CN115601005A - Intelligent auxiliary decision-making method and device for railway engineering equipment technical improvement and major repair establishment - Google Patents
Intelligent auxiliary decision-making method and device for railway engineering equipment technical improvement and major repair establishment Download PDFInfo
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Abstract
The application provides an intelligent auxiliary decision-making method and device for railway engineering equipment technical improvement and major repair establishment, wherein the method comprises the following steps: in order to determine whether a target device with a problem needs to perform technical revision, a target parameter of the target device is first acquired. And uploading the target parameters to an input target model obtained by training a sample containing the target parameters of the railway engineering equipment, and determining whether technical improvement and major repair are performed on the target equipment according to an output result of the target model. The method and the device for intelligently assisting in decision-making for technical improvement and major repair of the railway engineering equipment are used for judging whether the railway engineering equipment in use needs to establish the technical improvement and major repair or not so as to reasonably distribute maintenance expenses of the railway engineering equipment.
Description
Technical Field
The application relates to the field of technical improvement and overhaul establishment of railway engineering equipment, in particular to an intelligent auxiliary decision method and device for the technical improvement and overhaul establishment of the railway engineering equipment.
Background
With the increase of the operating mileage of the high-speed railway and the increase of the online operating time, the aging condition of the railway line equipment becomes more and more serious. Therefore, in order to ensure the safe operation of high-speed trains and general-speed trains, it is necessary to ensure that the railway work equipment along the railway is always in a stable and safe operation state.
In the related technology, aiming at the line section with more faults of railway engineering equipment, centralized maintenance and overhaul of the line equipment need to be organized and developed, and equipment diseases and hidden dangers appearing in the line are solved in time so as to ensure the operation safety of railway trains. The maintenance of railway engineering equipment requires a large amount of maintenance cost, and under the condition of limited maintenance cost, how to reasonably arrange the updating and the transformation of the equipment becomes a technical problem to be solved urgently.
Disclosure of Invention
The method and the device are used for judging whether the railway engineering equipment with problems needs to perform technical modification and major repair to reasonably distribute maintenance expenses of the railway engineering equipment.
The application provides an intelligent auxiliary decision-making method for railway engineering equipment technical improvement and major repair establishment, which comprises the following steps:
acquiring target parameters of target equipment; the target equipment is railway engineering equipment; inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating the times of technical improvement and overhaul of the equipment, maintaining the type of the equipment, operating time of the equipment, and disease problem information of the equipment, wherein at least one of the following steps: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of railway engineering equipment.
Optionally, the target model is trained based on the following steps: constructing a sample set based on target parameters of a plurality of railway engineering devices; and training the technological improvement and major modification intelligent judgment model by using the samples in the sample set, and optimizing the model parameters of the technological improvement and major modification intelligent judgment model by using a differential evolution algorithm.
Optionally, the constructing a sample set based on the target parameters of the plurality of railway service devices includes: under the condition that target railway business equipment has codes, acquiring target parameters of the target railway business equipment according to the equipment codes of the target railway business equipment; or under the condition that the target railway work equipment does not have the codes, carrying out interval division on the railway line according to preset railway mileage and acquiring target parameters of the target railway work equipment in a target interval; wherein the target railway service equipment is any one of the plurality of railway service equipment; and the target interval is a railway interval corresponding to the target railway work equipment.
Optionally, the training of the technical improvement major repair term intelligent discriminant model by using the samples in the sample set includes: inputting any target sample in the sample set into the technical improvement major repair establishment intelligent judgment model to obtain an output result; and the output result of the intelligent judgment model for the major improvement item is used for indicating whether the target sample is technically improved and the major improvement item is performed.
Optionally, the optimizing, by using a differential evolution algorithm, the model parameters of the technology major modification term intelligent discriminant model includes: in the process of training the technological improvement major repair intelligent judgment model by using the samples in the sample set, model parameters of the technological improvement major repair intelligent judgment model are optimized by using a differential evolution algorithm, so that the accuracy of an output result of the technological improvement major repair intelligent judgment model is improved.
