415 results about "Smart computing" patented technology

The invention relates to an intelligent computing power and storage scheduling method and system for a multi-service system, and belongs to the technical field of intelligent computing resource scheduling and storage management, and the method comprises the steps: collecting the real-time monitoring data of each node in the multi-service system, and carrying out the feature extraction; predicting a computing power demand value and a storage demand layering strategy in a future preset time window; dynamically correcting the prediction result, and generating a dynamic resource demand table and a cross-service priority weight matrix; generating a task allocation scheme through a hybrid scheduling algorithm; generating a storage data migration instruction through a storage scheduling engine; executing calculation node capacity expansion, task migration and storage data redistribution operation; and based on the executed latest state snapshot of the resource pool and the scheduling failure case library, updating a weight parameter of the time sequence prediction model and a scheduling strategy rule library to obtain an updated strategy version, and applying the updated strategy version to a next scheduling period. According to the invention, efficient scheduling of resources can be realized in a multi-service system environment.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The present application relates to the field of data processing, and in particular to an arithmetic unit chip configuration method, a computing subsystem, and an intelligent computing platform. The method comprises: in response to a configuration instruction for a target chip, generating an initial chip population corresponding to the target chip; performing strategy optimization on the initial chip population by using a dynamic evaluation and optimization model, so as to obtain an optimal initial individual in the initial chip population; performing crossover and mutation operations on the optimal initial individual so as to obtain an offspring chip population of the target chip; and performing iterative loop on the offspring chip population until an offspring individual meeting a chip optimization objective is selected, or until a preset iteration stop condition is met, and configuring chip parameters of the target chip on the basis of the optimal offspring individual in a chip population of a last generation. The method can improve the chip optimization efficiency, reduce the power consumption of arithmetic units, optimize the energy efficiency of arithmetic units, achieve the balance of low-energy-consumption and high-performance for arithmetic units, and improve the operation efficiency of devices.

The invention relates to the technical field of intelligent industrial manufacturing and automation, in particular to a carton production dynamic adjustment control system and a carton production dynamic adjustment method thereof. The problems of parameter lag, process coupling interference, quality tracing difficulty and the like in traditional carton production are solved.

The invention provides a method for debugging an intelligent agent through computing power of an intelligent computing center, which comprises the following steps: S1, a debugging intelligent agent receives task information provided by a user through an interactive interface, and the task information is used for indicating to debug a debugged object; the debugged object is a debugged intelligent agent or a function module or a tool function of the debugged intelligent agent; s2, the debugging agent determines a debugging requirement according to the task information; s3, the debugging agent determines a debugging method according to the debugging requirement, deploys a debugging environment according to the debugging method, generates a running instruction and debugs the debugged object; s4, the debugging agent obtains the operation information of the debugged object and analyzes the operation information, and whether the debugging requirement is met or not is evaluated according to the analysis result; s5, if not, the debugging agent checks the code of the debugged object, modifies the code according to the check result, and returns to the step S3 until the condition is met; and S6, the debugging agent feeds back a debugging result to the user through the interactive interface.

The invention provides a computing power routing addressing method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and the method comprises the steps: S1, obtaining the configuration information of the computing power service of a user; s2, generating computing power routing information based on the configuration information; and S3, based on the computing power routing information, distributing the flow corresponding to the computing power service to at least one computing power service instance corresponding to the computing power service. According to the invention, the efficiency of computing power routing addressing can be improved.

The invention provides a full-autonomous cell operation system based on body intelligence, and relates to the field of cell operation, the system comprises a system hardware architecture and a system software architecture; the system hardware architecture comprises a micromanipulation execution system, a precision positioning system, a multi-mode sensing system and an intelligent calculation and control center. The micromanipulation execution system is used for cell environment interaction, the precision positioning system is used for sample bearing and environment control, the multi-modal sensing system is used for providing key information, and the intelligent calculation and control center is used for operation decision making; the system software architecture comprises a sensing layer, a planning layer, an execution layer and an interaction layer. According to the application, cell recognition, tool selection, path planning, operation execution and result feedback can be automatically completed without manual intervention; through tool autonomous switching and strategy dynamic adjustment, various operations such as cell clamping, injection, controllable deformation and micro-assembly can be completed, and the problems that traditional cell operation depends on manual work, efficiency is low, and consistency is poor are solved.

The invention provides a computing power metering method and device of an intelligent computing center cloud platform based on a multi-dimensional performance index, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a first task type of a first computing power operation task; s2, determining a plurality of resource indexes corresponding to the first task type and a weight coefficient corresponding to each resource index; s3, collecting a parameter value corresponding to each resource index in each of the N periods; s4, calculating a computing power value corresponding to the first computing power operation task based on the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods; and S5, generating a first verification data packet based on the first task identifier of the first computing power operation task, the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods. The computing power credibility of the intelligent computing center cloud platform can be greatly improved.

