An Internet data processing method, system, device and medium

Abstract

The present invention provides an Internet data processing method, system, device and medium, belonging to the field of data processing. The method includes: in response to an access request from a data source node, generating a node identity identifier based on a multi-dimensional evaluation model; verifying the node identity identifier, and if the verification is passed, splitting the Internet data to be processed into multiple data segments and generating a verification tuple according to the real-time network load status; performing cross-layer verification through a heterogeneous consensus network, generating a dynamic verifiable data credential, and sending it to the requester. By generating a node identity identifier based on a multi-dimensional evaluation model, the credibility of the node can be dynamically evaluated, preventing the access of malicious nodes or unauthorized nodes, reducing the risk of network attacks. Through the dynamic sharding strategy, the single-point data processing pressure is reduced, and the efficiency of data processing and transmission is optimized. By performing cross-layer verification through a heterogeneous consensus network, combining the advantages of the edge layer and the blockchain layer, the efficiency and security of data verification are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to an Internet data processing method, system, device and medium. Background Art

[0002] With the rapid development of Internet technology, the scale, complexity and security requirements of data processing are increasing day by day. At present, the main industrial data transfer link is to build a local area network on Internet of Things devices. After the router completes the conversion of the device IP addresses in the local area network, the data interaction between the device and the data layer is realized. After the data enters the data layer, it is stored through a data storage tool such as a data warehouse, and then processed through a data middle platform. However, when the data processing method is applied to scenarios of large-scale, high-concurrency and cross-platform data processing, the following main problems are faced:

[0003] Traditional node identity authentication methods are usually based on static permission management, unable to dynamically evaluate the credibility of nodes, and are vulnerable to attacks from malicious nodes. In the scenario of surging data volume and high concurrency, traditional data processing methods, such as centralized data processing, are prone to become performance bottlenecks, resulting in processing delays and low efficiency. Moreover, the data verification methods adopted, such as a single consensus algorithm, are difficult to balance between security and efficiency and cannot meet the requirements of both low latency and high reliability at the same time. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an Internet data processing method, system, device and medium, which are used to solve all or at least part of the technical problems existing in the above-mentioned prior art, such as the inability to dynamically evaluate the credibility of nodes, data processing delays and low efficiency.

[0005] In a first aspect, an embodiment of the present application provides an Internet data processing method, including:

[0006] Responding to an access request from a data source node, generating a node identity identifier based on a pre-constructed multi-dimensional evaluation model;

[0007] Verifying the node identity identifier, and if the verification is passed, splitting the Internet data to be processed into multiple data segments according to the real-time network load status and generating a verification tuple;

[0008] Performing cross-layer verification through a heterogeneous consensus network, generating a dynamically verifiable data certificate, and distributing the dynamically verifiable data certificate to the requester.

[0009] Optionally, the pre-constructed multi-dimensional evaluation model is:

[0010]

[0011] Wherein, T is the comprehensive trust value, R is the historical behavior score, and H is the hardware security level. is the result of the i-th interactive verification. , , are dynamic weight coefficients, and n represents the total number of interactive verifications.

[0012] Optionally, verify the node identity identifier. If the verification is passed, split the Internet data to be processed into multiple data segments and generate a verification tuple according to the real-time network load status, including:

[0013] If the comprehensive trust value T is greater than or equal to the preset trust threshold, it is determined that the node passes the verification.

[0014] Real-time collect network bandwidth, edge node storage capacity, and request concurrency parameters, and calculate the load coefficient.

[0015] Determine the number of data segments according to the data sensitivity and the load coefficient.

[0016] Use an anti-quantum hash function to generate the hash value of the data segment, and generate a random polynomial array in combination with the threshold encryption algorithm.

[0017] Optionally, determine the number of data segments according to the data sensitivity and the load coefficient, including:

[0018] Determine the number of data segments K according to the following formula:

[0019]

[0020] Wherein, S represents the data sensitivity, L represents the load coefficient, a represents the sensitivity threshold, and b represents the load threshold.

[0021] Optionally, the generated verification tuple is represented as:

[0022] Wherein, is the i-th data segment, is the anti-quantum hash function, is the timestamp fingerprint, is the storage location identifier, and K is the number of data segments.

[0023] Optionally, perform cross-layer verification through a heterogeneous consensus network to generate a dynamic verifiable data credential, including:

[0024] In the edge layer, perform data integrity verification.

