Circuit board performance detection method and system

Abstract

The invention relates to the technical field of test circuits, in particular to a circuit board performance detection method and system. The method comprises the following steps: acquiring circuit board data; performing structure topology analysis on the circuit board data to obtain circuit board structure data; performing space mapping on the circuit board structure data to generate a circuit board structure model; performing circuit board environment feature extraction on the circuit board data to obtain circuit board environment data; performing dust influence analysis on the circuit board environment data to obtain circuit board dust influence data; and performing conductive track identification on the circuit board structure model to obtain conductive track data. Therefore, through the Internet of Things technology, the data mapping technology, the intelligent detection technology and the block chain technology, multi-dimensional correlation analysis is performed on the structural characteristics of the circuit board, so that the accuracy of circuit board detection is improved; and carrying out potential performance problem prediction on the performance data obtained by detection.

Description

Technical Field

[0001] The present invention relates to the technical field of test circuits, and in particular to a circuit board performance detection method and system. Background Art

[0002] The development of circuit board performance detection technology has experienced an evolution from basic manual visual inspection to automated and intelligent detection. In the early days, due to the relatively simple design of circuit boards, manual visual inspection was mainly relied on, but this method was inefficient and prone to errors. With the increasing complexity of electronic equipment, manual inspection could not meet production needs, prompting the industry to introduce automated testing equipment, such as bed of needle testers and flying probe testers, which identify manufacturing defects through electrical performance testing and test analog, digital and mixed signal components to perform circuit board performance testing; in recent years, with the development of deep learning algorithms, a variety of circuit board performance detection devices have emerged, such as multi-layer circuit board electrical performance testing devices, which automatically detect circuit board performance by integrating advanced sensors and automation technologies. However, the existing technology does not adequately analyze the structural characteristics of the circuit board. When performing circuit board performance testing, the structural characteristics are not fully considered as an important influencing factor in the evaluation, resulting in low detection accuracy and failure to predict potential problems with the performance data obtained from the test. Summary of the invention

[0003] Based on this, it is necessary to provide a circuit board performance detection method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a circuit board performance detection method is provided, the method comprising the following steps:

[0005] Step S1: acquiring circuit board data; performing structural topological analysis on the circuit board data to obtain circuit board structural data; performing spatial mapping on the circuit board structural data to generate a circuit board structural model;

[0006] Step S2: extracting circuit board environmental features from circuit board data to obtain circuit board environmental data; performing dust impact analysis on the circuit board environmental data to obtain circuit board dust impact data; performing conductive track identification on the circuit board structure model to obtain conductive track data; performing conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain circuit board conductive performance data;

[0007] Step S3: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; performing integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; performing heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data;

[0008] Step S4: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; determining the interlayer relationship of the transmission medium layer data to obtain interlayer relationship data; performing conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; performing signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain circuit board signal performance data;

[0009] Step S5: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

[0010] The present invention constructs a circuit board structure model through structural topology analysis and space mapping, which can provide physical and spatial information for subsequent performance testing and ensure the accuracy of the testing process; identifies environmental factors encountered by the circuit board in a specific environment, and performs dust impact analysis on it, which can reveal in detail the specific impact of dust on the performance of the circuit board, such as the resulting short circuit, poor contact and heat dissipation problems; identifies the conductive track of the circuit board structure model and clarifies the specific information of the conductive track; performs conductive performance detection on the conductive track data according to the dust impact data of the circuit board, which can verify the conductive performance of the circuit board, thereby avoiding circuit failures caused by dust. Microfluidic circuit identification of the circuit board structure model helps analyze the fluid transmission path on the circuit board; microchannel analysis of microfluidic circuit data can evaluate the efficiency and performance of the microchannels on the circuit board; integration evaluation of microfluidic circuit data based on circuit board microchannel data can clarify the space utilization and functional integration of the circuit board; heat dissipation parameter measurement of circuit board integration data is crucial for the subsequent stable operation of the circuit board under high load; heat dissipation performance testing of circuit board data based on heat dissipation parameter data to obtain circuit board heat dissipation performance data helps prevent performance degradation caused by overheating of the circuit board. Identifying the transmission medium layer of the circuit board structure model helps to understand the signal transmission path in the circuit board; determining the inter-layer relationship of the transmission medium layer data can analyze the signal transmission efficiency in the circuit board; analyzing the conductor medium characteristics of the transmission medium layer data based on the inter-layer relationship data can clarify the signal transmission quality and speed in the circuit board; testing the signal performance of the circuit board data based on the conductor medium characteristic data helps to verify the performance of the circuit board in communication; integrating the circuit board conductive performance data, circuit board heat dissipation performance data and circuit board signal performance data to obtain the circuit board performance data; constructing a circuit board performance problem prediction model for the circuit board data based on the circuit board performance data, providing a model basis for subsequent predictions; predicting performance problems of the circuit board data based on the circuit board performance problem prediction model ensures the comprehensiveness of the performance problem prediction, so as to more accurately identify potential circuit board performance problems; uploading the circuit board performance test report to the blockchain network distributed storage ensures the security and non-tamperability of the test report, while providing test report traceability and enhancing the credibility of the test report. Therefore, the present invention uses Internet of Things technology, data mapping technology, intelligent detection technology and blockchain technology to achieve multi-dimensional correlation analysis of the structural characteristics of the circuit board, thereby improving the accuracy of circuit board detection; and predicting potential performance problems based on the performance data obtained from the detection.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: extracting nonlinear features from the circuit board data to obtain circuit board nonlinear data; and identifying topological features from the circuit board nonlinear data to obtain circuit board topological feature data;

[0013] Step S12: performing circuit structure analysis on the circuit board topology feature data to obtain circuit board structure data; performing geometric analysis on the circuit board structure data to obtain circuit board structure analysis data;

[0014] Step S13: performing line spatial morphology positioning on the circuit board structure analysis data to obtain line spatial morphology data; performing multi-dimensional parameter determination on the line spatial morphology data to obtain spatial morphology parameter data;

[0015] Step S14: spatially mapping the circuit board data according to the spatial morphological parameter data to obtain a circuit board spatial mapping model; and visually rendering the circuit board spatial mapping model according to the circuit board structural data to generate a circuit board structural model.

[0016] The present invention extracts nonlinear features from circuit board data, which can deeply reveal the dynamic response of the circuit board under different working conditions, and is helpful to understand and predict the performance of the circuit board in practical applications; topological feature recognition is performed on the nonlinear data of the circuit board, which provides an accurate basis for analyzing the connection mode and structural relationship of the circuit board; line structure analysis is performed on the topological feature data of the circuit board, and the physical layout of the circuit of the circuit board is clarified; geometric analysis is performed on the structural data of the circuit board, which can accurately identify and quantify the physical layout of the circuit board, and can reveal the manufacturing defects of the circuit board; line spatial morphology positioning is performed on the structural analysis data of the circuit board, and accurate spatial information is provided for the performance detection of the circuit board; multi-dimensional parameter determination is performed on the line spatial morphology data, which is helpful to accurately evaluate the three-dimensional structure of the circuit board during the detection process, thereby ensuring the reliability of the detection result; the circuit board space mapping model and structural model generated by space mapping and structure visualization rendering can intuitively understand and analyze the complex structure of the circuit board.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: performing circuit board environmental feature recognition on the circuit board data to obtain circuit board environmental data; performing dust microscopic analysis on the circuit board environmental data to obtain circuit board dust data; performing motion state simulation on the circuit board dust data to obtain dust motion state simulation data;

[0019] Step S22: performing circuit board surface feature recognition on the circuit board structure model to obtain a circuit board surface image; performing coverage area calculation on the circuit board surface image according to the dust movement state simulation data to obtain dust coverage area data; performing electrical performance impact analysis on the dust coverage area data to generate circuit board dust impact data;

[0020] Step S23: performing current path analysis on the circuit board structure model to obtain current path data; determining the current flow direction on the current path data to obtain current flow direction data; and performing conductive track identification on the circuit board structure model according to the current path data and the current flow direction data to obtain conductive track data;

[0021] Step S24: performing contact impedance characteristic analysis on the conductive track data according to the circuit board dust impact data to obtain contact impedance characteristic data; performing conductive path continuity evaluation on the contact impedance characteristic data to obtain path continuity evaluation data; performing current distribution uniformity detection on the path continuity evaluation data to obtain distribution uniformity detection data;

[0022] Step S25: perform potential equalization identification on the distribution uniformity detection data to obtain potential equalization data; perform charge transfer efficiency calculation on the potential equalization data to obtain charge transfer efficiency data; perform electron transfer rate analysis on the charge transfer efficiency data to obtain electron transfer rate data; perform conductivity performance detection on the circuit board data according to the electron transfer rate data to obtain circuit board conductivity performance data.

[0023] The present invention identifies environmental features of circuit board data, providing a basis for analyzing performance changes of circuit boards in specific environments; further, microscopic analysis of dust is performed to help understand the specific impact of dust on circuit board performance. Through motion state simulation, the behavior of dust in an actual environment can be simulated, which is crucial for evaluating and improving the dust-proof performance of circuit boards; the identification of circuit board surface features and the calculation of dust coverage area can quantitatively analyze the impact of dust on the circuit board surface; the electrical performance impact degree analysis of dust coverage area data provides accurate quantitative indicators for evaluating the specific impact of dust on the electrical performance of circuit boards. Conducting current path analysis on the circuit board structure model can provide current flow information for identifying the conductive tracks in the circuit board; conducting conductive track identification on the circuit board structure model based on the current path data and current flow direction data can determine the conductive track information; conducting contact impedance characteristic analysis on the conductive track data and conducting conductive path continuity evaluation on the contact impedance characteristic data can reveal the integrity and stability of the conductive path in the circuit board; conducting current distribution uniformity detection on the path continuity evaluation data helps to identify and prevent overheating and performance degradation caused by uneven current distribution; potential balance identification and charge transfer efficiency calculation provide key indicators for evaluating the electrical performance of the circuit board; electron transfer rate analysis plays an important role in understanding the conductivity efficiency and speed of the circuit board; conducting conductive performance detection on the circuit board data based on the electron transfer rate data will provide a comprehensive and accurate evaluation of the conductive performance detection of the circuit board.

