Equipment remote control method and device, equipment and storage medium

Abstract

The invention provides an equipment remote control method and device, equipment and a storage medium, and the method comprises the steps: carrying out the layered wake-up processing of subway maintenance equipment after receiving a remote control request, and obtaining a wake-up confirmation signal and a network delay compensation parameter; performing dynamic allocation on the maintenance equipment operation authority according to an awakening confirmation signal and a delay compensation parameter in combination with a subway safety integrity level requirement to obtain an authority level and a hierarchical control channel; aiming at maintenance operation time window limitation, performing optimization generation on the control instruction according to the authority level and the hierarchical control channel to obtain a remote control instruction and an execution timing schedule; and performing closed-loop monitoring on the execution process of the remote control instruction according to the execution timing schedule, adjusting the execution process through state estimation and anomaly detection, and starting an emergency processing mechanism when an anomaly is detected. According to the invention, accurate remote control of maintenance equipment can be realized in a complex subway tunnel network environment, and the control efficiency and safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote control, and in particular to a device remote control method, device, apparatus and storage medium. BACKGROUND

[0002] With the rapid development of urban rail transit systems, remote control technology of subway maintenance equipment plays an important role in ensuring the safety of subway operation and improving the efficiency of maintenance. The traditional remote control method of subway maintenance equipment mainly relies on simple point-to-point communication and fixed permission allocation mechanism, and operates the maintenance equipment through a pre-set control instruction sequence.

[0003] However, the existing remote control method has significant technical defects: in a complex subway tunnel network environment, due to factors such as signal transmission delay, electromagnetic interference and network switching, the traditional method cannot achieve precise wake-up control and dynamic permission management of the maintenance equipment, resulting in low control instruction execution efficiency, which is difficult to meet the real-time and safety requirements of subway maintenance work. Especially in the case of limited maintenance operation time window, the existing technology lacks an optimized generation and closed-loop monitoring mechanism for control instructions, which is prone to control failure and safety hazards. SUMMARY

[0004] The main purpose of the present application is to solve the technical problem that the existing remote control method of subway maintenance equipment cannot achieve precise wake-up control and dynamic permission management in a complex tunnel network environment, resulting in low control instruction execution efficiency and difficulty in meeting the real-time and safety requirements of maintenance work.

[0005] The first aspect of the present application provides a device remote control method, the device remote control method comprising: After receiving a remote control request for a subway maintenance operation, performing a hierarchical wake-up process on the subway maintenance equipment based on a pre-built linear model to obtain a maintenance equipment wake-up confirmation signal and a network delay compensation parameter; According to the wake-up confirmation signal and the delay compensation parameter, dynamically allocating the operation permissions of the maintenance equipment in combination with the subway safety integrity level requirements to obtain the permission level and the hierarchical control channel; According to the permission level and the hierarchical control channel, performing an optimized generation process on the control instructions in the remote control request according to the time window limit of the subway maintenance operation to obtain a remote control instruction and an execution time sequence arrangement table; According to the execution time sequence arrangement table, performing a closed-loop monitoring process on the execution process of the remote control instruction, adjusting the execution process through state estimation and abnormality detection, and starting an emergency handling mechanism when an abnormality affecting the safety of subway operation is detected to obtain a remote control execution result of the subway maintenance equipment.

[0006] Optionally, in the first implementation manner of the first aspect of the present application, after receiving the remote control request of the subway maintenance operation, the layered wake-up processing based on the pre-built linear model is performed on the subway maintenance device to obtain the maintenance device wake-up confirmation signal and the network delay compensation parameter, including: After receiving the remote control request of the subway maintenance operation, the remote control request is parsed to extract the subway maintenance device identifier and the maintenance operation type, and the pre-stored linear dynamic model of the subway maintenance device is called to obtain the device state transition parameter adapted to the subway environment to obtain the initial parameter of the maintenance device wake-up control; An optimal wake-up trajectory from the maintenance device sleep state to the target working state is calculated according to the initial parameter and the subway tunnel signal transmission characteristics by solving a limited time domain optimization algorithm to obtain a multi-stage wake-up control instruction sequence adapted to the subway network environment; According to the current network state information of the subway tunnel and the base station switching frequency, the instruction sending time sequence in the multi-stage wake-up control instruction sequence is subjected to delay compensation processing for the subway mobile network to obtain the wake-up instruction after delay compensation; According to the wake-up instruction, the multi-stage wake-up control signals are sent in sequence and the device response state is monitored, and when the wake-up failure caused by the subway environment interference is detected, a retry mechanism is started to adjust the wake-up strategy until the successful wake-up is achieved to obtain the maintenance device wake-up confirmation signal and the network delay compensation parameter.

[0007] Optionally, in the second implementation manner of the first aspect of the present application, the optimal wake-up trajectory from the maintenance device sleep state to the target working state is calculated according to the initial parameter and the subway tunnel signal transmission characteristics by solving a limited time domain optimization algorithm to obtain a multi-stage wake-up control instruction sequence adapted to the subway network environment, including: According to the initial parameter, a pre-built limited time domain optimization model is called, the wake-up trajectory parameters of the subway maintenance device from the sleep state to the target working state are set in combination with the subway tunnel electromagnetic environment and signal attenuation characteristics, and the wake-up trajectory control point adapted to the subway environment is obtained; According to the wake-up trajectory control point, a hierarchical wake-up sequence planning conforming to the subway maintenance device start specification is performed, the wake-up process is divided into three stages of power pre-activation, subway special network connection and maintenance system complete activation, and the control strength is allocated according to the subway operation safety level to obtain the hierarchical wake-up control strategy of the subway maintenance device; According to the hierarchical wake-up control strategy, the corresponding control instruction is generated, the specific control signal parameter is allocated for each wake-up stage, and the multi-stage wake-up control instruction sequence optimized for the subway environment is obtained.

[0008] Optionally, in a third implementation form of the first aspect of the present application, the dynamic allocation processing of the operation permission of the maintenance equipment according to the wake-up confirmation signal and the delay compensation parameter, in combination with the metro safety integrity level requirement, comprises: analyzing the power state, CPU load and memory usage of the metro maintenance equipment according to the wake-up confirmation signal, calculating the actual operation permission range that can be borne by the equipment in combination with the metro operation environment constraint, and obtaining the capability evaluation result of the metro maintenance equipment; modeling the basic detection permission, the configuration modification permission and the key maintenance operation permission as continuous variables according to the capability evaluation result and the metro safety integrity level, and calculating the current allocable permission level, to obtain the permission level calculation result; configuring the local controller to directly communicate with the metro maintenance equipment for basic permission management according to the permission level calculation result and the delay compensation parameter, and configuring the remote controller to perform high-level permission allocation through the metro private network, to obtain the layered control channel; solving the coupled control problem according to the layered control channel, and simultaneously determining the permission allocation proportion of the local controller and the remote controller, to obtain the executable permission level of the metro maintenance equipment.

[0009] Optionally, in a fourth implementation form of the first aspect of the present application, the optimization generation processing of the control instruction in the remote control request according to the permission level and the layered control channel for the time window limit of the metro maintenance operation comprises: matching and verifying the original control instruction in the remote control request with the permission level through the permission demand vector, to obtain a candidate instruction set that passes the permission verification; solving a predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the time window limit of the metro maintenance operation through a multi-objective optimization algorithm, to obtain an optimized control instruction sequence; determining the best sending time of each instruction according to the optimized control instruction sequence and the layered control channel through a timing scheduling algorithm, and considering the train operation timetable constraint, to obtain the remote control instruction and the execution timing arrangement table.

[0010] Optionally, in a fifth implementation form of the first aspect of the present application, the solving of the predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the time window limit of the metro maintenance operation through a multi-objective optimization algorithm to obtain an optimized control instruction sequence comprises: constructing a control error penalty target and an execution cost target according to the candidate instruction set, setting the weight coefficients of each target function in combination with the time window limit of the metro maintenance operation, and establishing a multi-objective optimization function, to obtain a multi-objective optimization problem. According to the multi-objective optimization problem, a prediction control time domain and a constraint condition are set, the authority limit and the subway maintenance operation time limit are taken as the constraint conditions to join the optimization problem, and a multi-objective optimization problem with constraints is obtained; The multi-objective optimization problem with constraints is solved by a multi-objective optimization algorithm, a Pareto optimal solution is calculated, and a solution most suitable for the current subway network and the state of the subway maintenance equipment is selected, so that an optimized control instruction sequence is obtained.

