An optimization design method and system for laser rock breaking parameter scheme

Abstract

The present invention discloses an optimization design method and system for a laser rock breaking parameter scheme, and relates to the technical field of laser rock breaking for deep sea and deep land rock formations. First, an optimization design model for the laser rock breaking parameter scheme is constructed, and the optimization design model takes minimizing the laser rock breaking specific energy as an objective function, and takes a laser power value range, a laser irradiation time value range, a laser spot radius value range, and energy restrictions that must be met by laser gasification of rock as constraints. Then, an optimization algorithm is used to solve the optimization design model to obtain a laser rock breaking parameter scheme, and the laser rock breaking parameter scheme includes laser power, laser irradiation time, and laser spot radius, so that the optimization design of the laser rock breaking parameter scheme can be automatically completed, and high-efficiency, low-energy laser rock breaking operations can be achieved, and guidance is provided for decision-making on the laser rock breaking parameter scheme required for laser rock breaking of deep sea and deep land oil and gas reservoirs.

Description

Technical Field

[0001] The present invention relates to the technical field of laser rock breaking in deep sea and deep land rock formations, and in particular to an optimization design method and system for a laser rock breaking parameter scheme. Background Art

[0002] The development of deep sea and deep land oil and gas is an important successor field of my country's oil and gas resources. The deep sea and deep land oil and gas reservoirs are characterized by high rock hardness, large well depths and complex geological conditions, making the mechanical rock breaking technology of traditional drilling work increasingly difficult. Laser rock breaking technology has gradually attracted widespread attention due to its excellent linear performance and the ability to precisely control the geometry of the pores, and has achieved good results in rock breaking in deep sea and deep land oil and gas reservoirs.

[0003] In the process of using laser rock breaking technology, the design of laser rock breaking parameter scheme composed of laser power, laser irradiation time and laser spot radius is directly related to the energy consumption and efficiency of laser rock breaking. However, there is currently a lack of optimization design methods for laser rock breaking parameter schemes. The optimization design of laser rock breaking parameter schemes is to determine the optimal laser rock breaking parameter scheme through the optimization design method, so that the laser rock breaking speed is the fastest and the unit rock breaking energy (i.e., laser rock breaking specific energy) is the smallest, so as to achieve efficient and energy-saving laser rock breaking operations. It is still at the stage of relying on artificial experience to design laser rock breaking parameter schemes. Artificial design of laser rock breaking parameter schemes cannot guarantee the optimality of laser rock breaking parameter schemes, which can easily lead to problems such as energy waste, slow rock breaking footage, and low rock breaking efficiency. At the same time, the scheme design method that relies on artificial experience to make decisions on laser rock breaking parameter schemes has high requirements for engineers, and there are problems such as high learning cost and long training cycle, which restricts the development and popularization of laser rock breaking technology.

[0004] Based on this, there is an urgent need for an optimization design technology for laser rock breaking parameter schemes to automatically complete the design of laser rock breaking parameter schemes. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for optimizing the design of a laser rock breaking parameter scheme, which can automatically complete the design of a laser rock breaking parameter scheme and realize high-efficiency, low-energy laser rock breaking operations.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] An optimization design method for a laser rock breaking parameter scheme, the optimization design method comprising:

[0008] Constructing an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, the laser irradiation time value range, the laser spot radius value range, and the energy limit that the laser gasification rock must meet as the constraint conditions;

[0009] The optimization design model is solved by using an optimization algorithm to obtain a laser rock breaking parameter scheme; the laser rock breaking parameter scheme includes laser power, laser irradiation time and laser spot radius.

[0010] An optimization design system for a laser rock breaking parameter scheme, the optimization design system comprising:

[0011] A model building module is used to build an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, the laser irradiation time value range, the laser spot radius value range, and the energy limit that the laser gasification rock must meet as the constraint conditions;

[0012] The optimization solution module is used to solve the optimization design model using an optimization algorithm to obtain a laser rock breaking parameter solution; the laser rock breaking parameter solution includes laser power, laser irradiation time and laser spot radius.

