CN105657720A - System for taking out mucosa calculus in minimally invasive gallbladder surgery - Google Patents

Abstract

The present invention relates to a system for taking out a mucosa calculus in a minimally invasive gallbladder surgery. The system comprises a display apparatus, a power apparatus, a negative pressure apparatus, an extraction apparatus and a washing apparatus. The power apparatus is electrically connected with the display apparatus and the extraction apparatus. The negative pressure apparatus is arranged above the power apparatus. The washing apparatus is arranged below the power apparatus. The display apparatus specifically comprises wheels, a base, a hydraulic pump, an adjusting rod, a telescopic rod, a steering ball and a display screen. The wheels are arranged below the base. The hydraulic pump is arranged above the base. The steering ball is arranged above the telescopic rod. The display screen is arranged above the steering ball. According to the system, the calculus can be taken out effectively due to arrangement of a sucker and a scratch net; the surgery process can be detected effectively due to arrangement of a camera and the display screen; and the calculus and blood block can be effectively separated due to arrangement of the washing apparatus.

Description

Mucosal calculus taking system for minimally invasive gallbladder protection surgery

Technical Field

The invention belongs to the technical field of medical instruments, and particularly relates to a mucosal calculus taking system for minimally invasive gallbladder protection surgery.

Background

Calculus of intrahepatic duct is one of the common diseases of hepatobiliary department, the most effective method for treating calculus of intrahepatic duct is to take out calculus by operation, the most common calculus taking tool in operation is calculus taking forceps, and in the calculus taking process, the calculus is clamped by the calculus taking forceps and then taken out. In the operation process, the bile duct is required to be propped open, so that the bile duct can be damaged, and particularly when the calculus is large, the damage can be more serious; after the calculus is taken out, serum washing liquor is needed to wash the calculus, and then the calculus is collected by a brain surgery adhesive film, but the method forms blood clots after blood coagulation and the calculus are mixed together and cannot be completely removed, so that great inconvenience is brought to the collection of the calculus. The minimally invasive gallbladder-protecting operation is a novel method for taking out the calculus, and the existing calculus taking-out device has the problems of inconvenient carrying, complicated structure, unclean calculus cleaning and incapability of removing large calculus.

Disclosure of Invention

The invention provides a mucosal calculus removing device which is simple in structure, simple and convenient to operate, flexible to use and obvious in effect, and aims to solve the technical problems in a mucosal calculus removing system for minimally invasive gallbladder protection surgery.

The invention adopts the technical scheme to solve the technical problems in the orthopedic traction device: a mucosal calculus taking system for minimally invasive gallbladder-protecting surgery comprises a display device, a power device, a negative pressure device, an extraction device and a flushing device, wherein the power device is electrically connected with the display device and the extraction device; the negative pressure device is arranged above the power device; the flushing device is arranged below the power device;

the display device specifically comprises wheels, a base, a hydraulic pump, an adjusting rod, a telescopic rod, a steering ball and a display screen, wherein the wheels are arranged below the base; the hydraulic pump is arranged above the base; the adjusting rod is connected with the telescopic rod; the steering ball is arranged on the telescopic rod; the display screen is arranged on the steering ball; the display screen is provided with a wireless network module, and the wireless network module is provided with a multi-factor decision module and a wireless resource optimization allocation module;

the power device comprises a rotating motor, a rotating shaft, a graduated scale, a sleeve and a sleeve, and is arranged on the base; the rotating shaft is connected with the rotating motor and the extracting device; the graduated scale is arranged on the rotating shaft; the sleeve is arranged outside the rotating shaft; the sleeve is sleeved with the sleeve;

the negative pressure device specifically comprises a negative pressure shell, a vacuum meter, an air inlet, a pressure gauge, an adjusting button, a pump-out machine, an air outlet and a negative pressure pipe, wherein the vacuum meter is arranged on the negative pressure shell; the air inlet is arranged at the upper right part of the negative pressure shell; the pressure gauge is arranged on the upper surface of the negative pressure shell; the adjusting button is arranged on the outer wall of the negative pressure shell; the pumping-separating machine is arranged in the negative pressure shell; the air outlet is arranged at the left lower part of the negative pressure shell; the negative pressure pipe is arranged at the lower end of the negative pressure shell;

the extraction device specifically comprises a sucker, a scraping net, a camera, a collection bag and a detector, wherein the sucker is connected to the negative pressure pipe; the scraping net is arranged on the rotating shaft; the collecting bag is arranged below the rotating shaft; the camera is arranged at the front end of the rotating shaft; the detectors are arranged at two ends of the sucker;

the flushing device specifically comprises a water inlet, a shower head, a conveying pipe, a cutting blade, a flusher box, a water outlet and a stone taking position, wherein the water inlet is arranged on the flushing box; the shower head is connected with the water outlet; the conveying pipe is connected with a negative pressure pipe; the cutting blade is arranged on the inner wall of the flushing box; the water outlet is arranged below the flushing tank; the stone taking position is arranged at the left lower part of the flushing tank;

the camera is a fiber choledochoscope; the display screen is a 2MP medical diagnosis type gray scale liquid crystal display screen; the sucker is a self-suction type sucker; the steering ball body adopts a universal ball head.

Further, the implementation method of the multi-factor decision module comprises the following steps:

firstly, pre-allocating available frequency spectrums through a database to obtain the pre-allocation probability of a secondary user, and comprehensively deciding whether to participate in sensing according to the pre-allocation probability and the self condition of the secondary user; the spectrum pre-allocation specifically comprises:

step one, a query sending stage: the secondary user sends inquiry information to the base station, and the base station sends the inquiry information to the database;

step two, information extraction stage: the database extracts area information from query information sent by a secondary user, gives a frequency spectrum available information summary table in the area through a series of calculations, and sends the available frequency spectrum information summary table to the secondary user through a base station;

step three, a result submitting stage: after receiving the available frequency spectrum information list, the secondary user selects a frequency band and sends the frequency band to the database through the base station;

step four, a spectrum pre-allocation stage: the database calculates the probability that each secondary user can obtain the applied frequency band, namely the spectrum pre-allocation probability, according to the channel selection information sent by each secondary user in the step three, and sends the probability to each secondary user through the base station; the pre-assigned probability is specifically calculated as: if the number of the secondary users applied to one frequency band is m, calculating that the pre-distribution probability of each secondary user applying the frequency band is 1/m if the frequency band is available;

secondly, after comprehensive decision, determining secondary users participating in sensing to perform cooperative sensing, and performing frequency spectrum allocation according to a real-time sensing result; the comprehensive decision-making and real-time sensing and distribution stage specifically comprises the following steps:

step one, a comprehensive decision stage: after receiving the pre-distribution probability, the secondary user comprehensively decides the probability of participating in real-time perception through a set rule by combining the residual electric quantity condition of the secondary user and determines whether the secondary user participates in real-time perception or not according to the probability;

step two, a database supervision stage: the database also stores the residual energy and the pre-distribution probability of each secondary user, calculates the probability of each secondary user participating in perception, obtains the secondary users participating in perception according to network regulations, and then is responsible for supervising the participation of the secondary users needing to participate in perception in real time;

step three, a real-time perception stage: after pre-allocation, performing real-time cooperative sensing on secondary users needing to participate in sensing under the supervision of a database;

step four, a spectrum allocation stage: according to the result of real-time perception, the frequency spectrum allocation is carried out, and the network overall satisfaction meterA calculation stage: and after the frequency spectrum is distributed, the overall satisfaction degree of the secondary users of the whole network is considered, and the overall satisfaction degree of the network is calculated as follows: s secondary users participate in the sensing, wherein only t secondary users obtain the desired frequency band, and the satisfaction is defined as: omega_{Is full of}＝t/s；

And finally, observing the satisfaction degree of the secondary users in the whole network.

