Remote fault detection method and system for self-service printer

Abstract

The invention discloses a remote fault detection method and system for a self-service printer. The method comprises the steps that file information of a to-be-printed file is acquired; analyzing color block distribution information of each printing page according to the file information; in the printing process, when any printing page is completed, a thermal imaging camera is used for shooting the printing page, a thermal imaging image is obtained, and first heat distribution information is obtained according to the thermal imaging image; based on a pre-trained heat prediction model, second heat distribution information is obtained in combination with the current room temperature, the continuous printing duration and the color block distribution information; and generating a fault detection result by comparing the first heat distribution information with the second heat distribution information. The heat distribution in the printing process is monitored in real time and compared with the preset model prediction result, faults can be found and diagnosed in time, customer dissatisfaction and communication cost caused by the printing quality problem are reduced, the customer loss risk is reduced, the service quality and user experience of the self-service printer are improved, and meanwhile material waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a remote fault detection method and system for a self-service printer. Background Art

[0002] With the development of information technology, self-service printing equipment has been widely used in public places such as libraries, hotels, airports, etc. due to its convenience and efficiency. However, unattended self-service printers may encounter various faults during actual use, such as print head clogging, ink leakage, etc. These problems often lead to poor printing results and cause customer dissatisfaction. In particular, when problems occur during the printing process, customers cannot get technical support immediately, which can easily lead to customer churn, increase refund requests, and increase the cost of customer feedback communication.

[0003] Existing self-service printer fault detection methods usually rely on post-inspection or simple sensor monitoring, and cannot monitor fault information in real time during the printing process. This means that once a printing task starts, it is difficult to detect quality problems before printing is completed unless there is an obvious mechanical failure. This lag not only affects the user experience, but also increases the difficulty of equipment maintenance. Summary of the invention

[0004] In order to solve at least one of the above-mentioned technical problems, the present invention provides a remote fault detection method and system for a self-service printer.

[0005] In a first aspect, the present invention provides a remote fault detection method for a self-service printer, the method comprising: Get the file information of the file to be printed; According to the file information, analyze the color block distribution information corresponding to each printed page; Printing the document to be printed, when any printed page is printed, using a thermal imaging camera to shoot the corresponding printed page to obtain a thermal imaging image, and obtaining first heat distribution information according to the thermal imaging image and the color block distribution information; Based on the pre-trained heat prediction model, and according to the current room temperature, the continuous printing time, and the color block distribution information corresponding to the current printed page, the second heat distribution information is obtained; A fault detection result is generated according to the first heat distribution information and the second heat distribution information.

[0006] Preferably, generating a fault detection result according to the first heat distribution information and the second heat distribution information includes: Obtaining a total heat difference according to the first heat distribution information and the second heat distribution information; When the total heat difference is greater than a first preset value, an average change rate of the heat difference of the color blocks is obtained based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information; If the average change rate of the color block heat difference is less than the second preset value, reprint the current printed page. If the total heat difference of the new printed page is still greater than the first preset value, issue a fault reminder; if the average change rate of the color block heat difference is greater than or equal to the second preset value, issue a fault reminder.

[0007] Preferably, obtaining the average change rate of the color block heat difference based on the color block printing order and according to the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

[0008] Preferably, the heat prediction model training process includes: Collecting printing history data, including room temperature, continuous printing time, corresponding color block distribution information and heat distribution information; Room temperature, continuous printing time and color block distribution information are used as input features, heat distribution information is used as output label, and a heat prediction model based on neural network model is constructed based on printing history data.

[0009] In a second aspect, the present invention further provides a remote fault detection system for a self-service printer, the system comprising: A data acquisition module, used to acquire file information of a file to be printed; A color block distribution analysis module is used to analyze the color block distribution information corresponding to each printed page according to the file information; A printed page thermal analysis module is used to print the document to be printed. When any printed page is printed, a thermal imaging camera is used to shoot the corresponding printed page to obtain a thermal imaging image, and first thermal distribution information is obtained according to the thermal imaging image and the color block distribution information. A printed page heat prediction module, for obtaining second heat distribution information based on a pre-trained heat prediction model and according to current room temperature, continuous printing time and color block distribution information corresponding to the current printed page; The heat comparison detection module is used to generate a fault detection result according to the first heat distribution information and the second heat distribution information.

