CN115908871B - 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 method, a device, equipment and a medium for detecting data of a wearable device rail device, wherein the method for detecting the data of the wearable device rail device comprises the following steps: acquiring detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data; analyzing the inspection data of each device according to the protocol type to obtain the original inspection data; inputting the 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 transmitting the maintenance voice data to a corresponding wearing detection equipment terminal according to the wearing detection equipment data. The utility model provides an effect that promotes rail transit fortune dimension's efficiency.
Description
Technical Field
The invention relates to the technical field of rail transit operation and maintenance, in particular to a method, a device, equipment and a medium for detecting data of a wearable device rail device.
Background
At present, in the operation and maintenance of rail transit, each electromechanical device needs to be periodically inspected. At present, when each electromechanical device is inspected, people usually go to corresponding areas periodically to inspect, however, for the inspection of rail transit, various different devices are involved, and for each device, the structure of parts is extremely complex, the professional requirement is very high, maintenance personnel with less experience can hardly grasp the maintenance technology of the relevant device in a short time, a great deal of time is required to grasp the relevant maintenance technology or the maintenance personnel with great collaborative experience are required to perform maintenance together, so that the maintenance efficiency is affected.
Disclosure of Invention
In order to improve the efficiency of track traffic operation and maintenance, the application provides a method, a device, equipment and a medium for detecting data of track equipment of wearable equipment.
The first object of the present invention is achieved by the following technical solutions:
a wearable device rail device data detection method, the wearable device rail device data detection method comprising:
acquiring detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data;
analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
inputting the 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 transmitting the maintenance voice data to a corresponding wearing detection equipment terminal according to the wearing detection equipment data.
Through adopting the technical scheme, through the setting of the wearable equipment, the equipment can be worn by the patrol personnel to carry out patrol, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and further, the patrol personnel can timely acquire the scheme for maintaining the equipment on site through the wearable equipment, so that the patrol personnel with insufficient experience can independently execute the patrol task, more patrol personnel can be released to carry out patrol on the rail transit, and the patrol efficiency is improved; because the data detected by the wearable equipment has various data types, the data structure of each data type is different, the corresponding equipment inspection data can be split from the detection data of the wearable equipment through the preset protocol type, and the protocol type data is analyzed, so that the inspection original data with uniform data format can be obtained, the detection of the equipment detection model can be facilitated, and the detection efficiency of the wearable equipment is improved.
The present application may be further configured in a preferred example to: the method for acquiring the detection data of the wearable equipment comprises the steps of splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data, and specifically comprises the following steps:
acquiring inspection section information, acquiring an overhaul equipment image sent by the wearable detection equipment terminal according to the inspection section information, and identifying the overhaul equipment type from the overhaul equipment image;
and acquiring the protocol type according to the overhaul equipment type.
Through adopting above-mentioned technical scheme, through image recognition's technique, can utilize this wearable equipment to patrol and examine when examining personnel to patrol and examine, judge automatically and examine the type to can acquire corresponding protocol type, simultaneously, through the section information of patrolling and examining, can carry out image recognition according to the electromechanical device that this section was patrolled and examined to install, the precision when having promoted the discernment.
The present application may be further configured in a preferred example to: before the device detection result is input into the device maintenance model to obtain a device maintenance scheme, the device maintenance scheme is converted into maintenance voice data, and the wearable device track device data detection method further comprises the steps of:
acquiring historical maintenance data corresponding to the inspection section information, and acquiring equipment historical detection data and a corresponding equipment historical maintenance scheme from the historical maintenance scheme;
extracting detection data characteristics corresponding to each device history detection data, and associating the detection data characteristics with the device history maintenance scheme to obtain a corresponding history maintenance association packet;
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 expiration history maintenance data to will draw out corresponding detection data characteristic, thereby be convenient for follow-up and actual detection data to compare, simultaneously, through setting up history maintenance associated package, can compare with the history maintenance according to actual conditions, thereby can obtain corresponding equipment maintenance scheme.
