A method and system for real-time monitoring and response of fastener status

Abstract

The application provides a kind of fast clamp state real-time monitoring response method and system, which constructs four temperature threshold systems by setting first temperature threshold, second temperature threshold, third temperature threshold and fourth temperature threshold, sets a ladder response mechanism of "early warning→alarm→forced cooling→manual intervention", and realizes the hierarchical control of fast clamp temperature rise risk.The system adopts a three-level architecture design of "edge computing node + server + mobile terminal", wherein the edge computing node is deployed in the distribution room and connected with the wireless temperature measurement module to realize local data filtering, threshold judgment and cooling device control;The server serves as the data hub, responsible for storing full temperature data, and supports remote access and instruction relay of mobile terminal;Mobile terminal communicates with server, and can realize real-time monitoring and remote control function;It can realize accurate perception, fast response and efficient operation and maintenance of the temperature of the clamp.

Description

A method and system for real-time monitoring and response of fast clamp status Technical Field

[0001] This invention relates to the field of electronic power technology, and in particular to a method and system for real-time monitoring and response of fast clamp status based on a wireless temperature measurement module. Background Technology

[0002] In urban power distribution networks, especially in older urban areas, low-voltage overhead lines remain a common power supply method. In practice, when encountering temporary power transfers or emergency power needs, drawing power from low-voltage overhead lines via clamps is the primary and effective means of connection. Currently, the main methods for quickly connecting low-voltage overhead lines are piercing clamps and crimping clamps. Piercing clamps, made of high-strength metal, require pressure to pierce the conductor's insulation layer with a piercing blade, achieving direct contact and current transmission. This method is used in power lines and distribution systems, primarily connecting conductors while avoiding damage to the insulation. Crimping clamps, unlike piercing clamps, typically require stripping the conductor's insulation layer and securing it with bolts or crimping tools. The tightly contacting metal conductors mechanically and electrically connect two or more conductors, transmitting current through the clamp while maintaining the strength of the mechanical connection and ensuring tight contact between the conductor and the metal portion within the clamp. This is a widely used conductor connection device in power systems.

[0003] Traditional rapid clamp status monitoring mainly relies on manual inspection, periodically detecting temperature using infrared thermometers and manually comparing the ambient temperature with equipment thresholds, lacking real-time and continuous monitoring. When the temperature is abnormal, manual intervention is required to activate cooling equipment (such as temporarily installing fans), resulting in delayed response and susceptibility to human interference. At the communication level, traditional solutions often use wired connections or single gateways to forward data, leading to complex cabling, poor scalability, and difficulty in meeting the needs of large-scale equipment networking. Regarding threshold setting, traditional methods typically use a fixed single threshold, failing to adapt to the dynamic characteristics of equipment operation and environmental changes, easily leading to false alarms or missed alarms. For example, Chinese invention patent publication number CN116380285A discloses a cable terminal with temperature measurement function and its monitoring method, which includes the following steps: acquiring the internal temperature of the cable terminal; monitoring whether the cable terminal's operating status is abnormal based on the internal temperature, and issuing an alarm signal if abnormal. This method can only monitor the internal temperature of the cable terminal and cannot monitor the rapid clamps. Furthermore, this method uses a single threshold for monitoring, failing to achieve adaptive adjustment.

[0004] Therefore, in view of the problems existing in the prior art, it is of great importance to provide a real-time monitoring and response technology for the fast clamp status based on a wireless temperature measurement module, which has high monitoring accuracy, high flexibility, and can adaptively adjust a multi-level temperature threshold system. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time monitoring and response method and system for rapid clamp status. It constructs a four-level temperature threshold system (including dual warning values, i.e., minimum and maximum values) and sets up a tiered response mechanism of "early warning → alarm → forced cooling → manual intervention" to achieve graded control of the risk of rapid clamp temperature rise. Furthermore, the system adopts a three-level architecture of "edge computing nodes + cloud server + mobile terminal," significantly improving system reliability and operational efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a real-time monitoring and response method for fast wire clamp status, the real-time monitoring and response method comprising the following steps:

[0008] Step S1: After the wireless temperature measurement module initializes the temperature value of the fast clamp and the ambient temperature value, it pre-sets the range of multi-level temperature thresholds and the cooling rate; wherein, the multi-level temperature thresholds include at least four temperature thresholds, namely the first temperature threshold, the second temperature threshold, the third temperature threshold, and the fourth temperature threshold, and the range of each temperature threshold is set to increase progressively.

