CN114816917A - Monitoring data processing method, device, equipment and storage medium - Google Patents
Monitoring data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The present disclosure relates to the field of data processing technologies, and in particular, to a monitoring data processing method, apparatus, device, and storage medium. The method comprises the following steps: acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored; calculating the similarity of the real-time monitoring curve and the fault reference curve; and when the similarity is greater than a similarity threshold value, generating fault early warning information. The method and the device are used for solving the defect that alarm information can be sent after equipment failure in the prior art to cause equipment abnormality, and realize the process of early warning before the failure occurs.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a monitoring data processing method, apparatus, device, and storage medium.
Background
At present, various electronic devices occupy an important place in people's productive life. For example, data centers have a large number of asset devices to enable storage of important data. However, these devices inevitably have faults during the daily operation, such as cabinet overload, battery non-operation, UPS overload, full disk load, etc.
In the prior art, equipment is generally provided with a monitoring system, and the monitoring system monitors the operation of the equipment through sensors, measuring points and the like. When the terminal data collected by the monitoring system is abnormal, the equipment is indicated to have a fault, an alarm notice is immediately sent to operation and maintenance personnel, and after the operation and maintenance personnel receive the alarm notice, the equipment fault is repaired according to the details of the alarm information, so that the normal operation of the data center is ensured. However, the existing monitoring mechanism belongs to passive operation and maintenance, and an alarm can be sent only after the equipment fails, at this time, the equipment cannot normally operate, even the possibility of equipment damage caused by failure exists, and the operation cost of the equipment is increased.
Disclosure of Invention
The present disclosure provides a monitoring data processing method, apparatus, device and storage medium, which are used to solve the defect that in the prior art, an alarm message can only be sent after a device fails, resulting in device abnormality, and implement a process of performing early warning before the failure occurs.
The present disclosure provides a monitoring data processing method, including: acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored; calculating the similarity of the real-time monitoring curve and the fault reference curve; and when the similarity is greater than a similarity threshold value, generating fault early warning information.
According to the monitoring data processing method provided by the present disclosure, the calculating the similarity between the real-time monitoring curve and the fault reference curve includes: placing the fault reference curve and the real-time monitoring curve in a same plane rectangular coordinate system, wherein the horizontal axis of the plane rectangular coordinate system is time, and the vertical axis of the plane rectangular coordinate system is an index value of the target measuring point; dividing at least one time interval with the horizontal axis as a reference; and calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval.
According to the monitoring data processing method provided by the present disclosure, the calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval includes: calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; and according to each angle comparison result, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
According to the monitoring data processing method provided by the present disclosure, the calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval includes: processing the fault reference curve in each time interval as follows: acquiring two reference end points of the fault reference curve in the time interval; acquiring a reference straight line formed by the two reference end points; taking the angle of an included angle formed by the reference straight line and the transverse shaft as the reference angle; the calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval includes: processing the real-time monitoring curve in each time interval as follows: acquiring two real-time end points of the real-time monitoring curve in the time interval; acquiring a real-time straight line formed by the two real-time end points; and taking the angle of an included angle formed by the real-time straight line and the transverse shaft as the real-time angle.
According to a monitoring data processing method provided by the present disclosure, the comparing the reference angle and the real-time angle corresponding to each of the time intervals respectively to obtain an angle comparison result corresponding to each of the time intervals includes: for each of the time intervals, the following is performed: calculating an angle difference between the reference angle and the real-time angle; and when the angle difference is larger than or equal to a preset angle difference threshold value, generating an angle comparison result with dissimilar angles in the time interval, and when the difference is smaller than the difference threshold value, generating an angle comparison result with similar angles in the time interval.
According to a monitoring data processing method provided by the present disclosure, the obtaining a similarity between the real-time monitoring curve and the fault reference curve according to each angle comparison result includes: counting a first number of the time intervals with similar angles as the angle comparison result; and taking the proportion of the first number to the total number of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
According to the monitoring data processing method provided by the present disclosure, the calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval includes: calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval; calculating two reference end points of the fault reference curve in the time interval in each time interval to obtain a reference linear distance between the two reference end points, and calculating two real-time end points of the real-time monitoring curve in the time interval in each time interval to obtain a real-time linear distance between the two real-time end points; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; respectively comparing the reference linear distance and the real-time linear distance corresponding to each time interval to obtain a distance comparison result corresponding to each time interval; and according to each angle comparison result and each distance comparison result, obtaining the similarity of the real-time monitoring curve and the fault reference curve.
