Deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system

Abstract

The invention discloses a deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system, and particularly relates to the technical field of cerebral hemorrhage puncture, a cerebral hemorrhage puncture path planning module is arranged to provide a brand new cerebral hemorrhage puncture optimal puncture path generation method, the safety and the simple operability of the optimal puncture path can be considered, and the safety of the cerebral hemorrhage puncture is improved. The surgical risk can be effectively reduced; monitoring three-dimensional coordinates of an actual puncture point, three-dimensional coordinates of an inflection point, and puncture length and puncture angle, blood pressure, heart rate, respiratory rate, body temperature, pupil size, pupil shape and light response speed of each segmented puncture path, and judging whether adjustment measures need to be taken or not based on monitoring data; if needed, an adjusting scheme is generated and fed back to the mechanical arm auxiliary puncture control module or a voice prompt is sent to a surgeon, the monitoring data range is expanded, adjustment and judgment are more accurate, the dangerous situation duration time can be shortened, and the puncture operation success rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of puncture of cerebral hemorrhage, and more specifically to an auxiliary monitoring system for adjusting the puncture angle of deep cerebral hemorrhage. Background Art

[0002] With the vigorous development of medical technology, the traditional craniotomy and hematoma removal for the treatment of cerebral hemorrhage has gradually been eliminated, and the new stereotactic puncture for the treatment of cerebral hemorrhage has been widely promoted.

[0003] The existing deep cerebral hemorrhage puncture auxiliary monitoring system generates several puncture paths after determining the three-dimensional coordinates of the center of the hematoma, taking the three-dimensional coordinates of any point on the patient's scalp surface as the starting point and the coordinates of the center of the hematoma as the ending point, and selects the puncture path farthest from the important brain tissue as the target puncture path to perform the puncture task. When performing the puncture task, the patient's vital signs data are monitored, and when the patient's vital signs data are abnormal, adjustment measures are taken to ensure the normal progress of cerebral hemorrhage puncture, which can reduce the risk of cerebral hemorrhage puncture surgery to a certain extent.

[0004] However, the existing system still has some problems: the method for selecting the target puncture path is relatively simple, and the generated target path may be the farthest away from important brain tissue but the puncture path is the most complex and longest, which will delay the operation time to a certain extent and increase the surgical risk; when performing the puncture task, only monitoring the patient's vital signs data is not accurate enough, and the puncture operation data may be abnormal while the patient's vital signs data is normal, resulting in surgical risks due to untimely adjustments caused by abnormalities. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system, comprising: Brain structure 3D reconstruction module: retrieves the patient's brain MR images from the database in real time and performs image correction on them. Then, the patient's brain structure 3D model is reconstructed based on the corrected patient's brain MR images. After the model quality is tested, the hematoma, blood vessels and brain tissue in the brain structure model are marked. Intracerebral hemorrhage puncture path planning module: retrieve the annotated 3D model of the patient's brain structure, and determine the optimal 3D coordinates of the puncture path and scalp entry point based on the set puncture path generation rules and screening conditions; Puncture angle analysis module: after determining the best puncture path, segment the puncture path and calculate the puncture angle and puncture length of each puncture path; Robotic arm assisted puncture control module: when it is determined that cerebral hemorrhage puncture is required, the robot arm is controlled to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the puncture point and the three-dimensional coordinates of the inflection point of the optimal path, as well as the puncture length and puncture angle of each segment of the puncture path after segmentation, and the puncture operation is adjusted based on the feedback adjustment judgment result; Cerebral hemorrhage puncture monitoring module: used to monitor the puncture operation data of the robotic arm and the patient's vital signs during cerebral hemorrhage puncture and transmit the monitoring results to the cerebral hemorrhage puncture adjustment and judgment module in real time; Cerebral hemorrhage puncture adjustment judgment module: Based on the received real-time puncture operation data and the patient's vital signs parameters, it determines whether adjustment measures need to be taken. If necessary, it generates an adjustment plan and feeds it back to the robotic arm assisted puncture control module or sends a voice prompt to the surgeon; Cerebral hemorrhage puncture control effect analysis module: collects relevant data during cerebral hemorrhage puncture and calculates the puncture control stability coefficient after analyzing and processing it; Cerebral hemorrhage puncture efficacy analysis module: after the cerebral hemorrhage puncture operation is completed, the test results of the patient's relevant indicators are collected and analyzed and processed, and then the reliability coefficient of the puncture treatment is calculated; Intracerebral hemorrhage puncture-assisted effect evaluation module: Calculates the comprehensive evaluation index of intracerebral hemorrhage puncture-assisted effect based on the puncture control stability coefficient and puncture treatment reliability coefficient; Database: used to store data information of each module in the system.

[0007] Preferably, the brain structure three-dimensional reconstruction module includes a brain MR image retrieval unit, a brain MR image correction unit, a correction effect evaluation unit, a brain structure three-dimensional model reconstruction unit, a brain structure annotation unit and an automatic storage unit. The brain MR image retrieval unit retrieves the updated patient's brain MR image when updating the patient's brain MR image in the database; the brain MR image correction unit sequentially performs image denoising, image enhancement and image registration on the received brain MR image; the correction effect evaluation unit sequentially performs denoising effect evaluation, enhancement effect evaluation and registration effect evaluation after the image registration is completed, and issues an instruction to allow three-dimensional reconstruction after all evaluations are passed, and issues an instruction to continue image correction if any evaluation fails; the brain structure three-dimensional model reconstruction unit performs image clustering and segmentation based on pixels through a probabilistic adaptive neural network, and then reconstructs the brain structure three-dimensional model based on a volume rendering algorithm; the brain structure annotation unit uses an annotation tool to annotate the hematoma area, blood vessel area and other brain tissues in the brain structure three-dimensional model after reconstructing the brain structure three-dimensional model; the automatic storage unit automatically uploads the corrected brain MR image and the annotated brain structure three-dimensional model to the database for storage.

