Weak current equipment intelligent control system and method based on Internet of Things

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. Intelligent selection and switching of a main path are realized through topology modeling, path scoring, health feature analysis and supervised learning training and construction of a path health scoring function; the system can continuously monitor node states, identify abnormal nodes and carry out dynamic path replacement, and control continuity is guaranteed; and if path switching fails, fault root causes are automatically traced, high-risk nodes are isolated, and an operation and maintenance platform is linked to intervene in processing, so that the adaptivity, the self-healing capability and the engineering operation and maintenance efficiency of the weak current control system are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of weak current equipment control, and particularly relates to a weak current equipment intelligent control system and method based on Internet of Things. BACKGROUND

[0002] In the existing Internet of Things control system, the operation of weak current equipment depends on fixed communication topology and control link structure. Once a node in the system fails or communication is interrupted, the entire control path will be directly interrupted, which seriously affects the normal work of key equipment. In high-reliability scenarios such as hospitals, the continuous operation of weak current equipment such as lighting, security, and emergency broadcasting is crucial. However, the traditional system lacks real-time perception of node status and rapid recovery mechanism after failure, and the vulnerability of the control link is obvious. Current weak current control architecture mostly uses one-way control path or master-slave switching mechanism, which has problems such as response lag, unstable switching, and manual intervention in reconstruction process. The overall robustness and automatic recovery capability of the system are insufficient. Although some systems support node monitoring, they lack self-healing capability for dynamic reconstruction of control link. The system cannot achieve continuous control when facing complex dynamic environment, which poses a serious stability risk. Therefore, it is necessary to build an intelligent control mechanism that supports node health monitoring, path reconstruction, and adaptive adjustment of control logic, so that the system can automatically discover, re-plan control chains and quickly recover control functions when any control node or communication unit fails, thereby significantly improving the reliability, stability and self-recovery capability of the weak current control network. SUMMARY

[0003] The present application aims to provide a weak current equipment intelligent control system and method based on Internet of Things to solve the problems in the prior art.

[0004] To solve the above technical problems, the present application provides the following technical solution: a weak current equipment intelligent control method based on Internet of Things, the method comprising: Step S100: Set weak current equipment as nodes, aggregate the physical connection relationship and control logic channel of each node, generate a topology structure diagram of the weak current control network, and generate a control path set corresponding to different task types executed in different functional areas through historical records; Step S200: Calculate the path score of the historical records by obtaining the state table of the nodes, obtain the main control path according to the average value of the path score of each control path, obtain the historical records of each control path, calculate the path stability index, communication delay index and node health index of the historical records, and construct a path health feature set to fit and train the task execution results to generate a path health score function; Step S300: preset a monitoring period, calculate the health score of each stage when executing the current task, judge whether to trigger the path switching judgment process, if not, continue to collect, if trigger the path switching judgment process, screen the candidate control path, and synchronize the candidate control path that meets the condition to the control center; Step S400: in the control center, determine the new main control path, activate the strategy copy of the new main control path, set the time length of the initial switching, judge whether the new main control path meets the preset consistency condition, if yes, continue to execute the new main control path, if not, trigger the fault handling mechanism; Step S500: in the path switching and task recovery process, mark the to-be-confirmed abnormal node, determine whether the to-be-confirmed abnormal node is a high-risk node by monitoring the running state data of the to-be-confirmed abnormal node and drawing a fluctuation feature map, if it is a high-risk node, remind the maintenance personnel to handle it.

[0005] Further, step S100 includes: Step S101: set all weak current equipment deployed in the hospital building as nodes, obtain the physical connection relationship and control logic channel of each weak current equipment by analyzing the wiring drawing, control system configuration table and communication link log; Step S102: classify the devices with the same upper control node into the same functional group, construct the logical connection path between nodes, and generate the topology structure diagram of the weak current control network; Step S103: in the topology structure diagram, collect the basic attributes of each node, the basic attributes include node type, unique identification information, communication parameters, control load type, write the basic attributes into the state table of the node, and generate the state table of all nodes; Step S104: in the control center, obtain the historical record of task scheduling, extract the task type and the functional area corresponding to the weak current equipment executing the scheduling in the historical record, map out the task path corresponding relationship, and generate a feasible control path set in combination with the current topology structure; By analyzing the wiring drawing, control system configuration table and communication link log in the hospital building, the physical connection relationship and control channel mapping relationship between various weak current equipment are automatically extracted, avoiding the traditional manual configuration mode, realizing the structured and digital management of weak current system node relationship, and effectively improving the modeling efficiency and accuracy; By classifying the devices with the same upper control node into the same functional group, a functional grouping system based on control attribution logic is constructed, which makes the subsequent control strategy deployment and path planning more consistent with the actual control scene of the equipment, and improves the aggregation and effectiveness of the control strategy execution; By collecting the basic attributes of each weak electric node including node type, identification information, communication parameters, load type and the like and uniformly writing into a state table, the structured modeling and topology visualization of the entire weak electric control network are realized, thereby providing high-quality structural support for subsequent system state perception, health assessment and path scheduling. By obtaining the historical task scheduling records in the control center, the task type, scheduling device and the function area to which the scheduling device belongs are extracted, the mapping relationship between the historical tasks and the feasible control paths is generated in combination with the current topology structure, thereby constructing the task path set oriented to the function area, the set can provide input for the path scoring, health analysis and path switching modules, and the dynamic adaptability of the system is enhanced. Through the implementation of the method, the system can automatically complete the network topology modeling, state table construction and path set generation without manual intervention, thereby providing data support and structural support for subsequent control path scheduling, risk assessment and fault handling, and significantly improving the operation controllability, management transparency and intelligent degree of the overall weak electric control system.

