166 results about "Test algorithm" patented technology

The invention provides a road slope stability prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting the multi-dimensional environmental parameters of a road slope in real time, and forming an original parameter set; based on a Granger causal test algorithm, analyzing the original parameter set, determining a causal influence relationship among the parameters, constructing a parameter causal network, and screening out key parameters; calculating the similarity of each key parameter in time and space dimensions through a dynamic time warping algorithm, and generating a space-time incidence matrix; based on the space-time incidence matrix, performing weighted fusion on the key parameters to obtain fusion parameters; inputting the fusion parameters into a pre-trained slope stability prediction model to obtain a predicted value of the slope stability; and when the predicted value exceeds a dynamically updated early warning threshold value, outputting slope instability early warning information. According to the method, the defects of inaccurate prediction and data redundancy of a current road slope stability prediction method are overcome.

The invention discloses an enterprise credit risk assessment method based on big data collection, and relates to the technical field of big data analysis, and the method comprises the steps: constructing a space-time fusion engine, carrying out the fusion through combining a cross-modal attention mechanism, generating a space-time fusion feature, and carrying out the adversarial training through a gradient inversion layer and a domain classifier, eliminating space-time fusion feature distribution differences; based on space-time fusion features, constructing a causal graph skeleton by adopting a conditional independent test algorithm, quantifying causal effect intensity among nodes through a machine learning model, constructing a risk conduction dynamic model, and predicting risk conduction intensity based on the causal effect intensity; and based on the risk conduction intensity, calculating a risk index weight through a dynamic game network, generating an enterprise risk score in combination with the space-time fusion feature, and generating a dynamic risk score through a time sequence neural network. According to the invention, by constructing the space-time fusion engine and combining the cross-modal attention mechanism, efficient fusion of multi-modal data is realized, and the accuracy and robustness of feature expression are improved.

The invention relates to the technical field of information, and provides a power grid equipment information visualization method and system, equipment and a medium, and the method comprises the steps: carrying out the region division of main network equipment through employing a hierarchical clustering algorithm according to the attribute of the main network equipment; when it is monitored that the line loss rate of a certain line exceeds a preset line loss rate threshold value, automatically obtaining region division information of equipment to which the corresponding line belongs, and extracting historical operation data of the equipment from a cloud database of the equipment in the corresponding region; an Apriori association rule mining algorithm is adopted to analyze the relevance between the historical operation data of the equipment and the line loss rate abnormity, and an equipment defect and line loss rate relevance analysis result is obtained; and acquiring real-time monitoring data of equipment corresponding to the line with the abnormal line loss rate, and verifying and correcting an equipment defect reason judgment result by comparing and analyzing the real-time monitoring data with historical operation data of the equipment and adopting an isolation forest anomaly detection algorithm. According to the invention, rapid positioning of line loss rate abnormity and accurate prediction and visual management of equipment defects are realized.

The method for automatically and intelligently extracting the data of the atlas database comprises the following steps: performing data cleaning on original data to obtain cleaned data; performing cross-modal semantic alignment word segmentation processing on the cleaning data to obtain semantic alignment word segmentation data; performing entity relationship extraction on the semantic alignment word segmentation data, analyzing an influence relationship between entities through a Granger causality test algorithm, verifying the logicality of the influence relationship, and generating entity relationship pair data containing causality intensity; performing graph construction processing of incremental graph optimization on the entity relationship pair data to obtain a dynamic optimization knowledge graph; and carrying out migration fusion of field adaptation on the dynamic optimization knowledge graph to obtain a graph database of field fusion. According to the method, the defect that causal logic is difficult to mine at present, so that the map relationship lacks the deep decision support capability is overcome.

The invention discloses an air conditioner host load regulation and control parameter self-adaptive learning method. The method comprises the steps that multi-source real-time data of an air conditioner host is obtained and preprocessed; generating a steady-state identification sequence by adopting layered progressive steady-state analysis, wherein the short-term analysis adopts self-adaptive window variance calculation based on the instantaneous change state of the system; based on the steady-state identification sequence, constructing an event causal relationship graph by adopting an improved causal test algorithm, and determining causal association between regulation and response; integrating the causal atlas, and generating a self-adaptive regulation and control strategy library capable of online learning through density clustering and regression modeling; and responding to a power grid dispatching instruction, and matching and generating an optimal regulation and control instruction sequence from the strategy library. According to the method, through the technical means of hierarchical progressive steady-state analysis, improved causal test, density clustering and regression modeling, an online learning strategy library is constructed, the precision of steady-state detection and load prediction can be improved, and the generalization and self-adaption capability of the model under complex working conditions can be enhanced.

