Intelligent testing method based on chip algorithm and unmanned aerial vehicle interface

Abstract

The invention discloses an intelligent test method based on a chip algorithm and an unmanned aerial vehicle interface, and the method comprises the steps: carrying out the encryption of interface data obtained from an interface of a to-be-tested chip through a pre-configured chip algorithm in response to a test request, and obtaining encrypted data; sending the encrypted data to a test device for the test device to perform an intelligent test; the chip algorithm is configured to extract a first feature matrix from the interface data; reconstructing time domain information in the first feature matrix to form a second feature matrix; decomposing the second feature matrix to obtain K third feature matrixes; mapping information reconstructed in each third feature matrix to a time domain to form a fourth feature matrix; and encrypted data is determined based on the K fourth feature matrixes, and the interface data of the to-be-tested chip can be encrypted to improve the security of data transmission between the unmanned aerial vehicle and the test equipment, so that the accurate test of the unmanned aerial vehicle interface is realized, and the stability and reliability of the unmanned aerial vehicle system are ensured.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to an intelligent testing method based on chip algorithm and drone interface. Background Art

[0002] As the endurance of drones increases, the time drones stay in the air is getting longer and longer. It is necessary to perform interface testing on drones during long-term flight. Otherwise, when the interface of the drone fails, it is easy to affect the normal operation of the drone and cause safety hazards.

[0003] However, due to the size and weight limitations of drones, it is difficult to configure high-computing chips to provide testing services. Interface data can be transmitted to test equipment on the ground for testing to obtain more accurate and reliable test results. However, during the data transmission process, if the transmitted data is intercepted and successfully parsed, it will easily affect the data security of the drone. What's more, the intercepting party can also generate false data based on the parsed data and provide it to the test equipment to deceive the test equipment, making it impossible to make corresponding adjustments to the operation of the drone in a timely manner. Summary of the invention

[0004] In order to solve the above technical problems, the embodiment of the present application proposes an intelligent testing method based on chip algorithm and drone interface.

[0005] The embodiment of the present application provides an intelligent testing method based on chip algorithm and drone interface. The drone to be tested has a main control chip and a chip to be tested. The method is executed by the main control chip and includes:

[0006] In response to a test request, using a pre-configured chip algorithm, encrypting the interface data obtained from the interface of the chip to be tested to obtain encrypted data;

[0007] Sending the encrypted data to a test device so that the test device can perform intelligent testing;

[0008] Wherein, the chip algorithm is configured as follows:

[0009] extracting a first feature matrix from the interface data;

[0010] Reconstructing the time domain information in the first characteristic matrix to form a second characteristic matrix;

[0011] Decomposing the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1;

[0012] Mapping the information reconstructed in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix;

[0013] The encrypted data is determined based on the K fourth characteristic matrices.

[0014] Optionally, reconstructing the time domain information in the first feature matrix to form a second feature matrix includes:

[0015] Adding noise to the time domain information in the first characteristic matrix to obtain a fifth characteristic matrix;

[0016] The time domain information after adding the noise in the fifth characteristic matrix is ​​mapped to the frequency domain to form a second characteristic matrix.

[0017] Optionally, the noise is constructed in a manner including:

[0018] Determining a preset first noise seed;

[0019] Generate a second noise seed based on the time domain information in the first feature matrix and the first noise seed;

[0020] Vector quantization mapping is performed on the second noise seed to construct the noise.

[0021] Optionally, the first noise seed is noise data in the time domain, and generating the second noise seed based on the time domain information in the first feature matrix and the first noise seed includes:

[0022] Transforming the time domain information in the first feature matrix to obtain a time domain latent space feature vector;

[0023] Performing the transformation process on the first noise seed to obtain a noise latent space feature vector;

[0024] The second noise seed is obtained based on the time-domain latent space feature vector and the noise latent space feature vector.

