Digital integration method and system for physical control console for marine power verification

Abstract

This invention discloses a method and system for digital integration of physical control consoles for marine power verification, relating to the field of marine power verification technology. The method includes: for each verification task of the currently running parallel marine power system, calculating a comprehensive priority score for each verification task based on its urgency index, normalized data update frequency, and alarm level normalized value; allocating display channels to each verification task in descending order of comprehensive priority score; constructing a task-control console matching planning model and setting multiple constraints; minimizing the task-control console matching planning model to find the corresponding optimal decision variables; calculating a state deviation index based on the normalized state vector of the corresponding verification task in the main console's local cache and the normalized authoritative state vector of the corresponding verification task in the integration platform database; and triggering a three-level closed-loop correction mechanism based on the state deviation index for digital integration of the physical control console.

Description

Technical Field

[0001] This invention belongs to the field of marine power verification technology, and more specifically, relates to a digital integration method and system for a physical control console for marine power verification. Background Technology

[0002] Currently, when multiple marine power verification tasks, such as methanol engine bench testing, ammonia engine bench testing, hydrogen fuel cell bench testing, and DC power distribution simulation, are performed in parallel, the existing physical control console faces three core pain points: (1) Static binding of physical resources: The hardware resources of the main control station and each sub-control station are fixed and cannot be dynamically reallocated according to the verification task type, resulting in some station resources being idle and key test station resources being insufficient.

[0003] (2) Lack of credibility of multi-source data: The three sources of bench sensor data (such as engine speed and emission concentration), simulation model calculation data and platform command data are heterogeneous and lack a unified real-time quality quantification mechanism, making it difficult to support key verification decisions.

[0004] (3) The master-slave control coordination mechanism is imperfect: When multiple units operate concurrently, the routing of master console commands and slave console commands lacks optimization, which can easily lead to control conflicts. Furthermore, the consistency of the status between the control console and the integration platform lacks quantitative control measures.

[0005] Therefore, there is an urgent need for a technical solution that can solve the above technical problems. Summary of the Invention

[0006] To address the above technical problems, this invention proposes a digital integration method for a physical control console for marine power verification, used in simulation experiments, comprising: Step 101: For each verification task of the currently running marine power, calculate the comprehensive priority score of each verification task based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. Step 102: Construct a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. Minimize the task-console matching planning model and find the corresponding optimal decision variable. Step 103: Calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Trigger a three-level closed-loop correction mechanism based on the state deviation index to achieve digital integration of the physical control console.

[0007] Furthermore, before step 101, the method includes: abstracting all hardware resources in the physical console into a virtual resource unit, and calculating the overall resource availability of the physical console based on the health status flags and available capacity ratio of each virtual resource unit. Based on the historical resource consumption data of each verification task, the resource requirement vector of each verification task is analyzed.

[0008] Furthermore, several constraints include: the first constraint is that each verification task corresponds to one and only one console; the second constraint is that the sum of the demand intensities of all verification tasks currently assigned to a certain console for a certain virtual resource unit does not exceed the ratio of the available capacity of the virtual resource unit of that console; and the third constraint is that each decision variable takes only one of two integer values, zero and one.

[0009] Furthermore, the task-console matching planning model includes: in, For the total scheduling cost, To verify the task set, For console collection, To make the first The verification task was assigned to the first... The revised scheduling cost for each console, As decision variables, =1 indicates that the first... The verification task was assigned to the first... One console, otherwise 0. To make the first The verification task was assigned to the first... The basic scheduling cost of a console This is the credibility penalty strength coefficient. For a moment Time The verification task is in the first... The fusion credibility score under the data source of each console.

[0010] Furthermore, calculate the time. Time The verification task is in the first... Fusion credibility score under each console's data source include: in, This is the credibility sensitivity coefficient. For the first The fusion weight of the data source class For a moment Time The slippage anomaly rate of the data source.

[0011] Furthermore, the calculation of the state deviation index includes: in, For a moment Time The state deviation index of a verification task. For a moment The first in the local cache of the main console The normalized state vector of each verification task. For a moment The first time integration platform database The normalized authority state vector for each verification task.

