Power anomaly detection method, image forming apparatus, model training method and apparatus

Abstract

The embodiment of the present application provides a power anomaly detection method, an image forming device, a model training method and device. In the technical scheme provided by the embodiment of the present application, the power anomaly detection method comprises: acquiring current detection data of an image forming device, wherein the detection data comprises overall power of the image forming device, ambient temperature and working state; and inputting the current detection data into a power anomaly detection model to obtain an anomaly detection result. The power anomaly detection can be performed without adding a monitoring circuit to each power consumption module, thereby reducing the detection cost.

Description

[Technical Field]

[0001] This invention relates to the field of printer power detection, and more particularly to a power anomaly detection method, an image forming device, a model training method, and a device. [Background Technology]

[0002] Laser printers have relatively high overall power consumption, containing multiple power supplies and power-consuming modules such as the fuser heating module, image scanning engine, laser emitting unit, and imaging control module, each with its own power supply voltage and power consumption. Abnormal power consumption can lead to increased printer energy consumption and may even cause safety issues.

[0003] One solution is to add monitoring circuitry to these power-consuming modules, for example, by using series and parallel sensors to monitor the electrical signals of the branches to determine power consumption. However, this approach would increase the cost of the hardware circuitry. [Summary of the Invention]

[0004] In view of this, embodiments of the present invention provide a power anomaly detection method, an image forming device, a model training method and device, which can perform power anomaly detection without adding monitoring circuits to each power consumption module, thereby reducing detection costs.

[0005] In a first aspect, embodiments of the present invention provide a power anomaly detection method, the method comprising:

[0006] Acquire current detection data of the image forming device, including the overall power, ambient temperature, and operating status of the image forming device;

[0007] The current detection data is input into the power anomaly detection model to obtain the anomaly detection result.

[0008] Optionally, after inputting the current detection data into the power anomaly detection model to obtain the anomaly detection result, the method further includes:

[0009] If the anomaly detection result is determined to include an anomaly handling strategy, the operation corresponding to the anomaly handling strategy is executed.

[0010] Optionally, before executing the operation corresponding to the exception handling strategy, the method further includes:

[0011] If the exception handling strategy applies to the image forming device, the operation corresponding to the exception handling strategy is executed.

[0012] Optionally, the working state includes a main state and the working parameters corresponding to the main state.

[0013] Optionally, the working state may further include sub-states and working parameters corresponding to the sub-states.

[0014] Optionally, the detection data further includes: a fault code, which is used to indicate the type of abnormality corresponding to the main state or the type of abnormality corresponding to the sub-state.

[0015] On the other hand, embodiments of the present invention provide a model training method, the method comprising:

[0016] The AI ​​model is initially trained by inputting a large amount of historical detection data from when the image forming equipment is working properly.

[0017] The AI ​​model is corrected and trained using historical detection data of the image forming equipment malfunctions to obtain a power anomaly detection model. The power anomaly detection model is used to output anomaly detection results based on the current detection data of the image forming equipment, including: the overall power of the image forming equipment, ambient temperature, and operating status.

[0018] Optionally, before using historical detection data of malfunctions of the image forming device to correct and train the AI ​​model to obtain the power anomaly detection model, the method further includes:

[0019] In response to the scoring operation of the output of the AI ​​model, the AI ​​model is corrected and trained.

[0020] On the other hand, embodiments of the present invention provide an image forming apparatus, including a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that the program instructions, when loaded and executed by the processor, implement the steps of the above-described power anomaly detection method.

[0021] On the other hand, embodiments of the present invention provide a model training device, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. The feature is that when the program instructions are loaded and executed by the processor, the steps of the above-described model training method are implemented.

[0022] On the other hand, embodiments of the present invention provide a storage medium including a stored program, wherein the program controls the device where the storage medium is located to execute the above-mentioned power anomaly detection method or model training method when it is running.

