A fumigation device treatment path planning system and fumigation device

Abstract

The application discloses a fumigation equipment treatment path planning system and a fumigation equipment, and applies to the technical field of intelligent medical treatment and Internet of Things. In the system, the biophysical parameters of each detection point in the detection area are acquired first, and whether the detection point is a to-be-treated detection point is judged, and then the treatment path is planned in combination with the number, position and starting point of the mechanical arm of the to-be-treated detection point. If it is a single to-be-treated detection point, the path is determined according to the position and the starting point of the mechanical arm, and the treatment execution parameter is set according to the corresponding parameter. If it is two or more, the path is optimized in combination with the weight coefficient, position and starting point of the mechanical arm of each point, and the execution parameter is configured according to the parameter difference of each point. The application can adaptively identify the position and number of lesions, realize dynamic planning of the treatment path of the mechanical arm, simultaneously set the treatment parameter based on the biophysical parameter, break the closed loop from sensing to execution, realize adaptive adjustment based on the real-time state of the patient, and improve the treatment accuracy, drug utilization rate and curative effect controllability.

Description

Technical Field

[0001] This application relates to the fields of smart medical and Internet of Things technology, and in particular to a fumigation equipment treatment path planning system and fumigation equipment. Background Technology

[0002] Traditional Chinese medicine fumigation therapy, through the synergistic effect of heat and medicinal properties, has proven effective in treating bone and joint diseases, chronic pain, and other conditions. Currently, most clinical fumigation equipment employs basic temperature and time control technologies, and its core logic is a typical "open-loop" control system.

[0003] However, existing technologies suffer from a fundamental structural flaw: the devices are all fixed structures, lacking the ability to coordinate with high-precision robotic arms for treatment and unable to adaptively adjust based on real-time physiological feedback from patients. Specifically, even if the system can sense multimodal physiological information such as the patient's surface microcirculation and thermal response, the lack of a high-precision robotic arm as a spatial actuator prevents this information from being translated into dynamic adjustment commands for the position, angle, and distance of the treatment head. This results in a break in the "perception-decision-execution" closed loop, making it difficult to achieve precise targeted treatment and dynamic posture optimization for the lesion area.

[0004] In view of the above technologies, the problem that urgently needs to be solved by those skilled in the art is to find a traditional Chinese medicine fumigation method that deeply integrates multimodal physiological perception with high-precision robotic arm collaboration technology and can collaborate with high-precision robotic arms to perform adaptive treatment adjustment. Summary of the Invention

[0005] The purpose of this application is to provide a fumigation device treatment path planning system and a fumigation device. This can solve the problem of the inability to deeply integrate multimodal physiological sensing and high-precision robotic arm collaborative technology in existing technologies.

[0006] To address the aforementioned technical problems, this application provides a fumigation equipment treatment path planning system, applied to a fumigation equipment including a robotic arm. The system includes: The biophysical parameter acquisition module is used to acquire the biophysical parameters corresponding to each detection point in the detection area; The judgment module is used to determine whether the corresponding detection point is a detection point to be treated based on various biophysical parameters. The first treatment path planning module is used to determine the treatment path of the robotic arm based on the starting point of the robotic arm and the location of the target detection point when the detection area includes a target detection point to be treated, and to determine the treatment execution parameters of the robotic arm based on the biophysical parameters corresponding to the target detection point. The second treatment path planning module is used to determine the treatment path based on the weight coefficient of each treatment detection point, the location of each treatment detection point, and the starting point of the robotic arm when the detection area includes at least two treatment detection points. It also determines the treatment execution parameters of the robotic arm at each treatment detection point based on the biophysical parameters corresponding to each treatment detection point.

[0007] Preferably, the biophysical parameter acquisition module includes: Temperature acquisition unit is used to acquire the temperature field distribution map of the detection area; The blood perfusion value acquisition unit is used to acquire the blood perfusion value at each detection point; The tissue elasticity change value acquisition unit is used to acquire the tissue elasticity change value at each detection point; The environmental parameter acquisition unit is used to acquire the environmental parameters corresponding to the detection area; among them, the temperature field distribution map, blood perfusion value, tissue elasticity change value, and environmental parameters constitute biophysical parameters.

[0008] Preferably, the tissue elasticity change value acquisition unit includes: The gridded scanning subunit is used to control the robotic arm to perform gridded scanning of the detection area with a preset safe contact force; The signal acquisition subunit is used to acquire the echo signals generated by each detection point in the detection area after gridded scanning. The tissue elasticity change value determination subunit is used to determine the tissue elasticity change value corresponding to each detection point based on the echo signal.

[0009] Preferably, the judgment module includes: The temperature judgment submodule is used to determine whether there are hot spots in the temperature field distribution map with temperatures higher than the first threshold; if so, the detection point corresponding to the hot spot is the detection point to be treated. and / or; The blood perfusion value judgment submodule is used to determine whether the blood perfusion value corresponding to each detection point is lower than the second threshold; if so, the detection point corresponding to the blood perfusion value lower than the second threshold is the detection point to be treated. and / or; The tissue elasticity change value judgment submodule is used to determine whether the tissue elasticity change value corresponding to each detection point is higher than the third threshold; if so, the detection point corresponding to the tissue elasticity change value higher than the third threshold is the detection point to be treated.

[0010] Preferably, it further includes: The spatial coordinate system registration module is used to register the spatial coordinate system of the 3D point cloud corresponding to the detection area and the scanned medical image data to determine the location of each detection point.

[0011] Preferably, it further includes: The safety monitoring and arbitration module is used to control the robotic arm to stop the treatment operation when the treatment temperature of any detection point to be treated is higher than the temperature threshold or the treatment contact force is higher than the contact force threshold during the treatment process.

