Information visualization method and system based on industrial internet of things

Abstract

The application discloses an information visualization method and system based on Internet of Things, and relates to the technical field of information visualization. The application provides a method for a user to read and control the state of a device on a production object platform through a user terminal device on a user platform. The method comprises obtaining control instruction data, forming a space-time retrieval table, reasoning the device action using a device knowledge graph, converting it into specific control instructions and simulating execution through a virtual twin environment, processing device conflicts, and then actually executing the control instructions and displaying the device state in real time. In addition, the change rate is calculated according to the device state data to drive the visualization rendering, and static or dynamic visualization content is displayed. Dynamic data fusion through space-time alignment solves the problem of data fragmentation, change rate-driven static visualization rendering, eliminates visualization jitter, and a hierarchical priority arbitration mechanism is used to process device conflict pre-play, arbitration and execution of three-layer control chains, thereby ensuring real-time and collaboration.

Description

Technical Field

[0001] This application relates to the field of information visualization technology based on the Internet of Things, and more particularly to the field of information visualization system technology. Background Technology

[0002] With the rapid development of IoT technology, various IoT-based information visualization methods and systems have been widely applied in fields such as industry, urban management, and energy monitoring. However, existing technologies still suffer from problems such as insufficient data processing flexibility, poor user interaction experience, and weak system adaptability in the process of achieving information visualization, making it difficult to meet the needs for efficient, intelligent, and personalized information display in complex scenarios.

[0003] Existing technologies lack effective unification of multimodal data and device knowledge graph reasoning capabilities, leading to potential virtual state conflicts during control command execution and hindering the reasoning of device action sequences and dynamic rendering of visual content. Therefore, existing technologies urgently need improvement to address these issues. Summary of the Invention

[0004] In order to at least overcome the above-mentioned shortcomings in the prior art, the purpose of this application is to provide an information visualization method and system based on the Internet of Things to solve the above problems.

[0005] In a first aspect, this application provides an information visualization method based on the Internet of Things, comprising the following steps: Acquire control command data and device status data; Based on the control command data, obtain the user demand vector; based on the user demand vector, obtain the device action sequence through the device knowledge graph; The virtual execution result is obtained by simulating the execution of the device action sequence and performing conflict detection. If there is a virtual state conflict in the virtual execution result, the specific device control command will be arbitrated according to the preset arbitration rules to determine the command execution strategy. Based on the device status data, determine whether it exceeds the preset threshold, drive visualization rendering, and obtain dynamic or static visualization content.

[0006] In one possible implementation, a spatiotemporal lookup table with a unified spatiotemporal reference is obtained based on control command data and device status data, and in response to the broadcast reference time; wherein, forming the spatiotemporal lookup table specifically includes: Broadcast a reference time; control command data includes device ID, control command, and action parameters; device status data includes device ID, device coordinates, and device data acquisition timestamp. Based on the alignment relationship between the device data acquisition timestamps and the broadcast reference time, the time deviation of each device is calculated to unify the acquisition timestamps of all devices to the reference time. Applying an affine transformation to the device coordinates maps the local device coordinates to the global coordinate system to obtain the global coordinates; A multidimensional index is created based on the device ID, the aligned acquisition timestamp, and the global coordinates to obtain a spatiotemporal retrieval table.

[0007] In one possible implementation, the device knowledge graph is obtained by integrating the physical attributes, actions, and associated devices of all devices on the production object platform based on device logs; Among them, physical attributes include device model and action parameter range, actions include standard actions that the device can perform, and associated devices are the collaborative dependencies between devices; The following are included: Using device ID as the core node, actions, action parameter ranges, preceding actions, subsequent actions, associated devices, and core nodes are combined to obtain a mesh knowledge structure as a device knowledge graph. The mesh knowledge structure uses device ID as the node, and the associated edges of the node include action edges, parameter edges, dependency edges, and collaboration edges.

[0008] In one possible implementation, the method also includes: Based on the bidirectional mapping table, the conversion between abstract actions and specific equipment control commands is performed; The bidirectional mapping table is used to provide forward mapping and reverse mapping functions. Forward mapping is used to obtain specific device instructions based on abstract actions in the device knowledge graph, while reverse mapping is used to obtain the corresponding abstract actions based on the device's status data.

[0009] In one possible implementation, a virtual twin environment is obtained based on a spatiotemporal retrieval table and a device knowledge graph; the steps for obtaining the virtual twin environment include: Based on the global coordinates in the spatiotemporal retrieval table, construct the three-dimensional spatial layout of the device in the virtual environment according to the scale. Set the time base of the virtual twin environment based on the base time broadcast by the management platform; Based on the spatiotemporal retrieval table, the real-time status data of each device is imported into the virtual twin environment.

[0010] In one possible implementation, conflict detection includes resource conflict detection, spatial conflict detection, temporal conflict detection, and parameter conflict detection; Resource conflict detection checks whether the same device ID has been assigned multiple mutually exclusive actions within the same time period. Spatial conflict detection calculates whether the motion trajectories of different devices intersect in space; Temporal conflict detection verifies the actual occurrence time of the action sequence during virtual execution and whether it is consistent with the preceding and following actions on the dependency edges in the device knowledge graph. Parameter conflict detection verifies whether device status data exceeds the allowable range.

[0011] In one possible implementation, the pre-defined arbitration rules include a three-tiered priority arbitration rule, and the steps for conducting arbitration according to the pre-defined arbitration rules specifically include: The priority of the control command data is determined based on the command type; command types include emergency stop commands, production task commands, and optimization commands. If the instruction type is an emergency stop instruction, it is determined to be the highest priority, and an instruction execution strategy of immediately interrupting all tasks is obtained; If the instruction type is a production task instruction, it is determined to be of secondary priority, and an instruction execution strategy based on the device timing queue is obtained; If the instruction type is an optimized instruction, it is determined to be the lowest priority, and an instruction execution strategy based on the device's idle time period is obtained.

[0012] In one possible implementation, the steps driving the visualization rendering specifically include: Based on the equipment status data, calculate the rate of change of time-series sensor data for the set key equipment parameters; If the rate of change is greater than a preset threshold, dynamic mode is activated. Dynamic mode is used to mark data mutations at the corresponding device coordinate positions. If the rate of change is less than or equal to a preset threshold, static mode is activated. Static mode is used to freeze the current device status data at the corresponding device coordinate position.

