A browser-based automatic filling method

Abstract

The present invention relates to the field of data processing technology, and in particular to a browser-based automatic filling method, including: collecting filling time, correction frequency and selection frequency; determining temporary fields; determining automatic fields; determining filling fields; determining predicted fields; adjusting thresholds and filling. The present invention can intelligently identify and automatically fill in form fields by real-time collecting and analyzing the filling time, correction frequency and selection frequency of users in the process of browsing websites. By dynamically adjusting the time threshold, it can accurately predict and adapt to the filling habits of users, effectively improve the efficiency and accuracy of form filling, and is not only more personalized than traditional static automatic filling methods, but also can optimize the filling strategy in real time according to user behavior, reducing the cumbersome steps of user operations, improving user experience, and effectively solving the problems of slow response speed and poor filling adaptability due to reliance on server-side processing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a browser-based automatic filling method. Background Art

[0002] With the rapid development of the Internet, the popularity of online services and e-commerce, various applications that require users to fill out forms are gradually increasing. When users fill out web forms, they often face problems such as cumbersome information input, slow filling speed, and low accuracy of automatic filling of form fields. These problems not only affect the user experience, but may also lead to information entry errors or waste time. Therefore, how to improve the efficiency and accuracy of automatic form filling has become an important direction for improving user experience and work efficiency.

[0003] The patent document with publication number CN106897329A discloses an automatic filling method, device, browser, client device and server. The method includes: receiving web page data; detecting input items for automatic filling; sending user identification information to the server, wherein the user identification information is used to identify the user on the server; receiving automatic filling data from the server; and using the automatic filling data to automatically fill in the web page according to the input items.

[0004] It can be seen that this method relies on the server side to process the filling data, resulting in a slow response speed, especially when the network environment is poor or the server load is high, and poor adaptability. It is unable to adjust the filling strategy according to dynamic data such as real-time form filling time and correction frequency. It lacks real-time monitoring and optimization of user operation behavior and cannot effectively deal with complex or changeable web forms. Summary of the invention

[0005] To this end, the present invention provides a browser-based automatic filling method, which is used to optimize the filling strategy through prediction model prediction and dynamic adjustment mechanism to overcome the problems of slow response speed and poor filling adaptability in the prior art due to reliance on server-side processing.

[0006] To achieve the above object, the present invention provides a browser-based automatic filling method, comprising:

[0007] Collect the target's real-time filling time, real-time modification frequency, and real-time selection frequency of each form field while browsing the website;

[0008] Determine a number of temporary fields in the form field according to the real-time filling time and a preset filling time threshold;

[0009] Determining a number of automatic fields according to the real-time modification frequency of each of the temporary fields;

[0010] Determine a number of fill fields according to the real-time selection frequency of each automatic field within a preset first historical duration;

[0011] Determine a number of prediction fields according to a preset prediction model, the real-time filling duration of each form field within the preset first historical duration, and the real-time correction frequency;

[0012] Adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form an adjusted duration threshold;

[0013] The fill field formed based on the adjustment duration threshold is automatically filled.

[0014] Further, the determining of a plurality of fill-in fields according to the real-time selection frequency of each automatic field within a preset first historical duration includes:

[0015] Calculating the standard deviation of the real-time selection frequency to form a selection frequency fluctuation value;

[0016] When the selection frequency fluctuation value is greater than a preset selection fluctuation threshold, the automatic field is determined to be the filling field, and a plurality of filling fields are formed.

[0017] Further, adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form the adjusted duration threshold includes:

[0018] Determining a duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field;

[0019] determining a frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field;

[0020] The preset filling duration threshold is adjusted according to the duration change consistency and the frequency change consistency to form an adjusted duration threshold.

[0021] Further, the determining of the duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field includes:

[0022] Draw a filling time change curve according to the real-time filling time of the filling field;

[0023] Draw a prediction duration change curve according to the real-time filling duration of the prediction field;

[0024] The cosine similarity between the filling duration change curve and the predicted duration change curve is calculated to form a duration change consistency.

