CN115908871A - Wearable equipment track equipment data detection method, device, equipment and medium - Google Patents
Wearable equipment track equipment data detection method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of rail transit operation and maintenance, in particular to a wearable equipment rail equipment data detection method, a wearable equipment rail equipment data detection device, wearable equipment and a wearable equipment rail equipment data detection medium, wherein the wearable equipment rail equipment data detection method comprises the following steps: acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data; analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data; inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result; and inputting the equipment detection result into an equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to corresponding wearable detection equipment terminals according to the wearable detection data. This application has the effect that promotes track traffic fortune dimension's efficiency.
Description
Technical Field
The invention relates to the technical field of rail transit operation and maintenance, in particular to a wearable equipment rail equipment data detection method, a wearable equipment rail equipment data detection device, wearable equipment data detection equipment and a wearable equipment rail equipment data detection medium.
Background
At present, in rail transit operation and maintenance, each electromechanical device needs to be regularly inspected. At present, when each electromechanical device is patrolled and examined, the electromechanical device is usually patrolled and examined by people to the corresponding region regularly, however, for the patrolling and examining of rail transit, various different devices can be involved, and for each device, the structure of the parts is extremely complex, the professional requirement is very high, the maintenance technique of the related device is difficult to master by maintenance personnel with less abundant experience in a short time, a large amount of time is needed to master the related maintenance technique or the maintenance is needed to be carried out together with the maintenance personnel with abundant experience, so that the maintenance efficiency is influenced.
Disclosure of Invention
In order to improve the efficiency of rail transit operation and maintenance, the application provides a wearable equipment rail device data detection method, device, equipment and medium.
The above object of the present invention is achieved by the following technical solutions:
a wearable device track device data detection method comprises the following steps:
acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data;
analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and inputting the equipment detection result into an equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to a corresponding wearable detection equipment terminal according to the wearable equipment detection data.
By adopting the technical scheme, the equipment can be worn by the inspection personnel for inspection through the arrangement of the wearable equipment, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, the equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and the inspection personnel can acquire the scheme for maintaining the equipment on site through the wearable equipment in time, so that the inspection personnel with insufficient experience can independently execute inspection tasks, more inspection personnel can be released to inspect the rail transit, and the inspection efficiency is improved; because the data types of various different data exist in the data detected by the wearable device, and the data structure of each data type is different, the corresponding device inspection data can be split from the wearable device detection data through the preset protocol type, and the protocol type data is analyzed, so that the inspection original data with the uniform data format can be obtained, the device detection model can be conveniently detected, and the detection efficiency of the wearable device is improved.
The application may be further configured in a preferred example to: the method includes the steps of obtaining wearable device detection data, splitting the wearable device detection data according to a preset protocol type to obtain device inspection data, and specifically including:
acquiring patrol section information, acquiring a maintenance equipment image sent by the wearable detection equipment terminal according to the patrol section information, and identifying the type of maintenance equipment from the maintenance equipment image;
and acquiring the protocol type according to the type of the overhaul equipment.
Through adopting above-mentioned technical scheme, through image recognition's technique, can utilize this wearable equipment to patrol and examine when patrolling and examining personnel, judge the maintenance type automatically to can acquire the agreement type that corresponds, simultaneously, through acquireing section information of patrolling and examining, can carry out image recognition according to this electromechanical device who patrols and examines the section and install, precision when having promoted the discernment.
The present application may be further configured in a preferred example to: before inputting the device detection result into a device maintenance model to obtain a device maintenance scheme and converting the device maintenance scheme into maintenance voice data and sending the wearable device detection data to a corresponding wearable detection device terminal, the wearable device track device data detection method further includes:
acquiring historical maintenance data corresponding to the inspection section information, and acquiring historical equipment detection data and a corresponding historical equipment maintenance scheme from the historical maintenance scheme;
extracting detection data characteristics corresponding to each piece of historical detection data of the equipment, and associating the detection data characteristics with the historical maintenance scheme of the equipment to obtain a corresponding historical maintenance association package;
and training the historical maintenance association package to obtain the equipment maintenance model corresponding to the inspection section information.
Through adopting above-mentioned technical scheme, through exhaling historical maintenance data to will extract the detection data characteristic that corresponds, thereby be convenient for follow-up and actual detection data compare, simultaneously, through setting up historical maintenance association package, can examine and repair according to actual conditions and compare with historical, thereby can reachd the equipment maintenance scheme that corresponds.
