506 results about "PERQ" patented technology

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

The invention provides a Linux access control system based on attributes, and relates to the technical field of data access control. The system comprises a system monitor module, a data interaction module and a decision unit. The system monitor module collects attributes from a kernel and a user space and writes the attributes into the data interaction module; the access decision unit compiles the access control strategy into an eBPF program and mounts the eBPF program to a corresponding hook; executing the kernel to the hook, and triggering an eBPF program; an eBPF program queries a Flow rule; matching the attribute with the Flow rule, and if the matching is successful, executing a corresponding action; if all the Flow rules fail to match, executing a default action; the system can be expanded during operation, and can be loaded or unloaded based on dynamic loading characteristics and strategies of the eBPF program and the eBPF program during operation of the system, so that the problem that a kernel needs to be compiled in a traditional LSM scheme is solved; the method does not intrude the kernel, is completely based on an eBPF program, does not modify a kernel source code, and can guarantee the stability and compatibility.

The invention relates to the technical field of virtualization management, in particular to a virtual machine operation management system based on RISC-V. The system comprises an instruction set mapping module, a resource scheduling optimization module, a virtual machine monitoring integration module, an operation tuning update module and an operation configuration distribution module. According to the method, by analyzing the operation instruction of the virtual machine and combining the RISC-V underlying characteristics, accurate instruction mapping is achieved, the processing efficiency and accuracy are remarkably improved, the performance loss and conversion delay caused by too high abstraction level of a traditional virtual machine on the instruction level are overcome, the optimal allocation scheme is screened, the resource utilization rate and the system response speed are effectively improved, and the system performance is improved. The operation state and performance indexes of the virtual machine are integrated, anomaly detection and resource recovery are embedded, system stability and effective resource reutilization are ensured, basic configuration is continuously and incrementally updated, operation configuration refinement and automatic management and updating are achieved, the overall operation efficiency and management flexibility are improved, and the manual intervention cost is reduced.

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

The invention discloses a power consumption and performance balanced scheduling method and system for a heterogeneous multi-core processor, and belongs to the technical field of computers, and the method comprises the steps: obtaining historical load data and current system state parameters, generating a load sequence and carrying out load prediction analysis, generating a predicted load value for feature fusion and quantification, and generating a task feature vector; constructing a core capability portrait, calculating a matching degree between the task characteristic vector and the core capability portrait, and generating a scheduling strategy; and dynamically combining the physical cores into a logic calculation unit according to a scheduling strategy, configuring a power consumption state, and executing task allocation and migration. According to the method, the technical means of load prediction analysis, quantitative matching of tasks and cores and dynamic combination and configuration of physical cores are adopted, and pre-judgment and prospective distribution of system resources can be achieved, so that performance requirements are met, meanwhile, the system energy efficiency is effectively optimized, and deep balance of power consumption and performance of a processor is achieved.

The invention discloses a deployment method and device of a large model agent, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining agent configuration information of a to-be-deployed large model agent after an agent deployment request is received, and determining a target large model and model metadata; determining an available resource condition of a preset computing resource pool, and performing query matching according to the available resource condition and the model metadata to determine a plurality of hardware-compatible candidate computing resource nodes; performing load prediction operation on the to-be-deployed large model agent after the large model agent is online to obtain a predicted load, and generating a target deployment decision scheme based on the predicted load, a preset scheduling strategy, the available resource condition and the candidate computing resource node; and adjusting the runtime parameters of the target large model according to the resource characteristics of the target computing resource node, and loading the adjusted target large model on the target computing resource node so as to deploy and start the agent instance of the to-be-deployed large model agent.