The invention discloses an index transaction monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of transaction monitoring. The threshold range is dynamically generated based on the historical business data to adapt to the change of data distribution, the adaptive statistical test algorithm is selected in combination with the data sample size and the distribution characteristics to improve the accuracy of anomaly judgment, and personalized early warning output is performed according to the region and the business type of the auditing point. According to the technical scheme, data dynamic change and business scene diversity can be flexibly coped with, so that the technical problem that the reliability of an early warning result is reduced in the face of missing data, abnormal values and complex seasonal fluctuation, and then the real-time control and decision-making efficiency of an operator on a business operation state is influenced can be solved; the technical effects of improving the accuracy and reliability of business development index abnormity identification, reducing the false report and missing report rate, and enhancing the real-time control capability and decision-making efficiency of the operator for the business operation state are achieved.

The embodiment of the invention provides a memory controller, an error address positioning method and a computing system. The memory controller comprises an algorithm control circuit, a data generation circuit, a data comparison circuit and a monitoring circuit, and the algorithm control circuit is configured to generate control information for memory testing based on preset steps of a test algorithm; the data generation circuit is configured to generate test data used for being written into the memory to be tested and expected data obtained after the test data is read; the data comparison circuit is configured to detect whether target data read from the to-be-detected memory is consistent with expected data or not; and the monitoring circuit is configured to respond to the inconsistency of the read target data and the expected data, send a mark for stopping the memory test to the algorithm control circuit so as to stop the memory test and record a test address corresponding to an error and corresponding command data, so that stop and quick positioning are realized when the error occurs, and the repair efficiency is improved.

The invention relates to a storage test vector generation method and device, equipment and a storage medium, and is applied to the field of semiconductor testing, and the method comprises the steps: obtaining a chip manual and a chip test plan of a to-be-tested chip, and generating a chip description file according to the chip manual and the chip test plan; carrying out abstract processing according to the chip description file, and generating a chip abstract model; converting test logic in a test algorithm into a test sequence according to the chip abstract model; and obtaining a target file format requirement of a target ATE equipment platform, and converting the test sequence according to the target file format requirement to generate a storage test vector. The method has the technical effects that the reusability of cross-platform use of the test vector is improved, the test development efficiency is improved, and the time is saved.

The invention discloses a test method, device and equipment, a medium and a product. The method comprises the following steps: acquiring original CAN bus data acquired by a real vehicle road side; the original CAN bus data are screened based on a to-be-measured algorithm, a positive scene slice set and a negative scene slice set of at least one driving scene corresponding to the to-be-measured algorithm are obtained, scene slices in the positive scene slice set are correctly recognized based on the to-be-measured algorithm, and scene slices in the negative scene slice set are correctly recognized based on the to-be-measured algorithm. The scene slices in the negative scene slice set are scene slices with errors identified based on a to-be-measured algorithm; sending input data and an output result of a to-be-measured algorithm corresponding to the negative scene slice set to a target terminal, and receiving an updated to-be-measured algorithm fed back by the target terminal; verifying the updated to-be-measured algorithm based on the positive scene slice set and the negative scene slice set of the at least one driving scene; and if the verification is passed, replacing the to-be-measured algorithm deployed on the real vehicle with the updated to-be-measured algorithm.

A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include maneuvering, autonomously, a vehicle along a test track, monitoring, via at least one audio sensor, audio data from the vehicle during the maneuvering of the vehicle along the test track, and monitoring, via a monitoring system disposed along the test track, monitoring data of the vehicle along the test track. The operations also include executing, based on the monitored audio data, a sound detection function of an autonomous test algorithm, detecting, via the sound detection function, an error noise, categorizing, via a categorization function of the autonomous test algorithm, the detected error noise, identifying, based on the categorized error noise, a maintenance station and executing, in response to the identified maintenance station and categorized error noise, a spontaneous path planning function of the autonomous test algorithm.

