Flue gas cooler temperature wireless monitoring system

Abstract

The invention relates to the technical field of monitoring, in particular to a flue gas cooler temperature wireless monitoring system which comprises a data acquisition module, a data transmission module, an analysis decision module and a response control module. The data acquisition module acquires flue gas temperature data through a dual-redundancy wireless temperature sensor group, and acid mist erosion is reduced by adopting a corrosion-resistant alloy shell and an inert gas positive pressure barrier; the analysis decision module executes three-dimensional temperature field reconstruction and spatial distribution verification, time sequence trend verification and equipment state association verification, and outputs a corrosion risk index; and the response control module starts sampling strategy adjustment, atlas pushing or equipment linkage operation in a grading manner according to the risk index, and feeds back a control instruction to the acquisition and transmission module. Dynamic closed-loop control is formed through cooperation of multiple modules, the problem of temperature monitoring continuity interruption caused by sensor corrosion failure in a high-sulfur wet flue gas environment is solved, and the system operation reliability is improved.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology, and in particular to a wireless monitoring system for flue gas cooler temperature. Background Technology

[0002] In the power industry, flue gas coolers are heat exchange devices specifically designed to reduce the temperature of high-temperature flue gas. Their working principle is based on a heat exchange process, where a cooling medium absorbs waste heat from the flue gas, bringing it to a suitable temperature for subsequent treatment. Without cooling measures, high flue gas temperatures can lead to wasted heat energy, increased thermal stress on piping materials, and decreased performance of subsequent purification equipment. Therefore, the application of flue gas coolers can promote waste heat recovery to improve overall power generation efficiency, mitigate corrosion risks, and help meet stringent environmental protection regulations regarding emission temperatures.

[0003] Based on the operating environment and temperature monitoring requirements of flue gas coolers, there is a technical challenge: the reliability of wireless monitoring sensors decreases in high-temperature, highly corrosive flue gas environments. Specifically, as flue gas flows through heat exchange tubes for extended periods, sulfur oxides, unburned carbon particles, and condensate droplets form a highly corrosive ash mixture that directly coats or corrodes the sensor housing and signal acquisition unit. For instance, in the 90°C high-sulfur, humid flue gas section at the air preheater outlet, a wireless temperature sensor with a standard stainless steel housing may experience a short circuit after several months of operation due to housing seal failure and corrosion from sulfuric acid droplets. This not only leads to continuous abnormal data loss at that point but also distorts the temperature distribution curve across the entire flue gas flow path due to the hidden location of the failure, thus delaying early warning of ash buildup and blockage. This challenge directly impacts the integrity of the temperature monitoring system and reduces the accuracy of equipment condition assessment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a wireless temperature monitoring system for flue gas coolers, solving the problem of inaccurate continuous temperature distribution monitoring caused by corrosion failure of wireless temperature sensors in high-sulfur, humid flue gas environments.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] The present invention provides a wireless temperature monitoring system for a flue gas cooler, comprising:

[0007] The data acquisition module is equipped with a dual-redundant wireless temperature sensor group deployed on the flue gas cooler tube bank to acquire flue gas temperature data in real time. The dual-redundant wireless temperature sensor group includes a corrosion-resistant alloy shell and a ceramic-based composite sealing structure, and triggers an inert gas positive pressure barrier to reduce acid mist intrusion.

[0008] The data transmission module receives flue gas temperature data from the data acquisition module in real time, selects the transmission path through dynamic routing, adopts dual-band synchronous transmission and performs data verification, and is configured with an environmental sensing unit to activate neighboring nodes to relay transmission in case of anomalies.

[0009] The analysis and decision-making module performs three-dimensional temperature field reconstruction and multiple data verification on the flue gas temperature data verified by the data transmission module, and outputs corrosion risk index and abnormal area identification. The multiple data verification integrates spatial distribution verification, time-series trend verification and equipment status correlation verification.

[0010] The response control module, based on the corrosion risk index output by the analysis and decision-making module, initiates tiered data acquisition strategy adjustments, temperature distribution map pushes, or equipment linkage control operations, and feeds back control commands to the data acquisition module and data transmission module to dynamically optimize operating parameters.

