A full-link high-availability operation method and system for marine power verification

Abstract

The application discloses a kind of full-link high-availability operation method and system for marine power verification, the method includes: the integrated trust score is calculated to connection request and access decision is executed;Release request is generated data stream by message analysis and standardization.The response delay, resource occupancy and error rate of each application server are collected, the health score is calculated and normalized as routing weight, and the data stream is distributed to the server with the highest weight.After application server processing, the management class data and simulation model file are mapped to the number of check shards with the key degree index using the sigmoid function, and are written into the object storage in a dispersed manner.Management class data is written into a relational database.Physical sampling data is written into a time series database after adaptive time series compression.Interface clients prefetch simulation model files from the object storage to the simulation server, providing simulation access for multiple users.

Description

Technical Field

[0001] This invention belongs to the field of marine power verification technology, and more specifically, relates to a high-availability operation method and system for the entire chain of marine power verification. Background Technology

[0002] Existing marine power verification platforms generally adopt the following technical solutions during construction: Nginx master-slave configuration and dual-machine load balancing are deployed at the access and application layers, with Keepalived protocol used for virtual IP migration and failover. Routing weights are manually statically configured, and failover triggering relies entirely on heartbeat detection polling. This results in the system being unable to detect health trends and preventatively migrate traffic when application server resources are exhausted and approaching collapse, only passively switching over after a failure occurs. During failover, the test task command channel is interrupted. At the storage layer, a fixed three-replica or fixed-parameter erasure coding scheme is uniformly used for all data objects, without distinguishing between real-time test sampling data and system daily data. The difference in importance of data leads to low-value data occupying the same redundant resources as high-value data, resulting in low storage utilization. At the protocol conversion layer, only byte-level format mapping is performed without verifying the semantics of physical quantities. This causes the test bench's host computer to report speed data in rad / s format after a firmware upgrade, but the platform still parses it in r / min, resulting in an error of about 60 times at the simulation model input. At the time-series data storage layer, a fixed Delta-Delta differential encoding is uniformly applied to all sampled sequences without considering the drastic differences between adjacent sampling points under sudden changes in test conditions. This leads to VarInt encoding length expansion and compression ratio degradation when the second-order differential amplitude increases sharply. Therefore, a technical solution is urgently needed to solve the above technical problems. Summary of the Invention

[0003] To address the above technical problems, this invention proposes a high-availability end-to-end operation method for marine power verification, comprising: For each connection request arriving at the integration platform, a comprehensive trust score is calculated, and the corresponding access decision is executed. For connection requests that are allowed by the access decision, the corresponding original message is parsed and normalized to generate a normalized data stream. Collect the average response latency, resource utilization, and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. After processing by the application server, the management data and simulation model files generated are distributed and written to object storage after the criticality index is mapped to the number of verification fragments using the sigmoid function. The management data is written to the relational database. At the same time, the acquired physical sampling data is adaptively compressed in time series and written to the time series database. After the interface client prefetches the simulation model file from the object storage to the simulation server, it provides simulation access for multiple users.

[0004] Furthermore, calculating the comprehensive trust score includes: obtaining multiple sub-scores from the connection request, including device identity compliance score, behavioral baseline deviation score, environmental security assessment score, and historical trust record score, and then calculating the comprehensive trust score. in, For connection request Overall trust score For the first Weighting of individual scores For connection request The Individual score; Access decisions include: granting and denying, when When the access score is greater than or equal to the preset trust score threshold, the access decision is to allow access. If the score is below the preset trust score threshold, the access decision is to deny access.

[0005] Furthermore, the corresponding original message is parsed and normalized to generate a normalized data stream, including: parsing the original message into an intermediate representation consisting of a semantic identifier, a numerical value, and a unit identifier as triplets; verifying the unit identifier against the dimensional rule table one by one; and performing automatic conversion if the unit does not conform to the standard. The dimensional normalized intermediate representation is then used as the normalized data stream.

[0006] Furthermore, distributing the standardized data stream to the application server with the highest current routing weight for processing also includes: whenever a faulty application server occurs, using the mean square value of the health score of the faulty application server at a certain time before the failure as the loss function, and automatically updating the routing weight through gradient descent, so that the subsequent routing weight can more accurately reflect the true health trend of the application server.