[0011] Optionally, in the sixth implementation manner of the first aspect of the present application, the closed-loop monitoring processing of the execution process of the remote control instruction according to the execution time sequence arrangement table is performed, the execution process is adjusted through state estimation and anomaly detection, an emergency processing mechanism is started when an anomaly affecting the safety of subway operation is detected, and the remote control execution result of the subway maintenance equipment is obtained, including: The instruction execution process of the remote control instruction is modeled as a four-dimensional probability distribution vector through a Markov state transition model, and an instruction execution state transition matrix is obtained in combination with the operation characteristics of the subway maintenance equipment; The state observation of different types of instructions is processed according to the execution time sequence arrangement table and the state transition matrix through a multi-time scale Kalman filter, the delay change of the subway tunnel network is adapted, and an instruction execution state estimation value is obtained; The execution anomaly is divided into three levels through a residual analysis algorithm according to the instruction execution state estimation value, and an anomaly detection classification result is obtained; Different levels of recovery strategies are adopted according to the anomaly detection classification result, and control parameters are adjusted, so that the remote control execution result of the subway maintenance equipment is obtained.

[0012] The second aspect of the present application provides a device remote control device, which comprises: A wake-up control module is configured to perform hierarchical wake-up processing on the subway maintenance equipment based on a pre-built linear model after receiving a remote control request for a subway maintenance operation, and obtain a maintenance equipment wake-up confirmation signal and a network delay compensation parameter; An authority allocation module is configured to perform dynamic allocation processing on the operation authority of the maintenance equipment according to the wake-up confirmation signal and the delay compensation parameter in combination with the subway safety integrity level requirement, and obtain an authority level and a hierarchical control channel; An instruction generation module is configured to perform optimized generation processing on the control instruction in the remote control request according to the authority level and the hierarchical control channel for the time window limit of the subway maintenance operation, and obtain a remote control instruction and an execution time sequence arrangement table; An execution monitoring module is configured to monitor the execution process of the remote control instruction according to the execution time sequence table, to adjust the execution process through state estimation and abnormality detection, and to start an emergency processing mechanism when an abnormality affecting the safety of the subway operation is detected, thereby obtaining a remote control execution result of the subway maintenance equipment.

[0013] A third aspect of the present application provides a device remote control apparatus, comprising: a memory and at least one processor, the memory storing instructions, and the memory and the at least one processor being interconnected by a line; the at least one processor invoking the instructions in the memory to enable the device remote control apparatus to perform the steps of the device remote control method described above.

[0014] A fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium storing instructions, when the instructions are run on a computer, enabling the computer to perform the steps of the device remote control method described above.

[0015] The device remote control method, apparatus, device and storage medium described above, by performing hierarchical wake-up processing on the subway maintenance equipment after receiving a remote control request, obtaining a wake-up confirmation signal and a network delay compensation parameter; in combination with the subway safety integrity level requirement, dynamically allocating operation permissions of the maintenance equipment according to the wake-up confirmation signal and the delay compensation parameter, obtaining permission levels and hierarchical control channels; in view of the maintenance operation time window limit, optimizing the control instruction according to the permission levels and the hierarchical control channels, obtaining a remote control instruction and an execution time sequence table; monitoring the execution process of the remote control instruction in a closed loop according to the execution time sequence table, adjusting the execution process through state estimation and abnormality detection, and starting an emergency processing mechanism when an abnormality is detected. The present application can realize precise maintenance equipment remote control in a complex subway tunnel network environment, and improve control efficiency and safety.

[0016] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the description, claims and drawings.

[0017] In order to make the above objectives, features and advantages of the present application more apparent, the following preferred embodiments are specifically described with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A first embodiment of the device remote control method in the embodiments of the present application is shown in the figure; Figure 2 A second embodiment of the device remote control method in the embodiments of the present application is shown in the figure; Figure 3 Figure 1 is a schematic diagram of an embodiment of a device remote control apparatus according to the present application; Figure 4 Figure 2 is a schematic diagram of an embodiment of a device remote control apparatus according to the present application. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described below in detail with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of the present application.

[0020] The terms “comprising” and “having” and any variations thereof mentioned in the embodiments of the present application are intended to cover the inclusions without limitation. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but can optionally further comprise other steps or units not listed, or can optionally further comprise other steps or units inherent to the process, method, product or device.

[0021] In order to facilitate the understanding of the embodiments, first, a device remote control method disclosed in the embodiments of the present application will be described in detail. As shown in Figure 1 The method comprises the following steps: 101. After receiving the remote control request, performing hierarchical wake-up processing on the target device sleep state based on a pre-built linear model, performing device wake-up operation through a multi-level control sequence and network delay compensation, and obtaining a device wake-up confirmation signal and a network delay compensation parameter; It can be understood that the execution subject of the present application can be a device remote control apparatus, and can also be a terminal or a server, and the specific execution subject is not limited herein. The embodiments of the present application take a terminal as an execution subject for example.

[0022] In the embodiments, first, a device state transition graph is established, and a sleep state, a light sleep state, a deep sleep state and a working state are defined as four main nodes, each state node corresponds to different power consumption levels and response capabilities. The state machine determines the optimal state transition path according to the transition probability matrix in the pre-built linear model.

[0023] Layered wake-up processing employs a parallel wake-up strategy instead of a serial wake-up method. This strategy divides the device's functional modules into independent wake-up units, including communication modules, processor modules, and sensor modules, each with its own wake-up controller. During parallel wake-up, each unit simultaneously receives wake-up commands and independently executes state transitions, ensuring overall wake-up synchronization through inter-module handshake protocols. This approach significantly reduces the overall wake-up time and is particularly suitable for large, complex maintenance equipment.

[0024] Network latency compensation employs a predictive compensation algorithm, which establishes a latency prediction model based on historical network performance data. The compensation process monitors parameters such as network congestion status, base station load, and signal quality in real time, dynamically adjusting the transmission lead of control commands. For time-critical wake-up operations, the algorithm pre-calculates the latency characteristics of multiple transmission paths and selects the path with the lowest latency and highest stability for command transmission.

[0025] The device wake-up verification employs a multi-layered verification mechanism, comprising three levels: electrical characteristic testing, network connectivity testing, and functional integrity verification. Electrical characteristic testing determines the hardware wake-up status by measuring the device's voltage, current, and power consumption parameters. Network connectivity testing verifies the establishment of the communication link by sending test data packets. Functional integrity verification confirms the readiness status of each functional module of the device by executing a standard test sequence. This mechanism ensures the reliability and integrity of the wake-up process.

[0026] 102. Based on the wake-up confirmation signal and delay compensation parameters, dynamically allocate device operation permissions, calculate the permission allocation strategy through a two-layer control architecture, and obtain the device's executable permission level and hierarchical control channels; In this embodiment, the dynamic allocation of operation permissions for maintenance equipment based on the wake-up confirmation signal and delay compensation parameters, combined with the requirements of the metro safety integrity level, to obtain permission levels and hierarchical control channels includes: analyzing the power status, CPU load, and memory usage of the metro maintenance equipment based on the wake-up confirmation signal, and calculating the actual range of operation permissions that the equipment can withstand, combined with the constraints of the metro operating environment, to obtain the capability assessment result of the metro maintenance equipment; based on the capability assessment result, modeling basic detection permissions, configuration modification permissions, and critical maintenance operation permissions as continuous variables according to the metro safety integrity level and calculating the currently allocable permission level, to obtain the permission level calculation result; based on the permission level calculation result and delay compensation parameters, configuring the local controller to communicate directly with the metro maintenance equipment for basic permission management, and configuring the remote controller to allocate advanced permissions through the metro dedicated network, to obtain the hierarchical control channel; solving the coupling control problem based on the hierarchical control channel, and simultaneously determining the permission allocation ratio of the local controller and the remote controller, to obtain the executable permission level of the metro maintenance equipment.

