A LED operation shadowless lamp circuit control method and system

Abstract

The application provides an LED surgical shadowless lamp circuit control method and system, and belongs to the technical field of medical equipment circuit control.The method comprises the following steps: acquiring state data of each key node in an LED surgical shadowless lamp circuit, deeply analyzing the acquired circuit node state data, and constructing a circuit topology model of the LED surgical shadowless lamp by using a graph theory algorithm; according to the constructed circuit topology model, the circuit architecture of the LED surgical shadowless lamp is integrated and optimized; by the steps of constructing the circuit topology model, extracting electrical characteristic data, and simulating circuit transmission, the working state of the circuit can be deeply understood, intelligent and accurate control of the lighting parameters of the LED surgical shadowless lamp is realized, and the diversified needs of different surgical scenes are met.

Description

Technical Field

[0001] This invention proposes a circuit control method and system for LED surgical shadowless lamps, belonging to the field of medical equipment circuit control technology. Background Technology

[0002] During surgery, LED surgical shadowless lamps are crucial for providing surgeons with a clear, uniform, and shadow-free lighting environment. However, traditional LED surgical shadowless lamp circuit control methods are often relatively simple, making it difficult to flexibly and precisely adjust lighting parameters, such as brightness and color temperature, according to different surgical scenarios and needs. This results in suboptimal lighting effects, potentially affecting the surgeon's operation and judgment, and even posing potential risks to the patient's surgical safety. Therefore, an innovative LED surgical shadowless lamp circuit control method is needed to solve these problems. Summary of the Invention

[0003] This invention provides a method and system for controlling the circuit of an LED surgical shadowless lamp, in order to solve the problems mentioned in the background art above:

[0004] This invention proposes a circuit control method for an LED surgical shadowless lamp, the method comprising:

[0005] S1. Obtain the state data of each key node in the LED surgical shadowless lamp circuit, perform in-depth analysis on the obtained circuit node state data, and use graph theory algorithms to construct the circuit topology model of the LED surgical shadowless lamp; based on the constructed circuit topology model, integrate and optimize the circuit architecture of the LED surgical shadowless lamp.

[0006] S2. Extract the electrical features of the LED surgical shadowless lamp circuit node status data; obtain the electrical feature data of the LED surgical shadowless lamp; based on the extracted electrical feature data, perform unstructured division of the lighting data generated by the LED surgical shadowless lamp; according to the characteristics of the lighting data in the frequency domain, time domain, and spatial domain, divide it into different types, and further subdivide each type of lighting data to obtain video simulation lighting data and static image lighting data; for the video simulation lighting data and static image lighting data, calculate its minimum data transmission volume to obtain the unstructured minimum lighting data;

[0007] S3. Using the optimized LED surgical shadowless lamp circuit architecture, millimeter-wave transmission simulation is performed on the circuit signal containing unstructured minimum lighting data; the transmission of the simulated signal on different circuit paths is obtained to acquire equipment circuit communication transmission simulation data; based on the equipment circuit communication transmission simulation data, cluster analysis is used to classify the signal transmission range levels; the signal transmission range is divided into multiple levels to obtain signal transmission level range data.

[0008] S4. Obtain the circuit communication requirement data of the LED surgical shadowless lamp. Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, evaluate the flow margin of the circuit communication requirement data to obtain communication requirement flow evaluation data. Combine the unstructured minimum transmission data and the communication requirement flow evaluation data, use the optimization algorithm to calculate the minimum transmission flow of the communication requirement to obtain optimized requirement transmission flow data.

[0009] S5. Based on the signal transmission level range data, classify the communication requirement data of the LED surgical shadowless lamp circuit communication requirement data into communication requirement transmission levels; map different communication requirements to the corresponding signal transmission levels to obtain communication requirement transmission level data; based on the device circuit communication transmission simulation data, integrate the LED surgical shadowless lamp circuit resources with the communication requirement transmission level data and the optimized requirement transmission flow data; obtain the device circuit communication transmission strategy; transmit the generated device circuit communication transmission strategy to the LED surgical shadowless lamp circuit control platform.

[0010] The present invention proposes an LED surgical shadowless lamp circuit control system, comprising:

[0011] One or more processors;

[0012] Memory, used to store one or more programs.

[0013] Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above.

[0014] The beneficial effects of this invention are as follows: By constructing a circuit topology model, extracting electrical characteristic data, and simulating circuit transmission, a deep understanding of the circuit's operating state can be achieved, enabling intelligent and precise control of the LED surgical shadowless lamp's illumination parameters to meet the diverse needs of different surgical scenarios. By classifying signal transmission levels, assessing communication requirements, and calculating and optimizing transmission traffic, circuit resources can be rationally allocated, improving circuit transmission efficiency, reducing energy consumption, and extending equipment lifespan. This method can dynamically adjust illumination parameters according to the actual surgical situation, providing doctors with a clear, uniform, and shadow-free lighting environment, which helps improve the accuracy and safety of surgical operations and increase the success rate of surgery. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the steps of the method described in this invention;

[0016] Figure 2 As described in this invention Figure 1 Detailed steps for S2 are shown in the diagram. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] One embodiment of the present invention, such as Figure 1 As shown, a method for controlling the circuit of an LED surgical shadowless lamp includes:

[0019] S1. Obtain the state data of each key node in the LED surgical shadowless lamp circuit, perform in-depth analysis on the obtained circuit node state data, and use graph theory algorithms to construct the circuit topology model of the LED surgical shadowless lamp; based on the constructed circuit topology model, integrate and optimize the circuit architecture of the LED surgical shadowless lamp.

[0020] S2. Extract the electrical features of the LED surgical shadowless lamp circuit node status data; obtain the electrical feature data of the LED surgical shadowless lamp; based on the extracted electrical feature data, perform unstructured division of the lighting data generated by the LED surgical shadowless lamp; according to the characteristics of the lighting data in the frequency domain, time domain, and spatial domain, divide it into different types, and further subdivide each type of lighting data to obtain video simulation lighting data and static image lighting data; for the video simulation lighting data and static image lighting data, calculate its minimum data transmission volume to obtain the unstructured minimum lighting data;

[0021] S3. Using the optimized LED surgical shadowless lamp circuit architecture, millimeter-wave transmission simulation is performed on the circuit signal containing unstructured minimum lighting data; the transmission of the simulated signal on different circuit paths is obtained to acquire equipment circuit communication transmission simulation data; based on the equipment circuit communication transmission simulation data, cluster analysis is used to classify the signal transmission range levels; the signal transmission range is divided into multiple levels to obtain signal transmission level range data.

[0022] S4. Obtain the circuit communication requirement data of the LED surgical shadowless lamp. Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, evaluate the flow margin of the circuit communication requirement data to obtain communication requirement flow evaluation data. Combine the unstructured minimum transmission data and the communication requirement flow evaluation data, use the optimization algorithm to calculate the minimum transmission flow of the communication requirement to obtain optimized requirement transmission flow data.

[0023] S5. Based on the signal transmission level range data, classify the communication requirement data of the LED surgical shadowless lamp circuit communication requirement data into communication requirement transmission levels; map different communication requirements to the corresponding signal transmission levels to obtain communication requirement transmission level data; based on the device circuit communication transmission simulation data, integrate the LED surgical shadowless lamp circuit resources with the communication requirement transmission level data and the optimized requirement transmission flow data; obtain the device circuit communication transmission strategy; transmit the generated device circuit communication transmission strategy to the LED surgical shadowless lamp circuit control platform.

[0024] The working principle of the above technical solution is as follows: Acquire the state data of each key node in the LED surgical shadowless lamp circuit, including LED bead connection points, power input / output points, and control signal transmission points; the state data covers parameters such as voltage values, current values, and signal strength; perform in-depth analysis of the acquired circuit node state data, and use graph theory algorithms to construct a circuit topology model of the LED surgical shadowless lamp; this model can clearly present the connection relationships between each node in the circuit, signal flow direction, and energy transmission path, providing a basic architecture for subsequent circuit control; based on the constructed circuit topology model, integrate and optimize the circuit architecture of the LED surgical shadowless lamp; analyze possible redundant links, signal interference points, and energy loss areas in the circuit, and obtain a more efficient and stable LED surgical shadowless lamp circuit architecture by adjusting the layout of circuit components and optimizing the line connection method;

[0025] Electrical features of LED surgical shadowless lamp circuit nodes are extracted. By analyzing the voltage and current variation curves over time, characteristic parameters reflecting the circuit's operating state, such as voltage fluctuation range, current peak value, and signal frequency, are extracted to obtain the electrical feature data of the LED surgical shadowless lamp. Based on the extracted electrical feature data, the lighting data generated by the LED surgical shadowless lamp is unstructured and divided. According to the characteristics of the lighting data in the frequency, time, and spatial domains, it is divided into different types, such as basic lighting data and special effect lighting data (e.g., focused lighting data used to highlight specific surgical areas). Furthermore, each type of lighting data is further subdivided to obtain video simulation lighting data (simulating continuously changing lighting scenes) and static image lighting data (corresponding to fixed lighting states). For both video simulation lighting data and static image lighting data, the minimum data transmission volume is calculated to obtain unstructured minimum lighting data. This data can accurately express lighting information in the most concise form, providing efficient data support for subsequent circuit transmission and control.

[0026] Based on the optimized circuit architecture of the LED surgical shadowless lamp, millimeter-wave transmission simulation was performed on the circuit signals containing unstructured minimum illumination data. Considering the propagation characteristics of millimeter waves in the circuit, such as attenuation, reflection, and diffraction, the signal transmission along different circuit paths was simulated to obtain simulation data of device circuit communication transmission. This data includes parameters such as signal strength variation, transmission delay, and bit error rate. Based on the simulation data, cluster analysis was used to classify the signal transmission range into levels, such as short-range high-intensity transmission, medium-range medium-intensity transmission, and long-range low-intensity transmission, thus obtaining signal transmission level range data. Different levels correspond to different circuit operating parameters and transmission strategies to meet the illumination needs of different surgical scenarios.

