CN117787105A - Tunnel surrounding rock grading method, device, equipment and readable storage medium - Google Patents
Tunnel surrounding rock grading method, device, equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a readable storage medium for grading tunnel surrounding rocks, which relate to the technical field of surrounding rock prediction and comprise the steps of acquiring historical surrounding rock data of target surrounding rocks in a historical time period; initializing the sparrow population position based on chaotic mapping to obtain a plurality of initial position parameters; constructing a model based on the initial position parameters to obtain an initial prediction model; training an initial prediction model based on historical surrounding rock data and a sparrow algorithm to obtain a target classification model; inputting real-time surrounding rock data of the target surrounding rock into the target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock. According to the invention, parameters in the convolutional neural network are optimized through the sparrow search algorithm, so that the method can be more quickly and better adapted to the judgment of various working condition models, and a self-adaptive chaotic mapping is adopted, so that a correction step factor is established, the convergence speed of the sparrow search algorithm is improved, and the training efficiency of the model is greatly improved.
Description
Technical Field
The invention relates to the technical field of surrounding rock prediction, in particular to a method, a device and equipment for grading tunnel surrounding rock and a readable storage medium.
Background
The prior rock mass grading method is developed based on the past engineering experience, does not distinguish structural resistance, durability and maintainability, and cannot provide design reliability, so that the requirements of modern design specifications cannot be met.
Disclosure of Invention
The invention aims to provide a tunnel surrounding rock grading method, device and equipment and a readable storage medium, so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for grading surrounding rock of a tunnel, including:
acquiring historical surrounding rock data of target surrounding rock in a historical time period;
initializing the sparrow population position based on chaotic mapping to obtain a plurality of initial position parameters;
constructing a model based on the initial position parameters to obtain an initial prediction model;
training the initial prediction model based on the historical surrounding rock data and a sparrow algorithm to obtain a target classification model;
and inputting the real-time surrounding rock data of the target surrounding rock into the target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock.
In a second aspect, the present application further provides a tunnel surrounding rock grading device, including:
The first acquisition unit is used for acquiring historical surrounding rock data of the target surrounding rock in a historical time period;
the initialization unit is used for initializing the sparrow population positions based on the chaotic mapping to obtain a plurality of initial position parameters;
the construction unit is used for constructing a model based on the initial position parameters to obtain an initial prediction model;
the training unit is used for training the initial prediction model by the historical surrounding rock data and sparrow algorithm to obtain a target classification model;
the input unit is used for inputting the real-time surrounding rock data of the target surrounding rock into the target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock.
In a third aspect, the present application further provides a tunnel surrounding rock grading apparatus, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the tunnel surrounding rock grading method when executing the computer program.
In a fourth aspect, the present application also provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the tunnel surrounding rock based classification method described above.
The beneficial effects of the invention are as follows:
According to the invention, parameters in the convolutional neural network are optimized through the sparrow search algorithm, so that the method can be more quickly and better adapted to the judgment of various working condition models, and a self-adaptive chaotic mapping is adopted, so that a correction step factor is established, the convergence speed of the sparrow search algorithm is improved, and the training efficiency of the model is greatly improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for grading tunnel surrounding rock according to an embodiment of the invention;
FIG. 2 is a parameter map according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a tunnel surrounding rock grading device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a tunnel surrounding rock grading device according to an embodiment of the present invention.
The marks in the figure:
10000. a first acquisition unit; 20000. an initializing unit; 30000. a construction unit; 40000. a training unit; 50000. an input unit; 20100. a second acquisition unit; 20200. a first calculation unit; 20300. a second calculation unit; 20400. a third calculation unit; 20500. a fourth calculation unit; 20600. a repeating unit; 20700. a fifth calculation unit; 40100. a first determination unit; 40200. dividing units; 40300. a first updating unit; 40400. a third acquisition unit; 40500. a sixth calculation unit; 40600. a seventh calculation unit; 40700. an eighth calculation unit; 40800. a ninth calculation unit; 40900. a second determination unit; 41000. a fourth acquisition unit; 41100. a second updating unit; 41200. a third updating unit; 41300. an adjusting unit; 40301. a third determination unit; 40302. a first comparing unit; 40303. a fifth acquisition unit; 40304. a tenth calculation unit; 40305. a first carry-in unit; 40306. an eleventh calculation unit; 40307. a sixth acquisition unit; 40308. a fourth determination unit; 40309. a second comparing unit; 40310. a twelfth calculation unit; 40311. a second carry-in unit; 40312. a seventh acquisition unit; 40313. a thirteenth calculation unit; 40314. a third carry-in unit; 40901. an eighth acquisition unit; 40902. a ninth acquisition unit; 40903. a fifth determination unit; 40904. a fourteenth calculation unit; 40905. a fifteenth calculation unit;
800. A tunnel surrounding rock grading device; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a tunnel surrounding rock grading method.