[0006] Further, the step S200 comprises: Step S201: obtaining a historical record set of executing a certain task type in a certain function area, extracting the control path used for executing the task type in the historical record, classifying the historical records of the same control path, collecting the state table of each node involved in the historical record, extracting the communication parameters in the state table, and performing normalization processing, and calculating the path score according to the following formula: ; Wherein, A represents the path score, B da represents the normalized value of the a-th communication parameter in the d-th node, C a represents the weight of the a-th communication parameter, D d represents the weight of the d-th node, b represents the total number of communication parameters, and e represents the total number of nodes. Step S202: obtaining the path score corresponding to all historical records in a certain control path, calculating the average value of the path score of the control path, sorting the control paths used for executing a certain task type in a certain function area from high to low according to the average value of the path score, obtaining a candidate control path set, and setting the control path with the highest average value of the path score as the main control path. Step S203: Obtain the historical record of each control path, collect the task execution result, communication delay and node communication parameter of each historical record, count the number of successful task execution as H1 and the total number of historical records H2, calculate the path stability index as P1=H1 / H2, obtain the average communication delay of all communication links, the communication link is the connection between each two nodes, and calculate the communication delay index as , wherein t h represents the average communication delay of the hth communication link, j represents the total number of communication links, the communication parameters of the nodes are normalized, and the health score of the node is calculated according to the following node health score formula: ; , wherein E represents the health score of the node, B f represents the normalized value of the fth communication parameter, C f represents the weight of the fth communication parameter, the health scores of all nodes are summarized, the average value of the health score is calculated and set as the node health index; Step S204: The path stability index, communication delay index and node health index of the historical record are normalized and summarized to construct the path health feature set. The path health feature set of each historical record is taken as input, and the corresponding task execution result is taken as label to construct the supervised learning sample set. The path health score function is fitted and trained by the least square method, and the weight of the path health score function is trained. The path health score function is: K=Q1×P1+Q2×P2+Q3×P3, wherein K represents the path health score, Q1, Q2 and Q3 represent the weights of the path stability index, communication delay index and node health index trained respectively; By classifying, analyzing and modeling the performance data of each control path in the historical task execution record, a multi-dimensional evaluation system including path score, stability index, communication delay index and node health index is established, so that the advantages and disadvantages of the control path have a measurable and quantitative basis, which provides a scientific decision basis for path screening, scheduling and switching; In the path health evaluation process, not only the stability of the whole path such as success rate and average delay is counted, but also the node level communication parameter is introduced as input, and the node health score mechanism is constructed, so as to accurately reflect the potential local hidden danger in the path, improve the identification ability of the system to fine-grained abnormality, and enhance the comprehensiveness and sensitivity of the control path evaluation; The method collects key indexes such as path stability, communication delay, node health, and the like into a unified path health feature set, and combines historical task execution results as labels to construct a supervised learning data set, so that the path scoring process no longer depends on static configuration or experience weight, but is based on actual running effect for model training, and has higher scene adaptability and real-time accuracy. By supervised learning modeling on the health feature set, the path health scoring function is fitted and trained by the least square method, and dynamic weight values of different indexes in different task scenarios are obtained, so that the scoring result can be accurately matched according to the task type, and the adaptability and control reliability of the main path selection are improved. Using the path health score output by the scoring function, the system can dynamically select the path with the highest score as the main control path, and construct a candidate path set for scheduling and calling, thereby forming a path fault-tolerant mechanism based on health degree judgment, and effectively improving the self-healing ability and task continuity guarantee ability of the weak current control network in complex environments.

[0007] Further, the step S300 comprises: Step S301: presetting a monitoring period, acquiring a current execution task type and a functional area, and determining a current main control path; according to the preset monitoring period, collecting communication parameters of all nodes on the main control path, and performing normalization processing; and calculating the health score of the node according to the node health score formula; Step S302: presetting a health score threshold, comparing the health score of each node 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 there is any unhealthy node in the main control path, the system triggers a path switching judgment process, if there is no unhealthy node in the main control path, the system continues to collect according to the preset monitoring period; Step S303: when the system triggers the path switching judgment process, traversing the candidate control path set, acquiring the communication parameters of each node in the candidate control path, and calculating the health score of each node, eliminating the candidate control path with unhealthy nodes, calculating the path stability index, communication delay index and node health index in the remaining candidate control path, and inputting them into the path health score function, calculating the path health score of the remaining candidate control path, and sorting the remaining candidate control path according to the path health score from high to low, and synchronizing the sorted remaining candidate control path set to the control center; Through the preset monitoring period, the system can collect the communication parameters of all nodes on the main control path in real time, and analyze based on the standardization processing and the health score function, and construct an operation state quantitative model of the main path, so that the system can continuously perceive the path health condition in the task execution process, and effectively improve the state perception ability of the system operation. The node health score is compared with the preset threshold to realize independent evaluation of the running state of a single node in the main path. When the health score of a node is lower than the threshold, it is considered as a potential risk of failure, thereby triggering the path switching mechanism. Compared with the traditional "overall path abnormality judgment", it has higher sensitivity and local fault recognition ability, and reduces the risk of task interruption. The system can start the path switching judgment process in time when the main path has a risk, and through secondary screening of the node health of the candidate path, the path with unhealthy nodes is excluded, thereby improving the overall availability of the candidate path and ensuring that the switched path has higher execution reliability. By calculating the path stability index, communication delay index and node health index of the remaining candidate paths and inputting them into the trained path health score function, the system can comprehensively score and prioritize the candidate paths, select the optimal path and synchronize it to the control center. This mechanism ensures that the path switching is scientific, data-driven and scenario-adaptive. Through the whole process of real-time monitoring, intelligent identification of abnormal nodes, starting health path screening, optimal path sorting and synchronization to the control center, a complete closed-loop mechanism from problem discovery to fault avoidance is realized, which has high automation and self-healing, and significantly improves the task continuity protection capability of the weak current control network in the case of sudden node abnormality or communication fluctuation.

[0008] Further, step S400 comprises: Step S401: Select the control path with the highest path health score in the remaining candidate control path set as the new main control path in the control center, call the strategy template of the current task type through the control center, generate a control strategy copy according to the target control node in the new main control path, and distribute the strategy copy to the target control node through the fast deployment mechanism. The strategy copy is marked as an activated state; Step S402: Extract task context information from the nodes executing the current task in the original main control path, including task state variables, sensor feedback state and last action output, write the extracted task context information into the task context middleware for caching, and synchronize the task context information to the target control node deploying the strategy copy in the new main control path through the control channel; Step S403: Set the strategy copy in the new main control path to an activated state, restore task execution, and preset the time length of the switching initial stage. In the switching initial stage, the output consistency verification is performed by comparing the control instructions output by the new main control path with the output of the original main control path in the corresponding state. If the output consistency verification meets the preset consistency condition, the task execution of the new main control path is continued. If the output consistency verification does not meet the preset consistency condition, the fault handling mechanism is triggered. Step S404: After triggering the fault handling mechanism, it is judged whether the original main control path is in an available state. If the original main control path is available, the original main control path is switched back, the original control policy copy is reactivated, and task execution is resumed. If the original main control path is not available, a path with a second highest health score is selected as a new main control path based on a remaining candidate control path set, and steps of control policy copy deployment, task context synchronization and policy copy activation are repeatedly performed until task recovery is completed. By automatically selecting a path with the highest health score from the remaining candidate paths, and combining with the task type to call the corresponding policy template, a new control policy copy is generated and distributed to the target node, thereby realizing rapid replacement of the main path and policy deployment without interrupting task execution, and significantly improving the response speed and policy adaptability of the system. During path switching, the task context of the original path including state variables, sensor feedback and last action is extracted and synchronously transmitted to the new path, effectively avoiding the context breakpoint problem caused by path switching, realizing seamless migration of task state, and providing necessary support for continuous execution of complex tasks. In the initial stage of activating the new path policy copy, the system compares the control outputs of the new and old main paths under the same context state to perform consistency judgment. If the verification is passed, the new path is confirmed to be effective. If the verification is not passed, the fault handling mechanism is immediately triggered to effectively prevent the risk of miscontrol caused by policy mismatch or path distortion, and to improve the robustness and safety during the system switching process. When the new main path output verification fails, the system will first judge the availability of the original main path. If it is available, the original control policy is restored. If it is not available, the suboptimal path is tried in sequence according to the candidate path score, and the deployment, synchronization and activation steps are repeated until the task is successfully recovered, thereby forming a complete closed-loop path switching and task recovery mechanism. Under abnormal conditions such as node exception and communication fluctuation, the control guarantee strategy can realize uninterrupted task, non-failed policy and recoverable path, so that the weak current control network has high adaptive ability, fault tolerance ability and continuous control ability, and is especially suitable for scenes such as hospitals, rail transit and data centers which have high requirements for control stability and continuity.