[0024] Preferably, step S3 comprises the following steps:

[0025] Step S31: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel geometric feature extraction on the microfluidic circuit data to obtain microchannel geometric feature data;

[0026] Step S32: performing roughness fractal processing on the microchannel geometric feature data to obtain microchannel roughness data; performing fluid dynamic feature processing on the microchannel roughness data to obtain microchannel fluid dynamic data; performing flow resistance analysis on the microchannel fluid dynamic data to obtain microchannel flow resistance data;

[0027] Step S33: performing fractal dimension impact assessment on the microchannel flow resistance data to obtain fractal dimension impact assessment data; performing flow stability measurement on the microfluidic circuit data according to the fractal dimension impact assessment data to generate microchannel flow stability data; integrating the microchannel flow resistance data and the microchannel flow stability data to obtain circuit board microchannel data;

[0028] Step S34: performing heat flow analysis on the microfluidic loop data according to the circuit board microchannel data to obtain heat flow data; performing heat dissipation parameter measurement on the heat flow data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board heat dissipation performance data.

[0029] The present invention identifies the microfluidic circuit of the circuit board structure model, which provides a basis for analyzing the fluid flow in the circuit board. Further extraction of microchannel geometric features is helpful to evaluate the fluid transmission efficiency in the microfluidic circuit; roughness fractal processing of microchannel geometric feature data can clarify the fluid behavior in the microchannel; flow resistance analysis of microchannel fluid dynamic data can determine the flow resistance of the circuit board; fractal dimension impact assessment of microchannel flow resistance data is helpful to analyze the flow instability in microfluidics; heat flow analysis of microfluidic circuit data based on circuit board microchannel data is crucial for evaluating the thermal management capability of the circuit board under high power operation; heat dissipation parameter measurement of heat flow data is performed to clarify parameter information affecting heat dissipation; heat dissipation performance detection of circuit board data based on heat dissipation parameter data can accurately obtain circuit board heat dissipation performance data.

[0030] Preferably, step S34 includes the following steps:

[0031] Step S341: performing resistance effect determination on microfluidic circuit data according to microchannel flow resistance data to obtain resistance effect data; performing influence processing on microchannel flow stability data according to the resistance effect data to obtain microchannel flow influence data;

[0032] Step S342: performing heat conduction characteristic analysis on the microchannel flow influence data to obtain heat conduction efficiency data; performing heat flow measurement on the microfluidic loop data according to the heat conduction efficiency data to obtain heat flow data;

[0033] Step S343: performing thermal impedance minimization processing on the heat flow data to obtain thermal impedance minimization data; performing heat dissipation efficiency evaluation on the thermal impedance minimization data to obtain heat dissipation efficiency evaluation data; extracting heat dissipation parameters of the heat dissipation efficiency evaluation data to generate heat dissipation parameter data;

[0034] Step S344: Perform thermal response detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board thermal response data; perform heat flow rate measurement on the circuit board thermal response data to obtain the circuit board heat flow rate data; perform heat exchange efficiency calculation on the circuit board data according to the circuit board heat flow rate data to obtain the heat exchange efficiency data; integrate the heat flow rate data and the heat exchange efficiency data to obtain the circuit board heat dissipation performance data.

[0035] The present invention determines the resistance effect of microfluidic loop data, which is helpful to evaluate the resistance characteristics of fluid flow in the microfluidic system; performs influence processing on microchannel flow stability data according to the resistance effect data, which can provide important parameters for microfluidic fluid control; performs heat conduction characteristic analysis on microchannel flow influence data, which provides key indicators for the conduction of the microfluidic loop; performs thermal impedance minimization processing on heat flow data, which is helpful to detect the heat dissipation index of the circuit board; performs heat dissipation efficiency evaluation on thermal impedance minimization data, which can quantify the heat dissipation efficiency of the circuit board; performs thermal response detection on circuit board data according to heat dissipation parameter data, which is helpful to evaluate the response characteristics of the circuit board under heat load; performs heat flow rate measurement on circuit board thermal response data, which provides heat flow rate quantitative analysis for heat dissipation detection of the circuit board; integrates heat flow rate data and heat exchange efficiency data, and the heat dissipation performance data of the circuit board obtained provides a comprehensive evaluation for the heat dissipation performance detection of the circuit board.

[0036] Preferably, step S4 comprises the following steps:

[0037] Step S41: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; performing electromagnetic characteristic analysis on the transmission medium layer data to obtain electromagnetic characteristic data;

[0038] Step S42: determining the electromagnetic intensity of the electromagnetic characteristic data to obtain electromagnetic intensity data; performing interlayer electromagnetic interaction analysis on the transmission medium layer data according to the electromagnetic intensity data to obtain interlayer electromagnetic interaction data;

[0039] Step S43: determining the interlayer relationship of the transmission medium layer data according to the interlayer electromagnetic effect data to obtain medium interlayer relationship data;

[0040] Step S44: performing conductor signal characteristic analysis on the transmission medium layer data according to the medium layer relationship data to obtain conductor signal characteristic data; performing signal transmission analysis on the conductor signal characteristic data to obtain signal transmission data; performing conductor medium characteristic analysis on the transmission medium layer data according to the signal transmission data to obtain conductor medium characteristic data;

[0041] Step S45: Perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain circuit board signal performance data.

[0042] The present invention identifies the transmission medium layer of the circuit board structure model and obtains the transmission medium layer data; performs electromagnetic characteristic analysis on the transmission medium layer data, providing a basis for the subsequent determination of electromagnetic strength and ensuring the accuracy of the analysis; the electromagnetic characteristic data is used to determine the electromagnetic strength, and then obtains the electromagnetic strength data; the electromagnetic strength data is crucial for understanding the electromagnetic interaction between transmission medium layers; by analyzing the electromagnetic strength data, the interlayer electromagnetic action data can be accurately obtained, providing key information for the subsequent determination of the interlayer relationship; further, the interlayer relationship determination of the transmission medium layer data using the interlayer electromagnetic action data can clarify the internal structure and performance of the circuit board, thereby revealing the interaction and dependence between different medium layers; the conductor signal characteristic analysis of the transmission medium layer data based on the medium layer relationship data can understand the signal transmission characteristics of the conductor on the circuit board; the signal transmission analysis of the conductor signal characteristic data is performed to clarify the transmission efficiency characteristics of the signal on the circuit board; the conductor medium characteristic analysis of the transmission medium layer data based on the signal transmission data provides comprehensive information for signal performance detection; the signal performance detection of the circuit board data using the conductor medium characteristic data is performed to obtain the circuit board signal performance data, which directly reflects the signal performance level of the circuit board.

[0043] Preferably, step S43 includes the following steps:

[0044] Step S431: performing dielectric constant identification on the interlayer electromagnetic action data to obtain dielectric constant information; performing dielectric constant comparison on the interlayer electromagnetic action data according to the dielectric constant information to generate interlayer dielectric constant comparison data;

[0045] Step S432: performing data difference analysis on the interlayer dielectric constant comparison data to obtain interlayer dielectric difference data; performing interlayer electromagnetic interference measurement on the interlayer dielectric difference data to obtain interlayer electromagnetic interference data;

[0046] Step S433: performing interlayer isolation evaluation on the interlayer electromagnetic interference data to obtain electromagnetic interference isolation data; performing interlayer compatibility analysis on the electromagnetic interference isolation data to obtain medium interlayer compatibility data;

[0047] Step S434: determining the inter-layer consistency of the transmission medium layer data according to the inter-layer compatibility data to obtain inter-layer relationship data of the medium.

[0048] The present invention identifies the dielectric constant of interlayer electromagnetic action data, and provides key electromagnetic parameters for subsequent analysis; by comparing the dielectric constants of each layer, the dielectric constants between the layers inside the circuit board are clarified; the data difference analysis of the interlayer dielectric constant comparison data further reveals the electromagnetic characteristic differences between different dielectric layers; the interlayer electromagnetic interference is measured and calculated on the interlayer dielectric difference data, and the degree of electromagnetic interference inside the circuit board is clarified; the interlayer isolation evaluation of the interlayer electromagnetic interference data is helpful to judge the isolation effect between the layers inside the circuit board; the interlayer compatibility analysis of the electromagnetic interference isolation data provides a key indicator for determining the relationship between dielectric layers of the circuit board; the interlayer consistency of the transmission dielectric layer data is determined according to the dielectric layer compatibility data, and the interlayer relationship of the transmission dielectric layer is clarified, thereby obtaining the dielectric layer relationship data.

[0049] Preferably, step S45 includes the following steps:

[0050] Step S451: performing signal transmission efficiency analysis on the conductor medium characteristic data to obtain signal transmission efficiency data; performing noise suppression capability evaluation on the signal transmission efficiency data to obtain noise suppression capability data;

[0051] Step S452: performing signal distortion measurement on the noise suppression capability data to obtain signal distortion degree data; performing signal synchronization analysis on the signal distortion degree data to obtain signal synchronization data;

[0052] Step S453: measuring the transmission rate of the signal synchronization data to obtain signal transmission rate data; performing a signal stability test on the signal transmission rate data to obtain signal stability data;

[0053] Step S454: performing a signal coverage evaluation on the signal stability data to obtain signal coverage data; performing a signal attenuation measurement on the signal coverage data to obtain signal attenuation data; performing a spectrum analysis on the signal attenuation data to obtain signal attenuation spectrum data;

[0054] Step S455: performing modulation effect analysis on the signal attenuation spectrum data to obtain modulation effect data; performing impulse response correction on the modulation effect data to obtain impulse response correction data; performing time domain reflection measurement on the impulse response correction data to obtain time domain reflection data;

[0055] Step S456: perform signal integrity verification on the circuit board data according to the time domain reflection data to obtain signal integrity verification data; perform error vector amplitude detection on the circuit board data according to the signal integrity verification data to obtain error vector amplitude data; integrate the signal integrity verification data and the error vector amplitude data to generate circuit board signal performance data.

