CN113419493B - Method and device for detecting abnormality of industrial equipment, electronic equipment and storage medium - Google Patents
Method and device for detecting abnormality of industrial equipment, electronic equipment and storage medium Download PDFInfo
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- CN113419493B CN113419493B CN202110701913.6A CN202110701913A CN113419493B CN 113419493 B CN113419493 B CN 113419493B CN 202110701913 A CN202110701913 A CN 202110701913A CN 113419493 B CN113419493 B CN 113419493B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application provides an abnormality detection method and device for industrial equipment, electronic equipment and a storage medium, wherein the abnormality detection method comprises the following steps: the method comprises the steps of obtaining condition data to be played back of target industrial equipment, wherein the condition data to be played back comprises attention parameter information of the target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time, determining tracing points of the target industrial equipment on a map based on the position, forming a motion track of the target industrial equipment based on the determined tracing points, playing back the condition data to be played back of the target industrial equipment along the motion track at a preset playback speed, and determining an abnormal detection result of the target industrial equipment based on a comparison result of the attention parameter information corresponding to the tracing points and a preset alarm rule for each tracing point. Therefore, visual playback of the working condition data to be played back on the spatial dimension can be realized, and the running state and abnormal conditions of the industrial equipment are displayed more intuitively and accurately.
Description
Technical Field
The present disclosure relates to the field of anomaly detection technologies, and in particular, to an anomaly detection method and apparatus for an industrial device, an electronic device, and a storage medium.
Background
With the advance of industry 4.0, the intelligence and complexity of industrial equipment has greatly increased. The smooth proceeding of the industrial production needs the healthy and effective operation based on industrial equipment, and if the industrial equipment, particularly mechanical equipment, is abnormal, the influence on the production process is great, and the overall production value benefit is directly influenced.
In the prior art, the working condition attribute of the industrial equipment is displayed and played back based on the time dimension, and the working condition data of the industrial equipment is displayed and subjected to multi-curve comparative analysis in the form of a curve graph (the horizontal axis is the time dimension, and the vertical axis is the working condition attribute value), so that the equipment operation working condition and the equipment abnormal condition at certain specific positions and road sections cannot be detected in the abnormal detection of the industrial equipment, and the abnormal detection of the industrial equipment is not visual and accurate enough.
Disclosure of Invention
In view of this, an object of the present application is to provide an anomaly detection method and apparatus for an industrial device, an electronic device, and a storage medium, which can implement visual playback of to-be-played back operating condition data of the industrial device in a spatial dimension, and more intuitively and accurately show an operating state and an anomaly of the industrial device, so that a user can conveniently explore a fault section of the industrial device.
In a first aspect, the present application provides an abnormality detection method for an industrial device, the abnormality detection method including:
obtaining working condition data to be played back of target industrial equipment, wherein the working condition data to be played back comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time;
determining a point of the target industrial device on a map based on the position to form a motion track of the target industrial device based on the determined point;
replaying the working condition data to be replayed of the target industrial equipment along the motion track according to a preset replay speed;
and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
Preferably, before the obtaining of the condition data to be played back of the target industrial device, the anomaly detection method further includes:
determining preset playback time of target industrial equipment, attention parameter information collected in the preset playback time and the position of the target industrial equipment in the preset playback time;
acquiring to-be-processed working condition data which corresponds to the preset playback time and the position and is related to the attention parameter information from a working condition data pool based on the preset playback time, the position and the attention parameter information;
and preprocessing the acquired working condition data to be processed to obtain the working condition data to be played back of the target industrial equipment.
Preferably, the preprocessing the acquired to-be-processed working condition data to obtain the to-be-played back working condition data of the target industrial equipment includes:
extracting abnormal working condition data lacking longitude and latitude from the working condition data to be processed, and performing position compensation on the abnormal working condition data to obtain first position working condition data of the target industrial equipment;
extracting base station positioning information from the to-be-processed working condition data, and performing track deviation correction on the base station positioning information to obtain second position working condition data of the target industrial equipment;
and replacing part of abnormal data in the working condition data to be processed with the first position working condition data and the second position working condition data to obtain the working condition data to be played back of the target industrial equipment.
Preferably, the predetermined playback speed is determined by:
determining a target speed of the target industrial equipment based on the distance and the playback time difference between any two playback positions in the working condition data to be played back;
and determining the preset playback speed of the target industrial equipment based on the target speed and the preset playback magnification of the target industrial equipment.
Preferably, the playing back the data of the condition to be played back of the target industrial device along the motion track according to the predetermined playback speed includes:
creating an attribute value card aiming at the target industrial equipment, wherein the attribute value card is used for presenting attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and an abnormal detection result;
and controlling the attribute value card to move along the motion track according to a preset playback speed, wherein the acquisition time and the attention parameter information presented by the attribute value card change along with the tracing point of the motion track.
