Trusted controller dynamic measurement optimization method, system and device and storage medium

Abstract

The invention discloses a credible controller dynamic measurement optimization method, system and device and a storage medium. The method comprises the steps that dynamic measurement fragments are graded and sorted according to evaluation indexes; initial setting of a dynamic measurement priority strategy is carried out based on score sorting; adjusting the score sorting of the dynamic measurement fragments based on logic switching and IO parameter exception; optimizing a dynamic measurement priority strategy based on the adjusted score ranking; and based on a dynamic measurement priority strategy, in combination with dynamic measurement frequency period setting, performing period credible dynamic measurement. According to the invention, through dual mechanisms of initial setting of the dynamic measurement priority strategy and elastic adjustment of the dynamic measurement priority strategy, the problems of efficiency waste and insufficient real-time performance caused by static resource allocation in a traditional dynamic measurement method are solved, and collaborative optimization of dynamic measurement resource utilization and real-time performance requirements is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of trusted computing, in particular to a trusted controller dynamic measurement optimization method, system, device and storage medium. BACKGROUND

[0002] Under the background of rapid development of current information technology, dynamic measurement technology plays a crucial role in ensuring system security and data reliability. However, the existing dynamic measurement technology faces many challenges in actual application, and it is difficult to meet the requirements of efficient resource utilization and strict real-time performance at the same time.

[0003] On the one hand, the traditional dynamic measurement slice priority setting method is relatively single, which is often set according to a single factor or fixed rule, without fully considering the differences in tampering influence degree and business data importance involved in different dynamic measurement slices. This leads to the fact that during the operation of the distributed control system, for dynamic measurement slices with large tampering influence and important business data, it is difficult to obtain sufficient measurement resources in time, and there is a risk of missing the timely detection and processing of security threats or data anomalies. For relatively secondary dynamic measurement slices, too many measurement resources may be allocated, resulting in waste of resources and reducing the overall utilization efficiency of resources.

[0004] On the other hand, during the operation of the distributed control system, factors such as logic switching and IO parameter abnormality are in dynamic change, and the existing technology lacks a mechanism for real-time adjustment of dynamic measurement slice priority according to dynamic changes. When the distributed control system performs logic switching, if the inherent measurement priority is still used, the measurement task of critical business may be delayed due to insufficient resources, thereby affecting the real-time performance of the distributed control system. At the same time, when the IO parameter is abnormal, it is impossible to reasonably adjust the measurement strategy based on the IO parameter abnormality, which will also lead to the inefficiency or failure of the measurement process.

[0005] In summary, how to scientifically and reasonably set the priority of dynamic measurement slices based on dynamic measurement slice control, considering multiple factors, and how to real-time adjust the priority strategy according to the dynamic changes in the operation of the distributed control system, in order to effectively solve the contradiction between dynamic measurement resource utilization and real-time performance requirements, has become a technical problem to be solved. SUMMARY

[0006] In order to solve the above problems, the present application proposes a trusted controller dynamic measurement optimization method, system, device and storage medium which can improve the utilization efficiency of dynamic measurement resources.

[0007] In order to achieve the above purpose, the present application is realized by the following technical scheme:

[0008] The trusted controller dynamic measurement optimization method of the application comprises:

[0009] The dynamic measurement fragments are scored and ranked according to evaluation indexes.

[0010] The dynamic measurement priority strategy is initially set based on the score ranking.

[0011] The score ranking of the dynamic measurement fragments is adjusted based on logical switching and IO parameter abnormalities.

[0012] The dynamic measurement priority strategy is optimized based on the adjusted score ranking.

[0013] The periodic trusted dynamic measurement is performed based on the dynamic measurement priority strategy and the dynamic measurement frequency cycle setting.

[0014] The application is further improved in that the dynamic measurement fragments are scored and ranked according to evaluation indexes, and the scoring and ranking specifically comprises:

[0015] The dynamic measurement fragments are divided into ordinary and critical levels according to tampering influence degree and business data importance; for the ordinary dynamic measurement fragments, a score threshold is set, and the score value of the corresponding dynamic measurement fragment is calculated based on the calling frequency, and the calculation expression is:

[0016] S=T0+Ki(T1-T0)

[0017] wherein T0 is the low value of the score threshold set for the ordinary level, generally taking a value of 0, T1 is the high value of the score threshold set for the ordinary level, Ki is the relative value of the calling frequency of the dynamic measurement fragment of the ordinary level, and the value range is 0 to 1.

