720 results about "Dynamic testing" patented technology

The invention discloses an aerospace knowledge large model construction method based on progressive knowledge injection and retrieval enhancement generation, which comprises the following steps: collecting and preprocessing multi-source knowledge data, and constructing an aerospace field knowledge base; the method comprises the following steps: based on a DeepSeek-R1-8B model, carrying out continuous pre-training by adopting a three-stage progressive mixed course learning framework; performing supervision fine tuning through instruction data and a knowledge guiding mechanism to complete basic construction of a large model; a retrieval enhancement generation module is constructed, a'retrieval-filtering-generation 'process is formed, and the knowledge coverage and fact consistency of the model are improved through deep fusion of real-time retrieval and generation reasoning; and through a multi-dimensional quantitative index and a dynamic test mechanism, the performance of the model is evaluated, and a complete evaluation ecology from data construction to feedback optimization is formed. According to the method provided by the invention, deep integration and efficient application of knowledge in the aerospace field are realized through multi-stage collaborative optimization.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention relates to the technical field of industrial robots, in particular to an industrial robot automatic testing method and system, and the method comprises the following steps: based on a robot communication interface, collecting a communication signal of a robot, measuring the delay of signal transmission, verifying the integrity of transmission data, and evaluating the quality of a communication link; and generating a signal quality analysis result. According to the method, accurate evaluation of the communication quality of the robot and construction of a path planning task and a dynamic test environment are realized by collecting communication signals, deep evaluation of the path planning capability of the robot is provided, and measurement of the execution precision of the robot is realized by combining deviation identification of an actual execution path and a target path; a sensor is used for collecting vibration data under various loads, abnormal vibration of the robot under load changes is monitored in real time by analyzing vibration amplitude and frequency changes, the stability and performance of the robot under various load conditions are diagnosed, and the load bearing capacity of the robot is judged.

The invention relates to the technical field of structure health monitoring and bearing capacity evaluation, and discloses a heat absorption tower hybrid structure dynamic detection bearing capacity evaluation method, which comprises the following steps: S1, arranging various sensors on a heat absorption tower structure, including an acceleration sensor, a strain gauge, a temperature sensor and a displacement sensor, collecting dynamic response data of the heat absorption tower under different working conditions; s2, on the basis of data collected by the sensors, a digital twin model of the heat absorption tower is built, and the model comprises finite element three-dimensional models of a tower footing, a tower body and a tower top. Real-time monitoring and high-precision prediction of the heat absorption tower are realized through a sensor network and a digital twinning technology, structural problems can be intelligently identified and early warning can be carried out in advance, meanwhile, a maintenance strategy is optimized based on fault prediction of AI, the maintenance cost is reduced, the equipment operation efficiency is improved, and the accuracy of structural analysis is further improved through introduction of the digital twinning technology. And the long-term safety and stability of the heat absorption tower are ensured.

The invention belongs to the field of chip power consumption detection, relates to a data analysis technology, is used for solving the problem that optimization decision analysis cannot be carried out according to test data when the power consumption of a chip is abnormal in the prior art, and particularly relates to a chip power consumption evaluation method and system based on artificial intelligence and a storage medium. Comprising a static test module, a static analysis module, a dynamic test module, a dynamic analysis module and a database, the static test module is used for performing static test on the chip power consumption; the static analysis module is used for analyzing a static test result of the chip and marking a static test object i as a static normal object or a static abnormal object; according to the method, the basic power consumption performance of the chip in the static operation environment can be tested, then the static test result is subjected to data processing to obtain the static coefficient, then the static test object is subjected to differential marking according to the static coefficient, and data support is provided for the performance evaluation and analysis process.

