Medium voltage switch cabinet intelligent signal loop system and method based on wireless ad hoc network

Abstract

The application relates to the field of signal loop control, and specifically discloses a medium-voltage switch cabinet intelligent signal loop system and method based on a wireless ad hoc network, which constructs a cooperative diagnosis system composed of intelligent sensing nodes, edge computing gateways and a wireless ad hoc network. The intelligent sensing nodes immediately capture and encapsulate state data containing high-precision local time stamps at the moment when the state of a circuit breaker changes. After the data is transmitted to the edge computing gateway through the wireless ad hoc network, the gateway performs deep analysis on the received data packets through a special timing analysis algorithm, so that the inherent mechanical operation time delay and the variable wireless network transmission time delay can be effectively separated. In this way, the technical problem that the diagnosis result is unreliable due to the confusion of network time delay jitter and mechanical time delay is solved, and accurate judgment of the equipment state is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal loop control, and more particularly, to an intelligent signal loop system for a medium-voltage switchgear based on wireless ad hoc networks. BACKGROUND

[0002] In the operation monitoring of a medium-voltage switchgear, the reliability of the signal loop is of great importance, which is directly related to the safety and stability of the power system. The traditional signal loop generally adopts a hard-wired mode, and the connection and signal transmission of each unit in the switchgear are achieved through a large number of control cables. However, this mode not only leads to extremely complex wiring in the cabinet, long installation and debugging period, and high cost, but also lacks flexibility in the later operation and maintenance and function upgrade, and is difficult to adapt to the growing intelligent demand. In order to overcome the above-mentioned defects, the industry has begun to explore the introduction of wireless communication technology into the switchgear signal loop system, in the hope of constructing a new generation of intelligent signal loop scheme through wireless ad hoc networks and edge computing, so as to simplify the physical connection, reduce the engineering complexity and improve the intelligent level of the system.

[0003] However, although the scheme based on wireless ad hoc networks shows great potential, when it is applied to the field of switchgear state diagnosis with extremely strict timing requirements, it faces a major technical bottleneck. Under the harsh conditions of complex electromagnetic environment and dense metal structure inside the switchgear, the transmission path of the wireless signal may change dynamically, the data packet may pass through multi-hop routing, and it is easy to be affected by channel competition, conflict and external interference, which leads to the significant and inherent uncertainty and volatility of the wireless network transmission delay. The existing preliminary wireless scheme usually only takes the sensing node as a simple data reporting unit, and the edge gateway passively receives data, and there is a lack of effective coordination mechanism between the two to quantify and strip the uncertain network delay. This makes the total delay finally received by the gateway actually a mixture of the real mechanical operation delay of the device and the randomly fluctuating network transmission delay, and the two are coupled with each other and difficult to distinguish.

[0004] Therefore, a key technical problem to be solved in the prior art is how to accurately separate and evaluate the device mechanical operation delay and the wireless network transmission delay mixed in the total signal loop delay, so as to avoid false alarms or missed alarms caused by network jitter and other non-device fault factors. SUMMARY

[0005] To solve the problems in the background art, the present application is proposed. According to an aspect of the present application, a medium-voltage switch cabinet intelligent signal loop system based on a wireless ad hoc network comprises: an intelligent sensing node, an edge computing gateway, and a wireless ad hoc network; wherein, after the switch cabinet is powered on, the edge computing gateway is started and a wireless ad hoc network is created, the intelligent sensing node deployed in proximity to the circuit breaker joins the wireless ad hoc network to realize wireless communication between the edge computing gateway and the intelligent sensing node; the intelligent sensing node collects circuit breaker state data and transmits the data to the edge computing gateway through multi-hop transmission of the wireless ad hoc network; and the edge computing gateway analyzes and time-series analyzes the circuit breaker state data to determine whether to generate an alarm prompt signal.

[0006] According to another aspect of the present application, a central control method for a medium-voltage switch cabinet intelligent signal loop based on a wireless ad hoc network comprises: after the switch cabinet is powered on, a wireless ad hoc network is started and created by an edge computing gateway, an intelligent sensing node deployed in proximity to the circuit breaker joins the wireless ad hoc network to realize wireless communication between the edge computing gateway and the intelligent sensing node; circuit breaker state data is collected by the intelligent sensing node and transmitted to the edge computing gateway through multi-hop transmission of the wireless ad hoc network; and the edge computing gateway analyzes and time-series analyzes the circuit breaker state data to determine whether to generate an alarm prompt signal.

[0007] Compared with the prior art, the present application provides a medium-voltage switch cabinet intelligent signal loop system based on a wireless ad hoc network, which constructs a collaborative diagnosis system composed of an intelligent sensing node, an edge computing gateway, and a wireless ad hoc network. The intelligent sensing node immediately captures and encapsulates state data containing a high-precision local timestamp at the moment of a change in the state of the circuit breaker. After the data is transmitted to the edge computing gateway through multi-hop transmission of the wireless ad hoc network, the gateway analyzes the received data packet in depth through a special time-series analysis algorithm, so as to effectively separate the inherent mechanical operation time delay and the variable wireless network transmission time delay. This design combining accurate timing of the event source with intelligent analysis of the terminal solves the technical problem of unreliable diagnosis results caused by confusion between network time delay jitter and mechanical time delay, and realizes accurate judgment of the state of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0008] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application taken in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of embodiments of the present application and constitute a part of the specification, which together with the description, serve to explain the present application. The drawings are not intended to be an exhaustive description of the present application, and do not represent the only form in which the present application can be made or used. In the drawings, the same reference numerals are generally used to represent the same components or steps.

[0009] Figure 1A block diagram of a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application.

[0010] Figure 2 A block diagram of an edge computing gateway in a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application.

