CN115809497A - Intelligent line and slope adjusting design method, storage medium and equipment for urban rail transit - Google Patents
Intelligent line and slope adjusting design method, storage medium and equipment for urban rail transit Download PDFInfo
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Abstract
The invention discloses an intelligent line and slope adjusting design method, a storage medium and equipment for urban rail transit, wherein the method comprises the following steps of; setting weights and infringement tolerance values for the divided sections and the section positions of the infringement tunnel according to the limit requirements; establishing an optimization model according to the weight, the intrusion tolerance value and the constraint condition; obtaining an initial solution of the optimization model according to design parameters of a plane and a vertical section; and solving the optimal solution of the optimization model by adopting a particle swarm algorithm. According to the method, weights are respectively given to different positions of each section and each section, so that the overall consideration of the limit invasion mode can be realized, and the problem of limit invasion of the sections with larger limit invasion degree and higher engineering requirements can be solved preferentially. The optimization model is optimized through the particle swarm optimization, so that the convergence speed is higher in the process of solving the optimal solution of line regulation and slope regulation, and the design scheme of line regulation and slope regulation can be obtained quickly and accurately.
Description
Technical Field
The invention relates to the technical field of rail transit design, in particular to an intelligent line and slope adjusting design method, a storage medium and equipment for urban rail transit.
Background
The design of line and slope adjustment is the key work before track laying and equipment installation after the construction of the civil structure of the rail transit is finished. The inevitable error of civil engineering construction is specifically shown as the tunnel swings left and right, floats up, sinks, and deviates from the original design scheme. These deviations may cause problems in critical equipment installation space within the tunnel and at the same time conflict with the dynamic envelope of the vehicle, affecting the safety of train travel. Therefore, on the basis of the original design scheme, the design parameters of the line plane and the longitudinal section need to be finely adjusted, and the adverse effect caused by construction errors is reduced to an allowable range under the condition of meeting relevant constraints such as specifications.
The existing line and slope adjusting work is basically completed manually by designers, the measured data and the design data of an actual tunnel need to be repeatedly compared manually, then rough invasion limit condition analysis is carried out, and scheme adjustment is carried out manually. The method is not intuitive, has unclear targets, is always considered, has low efficiency and repeated flow, is difficult to obtain a better design scheme, influences the project progress and wastes the project investment. The existing intelligent line and slope regulating technology lacks the integral consideration of an intrusion limit mode, the intrusion limit mode has complex diversity, and the solution obtained by forcibly establishing a functional relation between the intrusion limit and a line parameter is often unsatisfactory. In actual engineering, the subway tunnel is formed by sections of different construction methods, and acceptable intrusion tolerance values of tunnel sections of different construction methods are different when a line is adjusted and a slope is adjusted. Meanwhile, the precision requirements of some sections on line and slope adjustment are not consistent under the requirements of engineering conditions, track vibration reduction and other factors.
Therefore, when the situation that the invasion limit forms are various is faced, how to realize the overall consideration of the invasion limit mode can quickly and accurately obtain a better solution, which is the problem to be solved by the line and slope adjusting design.
Disclosure of Invention
In order to overcome the technical defects, the invention provides an intelligent line and slope adjusting design method, a storage medium and equipment for urban rail transit, which can improve the efficiency and quality of line and slope adjusting design.
In order to solve the problems, the invention is realized according to the following technical scheme:
in a first aspect, the invention provides an intelligent line and slope adjusting design method for urban rail transit, which comprises the following steps:
setting weights and infringement tolerance values for the divided sections and the section positions of the infringement tunnel according to the limit requirements;
establishing an optimization model according to the weight, the intrusion tolerance value and the constraint condition;
obtaining an initial solution of the optimization model according to design parameters of a plane and a vertical section;
and solving the optimal solution of the optimization model by adopting a particle swarm algorithm.
