A spatiotemporal context-based spatial service providing method, device, and medium

Abstract

This invention relates to the field of spatial intelligence technology, and in particular to a method, device, and medium for providing spatial services based on spatiotemporal context. The method includes: when a target user enters an offline space, acquiring their on-site identity identifier, generating skill installation parameters and providing them to a personal intelligent agent; receiving the intelligent agent identifier returned by the personal intelligent agent and establishing a mapping with the on-site identity identifier to associate the collected raw behavioral data with the intelligent agent identifier; generating structured spatiotemporal events and aggregating them into a personal spatiotemporal context, which is then synchronized to the personal intelligent agent, forming a context with the intelligent agent identifier as the primary key and continuously accumulating as the target user moves across spaces; when the target user is in any offline space, providing spatial services based on the accumulated spatiotemporal context combined with real-time data; this invention enables offline behavioral data to be continuously accessed by the personal intelligent agent and personal spatiotemporal context to be accumulated across spaces, realizing personalized offline spatial services for users.

Description

Technical Field

[0001] This invention relates to the field of spatial intelligence technology, and in particular to a method, device and medium for providing spatial services based on spatiotemporal context. Background Technology

[0002] In recent years, offline spaces such as exhibitions, museums, parks, and shopping malls have generated a large amount of behavioral data related to physical spaces, such as entry, lingering at a booth, interacting with devices, and scanning QR codes for registration. Existing offline spaces typically have already deployed certain basic technological capabilities, such as RFID identification, Bluetooth / UWB positioning, access control, QR code check-in, and video analytics systems. These existing technologies can identify partial user identities within the venue's system, collect user behavioral data on-site, and use it for on-site management, visitor flow statistics, security control, or one-off services.

[0003] However, existing technologies still have the following shortcomings: Firstly, on-site behavioral data is difficult to integrate into personal intelligent agents. User behavior data in offline spaces is typically only stored in the venue or event organizer's system. Once the user leaves, the data is disconnected from the user and cannot be used as a basis for subsequent services. Secondly, temporary and long-term identities are separated. RFID numbers, ticket numbers, and location tag numbers used on-site are only valid in a single venue or event. Existing technologies lack a mechanism to securely, revocably, and with limited permissions bind temporary and long-term identities. Thirdly, historical data is difficult to reuse between different offline spaces. For example, when users enter different exhibitions, parks, or venues, they usually need to re-register and re-authorize. The behavioral history generated in different spaces is difficult to accumulate based on the same long-term identity, causing space services to remain in a single-point scenario and failing to form a continuous spatiotemporal context for individuals.

[0004] To address the above problems, this invention provides a method for providing spatial services based on spatiotemporal context. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, device, and medium for providing spatial services based on spatiotemporal context, which enables offline behavioral data to be continuously and securely accessed by a personal intelligent agent and accumulated across spaces into a reusable personal spatiotemporal context, thereby realizing personalized offline spatial services based on users' historical behavior.

[0006] According to a first aspect of the present invention, a method for providing spatial services based on spatiotemporal context is provided, comprising the following steps: S1. When a target user enters any offline space, obtain the target user's on-site identity identifier in the offline space, generate skill installation parameters containing the binding session identifier of the offline space, and provide them to the target user's personal intelligent body.

[0007] S2, receive the agent identifier returned by the personal agent in response to the skill installation parameters, and establish a mapping relationship between the on-site identity identifier and the agent identifier; the agent identifier is a unique identifier of the personal agent corresponding to the target user that remains unchanged in different offline spaces.

[0008] S3, based on the mapping relationship, the collected original behavioral data of the target user in the offline space is associated with the agent identifier, a structured spatiotemporal event associated with the agent identifier is generated, and the structured spatiotemporal event is aggregated into a personal spatiotemporal context and synchronized to the personal agent corresponding to the target user, so as to form a personal spatiotemporal context with the agent identifier as the primary key in the personal agent; the personal spatiotemporal context continues to accumulate with the target user's activities in different offline spaces.