According to a monitoring data processing method provided by the present disclosure, the comparing the reference angle and the real-time angle corresponding to each of the time intervals respectively to obtain an angle comparison result corresponding to each of the time intervals includes: for each of the time intervals, the following is performed: calculating an angle difference between the reference angle and the real-time angle; when the angle difference is larger than or equal to a preset angle difference threshold value, generating an angle comparison result with dissimilar angles in the time interval, and when the difference is smaller than the difference threshold value, generating an angle comparison result with similar angles in the time interval; the comparing the reference linear distance and the real-time linear distance corresponding to each time interval respectively to obtain the distance comparison result corresponding to each time interval includes: for each of the time intervals, the following is performed: calculating a distance difference between the reference linear distance and the real-time linear distance; and when the distance difference is larger than or equal to a preset distance difference threshold, generating a distance comparison result with dissimilar distances in the time interval, and when the distance difference is smaller than the distance difference threshold, generating a distance comparison result with similar distances in the time interval.
According to a monitoring data processing method provided by the present disclosure, the obtaining a similarity between the real-time monitoring curve and the fault reference curve according to each angle comparison result and each distance comparison result includes: counting the angle comparison result as the second number of the time intervals with similar angles and the distance comparison result as the distance; and taking the proportion of the second quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
According to the monitoring data processing method provided by the present disclosure, the acquiring of the fault reference curve corresponding to the target measuring point in the device to be monitored includes: acquiring the historical fault data of the equipment to be monitored, wherein the historical fault data comprises data of the equipment to be monitored in at least one fault; extracting relevant fault data of the target measuring point in the historical fault data, wherein the relevant fault data refers to monitoring data of the target measuring point within a preset time length by taking the fault moment as a final time point when the equipment to be monitored breaks down every time; performing curve fitting based on the related fault data to obtain a fault reference curve corresponding to the target measuring point; the acquiring of the real-time monitoring curve of the target measuring point in the device to be monitored comprises: acquiring real-time monitoring data of the target measuring point in the equipment to be monitored within the preset time length by taking the current moment as a final time point; and acquiring the real-time monitoring curve based on the real-time monitoring data.
According to the monitoring data processing method provided by the present disclosure, before acquiring a fault reference curve corresponding to a target measuring point in a device to be monitored and acquiring a real-time monitoring curve of the target measuring point in the device to be monitored, the method further includes: acquiring current monitoring data of the target measuring point at the current moment; and determining that the current monitoring data is larger than a preset data threshold value.
The present disclosure also provides a monitoring data processing apparatus, including: the system comprises an acquisition module, a fault detection module and a fault analysis module, wherein the acquisition module is used for acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, and the fault reference curve is acquired according to historical fault data of the equipment to be monitored; the calculation module is used for calculating the similarity between the real-time monitoring curve and the fault reference curve; and the early warning generation module is used for generating fault early warning information when the similarity is greater than a similarity threshold value.
The present disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the monitoring data processing method as described in any one of the above when executing the program.
The present disclosure also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the monitoring data processing method as any one of the above.
According to the monitoring data processing method, the monitoring data processing device, the monitoring data processing equipment and the storage medium, the fault reference curve corresponding to the target measuring point in the equipment to be monitored is obtained, wherein the fault reference curve is obtained according to historical fault data of the equipment to be monitored. Acquiring a real-time monitoring curve of a target measuring point in equipment to be monitored; calculating the similarity between the real-time monitoring curve and the fault reference curve; and when the similarity is greater than the similarity threshold, generating fault early warning information. In the operation process of the equipment to be monitored, if the similarity between the real-time monitoring curve of the target measuring point and the fault reference curve of the equipment to be monitored reaches a similar threshold value, the equipment to be monitored has a fault trend, and fault early warning information is generated at the moment, so that a worker can check the equipment to be monitored before the fault occurs, the fault hidden danger is eliminated, and the condition that the equipment to be monitored is abnormal in operation and even damaged is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the present disclosure or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow diagram of a monitoring data processing method provided by the present disclosure;
FIG. 2 is an exemplary diagram of processing a real-time monitoring curve and a fault reference curve in a planar rectangular coordinate system provided by the present disclosure;
fig. 3 is a schematic structural diagram of a monitoring data processing device provided by the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided by the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
The monitoring data processing method provided by the disclosure is realized by a software algorithm. The software algorithms may be implemented in any type of processing device such as a computer, processor, etc. The scope of the present disclosure is not limited to the specific implementation of the processing device.
The monitoring data processing method provided by the embodiment of the disclosure is described below with reference to fig. 1 to 2.
In one embodiment, as shown in fig. 1, the monitoring data processing method is implemented as follows:
In this embodiment, the device to be monitored refers to a device that needs to be monitored, and the device to be monitored may be any device that needs to be monitored, for example, a computer device or a power electronic device. The scope of the present disclosure is not limited by the particular type of device to be monitored.
