Data visualization recommendation method, electronic equipment and storage medium

Abstract

The invention discloses a data visualization recommendation method, electronic equipment and a storage medium, and relates to the technical field of data visualization. The data visualization recommendation method comprises the steps of obtaining interaction behavior information and historical behavior data of a target user for a visualization system; determining a region preference degree according to the interaction behavior information, and determining a preference display rule according to the region preference degree; determining user preferences according to the interactive behavior information and the historical behavior data; and dynamically adjusting a display recommendation strategy according to the user preference and the preference display rule. According to the embodiment of the invention, the user preference information is determined according to the interaction behavior information and the historical behavior data, and the recommendation strategy is displayed in a customized manner according to the user preference information, so that the flexibility of displaying the recommendation strategy is improved, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a data visualization recommendation method, electronic equipment and storage medium. Background Art

[0002] In the field of data visualization and intelligent analysis technology, although traditional data visualization tools have made some progress in displaying data, they usually have some limitations. These tools often require users to have certain data analysis knowledge in order to manually select and adjust the dimensions and perspectives of data display. However, for non-professional users, this can become quite complicated and time-consuming. In addition, when faced with large-scale and multi-dimensional data sets, it is difficult for users to quickly identify and understand the key information and potential patterns in the data.

[0003] Existing visualization systems usually lack in-depth understanding and analysis of user interaction behaviors, and are unable to automatically adjust the display content and methods according to the specific needs and preferences of users. At the same time, they rarely provide context-based intelligent recommendations to help users explore data more effectively. The lack of advanced analysis functions is also a problem, and users often need additional software or tools to conduct in-depth data analysis. Therefore, how to automatically adapt the visualization system to the user's interactive behavior and provide personalized display and recommendations has become a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a data visualization recommendation method, electronic device and storage medium to solve the problem that the existing visualization system is unable to deeply understand and analyze user interaction behaviors and automatically adjust the display content and method according to the user's specific needs and preferences.

[0005] According to one aspect of the present invention, a data visualization recommendation method is provided, wherein the method comprises:

[0006] Obtain the target user's interactive behavior information and historical behavior data for the visualization system;

[0007] Determining a region preference degree according to the interactive behavior information, and determining a preference display rule according to the region preference degree;

[0008] Determining user preferences based on the interactive behavior information and the historical behavior data;

[0009] The display recommendation strategy is dynamically adjusted according to the user preference and the preference display rule.

[0010] According to another aspect of the present invention, a data visualization recommendation device is provided, wherein the device comprises:

[0011] A data determination module is used to obtain the target user's interactive behavior information and historical behavior data for the visualization system;

[0012] A rule determination module, used to determine the region preference degree according to the interactive behavior information, and determine the preference display rule according to the region preference degree;

[0013] A preference recommendation module, used to determine user preferences based on the interaction behavior information and the historical behavior data;

[0014] The strategy recommendation module is used to dynamically adjust the display recommendation strategy according to the user preference and the preference display rule.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the data visualization recommendation method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the recommended method for data visualization described in any embodiment of the present invention when executed by a processor.

[0020] The technical solution of the embodiment of the present invention obtains the target user's interactive behavior information and historical behavior data for the visualization system, determines the regional preference level according to the interactive behavior information, determines the preference display rules according to the regional preference level, determines the user preference according to the interactive behavior information and the historical behavior data, and dynamically adjusts the display recommendation strategy according to the user preference and the preference display rules, thereby determining the user preference according to the interactive behavior information and the historical behavior data, and customizing the display recommendation strategy according to the user preference, thereby improving the flexibility of the display recommendation strategy and thus improving the user experience.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a data visualization recommendation method provided according to the first embodiment of the present invention;

[0024] Figure 2 is a flow chart of a data visualization recommendation method provided according to the second embodiment of the present invention;

[0025] Figure 3 is a structural schematic diagram of a data visualization recommendation device provided according to Embodiment 3 of the present invention;

[0026] Figure 4 The invention is a schematic diagram of the structure of an electronic device for implementing a recommended method for data visualization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1This is a flowchart of a data visualization recommendation method provided according to the first embodiment of the present invention. This embodiment is applicable to the case where the visualization system adjusts the display recommendation strategy according to the user's preference. The method can be executed by a data visualization recommendation device. The data visualization recommendation device can be implemented in the form of hardware and / or software. The data visualization recommendation device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110: Obtaining interactive behavior information and historical behavior data of a target user with respect to a visualization system.

[0032] Among them, the visualization system refers to a system that presents data using visualization means such as graphics, text, and charts. The visualization system interface can be divided into different display areas, such as the main display area, the auxiliary display area, and the control panel area. The visualization system can recommend data filtering conditions, color display schemes, and chart types according to user needs, so that users can conveniently customize display strategies. Interactive behavior information refers to the information generated by the user's interactive behavior with the visualization system. Generally speaking, the interactive behavior information may include but is not limited to the number of regional interactions, regional operation time, regional operation duration, regional gaze duration, and current operation area of ​​each area as interactive behavior information. Historical behavior data refers to the behavioral data of the user's historical interaction with the visualization system. Exemplarily, the historical behavior data may include but is not limited to the chart selection frequency of each chart type at historical moments, the color selection frequency of each color scheme, and the conditional selection frequency of each data filtering condition, determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the conditional weight coefficient of each data filtering condition, and historical interactive behavior information.

[0033] In an embodiment, based on the target user's operation behavior on the visualization system, the number of regional interactions, regional operation time, regional operation duration, regional gaze duration, and current operation area of ​​the target user in each area of ​​the visualization system can be determined, and the number of regional interactions, regional operation time, regional operation duration, regional gaze duration, and current operation area of ​​the target user in each area of ​​the visualization system are used as interaction behavior information. In addition, the chart selection frequency of each chart type, the color selection frequency of each color scheme, and the condition selection frequency of each data filtering condition at the user's historical moment are extracted, and the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the condition weight coefficient of each data filtering condition, and the historical interaction behavior information are determined, and the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient, and condition weight coefficient are used as historical behavior data.

[0034] In the actual operation process, a user interaction monitor can be pre-set to record information such as mouse clicks and scrolls in real time, determine the number of regional interactions, regional operation time, duration of regional operation, and current operation area of ​​each area of ​​the visualization system by the target user, and collect the gaze point position and gaze time of the target user through a pre-set eye tracking device (such as an eye tracker), and determine the target user's stay time for each area as the regional gaze duration. At the same time, extract the chart selection frequency of each chart type, the color selection frequency of each color scheme, and the conditional selection frequency of each data filtering condition at the pre-recorded user's historical moments, determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the conditional weight coefficient of each data filtering condition, and the historical interaction behavior information, and determine the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient, conditional weight coefficient, and historical interaction behavior information as historical behavior data.

