CN117873792A

CN117873792A - HDMI test method based on intelligent self-adaption technology

Info

Publication number: CN117873792A
Application number: CN202311704268.9A
Authority: CN
Inventors: 蔡欣华; 曾志; 吕俊
Original assignee: Shanghai Pance Information Technology Co ltd
Current assignee: Shanghai Pance Information Technology Co ltd
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-04-12

Abstract

The invention discloses an HDMI test method based on an intelligent self-adaption technology, which comprises the following steps: s1) signal access and channel selection: evaluating available test channels, and selecting the most suitable test channel through a machine learning classification algorithm; s2) adaptive signal analysis: according to the actual condition of the signal and default parameters, dynamically adjusting analysis parameters through a self-adaptive algorithm; s3) virtual channel extension: carrying out virtualization treatment on the physical test channels, and calculating the number of virtual channels according to the number of the physical channels and the virtual expansion coefficient; s4) intelligent fault diagnosis and advice: by analyzing the test result and comparing with the fault mode library, the possible fault cause is rapidly diagnosed by utilizing a rule reasoning algorithm; s5) cloud data analysis and storage: all the test data and results are uploaded to a cloud server for remote storage and analysis. The invention greatly improves the testing efficiency and the user experience while ensuring the testing accuracy.

Description

HDMI test method based on intelligent self-adaption technology

Technical Field

The invention relates to an HDMI test method, in particular to an HDMI test method based on an intelligent self-adaption technology.

Background

In modern electronic devices, a High-definition multimedia interface (HDMI for short) has become one of the mainstream standards for connecting video sources and display devices. HDMI technology supports the transmission of uncompressed high definition video and multi-channel audio data while also supporting various advanced functions such as 3D display, ethernet communication, and the like. With the improvement of the performance of electronic devices and the increase of the quality of multimedia content, the test requirement on the HDMI interface is also higher and higher.

Conventional HDMI signal testing methods typically rely on fixed test equipment and manual operation. The tester needs to manually select a proper test channel according to specific test requirements, and adjust corresponding test parameters. The method is complex in operation, low in test efficiency and obvious in disadvantage particularly under the condition that a large number of repeated tests are required.

Rule-based automated test method: by presetting a set of fixed rules and parameters, the test system can automatically select test channels and set parameters under specific conditions. This approach has some efficiency improvement over manual testing, but its flexibility and accuracy is still limited, especially in the face of complex and variable testing environments where fixed rules and parameters may be difficult to adapt.

In recent years, with the rapid development of artificial intelligence and machine learning technologies, some new HDMI test methods start to attempt to introduce intelligent elements to improve test efficiency and accuracy. These methods typically train a machine learning model by analyzing historical test data to achieve intelligent optimization of test parameters. However, these methods still have some drawbacks. First, they often still rely on fixed physical test channels, and the channel configuration cannot be dynamically adjusted according to the test requirements, which limits their test efficiency. Secondly, although machine learning models are introduced to perform parameter optimization, the models are easy, and complex and changeable test scenes are difficult to accurately cope with. Finally, these methods often lack deep analysis and fault diagnosis functionality of the test results and do not provide comprehensive test services to the user.

From the foregoing, it is apparent that, whether it is a conventional manual test method, a rule-based automatic test method, or a conventional machine learning-based test method, there is still a certain limitation in terms of efficiency, accuracy, or user experience, and further improvement is still required.

Disclosure of Invention

The technical problem to be solved by the invention is to provide the HDMI testing method based on the intelligent self-adaptive technology, so that the testing accuracy is ensured, the testing efficiency and the user experience are greatly improved, and the method has high practical value and wide application prospect.