[0011] Figure 3 A data flow schematic diagram of an edge computing gateway in a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application.

[0012] Figure 4 A logic judgment flowchart of an alarm prompt signal generation module in a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application.

[0013] Figure 5 A flowchart of a central control method of a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood.

[0015] It should be understood that the various steps in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0016] In view of the actual problems in the above technical solutions, the present application is proposed. Figure 1 A block diagram of a wireless ad hoc network-based intelligent signal loop system of a medium-voltage switch cabinet according to an embodiment of the present application. Specifically, as shown in FIG. 1, the system includes an edge computing gateway 100, a wireless ad hoc network 200, and a central control terminal 300. Figure 1As shown, the intelligent signal circuit system 100 for medium-voltage switchgear based on a wireless ad hoc network according to an embodiment of this application includes: an edge computing gateway 110, intelligent sensor nodes 120, and a wireless ad hoc network 130. After the switchgear is powered on, the edge computing gateway 110 starts and creates a wireless ad hoc network. Intelligent sensor nodes deployed near the circuit breakers join the wireless ad hoc network to achieve wireless communication between the edge computing gateway and the intelligent sensor nodes. The intelligent sensor nodes 120 collect circuit breaker status data and transmit it to the edge computing gateway via the wireless ad hoc network through multiple hops. The edge computing gateway 110 parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal. In one specific embodiment, the intelligent sensor node 120 includes a data acquisition unit 121, a data processing unit 122, a wireless communication module 123, and a power supply module 124.

[0017] It is worth mentioning that the intelligent signal circuit system for medium-voltage switchgear based on a wireless ad hoc network consists of intelligent sensor nodes, an edge computing gateway, and the wireless ad hoc network. After the system starts up, the edge computing gateway is responsible for creating and managing the entire wireless ad hoc network, providing the communication foundation for each node. Intelligent sensor nodes deployed near the circuit breakers collect the circuit breaker status data in real time after joining the network. The collected data is then reliably sent to the edge computing gateway through the multi-hop transmission mechanism of the wireless ad hoc network. After receiving the data, the edge computing gateway parses and performs time-series analysis, ultimately determining whether an alarm signal needs to be generated based on the analysis results. This forms a complete closed-loop monitoring and diagnostic process from data acquisition and wireless transmission to intelligent analysis.

[0018] In the specific workflow, the coordinated operation of the entire system begins with the power-on of the switchgear. After the system is activated, the various components begin to work closely together to achieve this complete monitoring and diagnostic closed loop. The following is a detailed explanation of each component and function.

[0019] The edge computing gateway plays a crucial role. It initiates and creates a wireless ad-hoc network, and nearby intelligent sensor nodes deployed on the circuit breaker join this network to enable wireless communication between the gateway and the sensor nodes. Essentially, the edge computing gateway is an embedded computing device installed in the secondary instrument room of the switchgear. It possesses powerful computing and storage capabilities, along with multiple communication interfaces, and serves as the core processing and management unit of the entire intelligent signal loop system. Its key role lies not only in handling the final data analysis and decision-making but also in building and maintaining the entire communication infrastructure.

[0020] Specifically, after the system powers on, the edge computing gateway 110 is activated first. Its built-in wireless communication module initializes and begins executing the network creation procedure. As the network coordinator, the gateway sets and broadcasts a unique wireless ad hoc network identifier and related network parameters, such as the operating channel and encryption key. It periodically sends beacon frames to announce to all devices within its communication range that an available and secure wireless network has been established. Simultaneously, nearby smart sensor nodes deployed on primary devices such as circuit breakers automatically enter network search mode after power-on. These nodes continuously scan preset wireless frequency bands to listen for beacon frames from the gateway. Once a smart sensor node successfully receives the network announcement signal from the gateway, it sends a request to join the network. Upon receiving the request, the edge computing gateway verifies the node's identity. If it confirms the node is an authorized device, it approves its joining, assigns it a unique network address, and updates its own routing table. Through this series of automated handshake and configuration processes, the intelligent sensor node successfully integrates into the wireless ad hoc network created by the gateway, thereby establishing a stable and reliable wireless communication link and laying the foundation for subsequent status data transmission and collaborative work.

[0021] With this communication link, the intelligent sensor node 120 collects circuit breaker status data and transmits it to the edge computing gateway via a multi-hop wireless ad hoc network. The intelligent sensor node is the sensing and execution unit deployed at the forefront of the field. Its core task is to accurately collect raw status data from primary equipment such as circuit breakers and transmit it to the edge computing gateway via a multi-hop wireless ad hoc network. These nodes are miniaturized, low-power intelligent devices, directly installed inside or near key equipment such as circuit breakers and disconnectors within the switch cabinet. As the source of information flow for the entire system, the accuracy and timeliness of their operation are the foundation for all subsequent intelligent analysis and diagnosis.

[0022] Specifically, this data acquisition and transmission process is a sophisticated workflow completed collaboratively by multiple internal units. In one specific implementation, the intelligent sensor node acquires circuit breaker status data and transmits it to the edge computing gateway via a multi-hop wireless ad hoc network. This includes: when the data acquisition unit of the intelligent sensor node detects that the circuit breaker has changed from open to closed or from closed to open, the voltage level of the data acquisition unit changes; after detecting the voltage level change, the data processing unit 122 of the intelligent sensor node generates circuit breaker status data, which includes a unique node ID, event type, timestamp of the event, and checksum; the wireless communication module 123 of the intelligent sensor node queries its own routing table and selects the path with the lowest gateway cost to transmit the circuit breaker status data to the edge computing gateway.

