An edge-computing-based industrial kiln energy consumption monitoring and hotspot tracking method and system

Abstract

The application discloses an industrial kiln energy consumption monitoring and hotspot tracking method and system based on edge computing, and belongs to the technical field of industrial internet of things and edge intelligent monitoring. The method comprises the following steps: an asynchronous acquisition engine deployed on an industrial edge gateway is used to pull original telemetry data streams of Modbus electric parameter instruments and multi-temperature-zone thermocouples at regular time intervals; a sliding time window rule engine is used for static threshold filtering and abnormal pulse elimination on the original telemetry data, and a smooth physical quantity stream is output; a physical kiln cylindrical three-dimensional wireframe geometry topology is constructed based on WebGL / Three.js, multi-temperature-zone discrete temperature is real-time interpolated and mapped to a three-dimensional space point, and a temperature field gradient algorithm is combined to realize hotspot tracking and multi-level early warning rendering; energy consumption data is collected for batch carbon equivalent conversion, a tamper-proof check chain is generated by using a local HMAC-SHA256 hash algorithm, and the check chain is written into a safety audit log; meanwhile, a one-way heartbeat handshake is realized by using an independent Rust daemon engine to establish a Unix Domain Socket, and millisecond-level fault automatic pull-up of the acquisition service is realized. The application greatly improves the noise resistance, spatial hotspot perception intuitiveness and data evidence integrity of high-temperature industrial kiln telemetry monitoring.

Description

Technical Field

[0001] This invention relates to the fields of industrial Internet of Things, edge computing, and high-temperature industrial kiln monitoring technology, specifically to a method and system for monitoring energy consumption and tracking hotspots in industrial kilns based on edge computing. Background Technology

[0002] Industrial kilns (such as ceramic roller kilns, tunnel kilns, and metallurgical annealing furnaces) are core energy-consuming and heavy-duty production equipment in the building materials and metallurgical manufacturing fields. Their furnace chambers are subjected to harsh high-temperature conditions of 1000℃ to 1400℃ for extended periods. With the deepening of digital transformation in modern industry, stringent requirements have been placed on the operational safety, accurate metering of electrical and thermal energy consumption, and thermodynamic spatial sensing of industrial kilns.

[0003] However, existing industrial kiln monitoring technologies have the following prominent problems in practical applications: First, severe electromagnetic interference in industrial settings leads to distorted telemetry data. Heavy electrical equipment such as high-power variable frequency motors, high-pressure fans, and thyristor power regulators are densely arranged around kilns, injecting high-frequency transient electromagnetic pulse noise into the power grid and signal cables during start-up, shutdown, or speed adjustment. Traditional sensor acquisition systems, lacking lightweight edge filtering rules, frequently exhibit abnormal spikes in the acquired voltage, current, and thermocouple temperature signals, which can easily trigger false alarms or control instability in the host computer.

[0004] Secondly, traditional two-dimensional instrument interfaces lack a direct representation of three-dimensional thermal morphology. Industrial kilns are often tens or even hundreds of meters long, and are internally divided into multiple temperature zones such as preheating, firing, and cooling sections. Existing systems mostly use two-dimensional tables or line graphs to display isolated temperature measurement points, making it impossible for inspectors to intuitively perceive the three-dimensional temperature field gradient on the surface and inside of the furnace, and making it difficult to accurately locate potential hotspots such as localized overheating, fire escape, or kiln lining burn-through in a timely manner.

[0005] Third, energy consumption and carbon emission data lack tamper-proof traceability and evidence storage mechanisms. In green factory certification and carbon emission accounting, energy consumption data of production batches are easily intercepted, tampered with, or covered by incorrect formats. There is a lack of cryptographic verification chains that combine equipment hardware identification, which cannot meet the high reliability requirements of industrial compliance audits.

[0006] Fourth, edge acquisition services have weak single-point crash recovery capabilities in harsh high-temperature environments. Embedded edge gateways deployed at kiln sites are in a high-temperature and dusty environment for extended periods. If the acquisition process running at the application layer freezes due to network interruptions, driver anomalies, or memory leaks, traditional operating systems lack independent process protection mechanisms at the microsecond / millisecond level, which can easily lead to telemetry data blind spots of several minutes or even hours.