[0013] In one possible implementation, the method also includes: Based on the dual-channel separation structure, control commands are transmitted; the dual-channel separation structure includes a real-time control channel and a data feedback channel. Among them, the user intent commands in the control command data are transmitted from the management platform to the production object platform via the real-time control channel; the data commands of the equipment are transmitted from the production object platform to the management platform via the data feedback channel.

[0014] Secondly, the information visualization system based on the Internet of Things includes a knowledge graph module with sequential electrical connections, a virtual twin environment module, an arbitration decision module, and a visualization module; The knowledge graph module is configured to acquire control command data and device status data; Then, based on the control command data, a user demand vector is obtained; based on the user demand vector, a device action sequence is obtained through the device knowledge graph. The virtual twin environment module is configured to simulate execution based on the sequence of device actions, perform conflict detection, and obtain virtual execution results. The arbitration decision module is configured to arbitrate specific device control commands according to preset arbitration rules if there is a virtual state conflict in the virtual execution result, so as to determine the command execution strategy. The visualization module is configured to determine whether the device status data exceeds a preset threshold, drive visualization rendering, and obtain dynamic or static visualization content.

[0015] Thirdly, this application provides a computer system device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the information visualization methods based on the Internet of Things.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the Internet of Things-based information visualization method.

[0017] In summary, the beneficial effects that this application can achieve are: This application provides an information visualization method and system based on the Internet of Things. The proposed method constructs a spatiotemporal retrieval table and solves the data fragmentation problem by aligning spatiotemporal labels. It realizes the unification of multi-source data from different devices onto the same time axis and solves the problem of spatiotemporal misalignment of cross-platform data. The proposed hierarchical rendering strategy solves the problem of visualization jitter by implementing a hierarchical rendering strategy based on the rate of data change. The paper proposes to use device knowledge graphs to infer device action sequences and to establish a virtual twin environment to rehearse control commands and detect conflicts. Through user intent parsing, rule matching, and virtual rehearsal, the control delay bottleneck is solved. The proposed multi-platform arbitration mechanism resolves priority conflicts of multi-source data through three levels of preset arbitration rules, enabling high-priority data to resolve conflicts first and improving the reliability of control command execution. The proposed five platforms solve the problem of low efficiency in cross-platform collaboration and the failure of inter-platform collaboration by using spatiotemporal synchronization frames. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the steps of an information visualization method based on the Internet of Things (IoT). Figure 2 This is a flowchart illustrating an information visualization method based on the Internet of Things (IoT). Figure 3 This is a flowchart illustrating the sequence of device actions using an information visualization method based on the Internet of Things (IoT). Figure 4 This is a flowchart illustrating the arbitration strategy based on an information visualization method for the Internet of Things. Figure 5 This is a schematic diagram of the structure of an information visualization system based on the Internet of Things (IoT). Figure 6 This is a schematic diagram of the five-platform system architecture for an information visualization system based on the Internet of Things. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0020] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The modules of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] Example 1 In this embodiment, refer to Figure 1 - Figure 5 This is the first embodiment of the present invention. This embodiment provides an information visualization method based on the Internet of Things, such as... Figure 1 As shown, the main steps include: Step S1: Obtain control command data from the user platform. The control command data includes the device ID of the control command, the control command, the action parameters, and the control command timestamp. The device ID of the control command, the control command, and the action parameters are used as the user demand vector. Simultaneously, the sensor network platform is accessed to obtain real-time status data of each device. The status data includes the device ID, device coordinates, time-series sensor data, and device data acquisition timestamps of the production object platform, and the corresponding device logs are stored in the database of the sensor network platform. The device ID is used as the unique identifier of the device. The management platform issues a reference time, aligns it with the reference time based on the device data collection timestamp, and calibrates the device coordinates to form a spatiotemporal retrieval table. Step S2: Input the user demand vector into the pre-trained device knowledge graph, infer the corresponding device actions based on the user demand vector, and output an abstract sequence of device actions. Furthermore, through a two-way mapping table, the device action sequence is converted into specific device control instructions, and the device status data is converted into an abstract device action sequence; Step S3: Execute device control commands in the virtual twin environment to obtain virtual execution results; If the virtual execution result results in a conflict with the virtual state of the device, proceed to step S4; otherwise, if no conflict occurs, proceed to step S5. Step S4: If a conflict occurs, arbitration is conducted according to the preset arbitration rules, and the equipment control commands are executed sequentially according to the corresponding arbitration strategy. Step S5: Calculate the rate of change of time-series sensor data based on the status data, and drive visualization rendering based on a preset threshold to generate static visualization content. Furthermore, static visual content is displayed on the user platform, and the flow status of user control commands is shown on the static visual content.

[0022] The purpose of this invention is to overcome the shortcomings of the prior art and provide an information visualization method and system based on the Internet of Things. The technical problem to be solved is that traditional industrial visualization systems mainly rely on static data models for display. When applied to continuous production scenarios or scenarios with dynamic changes in the production environment, there are large real-time errors, which may affect the accurate location of faults and emergency control.

[0023] The purpose of this invention is to specifically address the industrial IoT scenario involving five-platform collaboration. Building upon existing industrial IoT technologies, it improves upon existing methods by incorporating spatiotemporal alignment and fusion. This approach considers the impact of device relationships and command priorities during data processing, integrating control intent pre-simulation, conflict prediction, and dynamic resource scheduling into the visualization decision-making process. This significantly reduces control errors and response lags caused by factors such as data fragmentation, platform collaboration delays, and static rendering mechanisms. The invention provides a visualization control method and system that achieves unified spatiotemporal references for multi-platform data and improves the reliability of control command execution.

[0024] In existing technologies, industrial IoT monitoring systems have long faced the problem of spatiotemporal misalignment of multi-source heterogeneous data, leading to a disconnect between control commands and equipment feedback. Especially in continuous production scenarios, control commands issued by the user platform experience millisecond-level delays after passing through multiple platform intermediaries, causing the visualization of key parameters and control responses to exceed safety thresholds. For example, in equipment anomaly early warning scenarios, traditional systems, lacking a unified spatiotemporal reference, struggle to quickly locate the fault source and establish a control loop, directly impacting production safety and process stability.