Preferably, for each point, determining an abnormality detection result of the target industrial equipment based on a comparison result of the parameter information of interest corresponding to the point and a predetermined alarm rule includes:
acquiring a preset alarm rule corresponding to each concerned parameter information, wherein the preset alarm rule is used for indicating whether the concerned parameter information is abnormal or not;
judging whether the concerned parameter information is in an alarm interval corresponding to the preset alarm rule or not aiming at each concerned parameter information;
and aiming at each piece of attention parameter information, when the attention parameter information exceeds the alarm interval, determining that the attention parameter information is abnormal, and obtaining an abnormal detection result of the target industrial equipment.
Preferably, after the working condition data to be played back of the target industrial equipment is played back along the motion trajectory at the predetermined playback speed, the abnormality detection method further includes:
determining historical working condition data related to the abnormal detection result from a working condition data pool based on the abnormal detection result of the target industrial equipment; analyzing an abnormal detection result of the target industrial equipment according to the determined historical working condition data;
or controlling the target industrial equipment to move again according to the motion track, and analyzing the abnormal detection result of the target industrial equipment in real time.
In a second aspect, the present application provides an abnormality detection apparatus for an industrial device, the abnormality detection apparatus comprising:
the data acquisition module is used for acquiring the working condition data to be played back of the target industrial equipment, and the working condition data to be played back comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time;
a track forming module, which is used for determining the tracing points of the target industrial equipment on the map based on the positions so as to form the motion track of the target industrial equipment based on the determined tracing points;
the working condition playback module is used for playing back the working condition data to be played back of the target industrial equipment along the motion track according to the preset playback speed;
and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
In a third aspect, the present application provides an electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the method of anomaly detection for an industrial device as described above.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the abnormality detection method for an industrial device as described above.
The application provides an abnormality detection method and device for industrial equipment, electronic equipment and a storage medium, wherein the abnormality detection method comprises the following steps: acquiring to-be-played back condition data of target industrial equipment, wherein the to-be-played back condition data comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time; determining a point of the target industrial device on a map based on the position to form a motion track of the target industrial device based on the determined point; replaying working condition data to be replayed of the target industrial equipment along the motion track according to the preset replay speed; and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
Therefore, compared with the method for displaying and replaying the working condition attribute of the equipment based on the time dimension and displaying and carrying out multi-curve comparative analysis on the working condition data of the industrial equipment in the form of a curve graph in the prior art, the method for displaying and carrying out multi-curve comparative analysis on the working condition data of the industrial equipment at certain specific positions and road sections cannot be used for ascertaining the equipment operation working condition and the equipment abnormal condition at certain specific positions and road sections in the abnormal detection of the industrial equipment, the tracing point of the target industrial equipment on the map is determined based on the position of the target industrial equipment corresponding to the acquisition time, so that the motion track of the target industrial equipment is obtained.
In order to make the aforementioned objects, features and advantages of the present application comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of an abnormality detection method for an industrial device according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of another method for detecting anomalies in industrial equipment according to an embodiment of the present disclosure;
fig. 3 is a block flow diagram of an anomaly detection method for an industrial device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an abnormality detection apparatus for an industrial device according to an embodiment of the present disclosure;
fig. 5 is a second schematic structural diagram of an abnormality detection apparatus for industrial equipment according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
First, an application scenario to which the present application is applicable will be described. The method and the device can be applied to the technical field of anomaly detection, and the anomaly detection of typical industrial equipment has great scientific and engineering values. With the advance of industry 4.0, the intellectualization and the complexity of industrial equipment greatly rise, the smooth proceeding of industrial production needs to be based on the healthy and effective operation of the industrial equipment, if the industrial equipment, particularly mechanical equipment, is abnormal, the influence on the production process is huge, the whole production value benefit is directly influenced, and then how to more conveniently and more quickly detect the fault and the abnormality in the operation of the industrial equipment is the problem to be solved at present.
At present, for users in the industrial field, there are two main methods for analyzing attributes in the anomaly detection process of the existing industrial equipment, one method is shown and analyzed in a list form, and the serial number of the list is often a timestamp; the other type is shown in a graph form and multi-curve comparative analysis, wherein the horizontal axis is a time dimension, and the vertical axis is an attribute value. The disadvantages of these schemes are that the device condition attributes cannot be displayed and played back based on the spatial dimension, where the spatial dimension is the combination of position and time, and a two-dimensional spatial display is performed on a map, and the device condition attributes may be the single-time condition, reported voltage, speed, torque, oil amount, etc. of the device at different locations, and in the anomaly detection, the operation condition and device fault condition at some specific positions and road sections cannot be conveniently detected.