[0023] First, the data acquisition unit 121 within the intelligent sensing node is responsible for continuously monitoring the physical state of the circuit breaker. In practical applications, this is achieved by connecting to the auxiliary contacts of the circuit breaker. When the circuit breaker performs an operation, such as changing from an open state to a closed state, or vice versa, its internal mechanical linkage causes the auxiliary contacts to close or open. This physical action directly results in a clear change in the circuit level monitored by the data acquisition unit, such as a jump from a high level to a low level, or vice versa. This instantaneous change in level is the most basic signal that marks the occurrence of a valid mechanical event.

[0024] Immediately following the detection of this level change signal by the data acquisition unit, the data processing unit 122 inside the intelligent sensing node is triggered. This unit is the node's microcontroller, responsible for converting the raw level change signal into structured, information-rich circuit breaker status data. This conversion process includes several key steps: First, the data processing unit determines the specific event type according to preset logic rules. For example, the system can predefine a high-to-low level change to represent a closing event, and a low-to-high level change to represent a opening event. After determining the event type, the data processing unit encodes it, encoding closing as a specific binary value such as 0x01 and opening as another value such as 0x00, for efficient subsequent parsing. Subsequently, the data processing unit immediately acquires and encapsulates a complete data packet, namely the circuit breaker status data. This data packet contains the node's unique ID, event type, precise timestamp of the event occurrence, and checksum. The node's unique ID is a globally unique identifier burned into the chip during equipment manufacturing or system deployment, used to identify which specific circuit breaker the event originated from. The event type is the value determined and encoded in the previous step. The timestamp of the event is obtained by reading the count value of the high-precision real-time clock inside the node at the moment of the level transition. It accurately records the local raw time of the completion of the mechanical action and is the fundamental basis for subsequent timing analysis and separation of network latency and mechanical latency by the edge computing gateway. The checksum is a value calculated by the data processing unit using algorithms such as Cyclic Redundancy Check (CRC) on the node ID, event type, and timestamp. It is used to ensure the integrity of the data during transmission. For example, when a node with ID 0x1A2B3C4D detects that the circuit breaker is closed, its data processing unit may generate the following data packet, namely the circuit breaker status data: node ID is 0x1A2B3C4D, event type is 0x01, timestamp is 1678886400123456 (representing the number of microseconds at a specific moment), and calculate the checksum based on these data, such as 0xABCD.

[0025] After the circuit breaker status data packet is generated within the smart sensing node, its transmission is entrusted to the node's wireless communication module 123. This module ensures that this data packet, containing critical information, can safely, efficiently, and reliably traverse the complex switchgear environment, characterized by metallic structures and potential electromagnetic interference, ultimately reaching the edge computing gateway. The module first queries its own routing table and selects the path with the lowest gateway cost to transmit the circuit breaker status data to the edge computing gateway. This routing table is not a static, pre-set list but rather a product of the dynamic operation of the wireless ad hoc network protocol, representing each node's real-time understanding of the current network topology and link quality. To maintain this routing table, all nodes in the network, including the edge computing gateway, periodically exchange data packets containing link status information, such as received signal strength indicators or link quality indicators. Each node continuously evaluates the cost of each wireless link with its neighboring nodes based on this received information. This cost is a dynamically calculated comprehensive metric that quantifies multiple factors, such as hop count, signal strength, and link stability, into a single value. Typically, links with weaker signals and higher packet loss rates are assigned higher cost values. Furthermore, each additional hop increases the total cost of the path. When the wireless communication module needs to send data, it immediately consults this dynamically updated routing table, calculates the total cost of all possible paths to the final destination edge computing gateway, and selects the lowest-cost path as the optimal path for this transmission. For example, a wireless communication module carrying the aforementioned circuit breaker status data from a node with ID 0x1A2B3C4D needs to send a data packet to gateway G, but direct communication is impossible due to distance or obstacles. In this case, the routing table might show that the node can connect to the gateway through two nearby intermediate nodes B and C. The module evaluates two paths: path 1 (via B to G) and path 2 (via C to G). If the link from the current node to B has a strong and stable signal, its cost is calculated as 2, while the cost of the link from B to G is 3. Therefore, the total cost of path 1 is 5. Meanwhile, if the link from the current node to C is interfered with and has a weak signal, with a cost of 5, while the link from C to G is good with a cost of 1, then the total cost of path 2 is 6. In this case, the wireless communication module compares the total costs of the two paths, i.e., 5 and 6, and determines that path 1, with a cost of 5, is the optimal choice. After determining the optimal path, the data forwarding execution phase begins. In one specific implementation, the wireless communication module of the smart sensor node queries its own routing table and selects the path with the lowest gateway cost to transmit the circuit breaker status data to the edge computing gateway, including: if the edge computing gateway is not within the direct communication range of the smart sensor node, the wireless communication module of the smart sensor node sends the circuit breaker status data to the next node on the path.In the example above, since gateway G is not within the direct communication range of the starting node, and the optimal path is via node B, the wireless communication module will not attempt to send data to G or C. Instead, it will precisely send the complete data packet to the first intermediate node on the optimal path, namely node B. Upon successfully receiving this data packet, node B's own wireless communication module immediately initiates the exact same processing flow: it queries its local, dynamically maintained routing table, calculates and selects the optimal path from itself to gateway G, and then forwards the data packet to the next node on its selected path. This process constitutes a sequential intelligent forwarding mechanism, where data packets are relayed between nodes in the network based on the optimal local decision at each step, until they finally reach a node that can directly communicate with the edge computing gateway, completing the final hop. In this way, even in environments where network topology or link quality changes rapidly, data packets can always be reliably delivered to their destination along the path with the lowest current global cost.

[0026] All of these functions rely on the stable power provided by the power module 124. As the energy core of the entire node, it ensures the reliable execution of all functions such as data acquisition, processing, and communication.

