阅读说明：本技术 一种铅挤压复合旋叶式粘滞阻尼器及其应用方法 (Lead extrusion composite rotary vane type viscous damper and application method thereof ) 是由 颜学渊 郭桐含 杨国 黄晓锋 缪颖 汤昌环 于 2021-08-16 设计创作，主要内容包括：本发明涉及桥梁减震技术领域,公开了一种铅挤压复合旋叶式粘滞阻尼器及其应用方法,包括第一筒体和第二筒体,第一筒体设于第二筒体内部,第一筒体与第二筒体之间留有空腔,第一筒体和空腔内均填充有阻尼材料,第一筒体的上设有内螺纹套筒,第二筒体的一侧外侧壁上设有第一销头,另一侧外侧壁上设有活塞杆,活塞杆的一侧端部上设有第二销头,另一侧端部自内螺纹套筒穿入第一筒体内,活塞杆的杆壁开上设有外螺纹,活塞杆与内螺纹套筒螺接在一起,活塞杆穿入第一筒体内的杆壁上设有叶片,活塞杆的杆壁上还设有挤压凸环,挤压凸环设于活塞杆处于空腔内的杆壁上；本发明具有耗能机制多样、小震下耗能能力好、整体耗能效果好的特点。(The invention relates to the technical field of bridge shock absorption, and discloses a lead extrusion composite rotary vane type viscous damper and an application method thereof, wherein the lead extrusion composite rotary vane type viscous damper comprises a first cylinder and a second cylinder, the first cylinder is arranged in the second cylinder, a cavity is reserved between the first cylinder and the second cylinder, damping materials are filled in the first cylinder and the cavity, an internal thread sleeve is arranged on the first cylinder, a first pin head is arranged on the outer side wall of one side of the second cylinder, a piston rod is arranged on the outer side wall of the other side of the second cylinder, a second pin head is arranged on the end part of one side of the piston rod, the end part of the other side of the piston rod penetrates into the first cylinder from the internal thread sleeve, an external thread is arranged on the rod wall of the piston rod, the piston rod is in threaded connection with the internal thread sleeve, blades are arranged on the rod wall of the piston rod penetrating into the first cylinder, an extrusion convex ring is also arranged on the rod wall of the piston rod in the cavity; the invention has the characteristics of various energy consumption mechanisms, good energy consumption capability under small earthquake and good overall energy consumption effect.)

1. The utility model provides a compound rotary vane formula viscous damper of lead extrusion which characterized in that: comprises a first barrel body (1) and a second barrel body (2), the first barrel body (1) is arranged inside the second barrel body (2), a cavity (3) is reserved between the outer wall of the first barrel body (1) and the inner wall of the second barrel body (2), damping materials are filled in the first barrel body (1) and the cavity (3), an inner side wall of one side of the first barrel body (1) is provided with an inner thread sleeve (4), a lateral wall of one side of the second barrel body (2) is provided with a first pin head (5), a piston rod (6) which is movably arranged in a penetrating way is arranged on the lateral wall of the other side, the end part of the piston rod (6) far away from the second barrel body (2) is provided with a second pin head (7), the end part of the piston rod (6) close to the side of the second barrel body (2) penetrates into the first barrel body (1) from the inner thread sleeve (4), the rod wall of the piston rod (6) is provided with an outer thread (8) which is matched with the inner thread of the inner thread sleeve (4), the piston rod (6) is connected with the internal thread sleeve (4) in a threaded mode through the external thread (8), blades (9) are arranged on the rod wall of the piston rod (6) penetrating into the first barrel (1), an extrusion convex ring (10) is further arranged on the rod wall of the piston rod (6), and the extrusion convex ring (10) is arranged on the rod wall of the piston rod (6) in the cavity (3).

2. The lead extrusion compound rotary vane viscous damper of claim 1, characterized in that: first barrel (1) is the rubber protective sheath, and its both sides tip position is pressed close to in the inside of first barrel (1) all is equipped with and supports cover (11), supports the outer wall of cover (11) and is the setting of pasting closely with the inner wall of first barrel (1), and it has graphite alkene insulating layer still to coat on the inner wall of first barrel (1).