The present application may be further configured in a preferred example to: 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 wearing detection equipment terminal according to the wearing detection equipment data, wherein the method specifically comprises the following steps of:
extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each history maintenance association packet to obtain a corresponding comparison result;
and inputting the following formulas 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 feature points in the result feature data; a is the number of the alignment agreement; b is the inconsistent quantity of the comparison; beta is a weight parameter, S is the similarity score; k is a correction parameter;
and sequencing the historical maintenance association packages according to the sequence from high to low of the similarity scores, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance association package as the equipment maintenance scheme.
Through adopting above-mentioned technical scheme, through this formula, can calculate each history maintenance association package and actual audience degree to can obtain the association degree of each history maintenance association package and actual equipment condition, and then can select corresponding equipment maintenance scheme from each history maintenance association package.
The present 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 detected data features in each historical maintenance association packet;
and calculating the correction parameter k according to the number of the feature points of the detected data features and the number of the feature points in the result feature data.
By adopting the technical scheme, the number of the characteristic points of the actual result characteristic data is probably far larger than that of the corresponding detected characteristic data, so that errors are caused to the calculated associated scores, and therefore, the associated scores can be corrected by calculating the correction parameter k through the number of the characteristic points of the detected data characteristic and the number of the characteristic points in the result characteristic data, so that the accuracy of the equipment maintenance scheme obtained by matching is improved.
The second object of the present invention is achieved by the following technical solutions:
a wearable device rail device data detection apparatus, the wearable device rail device data detection apparatus comprising: the detection data acquisition module is used for acquiring detection data of the wearable equipment and splitting the detection data of the wearable equipment according to a preset protocol type to acquire equipment inspection data;
the data analysis module is used for analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
the equipment detection module is used for inputting the 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 transmitting the maintenance voice data to a corresponding wearing detection equipment terminal according to the wearing detection equipment data.
Through adopting the technical scheme, through the setting of the wearable equipment, the equipment can be worn by the patrol personnel to carry out patrol, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and further, the patrol personnel can timely acquire the scheme for maintaining the equipment on site through the wearable equipment, so that the patrol personnel with insufficient experience can independently execute the patrol task, more patrol personnel can be released to carry out patrol on the rail transit, and the patrol efficiency is improved; because the data detected by the wearable equipment has various data types, the data structure of each data type is different, the corresponding equipment inspection data can be split from the detection data of the wearable equipment through the preset protocol type, and the protocol type data is analyzed, so that the inspection original data with uniform data format can be obtained, the detection of the equipment detection model can be facilitated, and the detection efficiency of the wearable equipment is improved.
The third object of the present application is achieved by the following technical solutions:
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 described above when the computer program is executed.
The fourth object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the wearable device rail device data detection method described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. through the arrangement of the wearable equipment, the equipment can be worn by the patrol personnel to carry out patrol, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and further, the scheme for maintaining the equipment can be timely acquired on site by the patrol personnel through the wearable equipment, so that the patrol personnel with insufficient experience can independently execute the patrol task, more patrol personnel can be released to carry out patrol on the rail transit, and the patrol efficiency is improved;
2. because various data types exist in the data detected by the wearable equipment, and the data structure of each data type is different, corresponding equipment inspection data can be split from the detection data of the wearable equipment through a preset protocol type, and the protocol type data is analyzed, so that original inspection data with uniform data format can be obtained, the detection of the equipment detection model can be facilitated, and the detection efficiency of the wearable equipment is improved;
3. according to the formula, the degree of association between 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 the corresponding equipment maintenance scheme can be screened out from each historical maintenance association package;
4. because the number of the feature points of the actual result feature data is probably far greater than the number of the feature points of the corresponding detected feature data, errors are caused to the calculated associated scores, and therefore the associated scores can be corrected by calculating the correction parameter k by detecting the number of the feature points of the data feature and the number of the feature points in the result feature data, so that the accuracy of the equipment maintenance scheme obtained by matching is improved.
