Equipment security management and control method and system and storage medium

Abstract

The invention relates to the technical field of equipment security management and control, in particular to an equipment security management and control method and system and a storage medium. The method comprises the following steps: acquiring a user control instruction, constructing a communication diffusion network and an instruction secure channel in a preset equipment communication environment to carry out dynamic key conversion and generate a tamper-proof packaging instruction, then speculating equipment execution expected data based on the instructions, carrying out operation simulation, acquiring simulation equipment operation data, and sending the simulation equipment operation data to a server; the method comprises the following steps: analyzing and simulating equipment form change parameters through track recording, matching equipment execution expected data one by one, if the data are inconsistent, performing difference analysis, performing communication error backtracking and error point positioning according to form change differences, mapping error channels, redistributing channels, and generating dynamic instruction channels to optimize equipment safety management. According to the invention, a more efficient and safer equipment safety management and control method is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of device security management and control, and in particular to a device security management and control method, system and storage medium. Background Art

[0002] Traditional security management and control strategies are often unable to effectively respond to real-time changes in instructions and status. Many companies face the difficulty in ensuring the security of user instructions and the risk of information leakage during instruction transmission during the operation of automated equipment. The lack of a complete dynamic key management mechanism makes instructions vulnerable to attacks during transmission, resulting in increased equipment security risks. In fields such as industrial automation and intelligent manufacturing, the ability to predict and simulate equipment control instructions is insufficient. The existing system lacks effective analysis of real-time data feedback during equipment operation, and is often unable to promptly identify and respond to problems caused by changes in equipment morphology, resulting in instability in equipment operation. The imperfection of the prediction mechanism not only affects production efficiency, but also increases the probability of equipment failure. These problems further deepen the hidden dangers of equipment operation safety. Summary of the Invention

[0003] Based on this, it is necessary to provide a device security management method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a device security management method includes the following steps:

[0005] Step S1: Obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; perform dynamic key conversion on the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions;

[0006] Step S2: Calculating expected device execution data based on the tamper-proof package instruction; performing device operation simulation according to the tamper-proof package instruction to obtain simulated device operation data;

[0007] Step S3: Recording the trajectory based on the simulated device operation data to generate the instruction corresponding device trajectory; analyzing the simulated device morphological change parameters based on the instruction corresponding device trajectory and the simulated device operation data;

[0008] Step S4: Match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, directly return to the terminal; if they are inconsistent, analyze the parameter change differences of the simulated device form change parameters;

[0009] Step S5: Perform communication error backtracking based on the difference in morphological changes, and locate the error point according to the device trajectory corresponding to the instruction to obtain the communication error generation point; perform error channel mapping on the communication error generation point, and perform channel reallocation based on the communication diffusion network and error channel to generate a dynamic instruction channel.

[0010] By acquiring user control commands and building a communication diffusion network based on a preset device communication environment, the present invention effectively enhances the security of the command transmission process. The established command security channel ensures the privacy protection of user commands during transmission. Tamper-proof encapsulated commands generated through dynamic key conversion provide assurance of the validity and integrity of the commands. Expected device execution data is inferred based on the tamper-proof encapsulated commands, ensuring accurate prediction of device operations. The simulated device operation data generated by the device operation simulation function accurately reflects the device's potential behavior. The implementation of trajectory recording generates a device trajectory corresponding to the command, providing a clear basis for subsequent analysis. By analyzing the simulated device morphological change parameters, a comprehensive understanding of the device's morphological changes during operation is achieved. The consistency of expected data and simulated parameters is matched one by one, effectively improving the reliability of command execution. Difference analysis of morphological change parameters promotes a deeper understanding of potential communication problems. Communication error backtracking based on morphological change differences allows for rapid location of error points, providing precise direction for problem resolution. Error channel mapping and channel reallocation techniques ensure efficient and stable communication. The generated dynamic command channel enhances the device's responsiveness and adaptability, improving the overall efficiency and intelligence level of device security management and control.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Obtaining user operation instructions; extracting multimodal features of user operation instructions;

[0013] Step S12: mapping the radio frequency signal strength of the preset device communication environment and constructing a three-dimensional signal coverage environment;

[0014] Step S13: Grid partitioning is performed based on multimodal features and the three-dimensional signal coverage environment, and optimal path screening is performed to generate a communication diffusion network;

[0015] Step S14: configuring a link encryption tunnel for the communication diffusion network and building an end-to-end secure channel to obtain a command secure channel;

[0016] Step S15: performing dynamic key derivation processing on the user control instruction based on the instruction security channel to obtain dynamic key data;

[0017] Step S16: Perform block-level data obfuscation on the dynamic key data and user control instructions to generate tamper-proof encapsulated instructions.

[0018] The present invention achieves in-depth understanding and classification of instructions by extracting the multimodal features of user operation instructions, making instruction processing more accurate. The mapping of radio frequency signal strength provides detailed information for the device communication environment, effectively improving the reliability of signal coverage. The construction of a three-dimensional signal coverage environment ensures the stability and strength of the communication link. Grid partitioning and optimal path screening improve the efficiency of the communication diffusion network and enhance the security of instruction transmission. The configuration of the link encryption tunnel ensures the security of data during transmission. The constructed end-to-end secure channel effectively prevents external attacks and information leakage. Dynamic key derivation processing improves the flexibility of key management and ensures the effectiveness and security of keys during use. The implementation of block-level data obfuscation further increases the concealment of instructions. The generated tamper-proof encapsulated instructions effectively prevent improper access and tampering, thereby improving the overall security level of device management and the reliability of instruction execution.

[0019] Preferably, step S2 includes the following steps:

[0020] Step S21: Initialize the communication node state based on the instruction security channel, and perform instruction semantic conversion on the tamper-proof encapsulated instruction to obtain instruction abstract semantics, where;

[0021] Step S21: Predicting the state transition path based on the instruction abstract semantics and performing state transition constraints based on the initial communication node to obtain the expected data for device execution, wherein the delay threshold of the state transition constraint is controlled within 5ms;

[0022] Step S23: constructing a device virtual communication field according to the instruction secure channel, wherein the maximum number of nodes in the device virtual communication field is limited to 1024, and the processing capacity of each node is not less than 1 GHz;

[0023] Step S24: performing instruction transmission simulation on the anti-tampering encapsulation instruction based on the device virtual communication field, and performing device operation simulation based on the simulated transmission instruction to obtain simulated device operation data.

[0024] The present invention significantly improves the adaptability and responsiveness of the system to the communication environment through initialization of the communication node state. The instruction semantic conversion realizes the in-depth analysis of the tamper-proof encapsulated instructions, ensuring the validity and executability of the instructions. The prediction of the state migration path enhances the predictive ability of the device behavior. Combined with the constraints of the initialized communication nodes, the stability and reliability of the data flow are guaranteed. The construction of the device virtual communication field enables the diversas instructions and states to be effectively tested in the simulation environment, which improves the flexibility and adjustment space of the implementation. The instruction transmission simulation based on the virtual communication field ensures the adaptability of the instructions in the real communication environment. The simultaneous device operation simulation provides data support for the actual device operation, so that the system can make necessary adjustments according to the simulation data before implementation, thereby reducing the risks in the actual operation, and improving the overall intelligence and automation level of the device security management and control, making the device management more efficient and safe.

[0025] Preferably, the track recording based on the simulated device operation data in step S3 includes:

[0026] Record the device status change data of the simulated device operation data and perform time series segmentation to obtain device status time slices;

[0027] Analyze the device position change of the simulated device operation data based on the device status time slice to obtain the device change coordinates;

[0028] Construct three-dimensional equipment trajectory based on equipment change coordinates;

[0029] Mark the tamper-proof packaged instructions and the simulated device operation data in time sequence to obtain the device operation data corresponding to the instructions;

[0030] Perform time alignment on the three-dimensional device trajectory and the device operation data corresponding to the instruction to generate the device trajectory corresponding to the instruction.