[0025] Further, the determining the frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field includes:

[0026] Draw a filling frequency variation curve according to the real-time correction frequency of the filling field;

[0027] Draw a predicted frequency change curve according to the real-time corrected frequency of the predicted field;

[0028] The cosine similarity between the filling frequency change curve and the predicted frequency change curve is calculated to form a frequency change consistency.

[0029] Further, adjusting the preset filling duration threshold according to the duration change consistency and the frequency change consistency to form the adjusted duration threshold includes:

[0030] Calculating the absolute value of the relative deviation between the duration change consistency and the frequency change consistency to form a consistency deviation;

[0031] When the consistency deviation is greater than a preset deviation threshold, the preset filling time threshold is adjusted according to the consistency deviation to form an adjusted time threshold.

[0032] Further, adjusting the preset filling time threshold according to the consistency deviation to form the adjusted time threshold includes:

[0033] Calculating the standard deviation of the consistency deviation within the preset first historical time length to form a consistency deviation fluctuation value;

[0034] The preset filling time threshold is adjusted according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form an adjusted time threshold.

[0035] Further, the adjusting the preset filling time threshold according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form the adjusted time threshold includes:

[0036] When the consistency deviation fluctuation value is greater than the preset deviation fluctuation threshold, the preset filling time threshold is increased according to the relative deviation between the consistency deviation fluctuation value and the preset deviation fluctuation threshold and the preset adjustment coefficient to form an adjusted time threshold.

[0037] Further, the determining of several temporary fields in the form field according to the real-time filling time and the preset filling time threshold includes:

[0038] When the real-time filling time is greater than the preset filling time threshold, the form field is determined to be a temporary field.

[0039] Further, the determining of a number of automatic fields according to the real-time modification frequency of each temporary field includes:

[0040] Calculating the average value of the real-time correction frequency within a preset determined time period to form a correction frequency mean;

[0041] When the corrected frequency mean is greater than a preset frequency mean threshold, the temporary field is determined to be an automatic field, and a plurality of automatic fields are formed.

[0042] Compared with the prior art, the beneficial effect of the present invention lies in that, by real-time collection and analysis of the filling time, correction frequency and selection frequency of users in the process of browsing the website, it can intelligently identify and automatically fill in the form fields; by dynamically adjusting the time threshold, it can accurately predict and adapt to the user's filling habits, effectively improving the efficiency and accuracy of form filling, and compared with the traditional static automatic filling method, it is not only more personalized, but also can optimize the filling strategy in real time according to user behavior, reducing the tedious steps of user operations and improving user experience; in addition, through the learning and prediction of historical behavior data, the filling strategy can be automatically adjusted to avoid misjudgment caused by the complexity of form fields or fluctuations in user operation frequency, thereby realizing more flexible, efficient and intelligent form automatic filling, which is widely applicable to various web forms with dynamic and personalized needs, and effectively solves the problems of slow response speed and poor filling adaptability due to reliance on server-side processing.

[0043] Furthermore, by analyzing the volatility of the selection frequency through standard deviation, the system can automatically identify which fields show large fluctuations during the filling process, thereby optimizing and adjusting these fields to improve the accuracy of automatic filling and user experience. This dynamic determination method based on fluctuation values ​​can effectively reduce incorrect filling caused by frequent corrections and improve the efficiency of automatic filling.

[0044] Furthermore, by carefully analyzing the consistency of filling time and correction frequency, the system can accurately adjust the dynamic changes of filling behavior, thereby improving the automation level and accuracy of the filling process. By adjusting the time threshold, the automatic filling can maintain a high adaptability in different environments and user behaviors, avoid misjudgment, and improve user experience and work efficiency.

[0045] Furthermore, by drawing a time change curve, we can accurately capture the consistency of the change trend of the filled field and the predicted field over time, so that the system can more intelligently adjust the threshold to adapt to the filling behavior of different users. Evaluating consistency through cosine similarity helps to improve the system's sensitivity and accuracy to time change patterns, thereby optimizing the automatic filling effect, reducing incorrect filling caused by mismatches, and improving the user experience.