[0018] For the critical dynamic measurement fragments, a score threshold is set, and the score value of the corresponding dynamic measurement fragment is calculated based on the influence degree of the critical dynamic measurement fragments on the business.

[0019] The application is further improved in that the critical dynamic measurement fragments are process code segments, the process code segments are divided based on the influence degree on the business, and the weight value of the process code segment is set, wherein the divided process code segments include auxiliary algorithms, critical algorithms and core algorithms.

[0020] The application is further improved in that the calculation process of the score value of the critical dynamic measurement fragments comprises:

[0021] For the influence degree of the jth dynamic measurement fragment of the critical level on the business, i.e. the assignment expression of the weight value Wj is:

[0022] Wj=W0+Uj(W1-W0)

[0023] Wherein, W0 is the weight threshold low value of the algorithm type to which the process code segment of the critical level belongs, W1 is the weight threshold high value of the algorithm type to which the process code segment of the critical level belongs, Uj is the relative value of the dynamic metric slice call frequency of the algorithm type to which the process code segment of the critical level belongs, and the value range is 0 to 1;

[0024] The calculation expression of the score value of the jth dynamic metric slice of the critical level is:

[0025] S = T1 + Wj (T2 - T1)

[0026] Wherein, T2 is the score threshold high value set by the critical level, and generally takes the value of 100.

[0027] Further improvement of the application lies in that the score ranking of the dynamic metric slice is adjusted based on logical switching, and specifically includes:

[0028] Based on logical switching, the degree of influence of the dynamic metric slice on the service, that is, the weight value Wj, is adjusted, and the expression is:

[0029] Wj" = Wj' (1 + k * DeltaV * Wj')

[0030] Wherein: Wj' is the weight value before the dynamic metric slice is adjusted, Wj" is the weight value after the dynamic metric slice is adjusted, Wj" > 1, taking 1, Wj" < 0, taking 0, k is a variable value, being 1 when the priority is promoted and being -1 when the priority is restored, and DeltaV is a weight value adjustment parameter when logical switching;

[0031] The score value of the dynamic metric slice is calculated based on the weight value Wj" after the dynamic metric slice is adjusted.

[0032] Further improvement of the application lies in that the score ranking of the dynamic metric slice is adjusted based on IO parameter abnormality, and specifically includes:

[0033] Based on the IO parameter abnormality, the weight value of the corresponding dynamic metric slice is adjusted, and the weight value is set to the maximum value, that is, Wj" = 1.

[0034] After the IO parameter returns to normal, the weight value of the dynamic metric slice is adjusted to the initial value, that is, Wj" = Wj.

[0035] Further improvement of the application lies in that the periodic trusted dynamic metric is performed based on the dynamic metric priority strategy and in combination with the dynamic metric frequency period setting, and includes:

[0036] When the dynamic metric priority promotion causes the controller CPU load to exceed the preset limit value, the dynamic metric frequency period is increased.

[0037] When the controller CPU load is lower than a preset limit value, the initial dynamic metric frequency period is resumed;

[0038] The trusted controller performs periodic measurement according to the dynamic metric priority strategy and the dynamic metric frequency period; wherein the dynamic metric frequency period is divided into a low-frequency measurement period, a medium-frequency measurement period and a highest-frequency measurement period.

[0039] The trusted controller dynamic metric optimization system of the application comprises:

[0040] The scoring and sorting module is configured to score and sort the dynamic metric fragments according to the evaluation indexes.

[0041] The initial setting module is configured to initially set the dynamic metric priority strategy based on the scoring and sorting.

[0042] The score adjustment module is configured to adjust the scoring and sorting of the dynamic metric fragments based on the logical switching and the IO parameter abnormality.

[0043] The strategy adjustment module is configured to optimize the dynamic metric priority strategy based on the adjusted scoring and sorting.

[0044] The periodic measurement module is configured to perform periodic trusted dynamic measurement based on the dynamic metric priority strategy and the dynamic metric frequency period setting.

