Device point display method and device

Abstract

The application provides a device point display method and device, the method comprises the following steps: obtaining point attention degrees of each device point in a target scene, point importance degrees of each device point, and area importance degrees of areas to which each device point belongs; determining display strategies of each device point in the target scene according to at least one of the point attention degrees, the point importance degrees, and the area importance degrees; and displaying each device point in the target scene based on the display strategies. The application can intelligently optimize display content according to the point attention degrees related to real-time needs and behavior patterns of a user, the point importance degrees of device points, and the area importance degrees, avoid a traditional static aggregation display method, and thus improve user experience and system performance; and the application can more accurately grasp user needs and avoid display deviation caused by only relying on a single behavior.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and apparatus for displaying device positions. Background Technology

[0002] With the continuous development of the security field, the application of Internet of Things (IoT) services in 3D scenes is increasing, leading to a greater demand for displaying device locations within these scenes. Typically, a small to medium-sized campus has around 50,000 device locations, while large campuses can have as many as 100,000. Displaying all of them would impact map rendering and loading speeds, as well as the aesthetics of the display. Using aggregated displays, it would be difficult to distinguish the importance of each location, resulting in the omission of important ones. Therefore, an effective solution is urgently needed to address these issues. Summary of the Invention

[0003] To address the above problems, the present invention provides a method and apparatus for displaying device locations.

[0004] This invention provides a method for displaying device location, comprising: The system obtains the user's attention to each device location in the target scenario, the importance of each device location, and the regional importance of the area to which each device location belongs. Based on at least one of the attention level of each location, the importance of each location, and the importance of each region, determine the display strategy for each device location in the target scene; Based on the display strategy, each of the device locations is displayed in the target scene.

[0005] According to a device location display method provided by the present invention, the step of obtaining the user's attention level to each device location in a target scene includes: For each of the device locations, the user's attention level to the device location is determined based on the user's behavioral data in relation to the target scenario. The behavioral data includes visual data and / or operational data.

[0006] According to a device location display method provided by the present invention, the step of determining the user's attention level to the device location based on the user's behavioral data regarding the target scene includes: Based on the user's gaze duration at the device location in the visual data, determine the gaze factor corresponding to the device location; Based on each operation in the operation data, determine the operation factor corresponding to the device location; The user's attention level to the device location is determined based on the gaze factor and / or the operation factor.

[0007] According to a device location display method provided by the present invention, obtaining the location importance of each device location includes: For each device location in the target scenario, obtain the historical response value and importance level of the device location. The historical response value represents the usage frequency and / or alarm count of the device location. The importance of a device location is determined based on its historical response value, importance level, and attention level.

[0008] According to a device location display method provided by the present invention, obtaining the regional importance of the area to which each device location belongs includes: For each region in the target scenario, the region importance is determined based on the region attribute information, which includes the number of device points contained in the region and the device type of each device point contained in the region.

[0009] According to a device location display method provided by the present invention, determining a display strategy for each device location in a target scene based on at least one of the following: attention level of each location, importance level of each location, and importance level of each region, includes: For each of the device locations, the loading accuracy corresponding to the device location is calculated based on at least one of the device location attention, the device location importance, and the region importance of the region to which the device location belongs. Based on the attention level of the device location, determine the regional accuracy corresponding to the device location; Based on the loading precision and the area precision corresponding to the device location, the display strategy for the device location is determined.

[0010] According to a device location display method provided by the present invention, the step of calculating the loading accuracy corresponding to the device location based on at least one of the location attention, the location importance, and the region importance of the region to which the device location belongs includes: Based on the user's behavioral data for the target scenario, obtain the adjustment factor corresponding to the device location, and obtain the priority of the device location; The first product is obtained by multiplying the attention level, importance level, and regional importance level of the device location; and the second product is obtained by multiplying the adjustment factor and priority level corresponding to the device location. Calculate the ratio of the first product to the second product to obtain the loading accuracy corresponding to the device location.

[0011] According to a device location display method provided by the present invention, determining the area precision corresponding to the device location based on the location attention level includes: If the attention level of the device location is greater than or equal to the attention level threshold, the area accuracy corresponding to the device location is determined to be high area accuracy. High area accuracy indicates that the device location is fully displayed in the target scene. If the attention level of a device location is less than the attention threshold, the area precision corresponding to the device location is determined to be low area precision. Low area precision indicates whether the device location is displayed in the target scene or not.

[0012] According to a device location display method provided by the present invention, determining the display strategy of the device location based on the loading precision and the area precision corresponding to the device location includes: Determine whether the device location meets the loading conditions. The loading conditions are that the loading precision corresponding to the device location is high loading precision, and the area precision corresponding to the device location is high area precision. The high loading precision is greater than the precision threshold. The high area precision indicates that the device location is fully displayed in the target scene. If so, the display strategy for the device point is determined to be to display the device point according to the loading precision corresponding to the device point; If not, then the display strategy for the device locations is determined to be either to display them together or not to display them at all.

[0013] The present invention also provides a device for displaying device positions, comprising: The acquisition module is configured to acquire the user's attention to each device location in the target scene, the importance of each device location, and the regional importance of the area to which each device location belongs. The determination module is configured to determine the display strategy for each device point in the target scene based on at least one of the attention of each point, the importance of each point, and the importance of each region. The display module is configured to display each of the device locations in the target scene based on the display strategy.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the device dot display method as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the device dot display method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the device dot display method as described above.