[0027] This process involves acquiring circuit communication requirement data for LED surgical shadowless lamps. This data includes real-time requirements for different types of lighting during surgery and the frequency of lighting parameter adjustments. Based on the electrical characteristics of the LED surgical shadowless lamps and unstructured minimum lighting data, the flow margin of the circuit communication requirement data is assessed. The difference between the maximum data flow required to meet communication requirements and the actual available data flow under the current circuit conditions is analyzed to obtain communication requirement flow assessment data. Combining the unstructured minimum transmission data and the communication requirement flow assessment data, an optimization algorithm is used to calculate the minimum transmission flow for communication requirements. This calculation process comprehensively considers factors such as lighting effect, circuit transmission capacity, and energy consumption, aiming to find the minimum transmission flow that achieves the lowest energy consumption and highest transmission efficiency while meeting surgical lighting requirements, thereby obtaining optimized requirement transmission flow data.

[0028] Based on signal transmission level range data, the communication requirements of the LED surgical shadowless lamp circuit are classified into communication requirement transmission levels. Different communication requirements are mapped to corresponding signal transmission levels. For example, special effects lighting data with high real-time requirements and large data volume are classified into high-priority signal transmission levels, while basic lighting data is classified into relatively low-priority signal transmission levels, thus obtaining communication requirement transmission level data. Based on the device circuit communication transmission simulation data, the LED surgical shadowless lamp circuit resources are integrated based on the communication requirement transmission level data and the optimized requirement transmission flow data. Taking into account factors such as circuit transmission capacity, component performance, and energy consumption, circuit resources are rationally allocated, such as adjusting power output and optimizing signal transmission paths, thus obtaining the device circuit communication transmission strategy. The generated device circuit communication transmission strategy is transmitted to the LED surgical shadowless lamp circuit control platform. This platform adjusts circuit parameters in real time according to the strategy, controlling the brightness and color temperature of LED beads to execute precise communication control tasks and provide the best lighting environment for surgery.

[0029] The above technical solution achieves the following effects: by optimizing the circuit architecture of the LED surgical shadowless lamp, redundant components, signal interference, and energy loss are reduced, thereby improving the overall stability and energy efficiency of the circuit. Adopting an unstructured, minimal lighting data format reduces the amount of data transmitted, thus lowering energy consumption during lighting data transmission and improving transmission efficiency.

[0030] By simulating millimeter-wave transmission and classifying signal transmission range levels, appropriate signal transmission levels can be selected according to different transmission distances and requirements, improving the stability and reliability of signal transmission. Based on the real-time changing lighting needs during surgery, circuit parameters can be precisely adjusted to achieve more accurate lighting control, enhancing the lighting precision and real-time response capability in the surgical environment.

[0031] By optimizing algorithms to calculate minimum transmission traffic and comprehensively considering factors such as lighting requirements, circuit transmission capacity, and energy consumption, unnecessary data transmission is reduced, communication traffic is lowered, and system performance is optimized. Through the rational integration of circuit resources, power output and signal transmission paths are optimized, effectively improving system resource utilization.

[0032] By precisely controlling parameters such as the brightness and color temperature of the LED beads, an optimal lighting environment is ensured during surgery, improving surgical precision and safety. This method can automatically adjust lighting data and circuit control strategies according to different surgical scenarios, enhancing the system's adaptability to various surgical environments.

[0033] In one embodiment of the present invention, S1 includes:

[0034] S11. Identify each key node in the LED surgical shadowless lamp circuit, and use a high-precision voltage measuring instrument, current measuring instrument, and signal strength detector to collect the parameters of each key node in real time.

[0035] S12. Perform in-depth analysis on the collected circuit node state data, and use the adjacency matrix method or incidence matrix method in graph theory to abstract the nodes and connecting lines in the circuit into vertices and edges in graph theory.

[0036] S13. Based on the connection relationships between nodes, signal flow direction, and energy transmission path, construct the circuit topology model of the LED surgical shadowless lamp;

[0037] S14. Based on the constructed circuit topology model, conduct a comprehensive analysis of the circuit architecture of the LED surgical shadowless lamp; use circuit analysis software to identify possible redundant links in the circuit, locate signal interference points, analyze the causes and propagation paths of interference; determine the energy loss area, and assess the degree of energy loss and influencing factors.

[0038] S15. Based on the analysis results, optimize the measures to obtain a stable LED surgical shadowless lamp circuit architecture.

[0039] The working principle of the above technical solution is as follows: identify each key node in the LED surgical shadowless lamp circuit, including LED lamp bead connection points, power input and output points, control signal transmission points, etc.; use high-precision voltage measuring instruments, current measuring instruments, and signal strength detectors to collect parameters such as voltage values, current values, and signal strength of each key node in real time.

[0040] The collected circuit node state data is analyzed in depth, and the adjacency matrix method or incidence matrix method in graph theory are used to abstract the nodes and connecting lines in the circuit into vertices and edges in graph theory.

[0041] Based on the connection relationships between nodes, signal flow direction, and energy transmission path, a circuit topology model of the LED surgical shadowless lamp is constructed. This model is presented in a visual graphical way, which can clearly show the overall architecture of the circuit and the interrelationship between its parts, providing an intuitive basic architecture for subsequent circuit control.

[0042] Based on the constructed circuit topology model, a comprehensive analysis of the circuit architecture of the LED surgical shadowless lamp is conducted. Using circuit analysis software, potential redundant links in the circuit are identified, such as redundant line connections and duplicate component functions. Signal interference points are located, and the causes and propagation paths of interference are analyzed. Energy loss areas are determined, and the degree and influencing factors of energy loss are assessed.

[0043] Based on the analysis results, optimization measures were implemented; for example, adjusting the layout of circuit components to make the connections between components more compact and reasonable, reducing line length and signal transmission distance; optimizing line connection methods by using shielded wires, twisted pairs, and other lines with strong anti-interference capabilities to reduce signal interference; and replacing components with low-energy-consumption, high-efficiency ones to improve the overall performance of the circuit. Through these methods, a more efficient and stable LED surgical shadowless lamp circuit architecture was obtained.

[0044] The above technical solution achieves the following results: By deeply analyzing each key node, optimizing the circuit topology and signal transmission path, redundant components and signal interference are reduced, thereby improving the overall circuit stability and operating efficiency. By identifying and optimizing energy loss areas in the circuit and using high-efficiency, low-energy-consumption components, energy waste is reduced and power utilization is improved.

[0045] The wiring connections were optimized by using shielded cables and twisted-pair cables with strong anti-interference capabilities, effectively reducing signal interference and improving the stability and reliability of signal transmission. By constructing a detailed circuit topology model, the status of each node in the circuit can be monitored in real time, allowing for more precise adjustment of circuit parameters and improving the response speed and accuracy of the control system.

[0046] By comprehensively analyzing the circuit architecture and optimizing the design, potential fault points were identified and eliminated, reducing the risk of equipment failure during use. The optimized circuit architecture can flexibly adapt to different usage scenarios and requirements, improving the system's adaptability and scalability in different working environments.

[0047] By rationally integrating and allocating circuit resources, unnecessary components and wiring are reduced, improving the system's resource utilization efficiency. The optimized circuit architecture provides more uniform, stable, and efficient illumination, ensuring optimal lighting conditions during surgery and enhancing surgical precision and safety.

[0048] In one embodiment of the present invention, S13 includes:

[0049] Based on the graph theory abstraction of nodes and connection lines in S12, the direct connection relationships between nodes are further clarified; each node is recorded in detail as to which other nodes it is directly connected to, and for nodes with bidirectional connection relationships, their bidirectional transmission characteristics are clarified.

[0050] In-depth analysis of the signal transmission direction in the circuit, starting from the source of the signal, to trace its flow path in the circuit;

[0051] This study investigates the energy transfer process in a circuit, starting from the power input, and analyzes how electrical energy passes through various circuit components to ultimately provide the energy required for LED beads to emit light.

[0052] Based on the analysis results of the above node relationships, signal flow direction and energy transmission path, a circuit topology model of the LED surgical shadowless lamp is built using circuit design software or graphic drawing tools.

[0053] Nodes are used as the basic elements in the graph, and lines are used to represent the connections between nodes. The direction of the lines indicates the direction of signal flow. Different colors, shapes, or symbols are used to distinguish different types of nodes and connecting lines.

[0054] After completing the initial circuit topology model, verification is performed; the model is compared with the actual circuit to check whether the node connections, signal flow, and energy transmission paths in the model are consistent with the actual circuit.

[0055] The working principle of the above technical solution is as follows: Based on the graph theory abstraction of nodes and connecting lines in step S12, the direct connection relationships between each node are further clarified; each node is recorded in detail as to which other nodes it is directly connected to, and for nodes with bidirectional connections, their bidirectional transmission characteristics are clarified; for example, in the control signal transmission path, some nodes may both receive and send signals, and this bidirectional interaction relationship must be accurately labeled. At the same time, considering the connection differences between different types of nodes, such as power supply nodes and LED bead nodes, control signal nodes and power supply nodes, their unique connection methods in circuit function implementation are analyzed, providing comprehensive node connection information for subsequent topology construction.

[0056] A thorough analysis of signal transmission direction within the circuit is crucial. Starting from the signal's source, the flow path within the circuit is traced. For control signals, the process from the control chip, through various signal processing and transmission nodes, to the final arrival at the LED beads or other actuators is clearly defined. For power signals, the specific path from the power input, through voltage regulation and filtering, to provide stable power to various circuit components is determined. When determining signal flow, the branching and merging of signals at different nodes must be considered. For example, a control signal may simultaneously control multiple LED bead groups; the topology of such signal branches must be accurately depicted. Furthermore, the convergence of multiple signals at a particular node must be clearly recorded to ensure the accuracy and completeness of the signal flow.