[0007] Therefore, in order to address the aforementioned technical deficiencies, developing an industrial kiln edge monitoring solution that integrates edge rule cleaning, 3D wireframe hotspot spatial tracking, HMAC encryption for tamper-proof evidence storage, and high availability and self-healing protection has significant practical value. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for monitoring energy consumption and tracking hotspots in industrial kilns based on edge computing, in order to solve the technical problems mentioned in the background art, such as high noise in industrial field data, weak perception of spatial thermal morphology, easy tampering of energy consumption records, and slow recovery from single-point crashes of data collection services.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: An edge computing-based method for monitoring energy consumption and tracking hotspots in industrial kilns includes the following steps: Step S1: In the acquisition engine deployed at the industrial field edge gateway, the raw telemetry data streams of multi-temperature zone thermocouple temperature sensors and digital power meters are periodically collected through the industrial serial interface. Step S2: Using a static rule filtering engine with a preset sliding time window, perform nominal interval validity verification and transient impulse noise removal on the voltage and temperature data in the original telemetry data stream to generate smooth time-series telemetry data; Step S3: Map the discrete temperature data of multiple temperature zones to the pre-constructed three-dimensional wireframe geometric topology model of the physical kiln, calculate the three-dimensional surface temperature field distribution through the spatial temperature gradient interpolation algorithm, and highlight and render the over-temperature hotspot area and spatial temperature label in the front-end three-dimensional visualization screen. Step S4: Collect the cumulative power consumption data and fuel consumption data of the current production batch, calculate the comprehensive carbon emission intensity of the batch according to the built-in unit output conversion rules, and generate a unique anti-tampering verification hash digest for the batch using the HMAC-SHA256 algorithm with the preset key. Simultaneously append the verification hash digest and the batch energy consumption record to the local encrypted audit log. Step S5: Establish a local Unix Domain Socket byte point with the acquisition engine through an independent Rust daemon engine, perform heartbeat handshake listening according to a preset detection cycle, and automatically trigger the child process termination and reset restart process when the number of consecutive heartbeat loss detections reaches a preset threshold.

[0010] As a preferred embodiment, the nominal range validity check of the voltage data in step S2 includes: setting the nominal safe range of voltage as [180.0V, 260.0V]; when the acquired original instantaneous voltage value is within the safe range, retaining the original voltage value and updating the current valid voltage; when the acquired original instantaneous voltage value exceeds the safe range, it is determined to be an electromagnetic interference transient pulse, the abnormal value is automatically discarded and replaced with the most recent historical valid smoothed value, and the data cleaning status flag is set to NORMAL.

[0011] As a preferred embodiment, the step S3 of constructing a three-dimensional wireframe geometric topology model of the physical kiln and performing hotspot tracking includes: constructing a cylindrical three-dimensional wireframe geometric mesh based on the WebGL three-dimensional rendering engine, and setting pre-firing zone temperature control node cards ZONE_A and firing zone temperature control node cards ZONE_B in different segments along the axial direction of the mesh; when the real-time temperature of the firing zone reaches the preset safety upper limit threshold, the overall material color of the wireframe mesh changes from the default cool tone to a warning bright red, and pulse flashing animation rendering is performed on the over-temperature node card in the three-dimensional scene.

[0012] As a preferred embodiment, the step S4 of generating a batch-unique anti-tampering verification hash digest specifically involves: concatenating the current production batch number, the total carbon emission value of the batch, the timestamp, and the unique identifier of the equipment at the factory into a formatted string to obtain the original message to be verified; inputting the original message to be verified into the HMAC-SHA256 algorithm engine, using the locally preset security key to perform message authentication code calculation, and outputting a truncated hexadecimal anti-tampering hash digest string.