As an improvement of the above solution, the optimization model is expressed as:
wherein, W i Wi = W as the weight of the most unfavorable point on the ith section fi ×W si The product of the corresponding segment number and the section weight is obtained;
in the cut-off section k, f k For infringement limit, M is a penalty factor, T k The intrusion tolerance value of the Kth section is obtained;
P m the penalty term for the mth plane intersection parameter or the vertical plane change point parameter may violate the specification constraint.
As an improvement of the above scheme, the constraint conditions include:
plane specification constraint conditions: the minimum curve radius constraint, the minimum length constraint of the circular curve and the minimum straight line clamping constraint;
and (3) standard constraint conditions of the longitudinal section: maximum slope constraint, minimum slope length constraint and slope section inter-clamp straight line length constraint.
As an improvement of the above solution, the obtaining an initial solution of the optimization model according to the design parameters of the plane and the vertical section includes:
coding design parameters of the plane and the vertical section into vectors according to an initial design scheme, and enabling the parameter vectors to correspond to particles;
and solving the initial fitness function value of the particles according to the optimization model, and traversing the initial fitness function value to obtain the optimal fitness function value of each subgroup and the optimal fitness function value of the total group of the initial particle population.
As an improvement of the above scheme, the solving of the optimal solution of the optimization model by using the particle swarm algorithm includes:
setting an iteration model, maximum iteration times, particle number, maximum particle speed and a learning factor;
executing a circulation process until a preset condition is met; the circulation process comprises the following steps:
updating the particle data according to an iterative model;
calculating a fitness function value of the particle according to the optimization model;
judging whether the particle fitness is the historical optimal fitness or not, if the current fitness of the particle is superior to the historical optimal fitness, updating the current fitness of the particle to be the historical individual optimal fitness of the particle, and updating the current position to be the historical individual optimal position of the particle;
the preset condition is that the cycle number reaches the maximum iteration number set value;
traversing the fitness of each current subgroup of particles and the fitness in the total population to obtain the optimal fitness of the particle subgroup and the optimal fitness of the total population;
and obtaining the optimal solution according to the optimal fitness of the subgroups and the optimal fitness of the total group.
As an improvement of the above scheme, the iterative model is
Wherein,
is the velocity of particle i at time t + 1;
is the position of the particle i at time t;
the optimal solution of the particle i at the time t is obtained;
the optimal solution of the whole population at the time t is obtained; w is the velocity change weight; c. C 1 、c 2 Is a learning factor; r is 1 、r 2 Is a random number uniformly distributed in the interval (0, 1).
In a second aspect, the present invention provides a computer-readable storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for intelligent route and slope tuning of urban rail transit according to the first aspect.
In a third aspect, the present invention provides an apparatus, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor to implement the method for designing the intelligent tuning line and slope of the urban rail transit according to the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, weights are respectively given to different positions of each section and each section, so that the overall consideration of the limit invasion mode can be realized, and the problem of limit invasion of the sections with larger limit invasion degree and higher engineering requirements can be solved preferentially. The optimization model is optimized through the particle swarm optimization, so that the convergence speed is higher in the process of solving the optimal solution of line regulation and slope regulation, and the design scheme of line regulation and slope regulation can be obtained quickly and accurately.
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Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
fig. 1 is a schematic flow chart of an intelligent route and slope adjusting design method for urban rail transit in embodiment 1;
FIG. 2 is a schematic flow chart of step S3 described in example 1;
FIG. 3 is a schematic view of the flow chart of step S4 described in example 1;
FIG. 4 is a diagram of the overall weight design setup described in example 1;
FIG. 5 is a graph of the section A fitness optimization described in example 1;
FIG. 6 is a plane surface fitness optimization curve of the area A in example 1;
FIG. 7 is a comparison of the 36325.582 mileage profiling before and after the adjustment described in example 1.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
It should be noted that the reference numbers such as S1, S2 \8230 @, are merely used to distinguish steps from one another, and do not mean that the steps must be executed strictly according to the sequence numbers.