[0025] To address the aforementioned issues, this application identifies the fundamental cause of control delays as discrepancies in time references and spatial coordinates across multiple platforms. Analysis of timestamp distribution patterns in device logs reveals that differences in data acquisition cycles across different devices lead to misalignment of time-series data. Further investigation into the dependencies between device action sequences reveals that semantic differences in instructions from heterogeneous devices can trigger execution conflicts. Based on this, the application proposes establishing a global spatiotemporal reference for data alignment, constructing a device knowledge graph to eliminate semantic ambiguity, and pre-executing control instructions in a virtual environment. Specific approaches include: achieving millisecond-level time synchronization through reference time broadcasting, unifying the spatial coordinate system using affine transformations, designing a bidirectional mapping mechanism to convert abstract actions into concrete instructions, and detecting multi-dimensional conflicts through virtual pre-execution.

[0026] Therefore, this application proposes an information visualization method based on the Internet of Things, including the following steps: obtaining control command data containing device ID, control instructions, action parameters, and timestamps from the user platform to generate a user demand vector; accessing the sensor network platform to obtain device status data and forming a spatiotemporal retrieval table through reference time alignment and coordinate calibration; inputting the user demand vector into the device knowledge graph to infer the device action sequence; realizing the conversion between abstract actions and specific instructions through a bidirectional mapping table; executing control instructions and detecting conflicts in a virtual twin environment; and driving visualization rendering after handling conflicts according to arbitration rules.

[0027] The user demand vector refers to the mathematical expression that integrates the unique identifier of the device, operation instructions, and execution parameters. Specifically, it can be stored in a three-dimensional vector form to store the hash value of the device ID, the code of the control instruction, and the normalized value of the action parameters, used to represent the user's operational intent. The spatiotemporal retrieval table is a multi-dimensional index structure built based on a global spatiotemporal benchmark. Specifically, it can be achieved by broadcasting the benchmark time through the management platform, calculating the time deviation of each device, and then applying affine transformation to unify the coordinate system, forming a data structure indexed by device ID, aligned timestamp, and global coordinates. The device knowledge graph is a network knowledge model describing the physical attributes and relationships of devices. Specifically, it can integrate device models, action parameter ranges, standard actions, and collaborative dependencies to form a graph structure with device IDs as nodes. The virtual twin environment is a digital mirror system that synchronizes the physical device status. Specifically, it can construct a three-dimensional layout of devices according to the spatiotemporal retrieval table and import real-time status data to form a virtual space consistent with the physical environment.

[0028] Specifically, this method first eliminates spatiotemporal discrepancies among multiple devices through benchmark time broadcasting and coordinate transformation, integrating scattered sensor data into a unified spatiotemporal framework. User demand vectors are parsed using a knowledge graph to generate standard action sequences that conform to device physical constraints. A bidirectional mapping mechanism ensures that abstract actions are accurately converted into specific device instructions, while simultaneously mapping device states back into understandable semantic information. A virtual twin environment simulates the instruction execution process in advance, detecting potential problems such as resource conflicts and overlapping motion trajectories. When parameter limits are exceeded or timing errors are detected, the arbitration module adjusts the execution order according to preset priorities. Finally, a dynamic threshold mechanism triggers visual rendering, providing real-time feedback on the instruction execution status to the user interface.

[0029] Compared to existing technologies, traditional methods rely on a single platform's time synchronization mechanism, failing to address cross-platform data alignment issues. This solution achieves millisecond-level synchronization accuracy for cascaded devices across multiple platforms by actively broadcasting a reference time through the management platform. Existing technologies employ static device coordinate mapping, which is ill-suited for mobile device scenarios. This solution establishes a dynamic global coordinate system through affine transformation, supporting real-time updates of device positions. Traditional visualization systems lack instruction pre-rendering mechanisms; this solution fully reproduces device physical behavior in a virtual environment, proactively detecting spatial trajectory conflicts and parameter out-of-bounds risks.

[0030] This effectively solves the problem of spatiotemporal data misalignment across platforms in Industrial Internet of Things (IIoT) systems, enabling controllable end-to-end latency of control commands. The spatiotemporal lookup table ensures accurate alignment of multi-source heterogeneous data under a unified spatiotemporal benchmark, the device knowledge graph eliminates semantic ambiguity caused by device heterogeneity, and the virtual pre-execution mechanism proactively avoids execution conflicts. Ultimately, a visual tracking of command flow status is formed on the user interface, ensuring real-time response and reliable execution in the production process.

[0031] For the basic principles described in the embodiments of this application, please refer to [link / reference]. Figure 2 In this embodiment, the management platform is the data processing hub, which needs to process both user control intentions and device data. The visualization is static, but the data is dynamic. At the same time, it solves the arbitration mechanism in case of conflict.

[0032] On the user platform, users can read the real-time status of devices on the production object platform through the user terminal device provided on the user platform, and can control the specific actions of each device on the production object platform in real time.

[0033] The user inputs the original control intent, which is then transmitted to the service platform, where it generates control command data.

[0034] Step S1: The management platform obtains the user's control command data, which includes the device ID of the control command, the control command, the action parameters, and the control command timestamp. The device ID, control command, and action parameters of the control command are recorded as the user demand vector.

[0035] Simultaneously, the sensor network platform is accessed to obtain real-time status data of each device. The status data includes the device ID, device coordinates, time-series sensor data, and device data acquisition timestamps of the production object platform, and the corresponding device logs are stored in the database of the sensor network platform.

[0036] The device ID is used as the unique identifier of the device. The management platform issues a reference time, aligns it with the reference time based on the device data collection timestamp, and calibrates the device coordinates to form a spatiotemporal retrieval table. Sensor data and device logs are labeled as associated data on the device.