[0027] This communication system, jointly constructed by all intelligent sensor nodes and the edge computing gateway, and supporting the aforementioned intelligent routing and data transmission, is the Wireless Ad Hoc Network 130. Its core function is to provide a flexible, reliable, and efficient data transmission channel between intelligent sensor nodes and the edge computing gateway in the complex and electromagnetically interference-filled internal environment of the switch cabinet. The most critical feature of this network is its multi-hop transmission and self-healing capabilities. When an intelligent sensor node cannot directly communicate with the edge computing gateway due to distance or signal obstruction by metal partitions, it does not become an information island. Instead, the node's wireless communication module queries its internal routing table, intelligently selecting one or more paths to send data packets to neighboring nodes along the path. These intermediate nodes, upon receiving the data, continue to relay it until the data packet finally arrives safely at the edge computing gateway. This multi-hop mechanism allows the network to dynamically bypass obstacles and fault points, greatly expanding communication coverage and enhancing the robustness of the entire system, ensuring that the circuit breaker's status data is transmitted accurately under any circumstances.

[0028] As mentioned in the background, existing technologies face a critical bottleneck when applying wireless ad hoc networks to signal circuits in medium-voltage switchgear: the inability to effectively distinguish between the actual mechanical operation delay of the equipment and the randomly fluctuating wireless network transmission delay. This delay confusion makes it difficult for diagnostic systems to accurately determine the source of faults, and is prone to false alarms due to non-equipment factors such as network jitter, thus severely restricting the practical application and promotion of this technology in power systems requiring high reliability. Therefore, this application proposes an intelligent diagnostic method based on statistical analysis to solve the technical problem of the confusion between mechanical and network delays. Specifically, after receiving status data containing precise local timestamps, the edge computing gateway's parsing module first accurately separates the observed mechanical operation delay from the network transmission delay. Subsequently, the system does not directly use the original delay values, but instead, through normalization processing, compares the two delays with their historical statistical data (mean and standard deviation), converting them into standardized deviation values. Finally, by using a confidence calculation model based on Bayesian inference, the two standardized values ​​are quantitatively compared, which enables a scientific determination of whether the dominant factor causing the abnormal increase in total latency is equipment mechanical failure or network fluctuation, thus achieving highly reliable fault diagnosis.

[0029] Specifically, the edge computing gateway 110 parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal. In medium-voltage switchgear applications, which have extremely high reliability requirements, the introduction of wireless ad hoc network communication simplifies wiring but also brings new technical challenges. Wireless signals transmit in complex electromagnetic environments, and their latency exhibits inherent and unpredictable fluctuations. This uncertain network latency is coupled with the operational latency, which truly reflects the mechanical health of the circuit breaker, in the final data received by the edge computing gateway. Without precise parsing and timing analysis, these two cannot be effectively separated, leading to occasional network link jitter being misjudged as equipment failure, or early mechanical degradation being masked by network fluctuations. Therefore, to ensure that the final generated alarm signal is based on a reliable assessment of the equipment's physical state, rather than a misinterpretation of an uncertain communication process, it is necessary to parse and perform timing analysis on the circuit breaker status data.

[0030] In one specific implementation, Figure 2 This is a block diagram of an edge computing gateway in a medium-voltage switchgear intelligent signal circuit system based on a wireless self-organizing network, according to an embodiment of this application. Figure 3 This is a schematic diagram of the data flow of the edge computing gateway in a medium-voltage switchgear intelligent signal loop system based on a wireless ad hoc network, according to an embodiment of this application. Figure 2 and Figure 3As shown, the edge computing gateway 110 parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal. This includes: a circuit breaker status data parsing module 111, used to parse the circuit breaker status data to obtain the observed mechanical delay and the local observed network delay; a mechanical delay normalization module 112, used to normalize the observed mechanical delay based on the mean and standard deviation of the mechanical delay to obtain a standardized mechanical delay value; a network delay normalization module 113, used to normalize the local observed network delay based on the mean and standard deviation of the network delay to obtain a standardized network delay value; and an alarm signal generation module 114, used to perform timing analysis based on Bayesian inference on the standardized network delay value and the standardized mechanical delay value to determine whether to generate an alarm signal.