The invention relates to the field of digital marketing, and discloses a video understanding-oriented hierarchical spatio-temporal semantic extraction method, which analyzes video content through a three-stage progressive analysis framework: firstly, detecting an object instance in a key frame sequence and constructing a spatio-temporal trajectory to ensure object identity continuity; modeling an object state evolution law by using a recurrent neural network, and quantifying an interaction relationship between objects; and finally, constructing an event causal graph based on the state mutation points, and generating a directed causal chain through a statistical causal test algorithm. Video content is abstracted into event causal logic from pixel features, a semantic path of'track-state-causal 'is formed, structured semantic description of decisions can be directly driven, video semantics and user behavior data are coupled, user preference portraits are dynamically constructed, and personalized material generation is guided.

The invention relates to the field of artificial intelligence chips, and provides a memory aging test method, device and equipment, a storage medium and a program product.The method comprises the steps that according to the number of kernels of a processor to be tested, the memory space of the processor is divided into a plurality of memory address blocks to be distributed to the kernels of the processor; performing permutation and combination on the plurality of memory test algorithms to generate a plurality of first algorithm sequences; issuing the first algorithm sequence to a processor core by adopting a rotary full distribution mechanism; and each kernel runs the issued first algorithm sequence based on the allocated memory address block, and executes the aging test task. By permutation and combination of the memory test algorithms and issuing of the memory test algorithms to the kernels for operation and execution of the aging test task by adopting a rotation type full distribution mechanism, it can be ensured that memory address blocks of different kernels operate different algorithms at the same moment, the complexity of a memory operation scene is improved, and the efficiency is improved. And the test result can effectively guide the screening and quality control of the memory bank.

The utility model relates to a logic calculation board card used for memristor array testing, after the logic calculation board card is built, in actual use, a memristor array to be tested is installed on a base and then is powered on, firstly, the weight of the memristor array to be tested is subjected to reading and writing testing through a weight reading circuit and a weight writing circuit, and then the memristor array to be tested is tested through the weight reading circuit and the weight writing circuit; the logic calculation excitation signal generation circuit generates pulse signals needed by logic calculation, the calculation result measurement circuit measures the result of logic calculation, the bias circuit provides bias voltage for the memristor array to be measured through the gating switch module, and the clock circuit provides the calculation result measurement circuit and the logic calculation excitation signal generation circuit. The weight reading circuit and the weight writing circuit provide clock signals; compared with the prior art, manual operation links are greatly reduced, complex test algorithm development is avoided, the test of the memristor array to be tested is efficiently completed by completely utilizing the existing hardware components through an ingenious circuit connection relation, and finally the test efficiency is effectively improved.

The invention provides a fault-tolerant method for an SINS (strapdown inertial navigation system) and GNSS (global navigation satellite system) integrated navigation system based on an LSTM (long short-term memory) neural network, and provides a fault detection and system fault-tolerant method based on assistance of the LSTM neural network for sudden change faults and slow change faults in an SINS / GNSS loose integrated navigation system. An innovation mahalanobis distance assisted chi-square test algorithm is utilized to detect abrupt change faults in the system in real time, and then a sequential probability ratio algorithm is utilized to detect slow change faults in the system. And the robust Kalman filtering based on M estimation is used for completing robustness enhancement work of the system, so that the influence of the sudden change fault on the system is effectively inhibited. And an LSTM neural network substitution mode is used to effectively inhibit the influence of a slowly changing fault on the system, and signal restoration is realized. Through detection and fault tolerance of two faults existing in the GNSS signal, the precision of integrated navigation positioning is improved.

The invention discloses a data and mechanism model combined driven gas cut monitoring method. The method comprises the following steps: 1) acquiring basic input parameters required by a simulation well; (2) establishing a single-phase flow mechanical model of the shaft under the normal working condition on the basis of a drilling construction process; (3) an outlet flow correction factor and an annular pressure loss correction factor are introduced into the normal working condition single-phase flow mechanical model, and a gas cut working condition monitoring model is constructed; 4) acquiring pressure data and flow data, and estimating an outlet flow correction factor and an annular pressure loss correction factor through an unscented Kalman filtering algorithm; 5) calculating a decision function of an outlet flow correction factor and an annular pressure loss correction factor through a generalized likelihood ratio test algorithm; and 6) comparing the decision function value with a preset threshold value to judge whether gas cut may occur, and if so, sending out a gas cut early warning signal. The method is beneficial to early monitoring of complex working conditions, and meets the requirements for early warning and monitoring of gas cut overflow of deep wells, ultra-deep wells and complex stratums.