[0045] The computer device of the application comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the trusted controller dynamic metric optimization method when executing the program.

[0046] The computer readable storage medium of the application stores a computer program, and the computer program implements the steps of the trusted controller dynamic metric optimization method when executed by the processor.

[0047] The application has the following beneficial effects: by scoring and sorting the dynamic metric fragments according to the tampering influence degree and the importance of business data, and completing the initial setting of the dynamic metric priority strategy, the key dynamic metric fragments can be accurately identified. The dynamic metric fragments with large tampering influence and important business data are given high priority, and the measurement resources are preferentially allocated, so as to ensure that the data related to system security and core business are timely and sufficiently measured, and the detection and processing of security threats or data abnormalities due to improper resource allocation are effectively avoided. For secondary dynamic metric fragments, less resources are reasonably allocated to reduce resource waste, so that the distributed control system resources can be concentrated in the truly needed links, thereby significantly improving the overall utilization efficiency of dynamic metric resources.

[0048] Based on the logic switching, the IO parameter abnormality carries out the dynamic metric fragmentation priority adjustment, gives the dynamic adaptation ability to the distributed control system. When the distributed control system carries out the logic switching, can according to the logic switching fast identification resource shortage situation, timely adjusts the dynamic metric fragmentation priority, priority guarantee key business metric task's execution, avoids the task delay due to resource deficiency, ensures CPU under high load still can maintain the real-time performance of key business. When IO parameter is abnormal, based on the analysis of parameter influence, adjusts the metric strategy and fragmentation priority reasonably, makes the metric process can closely adhere to the change of system running state, avoids the inefficiency or failure of the metric process, further strengthens the real-time response capability of distributed control system, guarantees the stable and efficient operation of distributed control system. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is the method flow chart in the embodiment of the application;

[0050] Figure 2 It is the dynamic metric fragmentation priority strategy initial generation flow in the embodiment of the application;

[0051] Figure 3 It is the dynamic metric fragmentation score table adjustment rule schematic diagram in the embodiment of the application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. In addition, the technical features involved in each embodiment of the application described below can be combined with each other as long as they do not conflict with each other.

[0053] As shown in Figure 1 The dynamic metric optimization method of the trusted controller of the embodiment takes the periodic dynamic metric as the core closed loop, and through the cycle mechanism of "strategy initial setting→dynamic adjustment→execution strategy", adapts to the running state change of the distributed control system, and guarantees the efficient use and real-time performance of the metric resource. Specifically includes:

[0054] Step 1, score and sort the dynamic metric fragmentation according to the tampering influence degree and the importance of business data, to obtain the dynamic metric fragmentation score table. The dynamic metric fragmentation score table in the embodiment is used as the basis for the initial setting of the dynamic metric priority strategy, and the tampering influence degree and the importance of business data of the dynamic metric fragmentation are quantitatively scored, to provide the basis for the division of the dynamic metric frequency period, i.e. the metric period.

[0055] Step 2, initial setting of dynamic metric priority strategy based on score ranking. In this embodiment, based on the dynamic metric score table, the dynamic metric fragments are assigned to different metric periods, the initial setting of the dynamic metric priority strategy is completed, the initial dynamic metric priority strategy is formed, and the key dynamic metric fragments, i.e. the dynamic metric fragments with high scores, are prioritized for measurement. The secondary dynamic metric fragments, i.e. the dynamic metric fragments with low scores, are moderately reduced in measurement resource occupation.

[0056] Step 3, adjust the score ranking of the dynamic metric fragments based on logical switching and IO parameter abnormalities. In this embodiment, logical switching and IO parameter abnormalities are used as the trigger conditions for dynamic adjustment, and the running state of the distributed control system is monitored in real time to adjust the dynamic metric score table. When the trigger condition is reached, the dynamic metric score table is triggered for adjustment to achieve flexible adaptation of the dynamic metric priority strategy. For example, it includes:

[0057] When logical switching is performed, the non-critical dynamic metric fragments, i.e. the secondary dynamic metric fragments, are compressed in the measurement period to protect the critical dynamic metric fragment resources.

[0058] When the IO parameter is abnormal, the measurement priority of the related dynamic metric fragments is temporarily increased, i.e. the weight value of the related dynamic metric fragments is adjusted.