Drawings
FIG. 1 is a flow chart of a method for detecting data of a track device of a wearable device according to an embodiment of the present application;
fig. 2 is a flowchart of implementation of step S10 in a method for detecting data of a track device of a wearable device according to 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 according to an embodiment of the present application;
fig. 4 is a flowchart of implementation of step S40 in a method for detecting data of a track device of a wearable device according to an embodiment of the present application;
fig. 5 is a flowchart of implementation of step S42 in a method for detecting data of a track device of a wearable device according to an embodiment of the present application;
FIG. 6 is a schematic block diagram of a wearable device rail device data detection apparatus in an embodiment of the present application;
fig. 7 is a schematic view 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 method for detecting data of a track device of a wearable device, which specifically includes the following steps:
s10: the method comprises the steps of obtaining detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data.
In this embodiment, the wearable device detection data refers to data obtained by detection when the device worn on the body of the patrol personnel is patrol-checked on the device of the rail transit. The equipment inspection data refers to operation data obtained by detection of each piece of equipment.
Specifically, corresponding detection devices, such as a camera device and sensors with various functions, are arranged on equipment such as a helmet or a head ring worn by an inspection personnel during inspection, and when the inspection personnel inspect, operation data of rail transit electromechanical equipment or current images of the equipment detected during inspection are acquired through the detection devices, so that detection data of the wearing equipment are obtained.
Further, since the types of data detected by each device are different, for example, the sensors or the camera devices with different functions are different in the types of the acquired data, different protocols are needed to be analyzed, so that the data matched with the protocol type is acquired from the wearable device types through the corresponding protocol type, and the wearable device detection data is split to obtain the device inspection data corresponding to each protocol type.
S20: analyzing the inspection data of each device according to the protocol type to obtain the original inspection data.
In this embodiment, the patrol raw data refers to data corresponding to each piece of equipment patrol data obtained by analysis.
Specifically, an analysis protocol corresponding to each protocol type is adopted to analyze each equipment inspection device, namely format conversion is carried out, so that inspection original data with uniform format is obtained.
S30: and inputting the 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 electromechanical device has failed. The device detection results are current device operation data indicating the failed electromechanical device.
Specifically, training to obtain the equipment detection model according to the data of each electromechanical equipment in normal operation and abnormal operation in advance, further, taking one electromechanical equipment as a unit, inputting all inspection original data of one electromechanical equipment into the equipment maintenance model, judging whether the electromechanical equipment has faults, and taking the inspection original data of the faulty electromechanical equipment as an equipment detection result if the electromechanical equipment has faults.
S40: 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 transmitting the maintenance voice data to the corresponding wearing detection equipment terminal according to the wearing detection data.
In the present embodiment, the equipment maintenance model refers to a model for matching a scheme for maintaining the failed electromechanical equipment.
Specifically, after the models of the maintenance schemes of each electromechanical device when various faults occur are trained in advance, the maintenance results of the equipment are input into the equipment maintenance models, so that corresponding maintenance schemes are matched from the equipment maintenance models, and the corresponding maintenance schemes are used as the equipment maintenance schemes when the same or similar faults within an acceptable range occur in the same type of electromechanical devices within a period of time.
Further, the text part of the equipment maintenance scheme is converted into voice data, so that maintenance voice data are obtained and sent to the corresponding wearing detection equipment terminal. When the completion of the corresponding maintenance step is detected, the voice data corresponding to the next maintenance step is sent to the wearing detection equipment terminal.
In the embodiment, through the arrangement of the wearable equipment, the inspection personnel can wear the equipment to inspect, so that the detection data of the wearable equipment acquired by the equipment can be analyzed, an equipment maintenance scheme is generated and sent to the terminal of the wearable detection equipment, and further, the inspection personnel can acquire the scheme for maintaining the equipment on site in time through the wearable equipment, so that the inspection personnel with insufficient experience can independently execute the inspection task, more inspection personnel can be released to inspect the rail transit, and the inspection efficiency is improved; because the data detected by the wearable equipment has various data types, the data structure of each data type is different, the corresponding equipment inspection data can be split from the detection data of the wearable equipment through the preset protocol type, and the protocol type data is analyzed, so that the inspection original data with uniform data format can be obtained, the detection of the equipment detection model can be facilitated, and the detection efficiency of the wearable equipment is improved.