[0023] The power anomaly detection method, image forming apparatus, model training method, and apparatus provided in this invention include: acquiring current detection data of the image forming apparatus, the detection data including the overall power, ambient temperature, and operating status of the image forming apparatus; and inputting the current detection data into a power anomaly detection model to obtain anomaly detection results. This method eliminates the need to add monitoring circuits to each power consumption module, thus reducing detection costs. [Attached Image Description]

[0024] Figure 1 A flowchart of a power anomaly detection method provided in an embodiment of the present invention;

[0025] Figure 2 A flowchart of another power anomaly detection method provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the anomaly detection results in an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the power supply hierarchy of the image forming apparatus in an embodiment of the present invention;

[0028] Figure 5 A schematic diagram illustrating three types of power detection protection implemented by the power anomaly detection method provided in this embodiment of the invention;

[0029] Figure 6 This is a schematic diagram of the input and input of the power anomaly detection model in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of a self-test process for a complete machine component in an embodiment of the present invention;

[0031] Figure 8 A flowchart of a model training method provided in an embodiment of the present invention;

[0032] Figure 9 A flowchart illustrating yet another model training method provided in this embodiment of the invention;

[0033] Figure 10 This is a schematic diagram of the input data during the training of the AI ​​model in an embodiment of the present invention;

[0034] Figure 11 This is a schematic diagram of the structure of a power anomaly detection device provided in an embodiment of the present invention;

[0035] Figure 12 This is a schematic diagram of the structure of a model training device provided in an embodiment of the present invention;

[0036] Figure 13This is a schematic diagram of an electronic device provided in an embodiment of the present invention.

Detailed Implementation Methods

[0037] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0038] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0039] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0041] Laser printers have relatively high overall power consumption (generally above 500W), with the fuser unit accounting for the largest share of the total power. If the heating or detection circuit of the fuser unit malfunctions, it may cause abnormal heating of the fuser, leading to machine failure or even a fire.

[0042] The current mainstream solution is to add a sensor to detect the temperature of the fuser assembly and take corresponding protective measures. The biggest drawback of this solution is that it can only detect and protect the fuser assembly, not the entire machine. Improper installation of the temperature sensor can also lead to temperature detection errors, causing the protection to fail.

[0043] Printers are electrical devices, and their operating status can be reflected to some extent by their power consumption. For a printer that is not turned on, the power consumption should be zero or close to zero. When the printer is in standby mode, all the motors stop running, and the fuser is in a low-power output state or does not output. When the printer is printing, several motors start running, and the fuser heats up (at full power output for a certain period of time), and the overall power consumption of the machine will increase significantly.

[0044] One solution is to add monitoring circuitry to each power consumption module inside the printer, for example, by monitoring the electrical signals of the branches through series and parallel sensors to determine power consumption. However, this approach would increase the cost of the hardware circuitry.

[0045] To address the aforementioned technical problems, embodiments of the present invention provide a power anomaly detection method that can detect power anomalies without adding monitoring circuits to each power consumption module, thereby reducing detection costs.

[0046] Figure 1 This is a flowchart illustrating a power anomaly detection method provided in an embodiment of the present invention. The power anomaly detection method provided in this embodiment is applied to an image forming device that includes a power anomaly detection model, such as... Figure 1 As shown, the method includes steps 101-102.

[0047] Step 101: Obtain the current detection data of the image forming device. The detection data includes the overall power of the image forming device, ambient temperature, and operating status.

[0048] Step 102: Input the current detection data into the power anomaly detection model to obtain the anomaly detection result.

[0049] The present invention provides a power anomaly detection method, which includes: acquiring current detection data of an image forming device, the detection data including the overall power of the image forming device, ambient temperature, and operating status; and inputting the current detection data into a power anomaly detection model to obtain anomaly detection results. Power anomaly detection can be performed without adding monitoring circuits to each power consumption module, reducing detection costs.

[0050] Figure 2 A flowchart of another power anomaly detection method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the method includes steps 201-203.

[0051] Step 201: Obtain the current detection data of the image forming device.

[0052] In this embodiment of the invention, each step is performed by an image forming device. For example, the image forming device is a printer, etc.

[0053] For example, the detection data includes the overall power of the image forming equipment, ambient temperature, and operating status.

[0054] In this embodiment of the invention, a voltage detection element (such as a voltage sensor) and a current detection element (such as a current sensor) are added to the electrical circuit of the image forming apparatus to collect the device input voltage and device input current of the image forming apparatus.

[0055] Among them, the device input voltage and device input current of the image forming equipment are the voltage and current of the whole machine. They refer to the AC voltage and current input from the mains power to the image forming equipment, and sensors can be added to the AC input section of the image forming equipment for detection.

[0056] For example, a current sensor can be set at one end of the AC power supply, and a voltage sensor can be set at the other two ends of the AC power supply.