[0059] Step 4, optimize the dynamic metric priority strategy based on the adjusted score ranking.

[0060] Step 5, based on the dynamic metric priority strategy, combined with the dynamic metric frequency period setting, perform periodic trusted dynamic metric. In this embodiment, the optimized dynamic metric priority strategy guides the actual periodic trusted dynamic metric process to ensure that the measurement resources are accurately delivered to the key dynamic metric fragments of the current distributed control system, balancing resource utilization efficiency and real-time requirements. Through the closed loop of "monitoring→adjustment→execution", the safety and reliability of the distributed control system are continuously ensured.

[0061] In this embodiment, the kernel / process data to be measured is divided into fragments by type. When the data volume in the same type is large and occupies a long measurement time, the data in the same type is further divided into fragments to ensure that the data volume of a single dynamic metric fragment does not exceed the limit.

[0062] As shown in FIG. 1, in step 1, the dynamic metric fragments are divided into ordinary and critical levels according to the tampering influence degree and the importance of business data. Figure 2

[0063] Ordinary level:

[0064] ​The data segment with low tampering probability and small influence, such as the read-only data segment, has a low score, and a score threshold is set. In this embodiment, the score threshold is 0 to 29. The score of the dynamic metric slice is linearly adjusted according to the calling frequency.

[0065] The score value of the i-th dynamic metric slice is S, and the calculation expression of the score value of the ordinary-level dynamic metric slice is:

[0066] S = T0 + Ki(T1 - T0)

[0067] Wherein, T0 is the low value of the score threshold set for the ordinary level, generally taking a value of 0, T1 is the high value of the score threshold set for the ordinary level, Ki is the relative value of the calling frequency of the dynamic metric slice of the ordinary level, taking a value in the range of 0 to 1, the highest calling frequency taking a value of 1, and the lowest calling frequency taking a value of 0.

[0068] Key level:

[0069] The data segment with high tampering rate and large influence, the dynamic metric slice of the key level in this embodiment is the process code segment, has a high score, and a score threshold is set. In this embodiment, the score threshold is 30 to 100. The score value of the corresponding dynamic metric slice is calculated based on the influence degree of the key-level dynamic metric slice on the business, that is, the score value of the corresponding dynamic metric slice is calculated according to the importance of the process code segment.

[0070] In this embodiment, the key-level dynamic metric slices with different influence degrees on the business are sorted out according to the business logic in the distributed control system, that is, the link from collection to execution, that is, the process code segment is divided, and the weight value of the process code segment is set. This embodiment takes the distributed control system of a thermal power plant as an example for division:

[0071] Auxiliary algorithm: auxiliary system control algorithm module, auxiliary code for optimizing the operation of the core control algorithm, such as parameter compensation, debugging information, and other functional modules. A weight threshold is set, and in this embodiment, the weight threshold is set in the range of 0 to 0.39.

[0072] Key algorithm: code for monitoring the running state of the equipment, and triggering the safety protection mechanism in time when an abnormal condition occurs, such as over-temperature, over-pressure, and over-speed, such as emergency shutdown and alarm. A weight threshold is set, and in this embodiment, the weight threshold is set in the range of 0.4 to 0.79.

[0073] Core algorithm: code containing key control logic such as boiler combustion control algorithm and steam turbine speed regulation algorithm. A weight threshold is set, and in this embodiment, the weight threshold is set in the range of 0.8 to 1.

[0074] The influence degree of the j-th dynamic metric slice of the key level on the business, that is, the weight value Wj, is assigned as follows:

[0075] Wj = W0 + Uj(W1 - W0)

[0076] Where W0 is the low weight threshold of the algorithm type to which the critical process code segment belongs, W1 is the high weight threshold of the algorithm type to which the critical process code segment belongs, and Uj is the relative value of the dynamic measurement slice call frequency of the algorithm type to which the critical process code segment belongs, with a value range of 0 to 1.

[0077] The expression for calculating the score of the j-th dynamic metric segment at the critical level is:

[0078] S = T1 + Wj(100 - T1)

[0079] T1 is the high value of the scoring threshold set for the ordinary level.

