Equipment linkage method and system, equipment and storage medium

Abstract

The invention relates to the field of equipment linkage, and discloses an equipment linkage method and system, equipment and a storage medium, and the method comprises the steps: obtaining a query instruction sent by an AI alarm module through an in-space equipment recommendation module, and carrying out the analysis; according to the analyzed query instruction, obtaining target Internet of Things equipment and semantic space information associated with the target Internet of Things equipment from a space model library module; and obtaining a superior semantic space list, a related or adjacent semantic space list and a subordinate semantic space list of the semantic space information through a semantic space recommendation module to realize equipment linkage. By means of the event positioning method and device, the event positioning accuracy is greatly improved, more dimensions can be selected and analyzed, and commanding is more specific, comprehensive and accurate. A semantic-based information organization form provides a basis for command and dispatch based on artificial intelligence, and workflow and extended functions can be smoothly added.

Description

Technical Field

[0001] This disclosure relates to the field of equipment linkage technology, specifically to a method, system, device, and storage medium for equipment linkage. Background Technology

[0002] In today's era of rapid technological advancement, with the widespread application of IoT technology, the demand for intelligent management in parks, large buildings, and other similar venues is increasing daily. These scenarios deploy a large number of IoT devices, such as cameras, sensors, and access control systems, to achieve functions like security monitoring, environmental monitoring, and equipment management. However, existing park monitoring and equipment management systems have many shortcomings in practical applications and struggle to meet the complex and ever-changing real-world needs.

[0003] like Figure 2 The illustrated device linkage system based on area tags binds IoT sensors and cameras to pre-set fixed area tags (such as "Production Workshop A" and "Tank Area B") within the park. When the AI ​​alarm module detects an anomaly in a certain area, the system retrieves device data (such as camera footage and sensor readings) for that area based on the pre-set area tags and displays it centrally on the command center interface. Core principle: Area tag mapping: The physical location of devices is marked using static area tags (such as "Park A-Building 1-3rd Floor"), forming a one-to-one association between devices and areas. Alarm triggering mechanism: After an alarm is triggered by AI video analysis or sensor data anomalies, the system matches devices through area tags to achieve linked display of "area-device". Simplified hierarchical relationship: Only supports simple area hierarchies (such as park → building → floor), lacking precise modeling of the device's effective range (such as camera field of view radius and sensor monitoring range).

[0004] like Figure 3 The static device recommendation system shown generates a fixed list of recommended devices by analyzing their usage frequency in historical events. For example, when a fire alarm is detected, the system automatically recommends frequently used cameras or sensors from historical events, sorted by usage frequency. Core principles: Historical data statistics: The system records device usage history through a log system, calculating device "popularity" (e.g., usage frequency, response speed). Static recommendation logic: Preset fixed recommendation rules (e.g., "top 10 most popular devices are displayed first"), without dynamic adjustment based on real-time event spatial coordinates or semantic space. Manual intervention process: Operators need to manually select recommended devices or supplement information through secondary searches.

[0005] Furthermore, the equipment management system based on a static spatial model library pre-establishes a 3D spatial model library for the park, marking the physical location and operational range of equipment (such as camera field of view and sensor monitoring radius) in the model. When a spatial description (such as "coordinates (10,20,30)") is received, the system searches the model library for equipment that covers those coordinates and returns the result. Core principles: Spatial model construction: A spatial model of the park is created using CAD drawings or 3D modeling tools, marking the location, coordinates, and operational range of equipment. Coordinate matching algorithm: Equipment covering the target coordinates is selected through geometric calculations (such as coordinate range matching and radius coverage judgment). Static data updates: The spatial model library requires manual periodic updates and cannot reflect real-time changes in equipment status or the environment.

[0006] It is evident that traditional park monitoring systems primarily focus on the independent operation and data collection of individual devices, lacking effective linkage mechanisms between them. For example, in the event of a security incident, such as a fire or theft, the system can only provide information collected by a single device, failing to integrate and correlate information from devices in different locations and of different types. This forces managers to manually search for relevant information across multiple device systems when handling incidents, resulting in cumbersome procedures that waste significant time and effort, are prone to overlooking crucial information, and ultimately impact the efficiency and accuracy of incident handling.

