Laser equipment heat dissipation module frequency conversion temperature control system driven by AI algorithm

Abstract

The invention provides a laser equipment heat dissipation module frequency conversion temperature control system driven by an AI algorithm, and relates to the technical field of heat management, the laser equipment heat dissipation module frequency conversion temperature control system comprises a temperature control system, the temperature control system comprises a photon heat regulation frequency conversion heat dissipation module, a photon heat sensing module, an Al quantum heat control core module and a self-repairing heat flow regulation module, a photon band gap regulation engine is deployed in the photon heat regulation frequency conversion heat dissipation module, a photon heat flow collection engine is deployed in the photon heat sensing module, a heat flow topology analysis engine and a heat flow path optimization engine are deployed in the Al quantum heat control core module, and a photo-thermal synergistic repair engine is deployed in the self-repair heat flow regulation module. According to the system, infrared radiation photon and heat flow state data are collected through the photon thermal sensing module, a quantum heat flow optimization algorithm is operated in combination with the AI quantum thermal control core module, a high-precision three-dimensional heat flow field is generated in real time, and a heat dissipation strategy is dynamically adjusted.

Description

Technical Field

[0001] The present invention relates to the field of thermal management technology, and specifically to an AI algorithm-driven variable frequency temperature control system for a laser equipment heat dissipation module. Background Art

[0002] With the rapid development of laser technology, laser equipment is increasingly being used in industry, medicine, communications, scientific research, and other fields, becoming a vital tool for driving technological progress. In industry, high-power fiber lasers are widely used in metal cutting, welding, and additive manufacturing. In medicine, laser surgical instruments, with their high precision and minimally invasive properties, serve ophthalmology, dermatology, and other surgeries. In communications, lidar provides critical support for autonomous driving and high-speed data transmission. However, laser equipment generates significant heat during operation, particularly at high power, in pulsed mode, or for extended periods, significantly increasing the thermal load. Excessive temperatures not only lead to reduced beam quality and output power fluctuations, but can also cause optical component degradation, thermal stress damage, and even device failure. Therefore, developing efficient, precise, and reliable heat dissipation and temperature control technologies to ensure stable laser operation under complex operating conditions has become a research hotspot and a key technical bottleneck in the laser equipment field. As laser equipment continues to move towards miniaturization and higher power density, the limitations of traditional heat dissipation technologies are becoming increasingly prominent, necessitating innovative solutions to meet the demands for higher performance and reliability.

[0003] Although existing heat dissipation technology plays a certain role in laser equipment temperature control, it still has the following shortcomings: Problem 1: Traditional air-cooling and water-cooling heat dissipation systems are bulky and energy-intensive, failing to meet the compact design requirements of high-power lasers. Furthermore, temperature control accuracy is limited, making it difficult to achieve precise control within ±0.1°C, impacting laser performance in precision applications. Second, some laser equipment uses fixed-frequency heat dissipation modules that lack the ability to adapt to dynamic heat loads. During pulsed operation or ambient temperature fluctuations, the heat dissipation efficiency cannot be adjusted in real time, leading to the accumulation of localized hot spots and increasing the risk of equipment overheating. Problem three: Most existing cooling systems rely on a single heat conduction or radiation mechanism, failing to fully utilize advanced technologies such as photonic crystals and quantum heat flow to integrate multi-mode cooling paths. The overall cooling efficiency is low, typically 20%-30% lower than the theoretical value, limiting the continuous operation of the laser in high-load scenarios. Fourth, traditional cooling modules lack self-repair capabilities. Physical damage, such as microcracks or heat pipe blockages, requires downtime for maintenance, impacting continuous equipment operation. Furthermore, existing systems fail to fully integrate AI algorithms for real-time analysis of heat flow data, making dynamic optimization and anomaly prediction difficult, making it difficult to proactively intervene in potential failures.

[0004] Therefore, an AI algorithm-driven variable frequency temperature control system for the laser equipment heat dissipation module is needed to solve the above problems. Summary of the Invention

[0005] Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an AI algorithm-driven variable frequency temperature control system for a laser equipment heat dissipation module, which solves the problems in the above background technology.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an AI algorithm-driven laser equipment heat dissipation module variable frequency temperature control system, including a temperature control system, wherein the temperature control system includes a photon thermal control variable frequency heat dissipation module, a photon thermal sensing module, an Al quantum thermal control core module and a self-repairing heat flow control module, the photon thermal control variable frequency heat dissipation module is deployed with a photon bandgap control engine, the photon thermal sensing module is deployed with a photon thermal flow acquisition engine, the Al quantum thermal control core module is deployed with a heat flow topology analysis engine and a heat flow path optimization engine, the self-repairing heat flow control module is deployed with a photothermal collaborative repair engine, the photon thermal control variable frequency heat dissipation module, the photon thermal sensing module, the Al quantum thermal control core module and the self-repairing heat flow control module form a closed-loop heat flow ecosystem through hierarchical control flow to achieve real-time temperature control and dynamic heat flow optimization of the laser equipment.

[0009] Preferably, the photonic thermal control variable frequency heat dissipation module integrates photonic crystal materials and micro heat pipe arrays and is deployed on the surface of the laser to control infrared radiation heat dissipation through the dynamic refractive index of the photonic crystal material, and realizes heat transfer in combination with the micro heat pipe array. It collects photonic crystal operating status data and micro heat pipe array operating status data, and performs local variable frequency control, wherein the photonic crystal operating status data is a digital signal describing the band gap and refractive index of the photonic crystal, and the micro heat pipe array operating status data is a digital signal describing the heat pipe power and heat flow; The photon thermal sensing module integrates a photon counter, a quantum dot infrared detector, and a heat flow sensor to collect infrared radiation photons and heat flow quantum states from the laser surface to generate a three-dimensional heat flow field. The infrared radiation photons are photons emitted in the 8-14 micron band from the laser surface, and the heat flow quantum states are vectors that describe the microscopic quantum behavior of heat flow. The three-dimensional heat flow field includes a spatial vector field of temperature gradient, heat flux density, and hot spot position. The AI quantum thermal control core module integrates a quantum acceleration chip and an embedded storage unit to run an AI algorithm to analyze three-dimensional thermal flow field data, photonic crystal operating status data of the photonic thermal control variable frequency heat dissipation module, and micro heat pipe array operating status data, as well as the repair status report of the self-repair thermal flow control module, and generate a variable frequency control instruction. The AI algorithm includes a quantum thermal flow optimization algorithm, and the variable frequency control instruction is a digital signal generated by analyzing the heat flow distribution and operating status through the quantum thermal flow optimization algorithm and used to adjust the photonic crystal band gap and the micro heat pipe array power. The self-repairing heat flux control module integrates a photothermal actuator and a thermoelectric converter. The photothermal actuator receives laser waste light to stimulate a photothermal-responsive nanocoating, repairing microcracks and blockages in the photon thermal control variable frequency heat dissipation module. The module also uses laser waste light to drive thermoelectric conversion to provide backup heat dissipation and generate a repair status report. The photothermal-responsive nanocoating is a polymer material that self-repairs under photothermal stimulation. Laser waste light is defined as ineffective light with a wavelength greater than 1000 nanometers. The repair status report is structured data describing the repair progress and backup heat dissipation power. The photon thermal control variable frequency heat dissipation module, photon thermal perception module, AI quantum thermal control core module and self-repair heat flow control module operate collaboratively through hierarchical control flow. The photon thermal perception module prioritizes processing the hot spot data in the three-dimensional heat flow field. The AI quantum thermal control core module integrates the three-dimensional heat flow field data, photonic crystal operation status data, micro heat pipe array operation status data and repair status report to dynamically adjust the heat dissipation strategy.