[0009] S4. When the target user is detected to be in any offline space, spatial services are provided to the target user based on the personal spatiotemporal context accumulated in the target user's personal intelligent agent and combined with the real-time data collected in the current offline space.

[0010] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the above-described method for providing spatial services based on spatiotemporal context.

[0011] According to a third aspect of the present invention, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects: This invention provides a spatial service provision method based on spatiotemporal context. When a target user enters an offline space, the method acquires their on-site identity identifier, generates skill installation parameters including a bound session identifier, and provides them to the personal intelligent agent, enabling the user to autonomously control when the personal intelligent agent interacts with the offline space. Then, the method receives the intelligent agent identifier returned by the intelligent agent and establishes a mapping relationship between the on-site identity identifier and the intelligent agent identifier. By establishing a mapping relationship between the temporary on-site identity and this long-term stable identity, the offline space can identify the continuous behavior of the same user in different locations and at different times, providing an identity foundation for cross-space data aggregation and avoiding data fragmentation problems caused by identity confusion. Finally, based on this mapping relationship, the collected raw behavior data is... Linking data to intelligent agent identifiers enables the continuous and secure access of offline behavioral data to personal intelligent agents, generating structured spatiotemporal events and aggregating them into personal spatiotemporal contexts that are synchronized to the personal intelligent agent. This transforms the original, chaotic data into a personal spatiotemporal context that the personal intelligent agent can directly understand, query, and use, significantly improving data usability. In application, when a user is in any offline space, spatial services are provided to the user based on the accumulated context combined with real-time data. Service recommendations no longer rely on the user's active query or the venue's generalized statistics, but are instead generated proactively and accurately by the personal intelligent agent based on the user's historical interests and behavioral patterns, improving the intelligence level of offline spatial services and the user experience. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating a method for providing spatial services based on spatiotemporal context, as provided in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This invention provides a method for providing spatial services based on spatiotemporal context, such as... Figure 1 As shown, the method includes the following steps: S1, when a target user enters any offline space, the system acquires the target user's on-site identity identifier within that offline space, generates skill installation parameters containing the binding session identifier of the offline space, and provides these parameters to the target user's personal intelligent agent. This can be understood as: skill installation parameters. In specific implementations, data interaction with the personal intelligent agent can be achieved through the spatial intelligence system corresponding to the offline space.

[0017] Specifically, step S1 includes the following steps: S101: After obtaining the target user's on-site identity identifier in the offline space, the content to be authorized is displayed to the target user. In specific implementations, the content to be authorized can be displayed to the target user through a mini-program, webpage, on-site terminal, or personal smart agent installation page.

[0018] Specifically, the content to be authorized includes at least the venue identification corresponding to the offline space, the data type to be collected, the purpose of synchronizing the data to the personal intelligent agent, the authorization validity period, the method of revoking authorization, and the scope of permissions required for the skill. The data type to be collected refers to the types of raw behavioral data to be collected from the target user. Examples include: Radio Frequency Identification (RFID) read records, Ultra-Wideband (UWB) or Bluetooth positioning points, access control records, QR code scanning records, booth equipment interaction records, event check-in records, contact information exchange records, video analysis output records, etc.

[0019] Furthermore, on-site identification can be obtained through one or more of the following methods: RFID reading and writing, UWB tag reading, Bluetooth tag reading, access control credential reading, or ticket number reading. For example, an RFID reader can read the RFID exhibitor badge number worn or held by the user; a UWB or Bluetooth positioning tag system can obtain the positioning tag number; an access control system can read the user's entry pass number; the user can scan a QR code on-site and confirm their identity in a mini-program or webpage; or the ticket number and on-site credential number can be obtained from the ticketing system. In practice, the obtained on-site identification can be saved directly or after hashing. For example, a hash function can be used to de-identify the RFID exhibitor badge number to obtain a local identity hash, reducing the direct exposure of the original on-site number.

[0020] S102, after receiving confirmation and authorization feedback from the target user, an authorization record is generated; the authorization record includes at least the authorization time, scope of permissions, authorization validity period and authorization status.