[0009] Further, step S500 comprises: Step S501: During path switching and task recovery, if a number of output consistency verification failures, control task repeated rollback, path selection failure are detected, the system obtains the association relationship of the abnormal trigger node and the upstream and downstream nodes based on the path switching record, the abnormal log and the control path topology relationship, constructs a suspected fault node set, and marks the abnormal trigger node as a to-be-confirmed abnormal node. Step S502: Monitor the to-be-confirmed abnormal node, continuously collect running state data of the to-be-confirmed abnormal node, the running state data including communication delay, instruction response time, control output offset, upstream and downstream communication success rate, and draw a fluctuation feature map of the running state data within a preset judgment period; Step S503: According to the fluctuation feature map of the running state data, calculate the fluctuation slope of the running state data, preset a fluctuation slope threshold, when the fluctuation slope exceeds the fluctuation slope threshold, update the to-be-confirmed abnormal node to a high-risk node, and perform a logical isolation operation, and push the high-risk node to an operation and maintenance management platform to prompt a maintenance personnel to intervene in diagnosis and processing; When the control path repeatedly fails in the switching and task recovery process, the system no longer stays in the surface path switching logic, but actively traces back the path switching record and abnormal log, and extracts a key node set related to the abnormal trigger in combination with the control path topology relationship, to realize preliminary locking and to-be-confirmed marking of the abnormal root node, laying a foundation for subsequent refined diagnosis; For the to-be-confirmed abnormal node, the system can continuously collect its running state data, including communication delay, instruction response time, control output offset, upstream and downstream communication success rate and other multi-dimensional performance indicators, and construct a running state fluctuation map, so that the system can comprehensively reflect the node performance fluctuation trend, realize pre-monitoring and quantitative evaluation of potential risks; By calculating the fluctuation slope of the node running state data and comparing it with the preset threshold, whether the node has a continuous abnormal fluctuation trend in the time sequence is identified, compared with the traditional abnormal judgment method based on single threshold, this method has stronger trend analysis ability and dynamic abnormal identification ability, and can discover potential risk nodes earlier; Once a high-risk node is identified, the system can immediately perform a logical isolation operation to block its participation in the current control path, prevent it from continuously affecting task execution, and at the same time, the state of the node will be pushed to the operation and maintenance management platform to prompt the maintenance personnel to intervene and physically investigate on site, so as to realize a closed-loop operation and maintenance support system from automatic identification to automatic isolation to manual intervention; The weak current control system has intelligent operation and maintenance capabilities of self-diagnosis, self-identification and self-isolation, effectively reduces the system problems such as frequent path switching and task recovery failure caused by node failure, improves the elastic survival ability of the system in a fault environment, and provides structured technical support for engineering operation and maintenance of large-scale complex control networks.

[0010] In order to better realize the above method, a weak current equipment intelligent control system based on Internet of Things is also proposed, which comprises 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 sets the weak current equipment as a node, collects the physical connection relationship and control logic channel of each node, generates a topology structure diagram of the weak current control network, and generates a control path set corresponding to different task types under different functional areas through historical records; The control path analysis module obtains the state table of the node, calculates the path score of the historical record, obtains the main control path according to the average value of the path score of each control path, obtains the historical record of each control path, calculates the path stability index, communication time delay index and node health index of the historical record, and constructs a path health feature set to fit and train the task execution result to generate a path health score function; The task monitoring module presets a monitoring period, calculates the health score of each stage when executing the current task, judges whether to trigger the path switching judgment process, if the path switching judgment process is not triggered, the collection is continued, if the path switching judgment process is triggered, the candidate control path is screened, and the candidate control path meeting the condition is synchronized to the control center; The switching judgment module determines the new main control path in the control center, activates the strategy copy of the new main control path, sets the time length of the initial switching, judges whether the new main control path meets the preset consistency condition, if it meets, the new main control path is continued to be executed, if it does not meet, the fault handling mechanism is triggered; The abnormal node identification module marks the node as a to-be-confirmed abnormal node during the path switching and task recovery process, monitors the running state data of the to-be-confirmed abnormal node, draws a fluctuation feature diagram, determines whether the to-be-confirmed abnormal node is a high-risk node, and if it is a high-risk node, reminds the maintenance personnel to handle it.

[0011] Further, the topology modeling module includes a topology structure unit and a control path set unit: The topology structure unit sets all weak current equipment deployed in the hospital building as a node, obtains the physical connection relationship and control logic channel of each weak current equipment by analyzing the wiring drawing, control system configuration table and communication link log, classifies the devices with the same upper control node into the same functional group, constructs the logical connection path between the nodes, and generates a topology structure diagram of the weak current control network; The control path set unit collects the basic attributes of each node in the topology structure diagram, the basic attributes including node type, unique identification information, communication parameter and control load type, writes the basic attributes into the state table of the node, generates the state table of all nodes, obtains the historical record of task scheduling in the control center, extracts the task type and the functional area corresponding to the weak current equipment executing the scheduling in the historical record, maps out the task path corresponding relationship, and generates a feasible control path set in combination with the current topology structure Further, the task monitoring module comprises a path switching determination unit and a remaining candidate control path determination unit. The path switching determination unit: preset monitoring period, acquires the current execution task type and function area, and determines the current main control path; according to the preset monitoring period, collects the communication parameters of all nodes on the main control path, and performs normalization processing; according to the node health score formula, the health score of the node is calculated; a preset health score threshold is compared with the health score of each node; if the health score of a certain node is lower than the health score threshold, the node is set as an unhealthy node; if there is any unhealthy node in the main control path, the system triggers the path switching determination process; if there is no unhealthy node in the main control path, the system continues to collect according to the preset monitoring period. The remaining candidate control path determination unit: when the system triggers the path switching determination process, the candidate control path set is traversed, the communication parameters of each node in the candidate control path are acquired, and the health score of each node is calculated; the candidate control path with unhealthy nodes is eliminated; the path stability index, the communication delay index and the node health index in the remaining candidate control path are calculated and input into the path health score function; the path health score of the remaining candidate control path is calculated, and the remaining candidate control paths are sorted in descending order of path health score; the sorted remaining candidate control path set is synchronized to the control center.

