Method and device for evaluating life of toner cartridge based on state monitoring, equipment and medium

Abstract

The present application relates to the technical field of LED control circuit board manufacturing, solves the problem that the host computer cannot accurately learn the actual health status of the selenium drum in the prior art, which leads to inaccurate evaluation of the service life of the selenium drum and affects the reliability of the LED circuit control board, and provides a selenium drum service life evaluation method, device, equipment and medium based on state monitoring. The method comprises the following steps: obtaining the reference corresponding relationship between the printing data and the consumption of the selenium drum by performing test printing; monitoring the selenium drum to obtain actual state data; obtaining the actual corresponding relationship between the printing data and the consumption of the selenium drum; obtaining the consumption deviation value according to the reference corresponding relationship and the actual corresponding relationship; and evaluating according to the consumption deviation value and the actual state data to obtain a service life evaluation result. The present application comprehensively evaluates the service life of the selenium drum based on the consumption deviation and the actual state, thereby effectively improving the accuracy and real-time performance of the service life evaluation.

Description

Technical Field

[0001] This invention relates to the field of LED control circuit board manufacturing technology, and in particular to a method, apparatus, equipment and medium for evaluating the lifespan of a toner cartridge based on condition monitoring. Background Technology

[0002] In recent years, the rapid development of functional material printing technology has driven the application of toner cartridge electrostatic developing technology in the field of electronic device manufacturing, especially in the rapid prototyping and small-batch production of LED control circuit boards. Technologies exemplified by US Patent 8497057 B2 precisely transfer silver or copper-containing toner powder onto the surface of a flexible substrate using electrostatic imaging, and then form conductive patterns through hot pressing and sintering. This opens up a new path for printed circuit board manufacturing that is both low-cost and highly efficient. This path eliminates the need for traditional photolithography, etching, and electroplating processes, and does not rely on expensive masks or chemical developing solutions. When design changes occur, only the data file to be printed needs to be updated and the developing process re-developed. The new conductive pattern can then be deposited and the process proceeds to subsequent sintering and curing, significantly shortening the iteration cycle compared to photolithography processes that can take hours or even days. R&D engineers can complete multiple rounds of circuit layout modifications, printing, soldering, and functional verification in a short time, thereby improving the speed of prototype iteration.

[0003] Toner cartridges transfer image information from an optical system to a recording medium through electrostatic photosensitive imaging. A typical toner cartridge includes components such as a photosensitive drum, charger, developer, and cleaner. The photosensitive drum is the core component, and its surface is covered with a photosensitive material to form and retain an electrostatic latent image.

[0004] In the manufacturing process of LED control circuit boards, similar requirements for the precise deposition of conductive materials are faced in order to achieve electrode patterning, circuit interconnection, or flexible wire construction. Traditional technologies mostly rely on methods such as photolithography and sputtering, which are complex, costly, and have poor flexibility. However, applying toner cartridge imaging and electrostatic development technology to conductive material deposition can provide a new, low-cost, mass-producible process route for LED control circuit board manufacturing that is adaptable to flexible substrates.

[0005] In practical applications, the health of the photosensitive drum is crucial to the manufacturing quality and consistency of the LED control circuit board. Because LED driver circuits have extremely high requirements for conductor impedance, wiring accuracy, and current distribution, once the surface of the photosensitive drum ages or its potential drops, it will directly lead to a decrease in the quality of the electrostatic latent image and uneven transfer of conductive toner, resulting in missing details of local circuit lines, linewidth drift, and resistance mismatch on the circuit board, which seriously affects the driving balance and consistency of the LED string.

[0006] Therefore, in the manufacturing of LED control circuit boards, accurately knowing the actual lifespan of the toner cartridge and feeding back its health status to the host computer system in real time has become a key link in improving process reliability and intelligent production line management. If the host computer can collect toner cartridge lifespan parameters in real time, it can not only dynamically adjust the developing and transfer process parameters to compensate for drum aging, but also predictively schedule replacements to prevent sudden downtime and rework, ensuring the consistency and traceability of the quality of each batch of circuit boards.

[0007] Therefore, there is an urgent need for a real-time life assessment and status monitoring method suitable for toner cartridge units, in order to solve the problem that the host computer cannot accurately obtain the actual health status of the toner cartridge in the existing technology, which leads to inaccurate toner cartridge life assessment and affects the reliability of the LED circuit control board. Summary of the Invention

[0008] In view of this, embodiments of the present invention provide a method, apparatus, device and storage medium for evaluating the lifespan of a toner cartridge based on condition monitoring, which solves the problem in the prior art where the host computer cannot accurately obtain the actual health status of the toner cartridge, resulting in inaccurate toner cartridge lifespan evaluation and affecting the reliability of the LED circuit control board.

[0009] In a first aspect, embodiments of the present invention provide a method for evaluating the lifespan of a toner cartridge based on condition monitoring, wherein the toner cartridge is disposed in an image forming apparatus, and the method includes:

[0010] Test printing is performed according to a preset standard pattern set to obtain the baseline correspondence between the printing data and the toner consumption of the drum.

[0011] During the actual printing process, the toner cartridge is monitored to obtain actual status data;

[0012] At preset time intervals, obtain the actual correspondence between print data and toner consumption of the drum unit;

[0013] Based on the baseline correspondence and the actual correspondence, the consumption deviation value is obtained;

[0014] Based on the consumption deviation value and the actual state data, the toner cartridge is evaluated to obtain a lifespan assessment result.

[0015] Preferably, the step of performing test printing according to a preset standard pattern set to obtain a baseline correspondence between printing data and toner consumption of the drum includes:

[0016] Obtain a preset standard pattern set, wherein the preset standard pattern set includes a preset number of standard patterns, and the printing data of different standard patterns are different;

[0017] The preset standard pattern set is printed sequentially to obtain the toner consumption corresponding to each standard pattern.

[0018] By fitting the printing data and toner consumption corresponding to each standard pattern, a baseline correspondence between the printing data and toner consumption is obtained.

[0019] Preferably, the step of fitting the printing data and toner consumption corresponding to each standard pattern to obtain a baseline correspondence between the printing data and toner consumption includes:

[0020] Based on the printing data of each standard pattern, the image complexity features of each standard pattern are obtained, wherein the image complexity features include at least the amount of data, the density of point distribution, and the frequency of color changes.

[0021] Based on the image complexity characteristics, construct an image feature vector for each of the standard patterns;

[0022] Each of the image feature vectors is subjected to feature enhancement processing to obtain an enhanced feature vector;

[0023] The baseline correspondence is obtained by fitting the enhanced feature vector of each standard pattern with the corresponding toner consumption.

[0024] Preferably, the step of performing feature enhancement processing on each of the image feature vectors to obtain enhanced feature vectors includes:

[0025] The feature dimensions of the image feature vectors are combined in pairs to obtain several image feature pairs;

[0026] Calculate the Pearson correlation coefficient for each of the image feature pairs;

[0027] The image feature pairs are filtered based on the Pearson correlation coefficient and a preset correlation threshold to obtain initial weakly correlated feature pairs;

[0028] According to the preset filtering rules, the initial weakly correlated feature pairs are filtered to obtain the target weakly correlated feature pairs;

[0029] The image complexity features, excluding the weakly correlated feature pairs of the target in the image feature vector, are used as nonlinear transformation terms.

[0030] Based on the target weakly correlated feature pairs and the nonlinear transformation term, feature enhancement is performed on each of the image feature vectors to obtain enhanced feature vectors.

[0031] Preferably, the step of evaluating the toner cartridge based on the consumption deviation value and the actual state data to obtain a lifespan assessment result includes:

[0032] Based on the consumption deviation and the preset deviation threshold, the abnormal deviation type is obtained;

[0033] The evaluation weight of the actual state data is determined based on the abnormal deviation type, wherein the actual state data includes operating temperature, cumulative number of uses, and powder supply status;

[0034] The actual state data is weighted according to the evaluation weights to obtain weighted state data.

[0035] Based on the weighted state data and the consumption deviation, the lifespan assessment result of the toner cartridge is obtained.

[0036] Preferably, obtaining the abnormal deviation type based on the consumption deviation and a preset deviation threshold includes:

[0037] A preset deviation threshold is obtained based on the cumulative number of uses, wherein the preset deviation threshold is positively correlated with the cumulative number of uses;

[0038] Based on the difference between the preset deviation threshold and the consumption deviation, a defect detection image is obtained;

[0039] The defect detection image is printed to obtain a detection printed image;

[0040] The type of abnormal deviation is determined based on the type of defect found in the printed image.

[0041] Preferably, obtaining the lifespan assessment result of the toner cartridge based on the weighted state data and the consumption deviation includes:

[0042] Based on a preset time window, obtain the weighted state data sequence and the consumption deviation sequence;

[0043] Trend analysis is performed on the weighted state data sequence and the consumption deviation sequence to obtain the corresponding first trend change parameter and second trend change parameter, wherein the first trend change parameter and the second trend change parameter both include the rate of change, the direction of change, and the fluctuation amplitude.

[0044] Based on the first trend change parameter and the second trend change parameter, abnormal change data of the weighted state data sequence and the consumption deviation sequence are determined respectively;

[0045] The health trajectory curve is obtained based on the weighted state data sequence and consumption deviation sequence after removing abnormal change data;

[0046] The lifespan assessment results of the toner cartridge are obtained based on the health trajectory curve.