[0035] S120: Determine the regional preference degree according to the interactive behavior information, and determine the preference display rule according to the regional preference degree.

[0036] Among them, the regional preference degree refers to the degree of interest in each display area. The regional preference degree may include high preference areas and low preference areas. Generally speaking, the regions with higher regional operation frequency or higher regional gaze duration can be identified as high preference areas, and the remaining areas as low preference areas. The regional preference degree may also be quantified by a numerical value, and the higher the numerical value, the higher the regional preference degree is determined. The preference display rules may be understood as display rules adjusted for the regional preference degree. In the actual operation process, the preference display rules may include the data display area size and the data display content content of the region.

[0037] In an embodiment, the regional operation time, regional interaction times and regional gaze duration in the interactive behavior information can be extracted, and the region with a regional operation time greater than or equal to a preset threshold can be regarded as a high preference region; or, the region with a regional interaction times greater than or equal to a preset threshold can be regarded as a high preference region; or, the region preference corresponding to the region with a regional gaze duration greater than or equal to the preset region gaze duration can be identified as a high preference region; or, the regional operation frequency can be determined according to the regional operation time and the regional interaction times belonging to the same region, and the region preference corresponding to the region with a regional operation frequency greater than or equal to the preset frequency threshold can be identified as a high preference region. Then, the size of the data display area and the content of the data display content are adjusted according to the regional preference degree.

[0038] In the actual operation process, the preset data basic display size, preset gaze size weight coefficient, preset gaze information weight coefficient, data volume, preset regional display volume and preset operation frequency weight coefficient of the high preference area can be determined, and the product of the preset operation frequency weight coefficient and the regional operation frequency is determined as the first product, and the product of the preset gaze size weight coefficient and the regional gaze duration is determined as the second product, and the sum of the first product, the second product and the preset data basic display size is used as the data display area size of the high preference area; at the same time, the product of the data volume and the regional operation frequency is determined as the third product, and the product of the preset gaze information weight coefficient and the regional gaze duration is determined as the fourth product, and the sum of the third product, the fourth product and the preset regional display volume is used as the data display content content of the high preference area. In actual applications, the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient can be updated according to the regional preference degree. For example, when the regional preference degree is high, the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient can be increased. In one embodiment, after the data display area size and data display content content of the high preference area are determined, the data display area size and data display content content of other areas may also be adaptively adjusted according to the corresponding area gaze duration and area operation frequency.

[0039] S130: Determine user preference according to the interactive behavior information and historical behavior data.

[0040] Among them, user preferences can be understood as recommendation information for user preferences, which is the behavior of providing users with personalized content and functions based on the user's specific preferences and behavior patterns. Exemplarily, user preferences may include but are not limited to chart recommendations, color scheme recommendations, and filter condition recommendations.

[0041] In an embodiment, historical behavior data may be extracted, and user preferences may be determined according to the historical behavior data. In the actual operation process, when it is determined that the chart preference is greater than a preset preference threshold, it may be determined that the target user prefers to use the chart, and the chart may be recommended as a user preference; when it is determined that the color preference is greater than a preset color threshold, it may be determined that the target user prefers to change the color scheme, and the color scheme may be recommended as a user preference; when it is determined that the filter condition preference is greater than a preset filter condition preference threshold, it may be determined that the target user prefers to use the data filter condition, and the filter condition may be recommended as a user preference. Alternatively, a preset gaze weight coefficient and the regional gaze time in the interactive behavior information may be determined, and the user preference may be determined by combining the product of the regional gaze time and the preset gaze weight coefficient.

[0042] In one embodiment, when it is determined that the user preferences include chart preferences, the chart with the highest chart selection frequency for each chart can be determined as the recommended chart; when it is determined that the user preferences include color scheme preferences, the color scheme with the highest color selection frequency for each color scheme can be determined as the recommended color scheme; when it is determined that the user preferences include filter condition preferences, the data filter condition with the highest filter condition selection frequency for each data filter condition can be determined as the recommended data filter condition.

[0043] S140: Dynamically adjust the display recommendation strategy according to user preferences and preference display rules.

[0044] Among them, the display recommendation strategy refers to the recommendation display rules generated by the visualization system for users.

[0045] In an embodiment, after determining the user preferences and the preference display rules, the display recommendation strategy can be dynamically adjusted according to the user preferences and the preference display rules. In the actual operation process, the data display area size and the data display content can be adjusted according to the user preferences, and the charts, color schemes and filtering conditions can be recommended to the user according to the preference display rules. In an exemplary embodiment, if the user prefers to use a bar chart to analyze time series data, the visualization system can actively recommend the use of a bar chart according to the preference display rules, and automatically adjust the color and style of the chart according to the user's operating habits, so that the user can understand the data more intuitively and deeply.

[0046] The embodiments of the present invention obtain the target user's interactive behavior information and historical behavior data for a visualization system, determine the regional preference level according to the interactive behavior information, determine the preference display rules according to the regional preference level, determine the user preference according to the interactive behavior information and the historical behavior data, dynamically adjust the display recommendation strategy according to the user preference and the preference display rules, thereby determining the user preference according to the interactive behavior information and the historical behavior data, and customizing the display recommendation strategy according to the user preference, thereby improving the flexibility of the display recommendation strategy and thus improving the user experience.

[0047] In one embodiment, the data visualization recommendation method further includes:

[0048] The recommended operation area is determined according to the current operation area in the interactive behavior information and the historical interactive behavior information in the historical behavior data.

[0049] Among them, historical interaction behavior data refers to the interaction behavior data at historical moments. In the actual operation process, historical interaction behavior information may include the number of regional interactions at historical moments, regional operation time, regional operation duration, regional gaze duration, current operation area, and operation process, etc. The recommended operation area can be understood as the predicted user's future operation area.

[0050] In an embodiment, the user's current operation area can be determined, and the historical interaction behavior information in the historical behavior data can be determined. The user's future operation can be inferred according to the current operation area combined with the rational interaction behavior information, and the user can be provided with data views or analysis tools that they may be interested in in advance. In the actual operation process, the neural network model can be pre-trained, the user's future operation can be determined based on the neural network model, and the recommended operation area can be determined for the user's future operation. In one embodiment, the recommended operation area can be enlarged or highlighted to facilitate the user to perform further operations and improve the convenience of user operations.