[0057] This study investigates the energy transfer process in a circuit, starting from the power input. It analyzes how electrical energy passes through various circuit components, such as transformers, capacitors, and resistors, ultimately providing the energy needed for LED lights to emit light. It considers energy losses at different components; for example, resistors convert some electrical energy into heat, and it's crucial to identify the key points and locations of energy loss. Simultaneously, it focuses on power distribution during energy transfer. Different numbers of LEDs or LED groups operating in different states require different power; therefore, it's essential to plan how to rationally allocate energy according to actual needs to ensure efficient and stable energy transfer throughout the circuit. By comprehensively considering the energy transfer paths, losses, and distribution, it provides comprehensive energy transfer information for constructing a circuit topology model.

[0058] Based on the analysis results of the above node relationships, signal flow direction and energy transmission path, a circuit topology model of the LED surgical shadowless lamp is built using circuit design software or graphic drawing tools.

[0059] Nodes are used as the basic elements in the diagram, and lines represent the connections between nodes, with the direction of the lines indicating the signal flow. Different colors, shapes, or symbols are used to distinguish different types of nodes and connections to more clearly demonstrate the circuit's structure and function. For example, red represents power nodes, blue represents control signal nodes, and green represents LED nodes; solid lines represent main signal transmission lines, and dashed lines represent auxiliary or backup lines. During the topology construction process, continuous checking and adjustments are necessary to ensure the model accurately reflects the actual circuit's connection relationships, signal flow, and energy transmission paths, while also maintaining the model's aesthetics and readability.

[0060] After completing the initial circuit topology model, verification is performed. The model is compared with the actual circuit to check whether the node connections, signal flow, and energy transmission paths in the model are consistent with the actual circuit. The circuit's operation can be simulated in the model to observe whether the signal transmission and energy distribution meet expectations. Comparison with the circuit parameters obtained from actual measurements verifies the model's accuracy and reliability. If discrepancies are found between the model and the actual circuit, the reasons should be analyzed promptly, and the model should be adjusted and improved, such as correcting node connection errors, optimizing signal flow representation, or adjusting energy transmission path planning. Through multiple verifications and adjustments, it is ensured that the final circuit topology model can realistically and accurately reflect the circuit structure and functional characteristics of the LED surgical shadowless lamp.

[0061] The effects of the above technical solution are as follows: by meticulously analyzing the connections and signal flow between nodes, the circuit design can be made more accurate, reducing design errors and improving circuit reliability. By using graph theory to abstract and clearly define node connection information, the complexity of circuit design can be simplified, making it easier for designers to understand and manipulate the circuit structure.

[0062] By clearly recording the connection characteristics and signal transmission paths of each node, a clear reference can be provided for subsequent circuit debugging and maintenance, reducing the difficulty of troubleshooting. Optimized analysis of the energy transmission process helps reduce energy loss, rationally allocate power, ensure efficient and stable operation of LED beads, and improve the overall system's energy efficiency.

[0063] By using different colors, shapes, and symbols, the structure and function of a circuit can be clearly displayed, helping designers and users to understand the circuit's working principle more intuitively. Verifying the consistency between the model and the actual circuit ensures that the circuit design can operate stably in real-world applications, avoiding malfunctions caused by design errors.

[0064] With precise topology models, designers can quickly identify potential problems and optimization opportunities, reducing debugging and modification time. It allows for flexible design adjustments based on the requirements of different nodes and signal types, providing more customized design solutions to adapt to various application scenarios.

[0065] One embodiment of the present invention, such as Figure 2 As shown, S2 includes:

[0066] S21. Further process the status data of the LED surgical shadowless lamp circuit nodes, and use digital signal processing technology to preprocess the voltage and current change curves over time.

[0067] S22. By analyzing the processed voltage and current change curves, characteristic parameters reflecting the circuit's operating state are extracted; the characteristic parameters are integrated to obtain the electrical characteristic data of the LED surgical shadowless lamp.

[0068] S23. Based on the extracted electrical feature data, and according to the characteristics of the lighting data in the frequency domain, time domain, and spatial domain, cluster analysis and pattern recognition techniques are used to perform unstructured division of the lighting data generated by the LED surgical shadowless lamp.

[0069] S24. Divide the lighting data into different types, and further subdivide each type of lighting data;

[0070] S25. For dynamically changing surgical scenarios, obtain video simulation lighting data to simulate continuously changing lighting scenarios; for static surgical scenarios, obtain static image lighting data corresponding to fixed lighting states.

[0071] S26. For video simulation lighting data and static image lighting data, use data compression algorithms in information theory to calculate the minimum data transmission volume; obtain unstructured minimum lighting data by compressing the data.

[0072] The working principle of the above technical solution is as follows: further process the status data of the LED surgical shadowless lamp circuit nodes, and use digital signal processing technology to preprocess the voltage and current change curves over time;

[0073] By analyzing the processed voltage and current change curves, characteristic parameters reflecting the circuit's operating state are extracted; for example, the voltage fluctuation range is calculated to determine the maximum and minimum voltage values ​​under normal operating conditions; the current peak value is identified to analyze the instantaneous changes in current; the signal frequency is measured to understand the periodic change characteristics of the signal; and the characteristic parameters are integrated to obtain the electrical characteristic data of the LED surgical shadowless lamp.

[0074] Based on the extracted electrical feature data, and according to the characteristics of lighting data in the frequency domain, time domain, and spatial domain, cluster analysis and pattern recognition techniques are used to perform unstructured division of the lighting data generated by LED surgical shadowless lamps.

[0075] Lighting data is categorized into different types, such as basic lighting data, which provides basic illumination for the surgical area to ensure clear visibility of the surgical field; and special effects lighting data, such as focused lighting data used to highlight specific surgical areas, which can enhance the illumination of specific areas according to the needs of the surgical procedure, thereby improving the accuracy of the surgery; and each type of lighting data is further subdivided.

[0076] For dynamically changing surgical scenarios, video simulation lighting data is obtained to simulate continuously changing lighting conditions, such as tissue movement and instrument operation during surgery; for static surgical scenarios, static image lighting data is obtained, corresponding to fixed lighting states.

[0077] For video simulation lighting data and static image lighting data, data compression algorithms from information theory, such as Huffman coding and run-length coding, are used to calculate the minimum data transmission volume. By compressing the data, unstructured minimum lighting data is obtained. This data can accurately express lighting information in the most concise form, providing efficient data support for subsequent circuit transmission and control, and reducing the burden and energy consumption of circuit transmission.

[0078] The above technical solution achieves the following results: by preprocessing and extracting features from the voltage and current change curves, the working state of the circuit can be accurately analyzed, ensuring the stability and reliability of the LED surgical shadowless lamp under different working environments, reducing misjudgments caused by data noise, and improving the accuracy of the system.

[0079] By employing data compression algorithms (such as Huffman coding and run-length encoding), unstructured lighting data is compressed, reducing the amount of data that needs to be transmitted. This not only improves the transmission efficiency of the circuit but also effectively reduces the consumption of communication bandwidth and energy. By dividing the lighting data into unstructured segments and combining cluster analysis and pattern recognition techniques, the system can identify different types of lighting needs in real time, dynamically adjust lighting parameters, and provide the most suitable lighting conditions for the surgery, thereby improving the response speed during the surgical process and ensuring the convenience of the surgeon's operation.

[0080] By finely classifying lighting data, such as basic lighting data, special effects lighting data, video simulation lighting data, and static image lighting data, the system can more efficiently manage and schedule different types of lighting data, avoiding unnecessary changes in lighting conditions and making the surgical environment more stable and reliable. Data compression for dynamically changing lighting scenarios reduces the energy consumption of the circuit system when processing and transmitting lighting data. Especially in surgical scenarios, with the reduction in the amount of lighting data, the system can maintain efficient operation at low power consumption, extending the lifespan of the equipment.

[0081] By meticulously segmenting lighting data and dynamically adjusting it according to changes in the surgical scene, the system can adapt to different surgical environments and needs, meeting diverse surgical lighting requirements. It provides suitable lighting solutions for both dynamic and static surgical scenarios. Through real-time adjustments to special effects lighting data, it can enhance lighting on specific areas as needed during surgery, ensuring surgical precision, reducing surgical risks caused by improper lighting, and further improving surgical safety and accuracy.

[0082] By classifying and compressing the data, the overall system architecture becomes simpler, reducing performance issues caused by excessive data volume. This simplifies system maintenance and debugging, reducing subsequent manual intervention and debugging costs. The data compression and classification management approach allows the system to maintain good performance even when facing increased lighting demands, providing a flexible framework for future functional expansion and technological upgrades.

[0083] In one embodiment of the present invention, step S23 includes:

[0084] Lighting data is transformed from the time domain to the frequency domain using Fast Fourier Transform; in the frequency domain, the frequency component distribution of the lighting data is analyzed; the main frequency components are identified, and time series analysis of the lighting data is performed in the time domain; the lighting data is then mapped to the spatial domain for analysis.

[0085] The features obtained from the frequency, time, and spatial domains are fused together; a feature normalization method is used to map each feature to a unified numerical range, and principal component analysis is used to reduce the dimensionality of the fused features; through feature fusion and dimensionality reduction, feature vectors are obtained.

[0086] Cluster analysis is performed on the lighting data based on the dimensionality-reduced feature vectors; through cluster analysis, the lighting data is divided into several different clusters, each cluster representing a specific type of lighting data pattern;

[0087] Based on the clusters obtained from cluster analysis, pattern recognition technology is used to further analyze and identify each cluster to determine the specific meaning and characteristics of the lighting data patterns corresponding to each cluster;

[0088] The lighting dataset is divided into a training set and a test set. The training set is used to train the pattern recognition model, and the test set is used to test the trained model to evaluate the model's recognition accuracy and generalization ability. By continuously adjusting and optimizing the pattern recognition model, the unstructured partitioning of lighting data generated by LED surgical shadowless lamps is finally achieved.