[0010] Preferably, the temperature control system achieves the goal by the following steps: S1: The photon heat flow acquisition engine deployed in the photon thermal sensing module collects infrared radiation photon data, heat flow quantum state data and environmental thermal signal data, and runs the quantum heat flow mapping algorithm to generate a three-dimensional thermal flow field sequence; the photon heat flow acquisition engine uses infrared radiation photon data, heat flow quantum state data and environmental thermal signal data, where the environmental thermal signal data is the analog signal data of the ambient temperature and humidity; the infrared radiation photon data is stored in a ring buffer in a time series format, the heat flow quantum state data is parsed in a quantum state vector format, and the environmental thermal signal data is adaptively filtered to generate a feature vector; the quantum heat flow mapping algorithm takes infrared radiation photon data, heat flow quantum state data and environmental thermal signal data as input, reconstructs the heat distribution on the surface and inside of the laser through a quantum thermodynamic model, and outputs a three-dimensional thermal flow field sequence; the three-dimensional thermal flow field sequence is transmitted to the Al quantum thermal control core module via a high-speed optical fiber bus and is encrypted using a dynamic key; the photon heat flow acquisition engine operates in state machine mode, sampling every 20 milliseconds, and switches to 10 milliseconds high-frequency sampling and jumps to S2 when a hot spot is detected; S2: The heat flow topology analysis engine deployed in the Al quantum thermal control core module collects the photonic crystal operating status data and the micro heat pipe array operating status data of the photon thermal control variable frequency cooling module, as well as the three-dimensional heat flow field sequence of S1, runs the quantum tunneling prediction algorithm to generate a heat flow anomaly sequence, and broadcasts the heat flow summary in combination with the photon pulse heat flow sharing protocol; the heat flow topology analysis engine uses the photonic crystal operating status data, the micro heat pipe array operating status data and the three-dimensional heat flow field sequence, where the photonic crystal operating status data and the micro heat pipe array operating status data are stored in a key-value pair format, and the three-dimensional heat flow field sequence is generated. The columns are parsed in sparse matrix format; the quantum tunneling prediction algorithm takes the photonic crystal operating status data, the micro heat pipe array operating status data and the three-dimensional heat flow field sequence as input, detects heat flow anomalies by simulating the heat flow quantum tunneling behavior, and outputs a heat flow anomaly sequence, where the heat flow anomaly sequence is defined as a vector sequence describing the heat flow density exceeding the preset threshold; the heat flow summary is defined as the compressed heat flow anomaly description data, which is broadcast to the self-repair heat flow control module in pulse coding format through the photonic communication unit; the heat flow topology analysis engine runs in event-driven mode, prioritizes heat flow mutations, and jumps to S3 after confirming the heat flow anomaly; S3: Generate a photonic crystal bandgap control sequence through the photonic bandgap control engine deployed in the photonic thermal control variable frequency heat dissipation module, and run the photon radiation aggregation algorithm to detect the radiation efficiency deviation sequence; the photonic bandgap control engine uses the heat flow anomaly sequence of S2, where the heat flow anomaly sequence is stored in a vector sequence format; the photon radiation aggregation algorithm takes the heat flow anomaly sequence as input, detects the heat dissipation efficiency deviation by aggregating the photonic crystal radiation offset data, and outputs a radiation efficiency deviation sequence, where the photonic crystal bandgap control sequence is defined as a digital sequence for controlling the refractive index of the photonic crystal, and the radiation efficiency deviation sequence is a vector sequence of the difference between the actual radiation heat dissipation power and the expected power; the photonic crystal bandgap control sequence is stored in a time window encoding format, and the radiation efficiency deviation sequence is analyzed in a vector sequence format; the radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module through a post-quantum encrypted optical fiber channel; the photonic bandgap control engine runs the photon radiation aggregation algorithm at a fixed period of 50 milliseconds, and suspends low-priority heat dissipation tasks and jumps to S4 when a radiation efficiency deviation anomaly is detected; S4: The heat flow path optimization engine deployed in the Al quantum thermal control core module collects the heat flow interaction records from the micro heat pipe array to the photonic crystal, runs the heat flow ripple prediction algorithm based on the radiation efficiency deviation sequence of S3 to generate a heat flow optimization path sequence, and triggers the frequency conversion control instruction; the heat flow path optimization engine uses the heat flow interaction record and the radiation efficiency deviation sequence, wherein the heat flow interaction record is the time series data describing the direction and intensity of the heat flow, and is stored in the time series directed graph format; the heat flow ripple prediction algorithm takes the heat flow interaction record and the radiation efficiency deviation sequence as input, optimizes the heat flow transfer path by simulating the heat flow path ripple diffusion, and outputs the heat flow optimization path sequence, wherein the heat flow optimization path sequence is defined as the optimized heat flow transfer path vector; the frequency conversion control instruction is multicast to the photonic thermal control frequency conversion cooling module through the high-speed optical fiber bus; the heat flow path optimization engine runs in asynchronous mode and jumps to S5 when the prediction fails; S5: The photothermal collaborative repair engine deployed in the self-repair thermal flow control module collects the photothermal actuator operating data and the thermoelectric converter operating data, and combines the thermal flow optimization path sequence of S4 to run the cross-domain thermal flow fusion algorithm to generate a visual thermal flow report; the photothermal collaborative repair engine uses the photothermal actuator operating data, the thermoelectric converter operating data and the thermal flow optimization path sequence, wherein the photothermal actuator operating data is defined as the photothermal response signal data of the repair coating, which is stored in the spectrum sequence format, and the thermoelectric converter operating data is defined as the backup heat dissipation power curve data, which is saved in the power curve format; the cross-domain thermal flow fusion algorithm takes the photothermal actuator operating data, the thermoelectric converter operating data and the thermal flow optimization path sequence as input, detects microcracks and blockages by fusing thermal flow and physical anomaly data, and outputs a visual thermal flow report; the visual thermal flow report is transmitted to the Al quantum thermal control core module via a high-speed optical fiber bus in a compressed vector format; the photothermal collaborative repair engine runs in a multi-threaded pipeline mode, triggers the repair instruction and terminates the process after confirming the physical anomaly.

[0011] Preferably, the quantum heat flow mapping algorithm in S1 takes infrared radiation photon data, heat flow quantum state data and environmental thermal signal data as input, generates a three-dimensional heat flow field sequence through a quantum thermodynamic model, and drives the quantum tunneling prediction algorithm in S2 to analyze heat flow anomalies; the specific processing of the quantum heat flow mapping algorithm is: using infrared radiation photon data to extract the radiation heat flow characteristics of the laser surface, using heat flow quantum state data to evaluate the microscopic quantum behavior of the heat flow, using environmental thermal signal data to calibrate the environmental impact, reconstructing the three-dimensional heat flow field through the quantum thermodynamic model, and outputting a three-dimensional heat flow field sequence; the three-dimensional heat flow field sequence is stored in a fixed-length vector encoding format; the three-dimensional heat flow field sequence is transmitted to the heat flow topology analysis engine via a high-speed optical fiber bus using dynamic key encryption; the quantum heat flow mapping algorithm runs in state machine mode, switches to high-frequency sampling when a hot spot is detected, and notifies S2.

[0012] Preferably, the heat flow topology analysis engine in S2 is composed of an operation status acquisition unit, a heat flow data parser, a photon communication interface unit and a heat flow summary generation unit, and works as follows: the operation status acquisition unit analyzes the photon crystal band gap state and the micro heat pipe array power of the photon thermal control variable frequency heat dissipation module to generate operation status data, the heat flow data parser takes the three-dimensional heat flow field sequence and the operation status data of S1 as input, runs the quantum tunneling prediction algorithm to calculate the heat flow anomaly offset, the heat flow summary generation unit compresses the heat flow anomaly offset data into a heat flow summary of a fixed length, and then the operation status data and the three-dimensional heat flow field sequence are stored in a low-rank matrix format; the heat flow summary is transmitted to the heat flow path optimization engine of S4 and the photothermal collaborative repair engine of S5 through the photon communication interface unit in a pulse coding format; wherein the heat flow topology analysis engine runs in an event loop mode, and triggers S3 when a heat flow anomaly is detected, the quantum tunneling prediction algorithm in S2 takes the operation status data and the three-dimensional heat flow field sequence as input, generates a heat flow anomaly sequence by simulating the heat flow quantum tunneling behavior, and drives the photon radiation aggregation algorithm in S3 to detect the radiation efficiency deviation; The specific processing of the quantum tunneling prediction algorithm is as follows: extracting the photonic crystal bandgap state and the power of the micro heat pipe array using the operating status data, evaluating the heat flux density distribution using a three-dimensional heat flux field sequence, predicting the heat flux anomaly offset through the quantum tunneling model, and outputting the heat flux anomaly sequence; the heat flux anomaly sequence is stored as a sparse vector; the heat flux anomaly sequence is encrypted and transmitted to the photonic bandgap control engine via a high-speed optical fiber bus; the quantum tunneling prediction algorithm operates in a feedback loop mode and dynamically adjusts the prediction parameters. The photon pulse heat flux sharing protocol in S2 consists of a pulse coding unit, a photon emission unit, a quantum receiving unit, and an error checking unit. The working method is as follows: the pulse coding unit maps the heat flux summary into an interval coding sequence, the photon emission unit sends the interval coding sequence, the quantum receiving unit decodes the interval coding sequence, and the error checking unit verifies the integrity of the decoded data; the heat flux summary is stored in a sparse vector format; the photon pulse heat flux sharing protocol transmits the heat flux summary through the photon communication interface unit, supports the frequency conversion control instruction generation of S4 and the physical anomaly detection of S5; the photon pulse heat flux sharing protocol operates in a token scheduling mode, giving priority to transmitting high-risk heat flux summaries.

[0013] Preferably, the photon radiation aggregation algorithm in S3 takes the heat flux anomaly sequence as input, generates a radiation efficiency deviation sequence by aggregating the photonic crystal radiation offset data, and drives the heat flux ripple prediction algorithm in S4 to generate a heat flux optimization path sequence; the specific processing method of the photon radiation aggregation algorithm is: extract the heat flux density abnormal area with the heat flux anomaly sequence, evaluate the radiation heat dissipation efficiency with the photonic crystal radiation offset data, calculate the radiation efficiency deviation through the aggregation model, and output the radiation efficiency deviation sequence; the radiation efficiency deviation sequence is stored in a high-dimensional tensor encoding format; the radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module through a post-quantum encrypted optical fiber channel; the photon radiation aggregation algorithm runs in pipeline mode, notifies S4 to perform heat flow path optimization, and the photon radiation aggregation algorithm in S3 The sub-bandgap control engine consists of a bandgap sequence generation unit, a radiation efficiency analyzer, an encrypted communication interface unit and an anomaly storage unit. Its working mode is as follows: the bandgap sequence generation unit analyzes the heat flux anomaly sequence to generate a photonic crystal bandgap control sequence; the radiation efficiency analyzer takes the heat flux anomaly sequence as input, runs the photon radiation aggregation algorithm to calculate the radiation efficiency deviation sequence; the encrypted communication interface unit uploads the radiation efficiency deviation sequence; the anomaly storage unit caches the historical radiation efficiency deviation sequence; the radiation efficiency deviation sequence is stored in a sliding window format; the radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module in a fragmented format through a post-quantum encrypted optical fiber channel; the photonic bandgap control engine drives the photon radiation aggregation algorithm with a timer, and triggers S4 when a radiation efficiency deviation anomaly is detected.

[0014] Preferably, the heat flow path optimization engine in S4 is composed of a heat flow interaction acquisition unit, a heat flow ripple predictor, a frequency conversion control unit and a path cache unit, and works as follows: the heat flow interaction acquisition unit parses the heat flow interaction records from the micro heat pipe array to the photonic crystal, the heat flow ripple predictor takes the radiation efficiency deviation sequence and the heat flow interaction record of S3 as input, runs the heat flow ripple prediction algorithm to generate a heat flow optimization path sequence, the frequency conversion control unit sends a frequency conversion control instruction to the photonic thermal control frequency conversion heat dissipation module, and the path cache unit stores the historical heat flow optimization path sequence; the heat flow interaction records and the radiation efficiency deviation sequence are stored in a sparse matrix format; the heat flow optimization path sequence is multicast to the photothermal collaborative repair engine of S5 via a high-speed optical fiber bus; the heat flow path optimization engine runs in asynchronous mode and triggers S5 when the prediction fails.

[0015] Preferably, the heat flow ripple prediction algorithm in S4 takes the heat flow interaction record and the radiation efficiency deviation sequence as input, generates the heat flow optimization path sequence by simulating the heat flow path ripple diffusion, and drives the cross-domain heat flow fusion algorithm in S5 to detect physical anomalies; the specific processing method of the heat flow ripple prediction algorithm is: extracting the heat flow direction and intensity with the heat flow interaction record, evaluating the heat dissipation efficiency bottleneck with the radiation efficiency deviation sequence, optimizing the heat flow transfer path through the ripple diffusion model, and outputting the heat flow optimization path sequence; the heat flow optimization path sequence is stored in a time-sliced format; the heat flow optimization path sequence is multicast to the optical-thermal collaborative repair engine through a high-speed optical fiber bus using dynamic key encryption; the heat flow ripple prediction algorithm operates in a feedback loop mode and dynamically adjusts the prediction parameters.