[0025] Optionally, the time-domain latent space feature vector has M elements, the noise latent space feature vector has N elements, N and M are both positive integers, and N<M, and obtaining the second noise seed based on the time-domain latent space feature vector and the noise latent space feature vector includes:

[0026] Replace the N elements of the noise latent space feature vector with the N elements of the specified positions of the M elements of the time domain latent space feature vector;

[0027] The replaced time-domain latent space feature vector is subjected to an inverse transformation matching the transformation process to obtain the second noise seed.

[0028] Optionally, the transformation process is implemented by any of the following:

[0029] Autoencoders;

[0030] Variational Autoencoder;

[0031] Wavelet transform.

[0032] Optionally, sending the encrypted data to a test device includes:

[0033] Sending a connection request to the test device, wherein the connection request is used to request to establish a dedicated data channel between the main control chip and the test device, and the dedicated data channel is constructed so that the main control chip can unidirectionally transmit data to the test device;

[0034] It is determined that the dedicated data channel is successfully established, and the encrypted data is sent to the test device via the dedicated data channel.

[0035] Optionally, the encrypted data is the K fourth characteristic matrices, the bandwidth of the dedicated data channel is determined by the amount of data of the fourth characteristic matrix with the largest amount of data among the K fourth characteristic matrices, and sending the encrypted data to the test device via the dedicated data channel includes:

[0036] The K fourth characteristic matrices are sent one by one to the test device via the dedicated data channel.

[0037] Optionally, extracting a first feature matrix from the interface data includes:

[0038] Preprocessing the interface data;

[0039] Parse the timestamp in the preprocessed interface data;

[0040] Based on the timestamp, a first feature matrix is ​​extracted from the preprocessed interface data.

[0041] Optionally, extracting a first feature matrix from the preprocessed interface data based on the timestamp includes:

[0042] At a set time interval, dividing the pre-processed interface data into a plurality of time window data according to the timestamp;

[0043] Determine the data features corresponding to each time window data;

[0044] The data features corresponding to the multiple time window data are respectively used as elements in the first feature matrix to construct the first feature matrix.

[0045] In summary, the embodiments of the present application have at least the following beneficial effects:

[0046] According to an embodiment of the present application, in response to a test request, a pre-configured chip algorithm is used to encrypt the interface data obtained from the interface of the chip to be tested to obtain encrypted data; the encrypted data is sent to a test device for the test device to perform intelligent testing; wherein the chip algorithm is configured to: extract a first characteristic matrix from the interface data; reconstruct the time domain information in the first characteristic matrix to form a second characteristic matrix; decompose the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1; map the reconstructed information in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix; based on the K fourth characteristic matrices, the encrypted data is determined, and the interface data of the chip to be tested can be encrypted to improve the security of data transmission between the drone and the test device, thereby achieving accurate testing of the drone interface and ensuring the stability and reliability of the drone system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of an intelligent testing method based on chip algorithm and drone interface provided in an embodiment of the present application;

[0048] Figure 2 It is a schematic diagram of the flow of the chip algorithm provided in the embodiment of the present application;

[0049] Figure 3 It is a schematic diagram of an intelligent testing device based on chip algorithm and drone interface provided in an embodiment of the present application;

[0050] Figure 4 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. In the description of the present application, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "according to" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments".

[0053] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0054] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0055] See also Figure 1 , shows a flow chart of an intelligent testing method based on chip algorithm and UAV interface provided by an embodiment of the present application, the UAV to be tested has a main control chip and a chip to be tested, the method is executed by the main control chip and the method includes steps S101-S102, which are as follows:

[0056] S101, in response to a test request, using a pre-configured chip algorithm to encrypt interface data obtained from the interface of the chip to be tested to obtain encrypted data;

[0057] It should be noted that the test request in this embodiment may be a request initiated by the main control chip when it detects that there is an abnormality in the interface data of the chip under test, or may be a request periodically sent by the test device to the main control chip.

