CN116151788A - Method, device, equipment and storage medium for classifying electromechanical equipment in tunnel - Google Patents
Method, device, equipment and storage medium for classifying electromechanical equipment in tunnel Download PDFInfo
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Abstract
The application relates to the technical field of tunnel electromechanical device processing, and provides a method, a device, equipment and a storage medium for classifying electromechanical devices in tunnels. According to the method, the electromechanical equipment is calibrated from multiple dimensions to obtain corresponding multidimensional index values, so that fine classification of the electromechanical equipment is realized; mainly comprises the following steps: calibrating each electromechanical device according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the failure of the functions of the electromechanical device, the maintenance difficulty and the failure frequency of the electromechanical device, and the maintenance cost and the purchase price of the electromechanical device to obtain a multidimensional index value of each electromechanical device; based on the multidimensional index values of the electromechanical equipment, obtaining a plurality of multidimensional index value difference values of the electromechanical equipment; determining a distance between every two electromechanical devices based on a sum of a plurality of co-dimensional index value differences of every two electromechanical devices; and classifying the importance degree of each electromechanical device according to the distance between every two electromechanical devices.
Description
Technical Field
The present disclosure relates to the field of processing technologies of electromechanical devices in tunnels, and in particular, to a method and an apparatus for classifying electromechanical devices in tunnels, a computer device, and a storage medium.
Background
In a immersed tunnel, electromechanical equipment is more specialized and various, has large running environment difference and different state information, and part of the electromechanical equipment needs 24 hours of all-weather running; in order to carry out safety detection on electromechanical equipment in a tunnel and ensure driving safety in the tunnel, the electromechanical equipment needs to be managed and maintained. The key of accurate management and maintenance is the fine classification of electromechanical equipment; however, the conventional manner is to classify the electromechanical devices according to their power levels, which is relatively rough.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, and a storage medium for classifying electromechanical devices in a tunnel.
A method of electromechanical device classification within a tunnel, the method comprising:
calibrating each electromechanical device according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the failure of the functions of the electromechanical device, the maintenance difficulty and the failure frequency of the electromechanical device, and the maintenance cost and the purchase price of the electromechanical device to obtain a multidimensional index value of each electromechanical device;
based on the multidimensional index values of the electromechanical equipment, obtaining a plurality of multidimensional index value difference values of the electromechanical equipment;
determining the distance between the two electromechanical devices based on the sum of the difference values of the plurality of the same-dimensional index values of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices;
and classifying the importance degree of the electromechanical devices according to the distance between every two electromechanical devices.
In one of the embodiments of the present invention,
the greater the influence of the failure of the electromechanical equipment on the traffic safety of the vehicles in the tunnel, the higher the traffic safety risk of the vehicles in the tunnel;
the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency event, the higher the emergency rescue risk.
In one embodiment, the method further comprises:
obtaining maintenance difficulty of the electromechanical equipment according to the product of the maintenance level and the maintenance price of the electromechanical equipment;
obtaining the maintenance cost of the electromechanical equipment according to the product of the maintenance frequency and the maintenance price of the electromechanical equipment;
and determining the fault frequency of the electromechanical equipment according to the operation observation time length of the electromechanical equipment and the relative size of the fault time length of the electromechanical equipment in the operation observation time length.
In one embodiment, the mechatronic devices are pre-divided into a plurality of first mechatronic devices and a plurality of second mechatronic devices;
the classifying the importance degree of each electromechanical device according to the distance between every two electromechanical devices comprises the following steps:
determining a second electromechanical device closest to any first electromechanical device in the plurality of second electromechanical devices aiming at the any first electromechanical device, and obtaining the shortest distance corresponding to the any first electromechanical device;
determining an average value of the shortest distances corresponding to the first electromechanical devices;
if the average value is smaller than or equal to the preset distance, each first electromechanical device is classified into an electromechanical device cluster taking the second nearest electromechanical device of the first electromechanical device as a clustering center;
the similarity degree of the importance of two electromechanical devices respectively belonging to different electromechanical device clusters is smaller than that of the importance of two electromechanical devices belonging to the same electromechanical device cluster.
In one embodiment, one of the two mechatronic devices is a first mechatronic device and the other is a second mechatronic device.