The invention relates to the technical field of thermal equipment operation state dynamic test and compensation control, in particular to an AI-based boiler air pre-heater temperature uniformity regulation and control system, which comprises a collaborative closed-loop control network of a data acquisition module, an AI prediction module, a heat storage system control module, a temperature compensation control module and a thermal stress analysis module. The data acquisition module acquires flue gas temperature, coal quality parameters and load instructions in real time, the flue gas temperature, the coal quality parameters and the load instructions are transmitted to the AI prediction module through an industrial bus, XGBoost, LightGBM and a deep neural network model are used for carrying out spatial-temporal feature extraction on the evolution trend of a temperature field, and the heat storage system control module dynamically switches heat storage and release modes according to the load state. The heat distribution proportion among the multiple units is adjusted by combining the three-dimensional stress field distribution data of the thermal stress analysis module; the bottleneck of lack of thermal inertia regulation of an existing system is broken through, dynamic balance of a temperature field under the condition that thermal stress of a heat transfer element is controllable is achieved, and the heat exchange efficiency and operation stability of the air pre-heater are improved.

The invention relates to the technical field of software testing, and discloses a computer software performance testing method which comprises the following steps: S1, determining performance testing requirements: determining key performance indexes according to application requirements, and designing a layered load model; s2, constructing a dynamic test environment: realizing elastic adjustment of resources by utilizing a virtualization technology; s3, distributed performance testing: executing concurrent testing at a plurality of nodes, and simulating access of different users; s4, transverse extension testing: adjusting a virtual machine or a container, and evaluating the system extension capability; s5, collecting and analyzing performance data: analyzing and monitoring the performance data through a time sequence, and identifying a bottleneck; and S6, generating an optimization suggestion and a regression test: automatically generating the optimization suggestion, and verifying the effect through the regression test. Through combination of a virtualization technology, Kubernetes and OpenStack and cooperation with a lateral extension test, the stability and the extension capability of the system under a high concurrency condition are verified, and the overall operation efficiency is improved.

The invention belongs to the technical field of multi-modal learning, and discloses an operating system security evaluation system and method based on multi-modal learning and deep learning technology, the system uses the multi-modal learning technology, integrates vulnerability description, code structure and runtime data, extracts cross-modal features through a deep learning model, and constructs a unified evaluation framework. The system firstly collects vulnerability data from databases such as CVE, NVD and the like, and forms multi-dimensional input in combination with a calling path generated by static analysis and a running log of dynamic test. Secondly, predicting the availability and risk degree of the vulnerabilities through a multi-modal fusion network, and generating a comprehensive risk score; and finally, outputting a visual report by the system, and clearly displaying risk distribution and priority. Potential and actual security threats of the operating system are comprehensively evaluated, and the accuracy and reliability of evaluation are improved.

The invention relates to the technical field of engineering structure monitoring, in particular to a structure health monitoring method based on a distributed strain dynamic test. According to the technical scheme, the method comprises the steps of arrangement of a composite sensor array, acquisition of strain data, self-adaptive data transmission, operation of a data processing and analysis center and operation of a man-machine interaction interface, and the composite sensor array composed of multiple types of distributed strain sensors is arranged at an engineering structure part. The strain sensors comprise a fiber bragg grating strain sensor, a resistance strain gauge and a piezoelectric film strain sensor. According to the invention, various types of distributed strain sensors are adopted to form a composite sensor array, and comprehensive, real-time and accurate health monitoring and damage prediction of an engineering structure are realized in combination with a self-adaptive data transmission network and an advanced data processing and analysis technology; and more effective technical support is provided for safety guarantee of civil engineering structures.

The invention provides a wafer testing system and method, and belongs to the field of semiconductor testing. The wafer test system comprises a control host, a probe card and a test device, the probe card comprises a signal switch; the control host is used for generating a target mode test instruction and sending the target mode test instruction to the probe card; and the probe card is used for enabling the signal switch to select a corresponding target mode based on the target mode test instruction so as to acquire a test signal corresponding to the to-be-tested wafer device, and sending the test signal to the test device so as to obtain test data of the to-be-tested wafer device. Devices required for dynamic detection and static detection of wafer devices are integrated on the probe card, and mode selection is carried out through a signal switch. When a dynamic test and a static test need to be switched, the probe does not need to be switched, and only the signal switch needs to be switched on and off, so that the wafer test efficiency is improved.

