[0143] Example 2
[0144] On the basis of the configuration method for providing abnormality detection algorithm according to an embodiment of the, if it is determined the abnormality detection algorithm to detect an apparatus occurrence frequency stability abnormality can be detected in the abnormality detection algorithm to the equipment malfunction occurs, transmits alarm information to a user, by the the user equipment further confirm whether an abnormality actually occurred, and further to adjust the parameters of the abnormality detection algorithm according to the confirmation result of the user, thereby improving the accuracy of abnormality detection algorithm to detect an abnormality of the device.
[0145] figure 2 The present application is a diagram showing the abnormality detection method for adjusting parameters of an algorithm according to a second embodiment 200, as figure 2 , The data processing program 202 according to the configuration information 201 acquired operating data device, the data handler 202 of the data processing operation, to obtain variable information 203, variable information 203 is stored in the database 204, the algorithm runs the program from the database 205 204 reading variable information 203, and the read variable information 203 input to the abnormality detection algorithm, obtaining abnormality detection algorithm 206 outputs an abnormality detection result, stable frequency determining program 207 detects an abnormality detection algorithm according to the abnormality detection apparatus 206 determines the result of abnormal frequency is stable. When the frequency stability determination program 207 determines a frequency anomaly detection algorithm to detect an apparatus abnormal stable, if the abnormality detection result 206 characterization device abnormality, event logger 212 transmits alarm information 208 to a user, event logging program 212 receiving the user after labeling for alarm information 208 feedback information 209, the sample data 209 and 210 according to the tagging information variable information 203, and the formed sample data 210 stored in the database 204.
[0146]When the abnormality detection device detects the occurrence frequency algorithm stable abnormality, the abnormality detection algorithms have been described capable of accurately detecting device is abnormal, this time, if the abnormality detection result of abnormal characterization device 206, sending the corresponding alarm information to the user 208, to confirm whether the user equipment is really abnormal, then the user receives feedback information label 209, wherein the callout 209 is used to indicate the variable information corresponding to the state of the device 203 is operating normally or an abnormal operation, and according to variable and tagging information 209 form information 203 sample data 210, 210 and the sample data stored in the database 204. Sample data 210 includes positive and negative samples, the same as the confirmation result of the positive samples abnormality detection algorithm of the detection result of the user, the negative samples for different confirmation result of the detection result of the user's anomaly detection algorithm, the sample data 210 stored in the database , 210 can be analyzed by the sample data in the database, the anomaly detection algorithm to determine the device abnormality detection accuracy, facilitate the anomaly detection algorithms were evaluated and optimized.
[0147] In one possible implementation, if the label information indicates variable information corresponding to the apparatus operates abnormally, the abnormal label information further includes type information, the anomaly category classification information for indicating the device abnormality occurs, i.e., the abnormality detection algorithm apparatus abnormal, abnormal user equipment does confirm and verify the category of devices that appear abnormal. In this case, according to the abnormality type information and variable information including label information, the classification of the anomaly previously constructed models to optimize the train, wherein the variable information for classification model abnormality determination device according abnormality occurring category.
[0148] Specifically, when the device information indicates abnormal label, after the sample data stored in the database, run the algorithm program 205 reads 210 data from the sample database 204, according to the read sample data 210 includes variable information and abnormal categories information, abnormal classification model to optimize training.
[0149] After when the abnormality detection algorithm to equipment malfunction after sending alarm information to the user, the user receives the alarm information to confirm whether the device actually abnormal, and the feedback corresponding annotation information based on the confirmation result to confirm the variable information corresponding to the device operating status abnormal or normal running operation, thereby generating a sample data including variable information and annotation information, i.e., the sample data comprises variable information device and the operating state of the device is located at variable information (normal operation or abnormal operation). If the user equipment does confirm abnormal, not only the user feedback information label indicating abnormal device, the exception category label information further includes information indicating the device abnormality occurring, it may further include sample data based on the variable information and the exception type information, for abnormal classification model to optimize the training, so that unusual classification model capable of the type of exception appears to more accurately identify the device based on variable information, which is not only able to detect whether the device is abnormal, but also be able to accurately determine the device type of exception appears to realize car pinpoint abnormal production line, improving the user experience.
[0150] In one possible implementation, the algorithm determines the abnormality detecting apparatus according to variable information abnormality occurs, but the user does not confirm the unusual equipment, i.e. label information indicating abnormal device does not appear, then generate the sample data including variable information and annotation information the sample data after the anomaly detection algorithm parameters are adjusted so that the abnormality detection device algorithm does not appear abnormal based on the detection result of the variable information device status.
[0151] In the embodiment of the present application, such as figure 2 , The algorithm runs the program 205 will send the abnormality detection result of the program 206 determines 207 the frequency stability, frequency stability determination program 207 determines abnormal detection algorithm 206 detects an abnormality detection apparatus according to the result of the frequency abnormality is stable, the program determines if the frequency stability algorithm 207 determines that the abnormality detection device detects the abnormal frequency instability occurs, send feedback information 205 to the arithmetic operation program 211, the algorithm runs the program 205 to adjust the parameters of the feedback information 211 according to the abnormality detection algorithm.
[0152] Incidentally, the user can 202 sends the configuration to the data processing program by the user experience (User Experience, UX) tool information 201, event logger 212 can send alarm information 208 via the user experience tool to the user, via the user experience tool to the event recording program 212 transmits the information denoted by 209. The user experience can be a tool HMI (Human Machine Interaction, HMI).
[0153] It is further noted that, as figure 2 , The data processing program 202 comprises a data adapter 2021, data of the adapter 2021 may acquire data of different protocols to be able to collect various types of operational data, adapted to different types of field data to ensure the anomaly detection algorithm provided by the embodiment of the present application the configuration has strong applicability. Configuration information may be configured by data adapter 201 of access rules 2021, 2021 can be such that the data adapter according to the rules set by the subscription data. After data adapter 2021 to the operating data acquisition device, the data handler 202 may purge data 201 and the data processing operation data according to the configuration information 203 to obtain the variable information.