Example 1
The embodiment provides an intelligent line and slope adjusting design method for urban rail transit, and as shown in fig. 1, the intelligent line and slope adjusting design method for urban rail transit comprises the following steps:
s1: setting weights and infringement tolerance values for the divided sections and the section positions of the infringement tunnel according to the limit requirements;
in order to ensure that a designed line meets the requirements of equipment installation and driving safety of the current tunnel, the limit requirement of each section must be met during optimization and adjustment of a planar line, in actual engineering, a subway tunnel is formed by sections of different construction methods, and acceptable limit tolerance values of the tunnel sections of the different construction methods are different when a line is adjusted and a slope is adjusted. Meanwhile, the precision requirements of some sections on line and slope adjustment are not consistent under the requirements of engineering conditions, track vibration reduction and other factors. Therefore, as shown in table 1, considering the boundary requirements, in the design of track profile adjustment and slope adjustment, the division into sections and the section positions are set with weights and tolerance values for limit violation.
TABLE 1 weight and infringement tolerance segment table
| Segmented starting point mileage | Segmented end-point mileage | Weight of | Tolerance value of | Tolerance value of | Tolerance value left | Tolerance value of right |
| M 1 | M 2 | Ws 1 | Ts 1 | Tx 1 | Tz 1 | Ty 1 |
| M 2 | M 3 | Ws 2 | Ts 2 | Tx 2 | Tz 2 | Ty 2 |
| … | ||||||
| M k-1 | M k | Ws k | Ts k | Tx k | Tz k | Ty k |
Different requirements for the limit due to different construction equipment installation are different in the tunnel, so different weights are set for different point positions on the section, and important equipment is preferentially guaranteed to meet the limit requirement. Specifically, taking a shield tunnel as an example, 10 key points on a cross section are weighted and set, the transverse distance between the 10 key points on the cross section and the rail surface axis is usually used for representing a theoretical building limit, and the limit invasion condition of the cross section is analyzed by calculating the difference between the actually measured transverse distance and the theoretical limit transverse distance.
S2: establishing an optimization model according to the weight, the intrusion tolerance value and a constraint condition;
and (3) abstracting the line adjustment design of the horizontal and vertical sections into a mathematical model, namely solving the conditional extreme value of a multivariate function, and ensuring that the key sections and the sections meet the limit requirement on the premise of meeting design specifications, standards and the like to obtain a design scheme with the minimum total weight of the intrusion limit. The line optimization scheme needs to meet various standard constraint conditions and comprises the following steps: plane specification constraint conditions: (1) minimum curve radius constraint: r i ≥R min (ii) a (2) circular curve minimum length constraint: lc i ≥Lc min (ii) a (3) minimum clip line constraint: l is i ≥L min . The vertical section standard constraint condition is as follows: (1) maximum slope constraint: i is i ≥I max (ii) a (2) minimum slope constraint: i is i ≥I min (ii) a (3) minimum slope length constraint: l is si ≥L smin (ii) a (4) clamping straight line length between the slope sections for constraint: SL (Long-side) i ≥SL min 。
The limit penetration condition is characterized by the limit margin of the least favorable point on each section, and d is recorded i ＝D s -D i When d is i Not infringing limit when not less than 0, d i <The time of 0 represents the intrusion limit, so that the whole weighted intrusion limit value can be ensured to be minimum as long as the weighted total margin on each section is maximum, and the target function is set as follows:
in the formula: w i -the weight of the most unfavorable point on the ith section, W i ＝W fi ×W si The product of the number of corresponding segments and the section weight. The position of the worst point on the plane refers to the measuring point with the minimum margin in the measuring points on the left and the right of the tunnel, and the position of the worst point on the vertical section refers to the measuring point with the minimum margin in the measuring points at the top or the bottom of the tunnel;
f k ，T k m-in the cut-off section k, f k M is a penalty coefficient for exceeding the maximum tolerance value T k When (f) k <T k ) M takes a value with a large value, otherwise M is 0;
p m the m-th plane intersection point parameter or the vertical section slope point parameter may violate the penalty of the standard constraint, and a value with a large value is taken.