[0057] Common methods for voltage sampling include voltage dividers, optocouplers, and voltage transformers; common methods for current sampling include shunt resistors, Hall effect sensors, and current transformers.

[0058] Ambient temperature has a significant impact on the performance of internal components and the heating process of laser printers. For example, excessively low temperatures will increase the time it takes for the fusing assembly to heat up to the target temperature. Therefore, the influence of ambient temperature needs to be considered when detecting power anomalies.

[0059] There are many integrated modules available for ambient temperature detection, and in this embodiment of the invention, an ambient temperature sensor pre-set inside the image forming device can be used.

[0060] For example, the overall power of the device is calculated from the collected device input voltage and device input current. The formula for calculating power is: P = U * I * cosθ, where θ is the vector angle between voltage U and current I.

[0061] For example, the working state includes the main state and the working parameters corresponding to the main state.

[0062] For example, the working state also includes sub-states and the working parameters corresponding to the sub-states.

[0063] The operating parameters carried by the working status include, for example, the voltage and current of components such as motors involved in the module (not the voltage and current of the entire machine). For instance, when the main state is printing and the sub-state is developing, the developing power supply voltage and the voltage and current of the developing roller can be used as operating parameters. When the main state is scanning and the sub-state is Automatic Document Feeder (ADF) scanning, the power supply, motor voltage, and motor current of the ADF motor can be used as operating parameters. When the main state is printing and the sub-state is heating, the fixing target temperature or paper type can be used as operating parameters. It should be noted that the aforementioned voltage and current parameters can be determined based on signals from the controller, not on actual measured data. For example, if the controller determines the target developing voltage, the target developing voltage can be used as the operating parameter. This is because the target developing voltage also affects the printer power; using the target developing voltage as the operating parameter eliminates the need for additional voltage or current sensing elements to measure circuit parameters, thus helping to further save costs.

[0064] For printers, the main operating states are roughly: initialization, standby, hibernation, printing, scanning, and fault. Each main state is further subdivided into different sub-states. For example, the printing main state may have sub-states such as paper feed, calibration, developing, and fixing. The number of electrical components operating differs in different operating states, resulting in different power consumption.

[0065] For example, the detection data also includes fault codes. Fault codes are used to indicate the type of exception corresponding to the main state or the type of exception corresponding to the sub-state.

[0066] The image forming device can read the operating status and fault codes from the device firmware.

[0067] Step 202: Input the current detection data into the power anomaly detection model to obtain the anomaly detection result.

[0068] For example, a power anomaly detection model is a model trained from an artificial intelligence (AI) model, used to output anomaly detection results based on the input detection data.

[0069] Step 203: Determine the anomaly detection results, including the anomaly handling strategy, and execute the operation corresponding to the anomaly handling strategy.

[0070] In this embodiment of the invention, the image forming device can also issue an alarm based on the type of anomaly to alert the user that an anomaly has occurred.

[0071] In some possible embodiments, step 203 specifically includes: determining that the anomaly detection result includes an anomaly handling strategy; if the anomaly handling strategy is applicable to the image forming device, performing the operation corresponding to the anomaly handling strategy.

[0072] Before executing the operation corresponding to the exception handling strategy, it is necessary to determine whether the exception handling strategy is applicable to the image forming device. If so, the operation corresponding to the exception handling strategy is executed; otherwise, the operation corresponding to the exception handling strategy is not executed.

[0073] In this embodiment of the invention, among the operations corresponding to the exception handling strategy, the last operation to achieve a protective effect is disconnecting the power supply. Taking a printer as an example, Figure 4 This is a schematic diagram of the power supply hierarchy of the image forming apparatus in an embodiment of the present invention, as shown below. Figure 4 As shown, the power supply in the entire machine has a tree-like, hierarchical structure. The first level of power supply is the overall machine power supply; the second level of power supply includes: the main power supply and other power supplies. The third level of power supply under the main power supply branch includes: the print engine power supply and the scanner engine power supply; the third level of power supply under the other power supply branch includes: the data board power supply, the panel power supply, and others. The fourth level of power supply under the print engine power supply branch includes: the fuser power supply, the main motor power supply, the laser scanning unit (LSU) component power supply, and other component power supplies; the fourth level of power supply under the scanner engine power supply branch includes: the ADF motor power supply and other component power supplies.

