CN113569482B - Tunnel service performance evaluation method, device, terminal and storage medium - Google Patents
Tunnel service performance evaluation method, device, terminal and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, a terminal and a storage medium for evaluating service performance of a tunnel. The method comprises the following steps: obtaining a sample to be trained, inputting the sample to be trained into a multi-granularity cascade forest model, and dynamically weighting decision trees to determine a corrected multi-granularity cascade forest model if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number; acquiring a service performance evaluation index of a tunnel to be tested; and determining the tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model. According to the method, the tunnel service performance grade can be estimated through the corrected multi-granularity cascade forest model, and the accuracy of an estimation result is improved.
Description
Technical Field
The present invention relates to the field of tunnel performance detection technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for evaluating service performance of a tunnel.
Background
The induction factor of tunnel defect is very complex, and the state information of the operation tunnel structure is heterogeneous and dispersed in multiple sources, and the information redundancy and island phenomenon are serious. The method is based on the fact that the influence of multi-source information such as accurate cognitive monitoring data, disease detection results, design and construction, train load, external dynamic load, natural disasters and the like on the state of the tunnel structure is achieved, and the multi-source information is fully utilized.
The scientific and reasonable reflection of the service performance of the tunnel is very important for guiding the safe operation of the tunnel and the treatment and maintenance of diseases. At present, the tunnel service performance evaluation method is based on an empirical method and an expert scoring method,
however, the accuracy of the evaluation result obtained by the conventional tunnel service performance evaluation method is low.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a terminal and a storage medium for evaluating the service performance of a tunnel, which are used for solving the problem of low accuracy of an evaluation result obtained by the conventional method for evaluating the service performance of the tunnel.
In a first aspect, an embodiment of the present invention provides a method for evaluating service performance of a tunnel, including:
obtaining a sample to be trained, and inputting the sample to be trained into a multi-granularity cascade forest model;
if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, dynamically weighting the decision trees to determine a corrected multi-granularity cascade forest model;
acquiring a service performance evaluation index of a tunnel to be tested;
and determining the tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model.
In one possible implementation, if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number, dynamically weighting the decision trees to determine a modified multi-granularity cascade forest model, including:
if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, the voting weight of the decision trees is adjusted;
and if the voting weight of the decision tree is equal to the first preset weight, obtaining a corrected multi-granularity cascade forest model.
In one possible implementation, adjusting the voting weights of the decision tree includes:
calculating voting weights corresponding to all leaf nodes in the decision tree;
and if the voting weights corresponding to all the leaf nodes in the decision tree are not equal to the second preset weight, adjusting the voting weights corresponding to each leaf node in all the leaf nodes until the voting weights corresponding to all the leaf nodes in the decision tree are equal to the second preset weight.
In one possible implementation manner, the sample to be trained comprises a tunnel service performance evaluation index to be trained and a grade of the tunnel service performance to be trained;
obtaining a sample to be trained, and inputting the sample to be trained into a multi-granularity cascade forest model, wherein the method comprises the following steps of:
acquiring a tunnel service performance evaluation index to be trained;
grading the tunnel service performance evaluation indexes to be trained by using a K-Means clustering algorithm, and determining a plurality of tunnel service performance grades;
and inputting the tunnel service performance evaluation index to be trained and the plurality of tunnel service performance grades into a multi-granularity cascade forest model.
In one possible implementation manner, grading the tunnel service performance evaluation indexes to be trained by using a K-Means clustering algorithm, and determining a plurality of tunnel service performance grades includes:
obtaining k initial centroid vectors;
calculating the distance between each evaluation index in the evaluation indexes of the service performance of the tunnel to be trained and k initial centroid vectors, and determining the minimum distance corresponding to each evaluation index;
selecting the tunnel service performance grade corresponding to the minimum distance corresponding to each evaluation index as the tunnel service performance grade corresponding to each evaluation index;
and when the k initial centroid vectors are not changed any more, obtaining a plurality of tunnel service performance levels according to the tunnel service performance levels corresponding to each evaluation index.
In a second aspect, an embodiment of the present invention provides an apparatus for evaluating service performance of a tunnel, including:
the sample acquisition module is used for acquiring a sample to be trained and inputting the sample to be trained into the multi-granularity cascade forest model;
the model correction module is used for dynamically weighting the decision trees if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number so as to determine a corrected multi-granularity cascade forest model;
the evaluation index acquisition module is used for acquiring the evaluation index of the service performance of the tunnel to be tested;
the evaluation result determining module is used for determining the tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model.