[0010] Preferably, the heat comparison detection module includes: A total heat difference calculation unit, used for obtaining a total heat difference according to the first heat distribution information and the second heat distribution information; A first heat comparison detection unit is used to obtain an average change rate of the heat difference of the color blocks based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information when the total heat difference is greater than a first preset value; The second heat comparison detection unit is used to reprint the current printed page if the average change rate of the color block heat difference is less than the second preset value, and to issue a fault reminder if the total heat difference of the new printed page is still greater than the first preset value; if the average change rate of the color block heat difference is greater than or equal to the second preset value, a fault reminder is issued.

[0011] Preferably, obtaining the average change rate of the color block heat difference based on the color block printing order and according to the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

[0012] Preferably, the system further includes a heat prediction model training module, specifically including: A data collection unit, used to collect printing history data, the printing history data including room temperature, continuous printing time, corresponding color block distribution information and heat distribution information; The model training unit is used to take the room temperature, continuous printing time and color block distribution information as input features, the heat distribution information as output labels, and build a heat prediction model based on the neural network model based on the printing history data.

[0013] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1) The present invention can timely discover and diagnose faults in the printing process by real-time monitoring of the heat distribution during the printing process and comparing it with the prediction results of the preset model, thereby reducing customer dissatisfaction, customer communication costs and customer loss risks caused by printing quality problems, improving the service quality and user experience of self-service printers, further reducing unnecessary waste of printing materials, and improving the operating efficiency of the equipment.

[0016] 2) The present invention can timely detect and distinguish random faults from systematic faults during the printing process by real-time monitoring of the heat distribution of the printed page and performing multi-level fault analysis, thereby quickly locating the problem and taking corresponding measures, effectively reducing customer dissatisfaction and communication costs caused by printing quality problems, improving user experience and service reliability, while optimizing resource utilization and equipment maintenance efficiency, and reducing operating costs.

[0017] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0020] Figure 1 A flowchart of a remote fault detection method for a self-service printer provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a remote fault detection system for a self-service printer provided by an embodiment of the present invention; Figure 3 The embodiment of the present invention provides Figure 2 A schematic diagram of the structure of the submodules of the heat comparison detection module; Figure 4 The embodiment of the present invention provides Figure 2 Schematic diagram of the structure of the submodules of the medium calorie prediction model training module. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] Existing self-service printer fault detection methods are unable to monitor fault information in real time during the printing process, resulting in print quality problems causing customer dissatisfaction and increased communication costs, and easily causing customer churn.

[0024] See also Figure 1 , Figure 1 A flowchart of a remote fault detection method for a self-service printer provided by an embodiment of the present invention. Figure 1 As shown, the method includes: S100, obtaining file information of a file to be printed; Receive the file to be printed from the user through a network interface or a data storage medium such as a USB flash drive, and parse the file to be printed to obtain file information. The file information refers to metadata related to the file to be printed, including but not limited to file format (PDF, DOC, DOCX, PNG, etc.), size, number of pages, resolution, etc.

[0025] S200, analyzing the color block distribution information corresponding to each printed page according to the file information; Color block distribution information refers to the color distribution of different content areas on the printed page, which can be single-color blocks or mixed-color blocks, as well as their positions and area ratios. For PDF files, you can use libraries such as PyMuPDF or PDFMiner in Python to process PDF files. These libraries can extract text, images, and color information from PDFs, write scripts to traverse each element in PDF, and record its color properties to generate detailed data on color block distribution. For Word files, you can use the python-docx library to read the content of the Word document, analyze the color and position of the color blocks, and then obtain the color block distribution information.

[0026] In a possible embodiment, the file to be printed can be converted into an image file, and an image processing library (such as OpenCV) can be used to analyze the color block distribution information in the image. Image processing algorithms such as edge detection, threshold segmentation, watershed transformation, etc. are used to identify different color areas on the printed page, and color space conversion (such as from RGB to HSV or CMYK) is applied to better separate different color areas. After the color blocks are identified, the features of each color block, such as color value, position coordinates, area size, etc., are extracted to obtain the color block distribution information.