[0012] Preferably, it further includes: The treatment stop module is used to control the robotic arm to stop the treatment operation when the current treatment detection point meets any of the following conditions: the increase in blood perfusion value is higher than the fourth threshold, the maintenance time is greater than the first time threshold, the temperature uniformity is higher than the fifth threshold, and the treatment time is greater than the safe duration.

[0013] Preferably, it further includes: The upload module is used to record the parameter changes at each detection point during the treatment process and upload them synchronously to the cloud platform.

[0014] Preferably, it further includes: The information authentication module is used to obtain patient information corresponding to the detection area and to identify and authenticate the patient information in order to obtain historical treatment data corresponding to the detection area.

[0015] On the other hand, this application also provides a fumigation device, including the above-mentioned fumigation device treatment path planning system.

[0016] The fumigation equipment treatment path planning system provided in this application is specifically applied to fumigation equipment including a robotic arm. In this system, the biophysical parameters corresponding to each detection point in the detection area are first acquired; secondly, it is determined whether the corresponding detection point is a treatment point based on each biophysical parameter; finally, the treatment path of the robotic arm is determined based on the number and location of the treatment points and the starting point of the robotic arm. Specifically: when the treatment area includes one treatment point, the treatment path of the robotic arm is determined based on the starting point of the robotic arm and the location of the treatment point, and the treatment execution parameters of the robotic arm are determined based on the biophysical parameters corresponding to the treatment point; when the treatment area includes at least two treatment points, the treatment path is determined based on the weight coefficient of each treatment point, the location of each treatment point, and the starting point of the robotic arm, and the treatment execution parameters of the robotic arm at each treatment point are determined based on the biophysical parameters corresponding to each treatment point. Therefore, this application achieves adaptive planning and dynamic optimization of the robotic arm's treatment path by identifying the spatial location and number of detection points to be treated: for single-point lesions, it can directly reach the affected area; for multiple-point lesions, it can intelligently plan the optimal path based on weight coefficients, thereby transforming traditional static, blind fumigation into precise, targeted treatment. Simultaneously, by setting the robotic arm's treatment execution parameters at each detection point based on the differences in biophysical parameters, it effectively solves the closed-loop problem of "sensory information not being converted into execution instructions" in existing technologies, truly realizing personalized adaptive adjustment based on the patient's real-time physiological state, significantly improving the accuracy of treatment, drug utilization rate, and controllability of clinical efficacy. Attached Figure Description

[0017] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This application provides a structural diagram of a fumigation equipment treatment path planning system; Figure 2 A flowchart of a treatment path planning method for a fumigation device provided in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0020] The core of this application is to provide a fumigation equipment treatment path planning system and a fumigation equipment.

[0021] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Figure 1 A structural diagram of a fumigation equipment treatment path planning system provided in this application is shown below. Figure 1 As shown, the system includes: Biophysical parameter acquisition module 11 is used to acquire the biophysical parameters corresponding to each detection point in the detection area; The judgment module 12 is used to determine whether the corresponding detection point is a detection point to be treated based on each biophysical parameter; The first treatment path planning module 13 is used to determine the treatment path of the robotic arm based on the starting point of the robotic arm and the location of the detection point to be treated when the detection area includes a detection point to be treated, and to determine the treatment execution parameters of the robotic arm based on the biophysical parameters corresponding to the detection point to be treated. The second treatment path planning module 14 is used to determine the treatment path based on the weight coefficient of each treatment detection point, the position of each treatment detection point and the starting point of the robotic arm when the detection area includes at least two treatment detection points, and to determine the treatment execution parameters of the robotic arm at each treatment detection point based on the biophysical parameters corresponding to each treatment detection point.

[0023] The fumigation equipment treatment path planning system provided in this application is specifically applied to fumigation equipment including a robotic arm. In addition, the fumigation equipment also includes an infrared thermal imaging component, a radar component (e.g., millimeter-wave radar), a steam humidity / temperature sensor inside the treatment hood, a medicine separation chamber, an emergency physical button, an ambient temperature / humidity sensor, a treatment hood, a first steam nozzle, a second steam nozzle, a touchscreen, a voice prompt module, an IoT module, a medicine level sensor, a heating tank water temperature sensor, an electromagnetic heating tank, an ultrasonic atomizing plate, a status sensor, several temperature sensor arrays, a capacitive humidity sensor, a photoelectric level gauge, a precision metering pump, and a microcontroller. It is easy to understand that the current fumigation equipment, compared to traditional fumigation equipment, adds an infrared thermal imaging component, a radar component, and a robotic arm. Specifically, the robotic arm is a 6-axis collaborative robotic arm, and it includes a multi-functional treatment head integrating an ultrasonic transducer, a pressure sensor, and a temperature sensor.

[0024] In a specific embodiment, the biophysical parameter acquisition module 11 is mainly used to acquire the biophysical parameters corresponding to each detection point in the detection area. These biophysical parameters include a temperature field distribution map, blood perfusion value, tissue elasticity change value, and environmental parameters. The temperature field distribution map serves as a real-time or simulated visualization of the temperature distribution within and around the target area tissue in three-dimensional space during a specific treatment process. The blood perfusion value refers to the blood flow rate through a unit mass of tissue per unit time; in treatment, especially in the field of thermotherapy, it specifically refers to the microcirculatory blood flow level of the target lesion and its surrounding normal tissue. The tissue elasticity change value, commonly referred to as the elasticity value or stiffness value, is a physical parameter used to quantitatively assess the softness and hardness of biological tissue, measured by ultrasonic elastography technology. Therefore, the infrared thermal imaging component of the fumigation equipment acquires the temperature field distribution map of the detection area of ​​the patient currently using the fumigation equipment; the radar component monitors the blood perfusion value of each detection point in the detection area using a non-contact detection method; the ultrasonic transducer acquires the tissue elasticity change value of each detection point; and the temperature sensor array and capacitive humidity sensor acquire the environmental parameters corresponding to the detection area. It is therefore easy to understand that the biophysical parameter acquisition module 11 consists of an infrared thermal imaging component, a radar component, an ultrasonic transducer, a temperature sensor array, and a capacitive humidity sensor.

