Internet data processing method, system, equipment and medium

Abstract

The invention provides an internet data processing method, system and device and a medium, and belongs to the field of data processing. The method comprises the following steps: in response to an access request of a data source node, generating a node identity based on a multi-dimensional evaluation model; verifying the node identity, and if the verification is passed, segmenting the Internet data to be processed into a plurality of data segments according to the real-time network load state and generating a verification tuple; and executing cross-layer verification through the heterogeneous consensus network, generating a dynamic verifiable data voucher, and sending the dynamic verifiable data voucher to a requester. The node identity identification is generated based on the multi-dimensional evaluation model, the credibility of the node can be dynamically evaluated, the access of malicious nodes or unauthorized nodes is prevented, the network attack risk is reduced, the single-point data processing pressure is reduced through a dynamic fragmentation strategy, the data processing and transmission efficiency is optimized, cross-layer verification is executed through a heterogeneous consensus network, and the reliability of the node is improved. And by combining the advantages of the edge layer and the block chain layer, the high efficiency and security of data verification are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically 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. At present, the industrial data flow link is mainly to build a local area network for IoT devices. After the router completes the IP address conversion of the device 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 data storage tools such as data warehouses, and then processed through the data middle platform. However, the data processing method faces the following main problems when dealing with large-scale, high-concurrency, and cross-platform data processing scenarios: Traditional node authentication methods are usually based on static permission management, which cannot dynamically evaluate the credibility of nodes and are vulnerable to attacks by malicious nodes. In scenarios with surging data volumes and high concurrency, traditional data processing methods, such as centralized data processing, can easily become performance bottlenecks, leading to processing delays and inefficiencies. Moreover, the data verification methods used, such as a single consensus algorithm, are difficult to balance between security and efficiency, and cannot meet the requirements of low latency and high reliability at the same time. Summary of the invention

[0003] 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 fully or at least partially solve 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.

[0004] In a first aspect, an embodiment of the present application provides an Internet data processing method, including: In response to an access request from a data source node, a node identity is generated based on a pre-built multi-dimensional evaluation model; Verifying the node identity, if the node identity is verified, dividing the Internet data to be processed into multiple data segments according to the real-time network load status and generating a verification tuple; Cross-layer verification is performed through a heterogeneous consensus network, dynamically verifiable data credentials are generated, and the dynamically verifiable data credentials are distributed to the requesting party.

[0005] Optionally, the pre-built multi-dimensional evaluation model is:

[0006] In the formula, T is the comprehensive trust value, R is the historical behavior score, and H is the hardware security level. is the result of the ith interactive verification, , , is the dynamic weight coefficient, and n represents the total number of interactive verifications.

[0007] Optionally, verify the node identity. If the node identity is verified, segment 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, the node is determined to have passed the verification; Collect network bandwidth, edge node storage capacity, and concurrent request parameters in real time to calculate load factors; Determine the number of data fragments based on data sensitivity and load factor; Use quantum-resistant hash functions to generate hash values ​​for data fragments, and combine them with threshold encryption algorithms to generate random polynomial arrays.

[0008] Optionally, the number of data fragments is determined according to data sensitivity and load factor, including: The number of data segments K is determined according to the following formula:

[0009] Where S represents data sensitivity, L represents load factor, a represents sensitivity threshold, and b represents load threshold.

[0010] Optionally, generate a verification tuple represented as: In the formula, is the i-th data segment, For quantum-resistant hash functions, is the timestamp fingerprint, is the storage location identifier, and K is the number of data fragments.

[0011] Optionally, perform cross-layer verification 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 performed to generate a verification commitment.

[0012] Optionally, the following formula is used for data integrity verification:

[0013] 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, For Internet data sensitivity.

[0014] In a second aspect, an embodiment of the present application further provides an Internet data processing system, including: A generating unit, configured to generate a node identity based on a pre-built multi-dimensional evaluation model in response to an access request from a data source node; A verification unit, used to verify the node identity, and if the node identity is verified, divide the Internet data to be processed into multiple data segments according to the real-time network load status and generate a verification tuple; A processing unit is used to perform cross-layer verification through a heterogeneous consensus network, generate dynamically verifiable data credentials, and distribute the dynamically verifiable data credentials to the requesting party.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned Internet data processing method when executing the program.

[0016] In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned Internet data processing method are implemented.