Based on this, the embodiment of the application provides an anomaly detection method and apparatus for industrial equipment, an electronic device and a storage medium, which can realize flexible, convenient, visual and accurate equipment multi-attribute playback based on space dimensions, and perform anomaly detection in the equipment multi-attribute playback process, so that not only can the visual playback of the to-be-played back working condition data of the industrial equipment in the space dimensions be realized, but also the running state and the abnormal condition of the industrial equipment can be visually and accurately displayed, and a user can conveniently explore a fault section of the industrial equipment.
Referring to fig. 1, fig. 1 is a flowchart illustrating an abnormality detection method for an industrial device according to an embodiment of the present disclosure. As shown in fig. 1, an abnormality detection method provided in an embodiment of the present application includes:
s110, obtaining the working condition data to be replayed of the target industrial equipment, wherein the working condition data to be replayed comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time.
In this step, the target industrial device is an industrial device that the target user wants to detect, and the industrial device may be a forklift, an excavator, a cement tank truck, a rechargeable battery, a loader, or the like. The data of the working condition to be replayed is obtained after data in the data pool is preprocessed, the data of the working condition to be replayed is the working condition data after position compensation and track deviation correction, and meanwhile the data of the working condition to be replayed comprises concerned parameter information of the target industrial equipment, acquisition time of the concerned parameter information and the position of the target industrial equipment corresponding to the acquisition time. The attention parameter information is pre-selected, and the attention parameter information of the target user is different for different target industrial devices, for example, if the target industrial device has 1000 parameters, the attention parameter manually selected according to the actual requirement can further obtain 5 to 10 attention parameters, and the attention parameters can be regarded as important parameters of the target industrial device.
In addition, according to the acquisition time of the concerned parameter information and the position of the target industrial equipment corresponding to the acquisition time, the motion trail of the target industrial equipment can be drawn on the map, and therefore working condition analysis of the spatial dimension is conducted.
Specifically, the parameter information of interest may be oil pressure, temperature, for a rechargeable battery, current, voltage, speed, temperature, and the like; for a cement tanker, rotational speed, etc.
And S120, determining the point of the target industrial device on the map based on the position, and forming a motion track of the target industrial device based on the determined point.
In this step, the points are drawn on the map according to the position of the target industrial device corresponding to the acquisition time, and the motion trajectory of the target industrial device can be formed according to the determined points. The motion trajectory is a historical travel path of the target industrial device, and the travel path of the target industrial device can be reproduced according to the motion trajectory.
S130, replaying the working condition data to be replayed of the target industrial equipment along the motion track according to the preset replay speed; and aiming at each drawing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the drawing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the drawing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the concerned parameter information is played back to each drawing point.
In the step, the condition data to be played back of the target industrial equipment is played back, wherein the condition data to be played back comprises the attention parameter information of the target industrial equipment, the acquisition time of the attention parameter information and the position of the target industrial equipment corresponding to the acquisition time. In the playback process, aiming at each drawing point, corresponding to one piece of attention parameter information, the position and the acquisition time, according to a preset alarm rule, carrying out abnormity detection on the attention parameter information corresponding to each drawing point to obtain an abnormity detection result, and thus, when each drawing point is played back, the abnormity detection result of the target industrial equipment can be presented.
Furthermore, when the working condition data to be played back of the target industrial equipment is played back along the motion track according to the preset playback speed, the historical driving track of the target industrial equipment can be reproduced along the motion track, and the running state and the abnormal condition of the target industrial equipment can be displayed more visually, so that a target user can directly obtain the abnormal detection result of the target industrial equipment in the playback process, and the method is not only visual and convenient, but also can accurately probe the fault section of the industrial equipment.
The method for detecting the abnormality of the industrial equipment comprises the steps of obtaining working condition data to be played back of target industrial equipment, wherein the working condition data to be played back comprises the following steps: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time; determining a point of the target industrial device on the map based on the position to form a motion track of the target industrial device based on the determined point; replaying the working condition data to be replayed of the target industrial equipment along the motion track according to a preset replay speed; and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
Therefore, compared with the method for displaying and replaying the working condition attribute of the equipment based on the time dimension and displaying and analyzing the working condition data of the industrial equipment in a curve graph mode in the prior art, the method for displaying and analyzing the working condition data of the industrial equipment in a multi-curve comparison mode cannot detect the equipment operation working condition and the equipment abnormal condition at certain specific positions and road sections in the abnormal detection of the industrial equipment, the tracing point of the target industrial equipment on the map is determined based on the position of the target industrial equipment corresponding to the obtaining time, so that the motion track of the target industrial equipment is obtained, in the replaying process of the target industrial equipment, the concerned parameter information corresponding to the tracing point, the obtaining time of the concerned parameter information and the abnormal detection result can be presented when the target industrial equipment is replayed to each tracing point, the visual replaying of the working condition data to be replayed of the industrial equipment on the space dimension can be realized, the operation state and the abnormal condition of the industrial equipment can be intuitively and accurately displayed, and a user can conveniently explore the fault section of the industrial equipment.
