ICS computing power adaptive resource allocation method based on health evaluation

Abstract

The embodiment of the invention provides an ICS computing power adaptive resource allocation method based on health evaluation, and the method comprises the steps: extracting an initial allocation instruction of a platform, traversing a plurality of task flows and task equipment of the initial allocation instruction, and determining an equipment competition relation; acquiring a preset parameter range of the task equipment under the task flow, generating a computing power demand through computing power analysis, further generating a computing power health degree curve, and identifying the task flow and the task equipment with the lowest health degree in combination with an equipment competition relationship; on the basis of the service priority of the initial allocation instruction and the computing power health degree curve, dynamically configuring platform computing power, and determining a dynamic allocation scheme by combining the task flow with the lowest health degree, the task equipment and the equipment competition relationship as constraints; issuing a dynamic allocation scheme, detecting and evaluating multi-level dynamic data in the platform in the period, integrating the dynamic data, and comprehensively evaluating the dynamic allocation scheme through multiple indexes.

Description

Technical Field

[0001] This invention relates to the field of resource allocation technology, and in particular to an adaptive resource allocation method for ICS computing power based on health assessment. Background Technology

[0002] The ICS Intelligent Control Platform is a hardware and software platform deployed in the control area, designed for intelligent applications in power generation enterprises. It integrates data acquisition, algorithm execution, and monitoring display. Based on an industrial operating system platform, the system provides a "platform + industrial intelligent software" application model, solving the information silos and data integration problems caused by traditional siloed vertical applications. It offers enterprises sustainable, scalable, full lifecycle services centered on equipment big data. However, in existing technologies, when scheduling resources, the platform can only provide resource scheduling based on a single algorithm and cannot perform real-time resource scheduling and allocation according to the current status of the platform. Sometimes, when allocating computing power, it may be allocated to devices that are in a sub-healthy state or even experience sudden failures, which leads to a waste of computing power resources. Summary of the Invention

[0003] To address the problems existing in the prior art, embodiments of the present invention provide an adaptive resource allocation method and system for ICS computing power based on health assessment.

[0004] This invention provides an adaptive resource allocation method for ICS computing power based on health assessment, the method comprising: Extract the platform's initial allocation instruction, traverse multiple task flows and task devices of the initial allocation instruction, and determine the device competition relationship; Obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve. Combined with the device competition relationship, identify the task flow and task device with the lowest health. Based on the business priority of the initial allocation instruction and the computing power health curve, the platform computing power is dynamically configured, and the task flow with the lowest health and the task device and device competition relationship are used as constraints to determine the dynamic allocation scheme. A dynamic allocation scheme is issued, and dynamic data at multiple levels within the platform are monitored and evaluated within the evaluation period. The dynamic allocation scheme is then comprehensively evaluated using multiple indicators based on the dynamic data.

[0005] In one embodiment, the method further includes: The operating parameters of the task device are obtained, and the execution indicators of the task device are determined in combination with the preset parameter range. The health of the task device is judged based on the execution indicators. The execution indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. The baseline computing power requirement of the task device is obtained, and the dynamic computing power requirement is determined in combination with the health status of the task device, thereby determining the computing power health status curve.

[0006] In one embodiment, the method further includes: Dynamic computing power requirement = Base computing power requirement * (1 + α * (1 - health status)).

[0007] In one embodiment, the method further includes: The dynamic allocation scheme is comprehensively evaluated based on health improvement rate, resource utilization rate, task completion rate, energy efficiency ratio, and conflict resolution.

[0008] In one embodiment, the method further includes: The sensitivity coefficients of the dynamic allocation model and the computing power health curve are calibrated using the dynamic allocation scheme, the initial platform status, and evaluation indicators.

[0009] This invention provides an ICS computing power adaptive resource allocation system based on health assessment, the system comprising: The extraction module is used to extract the initial allocation instructions of the platform, traverse multiple task flows and task devices of the initial allocation instructions, and determine the device competition relationship; The computing power analysis module is used to obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve, and identifying the task flow and task device with the lowest health in combination with the device competition relationship; The allocation module is used to dynamically configure the platform's computing power based on the business priority of the initial allocation instruction and the computing power health curve, and to determine the dynamic allocation scheme by combining the task flow with the lowest health and the task device and device competition relationship as constraints. The evaluation module is used to issue dynamic allocation schemes, detect dynamic data at multiple levels within the platform during the evaluation period, and comprehensively evaluate the dynamic allocation schemes through multiple indicators based on the dynamic data.