[0031] The present invention realizes comprehensive monitoring and analysis of the equipment operation process by recording the state change data of the simulated equipment operation data. The time series segmentation provides a detailed time dimension for the equipment state change, which effectively improves the accuracy and traceability of data processing. The equipment position change analysis enables the system to clearly obtain the dynamic changes of the equipment during operation, laying the foundation for subsequent trajectory construction. The construction of three-dimensional equipment trajectory provides an intuitive visualization effect, which is convenient for understanding and judging the equipment behavior. The timing correspondence marking of anti-tampering packaged instructions and simulation data improves the correlation between instructions and operation data. The generation of instruction-corresponding equipment trajectory makes the instruction execution more traceable, enhances the system's evaluation ability and accuracy of the instruction implementation effect, and thus promotes the intelligence and efficiency of equipment management.

[0032] Preferably, the step S3 of analyzing the simulated device morphology change parameters based on the device trajectory corresponding to the instruction and the simulated device operation data includes:

[0033] Perform time-series segmented interpolation on the device trajectory corresponding to the instruction to obtain the device motion curve;

[0034] Construct the equipment dynamic response field based on the equipment motion curve;

[0035] Construct temperature gradient distribution field based on preset equipment physical characteristics and simulated equipment operation data;

[0036] Perform nonlinear thermal expansion analysis on the temperature gradient distribution field and construct the equipment thermal expansion stress field;

[0037] Perform comprehensive stress superposition on the equipment dynamic response field and the equipment thermal expansion stress field to generate a multi-physics coupled stress field;

[0038] Explicit dynamic iteration is performed based on the multi-physics coupled stress field to generate the surface deformation gradient field;

[0039] Calculate the contact pressure distribution according to the surface deformation gradient field, and locate the force-bearing area of the equipment based on the contact pressure distribution data;

[0040] The wear deformation of the stress-bearing area of the positioning equipment is predicted based on the preset equipment material parameters, and the simulated equipment morphological change parameters are generated.

[0041] The present invention provides an accurate representation of the device motion characteristics by performing time-series segmented interpolation on the device trajectory corresponding to the instruction. The construction of the device motion curve provides a basis for dynamic behavior for subsequent analysis. The formed device dynamic response field can deeply understand the behavioral response of the device under different conditions. The construction of the temperature gradient distribution field allows monitoring the impact of temperature changes on the device under different operating conditions. The nonlinear thermal expansion analysis reveals the specific effect of temperature changes on material stress. The generated device thermal expansion stress field provides the necessary data support for the subsequent comprehensive stress evaluation. The generation of multi-physics coupling stress field systematically considers the impact of multiple physical factors on the device, improving the accuracy of the prediction. The explicit dynamic iteration enables the generation of surface deformation gradient field to truly reflect the deformation of the device in actual operation. The calculation of contact pressure distribution improves the identification accuracy of the stress area. Positioning the stress area of the device provides a clear direction for equipment maintenance and optimization. The wear deformation prediction based on the equipment material parameters scientifically predicts the future morphological changes of the equipment, thereby improving the efficiency and pertinence of equipment management and maintenance work.

[0042] Preferably, step S4 includes the following steps:

[0043] Step S41: performing time extrapolation processing based on the expected equipment execution data and performing equipment wear prediction to obtain expected equipment wear data;

[0044] Step S42: unifying the expected equipment wear data and the simulated equipment morphology change parameters in terms of timeline, and matching the expected equipment wear data and the simulated equipment morphology change parameters one by one based on the same timeline to obtain a wear matching result;

[0045] Step S43: When the wear matching result is completely consistent, directly return to the terminal; when the wear matching result is not completely consistent, compare the expected equipment wear data and the simulated equipment morphological change parameters to generate parameter change differences.

[0046] The present invention realizes accurate prediction of equipment wear through time extrapolation processing, thereby providing a forward-looking assessment of equipment health status. The generation of expected equipment wear data makes equipment maintenance decisions more scientific. The unification of timelines provides a standardized basis for comparison between different data sets. Matching data one by one based on the same timeline improves the credibility of the matching results. When the wear is completely consistent, the operation process can be effectively simplified, and direct return to the terminal optimizes the system response efficiency. For cases of incomplete consistency, parameter difference comparison can deeply analyze the relationship between equipment wear and morphological changes. The generated parameter change differences provide data support for further optimization of equipment management, thereby realizing intelligent monitoring and early warning of equipment operation status, and greatly improving the accuracy and real-time performance of equipment management.

[0047] Preferably, the step S5 of performing communication error backtracking based on the difference in morphological changes and locating the error point according to the device trajectory corresponding to the instruction includes:

[0048] Backtracking is performed based on the difference in morphological changes to obtain the error propagation trajectory;

[0049] Perform time reversal reconstruction on the error propagation trajectory to generate error origin time series data;

[0050] Based on the error origin timing data, the device trajectory corresponding to the instruction is temporally and spatially aligned, and the error origin is matched to obtain the communication error generation point.

[0051] The present invention reveals the propagation path of communication errors during equipment operation by performing reverse tracing based on the differences in morphological changes, providing a reliable basis for locating the error source. The generation of error propagation trajectories helps analyze how errors accumulate and diffuse in the system. The application of time reversal reconstruction enables the identification of error origins with higher accuracy. The generated error origin time series data provides time dimension information for subsequent error analysis. The execution of spatiotemporal registration enables the precise alignment of the relationships between different data sets. The implementation of error origin matching provides a scientific basis for accurately locating the communication error generation point, which overall improves the system's analysis capability and response speed to communication errors, effectively reduces the uncertainty in equipment operation, and ensures the safety and stability of equipment operation.

[0052] Preferably, the step S5 of performing error channel mapping on the communication error generating point and performing channel reallocation based on the communication diffusion network and the error channel includes:

[0053] Conduct command path interference analysis on the communication error generating point to obtain the error propagation route;

[0054] Marking communication error channels based on error propagation routing and communication error generation points;

[0055] Perform conflict detection on the communication error channel to obtain channel conflict data;

[0056] According to the channel conflict data, the communication diffusion network is matched with conflict avoidance paths, and channels are reallocated based on the conflict avoidance paths to generate dynamic instruction channels.

[0057] The present invention clarifies the error propagation route by performing command path interference analysis on the communication error generation point, providing a direction for understanding the potential problems in the communication system. The identification of the error propagation route helps to optimize the existing signal transmission path. The communication error channel based on the route mark allows the system to focus on key interference points, thereby improving the overall communication quality. The execution of conflict detection provides relevant data for identifying conflicts in the channel, effectively avoiding communication delays caused by channel conflicts. The conflict avoidance path matching using channel conflict data optimizes the resource utilization efficiency and improves the stability of the communication network. The dynamic command channel generated by channel reallocation based on the conflict avoidance path improves the flexibility of communication and ensures smooth communication of the equipment under different working conditions, thereby significantly enhancing the safety and effectiveness of equipment operation.

[0058] The present invention also provides a device security management and control system for executing the device security management and control method described above, the device security management and control system comprising:

[0059] The key conversion module is used to obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; dynamically convert the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions;

[0060] An operation simulation module is used to infer expected device execution data based on the tamper-proof package instructions; and to simulate device operation according to the tamper-proof package instructions to obtain simulated device operation data;

[0061] The change analysis module is used to record the trajectory based on the simulated device operation data and generate the instruction corresponding device trajectory; based on the instruction corresponding device trajectory and the simulated device operation data, the simulated device morphological change parameters are analyzed;

[0062] The difference comparison module is used to match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, the module directly returns the result to the terminal. If they are inconsistent, the module analyzes the parameter change differences of the simulated device form change parameters.

[0063] The channel reallocation module is used to trace back communication errors based on morphological change differences and locate error points according to the device trajectory corresponding to the instruction to obtain the communication error generation point; error channel mapping is performed on the communication error generation point, and channel reallocation is performed based on the communication diffusion network and error channel to generate a dynamic instruction channel.