The technical scheme adopted for solving the technical problems is to provide an HDMI test method based on an intelligent self-adaptive technology, which comprises the following steps: s1) signal access and channel selection: evaluating available test channels, and selecting the most suitable test channel through a machine learning classification algorithm; s2) adaptive signal analysis: according to the actual condition of the signal and default parameters, dynamically adjusting analysis parameters through a self-adaptive algorithm; s3) virtual channel extension: carrying out virtualization treatment on the physical test channels, and calculating the number of virtual channels according to the number of the physical channels and the virtual expansion coefficient, so as to realize virtual expansion of the physical channels; s4) intelligent fault diagnosis and advice: by analyzing the test result and comparing with the fault mode library, the possible fault cause is rapidly diagnosed by utilizing a rule reasoning algorithm, and corresponding repair suggestions are given; s5) cloud data analysis and storage: all the test data and results are uploaded to a cloud server for remote storage and analysis.

Further, the step S1 selects the most suitable test channel according to the following formula:

C＝f(S1,T1,P1)；

wherein:

the classification variable C is a selected channel and represents different channels in the test system;

the signal characteristic vector S1 comprises various parameters describing the signal, including frequency, amplitude and eye pattern;

the test requirement vector comprises a description of a test task, wherein the description comprises a standard and a performance index which need to be met;

p1, testing a performance data matrix, wherein each row represents a previous test instance and comprises signal characteristics, test requirements and information of a selected channel;

f, predicting the most suitable channel C according to the input S1, T1 and P1 based on a machine learning classification algorithm; the classification algorithm is a decision tree, a support vector machine or a neural network.

Further, the process of intelligent channel switching in step S1 is divided into two stages: a training stage and a testing stage;

training phase: training a classification algorithm f using historical test data P1; each history test case comprises a signal characteristic vector S1, a test requirement T1 and a finally selected channel C, a classification algorithm f learns how to predict C according to S1 and T1, finds the relation between S1 and T1 and C, and constructs a model to describe the relation;

testing: when a new test task arrives, the signal characteristic vector S1 and the test requirement T1 are extracted and then input into a trained classification algorithm f, and the most suitable channel C is predicted according to the constructed model.

Further, the step S2 dynamically adjusts the analysis parameters according to the following adaptive algorithm formula:

P2＝g(S2,D)；

wherein:

the vector P2 is an analysis parameter which comprises various parameters required to be set when signal analysis is carried out, including a threshold value and a filter coefficient;

the vector or matrix S2 is signal data comprising sampling data of the signal to be analyzed;

the vector D is a default parameter, comprises a default value of the analysis parameter, and is set according to experience or standard;

and (3) a function g is an adaptive algorithm function, and the adjusted analysis parameter P2 is output according to the input signal data S2 and the default parameter D.

Further, the adaptive signal analysis process in step S2 includes two steps of parameter initialization and parameter adjustment;

parameter initialization: in this step, first, an initial value of the analysis parameter P2 is set according to the default parameter D; these default parameters are set empirically or by criteria, providing a starting point;

parameter adjustment: in this step, the analysis parameter P2 is dynamically adjusted by the adaptive algorithm g according to the input signal data S2; this process is an iterative process of evaluating the analysis result at the current parameter setting by continuously analyzing the signal data, and then adjusting the parameters according to the evaluation result until the analysis result reaches the set target.

Further, the step S3 calculates the number of virtual channels according to the following formula:

V＝h(C_phy,N)；

wherein:

the number of virtual channels represents the total number of channels which can be provided after the virtual channels are expanded;

c_phy, the number of physical channels, which represents the number of physical channels actually connected in the system;

virtual expansion coefficient, which represents the number of virtual channels that each physical channel can virtualize;

and the function h is a virtual expansion algorithm function, and the total number V of the virtual channels is output according to the input physical channel number C_phy and the virtual expansion coefficient N.

Further, the virtual expansion algorithm function allocates each physical channel to N virtual channels on average, and the total number V of virtual channels is equal to the number c_phy of physical channels multiplied by a virtual expansion coefficient N, v=c_phy.