[0031] The specific real-time process by which the edge computing gateway 110 parses and performs timing analysis on circuit breaker status data to determine whether to generate an alarm signal is as follows: It is understandable that in a wireless ad hoc network environment, data packets sent from smart sensor nodes undergo a multi-hop transmission process with uncertain timing and varying paths before reaching the edge computing gateway. The wireless network transmission delay introduced by this process has significant randomness and volatility, which, together with the actual mechanical operation delay of the device, constitutes the total delay observed by the gateway. If not distinguished, occasional jitter in the network link may be misjudged as a device failure. Therefore, the primary processing performed by the edge computing gateway after receiving data—parsing and timing analysis—aims to accurately separate the mechanical operation delay, which truly reflects the device's health status, and the network transmission delay, which reflects the current channel quality, from the mixed total delay information, laying a data foundation for subsequent accurate and reliable diagnosis. This is first implemented by the circuit breaker status data parsing module 111. When the edge computing gateway receives a data packet from the wireless ad hoc network, this module initiates a series of sophisticated parsing procedures. First, the module records the precise moment the data packet arrives at the gateway, designated as the arrival timestamp. Next, it performs an integrity check on the received data packet. Taking the aforementioned circuit breaker status data packet as an example, its checksum is 0xABCD. The module uses the same algorithm as the smart sensor node data processing unit to recalculate the checksum for the received node ID 0x1A2B3C4D, event type 0x01, and event timestamp 1678886400123456. Only when the calculated result perfectly matches the checksum 0xABCD carried in the data packet is the data considered error-free during transmission, the data packet is deemed valid, and proceeds to subsequent processing; otherwise, the data packet is discarded. After the data packet passes the checksum verification, the parsing module extracts its core information. The event timestamp 1678886400123456 is the local time recorded by the smart sensor node at the instant it detects a change in voltage level. The module calculates the time it takes for a data packet to travel from transmission to reception in the wireless network by subtracting the arrival timestamp from the event timestamp. This difference is the locally observed network latency. For example, if the gateway records an arrival timestamp of 1678886400123656, then the locally observed network latency = 1678886400123656 - 1678886400123456 = 200 microseconds. Simultaneously, to calculate the mechanical latency, the module also requires a critical starting time point: the moment the gateway issues the operation command. When the gateway sends an operation command, such as closing or opening, to a specific circuit breaker, it records the precise time of command issuance in its internal log, indexed by its node ID, and records this as the command issuance timestamp.Upon receiving the status data packet returned by the node, the parsing module queries the log based on the node ID 0x1A2B3C4D in the data packet to find the corresponding command issuance timestamp. By subtracting this command issuance timestamp from the event occurrence timestamp in the data packet, the complete time from command issuance to the device completing the mechanical action and being confirmed by the sensor can be obtained; this is the observed mechanical delay. For example, if the gateway finds that the closing command issuance timestamp for node 0x1A2B3C4D is 1678886400103456, then the observed mechanical delay = 1678886400123456 - 1678886400103456 = 20000 microseconds, or 20 milliseconds.

[0032] Correspondingly, simply obtaining the absolute value of the mechanical delay observed in this instance cannot directly determine whether the equipment performance is abnormal. Different models and service years of circuit breakers have different normal mechanical operation delay benchmarks; a normal value for one piece of equipment may be a symptom of a fault in another. For objective and standardized diagnosis, the observed value must be considered within its historical performance context. Therefore, introducing mechanical delay normalization processing can transform this absolute, isolated delay data into a relative, statistically significant standardized score, used to accurately quantify the deviation of this operation from its historical normal performance. This normalization processing is specifically performed by the mechanical delay normalization module 112. This module receives the observed mechanical delay output from the previous analysis module and calculates it based on pre-stored statistical parameters. These two key statistical parameters—the mean mechanical delay and the standard deviation of mechanical delay—are dynamically calculated and maintained by the edge computing gateway for each independent smart sensor node (i.e., each circuit breaker) during its long-term normal operation after commissioning. This is achieved through continuous collection and statistical analysis of a large amount of historical mechanical delay data. The mean represents the device's most typical performance, while the standard deviation quantifies the normal fluctuation range of its performance. In one specific implementation, the mechanical delay normalization module 112 is used to normalize the observed mechanical delay using the following formula: ;in, Due to the mechanical delay of this observation, This is the average mechanical delay. The standard deviation of mechanical time delay, This is the standardized value of the mechanical delay. It is based on the mechanical delay received from the previous module for this observation. For example, with a latency of 20 milliseconds, the edge computing gateway has calculated the historical average mechanical latency of the circuit breaker with node ID 0x1A2B3C4D based on long-term monitoring. The time was 18.5 milliseconds, with a standard deviation of 18.5%. The time delay is 0.5 milliseconds. When the mechanical delay normalization module receives the input value of 20 milliseconds, it will process it using the following formula: Mechanical delay normalization value. =(20-18.5) / 0.5=3. The essence of this formula is to calculate the distance of the current observation from its historical mean, and measure it in units of its own standard deviation. A value of 0... This indicates that the current operation is completely consistent with the historical average; a positive value indicates that it is slower than the average, and a negative value indicates that it is faster. The magnitude of its absolute value directly reflects the severity of the deviation. In this example, the result is... A value of 3 indicates that the mechanical operation of the circuit breaker was 3 standard deviations slower than its historical average. This is a relative indicator with clear statistical significance, as it eliminates the impact of individual equipment differences.

[0033] Similarly, as with the assessment of mechanical delay, the transmission delay of wireless ad hoc networks is also dynamically changing, affected by various factors such as channel congestion, electromagnetic interference, and changes in routing paths. An isolated network delay observation cannot reveal whether it represents an abnormal fluctuation in the current overall network operation. In order to fairly weigh the deviation between mechanical performance and network performance in subsequent diagnostics, network delay must also be standardized. Therefore, setting a network delay standardization module can eliminate the impact of instantaneous fluctuations in the network environment, transforming the absolute network delay value into a relative indicator that objectively reflects the deviation of the current communication link quality from its normal level. In a specific embodiment, the network delay standardization module 113 includes: normalizing the locally observed network delay using the following formula: ;in, To observe network latency locally, This represents the average network latency. The standard deviation of network latency. This is the network latency normalization value. After receiving the locally observed network latency output by the parsing module, the network latency normalization module performs normalization calculations. Taking the output of the previous process as an example, the input value received by this module is 200 microseconds. To process this data, the module calls the edge computing gateway to maintain two statistical parameters for this communication link from node 0x1A2B3C4D to the gateway: the mean network latency and the standard deviation of network latency. These two parameters are obtained through statistical analysis of a large amount of locally observed network latency data from historical normal communication and are dynamically updated to adapt to the slow changes in the network environment. For example, based on historical data, the mean network latency of this link is 160 microseconds, and the standard deviation is 10 microseconds. The module substitutes these values ​​into the formula for the network latency normalization value. =(200-160) / 10=4, which means that the network transmission delay is 4 standard deviations higher than the historical average. This standardized score clearly quantifies the degree of deviation of the current network communication quality.