[0013] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0014] The present invention provides an optimization design method and system for a laser rock breaking parameter scheme. First, an optimization design model for the laser rock breaking parameter scheme is constructed. The optimization design model takes minimizing the laser rock breaking specific energy as an objective function, takes a laser power value range, a laser irradiation time value range, a laser spot radius value range, and energy restrictions that must be met by laser gasification of rock as constraints, and then uses an optimization algorithm to solve the optimization design model to obtain a laser rock breaking parameter scheme. The laser rock breaking parameter scheme includes laser power, laser irradiation time, and laser spot radius, so that the optimization design of the laser rock breaking parameter scheme can be automatically completed, and high-efficiency, low-energy laser rock breaking operations can be achieved. Guidance is provided for the decision-making of the laser rock breaking parameter scheme required for laser rock breaking of deep-sea and deep-land oil and gas reservoirs, and the problems of high rock breaking energy consumption, low efficiency, slow footage, etc. caused by reliance on manual decision-making in laser rock breaking are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 This is a flow chart of the optimization design method provided in Example 1 of the present invention.

[0017] Figure 2 This is a detailed flow chart of the optimization design method provided in Example 1 of the present invention.

[0018] Figure 3 This is an iterative decline curve diagram provided in Example 1 of the present invention.

[0019] Figure 4 This is a schematic diagram of hole morphology under the first set of parameter schemes provided in Example 1 of the present invention.

[0020] Figure 5 A schematic diagram of hole morphology under the second set of parameter schemes provided in Example 1 of the present invention.

[0021] Figure 6 This is a schematic diagram of the hole morphology under the third set of parameter schemes provided in Example 1 of the present invention.

[0022] Figure 7 This is a schematic diagram of hole morphology under the fourth set of parameter schemes provided in Example 1 of the present invention.

[0023] Figure 8 This is a schematic diagram of the hole morphology under the fifth set of parameter schemes provided in Example 1 of the present invention.

[0024] Fig. 9 This is a system block diagram of the optimization design system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The purpose of the present invention is to provide an optimization design method and system for a laser rock breaking parameter scheme, which belongs to the technical field of drilling and completion of oil and gas development. By establishing an optimization design model of the laser rock breaking parameter scheme and solving the optimization design model using an optimization algorithm, the present invention can help drilling engineers complete the automatic optimization design of the laser rock breaking parameter scheme in the laser rock breaking process, realize high-efficiency and low-energy laser rock breaking operations, provide guidance for the decision-making of the laser rock breaking parameter scheme required for laser rock breaking of deep-sea and deep-land oil and gas reservoirs, and solve the problems of high rock breaking energy consumption, low efficiency, slow footage, etc. caused by laser rock breaking relying on manual decision-making.

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Embodiment 1:

[0029] This embodiment is used to provide an optimization design method for a laser rock breaking parameter solution. Figure 1 and Figure 2 As shown, the optimization design method includes:

[0030] S1: Construct an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, laser irradiation time value range, laser spot radius value range and the energy limit that laser gasification rock must meet as constraints.

[0031] In this embodiment, the key parameters such as laser power, laser irradiation time and laser spot radius in the laser rock breaking process are used as decision variables, and the minimization of the unit specific energy consumed by laser rock breaking (i.e., laser rock breaking specific energy) is used as the objective function. Laser rock breaking specific energy refers to the energy consumed to break a unit volume of rock when laser irradiates rock, and is an indicator for measuring the energy consumption and efficiency of rock breaking. The value range of laser power, laser irradiation time and laser spot radius and the energy limit satisfied by laser gasification of rock are used as constraints, and an optimization design model for the laser rock breaking parameter scheme is constructed. The optimization design model includes the objective function and the constraints.

[0032] This embodiment takes minimizing the laser rock breaking specific energy as the optimization goal, and the objective function is specifically:

[0033] (1)

[0034] In formula (1), min means minimization; SE ( P 0, t , r 0) is the laser rock breaking specific energy, in kJ / cm 3 ; P 0 is the laser power, in W / m 2 ; t is the laser irradiation time, in s; r 0 is the laser spot radius, in m; π is the ratio of pi; v is the laser rock breaking speed, in m / s.