[0080] In this embodiment, the dynamic measurement of the impact of data sharding on operations, i.e., the weight value Wj, is adjusted based on the actual operating conditions of the units in the distributed control system of the thermal power plant. During unit operation, due to factors such as equipment aging, equipment upgrades, maintenance adjustments, operating condition adjustments, and control strategy optimization, the initial weight value Wj may deviate from the actual operating requirements. Therefore, it is necessary to periodically correct the weight value Wj based on the actual unit monitoring process. The correction cycle can be determined according to the actual operating conditions of the unit. It can be corrected immediately after equipment upgrades or maintenance adjustments. For situations involving equipment aging, operating condition adjustments, or control strategy optimization, it can be set to be corrected daily, weekly, or monthly. Specifically:

[0081] 1) Modify trigger conditions

[0082] Timed triggering: When the preset cycle (such as daily, weekly, or monthly cycle) arrives, the adjustment relationship correction process is initiated to avoid the high load period of the unit and the frequent execution of critical business, thereby reducing interference with the real-time operation of the distributed control system of the thermal power plant.

[0083] 2) Correct data source

[0084] Collect historical operational data from the unit monitoring system, analyze the actual execution of business logic involved in dynamic measurement sharding under various application scenarios, such as equipment start-up and shutdown.

[0085] 3) Revise the logic and process

[0086] Based on historical data and following the dynamic measurement priority initial strategy generation process, the usage of key data segments of the unit is analyzed, including equipment aging, operating condition adjustment, and control strategy optimization. The initial weight value Wj of each dynamic measurement segment is then reset under different load scenarios.

[0087] like Figure 3 As shown, when a logical switch is performed or an IO parameter anomaly occurs, the dynamic metric sharding score table is adjusted.

[0088] Logic switching:

[0089] Business logic is divided according to business process. Taking a thermal power plant as an example, the entire process from the preparation stage before unit startup, the startup stage (from cold state to grid-connected power generation), the load increase stage (from initial load to rated load), the stable operation stage (rated load or variable load operation), the load reduction stage (from rated load to split), the shutdown stage (from split to complete shutdown), and the maintenance stage after shutdown (full life cycle extension) is covered.

[0090] Traverse all dynamic metric fragments of business logic in the distributed control system of the thermal power plant, such as boiler combustion control algorithm code segments, safety protection logic code segments, etc. For each dynamic metric fragment, analyze the business logic it belongs to. When the corresponding business logic is executed, the priority of the dynamic metric fragment is promoted, i.e. the weight value Wj of the dynamic metric fragment is promoted. When the execution of the corresponding business logic is completed, the weight value of the dynamic metric fragment is restored to the initial weight value, thereby adjusting the business impact degree of the dynamic metric fragment associated with the business logic, and the expression is:

[0091] Wj" = Wj' * (1 + k * AV * Wj')

[0092] Where: Wj' is the weight value before adjustment of the dynamic metric fragment, Wj" is the weight value after adjustment of the dynamic metric fragment, Wj" > 1, take 1, Wj" < 0, take 0, k is the variable value, 1 for priority promotion and -1 for priority restoration, AV is the weight value adjustment parameter during logic switching.

[0093] When the dynamic metric priority promotion causes the controller CPU load to exceed the preset limit, the dynamic metric frequency period is increased to ensure the real-time performance of the business system.

[0094] IO parameter anomaly:

[0095] IO parameters include device operating parameters and control instruction output states, where the device operating parameters include boiler temperature and turbine speed. The abnormal conditions of the embodiment include:

[0096] 1) Collection anomaly

[0097] Collection anomaly refers to data exceeding the normal range or experiencing severe fluctuations, and the specific triggering conditions are as follows:

[0098] a. Threshold overrun: triggered when sensor collected data breaks through the preset safety threshold. For example, the main steam temperature of the boiler exceeds 540℃ (upper threshold) or is lower than 520℃ (lower threshold); the turbine speed deviates from the rated value by ±5% (e.g. rated 3000r / min, exceeding 2850-3150r / min range).

[0099] b. Mutation detection: sliding window difference algorithm is used to analyze data fluctuation. If the change amplitude of a parameter within a short time (e.g. 1 second) exceeds ±20% of its historical mean, and the condition is met for 3 consecutive sampling periods, it is determined that the data is mutated. For example, the flow of a feedwater pump suddenly drops from 500m 3 / h to 300m 3 / h within 1 second, triggering an abnormal signal.