[0074] In scenarios with sufficiently granular power supply classification, embodiments of the present invention can selectively shut down corresponding power supplies, ensuring that the failure of some components does not affect the use of other components. For example, when a print engine failure is detected, shutting down only the print engine power allows the printer to continue using the scanning function, improving the overall availability of the machine.

[0075] In summary, this embodiment of the invention uses sensors to detect the voltage, current, and ambient temperature of the entire device, and reads the device's operating status and fault codes from the device firmware. The image forming device is placed under different parameter states, and manual annotation is used to identify the type of anomaly the printer is experiencing or the appropriate anomaly handling strategy. This information is then used as input to train an AI model, resulting in a power anomaly detection model. In actual use, the image forming device monitors the voltage, current, ambient temperature, operating status, and fault codes in real time, inputting these values ​​into the power anomaly detection model. The model then outputs the anomaly detection result. When the anomaly detection result is positive, it includes the anomaly type and the anomaly handling strategy.

[0076] Optionally, embodiments of the present invention may further include a humidity detection element and an atmospheric pressure detection component in the electrical circuit of the image forming apparatus. The detection data also includes ambient humidity and atmospheric pressure values.

[0077] For example, Figure 5 The following are schematic diagrams illustrating three types of power detection protection implemented by the power anomaly detection method provided in this embodiment of the invention: Figure 5 As shown, embodiments of the present invention achieve fixed threshold protection for power, AI model protection, and self-testing of all components by using factors such as overall power, ambient temperature and humidity, and atmospheric pressure.

[0078] Among them, the fixed threshold protection is a conventional threshold-type protection that determines in real time whether the current power is within the allowable power range of the current machine operating state. If it exceeds the range, an alarm is triggered and the main power supply is disconnected.

[0079] After the fixed threshold protection is activated, the main power supply will be disconnected. If the abnormality cannot be resolved after disconnecting the main power supply, the power supply to the entire machine will then be disconnected.

[0080] As shown in Table 1, the allowable power range varies depending on the operating state. The range of fixed threshold protection is derived from a combination of theoretical and experimental data obtained by the R&D personnel. A larger range, in principle, is more likely to avoid false alarms.

[0081] Table 1. Permissible power range of image forming equipment under different operating conditions.

[0082] Serial Number Work status Minimum allowable power Maximum allowable power 1 Power on A1 A2 2 preheating B1 B2 3 standby C1 C2 4 hibernation D1 D2 5 Fault E1 E2 6 Print F1 F2 7 scanning G1 G2 8 Print + Scan H1 H2

[0083] Due to differences in the materials used in printers and environmental factors, there will be individual differences in the printer's operating power. Therefore, strategies such as fixed threshold protection have their limitations. Figure 6 This diagram illustrates the input and output of the power anomaly detection model in this embodiment of the invention. Since the neural network-based AI model possesses data learning capabilities, the power anomaly detection model is obtained by training the AI ​​model, as shown below. Figure 6 As shown, in this embodiment of the invention, the real-time power of the whole machine, working status, ambient temperature and humidity values, and atmospheric pressure values ​​(altitude) are input into the power anomaly detection model. The power anomaly detection will analyze the current abnormal situation of the printer, that is, whether there is an anomaly. If there is an anomaly, the anomaly type is given. When there is an anomaly, the abnormal component is inferred, that is, which component is abnormal, and an anomaly handling strategy is given.

[0084] For example, the self-test of the entire machine components can be initiated and confirmed by the user. The printer independently turns on each component, allows it to work independently, and then performs a power test to determine whether the component is working properly. The normal power range for each component is determined by the R&D personnel based on actual measurements and is a program-defined value. After the individual component tests are completed, the entire machine test is initiated, and a copying or printing of a page of document is attempted to check whether the operating power is normal.

[0085] For example, Figure 7 This is a schematic diagram of a self-test process for a complete machine component according to an embodiment of the present invention, such as... Figure 7As shown, after confirming the start of the self-test, the main motor self-test is performed first. If an abnormality is detected in the main motor, an error message and suggestions are provided. If the main motor is detected as normal, the LSU motor self-test continues. If an abnormality is detected in the LSU motor, an error message and suggestions are provided. If the LSU motor is detected as normal, the self-test of other components continues. If any abnormality is found in other components, an error message and suggestions are provided. If all other components are detected as normal, a whole-machine self-test is performed. If an abnormality is found in the whole-machine self-test, an error message and suggestions are provided. If the whole-machine self-test is normal, the self-test is complete, and the process ends.