Optionally, after the training of the major improvement intellectual discrimination model by using the samples in the sample set and the optimization of the model parameters of the major improvement intellectual discrimination model by using the differential evolution algorithm, the method further includes: and stopping the training of the technical improvement and major repair term intelligent judgment model to obtain the target model under the condition that the accuracy of the output result of the technical improvement and major repair term intelligent judgment model reaches a preset value.
Optionally, the inputting the target parameter into a target model and determining whether to make a technical improvement and major repair term for the target device according to an output result of the target model includes: acquiring historical item setting information under the condition that the output result of the target model indicates that technical improvement and major repair item setting are required to be carried out on the target equipment; determining standing cost of reference equipment which is the same as or similar to the target equipment in each railway bureau according to the historical standing information; determining the standing cost of the target equipment after weighted calculation according to the standing cost of each railway bureau on the reference equipment; wherein the weight of the standing charge of the reference equipment belonging to the same railway bureau as the target equipment is higher; the standing cost of the reference equipment is positively correlated with the target similarity; the target similarity is the similarity between the reference device and the target device.
The application also provides an intelligent auxiliary decision-making device for the technical improvement and overhaul establishment of railway engineering equipment, which comprises:
the acquisition module is used for acquiring target parameters of the target equipment; the target equipment is railway engineering equipment; the determining module is used for inputting the target parameters into a target model and determining whether technical improvement and major repair are performed on the target equipment according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating the times of technical improvement and overhaul of the equipment, maintaining the type of the equipment, operating time of the equipment, and disease problem information of the equipment, wherein at least one of the following steps: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
Optionally, the apparatus further comprises: the device comprises a sample construction module and a training module; the sample construction module is used for constructing a sample set based on target parameters of a plurality of railway engineering devices; the training module is used for training the technological improvement major repair intelligent discrimination model by using the samples in the sample set and optimizing the model parameters of the technological improvement major repair intelligent discrimination model by using a differential evolution algorithm.
Optionally, the sample construction module is specifically configured to, when a target railway business device has a code, obtain a target parameter of the target railway business device according to the device code of the target railway business device; the sample construction module is specifically used for carrying out interval division on the railway line according to preset railway mileage under the condition that the target railway business equipment does not have codes, and acquiring target parameters of the target railway business equipment in a target interval; wherein the target railway service equipment is any one of the plurality of railway service equipment; and the target interval is a railway interval corresponding to the target railway service equipment.
Optionally, the training module is specifically configured to input any target sample in the sample set into the technical improvement major repair term intelligent discrimination model to obtain an output result; and the output result of the intelligent judgment model for the major improvement item is used for indicating whether the target sample is technically improved and the major improvement item is performed.
Optionally, the training module is specifically configured to optimize, by using a differential evolution algorithm, a model parameter of the improved major repair intelligent discriminant model in a process of training the improved major repair intelligent discriminant model by using the samples in the sample set, so as to improve accuracy of an output result of the improved major repair intelligent discriminant model.
Optionally, the model training module is further configured to stop the training of the major repair term intelligent discrimination model to obtain the target model when the accuracy of the output result of the major repair term intelligent discrimination model reaches a preset value.
Optionally, the obtaining module is further configured to obtain historical item information when the output result of the target model indicates that technical improvement and major repair item establishment needs to be performed on the target device; the determining module is further configured to determine standing charge of reference equipment, which is the same as or similar to the target equipment, in each railway bureau according to the historical standing information; the determining module is further used for determining the standing cost of the target equipment after weighted calculation according to the standing cost of each railway bureau for the reference equipment; wherein the weight of the standing charge of the reference equipment belonging to the same railway bureau as the target equipment is higher; the standing cost of the reference equipment is positively correlated with the target similarity; the target similarity is the similarity between the reference device and the target device.
The present application further provides a computer program product comprising a computer program/instructions which, when executed by a processor, implement the steps of the method for intelligent aid decision making for technical improvement and overhaul of railway service equipment as described in any of the above.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the program, the steps of the method for intelligently assisting decision-making for technical improvement and major repair of railway engineering equipment are realized.
The present application further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method for intelligent aid decision-making for technical modification and overhaul of railway service equipment as described in any one of the above.