[0046] Furthermore, by drawing the frequency change curve, we can accurately evaluate the consistency of changes in the correction frequency between the filled field and the predicted field, thereby optimizing the adjustment strategy during the auto-fill process. The calculation of cosine similarity helps to improve the sensitivity to frequency changes, ensuring that in different usage scenarios, auto-fill can more reasonably adapt to user behavior, reduce misjudgments and unnecessary delays, and improve the accuracy and efficiency of automatic filling.

[0047] Furthermore, the filling time threshold is effectively adjusted according to the consistency of the duration and frequency changes to improve the accuracy and flexibility of automatic filling. By introducing the calculation of consistency deviation, abnormal fluctuations can be monitored in real time to avoid slow response or errors caused by inconsistent filling behaviors. In addition, adjusting the filling time threshold can better adapt to the user's personalized filling habits, thereby improving the user experience and enhancing the intelligence level of automatic filling.

[0048] Furthermore, this dynamic adjustment mechanism can effectively cope with fluctuations in user behavior, especially frequent corrections or changes in selection duration. When the consistency deviation fluctuates too much, the filling duration threshold can be adjusted in time to avoid overfilling or untimely filling, improve the smoothness and accuracy of the filling process, improve the system's adaptability to various filling scenarios, and ensure the stability and intelligence of the user experience.

[0049] Furthermore, when the consistency deviation fluctuates greatly, it automatically adapts to these changes by appropriately increasing the filling time threshold to avoid excessive adjustment or untimely response, ensuring that the filling process is smoother and more efficient, improving the system's intelligence and adaptability to diverse user behaviors, and optimizing the user experience.

[0050] Furthermore, by comparing the real-time filling time with the preset filling time threshold, fields that may require more user interaction or adjustment can be effectively identified, so that they can be properly marked and optimized. This not only improves the accuracy of the filling process, but also reduces the system's misoperation when processing these fields, thereby improving user experience and operational efficiency.

[0051] Furthermore, by calculating and comparing the correction frequency, it is possible to effectively identify fields that need frequent correction, thereby marking them as automatic fields and optimizing the automatic filling process. By determining the automatic fields, it is possible to improve the efficiency of form filling, reduce human intervention, and achieve smarter browser automatic filling. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the browser-based automatic filling method of this embodiment;

[0053] Figure 2A decision logic diagram for adjusting the duration threshold is formed for this embodiment;

[0054] Figure 3 A decision logic diagram for determining a temporary field in this embodiment;

[0055] Figure 4 This is a decision logic diagram for determining the automatic field in this embodiment. DETAILED DESCRIPTION

[0056] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0058] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] See also Figure 1 As shown, it is a flow chart of the browser-based automatic filling method of this embodiment;

[0060] This embodiment provides a browser-based automatic filling method, including:

[0061] Collect the target's real-time filling time, real-time modification frequency, and real-time selection frequency of each form field while browsing the website;

[0062] Determine a number of temporary fields in the form field according to the real-time filling time and a preset filling time threshold;

[0063] Determining a number of automatic fields according to the real-time modification frequency of each of the temporary fields;

[0064] Determine a number of fill fields according to the real-time selection frequency of each automatic field within a preset first historical duration;

[0065] Determine a number of prediction fields according to a preset prediction model, the real-time filling duration of each form field within the preset first historical duration, and the real-time correction frequency;

[0066] Adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form an adjusted duration threshold;

[0067] The fill field formed based on the adjustment duration threshold is automatically filled.

[0068] The target collects the real-time filling time, real-time correction frequency, and real-time selection frequency of each form field during the browsing process of the website, which is achieved by embedding JavaScript scripts in the web page or using browser extension plug-ins. The specific method is that when the user interacts with the web page, the script monitors and records the user's input behavior in each form field in real time. The filling time can be obtained by recording the time difference between the focus and blur events of each field; the correction frequency can be calculated by capturing the number of times the user modifies the field content during the input process and the time interval between each modification; the selection frequency is counted by monitoring the frequency and time point of the user selecting items in the drop-down box, radio button or check box. All of this data will be stored in real time and uploaded to the server for further analysis and processing, so as to help the system dynamically adjust the auto-fill strategy.