[0064] The present invention provides secure and reliable user command processing through the design of a key conversion module. The constructed communication diffusion network ensures data security during command transmission. The implementation of a secure command channel strengthens the protection of user control commands. The tamper-proof encapsulated commands generated by dynamic key conversion effectively prevent the risk of command tampering during transmission. The operation simulation module accurately infers the expected device execution data, improving the predictability of device operation. The simulated device operation data provides a detailed basis for subsequent analysis. The change analysis module effectively associates commands with device behavior through trajectory recording. Analysis of simulated device morphological change parameters helps to fully understand the dynamic characteristics of the device during operation. The one-to-one matching mechanism of the difference comparison module ensures data consistency and significantly improves the accuracy of command execution. The difference analysis of simulated device morphological change parameters lays the foundation for identifying potential problems. The channel reallocation module quickly locates error points through communication error backtracking technology, ensuring the stability and accuracy of the communication system. The effective mapping of error channels optimizes the communication process. The dynamic command channel implements flexible channel management, improves the adaptability and response speed of the device in complex operating environments, and overall enhances the security management efficiency and intelligence level of the device.

[0065] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the device security management and control method as described in any one of the above.

[0066] The present invention, through the implementation of a computer-readable storage medium, makes the device security management method sustainable and efficient. The stored computer program ensures the rapid acquisition and processing of user control instructions. The communication diffusion network constructed based on the preset device communication environment effectively supports the establishment of a secure instruction channel. The generation of tamper-proof encapsulated instructions enhances the security of instructions during transmission. During program execution, the expected device execution data can be inferred based on the tamper-proof encapsulated instructions, thereby improving the ability to accurately predict device behavior. The introduction of the operation simulation module makes device operation simulation more efficient, thereby obtaining accurate simulated device operation data. Trajectory recording ensures effective consistency between instructions and device behavior. The change analysis module provides a detailed analysis of the simulated device morphological change parameters. The function of the difference comparison module effectively matches the consistency between the expected device execution data and the morphological change parameters, thereby improving the reliability of instruction execution. The use of the channel reallocation module facilitates communication error backtracing and error point location based on morphological change differences, enhancing the system's ability to handle emergencies. The generation of dynamic instruction channels optimizes instruction transmission efficiency, thereby overall improving the intelligence and automation level of device security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the steps of a device safety control method;

[0068] Figure 2 Detailed implementation flow chart of step S2;

[0069] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0070] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0072] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve this, please refer to Figures 1 to 2 , a device security management and control method, comprising the following steps:

[0074] Step S1: Obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; perform dynamic key conversion on the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions;

[0075] Step S2: Calculating expected device execution data based on the tamper-proof package instruction; performing device operation simulation according to the tamper-proof package instruction to obtain simulated device operation data;

[0076] Step S3: Recording the trajectory based on the simulated device operation data to generate the instruction corresponding device trajectory; analyzing the simulated device morphological change parameters based on the instruction corresponding device trajectory and the simulated device operation data;

[0077] Step S4: Match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, directly return to the terminal; if they are inconsistent, analyze the parameter change differences of the simulated device form change parameters;

[0078] Step S5: Perform communication error backtracking based on the difference in morphological changes, and locate the error point according to the device trajectory corresponding to the instruction to obtain the communication error generation point; perform error channel mapping on the communication error generation point, and perform channel reallocation based on the communication diffusion network and error channel to generate a dynamic instruction channel.

[0079] By acquiring user control commands and building a communication diffusion network based on a preset device communication environment, the present invention effectively enhances the security of the command transmission process. The established command security channel ensures the privacy protection of user commands during transmission. Tamper-proof encapsulated commands generated through dynamic key conversion provide assurance of the validity and integrity of the commands. Expected device execution data is inferred based on the tamper-proof encapsulated commands, ensuring accurate prediction of device operations. The simulated device operation data generated by the device operation simulation function accurately reflects the device's potential behavior. The implementation of trajectory recording generates a device trajectory corresponding to the command, providing a clear basis for subsequent analysis. By analyzing the simulated device morphological change parameters, a comprehensive understanding of the device's morphological changes during operation is achieved. The consistency of expected data and simulated parameters is matched one by one, effectively improving the reliability of command execution. Difference analysis of morphological change parameters promotes a deeper understanding of potential communication problems. Communication error backtracking based on morphological change differences allows for rapid location of error points, providing precise direction for problem resolution. Error channel mapping and channel reallocation techniques ensure efficient and stable communication. The generated dynamic command channel enhances the device's responsiveness and adaptability, improving the overall efficiency and intelligence level of device security management and control.

[0080] In an embodiment of the present invention, the device security management method includes the following steps:

[0081] Step S1: Obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; perform dynamic key conversion on the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions;

[0082] In this embodiment, when obtaining user control instructions, the instruction data input by the user is received through the external input interface. The instruction data includes the operation type, target device identification, execution parameters, etc. The communication parsing module is used to format the instruction data and convert it into structured instruction data. Subsequently, a communication diffusion network is constructed based on the preset device communication environment information. The communication diffusion network organizes the communication links between devices through a node hierarchical distribution structure, and adopts a dynamic link mapping method based on neighborhood channel perception to generate diffusion network topology information that conforms to the communication topology structure. Further based on the topology information, the optimal security path is selected within the network to construct an instruction security channel. The security channel adopts a multi-level authentication mechanism for channel access control, and is combined with an end-to-end encryption strategy to ensure the security of the communication process. After the instruction security channel is established, the dynamic key distribution mechanism is used to perform dynamic key conversion on the user control instructions to generate tamper-proof encapsulated instructions. The encapsulated instructions adopt an integrity verification method based on a hash chain for tamper-proof processing, and are combined with the device identification code for unique binding.

[0083] Step S2: Calculating expected device execution data based on the tamper-proof package instruction; performing device operation simulation according to the tamper-proof package instruction to obtain simulated device operation data;

[0084] In this embodiment, when inferring the expected execution data of the device based on the tamper-proof packaged instruction, the instruction parsing unit is used to extract key information such as the operation type and execution parameters from the packaged instruction, and the matching device operation model is found in combination with the device execution model library, the state inference module of the execution model is called, the instruction parameters are input, and the expected execution data of the device is generated. The data includes the operating state value that the device should reach after executing the instruction. At the same time, the device operation simulation unit is called according to the tamper-proof packaged instruction, the corresponding operating environment parameters of the device are loaded, the execution instruction is input, and the operation process of the device is simulated. The simulation process adopts a timing-driven simulation method. After executing the instruction, the real-time state parameters of the device are calculated according to the set physical constraints, and in the process of advancing the simulation time step, the changes in the key state parameters of the device are recorded, and finally the simulated device operation data is generated. The data includes the state information of the device at different time points, such as position, speed, angle, etc.

[0085] Step S3: Recording the trajectory based on the simulated device operation data to generate the instruction corresponding device trajectory; analyzing the simulated device morphological change parameters based on the instruction corresponding device trajectory and the simulated device operation data;

[0086] In this embodiment, when trajectory recording is performed based on the simulated device operation data, the trajectory acquisition module is used to perform time series segmentation processing on the simulated device operation data, extract the spatial position of the device at each time point, and construct a trajectory point sequence. The time interval data between the trajectory points is filled in by the trajectory interpolation method to generate the instruction corresponding device trajectory. The trajectory data is stored in the form of a spatiotemporal sequence. At the same time, based on the instruction corresponding device trajectory and the simulated device operation data, the morphological analysis module is called to extract the geometric morphological parameters involved in the device operation process, including changes in the device outline, the curvature of the motion trajectory, the spatial posture adjustment, etc. The morphological feature matching method is used to quantify the morphological change parameters of the simulated device and store them as a morphological change data set.

[0087] Step S4: Match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, directly return to the terminal; if they are inconsistent, analyze the parameter change differences of the simulated device form change parameters;

[0088] In this embodiment, when matching the expected data of device execution and the morphological change parameters of the simulated device one by one to see if they are consistent, the expected data of device execution is firstly decomposed into parameters, the target state parameter values are extracted, and the values are compared one by one with the corresponding parameter values in the morphological change parameters of the simulated device. During the comparison process, a matching method based on an error threshold is adopted to calculate the error range for each parameter value. If the error is within the set threshold range, it is determined to be consistent and directly returned to the terminal. If the error exceeds the threshold range, the morphological change difference analysis stage is entered. In this stage, the difference extraction module is used to perform a comparison operation on the morphological change parameters, calculate the parameter change amplitude, and generate parameter change difference data, which is used for subsequent communication error backtracking analysis.