The invention relates to a data processing method, a processor, a chip, a display card and electronic equipment, and relates to the field of intelligent computing, and the method comprises the following steps: reading a part of a first matrix from a local memory or a register of a computing unit in response to M tensor computing engines in a tensor computing engine cluster, reading the second matrix from the tensor memory, and executing matrix operation by the M tensor calculation engines according to part of the first matrix and the second matrix respectively to obtain operation results corresponding to the M tensor calculation engines; and the M tensor calculation engines respectively write operation results corresponding to the M tensor calculation engines into registers of the M tensor calculation engines. According to the embodiment of the invention, the computing power can be increased under the condition that the sizes and the bandwidths of the local memory and the register in the computing unit are not increased.

The invention relates to the technical field of vehicle-mounted intelligent computing, in particular to a hybrid key task scheduling method, system and equipment based on a virtualization technology. The method comprises the following steps: firstly, carrying out hybrid criticality grading on vehicle-mounted intelligent cabin tasks, and establishing a priority table; then, creating a dynamic resource partition based on a QNX Hypervisor platform, monitoring a system load state in real time through a hardware performance counter, and predicting a computing power demand of a large model; dynamically calculating an allocation scheme in combination with the task criticality level and the system energy efficiency state; then determining an optimal calculation unit selection strategy and a model precision grade by adopting an energy consumption-time delay joint optimization model; and finally, executing encrypted storage and integrity verification of large model parameters in a trusted environment, and completing end-side deployment and security verification. According to the method, the dynamic resource scheduling of the mixed key task is realized, the resource utilization rate is improved, the real-time requirement of the safety key task is ensured, and the dynamic balance between the performance and the energy efficiency is realized.

The invention provides a method and system for an intelligent computing center cloud platform to realize agent self-evolution through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, the method is executed by an embedded agent in a self-evolution agent, and the method comprises the following steps: S1, determining a function module needing to be developed and determining a development demand, the self-evolution intelligent agent is a target intelligent agent embedded with the development intelligent agent; s2, determining a design scheme of a functional module according to development requirements; s3, generating a code of the function module according to the development requirement and the design scheme; s4, debugging codes of the function modules; when the development requirement is not met, checking the design scheme and the code of the function module, returning to the step S2 if the design scheme needs to be modified, and returning to the step S3 if the code needs to be modified until the development requirement is met or the debugging frequency exceeds a preset threshold value; s5, feeding back development result information to the user; and S6, an intelligent agent online function module is embedded.

The invention relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and discloses a method and a device for distilling a reinforcement learning data set by an intelligent computing center for providing computing power, and the method comprises the steps: obtaining an original data set, generating at least one candidate answer corresponding to the original data set based on the original data set and a large model, respectively generating an answer thought corresponding to each candidate answer; based on the answer thought and the large model, generating an examination point corresponding to the answer thought; based on the answer thought, the examination point and the large model, generating a scoring standard corresponding to the examination point; and combining the original data set, the at least one candidate answer, the answer thought, the examination point and the scoring standard to obtain a reinforcement learning data set. Therefore, the reinforcement learning data set has diversity and complexity, and the large model can be trained subsequently according to the reinforcement learning data set, so that the expression and development potential of the large model in complex tasks and real application scenes is effectively enhanced, and the performance of the large model is improved.

The invention provides a computing power resource dynamic adjustment method and device of an intelligent computing center, and relates to the technical field of computing power infrastructure, and the method comprises the steps: receiving a computing power operation task execution request of a user; according to the type of the computing power operation task, a computing power resource adjustment strategy corresponding to the computing power operation task is configured, and the computing power resource adjustment strategy comprises a computing power instance number range and a computing power resource use threshold value; and dynamically adjusting the computing power resources allocated to the computing power operation task according to the computing power resource use condition, the computing power instance quantity range and the computing power resource use threshold value in the execution process of the computing power operation task. Therefore, based on the type of the computing power operation task, the computing power resource adjustment strategy of the computing power operation task is configured in a targeted manner, and furthermore, the dynamic adjustment of the computing power resource of the computing power operation task can be realized according to the use condition of the computing power resource in the execution process of the computing power operation task and the computing power resource adjustment strategy; the performance of the computing power operation task can be ensured, and the utilization rate of computing power resources can be improved.

