Printer action monitoring method, device and storage medium thereof

Abstract

The invention discloses a printer action monitoring method, which comprises the following steps: S1, installing a high-precision distance measuring sensor on a key transmission component of the printer; S2, uploading data collected by the sensor through a built-in wireless communication module; S3, designing a monitoring application to receive the distance measuring data and processing it by using an algorithm; S4, analyzing and transforming the data according to the change of the relative distance to obtain the real-time motion state of each transmission device of the printer; S5, introducing three-dimensional visualization technology, combining with a three-dimensional model of the printer, mapping the motion data obtained by the analysis onto the model, generating a dynamic three-dimensional animation, so as to intuitively display the working process and state of the printer; S6, integrating a remote control function into the monitoring application, allowing the user to perform basic operations of the printer; and solving the technical problem that the traditional monitoring technology cannot intuitively reflect the internal working state of the printer, especially the deficiencies in fault diagnosis and remote management.

Description

Technical Field

[0001] The present invention relates to the technical field of printing device control, in particular to a printer action monitoring method, device and storage medium thereof. Background Art

[0002] The action monitoring of the printer during the printing process usually requires manual observation, which is not only inefficient but also consumes a lot of human resources. Although modern technology can display the current status of the printer on the host computer and other tools through some variables, these methods often cannot provide detailed information about the specific actions of the printer. For example, the printer can read the raster value of the print carriage and the number of steps of the ink station motor. These data can be used to indirectly understand the movement of each module through formula calculation. However, these indirect data cannot directly reflect the real-time status of the printer.

[0003] Existing printer monitoring technologies mainly rely on simple status indicator lights or local sensors. These methods cannot intuitively reflect the working status of the printer's internal transmission mechanism, which brings inconvenience to fault diagnosis and remote management. Especially in scenarios where it is necessary to understand the printer's working status in real time or perform remote control, the limitations of traditional monitoring methods are particularly obvious. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, the present invention provides a printer action monitoring method, device and storage medium thereof.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A printer action monitoring method comprises the following steps:

[0007] S1. Install high-precision distance measuring sensors on the key transmission parts of the printer;

[0008] S2, upload the data collected by the sensor through the built-in wireless communication module;

[0009] S3. Design a monitoring application to receive the ranging data and process it using an algorithm;

[0010] S4. Analyze and transform data according to the change of relative distance to obtain the real-time motion status of each transmission device of the printer;

[0011] S5. Introduce 3D visualization technology, combine with the 3D model of the printer, map the analyzed motion data onto the model, generate dynamic 3D animation, and intuitively display the working process and status of the printer;

[0012] S6. Integrate remote control function in monitoring application to allow users to perform basic operations of the printer.

[0013] A printer action monitoring device includes a high-precision distance measuring sensor for measuring the position and moving distance of key transmission components of the printer in real time, and also includes a printer monitoring device, the printer monitoring device is equipped with a monitoring application, a data processing module, a wireless communication module and three-dimensional visualization software, and the three-dimensional visualization software is provided with a user operation interface; the high-precision distance measuring sensor is communicatively connected with the printer monitoring device.

[0014] A printer action monitoring method storage medium comprises a control chip, wherein the control chip is built into a printer monitoring device and is equipped with a program code. When the program code is executed, a printer action monitoring method is implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The present invention integrates a distance measurement module on the printer transmission device and combines it with a remote monitoring application to transform the printer transmission device, integrate a high-precision laser distance measurement sensor or other non-contact distance measurement technology, ensure that the sensor is closely combined with the transmission device, and accurately capture motion information;

[0017] 2. Design monitoring applications to realize data reception, processing, animation generation and remote control interface. The application needs to have a friendly user interface, intuitively display the working status of the printer, and provide a simple operation panel to execute remote commands;

[0018] 3. The data collected by the hardware is transmitted to the monitoring application server through a stable and reliable network protocol. The server performs efficient data processing and provides real-time feedback to the user terminal to achieve all-round remote monitoring and management. The present invention realizes real-time monitoring and animation simulation of the internal motion state of the printer, greatly improving the fault diagnosis efficiency and remote management capabilities. At the same time, the addition of the remote control function allows users to flexibly control and monitor the printer even if they are not on site, thereby improving the speed and accuracy of fault diagnosis and further enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a printer action monitoring method according to Example 1. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1

[0022] This embodiment aims to provide a printer action monitoring method, comprising the following steps:

[0023] S1. Install high-precision distance measuring sensors on the key transmission parts of the printer, such as the print carriage, paper feed motor, print platform and ink station;

[0024] S2, upload the data collected by the sensor through the built-in wireless communication module;

[0025] S3. Design a monitoring application to receive the ranging data and process it using an algorithm;

[0026] Specifically, the hidden Markov model is introduced to process the ranging data, and the processing process is as follows:

[0027] Sa1, define the state set S = {S1, S2, ..., S N}; For example, the possible states of the printer's print carriage are: stationary, moving at low speed, and moving at high speed;

[0028] Sa2, define the observation set O = {O1, O2, ..., O M}; For example, the position data of the printing carriage of the printer;

[0029] Sa3. Construct state transition probability matrix A:

[0030] A ij =P(q t =S j |q t-1 =S i )

[0031] A ij Indicates that it was in state S at the previous moment i Under the condition of j probability;

[0032] Construct the observation probability matrix B:

[0033] B j (k) = P(o t =o k |qt =S j )

[0034] B j (k) indicates that the state is S at time t j Under the condition of k probability;

[0035] Sa4, initial state probability vector, define the initial state probability vector π:

[0036] π i =P(q1=S i )

[0037] π i Indicates that at the initial time t = 1 the system is in state S i probability;

[0038] Sa5. Collect data, collect a series of observation data O 1：T =O1, O2, ..., O T ;

[0039] Sa6. Apply the forward algorithm and the backward algorithm to calculate the probability of the state sequence under a given observation sequence:

[0040] Initialization: α1(i) = π i ·B j (o t );β T (i)=1

[0041] For t = 2 to T, calculate

[0042] For t = T-1 to 1, calculate

[0043] Use the Viterbi algorithm to find the sequence of states that is most likely to produce the observed sequence:

[0044] Initialization: δ1(i) = π i ·B i (o1) and

[0045] For t = 2 to T, for each state j, calculate δ t (j) = max i δ t-1 (i) A ij ·B j (o t );

[0046] Update ψ t (j) is the most likely previous state i, such that δt (j) maximum;

[0047] Decode the most likely state path using the ψ vector;

[0048] Furthermore, the Baum-Welch algorithm is used to train the parameters of the hidden Markov model, including state transition probability, observation probability, and initial state probability;

[0049] Specifically:

[0050] ① Select initial parameters, such as randomly initialized state transition probability matrix A, observation probability matrix B and initial state probability vector π;

[0051] ② Implemented through the forward-backward algorithm, calculate the forward probability α t (i) and the backward probability β t (i);

[0052] ③Calculate the state-observation probability: For each observation o t and state q t , calculate the following probabilities:

[0053]

[0054] γ t (i) represents the probability of being in state i at time t, normalized to take into account all possible states;

[0055] ④ Calculate the state transition probability Calculate the probability of transitioning from one state to another between time t-1 and t:

[0056]

[0057] ⑤Update the model parameters to maximize the log-likelihood of the observed data:

[0058] Update the initial state probability vector π: π i =γ1(i)

[0059] Update the state transition probability matrix A:

[0060] Update the observation probability matrix B:

[0061] ⑥ Repeat steps ② to ⑤ until convergence, that is, the change in model parameters is very small or the preset number of iterations is reached;

[0062] After each iteration, check whether the change in the parameter is less than a certain threshold. If so, the algorithm converges;

[0063] Sa7. Apply the trained hidden Markov model to new observation data and use the Viterbi algorithm to predict the state of the printer;

[0064] Sa1-Sa7 can be used to evaluate the probability of the observation sequence and the probability of the state sequence under a given observation sequence. Through these probabilities, the state sequence can be further decoded to find the state path that is most likely to produce the observation sequence.

[0065] S4. Analyze and transform data according to the change of relative distance to obtain the real-time motion status of each transmission device of the printer;

[0066] The process of analyzing and transforming data is as follows:

[0067] 1. Collect the real-time position data of each transmission component of the printer from the high-precision distance measurement sensor;

[0068] 2. Clean the data, remove outliers and noise, and standardize the data to make it in a unified dimension;

[0069] 3. Calculate the relative distance change of each component, for example, the distance the print head moves from position A to position B; calculate the speed and acceleration through the relative position change at consecutive time points, and set the state judgment threshold of speed and acceleration;

[0070] 4. According to the state judgment threshold of speed and acceleration, the motion state of the printer components is identified;

[0071] 5. Convert position, velocity and acceleration data into parameters describing the printer status. The obtained parameters can be used to control the animation of the 3D model.