[0047] Secondly, embodiments of the present invention also provide a toner cartridge life assessment device based on condition monitoring, wherein the toner cartridge is disposed in an image forming apparatus, and the device includes:

[0048] The baseline relationship acquisition module is used to perform test printing based on a preset standard pattern set and obtain the baseline correspondence between the printing data and the consumption of the toner cartridge.

[0049] The status acquisition module is used to monitor the toner cartridge during the actual printing process and obtain actual status data;

[0050] The actual relationship acquisition module is used to acquire the actual correspondence between print data and toner cartridge consumption at preset time intervals.

[0051] The deviation calculation module is used to obtain the consumption deviation value based on the baseline correspondence and the actual correspondence.

[0052] The lifespan assessment module is used to assess the toner cartridge based on the consumption deviation value and the actual state data, and obtain the lifespan assessment result.

[0053] Thirdly, embodiments of the present invention provide an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect described above.

[0054] Fourthly, embodiments of the present invention provide a storage medium storing computer program instructions, which, when executed by a processor, implement the method of the first aspect described above.

[0055] In summary, the beneficial effects of the present invention are as follows:

[0056] The toner cartridge life assessment method, apparatus, device, and storage medium based on condition monitoring provided in this invention achieve real-time dynamic assessment of the toner cartridge's life status by periodically collecting actual printing data and consumption, avoiding the assessment lag problem caused by traditional methods that rely on fixed usage time or cumulative print count to estimate life. By introducing a standard pattern set to establish a benchmark consumption model, and based on the comprehensive analysis of consumption deviation and actual state data, it can accurately reflect the actual aging degree of the equipment, improving the consistency between life assessment results and the actual state of the equipment. By combining consumption deviation detection with condition monitoring, it can automatically identify the trend of equipment performance degradation without manual intervention, improving the intelligence level of equipment maintenance. By accurately identifying the equipment's life status, it can promptly prompt maintenance or replacement operations, reducing maintenance costs and downtime risks caused by sudden equipment failures. It is applicable to different types of toner cartridges and their consumption systems, and has good versatility and scalability.

[0057] Furthermore, by combining consumption deviation detection and condition monitoring, the system can automatically identify trends in equipment performance degradation without manual intervention, thus improving the intelligence level of equipment maintenance. For products like LED control circuit boards, which require extremely high resistance accuracy, precise toner cartridge life monitoring ensures the consistency of conductive patterns, thereby guaranteeing the normal operation and long-term stability of the circuit board. By accurately identifying the equipment's lifespan status, timely maintenance or replacement prompts can be provided, thereby reducing repair costs and downtime risks caused by sudden equipment failures. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0059] Figure 1 This is a schematic flowchart of a toner cartridge life assessment method based on condition monitoring according to an embodiment of the present invention.

[0060] Figure 2 This is another flowchart illustrating the toner cartridge life assessment method based on condition monitoring, as described in this embodiment of the invention.

[0061] Figure 3 This is a schematic diagram of the toner cartridge life assessment device based on condition monitoring according to an embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0063] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0065] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0066] Example 1

[0067] This invention provides a method for evaluating the lifespan of a toner cartridge based on condition monitoring. The toner cartridge is installed in an image forming apparatus and consists of a photosensitive drum, a drive motor, a charging assembly, a developing assembly, a transfer assembly, and a cleaning assembly. The surface of the photosensitive drum is covered with a photosensitive layer and can complete imaging cycles such as charging, exposure, developing, transfer, and cleaning under the control signals of a host computer. The host computer is interconnected with the toner cartridge in real time via a wired or wireless communication interface to issue working instructions, thereby driving the photosensitive drum to complete the pattern deposition of conductive material and simultaneously collecting status data from the toner cartridge to evaluate its lifespan.

[0068] Please see Figure 1 The method includes:

[0069] S1. Perform test printing according to the preset standard pattern set to obtain the baseline correspondence between the printing data and the toner consumption of the drum.

[0070] In this step, the preset standard pattern set refers to a set of test patterns designed in advance with representativeness and controllable complexity, used to standardize equipment performance testing, while the toner consumption refers to the amount of toner consumed during the image formation process.

[0071] The purpose of this step is to establish a baseline relationship between the printing data and consumption of the toner cartridge under ideal working conditions through standardized input (standard patterns), thereby providing a reference for comparison of conditions during subsequent actual printing processes. From a logical derivation, without baseline data, all subsequent deviation analyses would be without basis; therefore, establishing a stable and accurate baseline is the starting point and core of the entire evaluation process.

[0072] In the implementation process, a standard pattern set must first be selected to ensure that different patterns have different data complexity distributions, covering printing characteristics ranging from low density to high density and from simple colors to complex colors. Subsequently, printing tests are conducted using the toner cartridge to be evaluated, recording the printing data and corresponding toner consumption for each pattern in real time. Through statistical modeling, such as linear regression, multinomial fitting, or machine learning regression methods, a benchmark relationship between printing data and toner consumption is established.

[0073] S2. During the actual printing process, the toner cartridge is monitored to obtain actual status data;

[0074] In this step, actual status data refers to equipment health parameters collected in real time during actual printing operations, such as operating temperature, cumulative print count, toner supply status, and exposure energy level. This monitoring includes not only simple data reading but also sensor data acquisition and internal self-test data extraction. For example, a thermal sensor inside the image forming unit can record changes in the drum temperature in real time, and a toner supply sensor can monitor whether the toner supply is normal.

[0075] The purpose of this step is to obtain auxiliary characteristic data related to consumption changes in the actual working environment of the equipment for subsequent comprehensive analysis. By deduction, consumption deviations only reflect abnormal consumable usage, while actual condition data can provide more clues as to the causes of these abnormalities, such as drum degradation caused by high temperatures or abnormal consumption due to insufficient toner supply.

[0076] In practice, the device automatically collects various status data through its internal controller during each print job and records them to a log file or status database. The frequency and items of status data collection can be flexibly configured according to the device specifications; for example, temperature and toner supply status can be collected every 10 pages printed, and the usage count can be updated hourly.

[0077] S3. At preset time intervals, obtain the actual correspondence between print data and toner consumption of the drum unit;

[0078] In this step, the preset time period refers to a pre-defined time interval, such as collecting data every hour, every day, or every 1000 pages printed. The actual correspondence refers to the relationship between the actual printed data and the actual toner consumption within that time period. For example, after every 1000 pages printed, the actual amount of toner consumed is recorded, corresponding to the average print volume, color density, etc. By periodically updating the actual print consumption data, the device's operating status is continuously tracked, avoiding interference from occasional anomalies or short-term fluctuations in lifespan assessment. From a deductive perspective, a single data collection may lead to errors due to accidental circumstances, while accumulating data over a time period smooths out sampling fluctuations, ensuring the robustness of subsequent deviation calculations.

[0079] In one specific embodiment, a scheduled task or a data collection operation triggered based on the number of print jobs can be set to periodically collect print data and corresponding consumption over a period of time. The data can be categorized by job or accumulated overall. Through this step, a stable and continuous consumption pattern curve under actual operation can be established, forming a dynamic comparison basis with the initial baseline data, thereby enhancing the accuracy of life assessment.

[0080] S4. Obtain the consumption deviation value based on the benchmark correspondence and the actual correspondence;

[0081] In this step, the consumption deviation value refers to the difference between the actual and baseline correspondences. This deviation can be an absolute deviation (e.g., 5g more toner was consumed) or a relative deviation (e.g., a 10% increase in consumption). For example, if the baseline model predicts 30g of toner will be consumed when printing 1000 pages, but 35g is actually consumed, the deviation value is 5g or 16.7%.

[0082] The purpose of this step is to reflect changes in equipment performance through consumption deviation. By deduction, a continuously increasing consumption deviation usually indicates internal aging, decreased efficiency, or abnormal consumption phenomena within the equipment. This step quantifies the degree of deviation between the equipment's current state and its ideal state, providing a quantitative basis for lifespan assessment and enhancing the objectivity and operability of the assessment.

[0083] S5. Based on the consumption deviation value and the actual state data, the toner cartridge is evaluated to obtain a lifespan evaluation result.

[0084] In this step, the lifespan assessment result refers to the predicted remaining usable lifespan of the device, which can be the number of remaining printable pages, remaining usage time, or health level (such as healthy, warning, maintenance required, etc.). For example, when the toner cartridge lifespan assessment result shows that the remaining printable amount is less than 500 pages, the user can be prompted to prepare to replace the toner cartridge.

[0085] The purpose of this step is to comprehensively analyze the consumption deviation value and the actual status data to accurately determine the health status and future usability of the equipment. By deduction, looking only at the deviation value may lead to misjudgment of short-term anomalies, while looking only at the status data may overlook actual consumption changes. Combining the two allows for a more accurate and comprehensive assessment.

[0086] In the above embodiments, this invention achieves real-time dynamic evaluation of toner cartridge lifespan by periodically collecting actual printing data and consumption, combined with a benchmark consumption model and comprehensive analysis of actual state data. The host computer acquires the actual state data of the toner cartridge in real time and can dynamically adjust working parameters, such as developing voltage and exposure energy, according to the health status of the toner cartridge, thereby optimizing printing effects and extending toner cartridge lifespan. This overcomes the limitations of traditional lifespan estimation methods and avoids the lag problems that may occur when relying on fixed usage time or cumulative print count to evaluate toner cartridge lifespan. Secondly, by combining consumption deviation with actual state data, this invention can accurately predict the remaining service life of the equipment and promptly identify the trend of toner cartridge performance degradation. Consumption deviation essentially reflects abnormal consumption of the toner cartridge in actual use, while by monitoring the status (such as temperature, number of uses, toner supply status, etc.), other factors in actual operation can be combined to further analyze the root cause of performance changes. For example, excessively high temperature may accelerate the aging of the photosensitive drum, leading to a decrease in electrostatic latent image quality; abnormal toner supply may lead to poor transfer quality, affecting the accuracy of pattern deposition. In this way, comprehensive monitoring and analysis can identify potential equipment problems more comprehensively and sensitively than a single consumption deviation detection.