[0012] Furthermore, the three-level closed-loop correction mechanism includes: when Level 1 Correction: Push Time The first time integration platform database A snapshot of the authoritative state of each verification task, covering the time frame. The first in the local cache of the main console A snapshot of the status of each verification task, in which... To set a preset deviation threshold, for time... The first time integration platform database Normalize the authority state snapshots of each verification task to generate a normalized authority state vector, and then normalize the snapshots at time points. The first in the local cache of the main console The state snapshots of each verification task are normalized to generate a normalized state vector; when For Level 2 correction: Push a full state snapshot to the integration platform database and overwrite the local cache on the main console; when Level 3 Correction: Suspend the first... The control command for each verification task is issued and triggers a manual confirmation alarm on the main console. The operation can only be restored after the operator manually verifies the information.

[0013] Furthermore, each verification task occupies at most one display channel, and each display channel can only carry one verification task.

[0014] This invention also proposes a digitally integrated physical control console system for marine propulsion verification, used for simulation experiments, comprising: The display channel allocation module is used to calculate the comprehensive priority score of each verification task of the currently running marine power based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. The model setting module is used to build a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. The task-console matching planning model is minimized to find the corresponding optimal decision variables. The correction module is used to calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Based on the state deviation index, a three-level closed-loop correction mechanism is triggered to achieve digital integration of the physical control console.

[0015] Furthermore, before the display channel allocation module, it also includes: abstracting all hardware resources in the physical control console into a virtual resource unit, and calculating the overall resource availability of the physical control console based on the health status flags and available capacity ratio of each virtual resource unit; Based on the historical resource consumption data of each verification task, the resource requirement vector of each verification task is analyzed.

[0016] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: This invention solves the problem of local idleness and bottleneck caused by the fixed binding of resources between the main control console and the sub-control console by virtualizing all the hardware resources of the physical control console into virtual resource units carrying health status flags and available capacity ratios and forming a global virtual resource pool, so that the overall resource availability of the control console can be quantified in real time. By dynamically allocating display channels based on a multi-dimensional comprehensive priority score that is based on urgency index, normalized data update frequency, and alarm level normalized value, the problem of high-priority verification tasks being unable to obtain display resources in a timely manner due to reliance on manual switching by remote control is solved, thus ensuring the timeliness of display resources in response to changes in the priority of verification tasks. By calculating the slip anomaly rate from three heterogeneous data sources—bench sensor data, simulation calculation data, and platform command response data—and outputting a multi-source fusion credibility score, the problem of the inability to uniformly quantify the quality of multi-source data, which leads to a lack of credible basis for key verification decisions, is solved. This enables data quality anomalies to be detected in real time and automatically triggers fusion weight adjustments. By using the task-console matching planning model, the problem of task scheduling decisions being disconnected from data quality status, resulting in consoles with poor data quality still being assigned key verification tasks, is solved, and scheduling decisions are dynamically linked with the reliability of real-time data. An effectiveness evaluation index system is constructed by considering four dimensions: resource utilization, data quality, scheduling efficiency, and state synchronization. When the comprehensive score is lower than the effectiveness threshold, the parameters of the corresponding steps are adaptively adjusted. This solves the problem that the system cannot respond to changes in the operating state due to the fixed configuration of parameters in each step. This makes the entire integration method form a closed-loop operating mechanism that can be quantified and automatically adjusted. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention; Figure 3 This is a network architecture diagram of the integrated platform of this invention; Figure 4 This is a rendering of the physical control panel of the present invention. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0020] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0021] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0022] The display screen is used to show the user interface of each application.

[0023] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0024] The physical control console in this invention is a hardware assembly consisting of a main control console (including a telegraph, control button array, steering wheel, main control terminal, and display lifting device), six sub-control consoles (each containing a terminal and a display), a display wall (one 38.5-inch main screen + six secondary screens), a 16-in-16-out intelligent HDMI matrix, and a 19-inch server rack. Its function is to send verification commands to the integrated platform through the aforementioned input / output devices and to receive the calculation results returned by the integrated platform and display them on the front end. Figure 4 The image shown is a rendering of the physical control panel.

