CN113869373A - Equipment abnormality detection method and device, computer equipment and storage medium - Google Patents
Equipment abnormality detection method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a device abnormality detection method, a device, a computer device and a storage medium. The method comprises the following steps: acquiring state data of equipment to be detected; preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm; acquiring time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the current time point is taken as an end node in the preset time period; and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result. The method automatically realizes the abnormal detection of the equipment to be detected, avoids the problem that the false detection probability and the missed detection probability are increased along with the increase of the detection time in manual detection, has higher stability and accuracy, and reduces the workload of workers.
Description
Technical Field
The present application relates to the field of device anomaly detection technologies, and in particular, to a device anomaly detection method and apparatus, a computer device, and a storage medium.
Background
With the development of industrial technology, industrial equipment becomes more important in industrial production, and abnormality detection of industrial equipment also becomes a focus problem in industrial production.
In the conventional anomaly detection process, after state data of industrial equipment such as molecular beam epitaxy equipment is collected, the collected data is displayed on a human-computer interaction interface, and a worker observes the state data in real time and judges whether anomaly exists.
However, in this way, the worker is required to have enough experience, and as the working time of the worker increases, the probability of wrong detection and missed detection of the worker greatly increases.
Disclosure of Invention
In view of the above, it is necessary to provide a device abnormality detection method, apparatus, computer device, and storage medium capable of automatically detecting device abnormality.
An apparatus abnormality detection method includes:
acquiring state data of equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
In one embodiment, the preprocessing the acquired data includes:
carrying out normalization processing, noise reduction processing and time correction processing on the acquired data;
and performing time sequence classification on the processed acquired data by taking time as a classification standard.
In one embodiment, the time correction process includes:
and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule.
In one embodiment, the analyzing the acquired time series state characteristic parameters of each time node by using a preset algorithm, and determining whether the time series state characteristic parameters of each time node are abnormal includes:
obtaining a time sequence evaluation index of a corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value;
and judging whether the corresponding time sequence state characteristic parameters are abnormal or not according to the time sequence evaluation indexes of the time nodes.
In one embodiment, the formula for obtaining the time sequence evaluation index of the corresponding time node according to the time sequence state characteristic parameter of each time node is as follows:
wherein E represents the time-series evaluation index, and Y representsjA parameter that is characteristic of the time series state,
representing the average value of the normal state characteristic parameters, and j representing the dimension of the time sequence state characteristic parameters.
In one embodiment, the determining whether the corresponding time sequence state characteristic parameter is abnormal according to the time sequence evaluation index of each time node includes:
and judging whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
In one embodiment, the determining whether the device to be detected is in an abnormal state according to the determination result includes:
acquiring time nodes with abnormal time sequence state characteristic parameters, and recording the time nodes as abnormal time nodes;
and judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is greater than a preset threshold value, if so, judging that the equipment to be detected is in an abnormal state.
An apparatus abnormality detection device comprising:
the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring state data of equipment to be detected, and the acquired data comprises state parameters acquired for multiple times;
the processing module is used for preprocessing the acquired data to obtain time sequence state parameters of time sequence classification and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
the judging module is used for acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and the judging module is used for judging whether the equipment to be detected is in an abnormal state or not according to the judging result.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring state data of equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring state data of equipment to be detected;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
According to the equipment abnormality detection method, the device, the computer equipment and the storage medium, the time sequence state parameters of time sequence classification can be obtained by obtaining the state data of the equipment to be detected, such as molecular beam epitaxy equipment, and preprocessing the obtained data, the time sequence state parameters are used as input data, the time sequence state characteristic parameters corresponding to the time sequence of the time sequence state parameters are obtained through an abnormality detection algorithm, the time sequence state characteristic parameters are used for reflecting the running state characteristics of the equipment to be detected, then the time sequence state characteristic parameters of each time node in a preset time period are subjected to abnormality analysis, and whether the state of the equipment to be detected of each time node is abnormal or not can be determined according to the judgment result, so that whether the equipment to be detected is in an abnormal state or not is determined, and the abnormality detection of the equipment to be detected is automatically realized; the abnormity detection process does not need the participation of working personnel, the problems of false detection probability and missing detection probability rising caused by the increase of the detection time of manual detection are avoided, the stability and the accuracy are higher, and the workload of the working personnel is reduced.