[0058] S102, sending the encrypted data to a test device so that the test device can perform intelligent testing;

[0059] In one example, the intelligent test may refer to the process of automating and optimizing the test using artificial intelligence and machine learning techniques. This testing method aims to improve the efficiency, accuracy and coverage of the test while reducing manual intervention and errors. For example, a trained interface test model can be pre-deployed in the test device, and the interface test model can be trained using a general model training method based on historical test data. In this embodiment, the encrypted data sent to the test device can be decrypted by the test device using a decryption algorithm on the one hand, and the decrypted data can be input into the interface test model for testing; on the other hand, the interface test model can be configured accordingly so that the interface test model first uses a decryption algorithm to decrypt the encrypted data, and then tests according to the decrypted data. Among them, the decryption algorithm can be an algorithm that matches the chip algorithm, for example, it can be an inverse operation of the chip algorithm.

[0060] Among them, see Figure 2 , shows a schematic flow chart of a chip algorithm provided in an embodiment of the present application, wherein the chip algorithm is configured to include steps S201-S205:

[0061] S201, extracting a first characteristic matrix from the interface data;

[0062] In an example, step S201 may include: dividing the interface data into blocks, calculating the eigenvalues ​​of each of the divided data blocks, and constructing a first eigenmatrix according to all the eigenvalues.

[0063] S202, reconstructing the time domain information in the first characteristic matrix to form a second characteristic matrix;

[0064] In one example, step S202 may include: converting the time domain information in the first feature matrix into a target domain to form a second feature matrix, wherein the target domain is not the time domain and may include a frequency domain. When the target domain is the frequency domain, the time domain information in the first feature matrix may be converted into the frequency domain by Fourier transform.

[0065] S203, decomposing the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1;

[0066] In an example, step S203 may include: decomposing the second feature matrix into K third feature matrices, wherein the dimension of each third feature matrix is ​​equal to the dimension of the second feature matrix, and each third feature matrix has partial data in the second feature matrix, and the partial data can be represented by the element value of the corresponding position in the second feature matrix.

[0067] S204, mapping the information reconstructed in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix;

[0068] In one example, the reconstructed information can be mapped to the time domain in a manner corresponding to the reconstruction. For example, when the reconstruction is to convert the time domain information in the first feature matrix into the frequency domain through Fourier transform, the reconstructed information refers to the frequency domain information in the third feature matrix converted from the time domain information in the first feature matrix. At this time, the manner corresponding to the reconstruction may be an inverse Fourier transform.

[0069] It should be noted that, in this embodiment, any third characteristic matrix may contain reconstructed information, and for a third characteristic matrix that does not contain reconstructed information, it is not mapped and is directly used as the corresponding fourth characteristic matrix.

[0070] S205: Determine the encrypted data based on K fourth characteristic matrices.

[0071] In one example, step S205 may include: directly constructing encrypted data from K fourth characteristic matrices.

[0072] In an optional implementation manner, reconstructing the time domain information in the first feature matrix to form a second feature matrix includes:

[0073] Adding noise to the time domain information in the first characteristic matrix to obtain a fifth characteristic matrix;

[0074] The time domain information after adding the noise in the fifth characteristic matrix is ​​mapped to the frequency domain to form a second characteristic matrix.

[0075] In one example, the added noise may be a preset time domain noise, which is pre-stored in the drone and the test equipment respectively, so that it can be used for encryption in the drone and for decryption in the test equipment.

[0076] In an example, the time domain information after adding the noise in the fifth characteristic matrix can be mapped to the frequency domain through Fourier transform.

[0077] In an optional implementation manner, the noise is constructed in a manner including:

[0078] Determining a preset first noise seed;

[0079] Generate a second noise seed based on the time domain information in the first feature matrix and the first noise seed;

[0080] Vector quantization mapping is performed on the second noise seed to construct the noise.