[0100] 2) Output abnormality

[0101] Triggered when there is no obvious fluctuation in input collected data and business logic, but the output data has abnormal changes:

[0102] a. Output threshold abnormality: the output result of the control instruction exceeds the expected range. For example, when the unit load is stable, the opening degree instruction of the turbine regulating valve suddenly jumps from 50% to 80%, and there is no corresponding business logic adjustment trigger; or the output instruction frequency of the boiler soot blowing system abnormally increases when there is no planned task.

[0103] b. Output stability abnormality: under the condition that the input parameter is stable, the output data continuously fluctuates beyond the normal fluctuation range (e.g. 2 times the historical standard deviation as the fluctuation threshold). For example, the reactive power output of a generator fluctuates more than ±10% of the rated value within 3 minutes when the grid voltage is stable.

[0104] 3) Logic abnormality

[0105] Used to identify the logic contradiction between input and output data, specific scenarios include:

[0106] a. Instruction-feedback contradiction: the control instruction does not match the actual feedback state of the device. For example, the valve opening degree instruction is 30%, but the actual opening degree feedback from the sensor is only 10%; or the device start-stop instruction has been sent, but the corresponding device state monitoring signal still shows "running".

[0107] b. Data correlation contradiction: the parameter correlation relationship based on process logic is invalid. For example, according to the combustion control logic, the main steam pressure and the fuel quantity should be positively correlated. If it is monitored that the main steam pressure rises but the fuel quantity continuously decreases, and no other process adjustment is triggered, it is determined that there is a logic abnormality.

[0108] When an abnormality occurs, the influence degree weight value of the corresponding dynamic metric segment is adjusted, Wj" = 1, and the weight value is directly set to the maximum value. When the abnormality is restored, Wj" = Wj', which is restored to the initial value.

[0109] The dynamic metric frequency period set in this embodiment is:

[0110] a. Score 70~100→ highest frequency measurement period (e.g. 5s / time), ensure real-time protection of core business logic;

[0111] b. Score 40~70→ medium frequency measurement period (e.g. 30s / time), balance protection strength and resource consumption;

[0112] c. Score 0~40→ low frequency measurement period (e.g. 120s / time), release resources to focus on core.

[0113] According to the adjusted influence degree weight value, the dynamic measurement slice score table is recalculated, combined with the dynamic measurement frequency period, the dynamic measurement slice priority strategy is optimized, that is, the dynamic measurement slice priority adjustment strategy is formed.

[0114] When the dynamic measurement priority is improved to cause the controller CPU load to exceed the preset limit value, the dynamic measurement frequency period is increased to ensure the real-time performance of the business system; when the CPU load is lower than the preset value, the initial dynamic measurement frequency period is restored.

[0115] The trusted controller performs periodic measurement according to the dynamic measurement slice priority adjustment strategy, so that the measurement process can closely match the changes in the operation state of the power plant distributed control system, avoid inefficient or ineffective measurement process, further strengthen the real-time response capability of the power plant distributed control system, and ensure the stable and efficient operation of the power plant distributed control system.

[0116] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the prior art, unless otherwise defined, and should not be interpreted in an idealized or overly formal sense.

[0117] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A dynamic metric optimization method for a trusted controller, characterized in that: include: The dynamic metrics segments are scored and sorted according to the evaluation indicators; Initial setting of dynamic measurement priority strategy based on score ranking; The scoring and ranking of shards are dynamically measured based on logical switching and abnormal IO parameters. Optimize dynamic metric priority strategy based on adjusted score ranking; Based on a dynamic measurement priority strategy and combined with a dynamic measurement frequency period setting, a periodic reliable dynamic measurement is performed.