[0021] S103, a binding session is generated based on the on-site identity identifier and authorization record; the binding session also includes a binding session identifier, an activity identifier, and a binding random number; it can be understood that the binding session is used to establish a temporary bridging relationship between the on-site identity identifier and the subsequent intelligent agent identifier; wherein, the binding random number is a random string used to prevent the skill installation parameters from being reused or forged.

[0022] S104, generate skill installation parameters based on the binding session; the skill installation parameters also include a skill installation address, a skill description file address, a signature value, and a binding confirmation interface address, used to guide the personal intelligent agent to obtain and install or activate the skill corresponding to the offline space; the skill is a functional unit encapsulated according to a preset description file. In specific implementations, the skill installation parameters can be implemented using installation text, QR code links, or JSON format data.

[0023] Specifically, the signature value is obtained by performing a signature calculation on a string concatenated from several preset fields using an HMAC or asymmetric signature algorithm. These preset fields include at least a venue identifier, an activity identifier, a bound session identifier, a bound random number, and a validity period. In practice, the personal intelligent agent or skill installation service verifies the signature in subsequent steps to confirm that the skill installation parameters have not been tampered with.

[0024] The above-mentioned mechanism associates the authorization result with the skill installation parameters through the binding session mechanism, which makes it clear that the authorization boundary is known when collecting user behavior data in the future, preventing over-collection and synchronization. By obtaining the on-site identity identifier and generating skill installation parameters containing the binding session identifier, a traceable binding basis is established between the temporary identity in the offline space and the long-term identity of the personal intelligent agent. At the same time, the installation parameters are provided to the user side, so that the user can independently control the timing of the connection between the personal intelligent agent and the offline space, avoiding data connection without the user's active operation.

[0025] S2, receive the agent identifier returned by the personal agent in response to the skill installation parameters, and establish a mapping relationship between the on-site identity identifier and the agent identifier; the agent identifier is a unique identifier of the personal agent corresponding to the target user that remains unchanged in different offline spaces.

[0026] Specifically, receiving the agent identifier returned by the personal intelligent agent in response to the skill installation parameters includes the following steps: S201, Receive the verification request sent by the personal intelligent agent after parsing the skill installation parameters, query the corresponding binding session according to the binding session identifier, and perform at least the following verifications: verify whether the binding session identifier exists; verify whether the binding random number matches; verify whether the skill installation parameters are within the validity period; verify whether the signature value is correct; verify whether the binding session has not been used; verify whether the user authorization is valid.

[0027] Specifically, the parsing of skill installation parameters by the personal intelligent agent refers to obtaining various data information in the skill installation parameters; for example, the skill installation address and the skill description file address. In practice, the installation text obtained through the skill installation address is copied to the installation entry of the personal intelligent agent, or the installation is triggered by scanning a QR code, clicking a link, or one-click installation. Verification is performed before installation; if verification fails, the installation is terminated or the user is required to re-authorize.

[0028] S202, after successful verification, an instruction allowing the installation or activation of the skill is sent to the personal intelligent agent. This can be understood as the personal intelligent agent installing the skill by parsing the obtained skill description file. The skill description file includes at least: skill name, skill capability description, event query interface address, context query interface address, permission declaration, input / output field format, authentication method, and data format for context synchronization or callback. Through skill installation, data within the user's authorized scope is exchanged with the personal intelligent agent.

[0029] S203 After the skill installation or activation is completed on the personal intelligent agent side, the agent identifier and binding confirmation request are received from the personal intelligent agent side through the binding confirmation interface address; the agent identifier can be generated directly by the personal intelligent agent side, or a stable identifier can be generated by the target user account, public key or local intelligent agent instance.

[0030] Specifically, the binding confirmation request includes at least the binding session identifier, agent authorization token, skill installation status, and personal agent callback address. Additionally, the binding confirmation request also includes the venue identifier, activity identifier, and permission scope.