[0007] Furthermore, based on average response latency, resource utilization, and request error rate, a feature vector for health scoring is constructed, including: in, For time Time Feature vectors of application servers For time Time Average response latency of each application server The upper limit of response latency, For time Time Resource utilization of each application server For time Time Request error rate of each application server; The calculation of the health score for each application server includes: in, For time Time Health score of each application server For matrix transpose, For time Time The weights of the feature vectors of each application server; Normalized routing weights include: in, For time Time Routing weight of each application server For the first Health score of each application server For time A collection of application servers.

[0008] Furthermore, using the mean squared value of the health score of the failed application server at a certain time before failure as the loss function, the automatic updating of route weights through gradient descent includes: in, For time The loss function at that time For time A collection of application servers that occasionally malfunction. For the first The time before the application server failed Health score at the time, For time Time The weights of the feature vectors of each application server. For learning rate, For time Loss function at time right The partial derivative vector, The operator for Euclidean projection onto the probabilistic simplex; pass Calculation time Time Health score of application server , thus calculating This is to enable automatic updates of route weights.

[0009] Furthermore, the management data and simulation model files generated after application server processing are distributed and written to object storage after the criticality index is mapped to the number of verification fragments using the sigmoid function. in, To verify the number of fragments, As a criticality index, To verify the maximum number of shards, For the sigmoid function, For steepness parameter, The inflection point threshold, This is to verify the lower limit of the number of fragments.

[0010] Furthermore, the adaptive temporal compression of the acquired physical sampling data includes: performing second-order difference on the acquired physical sampling data, comparing the absolute value of the second-order difference result with a preset mutation threshold, and if the absolute value of the second-order difference result is greater than the preset mutation threshold, then the encoding mode is... The encoding mode is as follows: if the absolute value of the second-order difference result is less than or equal to a preset mutation threshold. model.

[0011] Furthermore, Everyday Economic News Each physical sampling data point, statistically analyzed Pattern usage , with step size Adaptive adjustment of mutation threshold : in, This is the lower limit of the utilization rate. This represents the upper limit of usage.

[0012] This invention also proposes a full-chain high-availability operating system for marine power verification, comprising: The normalization module is used to calculate a comprehensive trust score for each connection request that arrives at the integration platform, and execute the corresponding access decision. For connection requests that are allowed by the access decision, the corresponding original message is parsed and normalized to generate a normalized data stream. The distribution module is used to collect the average response latency, resource utilization and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. The writing module is used to write the management data and simulation model files generated after the application server is processed to the object storage after mapping the criticality index to the number of verification fragments using the sigmoid function, write the management data to the relational database, and simultaneously perform adaptive time-series compression on the acquired physical sampling data and write the compressed physical sampling data to the time-series database. The simulation module is used by the interface client to prefetch simulation model files from object storage to the simulation server, and then provide simulation access for multiple users.

[0013] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: This invention can identify access requests with legitimate IP addresses but abnormal behavior, reducing the false rejection rate of dynamic IP racks to near zero while retaining the ability to detect abnormal behavior; a gradient descent weight update mechanism driven by fault history enables the routing layer to complete preventative traffic migration before application server resources are exhausted, reducing application layer failover latency from seconds dependent on heartbeat polling to milliseconds; an erasure coding mechanism that dynamically maps the number of verification fragments using a sigmoid function based on data criticality index ensures that storage redundancy matches data importance; and a time-series compression mechanism that uses second-order differential amplitude to drive dynamic switching of coding modes maintains a compression rate of 30% to 40% during abrupt changes in experimental conditions, avoiding the problem of fixed Delta-Delta coding's compression efficiency degrading to near zero during abrupt changes. Attached Figure Description

[0014] 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 schematic diagram of the physical control panel of the present invention. Detailed Implementation

[0015] 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.

[0016] 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 3 The image shown is a rendering of the physical control panel.

[0017] The integrated platform is deployed on a host cluster within a rack, including an application server (Inspur NF5280M6), a one-dimensional simulation solver workstation (Lenovo ThinkServer SR660 V2), and a three-dimensional simulation solver workstation (Superior ServMAXTS40-X3). It supports the runtime environment of 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 data back to the physical console for display.

[0018] The benchtop agent is a software program deployed on the host computer of the physical bench, which acts as a communication intermediary layer between the physical bench and the integration platform.