[0089] Step S5: Perform communication error backtracking based on the difference in morphological changes, and locate the error point according to the device trajectory corresponding to the instruction to obtain the communication error generation point; perform error channel mapping on the communication error generation point, and perform channel reallocation based on the communication diffusion network and error channel to generate a dynamic instruction channel.

[0090] In this embodiment, when performing communication error backtracing based on morphological change differences, the error backtracing module is called to perform error chain tracking processing on the morphological change difference data. According to the error transmission path, the communication link is traced back to determine the source of the error signal. At the same time, combined with the trajectory of the device corresponding to the instruction, an error point positioning method based on trajectory offset analysis is adopted to calculate the abnormal offset area in the trajectory data, and the error signal source analysis results are integrated to mark the communication error generating point. Subsequently, error channel mapping is performed on the communication error generating point, and a channel abnormal area extraction method is adopted to extract features of the channel environment around the error point. Based on the topological structure of the communication diffusion network, error channel partition mapping is performed to form error channel mapping data. On this basis, combined with the error channel characteristics, a channel weight adjustment mechanism is adopted to perform channel reallocation on the error instruction, optimize the instruction transmission path, and finally generate a dynamic instruction channel.

[0091] Preferably, step S1 includes the following steps:

[0092] Step S11: Obtaining user operation instructions; extracting multimodal features of the user operation instructions;

[0093] Step S12: RF signal strength mapping is performed on the preset device communication environment, where the RF signal field strength range is limited to -120dBm to -40dBm, and a three-dimensional signal coverage environment is constructed;

[0094] Step S13: Grid partitioning is performed based on multimodal features and the three-dimensional signal coverage environment, where the grid size is set to 1m×1m×1m, and optimal paths are screened to generate a communication diffusion network;

[0095] Step S14: configuring a link encryption tunnel for the communication diffusion network and building an end-to-end secure channel to obtain a command secure channel;

[0096] Step S15: performing dynamic key derivation processing on the user control instruction based on the instruction security channel to obtain dynamic key data;

[0097] Step S16: Perform block-level data obfuscation on the dynamic key data and user control instructions to generate tamper-proof encapsulated instructions.

[0098] In this embodiment, when obtaining user control instructions and extracting multimodal features of user operation instructions, first, the device receives the control instructions from the user. The instructions can be text, voice, gesture or other input forms. The input data is transmitted to the instruction parsing module through the interface module. The parsing module performs preliminary preprocessing on the input text data, including removing irrelevant symbols and special characters to ensure the standardization of the instruction content. Then, the multimodal feature extraction module extracts features from the text instructions. The text features include word frequency, emotional tendency, etc. The feature extraction of voice instructions adopts MFCC (Mel-frequency cepstral The gesture instructions are processed using the Mel-frequency cepstral coefficients method, and the gesture instructions are processed using computer vision technology. The motion trajectory features are extracted through the image recognition algorithm. All extracted feature data, including the vectorized representation of text, voice, and gestures, are stored in the database for subsequent analysis and decision support. At the same time, in the process of instruction parsing, the features of different modes are fused to construct a comprehensive feature vector. The RF signal strength of the preset device communication environment is measured, where the RF signal field strength range is limited to -120dBm to -40dBm. When building a three-dimensional signal coverage environment, signal receiving devices are first deployed at multiple locations in the preset environment. These devices are equipped with spectrum analyzers, directional antennas, and receiving modules. The spectrum analyzer is used to capture RF signals in the 2.4GHz to 5.8GHz frequency band. The directional antenna accurately receives signals from different directions according to the pointing angle and position. The data collection interval is set to 0.5 seconds. The signal strength is recorded at each collection point and the data is uploaded to the data processing platform in real time. All data points are interpolated and combined with the known device position and measurement points to construct a three-dimensional signal strength distribution map. The intensity range is from -120dBm to -40dBm. Each point shown in the figure corresponds to a set of signal strength data. A continuous signal strength field is generated through the interpolation algorithm, and finally a complete three-dimensional signal coverage environment is formed. Grid partitioning is performed based on multimodal characteristics and three-dimensional signal coverage environment, where the grid size is set to 1m×1m×1m, and the optimal path screening is performed to generate a communication diffusion network. First, the three-dimensional signal coverage environment is divided into 1m×1m×1m grid units, and the signal strength and signal quality are calculated in each grid unit. The signal quality is measured by signal The signal-to-noise ratio (SNR) is evaluated. Grid cells with signal strength below -100dBm are marked as low-quality areas, areas with signal strength between -100dBm and -70dBm are marked as medium-quality areas, and areas with signal strength above -70dBm are marked as high-quality areas. Then, based on the user's command target and the actual location of the device, the path selection algorithm is used to optimize the path and select the path with the best signal quality to connect different grid cells. When screening paths, the signal quality of each grid cell, the availability of the path, and the topology of the network are taken into consideration.The Dijkstra algorithm is used to select the path and finally generate an optimal communication diffusion network. The link encryption tunnel configuration of the communication diffusion network is performed, and an end-to-end secure channel is constructed to obtain the instruction security channel. First, each link in the communication diffusion network is encrypted by an encryption algorithm. The selected encryption algorithm is AES-256 (Advanced Encryption Standard). Using the symmetric key encryption mode, a unique encryption key pair is generated at both ends of each link. The key generation process includes key exchange through the public key infrastructure (PKI) to ensure the secure distribution of keys at both ends of the link. After the configuration is completed, the communication data on each link is transmitted through an encrypted tunnel. The data transmission path of the entire network is encrypted to ensure the confidentiality and integrity of the data during transmission. Finally, an end-to-end secure channel is established. Based on the instruction security channel, dynamic key derivation processing is performed on the user control instructions to obtain dynamic key data. First, the key derivation adopts the HMAC (Hash-based Message Authentication A key derivation function (KDM) based on a hash-based message authentication code (MAC) generates a derived key by inputting the user's unique identifier, the instruction timestamp, and a pre-set initial key. Each step in the key derivation process uses a different salt value to enhance key security. The derived key data is used to encrypt subsequent instruction information, ensuring that the keys generated at different times and for different instructions are different. Block-level data obfuscation is performed on dynamic key data and user control instructions to generate tamper-proof encapsulated instructions. First, the user control instruction is divided into blocks, each of which is set to 128 bytes in size. Next, each data block is encrypted using the dynamic key. Simultaneously, a block-level obfuscation algorithm is used to perturb the content of each data block, disrupting the structure of the original data. The obfuscated data blocks are then rearranged in order. Finally, all data blocks are merged into a complete encrypted instruction, ensuring that even if the instruction is intercepted during transmission, the original content cannot be restored. Each data block of the tamper-proof encapsulated instruction contains an encryption checksum. After receiving the instruction, the receiving end decrypts and verifies the data block to confirm the integrity and legitimacy of the instruction.

[0099] Preferably, step S2 includes the following steps:

[0100] Step S21: Initialize the communication node state based on the instruction security channel, and perform instruction semantic conversion on the tamper-proof encapsulated instruction to obtain instruction abstract semantics, where;

[0101] Step S21: Predicting the state transition path based on the instruction abstract semantics and performing state transition constraints based on the initial communication node to obtain the expected data for device execution, wherein the delay threshold of the state transition constraint is controlled within 5ms;

[0102] Step S23: constructing a device virtual communication field according to the instruction secure channel, wherein the maximum number of nodes in the device virtual communication field is limited to 1024, and the processing capacity of each node is not less than 1 GHz;

[0103] Step S24: performing instruction transmission simulation on the anti-tampering encapsulation instruction based on the device virtual communication field, and performing device operation simulation based on the simulated transmission instruction to obtain simulated device operation data.