[0056] The present invention performs signal transmission efficiency analysis on conductor medium characteristic data, providing a basis for evaluating the rapidity and stability of circuit board signal transmission; further, performs noise suppression capability evaluation on signal transmission efficiency data, revealing the circuit board's ability to suppress noise interference; performs signal distortion measurement on noise suppression capability data, reflecting the fidelity of the circuit board during signal transmission; at the same time, performs signal synchronization analysis on signal distortion data, ensuring the timing consistency of the signal during transmission; performs transmission rate measurement on signal synchronization data, providing important parameters for evaluating the data transmission speed of the circuit board; performs signal stability test on data transmission rate data, further ensuring the reliability of the circuit board in continuous transmission. Signal coverage evaluation of signal stability data provides data support for determining the effective area of ​​signal transmission on the circuit board; signal attenuation measurement of signal coverage data helps to understand the attenuation characteristics of signals during transmission; spectrum analysis of signal attenuation data provides spectrum information for in-depth analysis of frequency domain characteristics of signal attenuation; modulation effect analysis of signal attenuation spectrum data helps to identify and correct modulation distortion in signal transmission; pulse response correction of modulation effect data further improves the transmission quality of signals; time domain reflection measurement of pulse response correction data provides accurate measurement results for evaluating the signal transmission path inside the circuit board; signal integrity verification of circuit board data based on time domain reflection data ensures the integrity and accuracy of circuit board signals; error vector amplitude detection of circuit board data based on signal integrity verification data detects the error vector amplitude of circuit board signals; circuit board signal performance data generated by integrating signal integrity verification data and error vector amplitude data provides comprehensive and accurate data support for performance evaluation of circuit boards.

[0057] Preferably, step S5 comprises the following steps:

[0058] Step S51: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain circuit board performance data;

[0059] Step S52: constructing a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain a circuit board performance problem prediction pre-model, and training the circuit board performance problem prediction pre-model using the circuit board performance data to obtain a circuit board performance problem prediction training model;

[0060] Step S53: performing a model cross-validation evaluation on the circuit board performance problem prediction training model to obtain circuit board performance problem prediction model evaluation data; adjusting the model parameters of the circuit board performance problem prediction training model using the circuit board performance problem prediction model evaluation data to obtain a circuit board performance problem prediction model;

[0061] Step S54: performing electric field simulation analysis on the conductive performance data of the circuit board to obtain the electric field distribution data of the circuit board; performing weak electric potential area prediction on the electric field distribution data of the circuit board based on the circuit board performance problem prediction model to generate electric potential prediction data; performing heat flow path identification on the heat dissipation performance data of the circuit board to obtain heat flow path data; performing hot spot concentration area prediction on the heat flow path data based on the circuit board performance problem prediction model to generate heat dissipation bottleneck prediction data; performing timing logic analysis on the signal performance data of the circuit board to obtain signal timing data; performing signal delay prediction on the signal timing data based on the circuit board performance problem prediction model to generate signal delay prediction data;

[0062] Step S55: Integrate the potential prediction data, the heat dissipation bottleneck prediction data and the signal delay prediction data to obtain potential performance problem data of the circuit board;

[0063] Step S56: Perform performance testing on the circuit board data according to the circuit board potential performance problem data and the circuit board performance data, and generate a circuit board performance testing report; use blockchain technology to upload the circuit board performance testing report to the blockchain node, and distribute the circuit board performance testing report through the blockchain node to execute the circuit board performance testing operation.

[0064] The present invention integrates the conductive performance, heat dissipation performance and signal performance data of the circuit board to form circuit board performance data, which lays a foundation for subsequent performance problem prediction and ensures the comprehensiveness and accuracy of the data; uses the circuit board performance data to build a circuit board performance problem prediction model, and uses a deep learning algorithm to learn the circuit board performance trend, thereby improving the accuracy and reliability of the prediction; cross-validates and evaluates the training model, and adjusts the model parameters according to the evaluation data, and finally obtains the circuit board performance problem prediction model, which ensures the generalization ability and prediction accuracy of the model and provides strong support for subsequent performance problem prediction. Analyzing the electric field distribution, heat flow path and signal timing of the circuit board and predicting the weak potential areas, hot spot concentration areas and signal delays in the circuit board are crucial to identifying potential weaknesses and risk areas of the circuit board; obtaining potential performance problem data of the circuit board by integrating the predicted data of electric potential, heat dissipation and signal delay helps to quickly locate and solve key performance problems of the circuit board; performing performance testing based on the potential performance problem data and circuit board performance data, and generating a circuit board performance test report; using blockchain technology to perform distributed storage of the test report ensures the immutability and traceability of the test report, and enhances the credibility and transparency of the test results.

[0065] In this specification, a circuit board performance detection system is provided, which is used to execute the above-mentioned circuit board performance detection method. The circuit board performance detection system includes:

[0066] The circuit board structure model module is used to obtain circuit board data; perform structural topology analysis on the circuit board data to obtain circuit board structure data; perform spatial mapping on the circuit board structure data to generate a circuit board structure model;

[0067] The circuit board conductive performance detection module is used to extract the circuit board environmental characteristics of the circuit board data to obtain the circuit board environmental data; perform dust impact analysis on the circuit board environmental data to obtain the circuit board dust impact data; perform conductive track identification on the circuit board structure model to obtain conductive track data; perform conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain the circuit board conductive performance data;

[0068] The circuit board heat dissipation performance detection module is used to perform microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; perform microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; perform integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; perform heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; perform heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data;

[0069] The circuit board signal performance detection module is used to identify the transmission medium layer of the circuit board structure model to obtain the transmission medium layer data; determine the interlayer relationship of the transmission medium layer data to obtain the interlayer relationship data; perform conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain the circuit board signal performance data;

[0070] The circuit board performance problem prediction module is used to integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

[0071] The present invention uses a circuit board structure model module to analyze the structure topology and space mapping, and constructs a circuit board structure model, which can provide physical and spatial information for subsequent performance testing, thereby ensuring the accuracy of the testing process; the circuit board conductive performance testing module identifies environmental factors encountered by the circuit board in a specific environment, and performs dust impact analysis on it, which can reveal in detail the specific impact of dust on the performance of the circuit board, such as the resulting short circuit, poor contact and heat dissipation problems; the circuit board structure model is used to identify the conductive track, and the specific information of the conductive track is clarified; the conductive performance of the conductive track data is tested according to the circuit board dust impact data, which can verify the conductive performance of the circuit board, thereby avoiding circuit failures caused by dust. The circuit board heat dissipation performance detection module is used to identify the microfluidic circuit of the circuit board structure model, which is helpful for analyzing the fluid transmission path on the circuit board; the microchannel analysis of the microfluidic circuit data can evaluate the efficiency and performance of the microchannel on the circuit board; the integration of the microfluidic circuit data is evaluated according to the circuit board microchannel data to clarify the space utilization and functional integration of the circuit board; the heat dissipation parameter determination of the circuit board integration data is crucial for the subsequent stable operation of the circuit board under high load; the heat dissipation performance detection of the circuit board data according to the heat dissipation parameter data to obtain the circuit board heat dissipation performance data is helpful for preventing the performance degradation or damage caused by overheating of the circuit board. The circuit board signal performance detection module is used to identify the transmission medium layer of the circuit board structure model, which is helpful for understanding the signal transmission path in the circuit board; the interlayer relationship of the transmission medium layer data is determined to analyze the signal transmission efficiency in the circuit board; the conductor medium characteristic analysis of the transmission medium layer data according to the medium interlayer relationship data can clarify the signal transmission quality and speed in the circuit board; the signal performance detection of the circuit board data according to the conductor medium characteristic data is helpful for verifying the performance of the circuit board in communication. The circuit board performance problem prediction module integrates the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; the circuit board performance problem prediction model is constructed for the circuit board data according to the circuit board performance data, providing a model basis for subsequent predictions; the circuit board data is predicted for performance problems based on the circuit board performance problem prediction model to ensure the comprehensiveness of the performance problem prediction, so that potential circuit board performance problems can be more accurately identified; the circuit board performance test report is uploaded to the blockchain network distributed storage to ensure the security and non-tamperability of the test report, while providing the test report traceability, enhancing the credibility of the test report. Therefore, the present invention realizes multi-dimensional correlation analysis of the structural characteristics of the circuit board through Internet of Things technology, data mapping technology, intelligent detection technology and blockchain technology, thereby improving the accuracy of circuit board detection; and the performance data obtained by the detection is predicted for potential performance problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the steps of a circuit board performance testing method;

[0073] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0074] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 , a circuit board performance detection method, the method comprising the following steps:

[0080] Step S1: acquiring circuit board data; performing structural topological analysis on the circuit board data to obtain circuit board structural data; performing spatial mapping on the circuit board structural data to generate a circuit board structural model;

[0081] Step S2: extracting circuit board environmental features from circuit board data to obtain circuit board environmental data; performing dust impact analysis on the circuit board environmental data to obtain circuit board dust impact data; performing conductive track identification on the circuit board structure model to obtain conductive track data; performing conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain circuit board conductive performance data;

[0082] Step S3: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; performing integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; performing heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data;

[0083] Step S4: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; determining the interlayer relationship of the transmission medium layer data to obtain interlayer relationship data; performing conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; performing signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain circuit board signal performance data;