[0009] Step S2: Monitor and acquire the temperature value of the quick clamp and the ambient temperature value in real time, and send them to the edge computing node for analysis. Determine whether the temperature value of the quick clamp is greater than the temperature value of the power supply. If it is greater, proceed to step S3. Otherwise, the wireless temperature measurement module continues to monitor and acquire the temperature value of the quick clamp and the ambient temperature value, and repeats step S2.

[0010] Step S3: Determine whether the temperature value of the quick clamp is greater than the ambient temperature value. If it is, proceed to step S4; otherwise, the wireless temperature measurement module continues to monitor the temperature value of the quick clamp and the ambient temperature in real time and provide feedback.

[0011] Step S4: Compare the temperature value of the quick clamp with the preset minimum value of the first-level temperature threshold. The edge computing node determines whether the temperature value of the quick clamp is greater than the minimum value of the first-level temperature threshold. If it is greater, an alarm signal is sent to the server and the process proceeds to step S5; otherwise, the alarm signal is deactivated, and the temperature value of the quick clamp is processed by data filtering before returning to step S2.

[0012] Step S5: Determine whether the temperature value of the quick clamp is greater than the highest value of the preset first-level temperature threshold. If it is greater, proceed to step S6 and switch to the second-level temperature threshold for comparison.

[0013] Step S6: Compare the temperature value of the quick clamp with the preset minimum value of the second-level temperature threshold, and determine whether the temperature value of the quick clamp is greater than the minimum value of the second-level temperature threshold. If it is greater, start the automatic cooling device to cool the quick clamp at the preset cooling rate and proceed to step S7; otherwise, return to step S2.

[0014] Step S7: Determine whether the temperature value of the quick clamp is greater than the highest value of the preset second-level temperature threshold. If it is greater, proceed to step S8 and switch to the third-level temperature threshold for comparison.

[0015] Step S8: Compare the temperature value of the quick clamp with the preset minimum value of the third-level temperature threshold. The edge computing node determines whether the temperature value of the quick clamp is greater than the minimum value of the third-level temperature threshold. If it is greater, the automatic cooling device is activated to cool the quick clamp at a preset cooling rate, and an alarm signal is sent to the server. Then proceed to step S9. Otherwise, return to step S2.

[0016] Step S9: The edge computing node determines whether the temperature value of the fast clamp is greater than the highest value of the preset third-level temperature threshold. If it is greater, it determines whether it is greater than the fourth-level temperature threshold. When the temperature value of the fast clamp is greater than the fourth-level temperature threshold, the automatic cooling device is activated to cool the fast clamp at a preset cooling rate and an alarm signal is sent to the server. Otherwise, return to step S2.

[0017] The above-mentioned real-time monitoring and response method also includes the following steps:

[0018] Step S10: After receiving the alarm signal, the server parses the alarm information carried by the signal and sends it to the matching mobile terminal according to the parsing result.

[0019] The above-mentioned real-time monitoring and response method also includes the following steps:

[0020] Step S11: Obtain the network connection status of the edge computing node. When the status is determined to be a network interruption, automatically enter the local mode, continue to execute temperature monitoring, judgment, and cooling commands, and store the data in the local database; and the edge computing node periodically initiates network connection commands until the network is reconnected.

[0021] In step S1, the wireless temperature measurement module initializes the temperature value of the fast clamp and the ambient temperature value to zero, and pre-sets the cooling rate and the first-level temperature threshold minimum value, the first-level temperature threshold maximum value, the second-level temperature threshold minimum value, the second-level temperature threshold maximum value, the third-level temperature threshold minimum value, the third-level temperature threshold maximum value, and the fourth-level temperature threshold in ascending order.

[0022] In step S4 above, the data filtering process for the temperature value of the quick clamp includes:

[0023] Filter out the maximum and minimum values ​​from the temperature values ​​of the quick clamp, and obtain the average and variance of the temperature values ​​of the quick clamp from the filtered temperature values;

[0024] The average value is compared with the variance of the fast clamp temperature value. If the average value is greater than the variance, the lowest value of the first-level temperature threshold is increased by one temperature step, and the highest value of the first-level temperature threshold is increased by one temperature step accordingly, and the filtering continues. If the average value is less than the variance, the lowest value of the first-level temperature threshold is decreased by one temperature step, and the highest value of the first-level temperature threshold is decreased by one temperature step accordingly, and the filtering continues.

[0025] The temperature step size is set by measurement, and the temperature step size is the smallest unit set according to temperature changes.