[0025] In the blockchain layer, perform zero-knowledge proof verification to generate a verification commitment.

[0026] Optionally, data integrity verification is performed using the following formula, including:

[0027]

[0028] In the formula, is the data integration factor of node i, is the verification strength of node i, is the weight factor of node i, is the root hash of the i-th node, is the dynamic threshold, is the sensitivity of Internet data.

[0029] Second, the embodiment of the present application further provides an Internet data processing system, including:

[0030] A generation unit, configured to generate a node identity identifier based on a pre-constructed multi-dimensional evaluation model in response to an access request from a data source node;

[0031] A verification unit, configured to verify the node identity identifier. If the verification is passed, the Internet data to be processed is divided into multiple data segments and a verification tuple is generated according to the real-time network load status;

[0032] A processing unit, configured to perform cross-layer verification through a heterogeneous consensus network, generate a dynamically verifiable data credential, and distribute the dynamically verifiable data credential to the requester.

[0033] Third, the embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned Internet data processing method are implemented.

[0034] Fourth, the embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned Internet data processing method are implemented.

[0035] From the above technical solutions, it can be seen that the present invention has the following advantages:

[0036] In the Internet data processing method, system, device, and medium provided by the present application, by generating a node identity identifier based on a multi-dimensional evaluation model, the credibility of the node can be dynamically evaluated, preventing the access of malicious nodes or unauthorized nodes, reducing the risk of network attacks. Through the dynamic sharding strategy, the single-point data processing pressure is reduced, and the efficiency of data processing and transmission is optimized. Through cross-layer verification by a heterogeneous consensus network, combining the advantages of the edge layer and the blockchain layer, the efficiency and security of data verification are ensured. Description of the Drawings

[0037] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0038] Figure 1 It is a flowchart of an Internet data processing method provided by an embodiment of the present invention;

[0039] Figure 2 It is a flowchart of splitting Internet data into multiple data segments and generating verification tuples provided by an embodiment of the present invention;

[0040] Figure 3 It is a schematic structural diagram of an Internet data processing system provided by an embodiment of the present invention;

[0041] Figure 4 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0042] In the following detailed description, various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions that fall within the spirit and scope of the various embodiments of the present disclosure.

[0043] In the following, the term "comprise" or "may comprise" that can be used in various embodiments of the present disclosure indicates the presence of the disclosed function or operation, and does not limit the addition of one or more functions or operations. In addition, as used in various embodiments of the present disclosure, the terms "comprise", "have" and their cognates are only intended to indicate a specific feature, number, step, operation, or combination of the foregoing items, and should not be construed as precluding the existence or addition of one or more other features, numbers, steps, operations, or combinations of the foregoing items.

[0044] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the listed words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Refer to Figure 1 The figure shows a flowchart of an Internet data processing method in a specific embodiment, including the following execution steps:

[0047] Step 100: In response to an access request from a data source node, generate a node identity identifier based on a pre-constructed multi-dimensional evaluation model.

[0048] Dynamically generating a node identity identifier through a multi-dimensional evaluation model can comprehensively evaluate the credibility of the node, ensuring that only strictly verified nodes can access the system, and effectively preventing identity forgery and malicious attacks.

[0049] Specifically, the pre-constructed multi-dimensional evaluation model is:

[0050]

[0051] In the formula, T is the comprehensive trust value, R is the historical behavior score, H is the node device security factor, is the result of the i-th interactive verification, , , are dynamic weight coefficients, and n represents the total number of interactive verifications.

[0052] The multi-dimensional evaluation model generates a comprehensive trust value by comprehensively considering multiple dimensional factors such as historical behavior scores, node device security factors, and multiple interactive verification results. This makes the evaluation of data source nodes more comprehensive and accurate, avoiding the one-sidedness of judging node credibility based on a single factor. For example, the historical behavior score can reflect the performance of the node in past data processing tasks, the device security factor can reflect the security of the node itself, and the interactive verification result records the verification situation during the interaction with the node. Considering all aspects ensures that the evaluation result is more reliable. Moreover, introducing dynamic weight coefficients can increase the weight corresponding to the node device security factor during periods of high network security risk, making the evaluation more focused on node security; the trust value obtained based on comprehensive and dynamic evaluation makes the generated node identity identifier better reflect the true situation and credibility of the node, thereby providing a reliable basis for subsequent data processing processes.