[0025] The integrated platform is deployed on a cluster of hosts within a rack, including an application server (Inspur NF5280M6, where simulation models for simulation experiments are deployed), a one-dimensional simulation solver workstation (Lenovo ThinkServer SR660 V2), and a three-dimensional simulation solver workstation (Superior ServMAX TS40-X3). It hosts the runtime environment for Syslink simulation models, COMSOL, and FLACS commercial software, and is responsible for receiving verification commands from the physical console, executing simulation calculations or scheduling physical bench tests, and transmitting the results back to the physical console for display. Figure 3 The diagram shows the network architecture of the integrated platform.

[0026] For example, the resource types of the physical control console used for marine power verification in this invention can be as shown in Table 1, and the configuration of the four-stroke ammonia fuel engine test bench is as shown in Table 2: Table 1 Table 2 Example 1 like Figure 1 As shown, this embodiment proposes a digital integration method for a physical control console for marine power verification, used for simulation experiments, including: Step 101: For each verification task of the currently running marine power, calculate the comprehensive priority score of each verification task based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. Specifically, each verification task occupies at most one display channel, and each display channel can carry at most one verification task.

[0027] Specifically, before step 101, the process also includes: abstracting all hardware resources in the physical console into a virtual resource unit, and calculating the overall resource availability of the physical console based on the health status flags and available capacity ratio of each virtual resource unit. Based on the historical resource consumption data of each verification task, the resource requirement vector of each verification task is analyzed.

[0028] Preferably, each physical resource in Table 1 is abstracted into a Virtual Resource Unit (VRU) and described uniformly using a 5-tuple: in, For the first One VRU, =1, 2,…, , The total number of VRUs. This is a resource type identifier, with values ​​ranging from one of the following: {terminal computing, display output, physical input, network communication}, such as main control console. =Terminal computing, vehicle telegraph =Physical Input Class, For the first The health status flags for each VRU are: 1 indicates online and normal, 0 indicates fault and offline, and are updated periodically via heartbeat detection (Keep-Alive=60s). For the first The available capacity ratio of a VRU is defined as the ratio of the current remaining available capacity to the rated capacity. For example, the available capacity ratio of the terminal computing class is 1 − (the weighted average of CPU utilization and memory utilization), and the available capacity ratio of the HDMI matrix port class is 1 (idle) or 0 (occupied). For the first The location numbering of each VRU in the physical control panel layout; for example, the main control panel is numbered 0, and the 6 sub-control panels are numbered 1-6 respectively. For the first A set of verification task identifiers bound to a VRU, for example, when the sub-controller terminal ① performs a hydrogen fuel cell bench test Λ k ={HFC-01}, all VRUs constitute a global virtual resource pool.

[0029] Preferably, calculating the overall resource availability of the physical console includes: in, For a moment Overall resource availability at that time For the first The importance weights of each VRU are configured offline by the system administrator based on business importance (e.g., from the main console). =0.20, Cart Clock =0.10, each secondary screen =0.05).

[0030] Preferably, by constructing a multi-dimensional priority scoring model, the limited display channels (7 secondary screens + 1 main screen, totaling...) are allocated... =8 channels) are dynamically assigned in real time to the highest priority verification task, replacing the static method that relies on manual switching by remote control, as shown below: At any time For the currently running in parallel For each verification task, calculate the overall priority score for the display channel. : in, As the weight of the urgency index, For a moment Time The urgency index of a verification task is defined as 1 minus the ratio of the task's current execution time to the planned total time. A higher value indicates that the task is closer to its deadline. For example: when there are 10 minutes remaining in a 100% load test of a methanol engine, and the planned total time is 60 minutes... =1−(50 / 60)≈ 0.83, The weights for normalizing data update frequency For a moment Time The normalized data update frequency of the verification task is defined as the frequency of the first verification task. The ratio of the real-time data update frequency of each verification task to the system's maximum supported update frequency. For example, if engine bench data is reported at a period of 100 ms (10 Hz), and the system's maximum supported frequency is 50 Hz, then... =10 / 50=0.20, for, For a moment Time The alarm level normalization value for each verification task is mapped from the alarm information reported by RackAgent: Normal = 0, Hint = 0.33, Warning = 0.67, Emergency = 1.00. For example, when ammonia emissions from an ammonia engine exceed the threshold, RackAgent pushes a Warning-level alarm. =0.67.