[0026] The present invention also provides a real-time monitoring and response system for fast clamp status, which applies the aforementioned real-time monitoring and response method for fast clamp status; the real-time monitoring and response system includes a wireless temperature measurement module, an automatic cooling device, an edge computing node, a server, and a mobile terminal; wherein, the wireless temperature measurement module and the automatic cooling device are both connected to the edge computing node, the edge computing node is connected to the server through a network, and the server is interactively connected to the mobile terminal.

[0027] The wireless temperature measurement module is configured to send the collected temperature data and its own status information to the edge computing node;

[0028] The automatic cooling device is configured to receive control commands from the edge computing node, so that the edge computing node can control it to turn on or off.

[0029] The edge computing node is configured to receive data and information transmitted from multiple wireless temperature measurement modules, analyze and judge them, and then send the corresponding information to the automatic cooling device or server.

[0030] The server is configured to receive alarm signals sent by edge computing nodes, analyze the alarm information carried by the alarm signals, and send the analysis results to the matching mobile terminal for interaction.

[0031] Specifically, the network topology of the aforementioned real-time monitoring and response system adopts a three-tier architecture of "edge computing nodes + servers + mobile terminals". The edge computing nodes are deployed in the power distribution room and connected to the wireless temperature measurement module through a ZigBee local area network to realize local data filtering, threshold judgment, and control of cooling devices. The server (e.g., a cloud server) serves as the data hub, responsible for storing all temperature data and supporting remote access and command relay by mobile terminals. The mobile terminals communicate with the cloud server based on 4G / 5G networks, are compatible with iOS / Android systems, and can realize real-time monitoring and remote control functions.

[0032] Specifically, the aforementioned real-time monitoring and response system can send alarm information down to mobile terminals. After receiving the alarm signal, its cloud server parses information such as device ID, real-time temperature value, and threshold level, and achieves precise notification through routing strategies. Based on the device location (e.g., "Power Distribution Room A in Factory Building"), it matches the mobile terminal ID of the responsible maintenance personnel and adjusts the push priority according to the alarm level (low / medium / high). High priority (e.g., level 4 threshold temperature alarm) is alerted through a triple notification via mobile terminal system notification, SMS, and voice call; medium priority (e.g., level 3 temperature threshold alarm) is alerted via a pop-up window in the mobile terminal APP and a red dot notification in the message center; low priority (e.g., level 1 / 2 temperature threshold alarm) is only recorded in the APP message center. The mobile terminal marks the location of the alarm device on the map interface, with a flashing red icon indicating a high-priority alarm. Clicking the device icon allows viewing detailed information including real-time temperature curves, historical alarm records, threshold configurations, etc.

[0033] Specifically, the aforementioned real-time monitoring and response system enables mobile terminals to transmit and execute control commands. Maintenance personnel can select the target device and send control commands such as "adjust threshold" or "restart cooling device" via a mobile terminal (e.g., a mobile app). After the control commands are transmitted to the cloud server, the system first verifies user permissions (e.g., engineers can adjust thresholds, administrators can perform power-off operations), and then checks the validity of the command parameters (e.g., the threshold adjustment range must be limited to between ambient temperature +15℃ and the material's temperature resistance limit -10℃). Commands that pass verification are sent to the edge computing nodes via MQTT topic / system / command / execute. After receiving the command, the edge computing node first executes its local logic (e.g., waiting 30 seconds for cooling time before restarting the cooling device), and then returns the command execution result (success / failure) and real-time temperature data to the cloud server, ultimately synchronizing it to the mobile terminal. When the edge computing node experiences a network outage with the cloud, it enters local autonomous mode, continuing threshold judgment and cooling control. It also stores unsent alarm events in a local SQLite database (maximum storage of 7 days) and attempts to reconnect to the cloud every 5 minutes. If the mobile terminal is offline, the cloud will resend unread alarm information after network recovery, and supports daily resending of historical alarm summaries via email. For command execution failure scenarios, if the edge computing node does not return an ACK confirmation packet, the cloud server automatically resends the command (up to 3 times, 1 minute interval). Three failures mark it as a "high-risk anomaly" and trigger a secondary alarm signal to notify the operations manager. At this time, the operations manager can view the command logs via a mobile terminal and manually send a "device inspection" command with camera capture requirements. The edge node collects on-site photos through the power distribution room camera and transmits them back to the mobile terminal via the MQTT topic / system / image / upload. In terms of performance optimization, temperature curve data is compressed using Gzip (compression ratio approximately 4:1) to reduce bandwidth consumption, and historical data is synchronized during off-peak hours from 0:00 to 6:00 AM to avoid bandwidth congestion. For security design, the communication layer uses AES-256 encryption to transmit sensitive data such as user permissions and threshold parameters. The application layer automatically refreshes API keys on mobile terminals every 30 minutes. The cloud server conducts periodic penetration tests and prohibits direct public access to edge computing node IPs. Simultaneously, the system records all mobile terminal operations (including operator ID, timestamp, device ID, instruction content, and execution result), and audit logs are stored for at least one year and support keyword retrieval and export.