[0081] It should be noted that the vector quantization mapping in this embodiment is a data compression and feature extraction technology. Vector quantization achieves data compression and feature extraction by mapping high-dimensional data to a low-dimensional discrete space (codebook), making the amount of noisy data smaller to facilitate subsequent data transmission.

[0082] In one example, the preset first noise seed may be the time domain noise preset in the above embodiment, and thus in this embodiment, the preset time domain noise is further processed so that the finally generated noise can simultaneously carry the information of the preset time domain noise and the time domain information in the first characteristic matrix, thereby improving the security of encryption.

[0083] In an optional implementation manner, the first noise seed is noise data in the time domain, and generating the second noise seed based on the time domain information in the first feature matrix and the first noise seed includes:

[0084] Transforming the time domain information in the first feature matrix to obtain a time domain latent space feature vector;

[0085] Performing the transformation process on the first noise seed to obtain a noise latent space feature vector;

[0086] The second noise seed is obtained based on the time-domain latent space feature vector and the noise latent space feature vector.

[0087] It should be noted that the latent space generally refers to a low-dimensional, abstract representation space, in which each point corresponds to a latent representation of the original data, because the latent space generally has a lower dimension than the original data space, thus helping to reduce the complexity of the data. And by mapping high-dimensional data to the latent space, the high-level features and intrinsic structure of the data can be captured, and by applying it to this embodiment, a low-dimensional latent space feature vector can be obtained, so that the amount of data of the second noise seed is smaller, and then the amount of noise data is smaller, so as to facilitate subsequent data transmission.

[0088] In an optional implementation, the time domain latent space feature vector has M elements, the noise latent space feature vector has N elements, N and M are both positive integers, and N<M, and obtaining the second noise seed based on the time domain latent space feature vector and the noise latent space feature vector includes:

[0089] Replace the N elements of the noise latent space feature vector with the N elements of the specified positions of the M elements of the time domain latent space feature vector;

[0090] The replaced time-domain latent space feature vector is subjected to an inverse transformation matching the transformation process to obtain the second noise seed.

[0091] It should be noted that, among the N elements of the specified position in the M elements of the time domain latent space feature vector in this embodiment, the specified position can be a number of positions pre-configured in the drone and the test equipment respectively to facilitate encryption and decryption of both parties, wherein one position can correspond to at least one element of the N elements of the noise latent space feature vector, and each position is used to integrate the information possessed by the corresponding elements.

[0092] In an optional implementation manner, the transformation process is implemented by any of the following:

[0093] Autoencoders;

[0094] Variational Autoencoder;

[0095] Wavelet transform.

[0096] In one example, the simplified autoencoder and the simplified variational autoencoder are both simplified encoder networks. Since only encryption (i.e., the encoding process) needs to be implemented on the drone side, the decoder networks contained in the autoencoder and the variational autoencoder can be removed, and the encoder networks corresponding to the autoencoder and the variational autoencoder can be pruned, so that the pruned encoder network is a simplified encoder network, which is convenient for operation by a low-computing-power drone main control chip.

[0097] In an optional implementation, sending the encrypted data to a test device includes:

[0098] Sending a connection request to the test device, wherein the connection request is used to request to establish a dedicated data channel between the main control chip and the test device, and the dedicated data channel is constructed so that the main control chip can unidirectionally transmit data to the test device;

[0099] It is determined that the dedicated data channel is successfully established, and the encrypted data is sent to the test device via the dedicated data channel.

[0100] In this embodiment, by establishing a dedicated data channel, it is possible to prevent the intercepting party from transmitting forged data to the test equipment for deception. In addition, since the dedicated data channel can only allow the main control chip to transmit data to the test equipment in one direction, there is no need to worry about other people taking advantage of the data transmission process of transmitting data for testing to send control signals to the drone to hijack the drone.