In one embodiment, the obtaining the difference value of the multiple multidimensional index values of the electromechanical device based on the multidimensional index values of the electromechanical device includes:
determining index values of the two-two electromechanical devices in the same dimension from the multi-dimension index values of the two-two electromechanical devices;
taking the difference value of the index values in the same dimension as the difference value of the index values in the same dimension, and obtaining a plurality of difference values of the index values in the same dimension corresponding to the multiple dimensions.
An electromechanical device classification apparatus within a tunnel, the apparatus comprising:
the electromechanical equipment calibration module is used for calibrating each electromechanical equipment according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the functional failure of the electromechanical equipment, the maintenance difficulty and the failure frequency of the electromechanical equipment, and the maintenance cost and the purchase price of the electromechanical equipment to obtain the multidimensional index value of each electromechanical equipment;
the index value difference value acquisition module is used for acquiring a plurality of same-dimensional index value differences of the electromechanical equipment based on the multi-dimensional index values of the electromechanical equipment;
the distance determining module is used for determining the distance between the two electromechanical devices based on the sum of a plurality of co-dimensional index value differences of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices;
and the electromechanical equipment classification module is used for classifying the importance degree of each electromechanical equipment according to the distance between every two electromechanical equipment.
In one embodiment, the greater the effect of the failure of the electromechanical device on the traffic safety in the tunnel, the higher the traffic safety risk in the tunnel;
the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency event, the higher the emergency rescue risk.
A computer device comprising a memory storing a computer program and a processor implementing the method described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method described above.
According to the method, the device, the computer equipment and the storage medium for classifying the electromechanical equipment in the tunnel, the electromechanical equipment is calibrated according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the functional failure of the electromechanical equipment, the maintenance difficulty and the failure frequency of the electromechanical equipment, and the maintenance cost and the purchase price of the electromechanical equipment, so that the multidimensional index value of each electromechanical equipment is obtained; based on the multidimensional index values of the electromechanical equipment, obtaining a plurality of multidimensional index value difference values of the electromechanical equipment; determining the distance between the two electromechanical devices based on the sum of the difference values of the plurality of the same-dimensional index values of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices; and classifying the importance degree of the electromechanical devices according to the distance between every two electromechanical devices. In the method, the electromechanical equipment is calibrated according to multiple dimensions (such as the traffic safety risk and emergency rescue risk of vehicles in a tunnel caused by the functional failure of the electromechanical equipment, the maintenance difficulty and the fault frequency of the electromechanical equipment, the maintenance cost and the purchase price of the electromechanical equipment) to obtain a multi-dimensional index value, the distance between every two electromechanical equipment is determined based on the sum of multiple same-dimensional index value differences of every two electromechanical equipment, the importance similarity degree of the electromechanical equipment is classified according to the distance, the electromechanical equipment is finely classified, and the fine management and the maintenance of the electromechanical equipment in the tunnel are ensured.
Drawings
FIG. 1 is a flow diagram of a method of classifying electromechanical devices within a tunnel in one embodiment;
FIG. 2 is a flow diagram of a method of classifying electromechanical devices within a tunnel in one embodiment;
FIG. 3 is a flow diagram of a method of classifying electromechanical devices within a tunnel in one embodiment;
FIG. 4 is a block diagram of an electromechanical device classification apparatus within a tunnel in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.
The method for classifying electromechanical devices in a tunnel, which is provided by the application, can be applied to computer devices, and is described below with reference to fig. 1.
Step S101, calibrating each electromechanical device according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the functional failure of the electromechanical device, the maintenance difficulty and the failure frequency of the electromechanical device, and the maintenance cost and the purchase price of the electromechanical device, and obtaining the multidimensional index value of each electromechanical device.
The step S101 is described below in terms of both the division index of each dimension of the electromechanical device and the calibration of the electromechanical device.
First aspect: each dimension index of the electromechanical device:
according to electromechanical equipment management in the relevant norms of highway industry, in combination with the condition of tunnel electromechanical equipment operation management, comprehensively consider the influence on the electromechanical equipment on the driving safety, the influence on the emergency rescue, the driving comfort, the operation cost and other factors in the tunnel operation process, extract the indexes affecting the normal operation of the tunnel from different dimensions, and comprise: (1) a functional index of the electromechanical device, (2) a maintenance index of the electromechanical device, and (3) an economic index of the electromechanical device.