[0017] The device location display method and apparatus provided by this invention acquires the user's attention level, the importance of each device location, and the regional importance of the area to which each device location belongs in a target scene; determines a display strategy for each device location in the target scene based on at least one of the attention level, importance, and regional importance; and displays each device location in the target scene based on the display strategy. This invention dynamically adjusts the device location display strategy by acquiring the attention level related to the user's real-time needs and behavioral patterns, as well as the importance and regional importance of the corresponding device locations. This intelligently optimizes the displayed content based on the user's real-time needs and behavioral patterns, avoiding traditional static aggregation display methods, thereby improving user experience and system performance. By jointly analyzing the attention level, importance, and regional importance, it associates the user's behavioral patterns with the device locations in the scene and optimizes the display strategy accordingly, enabling a more accurate grasp of user needs and avoiding display biases based solely on a single behavior. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the device location display method provided by the present invention.

[0020] Figure 2 This is an internal processing flowchart of the user behavior analysis unit provided by the present invention.

[0021] Figure 3 This is a flowchart of the internal processing of the three-dimensional scene segmentation unit provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the device position display device provided by the present invention.

[0023] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] The following is combined Figures 1-5 The device position display method and apparatus of the present invention are described. First, a brief description of the relevant content involved in this invention will be given.

[0026] Existing technologies typically handle device locations through simple classification and sorting. While this approach allows for device management, it overlooks factors such as the business relevance of devices and user behavior when dealing with a large number of devices, leading to inaccurate results. Furthermore, existing 3D scene generation technologies may also experience slow loading speeds and poor display quality when handling a large number of devices.

[0027] Figure 1 This is a flowchart illustrating the device location display method provided by the present invention, as shown below. Figure 1 As shown, the method includes steps 101 to 103.

[0028] Step 101: Obtain the user's attention to each device location in the target scene, the importance of each device location, and the regional importance of the area to which each device location belongs.

[0029] The device point display method provided in this embodiment of the invention is applied to a dynamic display resource point system in a two-dimensional or three-dimensional scene. The system consists of a user behavior analysis unit, a scene segmentation unit, and an adaptive rendering engine unit.

[0030] Specifically, the target scene refers to a two-dimensional or three-dimensional map used to display the target location, which can be a park, campus, community, etc., and can be displayed on a monitor. Device locations can be at least one of cameras, access control systems, alarms, and fire-fighting equipment. Location attention refers to the degree of attention currently paid to a device location; it reflects both the user's visual gaze (e.g., eye movement or dwell time) and their interactive behaviors (e.g., clicking, dragging). Therefore, location attention is a comprehensive indicator that directly reflects the user's current interests, meaning it is related to the user's real-time needs and behavioral patterns. Location importance characterizes the importance of a device location. Area importance characterizes the importance of the areas divided within the target scene.

[0031] In practical applications, the attention level of each device location in the target scene is obtained as follows: For each device location, its attention level can be obtained by matching it with a pre-set attention level matching library based on its position and / or dwell time on the display used to show the target scene. This attention level matching library contains attention levels corresponding to different positions and / or dwell times. The closer a device location is to the center of the display screen and / or the longer its dwell time, the higher its attention level; conversely, the further away a device location is from the center of the display screen and / or the shorter its dwell time, the lower its attention level. Furthermore, other feasible methods can also be used to obtain the attention level of each device location.

[0032] To obtain the importance of each device location in the target scenario: For each device location, the importance can be obtained based on its location attributes, such as the type, installation location, and quantity of the device location. Alternatively, the importance can be obtained based on other relevant data of the device location, such as data related to the device location in user behavior data.

[0033] Obtain the regional importance of each device location: For each region, the regional importance of the region can be obtained based on the region's regional attributes. For example, the regional importance of the region can be determined based on at least one of the following: the size of the region, its purpose, relevant data of the installed device locations (number of device locations and / or type of device locations), its location in the target scene, and the regional attributes of the surrounding areas.

[0034] Step 102: Determine the display strategy for each device location in the target scene based on at least one of the following: attention level, importance level, and regional importance level.

[0035] Based on the information obtained about the attention level, importance level, and regional importance of each device location, at least one of these factors can be analyzed to determine the display strategy for each device location.

[0036] For example, the region that a user is currently or will be interested in regarding a target scene can be identified based on the attention level of each device, the importance of each device, and the importance of each region. Different display strategies are adopted for device devices located in the regions of interest and for device devices located outside the regions of interest. For example, a high-precision rendering display strategy is used for device devices in the regions of interest, while a multi-device aggregate rendering or no rendering display strategy is used for device devices outside the regions of interest.

[0037] For example, for each device location, the loading precision and regional precision corresponding to each device location can be determined based on its location attention, location importance, and the regional importance of its area. Then, based on the loading precision and regional precision of the device location, the display strategy for that device location can be determined. Here, loading precision represents the rendering precision of the loaded device location; regional precision represents the rendering precision of the area where the device location is located.

[0038] Step 103: Based on the display strategy, display each of the device locations in the target scene.

[0039] Once the display strategy for each device location is determined, the device locations can be displayed in the target scene according to the display strategy.

[0040] The device location display method provided by this invention obtains the user's attention level, the importance of each device location, and the regional importance of the area to which each device location belongs in a target scene; determines the display strategy for each device location in the target scene based on at least one of the attention level, importance, and regional importance; and displays each device location in the target scene based on the display strategy. This invention dynamically adjusts the device location display strategy by acquiring the attention level related to the user's real-time needs and behavioral patterns, as well as the importance and regional importance of the corresponding device locations. This intelligently optimizes the displayed content based on the user's real-time needs and behavioral patterns, as well as the importance and regional importance of the device locations, avoiding traditional static aggregation display methods, thereby improving user experience and system performance. By jointly analyzing the attention level, importance, and regional importance of the device locations, and optimizing the display strategy accordingly, it can more accurately grasp user needs and avoid display deviations based on a single behavior.