[0025] The judgment module 12 is used to determine whether a corresponding detection point is a treatment target detection point based on various biophysical parameters. In other words, the working principle of the judgment module 12 is as follows: it receives and analyzes biophysical parameters in real time, compares, analyzes and quantifies the parameter values ​​corresponding to each detection point according to preset judgment thresholds and classification rules, identifies detection points with abnormal parameters that conform to the characteristics of lesions or target treatment areas, and finally determines whether the detection point is a treatment target detection point that needs targeted treatment, thereby realizing the automatic screening and positioning of detection areas.

[0026] The first treatment path planning module 13 is used to determine the treatment path of the robotic arm based on the starting point of the robotic arm and the position of the target detection point when the detection area includes one target detection point. It also determines the treatment execution parameters of the robotic arm based on the biophysical parameters corresponding to the target detection point. In other words, the working principle of the first treatment path planning module 13 is as follows: when there is only one target detection point in the detection area, it is input into the efficacy prediction model within the module. This model uses the current starting position of the robotic arm as the starting point and the spatial coordinates of the target detection point as the target point to complete the trajectory planning of the robotic arm from the starting point to the target point, generating a suitable treatment path. After treatment at the target point, the robotic arm returns to the starting point. Simultaneously, based on biophysical parameters such as the tissue elasticity change value corresponding to the target detection point, it matches and determines the treatment execution parameters of the robotic arm during the treatment process, such as the movement speed, intensity, and duration, thereby achieving integrated determination of path planning and treatment parameters for a single treatment target point.

[0027] The second treatment path planning module 14 is used to input at least two treatment detection points into the efficacy prediction model within the detection area. This model determines the treatment path based on the weight coefficients of each treatment detection point, its location, and the starting point of the robotic arm. It also determines the treatment execution parameters of the robotic arm at each treatment detection point based on the corresponding biophysical parameters. In other words, the working principle of the second treatment path planning module 14 is as follows: when there are at least two treatment detection points in the detection area, the module integrates the weight coefficients, spatial location information, and the starting point of the robotic arm to optimize and sort the paths for multiple treatment targets, determining the overall treatment path for the robotic arm to traverse each treatment detection point sequentially. Simultaneously, based on biophysical parameters such as the tissue elasticity change value corresponding to each treatment detection point, the module determines the treatment execution parameters of the robotic arm at the corresponding detection point, thereby achieving comprehensive planning of multi-target treatment paths and adaptive setting of treatment parameters at individual points.

[0028] The efficacy prediction model generates the robotic arm's treatment path through a path optimization algorithm, enabling high-weight regions to achieve longer dwell times or higher energy. Specifically, it comprises three layers: Layer 1: Weight Mapping and Initial Parameter Allocation: First, based on the identification results (e.g., hotspots, low-flow areas, stiff areas), each detection point in the detection region is assigned a weight coefficient wi, which directly reflects the treatment priority and demand intensity of that point. Then, based on this weight, an initial dwell time ti and output energy ei are linearly or non-linearly allocated to ensure that high-weight points receive longer treatment duration and / or stronger treatment stimulation. Layer 2: Path Sequence Optimization under Spatiotemporal Constraints: After obtaining the treatment parameters for each point, the algorithm models it as a weighted improved traveling salesman problem: the treatment utility (wi) of each detection point is assigned... f(ti, ei) is considered the "benefit," and the movement time of the robotic arm between points is considered the "cost." The algorithm aims to find a sequence that starts from the starting point, passes through all target points, and returns to the endpoint, maximizing this sequence while ensuring that the total time (treatment time + movement time) does not exceed the preset treatment duration. Considering real-time requirements, an efficient heuristic algorithm (such as a genetic algorithm or ant colony algorithm) is used for fast approximate solution, ensuring that the path is kinematically feasible and smooth for the robotic arm. Third layer: Online dynamic replanning and adjustment: The generated treatment path is not static. During treatment, the algorithm continuously receives real-time biophysical parameters (such as the temperature at a certain point reaching the safe upper limit, or the blood flow improvement reaching the expected level). Once the preset adjustment rules are triggered, the algorithm immediately initiates online replanning: Parameter fine-tuning: Based on the real-time therapeutic effect, dynamically increase or decrease ti and ei of the current point and subsequent similar points. Path insertion and deletion: When a new treatment target (such as a sudden pain point) is discovered, it can be inserted into the current optimal sequence position in real time; when the treatment at a certain point has reached the target or adverse reactions occur, it can be deleted from the path.

[0029] For example, in a specific implementation: Step 1: Multimodal baseline scanning yielded biophysical parameters: Temperature field distribution map (thermal imaging): The scan showed an overall knee joint temperature of approximately 35.5℃, with a "hot spot" on the lateral side of the patella at 37.2℃ (possibly an inflammatory reaction zone); Blood perfusion value (millimeter-wave radar): Overall blood perfusion value was low, especially in the medial joint space region, where the blood perfusion value was only 0.7 (lower than the average baseline of 0.9), indicating poor microcirculation; Tissue elasticity change value (robotic arm scan): In the region below the patella, the tissue elasticity value was significantly higher (i.e., tissue stiffness), suggesting possible soft tissue adhesions or cartilage degeneration.

[0030] Step 2: 3D Vision: Construct an accurate 3D model of the knee joint, including the bone outline and soft tissue surface.

[0031] Step 3: VR (Virtual Reality) Planning and AI (Artificial Intelligence) Initialization: The doctor sees a 3D model of the knee joint in VR, overlaid with a heat map (red hotspots), low blood perfusion areas (blue markers), and stiff areas (highlighted). The doctor confirms the system's recommended treatment plan: a base fumigation temperature of 58℃, with the robotic arm focusing on the three areas mentioned above.