The device to be monitored comprises at least one measuring point, and each measuring point can monitor a corresponding operating parameter, such as a temperature measuring point, a humidity measuring point or a current measuring point. The method provided by the disclosure can be implemented for different measuring points respectively.
In this embodiment, the device to be monitored has historically failed at least once, and the failure may be a test failure in a test stage or an actual failure in a use stage. Each failure will generate failure data and store it in the monitoring database. The fault reference curve is a curve obtained from historical fault data.
The real-time monitoring curve is a curve formed by the recently generated monitoring data when the device to be monitored is in operation, for example, a curve formed by the monitoring data of the target measuring point of the device to be monitored within 1 minute from the current time.
In one embodiment, a fault reference curve corresponding to a target measuring point in equipment to be monitored is obtained, and the specific implementation process is as follows: acquiring historical fault data of equipment to be monitored, wherein the historical fault data comprises data of the equipment to be monitored in at least one fault; extracting relevant fault data of a target measuring point in historical fault data, wherein the relevant fault data refers to monitoring data of the target measuring point within a preset time length by taking a fault moment as a final time point when equipment to be monitored breaks down each time; and performing curve fitting based on the related fault data to obtain a fault reference curve corresponding to the target measuring point. The real-time monitoring curve of target measurement point in the equipment to be monitored is obtained, and the method comprises the following steps: acquiring real-time monitoring data of a target measuring point in equipment to be monitored within a preset time length by taking the current moment as a final time point; and acquiring a real-time monitoring curve based on the real-time monitoring data.
In this embodiment, when the device to be monitored has a fault, the data monitored by each measuring point may change correspondingly, and the monitoring data of each measuring point is stored in the monitoring database. That is, the historical fault data may include fault data for multiple stations.
And for the target measuring point, related fault data of the target measuring point needs to be extracted separately. And in order to better process fault data, when data are extracted, only the monitoring data of the target measuring point within a preset time length by taking the fault moment as a final time point are extracted. For example, the failure time of one failure of the device to be monitored is 8: 00, extracting 7: 59 to 8: 00, the preset time of the fault is 1 minute. After the relevant fault data of each fault in the historical fault data are extracted, a plurality of fault data with the duration of 1 minute can be obtained.
Curve fitting is then performed based on the relevant fault data for each fault. Specifically, based on the relevant fault data, curve fitting is performed to obtain a fault reference curve corresponding to the target measuring point, and the implementation process is as follows: and inputting the related fault data into a preset machine learning platform, and acquiring a fault reference curve output by the machine learning platform, wherein the machine learning platform realizes curve fitting.
In this embodiment, the machine learning platform may be constructed and implemented according to any curve fitting principle, for example, a least square method or a neural network algorithm, and the scope of protection of the present disclosure is not limited by the principle basis of the machine learning platform.
In this embodiment, the fault reference curve may be generated once, stored in the memory after being generated, and acquired again when necessary, so that the occupation of processing resources may be reduced. The fault reference curve can also be generated according to the latest historical fault data when used every time, so that the fault reference curve can be ensured to be more consistent with the current state of the equipment to be monitored. The specific implementation can be set according to actual conditions and/or requirements.
In a particular embodiment, the associated fault data obtained directly from the monitoring database may include flare data for the device to be monitored. When the mutation data is generated, the equipment to be monitored does not have a fault, so the mutation data is ineffective and does not contribute to the fault monitoring. Therefore, before the relevant fault data are input into a preset machine learning platform, the relevant fault data are screened, and mutation data in the relevant fault data are removed. And then, inputting the screened related fault data to a machine learning platform to complete the process of curve fitting.
In this embodiment, the specific method for screening the related fault data may be selected according to actual conditions and/or needs, for example, the screening is performed by using the data dispersion, as long as the purpose of the screening can be achieved. The scope of the present disclosure is not limited to a particular screening method.
In this embodiment, through screening out the sudden change data, can avoid invalid sudden change data to cause the influence to the fitting process of fault reference curve, avoid the problem that fault reference curve degree of accuracy is low, and then reduce the accuracy of trouble early warning.
In one embodiment, the device to be monitored is in a normal operation state for most of the time during the operation process, and if the curves are compared during the whole operation process of the device to be monitored, processing resources are wasted. Therefore, the current monitoring data of the target measuring point at the current moment is obtained before the fault reference curve corresponding to the target measuring point in the equipment to be monitored and the real-time monitoring curve of the target measuring point in the equipment to be monitored are obtained; and determining that the current monitoring data is larger than a preset data threshold value.