[0035] In formula (1), the calculation expression of laser rock breaking speed is as follows:

[0036] (2)

[0037] In formula (2), B is a dimensionless parameter; τ is a dimensionless parameter; η is the dimensionless integration variable; ξ is a dimensionless parameter; Q is a dimensionless parameter.

[0038] In formula (2), the calculation expressions of each dimensionless parameter are as follows:

[0039] (3)

[0040] In formula (3), I 0 is the laser power density, in W / m 2 ; c P is the specific heat capacity of the stimulated rock (i.e. the rock irradiated by the laser), in J / (kg·K); ρ is the density of the stimulated rock, in kg / m 3 ; λ is the thermal conductivity of the stimulated rock, in W / (mK); L V is the gasification energy of the stimulated rock, in kJ / kg; a is the optical absorption coefficient, in cm -1 ; q is a constant; T V is the vaporization temperature of the stimulated rock, in K.

[0041] The specific constraints are:

[0042] (4)

[0043] In formula (4), P min The minimum feasible laser power that the laser can provide, in W / m 2 ; P 0 is the laser power, in W / m 2 ; P max The maximum feasible laser power that the laser can provide, in W / m 2 ; t min is the shortest feasible time for continuous laser irradiation, in seconds; t is the laser irradiation time, in s; t max The longest feasible time for continuous laser irradiation, in seconds; r0 is the laser spot radius, in m; is a feasible value set of the laser spot radius, including u A feasible value, r D,1 is the first feasible value of the laser spot radius, r D,2 is the second feasible value of the laser spot radius, r D,u is the radius of the laser spot u feasible values, in units of m; q is a constant; λ is the thermal conductivity of the stimulated rock, in W / (mK); c P is the specific heat capacity of the stimulated rock, in J / (kg·K); ρ is the density of the stimulated rock, in kg / m 3 ; π is pi; v is the laser rock breaking speed, in m / s; a is the optical absorption coefficient, in cm -1 ;∞ is infinity; η is the integration variable; T V is the rock gasification temperature, in K.

[0044] In this embodiment, the feasible range of laser parameters, the thermophysical parameters of rocks and the optical absorption coefficient are given in advance. Under the premise of knowing the laser parameters and the heat transfer parameters of rocks, the above-mentioned optimization design model is solved to determine the laser spot radius, laser irradiation time and laser power of the laser with the lowest laser rock breaking specific energy, so as to achieve efficient and energy-saving rock breaking and solve the problems of energy waste, slow rock breaking footage and low rock breaking efficiency that are easily caused by artificial design.

[0045] S2: solving the optimization design model using an optimization algorithm to obtain a laser rock breaking parameter solution; the laser rock breaking parameter solution includes laser power, laser irradiation time and laser spot radius.

[0046] The optimization algorithm can be an improved particle swarm-firefly hybrid algorithm, which uses a niche optimization operator and a density adjustment operator to update the population. This embodiment establishes a new particle swarm-firefly hybrid algorithm containing a niche optimization operator and a density adjustment operator, and realizes the effective solution of the optimization design model.

[0047] Specifically, the use of a niche optimization operator to update the population may include:

[0048] (1) Select m first individuals from the group to form a microhabitat group.

[0049] In this embodiment, when the niche optimization operator is used to update the population, the population refers to the contemporary population (i.e., the current generation particle swarm) of the improved particle swarm-firefly hybrid algorithm for solving the optimization design model, and m individuals are selected from the contemporary population to form a niche population.

[0050] The selection method specifically includes: for each first individual in the group, the roulette probability calculation formula is used to calculate the roulette probability of the first individual being selected to enter the microhabitat, and then the roulette probability is used as input and a judgment formula is used to determine whether to select the first individual, and the selected m first individuals constitute the microhabitat group.

[0051] Among them, the roulette probability calculation formula is:

[0052] (5)

[0053] In formula (5), P P，i For the i The roulette wheel probability of the first individual to be selected to enter the niche; F i ( x i ) is the i The objective function value of the first individual, x i For the group i The first individual, i =1, 2, ..., N , N is the total number of the first individuals in the group; F j ( x j ) is the j The objective function value of the first individual, x j For the group j The first individual, j =1, 2, ..., N .

