Karst area roadbed treatment method and device

Abstract

The invention discloses a karst area roadbed treatment method and device. The karst area roadbed treatment method comprises the steps that historical karst cave information and to-be-treated karst cave information are obtained; establishing a Kriging agent model according to the historical karst cave information; extracting karst cave shape information from the to-be-processed karst cave information, and determining a shape influence factor according to the karst cave shape information; the empirical parameters are input into a Kriging agent model, and a grouting influence factor, a burial depth influence factor and a load influence factor are determined; calculating an output value of a Sigmoid mapping function according to the shape influence factor, the grouting influence factor, the burial depth influence factor, the load influence factor and the initialized bias parameter; determining an optimal offset parameter; and according to the optimal bias parameter, determining an output value of the Sigmoid mapping function, and updating an empirical parameter. The roadbed treatment information can be output according to the updated empirical parameters, and the stability of the karst region roadbed is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of roadbed treatment, and in particular to a method and a device for treating a roadbed in a karst area. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] At present, there are many studies on the treatment methods of roadbed in karst areas, but the existing studies are generally aimed at the calculation of roadbed stability in karst areas, analysis of damage mechanisms, etc. Such studies are often qualitative analyses of certain aspects of roadbed in karst areas, and there are few roadbed treatment methods proposed for the response to the complex external environment on site. If the roadbed treatment is carried out using only qualitative analysis results, it is often impossible to predict the actual impact of the later complex external environment on the project, thus affecting the rationality and reliability of the roadbed treatment. Summary of the invention

[0004] The embodiment of the present invention provides a method for treating a roadbed in a karst area, which is used to output roadbed treatment information according to updated empirical parameters, thereby ensuring the stability of the roadbed in the karst area. The method includes:

[0005] Obtain historical cave information and cave information to be processed;

[0006] Establish a Kriging proxy model based on historical cave information;

[0007] Extracting cave shape information from the cave information to be processed, and determining a shape influencing factor based on the cave shape information; the shape influencing factor reflects the degree of influence of the spatial position distribution of the cave on the stress deformation of the roadbed;

[0008] Input the empirical parameters into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed;

[0009] According to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter, the output value of the Sigmoid mapping function is calculated;

[0010] Taking the maximum output value of the Sigmoid mapping function as the objective function, determine the optimal bias parameter;

[0011] According to the optimal bias parameter, the output value of the Sigmoid mapping function is determined; according to the output value of the Sigmoid mapping function, the empirical parameter is updated;

[0012] Output roadbed treatment information based on updated empirical parameters.

[0013] The embodiment of the present invention further provides a karst area roadbed treatment device, which is used to output roadbed treatment information according to updated empirical parameters, so as to ensure the stability of the roadbed in the karst area. The device includes:

[0014] The cave information acquisition module is used to obtain historical cave information and cave information to be processed;

[0015] Kriging proxy model building module, used to build Kriging proxy model based on historical cave information;

[0016] A shape influence factor determination module is used to extract cave shape information from the cave information to be processed, and determine a shape influence factor based on the cave shape information; the shape influence factor reflects the influence of the spatial position distribution of the cave on the stress deformation of the roadbed;

[0017] A module for determining grouting influence factor, burial depth influence factor and load influence factor, which is used to input empirical parameters into the Kriging proxy model to determine the grouting influence factor, burial depth influence factor and load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the karst cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed;

[0018] A Sigmoid mapping function calculation module is used to calculate the output value of the Sigmoid mapping function according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameters;

[0019] An optimal bias parameter determination module is used to determine the optimal bias parameter by taking the maximum output value of the Sigmoid mapping function as the objective function;

[0020] An empirical parameter updating module is used to determine the output value of the Sigmoid mapping function according to the optimal bias parameter; and update the empirical parameter according to the output value of the Sigmoid mapping function;

[0021] The roadbed treatment information output module is used to output the roadbed treatment information according to the updated empirical parameters.