[0087] Furthermore, the host computer can issue timely alarms when the equipment's health status deviates, and even proactively control the equipment to shut down when the toner cartridge is nearing the end of its lifespan. This prevents the equipment from continuing to operate in a poor condition and avoids quality problems or equipment damage caused by premature toner cartridge lifespan or performance degradation. In addition, the host computer can obtain the toner cartridge's lifespan based on actual monitoring results and automatically prompt for replacement, ensuring that the production process remains highly efficient and stable at all times.

[0088] In summary, this invention, by deriving a scheme for real-time dynamic evaluation of toner cartridge lifespan and combining consumption deviation and status monitoring, provides a high-precision, high-reliability equipment lifespan management mechanism. This not only significantly improves the accuracy of toner cartridge lifespan prediction but also optimizes the manufacturing process of the LED control circuit board, reducing equipment failure risks and production costs. Furthermore, real-time acquisition of toner cartridge status data and control of equipment shutdown provides an intelligent maintenance method, improving the efficiency and intelligence level of equipment management.

[0089] Preferably, in step S1, the step of performing test printing according to a preset standard pattern set to obtain a baseline correspondence between the printing data and the toner consumption of the drum includes:

[0090] S11. Obtain a preset standard pattern set, wherein the preset standard pattern set includes a preset number of standard patterns, and the printing data of different standard patterns are different;

[0091] Specifically, a pre-set standard pattern set refers to a fixed set of patterns specifically designed for performance testing before the device leaves the factory or during maintenance. Standard patterns typically cover different data densities, pattern complexities, and color variations to comprehensively reflect various performance indicators of the toner cartridge during testing. For example, they may include high-density black block patterns, low-density dot patterns, grayscale gradient patterns, and colored stripe patterns. By covering diverse print data, the standard pattern set can effectively simulate various printing loads that may occur in actual use. The main purpose of this step is to establish a benchmark dataset, enabling accurate assessment of the toner cartridge's basic consumption level through a standardized and reproducible printing process, ensuring a reliable basis for subsequent consumption deviation analysis. Without unified and standardized test patterns, the benchmark data will have large deviations, making it impossible to effectively compare trends in actual use.

[0092] In the implementation process, manufacturers typically pre-set standard pattern sets, which are stored in the device's local memory or on an external server. Alternatively, the standard pattern set can be dynamically updated according to the application scenario, such as adding large-area color block test patterns under high-load environments. This step ensures that the test prints have a high degree of consistency and representativeness, thereby ensuring the accuracy and applicability of the baseline consumption data and providing a reliable reference basis for subsequent lifespan assessments.

[0093] S12. Print the preset standard pattern set sequentially and obtain the consumption amount corresponding to each standard pattern.

[0094] By conducting actual printing tests, the actual consumption of consumables by different patterns under varying loads is obtained, providing real data support for establishing a consumption model. By printing standard patterns one by one, the resource usage characteristics of the equipment under different printing conditions can be accurately quantified, thereby identifying the device's energy consumption patterns and characteristics.

[0095] During implementation, standard patterns can be output sequentially in a preset order, while monitoring the consumption data corresponding to each pattern. Consumption can be obtained in real-time through internal sensors (such as toner level sensors and ink level detectors) or calculated by accumulating print jobs using the device control unit. To improve data accuracy, the same standard pattern can be printed multiple times and the average value taken, or repeated tests can be conducted under different environmental conditions (such as different temperatures and humidity) to obtain robust data. This step allows for fine-grained capture of the device's consumption characteristics under different printing loads, providing high-quality data for fitting the relationship between subsequent print data and consumption, significantly improving the fitting accuracy of the consumption baseline model.

[0096] S13. Fit the printing data and consumption amount corresponding to each standard pattern to obtain the baseline correspondence between the printing data and the consumption amount.

[0097] The purpose of this step is to establish a mathematical relationship between print data and consumption, i.e., a baseline correspondence. This allows for the prediction of theoretical consumption simply by acquiring print data during actual printing, and then the identification of equipment performance changes by comparing this data with actual consumption. The accuracy of the baseline correspondence directly affects the sensitivity and accuracy of subsequent deviation detection.

[0098] In the implementation process, various fitting methods can be employed, such as linear regression, multinomial regression, support vector regression (SVR), or neural network regression, to fit and model the print data feature vectors with the corresponding consumption data. To improve the fitting quality, feature enhancement processing can be introduced into the print data, such as adding nonlinear transformation terms or feature interaction terms, to enhance the model's ability to fit consumption variations under complex patterns. The fitting model can be a globally unified model or sub-models trained separately for different printing modes (monochrome / color, large-area coverage / small-area coverage). Through this step, the expected consumption can be quickly estimated from the print data. In actual use, a theoretical consumption benchmark can be obtained without each test, providing a foundation for subsequent rapid calculation of consumption deviations and significantly improving the real-time performance and accuracy of lifespan assessment.

[0099] Preferably, the step of fitting the printing data and consumption amount corresponding to each standard pattern to obtain the baseline correspondence between the printing data and the consumption amount includes:

[0100] S131. Based on the printing data of each standard pattern, obtain the image complexity features of each standard pattern, wherein the image complexity features include at least the amount of data, the point distribution density, and the color change frequency.

[0101] Specifically, image complexity features refer to data characteristics that reflect the complexity of the image's content, used to quantitatively describe the structure and variations of a pattern. Common complexity features include at least: data volume (e.g., file size, measured in KB or MB), dot density (the number of printed dots per unit area), and color change frequency (the number or magnitude of color changes in the image). For example, a large black block pattern has a high dot density and a low color change frequency, while a color gradient image has a high color change frequency and a large data volume.

[0102] In this step, the image complexity features of each standard pattern are extracted to lay the foundation for building a subsequent printing consumption prediction model. Image complexity features refer to several key factors that reflect the complexity of image content, including data volume, dot density, and color change frequency. Specifically, data volume refers to the size of the pattern file, such as in KB or MB; dot density refers to the number of printed dots per unit area, reflecting the richness of detail in the pattern; and color change frequency measures the frequency of color changes in the image, usually obtained by calculating the number of color level jumps or color differences.

[0103] By extracting these complexity features, we can more accurately predict the toner consumption of a pattern. This is because printing consumption is not only affected by file size; the complexity, detail density, and color variations of the pattern all influence the final consumption. For example, a simple black-filled pattern has high dot density but few color variations, while a color gradient pattern, due to its frequent color changes and larger data volume, will consume more material and energy.

[0104] In practice, the basic dimensional information of the pattern file is obtained by parsing it. Next, the pixel density of the image is statistically analyzed, that is, the number of active pixels per unit area is calculated. Then, image processing techniques, such as edge detection or histogram analysis, are used to evaluate the frequency of color changes in the image. In this way, the complexity of the image can be quantified from multiple perspectives, avoiding the limitations of relying solely on data volume for prediction. This method enables the establishment of a more comprehensive and accurate baseline consumption model, helping to accurately predict the toner consumption of different patterns.

[0105] S132. Construct an image feature vector for each of the standard patterns based on the image complexity characteristics;

[0106] Image feature vectors are multidimensional arrays or vectors formed by arranging image complexity features in a certain order. They serve as input for subsequent modeling. Based on the features extracted in S131, they are assembled into vectors with a fixed format according to a preset order. For example, the first element of the feature vector is always the data volume, the second is the point distribution density, and the third is the color change frequency. For missing or outlier data, preprocessing methods such as mean imputation and standardization can be used to ensure the consistency and numerical comparability of the feature vectors. In one embodiment, features can be normalized (e.g., Min-Max standardization) to prevent different units from affecting the fitting effect.

[0107] S133. Perform feature enhancement processing on each of the image feature vectors to obtain enhanced feature vectors;

[0108] Feature enhancement processing refers to expanding the feature space based on the original image feature vectors through feature interaction, nonlinear transformations, and other methods to improve the model's ability to fit complex relationships. The purpose of this step is to enrich the feature representation capabilities, enabling the model to capture the nonlinear relationship between image features and consumption, thereby improving the model's fitting accuracy. If only the original linear features are used, the model may only be able to fit simple relationships, making it difficult to accurately predict the actual consumption under highly complex patterns.

[0109] S134. Fit the enhanced feature vector of each standard pattern and the corresponding toner consumption to obtain the benchmark correspondence.

[0110] Fitting refers to using enhanced feature vectors as input and the actual toner consumption of a standard pattern as labels to train and obtain the mathematical relationship between the input features and the consumption, known as the baseline correspondence. Fitting methods can include linear regression, multinomial regression, support vector regression (SVR), gradient boosting tree (GBDT), etc.

[0111] The purpose of this step is to establish a precise mapping model between print data characteristics and consumption, enabling the estimation of theoretical consumption during subsequent actual printing. This accurate baseline correspondence is fundamental for subsequent detection of consumption deviations and assessment of equipment health.