[0032] Step 4: Path Generation and Collaborative Initiation: Based on the above identification results, the adaptive path planning algorithm automatically generates a weighted treatment path for the robotic arm.

[0033] The treatment weighting is as follows: Lateral patella (hotspot): Weight 0.3. The main strategy is mild stimulation and short-term cooling. The goal is not heating, but to promote heat dissipation and anti-inflammation in this area through gentle ultrasound micro-vibration, so lower ultrasound energy and shorter dwell time are allocated. Medial interarticular space (low blood flow): Weight 0.5. The main strategy is moderate stimulation and focused improvement of circulation. This is the main therapeutic target area, allocated with moderate ultrasound energy and a longer dwell time to promote vasodilation and blood flow restoration. Inferior patella (stiff tissue): Weight 0.2. The main strategy is deep release. Higher ultrasound energy (but controlled within certain safety limits) and a specific depth of focus are allocated to perform mechanical release of deep stiff tissue.

[0034] The algorithm meshes the surface of the knee joint treatment and calculates an "optimal coverage path" based on the weights.

[0035] For example: Starting point (safe position) - medial joint space area (circulate 10 grid points, stay at each point for 15 seconds) - lateral patellar area (circulate 3 grid points, stay at each point for 10 seconds) - infrapatellar area (circulate 5 grid points, stay at each point for 20 seconds) - return to safe position.

[0036] In addition, the fumigation equipment treatment path planning system provided in this application also has the ability to flexibly adjust steam. Specifically, it acquires the temperature field distribution map of the target area based on an infrared thermal imaging component; acquires the blood perfusion value and heart rate variability baseline of the target area based on a radar component; determines the target adjustment parameters corresponding to the temperature field distribution map, blood perfusion value, heart rate variability baseline, historical adjustment parameters, and environmental parameters during the adjustment cycle according to the steam adjustment model, records the changes of each parameter during the adjustment cycle, and synchronously uploads them to the cloud platform; adjusts the concentration of the medicinal liquid and the steam temperature in the fumigation equipment according to the target adjustment parameters so that the medicinal liquid and steam are combined in a preset ratio to generate mixed steam. In other words, the temperature field distribution map, blood perfusion value, and heart rate variability baseline are ultimately used to determine the corresponding target adjustment parameters under the corresponding steam adjustment model based on the acquired parameters, historical adjustment parameters, and environmental parameters, so as to adjust the concentration of the medicinal liquid and the steam temperature in the fumigation equipment according to the target adjustment parameters, thereby achieving the goal of adjusting relevant parameters during fumigation treatment to meet the needs of different patients, improving adjustment accuracy and medicinal liquid utilization. At the same time, it will upload relevant parameter changes during the current treatment process, providing a high-quality and reliable data source for clinical research and big data analysis.

[0037] The fumigation equipment treatment path planning system provided in this application is specifically applied to fumigation equipment including a robotic arm. In this system, the biophysical parameters corresponding to each detection point in the detection area are first acquired; secondly, it is determined whether the corresponding detection point is a treatment point based on each biophysical parameter; finally, the treatment path of the robotic arm is determined based on the number and location of the treatment points and the starting point of the robotic arm. Specifically: when the treatment area includes one treatment point, the treatment path of the robotic arm is determined based on the starting point of the robotic arm and the location of the treatment point, and the treatment execution parameters of the robotic arm are determined based on the biophysical parameters corresponding to the treatment point; when the treatment area includes at least two treatment points, the treatment path is determined based on the weight coefficient of each treatment point, the location of each treatment point, and the starting point of the robotic arm, and the treatment execution parameters of the robotic arm at each treatment point are determined based on the biophysical parameters corresponding to each treatment point. Therefore, this application achieves adaptive planning and dynamic optimization of the robotic arm's treatment path by identifying the spatial location and number of detection points to be treated: for single-point lesions, it can directly reach the affected area; for multiple-point lesions, it can intelligently plan the optimal path based on weight coefficients, thereby transforming traditional static, blind fumigation into precise, targeted treatment. Simultaneously, by setting the robotic arm's treatment execution parameters at each detection point based on the differences in biophysical parameters, it effectively solves the closed-loop problem of "sensory information not being converted into execution instructions" in existing technologies, truly realizing personalized adaptive adjustment based on the patient's real-time physiological state, significantly improving the accuracy of treatment, drug utilization rate, and controllability of clinical efficacy.

[0038] Based on the above embodiments, as a preferred embodiment, the biophysical parameter acquisition module 11 includes: Temperature acquisition unit is used to acquire the temperature field distribution map of the detection area; The blood perfusion value acquisition unit is used to acquire the blood perfusion value at each detection point; The tissue elasticity change value acquisition unit is used to acquire the tissue elasticity change value at each detection point; The environmental parameter acquisition unit is used to acquire the environmental parameters corresponding to the detection area; among them, the temperature field distribution map, blood perfusion value, tissue elasticity change value, and environmental parameters constitute biophysical parameters.

[0039] In a specific embodiment, the temperature acquisition unit is equivalent to an infrared thermal imaging component; the blood perfusion value acquisition unit is equivalent to a radar component; the tissue elasticity change value acquisition unit is equivalent to an ultrasonic transducer; and the environmental parameter acquisition unit is equivalent to a temperature sensor array and a capacitive humidity sensor.

[0040] It is easy to understand that the temperature acquisition unit generates and outputs the corresponding temperature field distribution map by collecting the temperature of the detection area; the blood perfusion value acquisition unit collects and analyzes the microcirculation blood flow information of each detection point to obtain the blood perfusion value of each detection point; the tissue elasticity change value acquisition unit detects the tissue mechanical properties of each detection point to obtain the corresponding tissue elasticity change value; and the environmental parameter acquisition unit collects the environmental parameters of the detection area. The above-mentioned temperature field distribution map, blood perfusion value, tissue elasticity change value, and environmental parameters together constitute the biophysical parameters of the system for subsequent analysis and processing, providing multi-dimensional data support for detection point determination, treatment path planning, and treatment execution parameter determination.