S3: obtaining an initial solution of the optimization model according to design parameters of a plane and a vertical section;
in this embodiment, as shown in fig. 3, the step S3 includes the following steps:
s31: coding design parameters of the plane and the vertical section into vectors according to an initial design scheme, and enabling the parameter vectors to correspond to particles;
the flat design line shape can be uniquely determined by the coordinates of each intersection point, the curve radius, and the easement curve length. On the plane, the length of the relaxation curve is preferably rounded by a multiple of 5 to 10, and considering that the line regulation and slope regulation are only small regulation of the line in a controllable range, and the regulation of the radius is only small-range fluctuation. In order to simplify the model, the invention does not include the length of the relaxation curve into the optimization parameter, and the length of the relaxation curve is correspondingAnd the value is dynamically taken according to the change of subway design specifications along with the radius. Thus, encoding the flat design line shape design parameters into a vector can be expressed as: [ x ] 1 ,y 1 ,R 1 ,…,x k ,y k ,R k ]And the aim of adjusting the line plane to the optimal position is achieved by modifying the design variables.
The designed linear shape of the longitudinal section can be uniquely determined by the mileage of a variable slope point, the elevation and the radius of a vertical curve. The vertical curve on the longitudinal section plays a role in smoothing the transition gradient, the radius of the vertical curve of the urban rail transit is generally fixed, and the radius of the vertical curve is not included in optimization parameters in order to simplify the model. The encoding of the design linear parameters of the vertical section into vectors can be expressed as: [ m ] of 1 ,h 1 ,R 1 ,…,m k ,h k ,R k ]。
S32: and solving the initial fitness function value of the particles according to the optimization model, and traversing the initial fitness function value to obtain the optimal fitness function value and the total population of each subgroup of the initial particle population.
Randomly initializing particle positions and particle speeds, and calculating a particle fitness value according to the optimization model to generate the fitness of each particle; through traversing the fitness of the particles, finding out the optimal fitness function value in each subgroup of the initial population and the optimal fitness function value in the total population, wherein the optimal fitness function value is the optimal solution P in the subgroup L The total population optimal fitness function value is the optimal solution P in the total population g 。
S4: and solving the optimal solution of the optimization model by adopting a particle swarm algorithm.
In this embodiment, as shown in fig. 4, the step S4 includes the following steps:
s41: setting the maximum iteration times, the particle number, the maximum particle speed and the learning factor of the iteration model;
optimizing the optimization model based on a particle swarm algorithm, wherein the particle swarm algorithm formula is
Wherein,
is the velocity of particle i at time t + 1;
is the position of the particle i at time t;
the optimal solution of the particle i at the time t is obtained;
the optimal solution of the whole population at the time t is obtained; c1 and c2 are learning factors; r1 and r2 are random numbers uniformly distributed in the interval of (0, 1); w is a speed change weight, also called an inertia factor, the value of which is nonnegative, the value of which is large, the global optimizing capability is strong, and the local optimizing capability is weak; the value is small, the global optimizing capability is weak, and the local optimizing capability is strong.
S42: executing a cyclic process until a preset condition is met; the circulation process comprises the following steps:
updating the particle data according to an iterative model;
calculating a fitness function value of the particle according to the optimization model;
judging whether the particle fitness is the historical optimal fitness or not, if the current fitness of the particle is superior to the historical optimal fitness, updating the current fitness of the particle to be the historical individual optimal fitness of the particle, and updating the current position to be the historical individual optimal position of the particle;
the preset condition is that the cycle number reaches the maximum iteration number set value;
specifically, according to set parameters, a particle swarm algorithm and an optimization model are used for performing iterative optimization on a particle swarm, the number of optimized iterations is based on the preset number of iterations, and the content of the specific particle swarm algorithm can be referred to in the prior art.
S43: traversing the fitness of each particle subgroup and the fitness of the total population to obtain the optimal fitness of the particle subgroup and the optimal fitness of the total population;
specifically, through iterative optimization of a particle swarm algorithm, a fitness function tends to converge, and at the moment, the particle swarm fitness and the fitness in the total swarm after iterative optimization are traversed to obtain the optimal fitness of a particle subgroup and the optimal fitness of the total swarm;
s44: and obtaining the optimal solution according to the optimal fitness of the subgroups and the optimal fitness of the total group.