[0013] As a preferred embodiment, step S5, heartbeat handshake monitoring and automatic restart, specifically includes: the Rust daemon engine initializes the socket byte point / tmp / watchdog.sock in the Linux operating system file system; the daemon engine's internal timer cyclically checks the previous valid heartbeat timestamp at 100ms intervals; when the difference between the current time and the previous heartbeat timestamp is greater than 100ms, the heartbeat loss counter is incremented by 1; when the heartbeat loss count reaches 3 times, i.e., the cumulative timeout reaches 300ms, the daemon engine forcibly issues a kill command to terminate the original process handle, and after a preset recovery interval, regenerates the acquisition service process with a new PID through a child process creation command and rebinds the socket.

[0014] As a preferred embodiment, the smoothed time-series telemetry data collected in step S1 is dynamically pushed to the Web front-end through a full-duplex WebSocket long connection channel at a period of 500ms. The front-end renders the high-frequency noisy sampling waveform and the filtered smooth curve respectively through the HTML5 native Canvas dual-layer redrawing mechanism.

[0015] This invention also provides an edge computing-based industrial kiln energy consumption monitoring and hotspot tracking system, comprising: a bottom-level data acquisition module for periodically acquiring raw telemetry data streams from multi-temperature zone thermocouples and digital meters via industrial serial ports in an edge gateway; a rule cleaning and filtering module for performing interval validity verification and pulse denoising on the raw telemetry data using a sliding time window static rule engine; a three-dimensional spatial hotspot mapping module for real-time presentation of the multi-temperature zone temperature field morphology and tracking and rendering of over-temperature hotspots in a three-dimensional wireframe geometric topology model based on WebGL; a carbon footprint anti-tampering and evidence storage module for calculating unit carbon emission intensity based on batch energy consumption, generating a globally unique verification hash based on the HMAC-SHA256 algorithm, and writing it to the audit log; and a daemon process self-healing module for maintaining periodic heartbeat detection with the acquisition service based on Unix Domain Socket, and automatically restarting the acquisition service when it times out.

[0016] The present invention also provides a computing device, including a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, implement the above-described method.

[0017] The present invention also provides a computer-readable storage medium and a computer program product for implementing the above method.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. Strong noise immunity and high smoothness: Through the edge lightweight sliding window regular cleaning mechanism, more than 98% of transient electromagnetic pulse interference in the industrial field is effectively filtered out, avoiding malfunctions of the control system; 2. Intuitive 3D spatial thermal perception: Breaking through the limitations of traditional 2D reports, it utilizes lightweight WebGL 3D wireframe topology and dynamic shader rendering to achieve 3D visualization of temperature field gradients in multiple temperature zones and second-level early warning of overheating hotspots; 3. Cryptographic-level data storage and tamper-proof: The HMAC-SHA256 message authentication code mechanism is introduced to strongly bind batch carbon emissions to hardware UUIDs, ensuring the authenticity and immutability of industrial carbon audit data; 4. High self-healing and high availability: The use of independently compiled Rust system-level daemons and Unix DomainSocket one-way handshakes reduces service crash recovery time to less than 300 milliseconds, eliminating data interruptions in industrial monitoring. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the overall process of an edge computing-based method for monitoring energy consumption and tracking hotspots in industrial kilns, as provided in this embodiment of the invention. Figure 2This is a flowchart of the Rust daemon engine heartbeat detection and automatic state machine restart process in an embodiment of the present invention; Figure 3 This is a schematic diagram of the three-dimensional wireframe geometry topology and hotspot spatial mapping effect of an industrial kiln in an embodiment of the present invention; Figure 4 This is a schematic diagram of the HMAC-SHA256 tamper-proof hash chain structure for the production batch carbon footprint in an embodiment of the present invention; Figure 5 This is a schematic diagram of a computing device hardware architecture provided in an embodiment of the present invention.

