Printer interaction interface adjusting method based on user operation habits

Abstract

The invention relates to the technical field of printer interaction optimization, and particularly discloses a user operation habit-based printer interaction interface adjustment method, which comprises the following steps of: 1, acquiring user operation data in real time by utilizing a high-precision sensor and a log recording module, comprehensively analyzing the function use frequency, the operation path, the time interval distribution, the equipment connection state and the environment parameters; 2, interface layout is dynamically adjusted based on the analysis result, high-frequency function preposition, operation path optimization and scenarized adaptation are achieved, and the scenarized adaptation comprehensively considers the use time period, the equipment connection state and the user identity factor; 3, memorizing historical parameters of the user, and providing intelligent recommendation based on a collaborative filtering algorithm and a deep learning recommendation algorithm; and 4, iteratively optimizing the interface model based on user feedback, and regularly updating the user operation model. And through high-frequency function preposition and path automatic filling, the click frequency of the user is reduced, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of printer interaction optimization, and in particular to a method for adjusting a printer interaction interface based on user operating habits. Background Art

[0002] With the increasing demand for office automation and home printing, printers, as important output devices, have become increasingly important for the convenience and ease of use of their interactive interfaces. However, existing printer interactive interfaces have many problems that need to be solved.

[0003] Traditional printer interfaces typically employ a fixed layout, unable to dynamically adjust to user preferences. In practice, different users vary significantly in their frequency of use of printer functions. For example, document processing users frequently use scanning and copying, while home users often use functions like photo printing and exam paper printing. However, the existing fixed interface layout requires frequent users of the scanning function to navigate through multiple menus, significantly reducing operational efficiency.

[0004] The existing interface lacks personalized settings and cannot effectively remember the user's historical operating parameters. Every time the user uses the printer, they need to repeatedly set parameters such as default paper size, print density, and output path, which not only wastes time but also leads to a poor user experience.

[0005] The layout of the existing printer interface isn't adaptable to different usage scenarios. In office settings, users tend to prefer features like "Scan to Email," "Batch Print," and "Encrypted Document Printing." In home settings, however, demand for features like "Photo Printing," "ID Copying," and "Fun Template Printing" is more prominent. However, the current interface lacks flexibility due to its inability to automatically adjust its functional layout based on changing scenarios.

[0006] With the development of Internet of Things technology, the interaction between printers, mobile devices and cloud services is becoming increasingly frequent, but the existing interactive interface fails to effectively integrate these functions and cannot meet the diverse operational needs of users. Summary of the Invention

[0007] The present invention aims to provide a method and system for adjusting the printer interaction interface based on user operating habits. By intelligently collecting and analyzing user operation data, the layout of the printer interaction interface is dynamically adjusted to achieve high-frequency function pre-positioning, operation path optimization, scenario adaptation, and personalized parameter memory and intelligent recommendation. It effectively solves the problems of fixed existing printer interaction interfaces, lack of personalized adaptation, and low operating efficiency, and significantly improves user experience and operating efficiency.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A method for adjusting a printer interaction interface based on user operating habits comprises the following steps:

[0010] Step 1: Use high-precision sensors and log recording modules to collect user operation data in real time, and conduct a comprehensive analysis of function usage frequency, operation path, time distribution, device connection status, and environmental parameters;

[0011] Step 2: Dynamically adjust the interface layout based on the analysis results to achieve high-frequency function pre-positioning, operation path optimization, and scenario-based adaptation. Scenario-based adaptation comprehensively considers usage time, device connection status, and user identity.

[0012] Step 3: Remember user historical parameters and provide intelligent recommendations based on collaborative filtering algorithms and deep learning recommendation algorithms;

[0013] Step 4: Iteratively optimize the interface model based on user feedback and regularly update the user operation model.

[0014] As a further solution of the present invention: the step 1 specifically includes:

[0015] Utilize sensors and log recording modules to collect user operation data on the printer interface in real time;

[0016] Assume that the collected operation data set is D = {d1, d2, ..., d n}, where d i Represents the i-th operation data;

[0017] Let the function set be F={f1,f2,...,f m}, for function f j , the number of clicks is recorded as C j , the total number of operations is

[0018] Then the function f j The usage frequency weight W j The calculation formula is:

[0019] W j =C j / C total ;

[0020] Construct the user operation path tree, and set the operation path set as P = {p1, p2, ..., p k}, where p s =(f s1 ,f s2 ,...,f sl ) represents the sth operation path, which consists of l function nodes.

[0021] As a further solution of the present invention: Step 2 includes:

[0022] Set the frequency weight threshold T1, when the function f j The weight W j When ≥T1, it will be promoted from the secondary menu to the main interface and arranged in descending order of weight;

[0023] Define the path similarity function Sim(p a ,p b ), used to measure the two operation paths p a and p b degree of similarity;

[0024] The operation paths whose similarity exceeds the preset threshold are defined as high-frequency similarity paths;

[0025] For a high-frequency similar path, when the starting operation of the high-frequency similar path is executed again, the subsequent steps are automatically filled in according to the repeated operation path in the high-frequency similar path.