[0027] S300, printing the document to be printed, when any printed page is printed, photographing the corresponding printed page with a thermal imaging camera to obtain a thermal imaging image, and obtaining first heat distribution information according to the thermal imaging image and the color block distribution information; When the self-service printer receives the print command, it starts printing according to the received file information. During the printing process, it is necessary to ensure that the printer is in good working condition to avoid printing quality problems caused by mechanical failure.

[0028] Install a thermal imaging camera at the output end of the printer, ensure that its viewing angle covers the entire printed page area, and adjust the parameters of the thermal imaging camera, such as sensitivity, measurement range, etc., to adapt to the material of the printing paper and the temperature range of the printing job.

[0029] Whenever a page is printed, the thermal imaging camera is immediately triggered to capture the thermal image of the page. To ensure the quality of the image, it is necessary to ensure that the printed page is stationary during the capture and that the distance between the camera and the paper is appropriate. Furthermore, the acquired thermal imaging image is preliminarily processed, such as denoising and contrast enhancement, and image processing algorithms are applied to identify hot spots in the thermal imaging image and quantify the temperature values ​​of these areas.

[0030] The color block distribution information obtained in step S200 is registered with the thermal imaging image, that is, the coordinate systems of the two are aligned through image processing technology. By comparing the color area in the color block distribution information with the temperature distribution in the thermal imaging image, the heat difference generated by different color areas during the printing process can be inferred. The color block distribution information and the thermal imaging image are combined to generate the first heat distribution information. The first heat distribution information should include the temperature distribution of the entire printed page, and especially mark those high-heat areas associated with the color block distribution. The generated first heat distribution information is analyzed to find any abnormal heat accumulation area, which may be a manifestation of print head blockage, excessive ink or other faults. If an abnormality is found, this information is recorded and prepared to be compared with the second heat distribution information in step S400.

[0031] S400, obtaining second heat distribution information based on a pre-trained heat prediction model and according to the current room temperature, the continuous printing time, and the color block distribution information corresponding to the current printed page; In a possible embodiment, the heat prediction model training process includes: Collecting printing history data, including room temperature, continuous printing time, corresponding color block distribution information and heat distribution information; Room temperature, continuous printing time and color block distribution information are used as input features, heat distribution information is used as output label, and a heat prediction model based on neural network model is constructed based on printing history data.

[0032] In a possible embodiment, the heat prediction model is trained by a large amount of historical data, which includes the heat distribution under different printing tasks and the corresponding environmental conditions (such as room temperature, printing speed, etc.). The input features of the heat prediction model include color block distribution information (such as the position, size, color type, etc. of the color block), current room temperature, continuous printing time, etc. The model selection can be a regression model based on machine learning, such as support vector machine (SVM), random forest (RF) or neural network (NN), or a deep learning model, such as convolutional neural network (CNN) or recurrent neural network (RNN).

[0033] A temperature sensor is integrated inside the printer to monitor the temperature changes of the printing environment in real time, and the temperature sensor is calibrated regularly to avoid errors.

[0034] When starting a print task, a timer is started to record the duration of each print task. For continuous printing, the duration of multiple print tasks needs to be accumulated to reflect the impact of the workload of the printing device on the heat of the printed page paper.

[0035] The color block distribution information of the current printed page is extracted from step S200 as an important input feature of the model, and the color block distribution information is preprocessed, such as normalization and standardization, to meet the requirements of the model input.

[0036] After obtaining all necessary input features, the pre-trained heat prediction model is called, the input features are passed to the model, and the prediction process is executed to obtain the predicted heat distribution, that is, the second heat distribution information.

[0037] S500: Generate a fault detection result according to the first heat distribution information and the second heat distribution information.

[0038] In this embodiment, in order to ensure the consistency of the first heat distribution information (actual value) and the second heat distribution information (predicted value) in spatial coordinates, the data points of the first heat distribution information and the second heat distribution information are matched, and the two data sets are preprocessed, such as removing noise, filling missing values, etc., to ensure data quality.

[0039] Compare each point in the two data sets and calculate the difference between them, using absolute difference, relative difference, or other metrics. Compare the average heat values ​​in a specific area to identify areas with inconsistent heat distribution.