[0041] Optionally, determining the display strategy for each device location in the target scene based on the attention level, importance, and regional importance of each location includes: For each of the device locations, the loading accuracy corresponding to the device location is calculated based on at least one of the device location attention, the device location importance, and the region importance of the region to which the device location belongs. Based on the attention level of the device location, determine the regional accuracy corresponding to the device location; Based on the loading precision and the area precision corresponding to the device location, the display strategy for the device location is determined.

[0042] Specifically, loading precision characterizes the rendering precision of the loading device point. Region precision characterizes the rendering precision of the region where the device point is located.

[0043] In practical applications, for each device location, user behavior data can be used to calculate the location's attention score according to a predefined location attention score determination strategy. Then, at least one of the device location's attention score, location importance, and regional importance is processed according to a preset loading accuracy determination strategy to obtain the loading accuracy corresponding to the device location. Finally, the location attention score of the device location is analyzed to determine the regional accuracy of that device location.

[0044] For example, the importance of the device location can be matched with various loading accuracies in a pre-defined loading accuracies library, and the successfully matched loading accuracies can be determined as the loading accuracies of the device location. Alternatively, the importance of the device location can be input into a pre-trained loading accuracies determination model for processing to obtain the loading accuracies of the device location.

[0045] Furthermore, based on the loading accuracy and area accuracy of the device location, a display strategy for that device location is determined. For example, the loading accuracy and area accuracy of the device location are matched with various display strategies in a preset display strategy library, and the successfully matched display strategy is adopted as the display strategy for that device location.

[0046] In this embodiment of the invention, the loading accuracy can be dynamically adjusted and the display strategy of the points can be optimized based on user behavior data, the importance of device locations, and scene requirements (regional importance), effectively balancing performance and display quality.

[0047] Optionally, obtaining the user's attention level to each device location in the target scenario includes: For each of the device locations, the user's attention level to the device location is determined based on the user's behavioral data in relation to the target scenario. The behavioral data includes visual data and / or operational data.

[0048] Specifically, user behavior data for a target scenario can be captured in real time: visual data of the user is acquired through eye-tracking devices or sensors, and user operation data is acquired through input devices (such as at least one of a mouse, keyboard, and touchscreen).

[0049] Among them, visual data can be the user's eye movement data; operation data can be the user's click, scroll, drag, zoom in, zoom out, etc., data on the target scene, and operation data can be multiple operation sequences.

[0050] In practical applications, for each device location, user behavior data can be used to calculate the location's attention level based on a predefined strategy. This improves the speed of acquiring location attention data.

[0051] Optionally, determining the user's attention level to the device location based on the user's behavioral data regarding the target scenario includes: Based on the user's gaze duration at the device location in the visual data, determine the gaze factor corresponding to the device location; Based on each operation in the operation data, determine the operation factor corresponding to the device location; Based on the gaze factor and / or the operation factor, the user's attention level to the device location is determined.

[0052] Specifically, gaze duration is the length of time a user focuses on a specific device location. The gaze factor is used to measure the length of time a user focuses on a specific device location. The operation data is set as follows: Each operation This includes the operation type (click, scroll, drag, etc.) and its attribute parameters (position, time, etc.). The number of operations contained in the operation data. From 1 to Any integer between 1 and 2. The operation factor is used to measure the user's operation on the device location.

[0053] In practical applications, eye-tracking data or video data of users can be obtained from visual data, and then the user's interaction with the device can be obtained through eye-tracking or video analysis. (No. gaze duration at each device location (The unit can be seconds). Furthermore, the maximum fixation duration is introduced. fixation duration Normalization is performed to obtain the equipment locations. Corresponding fixation factor Specifically, the normalization process is as follows: in, The range is between 0 and 1. If the user stares at the device location for an extended period of time... ,but The larger.

[0054] In addition, it is also necessary to process the operational data. Each operation The equipment locations are obtained by processing them according to the following formula. Corresponding operating factors .

[0055] .

[0056] in, It is an operation Equipment locations The influence function represents the correlation between the operation and the point, such as the distance between the click and the device point, the direction of the drag operation, etc.

[0057] Finally, equipment locations Corresponding fixation factor and / or operating factors The equipment locations are determined by processing according to the set location attention strategy. Points of attention .

[0058] For example, the device locations Corresponding fixation factor and operating factors Weighted processing is performed to obtain the equipment locations. Points of attention ,Right now ,in, fixation factor The corresponding weights Operational factors The corresponding weights.

[0059] In this way, by jointly analyzing the user's visual and operational intentions, and associating the user's behavioral data with the device locations in the scene, the user's attention to the device locations can be determined. This joint analysis method can more accurately grasp the user's needs and avoid the display bias caused by relying on a single behavior.

[0060] Optionally, calculating the loading accuracy corresponding to the device location based on at least one of the device location's attention level, the device location's importance, and the region importance of the region to which the device location belongs includes: Based on the user's behavioral data for the target scenario, obtain the adjustment factor corresponding to the device location, and obtain the priority of the device location; The first product is obtained by multiplying the attention level, importance level, and regional importance level of the device location; and the second product is obtained by multiplying the adjustment factor and priority level corresponding to the device location. Calculate the ratio of the first product to the second product to obtain the loading accuracy corresponding to the device location.

[0061] Specifically, the adjustment factor is used to measure the user's preference for device locations. The level of attention given to these locations.

[0062] In practical applications, to improve the accuracy of loading precision, it is necessary to consider the point attention, point importance, regional importance of the area to which the device belongs, adjustment factors, and priorities of the device locations. Therefore, after obtaining the point attention of the device locations... Importance of location Regional importance of the region Based on the regional importance of the j-th region, it is also necessary to obtain the adjustment factor and priority corresponding to the equipment location.

[0063] Since the adjustment factor is used to measure the attention given to a location, and this attention is based on behavioral data, similarly, the adjustment factor corresponding to a device location can be obtained based on behavioral data. And obtain the priority of the device location. The priority can be pre-set for the device location.