[0019] 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 full-link high-availability operation method for marine power verification, including: Step S1: For each connection request arriving at the integration platform, calculate the comprehensive trust score and execute the corresponding access decision. For connection requests that are allowed by the access decision, parse the corresponding original message and perform normalization processing to generate a normalized data stream. Preferably, two Nginx servers are deployed at the access layer in a master-slave configuration to achieve virtual IP failover. Specifically, the two Nginx servers maintain the virtual IP using the Keepalived protocol with a configurable heartbeat detection period. and maximum number of unresponsive events Monitor the master node's liveness; if it times out, the VIP will migrate to the slave node to ensure the access point remains available.

[0020] Specifically, calculating the comprehensive trust score includes: obtaining multiple sub-scores from the connection request, including device identity compliance score, behavioral baseline deviation score, environmental security assessment score, and historical trust record score, and then calculating the comprehensive trust score. in, For connection request Overall trust score For the first Weighting of individual scores For connection request The Individual score; Access decisions include: granting and denying, when When the access score is greater than or equal to the preset trust score threshold, the access decision is to allow access. If the score is below the preset trust score threshold, the access decision is to deny access.

[0021] Preferably, this implementation takes a single MQTT connection request from the host computer of the methanol engine test bench at Shanghai Maritime University as an example to illustrate the specific acquisition process of each sub-score dimension by dimension: Device identity compliance score: Acquisition Process: When the benchtop host computer initiates an MQTT CONNECT message, it carries the SHA-256 fingerprint of the device certificate issued by the integration platform's CA in the Password field. The interface server performs three checks: Extract the device serial number from the certificate, query the registered device table, and confirm that the device has been pre-registered (if the match is successful, the base score is 0.60). The PCR (Platform Configuration Register) metric of TPM 2.0 is compared with the baseline value at the time of device registration. A perfect match adds 0.30 points. Check the certificate validity period. 0.10 points are awarded if the certificate is more than 90 days from expiry, 0.05 points are awarded if it is between 30 and 90 days from expiry, and 0 points are awarded if it is less than 30 days from expiry.

[0022] Example: The device is registered, the TPM is consistent, and the certificate expires in 180 days. However, in actual engineering, due to the influence of floating-point precision and the calibration of various inspection weights, it is usually taken as... .

[0023] Behavioral baseline deviation score: The acquisition process consists of three steps: The first step is to construct the current session behavior feature vector. Using a statistical granularity of 60 seconds, three behavioral metrics are collected every minute within the first 5 minutes of the current session and concatenated into a 15-dimensional original vector. The 15-dimensional original vector is obtained after L2 normalization. ,satisfy .

[0024] The second step is to read the historical behavior baseline vector of the device. The baseline vector is obtained by retrieving the average behavior statistics of similar sessions over the past 30 days for this device (unique identifier by serial number) from Redis and processing them using the same dimension and normalization method. Similarly satisfied The historical baseline corresponds to a behavior pattern of "sampling rate of 50 Hz, approximately 200 bytes per record, and approximately 3,000 pushes per minute during normal testing".

[0025] The third step is to calculate the cosine similarity and map it to sub-scores: The current session's rate of 49 messages / second is highly similar to the baseline's 50 messages / second, with a cosine similarity of 0.97. .

[0026] Environmental safety assessment score: Acquisition process: The interface server queries three aspects: the source IP of the TCP connection, the system clock, and the IP geolocation database. Access network segment: If the source IP belongs to the Internet (not the CCS private network), assign a base score of 0.60; if it belongs to the CCS private network, assign 0.90; if it belongs to an unknown network segment, assign 0.20. Access time period compliance: Check the preset allowed access time period for this rack (weekdays 07:00-22:00). The current time is Tuesday 14:30. Within the allowed time period, add +0.20 points; Geographic location rationality: The IP address resolves to Shanghai, which matches the device's registration location (Shanghai Maritime University), adding +0.15 points; if it resolves to an overseas IP address, adding 0 points. This example: .

[0027] Historical credibility score: Acquisition process: Query the authentication log table of the relational database for the device over the past 30 days (time window). Records: Total number of authentications: 1002 Number of successful authentications: 1001; Authentication success rate: Number of security incidents: 1 (an authentication failure alert 30 days ago, verified as network jitter), Normalized Security Incident Rate Substitute into the formula: .