According to the intelligent auxiliary decision-making method and device for the technical improvement and major repair establishment of the railway engineering equipment, in order to judge whether the target equipment with problems needs to perform the technical improvement and major repair establishment, the target parameters of the target equipment are firstly obtained. And then, inputting the target parameters into a target model obtained by training based on a sample containing the target parameters of the railway engineering equipment, and determining whether to perform technical improvement and major repair on the target equipment according to an output result of the target model. Therefore, when the railway engineering equipment goes wrong, whether the equipment needs to be technically improved or not can be judged quickly and accurately, decision-making time is shortened, and maintenance cost can be saved to a certain extent.
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In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of an intelligent aid decision method for a technical improvement and major repair of railway engineering equipment provided by the present application;
FIG. 2 is a schematic flow chart of a model training process provided herein;
fig. 3 is a schematic structural diagram of an intelligent auxiliary decision-making device for a technical improvement and overhaul project of railway engineering equipment provided by the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application are capable of operation in sequences other than those illustrated or described herein, and that the terms "first," "second," etc. are generally used in a generic sense and do not limit the number of terms, e.g., a first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.
Aiming at the problems that the decision time for judging whether the railway engineering equipment needs to be technically improved and overhauled is long and the decision accuracy is low in the related technology, the embodiment of the application is conceived to construct an intelligent judgment model for the technically improved and overhauled item of the railway engineering equipment based on a neural network and a railway engineering equipment technically improved and overhauled item cost calculation and estimation algorithm based on the historical data of dynamic and static detection, maintenance inspection, overhaul scheme change, implementation plan and the like of a high-speed railway engineering track, help a high-speed railway/passenger special company, a railway bureau and a manager to decide whether to approve the technically improved and overhauled item of the railway engineering equipment or not, and solve the outstanding problems that the technically improved and overhauled item of the railway engineering equipment is repeatedly established, long-term maintenance is not changed, the cost has no unified standard and the like. On the one hand, the cost of equipment maintenance and reconstruction is saved, and on the other hand, the timeliness of railway engineering equipment updating and reconstruction can be ensured in an auxiliary guarantee manner.
The intelligent aid decision-making method for the technical improvement and major repair of railway engineering equipment provided by the embodiment of the present application is described in detail through specific embodiments and application scenarios thereof with reference to the attached drawings.
As shown in fig. 1, an intelligent aided decision-making method for a technical improvement and major repair of railway engineering equipment provided in an embodiment of the present application may include the following steps 101 and 102:
Wherein the target equipment is railway engineering equipment; the target parameters include: the method comprises the following steps of accumulating times of technical modification and overhaul of equipment, maintaining types of the equipment, operating time of the equipment, disease problem information of the equipment and at least one of the following items: the equipment aims at the detection large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment.
Illustratively, the target parameters must include the number of times of overhaul and overhaul of the equipment, the repair type of the equipment, the operation time of the equipment, and the fault problem information of the equipment. The target parameters can comprise one or more of detection large-value information of the equipment aiming at the track quality index, sedimentation change rate information of the equipment and maintenance specification information of the equipment.
In the embodiment of the present application, the target device is a railway service device having a problem on a railway line. The railway work equipment may include: track slabs, switches, rails, tracks, and the like. The technical improvement and overhaul comprises the following steps: technical improvement and equipment overhaul. Wherein, the technical improvement is used for upgrading and transforming the equipment; equipment overhaul is commonly used for equipment replacement.
It should be noted that the target parameters are used to describe relevant information of the railway work equipment, and different railway work equipment has corresponding target parameters. The device detects large-value information of a Track Quality Index (TQI) and is used for indicating relevant information of the Track Quality Index when the value of the Track Quality Index exceeds a preset threshold value.
Illustratively, the target parameters can be obtained by historical data of railway engineering overhaul projects, engineering inspection monitoring, question banks and the like.
And 102, inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment according to an output result of the target model.
The target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
For example, after the target information of the target device is acquired, the target information may be input into a target model, whether technical improvement and major repair are needed is predicted by using the target model, and whether technical improvement and major repair are needed is determined according to a result output by the target model.
Optionally, in the embodiment of the present application, in a case where it is determined that the technical modification and major repair of the target device need to be performed according to the output result of the target model, the royalty cost needs to be further determined.