[0031] right Each verification task is as follows Sort in descending order, construction time Time allocation decision matrix For example, if the elements of the decision matrix are assigned... , then indicates the time. The time will be the first The verification task was assigned to the first... Number display channel, To display the total number of channels, this embodiment is as follows: =8 channels; the channel allocation results simultaneously determine the data aggregation path for each verification task, guiding the direction of aggregation and merging of multi-source data.

[0032] Step 102: Construct a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. Minimize the task-console matching planning model and find the corresponding optimal decision variable. Specifically, by constructing a task-console matching planning model, the optimal allocation scheme for verification tasks between the main control console (1 console) and the sub-control consoles (6 consoles) is determined. A credibility correction mechanism dynamically incorporates data quality status into the scheduling decision, ultimately outputting an instruction routing mapping table. The task-console matching planning model includes: in, For the total scheduling cost, To verify the task set, For console collection, To make the first The verification task was assigned to the first... The revised scheduling cost for each console, As decision variables, =1 indicates that the first... The verification task was assigned to the first... One console, otherwise 0. To make the first The verification task was assigned to the first... The basic scheduling cost of a console This is the credibility penalty strength coefficient. For a moment Time The verification task is in the first... The fusion credibility score under the data source of each console.

[0033] Preferably, based on the data aggregation path, the three heterogeneous data sources (bench sensor, simulation model output, and platform command response) are uniformly normalized, and a real-time quantitative assessment of the current data quality is achieved through a sliding anomaly rate and exponential confidence model, as detailed below: The three types of data sources are defined as follows: The data is from bench sensors (reported by RackAgent via the MQTT protocol, including engine speed, power, fuel consumption rate, NO). x (physical quantities such as CO2 emissions and exhaust temperature). The simulation calculation data (from Syslink / COMSOL / FLACS solvers, including simulated rotational speed, simulated thrust, thermal field distribution, etc.) The platform command response data (integrating control parameters such as speed setpoints and simulated load torque issued by the platform) is used for time-based responses. Time The observation vectors of the data source are normalized. For the The data source class has a length of Within a sliding time window of sampling points, the Euclidean norm for calculating the difference between the normalized observed value and the normalized predicted value exceeds the threshold of the th sampling point. Exception detection threshold for data sources The proportion, as a time Time Slippage rate of data sources : in, For a moment Time Normalized observation vectors of a data source. For a moment Time Normalized predicted values ​​from similar data sources (obtained by fitting historical data) ), This is an indicator function.

[0034] Specifically, the sliding anomaly rate is calculated by combining the data from three types of data sources. Define the moment The fusion credibility score of multi-source fusion data at time, i.e., the calculation time Time The verification task is in the first... Fusion credibility score under each console's data source include: in, This is the credibility sensitivity coefficient. For the first The fusion weight of the data source class, when < (For example When the value is 0.60, the system triggers a data quality alarm to the main control panel and automatically reduces the fusion weight of the abnormal data source. .

[0035] Specifically, several constraints include: the first constraint is that each verification task corresponds to one and only one console; the second constraint is that the sum of the demand intensity of all verification tasks currently assigned to a certain console for a certain virtual resource unit does not exceed the available capacity ratio of the virtual resource units of that console; and the third constraint is that each decision variable takes only one of two integer values, zero or one.

[0036] Step 103: Calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Trigger a three-level closed-loop correction mechanism based on the state deviation index to achieve digital integration of the physical control console.

[0037] Regarding the normalized state vector of the corresponding verification task in the local cache of the main console, for example, the main console maintains a state snapshot of the current verification task locally. This snapshot comes from two sources: 1. The state snapshot recorded locally by the operator through control commands issued by physical input devices such as the vehicle telegraph and control buttons; 2. The state snapshot of the rack sensor data reported by the rack-end agent program (RackAgent) after processing is stored in the local cache, and then the state snapshot is normalized to obtain the normalized state vector.

[0038] Regarding the normalized authoritative state vector corresponding to the verification task in the integration platform database, for example, the integration platform is deployed on a host cluster (application server, simulation solver workstation). It stores a state snapshot of the "authoritative version" after task scheduling and data storage processing, which represents the benchmark of the current state snapshot recognized by the entire verification system.