Referring to fig. 2, fig. 2 is a flowchart of another method for detecting an abnormality of an industrial device according to an embodiment of the present disclosure. As shown in fig. 2, an abnormality detection method provided in an embodiment of the present application includes:
s210, determining the preset playback time of the target industrial equipment, the attention parameter information collected in the preset playback time, and the position of the target industrial equipment in the preset playback time.
Here, the preset playback time of the target industrial device, the parameter information of interest collected within the preset playback time, and the position where the target industrial device is located within the preset playback time are defined according to the requirements of the target user. Specifically, a parameter list may be set, where the parameter list is obtained by using the preset playback time and the location of the target industrial device as arguments and marking all parameter information corresponding to the preset playback time and location. And then selecting the concerned parameter information from the parameter list according to the requirement of the target user.
And S220, acquiring to-be-processed working condition data which corresponds to the preset playback time and the position and is related to the attention parameter information from a working condition data pool based on the preset playback time, the position and the attention parameter information.
Here, according to the preset playback time, the position, and the attention parameter information, to-be-processed condition data related to the parameters are searched from a condition data pool, where the to-be-processed condition data may have some defects and may further affect the final playback of the target industrial equipment, so that the to-be-processed condition data needs to be preprocessed.
Specifically, the to-be-processed working condition data may be a designated parameter value sequence including latitude and longitude of a location, a timestamp, and attention parameter information.
And S230, preprocessing the acquired working condition data to be processed to obtain the working condition data to be played back of the target industrial equipment.
The method comprises the following steps of preprocessing the acquired working condition data to be processed, ensuring that the position data is relatively accurate, wherein in the embodiment of the application, the preprocessing mode comprises position compensation and track deviation correction, and specifically comprises the following steps:
extracting abnormal working condition data lacking longitude and latitude from the working condition data to be processed, and performing position compensation on the abnormal working condition data to obtain first position working condition data of the target industrial equipment; extracting base station positioning information from the working condition data to be processed, and performing track deviation correction on the base station positioning information to obtain second position working condition data of the target industrial equipment; and replacing part of abnormal data in the working condition data to be processed with the first position working condition data and the second position working condition data to obtain the working condition data to be played back of the target industrial equipment.
Specifically, for the abnormal working condition data of the lost longitude and latitude, position compensation is carried out, and a compensation mode can select front value compensation. Because the working condition data are not always reported, some working condition data are uploaded one packet by one packet, and some data are overlapped, namely the oil pressure uploaded in the previous second is 100MPa, and the oil pressure uploaded in the next second is 101MPa. The first compensation mode is as follows: if the oil temperature uploaded in the previous second is 20 degrees, the oil temperature is not uploaded in the next second, and the oil temperature is considered to be unchanged if the oil temperature is not uploaded; the second compensation mode is as follows: if the oil temperature uploaded in the previous second is 50 degrees and the oil temperature uploaded in the next second is 60 degrees, since the oil temperature value cannot be changed to be large, calculating (50 + 60)/2 =55, and considering that the oil temperature uploaded in the next second is 55 degrees, the change process can be slowed down;
the track deviation correction can be carried out on the position information positioned by the base station. If the positioning mode is Beidou positioning, GPS positioning and the like, the correction is not needed because the positioning mode is very sensitive; for the positioning of the base station, because the base station is often inaccurate in positioning, for example, many vehicles walk on the road, and the positioned point is not a road and needs to be corrected on the road by force. One track deviation rectifying mode is to forcibly rectify the track, and the other track deviation rectifying mode is to: the base station location is corrected based on the empirical address base using a number of empirical address bases. For example, the last second of the automobile is at the east of the road, the next second is at the west of the road, and the automobile is considered not to generate large displacement within the two seconds, so that the automobile can be corrected to the middle position of the road, and the condition data to be played back is ensured to have position data and to be relatively accurate.
S240, obtaining the working condition data to be replayed of the target industrial equipment, wherein the working condition data to be replayed comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time.
And S250, determining the point of the target industrial equipment on the map based on the position, and forming a motion track of the target industrial equipment based on the determined point.