[0084] Step S5: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

[0085] The present invention constructs a circuit board structure model through structural topology analysis and space mapping, which can provide physical and spatial information for subsequent performance testing and ensure the accuracy of the testing process; identifies environmental factors encountered by the circuit board in a specific environment, and performs dust impact analysis on it, which can reveal in detail the specific impact of dust on the performance of the circuit board, such as the resulting short circuit, poor contact and heat dissipation problems; identifies the conductive track of the circuit board structure model and clarifies the specific information of the conductive track; performs conductive performance detection on the conductive track data according to the dust impact data of the circuit board, which can verify the conductive performance of the circuit board, thereby avoiding circuit failures caused by dust. Microfluidic circuit identification of the circuit board structure model helps analyze the fluid transmission path on the circuit board; microchannel analysis of microfluidic circuit data can evaluate the efficiency and performance of the microchannels on the circuit board; integration evaluation of microfluidic circuit data based on circuit board microchannel data can clarify the space utilization and functional integration of the circuit board; heat dissipation parameter measurement of circuit board integration data is crucial for the subsequent stable operation of the circuit board under high load; heat dissipation performance testing of circuit board data based on heat dissipation parameter data to obtain circuit board heat dissipation performance data helps prevent performance degradation or damage caused by overheating of the circuit board. Identifying the transmission medium layer of the circuit board structure model helps to understand the signal transmission path in the circuit board; determining the inter-layer relationship of the transmission medium layer data can analyze the signal transmission efficiency in the circuit board; analyzing the conductor medium characteristics of the transmission medium layer data based on the inter-layer relationship data can clarify the signal transmission quality and speed in the circuit board; testing the signal performance of the circuit board data based on the conductor medium characteristic data helps to verify the performance of the circuit board in communication; integrating the circuit board conductive performance data, circuit board heat dissipation performance data and circuit board signal performance data to obtain the circuit board performance data; constructing a circuit board performance problem prediction model for the circuit board data based on the circuit board performance data, providing a model basis for subsequent predictions; predicting performance problems of the circuit board data based on the circuit board performance problem prediction model ensures the comprehensiveness of the performance problem prediction, so as to more accurately identify potential circuit board performance problems; uploading the circuit board performance test report to the blockchain network distributed storage ensures the security and non-tamperability of the test report, while providing test report traceability and enhancing the credibility of the test report. Therefore, the present invention uses Internet of Things technology, data mapping technology, intelligent detection technology and blockchain technology to achieve multi-dimensional correlation analysis of the structural characteristics of the circuit board, thereby improving the accuracy of circuit board detection; and predicting potential performance problems based on the performance data obtained from the detection.

[0086] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a circuit board performance detection method of the present invention. In this example, the circuit board performance detection method includes the following steps:

[0087] Step S1: acquiring circuit board data; performing structural topological analysis on the circuit board data to obtain circuit board structural data; performing spatial mapping on the circuit board structural data to generate a circuit board structural model;

[0088] In an embodiment of the present invention, circuit board data is obtained, wherein the circuit board data includes parameters such as size, number of layers, wire width, spacing and aperture; professional EDA (electronic design automation) software is used to simulate the physical structure of the circuit board, and the circuit board data is subjected to structural topological analysis to obtain circuit board structural data. The circuit board structural data is spatially mapped. In this step, 3D modeling software such as Allegro or Altium Designer is used to create a three-dimensional model of the circuit board. The model will contain specific circuit board parameters such as size, number of layers, wire width, spacing and aperture; at the same time, during the model creation process, the 3D model of the circuit board components will be obtained from a special database, such as 3DContentCentral, to ensure that the model of each component matches the circuit board design and that all components can be correctly placed on the circuit board; finally, the circuit board model is exported as a STEP file for rendering and analysis; during the rendering process, professional rendering software is used to generate the visual effect of the circuit board to provide a more intuitive circuit board structure display.

[0089] Step S2: extracting circuit board environmental features from circuit board data to obtain circuit board environmental data; performing dust impact analysis on the circuit board environmental data to obtain circuit board dust impact data; performing conductive track identification on the circuit board structure model to obtain conductive track data; performing conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain circuit board conductive performance data;

[0090] In the embodiment of the present invention, a high-precision scanning device is used in combination with an image processing algorithm to extract the physical characteristics of the circuit board to obtain the circuit board environmental data; specifically, the YOLOv8 model technology is used for image recognition and processing to identify the circuit layout, welding point quality and other characteristics on the circuit board; the environmental release feature analysis technology is used to analyze the dust impact of the environmental data, and then evaluate the potential impact of dust on the performance of the circuit board; specifically, the persistent toxic substance (PTS) detection method is used to analyze the release characteristics of the dust; through computer vision technology, the conductive track of the circuit board structure model is identified, and the conductive track on the circuit board is extracted; combined with the dust impact data, the conductivity test method is used to detect the conductive performance of the conductive track; specifically, it is completed by measuring the resistivity of the circuit board under specific conditions, thereby obtaining the conductive performance data of the circuit board.

[0091] Step S3: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; performing integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; performing heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data;

[0092] In an embodiment of the present invention, image recognition technology is used to analyze the circuit board structure model, specifically identifying and mapping the layout and characteristics of the microfluidic circuit to obtain microfluidic circuit data; fluid dynamics simulation software is used to perform microchannel analysis on the microfluidic circuit data to evaluate the fluid flow characteristics and potential blockage points in the microchannel to obtain circuit board microchannel data; integration evaluation technology is used, and the microfluidic circuit data is analyzed based on the circuit board microchannel data to obtain circuit board integration data; specifically, multi-parameter analysis is performed on the size, shape and arrangement of the microchannel; thermal conductivity testing technology is used, and reference is made to thermal conductivity testing standards such as ASTM D5470 or ASTM E1461; heat dissipation parameters are determined for the circuit board integration data, and the thermal conductivity and thermal diffusivity of the material are measured to generate heat dissipation parameter data; heat dissipation performance detection technology is used to detect the circuit board data based on the heat dissipation parameter data, specifically including using a thermal imager or an infrared detector to measure the temperature distribution of the circuit board under different working conditions to obtain the circuit board heat dissipation performance data.

[0093] Step S4: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; determining the interlayer relationship of the transmission medium layer data to obtain interlayer relationship data; performing conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; performing signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain circuit board signal performance data;

[0094] In an embodiment of the present invention, firstly, image recognition technology is used to identify the transmission medium layer of the circuit board structure model to obtain transmission medium layer data; PCB stacking design technology is used to analyze the stacking structure of the circuit board; further, the interlayer relationship of the transmission medium layer data is determined to obtain medium interlayer relationship data, and the medium interlayer relationship data includes the relationship between each layer and the interconnection method; the conductor medium characteristic analysis technology is used to analyze the transmission medium layer data according to the medium interlayer relationship data to obtain the conductor medium characteristic data; specifically, by measuring the electromagnetic characteristics of the circuit board material, such as measuring the characteristic characterization of the circuit board material at millimeter wave frequency; according to the conductor medium characteristic data, the signal performance detection technology is used to detect the signal performance of the circuit board data to obtain the circuit board signal performance data; specifically, the signal integrity and transmission rate are verified through high-speed signal transmission test.

[0095] Step S5: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

[0096] In the embodiment of the present invention, data fusion technology is used to aggregate the conductive performance data, heat dissipation performance data and signal performance data of the circuit board to form a circuit board performance data set; the Dung Beetle Optimization DBO-LSTM-Attention algorithm is used, and a circuit board performance problem prediction model is constructed for the circuit board data according to the circuit board performance data to obtain a circuit board performance problem prediction model; and the parameters are optimized by the Dung Beetle Optimization Algorithm (DBO); during the model training process, the model is trained using the circuit board performance data, and the generalization ability of the model is evaluated by methods such as cross-validation; performance problem prediction is performed on the circuit board data based on the circuit board performance problem prediction model to identify potential performance problems; specifically, the performance trend of the circuit board under different working conditions is analyzed to predict failures or performance degradation; then, a comprehensive performance test is performed on the circuit board based on the predicted potential performance problem data and the existing circuit board performance data; finally, the circuit board performance test report is uploaded to the blockchain network for distributed storage; through the blockchain network, the integrity and traceability of the test report can be ensured, while facilitating data sharing and access.

[0097] Preferably, step S1 comprises the following steps:

[0098] Step S11: extracting nonlinear features from the circuit board data to obtain circuit board nonlinear data; and identifying topological features from the circuit board nonlinear data to obtain circuit board topological feature data;

[0099] Step S12: performing circuit structure analysis on the circuit board topology feature data to obtain circuit board structure data; performing geometric analysis on the circuit board structure data to obtain circuit board structure analysis data;

[0100] Step S13: performing line spatial morphology positioning on the circuit board structure analysis data to obtain line spatial morphology data; performing multi-dimensional parameter determination on the line spatial morphology data to obtain spatial morphology parameter data;

[0101] Step S14: spatially mapping the circuit board data according to the spatial morphological parameter data to obtain a circuit board spatial mapping model; and visually rendering the circuit board spatial mapping model according to the circuit board structural data to generate a circuit board structural model.