[0089] The working principle of the above technical solution is as follows: Lighting data is transformed from the time domain to the frequency domain using tools such as Fast Fourier Transform (FFT). In the frequency domain, the distribution of frequency components in the lighting data is analyzed; the main frequency components are identified, which often correspond to periodic variations in the lighting data, such as those caused by power frequency fluctuations or the flickering characteristics of LED beads. Simultaneously, noise components in the frequency domain are analyzed, including their frequency range and intensity, to determine their impact on the quality of the lighting data. Through frequency domain analysis, the characteristics of lighting data can be understood in depth from a frequency perspective, providing key frequency domain feature information for subsequent clustering and pattern recognition. In the time domain, time series analysis of the lighting data is performed; the changing trends of the lighting data over time are observed, including different stages such as rising, falling, and stable periods. Abrupt changes in lighting data at different time points are analyzed, such as jumps in lighting data caused by sudden changes in lighting demand due to surgical procedures. The statistical characteristics of the lighting data in the time domain, such as mean, variance, and standard deviation, are calculated. These statistical characteristics reflect the overall fluctuation and dispersion of the lighting data. Furthermore, this study investigates the autocorrelation and cross-correlation of lighting data. Autocorrelation reveals the intrinsic connections between lighting data at different time points, while cross-correlation helps analyze the relationships between different lighting areas or different types of lighting data. Through comprehensive temporal domain characteristic analysis, the rich features of lighting data in the time dimension are obtained. Considering the spatial distribution of LED surgical shadowless lamps, the lighting data is mapped to the spatial domain for analysis. For different locations within the surgical area, the distribution of lighting intensity is analyzed to identify high- and low-intensity areas, as well as the gradient changes in lighting intensity in space. The boundary characteristics between different lighting areas are studied, such as the clarity of the boundaries and the smoothness of transition areas. Simultaneously, the symmetry and asymmetry characteristics of lighting data in space are considered. Some surgical scenarios may require specific lighting symmetry, while complex surgical procedures may lead to asymmetrical lighting distributions. Exploring spatial domain characteristics allows for a spatial understanding of the characteristics of lighting data, providing an important supplement to a comprehensive understanding of the features of lighting data.

[0090] The features obtained from the frequency, time, and spatial domains are fused. Since features from different domains may have different dimensions and importance, a feature normalization method is used to map each feature to a unified numerical range, eliminating the impact of dimensional differences on subsequent analysis. Principal component analysis is then used to reduce the dimensionality of the fused features. The purpose of dimensionality reduction is to reduce the number of features, reduce computational complexity, and improve the efficiency of cluster analysis and pattern recognition while retaining the main feature information of the lighting data. Through feature fusion and dimensionality reduction, a set of concise and representative feature vectors is obtained.

[0091] Cluster analysis is performed on the lighting data based on the dimensionality-reduced feature vectors. During the clustering process, clustering parameters are appropriately set, such as the number of clusters K in K-means clustering. Through multiple trials and evaluations, the optimal parameter values ​​are selected so that the clustering results can best reflect the inherent structure and distribution patterns of the lighting data. The purpose of cluster analysis is to group lighting data with similar characteristics into one category, while different categories exhibit significant differences in lighting data. Through cluster analysis, the lighting data is divided into several different clusters, each representing a specific type of lighting data pattern.

[0092] Based on the clusters obtained from clustering analysis, pattern recognition technology is used to further analyze and identify each cluster, determining the specific meaning and characteristics of the lighting data pattern corresponding to each cluster. For example, one cluster is identified as corresponding to a basic lighting pattern, characterized by uniform and stable lighting intensity; another cluster corresponds to a special effects lighting pattern, with specific lighting intensity distribution and variation characteristics. To ensure the accuracy of pattern recognition, methods such as cross-validation are used to verify the recognition results.

[0093] The lighting dataset is divided into a training set and a test set. The training set is used to train the pattern recognition model, and the test set is used to test the trained model to evaluate the model's recognition accuracy and generalization ability. By continuously adjusting and optimizing the pattern recognition model, the recognition accuracy of lighting data patterns is improved, and finally, accurate unstructured partitioning of lighting data generated by LED surgical shadowless lamps is achieved.

[0094] The effects of the above technical solution are as follows: Through multi-dimensional analysis in the frequency, time, and spatial domains, the characteristics of lighting data can be understood in depth from different perspectives, ensuring that the data features are accurately captured and avoiding the limitations of single-domain analysis. By using feature fusion and dimensionality reduction, the number of features is reduced, effectively lowering computational complexity and thus improving the efficiency of cluster analysis and pattern recognition, while preserving the main information of the data.

[0095] Cluster analysis and pattern recognition can divide lighting data into patterns with distinct characteristics, helping users better understand the lighting features and variation patterns in different lighting scenarios. After selecting the optimal clustering parameters, cluster analysis can more accurately reflect the inherent structure and distribution patterns of lighting data, helping to identify different types of lighting patterns.

[0096] The pattern recognition results were validated using methods such as cross-validation, which improved recognition accuracy and ensured accurate unstructured partitioning of LED surgical shadowless lamp lighting data. Systematic analysis and pattern recognition of the lighting data enabled faster identification of lighting problems and optimization of the space, thereby reducing the time required for lighting design and adjustments.

[0097] By deeply analyzing the frequency, temporal, and spatial characteristics of lighting data, the performance of LED surgical shadowless lamps can be better predicted and managed, ensuring the stability and reliability of the equipment in different scenarios. Autocorrelation and cross-correlation analyses can identify abrupt changes and anomalies in the lighting data, facilitating real-time monitoring and timely response to sudden changes, thus ensuring stable lighting quality.

[0098] By employing spatial domain analysis and pattern recognition, the lighting distribution and intensity can be flexibly adjusted according to the needs of different surgical scenarios, enhancing the adaptability of the lighting system. Detailed time, frequency, and spatial domain analyses provide multi-dimensional information support, enabling more precise management of lighting data and contributing to improved optimization capabilities and effectiveness of the overall lighting system.

[0099] In one embodiment of the present invention, S26 includes:

[0100] Characteristic analysis was performed on video simulation lighting data and static image lighting data respectively; based on the above analysis results, the adaptability of various data compression algorithms in information theory was evaluated; by comprehensively evaluating the compression effect and computational complexity of different algorithms when processing different types of lighting data, the most suitable combination of compression algorithms was selected for each type of data.

[0101] After selecting the compression algorithm, the minimum data transmission volume of video simulation lighting data and still image lighting data is theoretically calculated using relevant theories of information theory.

[0102] Based on the selected compression algorithm and the theoretically calculated minimum data transmission target, actual compression processing is performed on video simulated lighting data and still image lighting data;

[0103] After compression, a comprehensive effect evaluation is performed on the compressed video simulation lighting data and static image lighting data; based on the evaluation results, the parameters of the compression algorithm are adjusted and optimized.

[0104] After multiple evaluations and optimizations, the compressed video simulation lighting data and static image lighting data were finally obtained.

[0105] The working principle of the above technical solution is as follows: Characteristic analysis is performed on both simulated video lighting data and static image lighting data. For simulated video lighting data, the correlation between frames is studied, and the changing patterns of lighting information in consecutive frames are analyzed. For example, the lighting intensity in certain areas may gradually or abruptly change with surgical operations. Simultaneously, the temporal redundancy characteristics of the video data are considered, i.e., there is a large amount of similar lighting information between adjacent frames. For static image lighting data, the spatial redundancy characteristics are analyzed, including the similarity of lighting intensity in different areas of the image and the repetition of textures. The statistical characteristics of the image, such as the distribution of pixel values, are also considered. This in-depth analysis of data characteristics provides a basis for selecting a suitable data compression algorithm. Based on the above analysis of data characteristics, the adaptability of various information theory-based data compression algorithms, such as Huffman coding and run-length encoding, is evaluated. For simulated video lighting data, if its temporal redundancy characteristics are prominent, algorithms with good temporal compression capabilities can be evaluated, such as a combination of inter-frame prediction compression algorithms and Huffman coding. Inter-frame prediction is used to reduce redundant information in the temporal dimension, and then Huffman coding is used to further compress the prediction error. For static image lighting data, if spatial redundancy is significant, evaluate the performance of algorithms such as run-length encoding that compress based on spatial similarity. Also consider some transform-based algorithms, such as Discrete Cosine Transform (DCT) combined with Huffman coding, to transform the image from the spatial domain to the frequency domain, remove spatial correlations, and then perform encoding compression. By comprehensively evaluating the compression performance and computational complexity of different algorithms when processing different types of lighting data, select the most suitable combination of compression algorithms for each type of data.

[0106] After selecting a compression algorithm, relevant theories of information theory are used to theoretically calculate the minimum data transmission volume for simulated video lighting data and static image lighting data. For simulated video lighting data, considering factors such as frame rate, resolution, and information entropy per frame, the minimum bit rate required to accurately transmit video lighting information, i.e., the minimum data transmission volume, is calculated based on rate-distortion theory in information theory, within a given distortion tolerance range. For static image lighting data, based on the number of pixels, pixel value distribution, and the information preservation characteristics of the compression algorithm used, the minimum data volume required to ensure that the image lighting information is essentially lossless or meets specific quality requirements is calculated. Theoretical calculations provide a target reference for practical compression processing, helping to evaluate the performance of compression algorithms and optimize compression parameters.

[0107] Based on the selected compression algorithm and the theoretically calculated minimum data transmission target, actual compression processing is performed on video simulation lighting data and still image lighting data. During compression, the data is preprocessed according to the specific requirements of the algorithm. For example, for Huffman coding, the frequency of occurrence of each symbol in the data needs to be counted to construct a Huffman tree; for run-length encoding, consecutive identical symbols need to be marked and counted. The data is encoded and compressed step by step according to the algorithm's steps, while the data volume changes and compression efficiency are monitored in real time to ensure that the compression processing is moving towards the goal of minimum data transmission. During compression, it is important to balance the compression ratio and image quality to avoid excessive compression that could lead to excessive loss of lighting information and affect subsequent circuit transmission and control effects.

[0108] After compression, a comprehensive evaluation of the compressed video simulation lighting data and static image lighting data is conducted. The evaluation considers multiple dimensions, including compression ratio, image quality, and data transmission efficiency. The compression ratio reflects the degree of data compression and is calculated by comparing the data volume before and after compression. Image quality is evaluated using a combination of subjective assessment and objective metrics. Subjective assessment involves professional visual evaluation of the compressed lighting images, while objective metrics include Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to assess the similarity between the compressed and original images. Data transmission efficiency considers the transmission speed of the compressed data under specific network conditions. Based on the evaluation results, the parameters of the compression algorithm are adjusted and optimized, such as adjusting the symbol partitioning method in Huffman coding and optimizing the marking rules in run-length encoding, to further improve the compression effect and bring it closer to the theoretically calculated minimum data transmission volume, while ensuring the quality of the lighting information.