[0027] Specifically, in this embodiment, the wake-up confirmation signal contains detailed information about the device's hardware state. Power supply state analysis assesses the device's power supply capability by analyzing voltage values, current values, and power consumption data. Voltage values reflect the stability of the power supply quality, with a standard operating voltage range of DC 24V ± 5%, current values show the actual power consumption level of the device, and power consumption data reflect the overall energy consumption status of the device.

[0028] CPU load analysis assesses the availability of computing resources by processing parameters such as processor usage, task queue length, and instruction execution frequency. Processor usage indicates the degree of CPU processing instructions, task queue length reflects the number of pending tasks, and instruction execution frequency shows the actual working frequency of the processor. Vibrations and temperature changes in the subway environment can affect the stability of the processor.

[0029] Memory usage analysis includes indicators such as physical memory occupancy, virtual memory swap frequency, and memory access delay. Physical memory occupancy indicates the proportion of used memory to total memory capacity, virtual memory swap frequency reflects the efficiency of memory management, and memory access delay directly affects the real-time performance of data processing.

[0030] Subway operating environment constraints include physical environment parameters such as temperature range, humidity limit, vibration level, and electromagnetic interference intensity. The temperature range is -10°C to +70°C, the humidity limit is a relative humidity of no more than 95%, the vibration level is classified according to IEC standards, and the electromagnetic interference intensity is affected by traction power supply and signal devices.

[0031] The actual range of operating permissions that the device can withstand is calculated by comprehensively analyzing hardware capabilities and environmental constraints. The calculation process establishes a multi-dimensional evaluation model, taking power supply capability, computing capability, storage capability, and environmental adaptability as the four main evaluation dimensions. The evaluation algorithm compares the actual measured value of each dimension with the standard threshold, calculates the deviation coefficient and safety margin, and obtains the comprehensive capability index through weighted fusion.

[0032] In this embodiment, the basic detection permission covers operations such as device state monitoring, parameter reading, and simple diagnosis. Configuration modification permissions include operations such as parameter adjustment, mode switching, and function start-stop. Key maintenance operation permissions cover advanced operations such as firmware upgrade, security configuration, and emergency handling.

[0033] The subway safety integrity level adopts the SIL standard, which divides safety requirements into four levels: SIL1 to SIL4. The modeling process determines the corresponding SIL level according to the device type and operating nature, and then converts the level to a safety coefficient. The conversion algorithm obtains the basic safety coefficient through table lookup, and then modifies it according to the current environmental conditions.

[0034] Continuous variable modeling transforms discrete permission levels into continuous numerical representations. The modeling algorithm first sets a baseline numerical range for each permission type, and then dynamically adjusts it based on device capability assessment results and security integrity level requirements. The adjustment process uses a linear interpolation method to maximize permission allocation while satisfying security constraints.

[0035] The permission level calculation process comprehensively considers factors such as device capabilities, security requirements, and operational complexity. The calculation algorithm employs fuzzy inference, using various influencing factors as input variables and determining the permission level through predefined inference rules. The inference process includes three steps: fuzzification, rule matching, and defuzzification, ultimately outputting a precise permission level value.

[0036] In this embodiment, the hierarchical control architecture divides access control into two layers: a local control layer and a remote control layer. The configuration process first analyzes network performance characteristics based on latency compensation parameters to determine the appropriate access operation types for local and remote processing.

[0037] The local controller configuration process determines the communication protocol and transmission parameters based on latency compensation parameters. The configuration algorithm analyzes parameters such as round-trip time, latency jitter, and packet loss rate to select the most suitable real-time communication protocol. For latency-sensitive operations, the algorithm configures the local controller to use direct communication, bypassing the network transmission stage.

[0038] The remote controller configuration process takes into account the specific requirements of the subway private network. The configuration algorithm selects the optimal communication path based on the network's topology and load conditions. For high-level privilege operations, the algorithm configures an end-to-end encrypted channel to ensure the security and integrity of command transmission.

[0039] The hierarchical control channel establishment process includes steps such as channel initialization, parameter negotiation, and state synchronization. The establishment algorithm first verifies the connectivity and compatibility between the two controllers, then negotiates technical parameters such as communication protocols, data formats, and synchronization mechanisms, and finally establishes a real-time state synchronization mechanism.

[0040] In this embodiment, the coupled control problem refers to the interdependent and mutually influential relationship between the local controller and the remote controller during the permission allocation process. The solution algorithm adopts a cooperative game model from game theory, modeling permission allocation as a multi-objective optimization problem. The algorithm constructs a utility function to describe the revenue and cost of each controller and seeks a Nash equilibrium solution.

[0041] The permission allocation ratio is determined based on load balancing and capacity matching principles. The allocation algorithm first evaluates the current load status and processing capacity of the two controllers, and then matches them according to the characteristic requirements of the permission operations. For permissions with high real-time requirements, the algorithm prioritizes allocation to the local controller; for permissions with high security requirements, the algorithm prioritizes allocation to the remote controller.

[0042] The allocation algorithm determines the final permission allocation scheme through iterative optimization. In each iteration, the algorithm evaluates the performance indicators and security risks of the current allocation scheme, adjusts the allocation strategy based on the evaluation results, and finds the optimal solution that meets all constraint conditions. The subway maintenance device executable permission level records the allocation results in the form of a permission matrix, which is dynamically updated according to changes in device status and network environment.

[0043] 103、According to the permission level and the hierarchical control channel, the control instruction in the remote control request is optimized and generated, the control instruction is generated through permission verification and timing scheduling, and the remote control instruction and the execution timing arrangement table are obtained; In this embodiment, according to the permission level and the hierarchical control channel, the control instruction in the remote control request is optimized and generated for the time window limit of the subway maintenance work, and the remote control instruction and the execution timing arrangement table are obtained, including: matching and verifying the original control instruction in the remote control request with the permission level through the permission demand vector, obtaining a candidate instruction set that passes the permission verification; Solve the predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the subway maintenance work time window limit through a multi-objective optimization algorithm, obtain the optimized control instruction sequence; determine the best sending time of each instruction according to the optimized control instruction sequence and the hierarchical control channel through a timing scheduling algorithm, consider the train operation timetable constraint, and obtain the remote control instruction and the execution timing arrangement table.

[0044] In this embodiment, the permission demand vector is a mathematical representation of the required permission level for each control instruction. The vector contains three components, corresponding to the demand intensity of basic detection permission, configuration modification permission and key maintenance operation permission respectively. The vector construction process determines the numerical value of each component according to the operation type, influence range and security level of the instruction.

[0045] The original control instruction analysis process extracts the key attribute information of the instruction. The analysis algorithm first identifies the operation code of the instruction, and then extracts information such as target device identification, operation parameters and execution conditions. The operation parameters include numerical setting, mode selection and time limit, etc.

[0046] The matching verification process uses a vector comparison algorithm to compare the instruction demand with the available permissions one by one. The comparison algorithm calculates the difference between the permission demand vector and the permission level vector. When the difference of all components is non-negative, it means that the permission meets the instruction execution requirements. The verification process also considers the timeliness and mutual exclusivity constraints of the permission, ensuring that the permission remains valid during instruction execution, and preventing the simultaneous execution of conflicting instructions.

[0047] In this embodiment, the multi-objective optimization algorithm optimizes multiple conflicting objective functions simultaneously. The algorithm employs the Pareto optimality theory to find solutions that achieve the best balance among all objective functions. Predictive control is a model-based control strategy that optimizes current control decisions by predicting future states of the device.

[0048] Metro maintenance operation time window constraints refer to the restriction that maintenance operations must be completed within a specified time period. Time windows are divided into two types: hard constraints, which cannot be violated, and soft constraints, which can be relaxed moderately.