[0007] Furthermore, existing systems handle the relationship between devices and space in a relatively simplistic way. They often merely record the physical location of devices without constructing a spatial model that includes multi-dimensional information. In real-world scenarios, information such as the scope of a device's operation, its relationships with other devices, and its semantic space are crucial for event analysis and processing. For example, in a large campus, a camera located on a floor of a building is not only associated with other devices on that floor (such as smoke sensors and access control systems), but also potentially connected to devices on adjacent floors and in adjacent areas. Existing systems, lacking effective management and utilization of these relationships, cannot quickly and accurately locate all devices related to an event, making it difficult to fully reconstruct the scene in which the event occurred.

[0008] Furthermore, the current system lacks the responsiveness to dynamically changing scenarios and new, complex events. As the park environment and business needs continue to evolve, the types and complexity of security incidents are also increasing. For example, during large-scale events, frequent personnel movement within the park can lead to multiple concurrent incidents. The existing system, lacking an intelligent recommendation mechanism based on real-time data and historical experience, cannot quickly provide effective decision support to managers based on the real-time situation of the event and experience in handling similar historical events, resulting in inefficiency when dealing with complex events.

[0009] Meanwhile, existing park monitoring systems also suffer from compatibility and scalability issues. IoT devices from different manufacturers often employ different communication protocols and data formats, making system integration and management difficult. Furthermore, when system functionalities need to be expanded or upgraded, the lack of standardized interfaces and unified data management mechanisms makes seamless integration of new functions with the existing system challenging, increasing maintenance costs and upgrade complexity.

[0010] In summary, existing park monitoring and equipment management systems have significant shortcomings in terms of equipment linkage, spatial information utilization, dynamic response capabilities, compatibility, and scalability. Therefore, developing a method and system for equipment linkage that can achieve efficient linkage of multiple devices, accurate utilization of spatial information, rapid response to dynamic changes, and good compatibility and scalability is of significant practical importance. Summary of the Invention

[0011] This disclosure provides a device linkage method, system, device, and storage medium to solve or alleviate one or more of the above-mentioned technical problems in the prior art.

[0012] According to one aspect of this disclosure, a device linkage method is provided, comprising: The system obtains and parses query commands sent by the AI ​​alarm module through the device recommendation module within the space. Based on the parsed query instructions, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module. The semantic space recommendation module obtains the upper-level semantic space list, related or adjacent semantic space list, and subordinate semantic space list of the semantic space information to achieve device linkage.

[0013] In one possible implementation, the query command sent by the AI ​​alarm module is obtained and parsed through the in-space device recommendation module, including: The spatial description information in the query instruction is parsed and converted into a target format to form parsed spatial description information.

[0014] In one possible implementation, based on the parsed query instruction, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module, including: Based on the parsed spatial description information, a query is performed in the spatial model library to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. Based on the correlation between IoT devices and events described by spatial description information, directly associated target IoT devices are determined based on the recommended IoT devices; Obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

[0015] In one possible implementation, the spatial model library module provides a standard API to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

[0016] In one possible implementation, the in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

[0017] In one possible implementation, the device linkage method also includes: Through the dynamic historical experience recommendation module, for a given IoT AI-analyzed event, the system can query the historical records of user question analysis. Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

[0018] According to one aspect of this disclosure, a device linkage system is provided, comprising: The in-space device recommendation module is used to obtain and parse query commands sent by the AI ​​alarm module; It is used to obtain the target IoT device and the semantic spatial information associated with the target IoT device from the spatial model library module according to the parsed query instructions; The semantic space recommendation module is used to obtain the upper-level semantic space list, related or adjacent semantic space list and subordinate semantic space list of the semantic space information to realize device linkage.

[0019] In one possible implementation, the in-space device recommendation module includes: The parsing submodule is used to parse the spatial description information in the query instruction, convert the spatial description information into a target format, and form parsed spatial description information.