The index (1) to (3) is subdivided according to the specific content of the index.
(1) Function index of electromechanical device
The function indexes comprise the traffic safety risk (expressed by vdr) and emergency rescue risk (expressed by err) of the vehicles in the tunnel; the larger the influence of the electromechanical equipment on the driving safety risk and the emergency rescue risk is, the higher the importance of the electromechanical equipment is, and the more the state evaluation should be carried out;
(2) Maintenance index for electromechanical devices
Maintenance metrics include maintenance difficulty (expressed in dem) and failure frequency (expressed in eff) of the electromechanical device. Comprehensively considering that the faults of the electromechanical equipment basically accord with the bathtub curve during the operation, the electromechanical equipment is more easily damaged and is easily broken in daily operation, and the maintenance difficulty is higher, so that the electromechanical equipment possibly has important influence on the functions of the electromechanical equipment in the operation process, and the service state of the electromechanical equipment is estimated;
(3) Economic index of electromechanical device
Economic indicators include maintenance costs (indicated by emc) and purchase prices (indicated by pe) for the electromechanical device; the higher the purchase price of the electromechanical equipment is, the higher the economic cost of the electromechanical equipment is for operation, and the higher the maintenance difficulty and the maintenance price are required, so that the state evaluation is required for ensuring the safe and economic operation of the electromechanical equipment.
Second aspect: calibrating electromechanical equipment:
in the running process of the electromechanical equipment, the factors of functional failure, maintenance difficulty and self faults of the electromechanical equipment can influence the safe operation of the tunnel, and the influence of the state changes of the electromechanical equipment with different degrees on the tunnel operation is different. Thus, calibration of the electromechanical device for different states is required from different indicators.
(1) Calibrating the electromechanical equipment according to the influence of the electromechanical equipment in the tunnel on the traffic safety risk vdr and the emergency rescue risk err of the vehicle in the tunnel, as shown in table 1;
table 1 calibration of content and results according to failure of function of electromechanical device
Based on the table 1, it can be determined that the greater the influence of the function failure of the electromechanical device on the traffic safety in the tunnel is, the higher the traffic safety risk in the tunnel is, and the greater the corresponding index value (i.e. the calibration result) is; the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency, the higher the emergency rescue risk, and the greater the corresponding index value (namely the calibration result).
(2) And acquiring the maintenance grade, the maintenance frequency and the maintenance price of the electromechanical equipment in the tunnel according to the management record of the electromechanical equipment of the tunnel operation maintenance unit. According to the maintenance level and the maintenance price in a certain time, acquiring the equipment maintenance difficulty dem, wherein the expression of the equipment maintenance difficulty dem is dem=l×q; where l represents the maintenance level of the electromechanical device and q represents the maintenance price of the electromechanical device. That is, the maintenance difficulty of the electromechanical device is obtained from the product of the maintenance level and the maintenance price of the electromechanical device.
Acquiring maintenance cost emc of the electromechanical equipment according to the maintenance frequency and the maintenance price in a certain time, wherein the expression is emc =f×q; where f represents the maintenance frequency of the electromechanical device. That is, the maintenance cost of the electromechanical device is obtained from the product of the maintenance frequency and the maintenance price of the electromechanical device.
(3) Acquiring the purchase price pe of the tunnel electromechanical equipment in a certain time according to the management record of the electromechanical equipment spare parts of the tunnel operation maintenance unit, and calibrating the purchase price of the electromechanical equipment according to the purchase price; illustratively, pe=1 when the purchase price of the electromechanical device is less than 0.5 ten thousand yuan; pe=2 when the purchase price is more than 0.5 ten thousand yuan and less than 2 ten thousand yuan; pe=3 when the purchase price is more than 2 ten thousand yuan and less than 5 ten thousand yuan; the purchase price is more than 5 ten thousand yuan and less than 10 ten thousand hours, pe=4; when the purchase price exceeds 10 ten thousand yuan, pe=5.