[0049] The control error penalty objective function quantifies the deviation between the execution results of control instructions and the expected target. The error calculation process establishes a device state prediction model that predicts future state trajectories based on the current device state and the sequence of instructions to be executed. The execution cost objective function evaluates the resource consumption required for the execution of the instruction sequence, including device resource occupation, network communication consumption, and manual intervention requirements.

[0050] The optimization algorithm solving process uses heuristic optimization methods such as genetic algorithms or particle swarm optimization. The algorithm first generates an initial instruction sequence population, then generates a new population through selection, crossover, and mutation operations. Constraint handling uses the penalty function method, which adds a penalty term for constraint violations in the objective function.

[0051] The optimized control instruction sequence is an instruction arrangement scheme that has undergone multi-objective optimization. This sequence achieves the best balance between control error and execution cost while meeting permission constraints and time window restrictions.

[0052] In this embodiment, the timing scheduling algorithm is a calculation method for determining task execution times. The algorithm takes into account factors such as task dependencies, resource constraints, and deadlines. The characteristics of the hierarchical control channel directly affect the transmission delay and execution timing of instructions.

[0053] The instruction sending time determination process uses the critical path method or priority scheduling algorithm. The critical path method constructs a task network graph by analyzing the dependencies between instructions, identifying the critical task sequence that affects the total execution time. The priority scheduling algorithm assigns priorities based on the importance, urgency, and resource requirements of instructions.

[0054] Train operation schedule constraints refer to the restriction that maintenance operations must avoid the normal operation period of trains. The constraint handling process obtains the current and future train operation plans, identifying available time windows for maintenance operations. The algorithm marks train operation periods as disabled time, and marks train intervals and night shutdown periods as available time.

[0055] The scheduling algorithm also considers the parallelism and serialism requirements of instruction execution. Instructions that can be executed in parallel can be sent and executed simultaneously, while instructions that are executed in series have a front-back dependency relationship. The algorithm determines the feasibility of parallel execution by analyzing the resource occupation and functional dependency of the instructions.

[0056] The execution timing table is the output result of the scheduling algorithm, which records in detail the sending time, expected execution time, responsible control channel and resource allocation of each instruction. The remote control instruction is the final instruction set after optimization and scheduling processing, which contains complete information such as the specific content of the instruction, execution parameters, sending time and routing information.

[0057] Further, the solving of the predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the subway maintenance operation time window limit by the multi-objective optimization algorithm to obtain the optimized control instruction sequence includes: constructing a control error penalty target and an execution cost target according to the candidate instruction set, setting the weight coefficients of each target function in combination with the subway maintenance operation time window limit, and establishing a multi-objective optimization function to obtain a multi-objective optimization problem; setting the prediction control time domain and the constraint condition according to the multi-objective optimization problem, adding the authority limit and the subway maintenance operation time limit as the constraint condition to the optimization problem to obtain a multi-objective optimization problem with constraints; solving the multi-objective optimization problem with constraints by a multi-objective optimization algorithm, calculating the Pareto optimal solution and selecting the solution most suitable for the current subway network and subway maintenance equipment state to obtain the optimized control instruction sequence.

[0058] Specifically, in this embodiment, the construction process of the control error penalty target is based on the deviation analysis of the device state prediction model and the expected control trajectory. The error penalty target quantifies the difference between the actual state of the device after instruction execution and the expected state. The construction process first establishes a device state space model, which describes the state evolution law of the device under different control inputs. The state variables include the running parameters, performance indicators and safety state of the device, and other key information.

[0059] The expected control trajectory is the ideal state change path determined according to the maintenance target. The trajectory planning process considers the physical constraints, safety specifications and performance requirements of the device. The error calculation adopts the form of weighted sum of squares of state deviation, and different state variables are assigned different weight coefficients according to their importance. The state variables related to safety are assigned higher weights to ensure that safety constraints are given priority.

[0060] The construction of the execution cost objective covers multiple dimensions, including resource consumption, time cost, and risk cost. Resource consumption includes computational resources, communication bandwidth, and device wear and tear. Computational resource consumption is quantified by instruction complexity and processing time, and communication bandwidth consumption considers instruction data volume and network transmission frequency. Time cost reflects the impact of instruction execution on maintenance job progress, including execution time, waiting time, and recovery time.

[0061] Risk cost evaluates the adverse consequences that may arise from instruction execution. The evaluation process establishes a risk assessment matrix to quantitatively analyze the failure probability and failure consequences of instructions. Failure probability is calculated based on historical data and device reliability models, and failure consequences are determined according to the impact on device safety, operation, and economy.

[0062] The weight coefficient setting process adjusts the relative importance of each objective according to the time window restrictions of metro maintenance work. When the time window is tight, the weight of the execution cost objective increases, prompting the algorithm to select instruction sequences with shorter execution times. When the time window is loose, the weight of the control error penalty objective increases, prompting the algorithm to select solutions with higher control accuracy. Weight adjustment uses an adaptive algorithm that dynamically adjusts weight proportions based on the remaining time of the time window.

[0063] The multi-objective optimization function is a weighted combination of the control error penalty objective and the execution cost objective. The function construction process uses linear weighting methods to sum the two objective functions multiplied by the corresponding weights. To overcome the subjectivity of weight selection, the construction process generates multiple candidate optimization functions using multiple sets of weight values.

[0064] In this embodiment, the prediction control time domain refers to the time range for which the optimization algorithm performs prediction and control. Time domain setting needs to balance control performance and computational complexity. The time domain length determination process considers device dynamic characteristics and maintenance job characteristics. Device dynamic characteristics are described by time constant and response speed, and maintenance job characteristics include job complexity and time sensitivity.

[0065] The time step within the prediction time domain affects prediction accuracy and computational load. Time step selection considers the minimum execution interval of control instructions and the time scale of device response. For devices with fast response, a smaller time step is used; for devices with slow response, a larger time step is used.

[0066] The constraint condition setting process converts various restrictions into mathematical inequalities or equality constraints. The authority limit constraint ensures that the authority usage at each time does not exceed the available authority level. The constraint implementation uses an authority margin check method to calculate the dynamic balance between authority consumption and authority replenishment in real time. The metro maintenance job time limit constraint ensures that all instructions are completed within the specified time window.

[0067] The constraints also include instruction execution order constraints and resource mutual exclusion constraints. The execution order constraints ensure that instructions with dependencies are executed in the correct order, and the resource mutual exclusion constraints prevent multiple instructions from occupying the same resource simultaneously.

[0068] In this embodiment, the multi-objective optimization algorithm adopts an evolutionary algorithm framework, which seeks the optimal solution by simulating the natural evolution process. The algorithm includes basic operations such as initialization, selection, crossover, mutation, and environmental selection. The initialization process generates a random initial population, and each individual represents a possible instruction sequence scheme. The individual encoding uses integer encoding, and each gene bit represents the execution time or execution order of an instruction.

[0069] The selection operation selects excellent individuals for reproduction according to the fitness values of the individuals. The fitness evaluation considers both the objective function value and the constraint violation degree. The crossover operation recombines the gene fragments of two parent individuals to generate offspring individuals with the characteristics of the parents. The mutation operation randomly changes part of the genes of the individuals to prevent premature convergence of the population.

[0070] The Pareto optimal solution refers to the solution set that cannot simultaneously improve all objectives in multi-objective optimization. The algorithm identifies the Pareto optimal solution through non-dominated sorting and maintains an external archive to save the discovered non-dominated solutions.

[0071] The solution selection process selects the solution that best fits the current subway network and maintenance equipment state from the Pareto optimal solution set. The selection criteria consider factors such as network performance, equipment state, and job priority. Network performance evaluation includes indicators such as delay, bandwidth, and reliability, and equipment state evaluation includes indicators such as health, load, and availability.

[0072] The optimized control instruction sequence is the final output of the selection process, which contains detailed information such as the execution order, time arrangement, and resource allocation of the instructions. Each instruction in the sequence is labeled with the expected start time, execution duration, and required permission level.

[0073] 104、According to the remote control instruction and the execution time arrangement table, the instruction execution process is monitored in a closed loop, and the execution process is adjusted through state estimation and abnormality detection to obtain the device remote control execution result.

