A door lock-based path guidance method

Abstract

The application provides a door lock-based path guiding method. The door lock-based path guiding method comprises the following steps: in the case that a target user initiates a navigation request, a target node in a path guiding system acquires target user identification information, source node information and destination node information according to the navigation request; the target node determines all feasible path information between the source node and the destination node from node topology information according to the source node information and the destination node information, and determines biological feature data of the target user according to the target user identification information; the target node determines an individualized path weight corresponding to each feasible path according to the biological feature data, acquires an optimal feasible path from all the feasible paths according to the individualized path weight, and issues the optimal feasible path information to all other nodes in the system; and the other nodes receive the optimal feasible path information and guide the target user according to the optimal feasible path information.

Description

Technical Field

[0001] This specification relates to the field of navigation technology, and in particular to a path guidance method based on door locks. Background Technology

[0002] Traditional outdoor positioning technologies, such as the Global Positioning System (GPS) and the BeiDou Navigation Satellite System, can provide users with accurate route guidance from their origin to their destination. However, existing navigation solutions are relatively inadequate when it comes to indoor environments. Unlike outdoor spaces, indoor structures are more complex and varied, including, but not limited to, the interiors of large buildings such as shopping malls, airports, hospitals, and museums. Due to the shielding provided by buildings and other facilities, traditional GPS signals cannot effectively penetrate buildings, leading to a significant decrease in positioning accuracy or even complete failure.

[0003] To achieve accurate indoor positioning, indoor positioning technology has emerged. While existing technologies based on devices such as Wi-Fi and Bluetooth beacons have solved the problem of GPS signal failure indoors, they generally rely on environmental information (such as distance and fixed obstacles) to provide the shortest or fastest path planning. This cannot meet the diverse needs of users. For example, for groups such as the elderly, people with mobility impairments, caregivers carrying heavy objects or infants, and certain health patients, the shortest path may not be optimal or may even be unsafe or infeasible. As a result, users cannot obtain personalized route guidance that truly meets their needs, thus limiting the navigation experience. Summary of the Invention

[0004] In view of this, one or more embodiments of this application provide a path guidance method based on a door lock.

[0005] To achieve the above objectives, one or more embodiments of this application provide the following technical solutions:

[0006] According to a first aspect of the embodiments of this specification, a path guidance method based on a door lock is provided, applied to a path guidance system. The path guidance system includes multiple nodes, each node maintaining node topology information and user identification information. The multiple nodes include a target node, and the target node also stores biometric data of a target user. The method includes: when a target user initiates a navigation request, the target node obtains target user identification information, source node information, and destination node information according to the navigation request; the target node determines all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information, and determines the biometric data of the target user based on the target user identification information; the target node determines a personalized path weight corresponding to each feasible path based on the biometric data, and obtains the optimal feasible path from all feasible paths based on the personalized path weight, and sends the optimal feasible path information to all other nodes in the path guidance system; the other nodes receive the optimal feasible path information and provide path guidance to the target user based on the optimal feasible path information.

[0007] According to a second aspect of the embodiments of this specification, a path guidance method is provided, applied to a target node of a path guidance system. The target node maintains biometric data, user identification information, and node topology information of a target user. The method includes: when a target user initiates a navigation request, obtaining target user identification information, source node information, and destination node information according to the navigation request; determining all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information; determining the target user's biometric data based on the target user identification information; determining a personalized path weight corresponding to each feasible path based on the biometric data; and obtaining the optimal feasible path from all feasible paths based on the personalized path weight and distributing the optimal feasible path information to all other nodes in the path guidance system, so as to guide the target user through all other nodes.

[0008] According to a third aspect of the embodiments of this specification, a path guidance method is provided, applied to any node of a path guidance system, wherein the node maintains target user identification information and node topology information. The method includes: receiving optimal feasible path information, the optimal feasible path information being sent to the cooperating node via the method described in the first aspect or the method described in the second aspect; when the cooperating node is determined to be a path node of the optimal feasible path based on the optimal feasible path information, monitoring the relative distance between the cooperating node and the target user; when the relative distance is less than a preset distance value, generating a first guidance mode based on the target user's biometric data and performing path guidance according to the first guidance mode; and generating a synchronization guidance instruction and sending the synchronization guidance instruction to neighboring path nodes according to the optimal feasible path information and the node topology information, causing the neighboring path nodes to perform synchronous guidance according to a second guidance mode, wherein the first guidance mode and the second guidance mode may be the same as or different from each other.

[0009] According to a fourth aspect of the embodiments of this specification, an electronic device is provided, including a processor and a memory for storing processor-executable instructions; wherein the processor is configured to implement the path bootstrapping method described in the first or second aspect.

[0010] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the path guidance method described in the first or second aspect.

[0011] According to a sixth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the path bootstrapping method described in the first or second aspect.

[0012] In the technical solution of this application, after calculating all feasible paths between the destination node and the source node, the target node in the path guidance system converts the biometric data of the target user into personalized path weights corresponding to each feasible path. This allows the numerical semantics of the personalized path weights to characterize the travel comfort of the corresponding feasible path. Based on the magnitude of the personalized path weights, the system formulates the optimal human-centered path for the target user and provides a suitable path guidance method for the target user, thus giving the path guidance system a human-centered function.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification.

[0015] Figure 1 This is a schematic diagram of the system architecture of a path guidance system provided in an exemplary embodiment;

[0016] Figure 2 This is a schematic diagram of the node topology of an indoor-outdoor cross-scene path guidance system provided in an exemplary embodiment;

[0017] Figure 3 This is a flowchart illustrating a path guidance method applicable to a path guidance system, provided in an exemplary embodiment.

[0018] Figure 4 This is a flowchart of a path guidance method applicable to a target node, provided in an exemplary embodiment;

[0019] Figure 5 This is a flowchart of a path guidance method applicable to any node, provided in an exemplary embodiment;

[0020] Figure 6 This is a schematic diagram of an electronic device provided in an exemplary embodiment;

[0021] Figure 7 This is a block diagram of a path guidance device applicable to a target node, provided in an exemplary embodiment;

[0022] Figure 8 This is a block diagram of a path guidance device applicable to any node, provided in an exemplary embodiment. Detailed Implementation

[0023] The user information (including but not limited to user identification information) and data (including but not limited to biometric data) involved in this application are all information and data authorized by the user or fully authorized by all parties. The collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals shall be provided for users to choose to authorize or refuse.

[0024] This application provides a path guidance system. Figure 1 This is a schematic diagram of a path guidance system provided in an exemplary embodiment, such as... Figure 1 As shown, the path guidance system 100 of this embodiment includes multiple nodes deployed in a distributed manner. These nodes are functionally divided into master nodes 110 and collaborating nodes 120, wherein:

[0025] The master node 110 is deployed near service access points, such as service desks, entrance registration areas, information centers, service rooms, or service windows, providing services to users. It is configured with global topology information and a user database. The user database includes user identification information, a biometric database, and service information. The global topology information stores complete node topology information centered on the master node, including the connection relationships of all reachable nodes and information about each reachable node, such as node type and node status. The user database registers and stores the biometric data of its service targets. Biometric data includes, but is not limited to, user tags such as mobility tags, environmental sensitivity tags, and access permission tags. Different master nodes serve different user targets. The master node 110 is primarily used to calculate all feasible paths between the source node and the destination node using the node topology information upon receiving a path planning request. It also queries the user's biometric data based on the user identification information and calculates the personalized path weight for each feasible path based on the biometric data to determine the optimal feasible path.

[0026] Collaborating nodes 120 are widely distributed throughout the area requiring navigation. They can be deployed at key path points such as room entrances, corridor entrances, elevator lobbies, building entrances, building exteriors, and important landmarks. Adjacent collaborative nodes are ensured to be within line of sight, and this extensive and strategic deployment ensures users receive timely and effective path guidance services throughout the navigation area. Each collaborative node 120 is configured with its own local node topology information and user identification information. The local node topology information typically only includes information about its directly connected neighboring nodes to meet the node's need for identifying the surrounding network structure when performing path guidance tasks. Of course, collaborative nodes 120 can also be configured with global node topology information, which can be configured according to the needs of those skilled in the art. Collaborating nodes only maintain user identification information, such as user ID and device ID, to determine whether a user is a service recipient of the system. In this embodiment, the collaborative nodes do not deploy biometric data to minimize information leakage. Collaboration node 120 mainly undertakes the functions of path execution and local guidance. After receiving the optimal feasible path information from the master node, when the target user arrives at or approaches the collaboration node, the collaboration node provides specific path guidance instructions to the user through various means such as lights, screens, and sounds, based on the received optimal feasible path information, and guides the user to move to the next node along the planned path.