[0037] The management platform broadcasts a reference time to the sensor network platform. Based on the alignment of the device data acquisition timestamps with the broadcast reference time, the time deviation between each device and the reference time is calculated, and the acquisition timestamps of all devices are unified to the reference time. Based on the device coordinates, an affine transformation is applied to map the local device coordinates to the global coordinate system, forming a spatiotemporal retrieval table. The time-series sensor data is labeled on the device as associated information, and the spatiotemporal retrieval table is created using the device ID, the aligned acquisition timestamps, and the global coordinates as multidimensional indexes.

[0038] Step S11: Real-time device status data acquisition and storage.

[0039] Sensors configured on the production platform collect real-time data on the status of each device, upload and store it in the device log of the sensor network platform, and input the device ID, device coordinates, time-series sensor data, and device data acquisition timestamp from the real-time data into the management platform.

[0040] The management platform inputs real-time status data from each device, which is then recorded. Where i is device i, and i is any value, i=1,2,…; It is the device ID of device i. These are the device coordinates, which are in their original spatial location; It is the time-series sensor data of device i, which is a sequence of monitoring values ​​that changes over time. k is the number of times the data is collected, and k can be any value, k=1, 2, ...; is the device data acquisition timestamp of device i, which records the acquisition time recorded locally on the device. k is the specific number of acquisitions. Because the acquisition timestamp is affected by the device's local clock deviation, it may be out of sync with other devices. Therefore, we unify the acquisition timestamps of all devices onto the same timeline. This timeline is the reference time broadcast by the management platform.

[0041] Step S12: Synchronize the reference time.

[0042] The management platform broadcasts a reference time to the sensor network platform. , k is any value, k = 1, 2, ...; It is the moment when the base time is issued. It is the minimum interval between moments on the reference axis of the base time.

[0043] Step S13: Align the collected timestamp with the reference time.

[0044] After receiving the reference time, the sensor network platform records the received time and calculates the time deviation between each device and the reference time; the received time of device i is denoted as... The time deviation of each device from the reference time is recorded as follows: , - .

[0045] To eliminate local clock skew, the timestamps of all devices are standardized to a reference time. The aligned timestamps are calculated using a time alignment formula based on the device data acquisition timestamps and their time deviations. This alignment formula is expressed as follows: , It is the aligned timestamp of device i. This aligned timestamp ensures that different collected data correspond to the same time identifier at the same physical moment.

[0046] Step S14: Calibrate the equipment coordinates.

[0047] Based on the device coordinates and using affine transformation, the original device coordinates are mapped to global coordinates.

[0048] Based on device coordinates It also pre-defines two or more reference anchor points for calibration, with the pre-define reference anchor point coordinates as follows: c is the calibration device used to calibrate the coordinates, c = 3, 4, ...

[0049] Local device coordinates are mapped to global coordinates through coordinate translation, rotation, and scaling transformations. On top of that, each device on its production object platform is converted into a globally unified coordinate system and stored in the management platform.

[0050] The function of a quadratic affine transformation is obtained by calculating the coordinates of the preset calibration point reference anchor point and its corresponding device coordinates. The reference anchor point coordinates used for calibration are... The original device coordinates of the reference anchor point are... Substituting the affine transformation function, we can solve for α and β, where α is the transformation matrix, corresponding to the rotation and scaling of the coordinates, and β is the translation vector.

[0051]

[0052] The calculated affine transformation function is used to calibrate the device coordinates to global coordinates.

[0053] Step S15: Construct a spatiotemporal retrieval table.

[0054] A spatiotemporal retrieval table is established using device ID, aligned acquisition timestamp, and global coordinates as multidimensional indexes, and time-series sensor data is labeled on the device as associated data.

[0055] Compared to existing technologies, traditional methods typically rely on a single timestamp or local coordinate system for data alignment, lacking a unified cross-platform benchmark, leading to spatiotemporal misalignment of data from multiple devices. For example, existing technologies may only use the device's local clock to record time without global synchronization, causing accumulated timing deviations; or they may only retain the original device coordinates without spatial transformation, resulting in mismatched location information during cross-platform collaboration. This solution, however, enforces calibration through broadcast benchmark time and combines it with affine transformation to achieve global coordinate mapping, unifying the data benchmark in both time and space dimensions, effectively solving the cross-platform data alignment problem.

[0056] This supports efficient queries. Range queries can be selected within a time window and spatial region. Related queries are time-consuming, and asynchronous device location updates can cause interface elements to jump. The spatiotemporal index table uses the device ID as the unique identifier of the device, ensuring the consistency of the device's spatial coordinates and guaranteeing stable interface rendering. The spatiotemporal index table can directly index the corresponding device location when the user makes a control, thus improving the speed of control command generation.

[0057] The spatiotemporal index table is the core bridge connecting the physical world and digital twins. It unifies the encoding of different device states and spatial topologies, enabling the unified fusion of multi-source data. Through clock synchronization and spatial calibration, it eliminates system-level errors and ensures real-time performance. It also provides underlying data support for advanced functions such as conflict simulation and rate of change calculation.

[0058] Please refer to Figure 3 In step S2, the control platform inputs the user demand vector into the pre-trained device knowledge graph, infers the corresponding device actions based on the user demand vector, and outputs an abstract sequence of device actions.

[0059] The pre-trained device knowledge graph is built by integrating the physical attributes, actions, and associated devices of all devices on the production object platform based on device logs.

[0060] This knowledge graph integrates physical attributes, actions, and associated device information from device logs to form a mesh-like knowledge structure centered on device IDs. Physical attributes include device model and action parameter range, with the action parameter range defining the allowed parameter intervals for each action. Actions refer to the set of standard operations supported by the device. Associated devices represent the collaborative dependencies between devices. The mesh-like knowledge structure contains four types of edges: action edges connect devices to their supported actions, parameter edges constrain the action parameter range, dependency edges regulate the temporal logic of actions before and after an action, and collaborative edges establish cross-device linkage relationships.

[0061] The control platform inputs the user demand vector into a pre-trained device knowledge graph for parsing. It then uses a breadth-first search to scan all associated edges of the device nodes, ensuring that all conditions are detected.

[0062] Step S21: Perform node matching, locate the target device node in the device knowledge graph using the device ID, and find the device ID001 node.

[0063] Step S22: Perform action retrieval, search for matching instructions in the action edge of device ID001 node, and confirm that rotation is an action supported by the device.