[0008] Preferably, the specific steps of the cerebral hemorrhage puncture path planning module to determine the optimal puncture path are as follows: B1. Retrieve the annotated 3D model of the brain structure to calculate the 3D coordinates of the center point of the hematoma and mark it as the second puncture point. At the same time, obtain the 3D coordinates of each point on the scalp surface and set the prohibited puncture area of ​​the brain. B2, generating several puncture paths with each point on the scalp surface as the starting point and the center point of the hematoma as the target point, deleting the puncture paths passing through the prohibited puncture area of ​​the brain, and marking the remaining puncture paths as the first puncture paths; B3. Calculate the shortest distance between the first puncture path and the forbidden area of ​​the brain and mark it as the first distance. Arrange the calculated first distances from high to low, using d a1 ,d a2 ,......,d an Indicates the first distance after arrangement, d a1 Indicates the first distance of the first position in the ranking order, d a2 Indicates the first distance of the second position in the ranking order, ..., d an Indicates the first distance of the nth position in the arrangement order and calculates the first proportional coefficient, using λ a1 , a2 ,......,λ an If it is expressed, then: ; B4. Calculate the path distance of the first puncture path and mark it as the second distance. Arrange the calculated second distances from low to high, using d b1 ,d b2 ,......,d bn Represents the second distance after arrangement, d b1 Indicates the second distance of the first position in the ranking order, d b2 Indicates the second distance of the 2nd position in the ranking order, ..., d bn Indicates the second distance of the nth position in the arrangement order, calculates the second proportional coefficient, and uses λ b1 , b2 ,......,λ bn If it is expressed, then: ; B5. Calculate the priority coefficient of the first puncture path based on the first proportional coefficient and the second proportional coefficient of the first puncture path. The puncture path corresponding to the maximum value of the priority coefficient of the first puncture path is the optimal puncture path. The scalp surface puncture point in the optimal puncture path is recorded as the first puncture point. The priority coefficient α of the kth puncture path in the first puncture path is yk The specific calculation formula is: ,λ akiis the first proportional coefficient of the kth puncture path in the first puncture path, i is the first distance arrangement order of the kth puncture path in the first puncture path, λ bkj为 The first proportional coefficient of the kth puncture path in the first puncture path, j is the second distance arrangement order of the kth puncture path in the first puncture path.

[0009] Preferably, the puncture angle analysis module divides the optimal puncture path into several puncture paths containing the maximum straight-line distance with the first puncture point as the starting point, and marks the two direct connection points as the first inflection point, the second inflection point, ..., the nth inflection point in sequence, and then calculates the first puncture angle and the first puncture length from the first puncture point to the first inflection point, the second puncture angle and the second puncture length from the first inflection point to the second inflection point, ..., the n+1th puncture angle and the n+1th puncture length from the nth inflection point to the second puncture point, and transmits the three-dimensional coordinates of the puncture points and the three-dimensional coordinates of the inflection points in the optimal puncture path, as well as the puncture length and puncture angle of each puncture path after division to the database for storage.

[0010] Preferably, the puncture operation data monitored in the cerebral hemorrhage puncture monitoring module include the actual three-dimensional coordinates of the first puncture point, the actual first puncture angle and the first puncture length, the actual three-dimensional coordinates of each inflection point, the puncture angle and puncture length between adjacent actual inflection points, the actual three-dimensional coordinates of the second puncture point, and the puncture angle and puncture length between the actual last inflection point and the actual second puncture point; the monitored patient's vital signs parameters include blood pressure, heart rate, respiratory rate, body temperature, and pupil size, shape, and light reaction speed.

[0011] Preferably, the cerebral hemorrhage puncture adjustment and judgment module includes a monitoring data receiving unit, an adjustment standard setting unit, an adjustment judgment unit, an adjustment scheme generating unit and an automatic storage unit, wherein the monitoring data receiving unit is used to receive real-time puncture operation data and real-time patient vital sign parameters; the adjustment standard setting unit is used to set the expected puncture angle offset coefficient, the expected puncture length offset coefficient, the expected inflection point position offset coefficient, the expected puncture point position offset coefficient, the expected fluctuation range of the patient's vital sign parameters and the expected rate of change coefficient of the patient's vital sign parameters; the adjustment judgment unit determines that the puncture operation is abnormal and needs to adjust the puncture operation when the actual puncture angle offset coefficient, the actual puncture length offset coefficient, the actual inflection point position offset coefficient, the actual puncture point position offset coefficient or the actual hematoma puncture point position offset coefficient is greater than or equal to the corresponding expected puncture angle offset coefficient, the expected puncture length offset coefficient, the expected inflection point position offset coefficient or the expected puncture point position offset coefficient. When the actual vital sign parameters of the patient are not within the fluctuation range of the expected patient vital sign parameters or the actual change rate coefficient of the patient vital sign parameters is greater than or equal to the change rate coefficient of the expected patient vital sign parameters, it is determined that the patient vital sign parameters are abnormal and the patient vital sign parameters need to be adjusted; when the puncture operation needs to be adjusted, the adjustment scheme generation unit generates a correction scheme that can adjust the actual puncture operation data that does not meet expectations back to the expected puncture operation data and sends it to the robotic arm assisted puncture control module; when the patient vital sign parameters need to be adjusted, a medical measure scheme that can adjust the actual vital sign parameters that do not meet expectations back to the expected vital sign parameters is generated and a voice prompt is sent to the surgeon; when adjustment is required, the automatic storage unit automatically transmits the received real-time monitoring data, the adjustment judgment result and the generated adjustment scheme to the database for storage, and when adjustment is not required, the received real-time monitoring data and the adjustment judgment result are transmitted to the database for storage.

[0012] Preferably, the cerebral hemorrhage puncture control effect analysis module includes a puncture control data collection unit, a puncture control data processing unit, a puncture control stability coefficient calculation unit and a data output unit. The specific control effect analysis process is as follows: Puncture control data collection unit: used to collect the expected number of feature points m during cerebral hemorrhage puncture az , the number of feature points that coincide with the expected feature points m at 、Expected number of puncture angles m bz , the number of puncture angles that are the same as the expected puncture angles in the same sequence m bt , optimal puncture path length d cz , the length of the puncture path that coincides with the optimal puncture path d ch , Vital sign parameter adjustment times m st , duration of intracerebral hemorrhage puncture surgery T a And the duration of abnormal vital sign parameters Tc , the characteristic points include inflection points and puncture points; Puncture control data processing unit: Calculate the puncture operation control accuracy coefficient β based on the collected data cx and the effectiveness coefficient of vital signs maintenance β wx , puncture operation control accuracy coefficient β cx The specific calculation formula is: , Vital signs maintenance effectiveness coefficient β wx The specific calculation formula is: ; Puncture control stability coefficient calculation unit: based on the puncture operation control accuracy coefficient β cx and the effectiveness coefficient of vital signs maintenance β wx Calculate the puncture control stability factor X cw , the specific formula is: ; Data output unit: sends the calculated puncture control stability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