After the step 102, the intelligent auxiliary decision-making method for the technical improvement and major repair of the railway engineering equipment provided by the embodiment of the present application may further include the following steps 103 to 105:
103, acquiring historical item setting information when the output result of the target model indicates that technical improvement and major repair item setting needs to be carried out on the target equipment.
And 104, determining standing charge of reference equipment which is the same as or similar to the target equipment in each railway bureau according to the historical standing information.
And 105, determining the standing cost of the target equipment after weighted calculation according to the standing cost of each railway bureau on the reference equipment.
Wherein the weight of the standing charge of the reference equipment belonging to the same railway bureau as the target equipment is higher; the standing cost of the reference equipment is positively correlated with the target similarity; the target similarity is the similarity between the reference device and the target device.
Illustratively, the historical standing information includes standing information of each railway bureau for other railway service equipment that is the same as or similar to the target equipment. And determining the project establishment cost of other railway engineering equipment according to the project establishment information of other railway engineering equipment, and taking the project establishment cost as a reference basis for determining the project establishment cost of the target equipment.
Illustratively, after weighted average is carried out on the standing money amount of each railway bureau for the reference equipment, the weighted average value of the standing money amount of each railway bureau for the reference equipment is obtained and is used as the standing money amount reference basis of the target equipment.
Optionally, in this embodiment of the present application, the target model may be obtained through the following training steps.
Illustratively, as shown in fig. 2, the intelligent aided decision-making method for a technical improvement and major repair of railway engineering equipment provided in the embodiment of the present application may further include the following steps 201 and 202:
Illustratively, device codes, interval positions, the number of times of technical improvement and overhaul, maintenance types, operating time, disease problem information, detection large value information, settlement change rate information, maintenance specification information (i.e., target parameters of devices), and the like are acquired as training samples, and one device corresponds to one training sample.
Illustratively, since the railway service equipment is divided into coding equipment and non-coding equipment, the target parameters of the equipment need to be acquired in different ways for different types of equipment.
For example, the samples in the sample set may be divided into a training set and a test set according to a preset ratio. For example, the ratio of training set to test set is 7.
Specifically, the step 201 may include the following step 201a or step 201b:
step 201a, under the condition that the target railway business equipment has the code, acquiring the target parameter of the target railway business equipment according to the equipment code of the target railway business equipment.
And step 201b, under the condition that the target railway work equipment does not have the codes, carrying out interval division on the railway line according to preset railway mileage, and acquiring target parameters of the target railway work equipment in a target interval.
Wherein the target railway work equipment is any one of the plurality of railway work equipment; and the target interval is a railway interval corresponding to the target railway work equipment.
For example, for the encoding device, the target information of the device may be obtained according to the device encoding; for the non-coding device, the railway line may be divided into a plurality of sections according to a preset length (i.e., the preset railway mileage, which may be 200 meters, for example), and the target parameter of the non-coding device in each section may be used as a sample.
It is to be understood that the above-described encoding apparatus may include: the track plate, the turnout, and the like, the non-coding device may include: rails, foundations, and the like. Since the non-coding device has a long length or a non-uniform length, when the target parameter is used as a sample for training the model, the target parameter may have an adverse effect on the prediction result of the model.
For example, after the technical major repair term intelligent discriminant model and the sample set are constructed, the training samples can be input into the technical major repair term intelligent discriminant model for training the model.
Specifically, the step 202 may include the following steps 202a:
step 202a, inputting any target sample in the sample set into the technical improvement major repair term intelligent judgment model to obtain an output result.
Wherein, the output result of the technical improvement and major repair term intelligent judgment model is used for indicating whether the target sample is subjected to the technical improvement and major repair term.
Illustratively, the output result of the above-mentioned technical improvement and major repair intelligent judgment model is whether the input railway engineering equipment has performed technical improvement and major repair in the current year.
Illustratively, in the process of training the technical improvement and major repair term intelligent judgment model, the model parameters also need to be continuously adjusted to improve the accuracy of the model prediction result.
Specifically, based on the step 202a, the step 202 may further include the following step 202b:
202b, in the process of training the technical improvement major repair intelligent judgment model by using the samples in the sample set, optimizing model parameters of the technical improvement major repair intelligent judgment model by using a differential evolution algorithm so as to improve the accuracy of the output result of the technical improvement major repair intelligent judgment model.