The invention provides a heterogeneous computing power normalization metering method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a parameter value of a first related parameter of a first GPU and a parameter value of a second related parameter of a first TPU, the first GPU and the first TPU are used for executing a first computing power operation task; s2, determining a first weighting coefficient of the first GPU, and determining a second weighting coefficient of the first TPU; s3, based on the first weighting coefficient, the parameter value of the first related parameter and a preset reference value, calculating to obtain a first computing power value of the first GPU; s4, based on the second weighting coefficient, the parameter value of the second related parameter and a preset reference value, calculating to obtain a second calculation force value of the first TPU; and S5, based on the first computing power value and the second computing power value, calculating to obtain a third computing power value of the first computing power operation task. According to the method, the computing power metering accuracy of the computing power operation task is greatly improved.

The invention discloses an intelligent collaborative scheduling system and method for a general computing and intelligent computing fusion scene, and relates to the technical field of computing power resource management and scheduling. In order to solve the problem that isomerous computing power resource islands are difficult to collaborate, the system comprises a computing power access layer used for executing specified operation on isomerous computing power resources through a standardized access template, abstracting the isomerous computing power resources into a unified logic computing power unit and registering the unified logic computing power unit into a computing power pool; the resource management layer is used for continuously monitoring and collecting static attributes and dynamic states of computing power resources to form a global resource real-time view; the business processing layer is used for receiving the business submitted by the user, analyzing and identifying the business demand, converting the business demand into a demand vector, and generating a dynamic arrangement scheme based on the real-time view and the multi-target strategy library; and the collaborative scheduling execution layer is used for converting the dynamic arrangement scheme into an instruction adaptive to various APIs and completing computing power resource allocation and service starting. According to the invention, unified management and intelligent cooperative scheduling of computing power resources can be realized.

The invention provides a method and a device for dynamically metering computing power based on computing power operation tasks of an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing cloud. The method for dynamically metering the computing power of the intelligent computing center cloud platform based on the computing power operation task comprises the following steps: S1, acquiring a parameter value of first operation data of a first GPU in a first time period, wherein the first GPU is a GPU for operating a first computing power operation task; s2, determining a first task type of the first computing power operation task based on a parameter value of the first operation data; s3, determining a weighting coefficient corresponding to the first task type; and S4, based on the weighting coefficient and the parameter value of the first operation data, calculating to obtain the calculation power value of the first calculation power operation task in the first time period. According to the method, the computing power metering accuracy of the computing power operation task in the intelligent computing center cloud platform can be greatly improved.

The invention discloses a fire-fighting smoke exhaust pipeline laying planning system based on an industrial cloud platform, and relates to the technical field of pipeline laying planning, and the system comprises a building information collection module which collects building data; the cloud edge cooperative computing module is used for constructing a smoke exhaust simulation model by using distributed computing resources of an industrial cloud platform and carrying out real-time parameter iteration through edge nodes; the pipeline path planning module is used for generating a plurality of candidate laying paths by using an improved algorithm and carrying out path optimization based on a pipeline resistance coefficient and a construction cost constraint condition; and the scheme verification and optimization module is used for carrying out smoke flow dynamics simulation verification on the optimal path scheme, identifying scheme defects and carrying out iterative optimization on path parameters through a genetic algorithm. According to the invention, through a cloud-edge collaborative intelligent computing architecture, a multi-dimensional constrained dual-target optimization mechanism and a dynamic adaptive path planning and verification system are fused, and collaborative improvement of fire-fighting smoke exhaust pipeline laying planning in accuracy, efficiency and compliance is realized.

The invention provides an intelligent computing center computing power resource container network card configuration method and device, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, creating a first virtual network card based on first information, the first information being used for indicating a first network segment and a first container, the first network segment is one of a plurality of network segments included in the container network, the container network is a network used when the container cluster distributes the network segments, the container cluster comprises a plurality of computing power resource containers, the first container is one of the computing power resource containers, and the first virtual network card is used for accessing the first network segment; s2, determining a first container in the plurality of computing power resource containers based on first information; and S3, migrating the first virtual network card into the first container, and executing network card configuration operation in the first container based on the first virtual network card. The network segment expansion efficiency of the container can be improved.

The invention provides an intelligent computing center path distribution method, a data transmission method and a network system. According to the technical scheme, based on the time sequence characteristics of the AI training data flow and the characteristic that each set communication library only selects one communication algorithm to drive data flow transmission in the same communication period, the data flow identification capable of identifying the data flow and the communication algorithm corresponding to the data flow identification are obtained, and path planning is carried out based on the communication algorithm. In this way, during actual communication, data streams driven by different communication algorithms on the same path cannot be transmitted at the same time, the link congestion risk is reduced, and idle links are reserved for subsequent data streams of the same communication algorithm; different links are allocated to data flow identifiers with the same source IP address or the same destination IP address corresponding to the same communication algorithm in the same set communication domain, multi-path load sharing is carried out, data flow dispersed transmission based on the same communication algorithm drive is ensured, the congestion risk of a single link is further reduced, and the transmission efficiency is improved. The communication efficiency of an intelligent computing center network is expected to be improved.