[0041] Its tissue elasticity change value acquisition unit includes: The gridded scanning subunit is used to control the robotic arm to perform gridded scanning of the detection area with a preset safe contact force; The signal acquisition subunit is used to acquire the echo signals generated by each detection point in the detection area after gridded scanning. The tissue elasticity change value determination subunit is used to determine the tissue elasticity change value corresponding to each detection point based on the echo signal.

[0042] In other words, the operating principle of the tissue elasticity change value acquisition unit is as follows: the gridded scanning subunit controls the robotic arm to perform gridded scanning on the detection area with a preset safe contact force, the signal acquisition subunit collects the echo signals generated by each detection point in the detection area during the scanning process, and the tissue elasticity change value determination subunit analyzes and calculates the echo signals to obtain the tissue elasticity change value corresponding to each detection point.

[0043] This unit controls a robotic arm to perform a gridded scan of the detection area using a preset safe contact force through a gridded scanning subunit. This enables full coverage, uniformity, and fine scanning of the detection area while ensuring detection safety and avoiding tissue damage. The signal acquisition subunit can accurately collect the echo signals corresponding to each detection point, providing reliable and stable raw data for tissue elasticity detection. The tissue elasticity change value determination subunit calculates the tissue elasticity change value of each detection point based on echo signal analysis, which can improve the detection accuracy and spatial resolution of tissue elasticity parameters. This enables quantitative, accurate, and automated acquisition of tissue mechanical properties, providing accurate and reliable data support for subsequent detection point identification, treatment path planning, and treatment parameter setting, thereby improving the detection accuracy and treatment safety of the entire system.

[0044] Based on the above embodiments, as a preferred embodiment, the determination module 12 includes: The temperature judgment submodule is used to determine whether there are hot spots in the temperature field distribution map with temperatures higher than the first threshold; if so, the detection point corresponding to the hot spot is the detection point to be treated. and / or; The blood perfusion value judgment submodule is used to determine whether the blood perfusion value corresponding to each detection point is lower than the second threshold; if so, the detection point corresponding to the blood perfusion value lower than the second threshold is the detection point to be treated. and / or; The tissue elasticity change value judgment submodule is used to determine whether the tissue elasticity change value corresponding to each detection point is higher than the third threshold; if so, the detection point corresponding to the tissue elasticity change value higher than the third threshold is the detection point to be treated.

[0045] In a specific embodiment, the temperature judgment submodule analyzes the temperature field distribution map to determine whether there are hotspot areas with temperatures higher than a first threshold. If such areas exist, it indicates inflammation in those areas, and the corresponding detection point is identified as a treatment target. The blood perfusion value judgment submodule compares the blood perfusion values ​​of each detection point to determine whether they are lower than a second threshold. If so, it indicates poor blood circulation in that area, and the corresponding detection point is identified as a treatment target. The tissue elasticity change value judgment submodule judges the tissue elasticity change values ​​of each detection point to determine whether they are higher than a third threshold. If so, it indicates stiffness in that area, and the corresponding detection point is identified as a treatment target. These submodules can work individually or in combination to achieve automatic and accurate screening and determination of treatment target detection points based on multimodal biophysical parameters.

[0046] Therefore, the judgment module in this application can accurately identify detection points with abnormal lesions from different physical characteristics by performing multi-dimensional threshold discrimination based on temperature field distribution map, blood perfusion value and tissue elasticity change value, thereby achieving reliable localization of the treatment area. By using multi-modal parameter judgment alone or in combination, the accuracy and robustness of target identification can be effectively improved, the risk of misjudgment caused by single parameter judgment can be reduced, and a stable and reliable target basis can be provided for subsequent treatment path planning and treatment execution, thereby improving the overall targeting and safety of treatment.

[0047] Based on the above embodiments, as a preferred embodiment, it further includes: The spatial coordinate system registration module is used to register the spatial coordinate system of the 3D point cloud corresponding to the detection area and the scanned medical image data to determine the location of each detection point.

[0048] The safety monitoring and arbitration module is used to control the robotic arm to stop the treatment operation when the treatment temperature of any detection point to be treated is higher than the temperature threshold or the treatment contact force is higher than the contact force threshold during the treatment process.

[0049] The treatment stop module is used to control the robotic arm to stop the treatment operation when the current treatment detection point meets any of the following conditions: the increase in blood perfusion value is higher than the fourth threshold, the maintenance time is greater than the first time threshold, the temperature uniformity is higher than the fifth threshold, and the treatment time is greater than the safe duration.

[0050] The upload module is used to record the parameter changes at each detection point during the treatment process and upload them synchronously to the cloud platform.

[0051] The information authentication module is used to obtain patient information corresponding to the detection area and to identify and authenticate the patient information in order to obtain historical treatment data corresponding to the detection area.

[0052] In a specific embodiment, the spatial coordinate system registration module performs a spatial coordinate system registration operation on the 3D point cloud data corresponding to the detection area and the scanned medical image data. This eliminates spatial offset errors from multi-source data and accurately calibrates the spatial position of each detection point in a unified coordinate system. This module achieves accurate fusion and position mapping of multimodal spatial data, providing a reliable spatial reference for subsequent treatment path planning and target localization, eliminating treatment errors caused by positional deviations, and improving the system's positioning accuracy and diagnostic reliability.

[0053] For the safety monitoring and arbitration module, the treatment temperature and contact force parameters at the treatment points are monitored in real time throughout the treatment process. When any parameter exceeds the preset temperature or contact force threshold, a control command is immediately issued to shut down the robotic arm treatment operation. This module achieves real-time safety protection and proactive arbitration during the treatment process, quickly responds to abnormal conditions, effectively avoids tissue overheating damage and excessive pressure damage, strengthens the treatment safety defense line, and improves the safety and stability of automated treatment equipment.