[0011] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the data acquisition module is configured as follows:

[0012] The main sensor and redundant sensors are fixed alternately on the windward and leeward sides of the flue gas cooler tube bank, and the corrosion-resistant alloy housing is installed by isolating the tube wall through ceramic fiber insulating gaskets.

[0013] The linkage flue gas sulfur concentration monitoring device triggers the high-speed sampling mode of the main sensor when the real-time sulfur concentration exceeds the threshold.

[0014] The primary / redundant sensor cross-verification program is initiated every 30 seconds. When the verification deviation exceeds the limit, the system automatically switches to the redundant sensor data channel and sends a node fault alarm to the response control module.

[0015] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the data transmission module is configured as follows:

[0016] During the initialization phase, a hybrid star and mesh topology network is constructed, and the optimal transmission path is calculated based on the real-time signal strength and remaining power of the sensor nodes.

[0017] During the transmission phase, the temperature data from the data acquisition module is split into 2.4GHz raw data packets and 868MHz check data packets and sent synchronously. After receiving the data, the gateway performs timestamp alignment and CRC matching verification.

[0018] The built-in vibration sensor monitors the node amplitude in real time. When the amplitude exceeds 5mm / s, it instructs the neighboring node to take over the data transmission channel and update the routing path synchronously.

[0019] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the analysis and decision module is configured as follows:

[0020] After receiving the verified data from the data transmission module, the timestamp break value is filtered out by the stream processing engine to compensate for short-term missing data points;

[0021] Using the flue section as the XY plane coordinate system, the temperature values ​​of each node are mapped into spatial vectors, and the CFD flue gas velocity model is applied to correct the thermal diffusion effect.

[0022] A dynamic isothermal contour map with 0.5℃ color gradation accuracy is output to the response control module every 5 minutes.

[0023] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the spatial distribution verification includes:

[0024] Temperature values ​​of node groups at the same cross section are extracted based on dynamic isothermal cloud maps, and the standard deviation of temperature gradient is calculated. Spatial anomalous nodes are marked when the standard deviation is >8℃.

[0025] The time-series trend verification includes: calling the historical operating condition database, comparing the current temperature value with the historical average of the same operating condition, marking time-series abnormal nodes when the deviation is ±20℃, and outputting the verification results to the response control module.

[0026] Furthermore, the wireless temperature monitoring system for the flue gas cooler described in this invention also includes:

[0027] Based on spatial anomaly nodes, temporal anomaly nodes, and physical logic anomaly nodes, respectively, gradient anomaly coefficient, trend deviation coefficient, and logical conflict coefficient are assigned;

[0028] Real-time sulfur concentration values ​​are synchronously acquired from the data acquisition module, and the sulfur concentration coefficient is calculated.

[0029] The gradient anomaly coefficient, trend deviation coefficient, logical conflict coefficient, and sulfur concentration coefficient are superimposed to generate a weighted corrosion risk index, which is then output to the response control module.

[0030] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the analysis and decision module is configured as follows:

[0031] When the corrosion risk index reaches 1.0-2.0, mark the coordinates of the yellow warning area and write them to the DCS system log;

[0032] When the corrosion risk index reaches 2.0-3.0, the dynamic isothermal cloud map generated by right 4 is invoked, and an orange warning signal is pushed to the mobile terminal;

[0033] When the corrosion risk index is greater than 3.0, a red warning command and a real-time corrosion rate curve are activated, triggering the response control module to execute equipment linkage.

[0034] Furthermore, in the wireless temperature monitoring system for the flue gas cooler described in this invention, the response control module is configured as follows:

[0035] For yellow alert areas, the data acquisition module is instructed to increase the sampling frequency to 5Hz and retrieve 72 hours of historical data from associated nodes.

[0036] Extract the dynamic isothermal cloud map pushed by the orange warning area with right 7, generate a real-time temperature trend map and push it to the operation and maintenance mobile terminal;

[0037] The following commands will be executed for red alert areas:

[0038] Activate the anti-clogging purging equipment and run it for 15 minutes;

[0039] Isolate the data channel of faulty sensor nodes;

[0040] The instruction data transmission module updates the topology network routing path.