[0064] Furthermore, calculate the location of the equipment. Corresponding loading precision .

[0065] in, This means at the equipment location. Importance of existing equipment Based on this, weighting is applied according to the regional importance of the area where the equipment location is situated. If If it is relatively large, then even Even at lower speeds, the device will still achieve high loading accuracy, ensuring that more detailed information can be displayed in critical areas.

[0066] In this way, the loading accuracy of the device locations will be dynamically adjusted according to the user's attention, the importance of the device, and the importance of the area. The higher the attention and the greater the importance, the higher the loading accuracy; conversely, device locations with low attention and low importance will use low loading accuracy.

[0067] Optionally, the behavioral data includes visual data and operational data; obtaining the adjustment factor corresponding to the device location based on the user's behavioral data for the target scene includes: Based on the visual data, determine the user's visual focus; Calculate the distance between the device location and the visual focus based on the coordinates of the visual focus and the coordinates of the device location. Based on the operational data, predict the future attention level of each of the aforementioned equipment locations; The ratio of the future attention to the device location to the distance is determined as the adjustment factor corresponding to the device location.

[0068] Specifically, visual focus refers to the location where the user concentrates their attention.

[0069] In practical applications, visual data can be analyzed to determine the location or area of ​​the device the user is currently focusing on: visual data can include the user's eye movement data, which can be analyzed to determine the user's visual focus. The coordinates are ,in and These are the horizontal and vertical coordinates of the user's visual focus, respectively. Further, based on the coordinates of the visual focus and the coordinates of each device location in the target scene, the focus of attention for each device location is calculated according to a predefined focus attention strategy.

[0070] Specifically, equipment locations Coordinates are ,in, and These are the equipment locations. The x and y coordinates are given. The location of the equipment can then be calculated using the following formula. With focus of view distance .

[0071] .

[0072] Furthermore, operational intent analysis can be performed on the operational data to determine the device locations or areas that users are currently or will be interested in in the future: the user's operational data is processed according to a set future attention prediction strategy to obtain the device locations. Future attention .

[0073] Finally, calculate the equipment locations. corresponding distance and future attention The ratio of the equipment locations is used to obtain the equipment location. Corresponding regulation factor .

[0074] It should be noted that, in order to prevent equipment location... With focus of view distance If it is 0, the set value can be used. Distance Fine-tuning was performed to obtain the adjusted distance. Then, the future attention The ratio of the distance to the fine-tuned distance is determined as the equipment location. Future attention As shown in the following formula. Where, the set value... It is a constant greater than zero, used to prevent division by zero.

[0075] .

[0076] It can be seen that the regulatory factor It is an indicator that integrates factors such as user operation intent and viewing distance, combining geometric and behavioral intent factors.

[0077] In this embodiment of the invention, the adjustment factor is determined by the distance between the device location and the visual focus, as well as the future attention of the device location. The closer the device location is to the visual focus, the higher the adjustment factor of the device location. Then, the loading accuracy is set based on the adjustment factor, thereby determining the display strategy for displaying the device location, which can better meet the needs of the user.

[0078] Optionally, predicting the future attention level of each of the device locations based on the operational data includes: Based on each operation in the operation data, determine the sub-future attention of the device location under each operation; The future attention of each sub-future attention corresponding to the device location is obtained by weighted summation.

[0079] Set user operation data as Each operation This includes the operation type (click, scroll, drag, etc.) and its attribute parameters (position, time, etc.). The number of operations contained in the operation data. From 1 to Any integer between 0 and 1.

[0080] Click actions: When a user clicks on a location, that location can be considered the area of ​​current interest. Scrolling and dragging actions: These actions can reveal areas the user is likely to focus on next. Information such as the direction and speed of scrolling and dragging actions can help predict the next area the user will focus on.

[0081] Based on user operation data, the device locations are calculated using the following weighted formula. Future attention .

[0082] .

[0083] in, It is the first One operation The weights are related to the type and frequency of the operation. It is an operation Equipment locations The influence function represents the correlation between the operation and the point, such as the distance between the click and the point, the direction of the drag operation, etc.

[0084] In this embodiment of the application, the future attention of the device location is determined by using operation data, including operation type and attribute parameters corresponding to at least one operation, thus ensuring the reliability and accuracy of the future attention.

[0085] Optionally, determining the regional precision corresponding to the device location based on the location attention level includes: If the attention level of the device location is greater than or equal to the attention level threshold, the area accuracy corresponding to the device location is determined to be high area accuracy. High area accuracy indicates that the device location is fully displayed in the target scene. If the attention level of a device location is less than the attention threshold, the area precision corresponding to the device location is determined to be low area precision. Low area precision indicates whether the device location is displayed in the target scene or not.

[0086] In practical applications, the rendering strategy can automatically switch between different precision levels based on the user's current area of ​​focus. For high-precision areas (such as the area the user is focused on), local high-precision rendering is used, while for low-precision areas (such as the surrounding area), global low-precision rendering is employed. The precision of high-precision areas is designated as high-area precision, and the precision of low-precision areas as low-area precision. Specifically, the determination of high area accuracy: if the equipment location Within the user's field of vision, and the device location Points of attention Then the equipment location Located in the high-precision area That is, equipment location The corresponding area precision is .

[0087] Determination of low area accuracy: If the equipment location If the device location is not within the user's high-precision focus area, then... If the location is in a low-precision area (not a high-precision area), the detailed information of the points in that area will be simplified or completely removed, i.e., the device points. The corresponding area precision is .

[0088] ; .

[0089] in, It is the set attention threshold, which represents the minimum attention required for the area affected by the viewpoint focus or operational intent.

[0090] For device points located in high-precision areas, the display method is determined to be detailed display, i.e., rendering is required; for device points located in low-precision areas, the display method is determined to be simplified display. As shown in the following formula: .