Referring to fig. 1, the method is shown to include step S10000, step S20000, step S30000, step S40000 and step S50000.
S10000, acquiring historical surrounding rock data of the target surrounding rock in a historical time period;
specifically, the grading design database for collecting and arranging tunnel surrounding rocks comprises engineering geological conditions of the tunnel surrounding rocks in different areas, namely, topography, stratum lithology, geological structure, meteorological hydrologic conditions, ground stress fields and the like, and qualitative indexes of the surrounding rocks, namely, rock hardness degree, rock integrity degree, jogging degree, rock structure, joint weathering condition, groundwater condition, ground stress condition and the like.
S20000, initializing the positions of the sparrow population based on chaotic mapping to obtain a plurality of initial position parameters;
in particular, use is made ofThe chaotic mapping initializes the sparrow population, can not repeatedly traverse the state of the sparrow population in a certain range, can relatively and uniformly distribute the sparrow population in the whole search space, not only increases the diversity of the initial sparrow population, but also avoids the situation of being in local optimum in the searching process of the sparrow algorithm.
Specifically, step S20000 specifically includes:
S20100, acquiring random position parameters and chaotic control parameters of any sparrow, wherein the initial position is located in a first set range, and the chaotic control parameters are located in a second set range;
step s20200, first calculation operation: calculating the product of the initial position and a first set threshold value to be used as a first product;
step s20300, second calculation operation: calculating a sine function value of the first product as a first value;
step S20400, third calculation operation: calculating the ratio of the chaotic control parameter to a second set threshold value to be used as a first ratio;
step s20500, fourth calculation operation: calculating the product of the first value and the first ratio as a second product;
s20600, repeating the first calculation operation, the second calculation operation, the third calculation operation and the fourth calculation operation until the preset repetition times are reached, and obtaining the target position parameters;
s20700, calculating target position parameters corresponding to all sparrows to obtain a plurality of initial position parameters of the sparrow population;
specifically, the chaotic mapping mathematical expression is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is sparrow->Is>A second chaos value; />Is sparrow->Is>The secondary chaos value is in the range of +.>；/>For the control parameters of the chaotic system, the value range is +. >；
In this embodiment, the chaotic value obtained by mapping each parameter twice is used as the initial position parameter of the sparrow, as shown in fig. 2, and is a parameter map obtained by mapping the chaotic value for two thousand iterations.
S30000, constructing a model based on initial position parameters to obtain an initial prediction model;
in particular, the model is initialized with initial position parameters of the sparrow algorithm, which will become the initial predictions of the model.
S40000, training an initial prediction model based on historical surrounding rock data and a sparrow algorithm to obtain a target classification model;
specifically, step S40000 specifically includes:
s40100, determining initial fitness of all sparrows based on initial position parameters of all sparrows;
specifically, the number of sparrow population individuals is assumed to beEach sparrow individual is taken as +.>One solution in the dimensional solution space, the fitness of the sparrow population is:
；
wherein,the fitness of the sparrow population; />Is the sparrow->In->The position of the dimensional space;is sparrow->Is suitable for individuals.