[0016] Preferably, the photothermal collaborative repair engine in S5 is composed of a photothermal actuator acquisition unit, a thermoelectric converter acquisition unit, a cross-domain thermal flow fusion reasoning core and a report generation unit, and works as follows: the photothermal actuator acquisition unit parses the photothermal actuator operation data, the thermoelectric converter acquisition unit extracts the thermoelectric converter operation data, the cross-domain thermal flow fusion reasoning core takes the thermal flow optimization path sequence, the photothermal actuator operation data and the thermoelectric converter operation data of S4 as input, runs the cross-domain thermal flow fusion algorithm to generate a visual thermal flow report, and the report generation unit generates a visual thermal flow report; the photothermal actuator operation data, the thermoelectric converter operation data and the thermal flow optimization path sequence are stored in a hierarchical cache format, and high-risk abnormal data are processed first; the visual thermal flow report is transmitted to the Al quantum thermal control core module via a high-speed optical fiber bus; the photothermal collaborative repair engine runs in a multi-threaded pipeline mode, and triggers the repair instruction after confirming the physical abnormality.

[0017] Preferably, the system includes the following hardware to support the coordinated operation of S1 to S5: An integrated sensing hardware module, comprising a single-photon avalanche diode counter, a quantum dot infrared detector, a heat flow sensor, and a photon sequence generator, configured to collect infrared radiation photons, heat flow quantum states, ambient thermal signals, and photon pulses; An edge computing hardware unit, including a quantum acceleration chip, configured to run a quantum heat flow mapping algorithm, a quantum tunneling prediction algorithm, and a cross-domain heat flow fusion algorithm; a photonic communication hardware module comprising a photon transmitter and a quantum receiver configured to execute a photon pulse heat flow sharing protocol; Perception data is stored in a hierarchical cache format, with signals containing hot spots prioritized. The hardware is interconnected via a high-speed fiber optic bus, transmitting perception data encrypted using quantum states. Edge computing hardware units operate in a master-slave architecture to coordinate sensor and communication tasks.

[0018] Beneficial effects

[0019] The present invention provides an AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules. It has the following beneficial effects: 1. The system of the present invention collects infrared radiation photons and heat flow quantum state data through the photon thermal sensing module, combines the AI quantum thermal control core module to run the quantum heat flow optimization algorithm, generates a high-precision three-dimensional heat flow field in real time and dynamically adjusts the heat dissipation strategy. The temperature control accuracy can reach ±0.1°C, significantly improving the beam quality and stability of lasers in precision applications such as industrial processing and medical surgery. It solves the problems of traditional air-cooling and water-cooling heat dissipation systems being bulky and having high energy consumption, unable to meet the requirements of compact design of high-power lasers, and having limited temperature control accuracy.

[0020] 2. This invention uses a photonic thermal control variable frequency heat dissipation module that integrates photonic crystals and micro heat pipe arrays. Through a photonic bandgap control engine and a heat flow path optimization engine, the bandgap (1-4 microns) and heat pipe frequency (5-30Hz) are adjusted in real time according to pulse operation or ambient temperature fluctuations. This effectively eliminates local hot spots and ensures the continuous and stable operation of the laser under high power or extreme environments. This solves the problem that some laser equipment uses fixed-frequency heat dissipation modules and lacks the ability to adapt to dynamic heat loads. During pulse operation or ambient temperature fluctuations, the heat dissipation efficiency cannot be adjusted in real time, resulting in the accumulation of local hot spots and increasing the risk of equipment overheating.

[0021] 3. The present invention regulates infrared radiation heat dissipation through the dynamic refractive index of the photon crystal of the photon thermal control variable frequency heat dissipation module, and combines it with a micro heat pipe array to optimize heat transfer, and synergistically runs the photon radiation aggregation algorithm and the heat flow ripple prediction algorithm, thereby improving the heat dissipation efficiency by 30%-40%, greatly reducing energy consumption, and extending the service life of the laser in high-load scenarios. It solves the problem that most existing heat dissipation systems rely on a single heat conduction or radiation mechanism, fail to fully utilize advanced technologies such as photonic crystals and quantum heat flow to integrate multi-mode heat dissipation paths, and have low overall heat dissipation efficiency.

[0022] 4. This invention introduces a self-repairing heat flow control module, utilizing photothermal actuators and thermoelectric converters to excite a nanocoating using waste laser light to repair microcracks and blockages, achieving a repair success rate exceeding 95%. It also provides 80W of standby heat dissipation power to ensure stable temperature control during repairs. Furthermore, the AI quantum thermal control core module uses a quantum tunneling prediction algorithm and a cross-domain heat flow fusion algorithm to analyze heat flow anomalies in real time and generate optimization instructions, significantly reducing the risk of false alarms and missed alarms, enabling early intervention in faults and improving the reliability of continuous equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a system flow chart of the present invention; Figure 2 It is a system framework diagram of the present invention; Figure 3This is a system simulation diagram of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Specific embodiment one: like Figure 1-Figure 3 As shown in the figure, the AI algorithm-driven variable-frequency temperature control system for laser equipment cooling modules achieves real-time temperature control and dynamic heat flow optimization for laser equipment through the coordinated operation of a photonic thermal control variable-frequency cooling module, a photonic thermal sensing module, an Al quantum thermal control core module, and a self-healing heat flow control module. The system utilizes a hierarchical control flow and closed-loop heat flow ecosystem architecture. Its core is to drive variable-frequency temperature control using Al algorithms (such as the quantum heat flow optimization algorithm), dynamically adjusting the photonic crystal band gap and micro-heat pipe array power to meet the heat dissipation requirements of the laser under different operating conditions, ensuring temperature control accuracy of ±0.1°C and improving heat dissipation efficiency by 30%-40%.

[0026] The functions of each module are implemented through hardware architecture and interconnection. The photonic thermal control variable frequency cooling module integrates photonic crystal materials (periodic optical structures with a bandgap range of 1-4 microns) and a micro heat pipe array (0.5 mm diameter, working fluid: water or ethanol). Deployed on the laser surface (approximately 0.01 m²), it modulates the photonic crystal bandgap using electro-variable refractive index materials (such as liquid crystals) to control the efficiency of infrared radiation heat dissipation in the 8-14 micron band. A micropump (frequency 5-30 Hz) drives the heat pipe fluid circulation to achieve heat transfer. This module collects operating status data for the photonic crystal (bandgap and refractive index, defined as digital signals describing optical properties) and the micro heat pipe array (power and flow, defined as digital signals describing heat transfer capabilities). It connects to the AI quantum thermal control core module via a high-speed fiber optic bus (bandwidth 10 Gbps) to receive variable frequency control commands. The photonic thermal sensing module integrates a single-photon avalanche diode counter (100 photons / s resolution), a quantum dot infrared detector (8-14 μm wavelength), and a heat flux sensor (0-1000 W / m² range). Encapsulated on the laser surface, it collects infrared radiation photons (defined as photons in the 8-14 μm wavelength range, reflecting radiative heat dissipation characteristics), heat flux quantum states (defined as vectors describing the microscopic quantum behavior of heat flux and characterizing the quantum state distribution), and ambient thermal signals (defined as analog signals of ambient temperature and humidity). It generates a three-dimensional heat flux field sequence (defined as a spatial vector field containing temperature gradient, heat flux density, and hot spot location) and transmits it to the AI quantum thermal control core module via an optical fiber bus. The AI quantum thermal control core module, which includes a quantum accelerator chip (10 TFLOPS computing power) and an embedded storage unit (1GB capacity), runs a quantum heat flux optimization algorithm, analyzes the three-dimensional heat flux field, operational status data, and repair status reports, and generates variable frequency control commands (defined as digital signals that adjust the photonic crystal band gap and heat pipe power). The module coordinates the operation of each module via the optical fiber bus. The self-healing thermal flow control module integrates a photothermal actuator (photosensitive polymer coating, response wavelength >1000 nm) and a thermoelectric converter (output power 50-100W). It repairs microcracks (defined as physical defects <100 microns) or blockages (defined as condensation of the working fluid within the heat pipe) in the photonic thermal control variable frequency cooling module. It utilizes laser waste light (defined as ineffective light with a wavelength >1000 nm) to provide backup cooling and generates a repair status report (structured data describing the repair progress and backup cooling power). The modules are interconnected via a fiber optic bus. The data flow (three-dimensional thermal flow field, operating status data, and repair status report) is initiated by the photonic thermal sensing module and processed by the AI quantum thermal control core module to generate variable frequency control instructions, driving the photonic thermal control variable frequency cooling module and the self-healing thermal flow control module to execute control and repair. The AI quantum thermal control core module is the primary control unit of the control flow, coordinating sampling, analysis, and execution.

[0027] Variable frequency temperature control is achieved through dynamic control of the photonic thermal control variable frequency cooling module. Its core is adjusting the band gap of the photonic crystal and the power of the micro-heat pipe array. The photonic thermal sensing module collects infrared radiation photons, heat flow quantum states, and ambient thermal signals at a frequency of 50-100Hz. It then runs a quantum heat flow mapping algorithm, reconstructs the heat distribution using a quantum thermodynamic model, and generates a three-dimensional heat flow field sequence. This sequence is stored as a fixed-length vector code (approximately 1KB per frame) and transmitted to the AI quantum thermal control core module via a fiber optic bus using dynamic key encryption. The AI quantum thermal control core module's heat flow topology analysis engine collects data on the operating status of the photonic crystal and heat pipes, runs a quantum tunneling prediction algorithm, simulates heat flow quantum tunneling behavior, detects heat flow anomalies (defined as areas with a heat flux density > 500W / m²), and outputs a heat flow anomaly sequence (defined as a sequence of vectors describing the anomaly areas), stored as a sparse vector. The heat flow anomaly sequence generates a heat flow summary (compressed anomaly description data, approximately 100 bytes) using the photon pulse heat flow sharing protocol (a communication mechanism that transmits heat flow summaries via pulse coding, comprising a pulse encoding unit, a photon emission unit, a quantum receiving unit, and an error checking unit). This summary is then broadcast to the self-repairing heat flow control module. The photonic bandgap control engine of the photonic thermal control variable frequency cooling module receives the heat flow anomaly sequence, runs a photon radiation aggregation algorithm, detects radiation efficiency deviation (defined as the difference between actual and expected heat dissipation power), and outputs a radiation efficiency deviation sequence (defined as a vector sequence of power differences). This sequence is then uploaded to the AI quantum thermal control core module via a post-quantum encrypted fiber channel. The AI quantum thermal control core module's heat flow path optimization engine collects heat flow interaction records (time series data describing heat flow direction and intensity), runs a heat flow ripple prediction algorithm, simulates heat flow path ripple diffusion, and generates a heat flow optimized path sequence (defined as the optimized heat flow transfer path vector), which is stored in time slices. The quantum heat flow optimization algorithm (defined as an AI algorithm based on topology optimization and quantum thermodynamics models) analyzes the three-dimensional heat flow field, operating status data, radiation efficiency deviation sequences, and optimized heat flow path sequences to calculate the optimal combination of photonic crystal band gap and heat pipe power, generating variable frequency control instructions. These instructions are multicast to the photonic thermal control variable frequency cooling module via an optical fiber bus, with a response time of <5ms. Specifically, the algorithm performs the following control actions: Photonic crystal band gap adjustment: By changing the band gap width (for example, from 2 microns to 3 microns) using electro-variable refractive index materials, the infrared radiation heat dissipation rate is controlled. Increasing the band gap in the hot spot area improves radiation efficiency by 20%. Micro-heat pipe array power regulation: By adjusting the micro-pump frequency (for example, from 10 Hz to 20 Hz), heat transfer is enhanced. In high-power mode, heat pipe power increases by 30%, reaching 500W. The photonic thermal sensing module continuously updates the three-dimensional heat flow field, and the AI quantum thermal control core module iteratively optimizes the instructions based on the feedback, forming a closed-loop control loop to ensure temperature control accuracy of ±0.1°C.The self-repairing thermal flow control module runs a cross-domain thermal flow fusion algorithm, integrating the photothermal actuator operating data (defined as the photothermal response signal of the repair coating), the thermoelectric converter operating data (defined as the backup heat dissipation power curve) and the heat flow optimization path sequence, detecting microcracks or blockages, triggering the photothermal actuator to use laser waste light to excite the nanocoating (defined as a polymer material that self-repairs under photothermal stimulation) to repair defects. The thermoelectric converter provides 50-100W of backup heat dissipation, generates a visual heat flow report (defined as image data describing the abnormal location and repair status), and transmits it to the AI quantum thermal control core module via the optical fiber bus to optimize the frequency conversion control strategy.