[0031] The above-mentioned multi-verification mechanism prevents the binding session from being forged, tampered with, or replayed, ensuring the security of identity binding. After successful installation, a confirmation is sent back, so that the agent authorization token and callback address carried in the binding confirmation request establish an authentic and callback-enabled data channel for subsequent data synchronization. This ensures that when synchronizing data to the personal agent later, the legitimacy of the request can be verified and the data can be accurately pushed to the corresponding personal agent.

[0032] Furthermore, establishing a mapping relationship between the on-site identity identifier and the intelligent agent identifier includes the following steps; S210, query the corresponding binding session based on the binding session identifier to obtain the on-site identity identifier, venue identifier, activity identifier, permission scope and validity period corresponding to the binding session.

[0033] S220, verify the binding confirmation request returned by the personal intelligent agent; wherein, the verification content includes: whether the intelligent agent identifier is empty; whether the intelligent agent authorization token is valid; whether the binding session identifier matches the session that has not been bound; whether the venue identifier and the activity identifier are consistent; whether the user authorization is still valid; and whether the skill installation status is successful.

[0034] S230, after successful verification, establish the mapping relationship and mapping record between the on-site identity identifier and the intelligent agent identifier.

[0035] Specifically, the mapping record includes, but is not limited to, mapping identifier, agent identifier, venue identifier, activity identifier, anonymized value of on-site identity identifier, type of on-site identity identifier, establishment time and validity period of mapping relationship, scope of permissions, data synchronization strategy, agent callback address, and mapping status. Among them, on-site identity identifier types include RFID, QR code, access card, etc.; mapping status includes, but is not limited to, valid, expired, revoked, pending, etc.

[0036] In one implementation, a combined unique index is established based on the desensitized values ​​of agent identifier, site identifier, and on-site identity identifier to avoid the same on-site identity being repeatedly bound to multiple valid mapping relationships within the same site.

[0037] Specifically, the synchronization strategy specifies how the spatial intelligence system synchronizes context with individual intelligent agents. Synchronization strategies include, but are not limited to: real-time synchronization (pushing data to the individual intelligent agent immediately after an important event); timed synchronization (batch synchronization at preset intervals); session-end synchronization (generating and synchronizing data upon user departure or event completion); pull-based synchronization (the individual intelligent agent actively queries data through a skill interface); and hybrid synchronization (real-time synchronization of important events and batch synchronization of ordinary events). In implementation, the spatial intelligence system automatically determines the criteria for judging important events based on venue configuration or activity type. Users can also select which event types are considered important during the authorization phase. For example, contact information exchange events, event check-in events, and payment or reservation-related interaction events are considered important events. User-initiated interactions such as scanning QR codes, device clicks, and service calls are considered more important than passively collected location data, and so on.

[0038] As described above, by receiving the agent identifier that remains unchanged in different offline spaces, a mapping relationship is established between the temporary identity on site and the long-term identity. This enables offline spaces to identify the continuous behavior of the same user in different locations and at different times, providing an identity basis for cross-space data collection. At the same time, the uniqueness and cross-space consistency of the agent identifier ensure that the subsequently accumulated data can be accurately attributed to the same user, avoiding data fragmentation caused by identity confusion.

[0039] S3, based on the mapping relationship, the collected raw behavioral data of the target user in offline spaces is associated with the agent identifier, generating structured spatiotemporal events associated with the agent identifier. These structured spatiotemporal events are then aggregated into a personal spatiotemporal context and synchronized to the target user's corresponding personal agent, forming a personal spatiotemporal context with the agent identifier as the primary key within the personal agent. This personal spatiotemporal context continuously accumulates as the target user's activities in different offline spaces continue. In specific implementations, user behavioral data in offline spaces is continuously collected using existing identification, positioning, and interaction devices. The data is then normalized and accumulated into the personal agent. For example, the names of spatial objects may differ in different venues. The system can use object tags or object types for normalization. For instance, medical robot booths, rehabilitation robot zones, and surgical robot manufacturers at different exhibitions may have different related object identifiers, but all can be categorized under the medical robot tag. This normalization does not require different venues to use the same map coordinates; instead, it uses venue identifiers, area identifiers, related object identifiers, and object tags for association.