In an embodiment, as shown in fig. 2, in step S10, wearable device detection data is obtained, and device inspection data is obtained by splitting from the wearable device detection data according to a preset protocol type, which specifically includes:
s11: and acquiring inspection section information, acquiring an inspection equipment image sent by the wearing detection equipment terminal according to the inspection section information, and identifying the type of the inspection equipment from the inspection equipment image.
In this embodiment, the inspection section information refers to information of an area where an inspector is responsible for inspection. The overhaul equipment type refers to the type of electromechanical equipment currently being patrolled by the inspector.
Specifically, the area currently being patrolled and examined is obtained from the patrol task of the patrol personnel and the positioning data of the patrol personnel, and is used as patrol section information, wherein the area can be one of stations in the rail transit or can be one of sections in the whole rail. After the inspection section information is acquired, the types of all electromechanical devices installed in the area and the images corresponding to the devices of each type are acquired from the corresponding construction scheme.
Further, when the inspection personnel inspect, the image of the equipment currently detected by the inspection personnel is obtained through the camera device arranged on the wearable equipment and used as an inspection equipment image, and the type of the inspection equipment is obtained through image identification of the inspection equipment image.
S12: and acquiring the protocol type according to the overhaul equipment type.
Specifically, all inspection indexes of the type of the inspection equipment are obtained when the type of the inspection equipment is inspected, and corresponding protocol types are 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 acquiring historical maintenance data corresponding to the inspection section information, and acquiring equipment historical detection data and a corresponding equipment historical maintenance scheme from the historical maintenance scheme.
In this embodiment, the historical maintenance data refers to maintenance data of all electromechanical devices in the area corresponding to the inspection section information in the past period of time. The device history detection data refers to data detected when each of the electromechanical devices fails in the area. The equipment history maintenance scheme is a corresponding maintenance scheme when the pointers are used for different faults of each electromechanical equipment.
Specifically, when each maintenance is performed, a maintenance record of each electromechanical device is recorded, and when the electromechanical device fails, the current running state and the data obtained by maintenance are recorded as the equipment history detection data and the corresponding equipment history maintenance scheme after the maintenance is completed.
S402: and extracting detection data characteristics corresponding to each device history detection data, and associating the detection data characteristics with a device history maintenance scheme to obtain a corresponding history maintenance association packet.
In the present embodiment, the detection data feature refers to data composed of each feature point in each device history detection data.
Specifically, corresponding feature points are extracted from the device history detection data, for example, if the structure of the electromechanical device is faulty due to the occurrence of a fault, the feature points of the corresponding image are extracted, or if the amplitude, sound or other aspects of the operation change when the fault occurs, the corresponding feature points are extracted, so that the detection data features are formed.
Further, after the detected data features are associated with the equipment history maintenance scheme, a corresponding history 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 each historical maintenance association package is statistically trained, an equipment maintenance model corresponding to the area is obtained.
In one embodiment, as shown in fig. 4, in step S40, the device detection result is input into the device maintenance model to obtain a device maintenance scheme, the device maintenance scheme is converted into maintenance voice data, and the maintenance voice data is sent to a corresponding wearable detection device terminal according to the wearable detection data, which specifically includes:
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 packet to obtain a corresponding comparison result.
Specifically, the mode of extracting the detection data features is adopted to extract corresponding result feature data from the device detection results.
Further, feature points of the result feature data are compared with each group of detected data feature points in the historical maintenance association packet of the equipment, so that whether the feature points are consistent or not is verified, and a corresponding comparison result is obtained.
S42: and (3) inputting the following formulas 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 feature points in the result feature data; a is the number of identical comparison; b is the inconsistent quantity of comparison; beta is a weight parameter, S is a similarity score; k is a correction parameter.