Specifically, parameter vector conversion is carried out according to the optimal fitness of the particle subgroup and the optimal fitness of the total population to obtain an optimal solution of an optimization model, wherein the optimal solution comprises a plane optimal solution and a vertical plane optimal solution.
The method for designing the urban rail transit intelligent line and slope regulation is further explained by combining one of the application examples:
in the line and slope adjustment design of an engineering A interval, the outline of the invasion limit of the original scheme is shown in Table 2, two sections of invasion limits exist on the plane of the A interval, wherein the invasion limit is serious on the left side of a range section from 36289.533 to 36370.566, the maximum invasion limit reaches-163 mm, and the installation of equipment in a tunnel is seriously influenced; meanwhile, the sand red interval has a plurality of 12 sections of lower side limit invasion on the longitudinal section, wherein the 36325.582-36352.593 mileage section limit invasion is serious, the maximum limit invasion value reaches-71 mm, and the special damping installation of the track of the section is seriously influenced.
TABLE 2 intrusion Profile before adjustment
| Infringement range (m) | Invade limit device | Maximum invasion limit (mm) | Maximum infringement limit mileage (m) |
| 36289.533-36370.566 | Left side of | -163 | 36325.582 |
| 36812.209-36875.296 | Right side of the | -77 | 36843.728 |
| 35902.235-35992.132 | Lower side | -53 | 35929.114 |
| 36050.687-36113.674 | Lower side | -59 | 36082.174 |
| 36181.393-36203.939 | Lower side | -32 | 36194.851 |
| 36244.500-36294.068 | Lower side | -25 | 36280.601 |
| 36325.582-36352.593 | Lower side | -71 | 36334.63 |
| 36541.949-36583.87 | Lower side | -48 | 36541.949 |
| 36607.98-36640.961 | Lower side | -33 | 36636.488 |
| 36708.602-36713.151 | Lower side | -27 | 36713.151 |
| 36744.637-36803.219 | Lower side | -27 | 36785.222 |
| 36834.756-36875.296 | Lower side | -53 | 36839.234 |
| 36938.466-37013.451 | Lower side | -71 | 37001.448 |
| 37037.515-37091.658 | Lower side | -24 | 37049.531 |
According to the specific conditions of the track damping design and the section limit violation between A, the weight and limit violation tolerance value design is performed according to the principle of preferentially solving the problem of the section limit violation with larger limit violation degree and higher engineering requirement and by referring to the tolerance limit violation value with the largest equipment rectification as shown in Table 3.
TABLE 3 weight and infringement tolerance segment Table
| Segmented starting point mileage | Segmented end-point mileage | Weight of | Tolerance value of | At tolerance value | Tolerance value of left | Tolerance value of right |
| 35902.000 | 36115.000 | 1 | -50 | -20 | -30 | -50 |
| 36115.000 | 36800.000 | 3 | -50 | -40 | -30 | -50 |
| 36800.000 | 37100.000 | 2 | -50 | -40 | -20 | -20 |
Besides, for the design of the cross-section weight of 10 points, the limit requirements of the evacuation platform and the track structure position are preferably guaranteed in the engineering, so the weights of the left middle 2 point and the bottom point are set to be 2, the weights of the other points are set to be 1, and the overall weight design is set as shown in fig. 4.
Taking a construction drawing design scheme as an initial scheme, and coding plane intersection point coordinates and radii, wherein corresponding plane initial particles are as follows:
X0＝{95686.9190,50003.8675,350,95579.9605,50262.9571,350,95564.6722,50751.5579,350}
coding the mileage and elevation of the slope changing point of the longitudinal section, wherein the initial particles corresponding to the longitudinal section are as follows:
Y0＝{35941,52.471,36231,45.221,35501,43.871,37081,47.931}
and respectively carrying out random disturbance on the design vectors of the flat sections and the vertical sections represented by the initial line to form a plurality of new flat section and vertical section schemes.