[0027] The nodes within the path guidance system can be any device capable of performing the aforementioned functions, including but not limited to IoT terminals with fixed deployment locations, such as public facility terminals, security terminals, and dedicated navigation terminals. Public facility terminals include, for example, smart light poles, advertising screens, and fire hydrants; security terminals include, for example, access control locks, turnstiles, and cameras; and dedicated navigation terminals include, for example, navigation signs. In practical applications, different hardware terminals can be flexibly configured according to the application scenario, and dynamic networking can be achieved through multimodal communication protocols. In this embodiment, the nodes support communication protocols such as NFC (Near Field Communication), ZigBee, and Bluetooth. The nodes have adaptive protocol switching capabilities, dynamically selecting the communication method according to scenario requirements. For example, the master node and collaborating nodes use the ZigBee high-reliability protocol for message broadcasting, while collaborating nodes and user terminals use low-latency protocols such as Bluetooth and NFC for real-time signal interaction. The application scenarios of the path guidance scheme in this embodiment include, but are not limited to, indoor navigation scenarios (e.g., airports, shopping malls), outdoor navigation scenarios (e.g., parking lots), and cross-scenario navigation between indoor and outdoor environments (e.g., rehabilitation hospitals), as well as various scenarios requiring personalized navigation.

[0028] Nodes periodically exchange neighbor data to dynamically maintain the node topology. Taking ZigBee communication between nodes as an example, the distance between the transmitter and receiver can be measured based on RSSI (Received Signal Strength Indication). Based on the principle that signal strength attenuates with propagation distance, the distance between nodes can be estimated using known transmit and receive power, combined with a signal attenuation model. In this embodiment, each node can act as both a transmitter and a receiver, allowing each node to estimate its distance to other nodes in the system using RSSI. Furthermore, each node periodically broadcasts path messages, carrying the node's topology identifier, node type, and node weight. After receiving a path message, any node stores the data locally and, based on RSSI, identifies one or more nodes that meet preset distance constraints to establish neighbor relationships. In some scenarios, it may be necessary to manually configure neighbor relationships and establish them, such as between nodes on adjacent floors and near elevators, or between nodes near building entrances and access locks at building entrances. After establishing neighbor relationships, neighboring nodes synchronize their neighbor data with each other. This neighbor data includes topology identifiers and node weight information. Each node gradually builds a node topology centered on itself, covering the entire navigation area, based on the synchronized neighbor data. In this way, the system's network topology can be automatically constructed through path messages.

[0029] In practical applications, manual verification can also be performed to determine whether the automatically constructed neighbor relationships are normal. For example, the number of neighbors of a node can be checked according to preset rules. These preset rules can be set according to the application scenario. In one scenario, the preset rules might include: nodes far from elevators or building entrances have a maximum of two neighbor nodes, while the number of neighbors of nodes near elevators or building entrances is unlimited. The accuracy of the neighbor relationships can also be checked. For example, if a door lock node in a corridor has established a neighbor relationship with a distant room door lock node but not with a nearby room door lock node, then the neighbor relationship of the door lock node in that corridor is incorrect and needs to be re-established. This could involve checking whether the door lock node successfully received the transmission signal from the nearby room door lock node and correctly estimated the relative distance between them. When the number of neighbor nodes is limited, nodes that conform to the above rules can be selected to maintain neighbor relationships based on the order of relative distance between nodes from closest to farthest. For example, the two neighboring nodes of an outdoor smart light pole node are the two nodes that are closest and second closest to the smart light pole node, respectively. Other nearby nodes that can exchange messages with the smart light pole node do not establish neighbor relationships, in order to reduce the computational complexity of path planning.

[0030] by Figure 2Taking the scenario shown as an example, there are two buildings in the navigation area. Building 1 has three floors, with an elevator and two building entrances between the three floors (the topology identifiers of the door lock nodes at the two building entrances are 1-0-0-1 and 1-0-0-2, respectively). The first and second floors have the same building structure, each with a corridor and five rooms. One corridor door lock node is deployed in the corridor on the first floor of Building 1 (the topology identifier of the corridor door lock node is 1-1-1-0), and one room door lock node is deployed in each room (the topology identifiers of the five room door lock nodes are 1-1-1-1, ... 3……1-1-1-6), deploy a node near the elevator (the topology identifier of the near elevator node is 1-1-1-2). The near elevator node needs to be manually configured. For example, configure the near elevator node 1-2-1-2 on the second floor and the two near elevator nodes 1-3-1-2 and 1-3-1-11 on the third floor as the near elevator node. Similarly, the near elevator node on the second floor of Building 1 (the topology identifier of the near elevator node is 1-2-1-2) includes the near elevator node 1-1-1-2 on the first floor and the two near elevator nodes 1-3-1-2 and 1-3-1-11 on the third floor. The architectural structure of the third floor of Building 1 differs from that of the first floor. The third floor contains two corridors and ten rooms. Each corridor has one corridor door lock node (topology identifiers 1-3-1-0 and 1-3-2-0, respectively). Each room has one room door lock node (topology identifiers 1-3-1-1, 1-3-1-3…1-3-1-12, respectively). Two nodes are deployed near the elevators (topology identifiers 1-3-1-2 and 1-3-1-11, respectively). Similarly, the elevator-adjacent node 1-2-1-2 on the second floor of Building 1 is manually configured as a close neighbor node. Each node in Building 1 sends a probe signal based on the ZigBee protocol and estimates its relative distance to other nodes based on the RSSI value of the received probe signal. According to the preset rules described above, the number of neighbors for the current node is determined from other nodes. If the number of neighbors is 2, the two nodes with the closest relative distance are selected as close neighbors. After obtaining its nearest neighbors, each node establishes neighbor relationships by sending path messages. The node distribution and neighbor establishment within Building 2 are as follows: Figure 2 As shown, the process of establishing neighbor relationships is similar to that of Building 1, and will not be described again in this embodiment.

[0031] Light poles were deployed around both buildings. Thirteen light poles were deployed near Building 1, and ten light poles were deployed near Building 2. Each light pole was equipped with a smart light pole node. The topology identifiers of the thirteen smart light pole nodes near Building 1 were 1-1, 1-2...1-13, and the topology identifiers of the ten smart light pole nodes near Building 2 were 2-1, 2-2...2-10. All outdoor smart light pole nodes sent probe signals based on the ZigBee protocol and estimated the relative positions of other smart light pole nodes based on the RSSI values ​​of the received probe signals. If each smart light pole node has two neighbors, the two closest smart light pole nodes are selected as nearby nodes. For example, building entrance door lock nodes and nearby smart light pole nodes are manually added through configuration. Specifically, nearby devices need to be manually added between building entrance door lock node 1-0-0-1 and smart light pole node 1-1, between building entrance door lock node 1-0-0-2 and smart light pole node 1-13, and between building entrance door lock node 2-0-0-1 and smart light pole node 2-1. Afterwards, a path is automatically established by sending path messages. Figure 2 The neighbor relationships shown are established from smart light pole nodes 1-1, 1-2, 1-3 to smart light pole nodes 1-13, 2-10, 2-9, and finally to smart light pole node 2-1. Each pair of adjacent smart light pole nodes establishes a neighbor relationship. Furthermore, smart light pole nodes in the outdoor environment establish neighbor relationships with corresponding nodes in the indoor environment. For example, smart light pole node 1-1 is neighbored to building entrance / exit door lock node 1-0-0-1, smart light pole node 2-1 is neighbored to building entrance / exit door lock node 2-0-0-1, and smart light pole node 1-13 is neighbored to building entrance / exit door lock node 1-0-0-2. Corresponding nodes within two buildings also establish neighbor relationships, such as building entrance / exit door lock nodes 1-0-0-1 and 2-0-0-1, enabling the path guidance system to have seamless navigation capabilities across different scenarios.

[0032] It should be understood that all nodes in the path guidance system have a unique topology identifier. Figure 2 In the scenario shown, the topology identifier of a node includes the node's own location information. Figure 2 The rectangular nodes shown are located inside the building. The topology identifier of the rectangular nodes is set as a combination of four fields: building number, floor number, corridor number, and room number. The specific values ​​of these four fields can determine the location of the corresponding rectangular node. For example, topology identifier 1-10-2-15 means that the node is located in room 1015 of corridor 2 on the 10th floor of building 1. Topology identifier 1-10-2-0 means that the node is located at the entrance / exit of corridor 2 on the 10th floor of building 1. Topology identifier 1-0-0-3 means that the node is located at the 3rd entrance / exit of building 1. Figure 2The columnar nodes shown are located outside the building. The topology identifier of each columnar node is a combination of two fields: building number and sequence number. The specific values ​​of these two fields determine the location of the corresponding columnar node. For example, a topology identifier of 1-5 indicates that the node is located on the 5th lamp post near building number 1. Using the topology identifier to indicate the node's location information facilitates verification of the node's neighbor relationships. It is understood that in practical applications, the topology identifier of nodes can be flexibly set, and this embodiment does not impose any particular limitations on this.