[0069] The preset filling time threshold refers to the maximum time limit that a user should use to fill in a form field under normal circumstances. It usually depends on the complexity of the form field, the user's filling habits, and the type of the filling field. It is usually set between a few seconds and tens of seconds. In this embodiment, it is set to 20 seconds, which can avoid abnormal time extension during the form filling process and help the system identify potential filling obstacles.

[0070] The preset first historical duration refers to the average duration of filling in the form field by users within a certain historical data range. This duration is usually set based on the average value of the user's historical behavior, with reference to the common filling time of the field. It is usually set between 30 seconds and 1 minute, and is set to 45 seconds in this embodiment, which can better judge the changing trend of the user's filling time and avoid overreacting to abnormal filling time.

[0071] The preset second historical duration refers to the duration of filling in the form field by the user within a longer historical data range. Usually, the range of this duration should include user behavior within a longer period, which can more accurately reflect the overall trend of the filling duration. It is usually set between 1 minute and 5 minutes, and is set to 3 minutes in this embodiment, which can comprehensively consider the impact of different user groups and long-term data, and ensure reasonable prediction and adjustment of the filling duration.

[0072] The preset prediction model refers to a statistical model or machine learning model based on historical data and user behavior analysis, which is used to predict the possible behavior patterns and filling time of users when filling in form fields. The specific model is a regression model, a time series prediction model, or a more complex machine learning model (such as a support vector machine, a random forest or a neural network, etc.), which is trained according to data such as historical filling time, correction frequency and selection frequency. This embodiment adopts a regression analysis model, such as support vector regression (SVR), to model the filling time and correction frequency of form fields through historical data to predict the user's future filling behavior. The model uses the real-time filling time, correction frequency and selection frequency of each field collected as input to learn the relationship in the historical data, and then predict the filling time and correction frequency of each form field. This method can not only adjust the prediction field in real time and improve the accuracy of automatic filling, but also has strong interpretability and adaptability, and can effectively optimize the form filling process.

[0073] The prediction process is to input the real-time filling time and correction frequency data of the form field within the preset first historical time. The model will analyze the user's past filling habits and combine the filling characteristics of other related fields to predict the user's possible filling time and behavior pattern in the future. According to the prediction results output by the model, several prediction fields can be determined. These fields show a filling trend consistent with the predicted time and correction frequency, thereby helping the system to perform more accurate filling and optimization adjustments.

[0074] By collecting the user's behavior data in real time while browsing the website, including the filling time, correction frequency, and selection frequency of each form field, etc. First, determine which form fields are temporary fields based on the user's real-time filling time and the preset threshold. Then, based on the correction frequency of these temporary fields, further identify which fields should be automatically filled. Next, based on the selection frequency within the first historical time, confirm the automatically filled fields. By combining the prediction model, use the user's filling time and correction frequency within the historical time to predict which fields are most likely to need to be filled. Finally, automatically fill in according to the adjusted time threshold, thereby improving filling efficiency and accuracy.

[0075] By real-time collection and analysis of the user's filling time, correction frequency and selection frequency during the website browsing process, it can intelligently identify and automatically fill in form fields. By dynamically adjusting the time threshold, it can accurately predict and adapt to the user's filling habits, effectively improving the efficiency and accuracy of form filling. Compared with the traditional static automatic filling method, it is not only more personalized, but also can optimize the filling strategy in real time according to user behavior, reducing the tedious steps of user operations and improving the user experience. In addition, through the learning and prediction of historical behavior data, the filling strategy can be automatically adjusted to avoid misjudgment caused by the complexity of form fields or fluctuations in user operation frequency, thereby achieving more flexible, efficient and intelligent form automatic filling. It is widely applicable to various web forms with dynamic and personalized needs, and effectively solves the problems of slow response speed and poor filling adaptability due to reliance on server-side processing.

[0076] Specifically, the determining of a number of fill-in fields according to the real-time selection frequency of each automatic field within a preset first historical duration includes:

[0077] Calculating the standard deviation of the real-time selection frequency to form a selection frequency fluctuation value;

[0078] When the selection frequency fluctuation value is greater than a preset selection fluctuation threshold, the automatic field is determined to be the filling field, and a plurality of filling fields are formed.