[0040] In one possible embodiment, a difference threshold is set based on historical data and experience to determine when the heat distribution is considered abnormal. The calculated difference is compared with the set threshold, and the part exceeding the threshold is regarded as an area where printing abnormalities may occur. Printing is stopped in time and the customer is compensated in time, reducing the time for customer communication and feedback. Then, by analyzing the area with the largest difference, the specific location where there may be problems in the printing process is determined. Furthermore, according to the characteristics of the difference distribution, the faults can be divided into different categories, such as print head clogging, ink leakage, heating element failure, etc.

[0041] The analysis results are summarized into a fault detection report, which should include the specific location, type, severity and recommended solutions of the fault. The difference distribution map is displayed through a graphical interface to intuitively point out which areas have abnormal heat distribution, so that technicians can quickly locate the problem.

[0042] In this embodiment, by real-time monitoring of the heat distribution during the printing process and comparing it with the prediction results of the preset model, faults can be discovered and diagnosed in a timely manner during the printing process, reducing customer dissatisfaction, customer communication costs and customer churn risks caused by printing quality issues, improving the service quality and user experience of the self-service printer, further reducing unnecessary waste of printing materials, and improving the operating efficiency of the equipment.

[0043] Preferably, generating a fault detection result according to the first heat distribution information and the second heat distribution information includes: Obtaining a total heat difference according to the first heat distribution information and the second heat distribution information; When the total heat difference is greater than a first preset value, an average change rate of the heat difference of the color blocks is obtained based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information; If the average change rate of the color block heat difference is less than the second preset value, reprint the current printed page. If the total heat difference of the new printed page is still greater than the first preset value, issue a fault reminder; if the average change rate of the color block heat difference is greater than or equal to the second preset value, issue a fault reminder.

[0044] The first heat distribution information (actual thermal imaging image) and the second heat distribution information (predicted heat distribution) of the current printed page are obtained from the previous steps (S300 and S400), and the two sets of data are preprocessed, such as normalization, to ensure that they are compared on the same scale, and the first heat distribution information is compared with the second heat distribution information point by point, and the temperature difference of each pixel is calculated, and the sum of the differences of all pixel points, that is, the total heat difference, is obtained.

[0045] The total heat difference refers to the total difference between the actual heat distribution and the predicted heat distribution of all color blocks on the current printed page, which is obtained by accumulating the temperature difference of each pixel.

[0046] Based on historical data and experience, a total heat difference threshold is set, called the first preset value. The total heat difference is compared with the first preset value to determine whether it exceeds the threshold. When the total heat difference exceeds the first preset value, it indicates that there may be a printing quality problem and subsequent analysis will continue.

[0047] Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, an average change rate of the color block heat difference is obtained.

[0048] According to the color block distribution information obtained in step S200, the printed page is divided into several color blocks, and the printing order of each color block is determined, usually arranged in the order of the print head scanning path. For each color block, the difference between the first heat distribution information and the second heat distribution information is calculated respectively. According to the printing order, the change rate of the difference between adjacent color blocks is calculated, and then the average value of all change rates is obtained, that is, the average change rate of the color block heat difference. It should be noted that the average change rate of the color block heat difference refers to the average rate at which the difference between the actual heat distribution and the predicted heat distribution of all color blocks changes with the printing order. The average change rate of the color block heat difference can help determine whether there is a random or systematic fault in the printing process.

[0049] By calculating the average rate of change of the color block heat difference, it is possible to further analyze whether there are random or fixed failure modes in the printing process, providing a basis for subsequent failure warnings.

[0050] Similarly, based on historical data and experience, another standard value is set, called the second preset value, and the average change rate of the color block heat difference is compared with the second preset value. If the average change rate of the color block heat difference is lower than the second preset value, it is considered that there may be a random fault. At this time, the current print page will be reprinted to verify the result of the initial print. It should be noted that the second preset value is a standard value used to determine whether the average change rate of the color block heat difference is within the normal range. If it is lower than this value, it is considered that there may be a fault caused by random factors.

[0051] When the rate of change is low, the system considers it to be a random fault and further verifies it by reprinting.

[0052] Reprint the current printed page and calculate the new total heat difference. If the new total heat difference still exceeds the first preset value, it is determined that there is a printing failure, and a fault reminder is issued through visual or sound methods, and the subsequent printing process is directly suspended. The staff can directly take the initiative to compensate for the printing order to appease the customer.