S260, replaying the working condition data to be replayed of the target industrial equipment along the motion track according to the preset replay speed; and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
The descriptions of S240 to S260 may refer to the descriptions of S110 to S130, and the same technical effects can be achieved, which are not described in detail.
In the embodiment of the present application, as a preferred embodiment, step S260 determines the predetermined playback speed by:
determining a target speed of the target industrial equipment based on the distance and the playback time difference between any two playback positions in the working condition data to be played back; and determining the preset playback speed of the target industrial equipment based on the target speed and the preset playback magnification of the target industrial equipment.
Here, a target speed of the target industrial device is calculated according to the distance between any two playback positions and the playback time difference, and the target speed can replace the actual speed of the target industrial device at the distance.
Specifically, the predetermined playback speed is found by the product between the target speed and the preset playback magnification.
In the embodiment of the present application, as a preferred embodiment, step S260 includes:
creating an attribute value card aiming at the target industrial equipment, wherein the attribute value card is used for presenting attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and an abnormal detection result; and controlling the attribute value card to move along the motion track according to a preset playback speed, wherein the acquisition time and the attention parameter information presented by the attribute value card change along with the tracing point of the motion track.
Here, the attribute value card presents some cases of the attention parameter information corresponding to the drawing point, the acquisition time of the attention parameter information, and the abnormality detection result, and it moves along with the target industrial equipment, the value on the attribute value card moves along with the motion trajectory, and the attribute value card is constantly being changed and played back.
In this way, the abnormality detection result is displayed through the attribute value card, so that the target user can intuitively know the abnormality of the target industrial equipment.
In the embodiment of the present application, as a preferred embodiment, step S260 includes:
acquiring a preset alarm rule corresponding to each piece of attention parameter information, wherein the preset alarm rule is used for indicating whether the attention parameter information is abnormal or not; judging whether the concerned parameter information is in an alarm interval corresponding to the preset alarm rule or not aiming at each concerned parameter information; and aiming at each piece of attention parameter information, when the attention parameter information exceeds the alarm interval, determining that the attention parameter information is abnormal, and obtaining an abnormal detection result of the target industrial equipment.
Here, each piece of attention parameter information corresponds to a predetermined alarm rule, and when the attention parameter information exceeds an alarm interval corresponding to the predetermined alarm rule, the attention parameter information is considered to be abnormal, and the abnormal result is determined as the abnormal detection result of the target industrial equipment within the acquisition time.
The predetermined alarm rules may be: and when the oil pressure is less than a certain value and the oil temperature is more than a certain value, a red alarm is sent out.
Further, in analyzing the abnormal situation of the target industrial equipment, it is performed on a per dot basis, so that when different types of failures occur in the target industrial equipment, colors between dots can be distinguished by different colors. For example, when the target industrial device has an oil temperature abnormality between a first trace point and a third trace point, a red line can be selected as a connecting line between the trace points; when the target industrial equipment has abnormal rotating speed between the ninth point drawing and the twelfth point drawing, the connecting line between the point drawing can be a yellow line, so that the fault interval and the fault type of the target industrial equipment in the whole operation process can be reflected.
In the embodiment of the present application, as a preferred embodiment, after step S260, the abnormality detecting method further includes:
determining historical working condition data related to the abnormal detection result from a working condition data pool based on the abnormal detection result of the target industrial equipment; analyzing an abnormal detection result of the target industrial equipment according to the determined historical working condition data;
or controlling the target industrial equipment to move again according to the motion track, and analyzing the abnormal detection result of the target industrial equipment in real time.
Here, the target industrial equipment in which the abnormality occurs is diagnosed in detail. The diagnosis mode comprises two types, wherein the first type is as follows: the time when the target industrial equipment is abnormal can be conveniently found out due to the time and the position, the parameter information on the corresponding position can be found out, and the problem of the target industrial equipment probably exists can be judged by combining the historical condition. The second method is as follows: and moving the target industrial equipment to the position where the abnormality occurs to reproduce the situation of the target industrial equipment, and observing the condition of the target industrial equipment to perform a real-time diagnosis process.
Referring to fig. 3, fig. 3 is a flowchart of an abnormality detection method for an industrial device according to an embodiment of the present application, and as shown in fig. 3, the abnormality detection for the industrial device is performed according to the following steps:
(1) And (3) user input: the user selects 1 industrial device (target industrial device) and single or multiple pieces of attention parameter information, the attention parameter information represents the attention attribute of the playback, and then selects the preset playback time and playback speed multiplying power, and simultaneously needs to establish a preset alarm rule.
(2) Acquiring working condition data: and acquiring a specified parameter value sequence of the industrial equipment within preset playback time from the working condition data pool, wherein the specified parameter value sequence comprises longitude and latitude of a position, a timestamp and attention parameter information of a user.