[0053] Step 101: Verify the identity identifier of the node. If the verification is passed, split the Internet data to be processed into multiple data segments according to the real-time network load status and generate a verification tuple.

[0054] Specifically, refer to Figure 2 As shown, when executing Step 101, the following steps can be specifically executed:

[0055] S1010: If the comprehensive trust value T is greater than or equal to the preset trust threshold, it is determined that the node passes the verification.

[0056] It should be noted that the preset trust threshold can be set according to the actual application scenario, for example, 0.8, which is not limited here.

[0057] S1011: Collect the network bandwidth, edge node storage capacity, and request concurrency parameter in real time, and calculate the load factor.

[0058] Specifically, the load factor = current load / maximum load capacity.

[0059] S1012: Determine the number of data segments according to the data sensitivity and the load factor.

[0060] Specifically, determine the number K of data segments according to the following formula:

[0061]

[0062] In the formula, S represents the data sensitivity, L represents the load factor, a represents the sensitivity threshold, and b represents the load threshold.

[0063] It should be noted that the sensitivity threshold a and the load threshold b can be set according to the specific application scenario. For example, the sensitivity threshold a is taken as 3, and the load threshold b is taken as 0.8, which is not limited here.

[0064] Data sensitivity is an index to measure the importance and confidentiality of data. By considering the relationship between the data segment and the sensitivity threshold, for highly sensitive data, it will be split into more segments. By increasing the number of data segments, the impact caused by the leakage or tampering of a single data segment can be reduced, and the data security can be improved. Moreover, different sensitive data requires different security levels and processing methods during the processing process. Dynamically adjusting the number of data segments according to the data sensitivity can make the data processing strategy more flexible and efficient. For low-sensitivity data, the number of segments can be appropriately reduced to reduce the overhead of data splitting and recombination and improve the processing speed; while for highly sensitive data, the number of segments is increased to enhance security and meet the processing requirements of different data.

[0065] In addition, when the network load is high, appropriately adjusting the number of data fragments can avoid network congestion caused by excessive transmission and processing of data fragments, enabling data processing tasks to be more evenly distributed across the network, thereby balancing the network load and improving the overall network performance.

[0066] S1013: Use an anti-quantum hash function to generate the hash value of the data fragment and generate a random polynomial array in combination with a threshold encryption algorithm.

[0067] An anti-quantum hash function is used to generate a unique hash value for each data fragment, like the "digital fingerprint" of the data. During data transmission or storage, by recalculating the hash value of the data fragment and comparing it with the original hash value, it can be quickly determined whether the data has been tampered with. For the same data fragment, the anti-quantum hash function always generates the same hash value, and the hash values of different data fragments are almost impossible to be the same. This ensures that the hash value of the data fragment can be used as its unique identifier for data traceability, positioning, and quick retrieval.

[0068] The random polynomial array generated by the threshold encryption algorithm is used to encrypt the data. Based on the secret sharing principle, this algorithm disperses the encryption key in a random polynomial array composed of multiple elements. Only when a certain number (threshold) of elements are gathered together can the complete key be restored to decrypt the data. This makes it impossible for attackers to obtain the decryption key even if some data fragments or encrypted information are leaked, greatly enhancing data confidentiality.

[0069] By combining the two, the anti-quantum hash function ensures data integrity, and the threshold encryption algorithm guarantees data confidentiality. The combination provides comprehensive security protection for Internet data throughout the entire process from storage, transmission to processing. Since in the complex and ever-changing Internet environment, data may be transmitted and processed at different nodes and under different network states. The combination of the high efficiency of the anti-quantum hash function and the distributed processing ability of the threshold encryption algorithm enables the system to operate stably in this environment, ensuring data security without affecting processing efficiency.

[0070] Specifically, the generation of the verification tuple is represented as:

[0071] In the formula, is the i-th data fragment, is the anti-quantum hash function, is the timestamp fingerprint, is the storage location identifier, and K is the number of data fragments.

[0072] Step 102: Perform cross-layer verification through a heterogeneous consensus network, generate a dynamic verifiable data credential, and distribute the dynamic verifiable data credential to the requester.