[0012] Further, the abnormal node identification module comprises a marked to-be-confirmed abnormal node unit and a high-risk node unit. The marked to-be-confirmed abnormal node unit: in the path switching and task recovery process, if the output consistency check fails for several times, the control task is repeatedly rolled back, and the path selection fails, the system acquires the association relationship of the abnormal trigger node and the upstream and downstream nodes based on the path switching record, the abnormal log and the control path topology relationship, constructs a suspected fault node set, and marks the abnormal trigger node as a to-be-confirmed abnormal node. The high-risk node unit: monitors the to-be-confirmed abnormal node, continuously collects the running state data of the to-be-confirmed abnormal node, the running state data includes communication delay, instruction response time, control output offset, upstream and downstream communication success rate, and draws the fluctuation characteristic map of the running state data in the preset judgment period; according to the fluctuation characteristic map of the running state data, the fluctuation slope of the running state data is calculated; a preset fluctuation slope threshold is set; when the fluctuation slope exceeds the fluctuation slope threshold, the to-be-confirmed abnormal node is updated to a high-risk node, and a logical isolation operation is performed; at the same time, the high-risk node is pushed to the operation and maintenance management platform, prompting the maintenance personnel to intervene in diagnosis and processing.

[0013] Compared with the prior art, the present application has the following advantages: 1. Unlike traditional weak current control systems that rely solely on statically configured paths, this invention builds a complete topology diagram and path health scoring mechanism, combining historical records to evaluate the stability, latency, and node health of each control path. This enables dynamic path optimization and automatic switching during task execution, effectively avoiding the risk of task interruption due to single path failures. 2. This invention, for the first time, integrates multi-dimensional parameters such as path stability, communication latency, and node health into a unified path scoring model. This model is trainable and adaptive, enabling a shift from rule-driven to data-driven path selection. Compared to fixed thresholds or manually configured strategies, this model offers greater flexibility and generalizability. 3. Traditional systems often rely on manual troubleshooting or passive alarms. This invention uses a suspected fault node identification mechanism based on the slope of operating status fluctuations. It can infer the set of problem nodes from abnormal events such as path switching failures, and combine it with time series data graphs for judgment. This allows equipment problems to be exposed and isolated proactively, greatly improving system stability and fault diagnosis efficiency. 4. Compared with the existing system that lacks a closed-loop linkage mechanism in path switching and fault handling, the present invention constructs a complete task scheduling closed loop through a combination of steps in the entire process. It can not only adaptively select the optimal control path, but also perform multi-level responses such as rapid recovery, alarm, and isolation when an abnormality occurs, thereby realizing the autonomous evolution capability of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of an intelligent control method for weak current equipment based on the Internet of Things according to the present invention; Figure 2 This is a structural schematic diagram of an intelligent control system for weak current equipment based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] See also Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent control method for weak current equipment based on the Internet of Things, the method comprising: Step S100: Set weak current devices as nodes, summarize the physical connection relationship and control logic channel of each node, generate a topology diagram of the weak current control network, and generate a set of control paths corresponding to different task types in different functional areas based on historical records; wherein, the step S100 comprises: Step S101: setting all weak current equipment deployed inside the hospital building as nodes, obtaining the physical connection relationship and control logic channel of each weak current equipment by analyzing the wiring drawing, control system configuration table and communication link log; Step S102: classifying the devices with the same upper control node into the same functional group, constructing the logical connection path between the nodes, and generating the topology structure diagram of the weak current control network; Step S103: collecting the basic attributes of each node in the topology structure diagram, the basic attributes including node type, unique identification information, communication parameter, control load type, writing the basic attributes into the state table of the node, and generating the state table of all nodes; Step S104: in the control center, obtaining the historical record of task scheduling, extracting the task type and the functional area corresponding to the weak current equipment performing the scheduling in the historical record, mapping out the task path corresponding relationship, and generating the feasible control path set in combination with the current topology structure; For example, taking a certain three-A comprehensive hospital inpatient building as a specific application scenario, the building is deployed with multiple weak electronic systems, including intelligent lighting system, security monitoring system, elevator control system, call intercom system, environmental monitoring system, etc. The terminal devices under each system are connected with the controller through physical communication lines, forming a complex weak current control network. Firstly, all weak current devices with control ability or being controllable in the hospital are set as network nodes, including light control module, camera, elevator control board, temperature and humidity sensor, call host, etc. The physical connection relationship and logical channel relationship between these nodes are obtained by analyzing the hospital intelligent construction wiring drawing, control system configuration list and communication link log, for example, it is found that the bedside lamp in the 5F ward area is connected to the 5F light controller through the KNX bus, and then gathered to the central control host. According to the hierarchical structure of the control system, the devices with the same upper control node are divided into the same functional group, for example, all the bedside lamps and corridor lamps connected to the 5F light controller are classified as "5F lighting subsystem". On this basis, the system generates the control logic path from the control center to each terminal node through logical deduction and connection tracking, and finally obtains a structured weak current control network topology diagram. The basic attributes of each node in the topology diagram are collected, including: node type such as switch, sensor, controller; unique identification information such as MAC address, device number; communication parameter such as communication protocol, baud rate; control load type such as lighting, audio and video, call; After the collection is completed, a node state table is generated for subsequent path construction and health analysis. For example, the bedside lamp A001 node in the 5F lighting subsystem records the following information: KNX protocol, address 1 / 0 / 15, type of lighting executor; The historical records of the control center scheduling platform are read, and the "night patrol task" scheduling process is extracted. It is found that the task involves controlling the corridor lights and cameras in the 5F and 6F ward areas. Combined with the existing topology structure, the system automatically calculates multiple feasible task control paths, such as: Control path 1: central host → 5F lighting controller → corridor light L051 Control path 2: central host → security master control platform → camera C061.