[0041] Furthermore, in the wireless temperature monitoring system for flue gas coolers described in this invention, the maintenance terminal is configured as follows:

[0042] Scan the QR code in the flue to retrieve the current early warning data from the response control module and associate it with the graded early warning indicators;

[0043] Displays a dual-sensor temperature comparison curve of the isolated node 72 hours before failure;

[0044] Send a firmware reset command to the isolated node. After a successful reset, the data channel isolation status will be automatically lifted.

[0045] Furthermore, in the wireless temperature monitoring system for the flue gas cooler described in this invention, after the analysis and decision module outputs a corrosion risk index, it triggers a four-level closed-loop control, which includes:

[0046] Send a tiered response command to the response control module;

[0047] Send a sampling rate adjustment command to the data acquisition module;

[0048] Based on the risk index, bandwidth priority for abnormal areas is allocated to the data transmission module, with bandwidth value = risk index × 10%;

[0049] After receiving the reset success signal from the isolated node, the instruction analysis and decision-making module resumes data verification for that node.

[0050] Beneficial effects of this invention;

[0051] This invention effectively solves the problem of inaccurate monitoring in high-sulfur humid flue gas environments through the synergistic effect of a dynamic protection system, a multi-dimensional verification mechanism, and a closed-loop control chain: the corrosion-resistant alloy shell and the inert gas positive pressure barrier form a physical protective layer, significantly reducing the risk of sensor failure caused by acid mist corrosion; the deployment of dual redundant sensors and the automatic switching mechanism between primary and backup channels maintain the integrity of data acquisition, and the combination of dual-band transmission verification and dynamic routing ensures continuous data transmission; the triple verification algorithm penetrates abnormal data interference and accurately identifies temperature distribution distortion caused by corrosion failure; the response control module triggers resource reconfiguration and equipment linkage according to the corrosion risk index, and simultaneously realizes remote repair of fault nodes through the operation and maintenance terminal, forming a closed-loop control chain from the identification of abnormal data acquisition to the dynamic optimization of system resources, ultimately achieving long-term stable operation of the temperature monitoring system in highly corrosive environments. Attached Figure Description

[0052] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0053] Figure 1 This is a system architecture diagram of a wireless temperature monitoring system for a flue gas cooler provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0055] Please see Figure 1 The present invention provides a wireless temperature monitoring system for a flue gas cooler, comprising:

[0056] A dual-redundant wireless temperature sensor array is fixed to the windward and leeward sides of the flue gas cooler tube array in a staggered layout, with a 1.5-meter distance between the main and redundant sensors. The corrosion-resistant alloy housing is physically isolated from the tube wall via ceramic fiber insulating gaskets, blocking the electrochemical corrosion pathway. The sensor array contains an inert gas storage tank; when the internal pressure sensor detects a pressure below a threshold, it automatically triggers a positive pressure barrier system to release nitrogen, forming an acid mist isolation layer. This module is linked to a flue gas sulfur concentration monitoring device to collect sulfur concentration data in real time; if the sulfur concentration exceeds 200 mg / m³... 3 At the critical value, the main sensor switches to 1Hz high-speed sampling mode. A main / redundant sensor data comparison program is executed every 30 seconds. If the verification deviation exceeds 2°C, it automatically switches to the redundant sensor data channel and sends a node fault code to the response control module via wireless protocol.

[0057] During the initialization phase, the gateway broadcasts network configuration commands, and each sensor node replies with signal strength and remaining battery power parameters. A dynamic routing algorithm constructs a hybrid star and mesh network based on the node topology, updating the optimal transmission path in real time. During transmission, temperature data is split into independent data packets: the 2.4GHz band transmits the raw temperature value, and the 868MHz band transmits the CRC-16 checksum and timestamp. Upon receiving the packets, the gateway performs timestamp alignment on both bands and verifies data integrity through checksum matching. A built-in triaxial vibration sensor continuously monitors the node's mechanical vibration. When the amplitude exceeds 5mm / s for 5 seconds, a nearby backup node is automatically activated to take over the data transmission channel, synchronously updating the network routing table.