The invention discloses an automatic integrated system from an asynchronous pipeline to an asynchronous circuit, which adopts a modular architecture, takes a high-level structure description file in a JSON / XML format as input, and gradually maps to generate a gate-level netlist and a standard delay format file so as to be in butt joint with a commercial back-end design process. According to the method, asynchronous design based on an asynchronous structure can be automatically mapped into an asynchronous circuit, the automatic mapping process can seamlessly process multi-level design representation, the mapping result is stable and cooperatively operated, asynchronous circuit time sequence detection and delay matching are automatically completed, the design efficiency is greatly improved, and the design cost is reduced. An automatic delay matching mechanism enables the circuit to have a good data ready detection and handshake mechanism, and the characteristics of low power consumption, high throughput and no global clock drift sensitivity are realized; in addition, automation of asynchronous design key links such as structure abstraction, time delay modeling and testability insertion is achieved while design readability, hierarchical consistency and time sequence controllability are guaranteed.

The invention relates to the field of industrial Internet of Things, in particular to an edge computing communication method and system for the industrial Internet of Things, and the method comprises the following steps: 1, a user issues a task to a service platform through a user platform, and after the service platform receives a task request, the service platform sends the task request to the user platform based on a device type and a task type associated with the task; classifying and marking the tasks, and determining real-time levels of the tasks and importance weights of associated production links; 2, the service platform transmits the classified tasks to a corresponding service sub-platform, the service sub-platform obtains historical operation data and edge node basic information of task associated equipment, generates a first calculation task carrying a task attribute tag according to a classification result, and sends the first calculation task to a management platform; and step 3, the management platform carries out unified management on the plurality of first computing tasks. By adopting the technical scheme of the invention, industrial scene characteristics can be fit, and task priority, resource dynamics and network adaptability are considered.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to indicate one or more computing resources to be used by one or more instructions based, at least in part, one or more characteristics of that software workload.

The invention relates to a method, a device, equipment and a medium for dynamically configuring a window control and layout, and the method comprises the following steps: firstly, obtaining and analyzing an XML configuration file to generate a data model tree of a structured window interface in a memory; then, objects are dynamically created based on a Qt reflection mechanism, attributes and signal slots are set, Qt reflection characteristics are fully utilized, object creation and attribute processing are flexible and efficient, hard coding interface elements in the compiling stage are not needed, needed controls and layouts can be dynamically generated according to a data model tree, and the dynamic nature and expandability of interface configuration are greatly enhanced; establishing a set membership tree between the controls and the layouts in the recursive traversal process to obtain a complete interface tree; and finally, performing layout calculation and rendering to generate a display interface, so that automation of interface layout is realized, interface element layout can be automatically adjusted according to a data model tree structure, and efficient dynamic configuration of window controls and layout is realized.

The invention provides a method, a system and equipment for improving the computing performance stability of a hardware platform and a medium, and belongs to the technical field of computer system performance optimization. The method comprises the following steps: acquiring architecture characteristic data of a bottom hardware platform through a hardware detection tool, and generating and loading a first-stage system kernel parameter configuration based on the architecture characteristic data; reading the architecture characteristic data, carrying out behavior analysis and type identification on the running process based on the architecture characteristic data, and generating a second-level process scheduling strategy according to the behavior analysis and type identification; reading cache structure information in the architecture characteristic data, and driving a file system to intelligently prefetch and reconstruct a storage layout of memory data based on the cache structure information; and continuously collecting performance index flow data when the system runs, performing real-time analysis on the performance index flow data by using a performance degradation model, generating a feedback control instruction according to an analysis result, and dynamically correcting the kernel parameter configuration of the first-stage system and the scheduling strategy of the second-stage process.

The invention discloses a heterogeneous resource energy-saving optimization scheduling method and system based on computing power perception, and relates to the technical field of modern computing infrastructure, the system comprises a computing power-energy consumption evaluation module, the computing power-energy consumption evaluation module is used for carrying out computing power and energy consumption evaluation on heterogeneous resources, generating and dynamically updating a computing power-energy consumption mapping table, the heterogeneous resources comprise ARM architecture resources, X86 architecture resources and GPU resources. According to the method, the computing power-energy consumption mapping table is generated and updated in real time, so that the scheduling decision is always based on the latest energy efficiency state of the resources, the accuracy of the basic data is ensured, intelligent task classification and dynamic constraint construction are combined, the system can flexibly balance the task performance and the energy consumption target, and particularly, the strategy can be adaptively adjusted for a high-emergency task; the timeliness of key services is guaranteed, the differentiated scheduling strategy serves as an efficient pre-screening mechanism, task requirements and resource architecture characteristics are accurately matched, and the pertinence and efficiency of resource allocation are remarkably improved.