[0034] The wireless temperature measurement module described above is connected to the edge computing node via a ZigBee wireless local area network.

[0035] The automatic cooling device is connected between the quick clamp and the power supply; the automatic cooling device is connected to the edge computing node via a wired connection or an industrial wireless connection.

[0036] The aforementioned edge computing nodes connect to the server via Ethernet, Wi-Fi, or cellular networks.

[0037] Preferably, the server sends alarm information to the mobile terminal via MQTT topic, system, or alert.

[0038] Preferably, the server transmits alarm information in JSON format, and the alarm information includes device ID, real-time temperature, triggered temperature threshold level, and suggested operation.

[0039] As described above, after receiving the alarm information, the mobile terminal triggers an audible and visual alarm according to the instruction priority and displays a structured data table.

[0040] Preferably, after receiving the alarm information sent by the server, the mobile terminal sends a second alarm signal, and the control command sent by the mobile terminal is transmitted to the server through the mobile or command topic; after receiving the command, the server verifies the user's authorization, executes the operation, and returns an ACK confirmation packet; after receiving the confirmation, the mobile terminal updates the command status.

[0041] Preferably, the server can be a cloud server.

[0042] The beneficial effects of this invention are:

[0043] This invention provides a method and system for real-time monitoring and response to fast clamp status. By employing a multi-level temperature threshold design and a dynamic threshold algorithm for data filtering, it can adapt to environmental changes and equipment aging, automatically adjusting the warning line, significantly improving monitoring accuracy and effectively avoiding false alarms and missed alarms caused by traditional fixed thresholds. This achieves intelligent precision and reduces false alarms. Furthermore, the system's edge computing nodes perform real-time judgment and control locally, achieving millisecond-level rapid response and immediately initiating automatic cooling. Even with network interruptions, the system can operate independently and stably, greatly improving power supply reliability. In addition, remote real-time monitoring and multi-priority alarm push notifications via mobile terminals enable maintenance personnel to quickly locate and handle problems, significantly reducing the intensity and lag of manual inspections, achieving efficient maintenance and freeing up manpower. Simultaneously, the system adopts a three-level collaborative architecture of "edge computing nodes + cloud servers + mobile terminals," ensuring both real-time local control and centralized data management and analysis. The use of wireless networking and standard communication protocols makes the system flexible in deployment and easy to scale up. This real-time monitoring and response method and system enables accurate sensing, rapid response, and efficient operation and maintenance of clamp temperature, solving problems such as slow response, reliance on manual labor, and susceptibility to false alarms in traditional methods. Attached Figure Description

[0044] Figure 1 is a flowchart illustrating the real-time monitoring and response method provided in an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of the structure of the real-time monitoring and response system provided in an embodiment of the present invention. Detailed Implementation

[0046] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0047] As shown in Figure 1, this embodiment provides a real-time monitoring and response method for fast clamp status, which includes the following steps:

[0048] Step S1: After initializing the temperature values ​​of the fast clamp and the ambient temperature, the wireless temperature measurement module pre-sets the range of multiple temperature thresholds and the cooling rate; wherein, the multiple temperature thresholds include four levels: the first level temperature threshold, the second level temperature threshold, the third level temperature threshold, and the fourth level temperature threshold, and the range of each temperature threshold is set to increase progressively; specifically:

[0049] The wireless temperature measurement module initializes the temperature values ​​of the fast clamp and the ambient temperature to zero, and pre-sets the cooling rate and the first-level temperature threshold minimum value, the first-level temperature threshold maximum value, the second-level temperature threshold minimum value, the second-level temperature threshold maximum value, the third-level temperature threshold minimum value, the third-level temperature threshold maximum value, and the fourth-level temperature threshold in ascending order.

[0050] In this embodiment, equipment deployment can be performed first, with a wireless temperature measurement module (with anti-electromagnetic interference capability) attached to the surface of each set of quick-connect clamps. A central control system is then installed in the power distribution room, connecting all wireless temperature measurement modules to a water-cooled automatic cooling device (using a combination of water spray cooling and fan heat dissipation). Specific parameter settings are as follows:

[0051] The minimum value for the first-level temperature threshold is 45℃, and the maximum value is 50℃.

[0052] The minimum value for the second-level temperature threshold is 55℃, and the maximum value is 60℃.