[0022] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned karst area roadbed treatment method when executing the computer program.

[0023] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned karst area roadbed treatment method is implemented.

[0024] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned karst area roadbed treatment method is implemented.

[0025] In the embodiment of the present invention, historical cave information and cave information to be processed are obtained; a Kriging proxy model is established according to the historical cave information; cave shape information is extracted from the cave information to be processed, and a shape influence factor is determined according to the cave shape information; the shape influence factor reflects the influence of the spatial position distribution of the cave on the stress and deformation of the roadbed; empirical parameters are input into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the cave on the stress and deformation of the roadbed; the The load influence factor reflects the influence of the roadbed driving vibration load on the roadbed stress and deformation; the output value of the Sigmoid mapping function is calculated according to the shape influence factor, grouting influence factor, burial depth influence factor and load influence factor, as well as the initialized bias parameters; the maximum output value of the Sigmoid mapping function is used as the objective function to determine the optimal bias parameters; according to the optimal bias parameters, the output value of the Sigmoid mapping function is determined; according to the output value of the Sigmoid mapping function, the empirical parameters are updated; according to the updated empirical parameters, the roadbed treatment information is output to ensure the stability of the roadbed in the karst area. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 creative work. In the drawings:

[0027] Figure 1 Flow chart of the method for treating karst area roadbed in an embodiment of the present invention;

[0028] Figure 2Flow chart of Sigmoid mapping function optimization in an embodiment of the present invention;

[0029] Figure 3 Schematic diagram of a karst area roadbed treatment device in an embodiment of the present invention;

[0030] Figure 4 It is a schematic diagram of a specific karst area roadbed treatment device in an embodiment of the present invention;

[0031] Figure 5 This is an example diagram of a method for treating a roadbed in a karst area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0033] Figure 1 This is a flow chart of a method for treating a karst area roadbed in an embodiment of the present invention, the method comprising:

[0034] Step 101, obtaining historical cave information and cave information to be processed;

[0035] Step 102, establishing a Kriging proxy model based on historical cave information;

[0036] Step 103, extracting cave shape information from the cave information to be processed, and determining a shape influence factor according to the cave shape information; the shape influence factor reflects the influence of the spatial position distribution of the cave on the stress deformation of the roadbed;

[0037] Step 104, input the empirical parameters into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed;

[0038] Step 105, calculating the output value of the Sigmoid mapping function according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter;

[0039] Step 106, taking the maximum output value of the Sigmoid mapping function as the objective function, determining the optimal bias parameter;

[0040] Step 107, determining the output value of the Sigmoid mapping function according to the optimal bias parameter; updating the empirical parameter according to the output value of the Sigmoid mapping function;

[0041] Step 108: outputting roadbed treatment information based on the updated empirical parameters.

[0042] Each step is described in detail below.

[0043] In step 101, historical cave information and to-be-processed cave information are obtained.

[0044] In a specific embodiment, a database of karst area roadbed treatment methods is established based on existing actual engineering data, and historical cave information and to-be-treated cave information are obtained from the database. The cave information includes cave shape information and cave location information.

[0045] In step 102, a Kriging proxy model is established based on historical cave information.

[0046] In a specific embodiment, the Kriging proxy model is composed of a global regression model and a nonparametric Gaussian random process based on local errors. In essence, the historical cave information near the cave information to be processed is linearly weighted combined to estimate the cave information to be processed. The Kriging proxy model shows good local estimation ability through the role of correlation functions with good continuity and differentiability, and has a good approximation effect on nonlinear complex problems.

[0047] In step 103, the shape information of the cave is extracted from the cave information to be processed, and the shape influence factor is determined according to the shape information of the cave; the shape influence factor reflects the influence degree of the spatial position distribution of the cave on the stress deformation of the roadbed.

[0048] In step 104, the empirical parameters are input into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed.

[0049] In a specific embodiment, the empirical parameters include the outer diameter, burial depth and tamping point distance of the reinforcement piles of the rock cave.