[0040] Specifically, before associating the collected raw behavioral data of the target user in the offline space with the intelligent agent identifier, the process further includes: S301, collect the raw behavioral data from multiple preset data sources, and perform time synchronization and spatial coordinate unification on the raw behavioral data from different data sources. For example, the raw behavioral data collected from multiple preset data sources can include: RFID tag number, antenna number, reading time, and signal strength output by RFID readers; tag number, coordinates, timestamp, and location confidence level output by UWB base stations; Bluetooth tag number, gateway number, signal strength, and timestamp output by Bluetooth gateways; access control number, entry time, exit time, and channel number output by access control systems; scanning user, QR code object, scanning time, and scanning location output by scanning systems; device number, interaction type, interaction time, and trigger status output by IoT devices; and area number, timestamp, dwell time, and confidence level output by video analysis systems when they have been associated with on-site identity identifiers. If the video analysis system has not been associated with on-site identity identifiers, the corresponding output is only used for anonymous site statistics and is not used to generate structured spatiotemporal events for individual intelligent agents.

[0041] Specifically, time synchronization refers to unifying the timestamps of different preset data sources. For example, each acquisition device can synchronize its time via NTP or a server, enabling events to be ordered.

[0042] Furthermore, spatial coordinate unification refers to converting coordinates, gateway numbers, or channel numbers from different sources into a unified site spatial model for location data. This site spatial model includes, but is not limited to, site markers, floor markers, map markers, area markers, coordinate points, and area types. For example, UWB coordinates can be converted into coordinate points for a specific exhibition hall, RFID antenna numbers can be mapped to a specific entrance or booth area, and access control channel numbers can be mapped to a specific spatial area.

[0043] S302, the unified data is cleaned; the cleansing process includes deleting invalid records lacking on-site identification or timestamps, merging records of the same target user repeatedly generated in the same area within a preset time window, and filtering out data with location confidence levels below a preset confidence threshold. For example, if the same RFID tag is read multiple times by the same antenna within 5 seconds, the system merges them into a single pass-through event. If the same location tag is continuously located in the same area within 1 minute, the system aggregates multiple location points into a single dwell event.

[0044] Specifically, the cleaning process also includes smoothing out locations with significant jumps and deduplicating data repeatedly reported by the same device within a preset time period. A significant jump refers to a situation where the target user's movement speed between two consecutive sampling times exceeds a preset speed threshold.

[0045] The above-mentioned process of synchronizing time, unifying spatial coordinates, and cleaning raw behavioral data from different data sources eliminates conflicts and redundancies caused by differences in acquisition devices, time bases, and coordinate systems. This ensures consistency and credibility of subsequent behavioral data associated with agent identifiers, providing a standardized data foundation for generating high-quality structured spatiotemporal events.

[0046] Furthermore, when associating raw behavioral data with agent identifiers, the mapping relationship is queried based on the target user's on-site identity identifier. If a valid mapping relationship is found, the corresponding agent identifier is written into the corresponding behavioral record. If no valid mapping relationship is found, the behavioral record is only used as anonymous on-site statistical data and is not synchronized to the individual agent.

[0047] Specifically, the structured spatiotemporal event includes event identifier, agent identifier, site identifier, activity identifier, region identifier, event type, start time, end time, associated object identifier, data source, confidence level, and permission scope.

[0048] The event types include visit, stay, path traversal, device interaction, activity participation, contact information exchange, and departure. For example, a visit event is generated when a user first appears in the entrance area of ​​a venue; a stay event is generated when a user remains in the same area for more than a preset stay threshold; a path traversal event is generated when a user moves from one area to another; an object interaction event is generated when a user scans a booth QR code, triggers a device, or clicks on a field service terminal; an activity participation event is generated when a user registers for an activity and appears in the activity area; a contact information exchange event is generated when two users exchange contact information with mutual confirmation; and a departure or session end event is generated when a user leaves the venue or the binding validity period expires.