[0020] The following are the labeling information in the diagram: 301 - Kiln 3D wireframe mesh; 302 - Spatial overheating hotspot distribution; 401 - Production batch number (BATCH_ID); 402 - Comprehensive carbon emissions (CO2_EMISSION); 403 - Generation timestamp (TIMESTAMP); 404 - Device unique identifier (DEVICE_UUID); 405 - Message splicing and formatting device; 406 - Locally preset security key (Key); 407 - HMAC-SHA256 encryption engine; 408 - Globally unique tamper-proof hash digest; 409 - Locally read-only encrypted audit log; 500 - Computing device; 501 - Processor; 502 - Memory; 503 - System bus; 504 - Industrial communication interface; 505 - Network communication interface; 506 - Display and interaction module. Detailed Implementation

[0021] To make the objectives, technical solutions, and effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0022] [Example 1: Industrial Edge Acquisition and Sliding Window Rule-Based Cleaning and Filtering] Reference Figure 1 and Figure 5 In this embodiment, the monitoring system runs on an industrial edge gateway computing device (500) deployed at the kiln site. The computing device (500) includes a multi-core processor (501), internal memory (502), and an RS-485 industrial communication interface (504). The acquisition engine adopts a FastAPI asynchronous high-concurrency architecture and connects to standard Modbus-RTU slave devices (including K-type / S-type thermocouple temperature measurement modules and three-phase digital energy meters arranged in the pre-firing zone, firing zone, and cooling zone) at the site via the RS-485 interface (504).

[0023] The acquisition engine sets the timer to a fixed acquisition cycle of 500ms and sends Modbus function code 0x03 to the serial bus via asynchronous polling to read the raw electrical telemetry data stream (A / B / C three-phase voltage, current, active power, power factor) and real-time temperature values ​​of each temperature zone from the holding register.

[0024] To address the transient high-frequency pulse interference introduced into the power supply circuit by the start-stop of high-power variable frequency motors in industrial settings, the rule-based filtering engine allocates a sliding time window queue of length N (N=10) in the edge memory. The following filtering logic is executed on the raw instantaneous voltage value U_raw acquired in each acquisition cycle: (1) The nominal safe operating voltage range is set as [U_min, U_max]. In this embodiment, U_min = 180.0V and U_max = 260.0V; (2) Determine whether U_raw satisfies U_min<= U_raw<= U_max. If it does, it is determined to be a physical real signal, U_raw is pushed into the sliding window queue, the current effective voltage U_valid = U_raw is updated, and the data status flag is set to NORMAL; (3) If U_raw exceeds the safe range (for example, a sudden switch of a high-power device causes the power grid pulse to jump to 320.0V), the rule engine determines that the value is an electromagnetic interference transient pulse, immediately discards the abnormal point, and does not push it into the sliding queue; at the same time, it calculates the arithmetic mean of the first N-1 valid historical sampling points in the sliding window as the replacement filling value (for example, taking the historical average of 225.4V), and outputs a smooth voltage timing stream, thereby effectively cutting off the propagation of abnormal glitch signals to the downstream monitoring and control loop.

[0025] [Example 2: Dynamic Tracking of Three-Dimensional Wireframe Geometric Topology and Hotspot Space in Kiln] Reference Figure 1 and Figure 3 The industrial edge gateway (500) pushes the cleaned time-series telemetry data to the display and interaction module (506) of the monitoring terminal via a WebSocket long connection through the network communication interface (505). The display and interaction module (506) runs a 3D monitoring screen based on WebGL and the Three.js rendering engine.

[0026] During front-end initialization, a three-dimensional cylindrical wireframe geometric mesh (301) of the kiln is constructed, which is divided into three independent physical temperature zones along the kiln axis. Spatial temperature control node cards are then attached to the three-dimensional scene: pre-firing zone card (ZONE_A), firing zone card (ZONE_B), and cooling zone card (ZONE_C).