Step S40200, dividing operation: dividing all sparrows into discoverers, followers and alertors based on the initial fitness;
step S40300, updating operation: updating initial position parameters of all sparrows based on a preset target formula to obtain a plurality of updated position parameters;
Specifically, step S40300 specifically includes:
s40301, determining a current early warning value based on all initial fitness;
s40302, comparing the current early warning value with a preset warning early warning value to obtain a first comparison result;
s40303, when the first comparison result meets a first set condition, obtaining an upper limit constraint of a finder, a lower limit constraint of the finder, a first optimal sparrow position parameter, a first worst sparrow position parameter, a constant factor and a first iteration number;
s40304, calculating to obtain self-adaptive weights based on upper limit constraint, lower limit constraint, first optimal sparrow position parameters, first worst sparrow position parameters, constant factors and first iteration times;
step S40305, the self-adaptive weight is brought into a preset first target formula, and updated position parameters updated by the discoverer are obtained through calculation;
s40306, calculating to obtain updated position parameters of the discoverer after updating based on a preset second target formula when the first comparison result meets a second setting condition;
specifically, the global search is too dependent on the position of a finder, and an adaptive weight factor is introduced by referring to a spiral ascending search mode of iterative optimization in a whale optimization algorithm, so that the finder searches for a next region after position update in the sparrow algorithm with a larger step length in the early stage and performs small step length convergence exploration in the later stage, and the position iterative formula of the sparrow finder is as follows:
；
；
Wherein,is->Sparrow +.>Is>A dimensional location; />For sparrow +.>Is the j-th dimensional position of (2); />Is an adaptive weight factor; />The maximum iteration number of the population; />Is a uniform random number, and the value range is +.>；/>The value range is +.>；/>For the alert threshold, the value range is +.>；/>Random numbers which are subjected to normal distribution; />Moment of 1 row d columnAn array; />Is->Sparrow +.>Is the worst position of (2); />Is->Sparrow +.>Is the optimal position of (a); />And->Is a constant factor; />Is sparrow->Upper limit constraints of (2); />Is sparrow->Lower limit constraints of (2);
when (when)When the early warning value is smaller than the safety value, no predator exists in the foraging environment, and the discoverer can perform wide search operation; when->When it means that there is a part in the populationThe sparrow is separated to find predators and give an early warning to other sparrows in the population, and all sparrows need to fly to a safe area to find food.
In the embodiment of the application, the sparrow follower position is updated as follows;
；
wherein,is->Sparrow +.>Is>A dimensional location; />Is->The position of the worst sparrow in the population after the iteration; / >Is->Sparrow +.>Is>A dimensional location; />Random numbers which are subjected to normal distribution; />Is->Sparrow +.>Is a position of (2); />Is a uniform random number with a value range of；/>Is a spatial dimension; />Is->Sparrow +.>Is>A dimensional location; />The number of individuals in the sparrow population is;
when (when)When the ith follower does not obtain food and is in a starvation state, the ith follower needs to fly to other places to find food so as to obtain more energy; when->This indicates->The follower can obtain food and can obtain energy without flying to other places to find food.
S40307, acquiring second iteration times;
s40308, determining the optimal fitness and the worst fitness based on all initial fitness;
s40309, comparing the fitness of the alerter with the optimal fitness to obtain a second comparison result;
s40310, calculating to obtain a step control parameter based on the optimal fitness, the worst fitness, the initial position parameter and the second iteration number when the second comparison result meets a third set condition;
s40311, bringing the step control parameters into a preset third target formula, and calculating to obtain updated position parameters of the alerter after updating;
S40312, when the second comparison result meets a fourth setting condition, acquiring a first random factor;
step S40313, calculating to obtain initial parameters based on the optimal fitness, the worst fitness, the initial position parameters, the first random factor and the second iteration times;
s40314, bringing the initial parameters into a preset fourth target formula, and calculating to obtain updated position parameters of the alerter after updating;
specifically, in order to improve the convergence speed and accuracy of the sparrow algorithm, a step factor is correctedAnd->The step factor correction formula is:
；
；
wherein,the adaptability of sparrows is optimized for the current population; />The adaptation degree of the current worst sparrow population is the adaptation degree of the current worst sparrow population; />The current iteration number; />Is an initialization parameter; />Is a uniformly distributed random factor;
the position update formula of sparrow alerter is:
；
wherein,is->Sparrow +.>Is>A dimensional location; />For sparrow +.>Is>A dimensional location; />Is->The position of the optimal sparrow in the population after the iteration; />Is->The position of the worst sparrow in the population after the iteration; />Is sparrow->Is a fitness of individuals; />The adaptability of the worst sparrow in the current population is; / >The adaptability of the optimal sparrow in the current population is achieved; />A minimum constant to prevent zero denominator; />And->Is a step size factor->The value range of (2) is +.>；
When (when)When the sparrow is at the edge of the population, the sparrow is extremely vulnerable to predators; when->Sparrows in the middle of the population are also at risk,in this case it is desirable to approach other sparrows to reduce the risk of predation.