According to the running condition of the electromechanical equipment in a period of time, combining the number of days of the electromechanical equipment failure, acquiring equipment failure frequency eff, wherein the expression of the equipment failure frequency eff is eff=t/T; wherein T represents the number of days for observing the operation of the electromechanical device, and T represents the number of days for failure of the electromechanical device in the number of days for observation. That is, the frequency of failure of the electromechanical device is derived from the operational observation period of the electromechanical device and the relative magnitude of the failure period of the electromechanical device within the operational observation period.
Step S102, obtaining a plurality of same-dimensional index value difference values of the electromechanical equipment based on the multi-dimensional index values of the electromechanical equipment.
Specifically, the computer equipment determines index values of the two-two electromechanical equipment in the same dimension from the multi-dimension index values of the two-two electromechanical equipment; taking the difference value of the index values in the same dimension as the difference value of the index values in the same dimension, and obtaining a plurality of difference values of the index values in the same dimension corresponding to the multiple dimensions.
Step S103, determining the distance between the two electromechanical devices based on the sum of a plurality of co-dimensional index value differences of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices.
The calibration value of the electromechanical device under the index of multiple dimensions can be called as a multidimensional index value, and can be the calibration value under the index of the dimensions of the traffic safety risk, the emergency rescue risk, the maintenance difficulty, the failure frequency, the maintenance cost and the purchase price of the vehicle in the tunnel.
For electromechanical devices O i And N j The difference in index values of the two electromechanical devices in the same dimension (such as the safety risk of vehicle traffic in a tunnel) may be referred to as the same-dimensional index value difference, and thus, in multiple dimensions, the electromechanical device O i And N j Corresponding to a plurality of co-dimensional index value differences.
Illustratively, the expression for the distance between two electromechanical devices is:
And step S104, classifying the importance degree of the electromechanical devices according to the distance between every two electromechanical devices.
In the method for classifying the electromechanical devices in the tunnel, the electromechanical devices are calibrated according to multiple dimensions (such as the passing safety risk and emergency rescue risk of vehicles in the tunnel caused by the functional failure of the electromechanical devices, the maintenance difficulty and the failure frequency of the electromechanical devices, and the maintenance cost and the purchase price of the electromechanical devices), so as to obtain multi-dimensional index values, the distance between every two electromechanical devices is determined based on the sum of the multiple same-dimensional index value differences of every two electromechanical devices, the importance similarity degree of the electromechanical devices is classified according to the distance, the fine classification of the electromechanical devices is realized, and the fine management and maintenance of the electromechanical devices in the tunnel are ensured.
In one embodiment, the mechatronic devices are pre-divided into a plurality of first mechatronic devices (denoted by O) and a plurality of second mechatronic devices (denoted by N). As shown in fig. 2, when the computer device performs step S104, the following steps may be specifically performed: step S201, determining, for any first electromechanical device, a second electromechanical device closest to the any first electromechanical device in the plurality of second electromechanical devices, to obtain a shortest distance corresponding to the any first electromechanical device; step S202, determining an average value of the shortest distances corresponding to the first electromechanical devices; step S203, if the average value is smaller than or equal to the preset distance, each first electromechanical device is classified into an electromechanical device cluster taking the second nearest electromechanical device of the first electromechanical device as a clustering center; the similarity degree of the importance of two electromechanical devices respectively belonging to different electromechanical device clusters is smaller than that of the importance of two electromechanical devices belonging to the same electromechanical device cluster.
Wherein the shortest distance between each first mechatronic device and the respective nearest second mechatronic device of the first mechatronic device may be characterized by the following expression: d (O) i ，N）＝min{d(O i ，N j ) J e (1, 2,., k); after obtaining the shortest distance corresponding to each first electromechanical device, calculating the average value of the shortest distances, wherein the corresponding expression is
D avg Mean (may be referred to as cluster result dissimilarity); i is the number of electromechanical devices; if the average value is smaller than the preset distance, determining that each second electromechanical device can be used as an optimal clustering center, classifying each first electromechanical device into an electromechanical device cluster with the nearest second electromechanical device of the first electromechanical device as the clustering center, and outputting a corresponding clustering result.
In the above embodiment, the cluster analysis is performed on the electromechanical devices according to the multidimensional index values of the electromechanical devices, so that the electromechanical devices with larger importance similarity are classified into one type, so that the distance between the electromechanical devices with smaller importance similarity is as large as possible, and the distance between the electromechanical devices with larger importance similarity is as small as possible, thereby realizing the classification of the importance similarity of the electromechanical devices.