[0086] Therefore, the embodiments of the present invention can perform fixed threshold protection for power, AI model protection, and self-testing of the whole machine components, which can better protect the image forming equipment and improve the availability of the printer to a certain extent.

[0087] In summary, the power anomaly detection method provided by the embodiments of the present invention has at least the following beneficial effects:

[0088] 1) The embodiments of the present invention do not require adding a monitoring circuit to each power consumption module, and can still perform power anomaly detection, thus reducing detection costs.

[0089] 2) This embodiment of the invention considers the current operating state of the image forming device when determining the anomaly handling strategy. For example, if the model output indicates a serious anomaly, the power can be shut off regardless of the current operating state of the image forming device. If a subunit has a non-serious fault and is currently performing a job, the power supply to the subunit can be shut off after the job is completed. If the anomaly detection result is not a normal value but cannot be classified into a specific anomaly, the current detection data is recorded in the memory for reporting to the server, etc. Since the training parameters include the current operating state, different handling strategies can be implemented when the image forming device is in different states. In contrast, in conventional image forming device technology, the entire machine usually stops operating when any module malfunctions, even if the problem is not serious. Therefore, this embodiment of the invention can improve user efficiency.

[0090] 3) The embodiments of the present invention take ambient temperature into account when determining the anomaly handling strategy. This is because there are a large number of motors inside the image forming device, and when the motor power is abnormally high, it is likely to accumulate heat due to rapid heating. Therefore, the embodiments of the present invention also take ambient temperature as a factor to consider in power anomalies, using the ambient temperature sensor inside the image forming device, instead of setting a contact-type temperature sensor for each motor. This can improve the accuracy of the machine learning model while saving hardware costs.

[0091] 4) This embodiment of the invention uses fault codes as input to the model. Firstly, the firmware of the image forming device already has pre-defined fault detection logic, allowing fault codes to replace the acquisition of other complex parameters and save on training costs. Secondly, different image forming devices may have different fault codes. For example, for fuser component missing insertion faults, some models categorize them all as fuser faults, causing a complete system shutdown upon occurrence; while others classify them as a separate fault called "fusing component missing insertion," which can be resolved by restarting or user reassembly without requiring after-sales service. Therefore, the relationship between fault codes and exception handling strategies is close; fault codes ensure that the solution strategies provided by the model are compatible with these types of devices.

[0092] To address the aforementioned power anomaly detection model, this embodiment of the invention provides a model training method.

[0093] Figure 8 A flowchart of a model training method provided in an embodiment of the present invention is shown below. Figure 8 As shown, the method includes steps 301-302.

[0094] Step 301: Perform preliminary training on the AI ​​model by inputting a large amount of historical detection data from when the image forming equipment is working properly.

[0095] Step 302: Use historical detection data of image forming equipment malfunctions to correct and train the AI ​​model to obtain a power anomaly detection model; the power anomaly detection model is used to output anomaly detection results based on the current detection data of the image forming equipment, including: the overall power of the image forming equipment, ambient temperature and operating status.

[0096] The technical solution of the model training method provided in this embodiment of the invention includes: initially training the AI ​​model by inputting a large amount of historical detection data of image forming equipment operating normally; correcting and training the AI ​​model using historical detection data of image forming equipment operating abnormally to obtain a power anomaly detection model; the power anomaly detection model is used to output anomaly detection results based on the current detection data of the image forming equipment, including: the overall power of the image forming equipment, ambient temperature, and operating status. Power anomaly detection can be performed without adding monitoring circuits to each power consumption module, reducing detection costs.

[0097] Figure 9 A flowchart of another model training method provided in an embodiment of the present invention is shown below. Figure 9 As shown, the method includes steps 401-403.

[0098] Step 301: Perform preliminary training on the AI ​​model by inputting a large amount of historical detection data from when the image forming equipment is working properly.

[0099] This step takes place at the factory stage, where R&D and testing personnel input a large number of images into the AI ​​model by setting up various usage environments and scenarios to form historical test data showing that the equipment is working properly.

[0100] For example, historical detection data refers to historical detection data.