[0017] Step S200: Calculate the path score of the historical records by obtaining the state table of the nodes. According to the average value of the path score of each control path, the main control path is obtained. At the same time, the historical records of each control path are obtained, and the path stability index, communication delay index, and node health index are calculated. The path health feature set is constructed and fitted with the task execution results to generate the path health score function; Step S200 includes: Step S201: Obtain a set of historical records of executing a certain task type in a certain functional area, extract the control path used to execute the task type in the historical records, classify the historical records of the same control path, and collect the state table of each node involved in the historical records. Extract the communication parameters in the state table and perform normalization processing. Calculate the path score according to the following formula: ; Where A represents the path score, B da represents the normalized value of the a-th communication parameter in the d-th node, C a represents the weight of the a-th communication parameter, D d represents the weight of the d-th node, b represents the total number of communication parameters, and e represents the total number of nodes. Step S202: Obtain the path score corresponding to all historical records in a certain control path, calculate the average value of the path score of the control path, and sort the control paths used to execute a certain task type in a certain functional area according to the average value of the path score from high to low to obtain a candidate control path set. The control path with the highest path score average value is set as the main control path. Step S203: Obtain the historical record of each control path, collect the task execution result, communication delay, and node communication parameter of each historical record, count the number of successful task execution as H1 and the total number of historical records H2, calculate the path stability index as P1=H1 / H2, obtain the average communication delay of all communication links, the communication link is the connection between each two nodes, and calculate the communication delay index as where t h represents the average communication delay of the hth communication link, j represents the total number of communication links, the communication parameters of the nodes are normalized, and the health score of the node is calculated according to the following node health score formula: ; where E represents the health score of the node, B f represents the normalized value of the fth communication parameter, C f represents the weight of the fth communication parameter, the health scores of all nodes are summarized, the average value of the health score is calculated and set as the node health index; Step S204: The path stability index, communication delay index, and node health index of the historical record are normalized and summarized to construct the path health feature set. The path health feature set of each historical record is taken as input, and the corresponding task execution result is taken as label to construct the supervised learning sample set. The path health score function is fitted and trained by the least square method, the weight of the path health score function is trained, and the path health score function is K=Q1XP1+Q2XP2+Q3XP3, where K represents the path health score, Q1, Q2, and Q3 represent the weights of the path stability index, communication delay index, and node health index trained respectively; For example, the collected communication parameter values are shown in Table 1:

[0018] Table 1 The normalized values are shown in Table 2:

[0019] Table 2 The path score is calculated by weighting to be 0.281; The average value of the path score of control path 1 is 0.285, and the average value of the path score of control path 2 is 0.276, so control path 1 is the main control path; ​Assuming that the system calls the historical execution record of control path 1, a total of 30 tasks: the number of successful times is 28, the total number of historical records is 30, and the path stability index is 0.933; control path 1 includes two communication links, the average communication delay of the central host→5F lighting controller communication link is 13, and the average communication delay of the 5F lighting controller→corridor lamp L051 communication link is 15, then the communication delay index is 14, and according to the data in table 2, the node health index is calculated to be 0.305; Assuming that the historical task execution record is table 3:

[0020] Table 3 Using the Min-Max normalization formula to process each column of feature values, using least squares regression to train the above samples, and fitting the weight examples Q1=0.55, Q2=−0.30 (indicating that the communication delay ratio is high, and the health score decreases), and Q3=0.45.

[0021] Step S300: preset monitoring period, calculate the health score of each stage when executing the current task, judge whether to trigger the path switching judgment process, if not, continue to collect, if trigger the path switching judgment process, screen the candidate control path, and synchronize the candidate control path meeting the conditions to the control center; Among them, step S300 includes: Step S301: preset monitoring period, obtain the current task type and function area, and determine the current main control path; according to the preset monitoring period, collect the communication parameters of all nodes on the main control path, and perform normalization processing; calculate the health score of the node according to the node health score formula; Step S302: preset health score threshold, compare the health score of each node with the health score threshold, if the health score of a node is lower than the health score threshold, the node is set as an unhealthy node, if there is any node in the main control path is an unhealthy node, the system triggers the path switching judgment process, if there is no unhealthy node in the main control path, the system continues to collect according to the preset monitoring period; Step S303: when the system triggers the path switching judgment process, traverse the candidate control path set, obtain the communication parameters of each node in the candidate control path, and calculate the health score of each node, eliminate the candidate control path with unhealthy nodes, calculate the path stability index, communication delay index and node health index of the remaining candidate control path, and input them into the path health score function, calculate the path health score of the remaining candidate control path, and sort the remaining candidate control path according to the path health score from high to low, synchronize the sorted remaining candidate control path set to the control center; For example, preset period: 10 minutes once, current task type: night patrol task, current functional area: 5F ward area, current main control path: control path 1; The collected communication parameters of this period are normalized and the data are shown in Table 4:

[0022] Table 4 The health score of the control center is 0.34, the health score of the 5F lighting controller is 0.685, and the health score of the corridor lamp L051 is 0.89. The preset health score threshold is 0.7. Therefore, the control center and the 5F lighting controller are unhealthy nodes, and the system triggers the path switching judgment process; The candidate control path set is: control path 2: control center→security main control platform→camera C061; control path 3: central host→environment controller→temperature and humidity detector TMP501; The normalized data of control path 2 are shown in Table 5:

[0023] Table 5 The health score of the control center is 0.34, the health score of the security main control platform is 0.42, and the health score of the camera C061 is 0.57. There is an unhealthy node, and this control path is excluded. The health score of the central host in control path 3 is 0.71, the health score of the environment controller is 0.78, and the health score of the temperature and humidity detector TMP501 is 0.82. Control path 3 is retained. The normalized values of the path stability index, the communication time delay index, and the node health index in control path 3 are 1, 0, and 0.833, respectively. The path health score is 0.925.

[0024] Step S400: In the control center, determine a new main control path, activate a strategy copy of the new main control path, set the time length of the initial switching, judge whether the new main control path meets the preset consistency condition, if yes, continue to execute the new main control path, if not, trigger a fault handling mechanism; Step S400 includes: Step S401: In the control center, select the control path with the highest path health score from the remaining candidate control path set as the new main control path, call the strategy template of the current task type through the control center, generate a control strategy copy according to the target control node in the new main control path, and distribute the strategy copy to the target control node through a rapid deployment mechanism. The strategy copy is marked as an activated state; Step S402: Extract task context information from the node executing the current task in the original master control path, the task context information including task state variables, sensor feedback state, last action output, write the extracted task context information into the task context middleware for caching, and synchronize the task context information to the target control node deploying the strategy copy in the new master control path through the control channel; Step S403: Set the strategy copy in the new master control path to an active state, resume task execution, and preset the time length of the switching initial stage. During the switching initial stage, the output consistency is verified by comparing the control instructions output by the new master control path with the output of the original master control path in the corresponding state. If the output consistency verification meets the preset consistency condition, the task execution of the new master control path is continued. If the output consistency verification does not meet the preset consistency condition, a fault handling mechanism is triggered. Step S404: When the fault handling mechanism is triggered, it is judged whether the original master control path is in an available state. If the original master control path is available, the original master control path is switched back, the original control strategy copy is reactivated, and the task execution is resumed. If the original master control path is not available, the path with the second highest path health score is selected as the new master control path based on the remaining candidate control path set, and the steps of control strategy copy deployment, task context synchronization and strategy copy activation are repeated until the task recovery is completed. For example, the functions of the system call task strategy template include patrol execution logic, lighting judgment, environment feedback collection, and feedback forwarding. According to the new path structure, the target control node is the temperature and humidity sensor TMP501, and the strategy copy will be deployed at the TMP501 node side and activated after context synchronization. The system extracts the task execution context in the original master path as the patrol stage variable: 2 patrol to the west section of the corridor; the last device output is light on for 5s; the sensor feedback is light on success, and the report delay = 120ms; the above data is written into the task context middleware and synchronized to the node where TMP501 is located through the control channel. The environment controller reads the context data and prepares for strategy activation. During the switching initial stage: set a 60-second observation window, The system compares the consistency of the execution output of control path 3 with the output of historical control path 1 in the same task state as shown in Table 6:

[0025] Table 6 Although the commands themselves are different, one is a light on command and one is an environment perception command, but both meet the task goal of "patrol state visualization + state confirmation", so they are considered to pass the functional consistency verification.

