Video data analysis method, device and computer equipment

Abstract

The application discloses a video data analysis method, device and computer equipment. The method comprises the following steps: in response to an analysis request sent by a client, receiving encrypted video data through a pre-established trusted tunnel; the encrypted video data is obtained by encrypting to-be-analyzed video data by using a first key; before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again by using a second key; the first key is a pre-acquired key; and the second key is a session key corresponding to the trusted tunnel; the encrypted video data is decrypted by using the second key to obtain the encrypted video data; a neural network model acquired from a cloud server is used to analyze the encrypted video data to obtain an analysis result; the analysis result at least comprises missing frames in the to-be-analyzed video data; and the analysis result is returned to the client.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edge computing, in particular to a video data analysis method and device and computer equipment. BACKGROUND

[0002] With the wide application of intelligent video analysis technology in the fields of security, transportation, retail, etc., intelligent video monitoring systems are becoming an indispensable part of modern society. However, there is growing concern about data privacy and information security. Traditional video monitoring systems have problems such as data leakage, privacy invasion, and model credibility in the process of data transmission and analysis, which not only threaten personal privacy, but also may lead to a decline in system efficiency and unnecessary security risks. At the same time, in the face of the challenges of data privacy and information security, the existing technology still has limitations in solving the problems in the field of intelligent video monitoring. Traditional encryption methods may have vulnerabilities in protecting data privacy, and relying solely on digital certificate authentication may not prevent threats such as man-in-the-middle attacks, resulting in low security of video data in the process of transmission and analysis. SUMMARY

[0003] The embodiments of the present application provide a video data analysis method, device and computer equipment to at least solve the technical problem of low security of video data in the process of transmission and analysis in the related art.

[0004] According to an aspect of an embodiment of the present application, a video data analysis method is provided, comprising: in response to an analysis request sent by a client, receiving encrypted video data through a pre-established trusted tunnel, the encrypted video data being obtained by encrypting to-be-analyzed video data using a first key by the client, wherein before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again using a second key, the first key being a pre-acquired key, and the second key being a session key corresponding to the trusted tunnel; decrypting the encrypted video data using the second key to obtain the encrypted video data, and analyzing the encrypted video data using a neural network model acquired from a cloud server to obtain an analysis result, the analysis result at least including missing frames in the to-be-analyzed video data; and returning the analysis result to the client.

[0005] Optionally, encrypting the to-be-analyzed video data to obtain encrypted video data comprises: receiving the encrypted video data through a pre-established trusted tunnel, wherein the encrypted video data is obtained by encrypting the to-be-analyzed video data using the first key by the client, and then performing differential privacy processing on the initial encrypted data according to a privacy protection strength and a privacy leakage probability.

[0006] Optionally, the decrypting the encrypted video data by using the second key comprises: decrypting the encrypted video data by using the second key, wherein the second key at least comprises: a client digital certificate, an edge computing node digital certificate and identity information of a target object.

[0007] Optionally, the analyzing the encrypted video data by using the neural network model obtained from the cloud server comprises: in a case where the neural network model exists in the edge computing node, directly analyzing the encrypted video data by using the pre-trained neural network model to obtain an analysis result; in a case where the neural network model does not exist in the edge computing node, synchronizing the encrypted video data to the cloud server for training the neural network model.

[0008] Optionally, the method further comprises: in a case where the neural network model does not exist in the edge computing node, synchronizing the encrypted video data to the cloud server, and training the neural network model by using a loss function, wherein the loss function is determined by a sum of a self-supervised loss function value, a long short-term memory loss function value and a differential privacy loss function value, the self-supervised loss function value is used to represent a difference between a missing frame and an actual frame in the encrypted video data, the long short-term memory loss function value is used to represent a difference between a time sequence of a predicted frame and a time sequence of an actual frame, and the differential privacy loss function value is used to represent a difference between an output result predicted on the initial encrypted data and an output result predicted on the encrypted video data.

[0009] Optionally, the analyzing the encrypted video data by using the neural network model obtained from the cloud server comprises: receiving the neural network model file, a digital signature of the neural network model and a device identifier sent by the cloud server; verifying the digital signature by using a public key corresponding to the digital signature of the neural network model, comparing the device identifier with an identifier of the edge computing node in a case where the digital signature is verified, and receiving the neural network model file to complete model deployment in a case where the device identifier is the same as the identifier of the edge computing node.