Further, the implementation method of the wireless resource optimization allocation module performs clustering processing on the users based on the position information of the users and the video request information counted in the current time period; according to the user clustering result and the position information of each user group, calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group; the base station antenna wave beam realizes the accurate alignment of the user group; the method comprises the steps that an active antenna beam forming model is adopted, a base station has a specific beam for each user group, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and a channel gain model from the base station to the user is determined;

the method specifically comprises the following steps:

taking the total throughput in the cell as the benefit U of the system:

$U=\underset{D_k\∈D}{\Σ}\underset{n\∈F}{\Σ}{\mathrm{\α}}_{n,k}{R}_n^k;$

wherein D and F represent multicast groups, respectivelyAnd a set of carriers, and a method for determining,represents the total data transmission rate obtained by users in user group k on carrier n, and is represented by:

$R_n^k=\underset{i\∈D_k}{\Σ}{B}_{0}log(1+\frac{G_{i,n}^k{p}^k}{{\mathrm{\σ}}^2});$

calculation of where B₀Is the bandwidth of the carrier, p^kFor the transmission power of the beam assigned to the user group k, σ²Power of Gaussian white noise, α_n，kUsing an indicator for the carrier, the condition being satisfied:

α_n，k＝{0，1}，D_k∈D，n∈F(1)

$\underset{D_k\∈D}{\Σ}{\mathrm{\α}}_{n,k}\≥1,n\∈F---\left(2\right)$

if condition (1) indicates that the carrier n is allocated to the user group k, α_n，k1, otherwise α_n，kCondition (2) indicates that all carriers are used and one carrier is multiplexed by a plurality of user groups;

a user group clustering algorithm is provided, and clustering processing is carried out on the user group according to the position information of the user group;

based on a carrier allocation algorithm that maximizes throughput.

Further, the clustering the users based on the position information of the users and the video request information counted in the current time period includes that the position information of the users is described by current position coordinates:

l_i＝(x_i，y_i)；

wherein x_i，y_iRespectively representing the horizontal and vertical coordinate values of the user i, and constructing a content request frequency vector for the user i:

n_i＝(n_i，1，n_i，2，…，n_i，c)；

wherein n is_i，cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;

clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:

$s(i,j)=\mathrm{\β}\frac{l_i\·l_j}{\left|\right|l_i\left|\right|\·\left|\right|l_j\left|\right|}+(1-\mathrm{\β})\frac{n_i\·n_j}{\left|\right|n_i\left|\right|\·\left|\right|n_j\left|\right|};$

wherein β is a weight coefficient between 0 and 1;

using a K-Means clustering method to cluster all users D in the cell, u_i＝{l_i，n_iDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }₁，…，D_CMathematically, the following formula is solvedMinimum value:

$\underset{k=1}{\overset{C}{\Σ}}\underset{u_i\∈D_k}{\Σ}\left|\right|u_i-{\mathrm{\γ}}_k\left|\right|;$

wherein gamma is_kIs the center of the user group;

the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:

step one, randomly taking C users from D as the centers of C user groups;

calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;

step three, updating the center gamma of the C user groups according to the clustering result_k＝{l_k，n_kUsing the following formula:

$l_k=(\frac{\underset{i\∈D_k}{\Σ}{m}_i{x}_i}{\underset{i\∈D_k}{\Σ}{m}_i},\frac{\underset{i\∈D_k}{\Σ}{m}_i{y}_i}{\underset{i\∈D_k}{\Σ}{m}_i});$

$n_k=(\frac{\underset{i\∈D_k}{\Σ}{m}_i{n}_{i,1}}{\underset{i\∈D_k}{\Σ}{m}_i},\mathrm{...},\frac{\underset{i\∈D_k}{\Σ}{m}_i{n}_{i,c}}{\underset{i\∈D_k}{\Σ}{m}_i});$

wherein m is_iThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;

the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:

the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0, 0, H)_BS) The centroid of the user group k is gamma_kPosition coordinates of (x)_k，y_k，z_k) Vertical elevation angle and horizontal azimuth angle of；

Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the centroid of the user group are calculated by the following formulas:

${\mathrm{\θ}}_k=ac\tan \left(\frac{H_{BS}-z_k}{\sqrt{(x_k^2+y_k^2)}}\right)+\mathrm{\π}/2;$

obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta₁∈(0，π)，

The precise alignment of the base station antenna beam to the user group specifically includes:

step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:

wherein,and theta_kCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;

step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:

$r_k=\underset{i\∈D_k}{\max }\sqrt{{(x_i-x_k)}^2+{(y_i-y_k)}^2};$

wherein (x)_k，y_k) Is the center gamma of the user group k_kThen the vertical half-power bandwidth of the kth beam is:

${\mathrm{\θ}}_{3dB}^k=\arctan \frac{\sqrt{(x_k^2+y_k^2)}+r_k}{H_{BS}}-\arctan \frac{\sqrt{(x_k^2+y_k^2)}-r_k}{H_{BS}};$

the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:

step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'_iIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:

step two, an antenna model of the active antenna array:

the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:

wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, w_m，nAnd v_m，nThe weight factor and the user offset phase are respectively expressed as follows:

m＝1，2，...N_H；n＝1，2，...N_V；

m＝1，2，...N_H；n＝1，2，...N_V；

wherein, theta_etiltRepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groups_etiltAnddifferent in configuration;

step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:

whereinRepresenting user i (i ∈ D)_k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:

where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;

the method for clustering the user group according to the position information of the user group comprises the following specific steps:

clustering a user group based on knowledge of graph theory, defining an interference graph G (V, E) among beams, wherein V represents a set of beams, the vertex of the interference graph is used as V, E represents an interference coefficient among beams, and an indicating function E (V, E) is defined as an edge of the interference graph_k，v_m) (k ≠ m) indicates the interference between beam k and beam m:

$e(v_k,v_m)=\left\{\begin{array}{cc}1,& \left|O_k-O_m\right|<r_k+r_m+r_{th}\\ 0,& \left|O_k-O_m\right|\&GreaterEqual;r_k+r_m+r_{th}\end{array}\right.;$

wherein O is_kAnd O_mRespectively representing the radius, r, of user group k and user group m_thA threshold distance representing a negligible interference between two beams, and e (v) is defined_k，v_k) And (0) indicating that no interference exists in the beam, and constructing a binary interference matrix according to an indication function:

interference degree of the defined beam:

$d_G\left(v_k\right)=\underset{m=1,m\&NotEqual;k}{\overset{C}{\&Sigma;}}e(v_k,v_m);$

when d is_G(v_k) When it is 0, it is called v_kIs a zero degree node;

the clustering method comprises the following specific steps:

step one, constructing an interference matrix A by using a vertex set V_GInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set

Step two, finding all zero-degree nodes v_kUpdate S-S ∪ v_k(ii) a The set of remaining nodes is recorded as Φ₁＝V-S；

Step three, clustering: a)find node k ═ argmax (d)_G(v_k) Let the k-th row and k-th column of the interference matrix be 0, update the node set B_h＝B_h∩v_k(ii) a b) Cyclically executing a) until A_G0; c) updating phi_h＝Φ_h-B_hThen phi is_hIs the h cluster;

step four, using the node to assemble B_hReconstruction of A_GNot equal to 0, updating the node set phi_h+1＝B_hUpdating the iteration factor h to h +1, and executing the step (3); if A is_G0 or | B_h1 if | B_hIf 1, then_h+1＝B_h；

Step five, distributing the isolated node set S to a cluster with least nodes;

after the clustering processing of the user group, the user group D ═ D₁，…，D_k，…，D_CThe data is divided into phi and phi through a clustering algorithm₁，…，Φ_h，…}，Φ_hAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:

$R_n^{{\mathrm{\&Phi;}}_h}=\underset{D_k\&Element;{\mathrm{\&Phi;}}_h}{\&Sigma;}{R}_n^k;$

the total throughput of the system is the sum of the transmission rates of all user clusters:

$U=\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}\underset{n\&Element;F}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}{R}_n^{{\mathrm{\&Phi;}}_h};$

whereinClustering phi for users_hThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:

${\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=\{0,1\},{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;},n\&Element;F---\left(1\right)$

$\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=1,n\&Element;F---\left(2\right)$

the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;

the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:

step one, according to a formula:

$R_n^{{\mathrm{\&Phi;}}_h}=\underset{D_k\&Element;{\mathrm{\&Phi;}}_h}{\&Sigma;}{R}_n^k,n\&Element;F,{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;};$

calculating the total transmission rate of the users in each cluster on the carrier n;

step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:

$(n,{\mathrm{\&Phi;}}_h)=\arg \underset{n\&Element;F,{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{max}{R}_n^{{\mathrm{\&Phi;}}_h};$

allocating carrier n to user cluster Φ_hThe maximum transmission rate is obtained, and carrier n is allocated to cluster phi_hSo that carrier n is allocated to user cluster phi_h；

Step three, the carrier n is selected from the carrier set FRemoving, while clustering users Φ_hRemoving from the set Φ;

and step four, repeatedly executing the step two and the step three until the carrier set or the user cluster set is an empty set.

The invention has the advantages and positive effects that: the stone can be effectively taken out through the arrangement of the sucking disc and the arrangement of the scraping net; the operation process can be effectively detected through the arrangement of the camera and the display screen; the calculus and the blood clot can be effectively separated by the arrangement of the flushing device. The method expands the existing research on the expense of cooperative perception, and considers the influence on the secondary user participation perception on the aspect of energy consumption of the secondary user, which is not considered by the existing research; the available frequency spectrum is pre-distributed by using Database (Database) for reference, and real-time sensing is performed after pre-distribution, so that the accuracy of a sensing result is ensured, the intention of a secondary user is considered, the satisfaction degree of the whole network is finally inspected, and the stability and other performances of the whole network are further ensured; comprehensively considering multiple factors, such as pre-distribution probability of a secondary user, electric quantity residual of the secondary user and the like, a decision method is provided, and the secondary user decides whether to participate in spectrum sensing or not according to the decision method; the sensing and the distribution are considered comprehensively, the influence of general spectrum sensing on spectrum distribution is considered, and the adverse effect of the spectrum distribution on the spectrum sensing is given, which does not exist in the prior art and belongs to a blank in the technical field; under the limitation of the user self condition, whether to participate in spectrum sensing is decided by estimating the resource acquisition probability, so that useless consumption of secondary users is reduced. The invention adopts the multicast technology based on the user preference, and improves the utilization rate of the frequency spectrum resources. In the invention, the situation that users may request the same video content is considered, user clustering is carried out according to the position information and the video request preference information of the users, the users with similar request preferences at similar positions can be divided into a multicast group, the same content is distributed in the multicast group by adopting a multicast technology, and all the users in the multicast group share one carrier resource, thereby improving the frequency resource utilization rate of the system; the accurate alignment of the active antenna array wave beam to the user group is realized, and the utilization rate of power and frequency resources is improved. Each multicast group is assigned an active antenna beam for a service by active array antenna beam forming techniques. Each beam points to the multicast group to be served, the directivity is strong, the bandwidth of the beam is narrower than that of the omnidirectional antenna, the energy is concentrated, the signal intensity is increased, and the utilization rate of the power is improved. In addition, because the main lobe of the wave beam is narrow, the side lobe is attenuated quickly, and the mutual interference among the wave beams in different directions is small, the frequency reuse of a user group among different wave beams can be realized, and the frequency resource utilization rate is improved; by clustering the user groups, the frequency reuse among the user groups is realized, and the frequency spectrum efficiency is improved. In the invention, the user groups are clustered according to the interference among the beams of the user groups, and the user groups with smaller interference are grouped into a cluster. The user groups in the same cluster can share one frequency resource due to small mutual interference, thereby realizing frequency reuse among the user groups; a carrier allocation algorithm for an active antenna multicast system is provided, and user performance and system capacity are improved. The multicast system adopting the active antenna has strong directivity of active array wave beams, narrower bandwidth and more concentrated energy, increases the intensity of received signals of users, simultaneously provides a carrier allocation algorithm based on maximized throughput, and improves the performance and the system capacity of the users.

Drawings

FIG. 1 is a schematic structural view of a mucosal calculus retrieval system for minimally invasive gallbladder surgery according to an embodiment of the present invention.

Fig. 2 is a schematic structural diagram of a display device according to an embodiment of the present invention.

Fig. 3 is a schematic structural diagram of a power plant provided by an embodiment of the invention.

Fig. 4 is a schematic structural diagram of a negative pressure device according to an embodiment of the present invention.

Fig. 5 is a schematic structural diagram of an extraction device according to an embodiment of the present invention.

Fig. 6 is a schematic structural diagram of a flushing device provided by an embodiment of the invention.