[0010] In one embodiment, the system further includes: The acquisition module is used to acquire the operating parameters of the task device, and in combination with the preset parameter range, determine the execution indicators of the task device, and judge the health of the task device based on the execution indicators. The execution indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. The demand module is used to obtain the baseline computing power demand of the task device, combine it with the health status of the task device, determine the dynamic computing power demand, and then determine the computing power health status curve.

[0011] In one embodiment, the system further includes: The comprehensive evaluation module is used to comprehensively evaluate dynamic allocation schemes based on health improvement rate, resource utilization rate, task completion rate, energy efficiency ratio, and conflict resolution.

[0012] This invention provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the methods described in one or more embodiments.

[0013] This invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described ICS computing power adaptive resource allocation method based on health assessment.

[0014] In view of the above, in one or more embodiments of this specification, the initial allocation instruction of the platform is extracted, multiple task flows and task devices of the initial allocation instruction are traversed, and the device competition relationship is determined; the preset parameter range of the task devices under the task flow is obtained, and through computing power analysis, computing power requirements are generated, thereby generating a computing power health curve. Combined with the device competition relationship, the task flow and task device with the lowest health are identified; based on the business priority of the initial allocation instruction and the computing power health curve, the platform computing power is dynamically configured, and combined with the task flow and task device with the lowest health and the device competition relationship as constraints, a dynamic allocation scheme is determined; the dynamic allocation scheme is issued, and dynamic data at multiple levels within the platform are detected and evaluated within the evaluation period. The dynamic data is integrated, and the dynamic allocation scheme is comprehensively evaluated through multiple indicators. This allows for dynamic adjustment of resources based on real-time health, adapting to changing workloads, improving system elasticity, and facilitating continuous optimization and decision support through multi-indicator evaluation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an ICS computing power adaptive resource allocation method based on health assessment, provided in one embodiment of this specification.

[0017] Figure 2This is a schematic diagram of the structure of an ICS computing power adaptive resource allocation system based on health assessment, provided in one embodiment of this specification.

[0018] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this specification. Detailed Implementation

[0019] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0020] As used herein, the term "comprising" and its variations are open terms meaning "including but not limited to". The term "based on" means "at least partially based on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. Unless explicitly indicated by the context, the definition of a term shall remain consistent throughout the specification.

[0021] like Figure 1 As shown, this embodiment of the invention provides an adaptive resource allocation method for ICS computing power based on health assessment, including: Step S102: Extract the initial allocation instruction from the platform, traverse multiple task flows and task devices of the initial allocation instruction, and determine the device competition relationship.

[0022] Specifically, the platform obtains initial allocation instructions from its task acquisition system and parses these instructions to obtain the corresponding instruction ID, timestamp, priority, source representation, etc. Then, it identifies the task flow definitions within the instructions and establishes dependencies between task flows, transforming the initial allocation instructions into a topology with an execution order. These dependencies can include sequential dependencies, parallel dependencies, and conditional dependencies (event-triggered), allowing tasks to be executed according to business logic. Each task flow is then decomposed into a specific device execution sequence. Based on the device execution sequence, the data input / output relationships between devices are determined. For example, the output port of device A is connected to the input port of device B.

[0023] Furthermore, the system analyzes device access patterns for exclusive and shared resources to identify potential device combinations with resource contention. When determining resource contention, it detects the existence of contention based on three dimensions: time overlap, resource overrun, and access conflicts. If contention exists, adjustments need to be made to the competing devices based on business priorities. This process involves parsing the initial allocation instructions, constructing a task flow topology, mapping it to specific devices, and identifying resource contention relationships between devices. The entire process provides foundational data for subsequent health assessments and dynamic allocation.

