports node monitoring but lacks self-recovery capability for dynamic reconstruction of a control link, so that the system cannot realize continuous control when facing a complex dynamic environment, and has serious potential stability hazards; Therefore, an intelligent control mechanism supporting node health monitoring, path reconstruction and control logic self-adaptive adjustment needs to be constructed, so that when any control node or communication unit fails, the system can automatically discover, re-plan a control chain and quickly recover a control function, thereby remarkably improving the reliability, stability and self-recovery capability of a weak current control network. Disclosure of Invention The invention aims to provide an intelligent control system and method for weak current equipment based on the Internet of things, so as to solve the problems in the prior art. In order to solve the technical problems, the invention provides the following technical scheme that the intelligent control method for the weak current equipment based on the Internet of things comprises the following steps: Step S100, setting weak current equipment as nodes, summarizing the physical connection relation and the control logic channel of each node, generating a topology structure diagram of a weak current control network, and generating a control path set corresponding to executing different task types in different functional areas through a history record; Step 200, calculating the path score of the history record by acquiring a state table of the node, obtaining a main control path according to the path score average value of each control path, simultaneously acquiring the history record of each control path, calculating the path stability index, the communication delay index and the node health index of the history record, constructing a path health feature set and a task execution result, performing fitting training, and generating a path health scoring function; Step 300, presetting a monitoring period, calculating health scores of each stage when executing a current task, judging whether to trigger a path switching judging process, if the path switching judging process is not triggered, continuing to collect, and if the path switching judging process is triggered, screening candidate control paths and synchronizing the candidate control paths meeting the conditions to a control center; step 400, determining a new main control path in a control center, activating a strategy copy of the new main control path, setting the duration of the initial switching period, judging whether the new main control path meets the preset consistency condition, if so, continuing to execute the new main control path, and if not, triggering a fault processing mechanism; And S500, marking the abnormal node to be confirmed in the path switching and task recovery process, and reminding maintenance personnel of processing if the abnormal node to be confirmed is a high-risk node by monitoring the running state data marked as the abnormal node to be confirmed and drawing a fluctuation characteristic diagram. Further, step S100 includes: Step S101, setting all weak current equipment deployed in a hospital building as nodes, and acquiring a physical connection relation and a control logic channel of each weak current equipment by analyzing a wiring drawing, a control system configuration table and a communication link log; Step S102, classifying devices with the same superior control nodes into the same functional group, constructing logic connection paths among the nodes, and generating a topology str