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In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of an exemplary embodiment of a device anomaly detection method;
FIG. 2 is a schematic diagram of state parameters of the molecular beam epitaxy apparatus in one embodiment;
FIG. 3 is a flowchart illustrating a method for detecting device anomalies according to one embodiment;
FIG. 4 is a schematic flow chart illustrating the preprocessing step performed on the acquired data in another embodiment;
FIG. 5 is a flowchart illustrating the time series state feature parameter abnormality determining step according to an embodiment;
FIG. 6 is a schematic flowchart of the step of determining the abnormality of the device under test in one embodiment;
FIG. 7 is a flowchart illustrating a method for detecting device anomalies according to another embodiment;
FIG. 8 is a block diagram showing the structure of an apparatus abnormality detection apparatus according to an embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Embodiments of the present application are set forth in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another.
As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises/comprising," "includes" or "including," etc., specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Also, as used in this specification, the term "and/or" includes any and all combinations of the associated listed items.
The equipment abnormity detection method can be directly applied to equipment to be detected. But also in the application environment as shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal may be, but is not limited to, the device to be detected and an upper computer of the device to be detected, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In an embodiment, as shown in fig. 3, a method for detecting device abnormality is provided, which is described by taking an example that the method is applied to a molecular beam epitaxy device, and includes:
s302, state data of the device to be detected are obtained, wherein the obtained data comprise state parameters collected for multiple times.
In the application, the data acquisition device acquires the state data of the equipment to be detected at regular intervals, the data acquisition device can be an upper computer of the equipment to be detected, and the acquired data is stored in the information acquisition device and/or the server. When the equipment abnormality detection is performed, the equipment abnormality detection device acquires all stored data or acquires stored data within a certain time range.
In an exemplary embodiment, the molecular beam epitaxy production process with stable operation includes daily debugging of equipment and epitaxy production. Typical daily commissioning involves equipment standby, bake out, and outgassing. And entering into the epitaxial production flow, the method comprises the following steps in time sequence: wafer loading, wafer degassing, wafer transfer, wafer pre-treatment, epitaxial growth according to a growth recipe, epitaxial wafer unloading. The status data includes one or more of the status parameters shown in fig. 2.
S304, preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node.
The time sequence state characteristic parameter is used for representing the operation state of the equipment to be detected. The time sequence state characteristic parameter and the corresponding time sequence state parameter correspond to the same time node, and then the time sequence state characteristic parameter can be divided into the time sequence state characteristic parameters of each time node.
Specifically, the data acquisition device acquires the state data of the equipment to be detected at regular intervals, the acquired data comprise state parameters at different times, the acquired data are preprocessed to obtain time sequence state parameters classified in a time sequence, and then the time sequence state characteristic parameters are obtained according to the time sequence state parameters and an anomaly detection algorithm, so that the running state of the equipment to be detected at corresponding time is determined.
Optionally, the obtaining of the time sequence state characteristic parameter according to the time sequence state parameter and the anomaly detection algorithm includes:
and inputting the time sequence state parameters into a trained machine learning model to obtain time sequence state characteristic parameters corresponding to the time sequence.
The machine learning model is trained in a supervised learning mode and obtained by labeling data training, and the training algorithm comprises a support vector machine, AdaBoost and the like.
S306, the time sequence state characteristic parameters of each time node in a preset time period are obtained, the obtained time sequence state characteristic parameters of each time node are analyzed through a preset algorithm, and whether the time sequence state characteristic parameters of each time node are abnormal or not is judged, wherein the preset time period takes the current time point as an end node.