For example, in the embodiment of the present application, a differential evolution algorithm may be used to optimize the model parameters of the technical improvement and major repair term intelligent discriminant model, so as to improve the accuracy of the output result of the technical improvement and major repair term intelligent discriminant model.
For example, in the training process of the major repair improvement intelligent discrimination model, if a preset termination condition is reached, the training process of the major repair intelligent discrimination model may be terminated, and the trained major repair intelligent discrimination model is used as the target model.
After the step 202, the intelligent auxiliary decision-making method for the technical improvement and major repair of the railway engineering equipment provided by the embodiment of the present application may further include the following step 203:
and 203, stopping training of the technical improvement and major repair term intelligent judgment model to obtain the target model under the condition that the accuracy of the output result of the technical improvement and major repair term intelligent judgment model reaches a preset value.
It should be noted that the target model is obtained by training a technical improvement and major modification intelligent judgment model. Namely, the technical improvement major repair term intelligent discrimination model is an original model, and the target model is a model obtained after training. The model parameters of the technical improvement major repair term intelligent discrimination model and the target model are different.
For example, when the target device needs to be technically improved and major-repair found by using the target model and is finally technically improved and major-repair found according to the prediction result of the target model, the found information of the target device may be supplemented to the training set.
For example, after the sample increment in the training set satisfies the preset increment threshold, the model parameters of the target model may be adjusted based on the updated training set.
In the intelligent auxiliary decision-making method for establishing the technical improvement and overhaul project of the railway engineering equipment, in order to judge whether the target equipment with problems needs to be established by technical improvement and overhaul, the target parameters of the target equipment are firstly obtained. And then, inputting the target parameters into a target model obtained by training based on a sample containing the target parameters of the railway engineering equipment, and determining whether to perform technical improvement and major repair on the target equipment according to an output result of the target model. Therefore, when the railway engineering equipment goes wrong, whether the equipment needs to be technically improved or not can be judged quickly and accurately, decision-making time is shortened, and maintenance cost can be saved to a certain extent.
It should be noted that, in the intelligent auxiliary decision-making method for railroad engineering equipment major repair establishment provided in the embodiment of the present application, the execution main body may be an intelligent auxiliary decision-making device for railroad engineering equipment major repair establishment, or a control module in the intelligent auxiliary decision-making device for railroad engineering equipment major repair establishment, for executing the intelligent auxiliary decision-making method for railroad engineering equipment major repair establishment. The embodiment of the present application takes an example where an intelligent auxiliary decision-making device for railway engineering equipment technical improvement and major repair setting executes an intelligent auxiliary decision-making method for railway engineering equipment technical improvement and major repair setting, and illustrates the intelligent auxiliary decision-making device for railway engineering equipment technical improvement and major repair setting provided in the embodiment of the present application.
In the embodiments of the present application, the above-described methods are illustrated in the drawings. The technical improvement and major repair item intelligent assistant decision-making method for railway engineering equipment is exemplarily described by combining one drawing in the embodiment of the application. In specific implementation, the railway engineering equipment technical improvement and major repair item intelligent assistant decision-making method shown in the above method drawings can also be implemented by combining with any other drawing which can be combined and is illustrated in the above embodiments, and details are not repeated here.
The following describes the intelligent auxiliary decision-making device for the technical improvement and major repair of railway engineering equipment provided by the present application, and the following description and the above-described intelligent auxiliary decision-making method for the technical improvement and major repair of railway engineering equipment can be referred to correspondingly.
Fig. 3 is a schematic structural diagram of an intelligent auxiliary decision-making device for a railroad engineering equipment technical improvement and major repair establishment according to an embodiment of the present application, and as shown in fig. 3, the intelligent auxiliary decision-making device specifically includes:
an obtaining module 301, configured to obtain a target parameter of a target device; the target equipment is railway engineering equipment; a determining module 302, configured to input the target parameter into a target model, and determine whether to perform a technical modification or major repair on the target device according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating times of technical modification and overhaul of equipment, maintaining types of the equipment, operating time of the equipment, disease problem information of the equipment and at least one of the following items: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
Optionally, the apparatus further comprises: a sample construction module and a training module; the sample construction module is used for constructing a sample set based on target parameters of a plurality of railway engineering devices; the training module is used for training the technological improvement major repair intelligent discrimination model by using the samples in the sample set and optimizing the model parameters of the technological improvement major repair intelligent discrimination model by using a differential evolution algorithm.