The clustering process described above is described below in conjunction with FIG. 3; the input of the cluster is: an electromechanical device index data set D comprising a multi-dimensional index value of the electromechanical device; the number of clusters k and the number of iterations m.
Step S301, randomly extracting k groups of data from the electromechanical facility index data set D as a clustering center [ N ] 1 ，N 2 ，...，N k ] T ；
Step S302, calculating non-center points O in the data set D i And a center point N j Classifying the non-center point data into center points according to the distance between the non-center point data and the center point data; that is, to increase the clustering efficiency, only the distance between the first mechatronic device and the second mechatronic device may be calculated, so that in step S103, one of the mechatronic devices is the first mechatronic device and the other mechatronic device is the second mechatronic device.
Step S303, calculating the distance between the non-center points in each category and the center points of the category, and calculating the average value of the distances corresponding to the non-center points to obtain the dissimilarity degree of the clustering results;
step S304, if the dissimilarity degree of the clustering result is smaller than the preset minimum value, replacing the current minimum value with the value, returning to the step S301 to iterate again until the iteration is finished, otherwise, storing k clustering centers as the optimal centers of the clusters; the condition for ending the iteration is that the dissimilarity degree of the clustering result is smaller than a preset minimum value or the iteration number reaches m;
and step S305, after the iteration is finished, outputting an optimal electromechanical device clustering result.
In the mode, the electromechanical equipment is calibrated according to the multiple dimensions to obtain the multi-dimensional index value, the distance between every two electromechanical equipment is determined based on the sum of the difference values of the multiple same-dimensional index values of every two electromechanical equipment, the electromechanical equipment is classified according to the distance to the degree of importance similarity, the electromechanical equipment is finely classified, the electromechanical equipment in a tunnel is finely maintained, and the method has important significance in reducing the potential safety risk of a immersed tunnel.
It should be understood that, although the steps in the flowcharts of fig. 1 to 3 are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of fig. 1-3 may include steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in other steps.
In one embodiment, as shown in fig. 4, there is provided an electromechanical device classification apparatus within a tunnel, comprising:
the electromechanical device calibration module 401 is configured to calibrate each electromechanical device according to a traffic safety risk and an emergency rescue risk of the vehicle in the tunnel caused by a functional failure of the electromechanical device, a maintenance difficulty and a failure frequency of the electromechanical device, and a maintenance cost and a purchase price of the electromechanical device, so as to obtain a multidimensional index value of each electromechanical device;
an index value difference obtaining module 402, configured to obtain a plurality of co-dimensional index value differences of two-to-two electromechanical devices based on a multi-dimensional index value of the electromechanical device;
a distance determining module 403, configured to determine a distance between the two electromechanical devices based on a sum of a plurality of co-dimensional index value differences of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices;
and the electromechanical device classification module 404 is configured to classify the importance degree of each electromechanical device according to the distance between every two electromechanical devices.
In one embodiment, the greater the effect of the electromechanical device functional failure on the traffic safety in the tunnel, the higher the traffic safety risk in the tunnel;
the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency event, the higher the emergency rescue risk.
In one embodiment, the apparatus further comprises: the index processing module is used for obtaining the maintenance difficulty of the electromechanical equipment according to the product of the maintenance grade and the maintenance price of the electromechanical equipment; obtaining the maintenance cost of the electromechanical equipment according to the product of the maintenance frequency and the maintenance price of the electromechanical equipment; and determining the fault frequency of the electromechanical equipment according to the operation observation time length of the electromechanical equipment and the relative size of the fault time length of the electromechanical equipment in the operation observation time length.
In one embodiment, the mechatronic devices are pre-divided into a plurality of first mechatronic devices and a plurality of second mechatronic devices;
the mechatronic device classification module 404 is further configured to determine, for any first mechatronic device, a second mechatronic device closest to the any first mechatronic device from the plurality of second mechatronic devices, to obtain a shortest distance corresponding to the any first mechatronic device; determining an average value of the shortest distances corresponding to the first electromechanical devices; if the average value is smaller than or equal to the preset distance, each first electromechanical device is classified into an electromechanical device cluster taking the second nearest electromechanical device of the first electromechanical device as a clustering center; the similarity degree of the importance of two electromechanical devices respectively belonging to different electromechanical device clusters is smaller than that of the importance of two electromechanical devices belonging to the same electromechanical device cluster.