The present application may be further configured in a preferred example to: the equipment detection result is input into the equipment maintenance model to obtain an equipment maintenance scheme, and the equipment maintenance scheme is converted into maintenance voice data, and according to wearing equipment detection data send to the wearing detection equipment terminal that corresponds, specifically include:
extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each historical maintenance association package to obtain a corresponding comparison result;
inputting the following formula according to the comparison result, and calculating the similarity score between each historical maintenance association package and the equipment detection result:
q＝a+b；
wherein q is the total number of the feature points in the result feature data; a is the number of the comparison consistence; b is the number of the inconsistent alignments; beta is a weight parameter, and S is the similarity score; k is a correction parameter;
and sequencing the historical maintenance related packages according to the sequence of the similarity score from high to low, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance related package as the equipment maintenance scheme.
By adopting the technical scheme, the audience degree of each historical maintenance association package and the actual audience degree can be calculated through the formula, so that the association degree of each historical maintenance association package and the actual equipment condition can be obtained, and the corresponding equipment maintenance scheme can be screened out from each historical maintenance association package.
The application may be further configured in a preferred example to: the method for calculating the correction parameter k comprises the following steps:
respectively acquiring the number of feature points of the detection data features in each historical maintenance related packet;
and calculating the correction parameter k according to the number of the feature points of the detection data features and the number of the feature points in the result feature data.
By adopting the technical scheme, because the number of the feature points of the actual result feature data is possibly far greater than the number of the feature points of the corresponding detected feature data, an error can be caused to the calculated association score, and therefore, the correction parameter k is calculated by the number of the feature points of the detected data feature and the number of the feature points in the result feature data, the association score can be corrected, and the accuracy of the matched equipment maintenance scheme is improved.
The second purpose of the invention of the application is realized by the following technical scheme:
a wearable device rail device data detection apparatus, the wearable device rail device data detection apparatus comprising: the wearable equipment detection data acquisition module is used for acquiring wearable equipment detection data and splitting the wearable equipment detection data according to a preset protocol type to obtain equipment inspection data;
the data analysis module is used for analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
the equipment detection module is used for inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and the maintenance recommendation module is used for inputting the equipment detection result into an equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
By adopting the technical scheme, the equipment can be worn by the inspection personnel for inspection through the arrangement of the wearable equipment, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, the equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and the inspection personnel can acquire the scheme for maintaining the equipment on site through the wearable equipment in time, so that the inspection personnel with insufficient experience can independently execute inspection tasks, more inspection personnel can be released to inspect the rail transit, and the inspection efficiency is improved; because the data types of various different data exist in the data detected by the wearable device, and the data structure of each data type is different, the corresponding device inspection data can be split from the wearable device detection data through the preset protocol type, and the protocol type data is analyzed, so that inspection original data with uniform data formats can be obtained, the device detection model can be conveniently detected, and the detection efficiency of the wearable device is improved.
The third purpose of the application is realized by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the wearable device rail device data detection method when executing the computer program.
The fourth purpose of the present application is achieved by the following technical solutions:
a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned wearable device track device data detection method.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the wearable equipment is arranged, so that an inspector can wear the equipment to inspect, the wearable equipment detection data obtained by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to a wearable detection equipment terminal, and the inspector can timely obtain a scheme for maintaining the equipment on site through the wearable equipment, so that the inspector with insufficient experience can independently perform inspection tasks, more inspectors can be released to inspect the rail transit, and the inspection efficiency is improved;
2. because various data types of different data exist in the data detected by the wearable device, and the data structure of each data type is different, the corresponding device inspection data can be split from the wearable device detection data through the preset protocol type, and the protocol type data is analyzed, so that inspection original data with uniform data formats can be obtained, the detection of the device detection model can be facilitated, and the detection efficiency of the wearable device is improved;
3. through the formula, the degree of each historical maintenance association package and the actual audience can be calculated, so that the degree of association between each historical maintenance association package and the actual equipment condition can be obtained, and a corresponding equipment maintenance scheme can be screened out from each historical maintenance association package;
4. because the number of feature points of the actual result feature data may be much larger than the number of feature points of the corresponding detected feature data, an error may be caused to the calculated association score, and therefore, the correction parameter k may be calculated by the number of feature points of the detected data feature and the number of feature points in the result feature data, and the association score may be corrected, thereby improving the accuracy of the matched equipment maintenance scheme.