3. The lead extrusion compound rotary vane viscous damper of claim 1, characterized in that: the end part of one side of the second cylinder body (2) is in an open structure, an end cover (12) covering the end part of the second cylinder body (2) is arranged on the end part of the second cylinder body (2), the end cover (12) is detachably connected to the end part of the second cylinder body (2) through a connecting bolt (13), and the first pin head (5) is arranged on the end cover (12).

4. The lead extrusion compound rotary vane viscous damper of claim 1, characterized in that: the inner wall of the second barrel (2) is provided with a fixing block (14), the fixing block (14) is located in the cavity (3), the fixing block (14) is provided with an SMA stranded wire (15), one end of the SMA stranded wire (15) is connected to the fixing block (14), and the other end of the SMA stranded wire is connected to the piston rod (6).

5. A method of using a lead extrusion compound rotary vane viscous damper as claimed in claims 1 to 4, characterized in that: the method comprises the following steps:

step S1: connecting a lead extrusion composite rotary vane type viscous damper to a stay cable of a cable-stayed bridge, and then arranging monitoring points on the cable-stayed bridge;

step S2: establishing a BP neural network model and setting each network parameter;

step S3: performing particle swarm optimization on the established BP neural network model to obtain an optimal network weight and a threshold;

step S4: training the established BP neural network model, establishing an error condition, judging the output error of the established BP neural network model according to the established error condition, if the output error meets the error condition, turning to the step S5, and if the output error does not meet the error condition, turning to the step S04;

step S5: after training, predicting a cable-stayed bridge displacement model by the trained BP neural network model, and performing alarm processing on a dangerous prediction result;

step S04: adjust the network threshold and go to step S4.

6. The method of applying a lead extrusion compound rotary vane viscous damper as claimed in claim 5, wherein: when monitoring points are arranged in the step S1, the monitoring points are divided into different priorities by using a weighting algorithm, and data with different priorities are preprocessed differently, where the algorithm for calculating priorities by the monitoring points is: and (3) considering the factors of terrain, external factors and stress conditions, taking the factors as input conditions, setting the weight of each account, and finally introducing a priority algorithm for calculation.

7. The method of applying a lead extrusion compound rotary vane viscous damper as claimed in claim 6, wherein: the algorithm for optimizing the particle group algorithm in step S3 is as follows: the weight is set for random selection in the early stage of searching, a variation operation is introduced in the later stage of searching, the global optimal position at the moment is endowed with randomness to prevent falling into a local minimum value, and the formula is as follows: pg' ═ Pg · ξ η;

wherein Pg' and Pg are global optimal positions, ξ is a value with a random value function, and η is a random vector of Gaussian distribution;

xi is calculated as: ξ ═ srad (0,1) × (1.5 ).

8. The method of applying a lead extrusion compound rotary vane viscous damper as claimed in claim 5, wherein: the method for training the established BP neural network model in the step S4 comprises the following steps:

step S401: the maximum error value is taken out for learning again after each training;

step S402: the method for determining the selection adaptivity of the hidden layer nodes specifically comprises the following steps: setting iteration times, after learning iteration is carried out for a plurality of times, if the difference between the number of the hidden layer nodes and the error condition is large, adding one to the number of the hidden layer nodes until the number of the training samples is reduced by one or the set iteration times is met;

step S403: when the input node is set, all the points of the first priority and the points adjacent to the first priority are input, and part of the points of the second priority are input.

9. The method of applying a lead extrusion compound rotary vane viscous damper as claimed in claim 5, wherein: the particle swarm optimization algorithm of the step S3 is as follows: setting an activation function of the established BP neural network model as a bipolar S-type function, randomly generating a first batch of particles in various places, storing the particles with highest fitness at the moment to be fixed, knowing the distance between the particles and the optimal value, drawing a circle by taking the particles with highest fitness at the moment as the center of the circle and taking the length which is 1.5 times of the distance with the optimal value as the radius, withdrawing the rest of the particles, randomly releasing the particles again in the drawn circle, storing the particles with highest fitness in the drawn circle to be fixed, repeating the circle drawing step, and continuously circulating until the optimal position is found.

10. The method of applying a lead extrusion compound rotary vane viscous damper as claimed in claim 6, wherein: the difference in preprocessing the data with different priorities in step S1 is specifically: the detection point alarm with high priority can restrain the detection point alarm with low priority, and the detection point alarm with low priority can not restrain the detection point alarm with high priority.