[0034] It is understandable that after obtaining standardized measures of both mechanical latency and network latency deviating from their normal levels, the diagnostic process enters the final decision-making stage. The core issue at this point is how to attribute and adjudicate when one or both metrics show significant deviations. A simple threshold judgment, such as triggering an alarm whenever the standardized mechanical latency value exceeds a certain number, can still be affected by extreme network anomalies. To establish an intelligent decision-making mechanism that comprehensively considers and weighs evidence, and provides the most credible conclusion in probabilistic terms, an alarm signal generation module is introduced. Based on the statistical principles of Bayesian inference, it performs in-depth time-series and correlation analysis on the two standardized values ​​to determine the root cause of the anomaly with the highest confidence and make the final decision on whether to generate an alarm.

[0035] In one specific embodiment, in one specific embodiment, Figure 4 This is a flowchart illustrating the logic judgment of the alarm signal generation module in a medium-voltage switchgear intelligent signal circuit system based on a wireless self-organizing network, according to an embodiment of this application. Figure 4 As shown, the alarm signal generation module includes: a circuit breaker status determination unit, used to determine that the circuit breaker is in normal condition if both the network delay standardization value and the mechanical delay standardization value are less than the normal state threshold; a diagnostic confidence calculation unit, used to calculate the confidence of mechanical fault as diagnostic confidence based on the network delay standardization value and the mechanical delay standardization value if both the network delay standardization value and the mechanical delay standardization value are greater than or equal to the normal state threshold; and a signal generation unit, used to determine to generate an alarm signal in response to the diagnostic confidence value being greater than the alarm confidence threshold.

[0036] Specifically, firstly, the circuit breaker status determination unit performs a first round of rapid filtering. This unit's function is to handle the most definitive situation: both the device and the network are functioning normally. It will then process the received... and The value is compared to a preset normal state threshold. This threshold is set based on long-term operational statistics and system reliability requirements, and a statistical significance level, such as 2.5, is selected to cover the vast majority of normal fluctuations. This means that any fluctuation within 2.5 standard deviations is considered normal. In the logic of this unit, only when... and Only when both values ​​are less than 2.5 will the operation be considered completely normal, and the diagnostic process will end without generating any alarms. In the current example, the input... =3 and Since both 4 and 2.5 are not less than 2.5, the judgment condition of this unit is not met, and the process continues to the next unit, passing these two values ​​to the next processing unit.

[0037] Next, the diagnostic confidence calculation unit is activated, entering the core probabilistic diagnostic phase. The logic of this unit is that, given the existence of at least one significant anomaly (i.e., a Z-value greater than or equal to the normal threshold), it is necessary to further quantify whether this anomaly is more likely caused by mechanical failure or network fluctuations. It calculates the confidence level of the mechanical failure using a formula based on Bayesian inference. In one specific implementation, the diagnostic confidence calculation unit is used to: calculate the confidence level of the mechanical failure as the diagnostic confidence level based on the network latency normalization value and the mechanical latency normalization value, using the following formula: ;in, It is the probability density function of the standard normal distribution. It refers to diagnostic confidence. Here... It is the probability density function of the standard normal distribution. Its physical meaning is that a standardized value represents... The relative probability of an event occurring. The further away from the center, the higher the probability. The larger the absolute value of an event, the lower its probability of occurrence; the corresponding probability density function value... The smaller the value, the better. Therefore, the essence of this formula is to compare observed values. Such mechanical time delay deviation and observation Such network latency deviates from the relative probabilities of these two events. With the aforementioned input... =3 and Taking a value of 4 as an example, this unit will perform the following calculation: Calculation and ,Right now and Consulting the standard normal distribution table or through calculation, we can find that... Approximately 0.00443, Approximately 0.000134. Substituting these values ​​into the formula: Diagnostic Confidence. =0.00443 / (0.00443+0.000134)≈0.97. The calculated value is... The Z-value, or probability value, is between 0 and 1. In this example, the confidence level is approximately 0.97, meaning that based on the currently observed data (a 3-standard-deviation mechanical delay deviation and a 4-standard-deviation network delay deviation), there is a 97% confidence level that the dominant factor causing this observational anomaly is mechanical performance degradation, rather than network transmission jitter. Because the probability of an event with a Z-value of 3 is much greater than that of an event with a Z-value of 4, the formula assigns greater weight to the relatively more likely anomaly by comparing their relative probabilities. After completing the calculation, this unit outputs this diagnostic confidence value of 0.97 to the final decision-making unit.

[0038] Specifically, when performing fault diagnosis, using the probability density function of a standard normal distribution implicitly assumes that both the original mechanical and network delays precisely follow an ideal normal distribution. However, in practical engineering applications, this assumption often deviates from reality. First, the mechanical delay distribution of circuit breakers is usually not symmetrical. As equipment ages and wears down, its mechanical operating time tends to increase, and it is difficult for it to become faster than when it was manufactured, leading to a significant right skew in its probability distribution. If a symmetrical normal distribution is used to fit this skewed distribution, the true probability of the long tail portion on the right—that is, the excessively long operating time, a key fault symptom—will be underestimated. Second, the delay distribution of wireless ad hoc networks is a typical non-Gaussian long-tailed distribution. The delay is stable most of the time, but occasionally, due to channel collisions, route reselection, or external interference, delay spikes far exceeding the average value will appear. If a normal distribution is used for modeling, these spikes would be considered extremely low-probability, impossible events. This will cause the model to incorrectly attribute the increase in total latency to mechanical parts when the probability of network anomalies approaches zero, resulting in false alarms of mechanical failures.