In one possible implementation, the model modification module includes:
the first judging sub-module is used for adjusting the voting weight of the decision trees if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number;
and the second judging sub-module is used for obtaining a corrected multi-granularity cascade forest model if the voting weight of the decision tree is equal to the first preset weight.
In one possible implementation manner, the first judging sub-module includes:
the weight calculation unit is used for calculating voting weights corresponding to all leaf nodes in the decision tree;
the weight adjusting unit is used for adjusting the voting weight corresponding to each leaf node in all the leaf nodes if the voting weight corresponding to all the leaf nodes in the decision tree is not equal to the second preset weight until the voting weight corresponding to all the leaf nodes in the decision tree is equal to the second preset weight.
In a third aspect, an embodiment of the present invention provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of the first aspect or any one of the possible implementations of the first aspect.
The embodiment of the invention provides a method, a device, a terminal and a storage medium for evaluating the service performance of a tunnel, wherein a to-be-trained sample is acquired and is input into a multi-granularity cascade forest model, if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number, the decision trees are dynamically weighted to determine a corrected multi-granularity cascade forest model, then a to-be-tested tunnel service performance evaluation index is acquired, and then the tunnel service performance grade corresponding to the to-be-tested tunnel service performance evaluation index is determined according to the to-be-tested tunnel service performance evaluation index and the corrected multi-granularity cascade forest model. According to the method, the decision tree in the forest of the multi-granularity cascade forest model is dynamically weighted to obtain the corrected multi-granularity cascade forest model, and the tunnel service performance grade is evaluated through the corrected multi-granularity cascade forest model, so that the accuracy of an evaluation result can be improved.
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 or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a method for evaluating service performance of a tunnel according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a multi-granularity cascade forest model provided by an embodiment of the invention;
FIG. 3 is a schematic structural diagram of an evaluation device for service performance of a tunnel according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation of a method for evaluating service performance of a tunnel according to an embodiment of the present invention is shown, and details are as follows:
step S101: obtaining a sample to be trained, and inputting the sample to be trained into a multi-granularity cascade forest model;
step S102: if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, dynamically weighting the decision trees to determine a corrected multi-granularity cascade forest model;
step S103: acquiring a service performance evaluation index of a tunnel to be tested;
step S104: and determining the tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model.
In one embodiment, the evaluation index includes: water leakage, breakage and crack, ballast bed disease, sedimentation deformation and convergence deformation. The service performance grades of the tunnel are divided into four grades, namely: good, better, general and worse, wherein good conditions do not require remediation, better conditions require enhanced monitoring, general conditions suggest remediation, and worse conditions require immediate remediation.
Further, after determining a sample to be trained, inputting the sample to be trained into a multi-granularity cascade forest model (the specific structure is shown in fig. 2), setting the number of forests and the number of decision trees in each forest, taking the grade of the service performance of the tunnel as the classification class of the model, and performing step-by-step training by adopting sampling sliding windows with different dimensionalities in the multi-granularity cascade forest model through a multi-granularity scanning module and then through the cascade forest module. If the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, dynamically weighting the decision trees to determine a corrected multi-granularity cascade forest model, and evaluating the tunnel service performance level through the corrected multi-granularity cascade forest model, so that the accuracy of an evaluation result can be improved. According to the invention, a dynamic weighted evaluation method is adopted, weighted random forests are utilized to correct votes, the prediction precision of the votes is continuously optimized and improved in the training process, the final prediction precision can be improved to a certain extent, the expansion level number can be reduced, the training duration and the calculation resource consumption are greatly reduced, the relevance of multi-source information is fully considered and is more objective, the evaluation subjectivity is reduced, and the accuracy is improved.
The embodiment of the invention provides a method for evaluating the service performance of a tunnel, which comprises the steps of obtaining a sample to be trained, inputting the sample to be trained into a multi-granularity cascade forest model, dynamically weighting decision trees to determine a corrected multi-granularity cascade forest model if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number, obtaining a tunnel service performance evaluation index to be tested, and determining the service performance grade of the tunnel corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model. According to the method, the decision tree in the forest of the multi-granularity cascade forest model is dynamically weighted to obtain the corrected multi-granularity cascade forest model, and the tunnel service performance grade is evaluated through the corrected multi-granularity cascade forest model, so that the accuracy of an evaluation result can be improved. In addition, the invention can evaluate the service performance of the tunnel under the condition of more than two influencing factors, and the evaluation accuracy is more than 70%.