Technical Field

The invention relates to the technical field of bridge damping, in particular to a lead extrusion composite rotary vane type viscous damper and an application method thereof.

Background

At present, people's traveling gradually develops from former walking to a series of roads such as a vehicle, a train, an airplane, a ship and the like instead of walking, corresponding roads, railways and the like begin to appear, and gradually develops from road surface construction to air construction in the development process, under the condition, the function of the bridge is more important, because in the long-distance road construction, rivers, valleys and other places are often crossed, and therefore the connection is needed through the bridge; in the bridge construction, make the bridge possess and have good shock attenuation performance, very big improvement bridge's that can life and the emergence of avoiding the incident.

The damper is a device for slowing down mechanical vibration and consuming kinetic energy by utilizing damping characteristics, mainly comprises a liquid damper, a gas damper and an electromagnetic damper, and is widely applied to the industries of aerospace, aviation, war industry, building and the like through development, and the application of the damper to bridge construction, particularly cable-stayed bridges is a common technical means.

However, the conventional damper has the problems of single energy consumption mechanism, insufficient energy consumption capability under small earthquake and poor energy consumption effect, for example, a utility model patent with the granted publication number of chinese patent CN2572130Y discloses a viscous damper, which comprises a cylinder body, a piston rod, a damping material cavity, a sealing end sleeve and a sealing ring, wherein the cylinder body is made of rigid material, the piston is arranged in the cylinder body, one end of the piston rod is connected with the piston, the other end extends out of the cylinder body, the damping material cavity is arranged in the cylinder body, the sealing end sleeve is arranged at the end part of the cylinder body, the sealing ring is arranged at the inner side of the sealing end sleeve, the viscous damper only consumes energy through the axial reciprocating motion of the piston, the energy consumption capability changes along with the speed of the piston, only in a speed type energy consumption mode, and the energy consumption mechanism is single, the problems of insufficient energy consumption capability and poor energy dissipation effect under small earthquake exist;

also like a utility model patent with the publication number of CN208981113U issued by chinese patent, it discloses a lead extrusion damper, which comprises a joint, a steel bar, an end cover, a bearing, a cylinder, and a sleeve; the cylinder body is of a hollow cylinder structure; the top end of the end cover is vertically extended to the side edge to form a clamping groove; the sleeve is a hollow cylinder; the bottom end of the sleeve is abutted against an end cover at the tail end of the cylinder body; the top end of the bearing vertically extends to the side edge to form a clamping groove, the outer diameter of the clamping groove is the same as the inner diameter of the cylinder, and the bearing forms a sealed space in the cylinder to prevent lead from leaking; the steel bar is a solid cylinder, the top end of the steel bar is connected with the joint through welding, a certain gap is formed between the steel bar and the inside of the cylinder, and lead is filled in the gap; the connector is of a conical steel structure, the number of the connectors is two, a circular hole is formed in the top end of each connector, and the connectors at the tail ends of the connectors are connected with the end covers through welding; the lead extrusion damper only adopts lead extrusion to dissipate earthquake energy, belongs to a displacement-related energy consumption mode, has a single energy consumption mechanism, and also has the problems of insufficient energy consumption capability and poor energy dissipation effect under small earthquakes.

Disclosure of Invention

In view of this, the invention provides a lead extrusion composite rotary vane type viscous damper and an application method thereof.

In order to solve the technical problems, the technical scheme of the invention is as follows: in a first aspect, the invention provides a lead extrusion composite rotary vane type viscous damper, which comprises a first cylinder and a second cylinder, wherein the first cylinder is arranged in the second cylinder, a cavity is reserved between the outer wall of the first cylinder and the inner wall of the second cylinder, damping materials are filled in the first cylinder and the cavity, an inner side wall of one side of the first cylinder is provided with an internal thread sleeve, a first pin head is arranged on the outer side wall of one side of the second cylinder, a piston rod movably arranged in a penetrating way is arranged on the outer side wall of the other side of the second cylinder, a second pin head is arranged on the end part of the piston rod far away from the side of the second cylinder, the end part of the piston rod close to the side of the second cylinder penetrates into the first cylinder from the internal thread sleeve, an external thread matched with the thread of the internal thread sleeve is arranged on the rod wall of the piston rod, the piston rod is screwed with the internal thread sleeve through the external thread, and blades are arranged on the rod wall of the first cylinder through which the piston rod penetrates, the rod wall of the piston rod is also provided with an extrusion convex ring which is arranged on the rod wall of the piston rod in the cavity.