Step S40400, a first acquisition operation: obtaining the maximum variation rate, the minimum variation rate, the current iteration number and the maximum iteration number;
step S40500, fifth calculation operation: calculating a difference value between the maximum variation rate and the minimum variation rate as a first difference value;
step S40600, sixth calculation operation: calculating the ratio of the current iteration times to the maximum iteration times as a second ratio;
step S40700, seventh calculation operation: calculating a difference value between the third set threshold value and the second ratio as a second difference value;
step S40800, eighth calculation operation: calculating the fourth power of the second difference value and multiplying the fourth power by the first difference value to obtain a target variation rate;
specifically, the mutation operation can enlarge the search space of the sparrow population, but not make each sparrow individual execute the operation in each iteration, and the mutation operation needs to be determined by the mutation rate, and the mutation rate calculation formula is as follows:
；
Wherein,is the mutation rate; />The maximum mutation rate is preset; />The minimum mutation rate is preset; />The number of individuals in the sparrow population; />Is the maximum iteration number of the population.
Step S40900, determining: determining a target sparrow based on the target mutation rate, and updating and replacing the updated position parameter based on a preset first formula to obtain a target position parameter;
specifically, step S40900 specifically includes:
s40901, obtaining a second random factor;
s40902, acquiring search ranges of all sparrows to obtain a plurality of range parameters;
s40903, determining a minimum value from a plurality of range parameters, and taking the minimum value as a target range parameter;
s40904, calculating the product of the target range parameter and the second random factor to be used as a third product;
and S40905, calculating the sum of the third product and the updated position parameter of the target sparrow to obtain the target position parameter.
And S40905, calculating the sum of the third product and the updated position parameter of the target sparrow to obtain the target position parameter.
Specifically, in order to prevent sparrows from being clustered prematurely, a position disturbance variation factor is fused, and a calculation formula is as follows:
；
；
wherein,is->Sparrow +.>Is >A dimensional location; />Is->Sparrow +.>Is>A dimensional location; />Is a variation factor; />Is a uniformly distributed random factor; />Is->Searching range of individual sparrows; />To take a small function.
In the embodiment of the application, in order to improve the optimization capacity of the sparrow algorithm and to solve the problem of local optimum jump, gaussian-cauchy variation is introduced to achieve the aim of replacing parameter variables in the sparrow algorithm to achieve the optimization algorithm, the invention combines the advantages of Gaussian distribution and cauchy distribution, considers the variation requirements of different search stages, designs a Gaussian-cauchy variation mechanism, facilitates keeping sparrow individuals with better adaptability positions, and enters the next iteration, and the expression is as follows:
；
；
wherein,to be good in adaptabilityThe position of the sparrow after individual variation; />The method is characterized in that the method is a sparrow individual position with better adaptability in the current population; />And->The dynamic parameters are adaptively adjusted along with the iteration times; />To satisfy the random variable of the cauchy distribution; />Random variables to satisfy a gaussian distribution; />The maximum iteration number of the population; />The current iteration number.
Step s41000, second obtaining operation: acquiring the number of alerters as a target number;
s41100, updating the number of alertors based on a preset second formula when the target number is larger than a preset minimum value of the alertors, and repeating the dividing operation to a second obtaining operation;
S41200, when the number of targets is not greater than the minimum value of the alerter, determining a target optimal sparrow based on the updated position parameters and the target position parameters, and determining a target optimal position parameter and a target optimal fitness of the target optimal sparrow;
s41300, adjusting an initial prediction model based on historical surrounding rock data, target optimal position parameters and target optimal fitness to obtain a target grading model;
specifically, the positions of the updated optimal population are determined by judging the proportion of the alertors, the more alertors are favorable for the algorithm to perform global search, the less alertors are favorable for accelerating convergence and performing local search in a small range, the higher proportion of the alertors can be given to the population in the early stage of the algorithm, the global searching capability of the population is enhanced, the proportion of the alertors is gradually reduced along with the increase of the iteration times of the population, and the algorithm convergence speed is accelerated.
The alerter proportion update formula is:
；
wherein,is the proportion of alertors; />Initial proportion for the alerter; />The current iteration number; />The maximum iteration number of the population; />The ratio is the minimum value of the preset alerter;
when (when)When the sparrow position updating iterative operation is carried out continuously until the condition is met; when (when) And when the conditions are met, ending the iteration, obtaining an optimal position and an optimal fitness value from the global, determining an optimal weight and a threshold of the convolutional neural network, and transmitting the optimal weight and the threshold back to the convolutional neural network for retraining to obtain a target hierarchical model.