[0033] The path guidance system in this embodiment may further include a user terminal 130. The user terminal 130 stores user identification information and is used to interact with nodes in the path guidance system, such as interacting with corridor door lock nodes or building entrance door lock nodes to complete unlocking authentication; or interacting with smart light pole nodes to initiate a path planning request. The user terminal 130 can be various mobile devices, such as wristband-type devices.

[0034] The embodiments described in this specification will now be described in detail.

[0035] This application provides a path guidance method based on a door lock. Figure 3 This is a flowchart of a path guidance method 300 applicable to a path guidance system, provided in an exemplary embodiment, as follows: Figure 3 As shown, method 300 includes at least the following steps S310 to S340:

[0036] Step S310: When the target user initiates a navigation request, the target node obtains the target user identification information, source node information, and destination node information according to the navigation request.

[0037] The path guidance system comprises multiple master nodes, each serving different users. The target node is the master node that stores the target user's biometric data. The source node is the node that receives the navigation request from the target user; it is typically the node closest to the target user. The destination node is the node closest to the target user's desired destination. During a single path guidance process, the source and destination nodes can simultaneously be different master nodes or different collaborating nodes; alternatively, the source node can be the master node and the destination node a collaborating node; or the source node can be a collaborating node and the destination node the master node.

[0038] After receiving a navigation request, the source node determines whether it is the target node corresponding to the target user based on the target user identifier information carried in the navigation request. If the source node is the target node corresponding to the target user, it can directly query the destination node corresponding to the destination information carried in the navigation request. If the source node is not the target node corresponding to the target user, it generates a path planning request carrying the target user identifier information, source node information, and destination information based on the navigation request, and encapsulates the path planning request into a broadcast message for broadcast. After receiving the broadcast message, all master nodes in the system obtain the path planning request by parsing the broadcast message, query their respective maintained user databases based on the target user identifier information carried in the path planning request, and determine whether the target user is their service object. If the service object information in the user database maintained by the current master node matches the target user identifier information, the current master node is determined to be the target node corresponding to the target user. At this time, the target node determines the destination node corresponding to the destination information based on the node topology information it maintains. Of course, if the source node is not the target node, it can also query the destination node.

[0039] In step S320, the target node determines all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information, and determines the biometric data of the target user based on the target user identification information.

[0040] After obtaining the source node information and the destination node information, the target node can traverse the global node topology information it maintains through a preset path search algorithm to query all feasible path information between the destination node and the source node. The path search algorithm includes, but is not limited to, depth-first search (DFS) and breadth-first search (BFS).

[0041] The biometric data in this embodiment includes, but is not limited to, mobility capability tags, environmental sensitivity tags, and access permission tags. Mobility capability tags describe a user's autonomous mobility and are typically related to the user's health status and user load (which includes carried items, pets, and infants). The values ​​of mobility capability tags can be flexibly set according to the application scenario. Taking a rehabilitation patient in a rehabilitation hospital scenario as an example, the mobility capability tag may include a first value indicating limited mobility and a second value indicating unrestricted mobility. Environmental sensitivity tags describe a user's sensitive response to real-world environmental stimuli and the user's impact on the real-world environment as a stimulus source. Real-world environmental stimuli include, for example, acoustic stimuli, optical stimuli, chemical stimuli (such as smoke, pollutant odors), and crowd density stimuli. The stimulus source corresponding to the user may include disease transmission sources. Environmental sensitivity tags are typically related to the user's health status and personal preferences. The sensitivity types included in the environmental sensitivity tags and the tag values ​​for each sensitivity type can be flexibly set according to the application scenario. The access permission label is used to describe a user's access permission to a specified path node. The specified path node refers to a node whose access permission can be changed, such as the access control lock node and the turnstile node mentioned above. The access status of the path where such a node is located is related to its own lock / unlock status. When the specified path node is in the locked state, the feasible path where it is located is in a restricted state. When the specified path node is in the unlocked state, the feasible path where it is located is in a passable state.

[0042] In step S330, the target node determines the personalized path weight corresponding to each feasible path based on the biometric data, and obtains the optimal feasible path from all feasible paths based on the personalized path weight, and sends the optimal feasible path information to all other nodes in the path guidance system.

[0043] The target node can be pre-configured with dynamic mapping rules. Based on the dynamic mapping rules, biometric data is transformed into personalized path weights corresponding to each feasible path. This allows the personalized path weights to be integrated with the target user's biometric data, realizing the numerical semantics of the personalized path weights and providing data support for subsequent decision-making on the optimal feasible path. The specific implementation steps for calculating personalized path weights based on dynamic mapping rules will be further described in subsequent embodiments.

[0044] In practical applications, the numerical semantics of personalized path weights can be flexibly designed. For example, the smaller the personalized path weight, the higher the comfort and safety of the corresponding feasible path; conversely, the larger the personalized path weight, the lower the comfort and safety of the corresponding feasible path. Under the above numerical semantic setting rules, this step can take the feasible path with the smallest personalized path weight as the optimal feasible path. Thus, based on the numerical semantics of personalized path weights, human-centered optimal paths can be formulated for target users. This realizes the transformation of optimal paths from geometric optimization based on environmental parameters (such as distance and time) to human-centered optimization based on biometric data, transforming the path guidance system from a mechanical recommendation of "one path for a thousand people" to an intelligent care system of "one path for one person."

[0045] In step S340, the other nodes receive the optimal feasible path information and guide the target user to a specific path based on the optimal feasible path information.

[0046] refer to Figure 3 The path guidance method shown in the system calculates all feasible paths between the source node and the destination node. Then, it transforms the biometric data of the target user into personalized path weights for each feasible path. The numerical semantics of the personalized path weights can represent the travel comfort of the corresponding feasible path. Based on the magnitude of the personalized path weights, the system formulates the optimal human-centered path for the target user and provides appropriate path guidance methods to guide the target user, thus giving the path guidance system a human-centered function.

[0047] In one illustrated embodiment, the feasible path information includes a base weight corresponding to each path node. The base weight reflects the inherent attributes of the path node. For example, different base weight values ​​can be set according to different node types, or the base weight values ​​can be set according to the distance between nodes. The greater the distance between adjacent nodes, the larger the base weight value. Of course, the base weight of all nodes can also be configured to the same value. This application embodiment does not impose any particular restrictions on the configuration method of the base weight.

[0048] Accordingly, in step S340 above, the target node determines the personalized path weight corresponding to each feasible path based on the biometric data, including: the target node obtains the basic weight corresponding to each path node based on the feasible path information; the target node determines the corresponding path monitoring indicators based on the biometric data to monitor each feasible path and obtains the monitoring data of each path node on each feasible path; the target node adjusts the basic weight of each path node based on the monitoring data corresponding to each path node; and the target node obtains the personalized path weight corresponding to each feasible path through the adjusted weight of each path node.

[0049] This embodiment calculates personalized path weights through a dual-weighting mechanism. The base weight reflects the inherent attributes of the path nodes, while the adjusted weight is calculated in real time based on the target user's biometric data. For example, a smart light pole node on a step can maintain a base weight of 10 when helping an ordinary person decide the optimal feasible path. However, when helping a wheelchair user decide the optimal feasible path, the dynamic weight of the smart light pole node needs to be adjusted to an infinite value. By significantly amplifying or reducing the base weight of the corresponding path node through biometric data, it is ensured that the dynamic weight of high-influence path nodes is not diluted by the weight of low-influence path nodes, thereby obtaining personalized path weights that can accurately represent biometric data.

[0050] It is understood that this embodiment shows one implementation of step S340. In other embodiments, the target node may also determine the corresponding path monitoring indicators based on the biometric data to monitor each feasible path and obtain the monitoring data of each path node on each feasible path; adjust the weight of each path node according to the monitoring data corresponding to each path node, and obtain the personalized path weight corresponding to each feasible path through the adjusted weight of each path node.