[0050] In step 105, the output value of the Sigmoid mapping function is calculated according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter.

[0051] In one embodiment, the output value of the Sigmoid mapping function is calculated according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter, including calculating the output value of the Sigmoid mapping function according to the following formula:

[0052]

[0053] Among them, S is the output value of the Sigmoid mapping function; i = 1, 2, 3, 4; α1 is the shape influence factor; α2 is the grouting influence factor; α3 is the burial depth influence factor; α4 is the load influence factor; and b is the bias parameter.

[0054] In step 106, the maximum output value of the Sigmoid mapping function is used as the objective function to determine the optimal bias parameter.

[0055] In step 107, the output value of the Sigmoid mapping function is determined according to the optimal bias parameter; and the empirical parameter is updated according to the output value of the Sigmoid mapping function.

[0056] like Figure 2 As shown, in one embodiment, the maximum output value of the Sigmoid mapping function is used as the objective function to determine the optimal bias parameter, including:

[0057] Step 201, using a preset optimization algorithm to perform multiple rounds of optimization on the bias parameters in the Sigmoid mapping function; wherein,

[0058] After each round of optimization, it is determined whether the objective function of the Sigmoid mapping function after this round of optimization converges. If it converges, the optimization is stopped. If not, the next round of optimization is performed based on the Sigmoid mapping function after this round of optimization until the objective function of the Sigmoid mapping function converges and the optimization is stopped to obtain the optimal bias parameter.

[0059] In step 108, the roadbed treatment information is output according to the updated empirical parameters.

[0060] In a specific embodiment, the roadbed treatment information includes the diameter, depth, and spacing of the piles; the spacing of the tamping points; the water content and density of the grouting, and the like.

[0061] In a specific embodiment, the relationship between the updated empirical parameter and the Sigmoid mapping function is as follows:

[0062]

[0063] in, is the updated empirical parameter, S is the output value of the Sigmoid mapping function, x nis the empirical parameter, and n is the number of empirical parameters.

[0064] The present invention also provides a karst area roadbed treatment device in the embodiment, as described in the following embodiment. Since the principle of the device to solve the problem is similar to the karst area roadbed treatment method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated. Figure 3 As shown, the device comprises:

[0065] The cave information acquisition module 301 is used to acquire historical cave information and to-be-processed cave information;

[0066] A Kriging proxy model building module 302 is used to build a Kriging proxy model based on historical cave information;

[0067] The shape influence factor determination module 303 is used to extract the shape information of the cave from the cave information to be processed, and determine the shape influence factor according to the shape information of the cave; the shape influence factor reflects the influence degree of the spatial position distribution of the cave on the stress deformation of the roadbed;

[0068] The grouting influence factor, burial depth influence factor and load influence factor determination module 304 is used to input the empirical parameters into the Kriging proxy model to determine the grouting influence factor, burial depth influence factor and load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the karst cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed;

[0069] Sigmoid mapping function calculation module 305, used to calculate the output value of the Sigmoid mapping function according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter;

[0070] The optimal bias parameter determination module 306 is used to determine the optimal bias parameter by taking the maximum output value of the Sigmoid mapping function as the objective function;

[0071] The empirical parameter updating module 307 is used to determine the output value of the Sigmoid mapping function according to the optimal bias parameter; and update the empirical parameter according to the output value of the Sigmoid mapping function;

[0072] The roadbed treatment information output module 308 is used to output the roadbed treatment information according to the updated empirical parameters.

[0073] In one embodiment, the Sigmoid mapping function calculation module 305 is specifically used for:

[0074] The output value of the Sigmoid mapping function is calculated according to the following formula:

[0075]

[0076] Among them, S is the output value of the Sigmoid mapping function; i = 1, 2, 3, 4; α1 is the shape influence factor; α2 is the grouting influence factor; α3 is the burial depth influence factor; α4 is the load influence factor; and b is the bias parameter.