Drawings
Fig. 1 is a flowchart of a wearable device track device data detection method according to an embodiment of the present application;
fig. 2 is a flowchart illustrating implementation of step S10 in a method for detecting data of a track device of a wearable device in an embodiment of the present application;
fig. 3 is a flowchart of another implementation of a method for detecting data of a track device of a wearable device in an embodiment of the present application;
fig. 4 is a flowchart illustrating implementation of step S40 in a data detection method for a track device of a wearable device in an embodiment of the present application;
fig. 5 is a flowchart illustrating implementation of step S42 in a data detection method for a track device of a wearable device in an embodiment of the present application;
fig. 6 is a schematic block diagram of a wearable device track device data detection apparatus according to an embodiment of the present application;
fig. 7 is a schematic diagram of an apparatus in an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1, the application discloses a wearable device track device data detection method, which specifically includes the following steps:
s10: and acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data.
In this embodiment, wearing equipment detection data refers to the wearing and patrols and examines personnel's equipment on one's body, when patrolling and examining rail transit's equipment, detects the data that obtain. The equipment inspection data refers to operation data detected by each piece of equipment.
Specifically, corresponding detection equipment, such as a camera device and sensors with various functions, is installed on equipment such as a helmet or a head ring worn by an inspection worker during inspection, and when the inspection worker performs inspection, the operation data of each rail transit electromechanical device detected during inspection or the current image of the equipment is acquired through the detection equipment, so that the detection data of the wearable equipment is obtained.
Further, since the types of data detected by each piece of equipment are different, for example, the types of data acquired by sensors or cameras with different functions are different, different protocols are required to be analyzed, and therefore, data matched with the protocol type is acquired from the wearable equipment type through the corresponding protocol type, and then the detected data of the wearable equipment is split, so that the equipment patrol data corresponding to each protocol type is obtained.
S20: and analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data.
In this embodiment, the original inspection data refers to data corresponding to each piece of equipment inspection data obtained through analysis.
Specifically, each equipment inspection device is analyzed by adopting an analysis protocol corresponding to each protocol type, namely format conversion is carried out, so that inspection original data with a uniform format are obtained.
S30: and inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result.
In the present embodiment, the device detection model refers to a model for detecting whether each of the mechatronic devices has a failure. The equipment detection result refers to the current equipment operation data of the electromechanical equipment with the fault.
Specifically, the equipment detection model is obtained through training in advance according to data of each electromechanical device in normal operation and abnormal operation, furthermore, all routing inspection original data of one electromechanical device are input into the equipment maintenance model by taking one electromechanical device as a unit, whether the electromechanical device breaks down or not is judged, and if the electromechanical device breaks down, the routing inspection original data of the broken electromechanical device is used as an equipment detection result.
S40: inputting the equipment detection result into the equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
In this embodiment, the equipment servicing model refers to a model for matching a scheme for servicing the malfunctioning mechatronic equipment.
Specifically, after a model of a maintenance scheme of each electromechanical device when various faults occur is trained in advance, the device maintenance result is input into the device maintenance model, so that a corresponding maintenance scheme is matched from the device maintenance model when the same type of electromechanical devices have the same fault or similar faults within an acceptable range within a past period of time, and the corresponding maintenance scheme is used as the device maintenance scheme.
And further, converting the text part of the equipment maintenance scheme into voice data so as to obtain maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal. When the maintenance voice data are sent to the wearable detection equipment terminal, maintenance steps are obtained from the equipment maintenance scheme, maintenance voice data corresponding to the maintenance steps are sent to the leaflet detection equipment terminal one by one from the first maintenance step, and after completion of the corresponding maintenance steps is detected, voice data corresponding to the next maintenance step are sent to the wearable detection equipment terminal.
In the embodiment, the wearable equipment is arranged, so that inspection personnel can wear the equipment to inspect, the wearable equipment detection data obtained by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to a wearable detection equipment terminal, and then the inspection personnel can timely obtain the scheme for maintaining the equipment on site through the wearable equipment, so that the inspection personnel with insufficient experience can independently perform inspection tasks, more inspection personnel can be released to inspect the rail transit, and the inspection efficiency is improved; because the data types of various different data exist in the data detected by the wearable device, and the data structure of each data type is different, the corresponding device inspection data can be split from the wearable device detection data through the preset protocol type, and the protocol type data is analyzed, so that the inspection original data with the uniform data format can be obtained, the device detection model can be conveniently detected, and the detection efficiency of the wearable device is improved.