In the figure: 1. a display device; 1-1, vehicle wheels; 1-2, a base; 1-3, a hydraulic pump; 1-4, adjusting a rod; 1-5 telescopic rods; 1-6, a steering ball; 1-7 display screens; 2. a power plant; 2-1, rotating the motor; 2-2, a rotating shaft; 2-3, a graduated scale; 2-4, sleeve; 2-5, sleeving; 3. a negative pressure device; 3-1, a negative pressure shell; 3-2, vacuum gauge; 3-3, an air inlet; 3-4, a pressure gauge; 3-5, adjusting a button; 3-6, extracting and separating; 3-7, an air outlet; 3-8 negative pressure tube; 4. an extraction device; 4-1, a sucker; 4-2, scraping the net; 4-3, a camera; 4-4, collecting bags; 4-5, a detector; 5. a flushing device; 5-1, a water inlet; 5-2, shower head; 5-3, a conveying pipe; 5-4, cutting slices; 5-5, a flusher tank; 5-6, a water outlet; 5-7, and removing stones.

Detailed Description

In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.

Please refer to fig. 1 to 6:

as shown in fig. 1 to 6: the invention provides a mucosal calculus taking system for minimally invasive gallbladder protection surgery, which comprises a display device 1, a power device 2, a negative pressure device 3, an extraction device 4 and a flushing device 5, wherein the power device 2 is electrically connected with the display device 1 and the extraction device 4; the negative pressure device 3 is arranged on the power device 2; the flushing device 5 is arranged below the power device 2.

The invention can also adopt the following technical measures:

the display device 1 specifically comprises wheels 1-1, a base 1-2, a hydraulic pump 1-3, an adjusting rod 1-4, a telescopic rod 1-5, a steering ball 1-6 and a display screen 1-7, wherein the wheels 1-1 are arranged below the base 1-2; the hydraulic pump 1-3 is arranged on the base 1-2; the adjusting rod 1-4 is connected with the telescopic rod 1-5; the steering balls 1-6 are arranged on the telescopic rods 1-5; the display screen 1-7 is arranged above the steering ball 1-6.

The power device 2 specifically comprises a rotating motor 2-1, a rotating shaft 2-2, a graduated scale 2-3, a sleeve 2-4 and a sleeve 2-5, and the power device is arranged on the base 1-2; the rotating shaft 2-2 is connected with the rotating motor 2-1 and the extraction device 4; the graduated scale 2-3 is arranged on the rotating shaft; the sleeves 2-4 are arranged outside the rotating shaft; the sleeve 2-4 and the sleeve 2-5 are sleeved together.

The negative pressure device 3 specifically comprises a negative pressure shell 3-1, a vacuum meter 3-2, an air inlet 3-3, a pressure gauge 3-4, an adjusting button 3-5, a pumping machine 3-6, an air outlet 3-7 and a negative pressure pipe 3-8, wherein the vacuum meter 3-2 is arranged on the negative pressure shell 3-1; the air inlet 3-3 is arranged at the upper right part of the negative pressure shell 3-1; the pressure gauge 3-4 is arranged on the upper surface of the negative pressure shell 3-1; the adjusting button 3-5 is arranged on the outer wall of the negative pressure shell 3-1; the extracting machine 3-6 is arranged inside the negative pressure shell 3-1; the air outlet 3-7 is arranged at the lower left of the negative pressure shell 3-1; the negative pressure pipe 3-8 is arranged at the lower end of the negative pressure shell 3-1.

The extraction device 4 specifically comprises a suction cup 4-1, a scraping net 4-2, a camera 4-3, a collection bag 4-4 and a detector 4-5, wherein the suction cup 4-1 is connected to a negative pressure pipe 3-8; the scraping net 4-2 is arranged above the rotating shaft; the collecting bag 4-4 is arranged below the rotating shaft; the camera 4-3 is arranged at the front end of the rotating shaft; the detectors 4-5 are arranged at two ends of the sucker 4-1.

The flushing device 5 specifically comprises a water inlet 5-1, a shower head 5-2, a conveying pipe 5-3, a cutting blade 5-4, a flusher box 5-5, a water outlet 5-6 and a stone taking position 5-7, wherein the water inlet 5-1 is arranged on the flushing box; the shower head 5-2 is connected with the water outlet 5-6; the transport pipe 5-3 is connected with the negative pressure pipe 3-8; the cutting blades 5-4 are arranged on the inner wall of the flushing box; the water outlets 5-6 are arranged below the flushing tank; the stone taking position 5-7 is arranged at the lower left of the flushing tank.

The camera 4-3 specifically adopts a fiber choledochoscope, and has the characteristics of high magnification, stable image, high definition, small disease and disease, and high success rate of calculus removal.

The display screen 1-7 specifically adopts a 2MP medical diagnosis type gray scale liquid crystal display screen 1-7, and has the characteristics of high resolution, stable image and no radiation. The display screens 1-7 are provided with wireless network modules, and the wireless network modules are provided with multi-factor decision modules and wireless resource optimization distribution modules;

the sucker 4-1 is a self-suction type sucker, and is beneficial to guaranteeing a negative pressure environment.

The steering balls 1-6 are universal ball heads, so that the display screen can rotate along any direction, and observation is further facilitated.

The implementation method of the multi-factor decision module comprises the following steps:

firstly, pre-allocating available frequency spectrums through a database to obtain the pre-allocation probability of a secondary user, and comprehensively deciding whether to participate in sensing according to the pre-allocation probability and the self condition of the secondary user; the spectrum pre-allocation specifically comprises:

step one, a query sending stage: the secondary user sends inquiry information to the base station, and the base station sends the inquiry information to the database;

step two, information extraction stage: the database extracts area information from query information sent by a secondary user, gives a frequency spectrum available information summary table in the area through a series of calculations, and sends the available frequency spectrum information summary table to the secondary user through a base station;

step three, a result submitting stage: after receiving the available frequency spectrum information list, the secondary user selects a frequency band and sends the frequency band to the database through the base station;

step four, a spectrum pre-allocation stage: the database calculates the probability that each secondary user can obtain the applied frequency band, namely the spectrum pre-allocation probability, according to the channel selection information sent by each secondary user in the step three, and sends the probability to each secondary user through the base station; the pre-assigned probability is specifically calculated as: if the number of the secondary users applied to one frequency band is m, calculating that the pre-distribution probability of each secondary user applying the frequency band is 1/m if the frequency band is available;

secondly, after comprehensive decision, determining secondary users participating in sensing to perform cooperative sensing, and performing frequency spectrum allocation according to a real-time sensing result; the comprehensive decision-making and real-time sensing and distribution stage specifically comprises the following steps:

step one, a comprehensive decision stage: after receiving the pre-distribution probability, the secondary user comprehensively decides the probability of participating in real-time perception through a set rule by combining the residual electric quantity condition of the secondary user and determines whether the secondary user participates in real-time perception or not according to the probability;

step two, a database supervision stage: the database also stores the residual energy and the pre-distribution probability of each secondary user, calculates the probability of each secondary user participating in perception, obtains the secondary users participating in perception according to network regulations, and then is responsible for supervising the participation of the secondary users needing to participate in perception in real time;

step three, a real-time perception stage: after pre-allocation, performing real-time cooperative sensing on secondary users needing to participate in sensing under the supervision of a database;

step four, a spectrum allocation stage: according to the result of real-time perception, spectrum allocation is carried out, and the network overall satisfaction degree calculation stage is as follows: and after the frequency spectrum is distributed, the overall satisfaction degree of the secondary users of the whole network is considered, and the overall satisfaction degree of the network is calculated as follows: s secondary users participate in the sensing, wherein only t secondary users obtain the desired frequency band, and the satisfaction is defined as: omega_{Is full of}＝t/s；

And finally, observing the satisfaction degree of the secondary users in the whole network.

