Multi-person interaction sharing information coordination and real-time resolving method

Abstract

The invention discloses a multi-user interaction shared information coordination and real-time resolving method, and relates to the technical field of multi-user interaction. In the prior art, the technical problems of large information transmission delay, low real-time resolving efficiency, complex equipment access, insufficient flexibility and difficulty in meeting the requirements under a large-space and multi-node high-dynamic cooperation scene generally exist in the aspects of multi-person interactive information sharing and real-time resolving. According to the technical scheme, the multi-person interaction sharing information coordination and real-time resolving method comprises the steps that observed quantity data and navigation message data are obtained, and a global self-description model containing physical states such as the position, the speed and the acceleration of equipment is constructed; the method comprises the steps of data preprocessing, initialization and dynamic updating; performing information interaction; distributing the data to different time slots for calculation and cache updating; predicting the future state of the equipment and performing error correction; based on multi-node state information, high-precision real-time position, speed and time are obtained. And the method can be used in multi-person interaction work.

Description

Technical Field

[0001] The invention relates to the field of multi-person interaction technology, and in particular to multi-person interaction based on large space computing technology. Background Art

[0002] In the field of multi-person interactive information sharing and real-time solution, existing technologies mainly focus on how to achieve efficient data sharing and synchronization between multiple devices, and perform positioning and status solution on this basis. For example, in energy storage equipment management, the existing sharing model is usually based on centralized data centers for data storage and processing. Although this model can support a certain degree of information sharing, it often has problems such as slow response speed and lack of real-time performance when facing dynamic changes in multiple nodes. In multi-satellite navigation and virtual reality scenarios, the number of nodes is large and the distribution range is wide. Traditional solution methods are mostly based on independent solution of each node, resulting in low overall system solution efficiency and difficulty in adapting to the needs of highly dynamic scenarios.

[0003] Taking navigation scenarios as an example, common multi-satellite positioning technology relies on independent calculations of each satellite. Although it can achieve high positioning accuracy within the range of a single node, when integrating information between multiple satellites, the lack of a unified model and real-time information sharing mechanism between nodes often leads to poor consistency in positioning results, making it difficult to achieve global accurate calculations. Similarly, in smart grid and shared energy storage scenarios, existing communication protocols and data interaction methods generally lack efficient plug-and-play mechanisms, the device access process is complex, information synchronization takes a long time, and system flexibility is limited.

[0004] In the interactive process of multi-user information sharing, the existing technology also faces the problem of information transmission security. In large-scale collaborative scenarios, multiple forwarding of information may bring security risks, and the traditional security mechanism based on static identity authentication cannot effectively cope with dynamic and changing device access requirements. At the same time, the synchronization problem in information interaction is also one of the main bottlenecks of the existing technology. Due to the frequent update of node status data, the traditional information synchronization method usually has the problems of low data verification efficiency and long synchronization delay. Especially in high-frequency data interaction scenarios, this delay will significantly reduce the overall efficiency of the system.

[0005] In summary, the existing technologies generally have the following problems in multi-person interactive information sharing and real-time solution: large information transmission delay, low real-time solution efficiency, complex and insufficient flexibility in device access, and lack of information synchronization and security, which makes it difficult to meet the needs of high-dynamic collaboration scenarios in large spaces and multiple nodes. Summary of the invention

[0006] In the prior art, in terms of multi-person interactive information sharing and real-time solution, there are generally technical problems such as large information transmission delay, low real-time solution efficiency, complex device access and insufficient flexibility, as well as lack of information synchronization and security, which make it difficult to meet the needs of large-space, multi-node high-dynamic collaboration scenarios. The technical solution provided by the present invention is:

[0007] A method for multi-person interactive shared information coordination and real-time solution, the method comprising:

[0008] Data acquisition and model building: Obtain observation data and navigation message data from multiple data sources, and build a global self-describing model that includes physical states such as device position, velocity, acceleration, etc. The model goes through data preprocessing, initialization, and dynamic update steps;

[0009] Information interaction and synchronization mechanism: Information interaction is carried out through the communication network between multiple mobile terminals and fixed terminals, including data collection, upload, synchronization verification and distribution; data matching is carried out through a shared self-description model, and a whitelist mechanism is used for security authentication;

[0010] Multi-threaded parallel information update: The first thread is used to process the device status information at the set scheduling frequency, and the data is allocated to different time slots for calculation and cache update; the second thread is used to perform extrapolation calculation of quadratic curve fitting, predict the future status of the device and perform error correction;

[0011] Data solution and positioning: Based on the status information of multiple nodes, the least squares method is used for global solution, and the data of all valid nodes are combined to obtain high-precision real-time position, speed and time.