Optionally, the sample building module is specifically configured to, when a target railway work equipment has a code, obtain a target parameter of the target railway work equipment according to the equipment code of the target railway work equipment; the sample construction module is specifically used for carrying out interval division on the railway line according to preset railway mileage under the condition that the target railway business equipment does not have codes, and acquiring target parameters of the target railway business equipment in a target interval; wherein the target railway service equipment is any one of the plurality of railway service equipment; and the target interval is a railway interval corresponding to the target railway service equipment.
Optionally, the training module is specifically configured to input any target sample in the sample set into the technical improvement major repair term intelligent discrimination model to obtain an output result; and the output result of the intelligent judgment model for the major improvement item is used for indicating whether the target sample is technically improved and the major improvement item is performed.
Optionally, the training module is specifically configured to optimize, by using a differential evolution algorithm, a model parameter of the improved major repair intelligent discriminant model in a process of training the improved major repair intelligent discriminant model by using the samples in the sample set, so as to improve accuracy of an output result of the improved major repair intelligent discriminant model.
Optionally, the model training module is further configured to stop the training of the major repair term intelligent discrimination model to obtain the target model when the accuracy of the output result of the major repair term intelligent discrimination model reaches a preset value.
Optionally, the obtaining module 301 is further configured to obtain historical item setting information when the output result of the target model indicates that technical modification and major repair item setting needs to be performed on the target device; the determining module 302 is further configured to determine standing charge of a reference device, which is the same as or similar to the target device, in each railway bureau according to the historical standing information; the determining module 302 is further configured to determine the standing cost of the target device after weighted calculation according to the standing cost of each railway bureau for the reference device; wherein the weight of the standing charge of the reference equipment belonging to the same railway bureau as the target equipment is higher; the standing cost of the reference equipment is positively correlated with the target similarity; the target similarity is the similarity between the reference device and the target device.
The application provides an intelligent auxiliary decision-making device for establishing technical improvement and overhaul items of railway engineering equipment, which is used for judging whether a target device with a problem needs to be established by technical improvement and overhaul or not, and firstly, acquiring target parameters of the target device. And then, inputting the target parameters into a target model obtained by training based on a sample containing the target parameters of the railway engineering equipment, and determining whether to perform technical improvement and major repair on the target equipment according to an output result of the target model. Therefore, when the railway engineering equipment goes wrong, whether the equipment needs to be technically improved or not can be judged quickly and accurately, decision-making time is shortened, and maintenance cost can be saved to a certain extent.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a railroad work equipment mechanic repair decision intelligent aid method comprising: acquiring target parameters of target equipment; the target equipment is railway engineering equipment; inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment or not according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating times of technical modification and overhaul of equipment, maintaining types of the equipment, operating time of the equipment, disease problem information of the equipment and at least one of the following items: the equipment aims at the detection large value information of the track quality index, the settlement change rate information of the equipment and the maintenance specification information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present application further provides a computer program product, which includes a computer program stored on a computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the method for intelligent aid decision-making for railroad work equipment technical modification major repair provided by the above methods, the method including: acquiring target parameters of target equipment; the target equipment is railway engineering equipment; inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating the times of technical improvement and overhaul of the equipment, maintaining the type of the equipment, operating time of the equipment, and disease problem information of the equipment, wherein at least one of the following steps: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
In yet another aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the above-mentioned provided railway work equipment technical modification major repair intelligent aid decision method, the method comprising: acquiring target parameters of target equipment; the target equipment is railway engineering equipment; inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment or not according to an output result of the target model; wherein the target parameters include: the method comprises the following steps of accumulating the times of technical improvement and overhaul of the equipment, maintaining the type of the equipment, operating time of the equipment, and disease problem information of the equipment, wherein at least one of the following steps: the equipment aims at the detection large value information of the track quality index, the settlement change rate information of the equipment and the maintenance specification information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An intelligent auxiliary decision-making method for establishing a technical improvement and major repair project of railway engineering equipment is characterized by comprising the following steps:
acquiring target parameters of target equipment; the target equipment is railway engineering equipment;
inputting the target parameters into a target model, and determining whether technical improvement and major repair are performed on the target equipment or not according to an output result of the target model;
wherein the target parameters include: the method comprises the following steps of accumulating times of technical modification and overhaul of equipment, maintaining types of the equipment, operating time of the equipment, disease problem information of the equipment and at least one of the following items: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
2. The method of claim 1, wherein the target model is trained based on the following steps:
constructing a sample set based on target parameters of a plurality of railway engineering devices;
and training the technical improvement and major repair standing intelligent discrimination model by using the samples in the sample set, and optimizing the model parameters of the technical improvement and major repair standing intelligent discrimination model by using a differential evolution algorithm.