[0028] The system achieves variable-frequency temperature control and heat flow optimization through the following process. The photonic thermal sensing module operates in state machine mode, sampling every 20ms. Upon detecting a hot spot (defined as an area where the local temperature exceeds a preset threshold), it switches to a 10ms high-frequency sampling rate, generating a three-dimensional heat flow field sequence and transmitting it to the AI quantum thermal control core module. The thermal flow topology analysis engine detects thermal flow anomalies, generates a thermal flow anomaly sequence and summary, and broadcasts it to the self-repair module. The photonic bandgap control engine adjusts the photonic crystal bandgap, detects radiation efficiency deviations, and uploads them to the AI quantum thermal control core module. The heat flow path optimization engine generates a heat flow optimization path sequence and variable-frequency control instructions, which drive the photonic thermal control variable-frequency cooling module to execute control. The self-repairing heat flow control module detects and repairs microcracks or blockages, generates a visual heat flow report, and updates the control strategy. The entire process forms a closed loop. The AI quantum thermal control core module coordinates various modules using a quantum heat flow optimization algorithm to ensure real-time temperature control and dynamic optimization.

[0029] The system is suitable for industrial fiber lasers (1kW-10kW, temperature control accuracy ±0.1°C), medical laser surgical instruments (bandgap adjustment time <10ms), and lidar (operable from -10°C to 50°C). In high-power mode, the bandgap increases to 3-4 microns, the heat pipe frequency is 20-30Hz, and the heat dissipation power is 500-1000W. In pulsed mode, high-frequency sampling and rapid bandgap adjustment (5ms) address transient hot spots. In low-power mode, the bandgap is 1-2 microns, and the heat pipe frequency is 5-10Hz, reducing energy consumption by 30%. 1000-hour testing has verified a 35% increase in heat dissipation efficiency and a 20% reduction in energy consumption. A quantum tunneling prediction algorithm detects heat flow anomalies in real time (response time <20ms). A photothermal responsive nanocoating repairs microcracks (95% success rate, repair time 10-30s). A thermoelectric converter provides 50-100W of backup heat dissipation to ensure stable temperature control in the event of a fault. The system utilizes adaptive filtering to resist environmental interference (-10°C to 50°C), and the fiber optic bus and quantum state encryption provide immunity to electromagnetic interference (bit error rate <10^-6). Vibration (10g, 20-2000Hz) and shock (50g, 11ms) tests verify hardware reliability.

[0030] The following terms may be vague or not clearly defined, so we would like to provide additional explanations: Heat flow quantum state: A vector describing the microscopic quantum behavior of heat flow. It is collected by a quantum dot infrared detector and characterizes the quantum state distribution of heat flow. The unit is a dimensionless vector and is stored in quantum state vector format.

[0031] Photon pulse heat flow sharing protocol: A communication mechanism that maps the heat flow summary into an interval coding sequence through a pulse coding unit. The photon emission unit sends the sequence, the quantum receiving unit decodes it, and the error checking unit verifies the data integrity. The data is stored as a sparse vector, and the transmission bandwidth is approximately 1Mbps.

[0032] Abnormal heat flux: The heat flux density exceeds the preset threshold (500W / m²), indicating local high temperature or insufficient heat dissipation.

[0033] Photonic crystal bandgap control sequence: a digital sequence that controls the refractive index of the photonic crystal, in micrometers, ranging from 1 to 4 micrometers, and stored in a time window encoding format.

[0034] Radiation efficiency deviation: The difference between the actual radiation heat dissipation power and the expected power, expressed in W / m², reflects the heat dissipation performance of the photonic crystal.

[0035] Heat flow optimization path sequence: The optimized heat flow transfer path vector, in m / s, guides the generation of variable frequency control instructions.

[0036] Visual heat flow report: Image data describing the anomaly location and repair status, with a resolution of 256x256 pixels, stored in a compressed vector format (approximately 10KB). Specific embodiment two: like Figure 1-Figure 3 As shown, the key algorithms mentioned in Example 1 are analyzed in detail below, including their core mathematical formulas and explanations: Quantum heat flow mapping algorithm (S1): The quantum heat flow mapping algorithm is used to generate a three-dimensional heat flow field sequence from infrared radiation photon data, heat flow quantum state data, and environmental thermal signal data, providing basic data for subsequent heat flow analysis and variable frequency temperature control.

[0038] The specific mathematical formula is as follows: ; ; in : Three-dimensional thermal flow field sequence, output vector field, contains the following components: : Temperature gradient (K / m), indicating the direction and intensity of heat flow; : Heat flux density (W / m²), which indicates the heat flux per unit area; : Hot spot position vector (m), marking the coordinates of the local high temperature area; : spatial coordinate (m), representing the three-dimensional position (x, y, z) of the laser surface; : Time (s), indicating the current sampling time; : integration domain, representing the spatial area of the laser surface; : Infrared radiation photon data vector, unit is photon counting rate ( ), represents the radiation intensity of photons in the 8-14 μm band; : heat flow quantum state data vector, dimensionless, representing the quantum state distribution of heat flow; : Ambient thermal signal feature vector, in normalized values (0-1), indicating the influence of ambient temperature and humidity; :weight coefficient, dimensionless, represents the contribution ratio of photon data, quantum state data and environmental signal, satisfying , the typical values are 0.4, 0.4, and 0.2 respectively; : Gaussian kernel function, used for spatial smoothing, is the smoothing scale (m), with a typical value of 0.01m; : Integration variable, representing a spatial point in the integration domain; : Euclidean distance (m), indicating the spatial distance between two points; : Differential volume element, representing a small spatial increment of the integration domain.

[0039] The quantum heat flow mapping algorithm is based on the quantum thermodynamics model and reconstructs the heat distribution on the surface and inside the laser by fusing multi-source data (infrared radiation photons, heat flow quantum states, and environmental thermal signals). The algorithm uses the Gaussian kernel smoothing method to integrate spatial data and generate a three-dimensional heat flow field sequence, including temperature gradient, heat flux density, and hot spot location, providing accurate input for subsequent variable frequency temperature control. Weight coefficient Adaptive adjustment through historical data ensures accuracy under different working conditions.

[0040] Quantum tunneling prediction algorithm (S2): The quantum tunneling prediction algorithm simulates the quantum tunneling behavior of heat flow to detect heat flow anomalies (heat flow density exceeds the threshold), providing location of abnormal areas for variable frequency control.

[0041] The specific mathematical formula is as follows: ; ; in : heat flow anomaly sequence, output vector sequence, representing the heat flow offset (W / m²) of the abnormal area; : space coordinates (m) and time (s), same as above; : Number of quantum states, typically 10, indicating the number of heat flow quantum states considered; :No. The weight coefficient of each quantum state is dimensionless and is trained through historical data. The typical value is 0.1. :No. The wave function of the heat flux quantum state, in units of m⁻³ / ², represents the quantum tunneling probability density; :No. The energy of each quantum state (J), calculated based on the microscopic behavior of heat flow; : Reduced Planck constant; : spatial wave function component, dimensionless, representing the spatial distribution of quantum states; : The square of the heat flow field gradient, in units of (W / m³)², reflecting the intensity of heat flow changes; : step function, 1 if yes, 0 otherwise. is the heat flux threshold (typical value 500W / m²); : Heat flux density, extracted from the three-dimensional thermal flow field sequence.

[0042] The quantum tunneling prediction algorithm is based on the tunneling effect in quantum thermodynamics and simulates the abnormal behavior of heat flux quantum states in high heat flux density areas. Calculate the tunneling probability of the quantum state, combined with the thermal flow field gradient Identify abnormal areas (heat flux exceeds the threshold). Step function Ensure that only data in abnormal areas are output to reduce the amount of calculation.

[0043] Algorithm principle: The quantum tunneling prediction algorithm is based on the tunneling effect in quantum thermodynamics and simulates the abnormal behavior of heat flux quantum states in high heat flux density areas. Calculate the tunneling probability of the quantum state, combined with the thermal flow field gradient Identify abnormal areas (heat flux exceeds the threshold). Step function Ensure that only data in abnormal areas are output to reduce the amount of calculation.

[0044] Photon radiation aggregation algorithm (S3): The photon radiation aggregation algorithm detects the deviation of the radiation heat dissipation efficiency of the photonic crystal and provides an optimization basis for variable frequency control.

[0045] The specific mathematical formula is as follows: ; ; in : Radiation efficiency deviation sequence, output vector sequence, representing the deviation between actual and expected heat dissipation power; : Photonic crystal surface area, integration domain, typical area 0.01m . : Radiation efficiency factor, dimensionless, represents the ratio of actual radiation intensity to ideal intensity; : Actual radiant heat dissipation power (W / m ), calculated through the photonic crystal operating state data; : Expected radiant heat dissipation power (W / m ), based on design parameter presets; : Wavelength (m), ranging from 8-14 microns (infrared band); : Infrared band boundaries, 8 microns and 14 microns respectively; : Actual radiation spectrum intensity (W / m m), extracted from the photonic crystal operating status data; : Ideal radiation spectrum intensity (W / m m), based on the blackbody radiation model; : differential area element, unit is m .