Preferably, first barrel is rubber protective sheath, and its both sides tip position is pressed close to in the inside of first barrel all is equipped with the support cover, supports the outer wall of cover and is the setting of pasting closely with the inner wall of first barrel, still coats on the inner wall of first barrel and has the graphite alkene insulating layer.

Preferably, the second cylinder is arranged in a manner that one side end of the second cylinder is in an open structure, an end cover covering the side end of the second cylinder is arranged on the side end of the second cylinder, the end cover is detachably connected to the side end of the second cylinder through a connecting bolt, and the first pin head is arranged on the end cover.

Preferably, the inner wall of the second cylinder is provided with a fixed block, the fixed block is positioned in the cavity, the fixed block is provided with an SMA stranded wire, one end of the SMA stranded wire is connected to the fixed block, and the other end of the SMA stranded wire is connected to the piston rod.

On the other hand, the invention provides an application method of a lead extrusion composite rotary vane type viscous damper, which comprises the following steps:

step S1: connecting a lead extrusion composite rotary vane type viscous damper to a stay cable of a cable-stayed bridge, and then arranging monitoring points on the cable-stayed bridge;

step S2: establishing a BP neural network model and setting each network parameter;

step S3: performing particle swarm optimization on the established BP neural network model to obtain an optimal network weight and a threshold;

step S4: training the established BP neural network model, establishing an error condition, judging the output error of the established BP neural network model according to the established error condition, if the output error meets the error condition, turning to the step S5, and if the output error does not meet the error condition, turning to the step S04;

step S5: after training, predicting a cable-stayed bridge displacement model by the trained BP neural network model, and performing alarm processing on a dangerous prediction result;

step S04: adjust the network threshold and go to step S4.

Preferably, when the monitoring points are arranged in step S1, a weighting algorithm is used to divide the monitoring points into different priorities, and the data with different priorities are preprocessed differently, where the algorithm for calculating the priorities by the monitoring points is as follows: and (3) considering the factors of terrain, external factors and stress conditions, taking the factors as input conditions, setting the weight of each account, and finally introducing a priority algorithm for calculation.

Preferably, the algorithm for optimizing the particle group algorithm in step S3 is: the weight is set for random selection in the early stage of searching, a variation operation is introduced in the later stage of searching, the global optimal position at the moment is endowed with randomness to prevent falling into a local minimum value, and the formula is as follows: pg' ═ Pg · ξ η;

wherein Pg' and Pg are global optimal positions, ξ is a value with a random value function, and η is a random vector of Gaussian distribution;

xi is calculated as: ξ ═ srad (0,1) × (1.5 ).

Preferably, the method for training the established BP neural network model in step S4 includes the following steps:

step S401: the maximum error value is taken out for learning again after each training;

step S402: the method for determining the selection adaptivity of the hidden layer nodes specifically comprises the following steps: setting iteration times, after learning iteration is carried out for a plurality of times, if the difference between the number of the hidden layer nodes and the error condition is large, adding one to the number of the hidden layer nodes until the number of the training samples is reduced by one or the set iteration times is met;

step S403: when the input node is set, all the points of the first priority and the points adjacent to the first priority are input, and part of the points of the second priority are input.

Preferably, the particle swarm optimization algorithm in step S3 is: setting an activation function of the established BP neural network model as a bipolar S-type function, randomly generating a first batch of particles in various places, storing the particles with highest fitness at the moment to be fixed, knowing the distance between the particles and the optimal value, drawing a circle by taking the particles with highest fitness at the moment as the center of the circle and taking the length which is 1.5 times of the distance with the optimal value as the radius, withdrawing the rest of the particles, randomly releasing the particles again in the drawn circle, storing the particles with highest fitness in the drawn circle to be fixed, repeating the circle drawing step, and continuously circulating until the optimal position is found.

Preferably, the difference of the preprocessing of the data with different priorities in step S1 is specifically: the detection point alarm with high priority can restrain the detection point alarm with low priority, and the detection point alarm with low priority can not restrain the detection point alarm with high priority.