[0104] In this embodiment, the communication node state is initialized based on the instruction security channel, and the instruction semantic conversion is performed on the tamper-proof encapsulated instruction. When the instruction abstract semantics are obtained, first, the received tamper-proof encapsulated instruction is processed by the encryption and decryption module, and the decrypted instruction content is extracted and converted into a standardized instruction format. The instruction format includes the operation type, operation parameters and target device. The grammatical structure of each instruction item conforms to the predefined format standard. Then, the instruction semantic conversion engine is used to perform semantic analysis on the instruction to parse out the key information in the instruction, such as the operation type is "start", "stop" or "set", and extract the specific parameters of each operation. These parameters include device ID, target operation time and required resources. Semantic information is mapped into an abstract semantic model. The instruction semantic model is represented by a graph structure, where each node in the graph represents an operation unit, and the edge represents the dependency relationship between operations. The vertex of the graph contains detailed information of each operation, including the operation sequence and execution conditions. The abstract semantic graph of the instruction is used for subsequent state migration path prediction. The state migration path prediction is performed based on the abstract semantics of the instruction, and the state transition constraints are performed according to the initialization communication node to obtain the expected data for the device execution. First, according to the abstract semantic graph of the instruction, the state transition model of the device is constructed. In the model, each state represents an execution stage of the device, such as "standby state", "execution state", and "completion state". Each state is connected by an edge, indicating the transition from The transition conditions from one state to another are determined. Then, the state transition algorithm is used to predict the state path of the device when executing instructions, combining the current working state of the device with the predefined state transition rules. During the prediction process, taking into account constraints such as network delay, resource usage and device capabilities, the algorithm optimizes the execution efficiency by dynamically adjusting the state transition path of the device. The state transition of each path is controlled by a delay threshold to ensure that the delay of all state changes does not exceed 5ms. Each step of the path prediction must meet the delay constraint. The delay time and resource usage of each node on the path are recorded. By comparing the delay values of different paths, the optimal path is selected to meet the execution requirements of the device, and the expected data of device execution is generated to ensure that the device can be highly efficient. In order to effectively complete the instruction task, a device virtual communication field is constructed according to the instruction security channel, where the maximum number of nodes in the device virtual communication field is limited to 1024, and the processing power of each node is not less than 1GHz. First, a virtual communication field is created on the device control platform through virtualization technology. Each node in the virtual communication field represents a virtual device. The computing power of each virtual node is set to be no less than 1GHz, and each node has independent memory and processing power to ensure that the nodes can process instruction data in parallel. Each node is interconnected through a high-speed communication network to form a global communication network. The total number of nodes in the virtual communication field is set to 1024 to ensure that the communication field can accommodate enough devices for parallel data transmission.During the creation of the virtual communication field, a distributed management architecture is adopted to ensure the load balancing of each node. Through the intelligent scheduling algorithm, resources are dynamically allocated according to the real-time load of each node to ensure that each virtual node can operate stably during the execution of the device. Finally, the communication links of all virtual nodes are securely encrypted to ensure the confidentiality and integrity of data transmission. The instruction transmission simulation of the tamper-proof encapsulated instruction is performed based on the device virtual communication field, and the device operation simulation is performed based on the simulated transmission instruction to obtain the simulated device operation data. First, the tamper-proof encapsulated instruction is transmitted through the nodes in the virtual communication field, and each node performs corresponding processing according to the content of the instruction. The instruction transmission process includes multiple stages such as encryption and decryption, verification, and execution. After receiving the instruction, the node first decodes it. The system receives encrypted instructions and parses out specific operations. Then, based on the device status information, it simulates the device's resource usage after receiving the instructions. The simulation platform simulates the transmission time and processing process of the instructions based on the processing power and communication delay of each node. Each node returns the results generated after the instruction is executed. Finally, the processing results of all nodes are summarized on the control platform. The simulation platform simulates the device operation data generated based on the instruction transmission simulation and analyzes the device's behavior under different operating states, including the device's response time, load changes, power consumption, etc. During the simulation, the processing process and execution status of each node are recorded in real time to evaluate the performance during the instruction execution process. The simulation data is used to verify the performance of the device in actual operation and further adjust the device's operation strategy based on the simulation results.

[0105] Preferably, the track recording based on the simulated device operation data in step S3 includes:

[0106] Record the device status change data of the simulated device operation data and perform time series segmentation to obtain device status time slices;

[0107] Analyze the device position change of the simulated device operation data based on the device status time slice to obtain the device change coordinates;

[0108] Construct three-dimensional equipment trajectory based on equipment change coordinates;

[0109] Mark the tamper-proof packaged instructions and the simulated device operation data in time sequence to obtain the device operation data corresponding to the instructions;

[0110] Perform time alignment on the three-dimensional device trajectory and the device operation data corresponding to the instruction to generate the device trajectory corresponding to the instruction.

[0111] In this embodiment, when recording the device state change data of the simulated device operation data, the device operation simulation database is called to retrieve the state information of the device at different time points. The state information includes parameters such as the motor speed, execution joint angle, load torque, current waveform, etc. of the device. The time series data structure is used to sort the state information according to the simulation timestamp and store it as a time series data table. The sliding window segmentation algorithm is used to segment the continuous time series. The window length is set to 50 milliseconds. Each window corresponds to a device state time slice. Within the time slice, the mean, variance, extreme value and other statistics of the device state parameters are calculated to form a complete device state time slice data. When analyzing the position change of the simulated device operation data based on the device state time slice, the device posture trajectory data is read. This data is generated by the device operation simulation system and contains the spatial coordinates and posture angle information of the device. The differential calculation method is used to derive the device coordinates of continuous time slices to obtain the speed and acceleration data of the device. At the same time, the spatial transformation matrix is called to normalize the device coordinates of different time slices to ensure that the coordinate data are compared in the same reference coordinate system. Finally, the device change coordinates are obtained. When constructing the three-dimensional device trajectory based on the device change coordinates, the spatial trajectory fitting module is called and the spline interpolation method (Spline The 3D device trajectory is fitted by using local weighted regression (LSR) to eliminate the sudden changes in the trajectory caused by noise interference. When marking the timing correspondence between the anti-tampering packaged instructions and the simulated device operation data, the instruction parsing module is called to read the execution timestamp of the anti-tampering packaged instructions, and the device status data closest to the timestamp is retrieved from the simulated device operation data. The bidirectional time alignment method is used to perform weighted average calculation on the device status data within 10 milliseconds before and after the instruction execution moment to obtain the device operation data corresponding to the instruction execution, and the timing mark is written into the device data index table to form the device operation data corresponding to the instruction. When performing timing alignment on the 3D device trajectory and the device operation data corresponding to the instruction, dynamic time warping (DTW) is used. The device trajectory is matched with the instruction timestamp sequence using the Time Warping (DTW) algorithm. The time deviation between the trajectory data and the instruction timestamp sequence is first calculated. The least squares optimization method is used to scale the trajectory time axis to minimize the time difference between the instruction timestamp and the trajectory data point. The optimal matching path is then constructed using the DTW algorithm. The matching results are constrained and optimized to ensure the temporal consistency of the trajectory. Finally, the device trajectory corresponding to the instruction is generated.

[0112] Preferably, the step S3 of analyzing the simulated device morphology change parameters based on the device trajectory corresponding to the instruction and the simulated device operation data includes:

[0113] Perform time-series segmented interpolation on the device trajectory corresponding to the instruction to obtain the device motion curve;

[0114] Construct the equipment dynamic response field based on the equipment motion curve;

[0115] Construct temperature gradient distribution field based on preset equipment physical characteristics and simulated equipment operation data;

[0116] Perform nonlinear thermal expansion analysis on the temperature gradient distribution field and construct the equipment thermal expansion stress field;

[0117] Perform comprehensive stress superposition on the equipment dynamic response field and the equipment thermal expansion stress field to generate a multi-physics coupled stress field;

[0118] Explicit dynamic iteration is performed based on the multi-physics coupled stress field to generate the surface deformation gradient field;

[0119] Calculate the contact pressure distribution according to the surface deformation gradient field, and locate the force-bearing area of the equipment based on the contact pressure distribution data;

[0120] The wear deformation of the stress-bearing area of the positioning equipment is predicted based on the preset equipment material parameters, and the simulated equipment morphological change parameters are generated.