Adaptation of each particle generated from the optimization modelAnd (4) degree of reaction. Finding out optimal solution P in each subgroup of initial population L And optimal solution P in the population g 。
And the intelligent optimization design of line and slope adjustment is carried out on the interval A by adopting a mode of optimizing the longitudinal section and then optimizing the plane. The parameters of the particle swarm were set as follows: number of particles N =30, c1= c2=1.5, and maximum number of iterations N =300. The maximum speed of the corresponding plane intersection point coordinate is 2, the maximum speed of the particles with the radius is 5, the maximum speed of the particles with the variable slope point mileage is 2, and the maximum speed of the particles with the variable slope point elevation is 0.1. And carrying out iterative updating on the positions and the speeds of the particles in the population according to a particle swarm algorithm until a convergence condition is met or the maximum iteration times is reached.
Obtaining the optimal fitness of the particle swarm and the optimal fitness of the total swarm according to the fitness of the particle swarm and the fitness of the total swarm after iterative optimization; and then, converting the parameter vectors according to the optimal fitness of the particle subgroups and the optimal fitness of the total group to obtain an optimal solution of an optimization model, wherein the optimal solution comprises a plane optimal solution and a longitudinal plane optimal solution.
Finally, the optimal solution of the optimization model is obtained as follows:
plane optimal solution: {95686.9294,50003.8963,349.5,95579.9538,50262.8346,344.5,95564.7002,50751.5363,349.8}
Optimal solution of the vertical section: {35943,52.475,36230.6,45.237,35501,43.985,37079.9,47.929}
As shown in fig. 5 and 6, a longitudinal section fitness optimization curve and a plane fitness optimization curve of the area a are respectively shown. The particle swarm optimization algorithm rapidly improves the fitness in the previous 100 iterations, and the optimization effect is very obvious. When the iteration times reach 200 times, the fitness function tends to converge, and the effectiveness of the model is verified.
TABLE 4 adjusted intrusion Profile
The adjusted invasion profile is shown in table 4, and in the plane intelligent line adjustment design, the algorithm obtains an optimization scheme meeting engineering requirements by finely adjusting the coordinates and the radius of three intersection points, wherein the radius optimization result is as follows: r1=350 → 349.5, R2=350 → 344.5, R2=350 → 349.8. The plane limit invasion problem is reduced from the original left side maximum limit invasion of-166 mm and right side maximum limit invasion of-77 mm to the left side maximum limit invasion and right side maximum limit invasion of-37 mm, and the feasibility of the project is ensured within the maximum limit invasion tolerance range of the project. In the intelligent slope adjustment design of the longitudinal section, the algorithm greatly reduces the invasion limit section and the invasion limit degree by finely adjusting the mileage and the elevation of the slope changing point, the maximum invasion limit problem of the longitudinal section is reduced to-40 mm from the original-71 mm, and the feasibility of the project is ensured within the maximum invasion limit tolerance range of the project.
As shown in fig. 7, the design tunnel and actual tunnel section diagrams before and after the mileage section adjustment of the section 36325.582 with the most serious limit of the original design are provided, and the problem of limit intrusion is effectively solved by the intelligent line and slope adjustment design method for urban rail transit provided by the invention.
According to the method and the device, weights are respectively given to different positions of each section and each section, so that the overall consideration of the limit invasion mode can be considered, and the problem of limit invasion of the section with a large limit invasion degree and high engineering requirements can be preferentially solved. The optimization model is optimized through the particle swarm optimization, so that the convergence speed is higher in the process of solving the optimal solution of line regulation and slope regulation, and the design scheme of line regulation and slope regulation can be obtained quickly and accurately.
Example 2
The embodiment of the invention provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the processor is enabled to realize the intelligent line and slope adjusting design method for urban rail transit provided by the embodiment 1 of the invention.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable storage media, which may include computer readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer-readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
For example, the computer readable storage medium may be an internal storage unit of the network management device in the foregoing embodiment, for example, a hard disk or a memory of the network management device. The computer readable storage medium may also be an external storage device of the network management device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the network management device.
