Tunnel electromechanical equipment twin network synchronous control system based on IPV6 +

Abstract

The invention discloses an IPV6 +-based twin network synchronous control system for tunnel electromechanical equipment, which relates to the technical field of digital twin and comprises a model construction module, a synchronous acquisition module, an information fusion module, a synchronous control module, an analysis module and a self-adaptive feedback module. According to the invention, the twin network synchronous control system synchronously acquires, analyzes and feeds back the operation data of the tunnel electromechanical equipment in real time, so that the accurate management of the operation state of the equipment and the safety improvement are realized, and the safe operation of the tunnel is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of twin network synchronization control systems, and in particular to an IPV6+-based twin network synchronization control system for tunnel electromechanical equipment. Background Art

[0002] With the development of tunnel safety, equipment digitization and information management, ensuring the effective convergence of facilities, traffic and environmental factors in the tunnel is the key to achieving normal equipment operation and safe passage. At present, broadcast services are developing towards full IP, and IP data networks are evolving towards fixed-mobile convergence, cloud-network collaboration and multi-service convergence. A series of "IPv6+" technologies represented by SRv6, FlexE, iFIT, etc. have become an important technical foundation for promoting tunnel informatization and intelligent control.

[0003] The development of digital twin technology has brought new opportunities for the intelligent management of tunnel electromechanical equipment. By establishing a digital twin model of the equipment, various types of equipment in the tunnel can be monitored in real time by combining virtual and real, and all-round perception and analysis of the equipment's operating status can be achieved. By establishing a digital twin of the tunnel's electromechanical equipment, the twin network synchronization control system can collect and synchronize the control signals and operating parameters of the equipment in real time, and then achieve precise control and risk warning of the equipment in the tunnel through synchronous control and analysis of virtual and actual equipment. This technology not only improves the level of intelligent equipment management, but also significantly improves the safety and reliability of the overall operation of the tunnel.

[0004] At present, the IPv6+-based twin network synchronization control system for tunnel electromechanical equipment is still in the exploratory stage, lacking specific networking practices and standardized methods for building equipment twin models. At the same time, the existing network architecture still has bottlenecks in achieving synchronous collection and processing of multi-source data, making it difficult to effectively meet the real-time data transmission and control needs of equipment in the tunnel. Therefore, establishing an efficient and reliable twin network synchronization control system to achieve comprehensive monitoring, data fusion analysis and intelligent tuning of tunnel electromechanical equipment is a key direction to solve the current technical bottleneck. Summary of the invention

[0005] The purpose of the present invention is to provide a tunnel electromechanical equipment twin network synchronization control system based on IPV6+ to solve the following technical problems:

[0006] How to achieve synchronous control and real-time data conversion of communication information and electrical signals, improve information management of tunnel electromechanical equipment and improve network security control.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The IPV6+-based tunnel electromechanical equipment twin network synchronization control system is characterized by including:

[0009] The model building module is used to collect the spatial data of the tunnel electromechanical area and establish a digital twin model of the electromechanical area based on the electromechanical area space;

[0010] A synchronous acquisition module is used to synchronously acquire real-time control signal data and real-time electrical performance parameters of tunnel electromechanical equipment through multi-sensors;

[0011] An information fusion module is used to perform fusion simulation analysis on the real-time control signal data obtained from each preset path according to a data fusion strategy and obtain standard electrical performance parameters of each preset path;

[0012] A synchronous control module, used to synchronously control the safe operation status of tunnel electromechanical equipment on a preset path according to real-time control signal data;

[0013] An analysis module is used to compare the real-time electrical performance parameters of the tunnel electromechanical equipment of each preset path under synchronous control with the standard electrical performance parameters, and judge the power supply status safety of the tunnel electromechanical equipment of the current path according to the comparison and analysis results;

[0014] The adaptive feedback module is used to adjust the data fusion strategy according to the feedback of the power supply status safety analysis results and realize the automatic tuning of the control signal data of the synchronous control module.

[0015] Preferably, the data fusion strategy is:

[0016] Set initial calibration instructions to calibrate the real-time control signal data on each path to obtain different calibration parameters;

[0017] Extracting a set of variation curves of different calibration parameters under continuous calibration cycles;

[0018] According to the characteristics of each curve change, weight values ​​are selected and assigned, and input into the deep learning model for data fusion;

[0019] Reconstruct the data based on the fused features and run the simulated circuit through electromechanical equipment to output standard electrical performance parameters.