[0053] In this embodiment, by real-time monitoring of the heat distribution of the printed page and performing multi-level fault analysis, random faults and systematic faults can be discovered and distinguished in a timely manner during the printing process, so that the problem can be quickly located and corresponding measures can be taken, effectively reducing customer dissatisfaction and communication costs caused by printing quality problems, improving user experience and service reliability, while optimizing resource utilization and equipment maintenance efficiency, and reducing operating costs.

[0054] Preferably, obtaining the average change rate of the color block heat difference based on the color block printing order and according to the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

[0055] In this embodiment, the embodiment arranges the color block heat difference sequence based on the color block printing order, and calculates the heat difference change rate between adjacent color blocks, and finally obtains the average change rate of the color block heat difference, so as to monitor the heat distribution changes of each color block in the printing process in real time, accurately identify random or systematic faults, and promptly locate and deal with printing quality problems, thereby improving the service quality and user experience of self-service printers, while optimizing resource utilization and reducing operating costs.

[0056] In summary, the method provided in this embodiment can at least achieve the following effects: 1) The present invention can timely discover and diagnose faults in the printing process by real-time monitoring of the heat distribution during the printing process and comparing it with the prediction results of the preset model, thereby reducing customer dissatisfaction, customer communication costs and customer loss risks caused by printing quality problems, improving the service quality and user experience of self-service printers, further reducing unnecessary waste of printing materials, and improving the operating efficiency of the equipment.

[0057] 2) The present invention can timely detect and distinguish random faults from systematic faults during the printing process by real-time monitoring of the heat distribution of the printed page and performing multi-level fault analysis, thereby quickly locating the problem and taking corresponding measures, effectively reducing customer dissatisfaction and communication costs caused by printing quality problems, improving user experience and service reliability, while optimizing resource utilization and equipment maintenance efficiency, and reducing operating costs.

[0058] See also Figure 2 In one embodiment, a remote fault detection system for a self-service printer is also provided, the system comprising: The data acquisition module 100 is used to acquire the file information of the file to be printed; The color block distribution analysis module 200 is used to analyze the color block distribution information corresponding to each printed page according to the file information; The printed page heat analysis module 300 is used to print the to-be-printed document. When any printed page is printed, a thermal imaging camera is used to shoot the corresponding printed page to obtain a thermal imaging image, and first heat distribution information is obtained according to the thermal imaging image and the color block distribution information. The printed page heat prediction module 400 is used to obtain second heat distribution information based on a pre-trained heat prediction model and according to the current room temperature, the continuous printing time and the color block distribution information corresponding to the current printed page; The heat comparison detection module 500 is used to generate a fault detection result according to the first heat distribution information and the second heat distribution information.

[0059] See also Figure 3 In one embodiment, the heat comparison detection module 500 includes: A total heat difference calculation unit 510 is used to obtain a total heat difference according to the first heat distribution information and the second heat distribution information; The first heat comparison detection unit 520 is used to obtain the average change rate of the heat difference of the color blocks based on the color block printing sequence and the first heat distribution information and the second heat distribution information when the total heat difference is greater than the first preset value; The second heat comparison detection unit 530 is used to reprint the current printed page if the average change rate of the color block heat difference is less than the second preset value, and to issue a fault reminder if the total heat difference of the new printed page is still greater than the first preset value; if the average change rate of the color block heat difference is greater than or equal to the second preset value, a fault reminder is issued.

[0060] In one embodiment, obtaining the average change rate of the color block heat difference based on the color block printing order and according to the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

[0061] See also Figure 2 and Figure 4 In one embodiment, the system further includes a heat prediction model training module 600, specifically including: A data collection unit 610 is used to collect printing history data, where the printing history data includes room temperature, continuous printing time, corresponding color block distribution information, and heat distribution information; The model training unit 620 is used to take the room temperature, continuous printing time and color block distribution information as input features and the heat distribution information as output labels, and to build a heat prediction model based on a neural network model based on the printing history data.