[0046] Heat flow ripple prediction algorithm (S4): The heat flow ripple prediction algorithm optimizes the heat flow transfer path by simulating the ripple diffusion of the heat flow path and generates variable frequency control instructions.

[0047] The specific mathematical formula is as follows: ; ; in : Heat flow optimization path sequence, output vector, representing the optimized heat flow transfer path (m / s); : Candidate heat flow path vector, in m / s, indicating the possible heat flow direction and speed; : laser surface and internal area, integration domain; : Target heat flux vector (m / s), comprehensive deviation and interaction record; : Radiation efficiency deviation sequence, obtained from S3; : Heat flow interaction record, describing the heat flow direction and intensity (m / s), collected from the micro heat pipe array; : scaling factor, dimensionless, typically 0.5, balancing the contributions of deviations and interaction records; : Regularization coefficient, unit is m^2, typical value is 0.001, controls the path smoothness; : square of path error, which indicates the deviation between the candidate path and the target path; : Path gradient squared, controls path smoothness. Algorithm principle: The heat flow ripple prediction algorithm is based on a ripple diffusion model, simulating the propagation path of heat flow in photonic crystals and heat pipe arrays. The algorithm optimizes the heat flow transfer path by minimizing the path error and regularization term, generating a heat flow optimized path sequence, and providing a basis for variable frequency control instructions. The target heat flow vector Comprehensive radiation efficiency deviation and heat flow interaction records ensure that path optimization takes into account cooling efficiency and heat transfer requirements.

[0048] Cross-domain heat flow fusion algorithm (S5): The cross-domain heat flow fusion algorithm fuses heat flow and physical anomaly data to detect microcracks or blockages, generate visual heat flow reports, and support self-repair and variable frequency temperature control optimization.

[0049] The specific mathematical formula is as follows: ; ; in : Visual heat flow report, output image data in pixel value (0-255), describing the abnormal location and repair status; : Number of data sources, typically 2 (photothermal actuators and thermoelectric converters); :No. The weight of each data source is dimensionless, with a typical value of 0.5, satisfying ; :No. A data source vector, the unit depends on the data type; : Photothermal actuator operating data (photothermal response signal, W / m²); : Thermoelectric converter operating data (power curve, W); : heat flow optimization path sequence, obtained from S4, unit is m / s; : Exponential kernel function, controls the locality of data fusion, is the fusion scale (m), with a typical value of 0.01m; : Euclidean norm of vector difference.

[0050] Based on multi-source data fusion theory, the cross-domain heat flow fusion algorithm integrates photothermal actuator operating data, thermoelectric converter operating data, and heat flow optimization path sequences using an exponential kernel function to detect physical anomalies (microcracks or blockages). The algorithm outputs a visual heat flow report, identifies anomaly locations, and assesses the repair status, guiding self-repair and variable frequency control optimization. Specific embodiment three: like Figure 1-Figure 3 As shown, the following is a detailed hardware composition and hardware description of each module in Example 1: The photon thermal control variable frequency cooling module consists of the following hardware: Photonic crystal material: A periodic optical structure (period 0.5-2 microns) composed of an electrovariable refractive index material (such as liquid crystal, with a refractive index range of 1.5-2.0), covering an area of approximately 0.01 m², deployed on the laser surface. Function: Controls the heat dissipation efficiency of infrared radiation in the 8-14 micron band by dynamically adjusting the band gap (1-4 microns).

[0052] Micro-type heat pipe array: Micro heat pipes with a diameter of 0.5 mm (copper material, working fluid: water or ethanol), arranged in a 10x10 array, are embedded in the laser substrate. A micro pump (frequency 5-30 Hz, power 10-50 W) controls fluid circulation. Function: Transfer heat from the laser surface to an external heat sink.

[0053] Bandgap controller: Embedded microcontroller (ARM Cortex-M4, 200MHz) with integrated D / A converter (12-bit, 1MHz sampling rate) controls the refractive index of the photonic crystal. Function: Receives frequency conversion control instructions and generates bandgap control signals.

[0054] Power sensor: Thermocouple array (range 0-1000W / m², resolution 1W / m²), monitors heat pipe power and flow. Function: Collects operating status data of the micro heat pipe array.

[0055] Communication Interface: Fiber optic transceiver (10Gbps bandwidth, TCP / IP protocol), supporting post-quantum encryption. Function: Communicates with the AI quantum thermal control core module, transmitting operating status data and receiving frequency conversion control instructions.

[0056] Hardware implementation algorithm functions: Photon Radiation Aggregation Algorithm (S3): Run by the bandgap controller, it calculates radiation efficiency deviations based on a heat flux anomaly sequence (input, sparse vector format, approximately 500 bytes / frame). The algorithm reads the photonic crystal operating status data (bandgap and refractive index, stored in internal registers, approximately 100 bytes) from the bandgap controller, calculates the difference between the actual radiation power and the expected power, and generates a radiation efficiency deviation sequence (vector sequence, approximately 1KB / frame). Hardware support: The bandgap controller performs matrix operations (floating point operation capability 1GFLOPS) and the single frame processing time is <5ms.

[0057] The optical fiber transceiver uploads the deviation sequence to the AI quantum thermal control core module through a post-quantum encrypted optical fiber channel (bit error rate <10^-6).

[0058] Frequency Control Execution: The bandgap controller receives the frequency control command (a digital signal, approximately 200 bytes) and generates an analog voltage (0-5V) via a D / A converter, adjusting the photonic crystal bandgap (for example, from 2 microns to 3 microns, with a response time of <1ms). The micropump adjusts its frequency based on the command (for example, from 10Hz to 20Hz), controlled by a PWM signal (frequency 1kHz), enhancing heat transfer efficiency (power increase by 30%).

[0059] Interface and collaboration: Interface: The optical fiber transceiver is connected to the AI quantum thermal control core module to transmit the bandgap control sequence and operating status data; the thermocouple array outputs power data through the I2C interface (rate 400kHz); and the bandgap controller controls the photonic crystal through the SPI interface (rate 10MHz).

[0060] Collaborative Operation: The module receives frequency conversion control commands from the AI quantum thermal control core module, performs bandgap and heat pipe power adjustments, and provides real-time operational status feedback. The output of the photon radiation aggregation algorithm (radiation efficiency deviation sequence) is uploaded via the optical fiber bus to guide the AI quantum thermal control core module in optimizing commands. The module collaborates with the photon thermal sensing module to determine control priorities based on a three-dimensional thermal flow field sequence (including hot spot locations).

[0061] The photon thermal sensing module consists of the following hardware: Single-photon avalanche diode counter: Silicon-based photodetector (sensitivity 100 photons / s, wavelength 8-14 μm), resolution 1 photon / µs. Function: Collects infrared radiation photon data, reflecting the radiation and heat dissipation characteristics of the laser surface.

[0062] Quantum dot infrared detector: InAs quantum dot array (32x32 pixels, wavelength range 8-14 μm, resolution 0.1 W / m²). Function: Collects heat flow quantum state data and characterizes the microscopic quantum behavior of heat flow.

[0063] Heat flow sensor: Thin-film thermopile (range 0-1000 W / m², resolution 1 W / m², response time 1 ms). Function: Collects ambient thermal signals (temperature and humidity).

[0064] Data acquisition unit: FPGA (Xilinx Zynq-7020, 50K logic units, 100MHz frequency), integrated A / D converter (12-bit, 1MHz sampling rate). Function: Process sensor data and generate 3D thermal flow field sequences.

[0065] Communication interface: Fiber optic transceiver (bandwidth 10Gbps, dynamic key encryption). Function: Transmit three-dimensional thermal flow field sequence to the AI quantum thermal control core module.

[0066] Hardware implementation algorithm functions: Quantum heat flow mapping algorithm (S1): Executed by the data acquisition unit (FPGA), it generates a three-dimensional heat flow field sequence based on infrared radiation photon data (time series, approximately 1 MB / s), heat flow quantum state data (quantum state vector, approximately 500 KB / s), and ambient thermal signal data (eigenvector, approximately 100 KB / s). The algorithm leverages the FPGA's parallel computing power (matrix operations, 10 GFLOPS) to perform Gaussian kernel smoothing and quantum thermodynamic model reconstruction.

[0067] The Al quantum thermal control core module consists of the following hardware: Quantum Accelerator Chip: A dedicated ASIC (10 TFLOPS computing power, 7nm process) integrating a quantum simulation unit (simulating quantum tunneling, 100 qubits) and a neural network accelerator (supporting deep learning, 1024 cores). Function: Runs quantum heat flow optimization algorithms, quantum tunneling prediction algorithms, and heat flow ripple prediction algorithms.

[0068] Embedded storage unit: DDR4 memory (1GB capacity, 25GB / s bandwidth) + NAND flash memory (16GB capacity). Function: Stores heat flow data, algorithm models, and historical instructions.

[0069] Main control unit: RISC-V processor (quad-core, 1GHz). Function: Coordinate module tasks and manage data and control flows.

[0070] Communication Interface: Fiber optic transceiver (10Gbps bandwidth, TCP / IP protocol, post-quantum encryption). Function: Communicates with each module, transmitting three-dimensional thermal flow fields, operating status data, frequency conversion control instructions, and repair status reports. Specific embodiment four: like Figure 1-Figure 3 As shown, the following are specific use cases of the entire system: Case 1: Industrial fiber laser (high power cutting application) Scenario: A manufacturing company uses a 10kW fiber laser for sheet metal cutting, operating in continuous high-power mode in an ambient temperature range of 15°C to 35°C and with a humidity of 50%-80%. The laser surface temperature must be controlled within 40±0.1°C to ensure beam quality and device life. The system must withstand high heat loads (heat flux up to 1000W / m²) and environmental fluctuations to verify application feasibility, stability, and reliability.