[0079] The preset selection fluctuation threshold refers to the critical value used by the system to measure the selection frequency fluctuation when determining whether to regard a certain automatic field as a fill-in field. It depends on factors such as the stability of the field filling time, the volatility of user behavior, and the importance of the field. It usually needs to be set through historical data analysis. The setting range is usually between 0.05 and 0.2. In this embodiment, it is set to 0.1, which can effectively balance sensitivity and accuracy, avoid misjudgment due to excessive sensitivity, and capture real filling fluctuations, thereby optimizing the judgment of automatic filling fields and improving user experience.

[0080] By collecting and analyzing the selection frequency data of each automatic field in real time, the standard deviation of the selection frequency is calculated within the preset first historical period to obtain the selection frequency fluctuation value. Then, the fluctuation value is compared with the preset selection fluctuation threshold. If the fluctuation value is greater than the threshold, the automatic field is judged as a fill field and is included in the subsequent automatic fill operation.

[0081] By analyzing the volatility of the selection frequency through standard deviation, the system can automatically identify which fields show large fluctuations during the filling process, and then optimize and adjust these fields to improve the accuracy of automatic filling and user experience. This dynamic judgment method based on fluctuation values ​​can effectively reduce incorrect filling caused by frequent corrections and improve the efficiency of automatic filling.

[0082] Specifically, adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form the adjusted duration threshold includes:

[0083] Determining a duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field;

[0084] determining a frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field;

[0085] The preset filling duration threshold is adjusted according to the duration change consistency and the frequency change consistency to form an adjusted duration threshold.

[0086] By comparing the filling time and correction frequency of the filled field and the predicted field within the preset second historical time, the consistency of the time change and the consistency of the frequency change are calculated. First, the consistency of the time change is determined by comparing the real-time filling time change trends of the filled field and the predicted field. Secondly, the consistency of the frequency change is obtained by comparing their real-time correction frequency change trends. When these two consistencies reach a certain standard, the system will adjust the preset filling time threshold according to the consistency level to form the final adjustment time threshold, thereby optimizing the automatic filling strategy during the form filling process.

[0087] By carefully analyzing the consistency of filling time and correction frequency, the system can accurately adjust the dynamic changes of filling behavior, thereby improving the automation level and accuracy of the filling process. By adjusting the time threshold, the automatic filling can maintain a high adaptability in different environments and user behaviors, avoid misjudgment, and improve user experience and work efficiency.

[0088] Specifically, determining the duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field includes:

[0089] Draw a filling time change curve according to the real-time filling time of the filling field;

[0090] Draw a prediction duration change curve according to the real-time filling duration of the prediction field;

[0091] The cosine similarity between the filling duration change curve and the predicted duration change curve is calculated to form a duration change consistency.

[0092] First, a filling duration change curve is drawn based on the real-time filling duration of the filling field, and a predicted duration change curve is drawn based on the real-time filling duration of the predicted field. Then, the cosine similarity between the two curves is calculated to quantify their similarity. The cosine similarity can reflect the consistency of the change trends of the two curves, and finally form the consistency of duration change, which is used to adjust the filling duration threshold of the system.

[0093] By drawing a time change curve, we can accurately capture the consistency of the change trend of the filled field and the predicted field over time, so that the system can adjust the threshold more intelligently to adapt to the filling behavior of different users. Evaluating consistency through cosine similarity helps to improve the system's sensitivity and accuracy to time change patterns, thereby optimizing the automatic filling effect, reducing incorrect filling caused by mismatches, and improving user experience.

[0094] Specifically, determining the frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field includes:

[0095] Draw a filling frequency variation curve according to the real-time correction frequency of the filling field;

[0096] Draw a predicted frequency change curve according to the real-time corrected frequency of the predicted field;

[0097] The cosine similarity between the filling frequency change curve and the predicted frequency change curve is calculated to form a frequency change consistency.