[0053] The minimum value for the third-level temperature threshold is 65℃, and the maximum value is 70℃.

[0054] The fourth-level temperature threshold is 75℃ (this value is the forced cooling and maximum alarm value);

[0055] Cooling rate: set to 8℃ / minute (for industrial-grade rapid cooling requirements).

[0056] The wireless temperature measurement module is initialized with a quick clamp temperature T0 = 0℃ and an ambient temperature T_env = 0℃.

[0057] In other embodiments, the values ​​of the above parameters may be adjusted according to specific circumstances.

[0058] Step S2: Monitor and acquire the temperature value of the quick clamp and the ambient temperature value in real time, and send them to the edge computing node for analysis. Determine whether the temperature value of the quick clamp is greater than the temperature value of the power supply. If it is greater, proceed to step S3. Otherwise, the wireless temperature measurement module continues to monitor and acquire the temperature value of the quick clamp and the ambient temperature value, and repeats step S2.

[0059] For example, if temperature data is received in real time and a temperature of 38℃ (> power supply temperature 35℃) is detected for the first time for a certain fast clamp, then proceed to step S3.

[0060] Step S3: Determine whether the temperature value of the quick clamp is greater than the ambient temperature value. If it is, proceed to step S4; otherwise, the wireless temperature measurement module continues to monitor the temperature value of the quick clamp and the ambient temperature in real time and provide feedback.

[0061] Step S4: Compare the temperature value of the quick clamp with the preset minimum value of the first-level temperature threshold. The edge computing node determines whether the temperature value of the quick clamp is greater than the minimum value of the first-level temperature threshold. If it is greater, an alarm signal is sent to the server and the process proceeds to step S5; otherwise, the alarm signal is deactivated, and the temperature value of the quick clamp is filtered before returning to step S2. In this embodiment, the data filtering process for the temperature value of the quick clamp includes:

[0062] Filter out the maximum and minimum values ​​from the temperature values ​​of the quick clamp, and obtain the average and variance of the temperature values ​​of the quick clamp from the filtered temperature values;

[0063] The average value is compared with the variance of the rapid clamp temperature value. If the average value is greater than the variance, the lowest value of the first-level temperature threshold is increased by one temperature step, and the highest value of the first-level temperature threshold is increased by one temperature step accordingly, and filtering continues. If the average value is less than the variance, the lowest value of the first-level temperature threshold is decreased by one temperature step, and the highest value of the first-level temperature threshold is decreased by one temperature step accordingly, and filtering continues. The temperature step is set by measurement, and the temperature step is the smallest unit set according to the temperature change.

[0064] Step S5: Determine whether the temperature value of the quick clamp is greater than the highest value of the preset first-level temperature threshold. If it is greater, proceed to step S6 and switch to the second-level temperature threshold for comparison.

[0065] Step S6: Compare the temperature value of the quick clamp with the preset minimum value of the second-level temperature threshold, and determine whether the temperature value of the quick clamp is greater than the minimum value of the second-level temperature threshold. If it is greater, start the automatic cooling device to cool the quick clamp at the preset cooling rate and proceed to step S7; otherwise, return to step S2.

[0066] Step S7: Determine whether the temperature value of the quick clamp is greater than the highest value of the preset second-level temperature threshold. If it is greater, proceed to step S8 and switch to the third-level temperature threshold for comparison.

[0067] Step S8: Compare the temperature value of the quick clamp with the preset minimum value of the third-level temperature threshold. The edge computing node determines whether the temperature value of the quick clamp is greater than the minimum value of the third-level temperature threshold. If it is greater, the automatic cooling device is activated to cool the quick clamp at a preset cooling rate, and an alarm signal is sent to the server. Then proceed to step S9. Otherwise, return to step S2.

[0068] Step S9: The edge computing node determines whether the temperature value of the fast clamp is greater than the highest value of the preset third-level temperature threshold. If it is greater, it determines whether it is greater than the fourth-level temperature threshold. When the temperature value of the fast clamp is greater than the fourth-level temperature threshold, the automatic cooling device is activated to cool the fast clamp at a preset cooling rate and an alarm signal is sent to the server. Otherwise, return to step S2.

[0069] Step S10: After receiving the alarm signal, the server parses the alarm information carried by the signal and sends it to the matching mobile terminal according to the parsing result.