[0102] In the embodiment of the present invention, the autoencoder technology is used to learn the low-dimensional representation of data from the circuit board data, so as to extract nonlinear features; then, a multi-channel adaptive weighted neural network is used to perform topological structure on the circuit board topological feature data to obtain circuit board structure data; computer-aided design (CAD) software is used to simulate and analyze the physical structure of the circuit board, and optical scanning and image processing technology are used to obtain detailed geometric information of the circuit board, so as to obtain circuit board structure data; spatial mapping technology is used to analyze the circuit board structure data to determine the spatial position and mutual relationship of each element in the circuit board, so as to obtain circuit space morphology data; through spatial mapping technology, the circuit space is analyzed. Specifically, a parameter mapping relationship is established between circuit simulation and electromagnetic simulation to obtain spatial morphological parameter data; finally, the circuit board data is spatially mapped according to the spatial morphological parameter data to obtain a circuit board spatial mapping model; then, the circuit board spatial mapping model is structurally visualized and rendered according to the circuit board structure data to generate a circuit board structure model; this step will be achieved by using professional 3D rendering software, such as the 3D rendering idea of ​​PCB circuit board 3D model, by importing the vector files of each layer into the 3D software, stretching the model, and modeling and assembling the electronic components, and finally rendering to obtain a 3D visualization model of the circuit board.

[0103] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0104] Step S21: performing circuit board environmental feature recognition on the circuit board data to obtain circuit board environmental data; performing dust microscopic analysis on the circuit board environmental data to obtain circuit board dust data; performing motion state simulation on the circuit board dust data to obtain dust motion state simulation data;

[0105] Step S22: performing circuit board surface feature recognition on the circuit board structure model to obtain a circuit board surface image; performing coverage area calculation on the circuit board surface image according to the dust movement state simulation data to obtain dust coverage area data; performing electrical performance impact analysis on the dust coverage area data to generate circuit board dust impact data;

[0106] Step S23: performing current path analysis on the circuit board structure model to obtain current path data; determining the current flow direction on the current path data to obtain current flow direction data; and performing conductive track identification on the circuit board structure model according to the current path data and the current flow direction data to obtain conductive track data;

[0107] Step S24: performing contact impedance characteristic analysis on the conductive track data according to the circuit board dust impact data to obtain contact impedance characteristic data; performing conductive path continuity evaluation on the contact impedance characteristic data to obtain path continuity evaluation data; performing current distribution uniformity detection on the path continuity evaluation data to obtain distribution uniformity detection data;

[0108] Step S25: perform potential equalization identification on the distribution uniformity detection data to obtain potential equalization data; perform charge transfer efficiency calculation on the potential equalization data to obtain charge transfer efficiency data; perform electron transfer rate analysis on the charge transfer efficiency data to obtain electron transfer rate data; perform conductivity performance detection on the circuit board data according to the electron transfer rate data to obtain circuit board conductivity performance data.

[0109] In an embodiment of the present invention, the circuit board data is subjected to environmental feature recognition, and machine vision detection technology is used, such as CCD or CMOS industrial camera to capture the detection image and convert it into a digital signal, and then the image digital signal is processed by computer software and hardware technology to obtain the circuit board environmental data; then, the circuit board environmental data is subjected to dust microscopic analysis by dust detection technology to obtain the circuit board dust data; then, the circuit board dust data is subjected to motion state simulation, and numerical simulation technology is used, such as the numerical simulation method of dust migration in the comprehensive excavation working surface, to simulate the movement of dust in the airflow to obtain the dust motion state simulation data; machine vision detection technology is used to identify the surface features of the circuit board structure model to obtain the circuit board surface image. According to the dust motion state simulation data, the circuit board surface image is subjected to coverage area calculation, and the dust coverage area is analyzed by the image processing algorithm to obtain the dust coverage area data. Analyze the degree of influence of dust on electrical performance on the dust-covered area data, use the electronic conductivity test principle and method, as described in the electronic conductivity test principle and method, to evaluate the influence of dust on electrical performance and generate circuit board dust influence data; analyze the current path of the circuit board structure model, and obtain the current path data through DC path and AC path analysis, as described in the static analysis of the amplifier circuit; determine the current flow direction of the current path data to obtain the current flow direction data; identify the conductive track of the circuit board structure model based on the current path data and the current flow direction data to obtain the conductive track data; use electrochemical impedance spectroscopy analysis technology, such as lithium-ion battery, based on the circuit board dust influence data. The electrochemical impedance spectroscopy analysis technology is used to obtain contact impedance characteristic data; the contact impedance characteristic data is subjected to conductive path continuity evaluation to obtain path continuity evaluation data; the path continuity evaluation data is subjected to current distribution uniformity detection to obtain distribution uniformity detection data; the distribution uniformity detection data is subjected to potential equilibrium identification to obtain potential equilibrium data; the differential charge analysis technology is used to calculate the charge transfer efficiency of the potential equilibrium data to obtain charge transfer efficiency data; the charge transfer efficiency data is subjected to electron transfer rate analysis to obtain electron transfer rate data; the circuit board data is subjected to conductivity detection based on the electron transfer rate data to obtain circuit board conductivity data.

[0110] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0111] Step S31: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel geometric feature extraction on the microfluidic circuit data to obtain microchannel geometric feature data;

[0112] Step S32: performing roughness fractal processing on the microchannel geometric feature data to obtain microchannel roughness data; performing fluid dynamic feature processing on the microchannel roughness data to obtain microchannel fluid dynamic data; performing flow resistance analysis on the microchannel fluid dynamic data to obtain microchannel flow resistance data;

[0113] Step S33: performing fractal dimension impact assessment on the microchannel flow resistance data to obtain fractal dimension impact assessment data; performing flow stability measurement on the microfluidic circuit data according to the fractal dimension impact assessment data to generate microchannel flow stability data; integrating the microchannel flow resistance data and the microchannel flow stability data to obtain circuit board microchannel data;

[0114] Step S34: performing heat flow analysis on the microfluidic loop data according to the circuit board microchannel data to obtain heat flow data; performing heat dissipation parameter measurement on the heat flow data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board heat dissipation performance data.

[0115] In an embodiment of the present invention, a circuit board structure model is analyzed using a microfluidic circuit recognition technology to obtain microfluidic circuit data; microchannel geometric feature extraction is performed on the microfluidic circuit data to obtain microchannel geometric feature data. Specifically, the three-dimensional geometric modeling and image processing technology of the microfluidic chip is implemented, including accurate measurement of the size, shape and surface characteristics of the microchannel to obtain microchannel geometric feature data; then, roughness fractal processing is performed on the microchannel geometric feature data, the concept of fractal dimension is used to describe the roughness of the microchannel surface, and the surface characteristics of the microchannel are evaluated by calculating the fractal dimension of the image, thereby obtaining microchannel roughness data; fluid dynamic feature processing is performed on the microchannel roughness data, and computational fluid dynamics (CFD) technology is used to simulate the fluid flow characteristics in the microchannel to obtain microchannel fluid dynamic data. Through CFD technology, the flow resistance and pressure drop of microchannel fluid dynamic data in the microchannel are calculated to obtain microchannel flow resistance data; the fractal dimension impact assessment is performed on the microchannel flow resistance data. Specifically, the fractal theory is used to analyze the impact of the roughness of the microchannel surface on the flow resistance to obtain fractal dimension impact assessment data; data fusion technology is used to measure the flow stability of the microfluidic circuit data based on the fractal dimension impact assessment data to generate microchannel flow stability data; through thermal analysis technology, heat flow analysis is performed on the microfluidic circuit data based on the circuit board microchannel data to obtain heat flow data; heat dissipation parameters are measured on the heat flow data to generate heat dissipation parameter data; heat dissipation performance of the circuit board data is tested based on the heat dissipation parameter data to obtain circuit board heat dissipation performance data.

[0116] Preferably, step S34 includes the following steps:

[0117] Step S341: performing resistance effect determination on microfluidic circuit data according to microchannel flow resistance data to obtain resistance effect data; performing influence processing on microchannel flow stability data according to the resistance effect data to obtain microchannel flow influence data;

[0118] Step S342: performing heat conduction characteristic analysis on the microchannel flow influence data to obtain heat conduction efficiency data; performing heat flow measurement on the microfluidic loop data according to the heat conduction efficiency data to obtain heat flow data;

[0119] Step S343: performing thermal impedance minimization processing on the heat flow data to obtain thermal impedance minimization data; performing heat dissipation efficiency evaluation on the thermal impedance minimization data to obtain heat dissipation efficiency evaluation data; extracting heat dissipation parameters of the heat dissipation efficiency evaluation data to generate heat dissipation parameter data;

[0120] Step S344: Perform thermal response detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board thermal response data; perform heat flow rate measurement on the circuit board thermal response data to obtain the circuit board heat flow rate data; perform heat exchange efficiency calculation on the circuit board data according to the circuit board heat flow rate data to obtain the heat exchange efficiency data; integrate the heat flow rate data and the heat exchange efficiency data to obtain the circuit board heat dissipation performance data.

[0121] In an embodiment of the present invention, the resistance effect is determined on the microfluidic loop data, and computational fluid dynamics (CFD) technology, such as Ansys Fluent software, is used to simulate the fluid flow in the microfluidic loop to obtain resistance effect data; then, fluid dynamics simulation technology is used to combine the fluid flow resistance data to perform influence processing on the microchannel flow stability data to obtain microchannel flow influence data; the steady-state heat conduction principle is used to perform heat conduction characteristic analysis on the microchannel flow influence data, and parameters such as the thermal conductivity and thermal diffusivity of the microchannel are calculated to obtain heat conduction efficiency data; based on the heat conduction efficiency data, the heat flow of the microfluidic loop data is measured to obtain heat flow data; next, the thermal impedance theory is used to perform thermal impedance minimization processing on the heat flow data to evaluate the influence of the microchannel flow resistance on the thermal responsiveness to obtain the thermal impedance minimum. data; then, the heat dissipation efficiency is evaluated on the thermal impedance minimization data to obtain the heat dissipation efficiency evaluation data; the heat dissipation parameters are extracted from the heat dissipation efficiency evaluation data to generate the heat dissipation parameter data, wherein the parameters include thermal conductivity, thermal diffusivity, heat capacity and specific heat capacity, etc.; the NTCRP series thermistor calculation method is used to perform thermal response detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board thermal response data; the hot wire or particle image velocimetry technology is used to measure the heat flow rate of the circuit board thermal response data to obtain the circuit board heat flow rate data; according to the circuit board heat flow rate data, the heat exchange efficiency of the circuit board data is calculated, and the heat flow rate data and the heat exchange efficiency data are integrated to obtain the circuit board heat dissipation performance data.