[0039] Therefore, using kernel density estimation instead of the standard normal distribution can construct a more accurate probabilistic model that better reflects the characteristics of real data, thereby improving the accuracy of fault diagnosis. Kernel density estimation, through a non-parametric method, does not presuppose any prior distribution shape, but directly learns and estimates the probability density function of variables from historical data samples, enabling it to truly capture the right-skewed characteristics of mechanical delay and the long-tailed characteristics of network delay.

[0040] Based on this, in a preferred embodiment, the diagnostic confidence calculation unit is used to: obtain the mechanical delay set of N historical normal operations of the circuit breaker and the network delay set of N historical measurements. It should be understood that to provide basic, realistic training data for subsequent probabilistic model construction, it is necessary to construct two empirical datasets that can reflect the long-term performance of specific devices and network environments. In specific implementation, the edge computing gateway continuously maintains and records two datasets in the background for each monitored circuit breaker: one is the mechanical delay set of N historical normal operations. The first set contains precise time records from the issuance of the command to the completion of the device action in each operation; the second set is a collection of network latency measurements from N historical data. This set contains the time elapsed from when a data packet is sent from the smart sensor node to when it is received by the gateway in each communication. These two sets are the foundation for all subsequent data-driven modeling. In particular, the method of obtaining these sets is the same as that used for obtaining the current network latency normalized value and mechanical latency normalized value, so it will not be described in detail here.

[0041] Based on the set of mechanical delays from N historical normal operations and the set of network delays from N historical measurements, a kernel density estimation model based on a Gaussian kernel function is constructed to obtain the network delay kernel density estimation model and the mechanical delay kernel density estimation model, namely: ;in, The input values ​​for which probability density needs to be calculated are, such as and , It's a kernel function, such as the Gaussian kernel function. It is pi. It is the number of values ​​in the set of mechanical delays from N historical normal operations and the set of network delays from N historical measurements. and This is a bandwidth smoothing parameter used to determine the smoothness of the final density curve. It can be determined using automated methods such as Scott's rule or Silverman's rule. It is 0.184. It is 16.53. It is a mechanical time delay kernel density estimation model. It is a network latency kernel density estimation model. That is, it transforms discrete historical data points into a continuous, smooth probability density function that can accurately describe the inherent distribution of the data. By placing a Gaussian kernel function for each historical data point, and then superimposing and averaging all these functions, the final result is the generation of two customized probability models that can accurately capture the right-skewed characteristics of mechanical latency and the long-tailed characteristics of network latency.

[0042] Based on the network latency kernel density estimation model and the mechanical latency kernel density estimation model, the confidence scores of the current network latency standardization value and mechanical latency standardization value are calculated to obtain the diagnostic confidence score, i.e.: ;in, This refers to diagnostic confidence. In other words, it involves applying a well-established, accurate model to make the final diagnostic decision. This is achieved by using currently observed... and Value as input Substitute into and The model calculates and These two values ​​represent the likelihood of the deviation between current mechanical and network latency under their respective true distributions. Subsequently, by replacing the original standard normal distribution probability density values ​​with these two likelihood values ​​to calculate the confidence level, the posterior probability of attributing the anomaly to mechanical failure can be obtained. Because the kernel density estimation model can capture the true distribution of data—for example, for network latency—the model correctly assigns a small but not negligible probability to the larger latency values ​​in the long tail. Thus, when network latency spikes occur, the model can identify this as likely network jitter, rather than easily misjudging it as mechanical failure, significantly improving diagnostic accuracy. Furthermore, since the model is driven by historical data, it can be updated periodically or on a rolling basis, automatically adapting to distribution drift caused by equipment aging or changes in the network environment.

[0043] Finally, the signal generation unit, acting as the final decision threshold, is responsible for deciding whether to issue an alarm. It receives the confidence value output by the diagnostic confidence calculation unit. The signal generation unit compares the input confidence level of 0.97 with a preset alarm confidence threshold. This threshold represents the minimum level of certainty required to trigger an alarm, and its setting needs to balance the sensitivity and accuracy of the alarm. It is usually set to a relatively high value, such as 0.9 or 0.95, to avoid false alarms due to insufficient evidence. For example, if the threshold is set to 0.9, the signal generation unit will compare the input confidence level of 0.97 with the threshold of 0.9. Since 0.97 is greater than 0.9, the triggering condition of the unit is met. Therefore, it ultimately determines that an alarm signal needs to be generated. This signal will then be presented in various forms through the interface of the edge computing gateway, such as a pop-up alarm on the monitoring center interface, recording the alarm information in the operation log, or driving the local audible and visual alarm of the switch cabinet to sound, thereby clearly indicating to the operation and maintenance personnel that the circuit breaker with ID 0x1A2B3C4D has a highly credible risk of mechanical failure and needs immediate attention and maintenance.

[0044] In summary, the intelligent signal loop system 100 for medium-voltage switchgear based on a wireless ad hoc network, as described in this application, constructs a collaborative diagnostic system consisting of intelligent sensing nodes, an edge computing gateway, and a wireless ad hoc network. The intelligent sensing nodes immediately capture and encapsulate status data containing a high-precision local timestamp the instant the circuit breaker status changes. This data is then transmitted via the wireless ad hoc network to the edge computing gateway. The gateway performs deep analysis of the received data packets using a specialized timing analysis algorithm, effectively separating the inherent mechanical operation delay from the variable wireless network transmission delay. This design, which combines precise timing at the event source with intelligent analysis at the terminal, solves the technical problem of unreliable diagnostic results caused by the confusion between network latency jitter and mechanical latency, achieving accurate judgment of equipment status.