[0010] Optionally, after the analysis result is returned to the client, the method further comprises: sending the encrypted video data to the cloud server, adjusting the neural network model; receiving the adjusted neural network model, and deleting the encrypted video data.

[0011] According to another aspect of the embodiments of the present application, a video data analysis apparatus is also provided, comprising: a receiving module configured to receive encrypted video data through a pre-established trusted tunnel in response to an analysis request sent by a client, the encrypted video data being obtained by encrypting video data to be analyzed by the client using a first key, wherein the encrypted video data is twice encrypted using a second key before being transmitted by the client through the trusted tunnel, the first key being a pre-acquired key, and the second key being a session key corresponding to the trusted tunnel; a decryption module configured to decrypt the encrypted video data using the second key to obtain the encrypted video data, and analyze the encrypted video data using a neural network model acquired from a server to obtain an analysis result, the analysis result at least including missing frames in the video data to be analyzed; and an output module configured to return the analysis result to the client.

[0012] According to still another aspect of the embodiments of the present application, a computer device is also provided, comprising a memory and a processor, wherein the memory is configured to store program instructions, and the processor, connected with the memory, is configured to execute the video data analysis method.

[0013] According to yet another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, comprising a stored computer program, wherein a device in which the non-volatile storage medium is located executes the video data analysis method by running the computer program.

[0014] According to still another aspect of the embodiments of the present application, a computer program product is also provided, comprising computer instructions which, when executed by a processor, implement the video data analysis method.

[0015] In the embodiment of the present application, by responding to the analysis request sent by the client, the encrypted video data is received through the pre-established trusted tunnel, the encrypted video data is obtained by encrypting the video data to be analyzed by the client using a first key, wherein before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again using a second key, the first key is a pre-obtained key, and the second key is a session key corresponding to the trusted tunnel; the encrypted video data is decrypted using the second key to obtain the encrypted video data, and the neural network model obtained from the cloud server is used to analyze the encrypted video data to obtain an analysis result, the analysis result at least includes: missing frames in the video data to be analyzed; the analysis result is returned to the client, thereby achieving the purpose of twice encrypting the video data to be analyzed and analyzing the video data to be analyzed based on the encrypted data, thereby realizing the technical effect of improving the security of video data in the transmission and analysis process, and further solving the technical problem of low security of video data in the transmission and analysis process in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a hardware structure block diagram of a computer terminal for implementing a video data analysis method according to an embodiment of the present application;

[0018] Figure 2 is a flowchart of a video data analysis method according to an embodiment of the present application;

[0019] Figure 3 is a flowchart of another video data analysis method according to an embodiment of the present application;

[0020] Figure 4 is a structure diagram of a video data analysis system according to an embodiment of the present application;

[0021] Figure 5 is a structure diagram of a video data analysis device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative efforts should fall within the protection scope of the present application.

[0023] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0024] The information collected by the embodiments of the present application is information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of related data comply with relevant laws, regulations, and standards in the relevant region, necessary security measures are taken, public order and good customs are not violated, and appropriate operation portals are provided for the user to choose to authorize or refuse automatic decision results; if the user chooses to refuse, the expert decision process is entered.

[0025] In the related art, the weighting coefficients of each perception index are mainly set by human, which is too simple, has poor interpretability, and the weighting coefficients are not accurately set, which may have a great impact on user network perception evaluation, or the index basic threshold is set based on business experience to perform evaluation, resulting in low accuracy of wireless network quality evaluation. In order to solve the problems in the related art, the embodiments of the present application provide a video data analysis method, which can be run in Figure 1 The computer terminal is explained and described below.

[0026] The video data analysis method provided by the embodiments of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing the video data analysis method is shown. As shown in FIG. 1, the computer terminal includes a processor 1001, a memory 1002, a communication interface 1003, and a bus 1004. Figure 1As shown, the computer terminal 10 may include one or more (illustrated by 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the video data analysis method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned video data analysis method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission module 106 is configured to receive or send data via a network. The network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network interface controller (NIC) that can connect to other network devices through a base station to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module configured to communicate with the Internet wirelessly.

[0030] The display can be a liquid crystal display (LCD) that is touch screen, for example, which can enable a user to interact with a user interface of the computer terminal 10.

[0031] It is noted that in some alternative embodiments, the above Figure 1 The computer terminal can include hardware elements (including circuitry), software elements (including computer code stored on a computer readable media), or a combination of both hardware and software elements. It should be noted that in some embodiments, the computer terminal can be a stand-alone computer terminal, or it can be a computer terminal that is part of a larger computer system. Figure 1 is merely one example of a particular implementation, and is intended to illustrate the types of components that can be present in the computer terminal described above.