[0101] In an optional implementation, the encrypted data is the K fourth characteristic matrices, the bandwidth of the dedicated data channel is determined by the amount of data of the fourth characteristic matrix with the largest amount of data among the K fourth characteristic matrices, and sending the encrypted data to the test device via the dedicated data channel includes:

[0102] The K fourth characteristic matrices are sent one by one to the test device via the dedicated data channel.

[0103] In one example, the bandwidth of the dedicated data channel can be configured according to the amount of data of the fourth characteristic matrix with the largest amount of data, so that the dedicated data channel can transmit the fourth characteristic matrix with the largest amount of data to the test equipment within a set time. In this embodiment, a dedicated data channel with a smaller bandwidth can be used to transmit each fourth characteristic matrix to the test equipment one by one, so as to reduce the impact of the test on the normal operation of the drone.

[0104] In an optional implementation, extracting the first feature matrix from the interface data includes:

[0105] Preprocessing the interface data;

[0106] Parse the timestamp in the preprocessed interface data;

[0107] Based on the timestamp, a first feature matrix is ​​extracted from the preprocessed interface data.

[0108] In an optional implementation, extracting a first feature matrix from the preprocessed interface data based on the timestamp includes:

[0109] At a set time interval, dividing the pre-processed interface data into a plurality of time window data according to the timestamp;

[0110] Determine the data features corresponding to each time window data;

[0111] The data features corresponding to the multiple time window data are respectively used as elements in the first feature matrix to construct the first feature matrix.

[0112] It should be noted that the set time interval in this embodiment can be pre-configured in both the drone and the test equipment to facilitate encryption / decryption by both parties.

[0113] Correspondingly, an embodiment of the present application also provides an intelligent testing device based on chip algorithm and drone interface, which can implement all processes of the intelligent testing method based on chip algorithm and drone interface provided in the above embodiment.

[0114] See also Figure 3 , shows a schematic diagram of the structure of an intelligent test device based on chip algorithm and UAV interface provided by an embodiment of the present application, the UAV to be tested has a main control chip and a chip to be tested, the intelligent test device based on chip algorithm and UAV interface is deployed on the main control chip, and the device includes:

[0115] The encryption module 301 is used to encrypt the interface data obtained from the interface of the chip to be tested using a pre-configured chip algorithm in response to a test request to obtain encrypted data;

[0116] A data sending module 302, used for sending the encrypted data to a test device, so that the test device can perform intelligent testing;

[0117] Wherein, the chip algorithm is configured as follows:

[0118] extracting a first feature matrix from the interface data;

[0119] Reconstructing the time domain information in the first characteristic matrix to form a second characteristic matrix;

[0120] Decomposing the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1;

[0121] Mapping the information reconstructed in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix;

[0122] The encrypted data is determined based on the K fourth characteristic matrices.

[0123] In an optional implementation manner, reconstructing the time domain information in the first feature matrix to form a second feature matrix includes:

[0124] Adding noise to the time domain information in the first characteristic matrix to obtain a fifth characteristic matrix;

[0125] The time domain information after adding the noise in the fifth characteristic matrix is ​​mapped to the frequency domain to form a second characteristic matrix.

[0126] In an optional implementation manner, the noise is constructed in a manner including:

[0127] Determining a preset first noise seed;

[0128] Generate a second noise seed based on the time domain information in the first feature matrix and the first noise seed;

[0129] Vector quantization mapping is performed on the second noise seed to construct the noise.

[0130] In an optional implementation manner, the first noise seed is noise data in the time domain, and generating the second noise seed based on the time domain information in the first feature matrix and the first noise seed includes:

[0131] Transforming the time domain information in the first feature matrix to obtain a time domain latent space feature vector;

[0132] Performing the transformation process on the first noise seed to obtain a noise latent space feature vector;

[0133] The second noise seed is obtained based on the time-domain latent space feature vector and the noise latent space feature vector.