Specifically, the acquired time sequence state characteristic parameters of each time node are analyzed through a preset algorithm, and whether the time sequence state characteristic parameters of each time node are abnormal or not can be determined, so that whether the running state of the equipment to be detected at the corresponding time is abnormal or not can be determined.
And S308, judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
According to the equipment abnormality detection method, the time sequence state parameters of time sequence classification can be obtained by obtaining the state data of equipment to be detected, such as molecular beam epitaxy equipment, and preprocessing the obtained data, the time sequence state parameters are used as input data, the time sequence state characteristic parameters corresponding to the time sequence of the time sequence state parameters are obtained through an abnormality detection algorithm, the time sequence state characteristic parameters are used for reflecting the running state characteristics of the equipment to be detected, then the time sequence state characteristic parameters of each time node in a preset time period are subjected to abnormality analysis, whether the state of the equipment to be detected of each time node is abnormal or not can be determined according to the judgment result, and whether the equipment to be detected is in an abnormal state or not is determined, so that the abnormality detection of the equipment to be detected is automatically realized; and the abnormal detection process does not need the participation of workers, so that the problem that the false detection probability and the missed detection probability are increased along with the increase of the detection time in manual detection is avoided, the stability and the accuracy are higher, and the workload of the workers is reduced.
In one embodiment, as shown in fig. 4, the preprocessing the acquired data includes:
s402, normalization processing, noise reduction processing and time correction processing are carried out on the acquired data.
Specifically, the acquired state data is limited within a certain range through normalization processing, so that subsequent data processing is facilitated. And removing the interference data through noise reduction processing to improve the accuracy of the acquired data. In application, if there may be a recording deviation in the recording acquisition time corresponding to the state parameter, time correction needs to be performed on the recording acquisition time corresponding to the corresponding state parameter.
And S404, performing time sequence classification on the processed acquired data by taking time as a classification standard.
Specifically, the acquired state data of the device includes multiple state parameters at different times, and the processed acquired data is subjected to time sequence classification by using the time as a classification standard, so that the state parameters acquired at the same time are classified into the same class, and the recorded acquisition times of the parameters in the same class are the same.
In one embodiment, the time correction process includes:
and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule.
Optionally, the time nodes are instantaneous values, the time nodes may be predetermined, the first time node is a time for initially acquiring the state parameters, and the time interval between adjacent time nodes is the same as the time interval for acquiring the state data, so that each time node may correspond to a set of state parameters acquired at the same time. For example, if the predetermined time period is one hour, and 1 minute is used as the time interval for acquiring the state data, the interval time between adjacent time nodes is also 1 minute, and the initial time point of the predetermined time period is the time of the first time node and the time of initially acquiring the state parameters, then there are 60 time nodes and 60 sets of state parameters in the predetermined time period, and each time node corresponds to one set of state parameters. In this case, the recording and acquiring time of the status parameter acquired at the nth time should correspond to the nth time node, but in the application, there may be a deviation in the recording and acquiring time of the status parameter acquired at the nth time, so the recording and acquiring time of all the status parameters acquired at the nth time is corrected to the nth time node, so that the recording and acquiring time of the status parameter acquired at the nth time is the same.
Optionally, the recording and acquiring time of the state parameters acquired at the same time is acquired, the mean value of the acquired recording and acquiring time is calculated, then the recording and acquiring time corresponding to the state parameters acquired at the same time is corrected to the mean value, the mean value is recorded as a time node, and each time node also corresponds to a group of state parameters.
In an embodiment, as shown in fig. 5, the analyzing the acquired time series state characteristic parameters of each time node by using a preset algorithm, and determining whether the time series state characteristic parameters of each time node are abnormal includes:
s502, obtaining a time sequence evaluation index of the corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value.