[0122] Preferably, step S4 comprises the following steps:

[0123] Step S41: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; performing electromagnetic characteristic analysis on the transmission medium layer data to obtain electromagnetic characteristic data;

[0124] Step S42: determining the electromagnetic intensity of the electromagnetic characteristic data to obtain electromagnetic intensity data; performing interlayer electromagnetic interaction analysis on the transmission medium layer data according to the electromagnetic intensity data to obtain interlayer electromagnetic interaction data;

[0125] Step S43: determining the interlayer relationship of the transmission medium layer data according to the interlayer electromagnetic effect data to obtain medium interlayer relationship data;

[0126] Step S44: performing conductor signal characteristic analysis on the transmission medium layer data according to the medium layer relationship data to obtain conductor signal characteristic data; performing signal transmission analysis on the conductor signal characteristic data to obtain signal transmission data; performing conductor medium characteristic analysis on the transmission medium layer data according to the signal transmission data to obtain conductor medium characteristic data;

[0127] In an embodiment of the present invention, the transmission medium layer of the circuit board structure model is identified, and the electromagnetic characteristic analysis technology is used to measure the dielectric constant and magnetic permeability of the medium layer to obtain the transmission medium layer data; then, the electromagnetic characteristic analysis is performed on the transmission medium layer data to obtain electromagnetic characteristic data; the electromagnetic intensity of the electromagnetic characteristic data is determined, and the electromagnetic field distribution in the circuit board is simulated using electromagnetic field simulation software, such as Ansys HFSS, to obtain electromagnetic intensity data; the electromagnetic compatibility (EMC) analysis technology is used to perform interlayer electromagnetic interaction analysis on the transmission medium layer data based on the electromagnetic intensity data to obtain interlayer electromagnetic action data; the interlayer relationship of the transmission medium layer data is determined based on the interlayer electromagnetic action data to obtain medium interlayer relationship data; specifically, the circuit simulation software is used to perform signal integrity analysis; next, the signal integrity analysis tool, such as Cadence Sigrity, performs conductor signal characteristic analysis on the transmission medium layer data according to the relationship data between medium layers to obtain conductor signal characteristic data; adopts the signal integrity analysis technology in high-speed digital design to perform signal transmission analysis on the conductor signal characteristic data to obtain signal transmission data; finally, performs conductor medium characteristic analysis on the transmission medium layer data according to the signal transmission data. Specifically, the electromagnetic wave propagation theory is used to analyze the signal attenuation and delay characteristics in the conductor medium to obtain the conductor medium characteristic data.

[0128] Preferably, step S43 includes the following steps:

[0129] Step S431: performing dielectric constant identification on the interlayer electromagnetic action data to obtain dielectric constant information; performing dielectric constant comparison on the interlayer electromagnetic action data according to the dielectric constant information to generate interlayer dielectric constant comparison data;

[0130] Step S432: performing data difference analysis on the interlayer dielectric constant comparison data to obtain interlayer dielectric difference data; performing interlayer electromagnetic interference measurement on the interlayer dielectric difference data to obtain interlayer electromagnetic interference data;

[0131] Step S433: performing interlayer isolation evaluation on the interlayer electromagnetic interference data to obtain electromagnetic interference isolation data; performing interlayer compatibility analysis on the electromagnetic interference isolation data to obtain medium interlayer compatibility data;

[0132] Step S434: determining the inter-layer consistency of the transmission medium layer data according to the inter-layer compatibility data to obtain inter-layer relationship data of the medium.

[0133] In an embodiment of the present invention, an electromagnetic characteristic analysis method is used to identify the dielectric constant of interlayer electromagnetic action data. Specifically, the dielectric constant information is determined by measuring the electromagnetic response at different frequencies; then, the dielectric constants of each layer of the interlayer electromagnetic action data are compared according to the dielectric constant information to generate interlayer dielectric constant comparison data; a statistical analysis technique is used to identify and quantify the differences in dielectric constants between different layers to obtain interlayer dielectric difference data; interlayer electromagnetic interference is measured for the interlayer dielectric difference data, and electromagnetic simulation software such as Ansys HFSS is used for simulation to obtain interlayer electromagnetic interference data; then, interlayer isolation is evaluated for the interlayer electromagnetic interference data, and electromagnetic compatibility (EMC) analysis technology is used to evaluate the isolation effect between different layers to obtain electromagnetic interference isolation data; interlayer compatibility analysis is performed on the electromagnetic interference isolation data, and signal integrity analysis is performed using circuit simulation software to obtain dielectric interlayer compatibility data; finally, interlayer consistency of the transmission medium layer data is determined according to the dielectric interlayer compatibility data, and electromagnetic simulation technology is used to verify the consistency and performance of the interlayer design to obtain dielectric interlayer relationship data.

[0134] Preferably, step S45 includes the following steps:

[0135] Step S451: performing signal transmission efficiency analysis on the conductor medium characteristic data to obtain signal transmission efficiency data; performing noise suppression capability evaluation on the signal transmission efficiency data to obtain noise suppression capability data;

[0136] Step S452: performing signal distortion measurement on the noise suppression capability data to obtain signal distortion degree data; performing signal synchronization analysis on the signal distortion degree data to obtain signal synchronization data;

[0137] Step S453: measuring the transmission rate of the signal synchronization data to obtain signal transmission rate data; performing a signal stability test on the signal transmission rate data to obtain signal stability data;

[0138] Step S454: performing a signal coverage evaluation on the signal stability data to obtain signal coverage data; performing a signal attenuation measurement on the signal coverage data to obtain signal attenuation data; performing a spectrum analysis on the signal attenuation data to obtain signal attenuation spectrum data;

[0139] Step S455: performing modulation effect analysis on the signal attenuation spectrum data to obtain modulation effect data; performing impulse response correction on the modulation effect data to obtain impulse response correction data; performing time domain reflection measurement on the impulse response correction data to obtain time domain reflection data;

[0140] Step S456: perform signal integrity verification on the circuit board data according to the time domain reflection data to obtain signal integrity verification data; perform error vector amplitude detection on the circuit board data according to the signal integrity verification data to obtain error vector amplitude data; integrate the signal integrity verification data and the error vector amplitude data to generate circuit board signal performance data.

[0141] In an embodiment of the present invention, a signal transmission efficiency analysis is performed on the conductor medium characteristic data, and a signal integrity analysis tool is used to evaluate the efficiency of the signal in the transmission medium layer; then, the signal transmission efficiency data is evaluated for noise suppression capability, and electromagnetic simulation software such as Ansys HFSS is used to identify the noise source and simulate the suppression effect to obtain noise suppression capability data; distortion measurement technology is used to measure the signal distortion of the noise suppression capability data, and specifically quantify the degree of distortion of the signal during the transmission process; signal synchronization analysis is performed on the signal distortion data, and synchronization technology in digital communication is used to obtain signal synchronization data; transmission rate measurement is performed on the signal synchronization data, and oscilloscope measurement technology, such as a signal transmission rate measurement method, is used to obtain signal transmission rate data; signal stability test is performed on the signal transmission rate data, and a stability test method is used to obtain signal stability data. Use signal coverage range test technology, such as coverage range simulation performed by electromagnetic simulation software, to evaluate signal coverage range of signal stability data and obtain signal coverage range data; use spectrum analysis technology, such as spectrum measurement function in signal processing toolbox, to measure signal attenuation of signal coverage range data and obtain signal attenuation data; perform spectrum analysis on signal attenuation data and obtain signal attenuation spectrum data; perform modulation effect analysis on signal attenuation spectrum data and use modulation effect analysis technology to obtain modulation effect data; perform pulse response correction on modulation effect data and use pulse response correction technology, such as pulse response correction method in signal integrity analysis tool to obtain pulse response correction data; perform time domain reflection measurement on pulse response correction data and obtain time domain reflection data. Perform signal integrity verification on circuit board data based on time domain reflection data and use signal integrity verification tool to obtain signal integrity verification data; perform error vector amplitude detection on circuit board data based on signal integrity verification data and obtain error vector amplitude data; integrate signal integrity verification data and error vector amplitude data to generate circuit board signal performance data.

[0142] Preferably, step S5 comprises the following steps:

[0143] Step S51: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain circuit board performance data;

[0144] Step S52: constructing a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain a circuit board performance problem prediction pre-model, and training the circuit board performance problem prediction pre-model using the circuit board performance data to obtain a circuit board performance problem prediction training model;

[0145] Step S53: performing a model cross-validation evaluation on the circuit board performance problem prediction training model to obtain circuit board performance problem prediction model evaluation data; adjusting the model parameters of the circuit board performance problem prediction training model using the circuit board performance problem prediction model evaluation data to obtain a circuit board performance problem prediction model;

[0146] Step S54: performing electric field simulation analysis on the conductive performance data of the circuit board to obtain the electric field distribution data of the circuit board; performing weak electric potential area prediction on the electric field distribution data of the circuit board based on the circuit board performance problem prediction model to generate electric potential prediction data; performing heat flow path identification on the heat dissipation performance data of the circuit board to obtain heat flow path data; performing hot spot concentration area prediction on the heat flow path data based on the circuit board performance problem prediction model to generate heat dissipation bottleneck prediction data; performing timing logic analysis on the signal performance data of the circuit board to obtain signal timing data; performing signal delay prediction on the signal timing data based on the circuit board performance problem prediction model to generate signal delay prediction data;

[0147] Step S55: Integrate the potential prediction data, the heat dissipation bottleneck prediction data and the signal delay prediction data to obtain potential performance problem data of the circuit board;

[0148] Step S56: Perform performance testing on the circuit board data according to the circuit board potential performance problem data and the circuit board performance data, and generate a circuit board performance testing report; use blockchain technology to upload the circuit board performance testing report to the blockchain node, and distribute the circuit board performance testing report through the blockchain node to execute the circuit board performance testing operation.