Example 3
An embodiment of the present invention provides an apparatus, including a processor and a memory, where the memory is used to store a computer program; the processor is used for executing the computer program and realizing the intelligent line and slope adjusting design method for the urban rail transit provided by the embodiment 1 of the invention when the computer program is executed.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The urban rail transit intelligent line and slope adjusting design method is characterized by comprising the following steps:
setting weights and infringement tolerance values for the divided sections and the section positions of the infringement tunnel according to the limit requirements;
establishing an optimization model according to the weight, the intrusion tolerance value and a constraint condition;
obtaining an initial solution of the optimization model according to design parameters of a plane and a vertical section;
and solving the optimal solution of the optimization model by adopting a particle swarm algorithm.
2. The urban rail transit intelligent line and slope adjusting design method according to claim 1, wherein the optimization model is expressed as:
wherein, W i Wi = W as the weight of the most unfavorable point on the ith section fi ×W si The product of the corresponding segment number and the section weight is obtained;
in the cut-off section k, f k For the intrusion value, M is the penalty factor, T k The intrusion tolerance value of the Kth section is obtained;
P m the penalty term for the mth plane intersection parameter or the vertical plane change point parameter may violate the specification constraint.
3. The urban rail transit intelligent line and slope adjusting design method according to claim 1, wherein the constraint condition comprises:
plane specification constraint conditions: the minimum curve radius constraint, the minimum length constraint of the circular curve and the minimum straight line clamping constraint;
and (3) standard constraint conditions of the longitudinal section: maximum slope constraint, minimum slope length constraint and slope section inter-clamp straight line length constraint.
4. The intelligent line and slope regulating design method for urban rail transit according to claim 1, wherein the obtaining of the initial solution of the optimization model according to the design parameters of the plane and the vertical section comprises:
coding design parameters of the plane and the vertical section into vectors according to an initial design scheme, and enabling the parameter vectors to correspond to particles;
and solving the initial fitness function value of the particles according to the optimization model, and traversing the initial fitness function value to obtain the optimal fitness function value of each subgroup and the optimal fitness function value of the total group of the initial particle population.
5. The intelligent route and slope adjusting design method for urban rail transit according to claim 4, wherein the solving of the optimal solution of the optimization model by adopting the particle swarm optimization comprises:
setting an iteration model, the maximum iteration times, the particle number, the maximum particle speed and a learning factor;
executing a cyclic process until a preset condition is met; the circulation process comprises the following steps:
updating the particle data according to an iterative model;
calculating a fitness function value of the particle according to the optimization model;
judging whether the particle fitness is the historical optimal fitness or not, if the current fitness of the particle is superior to the historical optimal fitness, updating the current fitness of the particle to be the historical individual optimal fitness of the particle, and updating the current position to be the historical individual optimal position of the particle;
the preset condition is that the cycle number reaches the maximum iteration number set value;
traversing the fitness of each particle subgroup and the fitness of the total population to obtain the optimal fitness of the particle subgroup and the optimal fitness of the total population;
and obtaining the optimal solution according to the optimal fitness of the subgroups and the optimal fitness of the total group.
6. The urban rail transit intelligent line and slope adjusting design method according to claim 5, wherein the iterative model is
Wherein, V i t+1 Is the velocity of particle i at time t + 1;
is the position of the particle i at time t;
the optimal solution of the particle i at the time t is obtained;
the optimal solution of the whole population at the time t is obtained; w is the velocity change weight; c. C 1 、c 2 Is a learning factor; r is a radical of hydrogen 1 、r 2 Is a random number uniformly distributed in the interval (0, 1).
7. A computer-readable storage medium, wherein at least one instruction, at least one program, a code set, or a set of instructions is stored in the computer-readable storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for intelligent route and slope tuning of urban rail transit according to any one of claims 1 to 6.
8. An apparatus, characterized in that the apparatus comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to realize the intelligent city rail transit line and slope design method according to any one of claims 1 to 6.
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