Further, the step S4 performs reasoning diagnosis according to the following formula:

the formula: r=i (T3, F);

wherein:

a text string R, repair advice describing how to repair the detected fault or anomaly;

t3, testing results, including various parameters and result data of HDMI signal test;

f, a fault mode library storing a series of known fault modes and corresponding repair suggestions;

the function i is an inference algorithm function based on rules, and according to an input test result T3 and a fault mode library F, a repair suggestion R is output, and the specific process is as follows:

extracting signal characteristics: extracting key signal characteristics from the test result T3, including peak values, average values and waveform shapes;

pattern matching: comparing the extracted signal characteristics with modes in a fault mode library F, and finding out a fault mode with highest similarity;

logical reasoning: according to the result of pattern matching, combining with the known fault logic relationship, deducing the most probable fault cause;

generating a repair suggestion: and according to the deduced fault cause, searching corresponding repair suggestions from the fault mode library F, and generating a final repair suggestion R.

Further, the step S5) performs cloud data analysis and storage according to the following formula:

D＝j(U,S3)；

wherein:

the cloud end stores data which comprises all test data and results uploaded to the cloud end;

user information comprising user identity information and authority settings;

s3, locally storing test data and results, wherein the test data and results comprise all data and results generated in the test process;

the function j is a data uploading algorithm function, and data D stored in the cloud is output according to the input user information U and the local test data S3; the method comprises the following specific steps:

user authentication: and authenticating the user according to the user information U to ensure that the user has the authority to upload data.

And (3) data packaging: converting the local test data S3 into a format suitable for uploading;

data compression: compressing the data;

data encryption: encrypting the data;

uploading data: uploading the processed data to a cloud server through a network;

and (3) data storage: after receiving the data, the cloud server stores the data in a cloud database, and waits for a user to access and analyze at any time.

Compared with the prior art, the invention has the following beneficial effects: the HDMI testing method based on the intelligent self-adaptive technology improves testing efficiency and accuracy, and ensures reliability of testing results. Specifically, the technical problems solved by the invention and the technical effects achieved are as follows:

1. the test efficiency is low, in the conventional HDMI test method, the selection of the test channel and the configuration of the test parameters often need to be performed manually, which is time-consuming and labor-consuming, and has extremely low efficiency when facing a large number of repetitive test tasks. According to the invention, by introducing an intelligent channel selection mechanism and a self-adaptive signal analysis technology, the automatic configuration of the test channel and parameters is realized, and the test efficiency is remarkably improved.

2. The configuration of the test parameters is complex, the characteristics of HDMI signals are various, and different types of signals can be accurately measured by different test parameters. In the traditional method, a tester needs to perform parameter configuration according to experience or a trial-and-error mode, which not only increases the workload, but also can cause inaccurate test results due to incorrect parameter configuration. According to the invention, the test parameters can be dynamically adjusted according to the actual conditions of the signals by the self-adaptive signal analysis technology, so that the accuracy of the test results is ensured.

3. The test result analysis is insufficient, that is, the conventional HDMI test method generally only provides basic test results and lacks deep analysis and fault diagnosis functions for test data. This makes it difficult for the user to quickly and accurately find the root of the problem when the test result is not in line with expectations, and to effectively solve the problem. The intelligent fault diagnosis and suggestion technology is introduced, the fault diagnosis can be automatically carried out according to the test result, the restoration suggestion is provided for the user, and the additional value of the test is greatly improved.

Drawings

Fig. 1 is a flow chart of HDMI testing based on the intelligent adaptive technology of the present invention.

Detailed Description

The invention is further described below with reference to the drawings and examples.