Optionally, step S102 includes: if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, the voting weight of the decision trees is adjusted; and if the voting weight of the decision tree is equal to the first preset weight, obtaining a corrected multi-granularity cascade forest model. Wherein adjusting the voting weight of the decision tree comprises: calculating voting weights corresponding to all leaf nodes in the decision tree; and if the voting weights corresponding to all the leaf nodes in the decision tree are not equal to the second preset weight, adjusting the voting weights corresponding to each leaf node in all the leaf nodes until the voting weights corresponding to all the leaf nodes in the decision tree are equal to the second preset weight.
In one embodiment, the process of dynamically weighting the decision tree is: after the samples to be trained are imported, if the number of decision trees in the forest is equal to the preset number, voting weight adjustment is carried out, firstly, the voting weight of each leaf node of the decision tree is set to be 0.5, then the voting weight is adjusted again according to the proportion of the number of the right samples judged by the leaf node and the total number of the arrived samples until the voting weights corresponding to all the leaf nodes in the decision tree are equal to the second preset weight, so that a corrected multi-granularity cascade forest model is obtained, and then a cascade forest classifier is generated, so that the model accuracy is improved. If the number of decision trees in the forest is not equal to the preset number, resetting the number of decision trees.
Optionally, step S101 includes: acquiring a tunnel service performance evaluation index to be trained; grading the tunnel service performance evaluation indexes to be trained by using a K-Means clustering algorithm, and determining a plurality of tunnel service performance grades; inputting the tunnel service performance evaluation index to be trained and the tunnel service performance grades into a multi-granularity cascade forest model, wherein the sample to be trained comprises the tunnel service performance evaluation index to be trained and the grade of the tunnel service performance to be trained.
In an embodiment, the to-be-trained sample includes a to-be-trained tunnel service performance evaluation index and a to-be-trained tunnel service performance grade, where the to-be-trained tunnel service performance evaluation index and the to-be-trained tunnel service performance grade belong to a corresponding relationship. The samples to be trained are obtained after grading by a K-Means clustering algorithm, namely, the evaluation index of the service performance of the tunnel to be trained corresponds to the grade of the service performance of the tunnel to be trained by the K-Means clustering algorithm. For example, the evaluation indexes of the service performance of the tunnel to be trained comprise water leakage and broken cracks, the grade of the service performance of the tunnel corresponding to the water leakage is general after the grade classification is carried out by a K-Means clustering algorithm, the grade of the service performance of the tunnel corresponding to the broken cracks is poor, and the obtained sample to be trained is [ water leakage-general, broken cracks-poor ].
Optionally, grading the tunnel service performance evaluation indexes to be trained by using a K-Means clustering algorithm, and determining a plurality of tunnel service performance grades includes: obtaining k initial centroid vectors; calculating the distance between each evaluation index in the evaluation indexes of the service performance of the tunnel to be trained and k initial centroid vectors, and determining the minimum distance corresponding to each evaluation index; selecting the tunnel service performance grade corresponding to the minimum distance corresponding to each evaluation index as the tunnel service performance grade corresponding to each evaluation index; and when the k initial centroid vectors are not changed any more, obtaining a plurality of tunnel service performance levels according to the tunnel service performance levels corresponding to each evaluation index.
In one embodiment, the classification process of the K-Means clustering algorithm is as follows:
in the first step, the disease grade of the evaluation index is divided into four types, namely, k is taken as 4.
Second, randomly selecting k samples to be trained from the input sample set d= { x1, x2, … xm } as initial k centroid vectors: { μ1, μ2,..mu.k }.
Third, for n=1, 2,..
a) Initializing cluster partition C tot＝1,2...k；
b) For i=1, 2..m, a sample xi and each centroid vector μj (j=1, 2,. distance of k):and marking xi as the category lambdaj corresponding to dij with the smallest label. Updating cλi=cλi { xi };
c) For j=1, 2,.. re-compute the new centroid for all sample points in Cj:
d) If all k centroid vectors have not changed, go to the fourth step.