[0070] The aforementioned real-time monitoring and response method differs from the crude control mode of a single threshold in existing technologies. It constructs a four-level temperature threshold system (including dual warning values, i.e., minimum and maximum values), and achieves graded control of the risk of rapid clamp temperature rise through a tiered response mechanism of "early warning (maximum value of the first-level temperature threshold) → alarm → forced cooling → manual intervention" (for example, the first-level minimum threshold is 45℃ to trigger an early warning, and the fourth-level temperature threshold is set to 75℃ to initiate forced cooling and the maximum alarm). Simultaneously, through data filtering and dynamic threshold adjustment algorithms (removing extreme values, calculating the mean and variance, and automatically correcting the threshold step size), the system can adapt to temperature fluctuation characteristics under different load scenarios (such as baseline drift caused by equipment aging and seasonal environmental temperature changes), avoiding false alarms or missed alarms caused by fixed thresholds, and significantly improving monitoring accuracy and response flexibility.

[0071] As shown in Figure 2, this embodiment also provides a real-time monitoring and response system for rapid clamp status, applying the real-time monitoring and response method for rapid clamp status provided in this embodiment. The real-time monitoring and response system includes a wireless temperature measurement module, an automatic cooling device, an edge computing node, a server, and a mobile terminal. The wireless temperature measurement module and the automatic cooling device are both connected to the edge computing node. The edge computing node connects to the server via a network, and the server interacts with the mobile terminal. The wireless temperature measurement module is configured to send the collected temperature data and its own status information to the edge computing node. The automatic cooling device is configured to receive control commands from the edge computing node, enabling the edge computing node to control its activation or deactivation. The edge computing node is configured to receive data and information transmitted from multiple wireless temperature measurement modules, analyze and judge them, and then send corresponding information to the automatic cooling device or the server. The server is configured to receive alarm signals sent by the edge computing node, analyze the alarm information carried by the alarm signals, and send the results to a matched mobile terminal for interaction.

[0072] In this embodiment, the wireless temperature measurement module is connected to the edge computing node via a ZigBee wireless local area network; the automatic cooling device is connected between the quick clamp and the power supply; the automatic cooling device is connected to the edge computing node via a wired connection or an industrial wireless connection.

[0073] In this embodiment, the edge computing node is connected to the server via Ethernet; the server transmits alarm information in JSON format, and the alarm information includes the device ID, real-time temperature, triggered temperature threshold level, and suggested actions (e.g., ...).

[0074] {"action_suggestion":"RESTART_COOLING"}).

[0075] In this embodiment, the mobile terminal and the server establish a communication connection via the MQTT protocol. The server transmits alarm information and control commands in JSON format, and its communication port uses a TLS encrypted channel by default (e.g., 8883). The server is configured to assign a unique device ID to each fast clamp (e.g., ...).

[0076] The server uses LINE_CLAMP_007 and binds it to the mobile terminal's device list to ensure accurate command delivery. The server sends alarm information to the mobile terminal via MQTT topics, system, or alert. Upon receiving the alarm information, the mobile terminal triggers an audible and visual alarm based on the command priority (default MEDIUM) and displays a structured data table, as shown below:

[0077]

[0078] In this embodiment, after receiving the alarm information sent by the server, the mobile terminal sends a second alarm signal. The control command sent by the mobile terminal is transmitted to the server through the mobile or command topic (for example, the command to restart the cooling device is {"command": "RESTART", "priority": "MEDIUM"}). After receiving the command, the server performs user permission verification, executes the operation, and returns an ACK confirmation packet. After receiving the confirmation, the mobile terminal updates the command status (for example, updates it to the 'executed' status).

[0079] In this embodiment, the server is a cloud server.

[0080] The aforementioned real-time monitoring and response system employs a three-tiered network topology: edge computing nodes, cloud servers, and mobile terminals. Edge computing nodes, deployed in the power distribution room, connect in real-time to wireless temperature measurement modules via a ZigBee LAN. This enables local data filtering, threshold judgment, and control of cooling devices. Even when disconnected from the cloud network, they can continue operating in a local autonomous mode (e.g., storing 7 days of data and initiating network reconnection commands every 5 minutes), mitigating the "loss of control" risk associated with traditional centralized cloud architectures during network outages. The cloud server, acting as the data hub, is responsible for storing all temperature data, relaying cross-regional maintenance commands, and intelligently routing alarm information (e.g., matching responsible personnel based on device location and adjusting notification priorities based on alarm levels). Combined with the localized real-time processing capabilities of the edge computing nodes, this creates a synergistic advantage of "fast near-field control response and wide far-field management coverage," significantly improving system reliability and operational efficiency. In this system, modules such as wireless temperature measurement modules, automatic cooling devices, and edge computing nodes are located in the power distribution room or at the installation site of the cable clamps. Their main functions are to collect data and control it locally in real time. The edge computing nodes are connected to the cloud server through the network, which serves as the channel for data uplink and command downlink. The cloud server acts as the "brain" of the system, and its main functions are to aggregate, store, analyze, and route commands. Finally, human-computer interaction is achieved through mobile terminals, providing maintenance personnel with visualized information and a remote control interface.