[0020] In one possible implementation, the in-space device recommendation module includes: The query submodule is used to query the spatial model library based on the parsed spatial description information to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. The determination submodule is used to determine the directly associated target IoT devices based on the correlation between IoT devices and events described by spatial description information, according to the recommended IoT devices; The acquisition submodule is used to obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

[0021] In one possible implementation, the spatial model library module provides a standard API to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

[0022] In one possible implementation, the in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

[0023] In one possible implementation, the equipment linkage system also includes: The dynamic historical experience recommendation module is used for: For a given IoT AI-analyzed event, query the historical records of user question analysis; Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

[0024] According to one aspect of this disclosure, an apparatus is provided, comprising: Processor and memory; The memory is used to store computer programs, and the processor calls the computer programs stored in the memory to execute any of the above-described device linkage methods.

[0025] According to one aspect of this disclosure, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the processor is able to perform the device linkage method described in any of the preceding claims.

[0026] This disclosure offers the following advantages: It significantly reduces the amount of data required for problem localization in the command and control center, allowing for one-click access to critical information and reducing information retrieval speed by several times to ten times. The interface is more intuitive, with user-friendly and logically intuitive prompts, lowering the skill requirements for command and control center personnel and preventing misjudgments and omissions. Event localization accuracy is greatly improved, and more dimensions can be selected for analysis, enabling more specific, comprehensive, and accurate command. The semantic-based information organization provides a foundation for AI-based command and dispatch, allowing for smooth workflow additions and functional expansion.

[0027] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features and advantages of this application will become apparent from the accompanying drawings. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not intended to limit this disclosure. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0029] Figure 1 This is a flowchart of a device linkage method according to an exemplary embodiment of the present invention; Figure 2 This is a schematic diagram of the device linkage method in an existing device linkage system based on region tags; Figure 3 This is a schematic diagram of the device recommendation method in an existing static device recommendation system based on historical data; Figure 4 This is a flowchart of a lightweight three-dimensional spatial dynamic device linkage method according to an exemplary embodiment of the present invention; Figure 5 This is a block diagram of a device linkage system according to an exemplary embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a device according to an exemplary embodiment of this invention. Detailed Implementation

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0031] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0033] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0034] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0035] It is worth noting that existing technologies in the field of park security monitoring have achieved basic device linkage and AI analysis, but generally suffer from the following problems: Insufficient utilization of spatial relationships: Device linkage relies on area labels or static coordinates, lacking multi-dimensional analysis of semantic spatial hierarchy and scope of action. Cumbersome operation process: Excessive manual intervention, lack of intelligent reuse of historical experience, and low efficiency when multiple events occur concurrently. Insufficient real-time performance: The interaction mechanism between AI alarms and device linkage is lagging, failing to meet the needs of rapid response to emergency events. This disclosure effectively solves the above problems by constructing a dynamic rule base, a multi-dimensional spatial association model, and a real-time interaction mechanism, significantly improving the intelligence level of park security monitoring and emergency response efficiency.

[0036] Figure 1 This is a flowchart of a device linkage method according to an exemplary embodiment of the present invention, such as... Figure 1 As shown, an exemplary embodiment of this disclosure provides a device linkage method, including: The system obtains and parses query commands sent by the AI ​​alarm module through the device recommendation module within the space. Based on the parsed query instructions, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module. The semantic space recommendation module obtains the upper-level semantic space list, related or adjacent semantic space list, and subordinate semantic space list of the semantic space information to achieve device linkage.

[0037] like Figure 4 As shown, this embodiment specifically discloses a lightweight three-dimensional spatial dynamic device linkage method, including the following functional modules: a spatial model library module for IoT devices, a spatial device recommendation module, a semantic space recommendation module based on semantics, and a log-based dynamic historical experience recommendation module. The core module interaction steps include: the AI ​​alarm module sending a query command containing spatial coordinates to the spatial device recommendation module; this module retrieving the device semantic space, coordinates, and relationships from the spatial model library module, and returning a list of recommended devices and semantic space information; the semantic space recommendation module generating a list of superior, subordinate, and adjacent spaces based on the current semantic space to assist in event reconstruction; and the dynamic historical experience recommendation module analyzing historical judgment records through a log system to provide recommendations for frequently used devices / spaces and popularity indicators.