[0026] Step S500: In the path switching and task recovery process, mark the to-be-confirmed abnormal node, determine whether the to-be-confirmed abnormal node is a high-risk node by monitoring the running state data of the to-be-confirmed abnormal node and drawing a fluctuation feature map, and if it is a high-risk node, remind the maintenance personnel to handle it; Wherein, step S500 comprises: Step S501: In the path switching and task recovery process, if the output consistency check fails several times, the control task is repeatedly rolled back, and the path selection fails, the system obtains the correlation relationship of the abnormal trigger node and the upstream and downstream nodes based on the path switching record, the abnormal log and the control path topology relationship, constructs a suspected fault node set, and marks the abnormal trigger node as a to-be-confirmed abnormal node; Step S502: Monitor the to-be-confirmed abnormal node, continuously collect the running state data of the to-be-confirmed abnormal node, the running state data includes communication delay, instruction response time, control output offset, upstream and downstream communication success rate, and draw a fluctuation feature map of the running state data in a preset judgment period; Step S503: According to the fluctuation feature map of the running state data, calculate the fluctuation slope of the running state data, preset the fluctuation slope threshold, when the fluctuation slope exceeds the fluctuation slope threshold, update the to-be-confirmed abnormal node to a high-risk node, and execute a logical isolation operation, at the same time, push the high-risk node to the operation and maintenance management platform, prompt the maintenance personnel to intervene in diagnosis and processing; For example, the environmental controller and the security main control platform are to-be-confirmed abnormal nodes, the collection period is set to 5 minutes, and the running state data is collected every 5 seconds to draw a fluctuation feature map of the running state data; The system automatically analyzes the fluctuation characteristics of the running data, and extracts the fluctuation slope as table 7:

[0027] Table 7 Set the delay fluctuation slope threshold to 30ms / min, the response time fluctuation slope threshold to 20ms / min, the output offset slope threshold to 0.1 offset / min, and the communication success rate change slope threshold to 5% communication success rate / min; The system automatically performs the following operations: remove the security main control platform from all path candidate sets, stop issuing control strategies to the node, and push the alarm to the operation and maintenance platform.

[0028] In order to better realize the above method, a weak current equipment intelligent control system based on Internet of Things is also proposed, which comprises 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 sets the weak current equipment as a node, collects the physical connection relationship and control logic channel of each node, generates a topology structure diagram of the weak current control network, and generates a control path set corresponding to different task types under different functional areas through historical records; The topology modeling module includes a topology structure unit and a control path set unit: The topology structure unit sets all weak current equipment deployed in the hospital building as a node, obtains the physical connection relationship and control logic channel of each weak current equipment by analyzing wiring drawings, control system configuration tables and communication link logs, classifies devices with the same upper control node into the same functional group, constructs the logical connection path between nodes, and generates a topology structure diagram of the weak current control network. The control path set unit collects the basic attributes of each node in the topology structure diagram, including node type, unique identification information, communication parameters, and control load type, writes the basic attributes into the state table of the node, generates a state table of all nodes, obtains the historical records of task scheduling in the control center, extracts the task type and the functional area of the weak current equipment corresponding to the historical records, maps the task path corresponding relationship, and generates a feasible control path set in combination with the current topology structure.

[0029] The control path analysis module calculates the path score of the historical records by obtaining the state table of the node, obtains the main control path according to the average value of the path score of each control path, obtains the path stability index, communication delay index and node health index of the historical records by calculating the historical records of each control path, and constructs the path health feature set and task execution result for fitting training to generate a path health score function. The task monitoring module presets a monitoring period, calculates the health score of each stage when executing the current task, judges whether to trigger the path switching judgment process, if the path switching judgment process is not triggered, the collection is continued, if the path switching judgment process is triggered, the candidate control path is screened, and the candidate control path meeting the condition is synchronized to the control center. The task monitoring module includes a path switching judgment unit and a remaining candidate control path determination unit: The path switching determination unit: preset monitoring period, obtain the current execution task type and function area, and determine the current main control path, collect the communication parameters of all nodes on the main control path according to the preset monitoring period, and perform normalization processing, calculate the health score of the node according to the node health score formula, preset the health score threshold, compare the health score of each node 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 there is any node in the main control path that is an unhealthy node, the system triggers the path switching determination process, if there is no unhealthy node in the main control path, the system continues to collect according to the preset monitoring period; The determination of the remaining candidate control path unit: when the system triggers the path switching determination process, traverse the candidate control path set, obtain the communication parameters of each node in the candidate control path, and calculate the health score of each node, eliminate the candidate control path with unhealthy nodes, calculate the path stability index, communication delay index and node health index in the remaining candidate control path, and input them into the path health score function, calculate the path health score of the remaining candidate control path, and sort the remaining candidate control paths according to the path health score from high to low, and synchronize the sorted remaining candidate control path set to the control center.

[0030] The switching judgment module: in the control center, determine the new main control path, activate the strategy copy of the new main control path, set the time length of the initial switching, judge whether the new main control path meets the preset consistency condition, if it meets, continue to execute the new main control path, if it does not meet, trigger the fault handling mechanism; The abnormal node identification module: in the process of path switching and task recovery, mark as a to-be-confirmed abnormal node, monitor the running state data of the to-be-confirmed abnormal node, draw a fluctuation feature map, determine whether the to-be-confirmed abnormal node is a high-risk node, if it is a high-risk node, remind the maintenance personnel to handle The abnormal node identification module includes a to-be-confirmed abnormal node marking unit and a high-risk node unit: The to-be-confirmed abnormal node marking unit: in the process of path switching and task recovery, if the output consistency check fails for several times, the control task is repeatedly rolled back, and the path selection fails, the system obtains the association relationship of the abnormal trigger node and the upstream and downstream nodes based on the path switching record, the abnormal log and the control path topology relationship, constructs a suspected fault node set, and marks the abnormal trigger node as a to-be-confirmed abnormal node; High-risk node unit: monitor the to-be-confirmed abnormal node, continuously collect running state data of the to-be-confirmed abnormal node, the running state data including communication delay, instruction response time, control output offset, upstream and downstream communication success rate, and draw a fluctuation characteristic map of the running state data within a preset judgment period, calculate the fluctuation slope of the running state data according to the fluctuation characteristic map of the running state data, preset a fluctuation slope threshold, when the fluctuation slope exceeds the fluctuation slope threshold, update the to-be-confirmed abnormal node to a high-risk node, and perform a logical isolation operation, at the same time, push the high-risk node to the operation and maintenance management platform, and prompt the maintenance personnel to intervene in diagnosis and processing.