And fourthly, outputting cluster division C= { C1, C2, &..Ck } to minimize total distance in the cluster, thereby obtaining the grade classification of the evaluation index with good effect.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a schematic structural diagram of an evaluation device for service performance of a tunnel according to an embodiment of the present invention, and for convenience of explanation, only the portions relevant to the embodiment of the present invention are shown, which is described in detail below:
as shown in fig. 3, an apparatus for evaluating service performance of a tunnel includes: a sample acquisition module 31, a model correction module 32, an evaluation index acquisition module 33, and an evaluation result determination module 34.
The sample acquisition module 31 is configured to acquire a sample to be trained, and input the sample to be trained into a multi-granularity cascade forest model;
the model correction module 32 is configured to dynamically weight the decision trees to determine a corrected multi-granularity cascade forest model if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number;
an evaluation index acquisition module 33, configured to acquire an evaluation index of service performance of a tunnel to be tested;
the evaluation result determining module 34 is configured to determine a tunnel service performance level corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the modified multi-granularity cascade forest model.
In one possible implementation, the model modification module 32 includes:
the first judging sub-module is used for adjusting the voting weight of the decision trees if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number;
and the second judging sub-module is used for obtaining a corrected multi-granularity cascade forest model if the voting weight of the decision tree is equal to the first preset weight.
In one possible implementation manner, the first judging sub-module includes:
the weight calculation unit is used for calculating voting weights corresponding to all leaf nodes in the decision tree;
the weight adjusting unit is used for adjusting the voting weight corresponding to each leaf node in all the leaf nodes if the voting weight corresponding to all the leaf nodes in the decision tree is not equal to the second preset weight until the voting weight corresponding to all the leaf nodes in the decision tree is equal to the second preset weight.
In one possible implementation manner, the sample to be trained comprises a tunnel service performance evaluation index to be trained and a grade of the tunnel service performance to be trained;
the sample acquisition module 31 includes:
the evaluation index acquisition sub-module is used for acquiring the evaluation index of the service performance of the tunnel to be trained;
the grading sub-module is used for grading the service performance evaluation indexes of the tunnels to be trained by using a K-Means clustering algorithm, and determining the service performance grades of the tunnels;
the model processing sub-module is used for inputting the tunnel service performance evaluation index to be trained and the plurality of tunnel service performance grades into the multi-granularity cascade forest model.
In one possible implementation, the ranking sub-module includes:
a vector acquisition unit for acquiring k initial centroid vectors;
the distance calculation unit is used for calculating the distance between each evaluation index and k initial centroid vectors in the evaluation indexes of the service performance of the tunnel to be trained and determining the minimum distance corresponding to each evaluation index;
the grade selecting unit is used for selecting the tunnel service performance grade corresponding to the minimum distance corresponding to each evaluation index as the tunnel service performance grade corresponding to each evaluation index;
and the grade determining unit is used for obtaining a plurality of tunnel service performance grades according to the tunnel service performance grades corresponding to each evaluation index when the k initial centroid vectors are not changed.
Fig. 4 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 4, the terminal 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The steps of the above-described embodiments of the method for evaluating the service performance of each tunnel are implemented when the processor 40 executes the computer program 42, for example, steps 101 to 104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules/units 31-34 shown in fig. 3.
By way of example, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to complete the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 42 in the terminal 4. For example, the computer program 42 may be split into the modules/units 31 to 34 shown in fig. 3.