[0109] After multiple evaluations and optimizations, compressed video simulation lighting data and static image lighting data were finally obtained, which constitute the unstructured minimal lighting data. This data accurately expresses the lighting information in the most concise form, minimizing the data volume while retaining key lighting features. The generated unstructured minimal lighting data is organized and stored to provide efficient data support for subsequent circuit transmission and control, reducing the burden and energy consumption of circuit transmission, while ensuring that the original lighting information can be accurately reproduced during transmission and control, meeting the lighting needs of LED surgical shadowless lamps in different surgical scenarios.

[0110] The above technical solution achieves the following results: By optimizing the compression algorithm, especially through characteristic analysis of video simulation lighting data and still image lighting data, the most suitable compression method was selected, effectively reducing data transmission requirements and bandwidth consumption. Through in-depth analysis of the redundancy characteristics of lighting data and combining information-theoretic compression algorithms (such as Huffman coding and run-length coding), storage space requirements and storage costs were successfully reduced.

[0111] Based on the different characteristics of video simulation and still image lighting data, specific combinations of compression algorithms were customized to achieve optimal compression results for each type of data, avoiding the shortcomings of general compression methods in complex data scenarios. Image quality was fully considered during compression, especially maintaining the accuracy of lighting information, avoiding image distortion caused by over-compression, and ensuring that the compressed data still met the requirements for lighting control precision.

[0112] By preprocessing lighting data, adapting compression algorithms, and optimizing compression steps, computational complexity is significantly reduced, making the compression process more efficient and improving processing speed in practical applications. During data compression, data loss and noise interference during transmission are avoided, ensuring that compressed data can be transmitted smoothly in different network environments and maintain high transmission efficiency.

[0113] Through repeated optimization and adjustment of algorithm parameters, the compressed data can more accurately reflect the original lighting information while ensuring optimal image quality, thus ensuring that the lighting information is not affected during circuit transmission and control. Through theoretical calculations and algorithm evaluation, compression schemes were developed for the different characteristics of video simulation data and still image data, improving the precision of data management and simplifying subsequent control and processing.

[0114] By reducing the bandwidth and storage space required during data transmission, the system burden is lessened, and overall energy efficiency is improved. This is especially beneficial in precision systems such as LED surgical shadowless lamps, ensuring more stable and sustained operation. Optimizing the processing speed of lighting data during compression and transmission facilitates real-time system control and response, guaranteeing a rapid and accurate response to lighting information in special scenarios such as surgery.

[0115] In one embodiment of the present invention, step S3 includes:

[0116] S31. Through the optimized LED surgical shadowless lamp circuit architecture, the circuit signal containing unstructured minimum lighting data is converted into a signal format that can be used for millimeter wave transmission;

[0117] S32. Use electromagnetic field simulation software to simulate the transmission of signals along different circuit paths; set different circuit parameters and environmental conditions to simulate signal transmission in various real-world scenarios.

[0118] S33. Through simulation experiments, obtain simulated data of equipment circuit communication transmission. Based on the simulated data of equipment circuit communication transmission, use the K-means clustering algorithm to divide the signal transmission range into multiple levels.

[0119] S34. Set cluster centers and cluster radii, and classify different transmission conditions into corresponding levels based on the similarity of signal parameters; obtain signal transmission level range data, and assign different circuit operating parameters and transmission strategies to different levels.

[0120] The working principle of the above technical solution is as follows: through the optimized LED surgical shadowless lamp circuit architecture, the circuit signal containing the minimum unstructured lighting data is converted into a signal format that can be used for millimeter wave transmission;

[0121] Considering the propagation characteristics of millimeter waves in circuits, such as attenuation, reflection, and diffraction, electromagnetic field simulation software is used to simulate the transmission of signals along different circuit paths. Different circuit parameters and environmental conditions are set, such as the material, size, and layout of circuit components, as well as the electromagnetic environment in the operating room, to simulate signal transmission in various real-world scenarios.

[0122] Through simulation experiments, simulated data of equipment circuit communication transmission was obtained. This data included parameters such as signal strength changes, transmission delay, and bit error rate. Analyzing this data allowed for understanding the performance changes of the signal during transmission, providing a basis for subsequent signal transmission level classification. Based on the simulated data of equipment circuit communication transmission, the K-means clustering algorithm was used to divide the signal transmission range into multiple levels.

[0123] Cluster centers and radii are set, and different transmission conditions are categorized into corresponding levels based on the similarity of parameters such as signal strength variation, transmission delay, and bit error rate. For example, a short-range high-intensity transmission level is defined, suitable for signal transmission near the surgical area, requiring high signal strength, low transmission delay, and low bit error rate; a medium-range medium-intensity transmission level is used for signal transmission in the central area of ​​the operating room; and a long-range low-intensity transmission level meets the signal transmission needs of areas further away from the operating room. By obtaining signal transmission level range data, different circuit operating parameters and transmission strategies are assigned to different levels to meet the lighting requirements of different surgical scenarios. For example, for the short-range high-intensity transmission level, the power output can be appropriately increased, and the signal amplification circuit optimized; for the long-range low-intensity transmission level, relay transmission technology can be used to enhance signal strength.

[0124] The above technical solution achieves the following effects: by optimizing the circuit architecture and millimeter-wave signal format conversion, it enhances signal transmission efficiency in the operating room environment, reduces signal attenuation and noise interference, thereby realizing more efficient lighting control. By simulating the electromagnetic environment and signal transmission characteristics, and precisely setting circuit parameters and environmental conditions, it effectively reduces the bit error rate during transmission and improves signal stability and reliability.

[0125] By utilizing the K-means clustering algorithm to divide signal transmission into multiple levels, the device can dynamically adjust circuit operating parameters and signal transmission strategies according to different transmission conditions and regional requirements, thereby improving the system's adaptability to various surgical environments. Simulations of signal transmission under different circuit paths and environmental conditions ensured the system's ability to flexibly respond to changes in the electromagnetic environment within the operating room, thus enhancing the stability of the lighting system in complex environments.

[0126] For long-distance, low-intensity transmission, relay transmission technology was employed to optimize the signal transmission path, reduce power consumption, ensure stable long-distance signal transmission, and lower energy consumption. Analysis of data such as signal transmission delay, intensity, and bit error rate, combined with real-time transmission strategy adjustments, improved the real-time response capability of the lighting system in dynamic surgical scenarios, ensuring that lighting data could be transmitted to the destination area in the shortest possible time.

[0127] By setting different circuit operating parameters for different transmission levels, accurate illumination signals were ensured to be provided at different locations in the surgical area, avoiding unnecessary illumination errors and improving surgical precision. Simulation and analysis of signal transmission characteristics allowed for early optimization of circuit design and operating parameters, reducing trial-and-error processes in actual circuit design and improving design efficiency.

[0128] By dividing signal transmission into different levels, the system can adapt to different operating room configurations, facilitating future expansion and system upgrades, and ensuring the compatibility of the lighting system in various environments. Analysis of key parameters such as signal transmission error rate and latency reduces the possibility of communication errors, thereby improving lighting safety during surgery and ensuring patient safety.

[0129] In one embodiment of the present invention, S34 includes:

[0130] The simulation data of device circuit communication transmission obtained from the simulation experiment are analyzed to obtain the inherent relationship and mutual influence between the data.

[0131] Based on the above analysis of clustering parameters, an intelligent algorithm combined with an expert experience base is used to set cluster centers; after setting the cluster centers, the cluster radius corresponding to each cluster center is dynamically determined.

[0132] Based on the predefined cluster centers and cluster radii, a similarity measurement method is used to accurately classify different transmission scenarios into corresponding levels. For each signal transmission data point obtained from the simulation experiment, the distance between it and each cluster center is calculated, and it is classified into the level corresponding to the nearest cluster center.

[0133] During the classification process, the classification status of data points is monitored in real time. For data points near the cluster boundary, in-depth analysis is conducted, and they are reclassified based on the special needs of the actual surgical scenario and the potential risks of signal transmission.

[0134] The signal transmission level range data after classification is recorded in detail, and the surgical scenario description applicable to each level is recorded. Based on the different signal transmission level range data, corresponding circuit operating parameters and transmission strategies are customized for each level.

[0135] The working principle of the above technical solution is as follows: analyze the simulated data of equipment circuit communication transmission obtained from the simulation experiment, and obtain the inherent relationship and mutual influence between the data;

[0136] Based on the above analysis of clustering parameters, an intelligent algorithm combined with an expert experience database is used to set cluster centers. After setting the cluster centers, the cluster radius corresponding to each cluster center is dynamically determined. According to the set cluster centers and cluster radii, a similarity measurement method is used to accurately classify different transmission situations into the corresponding levels. For each signal transmission data point obtained from the simulation experiment, the distance between it and each cluster center is calculated, and it is classified into the level corresponding to the nearest cluster center.

[0137] During the classification process, the classification status of data points is monitored in real time. For data points near the cluster boundary, in-depth analysis is conducted, and they are reclassified based on the specific needs of actual surgical scenarios and potential risks to signal transmission. The signal transmission level range data after classification is recorded in detail, along with a description of the surgical scenario applicable to each level. Based on the different signal transmission level range data, corresponding circuit operating parameters and transmission strategies are customized for each level.

[0138] The above technical solution achieves the following results: By setting circuit operating parameters and transmission strategies for different transmission levels according to the specific needs of actual surgical scenarios, signal transmission remains stable under various surgical environments, reducing the risk of signal fluctuations during transmission. Through optimized circuit design, signal modulation and demodulation algorithms, and relay transmission technology, signal distortion is reduced and anti-interference capabilities are improved at different transmission levels, effectively reducing the bit error rate and ensuring accurate signal transmission.