[0072] 6. Update the current status of the printer based on real-time data;

[0073] S5. Introduce 3D visualization technology, combine with the 3D model of the printer, map the analyzed motion data to the model, generate dynamic 3D animation, and intuitively display the working process and status of the printer. Specifically:

[0074] 1. Build a 3D model of the printer, including all moving parts;

[0075] 2. Associating the analyzed motion data with the corresponding components of the 3D model;

[0076] 3. Set keyframes in 3D animation according to the motion state of the transmission parts;

[0077] 4. Use interpolation algorithms to generate smooth transitions between key frames;

[0078] Specifically, the process of setting keyframes in 3D animation and using interpolation algorithms to generate smooth transitions can be divided into the following steps:

[0079] ① Identify the key moments in the animation and set keyframes at these time points. Keyframes contain information such as the position, rotation, and scale of the object at a specific time;

[0080] ②Select the linear interpolation method to generate smooth transitions between key frames;

[0081] Linear interpolation is used to change the state of an object evenly between two keyframes:

[0082]

[0083] Among them, value(t) is the interpolation result at time t, t start and t end is the time point of the key frame;

[0084] ③ Update the state of the object according to the calculated interpolation results;

[0085] ④ For each intermediate frame, repeat the interpolation process to generate a smooth animation effect;

[0086] ⑤ Use the rendering engine of the 3D software to render each calculated frame into an image sequence or video;

[0087] 5. Use rendering engine to generate dynamic 3D animation;

[0088] 6. Design a user interface that allows users to control animation playback and explore printer status;

[0089] 7. Synchronize real-time data stream with 3D model to achieve real-time animation update;

[0090] S6. Integrate remote control function in monitoring application to allow users to perform basic operations of the printer.

[0091] This embodiment implements a printer action monitoring method, which provides a comprehensive solution to accurately monitor and display the real-time motion status of each transmission device of the printer through high-precision sensors, hidden Markov model (HMM) data processing, and three-dimensional visualization technology. It solves the technical problem that traditional monitoring technology cannot intuitively reflect the internal working status of the printer, especially in terms of fault diagnosis and remote management. Through real-time data analysis, status prediction, and dynamic three-dimensional animation display, this embodiment greatly improves the understanding and control of the printer's working status, making remote operation and management more efficient and intuitive;

[0092] By adopting the hidden Markov model, the limitations of traditional printer monitoring technology in terms of accurate state identification, future state prediction, data fusion, anomaly detection, improvement of automation level, enhancement of user interaction experience, optimization of maintenance and repair processes, and improvement of data processing capabilities are effectively solved, which significantly improves the accuracy, reliability and intuitiveness of monitoring, and provides an advanced technical means for the intelligent monitoring and management of printers.

[0093] Example 2

[0094] A printer action monitoring device includes a high-precision distance measuring sensor for measuring the position and moving distance of key transmission components of the printer in real time, and also includes a printer monitoring device, the printer monitoring device is equipped with a monitoring application, a data processing module, a wireless communication module and three-dimensional visualization software, and the three-dimensional visualization software is provided with a user operation interface; the high-precision distance measuring sensor is communicatively connected with the printer monitoring device.

[0095] A printer action monitoring method storage medium comprises a control chip, wherein the control chip is built into a printer monitoring device and is equipped with a program code. When the program code is executed, a printer action monitoring method is implemented.

[0096] The printer motion monitoring device in this embodiment integrates high-precision distance measurement sensors, data processing modules, wireless communication modules and three-dimensional visualization software to achieve accurate measurement and analysis of the real-time position and movement distance of key transmission components of the printer.

[0097] With the program code built into the control chip, the device can not only execute advanced data processing algorithms such as hidden Markov models to identify and predict the movement status of the printer, but also convert the analysis results into intuitive three-dimensional animations, display them through the user interface and allow remote control operations, providing a comprehensive, efficient and user-friendly monitoring experience, greatly improving the intelligence level of printer monitoring, while simplifying fault diagnosis and remote management processes.

Claims

1. A printer action monitoring method, characterized in that: The following steps are involved: S1. Install high-precision distance measuring sensors on the key transmission parts of the printer; S2, upload the data collected by the sensor through the built-in wireless communication module; S3. Design a monitoring application to receive the ranging data and process it using an algorithm; S4. Analyze and transform data according to the change of relative distance to obtain the real-time motion status of each transmission device of the printer; S5. Introduce 3D visualization technology, combine with the 3D model of the printer, map the analyzed motion data onto the model, generate dynamic 3D animation, and intuitively display the working process and status of the printer; S6. Integrate remote control function in monitoring application, allowing users to perform basic operations of the printer; The S5 is specifically: Build a 3D model of the printer, including all moving parts; Associating the analyzed motion data with corresponding components of the three-dimensional model; According to the motion state of the transmission parts, key frames are set in the 3D animation; Use interpolation algorithms to generate smooth transitions between keyframes; Use rendering engines to generate dynamic 3D animations; Design user interfaces that allow users to control animation playback and explore printer status; Synchronize live data streams with 3D models to enable real-time animation updates.