The application provides a road slope stability prediction method and device, a storage medium and an electronic equipment, which comprises the following steps: collecting multi-dimensional environmental parameters of a road slope in real time to form an original parameter set; analyzing the original parameter set based on a Granger causality test algorithm to determine the causal influence relationship between parameters, construct a parameter causal network, and screen out key parameters; calculating the similarity of each key parameter in the time and space dimensions through a dynamic time warping algorithm to generate a space-time correlation matrix; weighting and fusing the key parameters based on the space-time correlation matrix to obtain a fused parameter; inputting the fused parameter into a pre-trained slope stability prediction model to obtain a predicted value of slope stability; and outputting a slope instability warning information when the predicted value exceeds a dynamically updated warning threshold. In the application, the defects of inaccurate prediction and data redundancy of the current road slope stability prediction method are overcome.

A method for performing an eye test includes the steps of: receiving a request from a user to perform the eye test; acquiring predetermined user identification data from the user; presenting or providing at least one test image to the user, the at least one test image including at least one test target contained therein; instructing the user to identify at least one of the at least one test target(s) contained within the at least one test image; acquiring response data associated with the user's attempt(s) to identify the at least one test target(s) contained within the at least one test image; aggregating and / or analysing the acquired user identification data and / or the response data utilising a test algorithm which determines result data; and, presenting or providing the result data to the user.

The invention relates to the technical field of memory chip testing, and particularly discloses a test algorithm for screening security indexes of a memory chip, which comprises the following steps of: firstly, establishing a plurality of groups of monitoring environments with different temperatures according to a fixed temperature difference interval, and determining the security indexes of the memory chip by combining read-write data of each read-write operation of the memory chip in a read-write test; according to the method, the read-write abnormal coefficient of each storage chip in the read-write operation process can be calculated, the data reflect whether the read-write state of the storage chip in one read-write operation in different test environments is qualified or not, and then the read-write stability is analyzed by combining the test data of each storage chip in one read-write test. According to the method and the device, the read-write frequency of each storage chip is estimated in combination with the data, and the security of the storage chips is analyzed in combination with the preset read-write operation frequency threshold, so that a large amount of read-write operation tests can be reduced, and the test efficiency of the security of the storage chips is improved.

The invention relates to the technical field of Web component testing, in particular to an automatic test case generation method, system and device for a Web component and a medium, and the method comprises the following steps: extracting a front-end frame type and code features to which the Web component belongs, constructing a visual state transition network, and constructing a unified data flow diagram; based on the code features, the visual state transition network and the unified data flow diagram, semantic reasoning is carried out by utilizing a large language model to generate a test path combination, and a minimum complete test case set is generated by applying a combination test algorithm; generating a composite assertion logic; frame type conversion is executed, and the minimum complete test case set and the composite assertion logic are converted into test codes capable of being executed by a front-end frame type; the test code is executed, and the minimum complete test case set and the composite assertion logic are updated and circulated based on Web component change or test failure in the execution process. According to the method and the device, the Web component automatic test which is high in accuracy, cross-frame adaptive and capable of realizing continuous self-healing can be realized.

The invention discloses an infrared camera low-power-consumption triggering method based on dynamic PIR detection and AI verification, and aims to solve the problem of high power consumption in a traditional infrared camera monitoring method. And carrying out dynamic PIR monitoring and AI target identification. Specifically, the method has a double-layer judgment mechanism of hardware-level PIR triggering and software-level AI verification, and a dynamic infrared triggering algorithm is a multi-mode self-adaptive triggering algorithm which integrates three data modes of PIR signal strength, time sequence analysis and environment temperature compensation and dynamically adjusts a triggering threshold value according to historical false triggering records. In addition, different field examples are used for testing algorithms and generating detailed monitoring reports, the availability and value of data are further improved, intelligent triggering of the infrared induction camera can be achieved through the method, the method is suitable for unattended field detection scenes, powerful support is provided for ecological protection and environment monitoring data collection, and the method is suitable for popularization and application. And deep research and wide application in the field of wild animal monitoring are promoted.