[0058] The stream processing engine receives verified data from the data transmission module. It first filters data packets with discontinuous timestamps and then uses a linear interpolation algorithm between adjacent nodes to compensate for short-term missing values. A two-dimensional coordinate system is established based on the flue cross-section, mapping the temperature values ​​of each node to spatial vector coordinates. Combined with flue gas velocity field data output from the computational fluid dynamics model, it corrects temperature distribution deviations caused by thermal diffusion. A dynamic isothermal cloud map is generated every 5 minutes with a color gradation accuracy of 0.5℃. The spatial distribution verification module extracts temperature data from node groups at the same cross-section and calculates the standard deviation of the temperature gradient. If the standard deviation exceeds 8℃, spatially abnormal nodes are marked. The time-series trend verification module calls the historical operating condition database and compares the current temperature value with the historical average under the same load and flue gas flow conditions. Nodes deviating by ±20℃ are marked as time-series abnormal nodes, and the verification results are transmitted to the response control module in real time.

[0059] Based on the received corrosion risk index, a tiered response is executed: when the index is in the range of 1.0-2.0, a command is sent to the data acquisition module to increase the sampling rate of the warning area to 5Hz and retrieve 72 hours of historical data from related nodes; when the index rises to 2.0-3.0, the dynamic isothermal cloud map generated by the analysis and decision-making module is extracted, and a temperature trend map is generated and pushed to the mobile terminal of maintenance personnel; when the index exceeds 3.0, the anti-blocking purging equipment is activated and runs for 15 minutes, while the data channel of the faulty node is isolated, and the data transmission module is instructed to update the network routing path. Control commands are fed back to the data acquisition module and the data transmission module in real time to dynamically optimize the sampling frequency and bandwidth allocation parameters.

[0060] The main sensor is fixed by drilling holes 2 meters apart on the windward side of the high-temperature section of the flue gas cooler tubes, and redundant sensors are installed 1.5 meters apart on the leeward side to form a cross-temperature measurement group. The corrosion-resistant alloy shell is electrically insulated from the metal tube wall using ceramic fiber insulating gaskets, eliminating the risk of electrochemical corrosion. This module receives signals from the flue gas sulfur concentration monitoring device in real time; when the SO2 concentration exceeds 200 mg / m³... 3 When the threshold is reached, the sampling frequency of the main sensor is automatically increased to 1Hz high-speed mode. The system starts the main / redundant sensor data comparison program every 30 seconds. If the deviation exceeds 2°C for three consecutive verifications, it automatically switches to the redundant sensor data channel and sends an alarm code containing node coordinates and fault type to the response control module.

[0061] During network initialization, the gateway node broadcasts network setup instructions, and each sensor replies with signal strength and remaining battery power parameters. A dynamic routing algorithm constructs a hybrid star and mesh topology based on node distance, signal attenuation rate, and battery status, updating the optimal transmission path every 5 minutes. During data transmission, the raw temperature value is encapsulated into a 2.4GHz frequency band data packet, while simultaneously generating and transmitting an 868MHz frequency band data packet containing a CRC-16 checksum and a millisecond-level timestamp. Upon receiving the packet, the gateway first aligns the timestamps of the two frequency band data packets, then performs checksum matching and verification, discarding packets that fail verification. When the built-in vibration sensor detects an amplitude exceeding 5mm / s for 10 seconds, it automatically activates the backup node with the strongest signal within a 3-meter radius to take over data transmission and broadcasts an update to the routing table across the entire network.

[0062] The stream processing engine receives the verified temperature data stream, first filtering out fragmented data packets with timestamp intervals exceeding 1 second, and then using an adjacent node spatial interpolation algorithm to compensate for missing values. A two-dimensional plane coordinate system is established with the center of the flue section as the origin, mapping the temperature values ​​of each node to a three-dimensional spatial vector (X / Y axis position + temperature value). Real-time flue gas velocity distribution data output from the computational fluid dynamics model is used to correct for thermal diffusion effects in the temperature vector, eliminating measurement deviations caused by uneven flow velocity. The system generates a dynamic isothermal contour map every 5 minutes, using a 0.5℃ color gradation accuracy to distinguish temperature gradient distributions. This contour map is transmitted in real-time to the data buffer of the response control module.