[0139] For short-range, high-intensity transmission, signal strength was enhanced by optimizing the signal amplification circuit and increasing the power supply output. Simultaneously, the optimized circuit design reduced signal transmission delay, making signal transmission more efficient. By dynamically adjusting the clustering radius and setting a fault tolerance coefficient, the system can adapt to signal transmission fluctuations in different scenarios, enhancing its tolerance to environmental changes and interference, and reducing the impact of unforeseen factors.

[0140] Personalized circuit operating parameters and transmission strategies were customized for different surgical scenarios (such as delicate surgery, general surgery, and auxiliary lighting), ensuring that the shadowless lamp achieves optimal illumination in various environments and improving the safety and accuracy of the surgical process. Through the combination of intelligent algorithms and an expert experience database, the system can automatically optimize and adjust under different working conditions, improving the flexibility and adaptability of the circuit design and providing precise support for different types of surgery.

[0141] In one embodiment of the present invention, step S4 includes:

[0142] S41. Obtain the circuit communication requirement data of the LED surgical shadowless lamp, and at the same time, record the adjustment frequency of the lighting parameters;

[0143] S42. Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, analyze the maximum data flow required to meet communication needs under the current circuit state; calculate the actual available data flow, obtain the amount of data that the current circuit can carry by monitoring the real-time transmission status of the circuit; analyze the difference between the maximum data flow and the actual available data flow to obtain communication demand flow assessment data.

[0144] S43. Combining unstructured minimum transmission data and communication demand traffic assessment data, a genetic algorithm is used to calculate the minimum transmission traffic for communication demand; thus obtaining optimized demand transmission traffic data.

[0145] The working principle of the above technical solution is as follows: It acquires the circuit communication requirements data of the LED surgical shadowless lamp, and through communication with surgical team members such as surgeons and nurses, understands the real-time requirements for different types of lighting data during the operation. For example, in complex neurosurgical procedures, the real-time requirements for special effect lighting data are high, requiring the ability to quickly adjust lighting parameters to adapt to changes in surgical procedures; simultaneously, it records the adjustment frequency of lighting parameters, such as the number of adjustments and time intervals for brightness and color temperature. This data reflects the dynamic needs for lighting control during the operation, providing a basis for subsequent flow margin assessment.

[0146] Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, the maximum data flow required to meet communication needs under the current circuit state is analyzed; the characteristics and transmission requirements of different types of lighting data, as well as the transmission capacity and bandwidth limitations of the circuit are considered; the actual available data flow is calculated, and the amount of data that the current circuit can carry is obtained by monitoring the real-time transmission status of the circuit; the difference between the maximum data flow and the actual available data flow is analyzed to obtain communication demand flow assessment data.

[0147] Combining unstructured minimum transmission data and communication demand traffic assessment data, a genetic algorithm is used to calculate the minimum transmission traffic for communication demand. During the calculation, factors such as lighting effect, circuit transmission capacity, and energy consumption are comprehensively considered. Lighting effect is the primary objective, ensuring that data transmission volume is minimized while meeting surgical lighting requirements. Simultaneously, circuit transmission capacity and bandwidth limitations are considered to avoid data congestion and transmission delays. Energy consumption is also addressed by optimizing transmission strategies to reduce circuit energy consumption and improve energy efficiency. Through continuous iterative optimization, the minimum transmission traffic that achieves the lowest energy consumption and highest transmission efficiency is found, thus obtaining optimized demand transmission traffic data.

[0148] The benefits of the above technical solution are as follows: By communicating with the surgical team, the dynamic needs for lighting control during the surgery were obtained, enabling a more accurate assessment of the real-time requirements for different types of lighting data. This improves the control over details such as the frequency of lighting parameter adjustments, brightness, and color temperature changes, ensuring that the lighting system can adapt to changes during the surgical process in a timely manner.

[0149] By calculating and analyzing the difference between the maximum data flow and the actual available flow based on the circuit's transmission capacity, bandwidth limitations, and real-time monitoring, congestion during data transmission was avoided. This helps prevent delays caused by transmission bottlenecks during surgery and improves the system's response speed.

[0150] Through optimization calculations using a genetic algorithm, multiple factors such as lighting effect, circuit transmission capacity, and energy consumption were comprehensively considered. While ensuring lighting performance, the data transmission volume was minimized, thus reducing energy consumption. The optimized transmission strategy improved the system's energy efficiency and enhanced the stability of the circuit system.

[0151] The system can quickly adjust lighting parameters, such as brightness and color temperature, according to surgical needs. This high real-time performance ensures rapid adaptation to changes in surgical procedures during complex operations, enhancing the accuracy and safety of lighting during surgery. By accurately calculating the minimum transmission flow, unnecessary data transmission is avoided, reducing bandwidth usage and resource consumption, thereby achieving efficient resource utilization and preventing waste caused by excessive data transmission.

[0152] By optimizing the assessment and transmission strategies for communication demand, LED surgical shadowless lamps can provide precise and efficient lighting support in different types of surgeries (such as neurosurgery and delicate surgeries), enhancing the system's adaptability and reliability. By ensuring precise adjustment of the lighting effect and optimizing communication transmission traffic, the system can reduce circuit power consumption and transmission latency while ensuring high-efficiency lighting. This optimization improves lighting safety during surgery and the ease of operation for surgical personnel.

[0153] By employing intelligent algorithms to calculate optimal transmission flow, the circuit design becomes more flexible, enabling automatic adjustments based on different surgical needs and lighting environments, thus enhancing the system's scalability and future upgrade potential.

[0154] In one embodiment of the present invention, step S5 includes:

[0155] S51. Based on the signal transmission level range data, classify the communication requirement transmission level of the circuit communication requirement data of the LED surgical shadowless lamp; map different communication requirements to the corresponding signal transmission level to obtain communication requirement transmission level data, and clarify the transmission priority and requirements of different communication requirements.

[0156] S52. Based on the equipment circuit communication transmission simulation data, integrate the LED surgical shadowless lamp circuit resources according to the communication requirement transmission level data and the optimization requirement transmission flow data; and rationally allocate circuit resources.

[0157] S53. The generated device circuit communication transmission strategy is transmitted to the circuit control platform of the LED surgical shadowless lamp. The circuit control platform adjusts the circuit parameters in real time according to the received transmission strategy, and at the same time monitors the circuit's operating status and adjusts the control strategy according to the monitoring results.

[0158] The working principle of the above technical solution is as follows: Based on the signal transmission level range data, the communication requirement data of the LED surgical shadowless lamp is divided into communication requirement transmission levels; different communication requirements are mapped to corresponding signal transmission levels. For example, special effect lighting data with high real-time requirements and large data volume, such as real-time adjustment of focused lighting data during critical surgical operations, is assigned to a high-priority signal transmission level to ensure fast and accurate transmission; basic lighting data, such as basic lighting in the surgical area, is assigned to a relatively low-priority signal transmission level, and circuit resources are rationally allocated while ensuring stable transmission; the communication requirement transmission level data is obtained, and the transmission priority and requirements of different communication requirements are clarified.

[0159] Based on the simulated data of equipment circuit communication transmission, LED surgical shadowless lamp circuit resources are integrated according to the communication demand transmission level data and the optimized demand transmission flow data. Taking into account factors such as circuit transmission capacity, component performance, and energy consumption, resource allocation algorithms, such as the maximum-minimum fairness algorithm or the proportional fairness algorithm, are used to rationally allocate circuit resources. For example, the power output is adjusted according to the needs of different transmission levels to provide more energy support for high-priority data transmission; signal transmission paths are optimized, selecting paths with low transmission delay and low bit error rate for data transmission; and circuit components are rationally configured to improve component utilization and working efficiency. Through these measures, efficient integration and utilization of circuit resources are achieved.

[0160] The generated device circuit communication transmission strategy is transmitted to the circuit control platform of the LED surgical shadowless lamp. The circuit control platform adjusts the circuit parameters in real time according to the received transmission strategy. It controls the power supply module to adjust the output voltage and current, and controls the LED driver circuit to adjust the brightness and color temperature of the LED beads. At the same time, it monitors the circuit's operating status, such as signal strength, transmission delay, and bit error rate. Based on the monitoring results, it adjusts the control strategy in a timely manner to ensure the stable operation of the circuit and the optimization of the lighting effect.

[0161] The effects of the above technical solution are as follows: By prioritizing communication needs based on transmission levels, high-real-time, high-data-volume lighting requirements (such as focused illumination) are given priority, ensuring that lighting for critical operations during surgery can be adjusted quickly and accurately, thus improving the precision and safety of the surgery. By rationally allocating circuit resources, low-priority basic lighting data receives adequate transmission support without consuming excessive circuit resources. This avoids resource waste and optimizes the overall utilization efficiency of circuit resources.

[0162] By using the max-min fairness algorithm or proportional fairness algorithm, circuit resources can be rationally allocated, especially in optimizing power output and signal transmission paths. This effectively reduces energy consumption and improves the stability and long-term reliability of the circuit system. By optimizing signal transmission paths and selecting paths with low latency and low bit error rate, the system can achieve more efficient communication while ensuring data transmission quality, avoiding signal loss or delay problems caused by poor transmission paths.

[0163] By optimizing power output and circuit component operation based on demand, excessive reliance on circuit components is reduced, enabling stable equipment operation while avoiding overload and lowering energy consumption. The circuit control platform receives the optimized transmission strategy in real time and adjusts the control strategy based on monitoring data (such as signal strength, transmission delay, and bit error rate) to ensure efficient system operation and optimized lighting effects, improving the lighting system's adaptability to changing demands.

[0164] By rationally configuring circuit components, the utilization rate and working efficiency of the components are improved, thereby reducing the wear and tear of circuit components, extending the service life of the equipment, and avoiding frequent equipment replacement and maintenance costs. The optimized resource allocation and transmission strategy effectively reduces system latency and error rate, especially during critical surgical operations, ensuring the rapid transmission and accuracy of shadowless lamp illumination data, and improving the response speed and illumination quality during surgery.