(3) Data preprocessing: carrying out position compensation on the working condition data losing the longitude and latitude, wherein the compensation mode can select previous value compensation, namely using the position data reported last time; for the position information positioned by the base station, track deviation correction can be carried out; in addition to this, a target speed may be generated, which is calculated from the time difference and the distance between the two location points, to replace the actual speed for this short distance.
(4) Sequentially playing back and rendering the preprocessed data: and extracting position information for each tracing point to be used for tracing the track on the map, and multiplying the target speed by a preset playback multiplying factor to control the playback speed between the two points. And displaying the industrial equipment and the current corresponding attribute value card at each trace point, calculating whether the current concerned parameter information triggers a preset alarm rule, if the red alarm is triggered, automatically pausing playback, and performing further detailed diagnosis by a user or selecting to continue the playback. The color of a connecting line between two tracing points is judged according to whether an alarm exists or not and the alarm category, and is divided into red, yellow and blue, so that the fault interval of the industrial equipment in the whole operation process can be embodied. And finally, repeating the steps until the whole condition data set to be played back is played back completely.
It is necessary to supplement, in the embodiments of the present application, the relevant electronic fences can be displayed synchronously on the map, and the execution instruction triggered when the industrial device crosses the fence can be analyzed conveniently. Specifically, when some industrial devices set the electronic fence, some execution instructions are set synchronously, and after the industrial devices cross the fence, the execution instructions are automatically executed, and the execution of the instructions is finally embodied as the relevant parameter information changes.
The method for detecting the abnormality of the industrial equipment, provided by the embodiment of the application, utilizes the map to analyze the working condition in the spatial dimension, and forms a uniform solution by combining methods such as track playback, predetermined alarm rule operation and the like. Based on the method, the visual playback of the abnormal detection results of the operation conditions and the attention parameter information of the industrial equipment on the space dimension can be realized, and the operation and abnormal states of the industrial equipment are displayed more intuitively.
Based on the same inventive concept, the embodiment of the present application further provides an apparatus for detecting an abnormality of an industrial device, which corresponds to the method for detecting an abnormality of an industrial device.
Referring to fig. 4 and 5, fig. 4 is a first schematic structural diagram of an abnormality detection apparatus for industrial equipment according to an embodiment of the present disclosure, and fig. 5 is a second schematic structural diagram of an abnormality detection apparatus for industrial equipment according to an embodiment of the present disclosure. As shown in fig. 4, the abnormality detection apparatus 400 includes:
a data obtaining module 410, configured to obtain to-be-played back condition data of a target industrial device, where the to-be-played back condition data includes: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time;
a trajectory forming module 420 for determining points of the target industrial device on the map based on the location to form a motion trajectory of the target industrial device based on the determined points;
the condition playback module 430 is configured to play back the condition data to be played back of the target industrial device along the motion trajectory according to a predetermined playback speed;
and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
Further, as shown in fig. 5, the abnormality detection apparatus 400 further includes:
the playback determining module 440 is configured to determine a preset playback time of the target industrial device, the parameter information of interest collected within the preset playback time, and a position of the target industrial device within the preset playback time;
a working condition obtaining module 450, configured to obtain to-be-processed working condition data, which corresponds to the preset playback time and the position and is related to the attention parameter information, from a working condition data pool based on the preset playback time, the position, and the attention parameter information;
and the data processing module 460 is configured to preprocess the acquired to-be-processed working condition data to obtain to-be-played back working condition data of the target industrial device.
Preferably, when the data processing module 460 is configured to perform preprocessing on the acquired to-be-processed operating condition data to obtain to-be-played back operating condition data of the target industrial device, the data processing module 460 is specifically configured to:
extracting abnormal working condition data lacking longitude and latitude from the working condition data to be processed, and performing position compensation on the abnormal working condition data to obtain first position working condition data of the target industrial equipment;
extracting base station positioning information from the to-be-processed working condition data, and performing track deviation correction on the base station positioning information to obtain second position working condition data of the target industrial equipment;
and replacing part of abnormal data in the working condition data to be processed with the first position working condition data and the second position working condition data to obtain the working condition data to be played back of the target industrial equipment.
Preferably, the condition playback module 430 is configured to determine the predetermined playback speed by:
determining a target speed of the target industrial equipment based on the distance and the playback time difference between any two playback positions in the working condition data to be played back;
and determining the preset playback speed of the target industrial equipment based on the target speed and the preset playback magnification of the target industrial equipment.