In the embodiment, before a real-time monitoring curve is obtained, current monitoring data acquired by a target measuring point at the current moment is compared with a preset data threshold, and when the current monitoring data is larger than the preset data threshold, the hidden danger in the operation of equipment to be monitored is indicated, and then a curve comparison process is started; when the current monitoring data is smaller than or equal to the data threshold, the device to be monitored is in a normal stable running state, the comparison of curves is not needed, and the current monitoring data only needs to be continuously monitored until the current monitoring data is larger than the preset data threshold.
In this embodiment, the preset data threshold may be set according to experimental data and/or needs, for example, a minimum value in the fault reference curve is used as the data threshold.
In this embodiment, only when it is determined that there is a hidden danger in the operation of the device to be monitored through the current monitoring data, the process of curve comparison is started. In the normal operation process of the equipment to be monitored, the curve comparison is not needed, so that the occupation of processing resources is greatly reduced, and the waste of the processing resources is avoided.
And 102, calculating the similarity between the real-time monitoring curve and the fault reference curve.
In this embodiment, the similarity between the real-time monitoring curve and the fault reference curve is calculated, and whether the equipment to be monitored has a fault tendency is determined according to the similarity.
In one embodiment, the similarity between the real-time monitoring curve and the fault reference curve is calculated, and the specific implementation process is as follows: placing the fault reference curve and the real-time monitoring curve in a same plane rectangular coordinate system, wherein the horizontal axis of the plane rectangular coordinate system is time, and the longitudinal axis of the plane rectangular coordinate system is an index value of a target measuring point; dividing at least one time interval with the horizontal axis as a reference; and calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval.
In this embodiment, the similarity between the real-time monitoring curve and the fault reference curve is calculated in a unitized manner by taking each time interval as a unit. The time interval is set according to the actual situation and needs, for example, 30 milliseconds (ms) according to the performance parameters of the device to be tested. The scope of the present disclosure is not limited to the specific values of the time intervals.
In one embodiment, the similarity between the real-time monitoring curve and the fault reference curve is determined according to the angle comparison result corresponding to each time interval. Specifically, the similarity between the real-time monitoring curve and the fault reference curve in each time interval is calculated, and the implementation process is as follows: calculating a reference angle formed by the fault reference curve and the transverse axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the transverse axis in each time interval; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; and according to the comparison result of each angle, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
In this embodiment, when the fault reference curve is generated from the related fault data of the preset time duration (for example, 1 minute), the implementation monitoring curve also intercepts the curve of the preset time duration closest to the current time, and places the two curves in the same plane rectangular coordinate system for processing.
In one embodiment, a reference angle formed by the fault reference curve and the horizontal axis in each time interval is calculated, and the specific implementation process is as follows: the fault reference curve in each time interval is processed as follows: acquiring two reference end points of a fault reference curve in a time interval; acquiring a reference straight line formed by two reference end points; taking the angle of an included angle formed by the reference straight line and the transverse shaft as a reference angle; calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval, wherein the real-time angle comprises the following steps: the real-time monitoring curve in each time interval is processed as follows: acquiring two real-time end points of a real-time monitoring curve in a time interval; acquiring a real-time straight line formed by two real-time end points; and taking the angle of an included angle formed by the real-time straight line and the transverse axis as a real-time angle.
In this embodiment, as shown in fig. 2, a solid curve represents a real-time monitoring curve, and a dashed curve represents a fault reference curve. The horizontal axis of the rectangular plane coordinate system is time, and the vertical axis of the rectangular plane coordinate system is an index value of the target measurement point, for example, a temperature value. On the horizontal axis, 1 minute is divided into 5 time intervals on average, a0, a1, a2, A3, a4, and a5 are respectively indicated by points of intersection of the end points of the time intervals and the real-time monitoring curve, and B0, B1, B2, B3, B4, and B5 are respectively indicated by points of intersection of the end points of the time intervals and the real-time monitoring curve.
Taking the first time interval as an example, a straight line between points A0 and A1 is obtained, and the straight line intersects the horizontal axis (X axis) to form an included angle alpha 1 ，α 1 The real-time angle corresponding to the first time interval. Obtaining a straight line between points B0 and B1, wherein the straight line intersects with a horizontal axis (X axis) to form an included angle alpha 2 ，α 2 The reference angle corresponding to the first time interval.
In one embodiment, after obtaining the reference angle and the real-time angle corresponding to each time interval, the reference angle and the real-time angle corresponding to each time interval are respectively compared to obtain an angle comparison result corresponding to each time interval, and the specific implementation process is as follows: for each time interval, the following is done: calculating the difference between the reference angle and the real-time angle; and when the difference value is greater than or equal to a preset angle difference value threshold value, generating angle comparison results with dissimilar angles in the time interval, and when the difference value is less than the angle difference value threshold value, generating angle comparison results with similar angles in the time interval.