[0051] In one illustrated embodiment, the nodes in the path guidance system include designated path nodes. The passage status of the path containing such a node is related to its own unlocking / locking status. When a designated path node is locked, the feasible path it belongs to is in a restricted state; when a designated path node is unlocked, the feasible path it belongs to is in a passable state. For such nodes, after obtaining the basic weight corresponding to each path node based on the feasible path information in step S340, method 300 further includes: the target node obtaining the current passage feasibility data of the designated path node among all feasible paths, where the designated path node refers to a node whose passage feasibility can be changed; and when the current passage feasibility data indicates that the designated path node is in a restricted state, the target node adjusts the basic weight of the designated path node to an infinite value. Taking a door lock node as an example, the current passage feasibility data indicates the status of the door lock node. If the current passage feasibility data is locked, it indicates that the door lock node is in a restricted state; if the current passage feasibility data is unlocked, it indicates that the door lock node is in a passable state. Here, "infinitely large value" can be understood as the adjusted weight value being N orders of magnitude greater than the base weight, where N can be a positive integer greater than 3. When a specified path node is in a restricted state, the adjusted weight corresponding to that specified path node will be much greater than its base weight. For example, if the base weight is 10, the "infinitely large value" is 30000. The "infinitely large value" ensures that the weight of high-influence path nodes will not be diluted by the weight of low-influence path nodes, thus ensuring that the personalized path weight can correctly represent the feasibility of the corresponding feasible path. After adjusting the base weight of the specified path node based on the current feasibility data of the specified path node, the target node then calculates the personalized path weight corresponding to each feasible path by combining it with biometric data.

[0052] In one illustrated embodiment, the path monitoring metrics include at least one of the following monitoring metrics:

[0053] The path type monitoring indicator is used to monitor the path type data of each feasible path, including two path types: the existence of a specified path node and the absence of a specified path node, so as to determine the feasibility of each feasible path through the path type data.

[0054] The node terrain monitoring index is used to monitor the node terrain data of each path node. The node terrain includes flat land, steps, underground passages and other terrain, so as to determine the accessibility of each path node through the node terrain data.

[0055] Surrounding environment monitoring indicators are used to monitor environmental perception data at each path node. Environmental perception types include environmental noise, light, smoke, environmental smell, and population density, in order to determine the travel comfort of each path node through environmental perception data.

[0056] The facility availability monitoring index is used to monitor the physical facility availability status data of each path node. The physical facility availability status includes whether physical facilities are available or not. Physical facilities include, for example, automated external defibrillators (AEDs), wheelchairs, etc., in order to determine the access safety of each path node through the physical facility availability data.

[0057] It is worth noting that the path type data required for path type monitoring indicators and the physical facility status data required for facility equipment monitoring indicators can be obtained through the local configuration information of the node devices. In practical applications, static information such as whether a node is a node of a specified path and whether physical devices such as AEDs are equipped in the specified area of ​​the node can be written into the local configuration table of the node during the node configuration phase. In this way, each node can obtain the monitoring data of the corresponding indicators by reading its local configuration table and send it to the target node. The node terrain data required for node terrain monitoring indicators can be collected in real time by specially configured cameras, or the terrain data can be written into the configuration table during the node configuration phase. The environmental perception data required for facility equipment monitoring indicators can be collected in real time by corresponding environmental sensors. The specific collection methods for various data that need to be collected in real time can be referred to relevant technologies, and will not be described in detail in this embodiment.

[0058] In one illustrated embodiment, the target node maps biometric data to corresponding path monitoring indicators according to dynamic mapping rules. By mapping biometric data to measurable physical quantities, the technical disconnect between the user's physiological and psychological characteristic data and the node sensors is resolved, enabling one or more monitoring data from the node sensors to characterize the suitability of the path node area relative to the target user. The dynamic mapping rules include any one of the following three mapping rules:

[0059] Rule 1: When the biometric data contains environmental sensitivity tags, the target node determines that the path monitoring indicators include the surrounding environmental monitoring indicators and obtains environmental perception data for each path node.

[0060] Rule 2: If the biometric data contains a mobility capability tag, the target node determines that the path monitoring indicators include the node terrain monitoring indicators and / or the facility equipment monitoring indicators, and obtains the node terrain data and / or physical facility equipment status data for each path node.

[0061] Rule 3: If the biometric data contains access permission tags, the target node determines that the path monitoring indicators include path type monitoring indicators and obtains the path type data for each feasible path.

[0062] As mentioned earlier, environmental sensitivity tags are used to describe a user's sensitive response to stimuli in the real environment and the user's impact on the real environment as a stimulus source. Therefore, when biometric data includes environmental sensitivity tags, it is necessary to monitor the comfort of each path node. The comfort of a path node can be assessed by monitoring its environmental perception data. The type of environmental perception can be determined based on the tag type of the environmental sensitivity tag. For example, if the tag type indicates that the target user is a source of disease transmission, then the environmental perception data should include crowd density perception data. In practical applications, crowd heat maps can be collected by infrared sensors at the path nodes and crowd density can be calculated. The calculated crowd density value can then be used as the crowd density perception data for that path node.

[0063] Mobility tags are used to describe a user's ability to move independently. Therefore, when biometric data includes mobility tags, it is necessary to monitor at least one of the following for each path node: accessibility safety and accessibility feasibility. Accessibility feasibility can be assessed by monitoring the node's terrain data, while accessibility safety can be assessed by monitoring the physical facility status data of the path node. The type of monitoring data can be determined based on the mobility tag value. For example, if the tag value indicates that the target user's mobility is limited (e.g., a wheelchair user), the monitoring data should include node terrain data; if the tag value indicates that the target user's mobility can change suddenly (e.g., a user with cardiovascular disease), the monitoring data should include physical facility status data.

[0064] Access permission tags describe a user's unlocking authority for a specified path node. Therefore, when biometric data includes access permission tags, it is necessary to monitor the path type data of each feasible path to assess its feasibility. If a user has unlocking authority, even if a feasible path is restricted due to a locked path node, the user can unlock the door to restore accessibility. Thus, in this embodiment, a specified path node can be configured with a corresponding status monitoring task. When its unlocking / locking status changes, it reports the changed status to the target node, allowing the target node to adjust the weight of the specified path node in a timely manner. Furthermore, during the path planning phase, the final weight of the specified path node can be determined by combining the access permission tag, ensuring that personalized path weights accurately represent the feasibility of the corresponding feasible path.

[0065] In one illustrated embodiment, the target node adjusts the base weight of each path node based on the monitoring information corresponding to each path node, including: when the node terrain data indicates that the corresponding path node is in a restricted state, the target node adjusts the base weight of the corresponding path node to an infinite value; and when the node terrain data indicates that the corresponding path node is in a passable state, the target node keeps the base weight of the corresponding path node unchanged; when the environmental perception data indicates that the corresponding path node is outside the allowable range of the comfort threshold, the target node increases the base weight of the corresponding path node according to a first preset adjustment step size; and when the environmental perception data indicates that the corresponding path node is within the allowable range of the comfort threshold, the target node keeps the base weight of the corresponding path node unchanged; the target node adjusts the base weight of the corresponding path node based on the physical facility equipment status data. When the corresponding path node is equipped with physical facilities, the base weight of the corresponding path node is reduced according to the second preset adjustment step size; when the physical facility equipment status data indicates that the corresponding path node is not equipped with physical facilities, the base weight value of the corresponding path node remains unchanged. When the path type data indicates that the corresponding feasible path contains the specified path node and the specified path node is in a passable state, the target node restores the weight value of the specified path node to the base weight value; when the path type data indicates that the corresponding feasible path contains the specified path node and the specified path node is in a restricted state, the base weight value of the specified path node is adjusted to an infinite value; and when the path type data indicates that the corresponding feasible path does not contain the specified path node, the current weight of each path node on the feasible path is maintained. The first preset adjustment step size and the second preset adjustment step size can be the same adjustment factor or different adjustment factors. After the base weight is adjusted by the first preset adjustment step size, its adjusted weight should be at least one order of magnitude less than the "infinite value". For example, if the base weight is 10 and the first preset adjustment step size is a proportional factor with a value of 5, the base weight of a certain path node will be 50 after one adjustment, which is much less than the "infinite value". This can ensure that the weight of high-influence path nodes will not be diluted by the weight of low-influence path nodes.

[0066] In one illustrated embodiment, the target node obtains the personalized path weight corresponding to each feasible path through the adjusted weights of each path node. This includes: the target node summing the final adjusted weights of all path nodes on each feasible path as the personalized path weight for each feasible path. The final weight value can be the base weight value or an infinitely large value, or a final value greater than or less than the base weight value. The personalized path weight is a linear sum of the final weights of all path nodes on the feasible path, and it characterizes the overall comfort of the feasible path.