Preferably, when the condition playback module 430 is configured to play back the condition data to be played back of the target industrial device along the motion trajectory according to the predetermined playback speed, the condition playback module 430 is specifically configured to:
creating an attribute value card aiming at the target industrial equipment, wherein the attribute value card is used for presenting attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and an abnormal detection result;
and controlling the attribute value card to move along the motion track according to a preset playback speed, wherein the acquisition time and the attention parameter information presented by the attribute value card change along with the tracing point of the motion track.
Preferably, when the condition playback module 430 is configured to determine, for each trace point, an abnormality detection result of the target industrial device based on a comparison result of the parameter information of interest corresponding to the trace point and a predetermined alarm rule, the condition playback module 430 is specifically configured to:
acquiring a preset alarm rule corresponding to each piece of attention parameter information, wherein the preset alarm rule is used for indicating whether the attention parameter information is abnormal or not;
judging whether the concerned parameter information is in an alarm interval corresponding to the preset alarm rule or not according to each concerned parameter information;
and aiming at each piece of attention parameter information, when the attention parameter information exceeds the alarm interval, determining that the attention parameter information is abnormal, and obtaining an abnormal detection result of the target industrial equipment.
Further, as shown in fig. 5, the abnormality detection apparatus 400 further includes an abnormality analysis module 470, where the abnormality analysis module 470 is specifically configured to:
determining historical working condition data related to the abnormal detection result from a working condition data pool based on the abnormal detection result of the target industrial equipment; analyzing an abnormal detection result of the target industrial equipment according to the determined historical working condition data;
or controlling the target industrial equipment to move again according to the motion track, and analyzing the abnormal detection result of the target industrial equipment in real time.
The anomaly detection device for the industrial equipment comprises a data acquisition module, a track forming module and a working condition playback module, wherein the data acquisition module is used for acquiring to-be-played back working condition data of the target industrial equipment, and the to-be-played back working condition data comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time; the track forming module is used for determining the tracing points of the target industrial equipment on the map based on the positions so as to form the motion track of the target industrial equipment based on the determined tracing points; the working condition playback module is used for playing back working condition data to be played back of the target industrial equipment along the motion track according to a preset playback speed; and aiming at each tracing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of the concerned parameter information corresponding to the tracing point and a preset alarm rule, and presenting the concerned parameter information corresponding to the tracing point, the acquisition time of the concerned parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each tracing point.
In this way, the tracing point of the target industrial equipment on the map is determined based on the position of the target industrial equipment corresponding to the acquisition time, so that the motion track of the target industrial equipment is obtained, in the playback process of the target industrial equipment, when the target industrial equipment is played back to each tracing point, the attention parameter information corresponding to the tracing point, the acquisition time of the attention parameter information and the abnormal detection result of the target industrial equipment can be presented, the visual playback of the to-be-played back working condition data of the industrial equipment on the spatial dimension can be realized, the running state and the abnormal condition of the industrial equipment can be visually and accurately displayed, and a user can conveniently explore the fault section of the industrial equipment.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the electronic device 600 includes a processor 610, a memory 620, and a bus 630.
The memory 620 stores machine-readable instructions executable by the processor 610, when the electronic device 600 runs, the processor 610 communicates with the memory 620 through the bus 630, and when the machine-readable instructions are executed by the processor 610, the steps of the method for detecting an abnormality of an industrial device in the method embodiments shown in fig. 1 and fig. 2 may be executed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for detecting an abnormality of an industrial device in the method embodiments shown in fig. 1 and fig. 2 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit the technical solutions, and the scope of the present application is not limited to the above-mentioned embodiments, although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. An abnormality detection method for an industrial device, characterized by comprising:
obtaining working condition data to be played back of target industrial equipment, wherein the working condition data to be played back comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time;
determining a point of the target industrial device on a map based on the position to form a motion track of the target industrial device based on the determined point;
replaying the working condition data to be replayed of the target industrial equipment along the motion track according to a preset replay speed;
aiming at each drawing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of attention parameter information corresponding to the drawing point and a preset alarm rule, and presenting the attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each drawing point;
after the working condition data to be played back of the target industrial equipment is played back along the motion track according to the preset playback speed, the abnormality detection method further comprises the following steps of:
determining historical working condition data related to the abnormal detection result from a working condition data pool based on the abnormal detection result of the target industrial equipment; analyzing an abnormal detection result of the target industrial equipment according to the determined historical working condition data;
or controlling the target industrial equipment to move to the position where the abnormality occurs again according to the motion track, and analyzing the abnormality detection result of the target industrial equipment in real time;
wherein the method further comprises:
when different types of failures occur in the target industrial equipment, colors between the dots are distinguished in different colors.