[0098] First, draw a filling frequency change curve based on the real-time correction frequency of the filling field, and draw a predicted frequency change curve based on the real-time correction frequency of the predicted field. Then, calculate the cosine similarity of the two curves to evaluate their consistency in frequency change. The cosine similarity reflects the similarity of the two curves, and then derives the consistency of frequency change, which is an important basis for adjusting the filling time threshold.

[0099] By drawing the frequency change curve, we can accurately evaluate the consistency of changes in the correction frequency of the filled field and the predicted field, thereby optimizing the adjustment strategy during the auto-fill process. The calculation of cosine similarity helps to improve the sensitivity to frequency changes, ensuring that in different usage scenarios, auto-fill can more reasonably adapt to user behavior, reduce misjudgments and unnecessary delays, and improve the accuracy and efficiency of automatic filling.

[0100] Please continue reading Figure 2 As shown, it is a decision logic diagram for forming an adjustment time threshold in this embodiment;

[0101] Specifically, adjusting the preset filling duration threshold according to the duration change consistency and the frequency change consistency to form the adjusted duration threshold includes:

[0102] Calculating the absolute value of the relative deviation between the duration change consistency and the frequency change consistency to form a consistency deviation;

[0103] When the consistency deviation is greater than a preset deviation threshold, the preset filling time threshold is adjusted according to the consistency deviation to form an adjusted time threshold.

[0104] The preset deviation threshold is a standard value used to determine whether the consistency deviation exceeds the acceptable range. It mainly determines when the filling time threshold needs to be adjusted. It depends on the system's tolerance for user filling behavior, the stability of historical data, and the required accuracy of the filling task. It is usually set between 0.1 and 0.3. In this embodiment, the preset deviation threshold is set to 0.2, which helps to adjust the filling time threshold in time while ensuring a reasonable error range, thereby balancing flexibility and stability and avoiding excessive adjustment or slow response.

[0105] First, the absolute value of the relative deviation between the consistency of duration change and the consistency of frequency change is calculated to form the consistency deviation. Then, if the consistency deviation is greater than the preset deviation threshold, the system will adjust the preset filling duration threshold according to this deviation, thereby forming a new adjustment duration threshold. This adjustment process optimizes the judgment of filling duration by considering the consistency of duration and frequency changes.

[0106] Effectively adjust the filling time threshold according to the consistency of duration and frequency changes to improve the accuracy and flexibility of automatic filling. By introducing the calculation of consistency deviation, abnormal fluctuations can be monitored in real time to avoid slow response or errors caused by inconsistent filling behaviors. In addition, adjusting the filling time threshold can better adapt to the user's personalized filling habits, thereby improving the user experience and enhancing the intelligence level of automatic filling.

[0107] Specifically, adjusting the preset filling time threshold according to the consistency deviation to form the adjusted time threshold includes:

[0108] Calculating the standard deviation of the consistency deviation within the preset first historical time length to form a consistency deviation fluctuation value;

[0109] The preset filling time threshold is adjusted according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form an adjusted time threshold.

[0110] The preset deviation fluctuation threshold is a set value used to determine whether the consistency deviation fluctuation value exceeds the acceptable range. It depends on the system's fault tolerance range, the volatility of user behavior, and the complexity of the task. It is usually set between 0.05 and 0.2 to balance the sensitivity to abnormal fluctuations and prevent overreaction. In this embodiment, it is set to 0.1 to avoid over-adjustment and ensure that the system can operate stably in most cases, while effectively responding to real abnormal situations and avoiding invalid adjustments.

[0111] First, the standard deviation of the consistency deviation within the preset first historical duration is calculated to form a consistency deviation fluctuation value. Next, the consistency deviation fluctuation value is compared with the preset deviation fluctuation threshold. If the fluctuation value exceeds the preset threshold, the preset filling duration threshold is adjusted according to this deviation, and finally an adjusted duration threshold is formed. This process ensures that the system can dynamically respond to changes in user behavior and optimize the accuracy of automatic filling.