The terminal 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The terminal 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the terminal 4 and is not intended to limit the terminal 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk provided on the terminal 4, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used to store computer programs and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the procedures in the methods of the above embodiments, or may be implemented by a computer program for instructing related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the methods of evaluating the service performance of each tunnel when executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (4)
1. The method for evaluating the service performance of the tunnel is characterized by comprising the following steps of:
obtaining a sample to be trained, and inputting the sample to be trained into a multi-granularity cascade forest model, wherein the sample to be trained comprises a tunnel service performance evaluation index to be trained and a grade of tunnel service performance to be trained, and the tunnel service performance evaluation index comprises: water leakage, breakage cracks, ballast bed diseases, sedimentation deformation and convergence deformation;
if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, dynamically weighting the decision trees to determine a corrected multi-granularity cascade forest model;
acquiring a service performance evaluation index of a tunnel to be tested;
determining a tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model;
wherein if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to a preset number, dynamically weighting the decision trees to determine a modified multi-granularity cascade forest model, comprising:
if the number of decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number, adjusting the voting weight of the decision trees;
if the voting weight of the decision tree is equal to a first preset weight, obtaining the corrected multi-granularity cascade forest model;
the adjusting the voting weight of the decision tree comprises:
calculating voting weights corresponding to all leaf nodes in the decision tree;
if the voting weights corresponding to all the leaf nodes in the decision tree are not equal to the second preset weights, the voting weights corresponding to each leaf node in the all the leaf nodes are adjusted until the voting weights corresponding to all the leaf nodes in the decision tree are equal to the second preset weights;
the obtaining the sample to be trained and inputting the sample to be trained into a multi-granularity cascade forest model comprises the following steps:
acquiring a tunnel service performance evaluation index to be trained;
grading the tunnel service performance evaluation indexes to be trained by using a K-Means clustering algorithm, and determining a plurality of tunnel service performance grades;
inputting the tunnel service performance evaluation index to be trained and the tunnel service performance grades into the multi-granularity cascade forest model;
the method for classifying the tunnel service performance evaluation indexes to be trained by using the K-Means clustering algorithm, and determining a plurality of tunnel service performance classes comprises the following steps:
obtaining k initial centroid vectors;
calculating the distance between each evaluation index of the tunnel service performance evaluation indexes to be trained and the k initial centroid vectors, and determining the minimum distance corresponding to each evaluation index;
selecting the tunnel service performance grade corresponding to the minimum distance corresponding to each evaluation index as the tunnel service performance grade corresponding to each evaluation index;
and when the k initial centroid vectors are not changed any more, obtaining the tunnel service performance levels according to the tunnel service performance levels corresponding to each evaluation index.
2. An apparatus for evaluating service performance of a tunnel, comprising:
the system comprises a sample acquisition module, a multi-granularity cascade forest model and a multi-granularity cascade forest model, wherein the sample acquisition module is used for acquiring a sample to be trained, the sample to be trained comprises a tunnel service performance evaluation index to be trained and a grade of tunnel service performance to be trained, and the tunnel service performance evaluation index comprises: water leakage, breakage cracks, ballast bed diseases, sedimentation deformation and convergence deformation;
the model correction module is used for dynamically weighting the decision trees if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number so as to determine a corrected multi-granularity cascade forest model;
the evaluation index acquisition module is used for acquiring the evaluation index of the service performance of the tunnel to be tested;
the evaluation result determining module is used for determining the tunnel service performance grade corresponding to the tunnel service performance evaluation index to be tested according to the tunnel service performance evaluation index to be tested and the corrected multi-granularity cascade forest model;
wherein, the model correction module includes: the first judging sub-module is used for adjusting the voting weight of the decision trees if the number of the decision trees in the forest of the multi-granularity cascade forest model is equal to the preset number; the second judging sub-module is used for obtaining the corrected multi-granularity cascade forest model if the voting weight of the decision tree is equal to the first preset weight; the first judging sub-module includes: the weight calculation unit is used for calculating voting weights corresponding to all leaf nodes in the decision tree; the weight adjusting unit is used for adjusting the voting weight corresponding to each leaf node in all the leaf nodes if the voting weight corresponding to all the leaf nodes in the decision tree is not equal to the second preset weight until the voting weight corresponding to all the leaf nodes in the decision tree is equal to the second preset weight;
the sample acquisition module includes: the evaluation index acquisition sub-module is used for acquiring the evaluation index of the service performance of the tunnel to be trained; the grading sub-module is used for grading the service performance evaluation indexes of the tunnels to be trained by using a K-Means clustering algorithm, and determining the service performance grades of the tunnels; the model processing submodule is used for inputting the tunnel service performance evaluation index to be trained and the plurality of tunnel service performance grades into a multi-granularity cascade forest model;
the grading submodule comprises: a vector acquisition unit for acquiring k initial centroid vectors; the distance calculation unit is used for calculating the distance between each evaluation index and k initial centroid vectors in the evaluation indexes of the service performance of the tunnel to be trained and determining the minimum distance corresponding to each evaluation index; the grade selecting unit is used for selecting the tunnel service performance grade corresponding to the minimum distance corresponding to each evaluation index as the tunnel service performance grade corresponding to each evaluation index; and the grade determining unit is used for obtaining a plurality of tunnel service performance grades according to the tunnel service performance grades corresponding to each evaluation index when the k initial centroid vectors are not changed.
3. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a method for evaluating tunnel service performance as claimed in claim 1 when the computer program is executed by the processor.
4. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a method for evaluating tunnel service performance according to claim 1.
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