[0054] For the treatment termination module, the treatment status of the current treatment point is monitored in real time. It determines whether any of the following termination conditions are met: the increase in blood perfusion value exceeds the fourth threshold (e.g., 30%) and the duration exceeds the first time threshold (e.g., 3 minutes); the temperature uniformity exceeds the fifth threshold (e.g., 90%); the treatment duration exceeds the safe duration (e.g., 30 minutes); or an external stop signal is received. If any of these conditions are met, the robotic arm is controlled to stop treatment. This module achieves intelligent and individualized determination of treatment termination, balancing treatment effectiveness and safety, avoiding overtreatment or undertreatment, accurately controlling the treatment endpoint, and improving the standardization of treatment and the overall therapeutic effect.

[0055] The upload module collects and records dynamic changes in biophysical parameters, treatment execution parameters, and spatiotemporal location at each detection point throughout the treatment process, and simultaneously uploads the data to the cloud platform for storage and backup. This module enables traceability and retention of data throughout the entire treatment process, facilitating subsequent diagnosis and treatment review, efficacy analysis, data traceability, and remote management, providing data support for personalized treatment optimization and data-driven scientific research.

[0056] For the information authentication module, it collects and verifies the patient's identity information corresponding to the testing area, completes information identification and authorization authentication, and retrieves the patient's historical treatment data after successful authentication. This module ensures the privacy and security of patient data and the accurate matching of medical information, quickly retrieves historical medical data to compare before and after treatment effects, helps to develop individualized treatment plans, and improves the continuity and efficiency of diagnosis and treatment.

[0057] In summary, the fumigation equipment treatment path planning system completes a full set of path planning stages as follows: Phase 1: Treatment Preparation and Planning (0-5 minutes) 1.1 Patient registration and identity authentication adopts RFID (Radio Frequency Identification) / facial recognition / QR code multi-mode authentication, and loads the patient's historical treatment data and personalized model.

[0058] 1.2 Multimodal reference scanning: Baseline data acquisition: 1. Thermal imaging scan: Acquire the reference temperature field of the detection area.

[0059] 2. Blood perfusion scan: Obtain blood perfusion values.

[0060] 3.3D Modeling: Depth camera acquires 3D point cloud of the detection area.

[0061] 4. Tissue elasticity assessment: The robotic arm is controlled to perform a grid-like scan of the detection area with a preset safe contact force (e.g., 2N). The tissue elasticity distribution is assessed through the echo signal from its integrated ultrasonic transducer, and the tissue elasticity change value is obtained.

[0062] 1.3 VR immersive treatment planning involves doctors or patients wearing VR headsets to enter a virtual treatment scene. The system automatically recommends treatment plans, and doctors can manually adjust: fumigation temperature curve, robotic arm scanning path, key treatment area marking, and treatment intensity grading.

[0063] Phase Two: Implementation of Collaborative Therapy (5-30 minutes)

[0064] 2.1 Start-up of the intelligent fumigation system

[0065] Feedforward temperature compensation: T_sp = T_base + (20 - T_env) × 0.3, where T_base is the basic treatment temperature set by the doctor, T_env is the ambient temperature, and T_sp is the "target setting value of steam temperature inside the treatment hood" to avoid confusion with thermal imaging temperature.

[0066] Atomization control: basic steam (PID control electromagnetic heating, accuracy ±0.5℃), liquid atomization (precise control of metering pump, error <±0.1ml / min) and mixing ratio (adjustable from 1:1 to 3:1).

[0067] 2.2 Robotic Arm Targeted Therapy

[0068] The adaptive path planning algorithm includes the following steps: Based on thermal imaging data, hotspot areas with temperatures exceeding a first threshold are identified.

[0069] Based on blood perfusion data, low perfusion areas with blood flow values ​​below a second threshold are identified.

[0070] Based on elastography data, stiff areas with tissue elasticity changes exceeding a third threshold are identified.

[0071] Different treatment weight coefficients are assigned to the identified regions, and based on the weight coefficients, a path optimization algorithm is used to generate the scanning path (treatment path) of the robotic arm, so that high-weight regions can obtain longer dwell time or higher treatment energy.

[0072] Real-time force control and safety protection: contact force control (maintained within a safe range of 5-15N), emergency obstacle avoidance (real-time 3D visual monitoring to avoid patient movement), and over-temperature protection (automatic lifting when the treatment head temperature >42℃).

[0073] 2.3 Multimodal Closed-Loop Control

[0074] Controller layer implementation steps: 1. Data synchronization and fusion, unified alignment of timestamps, registration of spatial coordinate systems, and generation of treatment status matrix.

[0075] 2. Feature extraction and real-time feature calculation (extracting average temperature, temperature uniformity, blood flow rate of change, and pressure index).

[0076] 3. AI model decision-making

[0077] The efficacy prediction model is deployed on a lightweight, fully connected neural network embedded in a main control chip, and it is built and run in the following way: 1. Training phase: Supervised training is performed on a cloud server using historical treatment data (input: multimodal feature vector; output: control parameters for the next time step as evaluated by experts) to obtain the initial model.

[0078] 2. Deployment phase: The trained model is pruned and quantized, and converted into a format model file that can run on the current controller.

[0079] 3. Inference phase: In each control cycle (e.g., 1Hz), the feature vector extracted in real time is input into the model. The model output layer directly provides the normalized value corresponding to the parameter, which is then denormalized and used as the control command.