[0165] Through flexible resource integration and transmission strategy adjustments, the system can be rapidly expanded and adjusted according to future needs, enhancing its ability to cope with different surgical environments and changing requirements, and ensuring adaptability and maintainability during long-term use.

[0166] In one embodiment of the present invention, the circuit communication requirement data of LED surgical shadowless lamp is analyzed and classified from multiple dimensions; for each category of communication requirement data, the data volume is calculated using data volume evaluation methods and tools.

[0167] We introduce a criticality quantification index to assess the importance of different communication needs data during the surgical process; we set assessment dimensions from multiple aspects and assign weights to each dimension; and we classify data according to real-time requirements and assign corresponding weights to data with different real-time requirements.

[0168] Taking into account both the criticality quantification value and the real-time requirement weight, a weighted summation method is used to calculate the overall priority of each type of communication requirement data;

[0169] Based on the signal transmission level range data, formulate matching rules between communication demand data and signal transmission levels; clarify the correspondence between data of different comprehensive priority ranges and corresponding signal transmission levels;

[0170] A dynamic adjustment mechanism is reserved in the transmission level matching rules; when the surgical scenario changes, the transmission level of communication demand data is adjusted in real time according to the actual situation.

[0171] After matching the communication requirement data with the signal transmission level, a verification test is conducted. In actual surgical scenarios or simulated surgical environments, various communication requirement data are transmitted according to the defined transmission levels, and indicators such as signal transmission quality, real-time performance, and accuracy are monitored. Based on the test results, the transmission level classification rules are optimized and adjusted.

[0172] The working principle of the above technical solution is as follows: The communication requirements data of the LED surgical shadowless lamp are analyzed and classified from multiple dimensions. Based on the functional characteristics of the data, it is divided into: lighting control requirements data, such as adjustment commands for parameters like LED brightness, color temperature, and focus position; lighting status feedback requirements data, such as real-time feedback of current lighting intensity and uniformity in the surgical area; and fault alarm requirements data, which are alarm signals issued when circuit abnormalities occur, such as short circuits, overloads, or component damage. Simultaneously, based on the real-time requirements of the data, it is further divided into: critical data with extremely high real-time requirements, such as real-time adjustment commands for focus lighting data during critical surgical operations, which must be accurately transmitted within a very short time to ensure the precision of the surgical operation; important data with relatively high real-time requirements, such as basic lighting data dynamically adjusted according to changes in the surgeon's position during the operation; and general data with relatively low real-time requirements, such as periodically fed back statistical information on the working status of circuit components. For each category of communication requirements data, data volume assessment methods and tools are used to calculate its size. For lighting control requirements data, factors such as the complexity of control commands, the number of LEDs involved, and the accuracy requirements of control parameters are considered. For example, if a high-precision PWM dimming method is used to control the brightness and color temperature of a large number of LED beads, the amount of control command data generated will be relatively large. For lighting status feedback data, the data volume is assessed based on the level of detail and update frequency of the feedback information. For instance, real-time feedback of lighting intensity and color temperature information at multiple key points in the surgical area, with a high update frequency, will significantly increase the data volume. For fault alarm data, although the frequency of occurrence is relatively low, the level of detail and encoding method of the alarm information will also affect the data volume; these factors need to be comprehensively considered for accurate assessment.

[0173] A criticality quantification index is introduced to assess the importance of different communication requirement data during surgery. Evaluation dimensions are set from multiple aspects, including surgical safety, ease of operation, and assurance of surgical outcomes, with each dimension assigned a weight. For example, the weight for surgical safety can be set to 0.5, the weight for ease of operation to 0.3, and the weight for assurance of surgical outcomes to 0.2. For each type of communication requirement data, professional surgeons, circuit engineers, and lighting experts are organized to score it. Based on the scoring results and the set weights, the criticality quantification value for each type of data is calculated. For example, real-time adjustment instructions for focused lighting during critical surgical operations have a significant impact on surgical accuracy and safety, and its criticality quantification value will be high; while periodically fed back statistical information on the working status of circuit components has a smaller direct impact on surgery, and its criticality quantification value is relatively low. According to the real-time requirement classification, corresponding weights are assigned to data with different real-time requirements: critical data with extremely high real-time requirements is assigned the highest weight, such as 0.7; important data with relatively high real-time requirements is assigned a medium weight, such as 0.5; and general data with relatively low real-time requirements is assigned a low weight, such as 0.3. By assigning weights to real-time requirements, the importance of real-time performance in determining transmission priority is highlighted, ensuring that critical data can be transmitted with priority.

[0174] Taking into account both the criticality quantification value and the real-time requirement weight, a weighted summation method is used to calculate the overall priority of each type of communication requirement data. The calculation formula is: Overall Priority = Criticality Quantification Value × Criticality Weight + Real-time Requirement Weight × Real-time Weight (Here, the criticality weight and real-time weight can be flexibly adjusted according to the actual situation, for example, they can be set to 0.6 and 0.4 respectively). The overall priority value for each type of data is obtained through calculation; the larger the value, the higher the transmission priority. For example, the real-time adjustment command for focused illumination data during critical surgical operations will have a significantly higher overall priority value than other data due to its high criticality quantification value and high real-time requirement weight, thus determining its high priority status in transmission.

[0175] Based on the signal transmission level range data, establish matching rules between communication requirement data and signal transmission levels; clarify the correspondence between data in different comprehensive priority ranges and their respective signal transmission levels; for example, data with a comprehensive priority value between 0.8 and 1.0 is assigned to a high-priority signal transmission level, which has the characteristics of high speed, low latency, and high reliability, and can meet the transmission needs of special effects lighting data with high real-time requirements and large data volume, such as the real-time adjustment of focused lighting data during critical surgical operations; data with a comprehensive priority value between 0.5 and 0.7 corresponds to a medium-priority signal transmission level, which is suitable for some important communication requirement data with relatively low real-time requirements; data with a comprehensive priority value between 0 and 0.4 corresponds to a low-priority signal transmission level, which is mainly used to transmit basic information data with low real-time requirements and small data volume, such as routine adjustment information of basic lighting data during non-critical operations.

[0176] Considering the complexity and uncertainty of the surgical procedure, as well as the dynamic changes in the operating environment of LED surgical shadowless lamps, a dynamic adjustment mechanism is reserved in the transmission level matching rules. When the surgical scenario changes, such as transitioning from routine surgical operations to a delicate operation stage, or when an emergency occurs in the surgical area requiring urgent lighting adjustments, the transmission level of communication demand data can be adjusted in real time according to the actual situation. For example, by setting up an intelligent monitoring module in the circuit control platform, the surgical operation status and changes in lighting requirements are monitored in real time. When a critical operation is detected, the relevant focused lighting data transmission level is automatically upgraded to a high priority to ensure that it can be transmitted quickly and accurately, thus guaranteeing the smooth progress of the surgery.

[0177] After matching communication requirement data with signal transmission levels, verification tests are conducted. In actual surgical scenarios or simulated surgical environments, various communication requirement data are transmitted according to the defined transmission levels, monitoring indicators such as signal transmission quality, real-time performance, and accuracy. Based on the test results, the transmission level classification rules are optimized and adjusted. If problems such as excessive delay or high bit error rate are found during transmission, the causes are analyzed, and the transmission priority and corresponding signal transmission level are reassessed to ensure that the communication requirement transmission level classification accurately adapts to actual surgical needs, achieving efficient utilization of circuit communication resources and optimized lighting effects. Finally, communication requirement transmission level data is obtained, clarifying the transmission priority and requirements for different communication needs.

[0178] The effect of the above technical solution is that by prioritizing the transmission of critical data with extremely high real-time requirements (such as real-time adjustment instructions for focused illumination data during critical surgical operations), the precise adjustment of illumination during the operation is ensured, effectively supporting the smooth progress of complex surgeries and reducing the risks caused by inaccurate illumination.

[0179] By assigning weights to different data types, the priority of data transmission was optimized, especially for lighting control and feedback data with high real-time requirements. This significantly reduced latency, ensuring that lighting in the surgical environment could respond to the doctor's needs in an instant and improving the convenience of surgical operations.

[0180] By conducting a detailed evaluation of the fault alarm requirement data, we ensured that alarms could be issued in a timely manner when circuit abnormalities occurred, thus guaranteeing the stable operation of the system, reducing safety accidents caused by equipment failures, and improving surgical safety.

[0181] By comprehensively considering data volume and real-time requirements, and scientifically classifying data transmission levels, efficient allocation of communication resources is achieved, avoiding unnecessary high bandwidth consumption and ensuring the transmission quality of various data in different surgical scenarios. A dynamic adjustment mechanism is incorporated into the transmission level matching rules, allowing for timely adjustments to data transmission priorities based on changes in the surgical environment. This ensures the system maintains high efficiency and stability even in the face of emergencies, adapting to diverse surgical needs.

[0182] By precisely adjusting and promptly responding to lighting control requirements, the complexity of the surgeon's operations during surgery is reduced, and the ease of operation is improved, allowing the surgeon to focus more on the surgery itself without being distracted by lighting issues. Optimized communication data partitioning and transmission priority allocation within the system reduce malfunctions and system performance problems caused by improper data transmission, thereby lowering the difficulty and cost of maintenance and debugging.

[0183] This matching mechanism between communication requirements and signal transmission levels provides a flexible framework for subsequent system upgrades and expansions, enabling support for more complex surgical environments and higher data processing demands, and ensuring that the system remains highly efficient and stable in long-term use.

[0184] One embodiment of the present invention provides an LED surgical shadowless lamp circuit control system, comprising:

[0185] One or more processors;

[0186] Memory, used to store one or more programs.

[0187] Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method as described in any one of the above statements.