[0020] Preferably, the comparative analysis process of the analysis module is:

[0021] By formula Calculate the operating status risk value Ris of the i-th preset path i :

[0022] Where N is the number of monitored electrical performance parameters, j∈[1,N]; Ls ij (t) is the real-time electrical performance parameter of the jth item of the i-th preset path; Lsij (t) ′ Ls is the standard electrical performance parameter of the jth item of the i-th preset path; (i-1)j (t) is the real-time electrical performance parameter of the jth item of the i-1th preset path; Ls (i-1)j (t) ′ is the standard electrical performance parameter of the jth item of the i-1th preset path; q i is the preset adjustment coefficient of the difference in the influence of the i-th preset path on various electrical performance parameters; q i-1 ΔLs is the preset adjustment coefficient of the difference in the impact of the i-1th preset path on various electrical performance parameters; j is the allowable value of the error of the jth electrical performance parameter; σ j is the preset weight coefficient of the jth electrical performance parameter.

[0023] Preferably, the operation status safety judgment process is:

[0024] The operating state risk value Ris i Compared with the preset standard threshold interval [Ris A ,Ris B ] for comparison:

[0025] If Ris A ≤Ris i ≤Ris B , it is judged that there is a risk in the power supply of the electromechanical equipment in the tunnel of the current path, and feedback is given to adjust the data fusion strategy;

[0026] If Ris i <Ris A , it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is normal;

[0027] If Ris i >Ris B , then it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is abnormal, and a maintenance instruction is generated.

[0028] Preferably, the synchronization control module further includes:

[0029] Monitoring unit, used to obtain information on the operating status of tunnel electromechanical equipment;

[0030] The operation status information includes temperature data of electromechanical equipment in each path, operation sound signal data of electromechanical equipment in each path, and voltage data of electromechanical equipment in each path;

[0031] The early warning unit is used to conduct safety monitoring of the operating status information of the tunnel electromechanical equipment according to the operating status of the tunnel electromechanical equipment.

[0032] Preferably, the process of the monitoring unit acquiring the operating status information of the tunnel electromechanical equipment is:

[0033] By formula Calculate the real-time equipment operation risk value Equ(t); where M is the total number of monitoring paths, l∈[1,M]; [t1,t2] is the monitoring time interval; T l (t) is the real-time electromechanical equipment temperature data of the lth monitoring path; V l (t) is the real-time electromechanical equipment operation sound signal data of the lth monitoring path; W l (t) is the real-time electromechanical equipment voltage data of the lth monitoring path; α is the first influence function, β is the second influence function, γ is the third influence function, and α, β, and γ are all greater than 0; τ l is the location coefficient of the lth monitoring path;

[0034] The real-time equipment operation risk value Equ(t) is compared with the preset threshold Equ thr To compare:

[0035] If Equ(t)>Equ thr , it is judged that there is a safety risk in the operation status of the tunnel electromechanical equipment, and the early warning unit sends out an early warning signal and generates a maintenance instruction.

[0036] Preferably, the twin network synchronization control system is connected to the electromechanical equipment communication control system through an IPV6+ converter, and the electromechanical equipment communication control system includes a device multi-service type interface, a service signal processing module and an ad hoc network fusion module;

[0037] The multi-service type interface is electrically connected to the service signal processing module, and the multi-service type interface is used to generate a multi-modal signal and perform electrical transmission;

[0038] The service signal processing module is used to receive the multimodal signal and perform digital-to-analog conversion into control signal data;

[0039] The self-organizing network fusion module transmits the control signal number through multiple preset paths of the wireless network.