[0032] In the above operating environment, the embodiments of the present application provide a method for analyzing video data. It is noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0033] Figure 2 is a flowchart of a method for analyzing video data according to an embodiment of the present application, as Figure 2 shown, the method includes the following steps:

[0034] In step S202, in response to the analysis request sent by the client, the encrypted video data is received through the pre-established trusted tunnel, the encrypted video data being obtained by encrypting the video data to be analyzed by the client using a first key, wherein before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again using a second key, the first key being a pre-acquired key, and the second key being a session key corresponding to the trusted tunnel;

[0035] In step S202, the encrypted video data can be included in the analysis request, or the encrypted video data can be sent by the client to the edge computing node after the client sends the analysis request and the edge computing node responds to the analysis request.

[0036] Step S204, the encrypted video data is decrypted using the second key to obtain the encrypted video data, and the encrypted video data is analyzed using the neural network model obtained from the cloud server to obtain an analysis result, the analysis result at least including: missing frames in the video data to be analyzed;

[0037] Step S206, the analysis result is returned to the client.

[0038] Through the above steps S202 to S206, by responding to the analysis request sent by the client, the encrypted video data is received through the pre-established trusted tunnel, the encrypted video data is obtained by encrypting the video data to be analyzed using the first key by the client, wherein before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted using the second key, the first key is a pre-obtained key, and the second key is a session key corresponding to the trusted tunnel; the encrypted video data is decrypted using the second key to obtain the encrypted video data, and the encrypted video data is analyzed using the neural network model obtained from the cloud server to obtain an analysis result, the analysis result at least including: missing frames in the video data to be analyzed; the analysis result is returned to the client, thereby achieving the purpose of encrypting the video data to be analyzed twice and analyzing the video data to be analyzed based on the encrypted data, thereby realizing the technical effect of improving the security of video data in the transmission and analysis process, and further solving the technical problem of low security of video data in the transmission and analysis process in the related art. The following will be described in detail.

[0039] In some embodiments of the present application, the specific steps of encrypting the video data to be analyzed to obtain encrypted video data include: receiving the encrypted video data through the pre-established trusted tunnel, wherein the encrypted video data is obtained by encrypting the video data to be analyzed using the first key to obtain initial encrypted data, and then performing differential privacy processing on the initial encrypted data according to the privacy protection strength and the privacy leakage probability.

[0040] Taking the video data to be analyzed of the local client as D as an example, D is encrypted and segmented to ensure that the data is not leaked in the transmission and storage process, the data is encrypted using a first key, wherein the first key is K e , and the initial encrypted data is E = Encrypt(D, K e ).

[0041] In order to further protect the data privacy, differential privacy is further added to E, and E ′= Differential_Privacy(E,∈,δ), wherein ∈ represents the privacy protection strength, and δ represents the privacy leakage probability.

[0042] In an optional manner, adding differential privacy mainly includes three main steps: step 1, determining a privacy budget, including: a privacy protection strength and a privacy leakage probability for controlling the degree of privacy protection in the data analysis process;

[0043] Step 2, according to the privacy budget parameters, selecting a suitable noise distribution (such as Laplace distribution or Gaussian distribution);

[0044] Step 3, based on the privacy budget parameters, calculating the noise scale that needs to be added, for the Laplace mechanism, the noise scale is inversely proportional to the privacy protection strength; for the Gaussian mechanism, it is related to the privacy protection strength and the privacy leakage;

[0045] Step 4, adding the generated noise to the initial encrypted data to obtain encrypted video data.

[0046] In some embodiments of the present application, decrypting the encrypted video data using the second key includes: decrypting the encrypted video data using the second key, wherein the second key at least includes: a client digital certificate, an edge computing node digital certificate and identity information of a target object.

[0047] Taking the second key (session key) as K s , for example, wherein,

[0048] K s = Multi_Factor_Authentication(Cert client ,Cert server ,Biometric, OTP)

[0049] In the formula, Cert client represents a client digital certificate, Cert server represents an edge computing node digital certificate, Biometric is identity information, including but not limited to fingerprint, face, voice and other information, and OTP represents a one-time password.