[0112] This dynamic adjustment mechanism can effectively cope with fluctuations in user behavior, especially frequent changes in corrections or selection duration. When the consistency deviation fluctuates too much, the filling duration threshold can be adjusted in time to avoid overfilling or untimely filling, improve the smoothness and accuracy of the filling process, improve the system's adaptability to various filling scenarios, and ensure the stability and intelligence of the user experience.

[0113] Specifically, adjusting the preset filling time threshold according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form the adjusted time threshold includes:

[0114] When the consistency deviation fluctuation value is greater than the preset deviation fluctuation threshold, the preset filling time threshold is increased according to the relative deviation between the consistency deviation fluctuation value and the preset deviation fluctuation threshold and the preset adjustment coefficient to form an adjusted time threshold.

[0115] First, determine whether the consistency deviation fluctuation value is greater than the preset deviation fluctuation threshold. If it is greater, the preset filling time threshold is increased by calculating the relative deviation between the consistency deviation fluctuation value and the preset deviation fluctuation threshold, and combining the preset adjustment coefficient. This adjustment makes the filling time more flexible and can adapt to the abnormal fluctuations and behavioral changes of users during the filling process, and finally forms the adjusted time threshold.

[0116] When the consistency deviation fluctuates greatly, it automatically adapts to these changes by appropriately increasing the filling time threshold to avoid excessive adjustment or untimely response, ensuring that the filling process is smoother and more efficient, improving the system's intelligence and adaptability to diverse user behaviors, and optimizing the user experience.

[0117] Please continue reading Figure 3As shown, it is a determination logic diagram for determining a temporary field in this embodiment;

[0118] Specifically, the determining of the temporary fields in the form field according to the real-time filling time and the preset filling time threshold includes:

[0119] When the real-time filling time is greater than the preset filling time threshold, the form field is determined to be a temporary field.

[0120] First, collect the real-time filling time of the user filling in the form and compare it with the preset filling time threshold. If the real-time filling time of a form field exceeds the preset time threshold, the field is determined to be a "temporary field". Temporary fields usually refer to fields that take an abnormally long time to fill in, which may mean that the user needs more adjustments or corrections on this field.

[0121] By comparing the real-time filling time with the preset filling time threshold, fields that may require more user interaction or adjustment can be effectively identified, so that they can be properly marked and optimized. This not only improves the accuracy of the filling process, but also reduces the system's misoperation when processing these fields, thereby improving user experience and operational efficiency.

[0122] Please continue reading Figure 4 As shown, it is a decision logic diagram for determining the automatic field in this embodiment;

[0123] Specifically, determining a number of automatic fields according to the real-time modification frequency of each temporary field includes:

[0124] Calculating the average value of the real-time correction frequency within a preset determined time period to form a correction frequency mean;

[0125] When the corrected frequency mean is greater than a preset frequency mean threshold, the temporary field is determined to be an automatic field, and a plurality of automatic fields are formed.

[0126] The preset frequency mean threshold is a standard value used to determine whether a temporary field meets the conditions for automated processing. It depends on the frequency fluctuation of field corrections in historical data, the complexity of form fields, and the filling accuracy required in actual business. It is usually set between 0.2 and 0.8. In this embodiment, it is set to 0.5, which can ensure that the conditions for determining the automatic fields are relatively strict, and effectively screen out those fields that require frequent corrections and are relatively stable, thereby reducing misjudgments and improving the accuracy and efficiency of automated filling.

[0127] First, the average value of the real-time correction frequency of each temporary field in a preset time period is calculated to obtain the correction frequency mean. Then, the correction frequency mean is compared with the preset frequency mean threshold. If the correction frequency mean is greater than the preset threshold, the temporary field is determined to be an automatic field, and further several automatic fields are formed.

[0128] By calculating and comparing the correction frequency, we can effectively identify those fields that need to be corrected frequently, so as to mark them as automatic fields and optimize the automatic filling process. By determining the automatic fields, we can improve the efficiency of form filling, reduce human intervention, and achieve smarter browser automatic filling.