[0012] Furthermore, it also includes:

[0013] Multi-user feedback and dynamic adjustment: In a multi-person interaction scenario, each user uploads status information as an independent node. The system dynamically adjusts its solution frequency based on the node's movement status and data upload frequency.

[0014] Furthermore, the step of constructing the global self-describing model includes a data preprocessing step to filter and correct the collected raw data.

[0015] Furthermore, in the information interaction and synchronization mechanism, data is transmitted between the mobile terminal and the fixed terminal via the IEC104 or IEC61850 GOOSE communication protocol.

[0016] Furthermore, the multi-threaded parallel information update includes a first thread processing device status information at a set scheduling frequency and updating a status cache after each calculation is completed.

[0017] Furthermore, the second thread uses quadratic curve fitting to perform state extrapolation calculation, extracts historical state data from the cache, and compares the fitting result with the actual state.

[0018] Furthermore, in the data solution and positioning, the least square method is used to combine the data of all valid nodes for joint calculation to obtain a global positioning result, and a time interval sampling method is used in the calculation process.

[0019] The benefits of the present invention are:

[0020] In this real-time solution method for multi-person interactive sharing of information, a global self-description model is used to uniformly process the device status through data acquisition and model construction, significantly improving the accuracy of data and the real-time performance of the system. Compared with the mode of isolated processing of device information in the prior art, this method avoids the problems of time delay and error accumulation in multi-source data integration, and effectively ensures the global consistency of large-scale dynamic systems.

[0021] The information matching and security authentication achieved through the shared self-description model and whitelist mechanism in the information interaction and synchronization mechanism ensures the security and consistency of data transmission in multi-person interaction scenarios. The existing technology often leads to information leakage or inconsistency due to the lack of a unified security authentication method, while the authentication matching method used in the present invention not only enhances the security of the system, but also optimizes the efficiency of data synchronization.

[0022] The multi-threaded parallel information update method processes the state data and performs cache updates through the first thread, while the second thread performs extrapolation calculations, which significantly improves the solution speed and data synchronization accuracy. Compared with the existing system that relies only on a single thread for solution, the present invention avoids solution conflicts and time delays between different nodes through parallel processing, especially in high-frequency data update scenarios, which can effectively shorten the information processing cycle.

[0023] The multi-node data solution method based on the least squares method reduces the influence of observation noise in the joint calculation of multi-node information and significantly improves the accuracy of global positioning results. The existing independent node solution mode is easily affected by single-point errors, resulting in low solution accuracy. However, through the multi-node joint calculation method, the present invention not only effectively reduces the influence of errors, but also improves the stability of the overall system in complex environments.

[0024] Through multi-user feedback and dynamic adjustment mechanism, the function of dynamically adjusting the solution frequency according to the state change of each node is realized, thereby optimizing the system resource allocation and maintaining the high accuracy of the solution. Compared with the fixed frequency solution method in the prior art, the dynamic adjustment strategy of the present invention can flexibly adjust the resource usage according to the movement speed and change of the node, effectively reducing the system burden while ensuring accuracy, and improving the adaptability of the system in a multi-user dynamic environment.

[0025] It can be used in multi-person interactive work. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a system diagram for implementing the method of multi-person interactive shared information coordination and real-time solution. DETAILED DESCRIPTION

[0027] In order to make the benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0028] Implementation method 1: The core of the technical solution provided by the present invention is:

[0029] 1. Multi-person interaction and information sharing coordination mechanism:

[0030] The invention provides a coordination mechanism for sharing information in a multi-person interaction scenario, so as to solve the problems of untimely information transmission and low calculation efficiency in the prior art.

[0031] It supports plug-and-play information interaction mode and is suitable for large-scale, dynamic collaboration scenarios such as smart grids, shared energy storage, navigation and virtual reality.

[0032] 2. Real-time solution method in large space environment:

[0033] Combine multiple mobile terminals with fixed terminals, and exchange information through communication networks (such as IEC104 and IEC61850 GOOSE protocols) to achieve high-precision real-time solutions.