[0091] in, Indicates equipment location Whether to display or render in detail. For low-precision areas, This indicates that the device locations in this area will be simplified or not displayed at all.

[0092] For example, for device points with low attention (located in low-precision areas), a point aggregation and culling strategy can be used to reduce unnecessary rendering burden. For multiple low-attention device points, they can be merged into a single symbol or icon for display, without showing their detailed data.

[0093] Optionally, determining the display strategy for the device location based on the loading precision and the area precision corresponding to the device location includes: Determine whether the device location meets the loading conditions. The loading conditions are that the loading precision corresponding to the device location is high loading precision, and the area precision corresponding to the device location is high area precision. The high loading precision is greater than the precision threshold. The high area precision indicates that the device location is fully displayed in the target scene. If so, the display strategy for the device point is determined to be to display the device point according to the loading precision corresponding to the device point; If not, then the display strategy for the device locations is determined to be either to display them together or not to display them at all.

[0094] In practical applications, to improve display and rendering efficiency, display points can be dynamically loaded and unloaded based on the user's perspective. The criteria for loading and unloading are the device display points. Points of attention and loading accuracy .

[0095] Specifically, see the following formula, if the equipment location It enters the high-precision region, and its loading precision is greater than the precision threshold. The system will load the detailed information for that location, thus determining the device location. Display strategy According to equipment location Corresponding loading precision Detailed display of equipment locations Otherwise, unload the equipment location. That is, determining the location of the equipment. Display strategy To combine or not display device locations .

[0096] .

[0097] In this way, the display strategy can be dynamically adjusted, effectively balancing performance and display quality, ensuring rendering accuracy in high-precision areas, while reducing rendering load in low-priority areas.

[0098] Optionally, obtaining the importance of each device location in the target scene includes: For each device location in the target scenario, obtain the historical response value and importance level of the device location. The historical response value represents the usage frequency and / or alarm count of the device location. The importance of a device location is obtained by weighting and summing its historical response value, importance level, and attention level.

[0099] Specifically, historical response values ​​characterize the event frequency (number of alarms) and monitoring time (usage frequency) of the equipment location.

[0100] In practical applications, device locations can be obtained. Historical response values and importance level Among them, historical response values Equipment location Related to historical data (such as event frequency, monitoring time, etc.); Importance level Equipment location The importance of this is related to factors such as the type of camera, its installation location, etc.

[0101] Furthermore, calculate the equipment locations using the following formula. Point importance .

[0102] .

[0103] in, It is based on the analysis results of user behavior data, preferably, For users to locate equipment points Current attention . , and These are historical response values, importance levels, and current attention levels. The weighting coefficients are used to adjust the influence of each factor.

[0104] Having determined the importance of each device location, a display strategy can be further determined based on this importance. Thus, by considering historical response values, importance levels, and location attention, the importance of each location is determined from multiple dimensions, and the display strategy is then based on this importance, ensuring the accuracy and comprehensiveness of the display strategy.

[0105] It should be noted that historical response values This can be quantified based on factors such as the usage frequency of the device location and the number of alarms triggered. For example, if a device location has triggered multiple alarms in the past 24 hours, the historical response value... The weight will be higher.

[0106] Optionally, the regional importance of the area to which each of the device locations belongs is obtained, including: For each region in the target scenario, the region importance is determined based on the region attribute information, which includes the number of device points contained in the region and the device type of each device point contained in the region.

[0107] In practical applications, 3D scenes need to be functionally partitioned. These areas can be divided into: security monitoring areas (such as the coverage area of ​​surveillance cameras, typically including critical facilities and important equipment) and non-monitoring areas (areas in the target scene that do not require real-time monitoring, such as office areas and rest areas); or, they can be divided into high-priority areas (such as monitoring rooms and control rooms, which typically have higher point-of-care attention) and low-priority areas (some peripheral areas or areas that do not require high-frequency inspection). Furthermore, the division between security monitoring areas and non-monitoring areas can be based on the actual layout of the scene or predefined areas, such as area markers (e.g., the extent in a map coordinate system).

[0108] Each region It will be assigned a function label This refers to the importance of a region. The goal of setting region importance is to provide different display strategies for different functional areas based on the actual needs of the scenario.

[0109] Setting the importance of a region can be expressed as: in: It is a region The functional label, namely the importance of the region. It is a region The term "regional attribute information" refers to the number of equipment points within a region and the equipment type of each equipment point.

[0110] This can improve the speed and accuracy of obtaining regional importance.

[0111] Optionally, before obtaining the importance of each device location in the target scene, the method further includes: The target scene is divided into regions based on the following formula: in, A collection of function labels for all regions; For the first One region; for The Middle equipment locations ; Equipment location The importance of the points, Equipment location The level of attention paid to the location; It is a region Internal equipment locations Loading load.

[0112] In practical applications, ensuring the rationality and dynamic response of scene partitioning is an optimization problem for the overall partitioning strategy. The optimization goal is to optimize scene partitioning by minimizing display load and ensuring priority display of important devices, as shown in the minimization formula above. Therefore, by optimizing the above minimization formula, it can be ensured that the scene partitioning and loading strategy can prioritize loading areas of user interest and important device locations while maintaining performance.

[0113] Optionally, predicting the future attention level of each of the device locations based on the operational data includes: The operation data is input into a pre-trained operation intention prediction model to obtain the future attention of each device location; the operation intention prediction model is periodically optimized based on historical behavior data, which includes the user's historical operation records and device location responses.

[0114] In practical applications, to improve the efficiency of acquiring future attention, a pre-trained operation intent prediction model can be used to process operation data and then output the future attention of each device location. This operation intent prediction model can be trained using historical behavior data.