The invention relates to the technical field of edge computing, electric automobile testing and the like, and provides an electric automobile instrument testing method based on end-cloud collaboration, which comprises the following steps of: constructing a lightweight virtualized resource isolation environment at an edge computing node, loading signal generation, communication agency and state monitoring micro-service; a test platform with elastic capacity expansion and contraction and fault self-recovery capability is formed; historical test data are collected at a cloud end, and an equipment health degree prediction model is trained and deployed to an edge node to support a local intelligent test; constructing a dynamic test service chain, generating and pre-distorting and compensating an enhanced test signal, and then acting the enhanced test signal on the instrument; through multi-modal signal acquisition and unified preprocessing, in combination with wavelet packet decomposition and a deep neural network, high-dimensional feature extraction is realized, a test report containing a health trend is finally output, and a test process is actively intervened based on a trend prediction result. According to the method, the authenticity of test signals, the health assessment precision and the safety and efficiency of the test process are effectively improved.

The invention relates to the technical field of GNSS (Global Navigation Satellite System) testing, in particular to a real-time dynamic testing method for a navigation type GNSS receiver, which comprises the following steps of: collecting GNSS signal data, carrying out time window sample segmentation on a received GNSS signal, analyzing signal intensity fluctuation and loss events of each sample, recording geometric coverage of each segment, calculating the number of coverage points and generating fractal dimension data. According to the invention, by collecting and analyzing the signal samples in different time windows, the fluctuation and loss events of the signal intensity can be evaluated more accurately, and the instant identification capability of signal loss and interference is enhanced. Through fractal dimension calculation and difference analysis, abnormal changes in a signal mode are effectively identified, and the understanding of navigation accuracy in a complex signal environment is improved. Besides, the signal complexity is analyzed and combined with climate and geographical environment data, so that the attribution of the signal change is more accurate, and the adaptability of the receiver under different environment conditions is optimized.

The invention discloses an automatic test case generation method and system based on a large model, and the method comprises the steps: multi-modal data fusion: receiving a natural language demand, a UML design drawing, an API document, a historical defect log and system monitoring data, aligning text and image features through a CLIP model, analyzing the interface constraint through CodeBERT, and carrying out the multi-modal data fusion; feature fusion is realized by adopting a cross-modal attention mechanism; a dynamic test scene modeling step: constructing an environment simulator based on reinforcement learning, and simulating high-concurrency, distributed abnormal and other complex scenes; and a layered test case generation step: generating a function test case, a boundary test case and an increment test case through GPT-4, GAN and LSTM respectively, and introducing an entropy evaluation model to quantify the effectiveness of the cases. According to the method, the multi-modal data is brought into the test case generation process for the first time, the test case generation time is shortened by 80%, the complex scene coverage rate is increased to 95%, the semantic matching degree reaches 92%, and the intelligent level and efficiency of software testing are remarkably improved.

The invention discloses a multifunctional angle module test bench which is composed of a bench base, a load simulation device and a pavement simulation device. The bench base is provided with a lifting sliding column, and the height of a loading platform can be adjusted to adapt to suspension deformation; the load simulation device realizes vertical load adjustment by increasing or decreasing balancing weights; the pavement simulation device integrates a belt pulley shaft, a motor, a magnetic powder brake and a pressure oil cylinder, and simulates pavement resistance through belt rolling; during testing, the angle module body is fixed to a loading platform, tires make contact with a belt to achieve dynamic testing, a magnetic powder brake increases resistance torque for a main belt wheel shaft so as to simulate different road conditions, a motor drives the belt to test regenerative braking performance, and a pressure oil cylinder is matched with a thrust bearing to measure axial loads. The steering resistance and tire mechanical property test is completed by changing the included angle between the wheel and the belt motion direction; according to the invention, driving, braking, steering and suspension system testing functions are integrated, and multi-working-condition comprehensive performance detection is realized.