[0062] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0063] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0064] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0065] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. A remote fault detection method for a self-service printer, characterized in that: The method comprises: Get the file information of the file to be printed; According to the file information, analyze the color block distribution information corresponding to each printed page; Printing the document to be printed, when any printed page is printed, using a thermal imaging camera to shoot the corresponding printed page to obtain a thermal imaging image, and obtaining first heat distribution information according to the thermal imaging image and the color block distribution information; Based on the pre-trained heat prediction model, and according to the current room temperature, the continuous printing time, and the color block distribution information corresponding to the current printed page, the second heat distribution information is obtained; A fault detection result is generated according to the first heat distribution information and the second heat distribution information.

2. The remote fault detection method for a self-service printer according to claim 1, characterized in that: The generating a fault detection result according to the first heat distribution information and the second heat distribution information includes: Obtaining a total heat difference according to the first heat distribution information and the second heat distribution information; When the total heat difference is greater than a first preset value, an average change rate of the heat difference of the color blocks is obtained based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information; If the average change rate of the color block heat difference is less than the second preset value, reprint the current printed page. If the total heat difference of the new printed page is still greater than the first preset value, issue a fault reminder; if the average change rate of the color block heat difference is greater than or equal to the second preset value, issue a fault reminder.

3. The remote fault detection method for a self-service printer according to claim 2, characterized in that: The method of obtaining the average change rate of the color block heat difference based on the color block printing sequence and the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

4. The remote fault detection method for a self-service printer according to claim 1, characterized in that: The heat prediction model training process includes: Collecting printing history data, including room temperature, continuous printing time, corresponding color block distribution information and heat distribution information; Room temperature, continuous printing time and color block distribution information are used as input features, heat distribution information is used as output label, and a heat prediction model based on neural network model is constructed based on printing history data.

5. A remote fault detection system for a self-service printer, characterized in that: The system comprises: A data acquisition module, used to acquire file information of a file to be printed; A color block distribution analysis module is used to analyze the color block distribution information corresponding to each printed page according to the file information; A printed page thermal analysis module is used to print the document to be printed. When any printed page is printed, a thermal imaging camera is used to shoot the corresponding printed page to obtain a thermal imaging image, and first thermal distribution information is obtained according to the thermal imaging image and the color block distribution information. A printed page heat prediction module, for obtaining second heat distribution information based on a pre-trained heat prediction model and according to current room temperature, continuous printing time and color block distribution information corresponding to the current printed page; The heat comparison detection module is used to generate a fault detection result according to the first heat distribution information and the second heat distribution information.

6. The remote fault detection system for a self-service printer according to claim 5, characterized in that: The heat comparison detection module includes: A total heat difference calculation unit, used for obtaining a total heat difference according to the first heat distribution information and the second heat distribution information; A first heat comparison detection unit is used to obtain an average change rate of the heat difference of the color blocks based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information when the total heat difference is greater than a first preset value; The second heat comparison detection unit is used to reprint the current printed page if the average change rate of the color block heat difference is less than the second preset value, and to issue a fault reminder if the total heat difference of the new printed page is still greater than the first preset value; if the average change rate of the color block heat difference is greater than or equal to the second preset value, a fault reminder is issued.

7. The remote fault detection system for a self-service printer according to claim 6, characterized in that: The method of obtaining the average change rate of the color block heat difference based on the color block printing sequence and the first heat distribution information and the second heat distribution information includes: Based on the color block printing sequence and according to the first heat distribution information and the second heat distribution information, a color block heat difference value sequence arranged based on the color block printing sequence is obtained; The change rate between the color block heat difference values ​​of any two adjacent items in the color block heat difference value sequence is calculated, and the obtained change rates are averaged to obtain the average change rate of the color block heat difference value.

8. The remote fault detection system for a self-service printer according to claim 5, characterized in that: The system also includes a heat prediction model training module, specifically including: A data collection unit, used to collect printing history data, the printing history data including room temperature, continuous printing time, corresponding color block distribution information and heat distribution information; The model training unit is used to take the room temperature, continuous printing time and color block distribution information as input features, the heat distribution information as output labels, and build a heat prediction model based on the neural network model based on the printing history data.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions. When the processor executes the computer instructions, the electronic device executes the remote fault detection method for a self-service printer as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the remote fault detection method for a self-service printer according to any one of claims 1 to 4.

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