[0054] The judgment formula is:

[0055] (6)

[0056] In formula (6), rand is a random number between the value interval [0, 1]. Taking the roulette probability of the first individual as input, if the above judgment formula is satisfied, the first individual is selected.

[0057] (2) Based on the m first individuals in the microhabitat population, 2m second individuals are generated; the 2m second individuals include m superior fireflies and m basic fireflies.

[0058] This embodiment specifically generates m superior fireflies according to the following formula (7) based on the m first individuals in the microhabitat group, and generates m basic fireflies according to the following formula (8).

[0059] (7)

[0060] In formula (7), For the i A firefly to the best, i= 1, 2, ..., m; x gb It is the best individual in the microhabitat group; γ is a random number vector between the value interval [-0.1, 0.1]; x Pop,max is a maximum value vector composed of the maximum values ​​of all first individuals in the microhabitat group in each dimension. In this embodiment, the dimension is 3, which are laser power, laser irradiation time and laser spot radius respectively; x Pop,min It is the minimum vector composed of the minimum values ​​of all the first individuals in the microhabitat group in each dimension.

[0061] (8)

[0062] In formula (8), For the i A basic firefly, i= 1, 2, ..., m; x i For the first i the first individual; is the optimal individual in the microhabitat population x gb and the first i The first individual x i The attraction between is the step size factor, ; For a small random amount, .

[0063] The calculation formula of attraction is as follows:

[0064] (9)

[0065] In formula (9), for lgb,i The attraction between individuals is equal to 0; a is the optical absorption coefficient; l gb,i is the optimal individual in the microhabitat group and the i The distance between the first individuals.

[0066] The distance is calculated as follows:

[0067] (10)

[0068] In formula (10), is the number of decision variables, which is 3 in this embodiment, including laser power, laser irradiation time and laser spot radius; x gb，d is the optimal individual in the microhabitat population. d The value of the dimension variable; x i，d For the first i The first individual d The value of the dimension variable.

[0069] m superior fireflies and m basic fireflies constitute 2m second individuals.

[0070] (3) Sort the 2m second individuals in ascending order of the objective function value, and use the first m second individuals to replace the m first individuals to update the population.

[0071] In this embodiment, 2m fireflies (i.e., the second individuals) formed by m superior fireflies and m basic fireflies are sorted by objective function values, and the fireflies with the top m objective function values ​​are selected to replace the m first individuals used to form the microhabitat population in the contemporary population to form a new population.

[0072] Specifically, the density adjustment operator is used to update the group, which may include:

[0073] (1) For each individual in the group, calculate the attraction between the individual and the best individual in the group; based on the attraction, calculate the membership of the individual to the density fuzzy set.

[0074] In this embodiment, when the density adjustment operator is used to update the group, the group refers to the group after the microhabitat optimization operator is executed, that is, the group updated by the microhabitat optimization operator. For each individual in the group, the attraction between the individual and the optimal individual is calculated using formula (9).

[0075] Calculating the membership of the individual to the density fuzzy set based on the attractiveness may include: taking the attractiveness as input and calculating the membership of the individual to the density fuzzy set using a membership calculation formula.

[0076] The membership calculation formula is:

[0077] (11)

[0078] In formula (11), For the i The membership degree of each individual to the intensive fuzzy set is For the i The attraction between the individual and the optimal individual is i The attraction of each individual to the best individual; is a gain coefficient with a value greater than 1; is the maximum value of the attraction between all individuals and the optimal individual, that is, the maximum attraction of the optimal individual to all individuals in the group. According to formula (11), the membership of each individual in the group to the density fuzzy set can be calculated.

[0079] (2) Based on the membership degree, multiple individuals in the group are adjusted to update the group.