[0038] This embodiment achieves precise and intelligent device linkage by constructing a multi-dimensional spatial association model: by integrating device semantic space, coordinates, scope of action, and hierarchical relationships into the spatial model library, and combining the spatial coordinate input of AI alarms, it recommends directly associated IoT sensors and their respective semantic spaces (including coordinates and names) in real time. This solves the problems of single device linkage dimensions, insufficient utilization of spatial relationships, and inaccurate positioning in existing technologies, improving the efficiency and accuracy of initial event judgment. For example, it solves the problems of single device linkage dimensions and insufficient utilization of spatial relationships in existing technologies: Existing technologies only use device location tags or simple semantic spaces for association display (such as "display of device associations at the incident location"), but lack multi-dimensional analysis of the hierarchical relationships (such as upper-level areas, adjacent areas, and subordinate sub-areas), precise matching of spatial coordinates, and scope of action of the semantic spaces to which the devices belong. This results in the inability to quickly associate surrounding associated devices and expand the analysis dimensions when locating events, affecting the efficiency of accurate location of fire sources / abnormal events.

[0039] Specifically, the system obtains and parses query commands sent by the AI ​​alarm module through the in-space device recommendation module, including: The spatial description information in the query instruction is parsed and converted into a target format to form parsed spatial description information.

[0040] Specifically, based on the parsed query command, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module, including: Based on the parsed spatial description information, a query is performed in the spatial model library to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. Based on the correlation between IoT devices and events described by spatial description information, directly associated target IoT devices are determined based on the recommended IoT devices; Obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

[0041] In this embodiment, the device recommendation module based on spatial coordinates includes the following functions: based on the spatial description of a query command, it returns recommended directly associated IoT sensors and their associated semantic spaces. The spatial description of the query request often comes from the AI's alarm module, and the returned information is divided into: the semantic space coordinates of the event estimation and the semantic space name. Detailed steps: Receive query command: The module receives a query command from the AI ​​alarm module. This command contains spatial description information of the event, such as the specific coordinate range and area name.

[0042] Parse query commands: Parse the spatial description information in the query command and convert it into a format that the module can process. For example, if the spatial description is a coordinate range, parse it into specific coordinate values; if it is a region name, find the corresponding coordinate range through the mapping relationship in the spatial model library.

[0043] Querying the Spatial Model Library: Based on the parsed spatial description information, a query is performed in the spatial model library. The spatial model library stores information such as the semantic space, spatial coordinates, and effective range of each IoT sensor. The specific query steps are as follows: 1. Determine the range of potentially associated sensors: Based on the spatial description information, filter out IoT sensors located within the spatial range or whose effective range covers the space. 2. Determine directly associated sensors: Further analyze the filtered sensors, and based on the correlation between the sensor and the event (e.g., sensor type, historical association records, etc.), determine the directly associated IoT sensors.

[0044] Obtain associated semantic space information: For a known directly associated IoT sensor, obtain its associated semantic space information from the spatial model library, including semantic space coordinates and semantic space name.

[0045] Return results: The estimated semantic space coordinates and semantic space name of the event are returned as information and provided to the calling module (such as the command center system) for subsequent event processing and decision-making.

[0046] Specifically, the spatial model library module provides standard APIs to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

[0047] In this embodiment, the spatial model library module for IoT devices includes the following functions: providing a standard API to enable querying of devices in the semantic space and recommending related semantic spaces. To achieve the above functions, in addition to storing the attributes of IoT devices, information such as the semantic space to which the device belongs, spatial coordinates, scope of operation, and semantic space relationships are also added. An exemplary spatial model library data structure is as follows: Device ID: DEV-001; Semantic space: "Building A-1, 3rd Floor, Park"; Spatial coordinates: [x, y, z]; "Area of ​​effect": "Radius 50 meters"; "Related Spaces": ["Building A-1, 2nd Floor", "Building A-1, 4th Floor", "Adjacent Corridor"]; Device type: Camera.