The embodiment of the invention discloses a rendering task rendering method and device, equipment and a medium. The rendering task rendering method comprises the steps that a specified rendering task is transferred to a Worker thread from a UI thread; dynamically configuring parameter information of a specified rendering task based on hardware characteristics of an environment where the Worker thread is located; setting a rendering mechanism of the specified rendering task based on the parameter information; and rendering the specified rendering task based on the rendering mechanism to obtain a rendering result of the specified rendering task. According to the method, the rendering task is stripped from the UI thread to the Worker thread, the blocking risk of the main thread is fundamentally eliminated, meanwhile, the rendering parameters are dynamically generated based on the real-time hardware characteristics of the Worker environment, the three major mechanisms of multi-stage buffering, tile rendering and layered rendering are coordinated and linked under unified configuration, a rendering strategy precisely matched with the hardware capability is formed, and the rendering efficiency is improved. Therefore, the cross-platform rendering robustness and the resource utilization efficiency of the high-end and low-end equipment are remarkably improved while smooth interaction experience is guaranteed.

The invention relates to a performance evaluation method for an ACE (Adaptive Cycle Engine) and electronic equipment. The method comprises the following steps: acquiring a to-be-evaluated ACE complete machine zero-dimensional simulation model, an adaptive fan high-fidelity simulation model, and working conditions and control rules of the to-be-evaluated ACE complete machine zero-dimensional simulation model; coupling the obtained models to obtain an ACE multi-dimensional coupling simulation model, and operating under the obtained working condition and control rule to obtain the ACE performance to be evaluated and the performance of each component in the ACE performance; according to the ACE multi-dimensional coupling simulation model, characteristic curves in an ACE complete machine zero-dimensional simulation model are adopted to represent characteristics of different ducts of a self-adaptive fan in the ACE complete machine zero-dimensional simulation model, and based on the difference of simulation results between the ACE complete machine zero-dimensional simulation model and the self-adaptive fan high-fidelity simulation model, the self-adaptive fan high-fidelity simulation model is obtained. The method is obtained after coupling is realized by continuously iteratively adjusting the scaling factor of the characteristic curve, so that the simulation precision of the self-adaptive fan is improved, and the overall performance evaluation accuracy is improved.

The invention discloses a controller programming method and device based on an equipment model, equipment, a medium and a product, the equipment model is used as a unified semantic anchor point, heterogeneous characteristics of physical equipment are converted into computable structured description, hardware adaptation and business logic are decoupled through a driving framework, a large model focuses on generation of a high-value control strategy, and the control strategy is optimized. Semantic drift and logic illusion of a program generation large model are remarkably inhibited by relying on double constraints of equipment document data and a knowledge graph, finally, an executable drive program meeting strict requirements of an industrial site is output through compiling verification of a closed loop, and due to the fact that manual development is not needed, development efficiency is improved, development cost is reduced, and development efficiency is improved. The equipment model provides a cross-protocol and cross-manufacturer standardized interface contract, so that the problem of poor reusability in traditional development is solved, and the reusability is improved.

The invention relates to a quantum measurement and control task compiling method and device, computer equipment and a storage medium. The method comprises the following steps: converting a quantum measurement and control task into an intermediate representation instruction matched with a quantum low-temperature measurement and control chip through a pre-constructed hardware behavior model of the quantum low-temperature measurement and control chip; the hardware behavior model is constructed according to hardware characteristics of the quantum low-temperature measurement and control chip; performing parallel scheduling on the intermediate representation instruction to obtain a scheduling result; the intermediate representation instruction is converted into hardware executable codes according to the scheduling result, compiling of the quantum measurement and control task is completed, and the hardware executable codes are used for indicating the quantum low-temperature measurement and control chip to conduct quantum calculation. By adopting the method, the high efficiency and reliability of the measurement and control chip in the low-temperature environment can be improved.