In an embodiment, as shown in fig. 2, in step S10, that is, acquiring wearable device detection data, splitting the wearable device detection data according to a preset protocol type to obtain device inspection data, specifically including:
s11: the inspection section information is acquired, the maintenance equipment image sent by the wearable detection equipment terminal is acquired according to the inspection section information, and the type of the maintenance equipment is identified from the maintenance equipment image.
In this embodiment, the patrol section information refers to information of an area where patrol personnel is responsible for patrol. The type of the maintenance equipment refers to the type of the electromechanical equipment which is currently inspected by an inspector.
Specifically, the area currently being inspected is obtained from the inspection task of the inspection personnel and the positioning data of the inspection personnel, and the area is used as the inspection section information, wherein the area can be one of the platforms in the rail transit or one of the sections in the whole rail. After the patrol section information is obtained, the types of the electromechanical equipment installed in the area and the image corresponding to each type of equipment are obtained from the corresponding construction scheme.
Further, when the inspection personnel inspect, the image of the equipment which is detected by the inspection personnel at present is acquired through the camera device installed on the wearable equipment and is used as the maintenance equipment image, and the maintenance equipment type is acquired through image recognition of the maintenance equipment image.
S12: and acquiring the protocol type according to the type of the overhaul equipment.
Specifically, all polling indexes are obtained when the type of the maintenance equipment is maintained, and a corresponding protocol type is obtained according to the indexes.
In an embodiment, as shown in fig. 3, before step S40, the wearable device track device data detection method further includes:
s401: and historical maintenance data corresponding to the patrol section information are obtained, and historical equipment detection data and a corresponding historical equipment maintenance scheme are obtained from the historical maintenance scheme.
In this embodiment, the historical maintenance data refers to maintenance data of all electromechanical devices in an area corresponding to the patrol section information in a past period of time. The device history detection data refers to data detected when each electromechanical device in the area has a fault. The equipment historical maintenance scheme refers to a corresponding maintenance scheme aiming at different faults of each electromechanical equipment.
Specifically, when the electromechanical device is overhauled every time, the overhaul record of each electromechanical device is recorded, and for the electromechanical device with a fault, the current running state and the data obtained by overhaul are recorded as historical equipment detection data, and a corresponding historical equipment maintenance scheme is obtained after the maintenance is completed.
S402: and extracting detection data characteristics corresponding to each equipment historical detection data, and associating the detection data characteristics with the equipment historical maintenance scheme to obtain a corresponding historical maintenance association package.
In this embodiment, the detected data feature refers to data composed of feature points in each device history detection data.
Specifically, corresponding feature points are extracted from the device history detection data, for example, if a structure of the electromechanical device fails due to a failure, the feature points of a corresponding image are extracted, or if the amplitude, sound or other aspects of the operation of the electromechanical device change during the failure, the corresponding feature points are extracted, so as to form the detection data features.
Furthermore, after the detection data characteristics are associated with the historical maintenance scheme of the equipment, a corresponding historical maintenance association package is obtained.
S403: and training the historical maintenance association package to obtain an equipment maintenance model corresponding to the inspection section information.
Specifically, after statistical training is performed on each historical maintenance association package, an equipment maintenance model corresponding to the area is obtained.
In an embodiment, as shown in fig. 4, in step S40, inputting a device detection result into the device maintenance model to obtain a device maintenance scheme, converting the device maintenance scheme into maintenance voice data, and sending the maintenance voice data to a corresponding wearable detection device terminal according to the wearable device detection data, specifically including:
s41: and extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each historical maintenance association package to obtain a corresponding comparison result.
Specifically, a mode of extracting detection data features is adopted, and corresponding result feature data is extracted from the equipment detection result.
Further, comparing the feature points of the result feature data with each group of detection data feature points in the historical maintenance association package of the equipment to verify whether the feature points are consistent or not, and obtaining a corresponding comparison result.