Further, the implementation method of the wireless resource optimization allocation module performs clustering processing on the users based on the position information of the users and the video request information counted in the current time period; according to the user clustering result and the position information of each user group, calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group; the base station antenna wave beam realizes the accurate alignment of the user group; the method comprises the steps that an active antenna beam forming model is adopted, a base station has a specific beam for each user group, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and a channel gain model from the base station to the user is determined;

the method specifically comprises the following steps:

taking the total throughput in the cell as the benefit U of the system:

$U=\underset{D_k\&Element;D}{\&Sigma;}\underset{n\&Element;F}{\&Sigma;}{\mathrm{\&alpha;}}_{n,k}{R}_n^k;$

where D and F denote the multicast group and the carrier set respectively,represents the total data transmission rate obtained by users in user group k on carrier n, and is represented by:

calculation of where B₀Is the bandwidth of the carrier, p^kFor the transmission power of the beam assigned to the user group k, σ²Power of Gaussian white noise, α_n，kUsing an indicator for the carrier, the condition being satisfied:

α_n，k＝{0，1}，D_k∈D，n∈F(1)

$\underset{D_k\&Element;D}{\&Sigma;}{\mathrm{\&alpha;}}_{n,k}\&GreaterEqual;1,n\&Element;F---\left(2\right)$

if condition (1) indicates that the carrier n is allocated to the user group k, α_n，k1, otherwise α_n，kCondition (2) indicates that all carriers are used and one carrier is multiplexed by a plurality of user groups;

a user group clustering algorithm is provided, and clustering processing is carried out on the user group according to the position information of the user group;

based on a carrier allocation algorithm that maximizes throughput.

Further, the clustering the users based on the position information of the users and the video request information counted in the current time period includes that the position information of the users is described by current position coordinates:

l_i＝(x_i，y_i)；

wherein x_i，y_iRespectively representing the horizontal and vertical coordinate values of the user i, and constructing a content request frequency vector for the user i:

n_i＝(n_i，1，n_i，2，…，n_i，c)；

wherein n is_i，cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;

clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:

$s(i,j)=\mathrm{\&beta;}\frac{l_i\&CenterDot;l_j}{\left|\right|l_i\left|\right|\&CenterDot;\left|\right|l_j\left|\right|}+(1-\mathrm{\&beta;})\frac{n_i\&CenterDot;n_j}{\left|\right|n_i\left|\right|\&CenterDot;\left|\right|n_j\left|\right|};$

wherein β is a weight coefficient between 0 and 1;

using a K-Means clustering method to cluster all users D in the cell, u_i＝{l_i，n_iDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }₁，…，D_CMathematically, the minimum is calculated as:

$\underset{k=1}{\overset{C}{\&Sigma;}}\underset{u_i\&Element;D_k}{\&Sigma;}\left|\right|u_i-{\mathrm{\&gamma;}}_k\left|\right|;$

wherein gamma is_kIs the center of the user group;

the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:

step one, randomly taking C users from D as the centers of C user groups;

calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;

step three, updating the center gamma of the C user groups according to the clustering result_k＝{l_k，n_kUsing the following formula:

$l_k=(\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{x}_i}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i},\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{y}_i}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i});$

$n_k=(\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{n}_{i,1}}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i},\mathrm{...},\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{n}_{i,c}}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i});$

wherein m is_iThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;

the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:

the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0, 0, H)_BS) The centroid of the user group k is gamma_kPosition coordinates of (x)_k，y_k，z_k) Vertical elevation angle and horizontal azimuth angle of；

Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the centroid of the user group are calculated by the following formulas:

${\mathrm{\&theta;}}_k=ac\tan \left(\frac{H_{BS}-z_k}{\sqrt{(x_k^2+y_k^2)}}\right)+\mathrm{\&pi;}/2;$

obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta₁∈(0，π)，

The precise alignment of the base station antenna beam to the user group specifically includes:

step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:

wherein,and theta_kCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;

step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:

$r_k=\underset{i\&Element;D_k}{\max }\sqrt{{(x_i-x_k)}^2+{(y_i-y_k)}^2};$

wherein (x)_k，y_k) Is the center gamma of the user group k_kThen the vertical half-power bandwidth of the kth beam is:

${\mathrm{\&theta;}}_{3dB}^k=\arctan \frac{\sqrt{(x_k^2+y_k^2)}+r_k}{H_{BS}}-\arctan \frac{\sqrt{(x_k^2+y_k^2)}-r_k}{H_{BS}};$

the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:

step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'_iIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:

step two, an antenna model of the active antenna array:

the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:

wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, w_m，nAnd v_m，nThe weight factor and the user offset phase are respectively expressed as follows:

m＝1，2，...N_H；n＝1，2，...N_V；

m＝1，2，...N_H；n＝1，2，...N_V；

wherein,θ_etiltrepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groups_etiltAnddifferent in configuration;

step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:

whereinRepresenting user i (i ∈ D)_k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:

where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;

the method for clustering the user group according to the position information of the user group comprises the following specific steps:

clustering a user group based on knowledge of graph theory, defining an interference graph G (V, E) among beams, wherein V represents a set of beams, the vertex of the interference graph is used as V, E represents an interference coefficient among beams, and an indicating function E (V, E) is defined as an edge of the interference graph_k，v_m) (k ≠ m) indicates the interference between beam k and beam m:

$e(v_k,v_m)=\left\{\begin{array}{cc}1,& \left|O_k-O_m\right|<r_k+r_m+r_{th}\\ 0,& \left|O_k-O_m\right|\&GreaterEqual;r_k+r_m+r_{th}\end{array}\right.;$

wherein O is_kAnd O_mRespectively representing the radius, r, of user group k and user group m_thA threshold distance representing a negligible interference between two beams, and e (v) is defined_k，v_k) And (0) indicating that no interference exists in the beam, and constructing a binary interference matrix according to an indication function:

interference degree of the defined beam:

$d_G\left(v_k\right)=\underset{m=1,m\&NotEqual;k}{\overset{C}{\&Sigma;}}e(v_k,v_m);$

when d is_G(v_k) When it is 0, it is called v_kIs a zero degree node;

the clustering method comprises the following specific steps:

step one, constructing an interference matrix A by using a vertex set V_GInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set

Step two, finding all zero-degree nodes v_kUpdate S-S ∪ v_k(ii) a The set of remaining nodes is recorded as Φ₁＝V-S；