[0013] Preferably, the cerebral hemorrhage puncture efficacy analysis module includes a patient index detection result acquisition unit, an expected postoperative patient index detection result setting unit, a data comparison unit, a puncture treatment reliability coefficient calculation unit and a data output unit. The patient index detection result acquisition unit is used to collect the GCS score, NIHSS score, ADL score and MRS score before cerebral hemorrhage puncture and the hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the expected postoperative patient index detection result setting unit is used to set the expected postoperative hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the data comparison unit collects the hematoma clearance rate α after cerebral hemorrhage puncture qx , GCS score F gx , NIHSS score F nx 、ADL score F ax and MRS score F mx Compared with the expected hematoma clearance rate after puncture of intracerebral hemorrhage qy , GCS score F gy , NIHSS score F ny 、ADL score F ay and MRS score F my Compare and calculate the hematoma clearance rate reaching standard coefficient θ qx 、GCS score achievement coefficient θ gx , NIHSS score compliance coefficient θ nx 、ADL score achievement coefficient θ ax And the MRS score achievement coefficient θ mx, the specific formula is as follows: The puncture treatment reliability coefficient calculation unit calculates the puncture treatment reliability coefficient X ck The specific formula is: ; The data output unit sends the calculated puncture treatment reliability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

[0014] Preferably, the specific formula for calculating the comprehensive evaluation index YFZ of puncture-assisted effect of cerebral hemorrhage based on the puncture control stability coefficient and the puncture treatment reliability coefficient of the cerebral hemorrhage puncture-assisted effect evaluation module is: .

[0015] Technical effects and advantages of the present invention: The present invention sets up a brain hemorrhage puncture path planning module to provide a new method for generating the best puncture path for brain hemorrhage puncture: the annotated three-dimensional model of the brain structure is retrieved to calculate the three-dimensional coordinates of the center point of the hematoma and mark it as the second puncture point, and the three-dimensional coordinates of each point on the scalp surface are obtained to set a prohibited puncture area of ​​the brain; a plurality of puncture paths are generated with each point on the scalp surface as the starting point and the center point of the hematoma as the target point, the puncture paths passing through the prohibited puncture area of ​​the brain are deleted, and the remaining puncture paths are marked as the first puncture paths; the shortest distance between the first puncture path and the prohibited area of ​​the brain is calculated and marked as the first distance, and the puncture paths are marked as the first puncture paths. The calculated first distances are arranged from high to low and the first proportional coefficient is calculated; the path distance of the first puncture path is calculated and marked as the second distance, the calculated second distances are arranged from low to high and the second proportional coefficient is calculated; the priority coefficient of the first puncture path is calculated based on the first proportional coefficient and the second proportional coefficient of the first puncture path, the puncture path corresponding to the maximum value of the priority coefficient of the first puncture path is the optimal puncture path, and the scalp surface puncture point in the optimal puncture path is recorded as the first puncture point; this method can take into account the safety and ease of operation of the optimal puncture path, and can effectively reduce surgical risks.

[0016] The present invention sets a puncture angle analysis module to segment the puncture path after determining the optimal puncture path, and calculates the puncture angle and puncture length of each puncture path; sets a robotic arm assisted puncture control module to control the robotic arm to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the puncture point of the optimal path, the three-dimensional coordinates of the inflection point, and the puncture length and puncture angle of each segment of the puncture path after segmentation when determining that cerebral hemorrhage puncture is required, and adjusts the puncture operation based on the feedback adjustment judgment result; sets a cerebral hemorrhage puncture monitoring module to monitor the puncture operation data of the robotic arm and the patient's vital sign parameters during cerebral hemorrhage puncture; sets a cerebral hemorrhage puncture adjustment judgment module to judge whether adjustment measures need to be taken based on the received real-time puncture operation data and the patient's vital sign parameters, and generates an adjustment plan if necessary and feeds it back to the robotic arm assisted puncture control module or sends a voice prompt to the surgeon; the monitoring data range is expanded, the adjustment judgment is more accurate, the duration of dangerous situations can be reduced, and the success rate of puncture surgery can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system structure block diagram of the present invention.

[0018] Figure 2 It is a flow chart of the system operation of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 The embodiment shown provides a deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system, including a brain structure three-dimensional reconstruction module, a cerebral hemorrhage puncture path planning module, a puncture angle analysis module, a robotic arm assisted puncture control module, a cerebral hemorrhage puncture monitoring module, a cerebral hemorrhage puncture adjustment judgment module, a cerebral hemorrhage puncture control effect analysis module, a cerebral hemorrhage puncture efficacy analysis module, a cerebral hemorrhage puncture auxiliary effect evaluation module and a database. The brain structure three-dimensional reconstruction module, the cerebral hemorrhage puncture path planning module, the puncture angle analysis module, the robotic arm assisted puncture control module, the cerebral hemorrhage puncture monitoring module, and the cerebral hemorrhage puncture adjustment judgment module are connected in sequence. The cerebral hemorrhage puncture adjustment judgment module is connected to the robotic arm assisted puncture control module and the cerebral hemorrhage puncture control effect analysis module. The cerebral hemorrhage puncture control effect analysis module and the cerebral hemorrhage puncture efficacy analysis module are connected to the cerebral hemorrhage puncture auxiliary effect evaluation module. All modules in the system are connected to the database.

[0021] The brain structure three-dimensional reconstruction module retrieves the patient's brain MR images from the database in real time and performs image correction on them, and then reconstructs the patient's brain structure three-dimensional model based on the corrected patient's brain MR images. After the model quality passes the inspection, the hematoma, blood vessels and brain tissue in the brain structure model are marked; Further, the brain structure three-dimensional reconstruction module includes a brain MR image retrieval unit, a brain MR image correction unit, a correction effect evaluation unit, a brain structure three-dimensional model reconstruction unit, a brain structure annotation unit and an automatic storage unit. The brain MR image retrieval unit retrieves the updated patient's brain MR image when updating the patient's brain MR image in the database; the brain MR image correction unit sequentially performs image denoising, image enhancement and image registration on the received brain MR image; the correction effect evaluation unit sequentially performs denoising effect evaluation, enhancement effect evaluation and registration effect evaluation after the image registration is completed, and issues an instruction to allow three-dimensional reconstruction after all evaluations are passed, and issues an instruction to continue image correction if any evaluation fails; the brain structure three-dimensional model reconstruction unit performs image clustering and segmentation based on pixels through a probabilistic adaptive neural network, and then reconstructs the brain structure three-dimensional model based on a volume rendering algorithm; the brain structure annotation unit uses an annotation tool to annotate the hematoma area, blood vessel area and other brain tissues in the brain structure three-dimensional model after reconstructing the brain structure three-dimensional model; the automatic storage unit automatically uploads the corrected brain MR image and the annotated brain structure three-dimensional model to the database for storage.