[0064] Step S23 parameter verification: Check whether the input parameter is within the range of the action parameter. If it is within the parameter range, proceed directly to the next step; otherwise, find the value within the nearest range. For example, verify that 120° is within the range of 0°-180°. If the user inputs 182°, then execute 180°.

[0065] Step S24: Finally, perform dependency derivation, backtracking the preceding and following actions along the dependency edges. For example, before performing rotation, the robotic arm must have already clamped the workpiece. Step S25: Find the associated collaborative device and link them together. Find the associated device through the collaborative edge. For example, the rotating conveyor belt ID002 needs to decelerate synchronously.

[0066] Step S26: Generate an action chain with dependencies as a device action sequence.

[0067] For example, if the user requirement vector is [Device ID: Welding Robot ID005, Action: Start Welding, Action Parameter: Welding Voltage = 220V], the target device, Device ID = ID005, is found in the knowledge graph, and the welding robot node is located. Then, it is checked whether the node is connected to the Start Welding edge to confirm that Device ID = ID005 supports the action. At the same time, it is also necessary to verify that the parameters are within the allowable range. The voltage = 220V is within the set 200-240V, and the verification is passed. The necessary conditions are found along the dependency edge, and the prerequisite action is set. The position of the conveyor belt is reached, and the positioning is completed. Then, the linkage device is found through the collaboration edge to generate the linkage instruction. The conveyor belt is a collaborative deceleration of the associated device to 1. Finally, the dependency relationship is transformed into an action chain as an ordered sequence of device actions. The conveyor belt decelerates to 1, and the welding robot is started to perform welding action under 220V voltage.

[0068] This process decouples the user demand vector into abstract actions through a semantic action layer, outputting a sequence of specific device instruction actions. Thus, even if the user demand vector contains different models of devices, this step transforms the dispersed device capabilities into a globally reasonable knowledge network.

[0069] Step S27: Through a bidirectional mapping table, the device action sequence is converted into specific device control instructions, and the device status data is converted into an abstract device action sequence.

[0070] The bidirectional mapping table provides forward mapping and reverse mapping functions. Forward mapping represents abstract actions in the device knowledge graph with specific device instructions, while reverse mapping identifies the corresponding abstract actions based on the device's state data.

[0071] In short, a bidirectional mapping table is a data structure that enables the mutual conversion between abstract actions and physical device instructions. Specifically, it can be described using a relational structure table. By identifying abstract actions, it establishes a correspondence between them and device instruction codes, thereby eliminating semantic differences between the control logic layer and the device execution layer.

[0072] Forward mapping specifically binds each abstract action in the device knowledge graph to a corresponding device instruction, and then the device instruction performs the corresponding workload according to the action parameters to form a specific device control instruction.

[0073] Reverse mapping determines the device ID and timing sensor data based on the device's status data. The timing sensor data then determines the specific device control command for that moment, followed by the action parameters, and finally outputs the corresponding abstract action. In practical engineering, registers are used to store the actions, so changes in register data immediately determine the action, and changes in the register address determine the action parameters.

[0074] Step S271: Output specific device control commands through forward mapping.

[0075] Actions are parsed by abstract action sequences in the device knowledge graph, and actions and action parameters are broken down. The corresponding device instructions for the actions are found in the bidirectional mapping table, and the action parameters are filled into the device instruction execution quantity. According to the device communication protocol, specific device control instructions that the device can execute are generated.

[0076] Step S272: Abstract the action by outputting device status data through reverse mapping.

[0077] The system finds the corresponding specific device control command in the bidirectional mapping table by using the device's status data, extracts the action parameters, generates an abstract action, and outputs the device action sequence.

[0078] Visualization, or reverse mapping, only requires processing abstract actions without parsing raw register values. It can also transform discrete sensor data into understandable actions, thus improving speed during the visualization process.

[0079] Step S3: Simulate the execution of device control commands in the virtual twin environment to obtain virtual execution results.

[0080] If the virtual execution result results in a conflict with the virtual state of the device, then proceed to step S5; otherwise, if no conflict occurs, then the device control command is transmitted to the sensor network platform, and the device on the production object platform executes the action according to the device control command, proceeding to step S6.

[0081] A virtual twin environment is created based on a spatiotemporal retrieval table and a device knowledge graph.

[0082] Based on the global coordinates in the spatiotemporal retrieval table, construct the three-dimensional spatial layout of the devices in the virtual environment on a proportional scale; ensure that the time base of the virtual environment is consistent with the base time broadcast by the management platform; and import the latest status data of each device in the spatiotemporal retrieval table into the virtual environment as the basis for the simulation.

[0083] Because the spatiotemporal retrieval table contains the device ID, global coordinates, aligned timestamps, and associated sensor data for each device, and the device knowledge graph contains the physical attributes of the devices, such as the arm length and range of motion of the robotic arm, the length and speed range of the conveyor belt, and the motion trajectory of the robotic arm when rotating.

[0084] Then, based on the global coordinates in the spatiotemporal retrieval table, the three-dimensional spatial layout of the device is constructed proportionally in the virtual environment. For example, based on the global coordinates of the robotic arm and its physical properties (such as an arm length of 2 meters), a corresponding robotic arm model is created in the virtual space, with its position and size consistent with the actual device.

[0085] The timeline of the virtual environment is synchronized with the base time of issuance, ensuring that the event time in the virtual environment is aligned with the actual physical time.

[0086] The latest status data of each device in the spatiotemporal retrieval table is imported into the virtual environment as the initial state for simulation execution.

[0087] This creates a virtual twin environment synchronized with the physical world, containing the spatial location, current state, and motion capability models of all devices.

[0088] Execute device control commands in a virtual twin environment to obtain virtual execution results. Step S31: Execute the specific device control command based on the specific device control command and the status data of the device in the current virtual twin environment.

[0089] Step S32: Gradually update the device status data on the timeline of the virtual twin environment.

[0090] Step S33: Perform conflict detection, which includes resource conflict detection, spatial conflict detection, temporal conflict detection, and parameter conflict detection.

[0091] Resource conflict detection checks whether the same device ID has been assigned multiple mutually exclusive actions within the same time period.

[0092] Spatial conflict detection calculates whether the motion trajectories of different devices intersect in space.