[0017] It can be seen from the above technical solutions that the present invention has the following advantages: In the Internet data processing method, system, device and medium provided by the present application, by generating node identity based on a multi-dimensional evaluation model, it is possible to dynamically evaluate the credibility of nodes, prevent the access of malicious nodes or unauthorized nodes, reduce the risk of network attacks, reduce the pressure of single-point data processing through dynamic sharding strategies, optimize the efficiency of data processing and transmission, perform cross-layer verification through heterogeneous consensus networks, and combine the advantages of the edge layer and the blockchain layer to ensure the efficiency and security of data verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 A flowchart of an Internet data processing method provided by an embodiment of the present invention; Figure 2 A flowchart of dividing Internet data into multiple data segments and generating verification tuples provided by an embodiment of the present invention; Figure 3A schematic diagram of the structure of an Internet data processing system provided by an embodiment of the present invention; Figure 4 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In the detailed description below, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may 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 rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0021] Hereinafter, the terms "include" or "may include" that may be used in various embodiments of the present disclosure indicate the presence of the disclosed functions or operations, and do not limit the addition of one or more functions or operations. In addition, as used in various embodiments of the present disclosure, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, or combinations of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing items.

[0022] 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 words listed at the same time. 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.

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 The figure is a flowchart of an Internet data processing method in a specific embodiment, which includes the following execution steps: Step 100: In response to an access request from a data source node, a node identity is generated based on a pre-built multi-dimensional evaluation model.

[0025] By dynamically generating node identities through a multi-dimensional evaluation model, the credibility of nodes can be comprehensively evaluated, ensuring that only nodes that have been strictly verified can access the system, and effectively preventing identity forgery and malicious attacks.

[0026] Specifically, the pre-built multi-dimensional evaluation model is:

[0027] In the formula, T is the comprehensive trust value, R is the historical behavior score, and H is the node device safety factor. is the result of the ith interactive verification, , , is the dynamic weight coefficient, and n represents the total number of interactive verifications.

[0028] The multi-dimensional evaluation model generates a comprehensive trust value by comprehensively considering multiple dimensional factors such as historical behavior scores, node device safety factors, and multiple interactive verification results. This makes the evaluation of data source nodes more comprehensive and accurate, and avoids the one-sidedness of judging the credibility of nodes based on a single factor. For example, historical behavior scores can reflect the performance of nodes in past data processing tasks, the device safety factor can reflect the security of the node itself, and the interactive verification results record the verification status during the interaction with the node. All-round considerations ensure that the evaluation results are more reliable. In addition, the introduction of dynamic weight coefficients can increase the weight corresponding to the node device safety factor during periods of high network security risks, so that the evaluation focuses more on node security; based on the trust value obtained from comprehensive and dynamic evaluation, the generated node identity can better reflect the true status and credibility of the node, thereby providing a reliable foundation for subsequent data processing processes.

[0029] Step 101: Verify the node identity. If the node identity passes the verification, divide the Internet data to be processed into multiple data segments according to the real-time network load status and generate a verification tuple.

[0030] For details, see Figure 2 As shown, when executing step 101, the following steps may be specifically performed: S1010: If the comprehensive trust value T is greater than or equal to the preset trust threshold, it is determined that the node has passed the verification.

[0031] It should be noted that the preset trust threshold can be set according to the actual application scenario, for example, 0.8, and is not limited here.

[0032] S1011: Collect network bandwidth, edge node storage capacity, and request concurrency parameters in real time, and calculate load factor.

[0033] Specifically, load factor = current load / maximum load capacity.

[0034] S1012: Determine the number of data segments according to data sensitivity and load factor.

[0035] Specifically, the number K of data segments is determined according to the following formula:

[0036] Where S represents data sensitivity, L represents load factor, a represents sensitivity threshold, and b represents load threshold.

[0037] 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 set to 3 and the load threshold b is set to 0.8, which is not limited here.

[0038] Data sensitivity is an indicator to measure the importance and confidentiality of data. By considering the relationship between data fragments and sensitivity thresholds, highly sensitive data will be segmented into more fragments. By increasing the number of data fragments, the impact caused by the leakage or tampering of a single data fragment can be reduced, and data security can be improved. Moreover, data of different sensitivities require different security levels and processing methods during processing. Dynamically adjusting the number of data fragments according to data sensitivity can make data processing strategies more flexible and efficient. For low-sensitivity data, the number of fragments can be appropriately reduced to reduce the overhead of data segmentation and reorganization and increase processing speed; while for highly sensitive data, the number of fragments can be increased to enhance security and meet the processing requirements of different data.