S50000, inputting real-time surrounding rock data of the target surrounding rock into a target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock;
specifically, in the surrounding rock grading prediction, the prediction result comprises the determination of the surrounding rock grade, so that a worker can master the surrounding rock condition of a tunnel in real time and adjust the surrounding rock grade in time, and the optimal scheme is selected from a large number of design parameters through intelligent scheme comparison and selection while the design requirement is met, thereby realizing informatization operation of the grading work of the surrounding rock of the tunnel, greatly saving the construction cost and conforming to the green sustainable development concept of China.
Example 2:
as shown in fig. 3, this embodiment provides a tunnel surrounding rock grading device, which includes:
a first acquiring unit 10000, configured to acquire historical surrounding rock data of a target surrounding rock in a historical time period;
an initialization unit 20000, configured to initialize the sparrow population location based on the chaotic map, and obtain a plurality of initial location parameters;
A construction unit 30000, configured to construct a model based on the initial position parameter, to obtain an initial prediction model;
the training unit 40000 is used for training the initial prediction model by using historical surrounding rock data and sparrow algorithm to obtain a target classification model;
the input unit 50000 is configured to input real-time surrounding rock data of the target surrounding rock into the target classification model, so as to obtain a real-time surrounding rock classification result of the target surrounding rock.
In a specific embodiment disclosed in the present application, the initialization unit 20000 includes:
a second obtaining unit 20100, configured to obtain random position parameters and chaotic control parameters of any sparrow, where an initial position is located in a first setting range, and the chaotic control parameters are located in a second setting range;
a first calculation unit 20200 for a first calculation operation: calculating the product of the initial position and a first set threshold value to be used as a first product;
a second calculation unit 20300 for a second calculation operation: calculating a sine function value of the first product as a first value;
third calculation unit 20400 for a third calculation operation: calculating the ratio of the chaotic control parameter to a second set threshold value to be used as a first ratio;
a fourth calculation unit 20500 for a fourth calculation operation: calculating the product of the first value and the first ratio as a second product;
A repeating unit 20600 for repeating the first calculating operation, the second calculating operation, the third calculating operation, and the fourth calculating operation until reaching the preset number of repetitions, to obtain the target position parameter;
and a fifth calculating unit 20700, configured to calculate target position parameters corresponding to all sparrows, so as to obtain a plurality of initial position parameters of the sparrow population.
In one embodiment disclosed herein, the training unit 40000 includes:
a first determining unit 40100, configured to determine initial fitness of all sparrows based on initial position parameters of all sparrows;
a dividing unit 40200 for dividing operations: dividing all sparrows into discoverers, followers and alertors based on the initial fitness;
a first updating unit 40300 for updating operations: updating initial position parameters of all sparrows based on a preset target formula to obtain a plurality of updated position parameters;
a third acquisition unit 40400 for the first acquisition operation: obtaining the maximum variation rate, the minimum variation rate, the current iteration number and the maximum iteration number;
a sixth calculation unit 40500 for a fifth calculation operation: calculating a difference value between the maximum variation rate and the minimum variation rate as a first difference value;
A seventh calculation unit 40600 for a sixth calculation operation: calculating the ratio of the current iteration times to the maximum iteration times as a second ratio;
an eighth calculation unit 40700 for seventh calculation operation: calculating a difference value between the third set threshold value and the second ratio as a second difference value;
a ninth calculation unit 40800 for eighth calculation operation: calculating the fourth power of the second difference value and multiplying the fourth power by the first difference value to obtain a target variation rate;
a second determination unit 40900 for determining the operation: determining a target sparrow based on the target mutation rate, and updating and replacing the updated position parameter based on a preset first formula to obtain a target position parameter;
a fourth acquisition unit 41000 for a second acquisition operation: acquiring the number of alerters as a target number;
a second updating unit 41100 for updating the number of alerters based on a preset second formula and repeating the dividing operation to the second acquiring operation when the target number is greater than the preset minimum number of alerters;
a third updating unit 41200, configured to determine, when the number of targets is not greater than the minimum value of the alerter, a target optimal sparrow based on the updated position parameter and the target position parameter, and determine a target optimal position parameter and a target optimal fitness of the target optimal sparrow;
The adjusting unit 41300 is configured to adjust the initial prediction model based on the historical surrounding rock data, the target optimal position parameter and the target optimal fitness to obtain a target classification model.