[0072] Operation process: The photonic thermal sensing module samples infrared radiation photon data (8-14 micron band, 1MB / s), heat flow quantum state data (quantum state vector, 500KB / s) and environmental thermal signal data (eigenvector, 100KB / s) at a period of 20ms, runs the quantum heat flow mapping algorithm, and generates a three-dimensional thermal flow field sequence through FPGA (1KB / frame, grid resolution 0.001m). After detecting a hot spot (local temperature >45°C), it switches to 10ms high-frequency sampling. The data is transmitted to the AI quantum thermal control core module via a high-speed fiber optic bus (10Gbps, dynamic key encryption). The AI quantum thermal control core module's quantum accelerator chip (10 TFLOPS) runs a quantum tunneling prediction algorithm. Based on the three-dimensional heat flow field and operating status data (photonic crystal bandgap and heat pipe power, 200 KB / s), it detects heat flow anomalies (heat flux density > 500 W / m²), generates a heat flow anomaly sequence (sparse vector, 500 bytes / frame), and broadcasts a heat flow summary (100 bytes) via a photon pulse heat flow sharing protocol (1 Mbps). The photonic thermal control variable frequency cooling module's photonic bandgap control engine (ARM Cortex-M4, 200 MHz) runs a photon radiation aggregation algorithm. Based on the heat flow anomaly sequence, it adjusts the photonic crystal bandgap (from 2 microns to 4 microns, with a response time of <1 ms), enhancing infrared radiation cooling efficiency by 20%. It then generates a radiation efficiency deviation sequence (1 KB / frame) and uploads it to the AI quantum thermal control core module. The heat flow path optimization engine runs a heat flow ripple prediction algorithm. Based on the deviation sequence and heat flow interaction records (a time-series directed graph, 500KB / s), it generates a heat flow optimization path sequence (2KB / frame). This generates a quantum heat flow optimization algorithm to calculate a 200-byte frequency conversion control instruction, which is multicast over the fiber optic bus. The photonic thermal control frequency conversion cooling module executes this instruction, adjusting the frequency of the micro-heat pipe array (from 10Hz to 30Hz, increasing power by 30%). The heat dissipation power reaches 1000W, and the laser surface temperature is stabilized at 40±0.1°C. The self-healing thermal flow control module runs a cross-domain thermal flow fusion algorithm based on photothermal actuator operating data (photothermal response signal, 500KB / s), thermoelectric converter operating data (power curve, 100KB / s), and optimized path sequences. It detects microcracks (size 50 microns) and uses laser waste light (>1000 nanometers) to stimulate photosensitive polymer coating repair (repair time 20 seconds, success rate 95%). The thermoelectric converter provides 80W of backup heat dissipation, generates a visual thermal flow report (256x256 pixels, 10KB), and provides feedback on optimization instructions.

[0073] Test results and analysis: Application Feasibility: The system achieves temperature control accuracy of ±0.1°C and a heat dissipation power of 1000W in 10kW high-power mode, meeting the beam quality requirements for metal cutting. Bandgap adjustment (2-4 microns) and increased heat pipe frequency (10-30Hz) effectively handle high heat loads, improving heat dissipation efficiency by 35%.

[0074] Stability: 1000 hours of continuous operation tested (ambient temperature 15°C-35°C, humidity 50%-80%), temperature control error maintained within ±0.1°C with no performance degradation. Adaptive filtering protects against environmental fluctuations, and heat flow anomaly detection response time <20ms.

[0075] Reliability: Microcrack repair success rate is 95%, and backup heat dissipation with thermoelectric converters ensures stable temperature control in the event of a failure. The fiber optic bus (bit error rate <10^-6) is resistant to electromagnetic interference, and vibration testing (10g, 20-2000Hz) verifies hardware reliability.

[0076] Case 2: Medical laser surgery instrument (pulse mode application) Scenario: A hospital uses a 2kW laser surgical instrument for ophthalmic surgery, operating in pulsed mode (pulse frequency 100Hz, single pulse energy 20mJ). The ambient temperature is 20°C ± 5°C, and the laser temperature must be controlled at 37°C ± 0.1°C to ensure surgical safety and accuracy. The system must quickly respond to transient hot spots (with an instantaneous heat flux density of 800W / m²) to verify the adaptability, stability, and reliability of the pulse mode.

[0077] Operation: The photonic thermal sensing module collects infrared radiation photons, heat flux quantum states, and ambient thermal signals at a high sampling rate of 10ms (pulse-triggered interrupts, controlled by an FPGA state machine). It then runs a quantum heat flux mapping algorithm to generate a three-dimensional heat flux field sequence (1KB / frame), detects transient hot spots (temperatures > 40°C), and transmits this data to the AI quantum thermal control core module via a fiber optic bus. A quantum tunneling prediction algorithm analyzes the heat flux field and operating status data, detects heat flux anomalies (>500W / m²), generates a heat flux anomaly sequence, and broadcasts a heat flux summary. The photonic thermal control variable frequency cooling module runs a photon radiation aggregation algorithm to adjust the photonic crystal band gap (from 1.5 microns to 3 microns, with a response time of 5ms), improving radiation efficiency by 15% and generating a deviation sequence. The heat flux ripple prediction algorithm generates an optimized path sequence, and the quantum heat flux mapping algorithm calculates variable frequency control instructions to adjust the heat pipe frequency (from 5Hz to 20Hz), achieving a heat dissipation power of 600W and a stable temperature of 37±0.1°C. The self-repair module detects heat pipe blockage (working fluid condensation), and the photothermal actuator uses waste light to clear it (repair time 15 seconds, success rate 95%). The thermoelectric converter provides 50W of backup heat dissipation and generates a heat flow report for feedback optimization.

[0078] Test results and analysis: Application Feasibility: The system rapidly responds to pulse-mode hot spots (bandgap adjustment time <5ms), with a temperature control accuracy of ±0.1°C, meeting the safety requirements of ophthalmic surgery. Heat dissipation efficiency is increased by 30%, while energy consumption is reduced by 20%.

[0079] Stability: 500 hours of pulse operation test (ambient temperature 15℃-25℃), temperature control error ±0.1℃, high-frequency sampling (100Hz) ensures real-time control of transient hot spots.

[0080] Reliability: 95% blockage repair success rate, redundant heat dissipation maintains stable temperature control. Fiber optic bus and quantum encryption are designed for interference resistance, and shock testing (50g, 11ms) verifies hardware durability.

[0081] Case 3: LiDAR (outdoor communication application) Scenario: An autonomous driving system uses a 500W lidar operating in low-power continuous mode, in an ambient temperature range of -10°C to 50°C and a humidity range of 30% to 90%. The laser temperature must be controlled at 25±0.1°C to ensure signal stability and device lifespan. The system must withstand extreme environments and long-term operation, verifying its stability and reliability for outdoor applications.

[0082] Operational Process: The photonic thermal sensing module samples at 20ms and runs a quantum heat flow mapping algorithm to generate a three-dimensional heat flow field sequence. It detects heat flow anomalies (>300W / m²) in a low-temperature environment (-10°C) and transmits them via a fiber optic bus. A quantum tunneling prediction algorithm generates a heat flow anomaly sequence and broadcasts a heat flow summary. The photonic thermal control variable frequency cooling module runs a photon radiation aggregation algorithm to adjust the band gap (from 1 micron to 2 microns, reducing energy consumption by 30%) and generates a deviation sequence. A heat flow ripple prediction algorithm generates an optimized path sequence. A quantum heat flow optimization algorithm adjusts the heat pipe frequency (from 5Hz to 10Hz), achieving a heat dissipation power of 400W and a stable temperature of 25±0.1°C. The self-repair module detects microcracks in the photonic crystal and repairs them using waste light (repair time 25s, 95% success rate). A thermoelectric converter provides 60W of backup heat dissipation and generates a heat flow report with optimization instructions.

[0083] Test results and analysis: Application feasibility: The system can adapt to environments ranging from -10°C to 50°C, with a temperature control accuracy of ±0.1°C in low-power mode and a 35% increase in heat dissipation efficiency, meeting the LiDAR signal stability requirements.

[0084] Stability: 2000 hours of outdoor operation test (humidity 30%-90%), temperature control error ±0.1°C, adaptive filtering to resist temperature fluctuations.

[0085] Reliability: 95% success rate for microcrack repair, and redundant heat dissipation ensures long-term stable operation. The fiber optic bus is resistant to electromagnetic interference, and environmental testing (-10°C to 50°C) verifies hardware reliability.

[0086] The three case studies cover industrial (10kW high power), medical (2kW pulse) and communications (500W low power) scenarios. The system copes with different heat loads through dynamic bandgap adjustment (1-4 microns) and heat pipe frequency regulation (5-30Hz), with a temperature control accuracy of ±0.1°C, a 30%-35% increase in heat dissipation efficiency, and a 20%-30% reduction in energy consumption. Specific embodiment five: like Figure 1-Figure 3 As shown, the following is the experimental data of the entire system: Case 1: Industrial fiber laser (high power cutting application) Test conditions: 10kW fiber laser, continuous high power operation, ambient temperature 15°C-35°C, humidity 50%-80%, target temperature 40°C, test duration 1000 hours, heat flux density up to 1000W / m²; ;

[0088] Data Analysis: Experimental data verified that the system achieved a temperature control accuracy of ±0.1°C and a heat dissipation power of 1000W in high-power industrial scenarios, with a 35.2% increase in efficiency and a 22.5% reduction in energy consumption. It operated for 1000 hours without any performance degradation, with a 95.3% repair success rate. The system also passed anti-interference and mechanical durability tests, demonstrating its feasibility, stability, and reliability.

[0089] Case 2: Medical laser surgery instrument (pulse mode application) Test conditions: 2kW laser surgical instrument, pulsed operation (frequency 100Hz, single pulse energy 20mJ), ambient temperature 20℃±5℃, target temperature 37℃, test duration 500 hours, transient heat flux density reaching 800W / m²; ;

[0090] Data Analysis: Experimental data shows that the system achieves a temperature control accuracy of ±0.1°C and a heat dissipation power of 600W in pulse mode, with an efficiency improvement of 30.8% and a power reduction of 20.3%. It also operates stably for 500 hours, responds quickly to transient hot spots (5ms), and has a repair success rate of 95.0%. Its anti-interference and mechanical durability have passed testing, verifying its feasibility, stability, and reliability in medical scenarios.

[0091] Case 3: LiDAR (outdoor communication application) Test conditions: 500W LiDAR, low-power continuous operation, ambient temperature -10°C to 50°C, humidity 30%-90%, target temperature 25°C, test duration 2000 hours, heat flux 300W / m²; ;

[0092] Data Analysis: Experimental data verified that the system achieved a temperature control accuracy of ±0.1°C and a heat dissipation power of 400W in outdoor low-power scenarios, increasing efficiency by 34.7% and reducing energy consumption by 28.6%. It also maintained stable operation for 2,000 hours, adapting to extreme environments (-10°C to 50°C, humidity between 30% and 90%), with a repair success rate of 95.5%. Its anti-interference and mechanical durability tests proved its feasibility, stability, and reliability in communication scenarios.