[0080] The updating process specifically includes: for each individual in the group, determine whether the individual's membership to the density fuzzy set is greater than or equal to the density fuzzy set cut-off level; if so, select the individual as the individual to be selected; sort all the individuals to be selected in descending order of membership, and select the individuals ranked in the front. κ ( I ) of the individuals to be selected, we get κ ( I ) selected individuals; for each selected individual, the optimal individual is randomly adjusted using a random number vector to obtain an adjusted individual, and the adjusted individual is used to replace the selected individual to update the population.

[0081] In this embodiment, the individuals to be selected are screened according to the following formula (12):

[0082] (12)

[0083] In formula (12), is the cut-off level of the intensive fuzzy set.

[0084] κ ( I ) is calculated as follows:

[0085] (13)

[0086] In formula (13), is a rounding function, which means rounding the calculation result in the brackets; κ min the minimum feasible number of individuals to be adjusted; κ max The maximum feasible number of individuals to be adjusted; I is the number of iterations; I max is the maximum number of iterations.

[0087] Using formula (12) and formula (13), we can filter out the dense fuzzy set with large membership value. κ ( I ) individuals, get κ ( I ) selected individuals.

[0088] The calculation formula for random adjustment of the optimal individual is as follows:

[0089] (14)

[0090] In formula (14), For the high density i The adjusted individuals corresponding to the selected individuals are i =1, 2, ..., κ ( I ); x gb The best individual in the group; The mean is 0 and the variance is 1. dimensional random number vector.

[0091] For each selected individual, use formula (14) to make adjustments to obtain the adjusted individual, and then use the adjusted individual to replace the selected individual to update the population.

[0092] Based on the updating process of the above-mentioned niche optimization operator and density adjustment operator, in this embodiment, the solving process of the improved particle swarm-firefly hybrid algorithm includes:

[0093] (1) Initialize the population of the improved particle swarm-firefly hybrid algorithm to obtain the initial population. Each individual in the initial population represents a laser rock breaking parameter scheme, including laser power, laser irradiation time, and laser spot radius that meet the constraints.

[0094] In each individual, the values ​​of laser power and laser irradiation time are random real numbers within the constraint range. The feasible values ​​of the laser spot radius within the constraint range are matched with integer codes, and different integer values ​​are used to represent different feasible values ​​of the laser spot radius. That is, the value of the laser spot radius in the individual code is within the integer code range, and the individual code length is the same as the number of decision variables of the optimization design model. The initialization code of the individual is as follows:

[0095] (15)

[0096] In formula (15), x i is the first in the initial population of the improved particle swarm-firefly hybrid algorithm i individual; P 0,i For the i The laser power of each individual is taken to satisfy A random real number; t i For the i The laser irradiation time of each individual is taken to satisfy A random real number; r 0,i For the i The individual laser spot radius is 1, 2, ···, u A random integer, 1, 2, ···, u The feasible value set corresponding to the laser spot radius The feasible values ​​of different laser spot radii.

[0097] (2) According to formula (1), calculate the objective function value of each individual in the initial population, compare the objective function values ​​of all individuals with the optimal individual, and if the objective function value of one individual is smaller than that of the optimal individual, update the optimal individual to that individual. Compare the objective function values ​​of all individuals with the stored historical optimal individual, and if the objective function value of one individual is smaller than that of the historical optimal individual, update the historical optimal individual to that individual.

[0098] (3) Update the initial population according to the velocity update operator and position update operator in the standard particle swarm algorithm to obtain the first updated population. The specific update formula is as follows:

[0099] (16)

[0100] In formula (16), V i ( I ) is the i The speed of each individual in the current iteration;w is the inertia weight, which represents the part of the information from the previous iteration that the individual retains to the current iteration. It is the main parameter that controls the convergence speed of the algorithm. V i ( I- 1) For i The speed of each individual in the previous iteration; is the individual cognitive learning factor, representing the individual's own learning; is a random number in the interval [0, 1]; x pb The best individual in history; x i For the i individual; It is a social cognitive learning factor, representing the information exchange between individuals and groups; is a random number in the interval [0, 1]; x gb is the best individual in the initial population.

[0101] (17)

[0102] In formula (17), The first i All individuals whose positions are updated form the first updated group.

[0103] (4) Use the niche optimization operator to update the first updated population to obtain the second updated population.