Specifically, when the equipment is in a normal state, a certain time period is selected, the state data of the equipment in the selected time period is acquired, then the acquired data is preprocessed to obtain time sequence state parameters of time sequence classification, normal time sequence state characteristic parameters can be obtained according to the time sequence state parameters and an anomaly detection algorithm, the time sequence state characteristic parameters of each time node in the selected time period are acquired, and a normal state characteristic parameter mean value is obtained according to the acquired time sequence state characteristic parameters of each time node. And traversing and calculating the difference between the time sequence state characteristic parameter of each time node and the average value of the normal state characteristic parameter to obtain the time sequence evaluation index of each time node.
S504, judging whether the corresponding time sequence state characteristic parameters are abnormal according to the time sequence evaluation indexes of the time nodes.
Specifically, the time sequence evaluation index is used for representing the degree of difference between the time sequence state characteristic parameter and the preset normal state characteristic parameter mean value. When the time sequence evaluation index of a certain time node is larger, the difference degree between the time sequence state characteristic parameter of the time node and the average value of the normal state characteristic parameter is larger, and the time sequence state characteristic parameter of the time node is obviously abnormal. On the contrary, when the time sequence evaluation index of a certain time node is smaller, the difference degree between the time sequence state characteristic parameter of the time node and the average value of the normal state characteristic parameters is smaller, and the probability of the time sequence state characteristic parameter of the time node is normal.
In an embodiment, the formula for obtaining the time sequence evaluation index of the corresponding time node according to the time sequence state characteristic parameter of each time node is as follows:
wherein E represents the time-series evaluation index, and Y representsjA parameter that is characteristic of the time series state,
and j represents the dimension of the time sequence state characteristic parameter, namely j represents the number of types of parameters in the time sequence state characteristic parameter.
In one embodiment, the determining whether the corresponding time sequence state characteristic parameter is abnormal according to the time sequence evaluation index of each time node includes:
and judging whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
The abnormal threshold is a test value obtained by multiple tests, and the abnormal threshold is related to the equipment to be detected and is not specifically limited herein.
Specifically, when the time sequence evaluation index of a certain time node is greater than a preset abnormal threshold, the difference between the time sequence state characteristic parameter of the time node and the average value of the normal state characteristic parameter exceeds an allowable range, and the time sequence state characteristic parameter of the time node is abnormal. When the time sequence evaluation index of a certain time node is smaller than or equal to a preset abnormal threshold value, the difference degree of the time node time sequence state characteristic parameter and the average value of the normal state characteristic parameter is within an allowable range, and the time node time sequence state characteristic parameter is judged to be normal. Through the above method, whether the time sequence state characteristic parameters of each time node are abnormal or not is determined in a traversing manner, and the abnormal time sequence state characteristic parameters and the corresponding time nodes are further determined.
In an embodiment, as shown in fig. 6, the determining whether the device to be tested is in an abnormal state according to the determination result includes:
and S602, acquiring the time node with the abnormal time sequence state characteristic parameter, and recording as the abnormal time node.
S604, judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is larger than a preset threshold value, and if so, judging that the equipment is in an abnormal state.
Specifically, the abnormal time sequence state characteristic parameter of a certain time node indicates that the running state of the equipment of the time node is abnormal. The proportion of the abnormal time of the equipment in the preset time period is determined by comparing the ratio of the number of abnormal time nodes in the preset time period to the number of total time nodes, and when the ratio is greater than a preset threshold value, the proportion of the abnormal time of the equipment in the preset time period exceeds a normal value, so that the equipment to be detected is judged to be in an abnormal state. When the ratio is smaller than or equal to the preset threshold, the proportion of the abnormal time of the equipment in the preset time period does not exceed the normal value, so that the equipment to be detected can be judged to be in a normal state.
In one embodiment, on the basis of the foregoing embodiment, as shown in fig. 7, there is provided a device abnormality detection method including:
s702, acquiring state data of the equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
s704, performing normalization processing and noise reduction processing on the acquired data, and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule;
s706, performing time sequence classification on the processed acquired data by taking time as a classification standard to obtain time sequence state parameters of the time sequence classification;
s708, obtaining a time sequence state characteristic parameter according to the time sequence state parameter and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
s710, acquiring the time sequence state characteristic parameters of each time node in a preset time period, wherein the preset time period takes the current time point as an end node;
s712, obtaining a time sequence evaluation index of the corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value;
s714, judging whether the time sequence evaluation index of each time node is larger than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal;
s716, acquiring the time node with the abnormal time sequence state characteristic parameter, and recording as the abnormal time node;
s718, judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is greater than a preset threshold value, and if so, judging that the equipment to be detected is in an abnormal state.