[0115] To ensure the accuracy of future attention predictions, historical behavioral data can be used periodically to optimize the operational intent prediction model, thereby improving prediction accuracy. By optimizing the operational intent prediction model, the areas of user attention in the future can be predicted more accurately.

[0116] Specifically, assuming the user's historical behavior data is each It includes user operation data and device location responses, among which, It is a positive integer. From 1 to Any positive number between 0 and 1.

[0117] Training process: The operation data from historical behavior data is used as input to machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.), and the responses of device locations are used as labels for training. Finally, a trained operation intention prediction model is obtained. It is used to predict future areas of interest, i.e., future levels of interest.

[0118] Optimization Process: The operation intent prediction model is optimized through a feedback mechanism based on historical behavior data, continuously updating the model to adapt to changing user behavior. Assume the user's behavior data is... Given new data at time t, the optimization process can be described as follows: in, It is an incremental model adjustment based on new behavioral data.

[0119] The device position display method provided by the present invention will be further described below with reference to specific embodiments.

[0120] The dynamic display resource point system comprises a user behavior analysis unit, a scene segmentation unit, and an adaptive rendering engine unit. The dynamic display resource point system executes the device point display method through these three parts: the user behavior analysis unit, the scene segmentation unit, and the adaptive rendering engine unit.

[0121] (a) User behavior analysis unit.

[0122] See Figure 2 , Figure 2 This is a flowchart of the internal processing of the user behavior analysis unit provided by the present invention: In the user behavior analysis unit, by capturing user behavior data in real time, the user's perspective focus, operation intention, and interaction pattern are analyzed to accurately identify the user's current area of ​​interest and predict their possible future areas of interest. This unit mainly includes: perspective focus detection, operation intention analysis, and behavior model optimization.

[0123] First, user behavior data is collected, including visual and operational data. Then, based on the visual and operational data, perspective focus detection and operational intent analysis are performed.

[0124] Visual focus detection determines the area of ​​a user's current attention by monitoring their gaze (visual data). It uses eye-tracking devices or sensors to acquire data on the user's eye movements and then uses this data to determine the location where the user's gaze is focused.

[0125] Assuming the system obtains the user's location on the device through eye tracking or video analysis. gaze duration (The unit can be seconds). To normalize, a maximum fixation duration is introduced. : in, This represents the normalized gaze factor, with a value ranging from 0 to 1. This is considered if a user gazes at the device's location for an extended period. ,but The larger.

[0126] Assume the user's operation data is Each operation This includes the operation type (click, scroll, drag, etc.) and its attribute parameters (position, time, etc.).

[0127] Click actions: When a user clicks on a location, that location can be considered the area of ​​current interest. Scrolling and dragging actions: These actions can reveal areas the user is likely to focus on next. Information such as the direction and speed of scrolling and dragging actions can help predict the next area the user will focus on.

[0128] For operation data Through a trained function To calculate behavior weights, this function can extract features from a user's historical and real-time actions based on a machine learning model.

[0129] definition Point-of-use attention (POI) is used to quantify the real-time level of user attention to a device location. It reflects both visual fixation (e.g., eye movements or dwell time) and user interaction behaviors (e.g., clicking, dragging, etc.). Therefore, It is considered a comprehensive indicator that directly reflects a user's current interests. The calculation formula is: During adaptive rendering Used to determine equipment location The rendering accuracy and display details.

[0130] Define the regulation factor Used to measure equipment locations attention, The calculation formula is: in, For the user's visual focus and device location The Euclidean distance between them.

[0131] Thus, when a user's actions indicate a high level of interest (significant interest) in a particular device location, This will increase accordingly; if the device location is close to the user's visual focus (smaller), it will also cause... Increase; conversely, if the device location is far away or user interaction is relatively infrequent, then A lower value indicates that the device location should not receive high rendering accuracy in the current presentation.

[0132] Assuming the focus of the viewpoint The coordinates are Equipment locations Coordinates are , It can be calculated using the following formula: The behavior model optimization unit further refines the operation intent analysis model by learning from users' historical behavior data, thereby improving prediction accuracy. By training on users' behavioral patterns, the system can more accurately predict users' future areas of interest.

[0133] Historical behavioral data: Assuming the user's historical behavioral data is as follows each It includes user operation data and device location responses.

[0134] Training process: The operation data from historical behavior data is used as input to machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.), and the responses of device locations are used as labels for training. Finally, a trained operation intention prediction model is obtained. It is used to predict future areas of interest, i.e., future levels of interest.

[0135] Model Optimization: The operation intent prediction model is optimized through a feedback mechanism based on historical behavior data, continuously updating the model to adapt to changing user behavior. Assume the user's behavior data is... Given new data at time t, the optimization process can be described as follows: .

[0136] (ii) Three-dimensional scene segmentation unit.

[0137] See Figure 3 , Figure 3 Here is the internal processing flowchart of the three-dimensional scene segmentation unit provided by the present invention: In the scene semantic segmentation unit, the three-dimensional scene can be intelligently divided according to different functional areas, the importance of devices and the real-time behavior of users, and the display strategy of device points in the scene can be dynamically adjusted.

[0138] First, functional areas are divided. The 3D scene is functionally partitioned, and these areas may include security monitoring areas and non-monitoring areas, or high-priority areas and low-priority areas. The division of security monitoring areas and non-monitoring areas can be based on the actual layout of the scene or predefined areas, such as area markers (e.g., ranges in a map coordinate system).

[0139] Each region It will be assigned a function label This refers to regional importance. Regional importance can be set as follows: .

[0140] Then, importance is assigned, that is, based on the historical data of the equipment, the importance level of the equipment location, and the results of user behavior analysis, each equipment location is assigned a location importance level. .

[0141] Point Importance The calculation formula is .in, It can be .