[0024] Step S104: Obtain the preset parameter range of the task devices under the task flow, generate computing power requirements through computing power analysis, and then generate a computing power health curve. Combined with the device competition relationship, identify the task flow and task device with the lowest health.

[0025] Specifically, for each task device in the task topology, key operating parameters (such as CPU utilization, memory usage, I / O latency, temperature, load rate, etc.) are collected and compared with preset health standard ranges for the device. This determines the execution quality of specific tasks on the device. Indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. This identifies devices in a sub-healthy state due to algorithm, configuration, or data flow issues (when parameters exceed standard ranges or task quality indicators decline, but have not yet led to failure). Furthermore, the associated device health and task health are aggregated according to their importance (weight) in the task flow to obtain the macro-health of the task flow, subsystems, and even the entire unit.

[0026] Further, determine the baseline computing power requirement of the equipment in a "fully healthy" state. This baseline requirement can be calculated from historical data, algorithm complexity analysis, or manufacturer specifications. Then, establish a functional relationship between health and computing power requirement adjustment coefficients. Generally, the lower the health, the more computing resources are required for compensation. For example, a server with degraded CPU performance may need more CPU time slices or cores to complete the same computing task. The computing power requirement can be expressed as: Dynamic computing power requirement = Baseline computing power requirement * (1 + α * (1 - Health)). Here, 'a' is a sensitivity coefficient, representing the degree of influence of health on resource requirements. Treat the dynamic computing power requirement of each task device as a time series, and apply prediction algorithms (such as exponential smoothing, ARIMA models, etc.) to predict its computing power requirement trend over a future period. Then, combine this with the sub-health status of the equipment to determine the computing power health curve. Based on the dynamic computing power requirement (computing power health curve), perform resource conflict detection to identify the equipment and tasks with the lowest overall health. The overall health can be determined by combining the equipment's health and priority.

[0027] Step S106: Based on the service priority of the initial allocation instruction and the computing power health curve, dynamically configure the platform computing power, and combine the task flow with the lowest health and the task device and device competition relationship as constraints to determine the dynamic allocation scheme.

[0028] Specifically, the types and quantities of resources to be allocated must be clearly defined, such as how many cores, memory, and bandwidth to allocate to each task device. Then, an objective function is established based on this. The objective function should maximize the globally weighted health (considering both business priority and the health curve) and maximize the task completion rate. Additionally, constraints must be defined, with the platform's available resources and computing power as the upper constraint. The task flow and device with the lowest overall health should be the lower constraint, ensuring they receive resources sufficient to meet their baseline computing power requirements. This also includes determining device competition relationships; for devices with competing relationships, resource allocation must adhere to mutual exclusion or sharing rules. Furthermore, for task flows with dependencies, resource allocation should not conflict in timing.

[0029] Furthermore, based on the model's complexity and real-time requirements, appropriate algorithms are selected. For example, for linear programming models with small scales, simplex methods or branch-and-bound methods can be used to determine resource allocation schemes. For large-scale, nonlinear models, genetic algorithms or particle swarm optimization can be used to determine specific resource allocation schemes. Additionally, after determining the resource allocation scheme, resources can be temporarily suspended without actually distributing them. Instead, in a simulation environment, the system behavior over a future scheduling cycle can be extrapolated based on candidate schemes and predicted computing power health curves. This avoids flawed allocation schemes impacting the real production system.

[0030] Step S108: Issue a dynamic allocation scheme, detect and evaluate multi-level dynamic data within the platform during the evaluation period, and comprehensively evaluate the dynamic allocation scheme through multiple indicators based on the dynamic data.

[0031] Specifically, the platform's management interface securely and accurately distributes dynamically allocated instructions to target devices or resource pools. Within an evaluation cycle of the solution's effectiveness, data is continuously collected at the device layer (raw parameters such as CPU, memory, and I / O), task layer (quality indicators such as execution cycle, throughput, and deadline hit rate), resource layer (actual resource usage of each device), and energy consumption layer (power consumption data of the entire platform or rack). Based on this data, corresponding comprehensive indicators are calculated. These indicators include health improvement rate (directly measuring the solution's contribution to improving the system's "health"), resource utilization rate (evaluating the efficiency of resource allocation), task completion rate (measuring the solution's effectiveness from a business perspective), energy efficiency ratio (evaluating the solution's efficiency in energy consumption), and conflict resolution. This multi-indicator comprehensive evaluation avoids the limitations of a single indicator.