[0039] In addition, when the network load is high, appropriately adjusting the number of data fragments can avoid network congestion caused by excessive data fragment transmission and processing, so that data processing tasks can be more evenly distributed in the network, thereby balancing the network load and improving overall network performance.

[0040] S1013: Use quantum-resistant hash functions to generate hash values ​​for data fragments, and combine them with threshold encryption algorithms to generate random polynomial arrays.

[0041] A quantum-resistant hash function is used to generate a unique hash value for each data fragment, which is like a "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 is possible to quickly determine whether the data has been tampered with. For the same data fragment, the quantum-resistant 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 tracing, positioning, and rapid retrieval.

[0042] The random polynomial array generated by the threshold encryption algorithm is used to encrypt data. The algorithm is based on the principle of secret sharing and 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 and the data be decrypted. This means that even if part of the data fragment or encrypted information is leaked, the attacker cannot obtain the decryption key, greatly improving data confidentiality.

[0043] By combining the two, the quantum-resistant hash function ensures data integrity, and the threshold encryption algorithm ensures data confidentiality. The combination of the two provides comprehensive security protection for Internet data from storage, transmission to processing. Due to the complex and ever-changing Internet environment, data may be transmitted and processed at different nodes and in different network states. The high efficiency of the quantum-resistant hash function combined with the distributed processing capability of the threshold encryption algorithm enables the system to maintain stable operation in this environment, ensuring data security without affecting processing efficiency.

[0044] Specifically, the generated verification tuple is represented as: In the formula, is the i-th data segment, For quantum-resistant hash functions, is the timestamp fingerprint, is the storage location identifier, and K is the number of data fragments.

[0045] Step 102: 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.

[0046] In a specific implementation, cross-layer verification is performed through a heterogeneous consensus network to generate dynamically verifiable data credentials, including: performing data integrity verification at the edge layer; performing zero-knowledge proof verification at the blockchain layer to generate a verification commitment.

[0047] The edge layer uses a dynamic weighted Byzantine fault-tolerant 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 system's fault tolerance.

[0048] Specifically, the following formula is used for data integrity verification:

[0049] 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, For Internet data sensitivity.

[0050] In this embodiment, factors such as the hardware configuration and processing algorithm efficiency of different nodes may lead to differences in their data processing capabilities. Nodes with strong processing capabilities may undertake more important tasks during the verification process, and their data processing results have a greater impact on the overall verification results. In addition, different verification scenarios may require different verification strengths. For example, for highly sensitive data, a higher verification strength is required to ensure the integrity of the data. The verification strength of each node can be adjusted according to actual needs, making the verification process more flexible and accurate.

[0051] In a heterogeneous consensus network, different nodes may have different roles and functions, and their influence on data integrity verification is also different. By setting appropriate weight factors, the role of important nodes can be highlighted and the reliability of verification results 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 value, the integrity of the data can be efficiently verified without transmitting a large amount of original data. This method greatly reduces the amount of data transmission and computing overhead during the verification process, and improves the verification efficiency.

[0052] Exemplarily, after verification, the master edge node broadcasts the tuple to the slave edge node. The slave node verifies the data integrity through the hash chain, writes the root hash of the verified tuple into the blockchain, generates 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.

[0053] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0054] like Figure 3 As shown, the following is an embodiment of the Internet data processing system provided by the embodiment of the present disclosure, which belongs to the same inventive concept as the Internet data processing method of the above-mentioned embodiments. For details not described in detail in the embodiment of the Internet data processing system, please refer to the embodiment of the above-mentioned Internet data processing method.

[0055] A generating unit, configured to generate a node identity based on a pre-built multi-dimensional evaluation model in response to an access request from a data source node; A verification unit, used to verify the node identity, and if the node identity is verified, divide the Internet data to be processed into multiple data segments according to the real-time network load status and generate a verification tuple; A processing unit is used to perform cross-layer verification through a heterogeneous consensus network, generate dynamically verifiable data credentials, and distribute the dynamically verifiable data credentials to the requesting party.

[0056] By generating node identities based on a multi-dimensional evaluation model, the credibility of nodes can be dynamically evaluated, malicious or unauthorized nodes can be prevented from accessing, and the risk of network attacks can be reduced. Through dynamic sharding strategies, the pressure of single-point data processing can be reduced, and the efficiency of data processing and transmission of the system can be optimized. Cross-layer verification can be performed through heterogeneous consensus networks, combining the advantages of the edge layer and the blockchain layer to ensure the efficiency and security of data verification.