The utility model discloses a DBC automatic test workstation, which comprises a test mechanism, a movable truss and a machine table, the test mechanism comprises a first test station, a second test station and a third test station, and the first test station, the second test station and the third test station are all installed on the machine table. The two ends of the movable truss are installed on the two sides of the machine table, the moving path of the movable truss is arranged above the first testing station, the second testing station and the third testing station, the movable truss is used for testing the bonded DBC module, the equipment can integrate various testing machines, and the testing efficiency is improved. Detection items such as safety testing, static testing and dynamic testing are automatically completed according to program setting, different testing processes can be achieved by calling testing programs, meanwhile, the equipment has the function of recording and storing testing data, the testing items are flexibly configured, and the testing efficiency is remarkably improved.

The invention relates to the technical field of satellite navigation, in particular to a real-time dynamic test method for a navigation type GNSS receiver, which comprises the following steps of: reconstructing a positioning point track by using positioning data of the GNSS receiver, calculating an error by comparing an actual track, and analyzing the influence of a motion state parameter on error change; and analyzing the influence of the environmental factors on the receiver performance in combination with the environmental data, predicting the receiver performance change trend based on the error fluctuation and the stability evaluation data, and obtaining a receiver performance evaluation result. According to the method, the positioning track of the receiver is reconstructed by acquiring the positioning data of the GNSS receiver, the error of each data point is accurately calculated, the influence of the motion state parameters on the error change is analyzed, the performance of the receiver in various dynamic environments is accurately identified, and the performance of the receiver in various dynamic environments is accurately identified through the fusion of the environment data and the motion state data. And predicting the performance change trend of the receiver under various environmental conditions, and realizing accurate prediction and evaluation of the performance of the receiver in combination with the precision performance data.

The invention provides a dynamic test system for a driving circuit of a motor driver. The dynamic test system comprises a control module, a driving module, a power supply module, a test tool and a data processing platform, the control module is used for controlling on and off of the tested power device and on and off of various relays, and receiving parameters and instructions sent by the data processing platform; the driving module is used for realizing various topologies and driving circuit forms of the inverter circuit; the power supply module outputs a driving voltage for the driving module and a power supply voltage for the control module; different driving voltages are used for driving signals of different power devices; the test tool comprises a heating tool, various voltage and current test devices and a structural member for fixing the devices at test points of the driving module; and the data processing platform is used for classifying voltage and current waveforms and identifying key data. According to the invention, the efficiency, the accuracy and the intelligent degree of the double-pulse test can be improved.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention relates to the field of software testing, in particular to a financial payment transaction testing method based on recording and playback. The method comprises the following steps: recording an interaction behavior of a user on a front-end interface, capturing back-end request and response data, generating an original operation sequence, and converting the original operation sequence into a parameterized test script; processing parameter variables in the parameterized test script through a dynamic test data generation algorithm, generating test parameters, and further obtaining a dynamic test parameter set; executing an operation instruction based on the parameterized test script and the dynamic test parameter set, and recording actual output; a verification result is generated for the test parameter based on the actual output, and an overall pass rate of the automated test is calculated. The problems that a traditional software test method depends on manual design of test cases and manual test execution, time consumption is long, efficiency is low, and key test scenes are prone to being omitted are solved; all input conditions and abnormal scenes are difficult to cover; a unified automatic process is lacked, and the test process is difficult to reuse.

The invention relates to an automatic driving intelligent safety multi-task dynamic test system based on a Carla simulation platform, and belongs to the field of artificial intelligence safety and automatic driving simulation test. The system comprises an automatic driving perception model integration and training module which comprises a multi-task data collection module, a fine tuning perception model and a compilation perception model Carla API interface; the attack and defense algorithm integration and training module is used for finely adjusting an attack and defense algorithm, writing an attack and defense algorithm Carla API (Application Program Interface) and replacing Carla object textures with an adversarial patch; and the real-time attack and defense deduction and evaluation recording module comprises a sensor for acquiring Carla real-time data, replacing textures, starting a perception model and starting an evaluation record of the success rate and the accuracy rate of defense and attack. According to the method, the robustness of the automatic driving perception model in a complex attack scene can be comprehensively evaluated, security holes of a perception system can be found in time, and the reliability and the security of an automatic driving technology in a complex environment are improved.