[0080] 4. Cooperative control algorithm

[0081] def collaborative_control(T_sp, PU_target, comfort_level):

[0082] # Fumigation System Control

[0083] if comfort_level < threshold_low:

[0084] steam_power = adjust_for_comfort(T_sp, comfort_level)

[0085] else:

[0086] steam_power = calculate_optimal_power(T_sp, PU_target)

[0087] # Robotic Arm Control Logic

[0088] if detect_focal_lesion(thermal_map):

[0089] robotic_path = replan_for_lesion(current_path, lesion_location)

[0090] robotic_power = increase_for_focal(lesion_severity)

[0091] return steam_power, robotic_path, robotic_power

[0092] 5. Security monitoring and arbitration, independent security thread, highest priority.

[0093] Hard safety rule: if T_steam > 65℃ or Force > 20N: EMERGENCY_STOP(); Soft security rule: Override AI instructions when there is local overheating.

[0094] Phase 3: Intelligent Termination and Data Management (30-35 minutes)

[0095] 3.1 Judgment of therapeutic efficacy targets

[0096] Termination condition (any one of the following must be met): 1. Blood perfusion value increases by ≥30% and remains elevated for 3 minutes; 2. Temperature uniformity ≥ 90%; 3. Reach the maximum safe duration (30 minutes); 4. The patient voluntarily stopped; 3.2 Soft shutdown sequence 1. Robotic arm return to position: The treatment head is raised and returns to the safe position.

[0097] 2. Fumigation system power reduction: liquid supply (immediately), heating power reduced in stages: 100%-60%-30%-0% (within 3 minutes), maintain 40℃ for 5 minutes.

[0098] 3. System cooling: The cooling fan operates until the internal temperature is below 40°C.

[0099] 3.3 Therapeutic Digital Twin Generation

[0100] {

[0101] Treatment ID: "T20240121-001", Patient Information: {"ID": "P001", "Name": "xxx"}, "Time Information": {"Start Time": "2024-01-21 10:00", "Duration": "25 minutes"}, "Control parameters": { "Temperature curve": [{"Time": "t", "Set value": "T_sp", "Actual value": "T_actual"}], "Atomization Curve": [{"Time": "t", "Set Flow Rate": "F_sp", "Actual Flow Rate": "F_actual"}], "Robotic arm path": {"Trajectory points": ["x,y,z", ...], "Dwell time": [...], "Applied energy": [...]} }, Physiological response: { "Temperature field change": {"Baseline": "T_base_map", "Final": "T_final_map", "Improvement rate":"ΔT%"}, "Blood Flow Changes": {"Baseline PU": "PU_base", "Final PU": "PU_final", "Improvement Rate": "ΔPU%"}, "Comfort Level Record": {"HRV Change Curve": [...], "User Feedback Rating": 4.5} }, "Security Incident": {"Record": [], "Handling Measures": []}, "Efficacy Assessment": { Overall rating: 8.7 AI Recommendation: "It is recommended to increase the time the robotic arm spends in area A during the next treatment." } } 3.4 Blockchain Evidence Preservation Process 1. Digital twin data is encrypted using the SM4 national cryptographic algorithm.

[0102] 2. Generate a Merkle tree and calculate the root hash.

[0103] 3. The hash value is uploaded to the blockchain network (consortium blockchain).

[0104] 4. Generate a certificate of evidence storage, which includes: [Treatment ID, timestamp, blockchain transaction ID, data hash].

[0105] The blockchain network adopts a consortium blockchain architecture based on Hyperledger Fabric, and the evidence storage nodes are composed of authorized medical institutions, regulatory agencies, and other entities.

[0106] Phase Four: Anomaly Handling and System Maintenance

[0107] 4.1 Real-time anomaly detection

[0108] def anomaly_detection(sensor_data):

[0109] anomalies = []

[0110] # Network interruption detection

[0111] if wifi_strength < -75dBm for 20s:

[0112] anomalies.append(("Network interrupted", "Start local caching"))

[0113] # Robotic arm anomaly detection

[0114] if force_sensor > 20N or torque > threshold:

[0115] anomalies.append(("Robot arm overload", "Emergency stop"))

[0116] # Sensor Failure Detection

[0117] if sensor_consistency_check() == FAIL:

[0118] anomalies.append(("Sensor malfunction", "Switch to redundant sensor"))

[0119] return anomalies

[0120] 4.2 Cross-device continuation of treatment

[0121] Breakpoint save: Local encrypted cache of complete treatment status.

[0122] Device addressing: Bluetooth Mesh network broadcast to find backup devices.

[0123] Status recovery: The backup device loads encrypted data packets with an error of <±0.3℃.

[0124] Patient confirms: Treatment will continue after recertification.

[0125] 4.3 System Self-Maintenance

[0126] Daily self-checks: sensor calibration, actuator testing.

[0127] Predictive maintenance: Predicting component lifespan based on robotic arm operation data.

[0128] Remote diagnostics: Remote fault diagnosis and firmware upgrade via cloud platform.

[0129] Therefore, the fumigation equipment treatment path planning system provided in this application has the following advantages: 1. Abandoning a single temperature sensor, it integrates multi-source data such as thermal imaging (spatial temperature field) and millimeter-wave radar (blood perfusion values, baseline heart rate variability) to comprehensively perceive the bio-thermal response and microcirculation status of the target area. Based on multi-source data, it identifies the treatment detection points and their spatial location and number, achieving adaptive planning and dynamic optimization of the robotic arm's treatment path: for single-point lesions, it can directly reach the affected area; for multiple-point lesions, it can intelligently plan the optimal path based on weight coefficients, thus transforming traditional static, blind fumigation into precise, targeted treatment.

[0130] 2. A complete "digital twin" record is generated for each treatment, including treatment execution parameters for the robotic arm at each treatment point, set according to the differences in biophysical parameters at each detection point. This record is encrypted and uploaded to a blockchain network for evidence storage. This achieves complete standardization and traceability of the treatment process, generates tamper-proof electronic efficacy certificates, provides evidence for medical disputes, and provides a high-quality, reliable data source for clinical research and big data analysis.