[0022] In this embodiment, it should be specifically explained that the MR image is a magnetic resonance image, and the MR image is obtained by an MRI examination (i.e., a magnetic resonance imaging examination). The patient's brain MR image obtained by the MRI examination will be transmitted in real time to the patient's brain MR image data set in the database, and the patient's brain MR image information in the database is updated accordingly. The brain MR image retrieval unit will automatically retrieve the updated patient's brain MR image. The patient's brain MR image data set stores a patient's brain MR image folder named with the patient's ID number + patient's name, and the patient's brain MR image folder stores the patient's brain MR image named with the MRI examination time. The corrected brain MR image will be transmitted in real time to the patient's brain MR in the database. In the image correction data set, the patient's brain MR image correction data set stores a patient's brain MR image correction record folder named with the patient's ID number + patient's name, and the patient's brain MR image correction record folder stores the corrected brain MR image and image correction text record named with the patient's brain MR image correction time. The annotated brain structure three-dimensional model will be transmitted in real time to the patient's brain structure three-dimensional model data set in the database, and the patient's brain structure three-dimensional model data set stores a patient's brain structure three-dimensional model folder named with the patient's ID number + patient's name, and the patient's brain structure three-dimensional model folder stores the patient's brain structure three-dimensional model file named with the completion time of the patient's brain structure three-dimensional model annotation.

[0023] In this embodiment, it is specifically necessary to explain that when the brain MR image correction unit performs image denoising on the received brain MR image, any one of the methods of mean filtering, median filtering, Wiener filtering and diffusion filtering may be used, which will not be specifically elaborated here; when the brain MR image correction unit performs image enhancement processing on the denoised brain MR image, the brain MR image correction unit may adjust the grayscale histogram of the image to make the grayscale distribution of the image more uniform, thereby improving the contrast of the image, and generating a high-resolution image from a low-resolution image to enhance the details and clarity of the image; when the brain MR image correction unit performs image registration on the brain MR image after image correction, the images at different levels are aligned to the same coordinate system through some transformation (such as translation, rotation, scaling, etc.).

[0024] What needs to be specifically explained in this embodiment is that, when performing denoising effect evaluation, the correction effect evaluation unit calculates the signal-to-noise ratio and the mean square error respectively and calculates the noise negative influence coefficient based on the above, and compares the calculated noise negative influence coefficient with the expected value. If the calculated value is less than or equal to the expected value, the denoising effect evaluation passes, and if the calculated value is greater than the expected value, the denoising effect evaluation fails; when performing enhancement effect evaluation, the correction effect evaluation unit calculates the contrast, information entropy and image clarity respectively and calculates the image display accuracy coefficient based on the above, and compares the calculated image display accuracy coefficient with the expected value. If the calculated value is greater than or equal to the expected value, the enhancement effect evaluation passes, and if the calculated value is less than the expected value, the enhancement effect evaluation fails; when performing registration effect evaluation, the correction effect evaluation unit calculates the feature point matching average error coefficient and compares the calculated feature point matching average error coefficient with the expected value. If the calculated value is less than or equal to the expected value, the registration effect evaluation passes, and if the calculated value is greater than the expected value, the registration effect evaluation fails.

[0025] Specifically, in this embodiment, a method for processing the data of the noise negative influence coefficient, the image display accuracy coefficient, and the feature point matching average error coefficient is provided, including the following steps: A1. Calculate the signal-to-noise ratio (SNR). The specific formula is: , M, N are the number of rows and columns of image pixels respectively, i, j are the i-th row and j-th column of image pixels respectively, f1(i, j), f2(i, j), f0 are the pixel value of the original image at point (i, j), the pixel value of the denoised image at point (i, j), and the average pixel value of the original image respectively, and the mean square error MSE is calculated. The specific formula is: ; A2. Calculate the noise negative impact coefficient W zf , the specific formula is: ; A3. Calculate the contrast μ d , the specific formula is: , calculate the information entropy μ s , the specific formula is: , L, k, p(k) are the total number of gray levels in the image, the kth gray level in the image, and the probability of occurrence of the kth gray level in the image, respectively. The image clarity μ is calculated. q , the specific formula is: , u and v are the frequency component indexes in the horizontal and vertical directions in the frequency domain respectively, D is the high-frequency area, F(u,v) is the complex value of the (u,v) frequency component of the image in the frequency domain, Re[F(u,v)] and Im[F(u,v)] are the real and imaginary parts of F(u,v), respectively, and the calculation formula of F(u,v) is: , b is an imaginary unit, satisfying b 2 =-1; A4. Calculate the image display accuracy coefficient W xd , the specific formula is: ; A5. Calculate the average error coefficient W of feature point matching pr , the specific formula is: , n t is the number of selected feature points, x ik 、x ij ,y ik ,y ij They are, in order, the projection coordinates of the kth image feature point containing the same feature point on the x-axis, the projection coordinates of the jth image feature point on the x-axis, the projection coordinates of the kth image feature point on the y-axis, and the projection coordinates of the jth image feature point on the y-axis.

[0026] It should be specifically noted in this embodiment that clustering and segmenting based on pixels through a probabilistic adaptive neural network is an existing technology and will not be elaborated on here. Three-dimensional reconstruction of brain structure based on a volume rendering algorithm is also an existing technology and will not be elaborated on here.

[0027] The cerebral hemorrhage puncture path planning module retrieves the annotated three-dimensional model of the patient's brain structure, and determines the optimal cerebral hemorrhage puncture path and the three-dimensional coordinates of the scalp puncture point based on the set cerebral hemorrhage puncture path generation rules and screening conditions; Furthermore, the specific steps of the cerebral hemorrhage puncture path planning module to determine the optimal puncture path are as follows: B1. Retrieve the annotated 3D model of the brain structure to calculate the 3D coordinates of the center point of the hematoma and mark it as the second puncture point. At the same time, obtain the 3D coordinates of each point on the scalp surface and set the prohibited puncture area of ​​the brain. B2, generating several puncture paths with each point on the scalp surface as the starting point and the center point of the hematoma as the target point, deleting the puncture paths passing through the prohibited puncture area of ​​the brain, and marking the remaining puncture paths as the first puncture paths; B3. Calculate the shortest distance between the first puncture path and the forbidden area of ​​the brain and mark it as the first distance. Arrange the calculated first distances from high to low, using d a1 ,d a2 ,......,d an Indicates the first distance after arrangement, d a1 Indicates the first distance of the first position in the ranking order, d a2 Indicates the first distance of the second position in the ranking order, ..., d an Indicates the first distance of the nth position in the arrangement order and calculates the first proportional coefficient, using λ a1 , a2 ,......,λ an If it is expressed, then: ; B4. Calculate the path distance of the first puncture path and mark it as the second distance. Arrange the calculated second distances from low to high, using d b1 ,d b2 ,......,d bn Represents the second distance after arrangement, d b1 Indicates the second distance of the first position in the ranking order, d b2 Indicates the second distance of the 2nd position in the ranking order, ..., d bn Indicates the second distance of the nth position in the arrangement order, calculates the second proportional coefficient, and uses λ b1 , b2 ,......,λ bn If it is expressed, then: ; B5. Calculate the priority coefficient of the first puncture path based on the first proportional coefficient and the second proportional coefficient of the first puncture path. The puncture path corresponding to the maximum value of the priority coefficient of the first puncture path is the optimal puncture path. The scalp surface puncture point in the optimal puncture path is recorded as the first puncture point. The priority coefficient α of the kth puncture path in the first puncture path is yk The specific calculation formula is: ,λ aki is the first proportional coefficient of the kth puncture path in the first puncture path, i is the first distance arrangement order of the kth puncture path in the first puncture path, λ bkj为 The first proportional coefficient of the kth puncture path in the first puncture path, j is the second distance arrangement order of the kth puncture path in the first puncture path.