[0039] Preferably, under the constraints of the instruction routing mapping table, a three-level closed-loop correction mechanism is used to continuously and actively monitor and correct the state consistency between the physical control console and the integration platform software, as detailed below: The calculation of the state deviation index includes: in, For a moment Time The state deviation index of a verification task. For a moment The first in the local cache of the main console The normalized state vector of each verification task. For a moment The first time integration platform database The normalized authority state vector for each verification task.

[0040] Specifically, the three-level closed-loop correction mechanism includes: when Level 1 Correction: Push Time The first time integration platform database A snapshot of the authoritative state of each verification task, covering the time frame. The first in the local cache of the main console A snapshot of the status of each verification task, in which... To set a preset deviation threshold, for time... The first time integration platform database Normalize the authority state snapshots of each verification task to generate a normalized authority state vector, and then normalize the snapshots at time points. The first in the local cache of the main console The state snapshots of each verification task are normalized to generate a normalized state vector; when For Level 2 correction: Push a full state snapshot to the integration platform database and overwrite the local cache on the main console; This should be classified as a Level 3 corrective action: suspend the [follow-up action]. The control command for each verification task is issued and triggers a manual confirmation alarm on the main console. The operation can only be restored after the operator manually verifies the information.

[0041] Preferably, this embodiment further includes: constructing a performance evaluation index system from four dimensions: resource utilization, data quality, scheduling efficiency, and state synchronization, and calculating the time through weighted summation. Overall score Identify performance bottlenecks and feed the evaluation results back to the corresponding execution steps to form a complete digital integration closed loop, specifically including: in, For the first Weights of each dimension For a moment Time Performance evaluation indicators in 10 dimensions (these are normalized values). This includes overall resource availability (representing resource utilization), fusion reliability score (representing data quality), and scheduling efficiency index. Represents the scheduling efficiency dimension and among all parallel verification tasks The percentage of verification tasks represents the state synchronization dimension, where, , The baseline scheduling cost (the scheduling cost when all verification tasks are assigned to the main console).

[0042] when (For example As a performance threshold, When the score is 0.65, the performance bottleneck is identified based on the score distribution of each dimension, triggering the following adaptive adjustment: 1. If the overall resource availability is the lowest among the four metrics, then rescan the status of all VRUs, update the overall resource availability, and also check the offline VRUs ( =0) Send hardware alarms, such as automatically migrating the verification task bound to a sub-control terminal to an available terminal when a terminal fails; 2. If the fusion credibility score is the lowest among the four indicators, the fusion weight of the abnormal data source will be automatically reduced. It also pushes data source degradation alarms to the main control console, such as automatically switching to simulation model data dominance when the bench sensor signal is interrupted. 3. Among the four indicators, if in all parallel verification tasks... If the percentage of verification tasks is high, it will trigger a shortening of the baseline synchronization period. (For example, reduce the synchronization frequency from 200 ms to 100 ms) to increase the synchronization frequency and reduce the accumulation of discrepancies between the state snapshots in the main console's local cache and the integration platform database. This can be done safely when the CCS private network bandwidth is sufficient.

[0043] Example 2 like Figure 2 As shown, this embodiment proposes a digitally integrated physical control console system for marine propulsion verification, used for simulation experiments, including: The display channel allocation module is used to calculate the comprehensive priority score of each verification task of the currently running marine power based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. The model setting module is used to build a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. The task-console matching planning model is minimized to find the corresponding optimal decision variables. The correction module is used to calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Based on the state deviation index, a three-level closed-loop correction mechanism is triggered to achieve digital integration of the physical control console.

[0044] Since the technical solution of Embodiment 2 is based on the technical solution of Embodiment 1, it will not be described again.

[0045] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0046] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0047] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0048] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0049] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0050] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A digital integration method for a physical control console for marine propulsion verification, used for simulation experiments, characterized in that, include: Step 101: For each verification task of the currently running marine power, calculate the comprehensive priority score of each verification task based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. Step 102: Construct a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. Minimize the task-console matching planning model and find the corresponding optimal decision variable. Step 103: Calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Trigger a three-level closed-loop correction mechanism based on the state deviation index to achieve digital integration of the physical control console.