[0142] It should be noted that historical response values This can be quantified based on factors such as the usage frequency of the device location and the number of alarms triggered. For example, if a device location has triggered multiple alarms in the past 24 hours, the historical response value... The weight will be higher.

[0143] In addition, it needs to be able to dynamically update partitions.

[0144] The partitioning of the target scenario needs to be dynamically updated based on real-time user behavior and environmental changes. Changes in the user's perspective focus, operational intent, and device status in the environment will all affect partition updates. For example, adding, deleting, or malfunctioning device points all require updating the partitions.

[0145] The dynamic update algorithm can be described as follows: .

[0146] in: It is time t for the region Functional tags. It is based on the change in function labels updated in real time, indicating the dynamic adjustment of the functional area division.

[0147] Update volume The following factors can be used to determine this: real-time changes in user behavior, such as when a user switches from a security monitoring area to a non-monitored area, causing a change in the function labels of the relevant areas; and environmental changes, such as when a device is disabled due to a malfunction, causing a change in the function labels of its area.

[0148] Furthermore, comprehensive optimization can be performed. To ensure the rationality and dynamic response of scene partitioning, the overall partitioning strategy is an optimization problem. The optimization goal is to optimize scene partitioning by minimizing display load and ensuring priority display for important devices. The optimization goal can be expressed as: By optimizing the above objectives, we can ensure that the scene partitioning and loading strategies can prioritize loading areas of interest to users and important device locations while maintaining performance.

[0149] (III) Adaptive Rendering Engine Unit Building upon the detailed user behavior analysis module and 3D scene segmentation module, the next module to be added is the adaptive loading model engine module. The core objective of the adaptive loading model engine is to dynamically adjust the display strategy of device points based on the analysis of user behavior and scene partitioning, thereby improving loading performance and ensuring accurate display of important areas.

[0150] The goal of an adaptive rendering engine is to reduce the rendering burden while ensuring high-quality display of critical areas by optimizing device-level display strategies. Its primary function is to adjust loading precision, dynamically adjusting loading precision and rendering details based on attention and importance.

[0151] First, perform loading accuracy calculations.

[0152] To effectively manage the rendering load of device locations, the adaptive rendering engine adjusts the rendering load based on the device locations. Points of attention Regulatory factors Importance of location Regional importance of the area and priority Calculate equipment locations Loading accuracy Specifically, loading accuracy Calculate using the following formula: The loading accuracy of the device locations will be dynamically adjusted based on the user's attention to and the importance of the locations. The higher the attention and importance of the location, the higher the loading accuracy; conversely, the lower the attention and importance of the device locations, the lower the loading accuracy will be.

[0153] Then, determine the local high-precision rendering and the global low-precision rendering.

[0154] The system can automatically switch between different rendering precision strategies based on the user's current area of ​​focus. For high-precision areas (such as the area where the user's gaze is focused), local high-precision rendering will be used, while for low-precision areas (such as the surrounding area), global low-precision rendering will be used.

[0155] Determination of high-precision areas: If the equipment location From the user's perspective, and Then the equipment location Located in the high-precision region.

[0156] Determination of low-precision areas: If the equipment location If a point is not within the user's high-precision focus area, it is considered a low-precision area, and the system will either simplify the display or completely remove the detailed information of the points in that area.

[0157] For device points located in high-precision areas, the display method is determined to be detailed display, i.e., rendering is required; for device points located in low-precision areas, the display method is determined to be simplified display. As shown in the following formula: For example, for device points with low attention (located in low-precision areas), a point aggregation and culling strategy can be used to reduce unnecessary rendering burden. For multiple low-attention device points, they can be merged into a single symbol or icon for display, without showing their detailed data.

[0158] To improve rendering efficiency, the system dynamically loads and unloads points based on the user's perspective. The decision to load or unload points is based on the device's point-of-use focus. and loading accuracy .

[0159] See the following formula, if the equipment location It enters the high-precision region, and its loading precision is greater than the precision threshold. The system will load the detailed information for that location, thus determining the device location. Display strategy According to equipment location Corresponding loading precision Detailed display of equipment locations Otherwise, unload the equipment location. That is, determining the location of the equipment. Display strategy To combine or not display device locations .

[0160] .

[0161] This invention, based on real-time capture of user behavior data (viewpoint focus, operational intent, etc.), dynamically adjusts the display strategy for device locations. This intelligently optimizes the displayed content according to the user's real-time needs and behavioral patterns, avoiding traditional static aggregation display methods and thus improving user experience and system performance. By intelligently dividing the scene, the display strategy is dynamically updated based on the scene's functional areas and the importance of the devices. This considers not only historical data and hardware importance of device locations but also user behavior analysis for dynamic adjustment, ensuring that the most relevant device locations are always displayed in different scenarios. Based on user behavior, the importance of device locations, and scene requirements... The system can dynamically adjust rendering precision and optimize the display hierarchy of points, effectively balancing performance and display quality. It ensures rendering precision in high-priority areas while reducing rendering load in low-priority areas. By analyzing historical user behavior data, the model can continuously optimize and predict future user focus areas, anticipate user interaction intentions, and make display adjustments in advance to improve user experience. By jointly analyzing user perspective focus and operation intentions (such as clicking, scrolling, dragging, etc.), it associates user behavior patterns with device points in the scene and optimizes display strategies accordingly. This allows for a more accurate understanding of user needs and avoids display deviations caused by relying on a single behavior.

[0162] In summary, the embodiments of the present invention can intelligently optimize the displayed content according to the user's real-time needs and behavioral patterns, avoiding the traditional static aggregation display method, thereby improving user experience and system performance; it can dynamically update the display strategy according to the functional areas of the scene and the importance of the device; it effectively balances performance and display quality, ensuring the rendering accuracy of high-priority areas while reducing the rendering load in low-priority areas; and it can predict the user's interaction intentions and make display adjustments in advance to improve user experience.