[0032] Furthermore, the dynamic allocation scheme, its corresponding initial system state (topology, competition relationships), and the final evaluation indicators are saved. The health model (dynamic allocation model) is adjusted using the saved data. If the actual health deviates significantly from the predicted value, the weight parameters in the health calculation model are retrained using new data. The sensitivity coefficient α can also be calibrated based on actual resource usage and health changes, allowing the system to adapt to constantly changing environments and workloads through a self-learning process.

[0033] This invention provides an adaptive resource allocation method for ICS computing power based on health assessment. The method extracts the platform's initial allocation instructions, traverses multiple task flows and task devices associated with these instructions, and determines device competition relationships. It obtains preset parameter ranges for task devices within each task flow and generates computing power requirements through computing power analysis, thereby generating a computing power health curve. Combining this with device competition relationships, it identifies the task flows and task devices with the lowest health. Based on the business priority of the initial allocation instructions and the computing power health curve, it dynamically configures the platform's computing power, using the task flows and task devices with the lowest health and device competition relationships as constraints to determine a dynamic allocation scheme. The dynamic allocation scheme is then issued, and dynamic data at multiple levels within the platform is monitored and evaluated within the assessment period. This comprehensive evaluation of the dynamic allocation scheme using multiple indicators allows for dynamic resource adjustment based on real-time health, adapting to changing workloads, improving system resilience, and facilitating continuous optimization and decision support through multi-indicator evaluation.

[0034] Please see Figure 2 , Figure 2This is a schematic diagram of the structure of an ICS computing power adaptive resource allocation system based on health assessment, provided in an embodiment of this application. Figure 2 As shown, the system includes: The extraction module S202 is used to extract the initial allocation instruction of the platform, traverse multiple task flows and task devices of the initial allocation instruction, and determine the device competition relationship; The computing power analysis module S204 is used to obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve, and identifying the task flow and task device with the lowest health in combination with the device competition relationship. The allocation module S206 is used to dynamically configure the platform computing power based on the business priority of the initial allocation instruction and the computing power health curve, and to determine the dynamic allocation scheme by combining the task flow with the lowest health and the task device and the device competition relationship as constraints. The evaluation module S208 is used to issue dynamic allocation schemes, detect dynamic data at multiple levels within the platform during the evaluation period, and comprehensively evaluate the dynamic allocation schemes through multiple indicators based on the dynamic data.

[0035] In another embodiment, an ICS computing power adaptive resource allocation system based on health assessment further includes: The acquisition module is used to acquire the operating parameters of the task device, and in combination with the preset parameter range, determine the execution indicators of the task device, and judge the health of the task device based on the execution indicators. The execution indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. The demand module is used to obtain the baseline computing power demand of the task device, combine it with the health status of the task device, determine the dynamic computing power demand, and then determine the computing power health status curve.

[0036] In another embodiment, an ICS computing power adaptive resource allocation system based on health assessment further includes: The comprehensive evaluation module is used to comprehensively evaluate dynamic allocation schemes based on health improvement rate, resource utilization rate, task completion rate, energy efficiency ratio, and conflict resolution.

[0037] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0038] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0039] See Figure 3 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0040] The communication bus 302 is used to enable communication between these components.

[0041] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0042] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0043] The processor 301 may include one or more processing cores. The processor 301 connects to various parts within the electronic device 300 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0044] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0045] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and obtain user input data; while the processor 301 can be used to call the image-based interactive application stored in the memory 305 and specifically perform the following operations: extract the platform's initial allocation instructions, traverse multiple task flows and task devices of the initial allocation instructions, and determine the device competition relationship; obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve, and identify the task flow and task device with the lowest health based on the device competition relationship; dynamically configure the platform's computing power based on the business priority of the initial allocation instructions and the computing power health curve, and determine the dynamic allocation scheme by combining the task flow and task device with the lowest health and the device competition relationship as constraints; issue the dynamic allocation scheme, detect and evaluate the dynamic data of multiple levels within the platform during the evaluation period, and comprehensively evaluate the dynamic allocation scheme through multiple indicators based on the dynamic data.