Step three, clustering: a)find node k ═ argmax (d)_G(v_k) Let the k-th row and k-th column of the interference matrix be 0, update the node set B_h＝B_h∩v_k(ii) a b) Cyclically executing a) until A_G0; c) updating phi_h＝Φ_h-B_hThen phi is_hIs the h cluster;

step four, using the node to assemble B_hReconstruction of A_GNot equal to 0, updateSet of nodes Φ_h+1＝B_hUpdating the iteration factor h to h +1, and executing the step (3); if A is_G0 or | B_h1 if | B_hIf 1, then_h+1＝B_h；

Step five, distributing the isolated node set S to a cluster with least nodes;

after the clustering processing of the user group, the user group D ═ D₁，…，D_k，…，D_CThe data is divided into phi and phi through a clustering algorithm₁，…，Φ_h，…}，Φ_hAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:

$R_n^{{\mathrm{\&Phi;}}_h}=\underset{D_k\&Element;{\mathrm{\&Phi;}}_h}{\&Sigma;}{R}_n^k;$

the total throughput of the system is the sum of the transmission rates of all user clusters:

$U=\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}\underset{n\&Element;F}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}{R}_n^{{\mathrm{\&Phi;}}_h};$

whereinClustering phi for users_hThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:

${\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=\{0,1\},{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;},n\&Element;F---\left(1\right)$

$\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=1,n\&Element;F---\left(2\right)$

the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;

the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:

step one, according to a formula:

$R_n^{{\mathrm{\&Phi;}}_h}=\underset{D_k\&Element;{\mathrm{\&Phi;}}_h}{\&Sigma;}{R}_n^k,n\&Element;F,{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;};$

calculating the total transmission rate of the users in each cluster on the carrier n;

step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:

$(n,{\mathrm{\&Phi;}}_h)=\arg \underset{n\&Element;F,{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{max}{R}_n^{{\mathrm{\&Phi;}}_h};$

allocating carrier n to user cluster Φ_hThe maximum transmission rate is obtained, and carrier n is allocated to cluster phi_hSo that carrier n is allocated to user cluster phi_h；

Step three, removing the carrier n from the carrier set F, and simultaneously, clustering the users to form a cluster phi_hRemoving from the set Φ;

and step four, repeatedly executing the step two and the step three until the carrier set or the user cluster set is an empty set.

Principle of operation

The invention is realized by that the scraping net 4-2 and the collecting bag 4-4 which are arranged at the front end of the rotating shaft 2-2 are driven by the rotating motor 2-1 to start working under the action of the negative pressure device 3. The suction cup 4-1 sucks the calculus into the washing device 5, and the calculus is washed, and all the work can be detected on the display screen 1-7.

The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (4)

1. The mucosal calculus taking system for minimally invasive gallbladder-protecting surgery is characterized in that the multifunctional orthopedic traction device comprises a display device, a power device, a negative pressure device, an extraction device and a flushing device, wherein the power device is electrically connected with the display device and the extraction device; the negative pressure device is arranged above the power device; the flushing device is arranged below the power device;

the display device specifically comprises wheels, a base, a hydraulic pump, an adjusting rod, a telescopic rod, a steering ball and a display screen, wherein the wheels are arranged below the base; the hydraulic pump is arranged above the base; the adjusting rod is connected with the telescopic rod; the steering ball is arranged on the telescopic rod; the display screen is arranged on the steering ball; the display screen is provided with a wireless network module, and the wireless network module is provided with a multi-factor decision module and a wireless resource optimization allocation module;

the power device comprises a rotating motor, a rotating shaft, a graduated scale, a sleeve and a sleeve, and is arranged on the base; the rotating shaft is connected with the rotating motor and the extracting device; the graduated scale is arranged on the rotating shaft; the sleeve is arranged outside the rotating shaft; the sleeve is sleeved with the sleeve;

the negative pressure device specifically comprises a negative pressure shell, a vacuum meter, an air inlet, a pressure gauge, an adjusting button, a pump-out machine, an air outlet and a negative pressure pipe, wherein the vacuum meter is arranged on the negative pressure shell; the air inlet is arranged at the upper right part of the negative pressure shell; the pressure gauge is arranged on the upper surface of the negative pressure shell; the adjusting button is arranged on the outer wall of the negative pressure shell; the pumping-separating machine is arranged in the negative pressure shell; the air outlet is arranged at the left lower part of the negative pressure shell; the negative pressure pipe is arranged at the lower end of the negative pressure shell;

the extraction device specifically comprises a sucker, a scraping net, a camera, a collection bag and a detector, wherein the sucker is connected to the negative pressure pipe; the scraping net is arranged on the rotating shaft; the collecting bag is arranged below the rotating shaft; the camera is arranged at the front end of the rotating shaft; the detectors are arranged at two ends of the sucker;

the flushing device specifically comprises a water inlet, a shower head, a conveying pipe, a cutting blade, a flusher box, a water outlet and a stone taking position, wherein the water inlet is arranged on the flushing box; the shower head is connected with the water outlet; the conveying pipe is connected with a negative pressure pipe; the cutting blade is arranged on the inner wall of the flushing box; the water outlet is arranged below the flushing tank; the stone taking position is arranged at the left lower part of the flushing tank;

the camera is a fiber choledochoscope; the display screen is a 2MP medical diagnosis type gray scale liquid crystal display screen; the sucker is a self-suction type sucker; the steering ball body adopts a universal ball head.

2. The system of claim 1, wherein the multi-factor decision module comprises:

firstly, pre-allocating available frequency spectrums through a database to obtain the pre-allocation probability of a secondary user, and comprehensively deciding whether to participate in sensing according to the pre-allocation probability and the self condition of the secondary user; the spectrum pre-allocation specifically comprises:

step one, a query sending stage: the secondary user sends inquiry information to the base station, and the base station sends the inquiry information to the database;

step two, information extraction stage: the database extracts area information from query information sent by a secondary user, gives a frequency spectrum available information summary table in the area through a series of calculations, and sends the available frequency spectrum information summary table to the secondary user through a base station;

step three, a result submitting stage: after receiving the available frequency spectrum information list, the secondary user selects a frequency band and sends the frequency band to the database through the base station;

step four, a spectrum pre-allocation stage: the database calculates the probability that each secondary user can obtain the applied frequency band, namely the spectrum pre-allocation probability, according to the channel selection information sent by each secondary user in the step three, and sends the probability to each secondary user through the base station; the pre-assigned probability is specifically calculated as: if the number of the secondary users applied to one frequency band is m, calculating that the pre-distribution probability of each secondary user applying the frequency band is 1/m if the frequency band is available;

secondly, after comprehensive decision, determining secondary users participating in sensing to perform cooperative sensing, and performing frequency spectrum allocation according to a real-time sensing result; the comprehensive decision-making and real-time sensing and distribution stage specifically comprises the following steps:

step one, a comprehensive decision stage: after receiving the pre-distribution probability, the secondary user comprehensively decides the probability of participating in real-time perception through a set rule by combining the residual electric quantity condition of the secondary user and determines whether the secondary user participates in real-time perception or not according to the probability;

step two, a database supervision stage: the database also stores the residual energy and the pre-distribution probability of each secondary user, calculates the probability of each secondary user participating in perception, obtains the secondary users participating in perception according to network regulations, and then is responsible for supervising the participation of the secondary users needing to participate in perception in real time;

step three, a real-time perception stage: after pre-allocation, performing real-time cooperative sensing on secondary users needing to participate in sensing under the supervision of a database;

step four, a spectrum allocation stage: according to the result of real-time perception, spectrum allocation is carried out, and the network overall satisfaction degree calculation stage is as follows: and after the frequency spectrum is distributed, the overall satisfaction degree of the secondary users of the whole network is considered, and the overall satisfaction degree of the network is calculated as follows: s secondary users participate in the sensing, wherein only t secondary users obtain the desired frequency band, and the satisfaction is defined as: omega_{Is full of}＝t/s；