2. The abnormality detection method according to claim 1, characterized in that, before said acquisition of the condition data to be played back of the target industrial equipment, the abnormality detection method further comprises:
determining preset playback time of target industrial equipment, attention parameter information collected in the preset playback time and the position of the target industrial equipment in the preset playback time;
acquiring to-be-processed working condition data which corresponds to the preset playback time and the position and is related to the attention parameter information from a working condition data pool based on the preset playback time, the position and the attention parameter information;
and preprocessing the acquired working condition data to be processed to obtain the working condition data to be played back of the target industrial equipment.
3. The anomaly detection method according to claim 2, wherein the preprocessing the acquired to-be-processed condition data to obtain the to-be-replayed condition data of the target industrial equipment comprises:
extracting abnormal working condition data lacking longitude and latitude from the working condition data to be processed, and performing position compensation on the abnormal working condition data to obtain first position working condition data of the target industrial equipment;
extracting base station positioning information from the to-be-processed working condition data, and performing track deviation correction on the base station positioning information to obtain second position working condition data of the target industrial equipment;
and replacing part of abnormal data in the working condition data to be processed with the first position working condition data and the second position working condition data to obtain the working condition data to be played back of the target industrial equipment.
4. The abnormality detection method according to claim 1, characterized in that the predetermined playback speed is determined by:
determining a target speed of the target industrial equipment based on the distance and the playback time difference between any two playback positions in the working condition data to be played back;
and determining the preset playback speed of the target industrial equipment based on the target speed and the preset playback magnification of the target industrial equipment.
5. The abnormality detection method according to claim 1, wherein said playing back the data of the condition to be played back of the target industrial equipment along the motion trajectory at a predetermined playback speed comprises:
creating an attribute value card aiming at the target industrial equipment, wherein the attribute value card is used for presenting attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and an abnormal detection result;
and controlling the attribute value card to move along the motion track according to a preset playback speed, wherein the acquisition time and the attention parameter information presented by the attribute value card change along with the tracing point of the motion track.
6. The abnormality detection method according to claim 1, wherein the determining, for each of the points, an abnormality detection result of the target industrial equipment based on a comparison result of the parameter information of interest corresponding to the point and a predetermined alarm rule includes:
acquiring a preset alarm rule corresponding to each piece of attention parameter information, wherein the preset alarm rule is used for indicating whether the attention parameter information is abnormal or not;
judging whether the concerned parameter information is in an alarm interval corresponding to the preset alarm rule or not aiming at each concerned parameter information;
and aiming at each piece of attention parameter information, when the attention parameter information exceeds the alarm interval, determining that the attention parameter information is abnormal, and obtaining an abnormal detection result of the target industrial equipment.
7. An abnormality detection device for an industrial equipment, characterized by comprising:
the data acquisition module is used for acquiring the working condition data to be played back of the target industrial equipment, and the working condition data to be played back comprises: the method comprises the steps of obtaining attention parameter information of target industrial equipment, obtaining time of the attention parameter information and the position of the target industrial equipment corresponding to the obtaining time;
a track forming module, which is used for determining the tracing points of the target industrial equipment on the map based on the positions so as to form the motion track of the target industrial equipment based on the determined tracing points;
the working condition playback module is used for playing back the working condition data to be played back of the target industrial equipment along the motion track according to the preset playback speed;
aiming at each drawing point, determining an abnormal detection result of the target industrial equipment based on a comparison result of attention parameter information corresponding to the drawing point and a preset alarm rule, and presenting the attention parameter information corresponding to the drawing point, acquisition time of the attention parameter information and the abnormal detection result of the target industrial equipment when the target industrial equipment is played back to each drawing point;
wherein, after the working condition data to be played back of the target industrial equipment is played back along the motion track according to the preset playback speed, the abnormality detection device further comprises:
the anomaly analysis module is used for determining historical working condition data related to the anomaly detection result from a working condition data pool based on the anomaly detection result of the target industrial equipment; analyzing an abnormal detection result of the target industrial equipment according to the determined historical working condition data;
or controlling the target industrial equipment to move to the position where the abnormality occurs again according to the motion track, and analyzing the abnormality detection result of the target industrial equipment in real time;
the condition playback module is further used for distinguishing colors among the points in different colors when different types of faults occur to the target industrial equipment.
8. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the processor executing the machine readable instructions to perform the steps of the abnormality detection method of the industrial device according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the abnormality detection method for an industrial device according to any one of claims 1 to 6.
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| JP2012026842A (en) * | 2010-07-22 | 2012-02-09 | Sony Corp | Information processor, information processing method and program |
| CN108181638B (en) * | 2017-12-21 | 2020-04-28 | 吉旗（成都）科技有限公司 | Solving device and method for accurately displaying positioning point and track data on electronic map |
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