[0112] In the implementation process, the enhanced feature vectors and their corresponding actual consumption can be combined into a training set, and the fitting model can be trained using cross-validation to avoid overfitting. In one embodiment, sub-models can be trained separately for different types of patterns (such as large-area color blocks or intricate graphics), or classification auxiliary information can be introduced to improve the fine-grained adaptability of the fitting.

[0113] This step allows for the acquisition of a high-precision baseline consumption prediction model. In practical use, only the characteristics of the printed data need to be input to quickly calculate the theoretical consumption. By comparing this model with the actual monitored consumption, the consumption deviation can be obtained in real time, thus providing efficient and accurate basic support for the life assessment of the toner cartridge.

[0114] Preferably, the step of performing feature enhancement processing on each of the image feature vectors to obtain enhanced feature vectors includes:

[0115] S1341. Combine the feature dimensions of the image feature vectors in pairs to obtain several image feature pairs;

[0116] Feature dimension refers to each individual feature term in the image feature vector, such as data volume, point distribution density, and color change frequency. Pairwise combination refers to randomly selecting two features from all feature dimensions and combining them to form new feature pairs. For example, data volume and point distribution density can form one pair, and point distribution density and color change frequency can form another. The purpose of this step is to explore the potential implicit connections or interactions between image features. A single feature often cannot fully express the complex relationship between pattern and consumption; by combining feature pairs, the synergistic variation patterns between features can be discovered.

[0117] S1342. Calculate the Pearson correlation coefficient for each of the image feature pairs;

[0118] The Pearson correlation coefficient is a statistic used to measure the degree of linear correlation between two variables. Its value ranges from -1 to 1, with an absolute value close to 1 indicating a strong correlation and close to 0 indicating a weak correlation. A value close to 0 indicates almost no linear relationship, while a value close to -1 indicates a negative correlation. By calculating the Pearson correlation coefficient for each pair of image features, the relationship between these feature pairs can be quantified, allowing for the assessment of which feature pairs are interdependent and which are independent. The purpose of this process is to quantitatively analyze the relationships between different feature pairs to provide a basis for subsequent feature selection.

[0119] The next step is to filter all feature pairs based on a preset relevance threshold. This threshold is usually a pre-defined value, such as 0.3 or 0.5, used to distinguish which feature pairs have a weak relationship and which have a strong relationship. Feature pairs whose absolute Pearson correlation coefficient is less than the preset threshold are considered weakly correlated. Weakly correlated feature pairs usually contain more complementary information; they can bring new, non-redundant features to the model without repeating information already included in other feature pairs.

[0120] This screening process removes highly correlated and redundant feature pairs, reducing the complexity of the feature space. This improves the efficiency of subsequent feature enhancement and modeling, enabling the final baseline consumption relationship to more accurately capture effective feature relationships.

[0121] S1344. According to the preset filtering rules, the initial weakly correlated feature pairs are filtered to obtain target weakly correlated feature pairs;

[0122] Preset filtering rules refer to the rules used to further filter weakly correlated feature pairs, which may include, but are not limited to, feature importance scoring, information gain analysis, and priority filtering based on business knowledge. Target weakly correlated feature pairs are the feature combinations ultimately selected for feature enhancement processing.

[0123] The purpose of this screening process is to avoid increasing the computational complexity of the model or causing overfitting due to an excessive number of feature pairs. Too many feature pairs may capture unnecessary noise, thus affecting accuracy. The selected weakly correlated features correspond to the most valuable feature combinations for improving the model. They can increase the amount of information while maintaining independence, without redundancy or duplication of existing information.

[0124] In the implementation process, statistical methods such as correlation scoring and analysis of variance can be used to further screen feature pairs that contribute significantly. Simultaneously, practical experience and domain knowledge can be combined to evaluate the relevance of certain features, especially for features with a known weak relationship to consumption; their contribution can be tested to determine whether to retain them.

[0125] S1345. Take the image complexity features other than the weakly correlated feature pairs of the target in the image feature vector as a nonlinear transformation term;

[0126] In this step, for the original image complexity features that are not involved in the construction of weakly correlated feature pairs, further nonlinear transformation processing can be performed. The so-called nonlinear transformation term involves processing these features using mathematical methods such as squaring, square root extraction, and logarithms, thereby expanding the representation of the features in the data space. For example, squaring the point distribution density can more clearly reflect the impact of high-density areas on printing costs; similarly, taking the logarithm of the data volume helps reduce the extreme impact of large files on cost calculations.

[0127] The aim is to utilize features not involved in the combination, supplementing and enriching their informational expression through different non-linear methods, enabling them to reflect the complex, non-linear relationships that may exist between features and printing consumption. In this way, while preserving the basic meaning of the original features, more potential patterns of change can be uncovered, helping to improve the accuracy of consumption prediction and making the connection between consumption trends and actual printing behavior clearer and more detailed.

[0128] S1346. Based on the target weakly correlated feature pair and the nonlinear transformation term, perform feature enhancement on each of the image feature vectors to obtain an enhanced feature vector.

[0129] In this step, the goal is to perform feature enhancement processing on each image feature vector. Specifically, this involves combining the previously selected weakly correlated feature pairs with new features obtained through nonlinear transformations into the original image feature vectors, forming a richer and more discriminative extended feature set. In this way, the enhanced feature vectors can contain information from the original, combined, and nonlinearly transformed features, making the features themselves more comprehensive and detailed.

[0130] The core of this approach is to calculate the features obtained from feature pairs and nonlinear transformations separately, and then add them to the original features in a certain order, making the overall feature set more dimensional and information-rich. If necessary, all enhanced features can be standardized to avoid the influence of different feature dimensions, ensuring that each feature plays its due role in the consumption prediction relationship.

[0131] This feature enhancement not only preserves the original complexity description but also fully utilizes the increased data information resulting from combinations and transformations, making the relationship between subsequent consumption and features more comprehensive and flexible. This provides a high-quality data foundation for improving the accuracy of printing consumption predictions and for accurately assessing toner cartridge life.

[0132] Preferably, the step of evaluating the toner cartridge based on the consumption deviation value and the actual state data to obtain a lifespan assessment result includes:

[0133] S51. Obtain the abnormal deviation type based on the consumption deviation and the preset deviation threshold;

[0134] The preset deviation threshold is a pre-defined allowable deviation range used to distinguish between normal fluctuations and abnormal changes. For example, a deviation of less than 5% can be considered normal, while a deviation greater than 5% may indicate equipment performance degradation. The purpose of this step is to quickly identify the degree and type of consumption anomalies by comparing the actual deviation with the preset threshold, thereby providing a basis for subsequent adjustments to the evaluation focus of status data. Without distinguishing between deviation types, it is impossible to adopt different evaluation strategies for different degrees of anomalies, which can easily lead to a decrease in evaluation accuracy.

[0135] During implementation, the preset deviation threshold can be dynamically adjusted based on the cumulative number of uses. For example, a stricter threshold can be set when the equipment is new, and the threshold can be appropriately relaxed as it ages. Then, the difference between the current consumption deviation and the threshold is calculated, and the abnormal deviation type is classified according to the magnitude of the difference, such as normal deviation, slight abnormal deviation, or severe abnormal deviation. The deviation classification standard can be flexibly set according to the actual application scenario, such as setting a three-level or four-level deviation level system.

[0136] This step allows for a quick preliminary assessment of the toner cartridge's health status based on changes in consumption, improving the real-time nature and sensitivity of lifespan evaluation.

[0137] S52. Determine the evaluation weight of the actual state data according to the abnormal deviation type, wherein the actual state data includes working temperature, cumulative number of uses, and powder supply status;

[0138] Actual status data refers to monitoring data reflecting the current operation and usage of the toner cartridge, including operating temperature (such as the ambient or internal temperature of the imaging component), cumulative usage count (such as cumulative number of prints or cycles), and toner supply status (such as remaining toner level or records of toner supply anomalies). Evaluation weight refers to the weighting of the degree of influence of different status data on the final evaluation result during comprehensive assessment.

[0139] The purpose of this step is to dynamically adjust the weight of each status data point in the lifespan assessment based on the type of abnormal deviation, enabling the assessment logic to adaptively optimize based on the current abnormal characteristics of the equipment. For example, if a powder supply-related abnormality is detected, the weight of the powder supply status should be increased to reflect its greater actual impact on the equipment's lifespan.

[0140] During implementation, a preset weight adjustment table can be invoked based on the type of abnormal deviation. For example, the weight of the working temperature index can be increased under abnormal temperature deviation, and the weight of the powder supply status can be increased under abnormal powder supply deviation. The weights can be allocated proportionally (e.g., 0.5 / 0.3 / 0.2) or dynamically fine-tuned according to the actual situation.

[0141] This step ensures that the life assessment model focuses on the most critical current state factors, thereby improving the accuracy and relevance of the assessment and avoiding assessment bias caused by a one-size-fits-all uniform weight allocation.

[0142] S53. The actual state data is weighted according to the evaluation weights to obtain weighted state data;

[0143] In this stage, evaluation weights are used to weight the actual state data, resulting in weighted state data. Weighting involves summing or combining the data according to their respective weights, ultimately yielding a unified indicator that comprehensively reflects the equipment's health status. The purpose of weighted state data is to compress the originally multi-dimensional state information into a result that is easy to compare and analyze, facilitating overall evaluation with other assessment data such as consumption deviations.