[0050] For example, Figure 3As shown, before the encrypted video data is analyzed by using the neural network model obtained from the cloud server, it is necessary to determine whether the neural network model is deployed in the edge computing node. In the case where the neural network model exists in the edge computing node, the pre-trained neural network model is directly used to analyze the encrypted video data to obtain an analysis result. In the case where the neural network model does not exist in the edge computing node, the encrypted video data is synchronized to the cloud server for training the neural network model.

[0051] In the case where the neural network model does not exist in the edge computing node, the encrypted video data is synchronized to the cloud server, and the neural network model is trained by using a loss function. The loss function is determined by a sum of a self-supervised loss function value, a long short-term memory loss function value and a differential privacy loss function value. The self-supervised loss function value is used to represent a difference between a missing frame and an actual frame in the encrypted video data. The long short-term memory loss function value is used to represent a difference between a time sequence of a predicted frame and a time sequence of an actual frame. The differential privacy loss function value is used to represent a difference between an output result predicted on the initial encrypted data and an output result predicted on the encrypted video data.

[0052] It can be understood that the encrypted video data and the pre-acquired training data set are used when training the neural network model, thereby avoiding data leakage in the process of training and analysis.

[0053] In the process of training, the long short-term memory model can be used to ensure the time sequence of the video data.

[0054] In the process of training, the self-supervised learning is introduced, the missing part (missing frame) in the video is predicted, the model is trained on unsupervised data, and the data utilization efficiency is improved. Specifically, the self-supervised loss can be calculated by using the following formula: self = Cross_Entropy(f(x t ), x t+k ), wherein f(·) represents the neural network model, x t represents the t-th frame, and x t+k represents the t+k-th frame

[0055] Step 6: training the model, wherein the total loss function L taotal is represented as

[0056] L total = L self + L LSTM + L Differential

[0057]

[0058] where M(E) represents the output result of a privacy mechanism or query running on data E. M(E') represents the output result of the same privacy mechanism or query running on data set E', and Pr[M(E)∈S] represents the probability of the output of the query executed on data set D falling into a certain set S.

[0059] It can be understood that the missing frame is a missing image frame.

[0060] To ensure that the trained model (neural network model) will be digitally signed Sign Model to ensure its integrity and credibility, for example: the edge computing node receives the neural network model file sent by the cloud server, the digital signature of the neural network model and the device identifier; the digital signature is verified by using the public key corresponding to the digital signature of the neural network model, and in the case that the digital signature verification is passed, the device identifier is compared with the identifier of itself, and in the case that the device identifier is the same as the identifier of itself, the file of the neural network model is received to complete the model deployment.

[0061] Specifically, the trained model is issued to the edge computing node, where the device id is denoted as Device i , and the model is verified by Device i and Sign Model ; the verified model is segmented into two parts, Model f and Model p , where Model f is used for feature extraction of the model, and Model p is used for prediction of different downstream task models, and the two are calculated in a trusted environment respectively; the client sends a request to send E' to the edge node, and the edge node temporarily stores the data in the storage module, and the edge node returns the final analysis result to the client through inference calculation.

[0062] In some embodiments of the present application, after the analysis result is returned to the client, the edge computing node also sends the encrypted video data to the cloud server to adjust the neural network model; the adjusted neural network model is received, and the encrypted video data is deleted.

[0063] For example: the edge computing node synchronizes the cloud server periodically, and deletes the temporary data; the cloud server re-optimizes the model according to the data, and reissues the optimized model to the edge node.

[0064] The embodiment of the application further provides another video data analysis method, comprising: a local client of a user encrypting and differentially-privately processing video data D to generate encrypted data E'; establishing a trusted channel and multi-factor authentication for information transmission; training the encrypted data through cloud-side time series modeling of the video and improving data utilization efficiency in a self-supervised manner, training a model through a gradient descent method and the like according to a comprehensive loss function comprising self-supervision, LSTM and differential privacy loss; dividing the verified model into feature extraction and prediction two parts, both of which are executed in a trusted TEE environment, a terminal user sending a request, and E ′ sending the request to an edge node for an inference task, while the edge node stores data and synchronizes with cloud data, and for the cloud, performing digital signature on the model to ensure the integrity and trustworthiness of the model according to the training of the encryption model.

[0065] Figure 4 is a structural diagram of a video data analysis system according to an embodiment of the application, the system comprising:

[0066] a cloud server, an edge computing node and a client.

[0067] Figure 5 is a structural diagram of a video data analysis device according to an embodiment of the application, the device comprising:

[0068] The receiving module 50 is configured to receive encrypted video data through a pre-established trusted tunnel in response to an analysis request sent by a client, wherein the encrypted video data is obtained by encrypting video data to be analyzed by the client using a first key, and wherein the encrypted video data is twice encrypted using a second key before being transmitted by the client through the trusted tunnel, the first key is a pre-obtained key, and the second key is a session key corresponding to the trusted tunnel.