The invention belongs to the technical field of integrated circuits, and particularly relates to a DDR3 self-test circuit based on an FPGA in a core particle system and a method of the DDR3 self-test circuit. Comprising a power supply module, a reset module, a clock module, an FPGA chip, a JTAG downloading module, a PC upper computer and a DDR3 memory. The FPGA chip is respectively connected with the power supply module, the reset module, the clock module, the JTAG downloading module and the DDR3 memory, and the PC upper computer is connected with the JTAG downloading module; wherein the FPGA chip comprises a BIST circuit, a hardware analyzer ILA and a memory interface generator MIG; wherein the BIST circuit is connected with the hardware analyzer ILA and the memory interface generator MIG; the BIST circuit comprises a BIST controller, an address generator, a vector generator, a control signal generator, a comparator and a selector MUX; switching of various test algorithms can be realized through the dynamic test circuit module, so that the flexibility is improved, the test efficiency is improved, and the test cost is reduced.

A remote radio head unit tester and a method for testing a remote radio head unit is shown and described. The remote radio head tester includes a chamber having an interior volume. The chamber encloses electrical components, at least one of the electrical components is a computer which has a testing algorithm installed thereon. A frame secured to the chamber. At least one set of wires operably coupled with the electrical components. The remote radio head unit tester is configured to test a remote radio head unit by booting up the remote radio head unit. The remote radio head unit can only be booted up after the correct power cables and data cables have been connected to the remote radio head unit. Checking the software version of the remote radio head unit and updating the software. Testing the power connections of the remote radio head unit.

The invention relates to the technical field of voiceprint monitoring, and discloses a fan monitoring method and system based on voiceprint recognition, and the method comprises the steps: deploying a plurality of voiceprint sensors at a blade root layer and a cabin layer of a to-be-monitored fan, constructing a fan monitoring identifier for the to-be-monitored fan according to the relation between voiceprint data and standard voiceprint data, the method comprises the steps of determining an association result between fan monitoring areas based on fan monitoring identifiers and a causal test algorithm, determining a risk association area and a risk independent area based on the association result, comparing voiceprint characteristics of voiceprint data corresponding to the risk independent area in a historical database, determining an alarm strategy or a model strategy according to a comparison result, and sending the alarm strategy or the model strategy to a server. And according to the convolutional neural network model of the attention mechanism, outputting a monitoring risk value for the voiceprint feature of the voiceprint data corresponding to the risk associated area or the voiceprint feature determined as the model strategy, and based on the monitoring risk value, judging whether to send out an early warning, thereby ensuring the reliability of fan monitoring.

The invention discloses an industrial multi-source data fusion method based on causal inference in the technical field of computer data processing, and the method is applied to an aero-engine, and comprises the steps: obtaining the original performance data of the aero-engine, carrying out the normalization processing of the original performance data, and obtaining a target variable, the original performance data comprises rotating speed, exhaust temperature, oil pressure, oil temperature and metal abrasive particle component data; constructing a causal inference algorithm to calculate a dynamic threshold value; the causal intensity of each variable pair is calculated from the candidate causal set, variables with the first M ranks of the causal intensity are selected to form an optimization condition set, and a test algorithm is constructed; constructing a symmetric causal intensity matrix; and on the basis of the target variable and the feature matrix, performing quadrature to obtain a new data set after dimension reduction. By constructing the causal inference algorithm, the interpretability and generalization ability of the model can be enhanced, the performance stability is improved, the application range in the industrial field is expanded, and the calculation burden is relieved.

The invention discloses a memory testing method, a memory testing device, a storage medium and electronic equipment, and the memory testing method comprises the steps: traversing each storage unit of a to-be-tested memory, performing data read-write operation on the traversed target storage unit and an adjacent storage unit adjacent to the target storage unit according to a preset advancing rule; performing logical operation on the read data of the target storage unit and the read data of the adjacent storage unit to obtain an actual logical relationship value; if the actual logic relation value is not consistent with the standard logic relation value, it is determined that the to-be-tested memory has a fault, by adopting the method, the fault condition of the memory can be detected through the actual logic relation value, the reliability of a test algorithm is high, meanwhile, the fault coverage rate and the test complexity are both considered, and the fault test efficiency is high.