[0163] The device location display device provided by the present invention is described below. The device location display device described below and the device location display method described above can be referred to in correspondence.

[0164] Figure 4 This is a schematic diagram of the device position display device provided by the present invention, as shown below. Figure 4 As shown, the device includes: The acquisition module 401 is configured to acquire the user's attention to each device location in the target scene, the importance of each device location, and the regional importance of the area to which each device location belongs. The determining module 402 is configured to determine the display strategy of each device point in the target scene based on at least one of the attention of each point, the importance of each point, and the importance of each region. Display module 403 is configured to display each of the device locations in the target scene based on the display strategy.

[0165] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a device location display method, which includes: acquiring the user's attention to each device location in a target scene, the importance of each device location, and the regional importance of the area to which each device location belongs; determining a display strategy for each device location in the target scene based on at least one of the attention to each device location, the importance of each device location, and the regional importance of each area; and displaying each device location in the target scene based on the display strategy.

[0166] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the device location display method provided by the above methods. The method includes: acquiring the user's attention to each device location in a target scene, the importance of each device location, and the regional importance of the area to which each device location belongs; determining a display strategy for each device location in the target scene based on at least one of the attention to each device location, the importance of each device location, and the regional importance of each area; and displaying each device location in the target scene based on the display strategy.

[0168] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the device location display method provided by the above methods. The method includes: acquiring the user's attention to each device location in a target scene, the importance of each device location, and the regional importance of the area to which each device location belongs; determining a display strategy for each device location in the target scene based on at least one of the attention to each device location, the importance of each device location, and the regional importance of each area; and displaying each device location in the target scene based on the display strategy.

[0169] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0170] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for displaying device location, characterized in that, include: The system obtains the user's attention to each device location in the target scenario, the importance of each device location, and the regional importance of the area to which each device location belongs. Based on at least one of the attention level of each location, the importance of each location, and the importance of each region, determine the display strategy for each device location in the target scene; Based on the display strategy, each of the device locations is displayed in the target scene.

2. The device position display method according to claim 1, characterized in that, The process of obtaining user attention to various device locations within a target scenario includes: For each of the device locations, the user's attention level to the device location is determined based on the user's behavioral data in relation to the target scenario. The behavioral data includes visual data and / or operational data.

3. The device location display method according to claim 2, characterized in that, The step of determining the user's attention level to the device location based on the user's behavioral data regarding the target scenario includes: Based on the user's gaze duration at the device location in the visual data, determine the gaze factor corresponding to the device location; Based on each operation in the operation data, determine the operation factor corresponding to the device location; The user's attention level to the device location is determined based on the gaze factor and / or the operation factor.

4. The device location display method according to claim 1, characterized in that, Obtain the importance of each of the aforementioned device locations, including: For each device location in the target scenario, obtain the historical response value and importance level of the device location. The historical response value represents the usage frequency and / or alarm count of the device location. The importance of a device location is determined based on its historical response value, importance level, and attention level.

5. The device location display method according to claim 1, characterized in that, Obtain the regional importance of the area to which each of the aforementioned device locations belongs, including: For each region in the target scenario, the region importance is determined based on the region attribute information, which includes the number of device points contained in the region and the device type of each device point contained in the region.

6. The device location display method according to claim 1, characterized in that, The step of determining the display strategy for each device location in the target scene based on at least one of the following: attention level, importance level, and area importance, includes: For each of the device locations, the loading accuracy corresponding to the device location is calculated based on at least one of the device location attention, the device location importance, and the region importance of the region to which the device location belongs. Based on the attention level of the device location, determine the regional accuracy corresponding to the device location; Based on the loading precision and the area precision corresponding to the device location, the display strategy for the device location is determined.

7. The device location display method according to claim 6, characterized in that, The step of calculating the loading accuracy corresponding to the device location based on at least one of the device location attention level, the device location importance level, and the region importance level of the region to which the device location belongs includes: Based on the user's behavioral data for the target scenario, obtain the adjustment factor corresponding to the device location, and obtain the priority of the device location; The first product is obtained by multiplying the attention level, importance level, and regional importance level of the device location; and the second product is obtained by multiplying the adjustment factor and priority level corresponding to the device location. Calculate the ratio of the first product to the second product to obtain the loading accuracy corresponding to the device location.

8. The device location display method according to claim 6, characterized in that, The step of determining the area precision corresponding to the device location based on the location attention level includes: If the attention level of the device location is greater than or equal to the attention level threshold, the area accuracy corresponding to the device location is determined to be high area accuracy. High area accuracy indicates that the device location is fully displayed in the target scene. If the attention level of a device location is less than the attention threshold, the area precision corresponding to the device location is determined to be low area precision. Low area precision indicates whether the device location is displayed in the target scene or not.

9. The device location display method according to claim 6, characterized in that, The step of determining the display strategy for the device location based on the loading precision and the area precision corresponding to the device location includes: Determine whether the device location meets the loading conditions. The loading conditions are that the loading precision corresponding to the device location is high loading precision, and the area precision corresponding to the device location is high area precision. The high loading precision is greater than the precision threshold. The high area precision indicates that the device location is fully displayed in the target scene. If so, the display strategy for the device point is determined to be to display the device point according to the loading precision corresponding to the device point; If not, then the display strategy for the device locations is determined to be either to display them together or not to display them at all.

10. A device for displaying equipment position, characterized in that, include: The acquisition module is configured to acquire the user's attention to each device location in the target scene, the importance of each device location, and the regional importance of the area to which each device location belongs. The determination module is configured to determine the display strategy for each device point in the target scene based on at least one of the attention of each point, the importance of each point, and the importance of each region. The display module is configured to display each of the device locations in the target scene based on the display strategy.

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