[0069] The decryption module 52 is configured to decrypt the encrypted video data using the second key to obtain the encrypted video data, and analyze the encrypted video data using the neural network model obtained from the server to obtain an analysis result, wherein the analysis result at least includes a missing frame in the video data to be analyzed.

[0070] The output module 54 is configured to return the analysis result to the client.

[0071] Through the analysis device of the video data, by responding to the analysis request sent by the client, the encrypted video data is received through the pre-established trusted tunnel, the encrypted video data is obtained by encrypting the video data to be analyzed by the client using a first key, wherein before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again using a second key, the first key is a pre-obtained key, and the second key is a session key corresponding to the trusted tunnel; the encrypted video data is decrypted using the second key to obtain the encrypted video data, and the neural network model obtained from the cloud server is used to analyze the encrypted video data to obtain an analysis result, the analysis result at least includes: missing frames in the video data to be analyzed; the analysis result is returned to the client, thereby achieving the purpose of encrypting the video data to be analyzed twice and analyzing the video data to be analyzed based on the encrypted data, thereby realizing the technical effect of improving the security of the video data in the transmission and analysis process, and further solving the technical problem of low security of the video data in the transmission and analysis process in the related art.

[0072] The receiving module 50 includes a receiving submodule for receiving encrypted video data through a pre-established trusted tunnel, wherein the encrypted video data is obtained by encrypting the video data to be analyzed by the client using the first key to obtain initial encrypted data, and then performing differential privacy processing on the initial encrypted data according to the privacy protection strength and the privacy leakage probability.

[0073] The decryption unit in the receiving submodule is used to decrypt the encrypted video data using the second key, including: decrypting the encrypted video data using the second key, wherein the second key at least includes: a client digital certificate, an edge computing node digital certificate and identity information of a target object.

[0074] The decryption module 52 includes an analysis submodule for analyzing the encrypted video data using the neural network model obtained from the cloud server, including: directly analyzing the encrypted video data using the pre-trained neural network model to obtain an analysis result in the case that the neural network model exists in the edge computing node; in the case that the neural network model does not exist in the edge computing node, synchronizing the encrypted video data to the cloud server for training the neural network model.

[0075] The analysis submodule comprises a training unit configured to, in a case where the neural network model does not exist in the edge computing node, synchronize the encrypted video data to the cloud server, and train the neural network model by using a loss function, wherein the loss function is determined by a sum of a self-supervised loss function value, a long short-term memory loss function value and a differential privacy loss function value, the self-supervised loss function value is used to represent a difference between a missing frame and an actual frame in the encrypted video data, the long short-term memory loss function value is used to represent a difference between a time sequence of a predicted frame and a time sequence of an actual frame, and the differential privacy loss function value is used to represent a difference between an output result predicted on the initial encrypted data and an output result predicted on the encrypted video data.

[0076] The training unit comprises a verification subunit configured to analyze the encrypted video data by using the neural network model obtained from the cloud server, comprising: receiving the neural network model file, the digital signature of the neural network model and the device identifier sent by the cloud server; verifying the digital signature by using a public key corresponding to the digital signature of the neural network model, in a case where the digital signature verification is passed, comparing the device identifier with the identifier of itself, in a case where the device identifier is the same as the identifier of itself, receiving the file of the neural network model to complete model deployment.

[0077] The analysis device of the video data further comprises an adjustment submodule configured to send the encrypted video data to the cloud server, adjust the neural network model, receive the adjusted neural network model, and delete the encrypted video data.

[0078] It should be noted that, Figure 5 The analysis device of the video data is configured to execute the analysis method of the video data as shown in Figure 2 The analysis method of the video data, and the related explanations and descriptions in the above analysis method of the video data are also applicable to the analysis device of the video data, which will not be described here in detail.

[0079] The embodiments of the present application further provide a computer device, comprising a memory and a processor, wherein the memory is configured to store program instructions, and the processor is connected with the memory and configured to execute the analysis method of the video data.

[0080] The embodiments of the present application further provide a non-volatile storage medium comprising a stored computer program, wherein a device in which the non-volatile storage medium is located executes the analysis method of the video data by running the computer program.