In one embodiment disclosed herein, the first updating unit 40300 includes:
a third determining unit 40301, configured to determine a current early warning value based on all initial fitness;
the first comparing unit 40302 is configured to compare the current early warning value with a preset warning early warning value to obtain a first comparison result;
a fifth obtaining unit 40303, configured to obtain, when the first comparison result meets the first setting condition, an upper limit constraint of the finder, a lower limit constraint of the finder, a first optimal sparrow position parameter, a first worst sparrow position parameter, a constant factor, and a first iteration number;
a tenth calculation unit 40304, configured to calculate an adaptive weight based on the upper limit constraint, the lower limit constraint, the first optimal sparrow position parameter, the first worst sparrow position parameter, the constant factor, and the first iteration number;
the first loading unit 40305 is configured to load the adaptive weight into a preset first target formula, and calculate an updated location parameter updated by the finder;
The eleventh calculating unit 40306 is configured to calculate, when the first comparison result meets the second setting condition, an updated location parameter updated by the finder based on a preset second target formula.
In a specific embodiment disclosed in the present application, the first updating unit 40300 further includes:
a sixth acquiring unit 40307, configured to acquire a second iteration number;
a fourth determining unit 40308 for determining an optimal fitness and a worst fitness based on all the initial fitness;
a second comparing unit 40309, configured to compare the fitness of the alerter with the optimal fitness to obtain a second comparison result;
a twelfth calculation unit 40310, configured to calculate, when the second comparison result meets the third setting condition, a step control parameter based on the optimal fitness, the worst fitness, the initial position parameter, and the second iteration number;
a second substituting unit 40311, configured to substituting the step control parameter into a preset third target formula, and calculate an updated position parameter updated by the alerter;
a seventh obtaining unit 40312, configured to obtain the first random factor when the second comparison result meets the fourth setting condition;
a thirteenth calculation unit 40313, configured to calculate an initial parameter based on the optimal fitness, the worst fitness, the initial position parameter, the first random factor, and the second iteration number;
The third substituting unit 40314 is configured to substituting the initial parameter into a preset fourth target formula, and calculate an updated position parameter of the alerter after updating.
In one embodiment disclosed herein, the second determining unit 40900 includes:
an eighth acquisition unit 40901 for acquiring a second random factor;
a ninth obtaining unit 40902, configured to obtain a plurality of range parameters by obtaining search ranges of all sparrows;
a fifth determining unit 40903 for determining a minimum value from the plurality of range parameters as a target range parameter;
a fourteenth calculation unit 40904 for calculating a product of the target range parameter and the second random factor as a third product;
the fifteenth calculating unit 40905 is configured to calculate a sum of the third product and the updated position parameter of the target sparrow to obtain the target position parameter.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
Example 3:
corresponding to the above method embodiment, a tunnel surrounding rock grading device is further provided in this embodiment, and a tunnel surrounding rock grading device described below and a tunnel surrounding rock grading method described above may be referred to correspondingly.
Fig. 4 is a block diagram of a tunnel surrounding rock grading apparatus 800, shown in accordance with an exemplary embodiment. As shown in fig. 4, the tunnel surrounding rock grading apparatus 800 may include: a processor 801, a memory 802. The tunnel surrounding rock grading apparatus 800 may also include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
Wherein the processor 801 is configured to control the overall operation of the tunnel surrounding rock grading apparatus 800 to perform all or part of the steps of the tunnel surrounding rock grading method described above. The memory 802 is used to store various types of data to support operation at the tunnel surrounding rock grading device 800, which may include, for example, instructions for any application or method operating on the tunnel surrounding rock grading device 800, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to provide wired or wireless communication between the tunnel surrounding rock classification apparatus 800 and other apparatuses. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the tunnel surrounding rock grading apparatus 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (DigitalSignal Processor, abbreviated as DSP), digital signal processing apparatus (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the tunnel surrounding rock grading method described above.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the tunnel surrounding rock grading method described above. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the tunnel surrounding rock classification apparatus 800 to perform the tunnel surrounding rock classification method described above.
Example 4:
corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a tunnel surrounding rock grading method described above may be referred to correspondingly.
A readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the tunnel surrounding rock grading method of the method embodiment described above.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for grading surrounding rocks of a tunnel, comprising:
acquiring historical surrounding rock data of target surrounding rock in a historical time period;
initializing the sparrow population position based on chaotic mapping to obtain a plurality of initial position parameters;
constructing a model based on the initial position parameters to obtain an initial prediction model;
training the initial prediction model based on the historical surrounding rock data and a sparrow algorithm to obtain a target classification model;
and inputting the real-time surrounding rock data of the target surrounding rock into the target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock.
2. The method of grading tunnel surrounding rock according to claim 1, wherein initializing the sparrow population location based on the chaotic map to obtain a plurality of initial location parameters comprises:
acquiring random position parameters and chaotic control parameters of any sparrow, wherein the initial position is positioned in a first setting range, and the chaotic control parameters are positioned in a second setting range;
first computing operation: calculating the product of the initial position and a first set threshold value to be used as a first product;
second calculation operation: calculating a sine function value of the first product as a first value;
Third calculation operation: calculating the ratio of the chaotic control parameter to a second set threshold value to be used as a first ratio;
fourth calculation operation: calculating the product of the first value and the first ratio as a second product;
repeating the first calculation operation, the second calculation operation, the third calculation operation and the fourth calculation operation until the preset repetition times are reached, and obtaining target position parameters;
and calculating target position parameters corresponding to all sparrows to obtain a plurality of initial position parameters of the sparrow population.
3. The tunnel surrounding rock grading method according to claim 1, wherein training the initial prediction model based on the historical surrounding rock data and sparrow algorithm to obtain a target prediction model comprises:
determining the initial fitness of all sparrows based on the initial position parameters of all sparrows;
dividing operation: dividing all sparrows into discoverers, followers and alertors based on the initial fitness;
updating operation: updating initial position parameters of all sparrows based on a preset target formula to obtain a plurality of updated position parameters;
first acquisition operation: obtaining the maximum variation rate, the minimum variation rate, the current iteration number and the maximum iteration number;
Fifth calculation operation: calculating the difference value of the maximum mutation rate and the minimum mutation rate as a first difference value;
sixth calculation operation: calculating the ratio of the current iteration times to the maximum iteration times as a second ratio;
seventh calculation operation: calculating the difference value between the third set threshold value and the second ratio as a second difference value;
eighth calculation operation: calculating the fourth power of the second difference value and multiplying the fourth power of the second difference value by the first difference value to obtain a target variation rate;
determination: determining a target sparrow based on the target mutation rate, and updating and replacing the updated position parameter based on a preset first formula to obtain a target position parameter;
second acquisition operation: acquiring the number of the alerters as a target number;
when the target number is greater than a preset minimum value of the alerters, updating the number of the alerters based on a preset second formula, and repeating the dividing operation to the second obtaining operation;
when the target number is not greater than the minimum value of the alerter, determining a target optimal sparrow based on the updated position parameter and the target position parameter, and determining a target optimal position parameter and a target optimal fitness of the target optimal sparrow;
And adjusting the initial prediction model based on the historical surrounding rock data, the target optimal position parameter and the target optimal adaptability to obtain the target grading model.
4. A method of grading surrounding rocks in a tunnel according to claim 3, wherein the updating operation updates initial position parameters of all sparrows based on a preset target formula to obtain a plurality of updated position parameters, and includes:
determining a current early warning value based on all initial fitness;
comparing the current early warning value with a preset warning early warning value to obtain a first comparison result;
when the first comparison result meets a first set condition, obtaining an upper limit constraint of the finder, a lower limit constraint of the finder, a first optimal sparrow position parameter, a first worst sparrow position parameter, a constant factor and a first iteration number;
calculating to obtain an adaptive weight based on the upper limit constraint, the lower limit constraint, the first optimal sparrow position parameter, the first worst sparrow position parameter, the constant factor and the first iteration number;
the self-adaptive weight is brought into a preset first target formula, and the updated position parameter of the discoverer after updating is calculated;
And when the first comparison result meets a second setting condition, calculating the updated position parameter updated by the finder based on a preset second target formula.
5. A tunnel surrounding rock grading device, comprising:
the first acquisition unit is used for acquiring historical surrounding rock data of the target surrounding rock in a historical time period;
the initialization unit is used for initializing the sparrow population positions based on the chaotic mapping to obtain a plurality of initial position parameters;
the construction unit is used for constructing a model based on the initial position parameters to obtain an initial prediction model;
the training unit is used for training the initial prediction model by the historical surrounding rock data and sparrow algorithm to obtain a target classification model;
the input unit is used for inputting the real-time surrounding rock data of the target surrounding rock into the target grading model to obtain a real-time surrounding rock grading result of the target surrounding rock.