[0144] The purpose of this approach is to organically integrate all factors affecting equipment health, preventing the overall judgment from being influenced by an excessively high or low weighting of any single data point during the evaluation process. Simply summing up unprocessed raw state data, with its varying dimensions and importance, can easily distort the evaluation results. To address this issue, each state data point can be normalized before weighting to ensure they are integrated on the same scale. Weighting methods can include the commonly used linear weighting, or methods such as weighted root mean square (RMS) to give more attention to extreme values ​​with large fluctuations in the state data.

[0145] This weighted processing effectively integrates and utilizes operational information from multiple devices, making the life assessment process simpler and more efficient. It also enhances the ability to adapt to the complex interactions of multiple factors, providing a more solid data foundation for subsequent life assessment.

[0146] S54. Based on the weighted state data and the consumption deviation, obtain the life assessment result of the toner cartridge.

[0147] Lifespan assessment results refer to the estimated remaining lifespan of a toner cartridge, calculated based on a comprehensive analysis of weighted condition data and consumption deviations. This lifespan can be expressed as remaining print count, remaining usage time, or health level. Lifespan assessment results are typically used to guide equipment maintenance and replacement planning.

[0148] The purpose of this step is to comprehensively calculate the actual lifespan of the equipment by fully utilizing status data and consumption behavior characteristics, thus providing a quantitative basis for equipment management. Relying solely on a single factor (such as the number of times it is used) can easily underestimate or overestimate the equipment's lifespan, affecting the scientific nature of maintenance decisions.

[0149] In the implementation process, health indicators (such as the weighted average of the deviation between weighted status data and consumption) can be set, and the remaining lifespan can be estimated by comparing the indicators with preset health level ranges. For example, a health indicator below 0.4 can be judged as a healthy state, 0.4 to 0.7 is a warning state, and above 0.7 enters a warning or maintenance state. Furthermore, the remaining service life can be predicted based on the rate of decline of the health indicator.

[0150] This step allows for real-time and accurate output of equipment lifespan status, greatly improving the scientific nature and timeliness of maintenance management, reducing the risk of sudden failures, optimizing consumable usage strategies, and extending the overall lifespan of the toner cartridge.

[0151] Preferably, obtaining the abnormal deviation type based on the consumption deviation and a preset deviation threshold includes:

[0152] S511. Obtain a preset deviation threshold based on the cumulative number of uses, wherein the preset deviation threshold is positively correlated with the cumulative number of uses;

[0153] Positive correlation means that as the cumulative number of uses increases, the preset deviation threshold also increases; that is, the older the equipment, the higher its tolerance for fluctuations in consumption. The purpose of this step is to dynamically adjust the deviation judgment standard based on the equipment's usage time, so that the life assessment method can adapt to the natural aging process of the equipment and avoid misjudging slight fluctuations caused by normal aging as abnormalities. If the deviation threshold is not adjusted according to the number of uses, the assessment standard will be too strict, leading to frequent false alarms during the equipment aging stage.

[0154] During implementation, a deviation threshold growth curve can be formulated based on the equipment's design life and historical data. For example, a strict setting can be implemented in the initial use phase (e.g., allowing a deviation of ±5%), moderately relaxed in the mid-term (e.g., ±8%), and further relaxed in the final phase (e.g., ±12%). In practice, the controller can determine the corresponding preset deviation threshold based on the cumulative number of uses read in real time, using a lookup table or interpolation calculation method.

[0155] S512. Obtain a defect detection image based on the difference between the preset deviation threshold and the consumption deviation;

[0156] Defect detection images refer to special test patterns used to detect and expose imaging anomalies in equipment, such as high-density black block images, uniform grayscale images, and fine-line test patterns. The difference value refers to the numerical difference between the actual consumption deviation and a preset deviation threshold, used to quantify the severity of the current deviation.

[0157] The purpose of this step is to select an appropriate defect detection image based on the severity of the deviation, and to trigger a more sensitive detection mode accordingly, so as to identify potential imaging problems in a timely manner. If the detection pattern is not adjusted according to the magnitude of the deviation, the detection sensitivity cannot match the severity of the anomaly, which can easily lead to missed or false detections.

[0158] During implementation, different levels of detection patterns can be preset according to the difference range. For example, if the difference is small, a normal grayscale image can be selected for detection; if the difference is large, a more demanding test image such as a high-contrast stripe image or a large-area solid color block image can be selected. The controller dynamically calls the corresponding pattern from the defect detection pattern library based on the currently calculated difference.

[0159] This step allows for flexible adjustment of the detection strategy based on the severity of the anomaly, enhancing the ability of the detected image to expose potential imaging problems, thereby improving the sensitivity and accuracy of anomaly detection.

[0160] S513. Print the defect detection image to obtain a printed detection image;

[0161] A printed inspection image refers to the physical printout obtained after selecting a defect inspection image is actually printed out using a toner cartridge. This printout can be used for visual inspection or for automated analysis using scanning equipment.

[0162] The purpose of this step is to demonstrate the current imaging performance of the device in an observable and detectable manner through actual printing. Actual printing can directly reflect the real working status of imaging units (such as photosensitive drums, developers, toner supply systems, etc.), and is a more direct means of health check than data monitoring.

[0163] During implementation, the printer can be controlled to directly print defect detection images onto standard media (such as ordinary paper or special testing paper) and execute the printing according to uniform printing parameters (such as standard mode and standard concentration) to ensure the consistency and comparability of the test results. After printing, the printed images can be captured by manual visual inspection or image acquisition devices (such as scanners or cameras) for subsequent analysis.

[0164] This step exposes potential aging, wear, and contamination issues inside the equipment in the form of actual images, providing intuitive evidence for subsequent anomaly type identification and improving the verifiability and accuracy of the diagnosis.

[0165] S514. Determine the type of abnormal deviation based on the type of defect appearing in the printed image.

[0166] Defect type refers to the abnormal appearance in the printed image, such as uneven density, streaks, spots, color shift, blurring, etc. Abnormal deviation type is the nature of the deviation classified according to the detected defect characteristics, such as abnormal toner supply, photosensitive drum aging, abnormal transfer, etc.

[0167] The purpose of this step is to further confirm the specific cause of the consumption deviation based on the actual imaging anomalies observed in the printed image, thereby accurately determining the type of anomaly. Inferring the deviation type solely based on consumption data is prone to misjudgment due to external environmental factors or occasional interference; combining image detection results can significantly improve the accuracy of anomaly classification.

[0168] During implementation, a rule table can be established to correspond defect features with abnormal deviation types. For example, if stripes are detected, it can be preferentially identified as damage to the surface of the photosensitive drum; if overall fading is detected, it may be identified as an abnormality in toner supply. Defect identification can be done manually or automatically by combining image recognition algorithms, thereby improving the degree of automation and identification accuracy.

[0169] This step allows for precise identification of the actual source of the fault causing abnormal consumption, further improving the relevance and accuracy of equipment life assessment and providing a valid basis for subsequent maintenance decisions.

[0170] Preferably, obtaining the lifespan assessment result of the toner cartridge based on the weighted state data and the consumption deviation includes:

[0171] S541. Obtain the weighted state data sequence and consumption deviation sequence according to the preset time window;

[0172] A preset time window refers to a segment of data collected continuously within a certain time range, such as the most recent day, week, or 100 print jobs. A weighted status data sequence refers to a sequence of comprehensive evaluation values ​​of the actual equipment status arranged in chronological order; a consumption deviation sequence refers to the consumption deviation value corresponding to each print job arranged in chronological order.

[0173] The purpose of this step is to introduce a time dimension, collecting a continuously changing range of data to reflect the trend characteristics of equipment operating status and consumption behavior over time. Single-point data is easily affected by occasional noise, while sequence data can more comprehensively describe the process of equipment health changes.

[0174] During implementation, the time window size can be set, such as the last 24 hours or the last 500 print records. Weighted status data and consumption deviations are monitored in real time, recording one data point at a time to form two time series. The series data can be stored in a local cache or on a remote server, supporting subsequent batch processing and analysis.

[0175] This step allows for the systematic collection of equipment operation data, providing a foundation for continuous changes and laying a solid data base for subsequent trend analysis, anomaly detection, and health trajectory modeling.

[0176] S542. Perform trend analysis on the weighted state data sequence and the consumption deviation sequence to obtain the corresponding first trend change parameter and second trend change parameter, wherein the first trend change parameter and the second trend change parameter both include the rate of change, the direction of change, and the fluctuation amplitude.

[0177] Trend analysis refers to the use of mathematical and statistical methods to extract the overall directionality and volatility characteristics of data changes from time series. The rate of change indicates how quickly the data changes over time (e.g., the magnitude of increase or decrease in an indicator per unit of time); the direction of change indicates whether the data is generally rising, falling, or remaining stable; and the magnitude of volatility indicates the range of fluctuation the data makes around the trend line.

[0178] The purpose of this step is to abstract the original time series into concise trend features, facilitating rapid identification of abnormal changes and reconstruction of healthy trajectories. Directly processing the original series is computationally intensive and noisy; extracting trend parameters effectively simplifies subsequent processing. Trend change parameters can be extracted using methods such as local linear regression (e.g., sliding window fitting), first-order differencing, and moving averages. The rate of change can be calculated from the slope, the direction of change is determined by the overall inclination angle of the trend line, and the fluctuation amplitude can be described using statistical measures such as standard deviation and range.

[0179] This step allows for the rapid extraction of key change features from massive amounts of raw data, improving the efficiency and accuracy of subsequent anomaly detection and health trajectory modeling.