[0077] like Figure 4 As shown, in one embodiment, a bias parameter optimization module 401 is also included, which is specifically used to:

[0078] The preset optimization algorithm is used to perform multiple rounds of optimization on the bias parameters in the Sigmoid mapping function;

[0079] After each round of optimization, it is determined whether the objective function of the Sigmoid mapping function after this round of optimization converges. If it converges, the optimization is stopped. If not, the next round of optimization is performed based on the Sigmoid mapping function after this round of optimization until the objective function of the Sigmoid mapping function converges and the optimization is stopped to obtain the optimal bias parameter.

[0080] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned karst area roadbed treatment method when executing the computer program.

[0081] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned karst area roadbed treatment method is implemented.

[0082] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned karst area roadbed treatment method is implemented.

[0083] like Figure 5 : is an example diagram of a method for treating a roadbed in a karst area according to an embodiment of the present invention. In the embodiment of the present invention, the shape information and location of the karst cave in the information of the karst cave to be processed are obtained, and the shape influencing factor α1 is determined according to the shape information of the karst cave; a Kriging proxy model is pre-established according to the historical karst cave information in the database; and the empirical parameters {x1, x2...x n}, including the outer diameter, burial depth and tamping point spacing of the reinforcement pile, input the Kriging proxy model to determine the grouting influence factor α2, burial depth influence factor α3 and load influence factor α4; according to the shape influence factor α1, grouting influence factor α2, burial depth influence factor α3 and load influence factor α4, as well as the initialized bias parameter b, calculate the Sigmoid mapping function The output value S of the Sigmoid mapping function is taken as the maximum output value S of the Sigmoid mapping function as the objective function, and the bias parameters in the Sigmoid mapping function are optimized for multiple rounds using a preset optimization algorithm. After each round of optimization, it is determined whether the objective function of the Sigmoid mapping function after this round of optimization converges. If it converges, the optimization is stopped. If it does not converge, the next round of optimization is performed based on the Sigmoid mapping function after this round of optimization until the objective function of the Sigmoid mapping function converges and the optimization is stopped to obtain the optimal bias parameters; according to the optimal bias parameters, the output value of the Sigmoid mapping function is determined; according to the output value of the Sigmoid mapping function, the empirical parameters, that is, the final parameters, are updated. The roadbed treatment information is output based on the updated empirical parameters to ensure the stability of the roadbed in karst areas.

[0084] In the embodiment of the present invention, historical karst cave information and karst cave information to be processed are obtained; a Kriging proxy model is established according to the historical karst cave information; karst cave shape information is extracted from the karst cave information to be processed, and a shape influence factor is determined according to the karst cave shape information; the shape influence factor reflects the influence of the spatial position distribution of the cave on the stress and deformation of the roadbed; empirical parameters are input into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the karst cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the roadbed driving The influence degree of vibration load on the stress and deformation of the roadbed; according to the shape influence factor, grouting influence factor, burial depth influence factor and load influence factor, as well as the initialized bias parameters, the output value of the Sigmoid mapping function is calculated; the maximum output value of the Sigmoid mapping function is used as the objective function to determine the optimal bias parameters; according to the optimal bias parameters, the output value of the Sigmoid mapping function is determined; according to the output value of the Sigmoid mapping function, the empirical parameters are updated; according to the updated empirical parameters, the roadbed treatment information is output, so as to formulate an effective karst area roadbed treatment plan through the roadbed treatment information to ensure the stability of the roadbed in the karst area.

[0085] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for treating roadbed in karst areas, characterized in that: include: Obtain historical cave information and cave information to be processed; Establish a Kriging proxy model based on historical cave information; Extracting cave shape information from the cave information to be processed, and determining a shape influencing factor based on the cave shape information; the shape influencing factor reflects the degree of influence of the spatial position distribution of the cave on the stress deformation of the roadbed; Input the empirical parameters into the Kriging proxy model to determine the grouting influence factor, the burial depth influence factor and the load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed; According to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameter, the output value of the Sigmoid mapping function is calculated; Taking the maximum output value of the Sigmoid mapping function as the objective function, determine the optimal bias parameter; According to the optimal bias parameter, determine the output value of the Sigmoid mapping function; Update the empirical parameters according to the output value of the Sigmoid mapping function; Output roadbed treatment information based on updated empirical parameters.