[0057] Figure 4 It is a schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0058] The Internet data processing method provided in the embodiment of the present application can be applied to electronic devices. It will be appreciated by those skilled in the art that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or less components than shown, or combine certain components, or arrange different components. In an embodiment 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the embodiments of the present application described herein and / or required.

[0059] 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, a button, a camera, a display, and a SIM card interface, etc.

[0060] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0061] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), 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. Different processing units may be independent devices or integrated into one or more processors.

[0062] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0063] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0064] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0065] The internal memory can be used to store computer executable program codes, which 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 may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0066] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0067] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (blue tooth, BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR), etc.

[0068] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0069] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0070] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0071] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0072] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0073] The storage medium provided in the present application stores a program product that can implement the Internet data processing method.

[0074] The Internet data processing method includes: responding to an access request from a data source node, generating a node identity based on a pre-built multi-dimensional evaluation model; verifying the node identity, and if the verification is passed, dividing the Internet data to be processed into multiple data fragments and generating a verification tuple according to the real-time network load status; performing cross-layer verification through a heterogeneous consensus network, generating a dynamically verifiable data credential, and distributing the dynamically verifiable data credential to the requester.

[0075] In some possible implementations, the subject matter of the present disclosure, namely, Internet data processing method and system, may be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above “Exemplary Method” section of this specification according to various exemplary implementations of the present disclosure.

[0076] 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, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0077] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing Internet data, characterized in that: include: In response to an access request from a data source node, a node identity is generated based on a pre-built multi-dimensional evaluation model; Verifying the node identity, if the node identity is verified, dividing the Internet data to be processed into multiple data segments according to the real-time network load status and generating a verification tuple; Cross-layer verification is performed through a heterogeneous consensus network, dynamically verifiable data credentials are generated, and the dynamically verifiable data credentials are distributed to the requesting party.

2. The Internet data processing method according to claim 1, characterized in that: The pre-built multi-dimensional evaluation model is: In the formula, T is the comprehensive trust value, R is the historical behavior score, and H is the node device safety factor. is the result of the ith interactive verification, , , is the dynamic weight coefficient, and n represents the total number of interactive verifications.

3. The Internet data processing method according to claim 2, characterized in that: Verify the node identity. If the node identity is verified, divide the Internet data to be processed into multiple data segments and generate verification tuples 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, the node is determined to have passed the verification; Collect network bandwidth, edge node storage capacity, and concurrent request parameters in real time to calculate load factors; Determine the number of data fragments based on data sensitivity and load factor; Use quantum-resistant hash functions to generate hash values ​​for data fragments, and combine them with threshold encryption algorithms to generate random polynomial arrays.

4. The Internet data processing method according to claim 3, characterized in that: Determine the number of data fragments based on data sensitivity and load factor, including: The number of data segments K is determined according to the following formula: Where S represents data sensitivity, L represents load factor, a represents sensitivity threshold, and b represents load threshold.

5. The Internet data processing method according to claim 4, characterized in that: The generated verification tuple is characterized as: In the formula, is the i-th data segment, For quantum-resistant hash functions, is the timestamp fingerprint, is the storage location identifier, and K is the number of data fragments.

6. The Internet data processing method according to claim 1, characterized in that: Perform cross-layer verification through a heterogeneous consensus network to generate dynamic verifiable data credentials, including: At the edge layer, data integrity verification is performed; At the blockchain layer, zero-knowledge proof verification is performed to generate a verification commitment.

7. The Internet data processing method according to claim 6, characterized in that: Data integrity verification includes: 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, For Internet data sensitivity.

8. An Internet data processing system, characterized in that: include: A generating unit, configured to generate a node identity based on a pre-built multi-dimensional evaluation model in response to an access request from a data source node; A verification unit, used to verify the node identity, and if the node identity is verified, divide the Internet data to be processed into multiple data segments according to the real-time network load status and generate a verification tuple; A processing unit is used to perform cross-layer verification through a heterogeneous consensus network, generate dynamically verifiable data credentials, and distribute the dynamically verifiable data credentials to the requesting party.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the Internet data processing method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the Internet data processing method according to any one of claims 1 to 7 are implemented.

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