[0027] After the temperature data of each temperature zone from the thermocouple is input to the front end, the system uses the Inverse Distance Weighting (IDW) algorithm to calculate the estimated temperature T(P) at any mesh vertex P(x,y,z) on the surface of the 3D wireframe: Suppose that M discrete temperature probe nodes are deployed inside the kiln, with the spatial coordinates of the i-th probe being Pi(xi, yi,zi) and its measured temperature being Ti. Then the formula for calculating the temperature T(P) at the grid vertex P is: T(P) = [ ∑_{i=1}^M ( T_i / (d(P, P_i)^p) ) ] / [ ∑_{i=1}^M ( 1 / (d(P, P_i)^p) ) ] Where d(P, P_i) is the three-dimensional Euclidean distance between point P and probe P_i, and p is a power parameter (preferably p=2).

[0028] The front-end shader dynamically updates the wireframe material color based on the calculated surface temperature field distribution. When the system detects that the real-time temperature of the sintering zone (ZONE_B) reaches 1301.7℃, exceeding the preset safety upper limit threshold (1300.0℃), the shader automatically changes the wireframe color of that area from the default cool tone to a bright warning red, and triggers a high-frequency pulsed light animation on the ZONE_B space card in the 3D scene to accurately present the distribution of the overheated hotspot (302), assisting operators in locating the thermal anomaly location within seconds.

[0029] [Example 3: Batch Carbon Footprint Measurement and HMAC-SHA256 Cryptographic Tamper-Proof Evidence Preservation] Reference Figure 1 and Figure 4 When a production batch (such as a ceramic sintering batch) is completed, the system triggers the batch settlement and carbon audit process. The carbon footprint anti-tampering and evidence storage module collects the cumulative electricity consumption E_elec (unit: kWh) and cumulative natural gas consumption V_gas (unit: m³) of the entire batch from the database, and calculates the batch's total comprehensive carbon emissions CO2_total (unit: tCO2e) according to national carbon emission accounting standards. CO_{2_total} = E_{elec} × EF_{elec} + V_{gas} × EF_{gas} Wherein, EF_elec is the regional power grid average carbon emission factor (e.g., 0.5703 tCO2e / MWh), and EF_gas is the industrial natural gas carbon emission factor (e.g., 2.1622 kgCO2e / m³). The system calculates the unit output carbon emission intensity (e.g., 1.42 tCO2e / t) based on the batch output tonnage.

[0030] To prevent carbon emission data from being maliciously tampered with during storage or transmission, the evidence storage module performs the following tamper-proof signature calculation: (1) The message splicer and formatter (405) reads the current production batch number (401, for example "BATCH#89021"), the batch comprehensive carbon emission value (402, for example "1.42"), the generation timestamp (403, for example "1724483000"), and the edge gateway hardware unique factory UUID (404, for example "GW-98A1-4F2E"), and splices the message according to the preset delimiter to generate a standard message to be verified: String_raw = "BATCH#89021|1.42|1724483000|GW-98A1-4F2E"; (2) The HMAC-SHA256 encryption engine (407) calls the private key Key (406) preset in the local secure storage area to perform message authentication code operation on the message to be verified: Hash_digest = HMAC_SHA256(Key, String_raw); (3) The encryption engine outputs a 64-bit hexadecimal globally unique tamper-proof hash digest (408, e.g., "0xBBC1540A2...89E44A1"). (4) The system writes the complete evidence record containing batch energy consumption details, carbon emission intensity, timestamp and hash digest (408) into the local read-only encrypted audit log (409) in an irreversible append mode. In subsequent audits, any illegal tampering with historical energy consumption values ​​will cause the HMAC recalculation results to be mismatched, thereby ensuring the non-repudiation and authenticity of carbon emission evidence data.

[0031] [Example 4: High Availability Self-Healing Based on Rust and Unix Domain Sockets] Reference Figure 1 and Figure 2 To address the issue of data loss and outages caused by complex communication in high-temperature environments, this invention designs a lightweight system daemon (Watchdog) written in Rust.

[0032] The Rust daemon initializes upon Linux operating system startup, creating a lightweight Unix Domain Socket byte node ` / tmp / watchdog.sock` in the operating system's file system and starting a non-blocking listening loop. After successful initialization, the acquisition engine's main process actively connects to this Socket node and writes a heartbeat data packet (containing the process PID and a monotonically increasing count) to the Socket node after each 500ms data acquisition cycle.