[0121] In this embodiment, the device trajectory corresponding to the instruction is interpolated in time segments. First, the trajectory data of the device is segmented in time, and the data is segmented according to the dynamic characteristics of the device trajectory. The length of each segment is set according to the device motion speed and control accuracy. The common segment length is 10 seconds. The device position and speed data are obtained in each segment using the equidistant interpolation method. The interpolation algorithm uses the cubic spline interpolation method to ensure that the trajectory is smooth and accurate. The obtained device motion curve can accurately reflect the position change and motion trend of the device during operation. The motion curve generated at this time can provide high-precision motion trajectory data for subsequent analysis. The device dynamic response field is constructed based on the device motion curve, and the acceleration of each part of the device is obtained through the device motion curve. The finite element analysis method is used to dynamically model the equipment based on the inertial parameters and external force conditions of the equipment, and the dynamic response field of the equipment is constructed. The number of nodes in the response field is set to 1000, and the spacing between the nodes is reasonably divided according to the size and motion state of the equipment. The dynamic loading and response algorithm is used to calculate within the node to obtain the dynamic response data of each node. The temperature gradient distribution field is constructed based on the preset physical characteristics of the equipment and the simulated equipment operation data. The heat conduction analysis model is used to construct the temperature distribution field in combination with the material, operation state and external environmental conditions of the equipment. By inputting the operation data of the equipment under different loads and environmental conditions, the temperature distribution on the surface and inside of the equipment is obtained. The calculation basis of the temperature gradient The thermal conductivity coefficient of the equipment material and the radiation characteristics of the equipment surface. When the temperature distribution field is generated, the temperature gradient interval is set to 1°C, and the temperature data accuracy is required to reach 0.1°C. A nonlinear thermal expansion analysis is performed on the temperature gradient distribution field, and the equipment thermal expansion stress field is constructed. Based on the physical characteristics and temperature gradient of the equipment, a nonlinear thermal expansion analysis is performed. The finite element method is used to model and calculate the thermal expansion of each part of the equipment. The thermal expansion coefficient of the equipment is set according to different materials. During the calculation process, the temperature change is segmented in units of 10°C. The deformation and stress changes of different parts of the equipment under temperature changes are analyzed. The thermal expansion stress field finally generated is accurate to each node, and the equipment dynamic response field and the equipment thermal expansion stress field are comprehensively superimposed to obtain the thermal expansion stress field. To generate a multi-physics coupled stress field, the dynamic response field and the thermal expansion stress field are first merged to obtain a comprehensive stress field. The combined stresses are superimposed using a weighted average method. The comprehensive stress at each node is a weighted combination of the dynamic response stress and the thermal expansion stress. The weight coefficient is set according to different operating conditions, and the specific coefficient is adjusted according to the equipment's operating environment and material properties. Finally, a multi-physics coupled stress field is generated. The node stress data accuracy requirement is 0.1 MPa. Explicit dynamic iteration is performed based on the multi-physics coupled stress field to generate a surface deformation gradient field. The explicit dynamic algorithm is then applied to iteratively calculate the combined stress field. The deformation value at each time step is calculated to generate the surface deformation gradient field. During the iteration process, the time step is set to 0.The system calculates the displacement and stress response of the device's surface deformation every 0.001 seconds, with each iteration accurate to 0.1mm. The contact pressure distribution is calculated based on the surface deformation gradient field, and the device's stress-bearing areas are located based on the contact pressure distribution data. A contact mechanics model is used to analyze the contact area within the surface deformation gradient field and calculate the pressure distribution at the contact points. Contact pressure calculations employ a displacement-based pressure inference method. The pressure value at each contact point is calculated based on the deformation and contact surface characteristics. The result is a contact pressure distribution map, which then locates areas of the device subject to significant pressure during operation. The positioning accuracy requirement is 0.01mm. Wear deformation predictions are performed on the stress-bearing areas of the located device based on preset device material parameters, generating simulated device morphological change parameters. Deformation predictions are performed using a wear model based on the material's hardness and wear resistance parameters, combined with pressure data from the device's stress-bearing areas. The wear model is calculated based on sliding distance and pressure distribution, and the predicted deformation is recorded with an accuracy of 0.1mm. The resulting simulated device morphological change parameters accurately describe the wear and deformation of the device under different operating conditions.

[0122] Preferably, step S4 includes the following steps:

[0123] Step S41: performing time extrapolation processing based on the expected equipment execution data and performing equipment wear prediction to obtain expected equipment wear data;

[0124] Step S42: unifying the expected equipment wear data and the simulated equipment morphology change parameters in terms of timeline, and matching the expected equipment wear data and the simulated equipment morphology change parameters one by one based on the same timeline to obtain a wear matching result;

[0125] Step S43: When the wear matching result is completely consistent, directly return to the terminal; when the wear matching result is not completely consistent, compare the expected equipment wear data and the simulated equipment morphological change parameters to generate parameter change differences.

[0126] In this embodiment, time extrapolation processing is performed based on the expected data of equipment execution, and equipment wear prediction is performed to obtain expected equipment wear data. First, based on the operating parameters and historical data of the equipment, the wear of the equipment in a certain period of time in the future is predicted through regression analysis method. In the regression analysis, the set parameters include equipment load, operating time, working environment and material properties. The extrapolation processing adopts linear extrapolation method to extend the historical data to the future time period. The extended time period is set according to the life cycle of the equipment and the expected maintenance cycle, usually set to 6 months. The time step in the extrapolation process is 1 day. The wear amount of each day is calculated and the wear number is recorded at each time point. According to the data, the wear data of the equipment in the expected time is finally obtained. The accuracy of the wear data is required to be 0.01mm. The expected equipment wear data and the simulated equipment morphological change parameters are unified on the timeline, and the expected equipment wear data and the simulated equipment morphological change parameters are matched one by one based on the same timeline to obtain the wear matching result. First, the expected equipment wear data and the simulated equipment morphological change parameters are aligned on the time axis. The time axis is unified by using the interpolation method to unify the time points of the two data sources to the same time axis. The interpolation method selects cubic spline interpolation. In the process of time axis unification, it is ensured that the wear data and morphological change parameters at each time point can accurately correspond to each other. The accuracy of the unified data is 0.1mm. When matching, the wear data of each time point is compared with the corresponding morphological change parameters one by one according to the time step of the equipment operation, and a corresponding relationship list is generated to finally obtain the wear matching result. When the wear matching result is completely consistent with the wear, it is directly returned to the terminal. At this time, the expected wear data of the equipment is completely consistent with the morphological change parameters of the simulated equipment, which means that the equipment is in a normal wear state and no additional intervention is required. The system will directly output the consistent status result. The action returned to the terminal does not involve additional calculations, and the output result is "the equipment wear state is normal". When the wear matching result is not completely consistent, the expected equipment wear is corrected. The parameter difference between the wear data and the simulated equipment morphological change parameters is compared to generate the parameter change difference. First, the difference between the expected equipment wear data and the simulated equipment morphological change parameters is calculated. The calculation method is to compare the difference between the wear amount and the morphological change amount at each time point. When the difference is greater than 0.1mm, it is marked as an abnormal difference. The calculation result is stored as a difference data table. Each row in the difference data table records the difference value at a time point. The specific difference value is generated by calculating the absolute value difference between the wear and morphological change parameters to generate a difference value chart. Finally, based on the difference data table, the wear repair suggestion of the equipment is generated. If the difference is large, the system will prompt that the equipment parts need to be further checked or replaced.

[0127] Preferably, the step S5 of performing communication error backtracking based on the difference in morphological changes and locating the error point according to the device trajectory corresponding to the instruction includes:

[0128] Backtracking is performed based on the difference in morphological changes to obtain the error propagation trajectory;

[0129] Perform time reversal reconstruction on the error propagation trajectory to generate error origin time series data;

[0130] Based on the error origin timing data, the device trajectory corresponding to the instruction is temporally and spatially aligned, and the error origin is matched to obtain the communication error generation point.