[0131] On the other hand, this application also provides a fumigation device, including the aforementioned fumigation device treatment path planning system, and has the same beneficial effects.

[0132] Since the embodiments of the fumigation equipment provided in this application are the same as the embodiments of the fumigation equipment treatment path planning system described above, this application will not repeat them here.

[0133] On the other hand, this application also provides a method for planning treatment pathways for fumigation equipment, such as... Figure 2 As shown, it includes the following steps: S10: Obtain the biophysical parameters corresponding to each detection point in the detection area.

[0134] S11: Determine whether the corresponding detection point is a detection point to be treated based on each biophysical parameter.

[0135] S12: When the detection area includes a detection point to be treated, the treatment path of the robotic arm is determined according to the starting point of the robotic arm and the location of the detection point to be treated, and the treatment execution parameters of the robotic arm are determined according to the biophysical parameters corresponding to the detection point to be treated.

[0136] S13: When the detection area includes at least two detection points to be treated, the treatment path is determined according to the weight coefficient of each detection point to be treated, the location of each detection point to be treated, and the starting point of the robotic arm. The treatment execution parameters of the robotic arm at each detection point to be treated are determined according to the biophysical parameters corresponding to each detection point to be treated.

[0137] Since the embodiments of the fumigation equipment treatment path planning method provided in this application are the same as the embodiments of the fumigation equipment treatment path planning system described above, this application will not repeat them here.

[0138] The above provides a detailed description of a fumigation device treatment path planning system and a fumigation device provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0139] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A fumigation equipment treatment path planning system, characterized in that, A fumigation system applied to a fumigation device including a robotic arm, the system comprising: The biophysical parameter acquisition module is used to acquire the biophysical parameters corresponding to each detection point in the detection area; The judgment module is used to determine whether the corresponding detection point is a detection point to be treated based on each of the biophysical parameters; The first treatment path planning module is used to determine the treatment path of the robotic arm based on the starting point of the robotic arm and the position of the detection point to be treated when the detection area includes a detection point to be treated; and to determine the treatment execution parameters of the robotic arm based on the biophysical parameters corresponding to the detection point to be treated. The second treatment path planning module is used to determine the treatment path based on the weight coefficient of each of the treatment detection points, the position of each treatment detection point and the starting point of the robotic arm when the detection area includes at least two treatment detection points, and to determine the treatment execution parameters of the robotic arm at each treatment detection point based on the biophysical parameters corresponding to each treatment detection point.

2. The fumigation equipment treatment path planning system according to claim 1, characterized in that, The biophysical parameter acquisition module includes: A temperature acquisition unit is used to acquire a temperature field distribution map of the detection area; A blood perfusion value acquisition unit is used to acquire the blood perfusion value of each of the detection points; The tissue elasticity change value acquisition unit is used to acquire the tissue elasticity change value of each of the detection points; An environmental parameter acquisition unit is used to acquire environmental parameters corresponding to the detection area; wherein the temperature field distribution map, the blood perfusion value, the tissue elasticity change value, and the environmental parameters constitute the biophysical parameters.

3. The fumigation equipment treatment path planning system according to claim 2, characterized in that, The tissue elasticity change value acquisition unit includes: A grid-based scanning subunit is used to control the robotic arm to perform grid-based scanning on the detection area with a preset safe contact force; The signal acquisition subunit is used to acquire the echo signal generated by each of the detection points in the detection area after the gridded scan. The tissue elasticity change value determination subunit is used to determine the tissue elasticity change value corresponding to each detection point based on the echo signal.

4. The fumigation equipment treatment path planning system according to claim 2, characterized in that, The judgment module includes: The temperature judgment submodule is used to determine whether there are hot spots in the temperature field distribution map with temperatures higher than a first threshold; if so, the detection point corresponding to the hot spot is the detection point to be treated. and / or; The blood perfusion value determination submodule is used to determine whether the blood perfusion value corresponding to each detection point is lower than a second threshold; if so, the detection point corresponding to the blood perfusion value lower than the second threshold is the detection point to be treated. and / or; The tissue elasticity change value judgment submodule is used to determine whether the tissue elasticity change value corresponding to each detection point is higher than the third threshold; if so, the detection point corresponding to the tissue elasticity change value higher than the third threshold is the detection point to be treated.

5. The fumigation equipment treatment path planning system according to claim 1, characterized in that, Also includes: The spatial coordinate system registration module is used to perform spatial coordinate system registration on the three-dimensional point cloud corresponding to the detection area and the scanned medical image data to determine the location of each detection point.

6. The fumigation equipment treatment path planning system according to claim 1, characterized in that, Also includes: The safety monitoring and arbitration module is used to control the robotic arm to stop the treatment operation when the treatment temperature of any of the treatment detection points is higher than the temperature threshold or the treatment contact force is higher than the contact force threshold during the treatment process.

7. The fumigation equipment treatment path planning system according to claim 1, characterized in that, Also includes: The treatment stop module is used to control the robotic arm to stop the treatment operation when the current treatment detection point meets any of the following conditions: the increase in blood perfusion value is higher than the fourth threshold, the maintenance time is greater than the first time threshold, the temperature uniformity is higher than the fifth threshold, and the treatment time is greater than the safe duration.

8. The fumigation equipment treatment path planning system according to claim 1, characterized in that, Also includes: The upload module is used to record the parameter changes of each detection point during the treatment process and upload them to the cloud platform simultaneously.

9. The fumigation equipment treatment path planning system according to any one of claims 1-8, characterized in that, Also includes: The information authentication module is used to obtain patient information corresponding to the detection area and to identify and authenticate the patient information in order to obtain historical treatment data corresponding to the detection area.

10. A fumigation device, characterized in that, The treatment path planning system for fumigation equipment as described in any one of claims 1-9.

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