[0134] In an optional implementation, the time domain latent space feature vector has M elements, the noise latent space feature vector has N elements, N and M are both positive integers, and N<M, and obtaining the second noise seed based on the time domain latent space feature vector and the noise latent space feature vector includes:

[0135] Replace the N elements of the noise latent space feature vector with the N elements of the specified positions of the M elements of the time domain latent space feature vector;

[0136] The replaced time-domain latent space feature vector is subjected to an inverse transformation matching the transformation process to obtain the second noise seed.

[0137] In an optional implementation manner, the transformation process is implemented by any of the following:

[0138] Autoencoders;

[0139] Variational Autoencoder;

[0140] Wavelet transform.

[0141] In an optional implementation, sending the encrypted data to a test device includes:

[0142] Sending a connection request to the test device, wherein the connection request is used to request to establish a dedicated data channel between the main control chip and the test device, and the dedicated data channel is constructed so that the main control chip can unidirectionally transmit data to the test device;

[0143] It is determined that the dedicated data channel is successfully established, and the encrypted data is sent to the test device via the dedicated data channel.

[0144] In an optional implementation, the encrypted data is the K fourth characteristic matrices, the bandwidth of the dedicated data channel is determined by the amount of data of the fourth characteristic matrix with the largest amount of data among the K fourth characteristic matrices, and sending the encrypted data to the test device via the dedicated data channel includes:

[0145] The K fourth characteristic matrices are sent one by one to the test device via the dedicated data channel.

[0146] In an optional implementation, extracting the first feature matrix from the interface data includes:

[0147] Preprocessing the interface data;

[0148] Parse the timestamp in the preprocessed interface data;

[0149] Based on the timestamp, a first feature matrix is ​​extracted from the preprocessed interface data.

[0150] In an optional implementation, extracting a first feature matrix from the preprocessed interface data based on the timestamp includes:

[0151] At a set time interval, dividing the pre-processed interface data into a plurality of time window data according to the timestamp;

[0152] Determine the data features corresponding to each time window data;

[0153] The data features corresponding to the multiple time window data are respectively used as elements in the first feature matrix to construct the first feature matrix.

[0154] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the intelligent testing method based on chip algorithm and drone interface described in any one of the above items are implemented.

[0155] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of any of the above-mentioned intelligent testing methods based on chip algorithm and drone interface.

[0156] See also Figure 4 An embodiment of the present application also provides a computer device, including a processor 401, a memory 402, and a computer program stored in the memory 402 and configured to be executed by the processor 401. When the processor 401 executes the computer program, the steps of the intelligent testing method based on chip algorithm and drone interface described in any one of the above items are implemented.

[0157] The computer device of this embodiment includes: a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401, such as an intelligent test program based on chip algorithm and drone interface. When the processor 401 executes the computer program, the steps in the above-mentioned intelligent test method embodiments based on chip algorithm and drone interface are implemented, such as Figure 1 Steps S101-102 are shown.

[0158] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0159] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0160] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor 401 may also be any conventional processor, etc. The processor 401 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0161] The memory 402 can be used to store the computer program and / or module, and the processor 401 realizes various functions of the computer device by running or executing the computer program and / or module stored in the memory 402, and calling the data stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 402 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0162] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 401. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0163] In summary, the embodiments of the present application have at least the following beneficial effects:

[0164] According to an embodiment of the present application, in response to a test request, a pre-configured chip algorithm is used to encrypt the interface data obtained from the interface of the chip to be tested to obtain encrypted data; the encrypted data is sent to a test device for the test device to perform intelligent testing; wherein the chip algorithm is configured to: extract a first characteristic matrix from the interface data; reconstruct the time domain information in the first characteristic matrix to form a second characteristic matrix; decompose the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1; map the reconstructed information in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix; based on the K fourth characteristic matrices, the encrypted data is determined, and the interface data of the chip to be tested can be encrypted to improve the security of data transmission between the drone and the test device, thereby achieving accurate testing of the drone interface and ensuring the stability and reliability of the drone system.