And finally, observing the satisfaction degree of the secondary users in the whole network.

3. The mucosal calculus retrieval system for minimally invasive gallbladder surgery according to claim 1, wherein the implementation method of the wireless resource optimization allocation module performs clustering processing on the users based on the position information of the users and the video request information counted in the current time period; according to the user clustering result and the position information of each user group, calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group; the base station antenna wave beam realizes the accurate alignment of the user group; the method comprises the steps that an active antenna beam forming model is adopted, a base station has a specific beam for each user group, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and a channel gain model from the base station to the user is determined;

the method specifically comprises the following steps:

taking the total throughput in the cell as the benefit U of the system:

$U=\underset{D_k\&Element;D}{\&Sigma;}\underset{n\&Element;F}{\&Sigma;}{\mathrm{\&alpha;}}_{n,k}{R}_n^k;$

where D and F denote the multicast group and the carrier set respectively,represents the total data transmission rate obtained by users in user group k on carrier n, and is represented by:

$R_n^k=\underset{i\&Element;D_k}{\&Sigma;}{B}_{0}log(1+\frac{G_{i,n}^k{p}^k}{{\mathrm{\&sigma;}}^2});$

calculation of where B₀Is the bandwidth of the carrier, p^kFor the transmission power of the beam assigned to the user group k, σ²Power of Gaussian white noise, α_n，kUsing an indicator for the carrier, the condition being satisfied:

α_n，k＝{0，1}，D_k∈D，n∈F(1)

$\underset{D_k\&Element;D}{\&Sigma;}{\mathrm{\&alpha;}}_{n,k}\&GreaterEqual;1,n\&Element;F---\left(2\right)$

if condition (1) indicates that the carrier n is allocated to the user group k, α_n，k1, otherwise α_n，kCondition (2) indicates that all carriers are used and one carrier is multiplexed by a plurality of user groups;

a user group clustering algorithm is provided, and clustering processing is carried out on the user group according to the position information of the user group;

based on a carrier allocation algorithm that maximizes throughput.

4. The method of claim 3, wherein the clustering the users based on the location information of the users and the video request information counted in the current time period comprises describing the location information of the users with current location coordinates:

l_i＝(x_i，y_i)；

wherein x_i，y_iRespectively representing the horizontal and vertical coordinate values of the user i, and constructing a content request frequency vector for the user i:

n_i＝(n_i，l，n_i，2，...，n_i，c)；

wherein n is_i，cRepresenting the number of times user i requests content c, each user corresponding to a content request vector reflecting the user's content request preferences;

clustering users based on the position information and the content request preference information of the users, dividing the users with similar content request preference and similar positions into a multicast group, calculating the similarity between the two users by using a cosine similarity criterion, and calculating by using the following formula:

$s(i,j)=\mathrm{\&beta;}\frac{l_i\&CenterDot;l_j}{\left|\right|l_i\left|\right|\&CenterDot;\left|\right|l_j\left|\right|}+(1-\mathrm{\&beta;})\frac{n_i\&CenterDot;n_j}{\left|\right|n_i\left|\right|\&CenterDot;\left|\right|n_j\left|\right|};$

wherein β is a weight coefficient between 0 and 1;

using a K-Means clustering method to cluster all users D in the cell, u_i＝{l_i，n_iDenotes the clustering information of user i, the purpose of clustering is to classify the original users into class C D ═ D }₁，…，D_CMathematically, the minimum is calculated as:

$\underset{k=1}{\overset{C}{\&Sigma;}}\underset{u_i\&Element;D_k}{\&Sigma;}\left|\right|u_i-{\mathrm{\&gamma;}}_k\left|\right|;$

wherein gamma is_kIs the center of the user group;

the specific steps of clustering the users based on the position information of the users and the video request information counted in the current time period are as follows:

step one, randomly taking C users from D as the centers of C user groups;

calculating the similarity from the rest users to the centers of the C user groups according to a calculation formula of the similarity, and dividing the users into the user groups with the highest similarity;

step three, updating the center gamma of the C user groups according to the clustering result_k＝{l_k，n_kUsing the following formula:

$l_k=\left(\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{x}_i}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i},\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{y}_i}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i}\right);$

$n_k=(\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{n}_{i,1}}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i},...,\frac{\underset{i\&Element;D_k}{\&Sigma;}{m}_i{n}_{i,c}}{\underset{i\&Element;D_k}{\&Sigma;}{m}_i});$

wherein m is_iThe weight coefficient is between 0 and 1, and the second step and the third step are repeated until the clustering center is not changed any more;

the calculating the horizontal azimuth angle and the vertical elevation angle of the central position of each user group according to the user clustering result and the position information of each user group specifically comprises:

the base station has a specific beam for each user group by adopting an active antenna beam forming model, namely, a specific electronic downward inclination angle and a beam with a vertical half-power bandwidth are set for each user group, and the coordinates of the base station are an origin O (0, 0, H)_BS) The centroid of the user group k is gamma_kPosition coordinates of (x)_k，y_k，z_k) Vertical elevation angle and horizontal azimuth angle of

Based on the clustered user group position information, the horizontal direction angle and the vertical elevation angle of the centroid of the user group are calculated by the following formulas:

${\mathrm{\&theta;}}_k=ac\tan (-\frac{H_{BS}-z_k}{\sqrt{(x_k^2+y_k^2)}})+\mathrm{\&pi;}/2;$

obviously, the vertical elevation angle and the horizontal azimuth angle have the value range of theta₁∈(0，π)，

The precise alignment of the base station antenna beam to the user group specifically includes:

step one, adjusting an electronic downtilt angle, an electronic horizontal angle and a half-power bandwidth of a beam, enabling a radiation direction of the beam to be aligned to a central position of a user group, enabling a half-power bandwidth range to cover all users in the user group, and adjusting the downtilt angle and the horizontal angle from a base station to the users as follows:

wherein,and theta_kCalculating the horizontal azimuth angle and the vertical elevation angle of the center of the user group by using the center position of the user group for the base station based on the user clustering result;

step two, determining the beam width, wherein the coverage area of the user group is a circle with a circular point at the center of the user group, and the radius of the circular area is the distance between the user farthest from the center position in the user group and the center, that is:

$r_k=\underset{i\&Element;D_k}{max}\sqrt{{(x_i-x_k)}^2+{(y_i-y_k)}^2};$

wherein (x)_k，y_k) Is the center gamma of the user group k_kThen the vertical half-power bandwidth of the kth beam is:

${\mathrm{\&theta;}}_{3dB}^k=\arctan \frac{\sqrt{(x_k^2+y_k^2)}+r_k}{H_{BS}}-\arctan \frac{\sqrt{(x_k^2+y_k^2)}-r_k}{H_{BS}};$

the determining the channel gain model from the base station to the user by using the antenna model of the active antenna array specifically includes:

step one, according to the position information of each user and the user group wave beam to which the user belongs, the actual horizontal azimuth angle and the vertical elevation angle of each user position are calculated, and the horizontal azimuth angle of a user i relative to a base station is calculatedAnd vertical elevation angle theta'_jIf user i belongs to multicast group k, the actual horizontal azimuth and vertical elevation of user i is equal to:

step two, an antenna model of the active antenna array:

the 3D antenna gain model adopts an active antenna array radiation model proposed in the 3GPP standard, and the antenna gain model is expressed as follows:

wherein,an antenna gain model of an active antenna element with a downtilt angle of 0,theta is the azimuth and vertical elevation at the actual location of the user, p is the correlation coefficient of the array antenna, w_m，nAnd v_m，nThe weight factor and the user offset phase are respectively expressed as follows:

m＝1，2，...N_H；n＝1，2，...N_V；

m＝1，2，...N_H；n＝1，2，...N_V；

wherein, theta_etiltRepresenting the downtilt angle of the antenna beam,indicating the horizontal steering angle of the antenna, theta of the antenna for different user groups_etiltAnddifferent in configuration;

step three, the channel gain model from the base station to the user adopts the multicast channel gain model, the user in a multicast group receives data at the same rate, the transmission rate of the base station exceeds the maximum bearing rate of a certain user in the group, then the user can not decode the data normally, the base station transmits the data at the minimum rate in the user group, therefore, the equivalent channel gain from the base station to the user in the user group k is equal to the worst channel gain of the user in the user group, namely:

whereinRepresenting user i (i ∈ D)_k) The channel gain on carrier n, consists of 3 parts: fast fading, base station to user path loss, and user 3D antenna gain, as follows:

where F and PL denote fast fading and path loss, respectively,represents the 3D antenna gain of the kth beam to user i;

the method for clustering the user group according to the position information of the user group comprises the following specific steps:

clustering a user group based on knowledge of graph theory, defining an interference graph G (V, E) among beams, wherein V represents a set of beams, the vertex of the interference graph is used as V, E represents an interference coefficient among beams, and an indicating function E (V, E) is defined as an edge of the interference graph_k，v_m) (k ≠ m) indicates the interference between beam k and beam m:

$e(v_k,v_m)=\left\{\begin{array}{c}1,|O_k-O_m|<r_k+r_m+r_{th}\\ 0,|O_k-O_m|\&GreaterEqual;r_k+r_m+r_{th}\end{array}\right.;$

wherein O is_kAnd O_mRespectively representing the radius, r, of user group k and user group m_thA threshold distance representing a negligible interference between two beams, and e (v) is defined_k，v_k) 0, indicating that the beam has no interference, and constructing a binary interference according to the indication functionMatrix:

interference degree of the defined beam:

$d_G\left(v_k\right)=\underset{m=1,m\&NotEqual;k}{\overset{C}{\&Sigma;}}e(v_k,v_m);$

when d is_G(v_k) When it is 0, it is called v_kIs a zero degree node;

the clustering method comprises the following specific steps:

step one, constructing an interference matrix A by using a vertex set V_GInitialization iteration factor h 1, set of isolated nodesClustering collectionsNode set

Step two, finding all zero-degree nodes v_kUpdate S-S ∪ v_k(ii) a The set of remaining nodes is recorded as Φ₁＝V-S；

Step three, clustering: a)find node k ═ argmax (d)_G(v_k) Let the k-th row and k-th column of the interference matrix be 0, update the node set B_h＝B_h∩v_k(ii) a b) Cyclically executing a) until A_G0; c) updating phi_h＝Φ_h-B_hThen phi is_hIs the h cluster;

step four, using the node to assemble B_hReconstruction of A_GNot equal to 0, updating the node set phi_h+1＝B_hUpdating the iteration factor h to h +1, and executing the step (3); if A is_G0 or | B_h1 if | B_hIf 1, then_h+1＝B_h；

Step five, distributing the isolated node set S to a cluster with least nodes;

after the clustering processing of the user group, the user group D ═ D₁，…，D_k，…，D_cThe data is divided into phi and phi through a clustering algorithm₁，…，Φ_h，…}，Φ_hAnd representing the h-th user cluster, wherein the total user transmission rate in each cluster is as follows:

$R_n^{{\mathrm{\&Phi;}}_h}=\underset{D_k\&Element;{\mathrm{\&Phi;}}_h}{\&Sigma;}{R}_n^k;$

the total throughput of the system is the sum of the transmission rates of all user clusters:

$U=\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}\underset{n\&Element;F}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h},R_n^{{\mathrm{\&Phi;}}_h};$

whereinClustering phi for users_hThe carrier n is used as an indicator, and correspondingly,the conditions are satisfied as follows:

${\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=\{0,1\},{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;},n\&Element;F---\left(1\right)$

$\underset{{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{\&Sigma;}{\mathrm{\&alpha;}}_{n,{\mathrm{\&Phi;}}_h}=1,n\&Element;F---\left(2\right)$

the condition (2) indicates that one carrier can only be allocated to one user group cluster, the user groups in the same cluster share one carrier resource, and the user groups in different clusters can not be multiplexed;

the carrier allocation algorithm based on the maximized throughput specifically comprises the following steps:

step one, according to a formula:

calculating the total transmission rate of the users in each cluster on the carrier n;

step two, in order to maximize the throughput of the system, find out the carrier and user cluster which obtain the maximum rate, distribute the carrier to the user cluster at first, according to the formula:

$(n,{\mathrm{\&Phi;}}_h)=\arg \underset{n\&Element;F,{\mathrm{\&Phi;}}_h\&Element;\mathrm{\&Phi;}}{max}{R}_n^{{\mathrm{\&Phi;}}_h};$

allocating carrier n to user cluster Φ_hThe maximum transmission rate is obtained, and carrier n is allocated to cluster phi_hSo that carrier n is allocated to user cluster phi_h；

Step three, removing the carrier n from the carrier set F, and simultaneously, clustering the users to form a cluster phi_hRemoving from the set Φ;

and step four, repeatedly executing the step two and the step three until the carrier set or the user cluster set is an empty set.