[0067] In one illustrated embodiment, where the minimum value of the personalized numerical weight indicates that the corresponding feasible path is the optimal feasible path, method 300 further includes: if there are multiple minimum personalized numerical weights among the feasible paths, the target node further determines the optimal feasible path based on the number of node paths and / or the user's historical paths, for example, taking the feasible path with the minimum number of node paths as the optimal feasible path, or taking the aforementioned feasible path that matches the user's historical path as the optimal feasible path.

[0068] The following example illustrates the decision-making process for the optimal feasible path for target user X in a rehabilitation hospital scenario. In this scenario, the node topology of the path guidance system is as follows: Figure 2 As shown, according to the device type of the nodes, the path guidance system includes smart light pole nodes, room door lock nodes, corridor door lock nodes, and building entrance door lock nodes. The weight adjustment rules for each type of path node are shown in Table 1.

[0069] Table 1:

[0070]

[0071] The feasibility of the paths containing the aforementioned corridor door lock nodes and building entrance / exit door lock nodes is restricted by the state of the door locks. When the door lock is locked, the feasible path is restricted; when the door lock is unlocked, the feasible path is passable. When the corridor door lock node and building entrance / exit door lock node are locked, their weight values ​​are adjusted to infinitely large values, for example, the weight of the corridor door lock node is 30,000, and the weight of the building entrance / exit door lock node is 60,000.

[0072] In a rehabilitation hospital setting, the room door lock node is the master node. Each room door lock node stores relevant information about all patients in that room, such as the patient's biometric data and basic patient information. Basic patient information includes the ward, bed, attending physician, nursing staff, and emergency contact. Biometric data includes disease information, gender, and age. Disease information includes historical medical records, current symptoms, treatment and rehabilitation recommendations. Based on the disease information, action capability tags and environmental sensitivity tags can be set for the biometric data, for example:

[0073] When a patient has an infectious disease, contact with crowds should be avoided, and the patient should travel through sparsely populated areas. In this case, the patient's biometric data should include an environmental sensitivity label, which indicates the patient's sensitivity to crowds. Nodes monitor for crowds using millimeter-wave radar. For example, the monitoring range of outdoor nodes is within a radius of 5 meters, and the monitoring range of indoor nodes is within a radius of 2 meters. If a crowd is detected, the base weight of the node that detects this situation is multiplied by 5. In addition, if the corridor door lock is locked, the weight of the corridor door lock node is reset to the base weight (10) when the user is an infectious patient. It does not need to be adjusted to 30000. If the path where the corridor door lock is located is the optimal feasible path, the user is guided to the corridor door lock node. The user can then obtain temporary access by scanning their face, fingerprint, or using the NFC function of their smart bracelet to open the corridor door lock, shortening the path back to the room and further reducing the possibility of contact with crowds.

[0074] In cases where patients suffer from cardiovascular disease, they may experience sudden attacks requiring emergency treatment, making proximity to AED devices along the route crucial. In such situations, the patient's biometric data should include a mobility capability tag indicating the patient's preferred route nodes equipped with AED devices. If an AED device is nearby—for example, within 10 meters of an indoor node or 30 meters of an outdoor node—then the relevant node information is configured. When guiding a cardiovascular patient back to their ward, the base weight of nodes with nearby AED devices is divided by 5, achieving a match between the optimal feasible route and high-density AED device areas.

[0075] When patients require rehabilitation training, a certain intensity of outdoor exercise is more conducive to rapid recovery. Therefore, the biometric data of such patients should include a mobility ability tag indicating their preference for long outdoor routes. Typically, since patients want to return to their wards, they have likely already finished their exercise; therefore, the optimal feasible route should be selected based on the lowest personalized route weight. For a few patients who want to increase their exercise, the smart light pole node can provide a voice prompt during the navigation request phase: "We have detected a suggestion to increase outdoor activities in your rehabilitation recommendations. If you wish to enjoy more scenery on your way back to your ward, please bring your smart bracelet close to the NFC contact area again. After hearing the beep, we will replan your route." If the user makes a selection, the feasible route with the "highest" personalized route weight value can be selected as the optimal feasible route. It is worth noting that when the personalized route weight is greater than 30,000, it means that there are locked doors along the route. Therefore, the optimal feasible route with the highest personalized route weight value should be selected from routes with a route weight less than 30,000.

[0076] In cases where the patient is critically ill, such as a person suffering from cancer, malignant tumors, or who has undergone organ surgery and is physically weakened, these patients cannot stay outdoors for extended periods and need to return to their ward via the shortest route. Therefore, the biometric data of such patients includes a mobility capability label, which indicates that the patient is physically weak and prefers the shortest path. When a critically ill patient initiates a navigation request, if the building entrance / exit door locks and corridor door locks are locked, the weights of these two types of nodes are reset to the base weight value (10), without needing to be adjusted to an infinite value. If the path where the corridor door lock and / or building entrance / exit door lock is located is the optimal feasible path, then when guiding the user to the corridor door lock node or building entrance / exit door lock node, the user can unlock the corridor door lock after completing identity authentication, shortening the path length back to the room.

[0077] Each node in the path guidance system stores node topology information centered on itself. This topology information includes all feasible paths between any two nodes, the basic weight of each path node, and the device type of each path node. Taking smart light pole node 1-13 as the starting point and the path to the destination room door lock 1-3-1-5 as an example, ... Figure 2 As shown, assuming the corridor door lock corresponding to node 1-3-1-0 and the building entrance door lock corresponding to node 1-0-0-2 are locked, then there are the following six feasible paths in the system:

[0078] The first feasible path: passing through smart light pole nodes 1-13, 1-12, 1-11...1-1, to the building entrance door lock node 1-0-0-1, then passing through the nearby elevator door lock nodes 1-1-1-2, 1-2-1-2, 1-3-1-11, then to the corridor door lock node 1-3-2-0, and then sequentially passing through room door lock nodes 1-3-1-10, 1-3-1-9, 1-3-1-8, 1-3-1-7, 1-3-1-6, to reach the destination room door lock node 1-3-1-5, for a total of 24 path nodes. Without considering the target user's biometric data, the path weight of the first feasible path is 240.

[0079] The second feasible path: passing through smart light pole nodes 1-13, 1-12, 1-11...1-1, to the building entrance door lock 1-0-0-1, passing the nearby elevator door locks 1-1-1-2, 1-2-1-2, 1-3-1-2, then to the corridor door lock 1-3-1-0, and then sequentially passing through room door lock nodes 1-3-1-3, 1-3-1-4, to reach the destination room door lock node 1-3-1-5, a total of 21 path nodes. Without considering the target user's biometric data, the path weight of the second feasible path is 30200.

[0080] The third feasible path: It passes through smart light pole nodes 1-13, 2-10, 2-9...2-1, 1-1, to the building entrance door lock 1-0-0-1, then through the nearby elevator door lock nodes 1-1-1-2, 1-2-1-2, 1-3-1-11, and then through the corridor door lock node 1-3-2-0. After that, it passes through the room door lock nodes 1-3-1-10, 1-3-1-9, 1-3-1-8, 1-3-1-7, 1-3-1-6, and finally reaches the destination room door lock node 1-3-1-5. There are a total of 23 path nodes. Without considering the target user's biometric data, the path weight of the third feasible path is 230.

[0081] The fourth feasible path: It passes through smart light pole nodes 1-13, 2-10, 2-9...2-1, 1-1, to the building entrance door lock node 1-0-0-1, then through the nearby elevator door locks 1-1-1-2, 1-2-1-2, 1-3-1-2, and then to the corridor door lock node 1-3-1-0, and then sequentially through the room door lock nodes 1-3-1-3, 1-3-1-4, to reach the destination room door lock node 1-3-1-5, for a total of 20 path nodes. Without considering the biometric data of the target user, the path weight of the fourth feasible path is 30190.

[0082] The fifth feasible path: From smart light pole node 1-13 to building entrance door lock node 1-0-0-2, after passing through room door lock nodes 1-1-1-5, 1-1-1-4, and 1-1-1-3, it reaches corridor door lock node 1-1-1-0, then passes through nearby elevator door lock nodes 1-1-1-2, 1-2-1-2, and 1-3-1-11, corridor door lock node 1-3-2-0, and then passes through room door lock nodes 1-3-1-10, 1-3-1-9, 1-3-1-8, 1-3-1-7, and 1-3-1-6, reaching the destination room door lock node 1-3-1-5. There are a total of 16 path nodes. Without considering the biometric data of the target user, the path weight of the fifth feasible path is 60150.