[0073] In a specific embodiment, cross-layer verification is performed through a heterogeneous consensus network to generate dynamically verifiable data credentials, including: at the edge layer, data integrity verification is performed; at the blockchain layer, zero-knowledge proof verification is executed to generate verification commitments.

[0074] The edge layer adopts a dynamic weight Byzantine fault tolerance algorithm to quickly verify data integrity and reduce latency. The blockchain layer introduces zero-knowledge proof to ensure data privacy and immutability. The cross-layer verification mechanism effectively prevents single-point failures and improves the fault tolerance of the system.

[0075] Specifically, the following formula is used for data integrity verification, including:

[0076]

[0077] In the formula, is the data integration factor of node i, is the verification strength of node i, is the weight factor of node i, is the root hash of the i-th node, is the dynamic threshold, is the sensitivity of Internet data.

[0078] In this embodiment, factors such as the hardware configuration and processing algorithm efficiency of different nodes will result in differences in their data processing capabilities. Nodes with stronger processing capabilities may undertake more important tasks during the verification process, and their data processing results have a greater impact on the overall verification result. Moreover, different verification scenarios may require different verification strengths. For example, for highly sensitive data, a higher verification strength is required to ensure data integrity. The introduction of in the formula can adjust the verification strength of each node according to actual needs, making the verification process more flexible and accurate.

[0079] In a heterogeneous consensus network, different nodes may have different roles and functions, and their influence on data integrity verification also varies. By setting appropriate weight factors, the role of important nodes can be highlighted, and the reliability of the verification result can be improved. Moreover, in data integrity verification, the root hash can quickly detect whether the data has changed. If the data is tampered with, its root hash value will also change accordingly. By comparing the root hash values, it is possible to efficiently verify the data integrity without transmitting a large amount of original data. This method greatly reduces the data transmission volume and computational overhead during the verification process and improves the verification efficiency.

[0080] Exemplarily, after the main edge node verifies, it broadcasts the tuple to the slave edge nodes. The slave nodes verify the data integrity through the hash chain, write the root hash of the verified tuple into the blockchain, generate a transaction hash, and the data requester verifies the transaction hash through the relay chain to ensure that the response data is consistent with the original data.

[0081] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0082] As Figure 3 shown, the following are embodiments of the Internet data processing system provided by the embodiments of the present disclosure. They belong to the same inventive concept as the Internet data processing methods of the above embodiments. For the details not described in detail in the embodiments of the Internet data processing system, reference can be made to the embodiments of the above Internet data processing methods.

[0083] A generation unit, configured to generate a node identity identifier based on a pre-constructed multi-dimensional evaluation model in response to an access request from a data source node;

[0084] A verification unit, configured to verify the node identity identifier. If the verification is passed, the Internet data to be processed is segmented into multiple data segments and a verification tuple is generated according to the real-time network load status;

[0085] A processing unit, configured to perform cross-layer verification through a heterogeneous consensus network, generate a dynamic verifiable data credential, and distribute the dynamic verifiable data credential to the requester.

[0086] By generating a node identity identifier based on a multi-dimensional evaluation model, the credibility of the node can be dynamically evaluated, access by malicious nodes or unauthorized nodes can be prevented, the network attack risk can be reduced. Through the dynamic sharding strategy, the single-point data processing pressure can be reduced, and the data processing and transmission efficiency of the system can be optimized. By performing cross-layer verification through a heterogeneous consensus network and combining the advantages of the edge layer and the blockchain layer, the efficiency and security of data verification can be ensured.

[0087] Figure 4 It is a schematic hardware structure diagram of an electronic device for implementing various embodiments of the present invention.

[0088] The Internet data processing method provided by the embodiments of this application can be applied to an electronic device. Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the figure, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0089] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, keys, a camera, a display screen, and a SIM card interface, etc.

[0090] It can be understood that the structure schematically shown in the embodiments of this application does not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0091] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), etc., an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0092] Among them, the processor can be the nerve center and command center of the electronic device. The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

[0093] A memory can also be set in the processor to store instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0094] The external memory interface can be used to connect to an external memory card, such as a MicroSD card, to implement the storage capacity expansion of the electronic device. The external memory card communicates with the processor through the external memory interface to achieve the data storage function. For example, files such as music and videos are saved in the external memory card.