[0074] In the embodiment, the closed-loop monitoring process of the execution process of the remote control instruction according to the execution time sequence table, the state estimation and the abnormality detection adjust the execution process, the emergency processing mechanism is started when the abnormality affecting the subway operation safety is detected, and the remote control execution result of the subway maintenance equipment includes: modeling the instruction execution process of the remote control instruction as a four-dimensional probability distribution vector through a Markov state transition model, obtaining an instruction execution state transition matrix in combination with the operation characteristics of the subway maintenance equipment; processing state observations of different types of instructions according to the execution time sequence table and the state transition matrix through a multi-time scale Kalman filter, adapting to changes in subway tunnel network delays, and obtaining instruction execution state estimation values; setting a multi-level detection threshold according to the instruction execution state estimation values through a residual analysis algorithm to divide execution abnormalities into three levels, and obtaining an abnormality detection classification result; and obtaining the remote control execution result of the subway maintenance equipment according to the abnormality detection classification result by adopting different levels of recovery strategies and adjusting control parameters.

[0075] In the embodiment, the Markov state transition model is a stochastic process model describing the transition probability between states. The instruction execution process modeling divides the execution state of each control instruction into four dimensions, including instruction transmission state, device response state, execution progress state and result confirmation state.

[0076] The instruction transmission state describes the transmission process of the control instruction from sending to reaching the target device, including four sub-states of instruction sending, network transmission, arrival confirmation and reception completion. The device response state reflects the processing of the received instruction by the maintenance device, including four sub-states of instruction analysis, authority verification, resource allocation and execution preparation. The execution progress state monitors the actual execution process of the instruction on the device, including four sub-states of execution start, execution, execution pause and execution completion. The result confirmation state tracks the feedback and confirmation process of the instruction execution result, including four sub-states of result generation, result verification, result transmission and confirmation completion.

[0077] The four-dimensional probability distribution vector is a mathematical vector representing the probability distribution of the instruction in the four state dimensions. Each component of the vector corresponds to a state dimension, and the component value represents the probability of the instruction being in each sub-state of the dimension. The operation characteristics of the subway maintenance equipment include response time characteristics, reliability characteristics and environmental adaptability characteristics. The response time characteristics describe the response speed of the device to different types of instructions, and the reliability characteristics reflect the success rate and failure rate of the device executing instructions.

[0078] The instruction execution state transition matrix is a matrix describing the transition probability between states, where the rows represent the current state and the columns represent the next state, and the matrix elements represent the probability of transitioning from the current state to the next state. The construction process of the transition matrix combines theoretical analysis and experimental data.

[0079] In this embodiment, the multi-timescale Kalman filter is an estimation algorithm that uses different filtering parameters for state variables with different time characteristics. The Kalman filter is a recursive filtering algorithm that estimates the optimal value of the state variable through two steps: prediction and update.

[0080] Instruction type classification is based on characteristics such as execution time, complexity, and resource requirements. Fast instructions have short execution times and frequent state changes, requiring smaller filtering time constants. Slow instructions have long execution times and slow state changes, requiring larger filtering time constants. The execution timing table provides the time base and synchronization information for instruction execution, and the filter corrects the time alignment of state observations based on the timestamps in the timing table.

[0081] Variations in subway tunnel network latency are a significant factor affecting the accuracy of condition observations. Network latency leads to uncertainty in the arrival time of condition observation data, causing timestamp discrepancies and data out-of-order processing. The filter employs a latency compensation algorithm to address this temporal uncertainty, adjusting the weights of the observation data and the filtering parameters based on the statistical characteristics of network latency.

[0082] The latency adaptation mechanism adjusts the filter configuration by monitoring network performance parameters in real time. When network latency increases, the filter reduces the weight of observed data and relies more on the state prediction model. The instruction execution state estimate is the optimal estimate of the state variables output by the filter, including the state mean and covariance matrix.

[0083] In this embodiment, the residual analysis algorithm is a method for detecting anomalies by analyzing the difference between observed values ​​and expected values. The residual refers to the difference between the actual observed state value and the estimated state value; the magnitude and pattern of the residual reflect anomalies in the execution process.

[0084] The residual calculation process involves subtracting the corresponding state estimate from the observed state value to obtain the residual vector for each dimension. Each component of the residual vector corresponds to the deviation of a state dimension; a positive value indicates that the observed value is greater than the estimate, and a negative value indicates that the observed value is less than the estimate. The algorithm also calculates statistics such as the mean, variance, and autocorrelation function of the residuals.

[0085] The multi-level detection threshold settings categorize the severity of anomalies into three levels: minor, moderate, and severe. Minor anomalies correspond to small deviations in equipment operation, moderate anomalies correspond to significant performance degradation or functional abnormalities, and severe anomalies correspond to major malfunctions or safety risks.

[0086] The threshold setting method is based on the statistical distribution of residuals and historical empirical data. The algorithm first analyzes the statistical distribution of residuals under normal operating conditions, calculates the mean and standard deviation of residuals. The slight abnormal threshold is set to the mean plus or minus 2 times the standard deviation, the medium abnormal threshold is set to the mean plus or minus 3 times the standard deviation, and the severe abnormal threshold is set to the mean plus or minus 4 times the standard deviation.

[0087] The anomaly detection process compares the current residual value with each level of threshold to determine the level and type of anomaly. The detection algorithm not only considers the instantaneous residual value, but also analyzes the trend and duration of the residual. The anomaly detection classification result includes anomaly level, anomaly type, occurrence time and impact range, etc.

[0088] In this embodiment, the recovery strategy is a processing scheme designed for different anomaly levels. Slight abnormality adopts parameter adjustment strategy, which improves the operating state by modifying control parameters or executing parameters. Medium abnormality adopts function reset strategy, which restarts the related function module or switches to standby mode. Severe abnormality adopts emergency shutdown strategy, which immediately stops the current operation and starts the safety protection mechanism.

[0089] The parameter adjustment process determines the adjustment direction and amplitude according to the specific performance of the anomaly. For execution speed anomaly, adjust the time parameter and priority setting. For communication anomaly, adjust network parameters and retransmission mechanism. The function reset process includes three steps of state saving, module restart and state recovery.

[0090] The emergency shutdown process gives priority to safety factors and executes according to the predefined safety shutdown process. The shutdown process first cuts off the operation that may cause danger, then saves the key data and state information, and finally puts the device into a safe state.

[0091] The execution result of the remote control of the subway maintenance equipment is the comprehensive output of the whole monitoring and recovery process. The execution result includes instruction completion status, execution quality evaluation, anomaly processing record and resource consumption statistics, etc. The instruction completion status indicates which instructions are successfully executed, which instructions are failed or partially completed. The execution quality evaluation measures the quality of the execution process through precision, efficiency and reliability, etc.

[0092] In the embodiment, after receiving the remote control request, the subway maintenance equipment is processed by hierarchical wake-up, the wake-up confirmation signal and network delay compensation parameters are obtained; combined with the subway safety integrity level requirements, the operation permission of the maintenance equipment is dynamically allocated according to the wake-up confirmation signal and the delay compensation parameters, the permission level and the hierarchical control channel are obtained; according to the maintenance operation time window limit, the control instruction is optimized and generated according to the permission level and the hierarchical control channel, the remote control instruction and the execution time sequence arrangement table are obtained; according to the execution time sequence arrangement table, the remote control instruction execution process is closed loop monitored, the execution process is adjusted through state estimation and abnormality detection, and the emergency processing mechanism is started when the abnormality is detected. The application can realize accurate maintenance equipment remote control in complex subway tunnel network environment, and improve control efficiency and safety.