2. The trusted controller dynamic metric optimization method according to claim 1, characterized in that: The dynamic metrics segments are scored and ranked according to evaluation indicators, specifically including: Dynamic metrics shards are divided into ordinary and critical levels based on the degree of tampering impact and the importance of business data. For ordinary level dynamic metrics shards, a scoring threshold is set, and the score value of the corresponding dynamic metrics shard is calculated based on the frequency of invocation. The calculation expression is as follows: S = T0 + Ki(T1 - T0) Where T0 is the low value of the scoring threshold set for the normal level, T1 is the high value of the scoring threshold set for the normal level, and Ki is the relative value of the dynamic measurement shard call frequency for the normal level, with a value range of 0 to 1. For critical dynamic metric shards, a scoring threshold is set, and the corresponding dynamic metric shard score is calculated based on the degree of impact of the critical dynamic metric shard on the business.

3. The reliable controller dynamic metric optimization method according to claim 2, characterized in that: The critical-level dynamic metric shards are process code segments. Based on the degree of impact on business, process code segments are divided and weight values ​​are set for each process code segment. The divided process code segments include auxiliary algorithms, key algorithms, and core algorithms.

4. The trusted controller dynamic metric optimization method according to claim 3, characterized in that: The calculation process for the score value of the critical-level dynamic metric slice includes: The expression for assigning the weight value Wj to the j-th dynamic metric shard at the critical level, representing its impact on the business, is as follows: Wj = W0 + Uj(W1 - W0) Where W0 is the low weight threshold of the algorithm type to which the critical process code segment belongs, W1 is the high weight threshold of the algorithm type to which the critical process code segment belongs, and Uj is the relative value of the dynamic measurement slice call frequency of the algorithm type to which the critical process code segment belongs, with a value range of 0 to 1. The expression for calculating the score of the j-th dynamic metric segment at the critical level is: S = T1 + Wj(T2 - T1) Where T2 is the high score threshold set for the critical level, and T1 is the high score threshold set for the normal level.

5. The trusted controller dynamic metric optimization method according to claim 4, characterized in that: The scoring and ranking of dynamic metric shards are adjusted based on logical switching, specifically including: Based on logical switching, the impact of dynamic sharding on business is adjusted, i.e., the weight value Wj, expressed as: Wj"=Wj′(1+k*ΔV*Wj′) Where: Wj′ is the weight value before dynamic metric sharding adjustment, Wj" is the weight value after dynamic metric sharding adjustment, when Wj">1, it is 1, when Wj"<0, it is 0, k is the variable value, it is 1 when priority is increased, and it is -1 when priority is restored, ΔV is the weight value adjustment parameter when logic switching; The score of the dynamic metric segment is calculated based on the adjusted weight value Wj".

6. The trusted controller dynamic metric optimization method according to claim 4, characterized in that: Adjusting the rating and ranking of dynamic metric shards based on IO parameter anomalies, specifically including: Based on the abnormal IO parameters, the weight values ​​of the corresponding dynamic metric partitions are adjusted and set to the maximum value, i.e., Wj" = 1; After the IO parameters return to normal, adjust the weight values ​​of the dynamic metric shards to their initial values, i.e., Wj" = Wj.

7. The trusted controller dynamic metric optimization method according to claim 4, characterized in that: Based on a dynamic measurement priority strategy and combined with a dynamic measurement frequency period setting, periodic reliable dynamic measurement is performed, including: When the priority of dynamic measurement increases, causing the controller CPU load to exceed the preset limit, the dynamic measurement frequency cycle is increased. When the controller CPU load is lower than the preset limit, the initial dynamic measurement frequency cycle is restored. The trusted controller performs periodic measurements based on the dynamic measurement priority strategy and the dynamic measurement frequency period; the dynamic measurement frequency period is divided into: low frequency measurement period, medium frequency measurement period, and highest frequency measurement period.

8. A trusted controller dynamic measurement optimization system, characterized in that: include: The scoring and sorting module is used to score and sort dynamic metric segments according to evaluation indicators. The initial setup module is used to initially set the dynamic measurement priority strategy based on the score ranking. The scoring adjustment module is used to adjust the scoring order of dynamic metric shards based on logical switching and abnormal IO parameters. The strategy adjustment module is used to optimize the dynamic metric priority strategy based on the adjusted score ranking. The periodic measurement module is used to perform reliable dynamic periodic measurement based on a dynamic measurement priority strategy and a dynamic measurement frequency period setting.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the steps of the trusted controller dynamic metric optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the trusted controller dynamic metric optimization method as described in any one of claims 1 to 7.

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