[0026] As a further solution of the present invention: the path similarity function Sim(p a ,p b ) is calculated using the following formula:

[0027]

[0028] Among them, EditDistance(p a ,p b ) represents the path p a and p b The edit distance, |p a | and |p b |represents the path p respectively a and path p b length.

[0029] As a further solution of the present invention: the step 2 further includes:

[0030] Adjust the display priority of different functions in function set F according to the usage scenario.

[0031] As a further solution of the present invention: in said step 3, specifically including:

[0032] Store user historical parameter settings, and set the user historical parameter set to H = {h1, h2, ..., h p};

[0033] When the user initiates the operation again, the historical parameters are automatically filled in;

[0034] Based on the collaborative filtering algorithm, related functions are recommended according to user operation habits and behaviors of similar user groups.

[0035] As a further solution of the present invention: the collaborative filtering algorithm is used to recommend related functions according to user operation habits and similar user group behaviors, specifically including:

[0036] Assume that the user set is U = {u1, u2, ..., u q};

[0037] The user-function operation matrix established based on historical data is A ij , where i represents the user index, j represents the function index, and A ij Represents user u i For function f j Operational status;

[0038] By calculating the similarity between users a ,u b ), find the target user u t Similar user groups recommend features that are commonly used by similar users but not used by the target users.

[0039] As a further solution of the present invention: the similarity (u a ,u b ) is calculated using the following formula:

[0040]

[0041] The beneficial effect of the present invention is that the number of user clicks is reduced by placing high-frequency functions in front and automatically filling in the path. Quantitative calculation such as function weight W j The application of enables high-frequency functions to be quickly presented to users, reducing the number of operational layers. Based on quantitative analysis of user operating habits, the interface layout is dynamically adjusted to meet the personalized needs of different users and reduce repeated settings. The memory and application of the user's historical parameter set H enables personalized parameter settings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings.

[0043] Figure 1 The present invention is a flowchart of a method for adjusting a printer interaction interface based on user operating habits. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, the present invention is a method for adjusting a printer interaction interface based on user operating habits, comprising the following steps:

[0046] 1. Operational data collection and quantitative analysis:

[0047] Using sensors and log recording modules, the user's operation data on the printer interface is collected in real time. Let the collected operation data set be D = {d1, d2, ..., d n}, where d i Indicates the i-th operation data, including function click records, operation path information, common parameter settings (such as paper type p type 、Number of copies p num 、Color Modep color etc.), usage period t, etc.

[0048] Use machine learning algorithms to analyze operational data and calculate the function usage frequency weights. Let the function set be F = {f1,f2,...,f m}, for function f j , the number of clicks is recorded as C j , the total number of operations is Then the function f j The usage frequency weight W j The calculation formula is:

[0049] W j =C j / C total ;

[0050] Construct the user operation path tree, and set the operation path set as P = {p1, p2, ..., p k}, where p s =(f s1 ,f s2 ,...,f sl ) represents the sth operation path, which consists of l function nodes. Common operation paths are analyzed by calculating indicators such as the frequency of occurrence of the path.

[0051] 2. Dynamic adjustment of interface layout:

[0052] High frequency function pre-position: Set the frequency weight threshold T1, when the function fj The weight W j When ≥T1, it will be promoted from the secondary menu to the main interface and arranged in descending order of weight.

[0053] Path optimization: define the operation path similarity function Sim(p a ,p b ), used to measure the two operation paths p a and p b For high-frequency similar paths, the similarity system automatically memorizes and fills in the intermediate steps. Let the user's common path be p common =(f c1 ,f c2 ,...,f cn ), when the user performs the path start operation f again c1 When the system automatically fills in the next steps, the user can confirm with one click. Path similarity function Sim(p a ,p b ) can be calculated using the following formula (taking similarity calculation based on edit distance as an example):

[0054]

[0055] Among them, EditDistance(p a ,p b ) represents the path p a and p b The edit distance, |p a | and |p b |represents the path p respectively a and path p b length.

[0056] The system can analyze a large collection of user operation paths to identify those with high similarity. When the similarity of certain paths exceeds a set threshold, these paths are identified as frequently similar paths. For example, if a user repeatedly executes the paths "Print → Duplex Print → A4 Paper" and "Print → Duplex Print → A3 Paper," and the path similarity calculation reveals a high degree of similarity, these paths can be identified as frequently similar paths.

[0057] Automatic path filling: The system automatically memorizes identified, frequently occurring similar paths. When a user repeats the initial operation for such a path, the system automatically fills in the subsequent steps based on the similarity relationship determined by the path similarity function. For example, when a user clicks "Print," the system automatically selects the double-sided printing option based on the analysis of previously frequently occurring similar paths and selects a commonly used paper size based on historical data. The user only needs to confirm with one click to complete the operation, eliminating the need to repeatedly select intermediate steps. This significantly reduces the number of operations and improves operational efficiency.