S42: inputting the following formula according to the comparison result, and calculating the similarity score between each historical maintenance association package and the equipment detection result:
q＝a+b；
wherein q is the total number of the characteristic points in the result characteristic data; a is the number of consistent comparison; b is the number of inconsistent comparison; beta is a weight parameter, and S is a similarity score; k is a correction parameter.
Specifically, after the correction parameter k is calculated and the weight parameter β is set, the comparison result is input to the above formula, so that the similarity score S is calculated.
S43: and sequencing the historical maintenance related packages according to the sequence of the similarity score from high to low, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance related package as an equipment maintenance scheme.
Specifically, the historical maintenance association package corresponding to the highest similarity score S is selected, and the corresponding historical maintenance scheme of the equipment is obtained from the historical maintenance association package and is used as the equipment maintenance scheme.
In one embodiment, as shown in fig. 5, in step S42, the method for calculating the correction parameter k includes:
s421: and respectively acquiring the number of feature points of the detection data features in each historical maintenance related packet.
Specifically, the number of feature points corresponding to each detection data feature in each historical maintenance association package is counted.
S422: and calculating a correction parameter k according to the number of the characteristic points of the detection data characteristic and the number of the characteristic points in the result characteristic data.
Specifically, the feature point quantity z of the detection data feature and the feature point quantity in the result feature data are input into the following formula, and a correction parameter k is calculated:
it should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In an embodiment, a wearable device track device data detection apparatus is provided, where the wearable device track device data detection apparatus corresponds to the wearable device track device data detection methods in the above embodiments one to one. As shown in fig. 6, the wearable device track device data detection apparatus includes a detection data acquisition module, a data analysis module, a device detection module, and a maintenance recommendation module. The functional modules are explained in detail as follows:
the wearable equipment detection data acquisition module is used for acquiring wearable equipment detection data and splitting the wearable equipment detection data according to a preset protocol type to obtain equipment inspection data;
the data analysis module is used for analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
the equipment detection module is used for inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and the maintenance recommendation module is used for inputting the equipment detection result into the equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
Optionally, the detection data obtaining module includes:
the type acquisition sub-module is used for acquiring the inspection section information, acquiring an overhaul equipment image sent by the wearable detection equipment terminal according to the inspection section information, and identifying the type of overhaul equipment from the overhaul equipment image;
and the protocol acquisition submodule is used for acquiring the protocol type according to the type of the overhaul equipment.
Optionally, the wearable device track device data detection apparatus further includes:
the historical data acquisition module is used for acquiring historical maintenance data corresponding to the inspection section information and acquiring historical detection data of the equipment and a corresponding historical maintenance scheme of the equipment from the historical maintenance scheme;
the data association module is used for extracting detection data characteristics corresponding to historical detection data of each piece of equipment, and associating the detection data characteristics with historical maintenance schemes of the equipment to obtain corresponding historical maintenance association packages;
and the model training module is used for training the historical maintenance association package to obtain an equipment maintenance model corresponding to the inspection section information.
Optionally, the repair recommendation module includes:
the characteristic comparison submodule is used for extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each historical maintenance association packet to obtain a corresponding comparison result;
and the score calculation submodule is used for inputting the following formula according to the comparison result and calculating the similarity score between each historical maintenance association packet and the equipment detection result:
q＝a+b；
wherein q is the total number of the characteristic points in the result characteristic data; a is the number of comparison consistence; b is the number of inconsistent alignments; beta is a weight parameter, and S is a similarity score; k is a correction parameter;
and the data recommendation submodule is used for sequencing the historical maintenance association packages according to the sequence of the similarity score from high to low, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance association package as an equipment maintenance scheme.
Optionally, the score calculating sub-module includes:
the quantity obtaining unit is used for respectively obtaining the quantity of the feature points of the detection data features in each historical maintenance related packet;
and the parameter calculation unit is used for calculating a correction parameter k according to the number of the feature points of the detection data features and the number of the feature points in the result feature data.
For specific definition of the wearable device track device data detection apparatus, reference may be made to the above definition of the wearable device track device data detection method, which is not described herein again. The modules in the wearable device track device data detection apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. 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 wearable device rail device data detection method.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data;
analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and inputting the equipment detection result into the equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data;
analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and inputting the equipment detection result into the equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting 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; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (10)
1. A wearable device track device data detection method is characterized by comprising the following steps:
acquiring wearable device detection data, and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data;
analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and inputting the equipment detection result into an equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to a corresponding wearable detection equipment terminal according to the wearable equipment detection data.