[0048] Specifically, the in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

[0049] In this embodiment, the semantic-based related semantic space recommendation module includes the following functions: querying and returning lists of parent semantic spaces, related or adjacent semantic spaces, and subordinate semantic spaces through the semantic space. The recommended semantic space provides specific dimensions for event reconstruction and analysis, helping users quickly access the information they need and prompting users whether they need more information to complete the profile.

[0050] This embodiment improves emergency response speed by optimizing the real-time interaction process between AI and devices: It achieves real-time data integration between the AI ​​alarm module and the spatial coordinate device recommendation module through a standardized API, automatically parsing the spatial descriptions output by AI (such as coordinates and area names), and returning associated devices and semantic spatial information within seconds, meeting the "rapid location-linked response" requirements for emergencies such as fires and intrusions. It solves the problem of insufficient real-time performance in existing AI alarm and device linkage technologies: Existing technologies lack a real-time data interaction mechanism between the AI ​​alarm module and the device recommendation module, making it impossible to quickly look up associated devices and their semantic spaces based on AI-recognized spatial coordinates (such as precise coordinates for fire location), still requiring manual input or secondary confirmation of spatial descriptions, resulting in a response speed lagging behind the needs of emergency event handling.

[0051] Specifically, the equipment linkage method also includes: Through the dynamic historical experience recommendation module, for a given IoT AI-analyzed event, the system can query the historical records of user question analysis. Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

[0052] In this embodiment, the log-based dynamic historical experience recommendation module includes the following functions: for a given IoT AI-analyzed event, it queries the historical records of user question analysis and provides statistics on the semantic space IoT devices used. When this data is displayed to the command and control center, it provides recommendations and popularity indicators for semantic space and devices. This list improves the speed at which users access information and reminds users to supplement relevant analysis dimensions to avoid omissions.

[0053] This embodiment simplifies the operation process by establishing a dynamic historical experience recommendation mechanism: based on the log system analysis of frequently used semantic spaces and devices in historical events, a recommendation list with heat indicators is generated to help operators quickly locate key information, avoid information omissions or duplicate operations when multiple events occur concurrently, and reduce the cost of manual decision-making. For example, it solves the problem of existing technologies relying on manual decision-making and not intelligently reusing historical experience: although existing technologies propose recommendations based on semantic spaces and sensors in historical events (such as "semantic spaces of interest in historical events"), these are only static recommendations and do not dynamically generate a list of frequently used devices / spaces in conjunction with real-time events, nor do they provide heat indicators to assist in priority judgment. When multiple events occur concurrently, operators need to manually retrieve multi-dimensional information, which can easily lead to the omission of key information or processing delays due to cumbersome operations.

[0054] In summary, this embodiment constructs a multi-dimensional spatial model library that can store device semantic space, coordinates, scope of action, and relationships, supporting device querying and space recommendation through standardized APIs. This embodiment implements AI-driven spatial coordinate recommendation: based on the spatial coordinates of AI alarms, it matches associated devices and semantic spaces in real time, outputting dual location information of coordinates and names. This embodiment constructs a hierarchical semantic space recommendation system: generating recommendation lists through semantic space hierarchical relationships (upper / lower / adjacent), supporting multi-dimensional event analysis. This embodiment can achieve dynamic historical experience matching: based on log statistics and historical analysis of high-frequency devices / spaces, it provides popularity indicators and intelligent recommendations.

[0055] Figure 5 This is a block diagram of a device linkage system according to an exemplary embodiment of the present invention, such as... Figure 5 As shown, an exemplary embodiment of this disclosure provides a device linkage system, including: The in-space device recommendation module is used to obtain and parse query commands sent by the AI ​​alarm module; It is used to obtain the target IoT device and the semantic spatial information associated with the target IoT device from the spatial model library module according to the parsed query instructions; The semantic space recommendation module is used to obtain the upper-level semantic space list, related or adjacent semantic space list and subordinate semantic space list of the semantic space information to realize device linkage.

[0056] Specifically, the in-space equipment recommendation module includes: The parsing submodule is used to parse the spatial description information in the query instruction, convert the spatial description information into a target format, and form parsed spatial description information.