Specifically, after the correction parameter k and the weight parameter β are calculated, the comparison result is input into the above formula, so as to calculate the similarity score S.
S43: and sequencing the history maintenance association packages according to the sequence from high to low of the similarity scores, and taking the equipment history maintenance scheme corresponding to the first sequenced history maintenance association package as an equipment maintenance scheme.
Specifically, a corresponding historical maintenance association package with the highest similarity score S is selected, and a corresponding equipment historical maintenance scheme 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 the characteristic points of the detected data characteristic in each historical maintenance association packet.
Specifically, the number of feature points corresponding to each detected data feature in each historical maintenance association packet is counted.
S422: and calculating a correction parameter k according to the number of the feature points of the detected data features and the number of the feature points in the result feature data.
Specifically, the number z of feature points of the detected data feature and the number of feature points in the result feature data are input into the following formula, and the correction parameter k is calculated:
it should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
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 method in the above embodiment one by one. As shown in fig. 6, the wearable equipment track equipment data detection device comprises a detection data acquisition module, a data analysis module, an equipment detection module and a maintenance recommendation module. The functional modules are described in detail as follows:
the detection data acquisition module is used for acquiring detection data of the wearable equipment and splitting the detection data of the wearable equipment according to a preset protocol type to acquire equipment inspection data;
the data analysis module is used for analyzing the data, for parsing each device inspection data according to protocol type, obtaining inspection original data;
the equipment detection module is used for inputting the 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 transmitting the maintenance voice data to the corresponding wearing detection equipment terminal according to the wearing detection equipment.
Optionally, the detection data acquisition module includes:
the type acquisition sub-module is used for acquiring inspection section information, acquiring an inspection equipment image sent by the wearing detection equipment terminal according to the inspection section information, and identifying the type of the inspection equipment from the inspection equipment image;
and the protocol acquisition sub-module is used for acquiring the protocol type according to the overhaul equipment type.
Optionally, the wearable equipment track equipment data detection device further includes:
the historical data acquisition module is used for acquiring historical maintenance data corresponding to the inspection section information and acquiring equipment historical detection data and a corresponding equipment historical maintenance scheme from the historical maintenance scheme;
the data association module is used for extracting detection data characteristics corresponding to each device history detection data, and associating the detection data characteristics with a device history maintenance scheme to obtain a corresponding history maintenance association packet;
the model training module is used for training the history maintenance association package, and obtaining an equipment maintenance model corresponding to the inspection section information.
Optionally, the maintenance recommendation module includes:
the feature comparison sub-module is used for extracting result feature data from the equipment detection result, and comparing the result feature data with the detection data features of each historical maintenance association packet to obtain a corresponding comparison result;
the score calculating sub-module is used for inputting the following formulas 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 result characteristic data, the total number of feature points; a is comparison a consistent number; b is not aligned a consistent number; beta is a weight parameter, S is a similarity score; k is a correction parameter;
and the data recommending sub-module is used for sequencing the historical maintenance association packages according to the sequence from high to low of the similarity scores, and taking the equipment historical maintenance scheme corresponding to the sequenced first historical maintenance association package as an equipment maintenance scheme.
Optionally, the score calculating submodule includes:
the quantity acquisition unit is used for respectively acquiring the quantity of the feature points of the detection data features in each historical maintenance association packet;
and the parameter calculation unit is used for calculating a correction parameter k according to the number of the characteristic points of the detected data characteristic and the number of the characteristic points in the result characteristic data.
For specific limitations of the wearable device track device data detection apparatus, reference may be made to the above limitation of the wearable device track device data detection method, and no further description is given here. The modules in the wearable equipment track equipment data detection device can be all or partially realized by software, hardware and a combination 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, 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 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 network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements 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 steps of when executing the computer program:
acquiring detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data;
analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
inputting the 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 transmitting the maintenance voice data to the corresponding wearing detection equipment terminal according to the wearing 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 detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data;
analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
inputting the 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 transmitting the maintenance voice data to the corresponding wearing detection equipment terminal according to the wearing detection data.