[0131] In this embodiment, reverse tracking is performed based on the morphological change difference to obtain the error propagation trajectory. First, the morphological change difference data is obtained and arranged in chronological order. The data is input into the error propagation modeling module. The error propagation modeling module uses the dynamic time warping (DTW) method to calculate the time correlation of the morphological change difference and establish an error propagation path. In the error propagation path, each error point contains a timestamp, spatial position, error amplitude and error direction. In order to improve the tracking accuracy of error propagation, the error propagation path uses the Markov Decision Process (MDP) for state transition analysis and combines the Long Short-Term Memory Network (LSTM) with the DTW method to perform state transition analysis. Memory, LSTM, long short-term memory network) predicts the propagation trend of the error. During the data processing process, the time window size is set to 0.1 seconds and the error amplitude threshold is set to 0.5 mm. Finally, the error propagation trajectory is obtained. The error propagation trajectory consists of the error state at multiple time points. Each time point includes the error value, error direction and propagation probability. The error propagation trajectory is reconstructed by time reversal to generate the error origin time series data. First, based on the error propagation trajectory, all error data are arranged in reverse chronological order, and the error propagation process is estimated by time reversal using the Bayesian filtering method. In order to improve the accuracy of time reversal reconstruction, the hidden Markov model (HMM) is used to establish the error state transfer matrix during the time reversal process, and the distribution probability of the error at different time points is calculated. In order to eliminate noise interference, the error trajectory data is smoothed by wavelet transform and a bidirectional recurrent neural network is used. The time characteristics of the error origin point are analyzed by using a bidirectional recurrent neural network (BRNN). In the process of determining the error origin point, the time matching threshold is set to 0.05 seconds and the error amplitude matching threshold is set to 0.2 mm. When the starting point of the error propagation trajectory meets the above conditions, the point is marked as the error origin point, and the error origin time series data is finally generated. The error origin time series data includes the error occurrence time, the error amplitude change trend and the device status parameters. Based on the error origin time series data, the device trajectory corresponding to the instruction is temporally and spatially aligned, and the error origin matching is performed to obtain the communication error generation point. First, the error origin time series data and the device trajectory data corresponding to the instruction are read and time-aligned. The time alignment uses the bidirectional dynamic time warping (BDTW) method for error time series matching. In order to ensure the accuracy of time alignment, the time error threshold is set to 0.02 seconds during the matching process. After time alignment, the spatial position of the error origin point is matched with the device trajectory. The spatial matching uses the nearest neighbor matching method and combines it with the rigid transformation (Rigid Transformation (rigid transformation) is used to transform the coordinates of the device trajectory so that the error origin point can be accurately aligned with the key points on the device trajectory. During the error matching process, the spatial error threshold is set to 0.5 mm. When the timestamp of a certain error origin point matches the timestamp on the device trajectory to a degree of matching greater than 98% and the spatial error is less than the threshold, the point is marked as the communication error origin point.

[0132] Preferably, the step S5 of performing error channel mapping on the communication error generating point and performing channel reallocation based on the communication diffusion network and the error channel includes:

[0133] Conduct command path interference analysis on the communication error generating point to obtain the error propagation route;

[0134] Marking communication error channels based on error propagation routing and communication error generation points;

[0135] Perform conflict detection on the communication error channel to obtain channel conflict data;

[0136] According to the channel conflict data, the communication diffusion network is matched with conflict avoidance paths, and channels are reallocated based on the conflict avoidance paths to generate dynamic instruction channels.

[0137] In this embodiment, the command path interference analysis is performed on the communication error generating point to obtain the error propagation route. First, the timestamp, spatial coordinates and error information of the communication error generating point are extracted, and the device command data stream is backtracked. During the backtracking process, the time synchronization network (TSN) is used to correct the command transmission time, and the shortest path backtracking algorithm is combined with the time synchronization network (TSN) to correct the command transmission time. Algorithm, SPBA, shortest path backtracking algorithm) reconstructs the error propagation path. During the error propagation path analysis process, the delay threshold is set to 0.1 milliseconds and the path backtracking step is 5 hops. When the delay error of a path exceeds the threshold and the error information intensity on the path is greater than 0.7, the path is marked as a potential error propagation path. Finally, all error propagation paths are connected to generate a complete error propagation route. The error propagation route contains path nodes, error information weights and instruction data stream characteristics. The communication error channel is marked based on the error propagation route and the communication error generation point. First, all path nodes in the error propagation route are obtained, and the communication channel of each path node is analyzed. During the analysis process, the channel feature extraction module (CFEM) is used to extract the channel frequency, channel bandwidth and error occupancy. In order to ensure the accurate marking of the error channel, the Gaussian mixture model (Gaussian Mixture Model) is used in the analysis process. A Gaussian mixture model (GMM) is used to fit the channel error distribution and calculate the channel error probability. A threshold of 0.6 is set for the error probability. When the error probability of a channel exceeds this threshold and the channel belongs to the critical path of the error propagation route, the channel is marked as a communication error channel. Ultimately, all communication error channels are stored in an error channel database. Conflict detection is performed on these channels to obtain channel conflict data. The error channel database is first traversed and the interference degree between each channel is calculated. The interference degree calculation uses the adjacency matrix model (AMM), where the matrix elements represent the signal interference strength between different channels. To improve the accuracy of conflict detection, the Fourier transform (FT) is used to extract the spectral characteristics of the channel signal during the interference degree calculation process. Mutual information analysis (MIA) is then used to calculate the conflict probability between channels. The conflict probability threshold is set to 0.When the collision probability of two or more channels exceeds this threshold and the bandwidth overlap between channels exceeds 50%, these channels are marked as conflicting channels and the channel conflict data is recorded. The channel conflict data includes the conflicting channel number, conflict probability, and interference intensity. Based on the channel conflict data, the communication diffusion network is matched with conflict-avoiding paths. Channels are then reallocated based on the conflict-avoiding paths to generate dynamic instruction channels. The Optimal Path Search Algorithm (OPSA) is first used to search for communication paths with minimal conflict in the communication diffusion network. During the search, the Dijkstra algorithm is used to calculate the communication interference weight of each path. The improved ant colony algorithm (IACA) is then used to optimize the channel allocation strategy. During the channel reallocation process, a channel switching threshold is set at 3 milliseconds. The backup channel with the least interference on the error propagation path is preferentially selected as the new instruction channel, ultimately generating a dynamic instruction channel.

[0138] The present invention also provides a device security management and control system for executing the device security management and control method described above, the device security management and control system comprising:

[0139] The key conversion module is used to obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; dynamically convert the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions;

[0140] An operation simulation module is used to infer expected device execution data based on the tamper-proof package instructions; and to simulate device operation according to the tamper-proof package instructions to obtain simulated device operation data;

[0141] The change analysis module is used to record the trajectory based on the simulated device operation data and generate the instruction corresponding device trajectory; based on the instruction corresponding device trajectory and the simulated device operation data, the simulated device morphological change parameters are analyzed;

[0142] The difference comparison module is used to match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, the module directly returns the result to the terminal. If they are inconsistent, the module analyzes the parameter change differences of the simulated device form change parameters.

[0143] The channel reallocation module is used to trace back communication errors based on morphological change differences and locate error points according to the device trajectory corresponding to the instruction to obtain the communication error generation point; error channel mapping is performed on the communication error generation point, and channel reallocation is performed based on the communication diffusion network and error channel to generate a dynamic instruction channel.

[0144] The present invention provides secure and reliable user command processing through the design of a key conversion module. The constructed communication diffusion network ensures data security during command transmission. The implementation of a secure command channel strengthens the protection of user control commands. The tamper-proof encapsulated commands generated by dynamic key conversion effectively prevent the risk of command tampering during transmission. The operation simulation module accurately infers the expected device execution data, improving the predictability of device operation. The simulated device operation data provides a detailed basis for subsequent analysis. The change analysis module effectively associates commands with device behavior through trajectory recording. Analysis of simulated device morphological change parameters helps to fully understand the dynamic characteristics of the device during operation. The one-to-one matching mechanism of the difference comparison module ensures data consistency and significantly improves the accuracy of command execution. The difference analysis of simulated device morphological change parameters lays the foundation for identifying potential problems. The channel reallocation module quickly locates error points through communication error backtracking technology, ensuring the stability and accuracy of the communication system. The effective mapping of error channels optimizes the communication process. The dynamic command channel implements flexible channel management, improves the adaptability and response speed of the device in complex operating environments, and overall enhances the security management efficiency and intelligence level of the device.

[0145] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the device security management and control method as described in any one of the above.