The invention requests to protect an ADC dynamic parameter test method based on spectrum correction, and the method comprises the following core steps: a primary spectrum correction stage: carrying out the spectrum error correction of an ADC output signal through employing a spectrum three-interpolation algorithm, and extracting a fundamental frequency; the sine wave amplitude, the phase and the direct current offset are solved through least square fitting, an initial ideal sine reference sequence is constructed, and noise separation is carried out for the first time. In the secondary spectrum optimization stage, a classic Hanning window function and a frequency domain two-point interpolation algorithm are adopted; and a sine wave error compensation sequence is constructed, and secondary noise extraction is carried out. In the coherent parameter reconstruction stage, the ADC output sequence is restored to a standard test state through amplitude and frequency conversion. According to the invention, the risk of test failure caused by the fact that the amplitude of the input signal exceeds the ADC range can be effectively avoided. On the basis, the secondary spectrum optimization significantly improves the recognition precision of the test algorithm on the harmonic distortion of the ADC output sequence, and effectively reduces the measurement error caused by nonlinear distortion.

The present invention relates to a programmable memory built-in self-test method based on a finite state machine, and the method is implemented by a programmable Mbist control module driven by three counters, an algorithm module, a comparison and analysis module, and a BRAM to be tested. In the present invention, the algorithm module integrates 8 test algorithms and generates characteristic signals according to the characteristics of each test algorithm, thereby improving the coverage of faults. Secondly, the programmable Mbist control module receives the characteristic signal of the test algorithm generated by the algorithm module, and generates a test vector, address and control signal to test the BRAM under the control of three counters, thereby improving the flexibility of the test. The present invention provides a new programmable memory built-in self-test method based on a finite state machine, which replaces the conventional test method. When the test algorithm needs to be replaced, the proposed test method does not need to reset the Mbist circuit, effectively reducing manpower and configuration time, thereby reducing the test cost.

The invention provides an abnormal time series data detection method and device and a storage medium, relates to the technical field of data processing, and can improve the accuracy of time series data anomaly detection. The method comprises the following steps: processing original time sequence data based on a spectrum entropy dynamic period detection algorithm, and determining whether the original time sequence data is period time sequence data; and under the condition that the original time series data are non-periodic time series data, determining a target anomaly detection model based on a normality test algorithm KS in combination with a first algorithm and the original time series data, performing anomaly detection on the original time series data based on the target anomaly detection model, and determining an anomaly detection result, the anomaly detection result comprises whether the original time sequence data is abnormal data or not; and under the condition that the original time sequence data is periodic time sequence data, performing anomaly detection on the original time sequence data based on a second algorithm, and determining an anomaly detection result.

The application provides a test scheme for checking data retention performance of NOR type Flash memory, focuses on the influence of erase-write cycles on the data retention performance, unifies the erase-write cycle number of the Flash memory test, stipulates clear temperature selection criteria, provides a reference for the Flash memory related test, and comprises the following steps: step one: determining the basic test algorithm of the NOR type Flash memory as the Checkerboard algorithm from the algorithm design complexity and the fault coverage; step two: improving the Checkerboard algorithm process and determining the data retention fault determination step; step three: carrying out a pre-experiment to determine the data retention performance test temperature, erase-write number and sample number of the NOR type Flash memory; step four: determining the erase-write number of the data retention test at normal temperature and the sample number under each erase-write number; step five: determining the test pattern, failure criterion, test duration and test cycle; step six: carrying out the data retention test combined with the erase-write cycle based on the non-replacement timing truncation test; and step seven: test data processing, fitting the average failure time under different erase-write numbers with the erase-write number to obtain the corresponding relationship between the average failure time and the erase-write number.

This invention relates to the field of collision detection technology, and in particular to a general performance evaluation system for visual collision detection algorithms. The system includes a video data module for calibrating optical variables and storing motion modal videos of approaching collision objects to obtain a standard video dataset; a data transmission module for exporting data from the standard video dataset to a test algorithm model to obtain test results, and also for importing the test results into an analysis module; and an analysis module for analyzing the test algorithm model using performance analysis and algorithm theory analysis to obtain analysis results. This invention can comprehensively evaluate collision detection algorithms, helping to analyze the specific advantages and disadvantages of visual collision detection algorithms, promoting the overall development and performance improvement of such algorithms, and increasing the practical applicability of visual collision detection algorithms.