[0093] Temporal conflict detection verifies the actual occurrence time of the action sequence during virtual execution and whether it is consistent with the preceding and following actions on the dependency edges in the device knowledge graph.

[0094] Parameter conflict detection verifies whether device status data exceeds the allowable range.

[0095] Step S34: If all execution device control instructions are completed and none of the above four conflicts are triggered, the virtual execution result is no conflict; if one or more conflicts occur, the virtual execution result is that there is a virtual state conflict of the device.

[0096] By proactively identifying potential conflicts in the virtual environment, such as mutually exclusive device actions due to resource contention, device collisions caused by overlapping motion trajectories, process interruptions due to timing misalignments, and abnormal device operation due to parameter out-of-bounds errors, the system eliminates potential conflicts before actual control commands are issued. This prevents execution errors caused by virtual state conflicts and ensures the reliability and security of the industrial IoT system under complex operating conditions.

[0097] Step S4: If a conflict occurs, arbitration is performed according to preset arbitration rules. Device control commands are executed sequentially according to the corresponding arbitration strategy, and the arbitration strategy is displayed. If the virtual execution result does not have a virtual state conflict, proceed directly to step S5.

[0098] Please refer to Figure 4 The preset arbitration rules include three-level priority arbitration rules. According to the preset arbitration rules, the types of control instructions are sorted to ensure that high-priority instruction types are processed first, thereby optimizing system response speed and user experience.

[0099] The control commands include emergency stop commands, production task commands, and optimization commands. The type of control command can be selected directly when the user inputs the control intention. Optimization commands simply adjust the parameters.

[0100] The management platform includes an arbitration module and a strategy module; wherein... The arbitration module is equipped with a three-tiered priority arbitration rule, including the highest priority (Priority0), the next highest priority (Priority1), and the lowest priority (Priority2).

[0101] When the instruction type is an emergency stop instruction, the output arbitration result is the highest priority, Priority0. At this time, the strategy module directly outputs the corresponding arbitration strategy, and the device immediately interrupts the execution of all tasks.

[0102] When the instruction type is a production task instruction, it is assigned as the secondary priority level (Priority1). At this time, the strategy module directly outputs the corresponding arbitration strategy and executes it according to the device timing queue.

[0103] When the instruction type is an optimization instruction, it is assigned the lowest priority (Priority2). In this case, the policy module directly outputs the corresponding arbitration policy and executes it according to the device's idle time period.

[0104] The arbitration results and execution status of conflicting instructions from the arbitration and strategy modules are displayed in real time, enhancing transparency.

[0105] Traditional visualizations flicker as data is refreshed. This implementation needs to present data stably like a dashboard, so a rate of change threshold is used to control the data refresh. Only data fluctuations exceeding the set threshold trigger an interface update. Daily fluctuations are represented by trend curves, which avoids jitter and reflects trends.

[0106] Step S5: The management platform receives the operating status and logs of each device in real time, and also displays the changes in the devices at any time when the user performs control.

[0107] Create a static layout diagram of the equipment based on the equipment ID and coordinates of the actual production object layer.

[0108] Based on the layout diagram, the rate of change of time-series sensor data is calculated according to the status data, and a preset threshold is used to drive the visualization rendering to generate static visualization content.

[0109] Furthermore, static visual content is displayed on the user platform, and control command flow arrows are superimposed on it to show the flow status of user control commands across various platforms.

[0110] Staticization is achieved using the rate of change. Based on state data, the rate of change of time-series sensor data is calculated, and a preset threshold is used to drive visualization rendering, generating static visualization content. Specifically, this includes: Based on real-time status data and user-defined key device parameters, the rate of change of the time-series sensor data for the key device parameters is calculated using real-time data transmitted from time-series sensors, and the user pre-sets a threshold for the rate of change.

[0111] When the rate of change of key device parameters exceeds a set threshold, a dynamic mode for visualization rendering is activated; the dynamic mode marks devices with data mutations at device coordinate positions above the set threshold.

[0112] When the rate of change of key device parameters is equal to or less than a set threshold, the static mode of visualization rendering is activated. The static mode freezes the current state data of the device at the device coordinate position that is equal to or less than the set threshold, and updates the trend curve only based on the real-time data transmitted back by the time-series sensor data.

[0113] Staticization is achieved using the rate of change. Based on a set rate of change threshold, the interface refresh rate is controlled to be reduced to 1Hz (static mode, which can also be set according to specific needs), reducing CPU load and eliminating visualization jitter. The rate of change of time-series sensor data refers to the fluctuation range of sensor measurements per unit time. Specifically, it can be achieved by calculating the average difference between data points at adjacent time points using a sliding window, used to quantify the degree of abrupt changes in the device's operating state. The preset threshold refers to the critical value set by the user according to device safety standards. Specifically, it can be achieved by inputting a numerical range through the configuration interface, serving as the basis for triggering the switching of visualization modes. Dynamic mode refers to the rendering method that highlights abnormal devices in the visualization interface. Specifically, it can be achieved by overlaying color codes or flashing icons on the device's coordinate position, used to quickly locate devices with data abrupt changes. Static mode refers to the rendering method that maintains the current data state in the visualization interface. Specifically, it can be achieved by pausing data refresh and retaining the last frame, used to reduce system resource consumption.

[0114] The control command transmission adopts a dual-channel separation structure, which includes a real-time control channel and a data return channel, and optimizes the control command link to reduce its weight.

[0115] in, The control commands include user intent commands and device data commands; Overlay control command flow arrows onto the heatmap on the user terminal device to show the flow status of user control commands across platforms; The user's intent commands are transmitted sequentially through the real-time control channel to the management platform, sensor network platform, and production object platform; the device's data commands are transmitted sequentially through the data feedback channel to the production object platform, sensor network platform, and management platform.

[0116] The control command transmission adopts a dual-channel separation structure. Through dual-channel transmission, the control command delay is much less than 100m steps S, ensuring real-time control.

[0117] Through the above innovative design and rigorous implementation steps, this solution achieves a unified high-stability visualization and real-time control in industrial scenarios by embedding a spatiotemporal alignment engine into the management platform, using the rate of change as a filter, and employing both pre-simulation and arbitration modules. This represents a significant technological breakthrough, effectively improves user satisfaction, and brings new breakthroughs to industry development.