[0063] In the spatial distribution verification phase, all node temperature values ​​for the same flue section are extracted from the dynamic isothermal cloud map, and the standard deviation of the temperature distribution at that section is calculated. If the standard deviation is greater than 8℃ for two consecutive periods, the section is marked as a spatial anomaly area, and the three-dimensional coordinates of the anomaly nodes are recorded. In the temporal trend verification phase, the average temperature under the same unit load and flue gas flow conditions in the historical database is retrieved, and the deviation between the current node temperature value and the historical average is compared. If the deviation exceeds ±20℃ for more than 5 minutes, it is marked as a temporal anomaly node. The spatial anomaly coordinates and temporal anomaly node data are pushed to the verification result queue of the response control module in real time via a dedicated protocol.

[0064] Spatial anomaly nodes are assigned a gradient anomaly coefficient of 0.3, and temporal anomaly nodes are assigned a trend deviation coefficient of 0.4. Physical logic anomaly nodes are determined through collaborative analysis of induced draft fan current data: if the temperature changes by more than 15°C within 10 seconds while the induced draft fan current fluctuation is less than 5%, a logic conflict coefficient of 0.3 is assigned. Sulfur concentration monitoring values ​​are acquired in real time from the data acquisition module; when the SO2 concentration exceeds 200 mg / m³... 3 At that time, the sulfur concentration coefficient is taken as 1.5. The gradient anomaly coefficient, trend deviation coefficient, and logical conflict coefficient are added together and multiplied by the sulfur concentration coefficient to generate a corrosion risk index with a score of 0-5. This index is updated every 2 minutes and written to the risk database of the response control module.

[0065] When the corrosion risk index is between 1.0 and 2.0, the three-dimensional coordinates of the yellow warning area are marked on the DCS system operation interface, with the coordinate information including the pipe row number and node location. When the index rises to 2.0-3.0, dynamic isothermal cloud map data is invoked to generate an orange warning report containing a 24-hour temperature change curve, which is pushed to the mobile terminals of maintenance personnel via the WeChat interface. When the index exceeds 3.0, a red warning command is activated, automatically generating an emergency report containing a corrosion rate curve. This curve is generated by analyzing the frequency of temperature fluctuations and the rate of change of sulfur concentration over the past hour, and simultaneously sending an equipment linkage trigger command to the response control module.

[0066] For yellow alert areas, a parameter adjustment command is sent to the data acquisition module to increase the sampling rate to 5Hz, and the temperature dataset for that node over the past 72 hours is retrieved from the historical database simultaneously. For orange alert areas, abnormal area data is extracted from the dynamic isotherm cloud map, and a temperature trend map with 10-minute intervals is generated and pushed to the maintenance mobile terminal via the MQTT protocol. A red alert response executes three levels of operations: the rotary purging equipment is started and runs for a 15-minute cycle; the data stream from the faulty node is isolated in the data transmission channel; and a topology update command is sent to the data transmission module to recalculate the optimal transmission path for the entire network. All operation records are written to the DCS operation log in real time.

[0067] Maintenance personnel use mobile devices to scan QR code labels in the flue area. This code is associated with the coordinates of a specific pipe row. The system automatically retrieves the current warning data for that area from the response control module, including the warning level and the abnormal node number. The terminal interface displays a comparison curve of the primary / backup sensor temperatures of the isolated node in the 72 hours prior to its failure, with the curves distinguishing between the two channels using blue and red. After clicking the reset button, the terminal sends a firmware reset command containing the node ID. After the node completes its self-test and restarts, it returns a status code. The system automatically releases the isolation status of the data transmission channel, and the reset result is displayed to the maintenance personnel via a pop-up window.

[0068] After the analysis and decision-making module outputs the corrosion risk index, it activates a four-level control chain: The first level sends a response command code corresponding to the warning level to the response control module; the second level sends sampling rate adjustment parameters to the data acquisition module, with the parameter value dynamically set according to the warning level; the third level allocates bandwidth resources according to the risk index value, with an allocation ratio of one-tenth of the index value; the fourth level monitors the reset status of isolated nodes in real time, and upon receiving a self-test success signal from the node, sends a data verification and recovery command to the analysis and decision-making module. The control chain performs a status check every 5 minutes.