[0051] Embodiment 2

[0052] Figure 2 is a flow chart of a data visualization recommendation method provided according to the second embodiment of the present invention. This embodiment is a further description of a data visualization recommendation method based on the above embodiment. Figure 2 As shown, the method includes:

[0053] S201. In response to the operation behavior of the target user, determine the number of regional interactions, regional operation time, regional operation duration, regional gaze duration, preset gaze weight coefficient and current operation area of ​​each area of ​​the target user in the visualization system as interaction behavior information.

[0054] Among them, the operation behavior refers to the operation behavior of the target user on the visualization system. Exemplarily, the operation behavior may include but is not limited to mouse clicks, dragging, and eye movements. The number of regional interactions refers to the number of interactions for each display area, such as the number of mouse clicks. The regional operation time refers to the operation time for each display area; the regional gaze duration refers to the length of time the eye stays in each area. Exemplarily, the gaze point position and gaze time of the target user can be collected through a pre-set eye tracking device (such as an eye tracker), and the target user's stay time in each area is used as the regional gaze duration; the current operation area refers to the display area currently operated by the user. The preset gaze weight coefficient refers to the pre-set weight coefficient for the regional gaze duration, which is used to characterize the impact of the regional gaze duration on data interaction.

[0055] In an embodiment, the number of area interactions, area operation time, area operation duration, area gaze duration and current operation area of ​​each area collected by preset sensors can be collected, and the number of area interactions, area operation time, area operation duration, area gaze duration, preset gaze weight coefficient and current operation area of ​​each area can be used as interaction behavior information.

[0056] S202, extracting the chart selection frequency of each chart type, the color selection frequency of each color scheme, and the condition selection frequency of each data filtering condition at the user's historical moments, and determining the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the condition weight coefficient of each data filtering condition, and historical interaction behavior information.

[0057] Among them, the chart selection frequency refers to the selection frequency for each chart type in the historical moment. The color selection frequency refers to the selection frequency for each color scheme in the historical moment. The filter condition selection frequency refers to the selection frequency for each data filter condition in the historical moment. In one embodiment, the chart selection frequency, color selection frequency and condition selection frequency can be counted for a period of time. The chart weight coefficient refers to the weight coefficient of each chart type; the color weight coefficient refers to the weight coefficient of each color scheme; the condition weight coefficient refers to the weight coefficient of each data filter condition; the historical interaction behavior information refers to the interaction behavior information at the historical moment.

[0058] The chart weight coefficient, color weight coefficient and condition weight coefficient are dynamically adjusted based on the chart selection frequency, color selection frequency, condition selection frequency and area fixation time. The greater the chart selection frequency and area fixation time for a chart type, the higher the chart weight coefficient of the chart type.

[0059] In an embodiment, the visualization system can record the chart selection frequency of each chart type at the user's historical moments, the color selection frequency of each color scheme, and the conditional selection frequency of each data filtering condition, determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the conditional weight coefficient of each data filtering condition, and historical interaction behavior information, and can extract the chart selection frequency of each chart type at the user's historical moments, the color selection frequency of each color scheme, and the conditional selection frequency of each data filtering condition, determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the conditional weight coefficient of each data filtering condition, and historical interaction behavior information.

[0060] S203 , taking the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient, condition weight coefficient and historical interaction behavior information as historical behavior data.

[0061] S204: Determine the regional operation time and the number of regional interactions belonging to the same region in the interactive behavior information, and determine the ratio of the regional operation time to the number of regional interactions belonging to the same region as the regional operation frequency.

[0062] In an embodiment, the regional operation time and the number of regional interactions belonging to the same region may be determined, and the ratio of the regional operation time to the number of regional interactions belonging to the same region may be used as the regional operation frequency.

[0063] S205: Confirm the region where the region operation frequency is greater than or equal to the preset frequency threshold, or the region where the region gaze duration in the interactive behavior information is greater than or equal to the preset region gaze duration, as a high preference region.

[0064] The high preference area can be understood as a display area with a high preference degree. The preset frequency threshold refers to a preset frequency threshold for determining the high preference area; the preset area fixation duration refers to a preset duration threshold for determining the high preference area.

[0065] In an embodiment, the regional operation frequency of each display area can be determined, and the area with a regional operation frequency greater than or equal to a preset frequency threshold can be regarded as a high preference area; and the regional gaze duration of each display area can be determined, and the regional preference corresponding to the area with a regional gaze duration greater than or equal to the preset regional gaze duration can be confirmed as a high preference area. In one embodiment, the display area other than the high preference area can be regarded as a low preference area.

[0066] S206, determining a preset data base display size, a preset gaze size weight coefficient, a data volume, a preset gaze information weight coefficient, a preset area display volume, and a preset operation frequency weight coefficient of the high preference area.

[0067] Among them, the preset data basic display size refers to the preset basic display size of each display area; the preset gaze size weight coefficient refers to the gaze time weight coefficient related to the display size; the preset gaze information weight coefficient refers to the gaze time weight coefficient related to the display quantity of the preset area; the preset area display quantity refers to the data display content content of each preset area; the preset operation frequency weight coefficient refers to the weight coefficient of the preset area operation frequency.

[0068] In an embodiment, a preset data base display size, a preset gaze size weight coefficient, a preset gaze information weight coefficient, a preset area display amount, and a preset operation frequency weight coefficient of a preset high preference area may be extracted.

[0069] S207 . Update a preset gaze size weight coefficient, a preset gaze information weight coefficient, and a preset operation frequency weight coefficient according to the region preference degree.

[0070] In an embodiment, when the degree of regional preference changes, the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient for each display area may change, and the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient may be updated based on the degree of regional preference.

[0071] In one embodiment, updating the preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient according to the region preference degree includes:

[0072] Determine the fixation duration of the area with high preference;

[0073] Determine the product of the learning rate of the preset update model and the regional fixation duration as the weight coefficient update amount;

[0074] The sum of the preset gaze size weight coefficient and the weight coefficient update amount is used as the updated preset gaze size weight coefficient;

[0075] The sum of the preset gaze information weight coefficient and the weight coefficient update amount is used as the updated preset gaze information weight coefficient;

[0076] The sum of the preset operating frequency weight coefficient and the weight coefficient update amount is used as the updated preset operating frequency weight coefficient.

[0077] The preset learning rate refers to a preset parameter for controlling the learning speed of the model in the visualization system. The preset update model refers to a preset model for updating the preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient. For example, the preset update model can be a neural network model or a machine learning model.

[0078] In an embodiment, the regional gaze duration corresponding to the high preference region can be extracted, the product of the learning rate of the preset update model and the regional gaze duration can be determined, and the product can be used as the weight coefficient update amount. The preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient are respectively increased by the weight coefficient update amount to obtain an updated preset gaze size weight coefficient, an updated preset gaze information weight coefficient, and an updated preset operation frequency weight coefficient.