2. A printer action monitoring method according to claim 1, characterized in that: The hidden Markov model is introduced in S3 to process the ranging data, and the processing process is as follows: Sa1, define the state set S = {S1, S2, ..., S N }; Sa2, define the observation set O = {O1, O2, ..., O M }; Sa3. Construct state transition probability matrix A: A ij =Pq t =S j q t-1 =S i A ij Indicates that it was in state S at the previous moment i Under the condition of j The probability of Construct the observation probability matrix B: B j (k)=Po t =o k q t =S j B j (k) indicates that the state is S at time t j Under the condition of k The probability of Sa4, initial state probability vector, define the initial state probability vector π: π i =P(q1=S i ) π i Indicates that at the initial time t = 1 the system is in state S i The probability of Sa5. Collect data, collect a series of observation data O 1：T =O1, O2, ..., O T ; Sa6. Apply the forward algorithm and the backward algorithm to calculate the probability of the state sequence under a given observation sequence: initialization:a1(i)=π i ·B j (the t );b T (i)=1 For t = 2 to T, calculate For t = T-1 to 1, calculate Use the Viterbi algorithm to find the sequence of states that is most likely to produce the observed sequence: Initialization: δ1(i) = π i ·B i (o1) and For t = 2 to T, for each state j, calculate δ t (j) = max i δ t-1 (i) A ij ·B j (o t ); Update ψ t (j) is the most likely previous state i, such that δ t (j) maximum; Decode the most likely state path using the ψ vector; Sa7. Apply the trained hidden Markov model to new observation data and use the Viterbi algorithm to predict the state of the printer.

3. A printer action monitoring method according to claim 2, characterized in that: The hidden Markov model is trained using the Baum-Welch algorithm, including state transition probability, observation probability and initial state probability.

4. A printer action monitoring method according to claim 3, characterized in that: The process of using the Baum-Welch algorithm to train the hidden Markov model is specifically as follows: ① Select initial parameters, such as randomly initialized state transition probability matrix A, observation probability matrix B and initial state probability vector π; ② Implemented through the forward-backward algorithm, calculate the forward probability α t (i) and the backward probability β t (i); ③Calculate the state-observation probability: For each observation o t and state q t , calculate the following probabilities: γ t (i) represents the probability of being in state i at time t, normalized to take into account all possible states; ④ Calculate the state transition probability Calculate the probability of transitioning from one state to another between time t-1 and t: ⑤Update the model parameters to maximize the log-likelihood of the observed data: Update the initial state probability vector π: π i =γ1(i) Update the state transition probability matrix A: Update the observation probability matrix B: ⑥ Repeat steps ② to ⑤ until convergence.

5. A printer action monitoring method according to claim 1, characterized in that: The process of analyzing and transforming data in S4 is as follows: Collect real-time position data of each transmission component of the printer from high-precision distance measurement sensors; Clean the data, remove outliers and noise, and standardize the data to make it in a unified dimension; Calculate the relative distance change of each component, the distance the print head moves from position A to position B; Calculate the speed and acceleration through the relative position changes at consecutive time points, and set the state judgment thresholds of speed and acceleration; According to the state judgment threshold of speed and acceleration, the motion state of the printer components is identified; Convert position, velocity and acceleration data into parameters describing the printer status, which can be used to control the animation of the 3D model; Update the current status of the printer based on real-time data.

6. A printer action monitoring method according to claim 1, characterized in that: The key transmission components of the printer in S1 include a printing carriage, a paper feeding motor, a printing platform and an ink station.

7. A printer action monitoring method according to claim 1, characterized in that: The process of setting key frames in the three-dimensional animation and using the interpolation algorithm to generate a smooth transition in S5 can be specifically divided into the following steps: ① Identify the key moments in the animation and set keyframes at these time points. Keyframes contain information such as the position, rotation, and scale of the object at a specific time; ②Select the linear interpolation method to generate smooth transitions between key frames; Linear interpolation is used to change the state of an object evenly between two keyframes: Among them, value(t) is the interpolation result at time t, t start and t end is the time point of the key frame; ③ Update the state of the object according to the calculated interpolation results; ④ For each intermediate frame, repeat the interpolation process to generate a smooth animation effect; ⑤ Use the rendering engine of the 3D software to render each calculated frame into an image sequence or video.

8. A printer action monitoring device based on the method according to any one of claims 1 to 7, characterized in that: It includes a high-precision distance measuring sensor for measuring the position and moving distance of key transmission parts of the printer in real time, and also includes a printer monitoring device. The printer monitoring device is equipped with a monitoring application, a data processing module, a wireless communication module and a three-dimensional visualization software. The three-dimensional visualization software is provided with a user operation interface; the high-precision distance measuring sensor is communicatively connected with the printer monitoring device.

9. A printer action monitoring method storage medium, characterized in that: The invention comprises a control chip, wherein the control chip is built into the printer monitoring device and the control chip is equipped with a program code, and when the program code is executed, a printer action monitoring method according to any one of claims 1 to 7 is implemented.

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