Compared with the prior art, the invention has the advantages that:

1. the invention has the advantages that the blades are arranged on the piston rod, when the piston rod moves by an external force, the piston rod rotates under the action of the internal thread sleeve, at the moment, the blades can rotate along with the piston rod, the blades can contact with the damping material in the first cylinder when rotating so as to realize energy consumption, the extrusion convex ring is arranged, when the piston rod moves, the extrusion convex ring can extrude the damping material in the cavity so as to deform the damping material in the cavity, the energy consumption mechanism of the damper is a speed-displacement dual energy consumption mechanism through the arrangement of the blades and the extrusion convex ring, under the condition that the whole reaction speed and the deformation of the damper are small, the extrusion convex ring can fully play the advantages of strong deformation tracking capability and displacement sensitivity degree by matching with the damping material in the first cylinder, and under the condition that the speed is large, the impeller can fully play the advantage of high energy consumption by matching with the damping material in the first cylinder, compared with the prior art, the energy consumption mechanism has the advantages of various energy consumption mechanisms, good energy consumption capability under small earthquake and good overall energy consumption effect.

2. According to the damper, the SMA stranded wires are arranged, so that the SMA stranded wires can generate counter force due to deformation caused by stretching when the piston rod rotates, energy consumption can be further realized, required force can be provided for resetting of the piston rod after the rotation of the piston rod is stopped, and the damper is convenient to use.

3. The invention can greatly enhance the safety of the damper applied to the cable-stayed bridge by providing the damping control algorithm of the damper.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic structural view of the present invention;

FIG. 2 is a sectional view taken along line A-A;

FIG. 3 is a cross-sectional view B-B;

FIG. 4 is a schematic flow chart of the BP neural network according to the present invention;

FIG. 5 is a flow chart of particle swarm optimization in the present invention.

Reference numerals: 1. a first cylinder; 2. a second cylinder; 3. a cavity; 4. an internally threaded sleeve; 5. a first pin head; 6. a piston rod; 7. a second pin head; 8. an external thread; 9. a blade; 10. extruding the convex ring; 11. a support sleeve; 12. an end cap; 13. a connecting bolt; 14. a fixed block; 15. and (3) SMA stranded wires.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1 to 3, the invention provides a lead extrusion composite rotary vane type viscous damper, which comprises a first cylinder 1 and a second cylinder 2, wherein the first cylinder 1 is arranged inside the second cylinder 2, the first cylinder 1 is a rubber protective sleeve, support sleeves 11 are arranged inside the first cylinder 1 and close to the end parts of the two sides of the first cylinder 1, the outer walls of the support sleeves 11 are closely attached to the inner wall of the first cylinder 1, the support sleeves 11 are arranged to support the first cylinder 1, a cavity 3 is left between the outer wall of the first cylinder 1 and the inner wall of the second cylinder 2, damping materials are filled in the first cylinder 1 and the cavity 3, wherein the damping material filled in the first cylinder 1 is polyether polyurethane liquid commonly used in the prior art, the damping material filled in the cavity 3 is lead commonly used in the prior art, and due to poor heat resistance of the polyether polyurethane liquid, in order to prevent the external temperature from influencing the performance of the polyether polyurethane liquid, the graphene thermal insulation layer is coated on the inner wall of the first cylinder 1, so that the influence of the external temperature on the inside of the first cylinder 1 is isolated.