[0045] Figure 5 This is a flowchart illustrating a central control method for a medium-voltage switchgear intelligent signal circuit based on a wireless ad hoc network, according to an embodiment of this application. Figure 5 As shown, the central control method for a medium-voltage switchgear intelligent signal circuit based on a wireless ad hoc network according to an embodiment of this application includes: S1, after the switchgear is powered on, a wireless ad hoc network is started and created through an edge computing gateway, and intelligent sensor nodes deployed nearby on the circuit breaker join the wireless ad hoc network to realize wireless communication between the edge computing gateway and the intelligent sensor nodes; S2, the circuit breaker status data is collected by the intelligent sensor nodes and transmitted to the edge computing gateway via the wireless ad hoc network in multiple hops; S3, the circuit breaker status data is parsed and time-series analyzed by the edge computing gateway to determine whether to generate an alarm signal.

[0046] Here, those skilled in the art will understand that the specific operations of each step in the above-described central control method for the intelligent signal circuit of a medium-voltage switchgear based on a wireless self-organizing network have been referenced above. Figures 1 to 4 The description of the intelligent signal circuit system for medium-voltage switchgear based on wireless self-organizing network is detailed here, and therefore, its repeated description will be omitted.

[0047] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive. Furthermore, it is not limited to the disclosed implementations, and many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations.

Claims

1. A smart signal circuit system for medium-voltage switchgear based on a wireless self-organizing network, characterized in that, include: Intelligent sensing nodes, edge computing gateways, and wireless ad hoc networks; When the switch cabinet is powered on, the edge computing gateway starts up and creates a wireless self-organizing network. The smart sensor nodes deployed nearby on the circuit breaker join the wireless self-organizing network to realize wireless communication between the edge computing gateway and the smart sensor nodes. Intelligent sensor nodes collect circuit breaker status data and transmit it to the edge computing gateway via a wireless self-organizing network through multiple hops. The edge computing gateway parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal. The edge computing gateway parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal, including: The circuit breaker status data parsing module is used to parse the circuit breaker status data to obtain the mechanical delay and local observation network delay of this observation. The mechanical delay normalization module is used to normalize the mechanical delay of this observation based on the mean and standard deviation of the mechanical delay to obtain a standardized value of the mechanical delay. The network latency standardization module is used to normalize the locally observed network latency based on the mean network latency and the standard deviation of network latency to obtain the standardized network latency value. The alarm signal generation module is used to perform time-series analysis based on Bayesian inference on the network delay normalization value and the mechanical delay normalization value to determine whether to generate an alarm signal.

2. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 1, characterized in that, The intelligent sensing node includes a data acquisition unit, a data processing unit, a wireless communication module, and a power supply module.

3. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 2, characterized in that, The intelligent sensing node collects circuit breaker status data and transmits it to the edge computing gateway via a multi-hop wireless ad-hoc network, including: When the data acquisition unit of the intelligent sensing node detects that the circuit breaker changes from open to closed or from closed to open, the level state of the data acquisition unit changes. After detecting a change in voltage level, the data processing unit of the intelligent sensing node generates circuit breaker status data, which includes a unique node ID, event type, timestamp of the event, and checksum. The wireless communication module of the smart sensor node queries its own routing table and selects the path with the lowest gateway cost to transmit the circuit breaker status data to the edge computing gateway.

4. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 3, characterized in that, The wireless communication module of the smart sensor node queries its own routing table and selects the path with the lowest gateway cost to transmit the circuit breaker status data to the edge computing gateway. This includes: if the edge computing gateway is not within the direct communication range of the smart sensor node, the wireless communication module of the smart sensor node sends the circuit breaker status data to the next node on the path.

5. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 1, characterized in that, The mechanical delay normalization module is used to normalize the mechanical delay of this observation using the following formula: ;in, Due to the mechanical delay of this observation, This is the average mechanical delay. The standard deviation of mechanical time delay, It is the standardized value of mechanical time delay; The network latency normalization module is used to normalize the locally observed network latency using the following formula: ;in, To observe network latency locally, This represents the average network latency. The standard deviation of network latency. It is the standardized value of network latency.

6. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 5, characterized in that, The alarm signal generation module includes: The circuit breaker status determination unit is used to determine that the circuit breaker is in normal condition if both the network delay standardization value and the mechanical delay standardization value are less than the normal state threshold. The diagnostic confidence calculation unit is used to calculate the confidence of mechanical faults as diagnostic confidence based on the network delay normalization value and the mechanical delay normalization value if both the network delay normalization value and the mechanical delay normalization value are greater than or equal to the normal state threshold. The signal generation unit is used to determine and generate an alarm signal in response to a diagnostic confidence level greater than an alarm confidence level threshold.

7. The intelligent signal circuit system for medium-voltage switchgear based on a wireless self-organizing network according to claim 6, characterized in that, The diagnostic confidence calculation unit is used to: calculate the confidence level of mechanical faults as diagnostic confidence based on the network delay normalization value and the mechanical delay normalization value using the following formula: in, It is the probability density function of the standard normal distribution. It is the diagnostic confidence level.

8. A central control method for a medium-voltage switchgear intelligent signal circuit based on a wireless ad hoc network, used to execute the medium-voltage switchgear intelligent signal circuit system based on a wireless ad hoc network as described in claim 1, characterized in that, The central control method for the intelligent signal circuit of medium-voltage switchgear based on wireless self-organizing network includes: After the switch cabinet is powered on, it starts and creates a wireless self-organizing network through the edge computing gateway. The smart sensor nodes deployed nearby on the circuit breaker join the wireless self-organizing network to realize wireless communication between the edge computing gateway and the smart sensor nodes. The status data of the circuit breaker is collected by intelligent sensor nodes and transmitted to the edge computing gateway via a multi-hop wireless self-organizing network. The edge computing gateway parses and performs timing analysis on the circuit breaker status data to determine whether to generate an alarm signal.

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