Machine learning techniques for traversal path optimization
Various embodiments of the present invention disclose techniques for traversal path optimization given a traversal network comprising a group of nodes comprising a plurality of navigation orchestration nodes and using a traversal path optimization machine learning model. In some embodiments, a path feature set is determined for each candidate traversal path of a plurality of candidate traversal paths. A traversal path optimization machine learning model is configured to generate path scores for each candidate traversal path based at least in part on the path feature set.
1 . A computer-implemented method comprising:
receiving, by one or more processors and from a user device, a traversal path optimization request that identifies an origin node and a destination node within a traversal network, wherein the traversal network comprises a navigation orchestration node that corresponds to an image device positioned within a proximity to the origin node or the destination node;
receiving, by the one or more processors, a candidate traversal path from the traversal network based at least in part on the origin node and the destination node;
receiving, by the one or more processors and from the image device, node feature data associated with an operational environment of the image device, wherein the node feature data identifies a camera field of view (FOV) of the image device;
processing, by the one or more processors, the node feature data with a node-wise coverage region determination machine learning model to receive a coverage region for the image device;
in response to determining that the coverage region overlaps with the candidate traversal path, generating, by the one or more processors and based at least in part on inputting a path feature set for the candidate traversal path to a traversal path optimization machine learning model, a path score for the candidate traversal path, wherein the path feature set is based at least in part on the node feature data; and
providing, by the one or more processors and to the user device, an optimal path traversal alert notification that displays the candidate traversal path based at least in part on the path score, wherein the optimal path traversal alert notification displays one or more biometric measures associated with a user and the candidate traversal path and at least one of (i) one or more nodes of the candidate traversal path and a first number of one or more secondary user devices within a proximity to the user device, or (ii) a second number of one or more image devices within a proximity to the user device.
2 . The computer-implemented method of claim 1 further comprising:
selecting the candidate traversal path from a plurality of candidate traversal paths in response to determining that the path score is a largest path score of a plurality of path scores respectively corresponding to the plurality of candidate traversal paths.
3 . The computer-implemented method of claim 1 further comprising:
determining a navigation orchestration node count associated with the candidate traversal path; and
generating the path score for the candidate traversal path based at least in part on the navigation orchestration node count.
4 . The computer-implemented method of claim 1 further comprising:
determining a computed positional intersectional degree with respect to a traversal network subset associated with the candidate traversal path; and
generating the path score for the candidate traversal path based at least in part on the computed positional intersectional degree.
5 . The computer-implemented method of claim 1 , wherein the path score for the candidate traversal path is generated based at least in part on an orchestration capability measure for the navigation orchestration node.
6 . The computer-implemented method of claim 1 further comprising:
performing one or more prediction-based actions based at least in part on a distance measure for the candidate traversal path.
7 . The computer-implemented method of claim 1 , wherein the traversal path optimization request further identifies one or more intermediary nodes.
8 . The computer-implemented method of claim 1 further comprising:
receiving an indication of an alternative traversal path request from the user device in response to providing an indication of the candidate traversal path to the user device;
selecting another candidate traversal path from a plurality of candidate traversal paths with a next largest path score; and
performing one or more prediction-based actions based at least in part on the selected traversal path.
9 . The computer-implemented method of claim 1 further comprising updating, using the node-wise coverage region determination machine learning model, the coverage region of the navigation orchestration node in response to receiving updated node feature data.
10 . The computer-implemented method of claim 1 further comprising:
initiating, at the user device, a check-in notification comprising one or more selectable buttons; and
in response to determining an absence of user input to the check-in notification within a predefined time period, providing an event notification to the one or more secondary user devices within the proximity to the user device.
11 . A system comprising:
one or more processors; and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a user device, a traversal path optimization request that identifies an origin node and a destination node within a traversal network, wherein the traversal network comprises a navigation orchestration node that corresponds to an image device positioned within a proximity to the origin node or the destination node;
receiving candidate traversal path from the traversal network based at least in part on the origin node and the destination node;
receiving, from the image device, node feature data associated with an operational environment of the image device, wherein the node feature data identifies a camera field of view (FOV) of the image device;
processing the node feature data with a node-wise coverage region determination machine learning model to receive a coverage region for the image device;
in response to determining that the coverage region overlaps with the candidate traversal path, generating, by the one or more processors and based at least in part on inputting a path feature set for the candidate traversal path to a traversal path optimization machine learning model, a path score for the candidate traversal path, wherein the path feature set is based at least in part on the node feature data; and
providing, to the user device, an optimal path traversal alert notification that displays the candidate traversal path based at least in part on the path score, wherein the optimal path traversal alert notification displays one or more biometric measures associated with a user and the candidate traversal path and at least one of (i) one or more nodes of the candidate traversal path and a first number of one or more secondary user devices within a proximity to the user device or (ii) a second number of one or more image devices within a proximity to the user device.
12 . The system of claim 11 , wherein the operations further comprise:
selecting the candidate traversal path from a plurality of candidate traversal paths in response to determining that the path score is a largest path score of a plurality of path scores respectively corresponding to the plurality of candidate traversal paths.
13 . The system of claim 11 , wherein the operations further comprise:
determining a navigation orchestration node count associated with the candidate traversal path; and
generating the path score for the candidate traversal path based at least in part on the navigation orchestration node count.
14 . The system of claim 11 , wherein the operations further comprise:
determining a computed positional intersectional degree with respect to a traversal network subset associated with the candidate traversal path; and
generating the path score for the candidate traversal path based at least in part on computed positional intersection degree.
15 . The system of claim 11 , wherein the path score for the candidate traversal path is generated based at least in part on an orchestration capability measure for the navigation orchestration node.
16 . The system of claim 11 , wherein the operations further comprise:
performing one or more prediction-based actions based at least in part on a distance measure for the candidate traversal path.
17 . The system of claim 11 , wherein the traversal path optimization request further identifies one or more intermediary nodes.
18 . The system of claim 11 , wherein the operations further comprise:
receiving an indication of an alternative traversal path request from the user device in response to providing an indication of the candidate traversal path to the user device;
selecting another candidate traversal path from a plurality of candidate traversal paths with a next largest path score; and
perform one or more prediction-based actions based at least in part on the selected traversal path.
19 . The system of claim 11 , wherein the operations further comprise:
update, using the node-wise coverage region determination machine learning model, the coverage region of the navigation orchestration node in response to receiving updated node feature data.
20 . One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a user device, a traversal path optimization request that identifies an origin node and a destination node within a traversal network, wherein the traversal network comprises a navigation orchestration node that corresponds to an image device positioned within a proximity to the origin node or the destination node;
receiving candidate traversal path from the traversal network based at least in part on the origin node and the destination node;
receiving, from the image device, node feature data associated with an operational environment of the image device, wherein the node feature data identifies a camera field of view (FOV) of the image device;
processing the node feature data with a node-wise coverage region determination machine learning model to receive a coverage region for the image device;
in response to determining that the coverage region overlaps with the candidate traversal path, generating, by the one or more processors and based at least in part on inputting a path feature set for the candidate traversal path to a traversal path optimization machine learning model, a path score for the candidate traversal path, wherein the path feature set is based at least in part on the node feature data; and
providing, to the user device, an optimal path traversal alert notification that displays the candidate traversal path based at least in part on the path score, wherein the optimal path traversal alert notification displays one or more biometric measures associated with a user and the candidate traversal path and at least one of (i) one or more nodes of the candidate traversal path and a first number of one or more secondary user devices within a proximity to the user device or (ii) a second number of one or more image devices within a proximity to the user device.