[0083] The sixth feasible path: From smart light pole node 1-13 to building entrance door lock node 1-0-0-2, after passing through room door lock nodes 1-1-1-5, 1-1-1-4, and 1-1-1-3 in sequence, it reaches corridor door lock node 1-1-1-0, then passes through nearby elevator door lock nodes 1-1-1-2, 1-2-1-2, and 1-3-1-2, corridor door lock 1-3-1-0, and then passes through room door lock nodes 1-3-1-3 and 1-3-1-4 in sequence, reaching the destination room door lock node 1-3-1-5, for a total of 13 path nodes. Without considering the target user's biometric data, the path weight of the sixth feasible path is 90110.

[0084] Patients in the rehabilitation hospital all wear smart bracelets issued by the hospital. The smart bracelets store the patient's basic information. When a target user X gets lost while outdoors, the target user X can reach the nearest smart light pole. Suppose the target user X presses the "One-Click Home" button on smart light pole node 1-13 and touches the smart bracelet to the NFC area next to the button, enabling the smart bracelet to interact with the smart light pole node. Smart light pole node 1-13 authenticates the patient's identity based on the patient's basic information stored in the smart bracelet. After successful authentication, smart light pole node 1-13 generates and broadcasts a path planning request carrying destination node information (assuming the destination node is room door lock node 1-3-1-5), source node information, and the patient's basic information.

[0085] When the target user X's room door lock node 1-3-1-5 receives a path planning request, it matches the patient's wristband ID in its maintained user database. If the user database contains patient information matching the wristband ID, it calculates six feasible paths from smart light pole node 1-13 to room door lock node 1-3-1-5 based on its maintained node topology information. Then, it further queries the patient's disease information and dynamically adjusts the current weights of the path nodes to obtain their final weights. Based on these final weights, it calculates the personalized path weight for each feasible path. Assuming the lock / unlock status of the relevant door remains unchanged, the calculation process for the optimal feasible path under each disease type is as follows:

[0086] If target user X is an infectious disease patient, and there is a crowd gathering at smart light pole node 2-4, then the final weight value will be 5 times the base weight of smart light pole node 2-4. Accordingly, the path weights of the third and fourth feasible paths containing smart light pole node 2-4 will change. Also, since target user X has the unlocking authority of the corridor door lock, the path weights of the second, fourth, and sixth feasible paths containing corridor door lock node 1-3-1-0 will change. The personalized path weights of the above six feasible paths are 240, 210, 270, 240, 60150, and 60120 respectively. At this time, the second feasible path is the optimal feasible path.

[0087] If the target user X is a cardiovascular patient, and AED devices are installed near smart light pole nodes 1-4 and 1-10, then 1 / 5 of the base weight of smart light pole node 1-4 will be used as its final weight value, and 1 / 5 of the base weight of smart light pole node 1-10 will be used as its final weight value. Accordingly, the path weights of the first and second feasible paths, which include smart light pole nodes 1-4 and 1-10, will change. The personalized path weights of the above six feasible paths are 224, 30184, 230, 30190, 60150, and 90110, respectively. At this time, the first feasible path is the optimal feasible path.

[0088] When the target user X is a rehabilitation patient, the personalized path weights of the six feasible paths are 240, 30200, 230, 30190, 60150, and 90110, respectively. In this case, the third feasible path is the optimal feasible path. If the target user X interacts with smart light pole nodes 1-13 via a smart bracelet and chooses to increase exercise, then the first feasible path will be the optimal feasible path to increase the outdoor activity distance.

[0089] If the target user X is a critically ill patient and has unlocking permissions for the corridor door lock and the building entrance door lock, then the path weights of the fifth and sixth feasible paths, which include the building entrance door lock node 1-0-0-2, change. The path weights of the second, fourth, and sixth feasible paths, which include the corridor door lock node 1-3-1-0, also change. The personalized path weights of the six feasible paths are 240, 210, 230, 200, 160, and 130, respectively. At this point, the sixth feasible path is the optimal feasible path.

[0090] After determining the optimal feasible path for target user X, room door lock node 1-3-1-5 generates a guidance path message based on the optimal feasible path information and broadcasts it.

[0091] In one illustrated embodiment, method 300 further includes: when the target node sends the optimal feasible path information, it also simultaneously sends the biometric data of the target user; correspondingly, in step S360, other nodes receiving the optimal feasible path information and guiding the target user according to the optimal feasible path information includes: when other nodes receive the optimal feasible path information and determine themselves as a path node of the optimal feasible path according to the optimal feasible path information, they monitor the relative distance between themselves and the target user; when the relative distance is less than a preset distance value, the other nodes generate a first guidance method according to the target user's biometric data and guide the path according to the first guidance method, and generate a synchronization guidance instruction and send the synchronization guidance instruction to neighboring path nodes according to the optimal feasible path information and the node topology information, so that the neighboring path nodes perform synchronous guidance according to a second guidance method, wherein the first guidance method and the second guidance method may be the same as or different from each other.

[0092] Other nodes in the system (including collaborating nodes and / or other master nodes) determine whether they are path nodes in the optimal feasible path. If not, they can cache the optimal feasible path information and biometric data for a preset time. This allows the non-path node to guide the target user in the correct direction if the target user moves to a non-path node. After receiving a guidance completion message from the target node, the non-path node can delete the cached optimal feasible path information. Each path node monitors its relative distance to the target user in real time. For example, a path node calculates its relative distance to the user terminal based on RSSI principles, or tracks the user terminal's trajectory using its positioning sensors, such as millimeter-wave radar. If the relative distance to the target user is less than a preset distance value, it determines that the target user is near the node; if the target user is not detected or the relative distance is consistently greater than the preset distance value, it determines that the target user has not yet moved to the node. The preset distance value can be set according to the node's distance detection capability. The preset distance values ​​for indoor and outdoor nodes can be the same or different. Figure 2 Taking the scenario shown as an example, the preset distance value for outdoor smart street light nodes is 5 meters, and the preset distance value for indoor door lock nodes is 3 meters, so that the target user can receive path guidance prompts.

[0093] In practical applications, the first guidance method can include multimodal guidance, such as light, screen, and sound. Light guidance includes the node and user device flashing the same color at the same frequency; screen guidance includes the node screen indicating the regional characteristics of the next node and the relative location of the next node; sound guidance includes node voice guidance, or it may also include the user device receiving the node's voice guidance content and broadcasting the guidance. In one example, multimodal guidance prompts can be generated based on the target user's biometric data. The prompts may include guiding the target user to interact with path nodes. For example, when the biometric data contains a pass permission tag, the prompts are used to instruct the target user to go to the specified path node for authentication to obtain unlocking permission; the prompts may also include guiding the target user's direction of travel, such as turning or going straight. The first guidance method can also include adaptive switching of guidance modalities. For example, based on the relative distance between the current node and the target user, if the user is close to the current node (e.g., within one meter), the guidance modality switches to any combination of sound and light, sound and screen, or sound, light, and screen; if the user is more than one meter away, the guidance modality switches to light guidance. The second guidance method includes light guidance, which instructs neighboring path nodes to flash the same color at the same frequency as the user device, and / or instructs neighboring path nodes to flash the same color at the same frequency as the current path node. In this way, the target user can know its direction of travel by the multimodal guidance prompts of the current path node, and determine in advance that it is on the correct travel route by the light guidance prompts of the next path node.

[0094] Continuing with the example of guiding target user X in a rehabilitation hospital scenario, after the target node broadcasts the guidance path message, the guiding lights of the starting smart light pole node and the next smart light pole node flash according to a set frequency and color, for example, flashing green every 2 seconds. Simultaneously, the starting smart light pole node broadcasts a voice prompt: "Dear X, please proceed along the direction of the light; the soft green guiding light will accompany you along the way." The starting smart light pole node continuously tracks the movement trajectory of target user X's smart bracelet. When the relative distance between target user X and the starting smart light pole node is greater than a preset distance, the light at the starting smart light pole node turns off. When the next smart light pole node detects that the relative distance to target user X is less than the preset distance, it controls the next smart light pole node to flash green every 2 seconds, while simultaneously broadcasting a voice prompt: "Dear X, please proceed along the direction of the light; the soft green guiding light will accompany you along the way." When user X arrives at the inpatient building following the lights, the smart light pole node near the building's entrance will end navigation and announce, "Dear X, you have arrived at Building 1. Indoor navigation will now begin." At this time, the building entrance door lock nodes will detect user X's proximity via millimeter-wave radar and announce, "Hello X, please walk approximately 30 meters along the corridor ahead to reach the elevator. Your floor is the 10th floor. Please take the elevator." As the user moves forward, the door lock nodes along the path will identify whether they are part of the path guidance based on interactions with the smart bracelet. If they are, the door lock nodes along the path will announce, "Hello X, please continue forward; you are about to reach the elevator." The door lock nodes near the elevator entrance will announce, "Hello X, the elevator is ahead (or in another direction). Your floor is the 10th floor. Please take the elevator." In practical applications, to enhance the guidance effect, small spotlights can be installed on the door handles of all room lock nodes along the route, flashing at the same frequency and color as the smart light pole nodes. After target user X arrives at the 10th floor by elevator, the nearest elevator node on the 10th floor will announce, "Hello X, you have arrived at your ward floor. Your room number is 1005. Please follow the lights." If target user X takes the wrong elevator, the nearest elevator node will interact with the smart bracelet and determine, based on the cached optimal feasible path information, that target user X is not on the optimal feasible path. The nearest elevator node will then announce, "Hello X, your floor is 10. You are currently on the 9th floor. Please continue taking the elevator to the 10th floor." When the target user arrives at their room, the target door lock node will announce, "Hello X, you have safely arrived at your room. Please open the door." After the patient unlocks the door using facial recognition, a password, or fingerprint, the guidance process ends, and the target door lock node will simply announce "Welcome home" and broadcast a message indicating the guidance is complete.