[0040] By adopting the above technical solution, the present invention mainly has the following technical effects:

[0041] The present invention uses a twin network synchronous control system including six components: a model building module, a synchronous acquisition module, an information fusion module, a synchronous control module, an analysis module and an adaptive feedback module. The twin network synchronous control system realizes the control signal data of multiple paths and the safe operation and data analysis process of related electromechanical equipment, and can timely perform real-time signal response according to the electromechanical equipment communication control system; through the generation of multimodal signals, the acquisition method of control signal data, the data fusion strategy, and the comparative analysis process, the process of timely early warning and signal monitoring of the safe operation status of tunnel electromechanical equipment is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 This is a schematic diagram of the twin network synchronization control system of tunnel electromechanical equipment based on IPV6+ of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, the present invention provides a twin network synchronization control system for tunnel electromechanical equipment based on IPV6+. When the twin network synchronization control system for tunnel electromechanical equipment based on IPV6+ is actually applied, it is adapted to the electromechanical equipment communication control system and is used for information management and network security control of tunnel electromechanical equipment.

[0046] In some embodiments, the electromechanical equipment communication control system may include an equipment multi-service type interface, a service signal processing module, and an ad hoc network fusion module;

[0047] The multi-service type interface is electrically connected to the service signal processing module, and the multi-service type interface is used to generate a multi-modal signal and perform electrical transmission;

[0048] The service signal processing module is used to receive multi-modal signals and perform digital-to-analog conversion into control signal data;

[0049] The ad hoc network fusion module transmits control signal data through multiple preset paths of the wireless network.

[0050] In some embodiments, the twin network synchronization control system includes:

[0051] The model building module is used to collect the spatial data of the tunnel electromechanical area and establish a digital twin model of the electromechanical area based on the electromechanical area space;

[0052] A synchronous acquisition module is used to synchronously acquire real-time control signal data and real-time electrical performance parameters of tunnel electromechanical equipment through multi-sensors;

[0053] An information fusion module is used to perform fusion simulation analysis on the real-time control signal data obtained from each preset path according to a data fusion strategy and obtain standard electrical performance parameters of each preset path;

[0054] A synchronous control module, used to synchronously control the safe operation status of tunnel electromechanical equipment on a preset path according to real-time control signal data;

[0055] An analysis module is used to compare the real-time electrical performance parameters of the tunnel electromechanical equipment of each preset path under synchronous control with the standard electrical performance parameters, and judge the power supply status safety of the tunnel electromechanical equipment of the current path according to the comparison and analysis results;

[0056] The adaptive feedback module is used to adjust the data fusion strategy according to the feedback of the power supply status safety analysis results and realize the automatic tuning of the control signal data of the synchronous control module.

[0057] In the above technical scheme, this embodiment uses a twin network synchronization control system and an electromechanical equipment communication control system to synchronously convert network signals and electrical signals to ensure the synchronous input and output process of signals of the tunnel electromechanical equipment multi-network fusion control system.

[0058] For example, the electromechanical equipment communication control system may include a device multi-service type interface, a service signal processing module and a self-organizing network fusion module; the electromechanical equipment communication control system realizes accurate collection, real-time processing and precise fusion of service communication control signals and digital output of control signals.

[0059] Specifically, the control data corresponding to the business communication signals transmitted by electromechanical equipment along different paths are different; specifically, the multi-business type interface is electrically connected to the business signal processing module, and the multi-business type interface is used to generate a multimodal signal and transmit it electrically; through the process of electrical transmission, the multi-business type signal is transmitted through the interface communication to perform a multimodal signal transmission process, and then a business signal processing module is set to receive the multimodal signal to perform digital-to-analog conversion into control signal data, and the electrical signal is converted into the data form of the control signal to ensure further analysis and processing of the equipment power data; finally, a self-organizing network fusion module is set to transmit the control signal data through multiple preset paths of the wireless network to achieve accurate transmission of the control signal data.

[0060] Furthermore, the twin network synchronous control system includes six components: model building module, synchronous acquisition module, information fusion module, synchronous control module, analysis module and adaptive feedback module. The twin network synchronous control system realizes the safe operation and data analysis process of control signal data of multiple paths and its related electromechanical equipment, and can make real-time signal response in time according to the electromechanical equipment communication control system.

[0061] Specifically, a model building module is set up to realize real-time collection of spatial data of the tunnel electromechanical area, and a digital twin model of the electromechanical area is established according to the electromechanical area space; the synchronization process of virtual and reality is realized through the digital twin model of the electromechanical area, and then, a synchronous acquisition module is set up to synchronously collect real-time control signal data and real-time electrical performance parameters of the tunnel electromechanical equipment through multi-sensors; and, through the information fusion module, the real-time control signal data obtained from each preset path is fused and simulated and analyzed according to the data fusion strategy, and the standard electrical performance parameters of each preset path are obtained.