[0034] Multi-threaded parallel information processing is adopted: the first thread is used for device status information processing and cache update, and the second thread is used for extrapolation calculation to improve the solution speed and system synchronization capability.

[0035] 3. Construction and update of information self-description model:

[0036] By collecting device status data, a unified global self-description model is constructed, covering physical status information such as position, speed, acceleration, etc., and updated dynamically.

[0037] Data preprocessing step: Filter and correct the collected raw data to ensure the accuracy of the model.

[0038] 4. Multi-node data solution and position determination:

[0039] Based on multi-node information interaction and dual-thread mechanism, phased solution is performed to obtain the global positioning result of the node.

[0040] The least square method is used in combination with the solution to integrate the data of all nodes, improve the positioning accuracy and ensure real-time performance.

[0041] 5. Security authentication and information synchronization mechanism:

[0042] Implement security authentication between mobile terminals and local terminals, based on unique identification information and whitelist mechanism to ensure the security and reliability of the connection.

[0043] Data encryption transmission is used to prevent data leakage and tampering.

[0044] 6. Real-time extrapolation and error correction:

[0045] The state information is extrapolated and calculated using the quadratic curve fitting method, and error correction is performed to ensure the accuracy of the state prediction.

[0046] Monitor the changes in the status of each node in real time and dynamically adjust its solution frequency to meet the needs of high-precision interaction in large space environments.

[0047] 7. Multi-user feedback and dynamic adjustment mechanism:

[0048] Each user can upload status information as an independent node. The system dynamically adjusts the solution frequency and optimizes the information interaction strategy according to the global status changes.

[0049] Through frequency adjustment and strategy optimization, the coordination of multi-person interactions and the optimal use of system resources are ensured.

[0050] Implementation Method 2: Combination Figure 1 The technical solution provided above is further described in detail:

[0051] 1. Data acquisition and model construction

[0052] Obtaining input data:

[0053] Obtain the observation data (obs data) and navigation message data (nav data) required by multiple data sources. These data include the equipment status information of each information node, such as energy storage station control layer information, battery management data, satellite ephemeris data, satellite clock error, etc.

[0054] Collection of equipment status information: The sensors at each information node collect the equipment's operating status data in real time. These data are uploaded to the central control system through a dedicated transmission protocol to ensure the accuracy and real-time nature of the data.

[0055] Constructing a self-describing information model:

[0056] Using the construction method of the movable shared energy storage self-description model, all device information and status data are processed in a unified manner to generate a global self-description model.

[0057] Model building steps:

[0058] 1. Data preprocessing: Filter and correct the collected raw data to remove noise and abnormal data to ensure data quality.

[0059] 2. Model initialization: Based on the preprocessed data, initialize the physical state model of each node, including parameters such as position, velocity, and acceleration.

[0060] 3. Model update: As data is continuously acquired, the self-describing model is dynamically updated to ensure that it reflects the latest device status.

[0061] The model not only covers physical state information such as position, velocity, acceleration, etc., but also includes the independent identification of each node to ensure the consistency and real-time synchronization of information.

[0062] 2. Information interaction and synchronization mechanism

[0063] Interaction between mobile terminals and fixed terminals:

[0064] Use multiple mobile terminal and fixed terminal models to exchange information through communication networks (such as IEC104, IEC61850 GOOSE protocols).

[0065] Information interaction process:

[0066] 1. Data collection and upload: Each mobile terminal obtains the status data of its internal equipment and uploads it to the local terminal via wireless communication.

[0067] 2. Data synchronization and verification: After receiving the data, the local terminal matches and verifies it with the global self-description model to ensure data consistency.

[0068] 3. Data distribution: Verified data is distributed to other nodes to achieve rapid information transmission between multiple nodes.

[0069] In a large space environment, mobile terminals and local terminals are matched through a shared self-description model to complete security authentication. This security authentication obtains the unique identification information of each terminal and performs a whitelist comparison to ensure that the connection between nodes is secure and reliable.

[0070] Multi-threaded parallel information update:

[0071] The first thread is used to process all device status information at a set scheduling frequency (such as 100 Hz), allocate device data to different time slots, and complete information calculation and cache update node by node.

[0072] Thread specific implementation:

[0073] 1. Scheduling and allocation: All nodes are allocated according to the set frequency to ensure that the data of each node is processed within a specific time interval.

[0074] 2. Status cache update: After each calculation is completed, the latest status information is saved to the global cache for use by other threads or nodes.