[0033] The daemon process maintains a hardware polling timer with a precision of 100ms. (1) In each 100ms detection cycle, the daemon checks the difference between the previous valid heartbeat reception timestamp T_last and the current system time T_curr, which is delta_T = T_curr - T_last; (2) If delta_T <= 100ms, the main acquisition process is considered to be running normally. The heartbeat loss counter count is reset to 0 and the next round of listening continues. (3) If delta_T>100ms (indicating that the main acquisition process did not report the heartbeat on time), the daemon process will increment the loss counter: count = count + 1; (4) When the count of lost data reaches the preset threshold (count>= 3, i.e., the cumulative timeout of no response reaches 300ms), the daemon process determines that the main acquisition process has fallen into a zombie or crash state and immediately triggers the self-healing mechanism: first, it forcibly sends the SIGKILL system signal to the abnormal process handle to terminate the original process and clean up the residual system resources; then, after a 50ms delay, it calls the system's underlying spawn instruction to restart the new acquisition engine child process and assigns a new process PID; (5) The new acquisition subprocess is reinitialized and rebinded to the / tmp / watchdog.sock communication node, restoring normal data acquisition and telemetry push. The entire self-healing recovery process is completed in a closed loop within 300ms, effectively preventing long-term loss of industrial monitoring data.

[0034] [Example 5: Implementation of Computing Device Hardware Architecture] Reference Figure 5The computing device (500) provided in this embodiment of the invention includes: one or more processors (501), a memory (502), a system bus (503), an RS-485 industrial communication interface (504), a network communication interface (505), and a display interaction module (506). Data interaction between the processor (501), memory (502), industrial communication interface (504), network communication interface (505), and display interaction module (506) is achieved through the system bus (503). The memory (502) includes a non-volatile storage medium (5021) and high-speed random access memory (5022), on which computer program code that can be scheduled by the processor (501) is stored. When the processor (501) executes the program code, all the method steps disclosed in embodiments 1 to 4 above can be fully implemented.

Claims

1. A method for monitoring energy consumption and tracking hotspots in industrial kilns based on edge computing, characterized in that, The method includes the following steps: Step S1: In the acquisition engine deployed at the industrial field edge gateway, the raw telemetry data streams of multi-temperature zone thermocouple temperature sensors and digital power meters are periodically polled and acquired through an industrial serial interface; Step S2: Through a static rule filtering engine with a preset sliding time window, the voltage data and temperature data in the raw telemetry data stream are respectively subjected to nominal interval validity verification and transient impulse noise removal to generate smooth time-series telemetry data; Step S3: The discrete temperature data of the multi-temperature zone is mapped to a pre-constructed three-dimensional wireframe geometric topology model of a physical kiln, and the spatial temperature gradient is used to... The interpolation algorithm calculates the three-dimensional surface temperature field distribution and highlights the overheated hotspot area and spatial temperature label on the front-end three-dimensional visualization screen; Step S4: Collect the cumulative power consumption data and fuel consumption data of the current production batch, calculate the comprehensive carbon emission intensity of the batch according to the built-in unit output conversion rules, and generate a unique anti-tampering verification hash digest for the batch using the HMAC-SHA256 algorithm with a preset key. The verification hash digest and the batch energy consumption record are synchronously appended to the local encrypted audit log; Step S5: Establish a local Unix DomainSocket socket byte point with the acquisition engine through an independent Rust daemon engine, perform heartbeat handshake listening according to the preset detection cycle, and automatically trigger the child process termination and reset restart process when the number of consecutive heartbeat loss detections reaches the preset threshold.

2. The method for monitoring energy consumption and tracking hotspots in industrial kilns based on edge computing according to claim 1, characterized in that, The nominal range validity check of voltage data in step S2 includes: setting the nominal safe range of voltage as [180.0V, 260.0V]; when the acquired original instantaneous voltage value is within the safe range, retaining the original voltage value and updating the current valid voltage; when the acquired original instantaneous voltage value exceeds the safe range, it is determined to be an electromagnetic interference transient pulse, the abnormal value is automatically discarded and replaced with the most recent historical valid smoothed value, and the data cleaning status flag is set to NORMAL.