The invention provides a method and device for realizing image detection based on computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and the method comprises the steps: S1, obtaining a first image; s2, identifying a first area where a first object indicated by the input first keyword is located in the first image; s3, acquiring information of the first area, and converting the information of the first area into first area information in a target format; s4, establishing a corresponding relationship between the first category information to which the first object belongs and the first region information; and S5, training a target detection model by using the first category information, the first region information and the corresponding relationship, wherein the target detection model is used for realizing image detection based on the computing power of the intelligent computing center. The trained target detection model is obtained in the above mode, and image detection is performed by using the target detection model based on the computing power of the intelligent computing center, so that the detection precision can be improved.

The invention provides a computing power routing gateway configuration method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, employing an agent module to collect the operation state data of a computing power routing gateway deployed on the same server node as the agent module, the operation state data of the computing power routing gateway is reported to the analysis service module; s2, the analysis service module analyzes the received operation state data of the computing power routing gateway, judges whether the computing power routing gateway has a crash risk or not based on an analysis result, takes the computing power routing gateway with the crash risk as a to-be-adjusted computing power routing gateway, and sends the to-be-adjusted computing power routing gateway to the computing power routing gateway; determining a configuration adjustment operation executed by the computing power routing gateway to be adjusted; and S3, the analysis service module calls the intelligent computing center cloud platform management system to perform configuration adjustment on the computing power routing gateway to be adjusted based on the configuration adjustment operation.

The invention provides a method for realizing agent thinking framework development through computing power of an intelligent computing center. The method comprises the following steps: S1, a thinking framework development agent receives task information for constructing a target agent thinking framework; s2, determining development requirements according to the task information; s3, determining a design scheme according to development requirements; s4, generating a code of a thinking framework and a thinking process output code according to the development requirement and the design scheme; s5, deploying a running environment, debugging codes of the thinking framework and outputting thinking process information; when the debugging result does not meet the development requirement, checking the design scheme and the code, returning to the step S3 if the design scheme needs to be modified, and returning to the step S4 if the code needs to be modified until the development requirement is met; s6, evaluating the thinking process information, if the thinking process information does not pass the evaluation and the design scheme and the codes are checked, returning to the step S3 if the design scheme needs to be modified, and returning to the step S4 if the codes need to be modified until the thinking process information passes the evaluation; and S7, feeding back result information.

The invention provides a multi-dimensional fault sensing method and device for computing power computing nodes of an intelligent computing center, and belongs to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure. The method comprises the steps that the operation state of at least one computing power computing node is monitored, and the computing power computing nodes with the abnormal operation state are sensed; taking the computing power computing node with the abnormal operation state as a target computing power computing node; obtaining target fault information of the target computing power computing node; the target fault information is compared with fault information in a preset database, the target fault dimension to which the fault of the target computing power computing node belongs is determined, the preset database comprises multiple pieces of fault information, and each piece of fault information corresponds to one fault dimension. The method can be used for solving the problem of how to realize multi-dimensional fault perception by an intelligent computing center.

The invention discloses a discrete manufacturing full-life-cycle intelligent computing system and method, the system comprises a data knowledge layer, a computing adaptation layer and a parallel service layer, a virtual matching mechanism is used between the data knowledge layer and the computing adaptation layer, that is, primary mapping is formed according to task demand description and manufacturing resource description in a twin world, an optimal algorithm is called for a specific task, the most reasonable resources are adapted, an optimal service strategy is obtained, and a mapping process from a service demand to the service strategy is realized; a virtual-real parallel computing mechanism is used between a computing adaptation layer and a parallel service layer, that is, a mutual linkage relation is established in a physical world according to a service execution strategy obtained in a twin world, so that the requirement of discrete manufacturing full-life-cycle intelligent service is met, and the mapping process from a service strategy to service execution is achieved. And through two times of mapping, autonomous computing adaptation of task-knowledge-algorithm-resource in the whole life cycle of discrete manufacturing is realized.