[0104] The specific updating method is as follows: m individuals are selected from the first updated population according to equations (5) and (6) to form a microhabitat population; based on the m individuals in the microhabitat population, m superior fireflies are generated according to equation (7); m basic fireflies are generated according to equation (8); the 2m fireflies formed by the m superior fireflies and the m basic fireflies are sorted by objective function values; the fireflies with the top m objective function values ​​are selected to replace the m individuals in the first updated population used to form the microhabitat population, thus forming the second updated population.

[0105] (5) Use the density adjustment operator to update the second updated population to obtain the third updated population.

[0106] The specific updating method is: according to formula (9), the attraction between each individual in the second updated group and the optimal individual is calculated. Taking the attraction as input, the membership of each individual in the second updated group to the density fuzzy set is calculated according to formula (11). According to formulas (12) and (13), the individuals with large membership values ​​of the density fuzzy set are selected. κ ( I) individuals, adjust the screened individuals according to formula (14), and use the adjusted individuals to replace the original screened individuals to obtain the third updated population.

[0107] (6) Determine whether the laser power, laser irradiation time and laser spot radius of each individual in the third updated group after executing the speed update operator, the position update operator, the niche optimization operator and the density adjustment operator meet the value range in the constraint conditions. If so, use the third updated group as the fourth updated group and execute step (7). If not, randomly generate new individuals within the value range that meets the constraint conditions to replace the original individuals until the constraint conditions are met, thereby obtaining the fourth updated group and executing step (7).

[0108] (7) Determine whether the current number of iterations has reached the termination number. If so, output the best individual in the fourth updated group (i.e., the individual with the smallest objective function value) as the optimal solution, and the parameter value in the optimal solution is the optimal laser rock breaking parameter scheme; if not, use the fourth updated group as the initial group for the next iteration, and return to step (2) to start execution.

[0109] Laser rock breaking is an emerging technology in the field of deep-sea and deep-land oil and gas reservoir rock breaking. The optimization design method of the laser rock breaking parameter scheme provided in this embodiment can realize the automatic optimization design of the laser rock breaking parameter scheme, avoiding the problems of traditional manual decision-making of laser rock breaking parameter schemes that are difficult to ensure the optimality of the parameter scheme, waste of laser energy and low rock breaking efficiency, improving the laser rock breaking efficiency, reducing the cost of use, and promoting the application of laser rock breaking technology in deep-sea and deep-land oil and gas reservoir rock breaking.

[0110] In order to prove the correctness of the optimization design method of the laser rock breaking parameter scheme proposed in this embodiment, the optimization design method of this embodiment is applied to solve the optimal laser rock breaking parameter scheme of granite, and a comparative analysis is carried out with the indoor test results.

[0111] The basic parameters in the optimization design model of the laser rock breaking parameter scheme are set as follows: The feasible value set of the laser spot radius is r D ={ r D,1 =4.5mm, r D,2 =4mm, r D,3 =5mm}, laser power range P max =900W, P min =500W, laser irradiation time range t max =5.0s,t min =1.0s, the overall thermal conductivity of granite is 2.61W / (mK), the specific heat capacity is 807J / (kg.K), and the thermal diffusivity is 1.16×10 -7 m 2 / s, gasification energy is 5.75×10 3 kJ / kg, optical absorption coefficient is 25cm -1 ,constant q 2. The gasification temperature is 2230℃.

[0112] Based on the above decision variable value constraints and rock physical parameters, the optimization design method of the laser rock breaking parameter scheme proposed in this embodiment is applied to optimize the solution. The number of iterations is set to 500. The method is used for 10 solutions. The relative error of the objective function value calculated within 10 times is 2.6%~5.8%, and the total calculation time is 2571.2s~3859.6s. The iterative decline curve is as follows: Figure 3 As shown, from Figure 3 It can be seen that all 10 optimization solutions converged in less than 200 iterations, with fast convergence speed and high solution accuracy. The average of the 10 solution results yields the optimal laser rock breaking parameter solution under the current constraints, which is a laser power of 756 W, a laser irradiation time of 2.8 s, and a laser spot radius of 4.5 mm.