2. The wearable device track device data detection method according to claim 1, wherein the acquiring wearable device detection data and splitting the wearable device detection data according to a preset protocol type to obtain device inspection data specifically includes:
acquiring inspection section information, acquiring maintenance equipment images sent by the wearable detection equipment terminal according to the inspection section information, and identifying maintenance equipment types from the maintenance equipment images;
and acquiring the protocol type according to the type of the overhaul equipment.
3. The method for detecting the data of the track equipment of the wearable equipment according to claim 2, wherein before the device detection result is input into a device maintenance model to obtain a device maintenance scheme, the device maintenance scheme is converted into maintenance voice data, and the data is sent to a corresponding wearable detection device terminal according to the detection data of the wearable equipment, the method further comprises:
acquiring historical maintenance data corresponding to the inspection section information, and acquiring historical equipment detection data and a corresponding historical equipment maintenance scheme from the historical maintenance scheme;
extracting detection data characteristics corresponding to each piece of historical detection data of the equipment, and associating the detection data characteristics with the historical maintenance scheme of the equipment to obtain a corresponding historical maintenance association package;
and training the historical maintenance association package to obtain the equipment maintenance model corresponding to the inspection section information.
4. The wearable device track device data detection method according to claim 3, wherein the inputting the device detection result into a device maintenance model to obtain a device maintenance scheme, converting the device maintenance scheme into maintenance voice data, and sending the maintenance voice data to a corresponding wearable detection device terminal according to the wearable device detection data specifically includes:
extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each historical maintenance association package to obtain a corresponding comparison result;
inputting the following formula according to the comparison result, and calculating the similarity score between each historical maintenance association package and the equipment detection result:
q＝a+b；
wherein q is the total number of the feature points in the result feature data; a is the number of the comparison consistence; b is the number of the inconsistent alignments; beta is a weight parameter, and S is the similarity score; k is a correction parameter;
and sequencing the historical maintenance related packages according to the sequence of the similarity score from high to low, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance related package as the equipment maintenance scheme.
5. The wearable device rail device data detection method of claim 4, wherein the method of calculating the correction parameter k comprises:
respectively acquiring the number of feature points of the detection data features in each historical maintenance related packet;
and calculating the correction parameter k according to the number of the feature points of the detection data features and the number of the feature points in the result feature data.
6. A wearable equipment rail device data detection device, characterized in that, wearable equipment rail device data detection device includes:
the wearable equipment detection data acquisition module is used for acquiring wearable equipment detection data and splitting the wearable equipment detection data according to a preset protocol type to obtain equipment inspection data;
the data analysis module is used for analyzing the routing inspection data of each device according to the protocol type to obtain routing inspection original data;
the equipment detection module is used for inputting the routing inspection original data into a preset equipment detection model for detection to obtain an equipment detection result;
and the maintenance recommendation module is used for inputting the equipment detection result into an equipment maintenance model to obtain an equipment maintenance scheme, converting the equipment maintenance scheme into maintenance voice data, and sending the maintenance voice data to the corresponding wearable detection equipment terminal according to the wearable detection data.
7. The wearable device rail device data detection apparatus of claim 6, wherein the detection data acquisition module comprises:
the type acquisition sub-module is used for acquiring polling section information, acquiring a maintenance equipment image sent by the wearable detection equipment terminal according to the polling section information, and identifying the type of maintenance equipment from the maintenance equipment image;
and the protocol acquisition submodule is used for acquiring the protocol type according to the type of the overhaul equipment.
8. The wearable device rail device data detection apparatus of claim 7, further comprising:
the historical data acquisition module is used for acquiring historical maintenance data corresponding to the inspection section information and acquiring historical detection data of equipment and a corresponding historical maintenance scheme of the equipment from the historical maintenance scheme;
the data association module is used for extracting detection data characteristics corresponding to the historical detection data of each piece of equipment, and associating the detection data characteristics with the historical maintenance scheme of the equipment to obtain a corresponding historical maintenance association package;
and the model training module is used for training the historical maintenance association package to obtain the equipment maintenance model corresponding to the inspection section information.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the wearable device rail device data detection method of any of claims 1 to 5.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the wearable device rail device data detection method according to any one of claims 1 to 5.
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