[0028] What needs to be specifically explained in this embodiment is that when the hematoma information changes, the optimal puncture path will also be updated. The first puncture path and the optimal puncture path will be stored in the patient's cerebral hemorrhage puncture path data set in the database each time the cerebral hemorrhage puncture path is updated. The patient's cerebral hemorrhage puncture path data set stores a patient's cerebral hemorrhage puncture path folder named after the patient's ID number + patient's name. The patient's cerebral hemorrhage puncture path folder stores a folder named after the time when the first puncture path was generated + the first puncture path. The folder stores the first puncture path and the specially marked optimal puncture path.

[0029] The puncture angle analysis module determines the optimal puncture path, segments the puncture path, and calculates the puncture angle and puncture length of each puncture path; Furthermore, the puncture angle analysis module divides the optimal puncture path into several puncture paths containing the maximum straight-line distance with the first puncture point as the starting point, and marks the two direct connection points as the first inflection point, the second inflection point, ..., the nth inflection point in sequence, and then calculates the first puncture angle and the first puncture length from the first puncture point to the first inflection point, the second puncture angle and the second puncture length from the first inflection point to the second inflection point, ..., the n+1th puncture angle and the n+1th puncture length from the nth inflection point to the second puncture point, and transmits the three-dimensional coordinates of the puncture points and the three-dimensional coordinates of the inflection points in the optimal puncture path, as well as the puncture length and puncture angle of each segmented puncture path to the database for storage.

[0030] What needs to be specifically explained in this embodiment is that the puncture angle can be directly calculated by the existing technology, which will not be elaborated here. The puncture length is actually the spatial distance between the first and last inflection points of the segmented puncture path, which can be calculated by the Euler formula, which will not be elaborated here. When the hematoma information changes, the three-dimensional coordinates of the puncture point, the three-dimensional coordinates of the inflection point, and the puncture length and puncture angle of each segment of the puncture path after segmentation will also be updated accordingly.

[0031] When determining that cerebral hemorrhage puncture is required, the robot-assisted puncture control module controls the robot to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the puncture point and the three-dimensional coordinates of the inflection point of the optimal path and the puncture length and puncture angle of each segment of the puncture path after segmentation, and adjusts the puncture operation based on the feedback adjustment judgment result; In this embodiment, it should be specifically explained that the robot-assisted puncture control module includes a cerebral hemorrhage puncture state selection unit, a patient cerebral hemorrhage puncture operation data retrieval unit, a robot operation control unit, an adjustment judgment result feedback unit and a robot puncture operation adjustment unit. The cerebral hemorrhage puncture state selection unit selects the cerebral hemorrhage puncture state by clicking the cerebral hemorrhage puncture start or cerebral hemorrhage puncture end button; the patient cerebral hemorrhage puncture operation data retrieval unit retrieves the three-dimensional coordinates of the puncture point in the patient's optimal puncture path, the three-dimensional coordinates of the inflection point, and the puncture length and puncture angle of each puncture path after segmentation from the database; the robot operation control unit is used to control the robot arm to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the scalp puncture point in the optimal path, the three-dimensional coordinates of the inflection point, and the puncture length and puncture angle of each puncture path after segmentation; the adjustment judgment result feedback unit is used to receive the puncture operation adjustment plan fed back by the cerebral hemorrhage puncture adjustment judgment module; the robot puncture operation adjustment unit adjusts the puncture operation based on the feedback puncture operation adjustment plan.

[0032] The cerebral hemorrhage puncture monitoring module is used to monitor the puncture operation data of the robot arm and the patient's vital sign parameters during the cerebral hemorrhage puncture and transmit the monitoring results to the cerebral hemorrhage puncture adjustment and judgment module in real time; Furthermore, the puncture operation data monitored in the cerebral hemorrhage puncture monitoring module include the actual three-dimensional coordinates of the first puncture point, the actual first puncture angle and the first puncture length, the actual three-dimensional coordinates of each inflection point, the puncture angle and puncture length between adjacent actual inflection points, the actual three-dimensional coordinates of the second puncture point, and the puncture angle and puncture length between the actual last inflection point and the actual second puncture point; the monitored patient's vital signs parameters include blood pressure, heart rate, respiratory rate, body temperature, and pupil size, shape, and light reaction speed.

[0033] The cerebral hemorrhage puncture adjustment judgment module determines whether adjustment measures need to be taken based on the received real-time puncture operation data and the patient's vital sign parameters, and generates an adjustment plan if necessary, and feeds it back to the robot arm assisted puncture control module or sends a voice prompt to the surgeon; Furthermore, the cerebral hemorrhage puncture adjustment and judgment module includes a monitoring data receiving unit, an adjustment standard setting unit, an adjustment judgment unit, an adjustment scheme generating unit and an automatic storage unit. The monitoring data receiving unit is used to receive real-time puncture operation data and real-time patient vital sign parameters; the adjustment standard setting unit is used to set an expected puncture angle offset coefficient, an expected puncture length offset coefficient, an expected inflection point position offset coefficient, an expected puncture point position offset coefficient, an expected fluctuation range of patient vital sign parameters and an expected change rate coefficient of patient vital sign parameters; the adjustment judgment unit determines that the puncture operation is abnormal and needs to be adjusted when the actual puncture angle offset coefficient, the actual puncture length offset coefficient, the actual inflection point position offset coefficient, the actual puncture point position offset coefficient or the actual hematoma puncture point position offset coefficient is greater than or equal to the corresponding expected puncture angle offset coefficient, the expected puncture length offset coefficient, the expected inflection point position offset coefficient or the expected puncture point position offset coefficient. When the actual vital sign parameters of the patient are not within the fluctuation range of the expected patient vital sign parameters or the actual change rate coefficient of the patient vital sign parameters is greater than or equal to the change rate coefficient of the expected patient vital sign parameters, it is determined that the patient vital sign parameters are abnormal and the patient vital sign parameters need to be adjusted; when the puncture operation needs to be adjusted, the adjustment scheme generation unit generates a correction scheme that can adjust the actual puncture operation data that does not meet expectations back to the expected puncture operation data and sends it to the robotic arm assisted puncture control module; when the patient vital sign parameters need to be adjusted, a medical measure scheme that can adjust the actual vital sign parameters that do not meet expectations back to the expected vital sign parameters is generated and a voice prompt is sent to the surgeon; when adjustment is required, the automatic storage unit automatically transmits the received real-time monitoring data, the adjustment judgment result and the generated adjustment scheme to the database for storage, and when adjustment is not required, the received real-time monitoring data and the adjustment judgment result are transmitted to the database for storage.