[0146] The present invention, through the implementation of a computer-readable storage medium, makes the device security management method sustainable and efficient. The stored computer program ensures the rapid acquisition and processing of user control instructions. The communication diffusion network constructed based on the preset device communication environment effectively supports the establishment of a secure instruction channel. The generation of tamper-proof encapsulated instructions enhances the security of instructions during transmission. During program execution, the expected device execution data can be inferred based on the tamper-proof encapsulated instructions, thereby improving the ability to accurately predict device behavior. The introduction of the operation simulation module makes device operation simulation more efficient, thereby obtaining accurate simulated device operation data. Trajectory recording ensures effective consistency between instructions and device behavior. The change analysis module provides a detailed analysis of the simulated device morphological change parameters. The function of the difference comparison module effectively matches the consistency between the expected device execution data and the morphological change parameters, thereby improving the reliability of instruction execution. The use of the channel reallocation module facilitates communication error backtracing and error point location based on morphological change differences, enhancing the system's ability to handle emergencies. The generation of dynamic instruction channels optimizes instruction transmission efficiency, thereby overall improving the intelligence and automation level of device security management.

[0147] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0148] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A device security management and control method, characterized in that: Applied to industrial equipment safety management and control, including the following steps: Step S1: Obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; perform dynamic key conversion on the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions; Step S2: Calculating expected device execution data based on the tamper-proof package instruction; performing device operation simulation according to the tamper-proof package instruction to obtain simulated device operation data; Step S3: Recording the trajectory based on the simulated device operation data to generate the instruction corresponding device trajectory; analyzing the simulated device morphological change parameters based on the instruction corresponding device trajectory and the simulated device operation data; Step S4: Match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, directly return to the terminal; if they are inconsistent, analyze the parameter change differences of the simulated device form change parameters; Step S5: Perform communication error backtracking based on the difference in morphological changes, and locate the error point according to the device trajectory corresponding to the instruction to obtain the communication error generation point; perform error channel mapping on the communication error generation point, and perform channel reallocation based on the communication diffusion network and error channel to generate a dynamic instruction channel.

2. The device security management and control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining user operation instructions; extracting multimodal features of user operation instructions; Step S12: RF signal strength mapping is performed on the preset device communication environment, where the RF signal field strength range is limited to -120dBm to -40dBm, and a three-dimensional signal coverage environment is constructed; Step S13: Grid partitioning is performed based on multimodal features and the three-dimensional signal coverage environment, where the grid size is set to 1m×1m×1m, and optimal paths are screened to generate a communication diffusion network; Step S14: configuring a link encryption tunnel for the communication diffusion network and building an end-to-end secure channel to obtain a command secure channel; Step S15: performing dynamic key derivation processing on the user control instruction based on the instruction security channel to obtain dynamic key data; Step S16: Perform block-level data obfuscation on the dynamic key data and user control instructions to generate tamper-proof encapsulated instructions.

3. The device safety management and control method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Initializing the communication node state based on the instruction security channel, and performing instruction semantic conversion on the tamper-proof encapsulated instruction to obtain instruction abstract semantics; Step S21: Predicting the state transition path based on the instruction abstract semantics and performing state transition constraints based on the initial communication node to obtain the expected data for device execution, wherein the delay threshold of the state transition constraint is controlled within 5ms; Step S23: constructing a device virtual communication field according to the instruction secure channel, wherein the maximum number of nodes in the device virtual communication field is limited to 1024, and the processing capacity of each node is not less than 1 GHz; Step S24: performing instruction transmission simulation on the anti-tampering encapsulation instruction based on the device virtual communication field, and performing device operation simulation based on the simulated transmission instruction to obtain simulated device operation data.

4. The device safety management and control method according to claim 1, characterized in that: The track recording based on the simulated device operation data in step S3 includes: Record the device status change data of the simulated device operation data and perform time series segmentation to obtain device status time slices; Analyze the device position change of the simulated device operation data based on the device status time slice to obtain the device change coordinates; Construct three-dimensional equipment trajectory based on equipment change coordinates; Mark the tamper-proof packaged instructions and the simulated device operation data in time sequence to obtain the device operation data corresponding to the instructions; Perform time alignment on the three-dimensional device trajectory and the device operation data corresponding to the instruction to generate the device trajectory corresponding to the instruction.

5. The device safety management and control method according to claim 1, characterized in that: The analysis of the simulated device morphology change parameters based on the device trajectory corresponding to the instruction and the simulated device operation data in step S3 includes: Perform time-series segmented interpolation on the device trajectory corresponding to the instruction to obtain the device motion curve; Construct the equipment dynamic response field based on the equipment motion curve; Construct temperature gradient distribution field based on preset equipment physical characteristics and simulated equipment operation data; Perform nonlinear thermal expansion analysis on the temperature gradient distribution field and construct the equipment thermal expansion stress field; Perform comprehensive stress superposition on the equipment dynamic response field and the equipment thermal expansion stress field to generate a multi-physics coupled stress field; Explicit dynamic iteration is performed based on the multi-physics coupled stress field to generate the surface deformation gradient field; Calculate the contact pressure distribution according to the surface deformation gradient field, and locate the force-bearing area of the equipment based on the contact pressure distribution data; The wear deformation of the stress-bearing area of the positioning equipment is predicted based on the preset equipment material parameters, and the simulated equipment morphological change parameters are generated.

6. The device safety management and control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing time extrapolation processing based on the expected equipment execution data and performing equipment wear prediction to obtain expected equipment wear data; Step S42: unifying the expected equipment wear data and the simulated equipment morphology change parameters in terms of timeline, and matching the expected equipment wear data and the simulated equipment morphology change parameters one by one based on the same timeline to obtain a wear matching result; Step S43: When the wear matching result is completely consistent, directly return to the terminal; when the wear matching result is not completely consistent, compare the expected equipment wear data and the simulated equipment morphological change parameters to generate parameter change differences.

7. The device safety management and control method according to claim 1, characterized in that: The communication error backtracking based on the morphological change difference and the error point positioning according to the instruction corresponding device trajectory described in step S5 include: Backtracking is performed based on the difference in morphological changes to obtain the error propagation trajectory; Perform time reversal reconstruction on the error propagation trajectory to generate error origin time series data; Based on the error origin timing data, the device trajectory corresponding to the instruction is temporally and spatially aligned, and the error origin is matched to obtain the communication error generation point.

8. The device safety management and control method according to claim 1, characterized in that: The step S5 of performing error channel mapping on the communication error generating point and performing channel reallocation based on the communication diffusion network and the error channel includes: Conduct command path interference analysis on the communication error generating point to obtain the error propagation route; Marking communication error channels based on error propagation routing and communication error generation points; Perform conflict detection on the communication error channel to obtain channel conflict data; According to the channel conflict data, the communication diffusion network is matched with conflict avoidance paths, and channels are reallocated based on the conflict avoidance paths to generate dynamic instruction channels.

9. A device safety management and control system, characterized in that: For executing the device security management and control method according to claim 1, the device security management and control system comprises: The key conversion module is used to obtain user control instructions; build a communication diffusion network based on the preset device communication environment and user control instructions, and establish a command security channel based on the communication diffusion network; dynamically convert the user control instructions based on the command security channel to generate tamper-proof encapsulated instructions; An operation simulation module is used to infer expected device execution data based on the tamper-proof package instructions; and to simulate device operation according to the tamper-proof package instructions to obtain simulated device operation data; The change analysis module is used to record the trajectory based on the simulated device operation data and generate the instruction corresponding device trajectory; based on the instruction corresponding device trajectory and the simulated device operation data, the simulated device morphological change parameters are analyzed; The difference comparison module is used to match the expected data of the device execution and the parameters of the simulated device form change one by one to see if they are consistent. If they are consistent, the module directly returns the result to the terminal. If they are inconsistent, the module analyzes the parameter change differences of the simulated device form change parameters. The channel reallocation module is used to trace back communication errors based on morphological change differences and locate error points according to the device trajectory corresponding to the instruction to obtain the communication error generation point; error channel mapping is performed on the communication error generation point, and channel reallocation is performed based on the communication diffusion network and error channel to generate a dynamic instruction channel.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the device security management and control method according to any one of claims 1 to 8 is implemented.

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