[0028] Specifically, the corresponding raw messages are parsed and normalized to generate a normalized data stream. This includes parsing the raw messages (MQTT, MODBUS, WebSocket, HTTP, etc.) into an intermediate representation consisting of a semantic identifier, a numerical value, and a unit identifier as triplets; verifying the unit identifier against the dimensional rule table; and performing automatic conversion if the unit does not conform to the standard (e.g., converting engine speed from r / min to rad / s). Dimensional verification: The product dimension is (Correct) The intermediate representation of the dimensionally normalized data is used as the normalized data stream.

[0029] Step S2: Collect the average response latency, resource utilization and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. Preferably, the application server deploys a Spring Boot microservice application, which is the backend program that actually responds to requests when users access it through a browser. All business logic for web-based operations—creating experimental tasks, configuring verification scenarios, viewing experimental progress, and generating evaluation reports—is executed on the application server.

[0030] Specifically, distributing the standardized data stream to the application server with the highest current routing weight for processing also includes: whenever a faulty application server occurs, using the mean square value of the health score of the faulty application server at a certain time before the failure as the loss function, and automatically updating the routing weight through gradient descent, so that the subsequent routing weight can more accurately reflect the true health trend of the application server.

[0031] Specifically, a feature vector for health scoring is constructed based on average response latency, resource utilization, and request error rate, including: in, For time Time Feature vectors of application servers For time Time Average response latency of each application server The upper limit of response latency, For time Time Resource utilization of each application server For time Time Request error rate of each application server; The calculation of the health score for each application server includes: in, For time Time Health score of each application server For matrix transpose, For time Time The weights of the feature vectors of each application server; Normalized routing weights include: in, For time Time Routing weight of each application server For the first Health score of each application server For time A collection of application servers.

[0032] Specifically, using the mean squared value of the health score of the failed application server at a certain time before failure as the loss function, the routing weights are automatically updated through gradient descent, including: in, For time The loss function at that time For time A collection of application servers that occasionally malfunction. For the first The time before the application server failed Health score at the time, For time Time The weights of the feature vectors of each application server. For learning rate, For time Loss function at time right The partial derivative vector, The operator for Euclidean projection onto the probabilistic simplex; pass Calculation time Time Health score of application server , thus calculating This is to enable automatic updates of route weights.

[0033] Step S3: After processing by the application server, the management data and simulation model files are mapped to the number of verification fragments using the sigmoid function and then written to the object storage (MinIO object storage master-slave node). The management data is written to the relational database. At the same time, the acquired physical sampling data is adaptively compressed in time series and written to the time series database. Specifically, the management data and simulation model files generated after application server processing are distributed and written to object storage after the criticality index is mapped to the number of verification fragments using the sigmoid function. in, To verify the number of fragments, As a criticality index, To verify the maximum number of shards, For the sigmoid function, For steepness parameter, The inflection point threshold, To verify the lower limit of the number of data shards, the number of data shards is used. After splitting the management data and simulation model files equally, the following generation process is performed. There are 10 verification fragments, totaling 1000 fragments. Each shard is distributed and stored across the MinIO nodes.

[0034] Preferably, in this embodiment, the criticality index It is assigned by the system based on the attribute rule table of the data object, and is essentially a category label mapping. The acquisition process consists of three steps: Step 1: Extract the attributes of the data object Each data object written to MinIO carries three meta-attributes when it is generated: Data type tag (type_tag): Attached by the business module that generates the data when writing it, such as TRIAL_REALTIME_SAMPLE, EVAL_REPORT, MODEL_FILE, etc. Scene tag: The type of test currently being performed, such as ENGINE_HIL (engine hardware in the loop) or FUEL_CELL_BENCH (hydrogen fuel cell test bench). Data lifecycle tag: Whether there is an alternative version of this data object, such as LATEST (current latest version, no alternative) or SUPERSEDED (has been replaced by a new version).