[0058] Scene-based adaptation: Switch the interface theme and function priorities according to factors such as the usage time period t. Let the scene set be S = {s1, s2,..., s n}, and establish a scene judgment function Scene(t) to determine the current usage scene. For example, when 9:00 < t < 18:00, Scene(t) = s offic e (office scene); when 19:00 < t < 22:00, Scene(t) = s home( (home scene). Under different scenes, the display priorities of the function set F are different, which can be represented by the scene-function priority matrix M ij , where i represents the scene index and j represents the function index. The larger the value of M ij , the higher the display priority of the function f j in the scene s i .

[0059] 3. Parameter memory and intelligent recommendation:

[0060] Store the user's historical parameter settings. Let the user's historical parameter set be H = {h1, h2,..., h p}, including the default paper size, printing concentration, default output path, etc. When the user发起 an operation again, the system automatically fills in the historical parameters.

[0061] Based on the collaborative filtering algorithm, recommend associated functions according to the user's operation habits and the behaviors of similar user groups. Let the user set be U = {u1, u2,..., u q}, and the user-function operation matrix be A ij , where i represents the user index and j represents the function index. A ij represents the operation situation (such as the number of operations, etc.) of the user u i on the function f j . By calculating the similarity Similarity(u a , u b ), find the user group similar to the target user u t , and recommend the functions commonly used by similar users but not used by the target user to the target user. The user similarity calculation can use the cosine similarity formula:

[0062]

[0063] 4. Feedback and iterative optimization

[0064] Set up a user feedback entry to collect the user's satisfaction score r for the interface layout and improvement suggestions.

[0065] Update the user operation model regularly and recalculate the function weight W based on the newly collected data j , path similarity Sim(p a ,p b ), scenario-function priority matrix M ij And other parameters to achieve dynamic iteration of the interface.

[0066] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for adjusting a printer interaction interface based on user operating habits, characterized in that: The following steps are involved: Step 1: Use high-precision sensors and log recording modules to collect user operation data in real time, and conduct a comprehensive analysis of function usage frequency, operation path, time distribution, device connection status, and environmental parameters; Step 2: Dynamically adjust the interface layout based on the analysis results to achieve high-frequency function pre-positioning, operation path optimization, and scenario-based adaptation. Scenario-based adaptation comprehensively considers usage time, device connection status, and user identity. Step 3: Remember user historical parameters and provide intelligent recommendations based on collaborative filtering algorithms and deep learning recommendation algorithms; Step 4: Iteratively optimize the interface model based on user feedback and regularly update the user operation model.

2. The method for adjusting the printer interactive interface based on user operating habits according to claim 1, characterized in that: The step 1 specifically includes: Utilize sensors and log recording modules to collect user operation data on the printer interface in real time; Assume that the collected operation data set is D = {d1, d2, ..., d n }, where d i Represents the i-th operation data; Let the function set be F={f1,f2,...,f m }, for function f j , the number of clicks is recorded as C j , the total number of operations is Then the function f j The usage frequency weight W j The calculation formula is: W j =C j / C total ; Construct the user operation path tree, and set the operation path set as P = {p1, p2, ..., p k }, where p s =(f s1 ,f s2 ,...,f sl ) represents the sth operation path, which consists of l function nodes.

3. The method for adjusting the printer interactive interface based on user operating habits according to claim 2, characterized in that: The second step includes: Set the frequency weight threshold T1, when the function f j The weight W j When ≥T1, it will be promoted from the secondary menu to the main interface and arranged in descending order of weight; Define the path similarity function Sim(p a ,p b ), used to measure the two operation paths p a and p b degree of similarity; The operation paths whose similarity exceeds the preset threshold are defined as high-frequency similarity paths; For a high-frequency similar path, when the starting operation of the high-frequency similar path is executed again, the subsequent steps are automatically filled in according to the repeated operation path in the high-frequency similar path.

4. The method for adjusting the printer interactive interface based on user operating habits according to claim 3, characterized in that: The path similarity function Sim(p a ,p b ) is calculated using the following formula: Among them, EditDistance(p a ,p b ) represents the path p a and p b The edit distance, |p a | and |p b |represents the path p respectively a and path p b length.

5. The method for adjusting the printer interactive interface based on user operating habits according to claim 3, characterized in that: The second step also includes: Adjust the display priority of different functions in function set F according to the usage scenario.

6. The method for adjusting the printer interactive interface based on user operating habits according to claim 1, characterized in that: In the step three, it specifically includes: Store user historical parameter settings, and set the user historical parameter set to H = {h1, h2, ..., h p }; When the user initiates the operation again, the historical parameters are automatically filled in; Based on the collaborative filtering algorithm, related functions are recommended according to user operation habits and behaviors of similar user groups.

7. The method for adjusting the printer interactive interface based on user operating habits according to claim 6, characterized in that: The collaborative filtering algorithm recommends related functions based on user operating habits and similar user group behaviors, specifically including: Assume that the user set is U = {u1, u2, ..., u q }; The user-function operation matrix established based on historical data is A ij , where i represents the user index, j represents the function index, and A ij Represents user u i For function f j Operational status; By calculating the similarity between users a ,u b ), find the target user u t Similar user groups recommend features that are commonly used by similar users but not used by the target users.

8. The method for adjusting the printer interactive interface based on user operating habits according to claim 7, characterized in that: The similarity(u a ,u b ) is calculated using the following formula:

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