[0049] Furthermore, the aggregation of structured spatiotemporal events into a personal spatiotemporal context includes: S310: Group structured spatiotemporal events according to one or more dimensions, including agent identifier, venue identifier, activity identifier, given time window, area identifier, and event type, to obtain event grouping results. For example, all events with the same agent identifier on the same day and at the same exhibition can be grouped into a single spatial access session, or consecutive location points and QR code scanning behaviors of the same user near a booth can be grouped into a single booth interaction segment.

[0050] S320, convert each group of data in the event grouping results into a context fragment; the context fragment includes at least a context identifier, agent identifier, site identifier, activity identifier, access session identifier, time range, location range, context type, fact list, list of associated event identifiers, list of associated object identifiers, confidence level, permission range, creation time, and validity period. The context type includes, but is not limited to, visit context, stay context, path traversal context; object interaction context; activity participation context; contact information exchange context; single activity summary context; and cross-spatial history context.

[0051] Specifically, the fact list consists of fact items extracted based on events. For example, a user was in booth A from 10:20 to 10:35, a user scanned a QR code to obtain information at booth B, a user participated in a forum activity at 14:00, and a user exchanged contact information with user C in area D.

[0052] S330, according to a preset synchronization strategy, the context fragments are synchronized to the personal intelligent agent corresponding to the target user, and the synchronization status of each context fragment is recorded. The synchronization strategy is the same as the one in the mapping record, and will not be elaborated further here. In specific implementations, to facilitate retrieval by the personal intelligent agent, an index is created for the context fragments. The index fields include, but are not limited to, information such as: intelligent agent identifier, venue identifier, and activity identifier. For example, when the personal intelligent agent queries "Which medical robot booths did I visit today?", it can retrieve the information based on the intelligent agent identifier, date, object tag, and booth interaction events.

[0053] Specifically, the synchronization status includes, but is not limited to, pending synchronization, synchronizing, synchronized, synchronization failed, revoked, and expired. If synchronization fails, it will be resent after a preset number of retries. If the user revokes authorization, synchronization will stop, and the corresponding mapping relationship will be set to revoked.

[0054] As described above, by associating raw behavioral data with agent identifiers based on mapping relationships and aggregating scattered raw data into structured spatiotemporal contexts, the original messy location points, access control records, and device logs are transformed into personal spatiotemporal contexts that can be directly understood, queried, and used by personal agents, greatly improving data availability. At the same time, this personal spatiotemporal context uses the agent identifier as the primary key and continues to accumulate with the user's cross-space activities, so that the behavioral data generated by the user in the offline space is no longer a one-time statistical log of the venue, but can be accumulated into knowledge assets that the personal agent can continuously possess.

[0055] S4. When the target user is detected to be in any offline space, spatial services are provided to the target user based on the personal spatiotemporal context accumulated in the target user's personal intelligent agent and combined with the real-time data collected in the current offline space.

[0056] Specifically, step S4 includes the following steps: S401, extract the target user's most recent location event from the personal spatiotemporal context, and combine it with the current time and the current offline spatial site map to determine the target user's current location and visited areas. For example, if the user has been continuously present in area A for the past 3 minutes and has not generated a departure event, it is determined that the user is still near area A.

[0057] S402, extract the target user's interest tags based on the personal spatiotemporal context; the extraction criteria for the interest tags include one or more of the target user's historical dwell time, number of QR code scans, activity participation records, and object interaction records.

[0058] S403, based on the current location and the interest tag, match corresponding spatial objects in the current offline space excluding already visited areas, and generate spatial service results based on the distance between the matched spatial objects and the target user's current location. For example, match the interest tag with booth, event, or device tags in the current venue to obtain matched spatial objects, and generate spatial service results in order of distance from the user's current location to the farthest location, taking into account the distance between the user's current location and the matched spatial object's location.