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 the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (4)
1. The wearable equipment track equipment data detection method is characterized by comprising the following steps of:
acquiring detection data of the wearable equipment, and splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data, wherein the method specifically comprises the following steps of:
acquiring inspection section information, acquiring an overhaul equipment image sent by a wearable detection equipment terminal according to the inspection section information, and identifying the overhaul equipment type from the overhaul equipment image;
acquiring the protocol type according to the overhaul equipment type;
analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
inputting the inspection original data into a preset equipment detection model to detect, thereby obtaining an equipment detection result, wherein the equipment detection model is used for detecting whether each electromechanical equipment has a fault, the equipment detection result is current equipment operation data of the electromechanical equipment with the fault, the equipment detection model is obtained by training according to the data of the normal operation and the abnormal operation of each electromechanical equipment in advance, all the inspection original data of one electromechanical equipment are input into the equipment inspection model by taking the electromechanical equipment as a unit, whether the electromechanical equipment has the fault is judged, and if the electromechanical equipment has the fault, the inspection original data of the electromechanical equipment with the fault is used as the equipment detection result;
acquiring historical maintenance data corresponding to the inspection section information, and acquiring equipment historical detection data and a corresponding equipment historical maintenance scheme from the historical maintenance data;
extracting detection data characteristics corresponding to each device history detection data, and associating the detection data characteristics with the device history maintenance scheme to obtain a corresponding history maintenance association packet;
training the historical maintenance association packet to obtain an equipment maintenance model corresponding to the inspection section information;
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 wearing detection equipment terminal according to the wearing detection equipment data, wherein the method specifically comprises the following steps of:
extracting result characteristic data from the equipment detection result, and comparing the result characteristic data with the detection data characteristics of each history maintenance association packet to obtain a corresponding comparison result;
and inputting the following formulas 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 feature points in the result feature data; a is the number of the alignment agreement; b is the inconsistent quantity of the comparison; beta is a weight parameter, S is the similarity score; k is a correction parameter, wherein the method for calculating the correction parameter k comprises the following steps:
respectively acquiring the number of feature points of the detected data features in each historical maintenance association packet;
inputting the number z of the feature points of the detected data features and the number of the feature points in the result feature data into the following formula to calculate a correction parameter k:
and sequencing the historical maintenance association packages according to the sequence from high to low of the similarity scores, and taking the equipment historical maintenance scheme corresponding to the first sequenced historical maintenance association package as the equipment maintenance scheme.
2. The utility model provides a wearable equipment track equipment data detection device which characterized in that, wearable equipment track equipment data detection device includes:
the detection data acquisition module is used for acquiring detection data of the wearable equipment, splitting the detection data of the wearable equipment according to a preset protocol type to obtain equipment inspection data, and comprises:
the type acquisition sub-module is used for acquiring inspection section information, acquiring an inspection equipment image sent by the wearing detection equipment terminal according to the inspection section information, and identifying the type of the inspection equipment from the inspection equipment image;
a protocol acquisition sub-module, configured to acquire the protocol type according to the overhaul equipment type;
the data analysis module is used for analyzing the inspection data of each device according to the protocol type to obtain the original inspection data;