[0149] In the embodiment of the present invention, the circuit board performance data, heat dissipation performance data and signal performance data are integrated through data fusion technology to obtain circuit board performance data. A circuit board performance problem prediction model is constructed using circuit board performance data, a machine learning algorithm such as random forest or gradient boosting machine (GBM) is selected, and model parameters such as the number of trees, depth and learning rate are determined; the specific operations of model construction include data preprocessing, feature selection, model initialization, and training using circuit board performance data. In the model initialization stage, n_estimators = 100 is set to indicate the number of trees in the forest, max_depth = None indicates that the depth of the tree is not limited, min_samples_split = 10 indicates the minimum number of samples required for node splitting, min_samples_leaf = 5 indicates the minimum number of samples for leaf nodes, and random_state = 42 is used to control the reproducibility of the model; the initialized model is trained using circuit board performance data. During the training process, the model parameters are adjusted to optimize the performance of the model. The StratifiedKFold method is used for cross-validation to ensure that the proportion of each category in each compromise is the same as the overall data set. For example, setting n_splits = 5 means that the data set is divided into 5 parts, and one part is set aside as a test set each time, and the rest is used as a training set. According to the results of cross-validation, the model parameters are adjusted. For example, the number of trees should be increased or the learning rate should be adjusted to achieve higher accuracy or faster inference speed. The adjusted model is cross-validated again to verify the effect of parameter adjustment. Finally, a circuit board performance problem prediction model is obtained. Conduct electric field simulation analysis on the conductive performance data of the circuit board, and use electromagnetic simulation software such as Ansys HFSS to obtain the electric field distribution data of the circuit board; based on the circuit board performance problem prediction model, predict the weak potential area of ​​the electric field distribution data to generate potential prediction data; at the same time, identify the heat flow path of the circuit board heat dissipation performance data to obtain heat flow path data, and predict the hot spot concentration area of ​​the heat flow path data based on the prediction model to generate heat dissipation bottleneck prediction data; in addition, perform timing logic analysis on the circuit board signal performance data to obtain signal timing data, and perform signal delay prediction to generate signal delay prediction data; integrate the potential prediction data, heat dissipation bottleneck prediction data and signal delay prediction data to obtain the potential performance problem data of the circuit board; perform performance testing based on the potential performance problem data and circuit board performance data to generate a circuit board performance test report. Use blockchain technology to upload the report to the blockchain node to achieve distributed storage to ensure data security and traceability.

[0150] In this specification, a circuit board performance detection system is provided, which is used to execute the above-mentioned circuit board performance detection method. The circuit board performance detection system includes:

[0151] The circuit board structure model module is used to obtain circuit board data; perform structural topology analysis on the circuit board data to obtain circuit board structure data; perform spatial mapping on the circuit board structure data to generate a circuit board structure model;

[0152] The circuit board conductive performance detection module is used to extract the circuit board environmental characteristics of the circuit board data to obtain the circuit board environmental data; perform dust impact analysis on the circuit board environmental data to obtain the circuit board dust impact data; perform conductive track identification on the circuit board structure model to obtain conductive track data; perform conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain the circuit board conductive performance data;

[0153] The circuit board heat dissipation performance detection module is used to perform microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; perform microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; perform integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; perform heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; perform heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data;

[0154] The circuit board signal performance detection module is used to identify the transmission medium layer of the circuit board structure model to obtain the transmission medium layer data; determine the interlayer relationship of the transmission medium layer data to obtain the interlayer relationship data; perform conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain the circuit board signal performance data;

[0155] The circuit board performance problem prediction module is used to integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

[0156] The present invention uses a circuit board structure model module to analyze the structure topology and space mapping, and constructs a circuit board structure model, which can provide physical and spatial information for subsequent performance testing, thereby ensuring the accuracy of the testing process; the circuit board conductive performance testing module identifies environmental factors encountered by the circuit board in a specific environment, and performs dust impact analysis on it, which can reveal in detail the specific impact of dust on the performance of the circuit board, such as the resulting short circuit, poor contact and heat dissipation problems; the circuit board structure model is used to identify the conductive track, and the specific information of the conductive track is clarified; the conductive performance of the conductive track data is tested according to the circuit board dust impact data, which can verify the conductive performance of the circuit board, thereby avoiding circuit failures caused by dust. The circuit board heat dissipation performance detection module is used to identify the microfluidic circuit of the circuit board structure model, which is helpful for analyzing the fluid transmission path on the circuit board; the microchannel analysis of the microfluidic circuit data can evaluate the efficiency and performance of the microchannel on the circuit board; the integration of the microfluidic circuit data is evaluated according to the circuit board microchannel data to clarify the space utilization and functional integration of the circuit board; the heat dissipation parameter determination of the circuit board integration data is crucial for the subsequent stable operation of the circuit board under high load; the heat dissipation performance detection of the circuit board data according to the heat dissipation parameter data to obtain the circuit board heat dissipation performance data is helpful for preventing the performance degradation or damage caused by overheating of the circuit board. The circuit board signal performance detection module is used to identify the transmission medium layer of the circuit board structure model, which is helpful for understanding the signal transmission path in the circuit board; the interlayer relationship of the transmission medium layer data is determined to analyze the signal transmission efficiency in the circuit board; the conductor medium characteristic analysis of the transmission medium layer data according to the medium interlayer relationship data can clarify the signal transmission quality and speed in the circuit board; the signal performance detection of the circuit board data according to the conductor medium characteristic data is helpful for verifying the performance of the circuit board in communication. The circuit board performance problem prediction module integrates the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; the circuit board performance problem prediction model is constructed for the circuit board data according to the circuit board performance data, providing a model basis for subsequent predictions; the circuit board data is predicted for performance problems based on the circuit board performance problem prediction model to ensure the comprehensiveness of the performance problem prediction, so that potential circuit board performance problems can be more accurately identified; the circuit board performance test report is uploaded to the blockchain network distributed storage to ensure the security and non-tamperability of the test report, while providing the test report traceability, enhancing the credibility of the test report. Therefore, the present invention realizes multi-dimensional correlation analysis of the structural characteristics of the circuit board through Internet of Things technology, data mapping technology, intelligent detection technology and blockchain technology, thereby improving the accuracy of circuit board detection; and the performance data obtained by the detection is predicted for potential performance problems.

[0157] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0158] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A circuit board performance detection method, characterized in that: The following steps are involved: Step S1: acquiring circuit board data; performing structural topological analysis on the circuit board data to obtain circuit board structural data; performing spatial mapping on the circuit board structural data to generate a circuit board structural model; Step S2: extracting circuit board environmental features from circuit board data to obtain circuit board environmental data; performing dust impact analysis on the circuit board environmental data to obtain circuit board dust impact data; performing conductive track identification on the circuit board structure model to obtain conductive track data; performing conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain circuit board conductive performance data; Step S3: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; performing integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; performing heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data; Step S4: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; Determining the inter-layer relationship of the transmission medium layer data to obtain the medium layer relationship data; Conductor medium characteristic analysis is performed on the transmission medium layer data according to the relationship data between the medium layers to obtain conductor medium characteristic data; Perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain the circuit board signal performance data; Step S5: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain a circuit board performance problem prediction model; Based on the circuit board performance problem prediction model, performance problems of the circuit board data are predicted to obtain potential performance problem data of the circuit board; performance detection of the circuit board data is performed based on the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance detection report; the circuit board performance detection report is uploaded to the blockchain network distributed storage to execute the circuit board performance detection operation.

2. The circuit board performance detection method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: extracting nonlinear features from the circuit board data to obtain circuit board nonlinear data; and identifying topological features from the circuit board nonlinear data to obtain circuit board topological feature data; Step S12: performing circuit structure analysis on the circuit board topology feature data to obtain circuit board structure data; performing geometric analysis on the circuit board structure data to obtain circuit board structure analysis data; Step S13: performing line spatial morphology positioning on the circuit board structure analysis data to obtain line spatial morphology data; performing multi-dimensional parameter determination on the line spatial morphology data to obtain spatial morphology parameter data; Step S14: spatially mapping the circuit board data according to the spatial morphological parameter data to obtain a circuit board spatial mapping model; and visually rendering the circuit board spatial mapping model according to the circuit board structural data to generate a circuit board structural model.

3. The circuit board performance detection method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing circuit board environmental feature recognition on the circuit board data to obtain circuit board environmental data; performing dust microscopic analysis on the circuit board environmental data to obtain circuit board dust data; performing motion state simulation on the circuit board dust data to obtain dust motion state simulation data; Step S22: performing circuit board surface feature recognition on the circuit board structure model to obtain a circuit board surface image; performing coverage area calculation on the circuit board surface image according to the dust movement state simulation data to obtain dust coverage area data; performing electrical performance impact analysis on the dust coverage area data to generate circuit board dust impact data; Step S23: performing current path analysis on the circuit board structure model to obtain current path data; determining the current flow direction on the current path data to obtain current flow direction data; and performing conductive track identification on the circuit board structure model according to the current path data and the current flow direction data to obtain conductive track data; Step S24: performing contact impedance characteristic analysis on the conductive track data according to the circuit board dust impact data to obtain contact impedance characteristic data; performing conductive path continuity evaluation on the contact impedance characteristic data to obtain path continuity evaluation data; performing current distribution uniformity detection on the path continuity evaluation data to obtain distribution uniformity detection data; Step S25: perform potential equalization identification on the distribution uniformity detection data to obtain potential equalization data; perform charge transfer efficiency calculation on the potential equalization data to obtain charge transfer efficiency data; perform electron transfer rate analysis on the charge transfer efficiency data to obtain electron transfer rate data; perform conductivity performance detection on the circuit board data according to the electron transfer rate data to obtain circuit board conductivity performance data.