[0081] In the aforementioned real-time monitoring and response system, the mobile terminal uses the MQTT protocol and JSON data format to achieve structured transmission of alarm information and control commands (e.g., JSON data packets containing device ID, threshold level, and suggested operations). The mobile terminal employs a multi-priority notification strategy (e.g., using high-priority alarms to trigger system notifications, SMS, and voice calls for triple alerts, while low-priority alarms are only recorded in the APP message center) and a map-based alarm display (high-risk devices are marked with flashing red icons; clicking them displays real-time curves and historical records), ensuring that maintenance personnel can quickly locate anomalies. Furthermore, the system supports remote command permission verification and closed-loop feedback (e.g., engineer-level adjustable thresholds, administrator-level power-off capabilities, and ACK confirmation packets returned after command execution), combined with full-process audit log recording (e.g., recording operator, timestamp, and command content, stored for ≥1 year), which improves operational security, meets industrial compliance requirements, and avoids the lag and risk of misoperation inherent in traditional manual inspections.

[0082] In the aforementioned real-time monitoring and response system, the connection methods of its various modules have the following distinct characteristics: ① It combines ZigBee (low-power local area network), cellular network / broadband (wide area network), and mobile data / Wi-Fi, allowing for the selection of the optimal connection method based on different scenarios, thus achieving heterogeneous network integration. ② The core communication of the mobile terminal uses MQTT and JSON, ensuring the system's openness, scalability, and ease of integration, and possessing the advantage of protocol standardization. ③ TLS encryption is used throughout the process, and authorization verification is performed on commands, ensuring the security of the industrial system. ④ Edge computing nodes are responsible for local control with high real-time requirements, while cloud servers are responsible for big data management and wide-area collaboration. The two are connected through standard network protocols, forming complementary advantages and enabling the edge computing nodes and cloud servers to work collaboratively. This system's connection design effectively solves the problems of complex wiring, poor scalability, and delayed response in traditional solutions, constructing a stable, efficient, and scalable intelligent monitoring and response system.

[0083] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A method for real-time monitoring and response to the status of a fast clamp, characterized in that, The real-time monitoring and response method includes the following steps: Step S1: After initializing the temperature value of the fast clamp and the ambient temperature value, the wireless temperature measurement module pre-sets the range of multi-level temperature thresholds and the cooling rate; wherein, the multi-level temperature thresholds include at least four levels, namely the first level temperature threshold, the second level temperature threshold, the third level temperature threshold, and the fourth level temperature threshold, and the range of each level temperature threshold is set to increase progressively; Step S2: The temperature value of the fast clamp and the ambient temperature value are acquired in real time and sent to the edge computing node for analysis to determine whether the temperature value of the fast clamp is greater than the temperature value of the power supply. If it is greater, proceed to step S3; otherwise, the wireless temperature measurement module continues to monitor and acquire the temperature value. Take the temperature value of the quick-connect clamp and the ambient temperature value, and repeat step S2; Step S3: Determine whether the temperature value of the quick-connect clamp is greater than the ambient temperature value. If it is, proceed to step S4; otherwise, the wireless temperature measurement module continues to monitor the temperature value of the quick-connect clamp and the ambient temperature in real time and provides feedback; Step S4: Compare the temperature value of the quick-connect clamp with the preset minimum value of the first-level temperature threshold. The edge computing node determines whether the temperature value of the quick-connect clamp is greater than the minimum value of the first-level temperature threshold. If it is, send an alarm signal to the server and proceed to step S5; otherwise, deactivate the alarm signal, perform data filtering on the temperature value of the quick-connect clamp, and return to step S2; Step S5: Determine whether the temperature value of the quick-connect clamp is greater than the minimum value of the first-level temperature threshold. If the temperature value of the quick clamp is greater than the preset maximum value of the first-level temperature threshold, proceed to step S6 and switch to the second-level temperature threshold for comparison; Step S6: Compare the temperature value of the quick clamp with the preset minimum value of the second-level temperature threshold to determine if the temperature value of the quick clamp is greater than the minimum value of the second-level temperature threshold. If it is greater, activate the automatic cooling device to cool the quick clamp at a preset cooling rate and proceed to step S7; otherwise, return to step S2; Step S7: Determine if the temperature value of the quick clamp is greater than the preset maximum value of the second-level temperature threshold. If it is greater, proceed to step S8 and switch to the third-level temperature threshold for comparison; Step S8: Compare the temperature value of the quick clamp with the preset maximum value of the third-level temperature threshold. Step S9: The edge computing node compares the minimum temperature threshold value with the maximum temperature threshold value of the fast clamp. If the temperature value is greater than the minimum temperature threshold value of the third level, the automatic cooling device is activated to cool the fast clamp at a preset cooling rate, and an alarm signal is sent to the server. Otherwise, the process returns to step S2. Step S9: The edge computing node checks whether the temperature value of the fast clamp is greater than the maximum temperature threshold value of the third level. If it is greater than the maximum temperature threshold value of the third level, the node checks whether the temperature value is greater than the fourth level temperature threshold value. When the temperature value of the fast clamp is greater than the fourth level temperature threshold value, the automatic cooling device is activated to cool the fast clamp at a preset cooling rate, and an alarm signal is sent to the server. Otherwise, the process returns to step S2.