The invention relates to the technical field of radiator monitoring, and particularly discloses a radiator heat dissipation performance dynamic test system, which is characterized in that a heat source simulation body is arranged on a heat transfer interface of a radiator, and a high-fidelity dynamic electric power load is applied to the heat source simulation body to simulate an actual working condition; radiating a microwave detection signal to a space where the heat source simulator is located and receiving a multi-path scattering response signal; the three-dimensional dielectric constant dynamic distribution of the heat source simulation body is reconstructed by solving the inverse scattering problem fused with the space-time constraint; according to the material dielectric constant-temperature relation mapping, three-dimensional temperature field dynamic distribution is generated; and finally, intelligently identifying and extracting a migration track and a temperature change curve of the highest temperature point in the space from the continuous time sequence temperature field data, and taking the migration track and the temperature change curve as a direct evaluation basis for the dynamic heat dissipation performance of the radiator. According to the method, lossless, full-field, real-time visualization and quantitative analysis of the migration process of the dynamic hot spots in the hidden heat source are realized, and the authenticity of the dynamic heat test is improved.

The invention discloses a Java code generation and self-repairing system, which comprises a natural language processing module, a data processing module, a self-repairing module and a self-repairing module, the code generation module is used for generating code snippets or complete codes according with Java grammar specifications and coding habits through a deep learning model; and the intelligent repair module is used for performing static analysis and dynamic test on the Java code to find errors and defects in the code. According to the method, advanced technologies such as deep learning and natural language processing are introduced, high-quality and maintainable codes are automatically generated according to natural language description or specific requirements of developers, and the flexibility and maintainability of code generation are improved.

The invention relates to the technical field of adhesives, in particular to a tensile test system for a structural adhesive, which is characterized in that a first sample group and a second sample group are prepared through a sample preparation unit; aiming at a plurality of sample subgroups in the sample group, standard tension test, dynamic tension test and cyclic tension test are respectively carried out through the standard test unit, the dynamic test unit and the cyclic test unit, and the data acquisition unit acquires test data in the tension test process in real time; in addition, the intelligent control unit controls the tension testing machine in the corresponding tension test through the standard tension increasing rate, the dynamic tension increasing rate and the circulating tension increasing rate; the intelligent control unit also controls the temperature of a test environment, and tension tests are carried out under different temperature conditions; the data analysis unit analyzes the tension test data to obtain a tension test result; the intelligent tension test system provided by the invention has high flexibility and adaptability, and can obtain a more accurate tension test result.

The invention provides an intelligent code generation method based on collaborative arrangement of a multi-source knowledge graph and a large model, and the method comprises the steps: uniformly converting the structured and semi-structured business information of multiple sources into a knowledge graph G after conflict resolution, converting a user demand into a structured intention object, and combining with the knowledge graph G after conflict resolution, thereby achieving the intelligent code generation. The method comprises the following steps: constructing a sub-task list, cooperatively driving a plurality of large language models to generate module-level codes according to sub-tasks t in the sub-task list T and initial contexts of the sub-tasks t, performing static check, dynamic test and business consistency check on generated candidate codes O, and generating a feedback prompt F to correct a Prompt template if the detection is not successful. And calling the corrected Prompt template for iteration through a large language model to realize end-to-end automatic generation from a demand intention to front and back end codes, interface documents and deployment scripts, and ensuring generation quality and business consistency through a closed-loop mechanism of ''graph driving-semantic reasoning-check feedback''.

The invention discloses a loadable folding rudder unfolding dynamic testing device and method.The device comprises a workbench, data sampling and analyzing equipment, a control surface limiting assembly, a loading assembly, a base assembly and a transmission assembly, and the control surface limiting assembly and the base assembly are arranged on the workbench; the loading assembly, the transmission assembly and the folding rudder are arranged on the base assembly, and the folding rudder is connected with the control surface limiting assembly and the transmission assembly. The device provided by the invention can continuously monitor the motion characteristics, the unfolding time and the vibration characteristics of the folding rudder in the unfolding process. In order to restore the real working condition of the folding rudder more truly, a loading assembly used for simulating pneumatic torque is additionally arranged, and loads can be increased when the folding rudder is unfolded and tested according to needs. In addition, through simple adjustment, the device and the method can be used for testing folding rudders with different interfaces, boundary dimensions, folding directions and angles.