[0062] Next, a synchronous control module is set up to synchronously control the safe operating status of the tunnel electromechanical equipment of the preset path according to the real-time control signal data; and the synchronous control module is used to further analyze the operating status of the tunnel electromechanical equipment, and then the acquisition method of the real-time control signal data of the equipment is adjusted according to the operating status of the tunnel electromechanical equipment; further, an analysis module is set up to compare the real-time electrical performance parameters and standard electrical performance parameters of the tunnel electromechanical equipment of each preset path under synchronous control, and judge the power supply status safety of the tunnel electromechanical equipment on the current path according to the comparison and analysis results; the power supply status is judged by the comparative analysis of the analysis module to realize the early prediction and judgment of the power supply status; finally, an adaptive feedback module is set up to adjust the data fusion strategy according to the feedback of the power supply status safety analysis results, and realize automatic tuning of the control signal data of the synchronous control module, thereby realizing the tuning process of the electrical performance parameters.

[0063] For example, the generation process of multimodal signals can be:

[0064] Converting mechatronic device control data inputs of different modalities into numerical vectors using pre-validated encoders;

[0065] The LLM language model is used to decode the input numerical vector, and a specific signal is selected and generated according to the pre-introduced "modal signal" mechanism;

[0066] Receive generation instructions from the LLM language model, map specific signals through a customized output layer, and then forward them to the corresponding multimodal decoder for multimodal signal generation operations;

[0067] The LLM language model is optimized by building a dataset in real time and instructing it based on the tuning signal.

[0068] In the above technical solution, the generation process of the multimodal signal in this embodiment is a process that relies on the encoder to convert the electromechanical device control data into a numerical vector and decode and generate related instructions; specifically, first, a pre-verified encoder is used to convert the electromechanical device control data input of different modes into a numerical vector; then, the input numerical vector is decoded using the LLM language model, and a specific signal is selected and generated according to the pre-introduced "modal signal" mechanism. The "modal signal" mechanism set here is a pre-defined rule, which is used to guide the model to convert the input numerical vector into a specific signal in a prescribed manner; then, Receive generation instructions from the LLM language model, map specific signals through a customized output layer, and then forward them to the corresponding multimodal decoder for multimodal signal generation operations; the goal of this setting is to generate corresponding multimodal outputs such as text descriptions, images, audio or video based on the input information and the guidance of the LLM; finally, optimize the LLM language model by combining the real-time constructed dataset and the tuning signal instructions; this process is to better understand and execute complex modal interleaving instructions, and help the LLM language model generate reasonable and coherent multimodal signals and output them through autoregression in a continuous feature space.

[0069] As an implementation mode of the present invention, the control signal data is obtained in the following manner:

[0070] Capture multimodal signals through multi-sensors and signal receiving equipment;

[0071] Preprocessing the received multimodal signals by removing noise, amplifying and filtering;

[0072] The preprocessed multimodal signal is converted into control signal data through an analog-to-digital converter (ADC).

[0073] In the above technical scheme, the control signal data in this embodiment is obtained by preprocessing and converting the multimodal signal of the collected sensor data, with the purpose of realizing the digitization process of the electrical signal. Specifically, the received multimodal analog signal type is first processed and analyzed by a multi-sensor and a signal receiving device. The multimodal analog signal type includes temperature signal, pressure signal, light signal intensity, sound signal, etc. Through the preprocessing of noise removal, amplification and filtering, the multimodal analog signal is accurately preprocessed to remove interference, which is convenient for further electrical signal conversion process. It is mainly converted into control signal data through an analog-to-digital converter (ADC). Here, the control signal is a comprehensive data form obtained based on the joint control of temperature signal, pressure signal, light signal intensity and sound signal, which is used as the current tunnel electromechanical equipment control signal data.