2. The real-time monitoring and response method according to claim 1, characterized in that, It also includes the following steps: Step S10: After receiving the alarm signal, the server parses the alarm information carried by the signal and sends it to the matching mobile terminal according to the parsing result.

3. The real-time monitoring and response method according to claim 2, characterized in that, It also includes the following steps: Step S11: Obtain the network connection status of the edge computing node. When the status is determined to be a network interruption, automatically enter the local mode, continue to execute temperature monitoring, judgment, and cooling commands, and store the data in the local database; and the edge computing node periodically initiates network connection commands until the network is reconnected.

4. The real-time monitoring and response method according to claim 1, characterized in that, In step S1, the wireless temperature measurement module initializes the temperature value of the fast clamp and the ambient temperature value to zero, and pre-sets the cooling rate and the first-level temperature threshold minimum value, the first-level temperature threshold maximum value, the second-level temperature threshold minimum value, the second-level temperature threshold maximum value, the third-level temperature threshold minimum value, the third-level temperature threshold maximum value, and the fourth-level temperature threshold in sequence.

5. The real-time monitoring and response method according to claim 1, characterized in that, In step S4, the data filtering process for the temperature value of the quick clamp includes: filtering out the maximum and minimum values ​​from the temperature value of the quick clamp, and obtaining the average and variance of the temperature value of the quick clamp from the filtered temperature value; comparing the average value with the variance of the temperature value of the quick clamp; if the average value is greater than the variance, increasing the minimum value of the first-level temperature threshold by one temperature step and correspondingly increasing the maximum value of the first-level temperature threshold by one temperature step, and continuing filtering; if the average value is less than the variance, decreasing the minimum value of the first-level temperature threshold by one temperature step and correspondingly decreasing the maximum value of the first-level temperature threshold by one temperature step, and continuing filtering; the temperature step is set by measurement, and the temperature step is the smallest unit set according to the temperature change.

6. A real-time monitoring and response system for rapid clamp status, characterized in that, The system employs the real-time monitoring and response method as described in any one of claims 1 to 5; the real-time monitoring and response system includes a wireless temperature measurement module, an automatic cooling device, an edge computing node, a server, and a mobile terminal; wherein the wireless temperature measurement module and the automatic cooling device are both connected to the edge computing node, the edge computing node is connected to the server via a network, and the server is interactively connected to the mobile terminal; the wireless temperature measurement module is configured to send the collected temperature data and its own status information to the edge computing node; the automatic cooling device is configured to receive control commands from the edge computing node so that the edge computing node can control it to turn on or off; the edge computing node is configured to receive data and information transmitted from multiple wireless temperature measurement modules, analyze and judge them, and then send corresponding information to the automatic cooling device or the server; the server is configured to receive alarm signals sent by the edge computing node, analyze the alarm information carried by the alarm signals, and send them to the matched mobile terminal according to the analysis results, and interact with the mobile terminal.

7. The real-time monitoring and response system according to claim 6, characterized in that, The wireless temperature measurement module is connected to the edge computing node via a ZigBee wireless local area network.

8. The real-time monitoring and response system according to claim 6, characterized in that, The automatic cooling device is connected between the quick clamp and the power supply; the automatic cooling device is connected to the edge computing node via a wired connection or an industrial wireless connection.

9. The real-time monitoring and response system according to claim 6, characterized in that, The edge computing nodes are connected to the server via Ethernet, Wi-Fi, or cellular networks.

10. The real-time monitoring and response system according to claim 6, characterized in that, The server transmits alarm information in JSON format, which includes device ID, real-time temperature, triggered temperature threshold level, and suggested actions.

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