[0075] The second thread is responsible for processing the extrapolation calculation of information with a higher priority, using quadratic curve fitting for state extrapolation, reducing the system burden and increasing the synchronization speed.

[0076] Implementation of quadratic curve fitting:

[0077] 1. Data fitting: Get historical status data from the cache and use the quadratic curve fitting method to predict the future status of the node.

[0078] 2. Error correction: After each fitting, compare the actual state with the predicted state, calculate the error and make corrections to gradually improve the extrapolation accuracy.

[0079] 3. Data solution and positioning

[0080] Data solving based on multiple nodes:

[0081] During the information interaction process, the data of each node is solved in stages through a dual-threaded parallel mechanism.

[0082] Status information calculation steps:

[0083] 1. Node data collection: Each node collects data independently and uploads it to the central control system.

[0084] 2. Global state update: Use the first thread to calculate the state information of all nodes, including position, velocity, and acceleration, and update the results to the global cache.

[0085] Subsequently, the second thread uses the least squares solution method to perform joint calculations on the data of all valid nodes to obtain a global positioning result.

[0086] Time interval sampling method: During the calculation process, the time interval sampling method is used to determine whether recalculation is needed for the status information of each node, thereby avoiding the inefficiency caused by repeated calculations.

[0087] 1. Time interval judgment: Before each state calculation, each node first checks the difference between the current time and the last calculation time. It will only recalculate if it exceeds the set time interval.

[0088] 2. Skip invalid calculations: If the time interval is insufficient, skip the calculation of the node directly to reduce the calculation burden of the system.

[0089] Quadratic curve fitting extrapolation:

[0090] The state information is extrapolated by quadratic curve fitting.

[0091] Extrapolation steps:

[0092] 1. State information extraction: Extract the latest state information from the global cache.

[0093] 2. Quadratic curve fitting calculation: Use the extracted data to perform quadratic curve fitting to predict the position and velocity of the node at a given moment.

[0094] 3. Result verification: Compare the fitting result with the actual state. If the error is within an acceptable range, use the result; otherwise, refit.

[0095] If the extrapolation time interval of a node is less than the set time threshold, the model is used to calculate its position and velocity at a given moment; if it is greater than the time threshold, the node is no longer used to ensure the real-time and accuracy of the solution.

[0096] The error of the extrapolated result is within a few centimeters to millimeters, which meets the accuracy requirements of most real-time interactive scenarios.

[0097] in,

[0098] 1. Real-time calculation formula of node position

[0099] The position formula describes the position of a node at time t and is calculated as follows:

[0100]

[0101] P(t): The position of the node at time t.

[0102] P0: The position of the node at the initial time t0.

[0103] V0: The velocity of the node at the initial time t0.

[0104] A: The acceleration of the node, assumed to be constant throughout the time interval.

[0105] t: current time.

[0106] t0: initial time.

[0107] This formula is a classic uniformly accelerated linear motion formula used to describe the displacement of a node, which includes the initial position, initial velocity, and the motion effect under constant acceleration A.

[0108] 2. Real-time calculation formula of node speed

[0109] The velocity formula is used to describe the velocity of a node at time t, and is calculated as follows:

[0110] V(t)=V0+A(t-t0)

[0111] V(t): The velocity of the node at time t.

[0112] V0: The velocity of the node at the initial time t0.

[0113] A: The acceleration of the node, assumed to be constant throughout the time interval.

[0114] t: current time.

[0115] t0: initial time.

[0116] This formula describes how the velocity of a node changes with time t. When the acceleration A is constant, the velocity increases or decreases linearly with time.

[0117] These two formulas are applicable to the case of uniformly accelerated linear motion, and can calculate the position and velocity of the node in real time. They are widely used to describe the motion trajectory and velocity changes of objects in mechanical systems, especially when the acceleration is constant.

[0118] Least squares combined solution: Use the least squares method to jointly solve the observation data of multiple nodes to reduce the impact of observation noise on the positioning results and improve positioning accuracy.

[0119] 4. Real-time solution and multi-user feedback

[0120] Real-time solution result output:

[0121] Combining the data extrapolation results of multiple nodes, the least squares method is used for global solution to obtain the real-time position, velocity and time (PVT) at each moment.

[0122] Solution steps:

[0123] Data integration: Integrate the status information of all nodes into a unified data set.

[0124] Least squares solution: Use the integrated data to perform the least squares solution to obtain the global PVT result.