[0079] S208, determining the data display area size and data display content content of the high preference area according to the preset data basic display size of the high preference area, the preset area display volume, and the updated preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient.

[0080] In an embodiment, the size change amount can be determined according to the updated preset gaze size weight coefficient, the regional operation frequency, and the preset operation frequency weight coefficient, and the total sum of the size change amount and the preset data base display size is used as the data display area size of the high preference area. The data volume change amount is determined according to the preset area display volume, the area operation frequency, and the preset gaze information weight coefficient, and the sum of the data volume change amount and the preset area display volume is used as the data display content content.

[0081] In one embodiment, the data display area size and data display content content of the high preference area are determined according to the preset data basic display size of the high preference area, the preset area display amount, and the updated preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient, including:

[0082] Determine the product of a preset operation frequency weight coefficient and the regional operation frequency as a first product, and determine the product of a preset gaze size weight coefficient and the regional gaze duration as a second product;

[0083] The sum of the first product, the second product and the preset data base display size is used as the data display area size of the high preference area;

[0084] Determine the product of the data volume and the regional operation frequency as the third product, and determine the product of the preset gaze information weight coefficient and the regional gaze duration as the fourth product;

[0085] The sum of the third product, the fourth product and the display volume of the preset area is used as the data display content content of the high preference area.

[0086] In an embodiment, the product of the preset operation frequency weight coefficient and the regional operation frequency can be used as the first product, the product of the preset gaze size weight coefficient and the regional gaze duration can be used as the second product, and the sum of the first product, the second product and the preset data basic display size can be determined as the data display area size of the high preference area. Then, the product of the data volume and the regional operation frequency can be used as the third product, the product of the preset gaze information weight coefficient and the regional gaze duration can be used as the fourth product, and the sum of the third product, the fourth product and the preset regional display volume can be determined as the data display content content of the high preference area.

[0087] S209: Using the data display area size and the data display content as the preference display rule.

[0088] S210, extracting the regional gaze time and preset gaze weight coefficient in the interactive behavior information, as well as the chart selection frequency of each chart type in the historical behavior data, the color selection frequency of each color scheme, the conditional selection frequency of each data filtering condition, the chart weight coefficient of each chart type, the color weight coefficient of each color, and the conditional weight coefficient of each data filtering condition.

[0089] In an embodiment, the regional gaze time and preset gaze weight coefficient in the pre-stored interactive behavior information, as well as the chart selection frequency of each chart type in the historical behavior data, the color selection frequency of each color scheme, the conditional selection frequency of each data filtering condition, the chart weight coefficient of each chart type, the color weight coefficient of each color, and the conditional weight coefficient of each data filtering condition can be extracted.

[0090] S211, determining the chart preference, color preference and filtering condition preference according to the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient and condition weight coefficient, area gaze time and preset gaze weight coefficient.

[0091] Among them, chart preference refers to the user's preference for using charts; color preference refers to the user's preference for using color schemes; and filter condition preference refers to the user's preference for using data filtering conditions.

[0092] In one embodiment, the chart preference, color preference and filtering condition preference are determined according to the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient and condition weight coefficient, area gaze time and preset gaze weight coefficient, including:

[0093] Determine the product of the regional gaze time and a preset gaze weight coefficient as the gaze information;

[0094] determining a sum of the gaze information and the chart selection frequency as the chart selection information, and determining a sum of the gaze information and the color selection frequency as the color selection information;

[0095] Determine the sum of the products of each chart selection information and the chart weight coefficient corresponding to the chart selection information as the chart preference degree;

[0096] Determine the sum of the products of each color selection information and the color weight coefficient corresponding to the color selection information as the color preference degree;

[0097] The sum of the products of the selection frequencies of each filtering condition and the filtering condition weight coefficients corresponding to the selection frequencies of the filtering condition is determined as the preference degree of the filtering condition.

[0098] In an embodiment, the product of the regional gaze time and the preset gaze weight coefficient may be determined as the gaze information, the sum of the gaze information and the chart selection frequency may be used as the chart selection information, and the sum of the gaze information and the color selection frequency may be used as the color selection information. The sum of the products of each chart selection information and the chart weight coefficient corresponding to the chart selection information may be determined as the chart preference degree, the sum of the products of each color selection information and the color weight coefficient corresponding to the color selection information may be used as the color preference degree, and the sum of the products of each filter condition selection frequency and the filter condition weight coefficient corresponding to the filter condition selection frequency may be determined as the filter condition preference degree.

[0099] S212: When the chart preference level is greater than a preset chart preference threshold, recommend the chart as a user preference.

[0100] The preset chart preference threshold may be understood as a preset threshold for determining whether the user prefers chart recommendations, and the preset chart preference threshold may be set according to user needs.

[0101] In an embodiment, the relationship between the chart preference degree and a preset chart preference threshold may be compared, and when it is determined that the chart preference degree is greater than the preset chart preference threshold, the chart may be recommended as a user preference.

[0102] S213: When the color preference degree is greater than a preset color preference threshold, recommend the color as the user preference.

[0103] The preset color preference threshold may be understood as a preset threshold for determining whether a user prefers a color recommendation, and the preset color preference threshold may be set according to user needs.

[0104] In an embodiment, the color preference degree may be compared with a preset color preference threshold, and when it is determined that the color preference degree is greater than the preset color preference threshold, the color may be recommended as the user preference.

[0105] S214: When the preference level of the filtering condition is greater than a preset filtering condition preference threshold, the filtering condition is recommended as a user preference.

[0106] The preset filtering condition preference threshold may be understood as a preset threshold for determining whether the user prefers the filtering condition recommendation, and the preset filtering condition preference threshold may be set according to user needs.

[0107] In an embodiment, the magnitude relationship between the filter condition preference degree and a preset filter condition preference threshold may be compared, and when it is determined that the filter condition preference degree is greater than the preset filter condition preference threshold, the filter condition may be recommended as a user preference.

[0108] S215: Dynamically adjust the display recommendation strategy according to user preferences and preference display rules.