[0031] It will be obvious to a person skilled in the art that the application is not limited to the details of the foregoing exemplary embodiments, but that the application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being defined by the appended claims rather than by the foregoing description, and it is therefore intended that all changes and modifications that fall within the meaning and range of equivalents of the claims be embraced therein. Any reference signs in the claims should not be construed as limiting the claims concerned.

Claims

1. A method for intelligent control of weak current equipment based on the Internet of Things, characterized in that: Methods include: Step S100: Set weak current devices as nodes, summarize the physical connection relationship and control logic channel of each node, generate a topology diagram of the weak current control network, and generate a set of control paths corresponding to different task types in different functional areas based on historical records; Step S200: Obtain the node's state table, calculate the path score of the historical records, and obtain the main control path based on the average path score of each control path. At the same time, obtain the historical records of each control path, calculate the path stability index, communication delay index, and node health index of the historical records, and construct a path health feature set to fit the task execution results and train them to generate a path health score function. Step S300: Preset a monitoring cycle, calculate the health score of each stage when executing the current task, and determine whether to trigger the path switching determination process. If the path switching determination process is not triggered, continue collecting data. If the path switching determination process is triggered, screen candidate control paths and synchronize the candidate control paths that meet the conditions to the control center; Step S400: In the control center, a new primary control path is determined, a policy copy of the new primary control path is activated, an initial switching duration is set, and a determination is made as to whether the new primary control path meets a preset consistency condition. If so, the new primary control path continues to be executed; if not, a fault handling mechanism is triggered. Step S500: During the path switching and task recovery process, the abnormal node is marked as a node to be confirmed. By monitoring the operating status data of the abnormal node marked as a node to be confirmed and drawing a fluctuation characteristic diagram, it is determined whether the abnormal node to be confirmed is a high-risk node. If it is a high-risk node, the maintenance personnel are reminded to handle it.

2. The method for intelligent control of weak current equipment based on the Internet of Things according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: All weak current devices deployed in the hospital building are set as nodes. By parsing the wiring drawings, control system configuration table and communication link log, the physical connection relationship and control logic channel of each weak current device are obtained; Step S102: Classify devices with the same upper-level control node into the same functional group, construct logical connection paths between the nodes, and generate a topological structure diagram of the weak current control network; Step S103: In the topology diagram, basic attributes of each node are collected, including node type, unique identification information, communication parameters, and control load type, and the basic attributes are written into the node's state table to generate a state table for all nodes; Step S104: In the control center, obtain the historical records of task scheduling, extract the task types in the historical records and the functional areas corresponding to the weak current equipment performing the scheduling, map the task path correspondence, and generate a feasible control path set in combination with the current topology structure.

3. The method for intelligent control of weak current equipment based on the Internet of Things according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: Obtain a set of historical records of executing a certain task type in a certain functional area, extract the control path used to execute the task type from the historical records, classify the historical records of the same control path, collect the state table of each node involved in the historical records, extract the communication parameters in the state table, perform normalization processing, and calculate the path score according to the following formula: ； Among them, A represents the path score, B da It is represented by the normalized value of the ath communication parameter in the dth node, C a Denotes the weight of the ath communication parameter, D d It represents the weight of the dth node, b represents the total number of communication parameters, and e represents the total number of nodes; Step S202: Obtain path scores corresponding to all historical records in a certain control path, calculate the average path score of the control path, sort the control paths used to perform a certain task type in a certain functional area from high to low according to the average path score, obtain a set of candidate control paths, and set the control path with the highest average path score as the primary control path; Step S203: Obtain the history records of each control path, collect the task execution results, communication delay, and communication parameters of each node in each history record, count the number of successfully executed tasks as H1 and the total number of history records as H2, calculate the path stability index as P1=H1 / H2, obtain the average communication delay of all communication links, where the communication link is the connection between every two nodes, and calculate the communication delay index as , where t h Denotes the average communication delay of the hth communication link, j denotes the total number of communication links, and the communication parameters of the node are normalized. The health score of the node is calculated according to the following node health score formula: ； Among them, E represents the health score of the node, B f It is expressed as the normalized value of the fth communication parameter, C f Expressed as the weight of the fth communication parameter, the health scores of all nodes are summarized, and the average health score is calculated and set as the node health index; Step S204: The path stability index, communication delay index, and node health index of the historical records are normalized and summarized to construct a path health feature set. The path health feature set of each historical record is used as input, and the corresponding task execution result is used as a label to construct a supervised learning sample set. The following path health scoring function is fitted and trained using the least squares method to train the weights of the path health scoring function. The path health scoring function is: K = Q1 × P1 + Q2 × P2 + Q3 × P3, where K represents the path health score, and Q1, Q2, and Q3 represent the weights of the trained path stability index, communication delay index, and node health index, respectively.

4. The method for intelligent control of weak current equipment based on the Internet of Things according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: Preset a monitoring cycle, obtain the currently executed task type and functional area, and determine the current main control path. According to the preset monitoring cycle, collect the communication parameters of all nodes on the main control path, perform normalization processing, and calculate the node health score according to the node health score formula; Step S302: A health score threshold is preset, and the health score of each node is compared with the health score threshold. If the health score of a 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 determination process. If there is no unhealthy node in the main control path, the system continues to collect data according to the preset monitoring period. Step S303: After the system triggers the path switching determination process, it traverses the candidate control path set, obtains the communication parameters of each node in the candidate control path, and calculates the health score of each node. The candidate control paths with unhealthy nodes are eliminated, and the path stability index, communication delay index, and node health index of the remaining candidate control paths are calculated and input into the path health scoring function. The path health scores of the remaining candidate control paths are calculated, and the remaining candidate control paths are sorted from high to low according to the path health scores. The sorted remaining candidate control path set is synchronized to the control center.