An internal thread sleeve 4 is arranged on the inner side wall of one side of the first cylinder 1, a first pin head 5 is arranged on the outer side wall of one side of the second cylinder 2, a piston rod 6 which is movably arranged in a penetrating manner is arranged on the outer side wall of the other side of the second cylinder, a second pin head 7 is rotatably connected to the end part, far away from the second cylinder 2, of the piston rod 6, the end part, close to the second cylinder 2, of the piston rod 6 penetrates into the first cylinder 1 from the internal thread sleeve 4, an external thread 8 which is adaptive to the thread of the internal thread sleeve 4 is arranged on the rod wall of the piston rod 6, the piston rod 6 is connected with the internal thread sleeve 4 in a threaded manner through the external thread 8, and blades 9 are arranged on the rod wall, penetrating into the first cylinder 1, of the piston rod 6; it should be noted that the pitch of the external thread 8 on the rod wall of the piston rod 6 and the pitch of the internal thread sleeve 4 are wide enough to meet the requirement that when the piston rod 6 has a movement trend of moving the piston along the direction of the internal thread sleeve 4, the piston rod 6 can rotate under the connection effect of the external thread 8 and the internal thread, and meanwhile, the connection tightness between the internal thread sleeve 4 and the piston rod 6 meets the requirement that the cavity 3 and the damping material in the first cylinder 1 are not communicated with each other; a fixed block 14 is arranged on the inner wall of the second cylinder 2, the fixed block 14 is positioned in the cavity 3, an SMA stranded wire 15 is arranged on the fixed block 14, one end of the SMA stranded wire 15 is connected to the fixed block 14, the other end of the SMA stranded wire is connected to the piston rod 6, an extrusion convex ring 10 is arranged on the rod wall of the piston rod 6, and the extrusion convex ring 10 is arranged on the rod wall of the piston rod 6 in the cavity 3; through the aforesaid setting, when receiving external force piston rod 6 and carrying out piston motion, at this moment, piston rod 6 still can rotate under the effect of internal thread sleeve 4, drive blade 9 with this and rotate in first barrel 1, make blade 9 and the damping material contact in the first barrel 1, thereby realize the power consumption, simultaneously, when piston rod 6 carries out piston motion, extrusion bulge loop 10 can extrude the damping material in cavity 3, thereby make the damping material in cavity 3 take place plastic deformation and further consume energy, and stranded conductor SMA 15 can produce the counter-force because of tensile production deformation when piston rod 6 rotates, not only can consume energy, can also provide required power for the restoration of piston rod 6.

In order to facilitate the disassembly and assembly of the damper, one side end of the second cylinder 2 is arranged in an open structure, an end cover 12 covering the side end of the second cylinder 2 is arranged on the side end of the second cylinder 2, the end cover 12 is detachably connected to the side end of the second cylinder 2 through a connecting bolt 13, the first pin head 5 is arranged on the end cover 12, and through the arrangement, the connecting bolt 13 can be taken down to take the end cover 12 down from the end of the second cylinder 2 in the open structure, so that the operations such as taking out the first cylinder 1 and replacing the damping material in the first cylinder 1 and the damping material in the cavity 3 are carried out.

Example (b): referring to fig. 1 to 5, the present embodiment provides an application method of a lead extrusion composite rotary vane type viscous damper, including the following steps:

step S1: connecting a lead extrusion composite rotary vane type viscous damper to a stay cable of a cable-stayed bridge, then arranging monitoring points on the cable-stayed bridge to obtain deformation sample data, and when needing to be explained, arranging measuring points in a full-length range as far as possible according to bridge characteristics when the monitoring points are arranged so as to expect to obtain information of a full-bridge structure on the whole, arranging more monitoring points on a cantilever section of the bridge to strengthen control of construction elevation of the cantilever end, and arranging priority division of the monitoring points at a bridge folding position;

the mode of acquiring deformation sample data is as follows: connecting the deformation sensor and the deformation collector to each selected monitoring point by using the deformation sensor and the deformation collector, setting the priority of each monitoring point, and then acquiring and predicting deformation sample data;

the priority setting algorithm of each monitoring point is as follows: dividing a plurality of monitoring points into different priorities by using a weighting algorithm, wherein the preprocessing of the data with different priorities is different, and the preprocessing of the data with different priorities is specifically as follows: the detection point alarm with high priority can inhibit the detection point alarm with low priority, and the detection point alarm with low priority can not inhibit the detection point alarm with high priority; the priority levels are divided into a first priority, a second priority, a third priority, … …, an nth priority and an n +1 th priority from high to low; the algorithm for calculating the priority of the monitoring points comprises the following steps: taking the terrain factors, the external factors and the stress condition factors into consideration, taking the factors as input conditions, setting each occupation weight, and finally introducing a priority algorithm for calculation, wherein each selected factor and each occupation weight need to be selected and brought in according to the actual geographical position of the bridge; the algorithm for calculating the priority of the monitoring points in the embodiment has the following formula:

wherein m is_n(x) Representing a specific priority value, the higher the value the higher the priority level, W_m(x) Weights, y, representing different factors_iRepresenting different factor values;