[0035] Step 2: Check the keyness rule table The system maintains a predefined criticality rule table, and searches for the corresponding basic criticality by data type label. Then, adjust the amount according to the correction rules. Finally obtained : Correction rules (overlaid on) Above): Step 3: Two concrete examples Example 1: Real-time sampling data packets from an engine test bench In the hardware-in-the-loop test of the methanol engine bench at Shanghai Maritime University, sampled data (speed, power, methanol consumption rate, NOx emissions, 50 Hz × 12 channels × 300 s = 180,000 records) was packaged every 5 minutes. The attributes written to MinIO were as follows: type_tag = TRIAL_REALTIME_SAMPLE, ; scene_tag = ENGINE_HIL (physical test bench, data cannot be reproduced). =+0.05; lifecycle_tag = LATEST, =+0.10; ; Mapping results: RS(6,3) encoding is used to store the amplification factor. It can tolerate the simultaneous failure of any three nodes.

[0036] Example 2: DC power distribution system simulation model file After simulation expert A completed the editing of the DC power distribution system model in Syslink, the model package was written to MinIO with the following attributes: type_tag = MODEL_FILE_SYSLINK, ; scene_tag = DC_POWER_SIM (pure simulation, model file can be reconstructed). =-0.05; lifecycle_tag = LATEST (Expert A just completed the modification, there is no alternative version yet). ; ; Mapping results: , It uses RS(6,2) encoding. .

[0037] Specifically, adaptive temporal compression of the acquired physical sampling data includes: performing second-order differencing on the acquired physical sampling data, comparing the absolute value of the second-order differencing result with a preset mutation threshold, and if the absolute value of the second-order differencing result is greater than the preset mutation threshold, then the encoding mode is... The encoding mode is as follows: if the absolute value of the second-order difference result is less than or equal to a preset mutation threshold. model.

[0038] Specifically, every day Each physical sampling data point, statistically analyzed Pattern usage , with step size Adaptive adjustment of mutation threshold : in, This is the lower limit of the utilization rate. This represents the upper limit of usage.

[0039] In step S4, after the interface client prefetches the simulation model file from the object storage to the simulation server, it provides simulation access for multiple users.

[0040] Preferably, seamless access to simulation software such as MWorks.Sysplorer, COMSOL, and FLACS is provided to web users through the Apache Guacamole remote desktop service.

[0041] Example 2 like Figure 2 As shown, this embodiment proposes a full-link high-availability operating system for marine power verification, including: The normalization module is used to calculate a comprehensive trust score for each connection request that arrives at the integration platform, and execute the corresponding access decision. For connection requests that are allowed by the access decision, the corresponding original message is parsed and normalized to generate a normalized data stream. The distribution module is used to collect the average response latency, resource utilization and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. The writing module is used to write the management data and simulation model files generated after the application server is processed to the object storage after mapping the criticality index to the number of verification fragments using the sigmoid function, write the management data to the relational database, and simultaneously perform adaptive time-series compression on the acquired physical sampling data and write the compressed physical sampling data to the time-series database. The simulation module is used by the interface client to prefetch simulation model files from object storage to the simulation server, and then provide simulation access for multiple users.

[0042] Since the technical solution of Embodiment 2 is based on the technical solution of Embodiment 1, it will not be described again.

[0043] 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.

[0044] 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.

Claims

1. A high-availability operation method for the entire chain of marine power verification, characterized in that, include: For each connection request arriving at the integration platform, a comprehensive trust score is calculated, and the corresponding access decision is executed. For connection requests that are allowed by the access decision, the corresponding original message is parsed and normalized to generate a normalized data stream. Collect the average response latency, resource utilization, and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. After processing by the application server, the management data and simulation model files generated are distributed and written to object storage after the criticality index is mapped to the number of verification fragments using the sigmoid function. The management data is written to the relational database. At the same time, the acquired physical sampling data is adaptively compressed in time series and written to the time series database. After the interface client prefetches the simulation model file from the object storage to the simulation server, it provides simulation access for multiple users.

2. The end-to-end high-availability operation method for marine power verification as described in claim 1, characterized in that, The calculation of the comprehensive trust score includes: obtaining multiple sub-scores for the connection request, including device identity compliance score, behavioral baseline deviation score, environmental security assessment score, and historical trust record score, and then calculating the comprehensive trust score. in, For connection request Overall trust score For the first Weighting of individual scores For connection request The Individual score; Access decisions include: granting and denying, when When the access score is greater than or equal to the preset trust score threshold, the access decision is to allow access. If the score is below the preset trust score threshold, the access decision is to deny access.