It should be understood that although the various steps in the flowcharts of fig. 1-7 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-7 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 8, an apparatus 800 for detecting device abnormality is provided, where the apparatus may adopt a software module or a hardware module, or a combination of the two modules to form a part of a computer device or a device to be detected, and the apparatus specifically includes: an obtaining module 801, a processing module 802, a judging module 803, and a judging module 804, wherein:
an obtaining module 801, configured to obtain state data of a device to be tested;
the processing module 802 is configured to pre-process the acquired data to obtain time sequence state parameters of time sequence classification, and obtain time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, where the time sequence state parameters of the same kind correspond to the same time node;
a determining module 803, configured to obtain the time sequence state characteristic parameters of each time node in a predetermined time period, analyze the obtained time sequence state characteristic parameters of each time node with a preset algorithm, and determine whether the time sequence state characteristic parameters of each time node are abnormal, where the predetermined time period takes a current time point as an end node;
and the judging module 804 is used for judging whether the equipment to be detected is in an abnormal state according to the judgment result.
In one embodiment, the processing module 802 includes a processing unit and a classifying unit, and the processing unit is configured to perform normalization processing, noise reduction processing, and time correction processing on the acquired data; and the classification unit is used for carrying out time sequence classification on the processed acquired data by taking time as a classification standard.
In one embodiment, the processing unit comprises a syndrome unit for correcting the recorded acquisition times of the state parameters acquired at the same time to the same value by a preset rule.
In one embodiment, the determining module 803 includes an evaluating unit and a first determining unit, where the evaluating unit is configured to obtain a time sequence evaluation index of a corresponding time node according to the time sequence state characteristic parameter of each time node, where the time sequence evaluation index is used to represent a difference between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value; and the first judging unit is used for judging whether the corresponding time sequence state characteristic parameters are abnormal or not according to the time sequence evaluation indexes of the time nodes.
In one embodiment, the first determining unit includes a determining subunit, where the determining subunit is configured to determine whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, determine that a time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
In one embodiment, the determining module 804 includes an obtaining unit and a second determining unit, where the obtaining unit is configured to obtain a time node with an abnormal time sequence state characteristic parameter, and record the time node as an abnormal time node; the second judging unit is configured to judge whether a ratio of the number of abnormal time nodes to the number of total time nodes in the predetermined time period is greater than a preset threshold, and if so, judge that the device to be detected is in an abnormal state.
For the specific limitations of the device abnormality detection apparatus, reference may be made to the above limitations of the device abnormality detection method, which are not described herein again. The modules in the device abnormality detection apparatus may be implemented wholly or partially by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an in-vehicle temperature adjustment method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring state data of equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
carrying out normalization processing, noise reduction processing and time correction processing on the acquired data; and performing time sequence classification on the processed acquired data by taking time as a classification standard.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining a time sequence evaluation index of a corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value; and judging whether the corresponding time sequence state characteristic parameters are abnormal or not according to the time sequence evaluation indexes of the time nodes.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and judging whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring time nodes with abnormal time sequence state characteristic parameters, and recording the time nodes as abnormal time nodes; and judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is greater than a preset threshold value, if so, judging that the equipment to be detected is in an abnormal state.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring state data of equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
carrying out normalization processing, noise reduction processing and time correction processing on the acquired data; and performing time sequence classification on the processed acquired data by taking time as a classification standard.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a time sequence evaluation index of a corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value; and judging whether the corresponding time sequence state characteristic parameters are abnormal or not according to the time sequence evaluation indexes of the time nodes.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and judging whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring time nodes with abnormal time sequence state characteristic parameters, and recording the time nodes as abnormal time nodes; and judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is greater than a preset threshold value, if so, judging that the equipment to be detected is in an abnormal state.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
In the description herein, references to the description of "some embodiments," "other embodiments," "desired embodiments," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, a schematic description of the above terminology may not necessarily refer to the same embodiment or example.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An apparatus abnormality detection method includes:
acquiring state data of equipment to be detected, wherein the acquired data comprises state parameters acquired for multiple times;
preprocessing the acquired data to obtain time sequence state parameters of time sequence classification, and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and judging whether the equipment to be detected is in an abnormal state or not according to the judgment result.