Referring to fig. 1, the method for testing HDMI based on intelligent adaptive technology provided by the present invention includes the following steps:

s1) signal access and channel selection: and evaluating available test channels, analyzing the preliminary characteristics and test requirements of signals, and selecting the most suitable test channel through a machine learning classification algorithm to ensure the accuracy and efficiency of a test process. First, an HDMI signal to be tested is accessed to a test system. In this step, the system will first perform preliminary signal detection to determine the basic characteristics and status of the signal. Based on this preliminary information, and an understanding of the test requirements, the system will then automatically select the most appropriate physical channel for testing. This selection process takes into account signal characteristics, test requirements, and past test performance data, ensuring that the selected channel meets the test requirements to the greatest extent

S2) adaptive signal analysis: after selecting the appropriate channel, the system will enter the signal analysis stage. In the step, the system dynamically adjusts analysis parameters through a self-adaptive algorithm according to the actual condition and default parameters of the signals, so that accurate and reliable test results can be obtained under different signal conditions.

S3) virtual channel extension: in order to improve the test efficiency, the system also introduces a virtual channel expansion technology. And carrying out virtualization processing on the physical test channel, and increasing the concurrent processing capacity of the test system, thereby improving the test efficiency. The method comprises the steps that the virtual channel number is calculated according to the physical channel number and the virtual expansion coefficient by a software virtual technology, and then virtual expansion of the physical channels is realized; thereby providing more test channels and meeting the requirement of high concurrency test. The process considers the number of physical channels and the requirement of virtual expansion, and realizes efficient virtual channel management and scheduling through an intelligent algorithm.

S4) intelligent fault diagnosis and advice: in the test process, if the signal is abnormal or the test result does not accord with the expectation, the intelligent fault diagnosis and suggestion module is automatically started; or after the test is finished, the system performs intelligent analysis on the test result, automatically performs fault diagnosis and gives a repair suggestion. And (3) through analyzing the test result and comparing with a fault mode library, rapidly diagnosing possible fault reasons by utilizing a rule reasoning algorithm, and giving corresponding repair suggestions. The process considers the test result and the existing fault mode library, rapidly locates possible fault reasons through a rule-based reasoning algorithm, provides targeted repair suggestions, and helps users rapidly solve the problems.

S5) cloud data analysis and storage: all test data and results are uploaded to the cloud server for remote storage and analysis, so that a user can conveniently access and analyze the data anytime and anywhere, and meanwhile, data support is provided for subsequent data mining and performance optimization. This not only facilitates the management and access of data, but also provides a user with powerful data analysis capabilities. The user can access the data through the network to analyze and process the data whenever and wherever, so that the efficiency and convenience of data utilization are greatly improved.

The present invention exhibits significant advantages, particularly in the following aspects:

1. efficiency and flexibility improvement: the HDMI test methods of the prior art typically rely on fixed test channels and parameter settings, which limit the efficiency and flexibility of the test. According to the invention, by introducing intelligent channel switching and self-adaptive signal analysis, the optimal test channel and parameters can be dynamically selected according to actual test requirements. The testing efficiency is greatly improved, the flexibility of the testing process is improved, and the optimal testing performance can be obtained under different testing situations.

2. Enhancement of accuracy and reliability: in the conventional test method, due to the fixity of parameter setting and the limitation of manual selection, the accuracy and reliability of the test result are often not sufficiently ensured. According to the invention, through self-adaptive signal analysis and intelligent fault diagnosis and suggestion, the test signal can be more accurately analyzed, and potential errors can be timely found and corrected, so that the accuracy of the test result and the reliability of the whole test process are remarkably improved.

3. Optimization of user experience: in the conventional method, a user needs to spend a lot of time for test setting and result analysis, and the storage and access of test data are also inconvenient. According to the cloud server, the test data and the results are uploaded to the cloud server through cloud data analysis and storage, so that the data storage is safer, and a user can conveniently access and analyze the test data at any time and any place. Meanwhile, through intelligent fault diagnosis and suggestion, the user can locate and solve the problem more quickly, and the use experience of the user is greatly optimized.

The main steps of the present invention will be further described below.

1. Signal access and channel selection

Signal access and channel selection are a key technical step in the invention, and the channel selection in the HDMI test process is optimized by using an advanced machine learning algorithm, so that the test efficiency and accuracy are improved. Next, the present invention will explain this step in detail.