In one embodiment, one of the two mechatronic devices is a first mechatronic device and the other is a second mechatronic device.
In one embodiment, the index value difference obtaining module 402 is further configured to determine, from the multidimensional index values of the two-by-two electromechanical devices, an index value of the two-by-two electromechanical devices in a same dimension; taking the difference value of the index values in the same dimension as the difference value of the index values in the same dimension, and obtaining a plurality of difference values of the index values in the same dimension corresponding to the multiple dimensions.
For a specific definition of the electromechanical device classification means in the tunnel, reference may be made to the definition of the electromechanical device classification method in the tunnel hereinabove, and no further description is given here. The respective modules in the electromechanical device classification apparatus in the tunnel may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing electromechanical device classification data in the tunnel. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of classifying electromechanical devices within a tunnel.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method embodiments described above when the processor executes the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the respective method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of classifying electromechanical devices within a tunnel, the method comprising:
calibrating each electromechanical device according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the failure of the functions of the electromechanical device, the maintenance difficulty and the failure frequency of the electromechanical device, and the maintenance cost and the purchase price of the electromechanical device to obtain a multidimensional index value of each electromechanical device;
based on the multidimensional index values of the electromechanical equipment, obtaining a plurality of multidimensional index value difference values of the electromechanical equipment;
determining the distance between the two electromechanical devices based on the sum of the difference values of the plurality of the same-dimensional index values of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices;
and classifying the importance degree of the electromechanical devices according to the distance between every two electromechanical devices.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the greater the influence of the failure of the electromechanical equipment on the traffic safety of the vehicles in the tunnel, the higher the traffic safety risk of the vehicles in the tunnel;
the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency event, the higher the emergency rescue risk.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
obtaining maintenance difficulty of the electromechanical equipment according to the product of the maintenance level and the maintenance price of the electromechanical equipment;
obtaining the maintenance cost of the electromechanical equipment according to the product of the maintenance frequency and the maintenance price of the electromechanical equipment;
and determining the fault frequency of the electromechanical equipment according to the operation observation time length of the electromechanical equipment and the relative size of the fault time length of the electromechanical equipment in the operation observation time length.
4. The method of claim 1, wherein each mechatronic device is pre-divided into a plurality of first mechatronic devices and a plurality of second mechatronic devices;
the classifying the importance degree of each electromechanical device according to the distance between every two electromechanical devices comprises the following steps:
determining a second electromechanical device closest to any first electromechanical device in the plurality of second electromechanical devices aiming at the any first electromechanical device, and obtaining the shortest distance corresponding to the any first electromechanical device;
determining an average value of the shortest distances corresponding to the first electromechanical devices;
if the average value is smaller than or equal to the preset distance, each first electromechanical device is classified into an electromechanical device cluster taking the second nearest electromechanical device of the first electromechanical device as a clustering center;
the similarity degree of the importance of two electromechanical devices respectively belonging to different electromechanical device clusters is smaller than that of the importance of two electromechanical devices belonging to the same electromechanical device cluster.
5. The method of claim 4, wherein one of the two mechatronic devices is a first mechatronic device and the other is a second mechatronic device.
6. The method according to claim 1, wherein the obtaining the values of the multidimensional index differences for the electromechanical devices based on the multidimensional index of the electromechanical devices comprises:
determining index values of the two-two electromechanical devices in the same dimension from the multi-dimension index values of the two-two electromechanical devices;
taking the difference value of the index values in the same dimension as the difference value of the index values in the same dimension, and obtaining a plurality of difference values of the index values in the same dimension corresponding to the multiple dimensions.