[0034] Specifically, in this embodiment, the actual puncture angle deviation coefficient calculation formula format is: The calculation formula format of the other actual puncture operation parameter offset coefficients is the same as the calculation formula of the actual puncture angle offset coefficient. The only difference is the corresponding puncture operation data. Therefore, they are not calculated one by one here. The calculation formula format of the actual blood pressure change rate coefficient at time i is: When i=1, the value of the actual blood pressure change rate coefficient is 0. The calculation formula format of the change rate coefficients of the remaining vital sign parameters at time i is the same as the calculation formula of the actual blood pressure change rate coefficient at time i. Only the corresponding vital sign parameters are different, so they are not calculated here one by one.

[0035] The cerebral hemorrhage puncture control effect analysis module collects relevant data during the cerebral hemorrhage puncture and calculates the puncture control stability coefficient after analyzing and processing the data; Furthermore, the cerebral hemorrhage puncture control effect analysis module includes a puncture control data collection unit, a puncture control data processing unit, a puncture control stability coefficient calculation unit and a data output unit. The specific control effect analysis process is as follows: Puncture control data collection unit: used to collect the expected number of feature points m during cerebral hemorrhage puncture az , the number of feature points that coincide with the expected feature points m at 、Expected number of puncture angles m bz , the number of puncture angles that are the same as the expected puncture angles in the same sequence m bt , optimal puncture path length d cz , the length of the puncture path that coincides with the optimal puncture path d ch , Vital sign parameter adjustment times m st , duration of puncture surgery for cerebral hemorrhage T a And the duration of abnormal vital sign parameters T c , the characteristic points include inflection points and puncture points; Puncture control data processing unit: Calculate the puncture operation control accuracy coefficient β based on the collected data cx and the effectiveness coefficient of vital signs maintenance β wx , puncture operation control accuracy coefficient β cx The specific calculation formula is: , Vital signs maintenance effectiveness coefficient β wx The specific calculation formula is: ; Puncture control stability coefficient calculation unit: based on the puncture operation control accuracy coefficient β cx and the effectiveness coefficient of vital signs maintenance β wx Calculate the puncture control stability factor X cw , the specific formula is: ; Data output unit: sends the calculated puncture control stability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

[0036] The cerebral hemorrhage puncture efficacy analysis module collects the test results of the patient's relevant indicators after the cerebral hemorrhage puncture operation is completed, analyzes and processes the results, and then calculates the reliability coefficient of the puncture treatment; Further, the cerebral hemorrhage puncture efficacy analysis module includes a patient index detection result collection unit, an expected postoperative patient index detection result setting unit, a data comparison unit, a puncture treatment reliability coefficient calculation unit and a data output unit. The patient index detection result collection unit is used to collect the GCS score, NIHSS score, ADL score and MRS score before cerebral hemorrhage puncture and the hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the expected postoperative patient index detection result setting unit is used to set the expected postoperative hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the data comparison unit collects the hematoma clearance rate α after cerebral hemorrhage puncture qx , GCS score F gx , NIHSS score F nx 、ADL score F ax and MRS score F mx Compared with the expected hematoma clearance rate after puncture of intracerebral hemorrhage qy , GCS score F gy , NIHSS score F ny 、ADL score F ay and MRS score F my Compare and calculate the hematoma clearance rate reaching standard coefficient θ qx 、GCS score achievement coefficient θ gx , NIHSS score compliance coefficient θ nx 、ADL score achievement coefficient θ ax And the MRS score achievement coefficient θ mx , the specific formula is as follows: The puncture treatment reliability coefficient calculation unit calculates the puncture treatment reliability coefficient X ck The specific formula is: ; The data output unit sends the calculated puncture treatment reliability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

[0037] Specifically, it should be noted that the GCS score refers to the Glasgow Coma Scale score, and the lower the score, the more severe the degree of consciousness disorder; the NIHSS score refers to the National Institutes of Health Stroke Scale score, and the higher the score, the more severe the degree of neurological damage; the ADL score refers to the Daily Living Activities Scale score, and the higher the score, the better the daily living activities ability; the MRS score refers to the Modified RANKIN Scale score, and the higher the score, the more severe the degree of disability.

[0038] The cerebral hemorrhage puncture auxiliary effect evaluation module calculates the cerebral hemorrhage puncture auxiliary effect comprehensive evaluation index based on the puncture control stability coefficient and the puncture treatment reliability coefficient; Furthermore, the specific formula for calculating the comprehensive evaluation index YFZ of puncture assistance effect of cerebral hemorrhage based on the puncture control stability coefficient and the puncture treatment reliability coefficient of the cerebral hemorrhage puncture assistance effect evaluation module is: .

[0039] The database is used to store data information of each module in the system.

[0040] It should be specifically noted in this embodiment that the expected values ​​and set values ​​used are selected based on actual needs and are not limited to specific values.

[0041] like Figure 2 This embodiment provides an operation flow of a deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system, including the following steps: S1: retrieve the patient's brain MR images from the database in real time and perform image correction on them, then reconstruct the patient's brain structure 3D model based on the corrected patient's brain MR images. After the model quality passes the inspection, the hematoma, blood vessels and brain tissue in the brain structure model are marked; S2: retrieve the annotated 3D model of the patient's brain structure, and determine the optimal 3D coordinates of the puncture path for cerebral hemorrhage and the scalp puncture point based on the set puncture path generation rules and screening conditions; S3: After determining the optimal puncture path, segment the puncture path and calculate the puncture angle and puncture length of each puncture path; S4: when it is determined that cerebral hemorrhage puncture is required, the robot arm is controlled to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the puncture point and the three-dimensional coordinates of the inflection point of the optimal path and the puncture length and puncture angle of each segment of the puncture path after segmentation, and the puncture operation is adjusted based on the feedback adjustment judgment result; S5: used to monitor the puncture operation data of the robotic arm and the patient's vital signs during puncture of cerebral hemorrhage; S6: judging whether adjustment measures need to be taken based on the received real-time puncture operation data and the patient's vital sign parameters, and if necessary, generating an adjustment plan and feeding it back to the robotic arm assisted puncture control module or sending a voice prompt to the surgeon; S7: Collect relevant data during puncture of cerebral hemorrhage and calculate the puncture control stability coefficient after analyzing and processing it; S8: After the puncture surgery for cerebral hemorrhage is completed, the test results of the patient's relevant indicators are collected and analyzed and processed to calculate the reliability coefficient of the puncture treatment; S9: Calculate the comprehensive evaluation index of puncture-assisted effect of cerebral hemorrhage based on the puncture control stability coefficient and puncture treatment reliability coefficient.