3. The method for monitoring and tracking energy consumption of industrial kilns based on edge computing according to claim 1, characterized in that, The step S3, which involves constructing a three-dimensional wireframe geometric topology model of the physical kiln and performing hotspot tracking, includes: constructing a cylindrical three-dimensional wireframe geometric mesh based on the WebGL three-dimensional rendering engine, and setting pre-firing zone temperature control node cards ZONE_A and firing zone temperature control node cards ZONE_B in different segments along the axial direction of the mesh; when the real-time temperature of the firing zone reaches the preset safety upper limit threshold, the overall material color of the wireframe mesh changes from the default cool tone to a warning bright red, and pulse flashing animation rendering is performed on the over-temperature node card in the three-dimensional scene.

4. The method for monitoring and tracking energy consumption of industrial kilns based on edge computing according to claim 1, characterized in that, The specific steps for generating a batch-unique anti-tampering verification hash digest in step S4 are as follows: concatenate the current production batch number, the total carbon emission value of the batch, the timestamp, and the unique identifier of the equipment at the factory into a formatted string to obtain the original message to be verified; input the original message to be verified into the HMAC-SHA256 algorithm engine, perform message authentication code calculation using the locally preset security key, and output a truncated hexadecimal anti-tampering hash digest string.

5. The method for monitoring and tracking energy consumption of industrial kilns based on edge computing according to claim 1, characterized in that, The specific steps of S5, heartbeat handshake monitoring and automatic restart, include: the Rust daemon engine initializes the socket byte point / tmp / watchdog.sock in the Linux operating system file system; the daemon engine's internal timer cyclically checks the previous valid heartbeat timestamp at 100ms intervals; when the difference between the current time and the previous heartbeat timestamp is greater than 100ms, the heartbeat loss counter is incremented by 1; when the heartbeat loss count reaches 3 times, i.e., the cumulative timeout reaches 300ms, the daemon engine forcibly issues a kill command to terminate the original process handle, and after a preset recovery interval, regenerates the acquisition service process with a new PID through a child process creation command and rebinds the socket.

6. The method for monitoring and tracking energy consumption of industrial kilns based on edge computing according to claim 1, characterized in that, The smoothed time-series telemetry data collected in step S1 is dynamically pushed to the Web frontend through a full-duplex WebSocket long connection channel at a period of 500ms. The frontend renders the high-frequency noisy sampling waveform and the filtered smooth curve respectively through the HTML5 native Canvas dual-layer redraw mechanism.

7. An industrial kiln energy consumption monitoring and hotspot tracking system based on edge computing, characterized in that, The system includes: a bottom-level data acquisition module for periodically acquiring raw telemetry data streams from multi-temperature zone thermocouples and digital meters via industrial serial ports in an edge gateway; a rule cleaning and filtering module for performing interval validity verification and pulse denoising on the raw telemetry data using a sliding time window static rule engine; a 3D spatial hotspot mapping module for real-time presentation of multi-temperature zone temperature field morphology and tracking and rendering of overheated hotspots in a 3D wireframe geometric topology model based on WebGL; a carbon footprint anti-tampering and evidence storage module for calculating unit carbon emission intensity based on batch energy consumption, generating a globally unique verification hash based on the HMAC-SHA256 algorithm, and writing it to the audit log; and a daemon process self-healing module for maintaining periodic heartbeat detection with the acquisition service based on Unix Domain Socket, and automatically restarting the acquisition service when it times out.

8. A computing device, characterized in that, include: One or more processors; Memory, used to store one or more computer program instructions; When the computer program instructions are executed by the processor, the method for monitoring and tracking energy consumption of industrial kilns based on edge computing as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the edge computing-based industrial kiln energy consumption monitoring and hotspot tracking method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the edge computing-based industrial kiln energy consumption monitoring and hotspot tracking method as described in any one of claims 1 to 6.

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