6. The tunnel surrounding rock grading device according to claim 5, wherein the initializing unit comprises:
the second acquisition unit is used for acquiring random position parameters and chaotic control parameters of any sparrow, wherein the initial position is positioned in a first setting range, and the chaotic control parameters are positioned in a second setting range;
A first calculation unit configured to perform a first calculation operation: calculating the product of the initial position and a first set threshold value to be used as a first product;
a second calculation unit configured to perform a second calculation operation: calculating a sine function value of the first product as a first value;
a third calculation unit configured to perform a third calculation operation: calculating the ratio of the chaotic control parameter to a second set threshold value to be used as a first ratio;
a fourth calculation unit configured to perform a fourth calculation operation: calculating the product of the first value and the first ratio as a second product;
a repeating unit, configured to repeat the first computing operation, the second computing operation, the third computing operation, and the fourth computing operation until a preset number of repetitions is reached, to obtain a target position parameter;
and the fifth calculation unit is used for calculating target position parameters corresponding to all sparrows to obtain a plurality of initial position parameters of the sparrow population.
7. The tunnel surrounding rock grading device according to claim 5, wherein the training unit comprises:
the first determining unit is used for determining initial fitness of all sparrows based on the initial position parameters of all sparrows;
A dividing unit for dividing operations: dividing all sparrows into discoverers, followers and alertors based on the initial fitness;
a first updating unit configured to update an operation: updating initial position parameters of all sparrows based on a preset target formula to obtain a plurality of updated position parameters;
a third acquisition unit configured to perform a first acquisition operation: obtaining the maximum variation rate, the minimum variation rate, the current iteration number and the maximum iteration number;
a sixth calculation unit configured to perform a fifth calculation operation: calculating the difference value of the maximum mutation rate and the minimum mutation rate as a first difference value;
a seventh calculation unit configured to: calculating the ratio of the current iteration times to the maximum iteration times as a second ratio;
an eighth calculation unit configured to: calculating the difference value between the third set threshold value and the second ratio as a second difference value;
a ninth calculation unit configured to: calculating the fourth power of the second difference value and multiplying the fourth power of the second difference value by the first difference value to obtain a target variation rate;
a second determination unit configured to determine: determining a target sparrow based on the target mutation rate, and updating and replacing the updated position parameter based on a preset first formula to obtain a target position parameter;
A fourth acquisition unit configured to perform a second acquisition operation: acquiring the number of the alerters as a target number;
a second updating unit configured to update the number of alerters based on a second preset formula and repeat the dividing operation to the second acquiring operation when the target number is greater than a preset minimum number of alerters;
a third updating unit, configured to determine, when the target number is not greater than the minimum value of the alerter, a target optimal sparrow based on the updated position parameter and the target position parameter, and determine a target optimal position parameter and a target optimal fitness of the target optimal sparrow;
and the adjusting unit is used for adjusting the initial prediction model based on the historical surrounding rock data, the target optimal position parameter and the target optimal adaptability to obtain the target grading model.
8. The tunnel surrounding rock grading device according to claim 7, wherein the first updating unit comprises:
the third determining unit is used for determining a current early warning value based on all initial fitness;
the first comparison unit is used for comparing the current early warning value with a preset warning early warning value to obtain a first comparison result;
A fifth obtaining unit, configured to obtain, when the first comparison result meets a first set condition, an upper limit constraint of the finder, a lower limit constraint of the finder, a first optimal sparrow position parameter, a first worst sparrow position parameter, a constant factor, and a first iteration number;
a tenth calculation unit, configured to calculate an adaptive weight based on the upper limit constraint, the lower limit constraint, the first optimal sparrow position parameter, the first worst sparrow position parameter, the constant factor, and the first iteration number;
the first carry-in unit is used for carrying the self-adaptive weight into a preset first target formula and calculating the updated position parameter updated by the finder;
an eleventh calculation unit, configured to calculate, based on a second preset target formula, the updated location parameter after updating the finder when the first comparison result meets a second setting condition.
9. A tunnel surrounding rock grading apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the tunnel surrounding rock grading method according to any of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the tunnel surrounding rock grading method according to any of claims 1 to 4.
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