In this embodiment, the angle difference threshold is set according to actual conditions and/or needs. And observing whether the fault reference curve and the implementation monitoring curve in each time interval are similar through the angle difference threshold value.
In one embodiment, the similarity between the real-time monitoring curve and the fault reference curve is obtained according to the comparison result of each angle, and the specific implementation process is as follows: counting a first number of time intervals with similar angles as an angle comparison result; and taking the proportion of the first quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
In this embodiment, the 5 time intervals are based on the example shown in fig. 2. And calculating the proportion of the first number of the time intervals with similar angles to the total number of the time intervals as the angle comparison result. If the first number is 3, the similarity is 3/5 × 100% — 60%; if the first number is 4, the similarity is 4/5 × 100% ═ 80%.
In one embodiment, the similarity between the real-time monitoring curve and the fault reference curve is determined according to the angle comparison result and the distance comparison result corresponding to each time interval. Specifically, the similarity between the real-time monitoring curve and the fault reference curve in each time interval is calculated, and the implementation process is as follows: calculating a reference angle formed by the fault reference curve and the transverse axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the transverse axis in each time interval; calculating two reference end points of the fault reference curve in each time interval in the time interval to obtain a reference linear distance between the two reference end points, and calculating two real-time end points of the real-time monitoring curve in each time interval in the time interval to obtain a real-time linear distance between the two real-time end points; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; respectively comparing the reference linear distance and the real-time linear distance corresponding to each time interval to obtain a distance comparison result corresponding to each time interval; and according to each angle comparison result and each distance comparison result, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
In the embodiment, the similarity between the real-time monitoring curve and the fault reference curve is calculated on the basis of distance and angle, and the comparison elements are added, so that interference factors can be further avoided, the comparison result is stable, and the reliability of data monitoring is improved.
In a specific example, still taking the example shown in fig. 2 as an example, the tables a0, a1, a2, A3, a4 and a5 show the intersections of the endpoints of the respective time intervals and the real-time monitoring curve, and the tables B0, B1, B2, B3, B4 and B5 show the intersections of the endpoints of the respective time intervals and the real-time monitoring curve. Taking the first time interval as an example, based on the above embodiment, α 1 is a real-time angle corresponding to the first time interval, and α 2 is a reference angle corresponding to the first time interval. Meanwhile, the linear distance between A0 and A1 is the real-time linear distance corresponding to the first time interval; the linear distance between B0 and B1 is the reference linear distance corresponding to the first time interval.
In one embodiment, the reference angle and the real-time angle corresponding to each time interval are respectively compared to obtain an angle comparison result corresponding to each time interval, and the specific implementation process is as follows: for each time interval, the following is done: calculating an angle difference value between the reference angle and the real-time angle; and when the angle difference value is greater than or equal to a preset angle difference value threshold value, generating angle comparison results with dissimilar angles in the time interval, and when the difference value is less than the angle difference value threshold value, generating angle comparison results with similar angles in the time interval. Respectively comparing the reference linear distance and the real-time linear distance corresponding to each time interval to obtain a distance comparison result corresponding to each time interval, wherein the specific implementation process is as follows: for each time interval, the following is done: calculating a distance difference value between the reference linear distance and the real-time linear distance; and when the distance difference is larger than or equal to a preset distance difference threshold, generating distance comparison results with dissimilar distances in the time interval, and when the distance difference is smaller than the distance difference threshold, generating distance comparison results with similar distances in the time interval.
In this embodiment, the distance difference threshold and the angle difference threshold are both set according to actual conditions and/or needs. And observing whether the fault reference curve and the implementation monitoring curve in each time interval are similar through the distance difference threshold and the angle difference threshold.
In one embodiment, the similarity between the real-time monitoring curve and the fault reference curve is obtained according to each angle comparison result and each distance comparison result, and the specific implementation process is as follows: counting the angle comparison result as the angle similarity, and the distance comparison result as the second number of the time intervals with similar distances; and taking the proportion of the second quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
In this embodiment, the angle comparison result is calculated as the angle similarity, and the distance comparison result is the time interval with similar distance, which is the proportion of the total number of the time intervals. If the first number is 2, the similarity is 2/5 × 100% ═ 40%.
And 103, generating fault early warning information when the similarity is greater than the similarity threshold.
In the embodiment, after the similarity is obtained, the similarity is compared with a similarity threshold, and when the similarity is greater than the similarity threshold, fault early warning information is generated; and when the similarity is less than or equal to the similarity threshold, not generating fault early warning information.
In this embodiment, the similarity threshold is set according to actual conditions and/or needs. For example, the similarity threshold is set to 70%, and if the similarity is 60% or 40%, the failure warning information is not generated; and if the similarity is 80%, generating fault early warning information.