[0074] As an implementation mode of the present invention, the data fusion strategy is:

[0075] Set initial calibration instructions to calibrate the real-time control signal data on each path to obtain different calibration parameters;

[0076] Extracting a set of variation curves of different calibration parameters under continuous calibration cycles;

[0077] According to the characteristics of each curve change, weight values ​​are selected and assigned, and input into the deep learning model for data fusion;

[0078] Reconstruct the data based on the fused features and run the simulated circuit through electromechanical equipment to output standard electrical performance parameters.

[0079] In the above technical scheme, the data fusion strategy in this embodiment is to realize the process of processing and analyzing the real-time control signal data on each path, mainly by first calibrating the real-time control signal data on each path. The calibration is implemented according to the initial calibration instruction, and the different calibration parameters on each path are output according to the initial calibration instruction. By obtaining a set of change curves of different calibration parameters under continuous calibration cycles, the continuous change of the calibration parameters is judged, and the weight value is selected and assigned according to the change characteristics of each curve, and the data is input into the deep learning model for data fusion; finally, the data is reconstructed according to the characteristics of the fused data, and the standard electrical performance parameters are output by running the simulation circuit of the electromechanical equipment. The types of electrical performance related to the obtained in the process of running the simulation circuit of the electromechanical equipment include data gain, noise coefficient, and broadband setting.

[0080] As an implementation mode of the present invention, the comparative analysis process of the analysis module is as follows:

[0081] By formula Calculate the operating status risk value Ris of the i-th preset pathi :

[0082] Where N is the number of monitored electrical performance parameters, j∈[1,N]; Ls ij (t) is the real-time electrical performance parameter of the jth item of the i-th preset path; Ls ij (t) ′ Ls is the standard electrical performance parameter of the jth item of the i-th preset path; (i-1)j (t) is the real-time electrical performance parameter of the jth item of the i-1th preset path; Ls (i-1)j (t) ′ is the standard electrical performance parameter of the jth item of the i-1th preset path; q i is the preset adjustment coefficient of the difference in the influence of the i-th preset path on various electrical performance parameters; q i-1 ΔLs is the preset adjustment coefficient of the difference in the impact of the i-1th preset path on various electrical performance parameters; j is the allowable value of the error of the jth electrical performance parameter; σ j is the preset weight coefficient of the jth electrical performance parameter.

[0083] In the above technical solution, the comparative analysis process of the analysis module in this embodiment is performed by calculating the formula Calculate the operating status risk value Ris of the i-th preset path i To judge and analyze the obtained, specifically by analyzing the size of the operating status risk value to judge the power supply status of the tunnel electromechanical equipment in the current path, and analyze the power supply risk situation to carry out the next operation and maintenance of related equipment in the power supply process.

[0084] Specifically, the calculation is performed by analyzing the operation of the signal during the power supply process of the previous path and the current signal operation, and by analyzing the changes in the corresponding power supply indicators, the size of all electrical performance parameters is judged to determine the size of the risk value of the current path operation status.

[0085] As an implementation mode of the present invention, the operation status safety judgment process is as follows:

[0086] The operating state risk value Ris i Compared with the preset standard threshold interval [Ris A ,Ris B ] for comparison:

[0087] If Ris A ≤Ris i ≤Ris B , it is judged that there is a risk in the power supply of the electromechanical equipment in the tunnel of the current path, and feedback is given to adjust the data fusion strategy;

[0088] If Ris i<Ris A , it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is normal;

[0089] If Ris i >Ris B , then it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is abnormal, and a maintenance instruction is generated.

[0090] As an implementation mode of the present invention, the synchronization control module further includes:

[0091] Monitoring unit, used to obtain information on the operating status of tunnel electromechanical equipment;

[0092] The operation status information includes the temperature data of the electromechanical equipment of each path, the operation sound signal data of the electromechanical equipment of each path, and the voltage data of the electromechanical equipment of each path;

[0093] The early warning unit is used to conduct safety monitoring of the operating status information of the tunnel electromechanical equipment according to the operating status of the tunnel electromechanical equipment.

[0094] In the above technical solution, the synchronous control module in this embodiment is also provided with a detection unit and an early warning unit to realize the process of timely early warning and signal monitoring of the safe operation status of the tunnel electromechanical equipment. Specifically, the monitoring unit calculates and obtains the operation status information of the tunnel electromechanical equipment, and the early warning unit determines the operation status of the tunnel electromechanical equipment according to the size of the operation status information to monitor the output of the early warning signal of the current equipment operation status information.