The invention provides a method and a device for performing large-model self-distillation absorption depth reasoning by an intelligent computing center cloud platform through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the following steps: S1, obtaining multiple groups of initial sample data; s2, constructing multiple groups of first sample data and multiple groups of second sample data based on the multiple groups of initial sample data; and S3, training the initial large model based on the multiple groups of first sample data and the multiple groups of second sample data to obtain a first large model. According to the method, the displayed natural language deep reasoning step is distilled to a continuous hidden space, and the first model can still ensure complete and excellent reasoning ability under the condition of not displaying and generating the middle deep reasoning step, so that resources occupied by a large model can be reduced; therefore, the large model service provided in the intelligent computing center cloud platform is greatly increased.

The invention provides a visual monitoring method and device for computing power resource system performance of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the following steps: S1, in the process of executing a computing power operation task, obtaining a system data set of computing power resources, the system data set comprises system performance data of the computing power resource at multiple moments in a target time period, and the system performance data comprises at least one of a CPU (Central Processing Unit) utilization rate, a GPU (Graphics Processing Unit) utilization rate, a memory utilization rate and a disk utilization rate; and S2, on the basis of the system data set, generating at least one first visual chart which is used for representing system performance data of the computing power resources in the target time period. According to the method, a user can intuitively obtain the change condition of system performance, and the operation and maintenance efficiency of computing power resources can be greatly improved.

The invention relates to a dynamic extensible heterogeneous collaborative intelligent computing platform and method for edge-end equipment, and relates to the technical field of edge intelligent computing and computer hardware, and the platform comprises a hardware infrastructure layer and a master control module for realizing signal transmission and resource sharing; the basic software layer is used for managing hardware resources and software services; the application support layer is used for running computing resources and realizing preliminary compatibility; the task management and resource scheduling layer is responsible for task arrangement and management and task scheduling decision; the application layer is used for providing various application services; the data transmission layer is responsible for transmitting data among different platforms; the computing resource interface layer provides an interface interacting with heterogeneous computing resources; and the computing resource platform is composed of different heterogeneous computing platforms and provides computing resources and computing power. The method has the capabilities of intelligent information and signal processing, diversification of application scenes, efficient parallel operation, rapid iterative development, universal flexible deployment and the like.

The invention provides a method and device for generating task planning information based on computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, taking a to-be-processed first business demand as a task planning target, and carrying out task planning based on a target model, a function name and / or a parameter name in the mask planning data are / is masked, and the target model is obtained by performing fine adjustment on the initial model based on multiple pieces of fine adjustment data; and S2, based on the mask mapping information corresponding to the multiple pieces of fine adjustment data, mask removal is performed on the function name and the parameter name in the mask planning data to obtain target planning data, and the target planning data is used for being executed by a task planning executor to realize the first business demand. In the model fine tuning stage, the function names and the parameter names are masked, so that the illusion problem of the large model can be inhibited, and the execution reliability of the target planning data output by the model is improved.

The multi-model photovoltaic module array control system comprises an inverter, an intelligent calculation module, a controller and a plurality of paths of photovoltaic module strings, the controller is used for reading the actually measured voltage of each path of photovoltaic string, comparing the actually measured voltage with a voltage threshold value, adjusting a pwm parameter and outputting the actually measured voltage to the inverter; calculating the time average voltage of each path of photovoltaic string, and transmitting the time average voltage to the intelligent calculation module; a voltage threshold update value transmitted by the intelligent calculation module is received, and the voltage threshold is updated; the inverter is used for receiving the actually measured voltage, calculating group average voltage and transmitting the group average voltage to the intelligent calculation module; and the intelligent calculation module is used for receiving the time average voltage and the group average voltage, calculating a voltage threshold update value and transmitting the voltage threshold update value to the controller. Pwm parameters are adjusted according to the comparison result of the actually measured voltage and the voltage threshold value, the intelligent calculation module calculates the updated value of the voltage threshold value, current backflow is actively inhibited, energy backflow caused by mismatching of component parameters is avoided, and the voltage threshold value is dynamically adjusted.

The invention provides a method and a device for an intelligent computing center cloud platform to realize intelligent agent development of an intelligent agent reflection system through computing power. The method comprises the following steps: S1, receiving task information for developing a target intelligent agent reflection system; s2, determining development requirements of the reflection system according to the task information; s3, determining a design scheme of the reflection system according to development requirements; s4, generating codes of the reflection system according to the development requirements and the design scheme; s5, deploying a running environment of the reflection system and verifying a code of the reflection system; the verification comprises the steps of checking the execution process of the target agent in the process of executing the test case, and judging whether the execution process comprises the reflection process or not; when the verification result indicates that the reflection process is not included, checking the design scheme and the code of the reflection system, returning to the step S3 if the design scheme needs to be modified, and returning to the step S4 if the code needs to be modified until the verification result indicates that the reflection process is included; and S6, feeding back development result information to the user.