In this embodiment, after the failure alarm information is generated, the user may be reminded in a preset manner, for example, any one or more of sound, image, light, text, and other manners. After the user receives the reminding information, the user can check the equipment to be monitored, so that hidden dangers are eliminated in advance, and normal operation of the equipment to be monitored is guaranteed.
The monitoring data processing method provided by the disclosure comprises the steps of obtaining a fault reference curve corresponding to a target measuring point in equipment to be monitored and obtaining a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is obtained according to historical fault data of the equipment to be monitored; calculating the similarity between the real-time monitoring curve and the fault reference curve; and when the similarity is greater than the similarity threshold, generating fault early warning information. In the operation process of the equipment to be monitored, if the similarity between the real-time monitoring curve of the target measuring point and the fault reference curve of the equipment to be monitored reaches a similar threshold value, the trend that the equipment to be monitored has faults is indicated, fault early warning information is generated at the moment, workers are provided to check the equipment to be monitored before the faults occur, the hidden fault danger is eliminated, and the condition that the equipment to be monitored is abnormal in operation and even damaged is avoided.
The following describes the monitoring data processing device provided by the embodiment of the present disclosure, and the monitoring data processing device described below and the monitoring data processing method described above may be referred to correspondingly. As shown in fig. 3, the monitoring data processing apparatus includes:
the acquisition module 301 is configured to acquire a fault reference curve corresponding to a target measurement point in a device to be monitored, and acquire a real-time monitoring curve of the target measurement point in the device to be monitored, where the fault reference curve is acquired according to historical fault data of the device to be monitored;
a calculating module 302, configured to calculate a similarity between the real-time monitoring curve and the fault reference curve;
and an early warning generation module 303, configured to generate fault early warning information when the similarity is greater than the similarity threshold.
In one embodiment, the calculating module 302 is specifically configured to place the fault reference curve and the real-time monitoring curve in a same plane rectangular coordinate system, where a horizontal axis of the plane rectangular coordinate system is time, and a vertical axis of the plane rectangular coordinate system is an index value of the target measuring point; dividing at least one time interval with the horizontal axis as a reference; and calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval.
In one embodiment, the calculating module 302 is specifically configured to calculate a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculate a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; and according to the comparison result of each angle, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
In an embodiment, the calculating module 302 is specifically configured to perform the following processing for the fault reference curve in each time interval: acquiring two reference end points of a fault reference curve in a time interval; acquiring a reference straight line formed by two reference end points; taking the angle of an included angle formed by the reference straight line and the transverse shaft as a reference angle; the real-time monitoring curve in each time interval is processed as follows: acquiring two real-time end points of a real-time monitoring curve in a time interval; acquiring a real-time straight line formed by two real-time end points; and taking the angle of an included angle formed by the real-time straight line and the transverse axis as a real-time angle.
In an embodiment, the calculating module 302 is specifically configured to perform the following processing for each time interval: calculating an angle difference value between the reference angle and the real-time angle; and when the angle difference value is greater than or equal to a preset angle difference value threshold value, generating angle comparison results with dissimilar angles in the time interval, and when the difference value is less than the angle difference value threshold value, generating angle comparison results with similar angles in the time interval.
In an embodiment, the calculating module 302 is specifically configured to count a first number of time intervals with similar angles as the angle comparison result; and taking the proportion of the first quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
In one embodiment, the calculating module 302 is specifically configured to calculate a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculate a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval; calculating two reference end points of the fault reference curve in each time interval in the time interval to obtain a reference linear distance between the two reference end points, and calculating two real-time end points of the real-time monitoring curve in each time interval in the time interval to obtain a real-time linear distance between the two real-time end points; respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval; respectively comparing the reference linear distance and the real-time linear distance corresponding to each time interval to obtain a distance comparison result corresponding to each time interval; and according to each angle comparison result and each distance comparison result, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
In an embodiment, the calculating module 302 is specifically configured to perform the following processing for each time interval: calculating an angle difference value between the reference angle and the real-time angle; when the angle difference is larger than or equal to a preset angle difference threshold value, generating angle comparison results with dissimilar angles in a time interval, and when the difference is smaller than the difference threshold value, generating angle comparison results with similar angles in the time interval; for each time interval, the following is done: calculating a distance difference value between the reference linear distance and the real-time linear distance; and when the distance difference is larger than or equal to a preset distance difference threshold, generating distance comparison results with dissimilar distances in the time interval, and when the distance difference is smaller than the distance difference threshold, generating distance comparison results with similar distances in the time interval.