[0095] As an implementation mode of the present invention, the process of the monitoring unit acquiring the operating status information of the tunnel electromechanical equipment is as follows:

[0096] By formula Calculate the real-time equipment operation risk value Equ(t); where M is the total number of monitoring paths, l∈[1,M]; [t1,t2] is the monitoring time interval; T l (t) is the real-time electromechanical equipment temperature data of the lth monitoring path; V l (t) is the real-time electromechanical equipment operation sound signal data of the lth monitoring path; W l (t) is the real-time electromechanical equipment voltage data of the lth monitoring path; α is the first influence function, β is the second influence function, γ is the third influence function, and α, β, and γ are all greater than 0; τ l is the location coefficient of the lth monitoring path;

[0097] The real-time equipment operation risk value Equ(t) is compared with the preset threshold Equ thr To compare:

[0098] If Equ(t)>Equthr , it is judged that there is a safety risk in the operation status of the tunnel electromechanical equipment, and the early warning unit sends out an early warning signal and generates a maintenance instruction.

[0099] In the above technical solution, the calculation process of the monitoring unit in this embodiment to obtain the operating status information of the tunnel electromechanical equipment is through the formula The real-time equipment operation risk value Equ(t) is calculated, and in the process of calculating the real-time equipment operation risk value, the electromechanical equipment temperature, electromechanical equipment operation sound signal and electromechanical equipment voltage are introduced to reflect the safety status of the tunnel electromechanical equipment operation. The equipment operation risk values ​​of multiple paths are accumulated through the different data changes of the tunnel electromechanical equipment to realize the early prediction of the operation status of the electromechanical equipment.

[0100] It should be further explained that the influence functions α, β, and γ are predetermined according to the influence proportion of the electromechanical equipment temperature, the electromechanical equipment operation sound signal, and the electromechanical equipment voltage on the operation of the electromechanical equipment under the current path, and are functions obtained in advance by fitting historical data; the position coefficient τ l It is determined in advance by fitting based on the location information of the historical monitoring path distribution, and is obtained by fitting based on the operating status data of the historical monitoring path equipment, which will not be described in detail here.

[0101] In some embodiments, the twin network synchronization control system can also be connected to the electromechanical equipment communication control system through an IPV6+ converter

[0102] In the above technical solutions, this system is based on the process of upgrading some common communication networks. By introducing new data network technologies such as SRv6, SDN, FlexE, Telemetry, iFIT, etc., it realizes the reconstruction of network architecture, the doubling of carrying capacity and the intelligent operation and maintenance management, and overall promotes the large-scale deployment and application of IPv6+ at all levels. This system often uses the IPV6+ converter to realize the connection processing with the communication control system of electromechanical equipment.

[0103] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0104] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. The twin network synchronization control system of tunnel electromechanical equipment based on IPV6+ is characterized by: include: The model building module is used to collect the spatial data of the tunnel electromechanical area and establish a digital twin model of the electromechanical area based on the electromechanical area space; A synchronous acquisition module is used to synchronously acquire real-time control signal data and real-time electrical performance parameters of tunnel electromechanical equipment through multi-sensors; An information fusion module is used to perform fusion simulation analysis on the real-time control signal data obtained from each preset path according to a data fusion strategy and obtain standard electrical performance parameters of each preset path; A synchronous control module, used to synchronously control the safe operation status of tunnel electromechanical equipment on a preset path according to real-time control signal data; An analysis module is used to compare the real-time electrical performance parameters of the tunnel electromechanical equipment of each preset path under synchronous control with the standard electrical performance parameters, and judge the power supply status safety of the tunnel electromechanical equipment of the current path according to the comparison and analysis results; The adaptive feedback module is used to adjust the data fusion strategy according to the feedback of the power supply status safety analysis results and realize the automatic tuning of the control signal data of the synchronous control module.

2. According to the IPV6+-based tunnel electromechanical equipment twin network synchronization control system of claim 1, it is characterized in that: The data fusion strategy is: Set initial calibration instructions to calibrate the real-time control signal data on each path to obtain different calibration parameters; Extracting a set of variation curves of different calibration parameters under continuous calibration cycles; According to the characteristics of each curve change, weight values ​​are selected and assigned, and input into the deep learning model for data fusion; Reconstruct the data based on the fused features and run the simulated circuit through electromechanical equipment to output standard electrical performance parameters.