Description of: in HDMI testing, it is often necessary to measure and analyze pairs of high-speed signals. These signals have different characteristics such as frequency, amplitude, eye pattern, etc. Meanwhile, the test requirements can be different according to different test targets and standards. In conventional testing methods, selecting which channel to test a particular signal often requires a test engineer to manually configure based on experience, which is not only time consuming, but also prone to error. The intelligent channel switching aims at automatically selecting the most suitable channel for testing by utilizing a machine learning algorithm according to the characteristics of signals and testing requirements, so that the testing efficiency and accuracy are improved.

The formula: c=f (S1, T1, P1);

wherein:

and C, selecting a channel. This is a classification variable that represents the different channels in the test system.

S1, signal characteristics. This is a vector that contains various parameters describing the signal, such as frequency, amplitude, eye pattern, etc.

T1, test requirement. This is also a vector containing descriptions of test tasks such as criteria and performance metrics that need to be met.

P1-previous test performance data. This is a matrix, with each row representing a previous test case, including information on signal characteristics, test requirements, and selected channels.

And f, a classification algorithm based on machine learning. This algorithm may be a decision tree, support vector machine, neural network, etc., with the purpose of predicting the most suitable channel C based on the inputs S1, T1 and P1.

Explanation: the process of intelligent channel switching can be divided into two phases: a training phase and a testing phase.

Training phase: at this stage, the system trains the classification algorithm f with historical test data (i.e., P1). Each historical test case contains a signal characteristic S1, a test requirement T1, and a final selected channel C. By analyzing these data, the classification algorithm f learns how to predict C from S1 and T1. In this process, the algorithm will try to find the relationship between S1 and T1 and C, and construct a model to describe this relationship.

Testing: at this stage, when a new test task arrives, the system extracts the signal features S1 and test requirements T1, which are then input into the trained classification algorithm f. The algorithm predicts the most appropriate channel C based on the learned model. Since this prediction is based on the learning result of a previously large number of test data, it will generally be more accurate and efficient than manual selection.

In general, the intelligent channel switching is realized by a machine learning algorithm, so that complex and variable channel selection tasks are automated, and the efficiency and accuracy of the testing process are improved. This not only reduces the need for manual operation and reduces the probability of errors, but also makes the testing process more flexible and intelligent.

2. Adaptive signal analysis

The self-adaptive signal analysis is an advanced signal processing technology, and can automatically adjust analysis parameters according to the actual condition of a test signal, so as to ensure the accuracy of a test result. This technique is particularly important in the testing of HDMI signals, because the characteristics of HDMI signals may vary from device to device and from transmission condition to transmission condition, requiring flexible analysis methods to cope with.

Description of: in HDMI signal testing, signal analysis is a critical step that involves measuring and evaluating various characteristics of the signal, such as amplitude, frequency, waveform, etc. In order to obtain an accurate test result, the analysis parameters need to be adjusted according to the actual condition of the signal. Adaptive signal analysis techniques have been designed to address this problem. The method dynamically adjusts analysis parameters by analyzing the actual data of the test signals, thereby ensuring the accuracy of test results.

The formula: p2=g (S2, D);

wherein:

and P2, analyzing parameters. This is a vector containing various parameters, such as thresholds, filter coefficients, etc., that need to be set when performing signal analysis.

S2, signal data. This is a vector or matrix containing the sampled data of the signal to be analyzed.

And D, default parameters. This is a vector that contains default values for the analysis parameters, typically set empirically or by criteria.

And g, self-adaptive algorithm. This is a function which outputs an adjusted analysis parameter P2 based on the input signal data S2 and the default parameter D.

Explanation: the process of adaptive signal analysis mainly comprises two steps: parameter initialization and parameter adjustment.

Parameter initialization: in this step, the system first sets an initial value of the analysis parameter P2 according to the default parameter D. These default parameters are typically set empirically or by criteria that provide a starting point to ensure that the analysis will normally proceed.