[0095] When the target user X is a user with access permission tags, such as an infectious disease patient or a critically ill patient, when the target user X approaches the vicinity of a corridor door lock or building entrance door lock, the corridor door lock node or building entrance door lock node interacts with the target user X's smart bracelet to determine that the target user X is the route guidance object. Then, a voice prompt will say, "Dear X, please use your facial recognition to open this door lock, which can shorten the distance back to your ward." In this way, personalized guidance can be provided to the user based on the target user X's biometric data, improving the user's navigation experience.

[0096] In one illustrated embodiment, the plurality of nodes includes a source node for receiving navigation requests issued by the target user. The navigation request carries the target user's identification information. The method 300 further includes: if the source node determines, based on the target user's identification information, that it is not the target node corresponding to the navigation request, it determines the target user's destination node information based on the target user's identification information, generates a path planning request carrying the source node information, the destination node information, and the target user's identification information, and broadcasts it. The source node in the system sends the path planning request to the target node corresponding to the target user via message broadcasting, enabling the target node to plan the optimal feasible path based on the target user's biometric data.

[0097] In one illustrated embodiment, the method 300 further includes: any one of the plurality of nodes determining the relative distance between itself and the signal transmitting node based on the signal strength of the received wireless communication signal; the current node determining its neighboring nodes from the signal transmitting nodes based on the relative distance and generating neighbor data, the neighbor data including at least the current node's base weight and the neighboring nodes' base weights; and the current node transmitting the wireless communication signal carrying the neighbor data to its surroundings. Each node in the system transmits the wireless communication signal carrying the neighbor data to its surroundings according to a set strategy to maintain the node topology.

[0098] This application also provides another path guidance method. Figure 4 This is a flowchart of a path guidance method 400 applicable to a target node, provided in an exemplary embodiment, as follows: Figure 4 As shown, method 400 includes at least the following steps S410 to S440:

[0099] Step S410: If the target user initiates a navigation request, obtain the target user identification information, source node information, and destination node information according to the navigation request.

[0100] Step S420: Determine all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information.

[0101] Step S430: Determine the biometric data of the target user based on the target user identification information.

[0102] Step S440: Determine the personalized path weight corresponding to each feasible path based on the biometric data.

[0103] Step S450: Obtain the optimal feasible path from all feasible paths according to the personalized path weight and send the optimal feasible path information to all other nodes in the path guidance system so as to guide the target user through all other nodes.

[0104] This application also provides another path guidance method. Figure 5 This is a flowchart of a path guidance method 500 applicable to any node, provided as an exemplary embodiment. Figure 5 As shown, method 500 includes at least the following steps S510 to S530:

[0105] Step S510: Receive the optimal feasible path information.

[0106] The optimal feasible path information is transmitted to the current node via method 300 or method 400. The current node determines whether it is a path node in the optimal feasible path. If it is not, the optimal feasible path information can be cached for a preset time. This allows the non-path node to guide the target user in the correct direction if the target user moves to a non-path node. For example, the non-path node can determine the nearest path node based on the optimal feasible path information and guide the user to that nearest path node with a voice prompt such as "Hello, you have deviated from the guidance path. Please proceed to the flashing green light pole to your left." The non-path node can delete the cached optimal feasible path information after receiving a guidance completion message from the target node.

[0107] Step S520: If the current node is determined to be a path node of the optimal feasible path based on the optimal feasible path information, monitor the relative distance between the current node and the target user.

[0108] Step S530: When the relative distance is less than a preset distance value, a first guidance method is generated based on the target user's biometric data and path guidance is performed according to the first guidance method. A synchronization guidance instruction is generated and sent to neighboring path nodes according to the optimal feasible path information and the node topology information, so that the neighboring path nodes perform synchronization guidance according to a second guidance method. The first guidance method and the second guidance method may be the same as or different from each other.

[0109] refer to Figure 5 The path guidance method shown in this technical solution allows any node to control itself and the next path node to guide the path when it receives the optimal feasible path and determines that it is the optimal feasible path. This helps users determine in advance whether they are on the correct guidance route and allows for the setting of appropriate guidance methods to improve the user's navigation experience.

[0110] Figure 6 This is a schematic diagram of an electronic device illustrated in this specification according to an exemplary embodiment. Please refer to... Figure 6 At the hardware level, the device includes a processor 602, an internal bus 604, a network interface 606, memory 608, a hardware acceleration device 610, and non-volatile memory 612, and may also include other hardware required for its functions. One or more embodiments of this application can be implemented in software, for example, the processor 602 reads the corresponding computer program from the non-volatile memory 612 into memory 608 and then runs it. Of course, in addition to software implementation, one or more embodiments of this application do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the above processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0111] Corresponding to the above embodiments of the path guidance method for target nodes, this application also provides corresponding apparatus embodiments. Please refer to... Figure 7 The path guidance device can be applied to target nodes as described in any of the above embodiments. Figure 6 When the electronic device shown is the aforementioned target node, the path guidance device based on the target node can be specifically applied to, for example... Figure 6 The electronic device shown implements the technical solution of this application. The path guidance device for the target node may include a first acquisition unit 710, a first calculation unit 720, a data acquisition unit 730, a weight calculation unit 740, and a second calculation unit 750, wherein:

[0112] The first receiving unit 710 is used to obtain target user identification information, source node information and destination node information according to the navigation request when the target user initiates a navigation request;

[0113] The first calculation unit 720 is used to determine all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information.

[0114] Data acquisition unit 730 is used to determine the biometric data of the target user based on the target user identification information;

[0115] The weight calculation unit 740 is used to determine the personalized path weight corresponding to each feasible path based on the biometric data.

[0116] The second calculation unit 750 is used to obtain the optimal feasible path from all feasible paths according to the personalized path weight and send the optimal feasible path information to all other nodes in the path guidance system so as to guide the target user through all other nodes.

[0117] Corresponding to the above embodiments of the path guidance method for any node, this application also provides corresponding apparatus embodiments. Please refer to... Figure 8 The path guidance device can be applied to any node as described in any of the above embodiments, in Figure 6 When the electronic device shown is any of the aforementioned nodes, the path guidance device based on any node can be specifically applied to, for example... Figure 6 The electronic device shown implements the technical solution of this application. The path guidance device for any node may include a receiving unit 810, a monitoring unit 820, a guidance unit 830, and a transmitting unit 840, wherein:

[0118] The second receiving unit 810 is used to receive the optimal feasible path information, which is sent to the current node through method 300 or method 400.

[0119] The monitoring unit 820 is used to monitor the relative distance between the current node and the target user when the current node is determined to be a path node of the optimal feasible path based on the optimal feasible path information.

[0120] The guidance unit 830 is configured to generate a first guidance method based on the biometric data of the target user and guide the path according to the first guidance method when the relative distance is less than a preset distance value, and to generate a synchronization guidance instruction and send the synchronization guidance instruction to neighboring path nodes according to the optimal feasible path information and the node topology information, so that the neighboring path nodes perform synchronization guidance according to a second guidance method, wherein the first guidance method and the second guidance method are the same or different.

[0121] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0122] Accordingly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described in any of the above embodiments. Accordingly, embodiments of this application also provide a computer program product configured to perform the methods described in any of the above embodiments.