[0109] In an embodiment of the present invention, in response to the operation behavior of the target user, the number of regional interactions, regional operation time, duration of regional operation, duration of regional gaze and current operation area of ​​each area of ​​the target user in the visualization system are determined as the interaction behavior information, and the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient, condition weight coefficient and historical interaction behavior information are used as historical behavior data, and a high preference area is determined according to the interaction behavior information and the historical behavior data, and the data display area size and data display content content of the high preference area are determined to adaptively determine the user preference according to the preference; by extracting the chart selection frequency of each chart type, the color selection frequency of each color scheme, the condition selection frequency of each data filtering condition, the chart weight coefficient of each chart type, the color weight coefficient of each color and the condition weight coefficient of each data filtering condition in the historical behavior data, the chart preference degree, the color preference degree and the filtering condition preference degree are determined according to the chart selection frequency, the color selection frequency, the condition selection frequency, the chart weight coefficient, the color weight coefficient and the condition weight coefficient, the user preference is determined, and the visualization system automatically adapts to the user's interaction behavior, provides personalized display and recommendation, and improves the user's use experience.

[0110] Embodiment 3

[0111] In one embodiment, this embodiment takes an intelligent multidimensional data interactive visualization analysis system as an example of a visualization system, wherein the intelligent multidimensional data interactive visualization analysis system includes: a user interaction module, a behavior analysis engine, an adaptive display logic module, an intelligent recommendation system, a context awareness module, a prediction model integration module, a custom recommendation algorithm interface, a real-time collaboration framework, and an advanced analysis plug-in library as an example to further illustrate a data visualization recommendation method.

[0112] Among them, the user interaction module monitors the clicks, drags and other operations of the target user in real time, and passes the data to the behavior analysis engine. The behavior analysis engine uses machine learning algorithms to analyze user behavior and intelligently infer the user's points of interest (high preference areas). Based on the analysis results, the adaptive display logic module dynamically adjusts the dimensions and detail levels of data display to adapt to user needs. The intelligent recommendation system further provides customized display perspectives and methods based on user behavior (interactive behavior information) and points of interest (high preference areas). The context-aware module deeply analyzes the original context of the data, provides analysis suggestions, and enhances user insight. The prediction model integration module allows users to adjust prediction parameters through interactive operations and displays prediction results in real time. The custom recommendation algorithm interface enables users to customize personalized recommendation strategies according to their own analysis needs. The real-time collaboration framework supports multi-user collaboration to achieve data sharing and communication. The advanced analysis plug-in library provides a variety of tools to meet users' needs for in-depth analysis.

[0113] In one embodiment, the user interaction monitor in the user interaction module can track and record various operations performed by the user on the data visualization interface in real time. Operation information may include basic clicks and drags, as well as more complex behaviors such as zooming and data selection. The user interaction monitor accurately captures every action of the user to ensure that the user's interaction intention is truly reflected. Exemplarily, the user interaction monitor can also determine the context information of the operation, such as the area operation time, the duration of the area operation, the frequency of the area operation, etc., thereby providing rich data for understanding the user's behavior patterns and preferences. In one embodiment, the interactive behavior information collected by the user interaction monitor can be stored as historical behavior data.

[0114] The behavior analysis engine performs in-depth analysis on the data collected by the user interaction monitor. This processor uses current advanced machine learning techniques, including Hidden Markov Model (HMM) and Deep Neural Network (DNN), to process and parse the user's interactive behavior information. In one embodiment, the behavior analysis engine can identify the user's behavior pattern by modeling and analyzing the user's interactive behavior information on the intelligent multidimensional data interactive visualization analysis system. These patterns may include the user's preference for a specific data set, operating habits, and specific behavioral trends shown during the analysis process. For example, if the user often performs in-depth analysis on a certain data area within a certain period of time, the processor can identify this behavior pattern. The behavior analysis engine can not only identify existing behavior patterns, but also predict the user's future operations by learning the user's historical interaction data. This predictive ability enables the system to more proactively adapt to the needs of users and provide users with data views or analysis tools that they may be interested in in advance. For example, if the system predicts that the user may want to view a detailed view of a specific data set next, the intelligent multidimensional data interactive visualization analysis system can automatically adjust the display parameters. The advanced analysis capabilities of the behavior analysis engine greatly enhance the personalized service level of the system. By accurately capturing and predicting user behavior, the system can provide a data analysis experience that is more in line with user needs, making users feel more convenient and intuitive during use.

[0115] The adaptive display logic module is an intelligent component in the intelligent multidimensional data interactive visualization analysis system. Its core task is to determine the degree of regional preference based on the user's interactive behavior information and determine the preference display rules based on the degree of regional preference. In the actual operation process, the adaptive display logic module first receives the interactive behavior information from the user interaction monitor. These data record in detail all the user's operations on the data visualization interface, including clicks, drags, zooms, selections, and eye gazes. Using the output of the behavior analysis processor, the engine identifies the user's behavior patterns and points of interest (high preference areas). If the user's operation frequency f on a certain area is significantly higher than that on the area, this may indicate that the user is particularly interested in this area. Based on the user's behavioral analysis, the engine uses a mathematical model to dynamically adjust the display parameters. The display size S can be calculated by the following formula: S = α·f+β+θ·g, where α is the weight coefficient related to the user's interaction frequency (region operation frequency), f is the region operation frequency, and β is the preset data base display size, θ is the weight coefficient related to the region gaze duration (preset gaze size weight coefficient), and g is the region gaze duration. Ensure that even display areas with low interaction frequency can obtain basic display. For data points that users are particularly interested in (high preference areas), the engine may need to display more detailed information. The data display content D can be calculated by the following formula: D = γ·I+δ+κ·g, where γ is the weight coefficient related to the user's interest point (regional operation frequency), I is the amount of data, and δ is the preset regional display volume, κ is the weight coefficient related to the regional gaze duration (preset gaze information weight coefficient), and g is the regional gaze duration to ensure the display of basic information. The adaptive display logic module also includes a feedback mechanism to continuously adjust α, β, γ, δ, θ, κ, η and μ based on the user's subsequent behavior and feedback. The feedback mechanism is not only based on the user's operational behavior on the recommendation results, but also can adjust the decision-making process of the recommendation algorithm based on the user's regional gaze duration to optimize the display effect.

[0116] In one embodiment, g recommend This will directly affect the learning process of the system. For example, the weights can be dynamically updated in the following ways: Among them, λ is the preset learning rate, g recommend It is the user's regional gaze market for the recommended results. In this way, the system will give priority to recommending content that users gaze at for a long time, further improving user satisfaction.

[0117] By introducing the gaze mechanism, the system can more accurately capture the user's interests and focus, making the dynamic adjustment of display and recommendation more intelligent and personalized.