[0042] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system, characterized by: include: Brain structure 3D reconstruction module: retrieves the patient's brain MR images from the database in real time and performs image correction on them. Then, the patient's brain structure 3D model is reconstructed based on the corrected patient's brain MR images. After the model quality is tested, the hematoma, blood vessels and brain tissue in the brain structure model are marked. Intracerebral hemorrhage puncture path planning module: retrieve the annotated 3D model of the patient's brain structure, and determine the optimal 3D coordinates of the puncture path and scalp entry point based on the set puncture path generation rules and screening conditions; Puncture angle analysis module: after determining the best puncture path, segment the puncture path and calculate the puncture angle and puncture length of each puncture path; Robotic arm assisted puncture control module: when it is determined that cerebral hemorrhage puncture is required, the robot arm is controlled to perform the cerebral hemorrhage puncture task according to the three-dimensional coordinates of the puncture point and the three-dimensional coordinates of the inflection point of the optimal path, as well as the puncture length and puncture angle of each segment of the puncture path after segmentation, and the puncture operation is adjusted based on the feedback adjustment judgment result; Cerebral hemorrhage puncture monitoring module: used to monitor the puncture operation data of the robotic arm and the patient's vital signs during cerebral hemorrhage puncture and transmit the monitoring results to the cerebral hemorrhage puncture adjustment and judgment module in real time; Cerebral hemorrhage puncture adjustment judgment module: Based on the received real-time puncture operation data and the patient's vital signs parameters, it determines whether adjustment measures need to be taken. If necessary, it generates an adjustment plan and feeds it back to the robotic arm assisted puncture control module or sends a voice prompt to the surgeon; Cerebral hemorrhage puncture control effect analysis module: collects relevant data during cerebral hemorrhage puncture and calculates the puncture control stability coefficient after analyzing and processing it; Cerebral hemorrhage puncture efficacy analysis module: after the cerebral hemorrhage puncture operation is completed, the test results of the patient's relevant indicators are collected and analyzed and processed, and then the reliability coefficient of the puncture treatment is calculated; Cerebral hemorrhage puncture-assisted effect evaluation module: Calculates the comprehensive evaluation index of cerebral hemorrhage puncture-assisted effect based on the puncture control stability coefficient and puncture treatment reliability coefficient.

2. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 1, characterized in that: The brain structure three-dimensional reconstruction module includes a brain MR image retrieval unit, a brain MR image correction unit, a correction effect evaluation unit, a brain structure three-dimensional model reconstruction unit, a brain structure annotation unit and an automatic storage unit. The brain MR image retrieval unit retrieves the updated patient's brain MR image when updating the patient's brain MR image in the database; the brain MR image correction unit sequentially performs image denoising, image enhancement and image registration on the received brain MR image; the correction effect evaluation unit sequentially performs denoising effect evaluation, enhancement effect evaluation and registration effect evaluation after the image registration is completed, and issues an instruction to allow three-dimensional reconstruction after all evaluations are passed, and issues an instruction to continue image correction if any evaluation fails; the brain structure three-dimensional model reconstruction unit performs image clustering and segmentation based on pixels through a probabilistic adaptive neural network, and then reconstructs the brain structure three-dimensional model based on a volume rendering algorithm; the brain structure annotation unit uses an annotation tool to annotate the hematoma area, blood vessel area and other brain tissues in the brain structure three-dimensional model after reconstructing the brain structure three-dimensional model; the automatic storage unit automatically uploads the corrected brain MR image and the annotated brain structure three-dimensional model to the database for storage.

3. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 2 is characterized in that: The specific steps of determining the optimal puncture path by the cerebral hemorrhage puncture path planning module are as follows: B1. Retrieve the annotated 3D model of the brain structure to calculate the 3D coordinates of the center point of the hematoma and mark it as the second puncture point. At the same time, obtain the 3D coordinates of each point on the scalp surface and set the prohibited puncture area of ​​the brain. B2, generating several puncture paths with each point on the scalp surface as the starting point and the center point of the hematoma as the target point, deleting the puncture paths passing through the prohibited puncture area of ​​the brain, and marking the remaining puncture paths as the first puncture paths; B3. Calculate the shortest distance between the first puncture path and the forbidden area of ​​the brain and mark it as the first distance. Arrange the calculated first distances from high to low, using d a1 ,d a2 ,......,d an Indicates the first distance after arrangement, d a1 Indicates the first distance of the first position in the ranking order, d a2 Indicates the first distance of the second position in the ranking order, ..., d an Indicates the first distance of the nth position in the arrangement order and calculates the first proportional coefficient, using λ a1 , a2 ,......,λ an If it is expressed, then: ； B4. Calculate the path distance of the first puncture path and mark it as the second distance. Arrange the calculated second distances from low to high, using d b1 ,d b2 ,......,d bn Represents the second distance after arrangement, d b1 Indicates the second distance of the first position in the ranking order, d b2 Indicates the second distance of the 2nd position in the ranking order, ..., d bn Indicates the second distance of the nth position in the arrangement order, calculates the second proportional coefficient, and uses λ b1 , b2 ,......,λ bn If it is expressed, then: ; B5. Calculate the priority coefficient of the first puncture path based on the first proportional coefficient and the second proportional coefficient of the first puncture path. The puncture path corresponding to the maximum value of the priority coefficient of the first puncture path is the optimal puncture path. The scalp surface puncture point in the optimal puncture path is recorded as the first puncture point. The priority coefficient α of the kth puncture path in the first puncture path is yk The specific calculation formula is: ,λ aki is the first proportional coefficient of the kth puncture path in the first puncture path, i is the first distance arrangement order of the kth puncture path in the first puncture path, λ bkj为 The first proportional coefficient of the kth puncture path in the first puncture path, j is the second distance arrangement order of the kth puncture path in the first puncture path.

4. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 3 is characterized in that: The puncture angle analysis module divides the optimal puncture path into several puncture paths with the maximum straight-line distance starting from the first puncture point, marks two direct connection points as the first inflection point, the second inflection point, ..., the nth inflection point in sequence, and then calculates the first puncture angle and the first puncture length from the first puncture point to the first inflection point, the second puncture angle and the second puncture length from the first inflection point to the second inflection point, ..., the n+1th puncture angle and the n+1th puncture length from the nth inflection point to the second puncture point, and transmits the three-dimensional coordinates of the puncture points and the three-dimensional coordinates of the inflection points in the optimal puncture path, as well as the puncture length and puncture angle of each segment of the puncture path after division to a database for storage.

5. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 4, characterized in that: The puncture operation data monitored in the cerebral hemorrhage puncture monitoring module include the actual three-dimensional coordinates of the first puncture point, the actual first puncture angle and the first puncture length, the actual three-dimensional coordinates of each inflection point, the puncture angle and puncture length between adjacent actual inflection points, the actual three-dimensional coordinates of the second puncture point, and the puncture angle and puncture length between the actual last inflection point and the actual second puncture point; the monitored patient's vital sign parameters include blood pressure, heart rate, respiratory rate, body temperature, and pupil size, shape, and light reaction speed.

6. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 5, characterized in that: The cerebral hemorrhage puncture adjustment and judgment module includes a monitoring data receiving unit, an adjustment standard setting unit, an adjustment judgment unit, an adjustment scheme generating unit and an automatic storage unit. The monitoring data receiving unit is used to receive real-time puncture operation data and real-time patient vital sign parameters; the adjustment standard setting unit is used to set an expected puncture angle offset coefficient, an expected puncture length offset coefficient, an expected inflection point position offset coefficient, an expected puncture point position offset coefficient, an expected fluctuation range of patient vital sign parameters and an expected change rate coefficient of patient vital sign parameters; the adjustment judgment unit is used to set an expected puncture angle offset coefficient, an expected puncture length offset coefficient, an expected inflection point position offset coefficient, an expected fluctuation range of patient vital sign parameters and an expected change rate coefficient of patient vital sign parameters; the adjustment judgment unit is used to set an expected puncture angle offset coefficient, an actual ... When the offset coefficient, the actual inflection point position offset coefficient, the actual puncture point position offset coefficient or the actual hematoma puncture point position offset coefficient is greater than or equal to the corresponding expected puncture angle offset coefficient, the expected puncture length offset coefficient, the expected inflection point position offset coefficient or the expected puncture point position offset coefficient, the puncture operation is determined to be abnormal and the puncture operation needs to be adjusted; when the actual vital sign parameters of the patient are not within the fluctuation range of the expected patient vital sign parameters or the actual change rate coefficient of the patient vital sign parameters is greater than or equal to the change rate coefficient of the expected patient vital sign parameters, the patient vital sign parameters are determined to be abnormal and the patient vital sign parameters need to be adjusted; When the puncture operation needs to be adjusted, the adjustment scheme generation unit generates a correction scheme that can adjust the actual puncture operation data that does not meet expectations back to the expected puncture operation data, and sends it to the robotic arm assisted puncture control module; when the patient's vital signs parameters need to be adjusted, the adjustment scheme generation unit generates a medical measure scheme that can adjust the actual vital signs parameters that do not meet expectations back to the expected vital signs parameters, and sends a voice prompt to the surgeon; when adjustment is required, the automatic storage unit automatically transmits the received real-time monitoring data, the adjustment judgment results and the generated adjustment scheme to the database for storage; when adjustment is not required, the received real-time monitoring data and the adjustment judgment results are transmitted to the database for storage.

7. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 1, characterized in that: The cerebral hemorrhage puncture control effect analysis module includes a puncture control data collection unit, a puncture control data processing unit, a puncture control stability coefficient calculation unit and a data output unit. The specific control effect analysis process is as follows: Puncture control data collection unit: used to collect the expected number of feature points m during cerebral hemorrhage puncture az , the number of feature points that coincide with the expected feature points m at 、Expected number of puncture angles m bz , the number of puncture angles that are the same as the expected puncture angles in the same sequence m bt , optimal puncture path length d cz , the length of the puncture path that coincides with the optimal puncture path d ch , Vital sign parameter adjustment times m st , duration of intracerebral hemorrhage puncture surgery T a And the duration of abnormal vital sign parameters T c , the characteristic points include inflection points and puncture points; Puncture control data processing unit: Calculate the puncture operation control accuracy coefficient β based on the collected data cx and the effectiveness coefficient of vital signs maintenance β wx , puncture operation control accuracy coefficient β cx The specific calculation formula is: , Vital signs maintenance effectiveness coefficient β wx The specific calculation formula is: ; Puncture control stability coefficient calculation unit: based on the puncture operation control accuracy coefficient β cx and the effectiveness coefficient of vital signs maintenance β wx Calculate the puncture control stability factor X cw , the specific formula is: ; Data output unit: sends the calculated puncture control stability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

8. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 7, characterized in that: The cerebral hemorrhage puncture efficacy analysis module includes a patient index detection result acquisition unit, an expected postoperative patient index detection result setting unit, a data comparison unit, a puncture treatment reliability coefficient calculation unit and a data output unit. The patient index detection result acquisition unit is used to collect the GCS score, NIHSS score, ADL score and MRS score before cerebral hemorrhage puncture and the hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the expected postoperative patient index detection result setting unit is used to set the expected postoperative hematoma clearance rate, GCS score, NIHSS score, ADL score and MRS score after cerebral hemorrhage puncture; the data comparison unit calculates the collected hematoma clearance rate α after cerebral hemorrhage puncture qx , GCS score F gx , NIHSS score F nx 、ADL score F ax and MRS score F mx Compared with the expected hematoma clearance rate after puncture of intracerebral hemorrhage qy , GCS score F gy , NIHSS score F ny 、ADL score F ay and MRS score F my Compare and calculate the hematoma clearance rate reaching standard coefficient θ qx 、GCS score achievement coefficient θ gx , NIHSS score compliance coefficient θ nx 、ADL score achievement coefficient θ ax And the MRS score achievement coefficient θ mx , the specific formula is as follows: The puncture treatment reliability coefficient calculation unit calculates the puncture treatment reliability coefficient X ck The specific formula is: ; The data output unit sends the calculated puncture treatment reliability coefficient to the cerebral hemorrhage puncture auxiliary effect evaluation module.

9. The deep cerebral hemorrhage puncture angle adjustment auxiliary monitoring system according to claim 8, characterized in that: The specific formula for calculating the comprehensive evaluation index YFZ of puncture assistance effect of cerebral hemorrhage based on the puncture control stability coefficient and the puncture treatment reliability coefficient of the cerebral hemorrhage puncture assistance effect evaluation module is: 。

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