[0180] S543. Based on the first trend change parameter and the second trend change parameter, determine the abnormal change data of the weighted state data sequence and the consumption deviation sequence, respectively.

[0181] Abnormal data refers to localized abrupt changes identified in trend parameter analysis that do not conform to the overall health evolution trend, such as sudden increases or decreases, or short-term drastic fluctuations. By identifying these abnormal data, occasional anomalies caused by external interference or short-term malfunctions can be eliminated, preserving the main trend of the equipment's natural aging process.

[0182] In the implementation process, abnormal data can be identified based on rules such as abrupt changes in rate of change (e.g., rate exceeding the mean ± 3 standard deviations), reversals in direction of change (e.g., rapid reversals in the short term), and abnormal fluctuation amplitudes (e.g., a sudden increase in fluctuation amplitude in a short period of time). Anomaly identification methods can employ statistical detection (e.g., Z-Score), rate of change detection (Delta detection), or machine learning-based anomaly detection (e.g., Isolation Forest).

[0183] This step effectively eliminates noise data caused by non-aging factors, ensuring that subsequent health trajectory curves are smooth, continuous, and accurate, thereby improving the robustness and reliability of the life assessment system.

[0184] In one specific embodiment, step S543 specifically includes:

[0185] S5431. Obtain the weighted state data sequence and consumption deviation sequence according to the preset time window;

[0186] During equipment operation, firstly, based on the set time window, continuous weighted status data and consumption deviation are collected to form two sets of time series data.

[0187] S5432. Based on the weighted state data sequence and the consumption deviation sequence, calculate the first trend change parameter and the second trend change parameter respectively, and obtain their respective change rate, change direction and fluctuation amplitude.

[0188] By employing methods such as local linear regression, moving average, or differencing, trends are extracted from the weighted state data series and the consumption deviation series to obtain trend parameters describing their changes, including the overall rate of change, whether the data is rising or falling, and the magnitude of fluctuations. This analysis can intuitively reflect the dynamic changes in equipment status and consumption.

[0189] S5433. Based on the trend change parameters and preset anomaly discrimination rules, identify and locate the abnormal change segments in the sequence to obtain abnormal change data;

[0190] In this step, based on the pre-set anomaly detection criteria (such as the rate of change exceeding a set threshold, a sudden reversal of the direction of change within a short period of time, or a significantly larger fluctuation amplitude), the trend parameters are analyzed, and data segments or data points with abnormal changes are identified from the weighted state data sequence and the consumption deviation sequence as abnormal change data.

[0191] For example:

[0192] If the rate of change of the weighted state index is greater than three times the standard deviation of the average value within a certain period of time, it is judged as a sudden anomaly.

[0193] If the consumption deviation sequence shows a continuous sharp increase or decrease, and the fluctuation range exceeds the threshold, it is also marked as an abnormal change segment.

[0194] If both sequences reverse direction within the same time window (e.g., from stable to rapidly declining), the weight of anomaly detection is further increased.

[0195] S5434. Based on the identified abnormal change data, the original data sequence is marked and isolated to obtain the health change data after removing the abnormalities;

[0196] The detected abnormal data points or segments are marked in the original sequence and isolated to remove abnormal fluctuations, which facilitates the smooth fitting of the health trajectory curve and the assessment of remaining lifespan.

[0197] S544. Obtain the health trajectory curve based on the weighted state data sequence and consumption deviation sequence after removing abnormal change data;

[0198] A health trajectory curve is a continuous curve obtained by fitting the remaining normal data points after removing abnormal fluctuations to show how the equipment's health level evolves over time. This curve truly reflects the natural performance degradation process of equipment from new to old.

[0199] The purpose of this step is to reconstruct a smooth, physically consistent health trajectory model based on cleaned, stable data, avoiding the impact of data noise on lifespan estimation results. Without reconstructing the health trajectory, lifespan prediction will heavily rely on unstable data, leading to reduced assessment accuracy. Various curve fitting methods can be employed, such as locally weighted regression, multinomial regression, exponential decay models, or piecewise linear fitting. The health trajectory can be represented by a numerical function, facilitating subsequent lifespan estimation. Smoothing processes can be incorporated during trajectory reconstruction to further improve the continuity and smoothness of the curve.

[0200] S545. Obtain the lifespan assessment result of the toner cartridge based on the health trajectory curve.

[0201] Life assessment results are the remaining lifespan of the equipment calculated based on the health trajectory curve, such as how many more pages can be printed, how many more hours can be run, or the health level (such as healthy, alert, warning, fault).

[0202] The purpose of this step is to scientifically calculate the remaining lifespan based on the equipment's health evolution trend, guiding maintenance decisions, resource allocation, and user prompts to prevent losses caused by sudden equipment failures. If lifespan is directly inferred from the current state, ignoring trend changes, it can easily lead to delayed or significantly inaccurate lifespan assessments.

[0203] During implementation, thresholds can be set for health indicators. For example, when an indicator drops to a set warning value, the toner cartridge's lifespan is considered nearing its end. Based on real-time data collection and calculation of the health trajectory curve, the host computer can not only predict how much printing volume or runtime is needed to reach the threshold, but also provide early warnings about the toner cartridge's aging trend by analyzing the rate of decline in the curve. Whenever a health indicator approaches or exceeds the warning threshold, the host computer proactively reminds the user to perform timely maintenance and replace the toner cartridge, effectively reducing the risk of equipment downtime and repairs due to unexpected toner cartridge failure, and ensuring production rhythm and material management. Through this method, the entire system achieves dynamic management and intelligent prompts for toner cartridge lifespan, greatly improving equipment safety and operational efficiency.

[0204] Example 2

[0205] In Example 1, the condition monitoring and lifespan assessment of the toner cartridge is performed on the entire machine. Testing is conducted using a standard pattern set to fit a baseline relationship between printing data and consumption, and based on this, the health status and lifespan of core components such as the photosensitive drum are comprehensively assessed. This holistic testing and assessment method can meet the needs of routine equipment maintenance and has good applicability to most devices with uniform performance.

[0206] However, with the diversification of toner cartridge applications and the increase in usage intensity, localized areas on the surface of the photosensitive drum may experience localized performance degradation and premature lifespan decline due to factors such as material inhomogeneity, mechanical wear, localized environmental differences, or long-term high single-point load. For example, certain areas may experience reduced charge retention capacity, abnormal localized pattern development, or banded or spot defects.

[0207] When using a holistic detection and evaluation approach, these local anomalies may be masked by the overall mean, leading to overly optimistic lifespan assessments and an inability to promptly detect and locate early-stage local failure risks. This can affect the overall quality of the equipment's output images and may also result in delayed equipment fault warnings, increasing unexpected downtime and subsequent maintenance costs.

[0208] Therefore, in order to more accurately reflect the true health status of core components such as the photosensitive drum and improve the sensitivity and response speed of equipment anomaly detection, this embodiment 2 proposes to introduce a zoned detection and evaluation mechanism based on overall assessment, building upon embodiment 1. This mechanism divides the surface of the photosensitive drum into several detection areas, collecting and analyzing the status data and consumption of each area separately. This allows for the timely detection, location, and evaluation of potential local failures, effectively preventing overall assessment from masking local anomalies and significantly improving the accuracy and reliability of equipment operation and maintenance. Specific improvements are as follows:

[0209] First, step S1 in Embodiment 1 is improved, and the improved step S1 further includes:

[0210] S2-11. According to the preset area division rules, the toner cartridge is divided into several independent detection areas;

[0211] Specifically, the area division rule refers to the spatial division method for core components of the toner cartridge, such as the photosensitive drum. For example, it divides the device into several detection areas based on axial direction, circumferential direction, or surface function. Detection areas can be equal-width strips, sector areas, rectangular grids, etc., with the number and shape flexibly determined according to equipment structure and monitoring requirements. This step divides the overall device into local units, enabling independent status monitoring of each area, facilitating the detection and location of local performance anomalies. It can accurately locate local failures, wear, or abnormal consumption points during equipment operation, improving the sensitivity and accuracy of fault detection and lifespan assessment.

[0212] S2-12. Obtain the target detection pattern for each detection area from a preset standard pattern set according to the detection area;

[0213] Specifically, the target detection pattern is a test pattern specifically designed for each detection area. Its characteristic is that only the target detection area contains actual printed content (such as solid color blocks, dense dot matrix, etc.), while other areas are completely white (no printed data). This ensures that resource consumption and imaging effects are strictly limited to the detection area during testing. For example, if the printed area is divided into four vertical strip areas, the target detection pattern in area A will only have a black rectangle drawn in area A, while areas B, C, and D will be entirely white (blank). During testing in area B, only area B will have a pattern, while the rest will be blank, and so on. Using a large-area or full-page pattern would lead to the superposition of resource consumption and imaging behavior across multiple areas, making it difficult to distinguish the true performance of a single area. By ensuring that only the target detection area contains printed content, while other areas are completely blank, variables can be isolated to the greatest extent possible, enabling "point-to-point" measurement of a single area.

[0214] S2-13. Print the target detection pattern for each detection area in turn to obtain the local consumption data of each detection area under the corresponding target detection pattern.

[0215] Specifically, target detection patterns are printed and tested sequentially for each detection area. Each target detection pattern is specifically designed for its corresponding detection area, with actual printed content only within that area, and the rest left blank. This design ensures that resource consumption during printing tests is concentrated primarily on the tested area, effectively avoiding interference from other areas. After each printing operation, the actual resource consumption data for the current detection area under the target detection pattern is obtained. This allows for the independent collection of high-quality resource consumption data for each area, accurately reflecting its performance under standard test loads, and laying a solid data foundation for subsequent regional modeling and analysis.