Parameter adjustment: in this step, the system dynamically adjusts the analysis parameter P2 by an adaptive algorithm g based on the input signal data S2. This process is an iterative process in which the system continuously analyzes the signal data, evaluates the analysis results at the current parameter setting, and then adjusts the parameters based on the evaluation results until the analysis results reach a satisfactory level. In the process, the self-adaptive algorithm g plays a key role, and intelligently adjusts parameters according to signal data and current parameter setting, so that an analysis result is more accurate and reliable.

In general, the adaptive signal analysis technology ensures that accurate test results can be obtained under different signal conditions by intelligently adjusting analysis parameters. The technology not only improves the flexibility and the robustness of the test, but also reduces the dependence on manual intervention and improves the test efficiency. By continually learning and adapting to changes in the signal, this technique is able to maintain a high level of performance and accuracy under a wide variety of complex and varying test conditions.

3. Virtual channel expansion

Virtual channel expansion is a method for increasing the number of available channels by using a software virtual technology, so that the test efficiency is improved. In HDMI testing, the number of channels is often limited by the physical limitations of the hardware devices, but through virtual channel expansion techniques, the present invention can break through this limitation and provide more channels for testing.

Description of: in conducting the testing of HDMI signals, different signals or test requirements may require different numbers and types of channels. Physical channels are channels that are directly connected by hardware, and are limited in number. In order to improve the flexibility and efficiency of the test, the invention can virtualize one physical channel into a plurality of virtual channels by using a virtual channel expansion technology in a software simulation mode, thereby increasing the number of available channels. The method can improve the performance and efficiency of the test system on the premise of not increasing the hardware cost.

The formula: v=h (c_phy, N);

wherein:

v is the number of virtual channels. This is an integer representing the total number of channels that the system can provide after expansion through the virtual channel.

C_phy, number of physical channels. This is also an integer representing the number of physical channels actually connected in the system.

And N is a virtual expansion coefficient. This is a positive real number, representing the number of virtual channels each physical channel can virtualize. The larger the value of N, the greater the number of virtual channels.

And h, virtual expansion algorithm. This is a function that outputs the total number of virtual channels V based on the number of physical channels c_phy and the virtual expansion coefficient N that are input.

Explanation: the core of the virtual channel expansion is a virtual expansion algorithm h. The purpose of this algorithm is to calculate the total number of virtual channels V based on the existing number of physical channels c_phy and the virtual expansion coefficient N. This process can be understood as a resource allocation problem, and the algorithm needs to intelligently allocate limited physical resources (physical channels) to more virtual channels to improve the test efficiency.

The specific implementation of the virtual expansion algorithm h can be designed according to different requirements and conditions. A simple implementation is to allocate each physical channel equally to N virtual channels, so that the total number V of virtual channels is equal to the number c_phy of physical channels multiplied by the virtual expansion coefficient N. The formula can be expressed as: v=c_phy. The advantage of this approach is that it is simple to implement, but may not fully utilize physical resources in some situations.

To more efficiently utilize physical resources, the virtual expansion algorithm h may employ more complex strategies, such as dynamically adjusting the allocation of virtual channels in consideration of signal characteristics, test requirements, and the like. For example, for high frequency signals, more virtual channels may be required for fine testing, while for low frequency signals, the number of virtual channels may be reduced. Through the dynamic adjustment, the invention can ensure that the test efficiency is improved to the maximum extent while the test requirement is met.

In general, the virtual channel expansion technology provides a flexible and efficient solution for HDMI signal testing, and can improve the performance and efficiency of a test system through a software virtual technology on the premise of not increasing hardware cost. The successful application of this technology demonstrates the great potential of software in modern test systems, providing valuable experience and insight into the development of future test technologies.

4. Intelligent fault diagnosis and suggestion

Intelligent fault diagnosis and advice is a technique for performing automatic fault analysis and providing repair advice based on test results. The technology is particularly suitable for testing and maintaining complex systems, such as HDMI signal testing, and can remarkably improve the efficiency and accuracy of fault processing.