5. The method for intelligent control of weak current equipment based on the Internet of Things according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: The control center selects the control path with the highest path health score from the remaining candidate control path set and sets it as the new primary control path. The control center calls the policy template of the current task type, generates a control policy copy based on the target control node in the new primary control path, and sends the policy copy to the target control node through the rapid deployment mechanism. The policy copy is marked as pending activation. Step S402: Extract task context information from the node executing the current task in the original primary control path. The task context information includes task state variables, sensor feedback status, and the last action output. The extracted task context information is written into the task context middleware for caching. The task context information is then synchronously transmitted to the target control node in the new primary control path where the policy copy is deployed via the control channel. Step S403: The policy copy in the new primary control path is set to the active state, and task execution is resumed. The initial switching time is preset. During the initial switching period, the control instructions output by the new primary control path are compared with the output of the original primary control path in the corresponding state to perform output consistency verification. If the output consistency verification meets the preset consistency conditions, task execution of the new primary control path is maintained. If the output consistency verification does not meet the preset consistency conditions, the fault handling mechanism is triggered. Step S404: When the fault handling mechanism is triggered, determine whether the original main control path is in an available state. If the original main control path is available, switch back to the original main control path, reactivate the original control policy copy, and resume task execution. If the original main control path is unavailable, based on the remaining candidate control path set, select the path with the second highest path health score as the new main control path, and repeat the control policy copy deployment, task context synchronization, and policy copy activation steps until task recovery is completed.

6. The method for intelligent control of weak current equipment based on the Internet of Things according to claim 5, characterized in that: The step S500 includes the following steps: Step S501: During the path switching and task recovery process, if several output consistency check failures, repeated control task rollbacks, or path selection failures are detected, the system obtains the association between the abnormal triggering node and upstream and downstream nodes based on the path switching records, abnormality logs, and control path topology, constructs a set of suspected fault nodes, and marks the abnormal triggering node as an abnormal node to be confirmed; Step S502: Monitor the abnormal node to be confirmed, continuously collect the operating status data of the abnormal node to be confirmed, the operating status data including communication delay, command response time, control output offset, and upstream and downstream communication success rate, and draw a fluctuation characteristic graph of the operating status data within a preset judgment period; Step S503: Calculate the fluctuation slope of the operating status data based on the fluctuation characteristic diagram of the operating status data, and preset a fluctuation slope threshold. When the fluctuation slope exceeds the fluctuation slope threshold, the abnormal node to be confirmed is updated to a high-risk node, and a logical isolation operation is performed. At the same time, the high-risk node is pushed to the operation and maintenance management platform, prompting maintenance personnel to intervene in diagnosis and processing.

7. An intelligent control system for weak current equipment based on the Internet of Things, used to implement the intelligent control method for weak current equipment based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: The system includes 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 sets weak current devices as nodes, summarizes the physical connection relationship and control logic channel of each node, generates a topology diagram of the weak current control network, and generates a set of control paths corresponding to different task types executed in different functional areas through historical records; The control path analysis module obtains the node status table, calculates the path score of the historical records, obtains the main control path based on the average path score of each control path, obtains the historical records of each control path, calculates the path stability index, communication delay index, and node health index of the historical records, and constructs a path health feature set to fit the task execution results for training, thereby generating a path health score function. The task monitoring module: presets a monitoring cycle, calculates the health score of each stage when executing the current task, determines whether to trigger the path switching determination process, continues to collect data if the path switching determination process is not triggered, and screens candidate control paths if the path switching determination process is triggered, and synchronizes the candidate control paths that meet the conditions to the control center; The switching judgment module: in the control center, determines the new primary control path, activates the policy copy of the new primary control path, sets the initial switching duration, and determines whether the new primary control path meets the preset consistency conditions. If so, the new primary control path continues to be executed. If not, the fault handling mechanism is triggered; The abnormal node identification module: During the path switching and task recovery process, it is marked as an abnormal node to be confirmed. By monitoring the operating status data of the abnormal node marked as the abnormal node to be confirmed and drawing a fluctuation characteristic diagram, it is determined whether the abnormal node to be confirmed is a high-risk node. If it is a high-risk node, the maintenance personnel are reminded to handle it.

8. The intelligent control system for weak current equipment based on the Internet of Things according to claim 7, characterized in that: The topology modeling module includes a topology structure unit and a control path collection unit: The topology unit sets all weak-current devices deployed in the hospital building as nodes, obtains the physical connection relationship and control logic channel of each weak-current device by parsing the wiring drawings, control system configuration table and communication link log, classifies devices with the same upper-level control node into the same functional group, constructs the logical connection path between each node, and generates a topology diagram of the weak-current control network; The control path collection unit: in the topology diagram, collects the basic attributes of each node, the basic attributes including node type, unique identification information, communication parameters, and control load type, writes the basic attributes into the node's status table, generates the status table of all nodes, obtains the historical records of task scheduling in the control center, extracts the task types in the historical records and the functional areas corresponding to the weak current equipment that performs the scheduling, maps the task path correspondence, and generates a feasible control path collection in combination with the current topology.

9. The intelligent control system for weak current equipment based on the Internet of Things according to claim 7, characterized in that: The task monitoring module includes a path switching determination unit and a remaining candidate control path determination unit: The path switching determination unit: presets a monitoring cycle, obtains the currently executed task type and functional area, and determines the current main control path. According to the preset monitoring cycle, the communication parameters of all nodes on the main control path are collected and normalized. The health score of the node is calculated according to the node health score formula, and a preset health score threshold is set. The health score of each node is compared with the health score threshold. If the health score of a 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 the path switching determination process. If there is no unhealthy node in the main control path, the system continues to collect data according to the preset monitoring cycle. The unit for determining the remaining candidate control paths: when the system triggers the path switching determination process, traverses the candidate control path set, obtains the communication parameters of each node in the candidate control path, calculates the health score of each node, eliminates the candidate control paths with unhealthy nodes, calculates the path stability index, communication delay index, and node health index in the remaining candidate control paths, and inputs them into the path health scoring function, calculates the path health scores of the remaining candidate control paths, and sorts the remaining candidate control paths from high to low according to the path health scores, and synchronizes the sorted remaining candidate control path set to the control center.

10. The intelligent control system for weak current equipment based on the Internet of Things according to claim 7, characterized in that: The abnormal node identification module includes a unit for marking abnormal nodes to be confirmed and a unit for marking high-risk nodes: The unit for marking abnormal nodes to be confirmed: During the path switching and task recovery process, if it detects several output consistency check failures, repeated rollbacks of control tasks, or path selection failures, the system obtains the association relationship between the abnormal trigger node and upstream and downstream nodes based on the path switching records, abnormal logs, and control path topology, constructs a set of suspected fault nodes, and marks the abnormal trigger node as an abnormal node to be confirmed; The high-risk node unit: monitors the abnormal nodes to be confirmed, continuously collects the operating status data of the abnormal nodes to be confirmed, the operating status data includes communication delay, instruction response time, control output offset, upstream and downstream communication success rate, and draws a fluctuation characteristic diagram of the operating status data within a preset judgment period. According to the fluctuation characteristic diagram of the operating status data, the fluctuation slope of the operating status data is calculated, and a fluctuation slope threshold is preset. When the fluctuation slope exceeds the fluctuation slope threshold, the abnormal node to be confirmed is updated to a high-risk node, and a logical isolation operation is performed. At the same time, the high-risk node is pushed to the operation and maintenance management platform, prompting maintenance personnel to intervene in diagnosis and processing.

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