[0113] Based on the laser rock-breaking parameter schemes obtained above, 5 groups of parameter schemes were set, of which the 5th group of parameter schemes adopted the laser rock-breaking parameter scheme obtained in this embodiment, and the other 4 groups of parameter schemes were control test schemes based on the optimal power, optimal irradiation time, and optimal laser spot radius settings. The test results of the 5 groups of parameter schemes were summarized in Table 1. It can be seen that the specific energy obtained by the optimal laser rock-breaking parameter scheme (the 5th group) is the smallest, which verifies the correctness of the optimization design method proposed in this embodiment. The numerical simulation software simulation found that the hole obtained under the 5th group of parameter schemes is relatively deep, that is, it can complete relatively efficient laser rock breaking with the minimum specific energy. The hole morphology comparison diagram under the 5 groups of parameter schemes is shown in the figure below. Figure 4-Figure 8 As shown, Figure 4-Figure 8 In the figure, R = 0 represents the distance from the rock excited centerline R is 0, which verifies the accurate evaluation and optimization of the laser rock breaking performance by the laser rock breaking parameter scheme optimization design model.

[0114] Table 1 Test results

[0115]

[0116] In Table 1, the second column to the sixth column respectively show the test results of the first group of parameter solutions to the fifth group of parameter solutions.

[0117] Embodiment 2:

[0118] This embodiment is used to provide an optimization design system for laser rock breaking parameter solutions, such as Fig. 9 As shown, the optimization design system includes:

[0119] The model building module M1 is used to build an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, the laser irradiation time value range, the laser spot radius value range and the energy limit that the laser gasification rock must meet as the constraint conditions;

[0120] The optimization solution module M2 is used to solve the optimization design model using an optimization algorithm to obtain a laser rock breaking parameter solution; the laser rock breaking parameter solution includes laser power, laser irradiation time and laser spot radius.

[0121] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0122] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for optimizing the design of laser rock breaking parameter scheme, characterized in that: include: Constructing an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, the laser irradiation time value range, the laser spot radius value range, and the energy limit that the laser gasification rock must meet as the constraint conditions; The optimization design model is solved by using an optimization algorithm to obtain a laser rock breaking parameter scheme; the laser rock breaking parameter scheme includes laser power, laser irradiation time and laser spot radius; The objective function is: Wherein, min is minimization; SE(P0, t, r0) is the laser rock breaking specific energy; P0 is the laser power; t is the laser irradiation time; r0 is the laser spot radius; π is the circumference; v is the laser rock breaking speed; The constraints are: Among them, P min is the minimum feasible laser power that the laser can provide; P0 is the laser power; P max is the maximum feasible laser power that the laser can provide; t min is the shortest feasible time for continuous laser irradiation; t is the laser irradiation time; t max is the longest feasible time for continuous irradiation of the laser; r0 is the radius of the laser spot; {r D，1 , r D，2 ,…,r D，u } is a set of feasible values ​​of the laser spot radius, including u feasible values ​​of the laser spot radius, r D，1 is the first feasible value of the laser spot radius, r D，2 is the second feasible value of the laser spot radius, r D，u is the uth feasible value of the laser spot radius; q is a constant; λ is the thermal conductivity of the stimulated rock; c P is the specific heat capacity of the stimulated rock; ρ is the density of the stimulated rock; π is the circumference of the circle; v is the laser rock breaking speed; a is the optical absorption coefficient; ∞ is infinity; η is the integral variable; T V is the rock gasification temperature; The optimization algorithm is an improved particle swarm-firefly hybrid algorithm; the improved particle swarm-firefly hybrid algorithm uses a niche selection operator and a density adjustment operator to update the population; The optimization design model is solved by using an optimization algorithm to obtain a laser rock breaking parameter solution, which specifically includes: Initialize the population of the improved particle swarm-firefly hybrid algorithm to obtain an initial population, each individual in the initial population represents a laser rock breaking parameter scheme, including laser power, laser irradiation time and laser spot radius that meet the constraint conditions; The initial group is updated according to the speed update operator and the position update operator in the particle swarm algorithm to obtain the first updated group; The first updated population is updated by using a niche optimization operator to obtain a second updated population; The second updated population is updated using a density adjustment operator to obtain a third updated population; Determine whether the laser power, laser irradiation time, and laser spot radius of each individual in the third updated group meet the constraint conditions; if so, use the third updated group as the fourth updated group; if not, randomly generate new individuals to replace the original individuals under the constraint conditions until the laser power, laser irradiation time, and laser spot radius of each individual in the third updated group meet the constraint conditions, thereby obtaining the fourth updated group; Determine whether the current number of iterations has reached the termination number; if so, output the optimal individual in the fourth updated group, which is the laser rock breaking parameter solution obtained by solution; if not, use the fourth updated group as the initial group for the next iteration, and return to the step of "updating the initial group according to the speed update operator and position update operator in the particle swarm algorithm"; The population is updated using the niche optimization operator, including: Select m first individuals from the group to form a microhabitat group; Based on the m first individuals in the microhabitat population, 2m second individuals are generated; the 2m second individuals include m superior fireflies and m basic fireflies; The 2m second individuals are sorted in ascending order of the objective function value, and the m second individuals ranked in the first m positions are used to replace the m first individuals, so as to update the population; The density adjustment operator is used to update the population, including: For each individual in the group, the attraction between the individual and the best individual in the group is calculated; based on the attraction, the membership of the individual to the density fuzzy set is calculated; Based on the membership degree, adjusting a plurality of individuals in the group to update the group; The adjusting of the plurality of individuals in the group based on the membership degree to update the group specifically includes: For each of the individuals, determine whether the membership of the individual to the intensive fuzzy set is greater than or equal to the intensive fuzzy set cut-off level; if so, select the individual as the individual to be selected; All the individuals to be selected are sorted in descending order of membership, and the individuals to be selected that are ranked in the first κ(I) to obtain κ(I) selected individuals; For each of the selected individuals, the optimal individual is randomly adjusted using a random number vector to obtain an adjusted individual; and the selected individual is replaced by the adjusted individual to update the population.