Description of: in performing HDMI signal testing, a wide variety of faults and anomalies may be encountered. In order to quickly and accurately find the root of the problem and give a solution, the present invention requires an intelligent fault diagnosis and suggestion technique. By analyzing the test result and combining the known fault mode library, the technology automatically diagnoses possible fault reasons by utilizing a rule reasoning algorithm and gives repair suggestions.

The formula: r=i (T3, F);

wherein:

and R, repairing advice. This is a text string that describes how to repair a detected fault or anomaly.

And T3, testing results. This is a data structure that contains parameters and result data of the HDMI signal test.

F, fault mode library. This is a database or data structure that stores a series of known failure modes and corresponding repair suggestions.

And i, a rule-based reasoning algorithm. This is a function, and based on the input test result T3 and the failure mode library F, the repair advice R is output.

Explanation: the core of intelligent fault diagnosis and suggestion is a rule-based reasoning algorithm i. The purpose of this algorithm is to deduce the possible cause of the fault from the test results T and the fault pattern library F and to give a repair recommendation R. The test result T3 contains abundant information such as signal strength, frequency, time delay and other parameters, and through analysis of the parameters, the invention can obtain important clues about the system state and performance. The fault pattern library F stores a series of known fault patterns and corresponding repair suggestions, which are accumulated based on historical data and expert experience.

The workflow of the inference algorithm i generally comprises the following steps:

extracting signal characteristics: key signal features such as peaks, averages, waveform shapes, etc. are extracted from the test result T3.

Pattern matching: and comparing the extracted signal characteristics with modes in a fault mode library F, and finding out the fault mode with the highest similarity.

Logical reasoning: and according to the result of pattern matching, combining the known fault logic relationship to deduce the most probable fault cause.

Through the series of steps, the intelligent fault diagnosis and suggestion system can rapidly and accurately diagnose the fault cause and give out a targeted repair suggestion, thereby greatly improving the efficiency and accuracy of fault processing. The successful application of this technology presents great potential for artificial intelligence in the field of modern testing and maintenance, providing valuable experience and insight for future development.

5. Cloud data analysis and storage

Cloud data analysis and storage are an important data management mode in the current technology development trend. By uploading locally generated data to the cloud server, remote storage and analysis of the data can be realized, and a user can access and process the data through a network at any time and any place. This is significant in improving the efficiency of data management, facilitating data sharing and collaboration.

Description of: during HDMI testing, a large amount of test data and results are generated. In order to facilitate management of such data and provide more powerful and flexible data analysis capabilities, the invention employs cloud data analysis and storage techniques. All the test data and results are uploaded to the cloud server through the network and stored in the cloud database. Users can access these data via the internet using various terminal devices (e.g., PCs, cell phones, tablet computers, etc.), and analyze and process the data.

The formula: d=j (U, S3);

wherein:

and D, data stored in the cloud. This is a data structure that contains all test data and results uploaded to the cloud.

U is user information. This is a data structure containing the identity information of the user, rights settings, etc.

S3, locally stored test data and results. This is a data structure that contains all the data and results generated during HDMI testing.

And j, a data uploading algorithm. This is a function, and according to the input user information U and the local test data S3, the cloud-stored data D is output.

Explanation: the main task of the data uploading algorithm j is to convert and package the locally generated test data and result S3 into a format D suitable for being stored in the cloud according to the user information U. In this process, data format conversion, data compression, encryption, etc. may be involved to ensure the security and efficiency of data during transmission and storage.

The method comprises the following specific steps:

And (3) data packaging: the local test data S3 is converted into a format suitable for uploading. This may include conversion of data structures, conversion of data types, and so forth.

Data compression: in order to improve the efficiency of data uploading and reduce the burden of network transmission, data is compressed.

Data encryption: in order to protect the security of the data during transmission, the data is encrypted.