[0069] This invention addresses the problem of inaccurate monitoring in high-sulfur humid flue gas environments through a triple technical mechanism:

[0070] The dual-redundant wireless temperature sensor array employs a corrosion-resistant alloy housing and a ceramic-based composite sealing structure, combined with an inert gas positive pressure barrier to actively isolate acid mist corrosion. Sensors are staggered on the windward and leeward sides of the pipe array, with ceramic fiber insulating gaskets blocking the electrochemical corrosion pathway. Real-time monitoring of sulfur concentration triggers a high-speed sampling mode, periodically performing cross-calibration between the primary and backup sensors. When deviations exceed limits, the data channel is automatically switched and a fault alarm is sent. This design mitigates the risk of sensor corrosion failure through both physical protection and data redundancy.

[0071] The transmission layer employs dual-band synchronous data packet transmission and dynamic routing switching, activating neighboring nodes to relay transmission when nodes vibrate or signal attenuates, maintaining data transmission continuity. The analysis layer performs three-dimensional field reconstruction on temperature data, fusing spatial distribution verification (temperature gradient analysis at the same cross-section), temporal trend verification (comparison with historical operating conditions), and equipment status correlation verification (coordinated analysis of induced draft fan current) to identify abnormal data caused by sensor corrosion. Multiple verification mechanisms penetrate interference from failed nodes to reconstruct the true temperature distribution.

[0072] The response control module triggers actions based on corrosion risk index grading: for low-risk areas, it increases the sampling frequency and retrieves historical data; for medium-risk areas, it pushes temperature trend maps to mobile terminals; and for high-risk areas, it activates purging equipment and isolates faulty nodes. Control commands are fed back to the acquisition and transmission module in real time, dynamically optimizing sampling frequency and network bandwidth allocation. The maintenance terminal supports remote reset of isolated nodes, and automatically resumes the data verification process after successful self-test.

Claims

1. A wireless temperature monitoring system for a flue gas cooler, characterized in that, include: The data acquisition module is equipped with a dual-redundant wireless temperature sensor group deployed on the flue gas cooler tube bank to acquire flue gas temperature data in real time. The dual-redundant wireless temperature sensor group includes a corrosion-resistant alloy shell and a ceramic-based composite sealing structure, and triggers an inert gas positive pressure barrier to reduce acid mist intrusion. The data transmission module receives flue gas temperature data from the data acquisition module in real time, selects the transmission path through dynamic routing, adopts dual-band synchronous transmission and performs data verification, and is configured with an environmental sensing unit to activate neighboring nodes to relay transmission in case of anomalies. The analysis and decision-making module performs three-dimensional temperature field reconstruction and multiple data verification on the flue gas temperature data verified by the data transmission module, and outputs corrosion risk index and abnormal area identification. The multiple data verification integrates spatial distribution verification, time-series trend verification and equipment status correlation verification. The response control module, based on the corrosion risk index output by the analysis and decision-making module, initiates tiered data acquisition strategy adjustments, temperature distribution map pushes, or equipment linkage control operations, and feeds back control commands to the data acquisition module and data transmission module to dynamically optimize operating parameters.

2. The wireless temperature monitoring system for flue gas coolers as described in claim 1, characterized in that, The data acquisition module is configured as follows: The main sensor and redundant sensors are fixed alternately on the windward and leeward sides of the flue gas cooler tube bank, and the corrosion-resistant alloy housing is installed by isolating the tube wall through ceramic fiber insulating gaskets. The linkage flue gas sulfur concentration monitoring device triggers the high-speed sampling mode of the main sensor when the real-time sulfur concentration exceeds the threshold. The primary and redundant sensor cross-verification program is initiated every 30 seconds. When the verification deviation exceeds the limit, the system automatically switches to the redundant sensor data channel and sends a node fault alarm to the response control module.

3. The wireless temperature monitoring system for flue gas coolers as described in claim 2, characterized in that, The data transmission module is configured as follows: During the initialization phase, a hybrid star and mesh topology network is constructed, and the optimal transmission path is calculated based on the real-time signal strength and remaining power of the sensor nodes. During the transmission phase, the temperature data from the data acquisition module is split into 2.4GHz raw data packets and 868MHz check data packets and sent synchronously. After receiving the data, the gateway performs timestamp alignment and CRC matching verification. The built-in vibration sensor monitors the node amplitude in real time. When the amplitude exceeds 5mm / s, it instructs the neighboring node to take over the data transmission channel and update the routing path synchronously.