[0093] Attachment Figure 3 This is a simulation diagram of the system, consisting of four sub-graphs: temperature field distribution, average temperature variation over time, band gap and heat pipe frequency control, and heat dissipation efficiency improvement and repair status. This diagram comprehensively demonstrates the system's operational performance under a 10kW industrial laser scenario (target temperature 40°C, ambient temperature 25°C). The temperature field distribution shows that the laser surface temperature ranges from 35°C to 45°C, with a central region of approximately 42°C, exhibiting a smooth Gaussian distribution. This reflects the three-dimensional heat flow field generated by the photonic thermal sensing module (S1) using a quantum heat flow mapping algorithm, accurately capturing the hot spot region (>45°C), consistent with the proposed fusion of infrared radiation photon and heat flow quantum state data. The average temperature variation over time graph shows that the temperature rapidly approaches 40°C from an initial 39°C within 200ms, then fluctuates around the target temperature. This demonstrates the ability of the AI quantum thermal control core modules (S2-S4) to generate variable frequency control commands through quantum tunneling prediction algorithms and heat flow path optimization, driving the real-time response of the heat dissipation module. The bandgap and heat pipe frequency control diagram shows a rapid increase in the photonic crystal bandgap from 1 micron to approximately 4 microns, while the heat pipe frequency increases from 5Hz to 30Hz. This demonstrates the dynamic adjustment of the bandgap and heat pipe power by the photonic thermal control variable frequency cooling module (S3) based on the sequence of heat flux anomalies. This is consistent with the design of the photon radiation aggregation algorithm to improve infrared radiation efficiency, and the control curve clearly responds to hot spot triggering. The heat dissipation efficiency improvement and repair status diagram shows an increase in efficiency from 0% to approximately 40%, meeting the 30%-40% improvement target in the scheme, validating the effectiveness of the heat flow ripple prediction algorithm (S4) in optimizing the heat flow path. The repair state is triggered twice (marked with *) at 500ms and 1500ms, consistent with the function of the self-repairing heat flow control module (S5) using a cross-domain heat flow fusion algorithm to detect microcracks / blockages and activate the photothermal actuator. The sparse triggering points reflect the high success rate (95.3%) of the repair mechanism.

[0094] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0095] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation module, including a temperature control system, characterized by: The temperature control system includes a photon thermal control variable frequency heat dissipation module, a photon thermal sensing module, an Al quantum thermal control core module and a self-repairing heat flow control module. The photon thermal control variable frequency heat dissipation module is deployed with a photon bandgap control engine, the photon thermal sensing module is deployed with a photon thermal flow acquisition engine, the Al quantum thermal control core module is deployed with a heat flow topology analysis engine and a heat flow path optimization engine, and the self-repairing heat flow control module is deployed with a photothermal collaborative repair engine. The photon thermal control variable frequency heat dissipation module, the photon thermal sensing module, the Al quantum thermal control core module and the self-repairing heat flow control module form a closed-loop heat flow ecosystem through hierarchical control flow to achieve real-time temperature control and dynamic heat flow optimization of laser equipment.

2. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 1 is characterized by: The photonic thermal control variable frequency heat dissipation module integrates photonic crystal materials and micro heat pipe arrays and is deployed on the surface of the laser to control infrared radiation heat dissipation through the dynamic refractive index of the photonic crystal material, and combines with the micro heat pipe array to achieve heat transfer. It collects photonic crystal operating status data and micro heat pipe array operating status data to perform local variable frequency control, wherein the photonic crystal operating status data is a digital signal describing the photonic crystal band gap and refractive index, and the micro heat pipe array operating status data is a digital signal describing the heat pipe power and heat flow. The photon thermal sensing module integrates a photon counter, a quantum dot infrared detector, and a heat flow sensor to collect infrared radiation photons and heat flow quantum states from the laser surface to generate a three-dimensional heat flow field. The infrared radiation photons are photons emitted in the 8-14 micron band from the laser surface, and the heat flow quantum states are vectors that describe the microscopic quantum behavior of heat flow. The three-dimensional heat flow field includes a spatial vector field of temperature gradient, heat flux density, and hot spot position. The AI quantum thermal control core module integrates a quantum acceleration chip and an embedded storage unit to run an AI algorithm to analyze three-dimensional thermal flow field data, photonic crystal operating status data of the photonic thermal control variable frequency heat dissipation module, and micro heat pipe array operating status data, as well as the repair status report of the self-repair thermal flow control module, and generate a variable frequency control instruction. The AI algorithm includes a quantum thermal flow optimization algorithm, and the variable frequency control instruction is a digital signal generated by analyzing the heat flow distribution and operating status through the quantum thermal flow optimization algorithm and used to adjust the photonic crystal band gap and the micro heat pipe array power. The self-repairing heat flux control module integrates a photothermal actuator and a thermoelectric converter. The photothermal actuator receives laser waste light to stimulate a photothermal-responsive nanocoating, repairing microcracks and blockages in the photon thermal control variable frequency heat dissipation module. The module also uses laser waste light to drive thermoelectric conversion to provide backup heat dissipation and generate a repair status report. The photothermal-responsive nanocoating is a polymer material that self-repairs under photothermal stimulation. Laser waste light is defined as ineffective light with a wavelength greater than 1000 nanometers. The repair status report is structured data describing the repair progress and backup heat dissipation power. The photon thermal control variable frequency heat dissipation module, photon thermal perception module, AI quantum thermal control core module and self-repair heat flow control module operate collaboratively through hierarchical control flow. The photon thermal perception module prioritizes processing the hot spot data in the three-dimensional heat flow field. The AI quantum thermal control core module integrates the three-dimensional heat flow field data, photonic crystal operation status data, micro heat pipe array operation status data and repair status report to dynamically adjust the heat dissipation strategy.

3. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 2 is characterized by: The temperature control system achieves its goal through the following steps: S1: The photon heat flow acquisition engine deployed in the photon thermal sensing module collects infrared radiation photon data, heat flow quantum state data and environmental thermal signal data, and runs the quantum heat flow mapping algorithm to generate a three-dimensional thermal flow field sequence; the photon heat flow acquisition engine uses infrared radiation photon data, heat flow quantum state data and environmental thermal signal data, where the environmental thermal signal data is the analog signal data of the ambient temperature and humidity; the infrared radiation photon data is stored in a ring buffer in a time series format, the heat flow quantum state data is parsed in a quantum state vector format, and the environmental thermal signal data is adaptively filtered to generate a feature vector; the quantum heat flow mapping algorithm takes infrared radiation photon data, heat flow quantum state data and environmental thermal signal data as input, reconstructs the heat distribution on the surface and inside of the laser through a quantum thermodynamic model, and outputs a three-dimensional thermal flow field sequence; the three-dimensional thermal flow field sequence is transmitted to the Al quantum thermal control core module via a high-speed optical fiber bus and is encrypted using a dynamic key; the photon heat flow acquisition engine operates in state machine mode, sampling every 20 milliseconds, and switches to 10 milliseconds high-frequency sampling and jumps to S2 when a hot spot is detected; S2: The heat flow topology analysis engine deployed in the Al quantum thermal control core module collects the photonic crystal operating status data and the micro heat pipe array operating status data of the photon thermal control variable frequency cooling module, as well as the three-dimensional heat flow field sequence of S1, runs the quantum tunneling prediction algorithm to generate a heat flow anomaly sequence, and broadcasts the heat flow summary in combination with the photon pulse heat flow sharing protocol; the heat flow topology analysis engine uses the photonic crystal operating status data, the micro heat pipe array operating status data and the three-dimensional heat flow field sequence, where the photonic crystal operating status data and the micro heat pipe array operating status data are stored in a key-value pair format, and the three-dimensional heat flow field sequence is generated. The columns are parsed in sparse matrix format; the quantum tunneling prediction algorithm takes the photonic crystal operating status data, the micro heat pipe array operating status data and the three-dimensional heat flow field sequence as input, detects heat flow anomalies by simulating the heat flow quantum tunneling behavior, and outputs a heat flow anomaly sequence, where the heat flow anomaly sequence is defined as a vector sequence describing the heat flow density exceeding the preset threshold; the heat flow summary is defined as the compressed heat flow anomaly description data, which is broadcast to the self-repair heat flow control module in pulse coding format through the photonic communication unit; the heat flow topology analysis engine runs in event-driven mode, prioritizes heat flow mutations, and jumps to S3 after confirming the heat flow anomaly; S3: Generate a photonic crystal bandgap control sequence through the photonic bandgap control engine deployed in the photonic thermal control variable frequency heat dissipation module, and run the photon radiation aggregation algorithm to detect the radiation efficiency deviation sequence; the photonic bandgap control engine uses the heat flow anomaly sequence of S2, where the heat flow anomaly sequence is stored in a vector sequence format; the photon radiation aggregation algorithm takes the heat flow anomaly sequence as input, detects the heat dissipation efficiency deviation by aggregating the photonic crystal radiation offset data, and outputs a radiation efficiency deviation sequence, where the photonic crystal bandgap control sequence is defined as a digital sequence for controlling the refractive index of the photonic crystal, and the radiation efficiency deviation sequence is a vector sequence of the difference between the actual radiation heat dissipation power and the expected power; the photonic crystal bandgap control sequence is stored in a time window encoding format, and the radiation efficiency deviation sequence is analyzed in a vector sequence format; the radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module through a post-quantum encrypted optical fiber channel; The photonic bandgap control engine runs the photon radiation aggregation algorithm at a fixed cycle of 50 milliseconds. When an abnormal deviation in radiation efficiency is detected, the low-priority heat dissipation task is suspended and the process jumps to S4. S4: The heat flow path optimization engine deployed in the Al quantum thermal control core module collects the heat flow interaction records from the micro heat pipe array to the photonic crystal, runs the heat flow ripple prediction algorithm based on the radiation efficiency deviation sequence of S3 to generate a heat flow optimization path sequence, and triggers the frequency conversion control instruction; the heat flow path optimization engine uses the heat flow interaction record and the radiation efficiency deviation sequence, wherein the heat flow interaction record is the time series data describing the direction and intensity of the heat flow, and is stored in the time series directed graph format; the heat flow ripple prediction algorithm takes the heat flow interaction record and the radiation efficiency deviation sequence as input, optimizes the heat flow transfer path by simulating the heat flow path ripple diffusion, and outputs the heat flow optimization path sequence, wherein the heat flow optimization path sequence is defined as the optimized heat flow transfer path vector; the frequency conversion control instruction is multicast to the photonic thermal control frequency conversion cooling module through the high-speed optical fiber bus; the heat flow path optimization engine runs in asynchronous mode and jumps to S5 when the prediction fails; S5: The photothermal collaborative repair engine deployed in the self-repair thermal flow control module collects the operating data of the photothermal actuator and the thermoelectric converter. Combined with the thermal flow optimization path sequence of S4, the cross-domain thermal flow fusion algorithm is used to generate a visual thermal flow report. The photothermal collaborative repair engine utilizes photothermal actuator operating data, thermoelectric converter operating data, and a heat flow optimization path sequence. The photothermal actuator operating data is defined as the photothermal response signal data of the repair coating, stored in a spectrum sequence format, and the thermoelectric converter operating data is defined as the backup heat dissipation power curve data, stored in a power curve format. The cross-domain heat flow fusion algorithm uses the photothermal actuator operating data, thermoelectric converter operating data, and a heat flow optimization path sequence as input, detects microcracks and blockages by fusing heat flow and physical anomaly data, and outputs a visual heat flow report. The visualized heat flow report is transmitted to the Al quantum thermal control core module via a high-speed fiber optic bus in a compressed vector format; the photothermal collaborative repair engine runs in a multi-threaded pipeline mode, triggering repair instructions and terminating the process after confirming physical anomalies.

4. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3 is characterized by: The quantum heat flow mapping algorithm in S1 takes infrared radiation photon data, heat flow quantum state data and environmental thermal signal data as input, generates a three-dimensional heat flow field sequence through a quantum thermodynamic model, and drives the quantum tunneling prediction algorithm in S2 to analyze heat flow anomalies; the specific processing of the quantum heat flow mapping algorithm is as follows: using infrared radiation photon data to extract the radiation heat flow characteristics of the laser surface, using heat flow quantum state data to evaluate the microscopic quantum behavior of the heat flow, using environmental thermal signal data to calibrate the environmental impact, reconstructing the three-dimensional heat flow field through the quantum thermodynamic model, and outputting a three-dimensional heat flow field sequence; the three-dimensional heat flow field sequence is stored in a fixed-length vector encoding format; the three-dimensional heat flow field sequence is transmitted to the heat flow topology analysis engine via a high-speed optical fiber bus using dynamic key encryption; the quantum heat flow mapping algorithm operates in state machine mode, switches to high-frequency sampling when a hot spot is detected, and notifies S2.

5. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3 is characterized by: The heat flow topology analysis engine in S2 is composed of an operation status acquisition unit, a heat flow data parser, a photon communication interface unit and a heat flow summary generation unit. The operation mode is as follows: the operation status acquisition unit analyzes the photonic crystal band gap state and the micro heat pipe array power of the photon thermal control variable frequency heat dissipation module to generate operation status data; the heat flow data parser takes the three-dimensional heat flow field sequence and the operation status data of S1 as input, runs the quantum tunneling prediction algorithm to calculate the heat flow anomaly offset; the heat flow summary generation unit compresses the heat flow anomaly offset data into a heat flow summary of a fixed length, and then stores the operation status data and the three-dimensional heat flow field sequence in a low-rank matrix format; The heat flow summary is transmitted in pulse-coded format via the photonic communication interface unit to the heat flow path optimization engine of S4 and the photothermal coordinated repair engine of S5; The heat flow topology analysis engine runs in event loop mode, and triggers S3 when a heat flow anomaly is detected. The quantum tunneling prediction algorithm in S2 takes the operating status data and the three-dimensional heat flow field sequence as input, generates a heat flow anomaly sequence by simulating the heat flow quantum tunneling behavior, and drives the photon radiation aggregation algorithm in S3 to detect the radiation efficiency deviation; the specific processing of the quantum tunneling prediction algorithm is: extracting the photonic crystal band gap state and the micro heat pipe array power with the operating status data, evaluating the heat flux density distribution with the three-dimensional heat flow field sequence, predicting the heat flow anomaly offset through the quantum tunneling model, and outputting the heat flow anomaly sequence; the heat flow anomaly sequence is stored as a sparse vector; the heat flow anomaly sequence is encrypted through a high-speed optical fiber bus Transmitted to the photonic bandgap control engine; the quantum tunneling prediction algorithm operates in a feedback loop mode and dynamically adjusts the prediction parameters. The photon pulse heat flow sharing protocol in S2 consists of a pulse coding unit, a photon emission unit, a quantum receiving unit, and an error checking unit. The working method is as follows: the pulse coding unit maps the heat flow summary into an interval coding sequence, the photon emission unit sends the interval coding sequence, the quantum receiving unit decodes the interval coding sequence, and the error checking unit verifies the integrity of the decoded data; the heat flow summary is stored in a sparse vector format; the photon pulse heat flow sharing protocol transmits the heat flow summary through the photon communication interface unit, supporting the frequency conversion control instruction generation of S4 and the physical anomaly detection of S5; The photon pulse heat flux sharing protocol operates in a token scheduling mode, giving priority to transmitting high-risk heat flux summaries.

6. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3 is characterized by: The photon radiation aggregation algorithm in S3 takes the heat flux anomaly sequence as input, generates a radiation efficiency deviation sequence by aggregating the photonic crystal radiation offset data, and drives the heat flux ripple prediction algorithm in S4 to generate a heat flux optimization path sequence; the specific processing method of the photon radiation aggregation algorithm is as follows: extract the heat flux density abnormal area with the heat flux anomaly sequence, evaluate the radiation heat dissipation efficiency with the photonic crystal radiation offset data, calculate the radiation efficiency deviation through the aggregation model, and output the radiation efficiency deviation sequence; the radiation efficiency deviation sequence is stored in a high-dimensional tensor encoding format; The radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module through a post-quantum encrypted optical fiber channel; the photon radiation aggregation algorithm runs in a pipeline mode, notifying S4 to perform heat flow path optimization. The photon bandgap control engine in S3 consists of a bandgap sequence generation unit, a radiation efficiency analyzer, an encrypted communication interface unit and an abnormality storage unit. The working method is: the bandgap sequence generation unit parses the heat flow abnormality sequence to generate a photonic crystal bandgap control sequence, the radiation efficiency analyzer takes the heat flow abnormality sequence as input, runs the photon radiation aggregation algorithm to calculate the radiation efficiency deviation sequence, the encrypted communication interface unit uploads the radiation efficiency deviation sequence, and the abnormality storage unit caches the historical radiation efficiency deviation sequence; the radiation efficiency deviation sequence is stored in a sliding window format; the radiation efficiency deviation sequence is uploaded to the Al quantum thermal control core module in a fragmented format through a post-quantum encrypted optical fiber channel; the photon bandgap control engine drives the photon radiation aggregation algorithm with a timer, and triggers S4 when a radiation efficiency deviation abnormality is detected.

7. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3, characterized in that: The heat flow path optimization engine in S4 is composed of a heat flow interaction acquisition unit, a heat flow ripple predictor, a frequency conversion control unit and a path cache unit. The working method is as follows: the heat flow interaction acquisition unit analyzes the heat flow interaction record from the micro heat pipe array to the photonic crystal, the heat flow ripple predictor takes the radiation efficiency deviation sequence and the heat flow interaction record of S3 as input, runs the heat flow ripple prediction algorithm to generate a heat flow optimization path sequence, the frequency conversion control unit sends a frequency conversion control instruction to the photonic thermal control frequency conversion heat dissipation module, and the path cache unit stores the historical heat flow optimization path sequence; the heat flow interaction record and the radiation efficiency deviation sequence are stored in a sparse matrix format; The heat flow optimized path sequence is multicast to the S5’s optical-thermal collaborative repair engine via a high-speed fiber bus; The heat flow path optimization engine runs in asynchronous mode and triggers S5 when the prediction fails.

8. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3, characterized in that: The heat flow ripple prediction algorithm in S4 takes the heat flow interaction record and the radiation efficiency deviation sequence as input, generates the heat flow optimization path sequence by simulating the heat flow path ripple diffusion, and drives the cross-domain heat flow fusion algorithm in S5 to detect physical anomalies; the specific processing method of the heat flow ripple prediction algorithm is: extracting the heat flow direction and intensity with the heat flow interaction record, evaluating the heat dissipation efficiency bottleneck with the radiation efficiency deviation sequence, optimizing the heat flow transfer path through the ripple diffusion model, and outputting the heat flow optimization path sequence; the heat flow optimization path sequence is stored in a time-sliced format; the heat flow optimization path sequence is multicast to the optical-thermal collaborative repair engine via a high-speed optical fiber bus using dynamic key encryption; the heat flow ripple prediction algorithm operates in a feedback loop mode and dynamically adjusts the prediction parameters.

9. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3, characterized in that: The photothermal collaborative repair engine in S5 is composed of a photothermal actuator acquisition unit, a thermoelectric converter acquisition unit, a cross-domain thermal flow fusion reasoning core, and a report generation unit. The working method is as follows: the photothermal actuator acquisition unit parses the photothermal actuator operation data, the thermoelectric converter acquisition unit extracts the thermoelectric converter operation data, the cross-domain thermal flow fusion reasoning core uses the thermal flow optimization path sequence, photothermal actuator operation data, and thermoelectric converter operation data of S4 as input, runs the cross-domain thermal flow fusion algorithm to generate a visual thermal flow report, and the report generation unit generates a visual thermal flow report. Photothermal actuator operating data, thermoelectric converter operating data, and heat flow optimization path sequence are stored in a hierarchical cache format, with high-risk abnormal data being processed first; The visual heat flow report is transmitted to the Al quantum thermal control core module via a high-speed fiber optic bus; the photothermal collaborative repair engine runs in a multi-threaded pipeline mode and triggers repair instructions after confirming physical anomalies.

10. The AI algorithm-driven variable frequency temperature control system for laser equipment heat dissipation modules according to claim 3, characterized in that: The system includes the following hardware to support the coordinated operation of S1 to S5: An integrated sensing hardware module, comprising a single-photon avalanche diode counter, a quantum dot infrared detector, a heat flow sensor, and a photon sequence generator, configured to collect infrared radiation photons, heat flow quantum states, ambient thermal signals, and photon pulses; An edge computing hardware unit, including a quantum acceleration chip, configured to run a quantum heat flow mapping algorithm, a quantum tunneling prediction algorithm, and a cross-domain heat flow fusion algorithm; a photonic communication hardware module comprising a photon transmitter and a quantum receiver configured to execute a photon pulse heat flow sharing protocol; Perception data is stored in a hierarchical cache format, prioritizing signals containing hot spots. The hardware is interconnected via a high-speed fiber optic bus, transmitting perception data encrypted using quantum states. Edge computing hardware units operate in a master-slave architecture, coordinating sensor and communication tasks.

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