[0118] In one embodiment, the intelligent recommendation system provides customized data display and analysis suggestions by analyzing the user's behavior patterns and preferences: the intelligent recommendation generator first needs to collect the user's interaction behavior information with the system. This interaction behavior information is not limited to the chart selection frequency f of the user's selection of the pending chart type. chart , preferred color scheme c pref , and commonly used data filtering conditions f filter The collected behavioral data can be used to build a user preference model. For example, the user's preference for different chart types can be calculated by the following weighted sum formula: Among them, P chart Indicates the user's comprehensive preference for chart types, w i is the chart weight coefficient of the i-th chart type, f chart,i is the chart selection frequency of the user selecting the i-th chart type, η is the weight coefficient related to the fixation duration (preset fixation weight coefficient), and g is the regional fixation duration. Similarly, the user's preference for color scheme can be calculated as follows: Among them, P color Indicates the user's preference for the color scheme, v j is the color weight coefficient of the j-color scheme, c pref,j is the user's choice rate of the jth color. The user's preference for data filtering conditions can also be determined in a similar way: Among them, P fiiiter Indicates the user's preference for data filtering conditions, s k is the condition weight coefficient of k filtering conditions, f filter,k is the frequency of the user applying the kth filter condition. Based on the user preference model, the intelligent recommendation generator applies a recommendation algorithm to generate display and analysis suggestions. Finally, the intelligent recommendation generator recommends the most appropriate chart type, color scheme, and data filter condition for the user based on the calculated preference degree. The recommended chart type can be based on the chart selection frequency f of the chart type chart To determine: Chart recommend = argmax(f chart ) Among them, Chart recommend is the type of chart recommended to the user. The intelligent recommendation generator also includes a feedback loop to adjust the weight coefficient w according to the user's actual use of the recommendation results and the satisfaction store feedback. i ,v j ,s k , to optimize the performance of the recommendation algorithm.

[0119] In an implemented context-aware module, detailed information related to the current operation area is provided, such as the source of the data, influencing factors, historical trends, etc., to help users understand the data in a broader context. The design goal of the context-aware module is to provide a richer and more comprehensive data analysis perspective.

[0120] The forecast model integration module can be an interactive forecast model that enables users to interact directly with the built-in forecast model. The forecast model integration module is used to provide intuitive control of model parameters through a user-friendly interface, such as adjusting the window size of time series analysis or feature weights in classification models. The interface is equipped with easy-to-operate controls such as sliders and drop-down menus, which allow users to fine-tune model parameters without professional knowledge. This design not only simplifies the user's operation process, but also makes model adjustment fast and straightforward. Users can see the specific impact of parameter changes on forecast results in real time by simply sliding or selecting operations. When users adjust parameters, the forecast model will recalculate and update the results instantly. This immediacy ensures that users can quickly evaluate the impact of different parameter settings on the accuracy and stability of model forecasts. For example, by adjusting the window size of the time series, users can observe the impact of different time ranges on trend forecasts. The interactive forecast model also includes a result displayer, which uses graphical means such as charts, curves and animations to present the forecast results in an intuitive way. This not only helps users better understand the output of the model, but also makes complex forecast data easier to digest and analyze. The design of the interactive forecast model also reflects the purpose of education and exploration. It encourages users to learn the behavior of the model through practice and gain a deep understanding of how different parameters affect the final prediction. This interactive learning experience not only improves users' data analysis capabilities, but also increases their understanding and trust in the predictive modeling process.

[0121] The real-time collaboration framework, or real-time multi-user collaboration platform, is an advanced feature in the intelligent multi-dimensional data interactive visualization analysis system. It allows multiple users to work on the same data set at the same time to achieve close collaboration. The real-time collaboration framework enables users to participate in the data analysis process no matter where they are by providing a shared data view and analysis result space. In order to promote effective communication among team members, the platform integrates a variety of real-time communication tools, including convenient chat functions and efficient video conferencing options. These tools not only strengthen information exchange among team members, but also support rapid decision-making and problem solving, thereby improving the collaboration efficiency of the entire team and the synergy of analytical work. Through this innovative way of collaboration, teams can focus on their respective expertise and perspectives to jointly drive data analysis projects forward.

[0122] The custom recommendation algorithm interface can be used for the advanced analysis plug-in system, which is equipped with a series of complex data analysis tools and algorithms. These plug-ins include but are not limited to advanced algorithms such as cluster analysis, classification, and regression, providing users with a rich algorithm library to meet various data analysis needs. In other words, users can flexibly select and apply these plug-ins according to their specific analysis goals, so as to conduct deeper data mining and insights. Whether it is exploring the inherent structure of data, performing pattern recognition, or predicting future trends, the advanced analysis plug-in system can provide the necessary support to make the data analysis process more efficient and accurate. Through this system, users can easily expand their analytical capabilities, deeply explore the potential of data, and obtain valuable business insights.

[0123] In one embodiment, the visualization system can be applied to a custom algorithm development environment, which is a highly flexible feature in the intelligent multidimensional data interactive visualization analysis system, and is designed for users with special or advanced analysis needs. In this environment, users can use various tools and application programming interfaces (APIs) provided by the system to develop personalized data analysis algorithms according to their specific needs. The friendliness of the system environment allows users to build and implement their own algorithm logic without having to deeply understand the complex underlying technical details during the development process. This means that even users who do not have a deep programming or data science background can use this environment to transform their ideas into practical data analysis solutions. In addition, this development environment also supports the testing and verification of algorithms to ensure that the algorithms developed by users are both effective and reliable in practical applications. In this way, the system not only improves the personalization and flexibility of data analysis, but also greatly expands its scope and depth of application.

[0124] In one embodiment, the visualization system also includes a result export tool, which enables users to easily export analysis results into a variety of different formats, such as Portable Document Format (PDF), Portable Network Graphics (PNG), Comma-Separated Values ​​(CSV), etc. Whether you need to save the results in the form of a static image file, or want to perform further data processing and analysis in a spreadsheet, or create an interactive web page to dynamically display data insights, the result export tool can meet these needs. The design of this tool focuses on convenience and diversity, ensuring that users can choose the most appropriate format based on their reporting or sharing needs.

[0125] In the embodiment of the present invention, the visualization system can conduct in-depth analysis based on the user's historical interaction data and real-time interaction behavior through an integrated intelligent recommendation system, and intelligently recommend the most appropriate data display perspective and analysis method. At the same time, when a user shows a strong interest in a specific data area, this behavior can be automatically identified, and the display parameters can be adjusted in real time, such as zooming in on the data area or providing more detailed information. In addition, users can not only receive intelligent recommendations based on personal preferences and behaviors, but also see in real time how these recommendations are converted into customized data views through the adaptive display engine. This seamless personalized experience enables users to focus more on data analysis itself, rather than on the adjustment and configuration of data, greatly improving the efficiency and depth of data analysis.