4. The wireless temperature monitoring system for a flue gas cooler as described in claim 3, characterized in that, The analysis and decision-making module is configured as follows: After receiving the verified data from the data transmission module, the timestamp break value is filtered out by the stream processing engine to compensate for short-term missing data points; Using the flue section as the XY plane coordinate system, the temperature values ​​of each node are mapped into spatial vectors, and the CFD flue gas velocity model is applied to correct the thermal diffusion effect. A dynamic isothermal contour map with 0.5℃ color gradation accuracy is output to the response control module every 5 minutes.

5. The wireless temperature monitoring system for a flue gas cooler as described in claim 4, characterized in that, The spatial distribution verification includes: Temperature values ​​of node groups at the same cross section are extracted based on dynamic isothermal cloud maps, and the standard deviation of temperature gradient is calculated. Spatial anomalous nodes are marked when the standard deviation is >8℃. The time-series trend verification includes: calling the historical operating condition database, comparing the current temperature value with the historical average of the same operating condition, marking time-series abnormal nodes when the deviation is ±20℃, and outputting the verification results to the response control module.

6. The wireless temperature monitoring system for a flue gas cooler as described in claim 5, characterized in that, Also includes: Based on spatial anomaly nodes, temporal anomaly nodes, and physical logic anomaly nodes, respectively, gradient anomaly coefficient, trend deviation coefficient, and logical conflict coefficient are assigned; Real-time sulfur concentration values ​​are synchronously acquired from the data acquisition module, and the sulfur concentration coefficient is calculated. The gradient anomaly coefficient, trend deviation coefficient, logical conflict coefficient, and sulfur concentration coefficient are superimposed to generate a weighted corrosion risk index, which is then output to the response control module.

7. The wireless temperature monitoring system for a flue gas cooler as described in claim 6, characterized in that, The analysis and decision-making module is configured as follows: When the corrosion risk index reaches 1.0-2.0, mark the coordinates of the yellow warning area and write them to the DCS system log; When the corrosion risk index reaches 2.0-3.0, the dynamic isothermal cloud map generated by right 4 is invoked, and an orange warning signal is pushed to the mobile terminal; When the corrosion risk index is greater than 3.0, a red warning command and a real-time corrosion rate curve are activated, triggering the response control module to execute equipment linkage.

8. The wireless temperature monitoring system for a flue gas cooler as described in claim 7, characterized in that, The response control module is configured as follows: For yellow alert areas, the data acquisition module is instructed to increase the sampling frequency to 5Hz and retrieve 72 hours of historical data from associated nodes. Extract the dynamic isothermal cloud map pushed by the orange warning area with right 7, generate a real-time temperature trend map and push it to the operation and maintenance mobile terminal; The following commands will be executed for red alert areas: Activate the anti-clogging purging equipment and run it for 15 minutes; Isolate the data channel of faulty sensor nodes; The instruction data transmission module updates the topology network routing path.

9. The wireless temperature monitoring system for a flue gas cooler as described in claim 8, characterized in that, The maintenance terminal is configured as follows: Scan the QR code in the flue to retrieve the current early warning data from the response control module and associate it with the graded early warning indicators; Displays a dual-sensor temperature comparison curve of the isolated node 72 hours before failure; Send a firmware reset command to the isolated node. After a successful reset, the data channel isolation status will be automatically lifted.

10. The wireless temperature monitoring system for a flue gas cooler as described in claim 9, characterized in that, After the analysis and decision-making module outputs the corrosion risk index, it triggers a four-level closed-loop control, which includes: Send a tiered response command to the response control module; Send a sampling rate adjustment command to the data acquisition module; Based on the risk index, bandwidth priority for abnormal areas is allocated to the data transmission module, with bandwidth value = risk index × 10%; After receiving the reset success signal from the isolated node, the instruction analysis and decision-making module resumes data verification for that node.

Images