[0093] Please refer to Figure 2 Another embodiment of the device remote control method in the embodiment includes: 201, after receiving the remote control request of the subway maintenance operation, the remote control request is parsed, the subway maintenance equipment identifier and the maintenance operation type are extracted, the pre-stored linear dynamic model of the subway maintenance equipment is called to obtain the equipment state transition parameter suitable for the subway environment, and the initial parameter of the maintenance equipment wake-up control is obtained; In the embodiment, the remote control request contains complete maintenance operation information, including device identifier, operation type, priority and time window and other key data. The parsing process first extracts the device identifier, which uses a hierarchical coding structure and contains three levels of line number, section number and device number, for example, "L01-S03-D015" represents the 15th device of the 3rd section of line 1. The maintenance operation type is divided into four types: preventive maintenance, fault diagnosis, parameter configuration and emergency repair, each type corresponds to different wake-up strategy and permission requirements.

[0094] The linear dynamic model of the subway maintenance equipment is a pre-established mathematical model that describes the state transition law of the equipment in the subway tunnel environment. The model considers the influence of the electromagnetic environment, temperature change, vibration influence and humidity condition of the subway tunnel on the equipment state. The equipment state transition parameters include transition matrix, noise covariance matrix and observation matrix, which reflect the probability distribution and transition speed of the equipment from one state to another. By calling the model, the state transition parameters optimized for the current subway environment conditions can be obtained, which are used as the initial parameters of the wake-up control, including wake-up time constant, power control parameter and communication protocol parameter.

[0095] 202、calculating an optimal wake-up trajectory from the maintenance equipment sleep state to the target working state according to the initial parameters and the subway tunnel signal transmission characteristics by solving a finite time domain optimization algorithm, obtaining a multi-stage wake-up control instruction sequence adapted to the subway network environment; In the embodiment, the calculating an optimal wake-up trajectory from the maintenance equipment sleep state to the target working state according to the initial parameters and the subway tunnel signal transmission characteristics by solving a finite time domain optimization algorithm, obtaining a multi-stage wake-up control instruction sequence adapted to the subway network environment includes: calling a pre-built finite time domain optimization model according to the initial parameters, setting the wake-up trajectory parameters of the subway maintenance equipment from the sleep state to the target working state in combination with the subway tunnel electromagnetic environment and signal attenuation characteristics, obtaining the wake-up trajectory control points adapted to the subway environment; planning a multi-stage wake-up sequence in accordance with the subway maintenance equipment start specification according to the wake-up trajectory control points, dividing the wake-up process into three stages of power pre-activation, subway private network connection and maintenance system complete activation, and distributing control strength according to the subway operation safety level, obtaining the multi-stage wake-up control strategy of the subway maintenance equipment; generating corresponding control instructions according to the multi-stage wake-up control strategy, assigning specific control signal parameters to each wake-up stage, and obtaining the multi-stage wake-up control instruction sequence optimized for the subway environment.

[0096] Specifically, in the embodiment, the pre-built finite time domain optimization model is a mathematical model constructed based on dynamic programming theory, which models the device wake-up process as a multi-stage decision problem. The finite time domain refers to optimization calculation within a limited time window, avoiding the computational complexity of infinite time domain optimization. The model includes three core components: state equation, output equation and constraint condition.

[0097] The electromagnetic environment of the subway tunnel has complex characteristics, mainly including the power frequency magnetic field generated by traction power supply, the radio frequency interference generated by signal devices, and the electromagnetic shielding effect formed by the metal tunnel structure. The frequency of the power frequency magnetic field is 50Hz, which interferes with low-frequency sensitive electronic devices. Radio frequency interference comes from train signals, communication devices and third rail power supply devices, with a frequency range covering tens of kHz to hundreds of MHz. The metal tunnel structure produces significant shielding and reflection effects on high-frequency signals.

[0098] The signal attenuation characteristics reflect the propagation loss law of electromagnetic signals in the subway tunnel. The free space path loss grows inversely with the square of the propagation distance, and the multipath propagation effect leads to rapid and slow fading of signal power. Frequency selective fading leads to different attenuation degrees of different frequency components, and spectral distortion occurs in the propagation process of wideband signals.

[0099] The wake-up trajectory parameter setting process takes into account the effects of the electromagnetic environment and signal attenuation on device response. Parameters include state transition time constants, control gain coefficients, and noise variance matrices. State transition time constants describe the time required for the device to transition from one state to the next, and this parameter is affected by the strength of electromagnetic interference. Control gain coefficients need to be adjusted according to signal attenuation characteristics to compensate for power loss during transmission.

[0100] The wake-up trajectory control points adapted to the subway environment are a series of key states defined in state space, including an initial sleep point, intermediate transition points, and a target working point. The initial sleep point corresponds to the lowest power consumption state of the device, the intermediate transition points include power activation points, network connection points, and protocol handshake points, and the target working point corresponds to the fully working state of the device.

[0101] In this embodiment, the subway maintenance device startup specification specifies the standard process and safety requirements for device startup. The hierarchical wake-up sequence planning divides the complex wake-up process into three independent stages, each with clear input conditions, execution steps, and output results.

[0102] The power pre-activation stage is the first stage of the wake-up process, and the main purpose is to wake up the device from a deep sleep state to a standby state. In this stage, the device is first sent a wake-up signal to activate the main power module of the device. After the main power module is started, it begins to power the core processing unit, memory, and basic I / O interface. The power pre-activation process needs to strictly control the power-on timing to avoid damage to the device caused by voltage surges.

[0103] The subway private network connection stage is the second stage of the wake-up process, and the main purpose is to establish the connection between the device and the subway dedicated communication network. The subway private network is a communication network specially designed for subway operation and maintenance, with characteristics such as high reliability, low delay, and strong security. In this stage, the network interface module of the device is activated, and the device begins to search for available network nodes and initiates a connection request. The connection establishment process includes steps such as identity authentication, key exchange, and QoS negotiation.

[0104] The maintenance device fully activated stage is the third stage of the wake-up process, and the main purpose is to activate all the maintenance function modules of the device. The maintenance function modules include data acquisition modules, signal processing modules, fault diagnosis modules, and parameter configuration modules, etc. The activation of each function module requires loading the corresponding software program and configuration parameters.

[0105] Subway operations are classified into three safety levels: A, B, and C. Level A corresponds to critical operations with the highest safety requirements, Level B corresponds to important operations with medium safety requirements, and Level C corresponds to routine operations with general safety requirements. The control intensity allocation determines the execution parameters for each stage based on the safety level. For Level A operations, the highest control intensity is used, including the shortest timeout, the most retries, and the most stringent error detection.

[0106] In this embodiment, the control instruction generation process transforms an abstract control strategy into specific executable instructions. Each control instruction includes information such as instruction type, target address, control parameters, and execution timing.

[0107] The control signal parameters for the power pre-activation phase include the wake-up pulse width, voltage rise slope, and current limit. The wake-up pulse width determines the duration of the wake-up signal, and this parameter needs to be adjusted according to the device type and ambient temperature. The voltage rise slope controls the rate of change of the power supply voltage, and the current limit prevents overcurrent during startup.

[0108] The control signal parameters for the subway dedicated network connection phase include carrier frequency, modulation scheme, and transmission power. The carrier frequency needs to avoid interference bands in the subway environment, selecting a frequency with good channel quality for communication. The modulation scheme employs digital modulation technology with strong anti-interference capabilities, and the transmission power is adjusted according to signal attenuation characteristics and noise levels.

[0109] The control signal parameters for the fully activated maintenance equipment phase include the functional module startup sequence, loading timeout time, and status check interval. The functional module startup sequence follows a dependency relationship, starting the basic modules first and then the application modules. The multi-level wake-up control command sequence optimized for the subway environment is a set of commands optimized based on the characteristics of the subway operating environment. This sequence takes into account the electromagnetic environment, network characteristics, and safety requirements of the subway tunnel.

[0110] 203. Based on the current network status information of the subway tunnel and the base station switching frequency, perform delay compensation processing on the instruction sending timing of the multi-level wake-up control instruction sequence for the subway mobile network to obtain the wake-up instruction after delay compensation. In this embodiment, the current network status information of the subway tunnel includes parameters such as network topology, bandwidth utilization, packet loss rate, and transmission delay. The base station handover frequency reflects the handover frequency of the mobile communication network during subway train operation, which directly affects the transmission stability of control commands. Subway mobile networks are characterized by high-speed movement, frequent handovers, and uneven signal coverage, leading to time-varying and uncertain network delays.