Uploading data: and uploading the processed data to a cloud server through a network.

Through the series of steps, the invention not only realizes the remote storage of the test data, but also provides the user with strong and flexible data analysis capability, and greatly improves the efficiency of data management and utilization. The data management mode based on cloud computing is one of important directions of modern information technology development, and has important significance for promoting sharing and collaboration of data and promoting scientific research and industrial development.

In summary, according to the HDMI testing method based on the intelligent self-adaptive technology, the intelligent channel selection mechanism is introduced, so that dynamic configuration of the testing channel is realized, and the testing efficiency is greatly improved. And secondly, the invention adopts a self-adaptive signal analysis technology, can dynamically adjust the test parameters according to the actual condition of the signal, and ensures the accuracy of the test result. In addition, the invention also introduces a virtual channel expansion technology, thereby further improving the test efficiency. In the aspect of test result analysis, the invention provides comprehensive test service for users through intelligent fault diagnosis and suggestion technology. Finally, the cloud data analysis and storage function is introduced, so that a user can conveniently access and analyze test data at any time and any place.

While the invention has been described with reference to the preferred embodiments, it is not intended to limit the invention thereto, and it is to be understood that other modifications and improvements may be made by those skilled in the art without departing from the spirit and scope of the invention, which is therefore defined by the appended claims.

Claims

1. The HDMI testing method based on the intelligent self-adapting technology is characterized by comprising the following steps:

s1) signal access and channel selection: evaluating available test channels, and selecting the most suitable test channel through a machine learning classification algorithm;

s2) adaptive signal analysis: according to the actual condition of the signal and default parameters, dynamically adjusting analysis parameters through a self-adaptive algorithm;

s3) virtual channel extension: carrying out virtualization treatment on the physical test channels, and calculating the number of virtual channels according to the number of the physical channels and the virtual expansion coefficient, so as to realize virtual expansion of the physical channels;

s4) intelligent fault diagnosis and advice: by analyzing the test result and comparing with the fault mode library, the possible fault cause is rapidly diagnosed by utilizing a rule reasoning algorithm, and corresponding repair suggestions are given;

s5) cloud data analysis and storage: all the test data and results are uploaded to a cloud server for remote storage and analysis.

2. The HDMI test method based on intelligent adaptation technology according to claim 1, wherein the step S1 selects the most suitable test channel according to the following formula:

C＝f(S1,T1,P1)；

wherein:

3. The HDMI test method based on intelligent adaptive technology as claimed in claim 2, wherein the process of intelligent channel switching in step S1 is divided into two stages: a training stage and a testing stage;

4. The HDMI test method based on intelligent adaptive technology as set forth in claim 1, wherein said step S2 dynamically adjusts the analysis parameters according to the following adaptive algorithm formula:

P2＝g(S2,D)；

wherein:

5. The HDMI test method based on intelligent adaptive technology as claimed in claim 4, wherein the adaptive signal analysis process in step S2 includes two steps of parameter initialization and parameter adjustment;

6. The HDMI test method based on intelligent adaptation technology according to claim 1, wherein the step S3 calculates the number of virtual channels according to the following formula:

V＝h(C_phy,N)；

wherein:

7. The HDMI testing method of claim 6, wherein the virtual expansion algorithm functions equally allocate each physical channel to N virtual channels, and the total number of virtual channels V is equal to the number of physical channels c_phy multiplied by a virtual expansion coefficient N, v=c_phy.

8. The HDMI test method based on intelligent adaptation technology according to claim 1, wherein the step S4 performs inference diagnosis according to the following formula:

the formula: r=i (T3, F);

wherein:

9. The HDMI test method based on intelligent adaptation technology according to claim 1, wherein step S5) performs cloud data analysis and storage according to the following formula:

D＝j(U,S3)；

wherein:

user information comprising user identity information and authority settings;

data compression: compressing the data;

data encryption: encrypting the data;