[0059] Specifically, the spatial service results include, but are not limited to, exhibition guidance, route recommendations, schedule reminders, booth or activity recommendations, on-site object retrieval, day review, contact information exchange assistance, historical trajectory review, and scenario summary and personalized suggestions. In specific implementation, the personal intelligent agent can output service results to users through dialogue interfaces, message reminders, voice prompts, mobile notifications, schedule cards, or on-site terminal interfaces. For example, if the user's time remaining until the start time of a registered activity is less than a preset threshold, a schedule reminder is triggered; if the user's time spent in a certain area exceeds the corresponding threshold, the area of ​​interest is recorded; if the user approaches a booth that matches their historical interest tags, a booth recommendation is triggered; if the user leaves, a day review is generated; if the user completes mutual authorization with another user, a contact information exchange record is triggered. For example, an implementation scenario could be as follows: The personal intelligent agent could prompt the user: You have spent 18 minutes in area A, and there is a booth nearby that matches your area of ​​interest, about a 3-minute walk away.

[0060] As described above, by providing spatial services based on accumulated personal spatiotemporal context combined with current real-time data, service recommendations no longer rely on users' active queries or venue generalization statistics. Instead, personalized spatial services are proactively and accurately generated by personal intelligent agents based on users' historical interests and behavioral patterns. This improves the intelligence level of offline spatial services and user experience. At the same time, because this context is continuously accumulated across spaces, the services that users receive in different places have continuity and consistency, transforming offline spatial services from a one-off, isolated response mode to a long-term, continuous, high-quality service mode.

[0061] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0062] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0063] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. It should also be understood that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method for providing spatial services based on spatiotemporal context, characterized in that, The method includes the following steps: S1, When a target user enters any offline space, obtain the target user's on-site identity identifier in the offline space, generate skill installation parameters containing the binding session identifier of the offline space, and provide them to the target user's personal intelligent body. S2, Receive the agent identifier returned by the personal agent in response to the skill installation parameters, and establish a mapping relationship between the on-site identity identifier and the agent identifier; the agent identifier is a unique identifier of the personal agent corresponding to the target user that remains unchanged in different offline spaces; S3, based on the mapping relationship, the collected original behavioral data of the target user in the offline space is associated with the agent identifier, a structured spatiotemporal event associated with the agent identifier is generated, and the structured spatiotemporal event is aggregated into a personal spatiotemporal context and synchronized to the personal agent corresponding to the target user, so as to form a personal spatiotemporal context with the agent identifier as the primary key in the personal agent; the personal spatiotemporal context continues to accumulate with the target user's activities in different offline spaces; S4. When the target user is detected to be in any offline space, spatial services are provided to the target user based on the personal spatiotemporal context accumulated in the target user's personal intelligent agent and combined with the real-time data collected in the current offline space.

2. The spatial service provision method based on spatiotemporal context according to claim 1, characterized in that, Step S1 includes the following steps: S101, after obtaining the target user's on-site identity identifier in the offline space, display the content to be authorized to the target user; the content to be authorized includes at least the venue identifier corresponding to the offline space, the data type to be collected, the purpose of synchronizing the data to the personal intelligent agent, the authorization validity period, the method of revoking authorization, and the scope of permissions required by the skill; S102, after receiving confirmation and authorization feedback from the target user, an authorization record is generated; the authorization record includes at least the authorization time, scope of permissions, authorization validity period, and authorization status; S103, Generate a binding session based on the on-site identity identifier and authorization record; the binding session also includes a binding session identifier, an activity identifier, and a binding random number; S104, generate skill installation parameters according to the binding session; the skill installation parameters also include skill installation address, skill description file address, signature value and binding confirmation interface address, which are used to guide the personal intelligent agent to obtain and install or activate the skill corresponding to the offline space; the skill is a functional unit encapsulated according to a preset description file.