2. The optimization design method of a laser rock breaking parameter scheme according to claim 1 is characterized in that: The step of selecting m first individuals from the group to form a microhabitat group specifically includes: For each first individual in the group, the roulette probability calculation formula is used to calculate the roulette probability of the first individual being selected to enter the microhabitat; using the roulette probability as input, a judgment formula is used to determine whether to select the first individual; the m first individuals selected form a microhabitat group; The roulette probability calculation formula is: Among them, P P，i is the roulette probability of the first individual of the i-th type being selected to enter the niche; F i (x i ) is the objective function value of the first individual, x i is the i-th first individual; N is the total number of first individuals in the group; F j ( x j ) is the objective function value of the jth first individual, x j is the jth first individual; The judgment formula is: Wherein, rand is a random number; if the judgment formula is satisfied, the first individual is selected.

3. The optimization design method of a laser rock breaking parameter scheme according to claim 1 is characterized in that: The step of calculating the membership of the individual to the density fuzzy set based on the attraction specifically includes: taking the attraction as input and using a membership calculation formula to calculate the membership of the individual to the density fuzzy set; The membership calculation formula is: in, is the membership of the ith individual to the density fuzzy set, is the attraction between the ith individual and the optimal individual; ζ μ is the gain coefficient; is the maximum value of the attraction between all individuals and the optimal individual.

4. A laser rock breaking parameter scheme optimization design system, which works by using a laser rock breaking parameter scheme optimization design method according to any one of claims 1 to 3, characterized in that: include: A model building module is used to build an optimization design model for the laser rock breaking parameter scheme; the optimization design model takes minimizing the laser rock breaking specific energy as the objective function, and takes the laser power value range, the laser irradiation time value range, the laser spot radius value range, and the energy limit that the laser gasification rock must meet as the constraint conditions; The optimization solution module is used to solve the optimization design model using an optimization algorithm to obtain a laser rock breaking parameter solution; the laser rock breaking parameter solution includes laser power, laser irradiation time and laser spot radius.

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