[0111] The delay compensation process adopts an adaptive prediction algorithm to establish a delay prediction model based on historical network state data. The model considers factors such as train position, moving speed, base station load, and network congestion affecting transmission delay. The compensation process first estimates the transmission delay of each control command, and then adjusts the sending timing of the command according to the delay prediction value. In specific implementation, for time-sensitive wake-up commands, an early sending strategy is adopted; for delay-tolerant configuration commands, a dynamic adjustment strategy is adopted. The wake-up command after delay compensation contains the corrected timestamp and retransmission parameters, ensuring that the command can reach the target device within the expected time.

[0112] 204、According to the wake-up command, a multi-level wake-up control signal is sent in turn and the device response state is monitored. When it is detected that the wake-up fails due to subway environment interference, a retry mechanism is started to adjust the wake-up strategy until the device is successfully woken up, obtaining a maintenance device wake-up confirmation signal and network delay compensation parameters; In this embodiment, the sending of multi-level wake-up control signals follows strict timing control, and each signal needs to wait for device response confirmation before sending the next level signal. Device response state monitoring is achieved through multiple detection methods, including signal strength detection, protocol handshake detection, and function state detection. Signal strength detection is used to determine the power state of the device, protocol handshake detection is used to verify the network connection state, and function state detection is used to confirm the working preparation state of the device.

[0113] Subway environment interference mainly includes electromagnetic interference, mechanical vibration, and temperature change. Electromagnetic interference comes from train traction power supply and signal devices, mechanical vibration comes from train operation and tunnel structure, and temperature change affects the electrical characteristics and response speed of the device. When it is detected that the wake-up fails, the retry mechanism adjusts the wake-up strategy according to the failure reason. For failures caused by electromagnetic interference, the signal power and retransmission times are increased; for failures caused by network congestion, the sending timing and routing strategy are adjusted; for failures caused by device failure, the standby device is switched to or the service mode is downgraded.

[0114] The maintenance device wake-up confirmation signal contains device state information, network connection quality, and function readiness state data. Network delay compensation parameters are obtained by real-time measurement, including end-to-end transmission delay, delay jitter, and bandwidth availability. These parameters reflect the actual transmission performance of the current network environment, providing a reference for subsequent control command sending. The successful completion of the wake-up process indicates that the device is ready to receive and execute remote control commands, and the network delay compensation parameters obtained provide important network performance data for permission allocation and command optimization.

[0115] 205、according to the wake-up confirmation signal and the delay compensation parameter, dynamically allocating the device operation permission, calculating the permission allocation strategy through a double-layer control architecture, obtaining a device executable permission level and a layered control channel; 206、according to the permission level and the layered control channel, optimizing and generating a control instruction in a remote control request, generating the control instruction through permission verification and timing scheduling, obtaining a remote control instruction and an execution timing arrangement table; 207、according to the remote control instruction and the execution timing arrangement table, performing closed-loop monitoring on an instruction execution process, adjusting the execution process through state estimation and abnormality detection, and obtaining a device remote control execution result.

[0116] In the embodiment, steps 205-207 are similar to steps 102-104 in the first embodiment, and will not be described here.

[0117] In the embodiment, after receiving a remote control request, layered wake-up processing is performed on a subway maintenance device to obtain a wake-up confirmation signal and a network delay compensation parameter; in combination with a subway safety integrity level requirement, the operation permission of the maintenance device is dynamically allocated according to the wake-up confirmation signal and the delay compensation parameter, to obtain a permission level and a layered control channel; in view of a maintenance operation time window limit, a control instruction is optimized and generated according to the permission level and the layered control channel, to obtain a remote control instruction and an execution timing arrangement table; according to the execution timing arrangement table, a closed-loop monitoring is performed on a remote control instruction execution process, the execution process is adjusted through state estimation and abnormality detection, and an emergency processing mechanism is started when an abnormality is detected. The present application can realize precise maintenance device remote control in a complex subway tunnel network environment, and improve control efficiency and safety.

[0118] The above describes a device remote control method in the embodiment of the present application, and the following describes a device remote control apparatus in the embodiment of the present application, which refers to Figure 3 The device remote control apparatus in the embodiment of the present application includes: A wake-up control module 301 is configured to, after receiving a remote control request, perform layered wake-up processing on a target device sleep state based on a pre-built linear model, perform device wake-up operation through a multi-level control sequence and network delay compensation, and obtain a device wake-up confirmation signal and a network delay compensation parameter; A permission allocation module 302 is configured to, according to the wake-up confirmation signal and the delay compensation parameter, dynamically allocate the device operation permission, calculate the permission allocation strategy through a double-layer control architecture, and obtain a device executable permission level and a layered control channel; The instruction generation module 303 is used for performing optimized generation processing on the control instruction in the remote control request according to the permission level and the hierarchical control channel, generating the control instruction through permission verification and timing scheduling, and obtaining the remote control instruction and an execution timing arrangement table; The execution monitoring module 304 is used for performing closed-loop monitoring processing on the instruction execution process according to the remote control instruction and the execution timing arrangement table, adjusting the execution process through state estimation and abnormality detection, and obtaining a device remote control execution result.

[0119] In the embodiment of the application, the device remote control apparatus runs the above device remote control method, and after receiving the remote control request, the device remote control apparatus performs hierarchical wake-up processing on the subway maintenance device, obtains a wake-up confirmation signal and a network delay compensation parameter; in combination with the subway safety integrity level requirement, dynamically allocates the operation permission of the maintenance device according to the wake-up confirmation signal and the delay compensation parameter, obtains the permission level and the hierarchical control channel; in view of the maintenance operation time window limit, optimizes the generation of the control instruction according to the permission level and the hierarchical control channel, obtains the remote control instruction and the execution timing arrangement table; according to the execution timing arrangement table, performs closed-loop monitoring on the execution process of the remote control instruction, adjusts the execution process through state estimation and abnormality detection, and starts an emergency processing mechanism when an abnormality is detected. The application can realize accurate maintenance device remote control in a complex subway tunnel network environment, and improve the control efficiency and safety.

[0120] The above Figure 3 The device remote control apparatus in the embodiment of the application is described in detail from the perspective of a modular functional entity, and the device remote control apparatus in the embodiment of the application is described in detail from the perspective of hardware processing.

[0121] Figure 4is a structural schematic diagram of a device remote control device provided by an embodiment of the present application. The device remote control device 300 can have great differences due to different configurations or performances, and can include one or more central processing units (CPUs) 410 (for example, one or more processors) and a memory 420, one or more storage media 430 (for example, one or more mass storage device ends) storing an application program 333 or data 432. The memory 420 and the storage media 430 can be temporary storage or persistent storage. The program stored in the storage media 430 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the device remote control device 400. Further, the processor 410 can be configured to communicate with the storage media 430 and execute the series of instruction operations in the storage media 430 on the device remote control device 400 to implement the steps of the device remote control method described above.

[0122] The device remote control device 400 can also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input / output interfaces 460, and / or one or more operating systems 431, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the device remote control device 400 can include more or fewer components than those shown, or some components can be combined, or different components can be arranged. Figure 4 The device remote control device structure shown does not constitute a limitation on the device remote control device provided by the present application, and can include more or fewer components than those shown, or some components can be combined, or different components can be arranged.

[0123] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium. The computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the device remote control method.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system or device, unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0125] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0126] The above description and the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features. These modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A device remote control method characterized by, The device remote control method comprises: After receiving the remote control request of the subway maintenance work, the subway maintenance device is subjected to hierarchical wake-up processing based on a pre-built linear model, to obtain a maintenance device wake-up confirmation signal and a network delay compensation parameter; According to the wake-up confirmation signal and the delay compensation parameter, the operation authority of the maintenance device is subjected to dynamic allocation processing in combination with the subway safety integrity level requirement, to obtain a permission level and a hierarchical control channel; According to the permission level and the hierarchical control channel, the control instruction in the remote control request is subjected to optimized generation processing in view of the time window limit of the subway maintenance work, to obtain a remote control instruction and an execution time sequence arrangement table; According to the execution time sequence arrangement table, the execution process of the remote control instruction is subjected to closed-loop monitoring processing, the execution process is adjusted through state estimation and abnormality detection, and an emergency processing mechanism is started when an abnormality affecting the safety of subway operation is detected, to obtain a remote control execution result of the subway maintenance device.