In an embodiment, the calculating module 302 is specifically configured to count that the angle comparison result is angle similarity, and the distance comparison result is a second number of time intervals with similar distances; and taking the proportion of the second quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
In one embodiment, the obtaining module 301 is specifically configured to obtain the historical fault data of the device to be monitored, where the historical fault data includes data of the device to be monitored during at least one fault; extracting relevant fault data of the target measuring point in the historical fault data, wherein the relevant fault data refers to monitoring data of the target measuring point within a preset time length by taking a fault moment as a final time point when the equipment to be monitored breaks down each time; performing curve fitting based on the related fault data to obtain a fault reference curve corresponding to the target measuring point; the acquiring of the real-time monitoring curve of the target measuring point in the device to be monitored comprises: acquiring real-time monitoring data of the target measuring point in the equipment to be monitored within the preset time length by taking the current moment as a final time point; and acquiring the real-time monitoring curve based on the real-time monitoring data.
In one embodiment, the monitoring data processing apparatus further comprises a preprocessing module 304. The preprocessing module 304 is configured to obtain current monitoring data of a target measurement point at a current moment before obtaining a fault reference curve corresponding to the target measurement point in the device to be monitored and obtaining a real-time monitoring curve of the target measurement point in the device to be monitored; and determining that the current monitoring data is larger than a preset data threshold value.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a communication bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the communication bus 404. Processor 401 may call logic instructions in memory 403 to perform a method of monitoring data processing, the method comprising: acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored; calculating the similarity between the real-time monitoring curve and the fault reference curve; and when the similarity is greater than the similarity threshold, generating fault early warning information.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present disclosure 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 disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present disclosure also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the monitoring data processing method provided by the above methods, the method comprising: acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored; calculating the similarity between the real-time monitoring curve and the fault reference curve; and when the similarity is greater than the similarity threshold, generating fault early warning information.
In yet another aspect, the present disclosure also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the monitoring data processing method provided above, the method including: acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored; calculating the similarity between the real-time monitoring curve and the fault reference curve; and when the similarity is greater than the similarity threshold, generating fault early warning information.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (14)
1. A method for processing monitoring data, comprising:
acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, wherein the fault reference curve is acquired according to historical fault data of the equipment to be monitored;
calculating the similarity of the real-time monitoring curve and the fault reference curve;
and when the similarity is greater than a similarity threshold value, generating fault early warning information.
2. The method according to claim 1, wherein the calculating the similarity between the real-time monitoring curve and the fault reference curve comprises:
placing the fault reference curve and the real-time monitoring curve in a same plane rectangular coordinate system, wherein the horizontal axis of the plane rectangular coordinate system is time, and the vertical axis of the plane rectangular coordinate system is an index value of the target measuring point;
dividing at least one time interval with the horizontal axis as a reference;
and calculating the similarity between the real-time monitoring curve and the fault reference curve in each time interval.
3. The method according to claim 2, wherein the calculating the similarity between the real-time monitoring curve and the fault reference curve in each of the time intervals comprises:
calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval;
respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval;
and according to each angle comparison result, obtaining the similarity between the real-time monitoring curve and the fault reference curve.
4. The method according to claim 3, wherein the calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval comprises:
processing the fault reference curve in each time interval as follows: acquiring two reference end points of the fault reference curve in the time interval; acquiring a reference straight line formed by the two reference end points; taking the angle of an included angle formed by the reference straight line and the transverse shaft as the reference angle;
the calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval includes:
processing the real-time monitoring curve in each time interval as follows: acquiring two real-time end points of the real-time monitoring curve in the time interval; acquiring a real-time straight line formed by the two real-time end points; and taking the angle of an included angle formed by the real-time straight line and the transverse shaft as the real-time angle.
5. The method according to claim 3, wherein the comparing the reference angle and the real-time angle corresponding to each of the time intervals to obtain the angle comparison result corresponding to each of the time intervals comprises:
for each of said time intervals, the following is done:
calculating an angle difference between the reference angle and the real-time angle; and when the angle difference is larger than or equal to a preset angle difference threshold value, generating an angle comparison result with dissimilar angles in the time interval, and when the difference is smaller than the angle difference threshold value, generating an angle comparison result with similar angles in the time interval.
6. The method according to claim 5, wherein the obtaining the similarity between the real-time monitoring curve and the fault reference curve according to each angle comparison result comprises:
counting a first number of the time intervals with similar angles as the angle comparison result;
and taking the proportion of the first number to the total number of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
7. The method according to claim 2, wherein the calculating the similarity between the real-time monitoring curve and the fault reference curve in each of the time intervals comprises:
calculating a reference angle formed by the fault reference curve and the horizontal axis in each time interval, and calculating a real-time angle formed by the real-time monitoring curve and the horizontal axis in each time interval;
calculating two reference end points of the fault reference curve in the time interval in each time interval to obtain a reference linear distance between the two reference end points, and calculating two real-time end points of the real-time monitoring curve in the time interval in each time interval to obtain a real-time linear distance between the two real-time end points;
respectively comparing the reference angle and the real-time angle corresponding to each time interval to obtain an angle comparison result corresponding to each time interval;
respectively comparing the reference linear distance and the real-time linear distance corresponding to each time interval to obtain a distance comparison result corresponding to each time interval;
and according to each angle comparison result and each distance comparison result, obtaining the similarity of the real-time monitoring curve and the fault reference curve.