4. The circuit board performance detection method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; performing microchannel geometric feature extraction on the microfluidic circuit data to obtain microchannel geometric feature data; Step S32: performing roughness fractal processing on the microchannel geometric feature data to obtain microchannel roughness data; performing fluid dynamic feature processing on the microchannel roughness data to obtain microchannel fluid dynamic data; performing flow resistance analysis on the microchannel fluid dynamic data to obtain microchannel flow resistance data; Step S33: performing fractal dimension impact assessment on the microchannel flow resistance data to obtain fractal dimension impact assessment data; performing flow stability measurement on the microfluidic circuit data according to the fractal dimension impact assessment data to generate microchannel flow stability data; integrating the microchannel flow resistance data and the microchannel flow stability data to obtain circuit board microchannel data; Step S34: performing heat flow analysis on the microfluidic loop data according to the circuit board microchannel data to obtain heat flow data; performing heat dissipation parameter measurement on the heat flow data to generate heat dissipation parameter data; performing heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board heat dissipation performance data.

5. The circuit board performance detection method according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: performing resistance effect determination on microfluidic circuit data according to microchannel flow resistance data to obtain resistance effect data; performing influence processing on microchannel flow stability data according to the resistance effect data to obtain microchannel flow influence data; Step S342: performing heat conduction characteristic analysis on the microchannel flow influence data to obtain heat conduction efficiency data; performing heat flow measurement on the microfluidic loop data according to the heat conduction efficiency data to obtain heat flow data; Step S343: performing thermal impedance minimization processing on the heat flow data to obtain thermal impedance minimization data; performing heat dissipation efficiency evaluation on the thermal impedance minimization data to obtain heat dissipation efficiency evaluation data; extracting heat dissipation parameters of the heat dissipation efficiency evaluation data to generate heat dissipation parameter data; Step S344: Perform thermal response detection on the circuit board data according to the heat dissipation parameter data to obtain the circuit board thermal response data; perform heat flow rate measurement on the circuit board thermal response data to obtain the circuit board heat flow rate data; perform heat exchange efficiency calculation on the circuit board data according to the circuit board heat flow rate data to obtain the heat exchange efficiency data; integrate the heat flow rate data and the heat exchange efficiency data to obtain the circuit board heat dissipation performance data.

6. The circuit board performance detection method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing transmission medium layer identification on the circuit board structure model to obtain transmission medium layer data; performing electromagnetic characteristic analysis on the transmission medium layer data to obtain electromagnetic characteristic data; Step S42: determining the electromagnetic intensity of the electromagnetic characteristic data to obtain electromagnetic intensity data; performing interlayer electromagnetic interaction analysis on the transmission medium layer data according to the electromagnetic intensity data to obtain interlayer electromagnetic interaction data; Step S43: determining the interlayer relationship of the transmission medium layer data according to the interlayer electromagnetic effect data to obtain medium interlayer relationship data; Step S44: performing conductor signal characteristic analysis on the transmission medium layer data according to the medium layer relationship data to obtain conductor signal characteristic data; performing signal transmission analysis on the conductor signal characteristic data to obtain signal transmission data; performing conductor medium characteristic analysis on the transmission medium layer data according to the signal transmission data to obtain conductor medium characteristic data; Step S45: Perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain circuit board signal performance data.

7. The circuit board performance detection method according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: performing dielectric constant identification on the interlayer electromagnetic action data to obtain dielectric constant information; performing dielectric constant comparison on the interlayer electromagnetic action data according to the dielectric constant information to generate interlayer dielectric constant comparison data; Step S432: performing data difference analysis on the interlayer dielectric constant comparison data to obtain interlayer dielectric difference data; performing interlayer electromagnetic interference measurement on the interlayer dielectric difference data to obtain interlayer electromagnetic interference data; Step S433: performing interlayer isolation evaluation on the interlayer electromagnetic interference data to obtain electromagnetic interference isolation data; performing interlayer compatibility analysis on the electromagnetic interference isolation data to obtain medium interlayer compatibility data; Step S434: determining the inter-layer consistency of the transmission medium layer data according to the inter-layer compatibility data to obtain inter-layer relationship data of the medium.

8. The circuit board performance detection method according to claim 6, characterized in that: Step S45 includes the following steps: Step S451: performing signal transmission efficiency analysis on the conductor medium characteristic data to obtain signal transmission efficiency data; performing noise suppression capability evaluation on the signal transmission efficiency data to obtain noise suppression capability data; Step S452: performing signal distortion measurement on the noise suppression capability data to obtain signal distortion degree data; performing signal synchronization analysis on the signal distortion degree data to obtain signal synchronization data; Step S453: measuring the transmission rate of the signal synchronization data to obtain signal transmission rate data; performing a signal stability test on the signal transmission rate data to obtain signal stability data; Step S454: performing a signal coverage evaluation on the signal stability data to obtain signal coverage data; performing a signal attenuation measurement on the signal coverage data to obtain signal attenuation data; performing a spectrum analysis on the signal attenuation data to obtain signal attenuation spectrum data; Step S455: performing modulation effect analysis on the signal attenuation spectrum data to obtain modulation effect data; performing impulse response correction on the modulation effect data to obtain impulse response correction data; performing time domain reflection measurement on the impulse response correction data to obtain time domain reflection data; Step S456: perform signal integrity verification on the circuit board data according to the time domain reflection data to obtain signal integrity verification data; perform error vector amplitude detection on the circuit board data according to the signal integrity verification data to obtain error vector amplitude data; integrate the signal integrity verification data and the error vector amplitude data to generate circuit board signal performance data.

9. The circuit board performance detection method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain circuit board performance data; Step S52: constructing a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain a circuit board performance problem prediction pre-model, and training the circuit board performance problem prediction pre-model using the circuit board performance data to obtain a circuit board performance problem prediction training model; Step S53: performing a model cross-validation evaluation on the circuit board performance problem prediction training model to obtain circuit board performance problem prediction model evaluation data; adjusting the model parameters of the circuit board performance problem prediction training model using the circuit board performance problem prediction model evaluation data to obtain a circuit board performance problem prediction model; Step S54: performing electric field simulation analysis on the conductive performance data of the circuit board to obtain the electric field distribution data of the circuit board; performing weak electric potential area prediction on the electric field distribution data of the circuit board based on the circuit board performance problem prediction model to generate electric potential prediction data; performing heat flow path identification on the heat dissipation performance data of the circuit board to obtain heat flow path data; performing hot spot concentration area prediction on the heat flow path data based on the circuit board performance problem prediction model to generate heat dissipation bottleneck prediction data; performing timing logic analysis on the signal performance data of the circuit board to obtain signal timing data; performing signal delay prediction on the signal timing data based on the circuit board performance problem prediction model to generate signal delay prediction data; Step S55: Integrate the potential prediction data, the heat dissipation bottleneck prediction data and the signal delay prediction data to obtain potential performance problem data of the circuit board; Step S56: Perform performance testing on the circuit board data according to the circuit board potential performance problem data and the circuit board performance data, and generate a circuit board performance testing report; use blockchain technology to upload the circuit board performance testing report to the blockchain node, and distribute the circuit board performance testing report through the blockchain node to execute the circuit board performance testing operation.

10. A circuit board performance detection system, characterized in that: Used to execute the circuit board performance detection method according to claim 1, the circuit board performance detection system comprises: The circuit board structure model module is used to obtain circuit board data; perform structural topology analysis on the circuit board data to obtain circuit board structure data; perform spatial mapping on the circuit board structure data to generate a circuit board structure model; The circuit board conductive performance detection module is used to extract the circuit board environmental characteristics of the circuit board data to obtain the circuit board environmental data; perform dust impact analysis on the circuit board environmental data to obtain the circuit board dust impact data; perform conductive track identification on the circuit board structure model to obtain conductive track data; perform conductive performance detection on the conductive track data according to the circuit board dust impact data to obtain the circuit board conductive performance data; The circuit board heat dissipation performance detection module is used to perform microfluidic circuit identification on the circuit board structure model to obtain microfluidic circuit data; perform microchannel analysis on the microfluidic circuit data to obtain circuit board microchannel data; perform integration evaluation on the microfluidic circuit data according to the circuit board microchannel data to obtain circuit board integration data; perform heat dissipation parameter measurement on the circuit board integration data to generate heat dissipation parameter data; perform heat dissipation performance detection on the circuit board data according to the heat dissipation parameter data to obtain circuit board heat dissipation performance data; The circuit board signal performance detection module is used to identify the transmission medium layer of the circuit board structure model to obtain the transmission medium layer data; determine the interlayer relationship of the transmission medium layer data to obtain the interlayer relationship data; perform conductor medium characteristic analysis on the transmission medium layer data according to the interlayer relationship data to obtain conductor medium characteristic data; perform signal performance detection on the circuit board data according to the conductor medium characteristic data to obtain the circuit board signal performance data; The circuit board performance problem prediction module is used to integrate the circuit board conductive performance data, the circuit board heat dissipation performance data and the circuit board signal performance data to obtain the circuit board performance data; construct a circuit board performance problem prediction model for the circuit board data according to the circuit board performance data to obtain the circuit board performance problem prediction model; predict performance problems on the circuit board data based on the circuit board performance problem prediction model to obtain potential performance problem data of the circuit board; perform performance testing on the circuit board data according to the potential performance problem data of the circuit board and the circuit board performance data to generate a circuit board performance test report; upload the circuit board performance test report to the blockchain network distributed storage to execute the circuit board performance test operation.

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