the algorithm for acquiring and predicting the deformation sample data comprises the following steps: acquiring deformation monitoring data of each monitoring point in a first time period from the moment, taking the deformation monitoring data as sample data, predicting the displacement deformation of the monitoring points in a second time period, and then preprocessing the sample data, wherein the preprocessing mode is as follows: preprocessing sample data to eliminate results caused by special factors, focusing on data of monitoring points with front priorities, analyzing certain unreasonable data, and performing normalization processing on the processed data to obtain a required sample data set;

the normalization process uses the formula:

wherein, X' is the value after the sample normalization treatment, the value range is-1, and X_maxIs the maximum value, X, in the sample data_minIs the minimum value in the sample data, X is the sample data value;

step S2: establishing a BP neural network model and setting each network parameter; the concrete mode is as follows: adopting a newff function in MATLAB software to construct a BP neural network, firstly determining specific structural parameters of an input layer, a hidden layer and an output layer, then inputting data to be trained as the input layer, and giving an expected output value; BTF is used as a training function and is set as "trai nbr", BLF is used as a learning algorithm and is set as "l earngdm"; the BP neural network is mainly composed of an input layer LA, one or more hidden layers LB and an output layer LC, the connection weight from the input layer to the hidden layers is set as v, the connection weight from the hidden layers to the output layers is set as w, the threshold value of the hidden layers is set as theta, the threshold value of the output layers is set as gamma, the connection weight v and the threshold value theta between transmission layers are adjusted by using the generalization error of each unit in the layers and the output correction weight w and the threshold value gamma of the hidden layers in the training process, and when the global error of the network reaches a preset value, the network convergence training is finished;

then, network training is carried out, the deformation value, the time domain characteristic and the space domain characteristic of the first priority monitoring point on the fifth day are predicted by taking the data of the first three days with the deformation change of the first priority monitoring point and 6 samples of the space domain characteristic, the 6 samples of the space domain characteristic are the deformation change of the adjacent point of the first priority monitoring point, the prediction algorithm is that 9 samples of the data of the first three days with the deformation change of the first priority monitoring point and the 6 samples of the space domain characteristic are taken as network input samples, when the time sequence and the adjacent point are taken as the influence factors of the input samples, the number of the input layer units is 9, the number of the output layer units is 1, the learning rate is 0.05, the training target is 0.001, meanwhile, the minimum root mean square error training parameter is set, BP neural network training is carried out, and the activation function adopts a bipolar S-type function;

step S3: performing particle swarm optimization on the established BP neural network model to obtain an optimal network weight and a threshold;

the particle swarm optimization BP neural network comprises the following specific steps:

step S301: initializing parameters of the particle swarm, wherein PSO parameters are set as follows: setting the population size N to 50, wherein the convergence speed is fastest at the moment; the spatial dimension D is 2; learning factor c1 ═ c2 ═ 2; the maximum iteration number is 150; the particle dimension is 121; position boundaries-1, 1; setting the inertia weight and the particle speed within the range of-5 to 5;

step S302: initializing an initial position and an initial speed of a particle swarm, setting parameters of the particle swarm, ensuring that the motion track and the running speed of the particles are within a set range at the stage, and endowing the particle swarm with a global optimal position and a self optimal position;

step S303: calculating the fitness value of the population particles; the fitness function is a function for evaluating the individual quality, and the fitness function of the PSO-BP neural network model is defined as the mean square error of an output value and a sample value:

in the formula: delta_MSEIs the fitness value of the population particle, N is the training sampleThe number of the chips; y is_iIs the measured value of the ith sample; s_iIs the predicted value of the ith sample;

step S304: randomly generating a first batch of particles at each place, storing the particles with the highest fitness at the moment, fixing the particles, recording the positions and the fitness values at the moment, and recording the distance between the globally optimal particles and the optimal values at the moment;

step S305: and setting a speed and position updating formula of the particles, and updating the speed and the position of the particles by using the speed and position updating formula of the particles.