The invention provides a cloud edge collaboration algorithm arrangement and deployment method, system and device and a storage medium, and relates to the technical field of cloud edge collaboration.The method comprises the steps that algorithm task requirements input by a user are analyzed, and after corresponding task information is obtained, an intermediate representation file is generated; collecting computing power state information of the plurality of edge devices to determine a target edge device used for executing an algorithm link, and generating an operation parameter configuration file according to a hardware architecture of the target edge device and algorithm characteristics of the intermediate representation file; and issuing the intermediate representation file and the operation parameter configuration file to the target edge device, and performing automatic compiling and deployment according to the chip type of the target edge device. By issuing the intermediate representation file and the operation parameter configuration file to the target edge device and automatically executing compiling and deployment, the effects of cross-platform automatic adaptation, rapid deployment and efficient operation are achieved, and the problem that in the prior art, task organization and deployment flexibility is insufficient is solved.

The invention relates to the technical field of engineering simulation, and discloses a GPU heterogeneous resource scheduling method based on mixed execution of multiple simulation jobs, which considers the calculation characteristics of different simulation tasks, also introduces a calculation time prediction model and a video memory usage amount prediction model, and prejudges the resource consumption condition during job execution in advance. And accurate depiction of resource demands in a complex multi-physics field simulation scene is realized. When potential conflicts are found, a Monte Carlo tree search technology is utilized to explore a plurality of scheduling strategies, and a scheduling scheme which achieves optimal balance among calculation delay, throughput and energy consumption is screened out from the scheduling strategies. According to the method, historical data and real-time job requirements of a simulation platform are comprehensively analyzed, accurate prediction and dynamic allocation of GPU heterogeneous resources are achieved, the utilization rate and the overall performance of the GPU heterogeneous resources in a multi-simulation job mixed execution environment are effectively improved, and support can be provided for efficient resource scheduling in a complex simulation scene.

The invention discloses a hierarchical self-adaptive full block pre-filling scheduling method and system for large language model reasoning, and the method comprises the steps: carrying out the hierarchical portrait analysis of a to-be-served model, and dividing the to-be-served model into partitions with different calculation characteristics according to the calculation intensity and memory access characteristics of each layer; then, making a layering and partitioning strategy based on a partitioning result, allocating a larger partitioning size to a calculation-intensive partition, allocating a smaller partitioning size to a memory bandwidth-intensive partition, and generating a layering and partitioning mapping table; and finally, when the online scheduling is executed, querying the mapping table according to the request processing progress to determine the block target size, and jointly forming a batch processing unit by the decoding task and the pre-filled block with the heterogeneous size under the constraint of the iteration time budget to be executed. According to the method, accurate matching of calculation and bandwidth resources is achieved, the system throughput can be effectively improved, tail delay and fluctuation thereof can be remarkably reduced, bubbles under pipeline parallelism are reduced, and the method is suitable for various attention mechanisms and distributed reasoning architectures.

The invention discloses a digital twinning three-dimensional model dynamic synthesis system based on multi-modal feature fusion, and belongs to the technical field of digital twinning, computer processing and three-dimensional reconstruction crossing. Data flow characteristics are divided into high-frequency real-time data flow in a machine tool running state and low-frequency / event triggering data in a machine tool static state, a specific processing fusion path is intelligently selected based on a self-adaptive double-path processing mechanism, a state parameter set for driving a three-dimensional basic model is generated, and the three-dimensional basic model is obtained. The method not only optimizes the dynamic synchronization and prediction of the operation state, but also achieves the modeling and tracking of the static state (such as cooling recovery, maintenance calibration and environmental evolution) of the machine tool, constructs a digital twin system covering the whole scene of operation-shutdown-maintenance of the numerical control machine tool, and supports the real whole life cycle management.