3. According to the IPV6+-based tunnel electromechanical equipment twin network synchronization control system of claim 2, it is characterized in that: The comparative analysis process of the analysis module is as follows: By formula Calculate the operating status risk value Ris of the i-th preset path i : Where N is the number of monitored electrical performance parameters, j∈[1,N]; Ls ij (t) is the real-time electrical performance parameter of the jth item of the i-th preset path; Ls ij (t) ′ Ls is the standard electrical performance parameter of the jth item of the i-th preset path; (i-1)j (t) is the real-time electrical performance parameter of the jth item of the i-1th preset path; Ls (i-1)j (t) ′ is the standard electrical performance parameter of the jth item of the i-1th preset path; q i is the preset adjustment coefficient of the difference in the influence of the i-th preset path on various electrical performance parameters; q i-1 ΔLs is the preset adjustment coefficient of the difference in the impact of the i-1th preset path on various electrical performance parameters; j is the allowable value of the error of the jth electrical performance parameter; σ j is the preset weight coefficient of the jth electrical performance parameter.

4. According to the IPV6+-based tunnel electromechanical equipment twin network synchronization control system of claim 1, it is characterized in that: The operation status safety judgment process is as follows: The operating state risk value Ris i Compared with the preset standard threshold interval [Ris A ,Ris B ] for comparison: If Ris A ≤Ris i ≤Ris B , it is judged that there is a risk in the power supply of the electromechanical equipment in the tunnel of the current path, and feedback is given to adjust the data fusion strategy; If Ris i <Ris A , it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is normal; If Ris i >Ris B , then it is judged that the power supply status of the electromechanical equipment in the tunnel of the current path is abnormal, and a maintenance instruction is generated.

5. According to the IPV6+-based tunnel electromechanical equipment twin network synchronization control system of claim 1, it is characterized in that: The synchronization control module also includes: Monitoring unit, used to obtain information on the operating status of tunnel electromechanical equipment; The operation status information includes temperature data of electromechanical equipment in each path, operation sound signal data of electromechanical equipment in each path, and voltage data of electromechanical equipment in each path; The early warning unit is used to conduct safety monitoring of the operating status information of the tunnel electromechanical equipment according to the operating status of the tunnel electromechanical equipment.

6. The IPV6+-based tunnel electromechanical equipment twin network synchronization control system according to claim 5 is characterized in that: The process of the monitoring unit acquiring the operating status information of the tunnel electromechanical equipment is as follows: By formula Calculate the real-time equipment operation risk value Equ(t); where M is the total number of monitoring paths, l∈[1,M]; [t1,t2] is the monitoring time interval; T l (t) is the real-time electromechanical equipment temperature data of the lth monitoring path; V l (t) is the real-time electromechanical equipment operation sound signal data of the lth monitoring path; W l (t) is the real-time electromechanical equipment voltage data of the lth monitoring path; α is the first influence function, β is the second influence function, γ is the third influence function, and α, β, and γ are all greater than 0; τ l is the location coefficient of the lth monitoring path; The real-time equipment operation risk value Equ(t) is compared with the preset threshold Equ thr To compare: If Equ(t)>Equ thr , it is judged that there is a safety risk in the operation status of the tunnel electromechanical equipment, and the early warning unit sends out an early warning signal and generates a maintenance instruction.

7. The IPV6+-based tunnel electromechanical equipment twin network synchronization control system according to claim 1 is characterized in that: The twin network synchronization control system is connected to the electromechanical equipment communication control system through an IPV6+ converter, and the electromechanical equipment communication control system includes a device multi-service type interface, a service signal processing module, and an ad hoc network fusion module; The multi-service type interface is electrically connected to the service signal processing module, and the multi-service type interface is used to generate a multi-modal signal and perform electrical transmission; The service signal processing module is used to receive the multimodal signal and perform digital-to-analog conversion into control signal data; The self-organizing network fusion module transmits the control signal number through multiple preset paths of the wireless network.

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