2. The method for digital integration of a physical control console for marine power verification as described in claim 1, characterized in that, Before step 101, the process also includes: abstracting all hardware resources in the physical console into a virtual resource unit, and calculating the overall resource availability of the physical console based on the health status flags and available capacity ratio of each virtual resource unit. Based on the historical resource consumption data of each verification task, the resource requirement vector of each verification task is analyzed.

3. The method for digital integration of a physical control console for marine power verification as described in claim 2, characterized in that, Several constraints include: the first constraint is that each verification task corresponds to one and only one console; the second constraint is that the sum of the demand intensity of all verification tasks currently assigned to a certain console for a certain virtual resource unit does not exceed the ratio of the available capacity of the virtual resource units of that console; the third constraint is that each decision variable takes only one of two integer values, zero and one.

4. The method for digital integration of a physical control console for marine power verification as described in claim 1, characterized in that, The task-console matching planning model includes: in, For the total scheduling cost, To verify the task set, For console collection, To make the first The verification task was assigned to the first... The revised scheduling cost for each console, As decision variables, =1 indicates that the first... The verification task was assigned to the first... One console, otherwise 0. To make the first The verification task was assigned to the first... The basic scheduling cost of a console This is the credibility penalty strength coefficient. For a moment Time The verification task is in the first... The fusion credibility score under the data source of each console.

5. The method for digital integration of a physical control console for marine power verification as described in claim 4, characterized in that, Calculation time Time The verification task is in the first... Fusion credibility score under each console's data source include: in, This is the credibility sensitivity coefficient. For the first The fusion weight of the data source class For a moment Time The slippage anomaly rate of the data source.

6. The method for digital integration of a physical control console for marine power verification as described in claim 1, characterized in that, The calculation of the state deviation index includes: in, For a moment Time The state deviation index of a verification task. For a moment The first in the local cache of the main console The normalized state vector of each verification task. For a moment The first time integration platform database The normalized authority state vector for each verification task.

7. The method for digital integration of a physical control console for marine power verification as described in claim 6, characterized in that, The three-level closed-loop correction mechanism includes: when Level 1 Correction: Push Time The first time integration platform database A snapshot of the authoritative state of each verification task, covering the time frame. The first in the local cache of the main console A snapshot of the status of each verification task, in which... To set a preset deviation threshold, for time... The first time integration platform database Normalize the authority state snapshots of each verification task to generate a normalized authority state vector, and then normalize the snapshots at time points. The first in the local cache of the main console The state snapshots of each verification task are normalized to generate a normalized state vector; when For Level 2 correction: Push a full state snapshot to the integration platform database and overwrite the local cache on the main console; when Level 3 Correction: Suspend the first... The control command for each verification task is issued and triggers a manual confirmation alarm on the main console. The operation can only be restored after the operator manually verifies the information.

8. The method for digital integration of a physical control console for marine power verification as described in claim 1, characterized in that, Each verification task occupies at most one display channel, and each display channel can carry at most one verification task.

9. A digitally integrated physical control console system for marine propulsion verification, used for simulation experiments, characterized in that, include: The display channel allocation module is used to calculate the comprehensive priority score of each verification task of the currently running marine power based on the urgency index, normalized data update frequency and alarm level normalized value of each verification task, and allocate the display channel to each verification task in order of comprehensive priority score from high to low. The model setting module is used to build a task-console matching planning model and set multiple constraints to verify that the allocation relationship between tasks and consoles is a decision variable. The task-console matching planning model is minimized to find the corresponding optimal decision variables. The correction module is used to calculate the state deviation index based on the normalized state vector of the corresponding verification task in the local cache of the main console and the normalized authoritative state vector of the corresponding verification task in the integration platform database. Based on the state deviation index, a three-level closed-loop correction mechanism is triggered to achieve digital integration of the physical control console.

10. The method for digital integration of a physical control console for marine power verification as described in claim 9, characterized in that, Before the display channel allocation module, it also includes: abstracting all hardware resources in the physical control console into a virtual resource unit, and calculating the overall resource availability of the physical control console based on the health status flags and available capacity ratio of each virtual resource unit; Based on the historical resource consumption data of each verification task, the resource requirement vector of each verification task is analyzed.

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