[0095] The internal memory can be used to store computer-executable program codes, and the computer-executable program codes include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory can include a program storage area and a data storage area. The internal memory can include a high-speed random access memory and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0096] The wireless communication function of the electronic device can be implemented through an antenna, a wireless communication module, a modulation and demodulation processor, a baseband processor, etc.

[0097] The wireless communication module can provide wireless communication solutions applied to the electronic device, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0098] The electronic device can implement audio functions, etc. through an audio module, a speaker, a receiver, a microphone, a headphone interface, an application processor, etc.

[0099] An electronic device can implement a shooting function through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, etc.

[0100] An electronic device can implement a display function through a GPU, a display screen, an application processor, etc.

[0101] The GPU is a microprocessor for image processing, connecting the display screen and the application processor. The GPU is used to execute mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs, which execute program instructions to generate or change display information.

[0102] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0103] In the storage medium provided by this application, there is a program product capable of implementing an Internet data processing method.

[0104] The Internet data processing method includes: in response to an access request from a data source node, generating a node identity based on a pre-constructed multi-dimensional evaluation model; verifying the node identity, and if the verification passes, splitting the Internet data to be processed into multiple data segments and generating a verification tuple according to the real-time network load status; performing cross-layer verification through a heterogeneous consensus network to generate a dynamic verifiable data credential, and distributing the dynamic verifiable data credential to the requester.

[0105] In some possible implementation manners, the subject name Internet data processing method and system of the present disclosure can be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0106] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0107] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An Internet data processing method, characterized in that, including: In response to the access request of the data source node, generate a node identity identifier based on a pre-constructed multi-dimensional evaluation model; Verify the node identity identifier. If the verification is passed, divide the Internet data to be processed into multiple data segments and generate a verification tuple according to the real-time network load status; Execute cross-layer verification through a heterogeneous consensus network, generate a dynamic verifiable data credential, and distribute the dynamic verifiable data credential to the requester; The pre-constructed multi-dimensional evaluation model is: where T is the comprehensive trust value, R is the historical behavior score, and H is the node device security factor. is the result of the i-th interactive verification. , , are dynamic weight coefficients, and n represents the total number of interactive verifications. Verify the node identity identifier. If the verification is passed, divide the Internet data to be processed into multiple data segments and generate a verification tuple according to the real-time network load status, including: If the comprehensive trust value T is greater than or equal to the preset trust threshold, determine that the node passes the verification; Collect the network bandwidth, edge node storage capacity, and request concurrency parameter in real time, and calculate the load coefficient; Determine the number of data segments according to the data sensitivity and the load coefficient; Use an anti-quantum hash function to generate the hash value of the data segment, and generate a random polynomial array in combination with a threshold encryption algorithm; Determine the number of data segments according to the data sensitivity and the load coefficient, including: Determine the number of data segments K according to the following formula: In the formula, S represents the data sensitivity, L represents the load coefficient, a represents the sensitivity threshold, and b represents the load threshold.

2. The Internet data processing method according to claim 1, wherein The generated verification tuple is characterized as: In the formula, is the i-th data segment, is the anti-quantum hash function, is the timestamp fingerprint, is the storage location identifier, and K is the number of data segments.

3. The Internet data processing method according to claim 1, characterized in that Execute cross-layer verification through a heterogeneous consensus network to generate a dynamic verifiable data credential, including: In the edge layer, perform data integrity verification; In the blockchain layer, execute zero-knowledge proof verification to generate a verification commitment.

4. The Internet data processing method according to claim 3, wherein Performing data integrity verification includes: Wherein, is the data integration factor of node i, is the verification strength of node i, is the weight factor of node i, is the root hash of the i-th node, is the dynamic threshold, is the sensitivity of Internet data.

5. An Internet data processing system, characterized in that, The system is used to implement the Internet data processing method described in any one of claims 1 to 4; including: A generation unit, configured to generate a node identity identifier based on a pre-constructed multi-dimensional evaluation model in response to an access request of a data source node; A verification unit, configured to verify the node identity identifier. If the verification is passed, divide the Internet data to be processed into multiple data segments and generate a verification tuple according to the real-time network load status; A processing unit, configured to execute cross-layer verification through a heterogeneous consensus network, generate a dynamic verifiable data credential, and distribute the dynamic verifiable data credential to the requester.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the Internet data processing method described in any one of claims 1 to 4.

7. A storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the steps of the Internet data processing method described in any one of claims 1 to 4.

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