The invention discloses a satellite communication terminal application software modular design method, which is suitable for an ARM (Advanced RISC Machines) architecture Linux platform and aims to solve the core defects of traditional satellite terminal software. The method comprises the following steps of: firstly, dividing 14 independent modules by combining platform characteristics and business requirements, and interacting the modules through a standard interface adaptive to the bottom specification of the platform; secondly, a main process and multiple sub-threads framework is adopted, the main process initializes system resources and pulls up the sub-threads of all modules, and the sub-threads support dynamic loading / unloading and fault restarting through IPC cooperation, so that overall collapse is avoided; and finally, abstracting driving logic through a hardware interface module, realizing multi-hardware compatibility in combination with a common function of a common module, and monitoring the state by means of an alarm and log module to ensure stable operation.

The invention provides a heterogeneous multi-agent autonomous collaborative optimization method for production and maintenance integrated scheduling of a mixed flow manufacturing workshop. The heterogeneous multi-agent autonomous collaborative optimization method comprises the steps of limited buffer region constraint, feature screening, heterogeneous multi-agent and the like. The method is optimized on the basis of a TD3 algorithm, and comprises the following steps: firstly, constructing a heterogeneous multi-agent according to the characteristics of each processing site; secondly, in order to improve effective characterization of feature extraction, a feature screening mechanism based on NSGAIII is designed; and finally, in combination with the actual scheduling condition of the mixed-flow manufacturing workshop and aiming at the actual situation that the performance of the scheduling rule of the first-selected workpiece and the second-selected machine is poor, a composite scheduling rule based on the first-selected machine and the second-selected workpiece is adopted. According to the method provided by the invention, the effectiveness and superiority of the NSGAII-MATD3 algorithm are respectively demonstrated by comparing a traditional single scheduling rule, a feature-free screening MATD3 algorithm and five advanced deep reinforcement learning algorithms.

The invention discloses a numerical control machine tool synchronous evolution control method and system associated with dynamic and static errors, relates to the technical field of information processing management, and is used for solving the problem that a machine tool structure optimization method is poor in structural integrity, structural connectivity and dynamic response characteristic analysis. By monitoring and analyzing geometric structure parameter changes of all parts in the machine tool operation process, effective geometric structure parameters with the high influence degree are screened out, preliminary optimization is carried out, a geometric structure optimization evaluation model is established, and an optimization result is evaluated; the method comprises the following steps of: acquiring local rigidity and weight, establishing an optimization evaluation model, defining geometric structure parameters of a plurality of components as an integral assembly structure, constructing a parallel optimization model by utilizing a grid division and rigidity matrix calculation method, and finally obtaining optimization results of a macroscopic unit and a connecting unit by taking minimization of structural flexibility as a target. Key parameters can be accurately monitored and optimized, the structural connectivity is considered, and the overall performance and stability are improved.

The invention provides a test program generation method, electronic equipment and a storage medium, relates to the field of chips, and is mainly technically characterized by comprising the following steps: generating a plurality of subprograms corresponding to each module in at least two modules according to instruction characteristics of the at least two modules in a target processor; wherein each subprogram comprises dependency information with other subprograms, each subprogram comprises a plurality of instruction fragments, each instruction fragment comprises at least one instruction, and the dependency information of the subprogram is used for indicating whether the instruction fragment in the subprogram conflicts with the instruction fragments in other subprograms or not; the to-be-used instruction fragments are continuously selected until a preset ending condition is met, and the next to-be-used instruction fragment in every two adjacent to-be-used instruction fragments is the to-be-used instruction fragment which is sequentially selected from any subprogram of any module and does not conflict with the previous to-be-used instruction fragment; and obtaining a target test program according to a selection result of the to-be-used instruction fragment.