[0057] Specifically, the in-space equipment recommendation module includes: The query submodule is used to query the spatial model library based on the parsed spatial description information to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. The determination submodule is used to determine the directly associated target IoT devices based on the correlation between IoT devices and events described by spatial description information, according to the recommended IoT devices; The acquisition submodule is used to obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

[0058] Specifically, the spatial model library module provides standard APIs to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

[0059] Specifically, the in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

[0060] Specifically, the equipment linkage system also includes: The dynamic historical experience recommendation module is used for: For a given IoT AI-analyzed event, query the historical records of user question analysis; Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

[0061] This embodiment constructs a model library containing diverse spatial information to achieve dynamic and accurate mapping of device-space relationships, meeting real-time positioning needs in complex scenarios. It addresses the problems of existing technologies, including: device association based solely on region labels or static coordinates, lacking integrated analysis of the device's semantic space (e.g., "Building A-1, 3rd Floor"), spatial coordinates (x / y / z 3D coordinates), effective range (e.g., camera field of view radius, sensor monitoring range), and hierarchical relationships (superior / inferior, adjacent spaces). When the AI ​​alarm module outputs an event space description (e.g., fire source coordinates), traditional systems cannot quickly match associated devices and expand analysis dimensions, resulting in long positioning times and low accuracy.

[0062] This embodiment analyzes historical judgment records through a log system to establish a dynamic intelligent recommendation mechanism, providing the command center with a list of related devices and spaces marked with heat indicators. This enables "one-click access" to key information and reduces the cost of manual decision-making. It addresses the problems of existing technologies, including: existing systems rely on manual retrieval of multi-dimensional device data or recommend limited information based on static historical experience, failing to dynamically generate frequently used semantic spaces and device lists in conjunction with real-time events. When multiple events occur concurrently, operators are prone to overlooking key information due to cumbersome operations (such as omitting sensor data from adjacent areas), affecting the completeness of event judgment.

[0063] This embodiment achieves real-time data integration between the AI ​​alarm and the spatial coordinate device recommendation module through a standardized API. It automatically parses the spatial description (coordinates, area name, etc.) output by the AI ​​and returns associated device and semantic spatial information (coordinates, name) within seconds, forming a closed-loop rapid response chain of "alarm-location-linkage". This solves problems in existing technologies, including: the spatial description output by the AI ​​alarm module (such as fire location coordinates) cannot directly drive the device recommendation module, requiring manual secondary input or confirmation, resulting in response delays (e.g., more than 10 seconds from alarm to retrieval of camera footage). For emergencies such as fires and intrusions, second-level response is crucial.

[0064] This embodiment constructs a dynamic recommendation model based on the hierarchical relationships (superior, subordinate, adjacent) in semantic space, providing the command center with a multi-dimensional analytical perspective and assisting decision-makers in quickly formulating comprehensive response plans (such as simultaneously accessing cameras on adjacent floors and initiating cross-regional fire linkage). It addresses existing technical problems, including: existing systems only support simple hierarchical semantic space relationships (e.g., park → building), lacking intelligent recommendations for "related semantic spaces" (e.g., adjacent corridors, upper and lower floors), resulting in event analysis being limited to a single dimension (e.g., only viewing data from the floor where the incident occurred), making it difficult to reconstruct the full picture of the event (e.g., determining whether the fire has spread to adjacent areas).

[0065] Figure 6 This is a schematic diagram of the structure of a device according to an exemplary embodiment of this invention. Figure 6 As shown, corresponding to the device linkage method provided above, this disclosure also provides a device. Since the embodiment of this device is similar to the above method embodiment, the description is relatively simple; please refer to the description in the above method embodiment section for relevant details. The device described below is merely illustrative. This device may include: a processor 1, a memory 2, a communication bus (i.e., the aforementioned device bus), and a lookup engine. The processor 1 and memory 2 communicate with each other through the communication bus and communicate with external systems through a communication interface. The processor 1 can call logical instructions in the memory 2 to execute the device linkage method.

[0066] Furthermore, the logical instructions in the aforementioned memory 2 can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] On the other hand, this disclosure also provides a processor-readable storage medium storing a computer program 3, which, when executed by a processor 1, is implemented to perform the device linkage method provided in the above embodiments.