7. An electromechanical device classification apparatus within a tunnel, the apparatus comprising:
the electromechanical equipment calibration module is used for calibrating each electromechanical equipment according to the traffic safety risk and emergency rescue risk of the vehicle in the tunnel caused by the functional failure of the electromechanical equipment, the maintenance difficulty and the failure frequency of the electromechanical equipment, and the maintenance cost and the purchase price of the electromechanical equipment to obtain the multidimensional index value of each electromechanical equipment;
the index value difference value acquisition module is used for acquiring a plurality of same-dimensional index value differences of the electromechanical equipment based on the multi-dimensional index values of the electromechanical equipment;
the distance determining module is used for determining the distance between the two electromechanical devices based on the sum of a plurality of co-dimensional index value differences of the two electromechanical devices; the distance between every two electromechanical devices is inversely related to the importance degree of every two electromechanical devices;
and the electromechanical equipment classification module is used for classifying the importance degree of each electromechanical equipment according to the distance between every two electromechanical equipment.
8. The apparatus of claim 7, wherein the greater the impact of the electromechanical device failure on the safety of vehicle traffic in the tunnel, the greater the risk of vehicle traffic in the tunnel;
the greater the influence of the mechanical and electrical equipment functional failure on the early warning and/or rescue of the tunnel emergency event, the higher the emergency rescue risk.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 6 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any one of claims 1 to 6.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103471870A (en) * | 2013-10-08 | 2013-12-25 | 中国矿业大学(北京) | Method for researching sensibility and clustering performance for recognizing states of coal mine equipment based on two-dimensional projection characteristic indexes |
| CN106897945A (en) * | 2017-03-02 | 2017-06-27 | 新疆金风科技股份有限公司 | The clustering method and equipment of wind power generating set |
| CN110609524A (en) * | 2019-08-14 | 2019-12-24 | 华中科技大学 | Industrial equipment residual life prediction model and construction method and application thereof |
| CN110825826A (en) * | 2019-11-07 | 2020-02-21 | 深圳大学 | Clustering calculation method, device, terminal and storage medium |
| KR102141391B1 (en) * | 2019-12-16 | 2020-08-05 | 주식회사 한국가스기술공사 | Failure data management method based on cluster estimation |
| US20210073060A1 (en) * | 2019-09-11 | 2021-03-11 | International Business Machines Corporation | Managing device maintenance via artificial intelligence |
| CN113466597A (en) * | 2021-07-19 | 2021-10-01 | 江苏金鑫信息技术有限公司 | Intelligent detection method for equipment state of rail transit power supply system |
| CN113869721A (en) * | 2021-09-27 | 2021-12-31 | 广东电网有限责任公司 | Substation equipment health state classification method and apparatus |
-
2022
- 2022-04-01 CN CN202210338338.2A patent/CN116151788B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103471870A (en) * | 2013-10-08 | 2013-12-25 | 中国矿业大学(北京) | Method for researching sensibility and clustering performance for recognizing states of coal mine equipment based on two-dimensional projection characteristic indexes |
| CN106897945A (en) * | 2017-03-02 | 2017-06-27 | 新疆金风科技股份有限公司 | The clustering method and equipment of wind power generating set |
| CN110609524A (en) * | 2019-08-14 | 2019-12-24 | 华中科技大学 | Industrial equipment residual life prediction model and construction method and application thereof |
| US20210073060A1 (en) * | 2019-09-11 | 2021-03-11 | International Business Machines Corporation | Managing device maintenance via artificial intelligence |
| CN110825826A (en) * | 2019-11-07 | 2020-02-21 | 深圳大学 | Clustering calculation method, device, terminal and storage medium |
| KR102141391B1 (en) * | 2019-12-16 | 2020-08-05 | 주식회사 한국가스기술공사 | Failure data management method based on cluster estimation |
| CN113466597A (en) * | 2021-07-19 | 2021-10-01 | 江苏金鑫信息技术有限公司 | Intelligent detection method for equipment state of rail transit power supply system |
| CN113869721A (en) * | 2021-09-27 | 2021-12-31 | 广东电网有限责任公司 | Substation equipment health state classification method and apparatus |
Non-Patent Citations (3)
| Title |
|---|
| 朱建新;陈学东;吕宝林;王溢芳;乔松;陈嘉宏;: "基于多维高斯贝叶斯的机械设备失效/故障智能诊断及参数影响分析", 机械工程学报, no. 04 * |
| 陈晓利;郭兴隆;: "公路隧道运营管理分类方法研究", 公路交通技术, no. 05 * |
| 马慧;魏立力;: "基于多维标度和聚类的CPI数据结构分析", 兰州文理学院学报(自然科学版), no. 03 * |
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