[0126] Embodiment 4

[0127] Figure 3 is a schematic diagram of the structure of a data visualization recommendation device provided according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data determination module 31, a rule determination module 32, a preference recommendation module 33 and a strategy recommendation module 34.

[0128] The data determination module 31 is used to obtain the target user's interactive behavior information and historical behavior data for the visualization system;

[0129] A rule determination module 32, used to determine the regional preference degree according to the interactive behavior information, and determine the preference display rule according to the regional preference degree;

[0130] A preference recommendation module 33, used to determine user preferences based on interaction behavior information and historical behavior data;

[0131] The strategy recommendation module 34 is used to dynamically adjust the display recommendation strategy according to user preferences and preference display rules.

[0132] In an embodiment of the present invention, the interactive behavior information and historical behavior data of the target user for the visualization system are obtained through a data determination module. The rule determination module determines the regional preference degree according to the interactive behavior information, and determines the preference display rules according to the regional preference degree. The preference recommendation module determines the user preference according to the interactive behavior information and historical behavior data. The strategy recommendation module dynamically adjusts the display recommendation strategy according to the user preference and the preference display rules, thereby determining the user preference according to the interactive behavior information and historical behavior data, and customizing the display recommendation strategy according to the user preference, thereby improving the flexibility of the display recommendation strategy and thus improving the user experience.

[0133] In one embodiment, the data determination module 31 includes:

[0134] An interaction information determination unit, for determining, in response to the operation behavior of the target user, the number of regional interactions, the regional operation time, the duration of the regional operation, the regional gaze duration, the preset gaze weight coefficient, and the current operation area of ​​each area of ​​the visualization system of the target user as the interaction behavior information;

[0135] An information extraction unit, used to extract the chart selection frequency of each chart type, the color selection frequency of each color scheme, and the condition selection frequency of each data filtering condition at the user's historical moment, and determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the condition weight coefficient of each data filtering condition, and historical interaction behavior information;

[0136] Among them, the chart weight coefficient, color weight coefficient and condition weight coefficient are dynamically adjusted based on the chart selection frequency, color selection frequency, condition selection frequency and area fixation time;

[0137] The historical data determination unit is used to use the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient, condition weight coefficient and historical interaction behavior information as historical behavior data.

[0138] In one embodiment, the rule determination module 32 includes:

[0139] an operation frequency determination unit, configured to determine the regional operation time and the number of regional interactions belonging to the same region in the interaction behavior information, and determine the ratio of the regional operation time to the number of regional interactions belonging to the same region as the regional operation frequency;

[0140] A preference area determination unit, configured to identify an area whose area operation frequency is greater than or equal to a preset frequency threshold, or an area whose area gaze duration in the interactive behavior information is greater than or equal to a preset area gaze duration, as a high preference area;

[0141] A coefficient extraction unit, used to determine a preset data base display size, a preset gaze size weight coefficient, a data volume, a preset gaze information weight coefficient, a preset area display volume, and a preset operation frequency weight coefficient for a high preference area;

[0142] A coefficient updating unit, used for updating a preset gaze size weight coefficient, a preset gaze information weight coefficient and a preset operation frequency weight coefficient according to the region preference degree;

[0143] A display determination unit, configured to determine a data display area size and data display content content of a high preference area according to a preset data base display size of the high preference area, a preset area display amount, and an updated preset gaze size weight coefficient, a preset gaze information weight coefficient, and a preset operation frequency weight coefficient;

[0144] The display rule determination unit is used to use the data display area size and the data display content as the preferred display rule.

[0145] In one embodiment, the coefficient updating unit is specifically used for:

[0146] Determine the fixation duration of the area with high preference;

[0147] Determine the product of the learning rate of the preset update model and the regional fixation duration as the weight coefficient update amount;

[0148] The sum of the preset gaze size weight coefficient and the weight coefficient update amount is used as the updated preset gaze size weight coefficient;

[0149] The sum of the preset gaze information weight coefficient and the weight coefficient update amount is used as the updated preset gaze information weight coefficient;

[0150] The sum of the preset operating frequency weight coefficient and the weight coefficient update amount is used as the updated preset operating frequency weight coefficient.

[0151] In one embodiment, a determination unit is shown, specifically configured to:

[0152] Determine the product of a preset operation frequency weight coefficient and the regional operation frequency as a first product, and determine the product of a preset gaze size weight coefficient and the regional gaze duration as a second product;

[0153] The sum of the first product, the second product and the preset data base display size is used as the data display area size of the high preference area;

[0154] Determine the product of the data volume and the regional operation frequency as the third product, and determine the product of the preset gaze information weight coefficient and the regional gaze duration as the fourth product;

[0155] The sum of the third product, the fourth product and the display volume of the preset area is used as the data display content content of the high preference area.

[0156] In one embodiment, the preference recommendation module 33 includes:

[0157] a weight coefficient extraction unit, used to extract the regional gaze time and the preset gaze weight coefficient in the interactive behavior information, as well as the chart selection frequency of each chart type, the color selection frequency of each color scheme, the condition selection frequency of each data filtering condition, the chart weight coefficient of each chart type, the color weight coefficient of each color, and the condition weight coefficient of each data filtering condition in the historical behavior data;

[0158] A preference degree determination unit, used to determine the chart preference degree, color preference degree and filtering condition preference degree according to the chart selection frequency, color selection frequency, condition selection frequency, chart weight coefficient, color weight coefficient and condition weight coefficient, area gaze time and preset gaze weight coefficient;

[0159] a first preference determination unit, configured to recommend a chart as a user preference when the chart preference level is greater than a preset chart preference threshold;

[0160] a second preference determination unit, configured to recommend a color as a user preference when the color preference degree is greater than a preset color preference threshold;

[0161] The third preference determining unit is configured to recommend the filtering condition as the user preference when the preference degree of the filtering condition is greater than a preset filtering condition preference threshold.

[0162] In one embodiment, the preference level determination unit is specifically configured to:

[0163] Determine the product of the regional gaze time and a preset gaze weight coefficient as the gaze information;

[0164] determining a sum of the gaze information and the chart selection frequency as the chart selection information, and determining a sum of the gaze information and the color selection frequency as the color selection information;

[0165] Determine the sum of the products of each chart selection information and the chart weight coefficient corresponding to the chart selection information as the chart preference degree;

[0166] Determine the sum of the products of each color selection information and the color weight coefficient corresponding to the color selection information as the color preference degree;

[0167] The sum of the products of the selection frequencies of each filtering condition and the filtering condition weight coefficients corresponding to the selection frequencies of the filtering condition is determined as the preference degree of the filtering condition.