[0216] S2-14. Based on the local consumption data and the corresponding target detection pattern printing data, obtain the local reference correspondence between the printing data of each detection area response and the local consumption data.

[0217] Based on the collected local consumption data of each detection area and the corresponding printed data of the target detection pattern, a local baseline correspondence is established between the printed data and local consumption data of each detection area. The method for establishing this baseline correspondence can refer to the modeling process of the overall baseline model in Example 1, which will not be repeated here. The difference is that Example 1 focuses on the relationship between the overall printed data and consumption of the equipment, while this step establishes the correspondence for each detection area separately, achieving a refined and distributed baseline establishment from the overall to the local. In this way, local performance changes, local aging, or abnormal consumption phenomena can be more accurately identified and located, thereby significantly improving the scientific rigor and reliability of drum life management and fault warning.

[0218] The improved step S3 in Example 1 specifically includes:

[0219] S2-31. Every preset time period, each detection area is selected as the current test object according to the area division rules.

[0220] Specifically, at preset time intervals (e.g., every 1000 pages printed, daily, or weekly), the system automatically selects each detection area as the current test target according to the area division rules. This ensures that all detection areas are covered sequentially within the device's operating cycle, thereby obtaining their latest health status data. By systematically polling the detection areas, it avoids focusing on the whole or a specific part for extended periods, ensuring balanced and systematic area monitoring.

[0221] S2-32. For the current detection area, perform a printing test operation on the target detection pattern of the object to obtain the actual local consumption data under the current detection area.

[0222] For the selected detection area, the system will use a dedicated target detection pattern for that area to perform a printing test. The key feature of this target detection pattern design is that only the selected detection area has printed content, while the remaining areas are blank. Therefore, resource consumption and imaging performance in this test are almost entirely attributed to the current detection area. After printing, the system uses built-in or external consumable sensors and energy consumption statistics to collect and record the actual local consumption data for that area in real time during this round of testing. This step ensures the purity and high comparability of the local consumption data, eliminating the influence of non-target areas.

[0223] S2-33. Pair the printed data with the corresponding local consumption data to establish the actual local correspondence of the detection area;

[0224] Finally, the printing data from this round of printing tasks will be paired one-to-one with the corresponding local consumption data to establish the latest local actual correspondence for the current inspection area. This data will serve as the basis for regional health management, being continuously recorded and updated for comparison with previously established regional benchmarks, calculation of consumption deviations, and trend analysis. Through this series of operations, the equipment can periodically obtain the real operational performance of each inspection area, providing reliable data support for zonal life assessment, anomaly monitoring, and precise maintenance decisions.

[0225] The improved step S4 in Example 1 is as follows:

[0226] S2-4. Based on the local reference correspondence and the local actual correspondence, obtain the local consumption deviation value of each detection area;

[0227] The improved step S5 in Example 1 is as follows:

[0228] S2-5. The toner cartridge is evaluated based on the consumption deviation value, the local consumption deviation value, and the actual state data to obtain a lifespan evaluation result.

[0229] Step S2-5 further includes:

[0230] S2-51. Based on the consumption deviation and the preset deviation threshold, obtain the abnormal deviation type and the overall deviation level;

[0231] The process for obtaining the abnormal deviation type is the same as step S51 in Example 1, and will not be repeated here. The overall deviation level refers to determining the overall health status of the equipment based on the range of the overall deviation. For example, a deviation of less than 5% is considered normal, while a deviation exceeding 5% is classified as abnormal, and the severity (mild or severe) of the abnormal deviation will affect the weighting of subsequent assessments. The goal of this step is to provide a health assessment based on overall consumption to help identify potential problems in equipment operation.

[0232] S2-52. Based on the comparison between the local consumption deviation value of each detection area and the corresponding local deviation threshold, obtain the local deviation level of each detection area;

[0233] The core of this step is to analyze by region. This step compares the local consumption deviation value of each detection region with the local deviation threshold for that region. This helps identify which regions have consumption exceeding the normal range. Based on the magnitude of the local deviation value, each region is assigned a local deviation level to determine the health status of that region. For example, if the consumption deviation of a region exceeds the set local deviation threshold, that region will be rated as poor or abnormal, which helps to further locate and resolve local equipment problems. The local deviation threshold for each monitoring region can be set differently depending on the importance of the region (e.g., core component regions, peripheral component regions). For example, core component regions may have a lower threshold set for higher sensitivity, while peripheral regions may have a slightly higher threshold set.

[0234] S5-53. Based on the overall deviation level and the local deviation level of each detection area, dynamically allocate the local state evaluation weight of each detection area.

[0235] In this step, the local condition evaluation weight of each detection area is dynamically adjusted by combining the overall deviation level and the local deviation level of each detection area. The overall deviation level reflects the macroscopic health status of the equipment, while the local deviation level reflects the severity of regional consumption anomalies.

[0236] The overall deviation level is calculated based on the overall consumption deviation of the entire device. If the overall consumption deviation exceeds a preset deviation threshold (e.g., exceeding 10%), it indicates that the overall health of the device requires close monitoring. Therefore, the weight of local condition evaluation for each detection area will be increased accordingly, especially for areas with large local consumption deviations. In other words, the overall health of the device directly affects the sensitivity of area assessment through the deviation threshold. The higher the overall deviation level (i.e., the greater the overall consumption deviation), the more the system will enhance the monitoring and evaluation of each area, especially for areas with significant local deviations, ensuring the detection and resolution of potential problems.

[0237] The local deviation level is determined by comparing the consumption deviation of each detection area with its local deviation threshold. For example, if the local consumption deviation of a certain area exceeds the local deviation threshold for that area (e.g., exceeding 5%), the local state evaluation weight of that area will be increased accordingly to reflect the severity of abnormal consumption in that area. Conversely, if the deviation value of that area is lower than the local deviation threshold, the weight of that area is relatively low, indicating that its consumption is normal.

[0238] The specific dynamic weight allocation rules are as follows:

[0239] When the overall deviation level exceeds the preset deviation level, the weight of local state evaluation in all detection areas is increased, especially in areas with large local consumption deviations. This allows for more sensitive monitoring of local anomalies even when overall consumption is abnormal, by enhancing the weight of each area.

[0240] When the overall deviation level does not exceed the preset deviation level, the weight is adjusted according to the consumption deviation of local areas, giving priority to areas with large local consumption deviations and ignoring slight fluctuations in overall consumption.

[0241] This approach allows for flexible adjustment of the influence of each detection area in the overall assessment, ensuring a more accurate and adaptable evaluation. Changes in the weight of each area reflect the overall health of the equipment and the impact of abnormal consumption in each area in real time, ultimately providing a more accurate basis for lifespan assessment.

[0242] S2-54. Weight the overall actual state data with the local state evaluation weight of each detection area to obtain the weighted state index of each detection area.

[0243] The specific amount is determined by weighting the overall actual status data (such as operating temperature, cumulative usage times, powder supply status, etc.) with the local status evaluation weights of each detection area, based on the local status evaluation weights allocated in the previous steps. Ultimately, the system obtains a weighted status index for each detection area. This index not only reflects the overall health of the equipment but also specifically displays the health status of each area under different weight conditions. Through weighting, the operating status of each area can be more accurately assessed, ensuring that different influencing factors in all areas are fully considered. This step is similar to step S53 in Example 1 and will not be repeated here.

[0244] S2-55. Based on the weighted state index of each detection area and the deviation value of the local consumption of that detection area, obtain the lifetime estimate of each detection area.

[0245] Specifically, the lifetime estimate for each detected area is calculated by combining the weighted status index of each area with the local consumption deviation value of that area. This lifetime estimate reflects the remaining lifetime or service life of each area. By combining the weighted status with the consumption deviation, the system can obtain a more accurate lifetime prediction, especially for areas with abnormal local consumption, which can accurately determine their service life. The lifetime estimate may include the number of remaining printable pages, remaining working time, etc.

[0246] S2-56. Mark the detection area with the smallest lifetime estimate as a weak area;

[0247] The weakest area, i.e., the area with the lowest estimated lifespan, is determined based on the lifespan estimates of each testing area. This weakest area is typically the most prone to failure, possibly due to significant deviations in consumption or poor local conditions. This method allows for the identification of potential fault points, enabling timely repair or replacement measures to prevent wider equipment failure.

[0248] S2-57. Based on the estimated lifespan of the weak area, obtain the lifespan assessment result of the toner cartridge.

[0249] Finally, the overall machine lifespan assessment is derived based on the lifespan estimate of the vulnerable areas. Traditional lifespan assessment methods typically extrapolate remaining lifespan based on the average wear and tear of the entire device, which can easily overlook the impact of localized failures. By prioritizing the lifespan estimate of vulnerable areas, a more accurate prediction of remaining lifespan can be obtained. This is because the health of vulnerable areas is often a determining factor in device lifespan. If a vulnerable area (such as the photosensitive drum) is nearing the end of its lifespan, the overall remaining lifespan of the device will be severely affected. In this way, the lifespan assessment results can more accurately reflect the impending failures of the equipment, helping users make replacement or maintenance decisions earlier.