The invention provides an intelligent data management method and system, and relates to the technical field of data processing. Standardized packaging and automatic execution of the data governance instruction are achieved, and the problem that a traditional storage medium cannot dynamically adapt to the multi-source data processing requirement is solved. Through the decoupling design of the pre-compilation instruction and the runtime environment, the same storage medium can adapt to data management nodes of different hardware architectures, and the deployment complexity during system upgrading is effectively reduced. Furthermore, based on an instruction execution mechanism of a streaming computing engine, the real-time performance of data quality evaluation and early warning triggering is ensured, and decision lag caused by batch processing delay is avoided. Through the on-demand loading characteristic of the modular instruction set, the function expansion requirement of a specific business scene is supported while the core governance function is ensured.

The invention relates to an RRC (Radio Resource Control) layer enhanced mobile model simulation method and system based on a 3GPP (3rd Generation Partnership Project) NTN (Network Temporary Network) standard. The method comprises the following steps: S1, constructing a simulation environment conforming to the 3GPP NTN standard; s2, constructing an RRC state machine in the NTN scene, wherein the RRC state machine comprises a user RRC state machine and a satellite RRC state machine; s3, configuring a dynamic conversion condition of the RRC state machine and a corresponding behavior logic; s4, scheduling events and injecting NTN parameters, and performing NTN propagation characteristic simulation based on the simulation environment; and S5, counting performance indexes generated in the simulation process, and outputting an NTN scene simulation report. According to the method, the simulation environment conforming to the 3GPP NTN standard is constructed, the RRC state machine in the NTN scene is constructed, the characteristics of long delay, high mobility and orbit prediction of the satellite are adapted, and the problem that a state machine model of a traditional simulation method is not applicable is solved.

The invention relates to the technical field of power equipment inspection data processing methods, in particular to a Grouping-DINO model acceleration method combined with a multi-stage progressive multi-mode distillation mechanism. According to the method, through the whole process of data set construction, lightweight model design, three-stage progressive multi-mode distillation and deployment optimization, the core pain point of electric power inspection is systematically solved. Firstly, a training foundation is built by using an electric power scene adaptive data set, and then a lightweight student model conforming to edge equipment computing power limitation is designed in a targeted manner, so that the problem of computing power mismatching of a general model is avoided; key points are that teacher model capability is migrated by stages by means of a three-stage distillation mechanism, lightweight precision loss is made up in combination with special optimization for an electric power scene, and edge equipment is further adapted through deployment optimization; finally, the technical problem that the routing inspection detection precision is reduced due to the fact that adaptation optimization is not carried out for the layered structure, the shielding characteristic and the terminology instruction of the power equipment is solved.

The invention provides an HIL test system and method based on an IBC controller and a medium. The system comprises a software platform, a hardware platform, the IBC controller and an IBC actuator. Wherein the software platform comprises a whole vehicle dynamics model, the hardware platform comprises an actuator virtual model, the IBC controller and the IBC actuator are in intrusive hard wire connection through a fault injection board card, and the actuator virtual model is used for simulating the working characteristics of the IBC actuator. Interventional hardwire connection between the IBC controller and the IBC actuator is established through the fault injection board card, and the HIL test system can control functions needing to be tested in a mode of remotely controlling a fault injection mode. Meanwhile, the actuator virtual model can specifically simulate the working characteristics of the IBC actuator, so that the limitation of traditional hardware is eliminated, the whole test method can be completed through a pure model, and the conventional function test requirements can be effectively met.

The invention discloses a stabilization and hierarchical cooperative stabilization control method for a heterogeneous inverter parallel system, and belongs to the field of grid-connected inverters. The method is characterized in that the capacity ratio of the heterogeneous inverter parallel system and cooperative regulation and control of key control parameters are fully considered, and the stability margin of the system can be effectively increased. The method comprises the following two layers of control: 1, when the operation condition of the system is changed, adjusting the capacity of a network-following inverter and a network-constructing inverter in the heterogeneous system; and 2, if the system still loses stability after the network following / constructing capacity ratio is changed, the stability of the system is improved by adjusting key control parameters of the heterogeneous parallel system. Based on the method, through reasonable configuration of the two types of inverters, the system achieves better dynamic and steady state characteristics, so that performance optimization and stability enhancement of the parallel system are realized.