2. The device remote control method according to claim 1, wherein, After receiving the remote control request of the subway maintenance work, the subway maintenance device is subjected to hierarchical wake-up processing based on a pre-built linear model, to obtain a maintenance device wake-up confirmation signal and a network delay compensation parameter, which comprises: After receiving the remote control request of the subway maintenance work, the remote control request is analyzed, the identification of the subway maintenance device and the type of the maintenance work are extracted, a pre-stored linear dynamic model of the subway maintenance device is called to obtain device state transition parameters adapted to the subway environment, and initial parameters of maintenance device wake-up control are obtained; An optimal wake-up trajectory from a maintenance device sleep state to a target working state is calculated according to the initial parameters and the subway tunnel signal transmission characteristics by solving a finite time domain optimization algorithm, to obtain a multi-stage wake-up control instruction sequence adapted to the subway network environment; According to the current network state information of the subway tunnel and the base station switching frequency, the instruction sending time sequence in the multi-stage wake-up control instruction sequence is subjected to delay compensation processing for the subway mobile network, to obtain wake-up instructions after delay compensation; According to the wake-up instructions, multi-stage wake-up control signals are sent in sequence and the device response state is monitored, a retry mechanism is started to adjust the wake-up strategy when wake-up failure caused by subway environment interference is detected, until successful wake-up, to obtain a maintenance device wake-up confirmation signal and a network delay compensation parameter.

3. The device remote control method according to claim 2, wherein, The optimal wake-up trajectory from a maintenance device sleep state to a target working state is calculated according to the initial parameters and the subway tunnel signal transmission characteristics by solving a finite time domain optimization algorithm, to obtain a multi-stage wake-up control instruction sequence adapted to the subway network environment, which comprises: A pre-built finite time domain optimization model is called according to the initial parameters, the wake-up trajectory parameters of the subway maintenance device from the sleep state to the target working state are set in combination with the electromagnetic environment and signal attenuation characteristics of the subway tunnel, to obtain wake-up trajectory control points adapted to the subway environment; According to the wake-up trajectory control points, a hierarchical wake-up sequence planning conforming to the startup specification of the subway maintenance device is carried out, the wake-up process is divided into three stages of power pre-activation, subway special network connection and maintenance system complete activation, and the control strength is allocated according to the subway work safety level, to obtain a hierarchical wake-up control strategy of the subway maintenance device; According to the hierarchical wake-up control strategy, corresponding control instructions are generated, specific control signal parameters are allocated to each wake-up stage, and a multi-stage wake-up control instruction sequence optimized for the subway environment is obtained.

4. The device remote control method of claim 1, wherein, According to the wake-up confirmation signal and the delay compensation parameter, the operation authority of the maintenance equipment is dynamically allocated and processed in combination with the subway safety integrity level requirement to obtain the authority level and the hierarchical control channel, which includes: According to the wake-up confirmation signal, the power state of the subway maintenance equipment, the CPU load and the memory usage are analyzed, the actual operation authority range that the equipment can withstand is calculated in combination with the subway operation environment constraints, and the capability evaluation result of the subway maintenance equipment is obtained. According to the capability evaluation result, the basic detection authority, the configuration modification authority and the key maintenance operation authority are modeled as continuous variables according to the subway safety integrity level, and the current assignable authority level is calculated, to obtain the authority level calculation result. According to the authority level calculation result and the delay compensation parameter, the local controller is configured to directly communicate with the subway maintenance equipment for basic authority management, and the remote controller is configured to perform high-level authority allocation through the subway private network, to obtain the hierarchical control channel. According to the hierarchical control channel, the coupled control problem is solved, and the authority allocation proportion of the local controller and the remote controller is determined, to obtain the executable authority level of the subway maintenance equipment.

5. The device remote control method of claim 1, wherein, According to the authority level and the hierarchical control channel, the control instructions in the remote control request are optimized and generated in combination with the time window limit of the subway maintenance operation, to obtain the remote control instruction and the execution time sequence arrangement table, which includes: The original control instructions in the remote control request are matched and verified with the authority level through the authority demand vector, to obtain a candidate instruction set that passes the authority verification; A multi-objective optimization algorithm is used to solve a predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the subway maintenance operation time window limit, to obtain an optimized control instruction sequence; A timing scheduling algorithm is used to determine the best sending time of each instruction according to the optimized control instruction sequence and the hierarchical control channel, considering the train operation timetable constraints, to obtain the remote control instruction and the execution time sequence arrangement table.

6. The device remote control method according to claim 5, wherein The multi-objective optimization algorithm is used to solve a predictive control problem containing control error penalty and execution cost according to the candidate instruction set and the subway maintenance operation time window limit, to obtain an optimized control instruction sequence, which includes: Control error penalty objectives and execution cost objectives are constructed according to the candidate instruction set, and weight coefficients of each objective function are set in combination with the subway maintenance operation time window limit to establish a multi-objective optimization function, to obtain a multi-objective optimization problem; The prediction control time domain and the constraint condition are set according to the multi-objective optimization problem, and the authority limit and the subway maintenance operation time limit are added as constraint conditions to the optimization problem, to obtain a multi-objective optimization problem with constraints; A multi-objective optimization algorithm is used to solve the multi-objective optimization problem with constraints, to calculate the Pareto optimal solution and select the solution that best fits the current subway network and subway maintenance equipment state, to obtain the optimized control instruction sequence.

7. The device remote control method of claim 1, wherein, The closed-loop monitoring process of the execution process of the remote control instruction according to the execution time sequence table adjusts the execution process through state estimation and abnormality detection, starts an emergency processing mechanism when an abnormality affecting the safe operation of the subway is detected, and obtains the remote control execution result of the subway maintenance equipment, including: The instruction execution process of the remote control instruction is modeled as a four-dimensional probability distribution vector through a Markov state transition model, and a state transition matrix of instruction execution is obtained in combination with the operation characteristics of the subway maintenance equipment; The state observation of different types of instructions is processed through a multi-time scale Kalman filter according to the execution time sequence table and the state transition matrix, the delay variation of the subway tunnel network is adapted, and the state estimation value of instruction execution is obtained; The execution abnormality is divided into three levels through a residual analysis algorithm according to the state estimation value of instruction execution, and the abnormality detection classification result is obtained; Different levels of recovery strategies are adopted according to the abnormality detection classification result, and the control parameters are adjusted, and the remote control execution result of the subway maintenance equipment is obtained.

8. A device remote control apparatus, characterized by comprising: The device remote control device includes: A wake-up control module for receiving a remote control request for subway maintenance work, performing hierarchical wake-up processing on the subway maintenance equipment based on a pre-built linear model, obtaining a maintenance equipment wake-up confirmation signal and a network delay compensation parameter; A permission allocation module for dynamically allocating the operation permissions of the maintenance equipment according to the wake-up confirmation signal and the delay compensation parameter in combination with the subway safety integrity level requirements, obtaining the permission level and the hierarchical control channel; An instruction generation module for optimizing and generating the control instructions in the remote control request according to the permission level and the hierarchical control channel, obtaining the remote control instruction and the execution time sequence table; An execution monitoring module for performing closed-loop monitoring processing on the execution process of the remote control instruction according to the execution time sequence table, adjusting the execution process through state estimation and abnormality detection, starting an emergency processing mechanism when an abnormality affecting the safe operation of the subway is detected, and obtaining the remote control execution result of the subway maintenance equipment.

9. A device remote control device, characterized by The device remote control device includes a memory and at least one processor, and the memory stores instructions; The at least one processor invokes the instructions in the memory to enable the device remote control device to perform the steps of the device remote control method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon instructions, the instructions comprising, The instructions are executed by the processor to implement the steps of the device remote control method of any one of claims 1-7.

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