[0101] For example, Figure 10 This is a schematic diagram of the input data during the training of the AI ​​model in an embodiment of the present invention, such as... Figure 10 As shown, the input data during AI model training includes overall power consumption, ambient temperature, operating status, fault codes, and policy / abnormality type. Historical detection data includes overall power consumption, ambient temperature, operating status, and fault codes.

[0102] Different operating states of an image forming device result in varying numbers of electrical components and consequently different power consumption. Therefore, to better train an AI model, it is necessary to subdivide the operating states as much as possible. Each specific operating state can carry operating parameters as needed, which can be provided to the AI ​​model to more accurately identify anomalies.

[0103] Figure 10 In this system, the "strategy / exception type" is determined manually in advance during the training phase. For example, engineers can observe and determine the printer's current state and the appropriate strategy to execute during training. In actual use, the "strategy / exception type" becomes the output value.

[0104] Optionally, historical monitoring data may also include ambient humidity and atmospheric pressure values.

[0105] Step 402: In response to the scoring operation of the AI ​​model's output, perform the first correction training on the AI ​​model.

[0106] This step takes place at the factory stage, where R&D and testing personnel score the output of the AI ​​model by simulating failure scenarios, thereby correcting and training the AI ​​model.

[0107] During AI model training, a mechanism is needed to "score" the output results. Printers have their own fault detection mechanisms; when trigger conditions are met, they report fault information, causing the printer to enter a fault (error) state. Different faults are distinguished by different fault code values; a value of 0 indicates no fault. Whether the printer ultimately malfunctions is a crucial basis for the AI ​​model's scoring mechanism.

[0108] For example, such as Figure 10As shown, the input data for AI model training also includes "scoring". "Scoring" is optional and aims to avoid inaccuracies caused by training solely with "policy / anomaly type". After the initial training of the model, developers and testers can score the AI ​​model's output to help adjust the output.

[0109] Step 403: Use historical detection data of image forming equipment malfunctions to perform a second correction training on the AI ​​model to obtain the power anomaly detection model.

[0110] This step involves collecting usage data from user machines during the user phase, filtering out historical detection data of malfunctions from the machines that eventually fail, and then using the historical detection data of malfunctions of the image forming equipment to further refine and train the AI ​​model, resulting in a power anomaly detection model.

[0111] Figure 11 This is a schematic diagram of a power anomaly detection device provided in an embodiment of the present invention, as shown below. Figure 11 As shown, the device includes: a first processing module.

[0112] The first processing module is used to acquire the current detection data of the image forming device, the detection data including the overall power, ambient temperature and operating status of the image forming device; and input the current detection data into the power anomaly detection model to obtain anomaly detection results.

[0113] Optionally, the processing module is further configured to determine that the anomaly detection result includes an anomaly handling strategy, and execute the operation corresponding to the anomaly handling strategy.

[0114] Optionally, the processing module is further configured to execute the operation corresponding to the exception handling strategy if the exception handling strategy applies to the image forming device.

[0115] Optionally, the working state includes a main state and the working parameters corresponding to the main state.

[0116] Optionally, the working state may further include sub-states and working parameters corresponding to the sub-states.

[0117] Optionally, the detection data further includes: a fault code, which is used to indicate the type of abnormality corresponding to the main state or the type of abnormality corresponding to the sub-state.

[0118] The power anomaly detection device provided in this embodiment of the invention can be used to achieve the above. Figures 1 to 7 For a detailed description of the power anomaly detection method described above, please refer to the embodiments of the power anomaly detection method, which will not be repeated here.

[0119] The technical solution of the power anomaly detection device provided in this embodiment of the invention acquires the current detection data of the image forming equipment, including the overall power of the image forming equipment, ambient temperature, and operating status; the current detection data is then input into a power anomaly detection model to obtain the anomaly detection result. Power anomaly detection can be performed without adding monitoring circuits to each power consumption module, reducing detection costs.

[0120] Figure 12 This is a schematic diagram of the structure of a model training device provided in an embodiment of the present invention, as shown below. Figure 12 As shown, the device includes a second processing module.

[0121] The second processing module is used to initially train the AI ​​model by inputting a large amount of historical detection data of the image forming equipment working normally; and to correct and train the AI ​​model using historical detection data of the image forming equipment working abnormally, so as to obtain a power anomaly detection model; the power anomaly detection model is used to output anomaly detection results based on the current detection data of the image forming equipment, wherein the detection data includes: the overall power of the image forming equipment, the ambient temperature, and the working status.