the equipment detection module is used for inputting the inspection original data into a preset equipment detection model to detect, so as to obtain an equipment detection result, wherein the equipment detection model is used for detecting whether each electromechanical device has a fault or not, the equipment detection result is current equipment operation data of the electromechanical device with the fault, the equipment detection model is obtained through training according to the data of the normal operation and the abnormal operation of each electromechanical device in advance, all the inspection original data of one electromechanical device are input into the equipment inspection model by taking one electromechanical device as a unit, whether the electromechanical device has the fault or not is judged, and if the electromechanical device has the fault, the inspection original data of the electromechanical device with the fault is used as the equipment detection result;
the historical data acquisition module is used for acquiring historical maintenance data corresponding to the inspection section information, and acquiring equipment historical detection data and a corresponding equipment historical maintenance scheme from the historical maintenance data;
the data association module is used for extracting detection data characteristics corresponding to each equipment history detection data, and associating the detection data characteristics with the equipment history maintenance scheme to obtain a corresponding history maintenance association packet;
the model training module is used for training the historical maintenance association packet to obtain an equipment maintenance model corresponding to the inspection section information;
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 wearing detection equipment terminal according to the wearing detection equipment detection data, and the maintenance recommendation module comprises:
the feature comparison sub-module is used for extracting result feature data from the equipment detection result, and comparing the result feature data with the detection data features of each historical maintenance association packet to obtain a corresponding comparison result;
the score calculating sub-module is used for inputting the following formulas 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 feature points in the result feature data; a is the number of identical comparison; b is the inconsistent quantity of comparison; beta is a weight parameter, S is a similarity score; k is a correction parameter, and the score calculating submodule comprises:
the quantity acquisition unit is used for respectively acquiring the quantity of the feature points of the detection data features in each historical maintenance association packet;
the parameter calculation unit is used for inputting the number z of the feature points of the detected data features and the number of the feature points in the result feature data into the following formula to calculate a correction parameter k:
and the data recommending sub-module is used for sequencing the historical maintenance association packages according to the sequence from high to low of the similarity scores, and taking the equipment historical maintenance scheme corresponding to the sequenced first historical maintenance association package as an equipment maintenance scheme.
3. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the wearable device rail device data detection method of claim 1 when the computer program is executed by the processor.
4. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the wearable device rail device data detection method of claim 1.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109754094A (en) * | 2018-12-20 | 2019-05-14 | 广州航天海特系统工程有限公司 | Electromechanical equipment intelligence O&M method, system, equipment and storage medium |
| CN114299030A (en) * | 2021-12-29 | 2022-04-08 | 中国建设银行股份有限公司 | Object detection model processing method, device, equipment and storage medium |
| CN114565845A (en) * | 2022-02-23 | 2022-05-31 | 青岛图灵科技有限公司 | Intelligent inspection system for underground tunnel |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106845502B (en) * | 2017-01-23 | 2020-07-07 | 东南大学 | Wearable auxiliary device for equipment maintenance and visual equipment maintenance guiding method |
| CN206672041U (en) * | 2017-03-14 | 2017-11-24 | 北京大合创新科技发展有限公司 | A kind of mining equipment intelligent patrol detection based on augmented reality and safeguard accessory system |
| US11152098B2 (en) * | 2018-03-13 | 2021-10-19 | International Business Machines Corporation | System and method for health data management with wearable devices |
| CN109753893A (en) * | 2018-12-20 | 2019-05-14 | 广州航天海特系统工程有限公司 | Video detecting method, system, computer equipment and storage medium along track |
| CN111507147A (en) * | 2019-10-28 | 2020-08-07 | 深圳市海洋王照明工程有限公司 | Intelligent inspection method and device, computer equipment and storage medium |
| CN112288761B (en) * | 2020-07-07 | 2022-08-30 | 国网江苏省电力有限公司常州供电分公司 | Abnormal heating power equipment detection method and device and readable storage medium |
| CN115086391A (en) * | 2022-06-16 | 2022-09-20 | 国网重庆市电力公司电力科学研究院 | Fault expert system based on wearable distribution network inspection equipment and use method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109754094A (en) * | 2018-12-20 | 2019-05-14 | 广州航天海特系统工程有限公司 | Electromechanical equipment intelligence O&M method, system, equipment and storage medium |
| CN114299030A (en) * | 2021-12-29 | 2022-04-08 | 中国建设银行股份有限公司 | Object detection model processing method, device, equipment and storage medium |
| CN114565845A (en) * | 2022-02-23 | 2022-05-31 | 青岛图灵科技有限公司 | Intelligent inspection system for underground tunnel |
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