The invention discloses a test system and method for generating capability evaluation based on a large model, and the method comprises the steps: S1, carrying out threat modeling, and generating a test case, including classifying a threat scene and generating an automatic test case, the generation content of the test case including generation of an adversarial sample and generation of a privacy test case; s2, a multi-dimensional test is executed, the multi-dimensional test comprises a dynamic test engine and real-time defense detection, and test indexes of the dynamic test engine comprise a sensitive information leakage rate, a malicious instruction execution rate and a prejudice index; s3, performing vulnerability diagnosis, including analyzing attention weight and tracing privacy leakage; and S4, performing verification and iterative repair, including collecting vulnerability samples, performing mixed training, adjusting a loss function and performing regression testing. Through threat modeling and attack scene classification, various potential attacks can be identified and covered, test comprehensiveness is ensured, neglected potential safety hazards can be found, and test effectiveness is improved.

The invention relates to the technical field of strength detection, in particular to a TPU particle strength detection device. The invention provides a TPU particle strength detection device capable of simulating an extreme environment and realizing dynamic pressure simulation, which comprises a rack, a hydraulic cylinder mounted in the rack, a piston part of the hydraulic cylinder fixedly connected with a lifting frame, a pressing frame slidably connected in the lifting frame, and an impact frame slidably connected on the pressing frame, and telescopic rods which are symmetrically distributed are fixedly connected to the pressing frame. According to the device, conventional testing is conducted on TPU particles through the hydraulic cylinder, the lifting frame, the pressing frame, the pressure sensor and the like, then dynamic pressure testing is simulated through the impact frame, then the compression condition of the TPU particles in the extreme environment is simulated through the cold air channel and the heating wire, and the device integrates conventional testing, dynamic testing and extreme temperature testing; the performance of the TPU particles under different conditions can be comprehensively evaluated, the tedious process of respective testing is avoided, and the testing efficiency is improved.

The invention relates to the technical field of energy testing, in particular to a self-adaptive dynamic energy testing method based on combined heat and power generation, which comprises the following steps: constructing a thermoelectric coupling transfer model and a working condition identification model, and inputting power generation power and heat supply power into the thermoelectric coupling transfer model to correct model parameters, inputting the environmental parameters into a working condition recognition model, recognizing a current working condition type based on a preset working condition feature library, calling an initial test scheme from a test scheme library according to the current working condition type, and generating a dynamic test scheme through a machine learning algorithm in combination with the parameters corrected by the thermoelectric coupling transfer model; and controlling the test equipment to execute a test based on the dynamic test scheme, collecting test data in real time, calculating a system performance index according to the test data, comparing the system performance index with a preset threshold value, and generating feedback information for adjusting the test scheme. Therefore, the problems that in the prior art, a test scheme is fixed and cannot adapt to complex working conditions, and performance evaluation is incomplete are solved.

The invention belongs to the technical field of compressor performance prediction, and particularly provides a method and system for determining a surge judgment critical value of a compressor based on multi-source data fusion. Comprising the steps that a compressor surge test system is built and operated, and a first surge critical value is calculated; obtaining multi-source historical data related to surge of the compressor, selecting characteristic variables to construct historical input vectors, and fitting the multiple regression model by using the historical input vectors to obtain a discrimination model; training the machine learning model to obtain a trained machine learning model; a real-time input vector is constructed based on data, collected in real time, of the to-be-monitored compressor, and then a second surge critical value and a third surge critical value are obtained; and fusing the first surge critical value, the second surge critical value and the third surge critical value to obtain a predicted surge critical value of the to-be-monitored compressor. According to the method, high-precision dynamic testing and historical data regression analysis are combined, and the actual value of the surge critical value is finally and quantitatively determined through multi-parameter coupling measurement and data mining.