[0129] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A browser-based automatic filling method, characterized in that: include: Collect the target's real-time filling time, real-time modification frequency, and real-time selection frequency of each form field while browsing the website; Determine a number of temporary fields in the form field according to the real-time filling time and a preset filling time threshold; Determining a number of automatic fields according to the real-time modification frequency of each of the temporary fields; Determine a number of fill-in fields according to the real-time selection frequency of each automatic field within a preset first historical duration; Determine a number of prediction fields according to a preset prediction model, the real-time filling duration of each form field within the preset first historical duration, and the real-time correction frequency; Adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form an adjusted duration threshold; The fill field formed based on the adjustment duration threshold is automatically filled.

2. The browser-based automatic filling method according to claim 1, characterized in that: The determining of a plurality of fill-in fields according to the real-time selection frequency of each automatic field within a preset first historical duration comprises: Calculating the standard deviation of the real-time selection frequency to form a selection frequency fluctuation value; When the selection frequency fluctuation value is greater than a preset selection fluctuation threshold, the automatic field is determined to be the filling field, and a plurality of filling fields are formed.

3. The browser-based automatic filling method according to claim 2, characterized in that: The adjusting the preset filling duration threshold according to the filling field and the prediction field within the preset second historical duration to form the adjusted duration threshold comprises: Determining a duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field; determining a frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field; The preset filling duration threshold is adjusted according to the duration change consistency and the frequency change consistency to form an adjusted duration threshold.

4. The browser-based automatic filling method according to claim 3, characterized in that: The determining of the duration change consistency according to the real-time filling duration of the filling field and the real-time filling duration of the prediction field comprises: Draw a filling time change curve according to the real-time filling time of the filling field; Draw a prediction duration change curve according to the real-time filling duration of the prediction field; The cosine similarity between the filling duration change curve and the predicted duration change curve is calculated to form a duration change consistency.

5. The browser-based automatic filling method according to claim 3, characterized in that: The determining of the frequency change consistency according to the real-time revised frequency of the filling field and the real-time revised frequency of the prediction field comprises: Draw a filling frequency variation curve according to the real-time correction frequency of the filling field; Draw a predicted frequency change curve according to the real-time corrected frequency of the predicted field; The cosine similarity between the filling frequency change curve and the predicted frequency change curve is calculated to form a frequency change consistency.

6. The browser-based automatic filling method according to claim 3, characterized in that: The adjusting the preset filling duration threshold according to the duration change consistency and the frequency change consistency to form the adjusted duration threshold comprises: Calculating the absolute value of the relative deviation between the duration change consistency and the frequency change consistency to form a consistency deviation; When the consistency deviation is greater than a preset deviation threshold, the preset filling time threshold is adjusted according to the consistency deviation to form an adjusted time threshold.

7. The browser-based automatic filling method according to claim 6, characterized in that: The step of adjusting the preset filling time threshold according to the consistency deviation to form the adjusted time threshold comprises: Calculating the standard deviation of the consistency deviation within the preset first historical time length to form a consistency deviation fluctuation value; The preset filling time threshold is adjusted according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form an adjusted time threshold.

8. The browser-based automatic filling method according to claim 7, characterized in that: The adjusting the preset filling time threshold according to the consistency deviation fluctuation value and the preset deviation fluctuation threshold to form the adjusted time threshold comprises: When the consistency deviation fluctuation value is greater than the preset deviation fluctuation threshold, the preset filling time threshold is increased according to the relative deviation between the consistency deviation fluctuation value and the preset deviation fluctuation threshold and the preset adjustment coefficient to form an adjusted time threshold.

9. The browser-based automatic filling method according to claim 8, characterized in that: The determining of the plurality of temporary fields in the form field according to the real-time filling time and the preset filling time threshold comprises: When the real-time filling time is greater than the preset filling time threshold, the form field is determined to be a temporary field.

10. The browser-based automatic filling method according to claim 9, characterized in that: The determining of a number of automatic fields according to the real-time modification frequency of each temporary field comprises: Calculating the average value of the real-time correction frequency within a preset determined time period to form a correction frequency mean; When the corrected frequency mean is greater than a preset frequency mean threshold, the temporary field is determined to be an automatic field, and a plurality of automatic fields are formed.

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