3. The method of claim 2, wherein constructing the sample set based on the target parameters of the plurality of railroad work devices comprises:
under the condition that target railway business equipment has codes, acquiring target parameters of the target railway business equipment according to the equipment codes of the target railway business equipment;
alternatively, the first and second electrodes may be,
under the condition that the target railway work equipment does not have codes, carrying out interval division on a railway line according to preset railway mileage, and acquiring target parameters of the target railway work equipment in a target interval;
wherein the target railway service equipment is any one of the plurality of railway service equipment; and the target interval is a railway interval corresponding to the target railway service equipment.
4. The method of claim 2, wherein the training of the crafting major repair intelligent discriminant model using the samples in the set of samples comprises:
inputting any target sample in the sample set into the technological improvement major repair term intelligent judgment model to obtain an output result; and the output result of the intelligent judgment model for the major improvement item is used for indicating whether the target sample is technically improved and the major improvement item is performed.
5. The method of claim 4, wherein the optimizing model parameters of the technical major improvement term intelligent discriminant model using a differential evolution algorithm comprises:
in the process of training the technological improvement major repair intelligent judgment model by using the samples in the sample set, model parameters of the technological improvement major repair intelligent judgment model are optimized by using a differential evolution algorithm, so that the accuracy of an output result of the technological improvement major repair intelligent judgment model is improved.
6. The method of claim 5, wherein after training the crafting major intelligence discriminant model using the samples in the sample set and optimizing model parameters of the crafting major intelligence discriminant model using a differential evolution algorithm, the method further comprises:
and stopping the training of the technical improvement and major repair term intelligent judgment model to obtain the target model under the condition that the accuracy of the output result of the technical improvement and major repair term intelligent judgment model reaches a preset value.
7. The method of claim 1, wherein inputting the target parameters into a target model and determining whether to make a technical revision decision for the target device according to an output of the target model comprises:
acquiring historical item establishment information under the condition that the output result of the target model indicates that technical modification and major repair item establishment needs to be carried out on the target equipment;
determining the standing cost of reference equipment which is the same as or similar to the target equipment in each railway bureau according to the historical standing information;
determining the standing cost of the target equipment after weighted calculation according to the standing cost of each railway bureau on the reference equipment;
wherein the weight of the standing charge of the reference equipment belonging to the same railway bureau as the target equipment is higher; the standing cost of the reference equipment is positively correlated with the target similarity; the target similarity is the similarity between the reference device and the target device.
8. An intelligent auxiliary decision-making device for railway engineering equipment technical improvement and major repair establishment, which is characterized by comprising:
the acquisition module is used for acquiring target parameters of the target equipment; the target equipment is railway engineering equipment;
the determining module is used for inputting the target parameters into a target model and determining whether technical improvement and major repair are performed on the target equipment or not according to an output result of the target model;
wherein the target parameters include: the method comprises the following steps of accumulating times of technical modification and overhaul of equipment, maintaining types of the equipment, operating time of the equipment, disease problem information of the equipment and at least one of the following items: the equipment detects the large value information of the track quality index, the settlement change rate information of the equipment and the maintenance standard information of the equipment; the target model is obtained by training based on a sample containing target parameters of the railway engineering equipment.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for intelligent aid decision making for technical improvement and major repair of railroad work equipment as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method for intelligently assisting decision making in technical improvement and major repair of railway working equipment according to any one of claims 1 to 7.
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| CN116895338B (en) * | 2023-08-08 | 2024-02-20 | 盐城师范学院 | Method and system for improving dendrimer research model |
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