3. The end-to-end high-availability operation method for marine power verification as described in claim 1, characterized in that, The corresponding original message is parsed and normalized to generate a normalized data stream. This includes parsing the original message into an intermediate representation consisting of a semantic identifier, a numerical value, and a unit identifier as triplets, verifying the unit identifier against the dimensional rule table one by one, and performing automatic conversion if the unit does not conform to the standard. The dimensional normalized intermediate representation is then used as the normalized data stream.

4. The end-to-end high-availability operation method for marine power verification as described in claim 1, characterized in that, Distributing the standardized data stream to the application server with the highest current routing weight for processing also includes: whenever a faulty application server occurs, using the mean square value of the health score of the faulty application server at a certain time before the failure as the loss function, and automatically updating the routing weight through gradient descent, so that the subsequent routing weight can more accurately reflect the true health trend of the application server.

5. The end-to-end high-availability operation method for marine power verification as described in claim 4, characterized in that, Based on average response latency, resource utilization, and request error rate, a feature vector for health scoring is constructed, including: in, For time Time Feature vectors of application servers For time Time Average response latency of each application server The upper limit of response latency, For time Time Resource utilization of each application server For time Time Request error rate of each application server; The calculation of the health score for each application server includes: in, For time Time Health score of each application server For matrix transpose, For time Time The weights of the feature vectors of each application server; Normalized routing weights include: in, For time Time Routing weight of each application server For the first Health score of each application server For time A collection of application servers.

6. The end-to-end high-availability operation method for marine power verification as described in claim 5, characterized in that, Using the mean squared value of the health score of the failed application server at a certain point before failure as the loss function, the automatic updating of route weights through gradient descent includes: in, For time The loss function at that time For time A collection of application servers that occasionally malfunction. For the first The time before the application server failed Health score at the time, For time Time The weights of the feature vectors of each application server. For learning rate, For time Loss function at time right The partial derivative vector, The operator for Euclidean projection onto the probabilistic simplex; pass Calculation time Time Health score of each application server , thus calculating This is to enable automatic updates of route weights.

7. The end-to-end high-availability operation method for marine power verification as described in claim 1, characterized in that, After processing by the application server, the management data and simulation model files are mapped to the number of verification fragments using the sigmoid function and then distributed and written to object storage, including: in, To verify the number of fragments, As a criticality index, To verify the maximum number of shards, For the sigmoid function, For steepness parameter, The inflection point threshold, This is to verify the lower limit of the number of fragments.

8. The end-to-end high-availability operation method for marine power verification as described in claim 1, characterized in that, Adaptive temporal compression of the acquired physical sampling data includes: performing second-order differencing on the acquired physical sampling data, comparing the absolute value of the second-order differencing result with a preset mutation threshold, and if the absolute value of the second-order differencing result is greater than the preset mutation threshold, then the encoding mode is... The encoding mode is as follows: if the absolute value of the second-order difference result is less than or equal to a preset mutation threshold. model.

9. A high-availability operation method for the entire chain of marine power verification as described in claim 8, characterized in that, Every Jing Each physical sampling data point, statistically analyzed Pattern usage , with step size Adaptive adjustment of mutation threshold : in, This is the lower limit of the utilization rate. This represents the upper limit of usage.

10. A fully-linked high-availability operating system for marine power verification, characterized in that, include: The normalization module is used to calculate a comprehensive trust score for each connection request that arrives at the integration platform, and execute the corresponding access decision. For connection requests that are allowed by the access decision, the corresponding original message is parsed and normalized to generate a normalized data stream. The distribution module is used to collect the average response latency, resource utilization and request error rate of each application server, calculate the health score of each application server and normalize it into a routing weight, and distribute the normalized data stream to the application server with the highest current routing weight for processing. The writing module is used to write the management data and simulation model files generated after the application server is processed to the object storage after mapping the criticality index to the number of verification fragments using the sigmoid function, write the management data to the relational database, and simultaneously perform adaptive time-series compression on the acquired physical sampling data and write the compressed physical sampling data to the time-series database. The simulation module is used by the interface client to prefetch simulation model files from object storage to the simulation server, and then provide simulation access for multiple users.

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