[0118] In Example 2: This is the second embodiment of the present invention. This embodiment provides an information visualization system based on the Internet of Things. This system is based on a five-platform architecture. Please refer to [link / reference needed]. Figure 5This embodiment focuses on data processing on the management platform to achieve information visualization, including an electrically connected user platform, service platform, management platform, sensor network platform, and production object platform, wherein: The user platform is responsible for receiving and transmitting user input requests and displaying device status. The user platform is configured to provide front-end services to users; users obtain the necessary sensory service information through the user platform, process this information, and transform it into user-perceived information; users analyze this user-perceived information and make corresponding decisions based on their own wishes, then convert the user-perceived information into user control information through the corresponding information system and send it to the service platform, thereby demonstrating the user's corresponding service needs and wishes.

[0119] The service platform is responsible for generating user control command data and transmitting it to other modules through the management platform.

[0120] The management platform processes user control command data, monitors the real-time status of devices, completes spatiotemporal alignment to build a spatiotemporal retrieval table, analyzes user needs to generate device action sequences, and generates device action sequences in a virtual twin environment.

[0121] The sensor network platform completes and stores the monitoring of device status data.

[0122] The production object platform controls the equipment to perform actual operations according to the specific equipment control instructions received, collects real-time status data of the equipment, and transmits it to the sensor network platform.

[0123] The management platform includes a knowledge graph module, a virtual twin environment module, an arbitration module, and a visualization module.

[0124] The knowledge graph module is responsible for constructing the device knowledge graph and parsing user control command data.

[0125] The virtual twin environment module creates a simulated virtual twin environment through a spatiotemporal retrieval table and a device knowledge graph to execute control commands and detect conflicts.

[0126] The arbitration module is used to implement preset arbitration strategies when conflicts occur.

[0127] The visualization module generates an intuitive interface display based on the current virtual twin environment and virtual test results.

[0128] Please refer to Figure 6 The system architecture consists of five platforms: user platform, service platform, management platform, sensor network platform, and production object platform.

[0129] The user platform is the dominant platform for the operation of the Internet of Things (IoT) system, serving as the initiation and decision-making center for user needs. It receives and processes the sensing service information transmitted from the service platform, transforming it into user sensing information. Combining user intentions with analysis and decision-making, it generates user control information and sends it to the service platform to express service needs. It is configured as various user terminals, such as mobile phones, computers, and dedicated terminals, and through integration with user information system software, it realizes user-end services.

[0130] The service platform is the hub platform for service communication, connecting the user platform and the management platform; it enables two-way communication of perceived service information (user → management) and control service information (management → user); independent of the management platform, it provides user service demand calculations to form a service-related database; it can be configured as an API server or other server used to establish communication between the management platform and the user platform to achieve corresponding functions; the physical entities of the service platform include various servers.

[0131] The management platform is the overall management platform for the operation of the Internet of Things (IoT) system. It is responsible for the connection and control of information interaction; it processes, identifies, parses, stores, and classifies sensed and control information to understand the operational status of objects; it connects the information interaction between users and objects to realize sensed management (acquiring object status) and control management (issuing control commands); it is configured to perform at least one of the following: device operation status monitoring management, data monitoring management, device parameter management, and lifecycle management; the management platform is the overall operation platform for the IoT and may include various management sub-platforms, with different management sub-platforms performing different management tasks; the physical entities of the management platform include various servers.

[0132] The sensor network platform is the core platform for sensor communication, connecting the management platform and the object platforms to avoid redundancy caused by direct data aggregation. It enables bidirectional communication of sensing information (object → management) and control sensing information (management → object). It provides functions such as data transmission, parsing, identification, and classification, reducing the processing load on the management platform. It is configured to perform at least one of the following: network management, command management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides functions such as data communication transmission, parsing, identification, and classification, preventing data from various object platforms from directly aggregating on the management platform, which would cause data redundancy and low data processing efficiency. The physical entities of the sensor network platform include various gateways and edge computing devices.

[0133] The production object platform is the execution platform for perception and control, and the final implementation link of user intentions. Based on user perception needs, it collects object perception information (such as equipment status and production data) through sensors and other devices. Based on user control needs, it executes control commands (such as equipment start-up and shutdown, parameter adjustment). It is configured to perform specific production control, detection, measurement and other production tasks. The physical entities of the production objects include various production equipment, sensors and so on.

[0134] The service platform includes a general service database that communicates with the management platform, and at least one service sub-platform that communicates with both the general service database and the user platform. Each service sub-platform includes a service sub-database.

[0135] Through the collaborative work of multiple platforms, this architecture not only optimizes the visualization process but also improves the efficiency of the Industrial Internet of Things (IIoT) and promotes transparency and flexibility in data and control.

[0136] Example 3 The third embodiment of the present invention differs from the previous embodiments in that: Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or modules may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or electrical connection shown or discussed may be indirect coupling or electrical connection through some interfaces and their systems or units, or it may be an electrical, mechanical, or other form of connection.

[0138] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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 grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An information visualization method based on the Internet of Things, characterized in that, The method is executed by the management platform and includes the following steps: Acquire control command data and device status data; Based on the control command data, a user demand vector is obtained; based on the user demand vector, a device action sequence is obtained through a device knowledge graph. Virtual execution is performed according to the sequence of device actions, and conflict detection is performed to obtain the virtual execution result; If the virtual execution result has a virtual state conflict, the device control command is arbitrated according to the preset arbitration rules to determine the command execution strategy; Based on the device status data, determine whether it exceeds a preset threshold, drive visualization rendering, and obtain dynamic or static visualization content. Based on the control command data and device status data, and in response to the broadcast reference time, a spatiotemporal lookup table with a unified spatiotemporal reference is obtained, specifically including: The control command data includes device ID, control command, and action parameters; the device status data includes device ID, device coordinates, and device data acquisition timestamp. Based on the alignment relationship between the device data acquisition timestamp and the broadcast reference time, the time deviation of each device is calculated, and the device data acquisition timestamps of all devices are unified to the reference time to obtain the aligned acquisition timestamps. Based on the device coordinates, an affine transformation is applied to map the device coordinates to the global coordinate system to obtain the global coordinates; A spatiotemporal retrieval table is obtained based on the device ID, the aligned acquisition timestamp, and the global coordinates; The preset arbitration rules include a three-level priority arbitration rule. The arbitration of device control commands according to the preset arbitration rules specifically includes: The priority of the control command data is determined based on the command type; the command types include emergency stop commands, production task commands, and optimization commands. If the instruction type is an emergency stop instruction, it is determined to be of the highest priority, and an instruction execution strategy to immediately interrupt all tasks is obtained; If the instruction type is a production task instruction, it is determined to be of secondary priority, and an instruction execution strategy based on the device timing queue is obtained; If the instruction type is an optimized instruction, it is determined to be the lowest priority, and an instruction execution strategy based on device idle time periods is obtained.