[0068] The processor-readable storage medium can be any available medium or data storage device that the processor 1 can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0069] The above are merely preferred embodiments of this disclosure. The scope of protection of this disclosure is not limited to the above embodiments. All technical solutions falling within the scope of this disclosure are protected. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of this disclosure should be considered within the scope of protection of this disclosure.

Claims

1. A method for linking equipment, characterized in that, include: The system obtains and parses query commands sent by the AI ​​alarm module through the device recommendation module within the space. Based on the parsed query instructions, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module. The semantic space recommendation module obtains the upper-level semantic space list, related or adjacent semantic space list, and subordinate semantic space list of the semantic space information to achieve device linkage.

2. The equipment linkage method according to claim 1, characterized in that, The system obtains and parses query commands sent by the AI ​​alarm module through the in-space device recommendation module, including: The spatial description information in the query instruction is parsed and converted into a target format to form parsed spatial description information.

3. The equipment linkage method according to claim 1, characterized in that, Based on the parsed query command, the target IoT device and its associated semantic spatial information are obtained from the spatial model library module, including: Based on the parsed spatial description information, a query is performed in the spatial model library to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. Based on the correlation between IoT devices and events described by spatial description information, directly associated target IoT devices are determined based on the recommended IoT devices; Obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

4. The equipment linkage method according to claim 1, characterized in that, The spatial model library module provides standard APIs to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

5. The equipment linkage method according to claim 1, characterized in that, The in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

6. The equipment linkage method according to any one of claims 1-5, characterized in that, Also includes: Through the dynamic historical experience recommendation module, for a given IoT AI-analyzed event, the system can query the historical records of user question analysis. Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

7. A device linkage system, characterized in that, include: The in-space device recommendation module is used to obtain and parse query commands sent by the AI ​​alarm module; It is used to obtain the target IoT device and the semantic spatial information associated with the target IoT device from the spatial model library module according to the parsed query instructions; The semantic space recommendation module is used to obtain the upper-level semantic space list, related or adjacent semantic space list and subordinate semantic space list of the semantic space information to realize device linkage.

8. The equipment linkage system according to claim 7, characterized in that, The in-space equipment recommendation module includes: The parsing submodule is used to parse the spatial description information in the query instruction, convert the spatial description information into a target format, and form parsed spatial description information.

9. The equipment linkage system according to claim 7, characterized in that, The in-space equipment recommendation module includes: The query submodule is used to query the spatial model library based on the parsed spatial description information to obtain recommended IoT devices that are located within the spatial range described by the spatial description information or whose scope of action covers the spatial range. The determination submodule is used to determine the directly associated target IoT devices based on the correlation between IoT devices and events described by spatial description information, according to the recommended IoT devices; The acquisition submodule is used to obtain the semantic space information associated with the target IoT device from the spatial model library, including semantic space coordinates and semantic space name.

10. The equipment linkage system according to claim 7, characterized in that, The spatial model library module provides standard APIs to enable external queries of devices in the semantic space and semantic space-related recommendations. The spatial model library module includes a spatial model library, which stores the attributes of IoT devices, the semantic space to which the devices belong, the spatial coordinates of the devices, the scope of operation of the devices, and the semantic space relationships.

11. The equipment linkage system according to claim 7, characterized in that, The in-space device recommendation module achieves real-time data connection with the AI ​​alarm module through a standardized API.

12. The equipment linkage system according to any one of claims 7-11, characterized in that, Also includes: The dynamic historical experience recommendation module is used for: For a given event identified by IoT AI analysis, query the historical records of user question analysis; Based on the historical records of user problem analysis, statistical data on the semantic space IoT devices used are provided respectively; Based on statistical data of IoT devices used in the semantic space, recommended labels and popularity labels for semantic space and IoT devices are generated.

13. A device, characterized in that, include: Processor and memory; The memory is used to store computer programs, and the processor calls the computer programs stored in the memory to execute the device linkage method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the device linkage method according to any one of claims 1 to 6.

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