[0122] Optionally, before the second processing module corrects and trains the AI ​​model using historical detection data of the image forming device malfunction to obtain the power anomaly detection model, it is further configured to: correct and train the AI ​​model in response to a scoring operation on the output of the AI ​​model.

[0123] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 13 As shown, the electronic device described above may include at least one processor; and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor can execute this specification by calling the program instructions. Figures 1 to 7 The power anomaly detection method or embodiment shown provides Figure 8-10 The illustrated embodiment provides a model training method. The electronic device can be the image forming device or the model training device described above.

[0124] Figure 13 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown. Figure 13 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0125] like Figure 13As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 21, memory 23, and communication bus 24 connecting different system components (including memory 23 and processor 21).

[0126] Communication bus 24 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0127] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0128] Memory 23 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 23 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0129] A program / utility having a set (at least one) of program modules may be stored in memory 23. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this specification.

[0130] Processor 21 executes various functional applications and data processing by running programs stored in memory 23, such as implementing the specifications herein. Figures 1 to 7The power anomaly detection method or embodiment shown provides Figure 8-10 The model training method provided in the illustrated embodiment.

[0131] This invention provides a non-transitory computer-readable storage medium that stores computer instructions that cause the computer to execute the contents of this specification. Figures 1 to 7 The power anomaly detection method provided in the illustrated embodiment Figure 8-10 The model training method provided in the illustrated embodiment.

[0132] The aforementioned non-transitory computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0133] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0134] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0135] Computer program code for performing the operations described herein can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0137] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0138] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this specification, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0139] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this specification includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of this specification pertain.

[0140] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0141] It should be noted that the terminals involved in the embodiments of this specification may include, but are not limited to, personal computers (hereinafter referred to as PCs), personal digital assistants (hereinafter referred to as PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 players, MP4 players, etc.

[0142] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0143] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0144] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

Claims

1. A method for detecting power anomalies, characterized in that, The method includes: Acquire current detection data of the image forming device, including the overall power, ambient temperature, and operating status of the image forming device; The current detection data is input into the power anomaly detection model to obtain the anomaly detection result; The model training method for the power anomaly detection model includes: initially training the AI ​​model by inputting a large amount of historical detection data of the image forming equipment working normally; and correcting and training the AI ​​model using historical detection data of the image forming equipment working abnormally, thereby obtaining the power anomaly detection model.

2. The method according to claim 1, characterized in that, After inputting the current detection data into the power anomaly detection model to obtain the anomaly detection result, the method further includes: If the anomaly detection result is determined to include an anomaly handling strategy, the operation corresponding to the anomaly handling strategy is executed.

3. The method according to claim 2, characterized in that, Before executing the operation corresponding to the exception handling strategy, the method further includes: If the exception handling strategy applies to the image forming device, the operation corresponding to the exception handling strategy is executed.

4. The method according to any one of claims 1-3, characterized in that, The working state includes the main state and the working parameters corresponding to the main state.

5. The method according to claim 4, characterized in that, The working state also includes sub-states and the working parameters corresponding to the sub-states.

6. The method according to claim 1, characterized in that, The detection data also includes: fault codes, which are used to indicate the type of abnormality corresponding to the main state or the type of abnormality corresponding to the sub-state.

7. A model training method, characterized in that, The method includes: The AI ​​model is initially trained by inputting a large amount of historical detection data from when the image forming equipment is working properly. The AI ​​model is corrected and trained using historical detection data of the image forming equipment malfunctions to obtain a power anomaly detection model. The power anomaly detection model is used to output anomaly detection results based on the current detection data of the image forming equipment, including: the overall power of the image forming equipment, ambient temperature, and operating status.

8. The method according to claim 7, characterized in that, Before using historical detection data of malfunctions of the image forming device to correct and train the AI ​​model to obtain the power anomaly detection model, the method further includes: In response to the scoring operation of the output of the AI ​​model, the AI ​​model is corrected and trained.

9. An image forming apparatus, comprising a memory and a processor, the memory for storing information including program instructions, and the processor for controlling the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the method described in any one of claims 1-6.

10. A model training device, comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the method of claim 7 or 8.

11. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1-8.

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