[0125] Result broadcast: The calculated PVT results are broadcast to all nodes to ensure that each user node obtains high-precision position and velocity information within milliseconds.

[0126] Multi-user feedback and dynamic adjustment:

[0127] In a multi-person interaction scenario, each user can act as an independent node and upload his or her status information through a mobile terminal.

[0128] Dynamic adjustment mechanism:

[0129] Status monitoring: The system monitors the status changes of each node in real time, including movement speed, data upload frequency, etc.

[0130] Frequency adjustment: Dynamically adjust the resolution frequency according to the change of node status. For example, for nodes with faster movement speed, increase their data collection and resolution frequency to ensure positioning accuracy.

[0131] Strategy optimization: Optimize the information interaction strategy according to the changes in the global state to ensure the optimal allocation of system resources.

[0132] 5. System security and protection mechanism

[0133] Security authentication matching:

[0134] All information interactions between nodes are verified through security authentication to prevent unauthorized node access.

[0135] Security authentication steps:

[0136] 1. Identity verification: Before accessing the system, each node must submit unique identification information for identity verification by the system.

[0137] 2. Whitelist comparison: Through the whitelist mechanism, ensure that only authorized terminal devices can join the shared information network.

[0138] 3. Data encryption: During data transmission, encryption protocols are used to encrypt information to prevent data leakage and tampering.

[0139] Mutex protection:

[0140] In multi-threaded parallel computing, any operation on the global cache data is protected by a mutex to ensure data integrity and consistency.

[0141] Implementation of the mutex mechanism:

[0142] 1. Lock the global cache: Before reading or writing to the global cache, first lock the cache to prevent other threads from accessing it at the same time.

[0143] 2. Release the lock after the operation is completed: After the operation is completed, the lock is released immediately so that other threads can continue to access the cache, ensuring efficient operation of the system.

Claims

1. A multi-person interactive shared information coordination and real-time solution method, characterized in that: Methods include: Data acquisition and model building: Obtain observation data and navigation message data from multiple data sources, and build acceleration including device position and velocity as a global self-describing model of the physical state; the model goes through data preprocessing, initialization and dynamic update steps; Information interaction and synchronization mechanism: Information interaction is carried out through the communication network between multiple mobile terminals and fixed terminals, including data collection, upload, synchronization verification and distribution; data matching is carried out through a shared self-description model, and a whitelist mechanism is used for security authentication; Multi-threaded parallel information update: The first thread is used to process the device status information at the set scheduling frequency, and the data is allocated to different time slots for calculation and cache update; the second thread is used to perform extrapolation calculation of quadratic curve fitting, predict the future status of the device and perform error correction; Data solution and positioning: Based on the status information of multiple nodes, the least squares method is used for global solution, and the data of all valid nodes are combined to obtain high-precision real-time position, speed and time.

2. The method for multi-person interactive shared information coordination and real-time solution according to claim 1 is characterized in that: Also includes: Multi-user feedback and dynamic adjustment: In a multi-person interaction scenario, each user uploads status information as an independent node. The system dynamically adjusts its solution frequency based on the node's movement status and data upload frequency.

3. The method for multi-person interactive shared information coordination and real-time solution according to claim 1, characterized in that: The step of constructing the global self-describing model includes a data preprocessing step, in which the collected raw data are filtered and corrected.

4. The method for multi-person interactive shared information coordination and real-time solution according to claim 1, characterized in that: In the information interaction and synchronization mechanism, data is transmitted between the mobile terminal and the fixed terminal via the IEC104 or IEC61850 GOOSE communication protocol.

5. The method for multi-person interactive shared information coordination and real-time solution according to claim 1, characterized in that: The multi-threaded parallel information update includes a first thread processing device status information at a set scheduling frequency and updating a status cache after each calculation is completed.

6. The method for multi-person interactive shared information coordination and real-time solution according to claim 1, characterized in that: The second thread uses quadratic curve fitting to perform state extrapolation calculation, extracts historical state data from the cache, and compares the fitting result with the actual state.

7. The method for multi-person interactive shared information coordination and real-time solution according to claim 1, characterized in that: In the data solution and positioning, the least square method is used to combine the data of all valid nodes for joint calculation to obtain a global positioning result, and a time interval sampling method is used in the calculation process.

8. A large space for multi-person interactive sharing of information, characterized by: The large space is constructed according to the solution method of claim 1.

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