[0165] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary hardware platform, and of course, it can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present application to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0166] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. An intelligent testing method based on chip algorithm and drone interface, characterized in that: The unmanned aerial vehicle to be tested has a main control chip and a chip to be tested, and the method is executed by the main control chip and includes: In response to a test request, using a pre-configured chip algorithm, encrypting the interface data obtained from the interface of the chip to be tested to obtain encrypted data; Sending the encrypted data to a test device so that the test device can perform intelligent testing; Wherein, the chip algorithm is configured as follows: extracting a first feature matrix from the interface data; Reconstructing the time domain information in the first characteristic matrix to form a second characteristic matrix; Decomposing the second characteristic matrix to obtain K third characteristic matrices, where K is greater than 1; Mapping the information reconstructed in each third characteristic matrix to the time domain to form a corresponding fourth characteristic matrix; The encrypted data is determined based on the K fourth characteristic matrices.

2. The method according to claim 1, characterized in that The reconstructing the time domain information in the first characteristic matrix to form a second characteristic matrix includes: Adding noise to the time domain information in the first characteristic matrix to obtain a fifth characteristic matrix; The time domain information after adding the noise in the fifth characteristic matrix is ​​mapped to the frequency domain to form a second characteristic matrix.

3. The method according to claim 2, characterized in that The noise is constructed in a manner including: Determining a preset first noise seed; Generate a second noise seed based on the time domain information in the first feature matrix and the first noise seed; Vector quantization mapping is performed on the second noise seed to construct the noise.

4. The method according to claim 3, characterized in that The first noise seed is noise data in the time domain, and the generating the second noise seed based on the time domain information in the first feature matrix and the first noise seed includes: Transforming the time domain information in the first feature matrix to obtain a time domain latent space feature vector; Performing the transformation process on the first noise seed to obtain a noise latent space feature vector; The second noise seed is obtained based on the time-domain latent space feature vector and the noise latent space feature vector.

5. The method according to claim 4, characterized in that The time domain latent space feature vector has M elements, the noise latent space feature vector has N elements, N and M are both positive integers, and N<M, and the second noise seed is obtained based on the time domain latent space feature vector and the noise latent space feature vector, including: Replace the N elements of the noise latent space feature vector with the N elements of the specified positions of the M elements of the time domain latent space feature vector; The replaced time-domain latent space feature vector is subjected to an inverse transformation matching the transformation process to obtain the second noise seed.

6. The method according to claim 4, characterized in that The transformation process is implemented by any of the following: Autoencoders; Variational Autoencoder; Wavelet transform.

7. The method according to claim 1, characterized in that The sending the encrypted data to the test device comprises: Sending a connection request to the test device, wherein the connection request is used to request to establish a dedicated data channel between the main control chip and the test device, and the dedicated data channel is constructed so that the main control chip can unidirectionally transmit data to the test device; It is determined that the dedicated data channel is successfully established, and the encrypted data is sent to the test device via the dedicated data channel.

8. The method according to claim 7, characterized in that The encrypted data is the K fourth characteristic matrices, the bandwidth of the dedicated data channel is determined by the amount of data of the fourth characteristic matrix with the largest amount of data among the K fourth characteristic matrices, and sending the encrypted data to the test device via the dedicated data channel includes: The K fourth characteristic matrices are sent one by one to the test device via the dedicated data channel.

9. The method according to claim 1, characterized in that The step of extracting a first characteristic matrix from the interface data comprises: Preprocessing the interface data; Parse the timestamp in the preprocessed interface data; Based on the timestamp, a first feature matrix is ​​extracted from the preprocessed interface data.

10. The method according to claim 9, characterized in that The step of extracting a first feature matrix from the preprocessed interface data based on the timestamp includes: At a set time interval, dividing the pre-processed interface data into a plurality of time window data according to the timestamp; Determine the data features corresponding to each time window data; The data features corresponding to the multiple time window data are respectively used as elements in the first feature matrix to construct the first feature matrix.

Images