8. The method according to claim 7, wherein the comparing the reference angle and the real-time angle corresponding to each of the time intervals to obtain the angle comparison result corresponding to each of the time intervals comprises:
for each of the time intervals, the following is performed:
calculating an angle difference between the reference angle and the real-time angle; when the angle difference is larger than or equal to a preset angle difference threshold value, generating an angle comparison result with dissimilar angles in the time interval, and when the difference is smaller than the difference threshold value, generating an angle comparison result with similar angles in the time interval;
the comparing the reference linear distance and the real-time linear distance corresponding to each time interval respectively to obtain the distance comparison result corresponding to each time interval includes:
for each of the time intervals, the following is performed:
calculating a distance difference between the reference linear distance and the real-time linear distance; and when the distance difference is greater than or equal to a preset distance difference threshold, generating distance comparison results with dissimilar distances in the time interval, and when the distance difference is smaller than the distance difference threshold, generating distance comparison results with similar distances in the time interval.
9. The method according to claim 8, wherein the obtaining the similarity between the real-time monitoring curve and the fault reference curve according to each angle comparison result and each distance comparison result comprises:
counting the angle comparison result as the second number of the time intervals with similar angles and the distance comparison result as the distance;
and taking the proportion of the second quantity to the total quantity of the time intervals as the similarity of the real-time monitoring curve and the fault reference curve.
10. The monitoring data processing method according to claim 1, wherein the obtaining of the fault reference curve corresponding to the target measuring point in the device to be monitored comprises:
acquiring the historical fault data of the equipment to be monitored, wherein the historical fault data comprises data of the equipment to be monitored in at least one fault;
extracting relevant fault data of the target measuring point in the historical fault data, wherein the relevant fault data refers to monitoring data of the target measuring point within a preset time length by taking a fault moment as a final time point when the equipment to be monitored breaks down each time;
performing curve fitting based on the related fault data to obtain a fault reference curve corresponding to the target measuring point;
the acquiring of the real-time monitoring curve of the target measuring point in the device to be monitored comprises:
acquiring real-time monitoring data of the target measuring point in the equipment to be monitored within the preset time length by taking the current moment as a final time point;
and acquiring the real-time monitoring curve based on the real-time monitoring data.
11. The monitoring data processing method according to claim 1, wherein before acquiring the fault reference curve corresponding to the target measurement point in the device to be monitored and acquiring the real-time monitoring curve of the target measurement point in the device to be monitored, the method further comprises:
acquiring current monitoring data of the target measuring point at the current moment;
and determining that the current monitoring data is larger than a preset data threshold value.
12. A monitoring data processing apparatus, comprising:
the system comprises an acquisition module, a fault detection module and a fault analysis module, wherein the acquisition module is used for acquiring a fault reference curve corresponding to a target measuring point in equipment to be monitored and acquiring a real-time monitoring curve of the target measuring point in the equipment to be monitored, and the fault reference curve is acquired according to historical fault data of the equipment to be monitored;
the calculation module is used for calculating the similarity between the real-time monitoring curve and the fault reference curve;
and the early warning generation module is used for generating fault early warning information when the similarity is greater than a similarity threshold value.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of processing monitoring data according to any of claims 1 to 11 are implemented when the program is executed by the processor.
14. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the monitoring data processing method according to any one of claims 1 to 11.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115576733A (en) * | 2022-11-17 | 2023-01-06 | 广州信诚信息科技有限公司 | Intelligent equipment fault diagnosis system based on deep reinforcement learning |
| CN117788046A (en) * | 2024-01-25 | 2024-03-29 | 广东美电国创科技有限公司 | Power consumption monitoring and early warning method and device based on Internet of things |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115576733A (en) * | 2022-11-17 | 2023-01-06 | 广州信诚信息科技有限公司 | Intelligent equipment fault diagnosis system based on deep reinforcement learning |
| CN115576733B (en) * | 2022-11-17 | 2023-03-10 | 广州信诚信息科技有限公司 | Intelligent equipment fault diagnosis system based on deep reinforcement learning |
| CN117788046A (en) * | 2024-01-25 | 2024-03-29 | 广东美电国创科技有限公司 | Power consumption monitoring and early warning method and device based on Internet of things |
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