[0250] In another embodiment, the lifespan estimates of all regions can be weighted and averaged, and the average lifespan estimate of the entire machine can be calculated by combining the lifespan estimates of each region with the weighting coefficient. This process ensures the accuracy of the overall machine lifespan assessment while taking into account the different impacts of the health status of each region, avoiding the limitations of a single evaluation method.

[0251] In Example 2, the combination of overall assessment and zoned detection assessment can significantly improve the accuracy of toner cartridge life assessment, identify potential faults in advance, optimize maintenance resource allocation, and improve equipment reliability. Specifically, Example 2 has the following significant advantages over Example 1:

[0252] By dividing core components such as the photosensitive drum into multiple inspection zones and independently evaluating the status of each zone, performance changes and potential faults in localized areas can be captured in real time. This regionalized inspection can identify localized failure risks that might be masked in traditional overall assessments, such as decreased local charge retention, abnormal pattern development, or regional wear. This makes lifespan assessment more detailed, enabling earlier detection and resolution of problems, and reducing global equipment downtime caused by localized failures.

[0253] When using a holistic assessment, anomalies in certain localized areas may be masked by the overall average, leading to an overly optimistic equipment lifespan assessment. For example, some areas may prematurely decline due to prolonged high loads or other factors, but if the overall equipment consumption is relatively uniform, these early failure risks cannot be detected in time. By using a zoned assessment, the system ensures that each zone can be assessed for health independently, preventing localized faults from being masked by the good condition of other zones, and ensuring that the actual health status of each zone is accurately reflected.

[0254] In summary, Example 2 combines local and overall lifespan assessment by dividing core components such as the photosensitive drum into multiple detection areas. This method not only more accurately identifies and handles local wear anomalies, improving the accuracy of fault warnings, but also helps users optimize maintenance plans and extend equipment lifespan through more precise lifespan predictions. Compared to traditional overall assessment methods, Example 2 significantly improves equipment health management capabilities, reduces maintenance costs and the risk of sudden downtime, and provides stronger support for the long-term stable operation of equipment.

[0255] Example 3

[0256] Please see Figure 3 This invention provides a toner cartridge life assessment device based on condition monitoring, the device comprising:

[0257] The baseline relationship acquisition module is used to perform test printing based on a preset standard pattern set and obtain the baseline correspondence between the printing data and the consumption of the toner cartridge.

[0258] The status acquisition module is used to monitor the toner cartridge during the actual printing process and obtain actual status data;

[0259] The actual relationship acquisition module is used to acquire the actual correspondence between print data and toner cartridge consumption at preset time intervals.

[0260] The deviation calculation module is used to obtain the consumption deviation value based on the baseline correspondence and the actual correspondence.

[0261] The lifespan assessment module is used to assess the toner cartridge based on the consumption deviation value and the actual state data, and obtain the lifespan assessment result.

[0262] It should be noted that each module and unit in the condition monitoring-based drum life assessment device in this embodiment corresponds one-to-one with each step in the condition monitoring-based drum life assessment method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned condition monitoring-based drum life assessment method, and will not be repeated here.

[0263] Example 3

[0264] In addition, combined Figure 1 The condition monitoring-based drum life assessment method described in this embodiment of the invention can be implemented by an electronic device. Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown.

[0265] Electronic devices may include processors and memory storing computer program instructions.

[0266] Specifically, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0267] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0268] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated communication signals and carrier waves.

[0269] The processor reads and executes computer program instructions stored in the memory to implement any of the toner cartridge life assessment methods based on condition monitoring in the above embodiments.

[0270] In one example, the electronic device may also include a communication interface and a bus. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0271] The communication interface is mainly used to enable communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0272] A bus, including hardware, software, or both, couples components of an electronic device together. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0273] Example 4

[0274] Furthermore, in conjunction with the condition monitoring-based drum life assessment method in the above embodiments, this invention can be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the condition monitoring-based drum life assessment methods described in the above embodiments.

[0275] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0276] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0277] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0278] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0279] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0280] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0281] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for assessing the lifespan of a toner cartridge based on condition monitoring, characterized in that, The toner cartridge is disposed in the image forming apparatus, and the method includes: Test printing is performed according to a preset standard pattern set to obtain the baseline correspondence between the printing data and the toner consumption of the drum. During the actual printing process, the toner cartridge is monitored to obtain actual status data; At preset time intervals, obtain the actual correspondence between print data and toner consumption of the drum unit; Based on the baseline correspondence and the actual correspondence, the consumption deviation value is obtained; Based on the consumption deviation value and the actual state data, the toner cartridge is evaluated to obtain a lifespan assessment result.

2. The method for assessing the lifespan of a toner cartridge based on condition monitoring according to claim 1, characterized in that, The step of performing test printing according to a preset standard pattern set to obtain the baseline correspondence between the printing data and the toner consumption of the drum includes: Obtain a preset standard pattern set, wherein the preset standard pattern set includes a preset number of standard patterns, and the printing data of different standard patterns are different; The preset standard pattern set is printed sequentially to obtain the toner consumption corresponding to each standard pattern. By fitting the printing data and toner consumption corresponding to each standard pattern, a baseline correspondence between the printing data and toner consumption is obtained.

3. The method for assessing the lifespan of a toner cartridge based on condition monitoring according to claim 2, characterized in that, The step of fitting the printing data and toner consumption corresponding to each standard pattern to obtain a baseline correspondence between the printing data and toner consumption includes: Based on the printing data of each standard pattern, the image complexity features of each standard pattern are obtained, wherein the image complexity features include at least the amount of data, the density of point distribution, and the frequency of color changes. Based on the image complexity characteristics, construct an image feature vector for each of the standard patterns; Each of the image feature vectors is subjected to feature enhancement processing to obtain an enhanced feature vector; The baseline correspondence is obtained by fitting the enhanced feature vector of each standard pattern with the corresponding toner consumption.

4. The method for evaluating the lifespan of a toner cartridge based on condition monitoring according to claim 3, characterized in that, The step of performing feature enhancement processing on each of the image feature vectors to obtain enhanced feature vectors includes: The feature dimensions of the image feature vectors are combined in pairs to obtain several image feature pairs; Calculate the Pearson correlation coefficient for each of the image feature pairs; The image feature pairs are filtered based on the Pearson correlation coefficient and a preset correlation threshold to obtain initial weakly correlated feature pairs; According to the preset filtering rules, the initial weakly correlated feature pairs are filtered to obtain the target weakly correlated feature pairs; The image complexity features, excluding the weakly correlated feature pairs of the target in the image feature vector, are used as nonlinear transformation terms. Based on the target weakly correlated feature pairs and the nonlinear transformation term, feature enhancement is performed on each of the image feature vectors to obtain enhanced feature vectors.

5. The method for assessing the lifespan of a toner cartridge based on condition monitoring according to any one of claims 1-4, characterized in that, The process of evaluating the toner cartridge based on the consumption deviation value and the actual condition data to obtain a lifespan assessment result includes: Based on the consumption deviation value and the preset deviation threshold, the abnormal deviation type is obtained; The evaluation weight of the actual state data is determined based on the abnormal deviation type, wherein the actual state data includes operating temperature, cumulative number of uses, and powder supply status; The actual state data is weighted according to the evaluation weights to obtain weighted state data. The lifespan assessment result of the toner cartridge is obtained based on the weighted state data and the consumption deviation value.

6. The method for assessing the lifespan of a toner cartridge based on condition monitoring according to claim 5, characterized in that, The step of obtaining the abnormal deviation type based on the consumption deviation value and the preset deviation threshold includes: A preset deviation threshold is obtained based on the cumulative number of uses, wherein the preset deviation threshold is positively correlated with the cumulative number of uses; Based on the difference between the preset deviation threshold and the consumption deviation value, a defect detection image is obtained; The defect detection image is printed to obtain a detection printed image; The type of abnormal deviation is determined based on the type of defect found in the printed image.

7. The method for assessing the lifespan of a toner cartridge based on condition monitoring according to claim 5, characterized in that, The step of obtaining the lifespan assessment result of the toner cartridge based on the weighted state data and the consumption deviation value includes: Based on a preset time window, obtain the weighted state data sequence and the consumption deviation sequence; Trend analysis is performed on the weighted state data sequence and the consumption deviation sequence to obtain the corresponding first trend change parameter and second trend change parameter, wherein the first trend change parameter and the second trend change parameter both include the rate of change, the direction of change, and the fluctuation amplitude. Based on the first trend change parameter and the second trend change parameter, abnormal change data of the weighted state data sequence and the consumption deviation sequence are determined respectively; The health trajectory curve is obtained based on the weighted state data sequence after removing abnormally changing data and the consumption deviation sequence; The lifespan assessment results of the toner cartridge are obtained based on the health trajectory curve.

8. A toner cartridge life assessment device based on condition monitoring, characterized in that, The toner cartridge is disposed in the image forming apparatus, the apparatus comprising: The baseline relationship acquisition module is used to perform test printing based on a preset standard pattern set and obtain the baseline correspondence between the printing data and the toner consumption of the drum. The status acquisition module is used to monitor the toner cartridge during the actual printing process and obtain actual status data; The actual relationship acquisition module is used to acquire the actual correspondence between printing data and toner consumption of the drum at preset time intervals. The deviation calculation module is used to obtain the consumption deviation value based on the baseline correspondence and the actual correspondence. The lifespan assessment module is used to assess the toner cartridge based on the consumption deviation value and the actual state data, and obtain the lifespan assessment result.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The method as described in any one of claims 1-7 is implemented when the computer program instructions are executed by the processor.

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