2. The method according to claim 1, characterized in that According to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, and the initialized bias parameter, the output value of the Sigmoid mapping function is calculated, including calculating the output value of the Sigmoid mapping function according to the following formula: Among them, S is the output value of the Sigmoid mapping function; i = 1, 2, 3, 4; α1 is the shape influence factor; α2 is the grouting influence factor; α3 is the burial depth influence factor; α4 is the load influence factor; and b is the bias parameter.

3. The method according to claim 1, characterized in that Taking the maximum output value of the Sigmoid mapping function as the objective function, determine the optimal bias parameters, including: The preset optimization algorithm is used to perform multiple rounds of optimization on the bias parameters in the Sigmoid mapping function; After each round of optimization, it is determined whether the objective function of the Sigmoid mapping function after this round of optimization converges. If it converges, the optimization is stopped. If not, the next round of optimization is performed based on the Sigmoid mapping function after this round of optimization until the objective function of the Sigmoid mapping function converges and the optimization is stopped to obtain the optimal bias parameter.

4. A karst area roadbed treatment device, characterized in that: include: The cave information acquisition module is used to obtain historical cave information and cave information to be processed; Kriging proxy model building module, used to build Kriging proxy model based on historical cave information; A shape influence factor determination module is used to extract cave shape information from the cave information to be processed, and determine a shape influence factor based on the cave shape information; the shape influence factor reflects the influence of the spatial position distribution of the cave on the stress deformation of the roadbed; A module for determining grouting influence factor, burial depth influence factor and load influence factor, which is used to input empirical parameters into the Kriging proxy model to determine the grouting influence factor, burial depth influence factor and load influence factor; the grouting influence factor reflects the influence of the grouting of the rock cave on the stress and deformation of the roadbed; the burial depth influence factor reflects the influence of the burial depth of the karst cave on the stress and deformation of the roadbed; the load influence factor reflects the influence of the vibration load of the roadbed on the stress and deformation of the roadbed; A Sigmoid mapping function calculation module is used to calculate the output value of the Sigmoid mapping function according to the shape influence factor, the grouting influence factor, the burial depth influence factor and the load influence factor, as well as the initialized bias parameters; An optimal bias parameter determination module is used to determine the optimal bias parameter by taking the maximum output value of the Sigmoid mapping function as the objective function; An empirical parameter updating module is used to determine the output value of the Sigmoid mapping function according to the optimal bias parameter; Update the empirical parameters according to the output value of the Sigmoid mapping function; The roadbed treatment information output module is used to output the roadbed treatment information according to the updated empirical parameters.

5. The device according to claim 4, characterized in that Sigmoid mapping function calculation module, specifically used for: The output value of the Sigmoid mapping function is calculated according to the following formula: Among them, S is the output value of the Sigmoid mapping function; i = 1, 2, 3, 4; α1 is the shape influence factor; α2 is the grouting influence factor; α3 is the burial depth influence factor; α4 is the load influence factor; and b is the bias parameter.

6. The device according to claim 4, characterized in that It also includes a bias parameter optimization module, which is specifically used to: The preset optimization algorithm is used to perform multiple rounds of optimization on the bias parameters in the Sigmoid mapping function; After each round of optimization, it is determined whether the objective function of the Sigmoid mapping function after this round of optimization converges. If it converges, the optimization is stopped. If not, the next round of optimization is performed based on the Sigmoid mapping function after this round of optimization until the objective function of the Sigmoid mapping function converges and the optimization is stopped to obtain the optimal bias parameter.

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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