[0046] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0047] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0048] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0049] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, 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 service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0050] 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.

[0051] Furthermore, the functional units in the various embodiments of this application 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.

[0052] 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 device (CMD). Based on this understanding, the technical solution of this application, 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 memory 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 this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0053] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0054] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A method for adaptive resource allocation of ICS computing power based on health assessment, the method comprising: Extract the platform's initial allocation instruction, traverse multiple task flows and task devices of the initial allocation instruction, and determine the device competition relationship; Obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve. Combined with the device competition relationship, identify the task flow and task device with the lowest health. Based on the business priority of the initial allocation instruction and the computing power health curve, the platform computing power is dynamically configured, and the task flow with the lowest health and the task device and device competition relationship are used as constraints to determine the dynamic allocation scheme. A dynamic allocation scheme is issued, and dynamic data at multiple levels within the platform are monitored and evaluated within the evaluation period. The dynamic allocation scheme is then comprehensively evaluated using multiple indicators based on the dynamic data.

2. The method according to claim 1, characterized in that, The process of obtaining the preset parameter range of the task devices under the task flow, generating computing power requirements through computing power analysis, and then generating a computing power health curve includes: The operating parameters of the task device are obtained, and the execution indicators of the task device are determined in combination with the preset parameter range. The health of the task device is judged based on the execution indicators. The execution indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. The baseline computing power requirement of the task device is obtained, and the dynamic computing power requirement is determined in combination with the health status of the task device, thereby determining the computing power health status curve.

3. The method according to claim 2, characterized in that, The dynamic computing power requirements include: Dynamic computing power requirement = Base computing power requirement * (1 + α * (1 - health status)).

4. The method according to claim 1, characterized in that, The dynamic allocation scheme, which comprehensively evaluates multiple indicators, includes: The dynamic allocation scheme is comprehensively evaluated based on health improvement rate, resource utilization rate, task completion rate, energy efficiency ratio, and conflict resolution.

5. The method according to claim 4, characterized in that, The method further includes: The sensitivity coefficients of the dynamic allocation model and the computing power health curve are calibrated using the dynamic allocation scheme, the initial platform status, and evaluation indicators.

6. An ICS computing power adaptive resource allocation system based on health assessment, characterized in that, The system includes; The extraction module is used to extract the initial allocation instructions of the platform, traverse multiple task flows and task devices of the initial allocation instructions, and determine the device competition relationship; The computing power analysis module is used to obtain the preset parameter range of the task devices under the task flow, and generate computing power requirements through computing power analysis, thereby generating a computing power health curve, and identifying the task flow and task device with the lowest health in combination with the device competition relationship; The allocation module is used to dynamically configure the platform's computing power based on the business priority of the initial allocation instruction and the computing power health curve, and to determine the dynamic allocation scheme by combining the task flow with the lowest health and the task device and device competition relationship as constraints. The evaluation module is used to issue dynamic allocation schemes, detect dynamic data at multiple levels within the platform during the evaluation period, and comprehensively evaluate the dynamic allocation schemes through multiple indicators based on the dynamic data.

7. The system according to claim 6, characterized in that, The system also includes: The acquisition module is used to acquire the operating parameters of the task device, and in combination with the preset parameter range, determine the execution indicators of the task device, and judge the health of the task device based on the execution indicators. The execution indicators include: task execution cycle stability, data processing throughput, accuracy / reasonableness of output results, and deadline hit rate. The demand module is used to obtain the baseline computing power demand of the task device, combine it with the health status of the task device, determine the dynamic computing power demand, and then determine the computing power health status curve.

8. The system according to claim 6, characterized in that, The system also includes: The comprehensive evaluation module is used to comprehensively evaluate dynamic allocation schemes based on health improvement rate, resource utilization rate, task completion rate, energy efficiency ratio, and conflict resolution.

9. An electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, in order to perform the method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-5.

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