3. The spatial service provision method based on spatiotemporal context according to claim 2, characterized in that, Step S2, receiving the agent identifier returned by the personal intelligent agent in response to the skill installation parameters, includes the following steps: S201, Receive the verification request sent by the personal intelligent agent after parsing the skill installation parameters, query the corresponding binding session according to the binding session identifier, and perform at least the following verifications: verify that the binding session identifier exists; verify that the binding random number matches; verify that the skill installation parameters are within the validity period; verify that the signature value is correct; verify that the binding session has not been used; verify that the user authorization is valid. S202, After the verification is successful, an instruction allowing the installation or activation of the skill is sent to the personal intelligent agent; S203, after the skill installation or activation is completed on the personal intelligent agent side, the intelligent agent identifier and binding confirmation request are received from the personal intelligent agent side through the binding confirmation interface address; the binding confirmation request includes at least the binding session identifier, intelligent agent authorization token, skill installation status and personal intelligent agent callback address.

4. The spatial service provision method based on spatiotemporal context according to claim 3, characterized in that, In step S2, a mapping relationship is established between the on-site identity identifier and the intelligent agent identifier, including the following steps; S210, query the corresponding binding session based on the binding session identifier to obtain the on-site identity identifier, venue identifier, activity identifier, permission scope and validity period corresponding to the binding session; S220, verify the binding confirmation request returned by the personal intelligent agent; wherein, the verification content includes: whether the intelligent agent identifier is empty; whether the intelligent agent authorization token is valid; whether the binding session identifier matches the session that has not been bound; whether the venue identifier and the activity identifier are consistent; whether the user authorization is still valid; and whether the skill installation status is successful. S230, after successful verification, establish the mapping relationship and mapping record between the on-site identity identifier and the intelligent agent identifier.

5. The method for providing spatial services based on spatiotemporal context according to claim 1, characterized in that, In step S3, before associating the collected raw behavioral data of the target user in the offline space with the intelligent agent identifier, the following steps are also included: S301, Collect the raw behavioral data from multiple preset data sources, and synchronize the time and unify the spatial coordinates of the raw behavioral data from different data sources; S302, perform cleaning processing on the unified data; the cleaning processing includes deleting invalid records that lack on-site identification or timestamps, merging the same area records repeatedly generated by the same target user within a preset time window, and filtering out data whose location reliability is lower than a preset reliability threshold.

6. The method for providing spatial services based on spatiotemporal context according to claim 1, characterized in that, The structured spatiotemporal event includes event identifier, agent identifier, site identifier, activity identifier, region identifier, event type, start time, end time, associated object identifier, data source, confidence level, and permission scope; The event types include visiting, staying, passing through a route, interacting with devices, participating in activities, exchanging contact information, and leaving.

7. The method for providing spatial services based on spatiotemporal context according to claim 1, characterized in that, In step S3, the aggregation of structured spatiotemporal events into a personal spatiotemporal context includes: S310, group structured spatiotemporal events according to one or more dimensions of agent identifier, site identifier, activity identifier, given time window, region identifier and event type to obtain event grouping results; S320, convert each group of data in the event grouping result into a context fragment; the context fragment includes at least the context identifier, agent identifier, venue identifier, activity identifier, access session identifier, time range, location range, context type, fact list, associated event identifier list, associated object identifier list, confidence level, permission range, creation time and validity period; S330, according to the preset synchronization strategy, synchronize the context fragments to the personal intelligent agent corresponding to the target user and record the synchronization status of each context fragment.

8. The method for providing spatial services based on spatiotemporal context according to claim 1, characterized in that, Step S4 includes the following steps: S401, extract the target user's most recent location event from the personal spatiotemporal context, and combine the current time and the current offline space site map to determine the target user's current location and visited areas; S402, extract the target user's interest tags based on the personal spatiotemporal context; the extraction criteria for the interest tags include one or more of the target user's historical dwell time, number of QR code scans, activity participation records, and object interaction records; S403, based on the current location and the interest tag, match the corresponding space object in the current offline space excluding the visited area, and generate a space service result based on the distance between the matched space object and the target user's current location.

9. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the spatiotemporal context-based spatial service provision method as described in any one of claims 1-8.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

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