[0168] In one embodiment, the data visualization recommendation device further includes:

[0169] The operation recommendation module is used to determine the recommended operation area according to the current operation area in the interactive behavior information and the historical interactive behavior information in the historical behavior data.

[0170] The data visualization recommendation device provided in the embodiment of the present invention can execute the data visualization recommendation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0171] Embodiment 5

[0172] Figure 4Schematic diagram of the structure of an electronic device for implementing a recommended method of data visualization of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0173] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0174] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0175] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the recommended method for data visualization.

[0176] In some embodiments, the data visualization recommendation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data visualization recommendation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data visualization recommendation method in any other appropriate manner (e.g., by means of firmware).

[0177] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0178] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0179] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0180] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0181] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0182] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0183] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0184] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data visualization recommendation method, characterized in that: include: Obtain the target user's interactive behavior information and historical behavior data for the visualization system; Determining a region preference degree according to the interactive behavior information, and determining a preference display rule according to the region preference degree; Determining user preferences based on the interactive behavior information and the historical behavior data; The display recommendation strategy is dynamically adjusted according to the user preference and the preference display rule.

2. The method according to claim 1, characterized in that: The obtaining of the target user's interactive behavior information and historical behavior data for the visualization system includes: In response to the operation behavior of the target user, determining the number of regional interactions, regional operation time, regional operation duration, regional gaze duration, preset gaze weight coefficient and current operation area of ​​the target user in each area of ​​the visualization system as the interaction behavior information; Extract the chart selection frequency of each chart type, the color selection frequency of each color scheme, and the condition selection frequency of each data filtering condition at the user's historical moment, and determine the chart weight coefficient of each chart type, the color weight coefficient of each color scheme, the condition weight coefficient of each data filtering condition, and historical interaction behavior information; The chart weight coefficient, the color weight coefficient and the condition weight coefficient are dynamically adjusted based on the chart selection frequency, the color selection frequency, the condition selection frequency and the area fixation duration; The chart selection frequency, the color selection frequency, the condition selection frequency, the chart weight coefficient, the color weight coefficient, the condition weight coefficient and the historical interaction behavior information are used as historical behavior data.

3. The method according to claim 1, characterized in that The determining of the region preference degree according to the interactive behavior information, and determining the preference display rule according to the region preference degree, includes: Determine the area operation time and the number of area interactions belonging to the same area in the interaction behavior information, and determine the ratio of the area operation time to the number of area interactions belonging to the same area as the area operation frequency; Confirm the area preference corresponding to the area whose area operation frequency is greater than or equal to the preset frequency threshold, or the area whose area fixation time in the interaction behavior information is greater than or equal to the preset area fixation time as a high preference area; Determine a preset data base display size, a preset gaze size weight coefficient, a data volume, a preset gaze information weight coefficient, a preset area display volume, and a preset operation frequency weight coefficient of the high preference area; Update the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient according to the region preference degree; Determine the data display area size and data display content content of the high preference area according to the preset data basic display size of the high preference area, the preset area display amount, and the updated preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient; The data display area size and the data display content content are used as preference display rules.

4. The method according to claim 3, characterized in that: The updating of the preset gaze size weight coefficient, the preset gaze information weight coefficient and the preset operation frequency weight coefficient according to the region preference degree includes: Determine the fixation duration of the area with a high preference degree; Determine the product of the learning rate of the preset update model and the fixation time of the area as the weight coefficient update amount; The sum of the preset gaze size weight coefficient and the weight coefficient update amount is used as the updated preset gaze size weight coefficient; The sum of the preset gaze information weight coefficient and the weight coefficient update amount is used as the updated preset gaze information weight coefficient; The sum of the preset operating frequency weight coefficient and the weight coefficient update amount is used as the updated preset operating frequency weight coefficient.

5. The method according to claim 3, characterized in that: The determining of the data display area size and data display content content of the high preference area according to the preset data base display size of the high preference area, the preset area display amount, and the updated preset gaze size weight coefficient, the preset gaze information weight coefficient, and the preset operation frequency weight coefficient includes: Determine the product of the preset operation frequency weight coefficient and the regional operation frequency as a first product, and determine the product of the preset gaze size weight coefficient and the regional gaze duration as a second product; The sum of the first product, the second product and the preset data basic display size is used as the data display area size of the high preference area; Determine the product of the data volume and the regional operation frequency as a third product, and determine the product of the preset gaze information weight coefficient and the regional gaze duration as a fourth product; The sum of the third product, the fourth product and the display amount of the preset area is used as the data display content content of the high preference area.

6. The method according to claim 1, characterized in that The determining the user preference according to the interactive behavior information and the historical behavior data includes: Extracting the regional gaze time and the preset gaze weight coefficient in the interactive behavior information, as well as the chart selection frequency of each chart type, the color selection frequency of each color scheme, the condition selection frequency of each data filtering condition, the chart weight coefficient of each chart type, the color weight coefficient of each color, and the condition weight coefficient of each data filtering condition in the historical behavior data; Determine the chart preference, color preference and filtering condition preference according to the chart selection frequency, the color selection frequency, the condition selection frequency, the chart weight coefficient, the color weight coefficient and the condition weight coefficient, the area gaze time and the preset gaze weight coefficient; When the chart preference level is greater than a preset chart preference threshold, recommending the chart as a user preference; When the color preference degree is greater than a preset color preference threshold, recommending the color as the user preference; When the filtering condition preference degree is greater than a preset filtering condition preference threshold, the filtering condition is recommended as the user preference.

7. The method according to claim 6, characterized in that The determining of the chart preference, color preference and filtering condition preference according to the chart selection frequency, the color selection frequency, the condition selection frequency, the chart weight coefficient, the color weight coefficient and the condition weight coefficient, the area gaze time and the preset gaze weight coefficient includes: Determine the product of the regional gaze time and a preset gaze weight coefficient as the gaze information; determining a sum of the gaze information and a chart selection frequency as chart selection information, and determining a sum of the gaze information and a color selection frequency as color selection information; determining a sum of products of each of the chart selection information and the chart weight coefficient corresponding to the chart selection information as a chart preference degree; determining a sum of products of each of the color selection information and a color weight coefficient corresponding to the color selection information as a color preference degree; The sum of the products of the filter condition selection frequencies and the filter condition weight coefficients corresponding to the filter condition selection frequencies is determined as the filter condition preference degree.

8. The method according to claim 1, characterized in that: The method further comprises: The recommended operation area is determined according to the current operation area in the interactive behavior information and the historical interactive behavior information in the historical behavior data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the data visualization recommendation method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the data visualization recommendation method according to any one of claims 1 to 8 when executed by a processor.

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