Step S306: if the objective function value cannot be met, drawing a circle by taking the length which is 1.5 times of the linear length of the objective function value from the optimal distance position as a radius, withdrawing the rest particles except the global optimal particles, randomly throwing the particles in the circumferential range, and not throwing the particles outside the circumferential range, and continuously and circularly iterating until the objective function value is met or the maximum iteration number is reached;

wherein the iterative process comprises the steps of:

step S3061: in the early stage of iteration, weight random selection is set, namely the initial position and the initial speed of the particles are random, and the calculation formula is as follows:

ω＝μ+σ*N(0,1)

μ＝μ_min+(μ_max-μ_min)*rand(0,1)

where ω represents an inertial weight, N (0,1) represents a normally distributed random number, rand (0,1) represents a random number between 0 and 1, μ_maxRepresents the maximum value of the random inertial weight, mu_minRepresents the minimum value of the random inertia weight, mu represents the random inertia weight value, and sigma variance is used to measure the deviation value between the random variable weight and the mathematical expectation;

step S3062: and in the later iteration stage, introducing a mutation operation, assigning the global optimal position at the moment to randomness to prevent from falling into a local minimum value, and the formula is as follows: pg' ═ Pg xi eta,

wherein Pg' and Pg are global optimal positions, ξ is a value with a random value function, η is a random vector with Gaussian distribution, and the formula is mainly used for endowing randomness thereof;

and xi has the formula: ξ — srad 0,1 × rand-1.5, 1.5;

wherein, srad and rand are both two random functions, which aim to endow the numerical value with a randomness, namely, srad 0,1 is a number randomly selected in the range of 0 to 1, and rand-1.5, 1.5 is a number randomly selected in the range of-1.5 to 1.5;

then, setting a particle speed and position updating formula, updating the speed and the position of the particle by using the particle speed and position updating formula by using the formula 1-2 to obtain a particle fitness updating value, and repeatedly updating the individual extreme value and the global extreme value of the particle according to a new fitness value;

formula 1 is specifically: v. of_i.j(t+1)＝wv_i.j(t)+c1r1[p_i.j-x_i.j(t)]+c2r2[p_g.j-x_i.j(t)]

Formula 2 specifically is: x is the number of_i.j(t+1)＝x_i.j(t)+v_i.j(t+1)

Wherein t represents the number of iterations of the program; ω represents the inertial weight; c1 and c2 both represent learning factors, are also called acceleration constants, and are c 1-c 2-2; r1 and r2 are [0, 1 ]]A uniform random number within a range; p is a radical of_i,jRepresenting the optimal solution searched by the current individual; p is a radical of_g,jRepresenting the optimal solution searched by the whole population; v. of_i,jVelocity of movement of the particles, v_i,j∈[-v_max,v_max],v_maxIs a constant, limiting the velocity of the particles according to user settings; x is the number of_i,jIndividual parameters representing the particles, i.e. the values of the decision variables;

after repeated iteration, when the error reaches an expected value or reaches a set maximum iteration number, ending the particle swarm algorithm, and taking the obtained global extremum as the weight and the threshold of the BP neural network;

step S4: the method for training the established BP neural network model comprises the following steps:

step S401: the maximum error value is taken out for learning again after each training;

step S402: the method for determining the selection adaptivity of the hidden layer nodes specifically comprises the following steps: setting iteration times, after learning iteration is carried out for a plurality of times, if the difference between the number of the hidden layer nodes and the error condition is large, adding one to the number of the hidden layer nodes until the number of the training samples is reduced by one or the set iteration times is met;

step S403: when the input node is set, all the points with the first priority and the points adjacent to the first priority are input, and part of the points with the second priority are input;

then establishing an error condition, judging the output error of the established BP neural network model according to the established error condition, if the output error meets the error condition, turning to the step S5, and if the output error does not meet the error condition, turning to the step S04;

after the set iteration times are met, assigning the position vector value of the optimal particle to a weight and a threshold of a BP neural network, wherein the performance of the BP neural network is close to the optimal value, in order to avoid overfitting, excessive training times are not required to be set when the BP neural network is trained, the maximum training cycle time is set to be 200, the learning efficiency is 0.01, the target precision is 0.00001, a trai n lm is selected as a training function, the obtained optimal weight and the threshold are assigned to the BP neural network, and then a training data set is adopted for network training, so that the trained BP neural network can be obtained;

step S5: after training, predicting a cable-stayed bridge displacement model by the trained BP neural network model, and performing alarm processing on a dangerous prediction result;

step S04: adjust the network threshold and go to step S4.

The above are only typical examples of the present invention, and besides, the present invention may have other embodiments, and all technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of the present invention as claimed.