2. The method of claim 1, wherein preprocessing the acquisition data comprises:
carrying out normalization processing, noise reduction processing and time correction processing on the acquired data;
and performing time sequence classification on the processed acquired data by taking time as a classification standard.
3. The method according to claim 2, wherein the time correction process includes:
and correcting the recorded acquisition time of the state parameters acquired at the same time to the same value according to a preset rule.
4. The method according to claim 1, wherein the analyzing the acquired time sequence state characteristic parameters of each time node by a preset algorithm, and the determining whether the time sequence state characteristic parameters of each time node are abnormal comprises:
obtaining a time sequence evaluation index of a corresponding time node according to the time sequence state characteristic parameter of each time node, wherein the time sequence evaluation index is used for representing the difference degree between the time sequence state characteristic parameter and a preset normal state characteristic parameter mean value;
and judging whether the corresponding time sequence state characteristic parameters are abnormal or not according to the time sequence evaluation indexes of the time nodes.
5. The method according to claim 4, wherein the formula for obtaining the time sequence evaluation index of the corresponding time node according to the time sequence state characteristic parameter of each time node is as follows:
wherein E represents the time-series evaluation index, and Y representsjA parameter that is characteristic of the time series state,
representing the average value of the normal state characteristic parameters, and j representing the dimension of the time sequence state characteristic parameters.
6. The method according to claim 3, wherein the determining whether the corresponding time sequence state characteristic parameter is abnormal according to the time sequence evaluation index of each time node comprises:
and judging whether the time sequence evaluation index of each time node is greater than a preset abnormal threshold, and if so, judging that the time sequence state characteristic parameter corresponding to the time sequence evaluation index is abnormal.
7. The method according to any one of claims 1 to 6, wherein the determining whether the device to be detected is in an abnormal state according to the determination result comprises:
acquiring time nodes with abnormal time sequence state characteristic parameters, and recording the time nodes as abnormal time nodes;
and judging whether the ratio of the abnormal time node number to the total time node number in the preset time period is greater than a preset threshold value, if so, judging that the equipment to be detected is in an abnormal state.
8. An apparatus abnormality detection device characterized by comprising:
the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring state data of equipment to be detected, and the acquired data comprises state parameters acquired for multiple times;
the processing module is used for preprocessing the acquired data to obtain time sequence state parameters of time sequence classification and obtaining time sequence state characteristic parameters according to the time sequence state parameters and an anomaly detection algorithm, wherein the time sequence state parameters of the same kind correspond to the same time node;
the judging module is used for acquiring the time sequence state characteristic parameters of each time node in a preset time period, analyzing the acquired time sequence state characteristic parameters of each time node by using a preset algorithm, and judging whether the time sequence state characteristic parameters of each time node are abnormal or not, wherein the preset time period takes the current time point as an end node;
and the judging module is used for judging whether the equipment to be detected is in an abnormal state or not according to the judging result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN116108086A (en) * | 2023-02-27 | 2023-05-12 | 广州汇通国信科技有限公司 | Time sequence data evaluation method and device, electronic equipment and storage medium |
| CN116108086B (en) * | 2023-02-27 | 2023-09-26 | 广州汇通国信科技有限公司 | Time sequence data evaluation method and device, electronic equipment and storage medium |
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