[0188] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for controlling the circuit of an LED surgical shadowless lamp, characterized in that, The method includes: S1. Obtain the state data of each key node in the LED surgical shadowless lamp circuit, perform in-depth analysis on the obtained circuit node state data, and use graph theory algorithms to construct the circuit topology model of the LED surgical shadowless lamp; based on the constructed circuit topology model, integrate and optimize the circuit architecture of the LED surgical shadowless lamp. S2. Extract the electrical features of the LED surgical shadowless lamp circuit node status data; obtain the electrical feature data of the LED surgical shadowless lamp; based on the extracted electrical feature data, perform unstructured division of the lighting data generated by the LED surgical shadowless lamp; according to the characteristics of the lighting data in the frequency domain, time domain, and spatial domain, divide it into different types, and further subdivide each type of lighting data to obtain video simulation lighting data and static image lighting data; for the video simulation lighting data and static image lighting data, calculate its minimum data transmission volume to obtain the unstructured minimum lighting data; S3. Using the optimized LED surgical shadowless lamp circuit architecture, millimeter-wave transmission simulation is performed on the circuit signal containing unstructured minimum lighting data; the transmission of the simulated signal on different circuit paths is obtained to acquire equipment circuit communication transmission simulation data; based on the equipment circuit communication transmission simulation data, cluster analysis is used to classify the signal transmission range levels; the signal transmission range is divided into multiple levels to obtain signal transmission level range data. S4. Obtain the circuit communication requirement data of the LED surgical shadowless lamp. Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, evaluate the flow margin of the circuit communication requirement data; obtain the communication requirement flow evaluation data; combine the unstructured minimum transmission data and the communication requirement flow evaluation data, use the optimization algorithm to calculate the minimum transmission flow of the communication requirement; obtain the optimized requirement transmission flow data. S5. Based on the signal transmission level range data, classify the communication requirement data of the LED surgical shadowless lamp circuit communication requirement data into communication requirement transmission levels; map different communication requirements to the corresponding signal transmission levels to obtain communication requirement transmission level data; based on the device circuit communication transmission simulation data, integrate the LED surgical shadowless lamp circuit resources with the communication requirement transmission level data and the optimized requirement transmission flow data; obtain the device circuit communication transmission strategy; transmit the generated device circuit communication transmission strategy to the LED surgical shadowless lamp circuit control platform. S1 includes: S11. Identify each key node in the LED surgical shadowless lamp circuit, and use a high-precision voltage measuring instrument, current measuring instrument, and signal strength detector to collect the parameters of each key node in real time. S12. Perform in-depth analysis on the collected circuit node state data, and use the adjacency matrix method or incidence matrix method in graph theory to abstract the nodes and connecting lines in the circuit into vertices and edges in graph theory. S13. Based on the connection relationships between nodes, signal flow direction, and energy transmission path, construct the circuit topology model of the LED surgical shadowless lamp; S14. Based on the constructed circuit topology model, conduct a comprehensive analysis of the circuit architecture of the LED surgical shadowless lamp; use circuit analysis software to identify possible redundant links in the circuit, locate signal interference points, analyze the causes and propagation paths of interference; determine the energy loss area, and assess the degree of energy loss and influencing factors. S15. Based on the analysis results, optimize the measures to obtain a stable LED surgical shadowless lamp circuit architecture.

2. The LED surgical shadowless lamp circuit control method according to claim 1, characterized in that, S13 includes: Based on the graph theory abstraction of nodes and connection lines in S12, the direct connection relationships between nodes are further clarified; each node is recorded in detail as to which other nodes it is directly connected to, and for nodes with bidirectional connection relationships, their bidirectional transmission characteristics are clarified. In-depth analysis of the signal transmission direction in the circuit, starting from the source of the signal, to trace its flow path in the circuit; This study investigates the energy transfer process in a circuit, starting from the power input, and analyzes how electrical energy passes through various circuit components to ultimately provide the energy required for LED beads to emit light. Based on the analysis results of the above node relationships, signal flow direction and energy transmission path, a circuit topology model of the LED surgical shadowless lamp is built using circuit design software or graphic drawing tools. Nodes are used as the basic elements in the graph, and lines are used to represent the connections between nodes. The direction of the lines indicates the direction of signal flow. Different colors, shapes, or symbols are used to distinguish different types of nodes and connecting lines. After completing the initial circuit topology model, verification is performed; the model is compared with the actual circuit to check whether the node connections, signal flow, and energy transmission paths in the model are consistent with the actual circuit.

3. The LED surgical shadowless lamp circuit control method according to claim 1, characterized in that, S2 includes: S21. Further process the status data of the LED surgical shadowless lamp circuit nodes, and use digital signal processing technology to preprocess the voltage and current change curves over time. S22. By analyzing the processed voltage and current change curves, characteristic parameters reflecting the circuit's operating state are extracted; the characteristic parameters are integrated to obtain the electrical characteristic data of the LED surgical shadowless lamp. S23. Based on the extracted electrical feature data, and according to the characteristics of the lighting data in the frequency domain, time domain, and spatial domain, cluster analysis and pattern recognition techniques are used to perform unstructured division of the lighting data generated by the LED surgical shadowless lamp. S24. Divide the lighting data into different types, and further subdivide each type of lighting data; S25. For dynamically changing surgical scenarios, obtain video simulation lighting data to simulate continuously changing lighting scenarios; for static surgical scenarios, obtain static image lighting data corresponding to fixed lighting states. S26. For video simulation lighting data and static image lighting data, use data compression algorithms in information theory to calculate the minimum data transmission volume; obtain unstructured minimum lighting data by compressing the data.

4. The LED surgical shadowless lamp circuit control method according to claim 3, characterized in that, S23 includes: Lighting data is transformed from the time domain to the frequency domain using Fast Fourier Transform; in the frequency domain, the frequency component distribution of the lighting data is analyzed; the main frequency components are identified, and time series analysis of the lighting data is performed in the time domain; the lighting data is then mapped to the spatial domain for analysis. The features obtained from the frequency, time, and spatial domains are fused together; a feature normalization method is used to map each feature to a unified numerical range, and principal component analysis is used to reduce the dimensionality of the fused features; through feature fusion and dimensionality reduction, feature vectors are obtained. Cluster analysis is performed on the lighting data based on the dimensionality-reduced feature vectors; through cluster analysis, the lighting data is divided into several different clusters, each cluster representing a specific type of lighting data pattern; Based on the clusters obtained from cluster analysis, pattern recognition technology is used to further analyze and identify each cluster to determine the specific meaning and characteristics of the lighting data patterns corresponding to each cluster; The lighting dataset is divided into a training set and a test set. The training set is used to train the pattern recognition model, and the test set is used to test the trained model to evaluate the model's recognition accuracy and generalization ability. By continuously adjusting and optimizing the pattern recognition model, the unstructured partitioning of lighting data generated by LED surgical shadowless lamps is finally achieved.

5. The LED surgical shadowless lamp circuit control method according to claim 3, characterized in that, S26 includes: Characteristic analysis was performed on video simulation lighting data and static image lighting data respectively; based on the above analysis results, the adaptability of various data compression algorithms in information theory was evaluated; by comprehensively evaluating the compression effect and computational complexity of different algorithms when processing different types of lighting data, the most suitable combination of compression algorithms was selected for each type of data. After selecting the compression algorithm, the minimum data transmission volume of video simulation lighting data and still image lighting data is theoretically calculated using relevant theories of information theory. Based on the selected compression algorithm and the theoretically calculated minimum data transmission target, actual compression processing is performed on video simulated lighting data and still image lighting data; After compression, a comprehensive effect evaluation is performed on the compressed video simulation lighting data and static image lighting data; based on the evaluation results, the parameters of the compression algorithm are adjusted and optimized. After multiple evaluations and optimizations, the compressed video simulation lighting data and static image lighting data were finally obtained.

6. The LED surgical shadowless lamp circuit control method according to claim 1, characterized in that, The S3 includes: S31. Through the optimized LED surgical shadowless lamp circuit architecture, the circuit signal containing unstructured minimum lighting data is converted into a signal format that can be used for millimeter wave transmission; S32. Use electromagnetic field simulation software to simulate the transmission of signals along different circuit paths; set different circuit parameters and environmental conditions to simulate signal transmission in various real-world scenarios. S33. Through simulation experiments, obtain simulated data of equipment circuit communication transmission. Based on the simulated data of equipment circuit communication transmission, use the K-means clustering algorithm to divide the signal transmission range into multiple levels. S34. Set cluster centers and cluster radii, and classify different transmission conditions into corresponding levels based on the similarity of signal parameters; obtain signal transmission level range data, and assign different circuit operating parameters and transmission strategies to different levels.

7. The LED surgical shadowless lamp circuit control method according to claim 1, characterized in that, The S4 includes: S41. Obtain the circuit communication requirement data of the LED surgical shadowless lamp, and at the same time, record the adjustment frequency of the lighting parameters; S42. Based on the electrical characteristic data and unstructured minimum lighting data of the LED surgical shadowless lamp, analyze the maximum data flow required to meet communication needs under the current circuit state; calculate the actual available data flow, obtain the amount of data that the current circuit can carry by monitoring the real-time transmission status of the circuit; analyze the difference between the maximum data flow and the actual available data flow to obtain communication demand flow assessment data. S43. Combining unstructured minimum transmission data and communication demand traffic assessment data, a genetic algorithm is used to calculate the minimum transmission traffic for communication demand; thus obtaining optimized demand transmission traffic data.

8. The LED surgical shadowless lamp circuit control method according to claim 1, characterized in that, The S5 includes: S51. Based on the signal transmission level range data, classify the communication requirement transmission level of the circuit communication requirement data of the LED surgical shadowless lamp; map different communication requirements to the corresponding signal transmission level to obtain communication requirement transmission level data, and clarify the transmission priority and requirements of different communication requirements. S52. Based on the equipment circuit communication transmission simulation data, integrate the LED surgical shadowless lamp circuit resources by analyzing the communication requirement transmission level data and the optimization requirement transmission flow data; and rationally allocate circuit resources. S53. The generated device circuit communication transmission strategy is transmitted to the circuit control platform of the LED surgical shadowless lamp. The circuit control platform adjusts the circuit parameters in real time according to the received transmission strategy, and at the same time monitors the circuit's operating status and adjusts the control strategy according to the monitoring results.

9. A circuit control system for an LED surgical shadowless lamp, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 8.

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