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CN-119535922-B - Power abnormality detection method, image forming apparatus, model training method and apparatus

CN119535922BCN 119535922 BCN119535922 BCN 119535922BCN-119535922-B

Abstract

The embodiment of the invention provides a power abnormality detection method, image forming equipment, a model training method and equipment. In the technical scheme provided by the embodiment of the invention, the power abnormality detection method comprises the steps of obtaining current detection data of image forming equipment, wherein the detection data comprise the whole power, the ambient temperature and the working state of the image forming equipment, and inputting the current detection data into a power abnormality detection model to obtain an abnormality detection result. The power abnormality detection can be performed without adding a monitoring circuit to each power consumption module, and the detection cost is reduced.

Inventors

  • WEN XIAOHONG

Assignees

  • 珠海奔图电子有限公司

Dates

Publication Date
20260508
Application Date
20241025

Claims (11)

  1. 1. A method for detecting power anomalies, the method comprising: Acquiring current detection data of image forming equipment, wherein the detection data comprise the whole machine power, the environment temperature and the working state of the image forming equipment; inputting the current detection data into a power abnormality detection model to obtain an abnormality detection result; The model training method of the power abnormality detection model comprises the steps of carrying out preliminary training on an AI model by inputting a large amount of historical detection data of normal operation of image forming equipment to the AI model, and carrying out correction training on the AI model by using the historical detection data of abnormal operation of the image forming equipment to obtain the power abnormality detection model.
  2. 2. The method of claim 1, wherein after inputting the current detection data into the power anomaly detection model to obtain an anomaly detection result, further comprising: and determining that the abnormality detection result comprises an abnormality processing strategy, and executing an operation corresponding to the abnormality processing strategy.
  3. 3. The method according to claim 2, wherein before the performing the operation corresponding to the exception handling policy, further comprises: and if the exception handling policy is applicable to the image forming equipment, executing the operation corresponding to the exception handling policy.
  4. 4. A method according to any one of claims 1-3, wherein the operating state comprises a main state and an operating parameter corresponding to the main state.
  5. 5. The method of claim 4, wherein the operating state further comprises a sub-state and an operating parameter corresponding to the sub-state.
  6. 6. The method of claim 1, wherein the detection data further comprises a fault code, the fault code indicating an exception type corresponding to the main state or an exception type corresponding to the sub-state.
  7. 7. A method of model training, the method comprising: Preliminary training of an AI model by inputting a large amount of history detection data of normal operation of an image forming apparatus to the AI model; And correcting and training the AI model by using historical detection data of the working abnormality of the image forming equipment to obtain a power abnormality detection model, wherein the power abnormality detection model is used for outputting an abnormality detection result according to the current detection data of the image forming equipment, and the detection data comprises the whole machine power, the environment temperature and the working state of the image forming equipment.
  8. 8. The method of claim 7, wherein the training the AI model with the historical detection data of the image forming apparatus operational anomalies to obtain a power anomaly detection model is preceded by: and performing correction training on the AI model in response to scoring operation on the output result of the AI model.
  9. 9. An image forming apparatus comprising a memory for storing information including program instructions and a processor for controlling execution of the program instructions, wherein the program instructions when loaded and executed by the processor implement the method of any one of claims 1-6.
  10. 10. Model training device comprising a memory for storing information comprising program instructions and a processor for controlling the execution of the program instructions, characterized in that the program instructions, when loaded and executed by the processor, implement the method of claim 7 or 8.
  11. 11. A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method of any one of claims 1-8.

Description

Power abnormality detection method, image forming apparatus, model training method and apparatus [ Field of technology ] The present invention relates to the field of printer power detection, and in particular, to a power abnormality detection method, an image forming apparatus, a model training method, and an apparatus. [ Background Art ] The laser printer has relatively high overall power, and various power sources and various power consumption modules, such as a fixing heating module, an image scanning engine, a laser emission unit, an imaging control module and the like, are arranged in the printer and have respective power supply voltages, so that power consumption can be generated. If the power is abnormal, this may lead to an increase in printer power consumption and may even create safety issues. One solution is to add monitoring circuitry to these power consumption modules, for example by monitoring the electrical signals of the branches by means of series-parallel sensors, to determine the power consumption, which however would add additional hardware circuit costs. [ Invention ] In view of this, the embodiments of the present invention provide a power anomaly detection method, an image forming apparatus, a model training method, and an apparatus, which can perform power anomaly detection without adding a monitoring circuit to each power consumption module, thereby reducing detection cost. In a first aspect, an embodiment of the present invention provides a power anomaly detection method, where the method includes: Acquiring current detection data of image forming equipment, wherein the detection data comprise the whole machine power, the environment temperature and the working state of the image forming equipment; And inputting the current detection data into a power abnormality detection model to obtain an abnormality detection result. Optionally, after the current detection data is input into the power abnormality detection model to obtain an abnormality detection result, the method further includes: and determining that the abnormality detection result comprises an abnormality processing strategy, and executing an operation corresponding to the abnormality processing strategy. Optionally, before the executing the operation corresponding to the exception handling policy, the method further includes: and if the exception handling policy is applicable to the image forming equipment, executing the operation corresponding to the exception handling policy. Optionally, the working state includes a main state and a working parameter corresponding to the main state. Optionally, the working state further includes a sub-state and a working parameter corresponding to the sub-state. Optionally, the detection data further comprises a fault code, wherein the fault code is used for indicating an abnormal type corresponding to the main state or an abnormal type corresponding to the sub state. In another aspect, an embodiment of the present invention provides a model training method, including: Preliminary training of an AI model by inputting a large amount of history detection data of normal operation of an image forming apparatus to the AI model; And correcting and training the AI model by using historical detection data of the working abnormality of the image forming equipment to obtain a power abnormality detection model, wherein the power abnormality detection model is used for outputting an abnormality detection result according to the current detection data of the image forming equipment, and the detection data comprises the whole machine power, the environment temperature and the working state of the image forming equipment. Optionally, before performing correction training on the AI model by using the historical detection data of the abnormal operation of the image forming apparatus to obtain a power abnormality detection model, the method further includes: and performing correction training on the AI model in response to scoring operation on the output result of the AI model. In another aspect, an embodiment of the present invention provides an image forming apparatus, including a memory for storing information including program instructions and a processor for controlling execution of the program instructions, wherein the program instructions implement steps of the above-described power abnormality detection method when loaded and executed by the processor. In another aspect, an embodiment of the present invention provides a model training apparatus, including a memory and a processor, where the memory is configured to store information including program instructions, and the processor is configured to control execution of the program instructions, where the program instructions, when loaded and executed by the processor, implement steps of the model training method described above. On the other hand, the embodiment of the invention provides a storage medium, which comprises a stored program, wherein