2. The information visualization method based on the Internet of Things according to claim 1, characterized in that, The device knowledge graph is obtained by integrating the physical attributes, actions, and associated devices of all devices on the production object platform based on device logs; The physical attributes include the device model and the range of action parameters; the actions include standard actions that the device can perform; and the associated devices are collaborative dependencies between devices. The acquisition includes: Using the device ID as the core node, the action, the range of action parameters, the preceding action, the following action, the associated device, and the associated device are combined with the core node to obtain a mesh knowledge structure as the device knowledge graph. The mesh knowledge structure uses the device ID as the core node, and the associated edges of the core node include action edges, parameter edges, dependency edges, and collaboration edges.

3. The information visualization method based on the Internet of Things according to claim 1, characterized in that, The method further includes: Based on the bidirectional mapping table, the conversion between abstract actions and specific equipment control commands is performed; The bidirectional mapping table is used to provide forward mapping and reverse mapping functions. The forward mapping is used to obtain specific device control instructions based on the abstract actions in the device knowledge graph, and the reverse mapping is used to obtain the corresponding abstract actions based on the device status data.

4. The information visualization method based on the Internet of Things according to claim 1, characterized in that, A virtual twin environment is obtained based on the spatiotemporal retrieval table and the device knowledge graph; The steps for obtaining the virtual twin environment include: Based on the global coordinates in the spatiotemporal retrieval table, the three-dimensional spatial layout of the device is constructed proportionally in the virtual environment. The time base of the virtual twin environment is set according to the base time of the broadcast; According to the spatiotemporal retrieval table, the real-time status data of each device is imported into the virtual twin environment.

5. The information visualization method based on the Internet of Things according to claim 1, characterized in that, The conflict detection includes resource conflict detection, spatial conflict detection, temporal conflict detection, and parameter conflict detection; Resource conflict detection checks whether the same device ID has been assigned multiple mutually exclusive actions within the same time period. Spatial conflict detection calculates whether the motion trajectories of different devices intersect in space; Temporal conflict detection verifies whether the actual occurrence time of the device action sequence during virtual execution is consistent, and verifies whether the device action sequence during virtual execution is consistent with the preceding and following actions of the dependency edges in the device knowledge graph; Parameter conflict detection verifies whether the device status data exceeds the set allowable range.

6. The information visualization method based on the Internet of Things according to claim 1, characterized in that, The steps for driving visualization rendering specifically include: Based on the device status data, calculate the rate of change of time-series sensor data for the set key device parameters; If the rate of change is greater than a preset threshold, a dynamic mode is activated, which is used to mark data mutations at the corresponding device coordinate positions. If the rate of change is less than or equal to a preset threshold, a static mode is activated. The static mode is used to freeze the current device status data at the corresponding device coordinate position.

7. The information visualization method based on the Internet of Things according to claim 1, characterized in that, The method further includes: According to the dual-channel separation structure, control commands are transmitted; the dual-channel separation structure includes a real-time control channel and a data feedback channel. The control command data is transmitted from the management platform to the production object platform via the real-time control channel; the equipment status data command is transmitted from the production object platform to the management platform via the data feedback channel.

8. An information visualization system based on the Internet of Things, characterized in that, The system is applied to the Internet of Things-based information visualization method according to any one of claims 1 to 7, including a knowledge graph module, a virtual twin environment module, an arbitration decision module, and a visualization module connected in sequence; The knowledge graph module is configured to: acquire control command data and device status data; Based on the control command data, a user demand vector is obtained; based on the user demand vector, a device action sequence is obtained through a device knowledge graph. The virtual twin environment module is configured to: perform virtual execution according to the device action sequence, perform conflict detection, and obtain virtual execution results; The arbitration decision module is configured to: if there is a virtual state conflict in the virtual execution result, arbitrate the device control command according to the preset arbitration rules to determine the command execution strategy; The visualization module is configured to: determine whether the device status data exceeds a preset threshold, drive visualization rendering, and obtain dynamic or static visualization content. The knowledge graph module is further configured to: obtain a spatiotemporal retrieval table with a unified spatiotemporal reference based on the control command data and device status data, and in response to the broadcast reference time, specifically including: The control command data includes device ID, control command, and action parameters; the device status data includes device ID, device coordinates, and device data acquisition timestamp. Based on the alignment relationship between the device data acquisition timestamp and the broadcast reference time, the time deviation of each device is calculated, and the device data acquisition timestamps of all devices are unified to the reference time to obtain the aligned acquisition timestamps. Based on the device coordinates, an affine transformation is applied to map the device coordinates to the global coordinate system to obtain the global coordinates; A spatiotemporal retrieval table is obtained based on the device ID, the aligned acquisition timestamp, and the global coordinates; The arbitration decision module is further configured such that: the preset arbitration rules include three-level priority arbitration rules; and the arbitration of device control commands according to the preset arbitration rules specifically includes: The priority of the control command data is determined based on the command type; the command types include emergency stop commands, production task commands, and optimization commands. If the instruction type is an emergency stop instruction, it is determined to be of the highest priority, and an instruction execution strategy to immediately interrupt all tasks is obtained; If the instruction type is a production task instruction, it is determined to be of secondary priority, and an instruction execution strategy based on the device timing queue is obtained; If the instruction type is an optimized instruction, it is determined to be the lowest priority, and an instruction execution strategy based on device idle time periods is obtained.

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