[0123] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0124] In a typical configuration, a computer includes one or more processors (CPUs), input / output interfaces, a network interface, and memory. Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0125] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0126] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A path guidance method based on a door lock, characterized in that, The method is applied to a path guidance system, which includes multiple nodes, each node including a door lock. Each node maintains node topology information and user identification information. The multiple nodes are functionally divided into multiple master nodes and multiple collaborating nodes. The service objects of the multiple master nodes are different from each other. The multiple collaborating nodes are distributed throughout the entire area requiring navigation to perform local guidance functions. The multiple nodes include a target node, which refers to a master node that stores the biometric data of the target user. The method includes: When a target user initiates a navigation request, the target node obtains the target user identification information, source node information, and destination node information based on the navigation request. The target node determines all feasible path information between the source node and the destination node from the node topology information based on the source node information and the destination node information, and determines the biometric data of the target user based on the target user identification information; The target node determines the personalized path weight corresponding to each feasible path based on the biometric data, and obtains the optimal feasible path from all feasible paths based on the personalized path weight, and sends the optimal feasible path information to all other nodes in the path guidance system. The other nodes receive the optimal feasible path information and guide the target user according to the optimal feasible path information; The feasible path information includes a basic weight corresponding to each path node. The target node determines the personalized path weight corresponding to each feasible path based on the biometric data, including: the target node obtaining the basic weight corresponding to each path node based on the feasible path information; the target node determining corresponding path monitoring indicators based on the biometric data to monitor each feasible path and obtaining monitoring data for each path node on each feasible path; the target node adjusting the basic weight of each path node based on the monitoring data; and the target node obtaining the personalized path weight corresponding to each feasible path through the adjusted weight of each path node. After the target node obtains the basic weight corresponding to each path node based on the feasible path information, the method further includes: the target node obtains the current traffic feasibility data of a specified path node among all feasible paths, wherein the specified path node refers to a node whose traffic feasibility can be changed; when the current traffic feasibility data indicates that the specified path node is in a restricted state, the target node adjusts the basic weight of the specified path node to an infinite value.

2. The method according to claim 1, characterized in that, The path monitoring indicators include at least one of the following monitoring indicators: The path type monitoring indicator is used to monitor the path type data of each feasible path in order to determine the feasibility of each feasible path. Node terrain monitoring indicators are used to monitor the node terrain data of each path node in order to determine the passability of each path node; Surrounding environment monitoring indicators are used to monitor environmental perception data at each path node in order to determine the travel comfort of each path node; Facilities are equipped with monitoring indicators to monitor the physical facilities status data of each path node in order to determine the passage safety of each path node.

3. The method according to claim 2, characterized in that, The biometric data includes at least one of environmental sensitivity tags, mobility tags, and access permission tags. The target node determines corresponding path monitoring indicators based on the biometric data to monitor each feasible path and obtains monitoring information for each path node on each feasible path, including: When the biometric data contains environmental sensitivity tags, the target node determines that the path monitoring indicators include the surrounding environment monitoring indicators and acquires environmental perception data for each path node. When the biometric data contains a mobility capability tag, the target node determines that the path monitoring indicators include the node terrain monitoring indicators and / or the facility equipment monitoring indicators, and acquires the node terrain data and / or physical facility equipment status data for each path node. When the biometric data contains access permission tags, the target node determines that the path monitoring indicators include path type monitoring indicators and obtains the path type data for each feasible path.

4. The method according to claim 3, characterized in that, The target node adjusts the basic weight of each path node based on the monitoring information corresponding to each path node, including: When the terrain data indicates that the corresponding path node is under traffic restrictions, the target node adjusts the base weight of the corresponding path node to an infinite value. When the environmental perception data indicates that the corresponding path node is outside the allowable range of the comfort threshold, the target node increases the basic weight of the corresponding path node according to the first preset adjustment step size. When the physical facility configuration status data indicates that the corresponding path node is equipped with physical facilities, the target node reduces the base weight of the corresponding path node according to the second preset adjustment step size; When the path type data indicates that the corresponding feasible path contains a specified path node and the specified path node is in a passable state, the target node restores the weight of the specified path node to the base weight. The specified path node refers to a node whose passability can be changed.

5. The method according to claim 1, characterized in that, The target node adjusts the basic weight of each path node based on the monitoring data corresponding to each path node, including: The target node adjusts the current weight of each path node based on the monitoring data corresponding to each path node.

6. The method according to claim 1, characterized in that, The target node obtains the personalized path weight corresponding to each feasible path through the adjusted weight of each path node, including: The target node uses the sum of the final adjusted weights of all path nodes on each feasible path as the personalized path weight for each feasible path.

7. The method according to claim 1, characterized in that, When the target node sends out the optimal feasible path information, it also simultaneously sends out the biometric data of the target user. The other nodes receive the optimal feasible path information and guide the target user's path based on it, including: The other nodes receive the optimal feasible path information and, if they determine themselves to be a path node of the optimal feasible path based on the optimal feasible path information, monitor their relative distance to the target user. When the relative distance is less than a preset distance value, the other nodes generate a first guidance method based on the target user's biometric data and guide the path according to the first guidance method. They also generate a synchronization guidance instruction and send the synchronization guidance instruction to neighboring path nodes according to the optimal feasible path information and the node topology information, so that the neighboring path nodes perform synchronous guidance according to a second guidance method. The first guidance method and the second guidance method may be the same as or different from each other.

8. The method according to claim 1, characterized in that, The plurality of nodes includes a source node for receiving navigation requests issued by the target user, the navigation requests carrying the target user's identification information, and the method further includes: If the source node determines that it is not the target node based on the target user identification information, it determines the destination node information of the target user based on the target user identification information, generates a path planning request carrying the source node information, the destination node information, and the target user identification information, and broadcasts it.

9. The method according to claim 1, characterized in that, The method further includes: Each of the plurality of nodes determines the relative distance between the current node and the signal transmitting node based on the signal strength of the received wireless communication signal; The current node determines its neighboring nodes from the signal transmitting nodes based on the relative distance and generates neighbor data, which includes at least the current node's base weight and the neighboring nodes' base weights. The current node transmits wireless communication signals carrying neighbor data to its surroundings to maintain the node topology information of the current node.

10. A path guidance method, characterized in that, A target node is applied to a path guidance system. The target node maintains the target user's biometric data, user identification information, and node topology information. The path guidance system includes multiple nodes, each maintaining node topology information and user identification information. These multiple nodes are functionally divided into multiple master nodes and multiple collaborating nodes. The service objects of the multiple master nodes are different from each other. The multiple collaborating nodes are distributed throughout the entire area requiring navigation. The multiple nodes include a target node, which is a master node that stores the target user's biometric data. The method includes: When a target user initiates a navigation request, the target user identification information, source node information, and destination node information are obtained based on the navigation request. Based on the source node information and the destination node information, determine all feasible path information between the destination node and the source node from the node topology information; The biometric data of the target user are determined based on the target user identification information; The personalized path weight corresponding to each feasible path is determined based on the biometric data. The optimal feasible path is obtained from all feasible paths based on the personalized path weight, and the optimal feasible path information is sent to all other nodes in the path guidance system so as to guide the target user through all other nodes. The feasible path information includes a basic weight corresponding to each path node. The target node determines the personalized path weight corresponding to each feasible path based on the biometric data, including: the target node obtaining the basic weight corresponding to each path node based on the feasible path information; the target node determining corresponding path monitoring indicators based on the biometric data to monitor each feasible path and obtaining monitoring data for each path node on each feasible path; the target node adjusting the basic weight of each path node based on the monitoring data; and the target node obtaining the personalized path weight corresponding to each feasible path through the adjusted weight of each path node. After the target node obtains the basic weight corresponding to each path node based on the feasible path information, the method further includes: the target node obtains the current traffic feasibility data of a specified path node among all feasible paths, wherein the specified path node refers to a node whose traffic feasibility can be changed; when the current traffic feasibility data indicates that the specified path node is in a restricted state, the target node adjusts the basic weight of the specified path node to an infinite value.

11. A path guidance method, characterized in that, The method, applied to any node in a path guidance system, wherein the node maintains target user identification information and node topology information, includes: The optimal feasible path information is received, wherein the optimal feasible path information is sent to the current node by the method described in any one of claims 1 to 10; If the current node is determined to be a path node of the optimal feasible path based on the optimal feasible path information, the relative distance between the current node and the target user is monitored. When the relative distance is less than a preset distance value, a first guidance method is generated based on the target user's biometric data and path guidance is performed according to the first guidance method. A synchronization guidance instruction is generated and sent to neighboring path nodes according to the optimal feasible path information and the node topology information, so that the neighboring path nodes perform synchronization guidance according to a second guidance method. The first guidance method and the second guidance method may be the same as or different from each other.

12. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method as described in any one of claims 1 to 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 11.

14. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method as described in any one of claims 1 to 11.

Images