[0081] The embodiment of the present application further provides a computer program product, comprising a nonvolatile computer readable storage medium, and the nonvolatile computer readable storage medium stores a computer program.

[0082] The embodiment of the present application further provides a computer program product, comprising computer instructions, which are executed by a processor to implement the steps of the method for analyzing video data in the present application.

[0083] The embodiment of the present application further provides a computer program, which is executed by a processor to implement the steps of the method for analyzing video data in the present application.

[0084] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0085] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the embodiment described above is only illustrative, and for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0087] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0088] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0089] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0090] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method of analyzing video data, characterized by, The method comprises the following steps: In response to an analysis request sent by a client, encrypted video data is received through a pre-established trusted tunnel, wherein the encrypted video data is obtained by encrypting video data to be analyzed by the client using a first key, and before the client transmits the encrypted video data through the trusted tunnel, the encrypted video data is encrypted again using a second key, wherein the first key is a pre-obtained key, and the second key is a session key corresponding to the trusted tunnel; The encrypted video data is decrypted using the second key to obtain the encrypted video data, and a neural network model obtained from a cloud server is used to analyze the encrypted video data to obtain an analysis result, wherein the analysis result at least includes missing frames in the video data to be analyzed; The analysis result is returned to the client.

2. The method of claim 1, wherein, The encrypted video data is obtained by encrypting the video data to be analyzed, comprising: The encrypted video data is received through a pre-established trusted tunnel, wherein the encrypted video data is obtained by encrypting the video data to be analyzed by the client using the first key to obtain initial encrypted data, and then performing differential privacy processing on the initial encrypted data according to the privacy protection strength and the privacy leakage probability.

3. The method of claim 2, wherein, Decrypting the encrypted video data using the second key, comprising: Decrypting the encrypted video data using the second key, wherein the second key at least includes a client digital certificate, an edge computing node digital certificate, and identity information of a target object.

4. The method of claim 1, wherein, Using a neural network model obtained from a cloud server to analyze the encrypted video data, comprising: In the case that the neural network model exists in the edge computing node, directly using the neural network model to analyze the encrypted video data to obtain an analysis result; In the case that the neural network model does not exist in the edge computing node, synchronizing the encrypted video data to the cloud server for training the neural network model.

5. The method of claim 4, wherein, The method further comprises: In the case that the neural network model does not exist in the edge computing node, synchronizing the encrypted video data to the cloud server, and training the neural network model using a loss function, wherein the loss function is determined by the sum of a self-supervised loss function value, a long short-term memory loss function value, and a differential privacy loss function value, the self-supervised loss function value is used to represent the difference between missing frames and actual frames in the encrypted video data, the long short-term memory loss function value is used to represent the difference between the time sequence of predicted frames and the time sequence of actual frames, and the differential privacy loss function value is used to represent the difference between the output result predicted on the initial encrypted data and the output result predicted on the encrypted video data.

6. The method of claim 5, wherein, Using the neural network model obtained from the cloud server to analyze the encrypted video data, comprising: Receiving the neural network model file, the digital signature of the neural network model, and the device identifier sent by the cloud server; The digital signature is verified by using a public key corresponding to the digital signature of the neural network model, and in the case where the digital signature verification is passed, the device identity is compared with the identity of itself, and in the case where the device identity is the same as the identity of itself, a file of the neural network model is received to complete model deployment.

7. The method of claim 1, wherein, After returning the analysis result to the client, the method further comprises: sending the encrypted video data to the cloud server, and adjusting the neural network model; receiving the adjusted neural network model, and deleting the encrypted video data.

8. An analysis apparatus of video data, characterized by comprising: Comprise: a receiving module, configured to receive encrypted video data through a pre-established trusted tunnel in response to an analysis request sent by a client, the encrypted video data being obtained by encrypting video data to be analyzed by the client by using a first key, wherein the encrypted video data is secondarily encrypted by using a second key before the client transmits the encrypted video data through the trusted tunnel, the first key being a pre-acquired key, and the second key being a session key corresponding to the trusted tunnel; a decryption module, configured to decrypt the encrypted video data by using the second key to obtain the encrypted video data, and analyze the encrypted video data by using a neural network model acquired from a server to obtain an analysis result, the analysis result at least including a missing frame in the video data to be analyzed; an output module, configured to return the analysis result to the client.

9. A computer device, comprising: Comprise: a memory and a processor, wherein the memory is used to store program instructions; the processor, connected with the memory, is used to execute the video data analysis method in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the video data analysis method in any one of claims 1 to 7.

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