Hardware equipment fingerprint verification method and system

Abstract

The invention discloses a hardware equipment fingerprint verification method and system. The method comprises the following steps: S1, collecting and calculating equipment fingerprint information of target hardware equipment; s2, broadcasting the processed equipment fingerprint information to a block chain network, and verifying the equipment fingerprint information based on a consensus node in the block chain network; and S3, based on the verification result of the consensus node, determining whether the device fingerprint of the target hardware device passes. According to the method, the reference information and the verification logic are stored in the decentralized block chain network, and verification is automatically executed by using the smart contract, so that algorithm cracking, data tampering and equipment identity counterfeiting are effectively prevented. And a multi-weight factor algorithm is adopted to better adapt to diversified hardware configuration in PC open ecology. In addition, a judgment mechanism based on a consensus node response proportion ensures that the verification service can still run stably and reliably under the condition that the network environment is complex and changeable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of security, in particular to a hardware device fingerprint verification method and system. BACKGROUND

[0002] In large network games, users generally operate through hardware devices such as PC, so the PC fingerprint is an important infrastructure in the game field, which is commonly used in the analysis and management of network game security issues, such as user identity authentication and access control, black production anti-cheating tracking, and is also involved in the field of digital marketing, such as user behavior analysis and advertising marketing.

[0003] Due to the open nature of PC devices, the collection and verification mode of PC device fingerprints is diversified, and the device adaptation is difficult. At present, the technical implementation is mainly concentrated on local collection and verification of the client, and there are certain security risks. The existing device fingerprint verification algorithm based on local PC hardware collection collects the inherent identification information of various PC hardware and processes it into the same device fingerprint, which is used for device tracking and verification. However, this technical solution has the following defects and deficiencies: Standard uniformity is difficult: compared with mobile terminals, the PC ecosystem is more open, and the standards of hardware and software are difficult to unify, so it is difficult to comprehensively cover compatibility. At the same time, due to the differences in game types, there are different demands on hardware specifications, which makes it difficult to unify the device fingerprint collection algorithm.

[0004] Insufficient security: existing PC device algorithms rely on local verification, which makes the device fingerprint algorithm easy to be cracked, tampered with and forged, making it impossible to track.

[0005] High stability requirement: PC device fingerprint is part of the game infrastructure, and is dependent on security, marketing, and asset requirements. However, considering the complexity of the network and the sensitivity of user privacy data, the robustness of fingerprint collection is poor. SUMMARY

[0006] To solve the technical problems existing in the prior art, the present application provides a hardware device fingerprint verification method, comprising the following steps: A hardware device fingerprint verification method, comprising the following steps: S1: collecting and calculating the device fingerprint information of the target hardware device; S2: broadcasting the processed device fingerprint information to a blockchain network, and verifying the device fingerprint information based on the consensus nodes in the blockchain network; S3: determining whether the device fingerprint of the target hardware device passes based on the verification result of the consensus nodes.

[0007] Further, S1 specifically comprises: Obtaining inherent identification information of a plurality of hardware of a target hardware device; Based on a preset weight factor, the inherent identification information of each hardware is calculated to obtain a device fingerprint verification result, and the calculation formula is:

[0008] Wherein, Res is the device fingerprint verification result; n is the number of hardware devices participating in verification; si is the weight of the i-th hardware device; ri is the difference between the MD5 hash storage value corresponding to the identification information of the i-th hardware device and the current verification value; and σ is the threshold value for passing the device fingerprint verification.

[0009] Further, after obtaining the inherent identification information of the hardware, MD5 hash processing is performed thereon to obtain a hash value, which is then used for subsequent calculation to protect the privacy of the original device information.

[0010] Further, the hardware devices participating in verification include but are not limited to motherboard smBIOS UUID, CPU ID, MAC address, hard disk serial number, BIOS serial number, motherboard serial number, MachineGUID, PCI ID, and Windows product ID.

[0011] Further, in step S2, the device fingerprint information is verified based on consensus nodes in the blockchain network, specifically comprising: The target hardware device acts as a local node and broadcasts the processed device fingerprint information to all consensus nodes in the blockchain network; After receiving the device fingerprint information, each consensus node calls the locally stored smart contract, verifies based on a preset device fingerprint verification algorithm, and returns the verification result to the local node.

[0012] Further, step S3 specifically comprises: The local node receives the verification results returned by each consensus node in the blockchain network within a predetermined waiting period; The number of consensus nodes that pass the verification is counted, and the proportion of the number in the total number of consensus nodes is calculated; If the proportion is greater than or equal to a preset consensus node threshold β, it is determined that the device fingerprint verification of the target hardware device passes; otherwise, it is determined as not passing.

[0013] Further, the calculation formula of the preset consensus node threshold β is:

[0014] Wherein, b is the number of consensus nodes that pass the verification and respond to the local node, and m is the total number of consensus nodes participating in the verification in the blockchain network.

[0015] Further, it further comprises S4: if a new device joins the blockchain network, a consensus is reached with each consensus node in the blockchain network and the fingerprint verification smart contract application is downloaded, and the node initialization is completed.

[0016] Further, it further comprises S5: when the PC device hardware changes or the fingerprint verification is abnormal, a fingerprint reset request is initiated through the master node in the blockchain network, and after the preset consensus mechanism is confirmed, the fingerprint mapping relationship of the device stored in each consensus node is updated, and the fingerprint reset is completed.

[0017] The application also provides a hardware device fingerprint verification system for implementing the method as described, characterized in that the system comprises a fingerprint collection and calculation unit, a blockchain node verification unit, and a verification judgment unit, wherein: The fingerprint collection and calculation unit is used to collect and calculate the device fingerprint information of the target hardware device; The blockchain node verification unit is used to broadcast the processed device fingerprint information to the blockchain network and verify the device fingerprint information based on the consensus nodes in the blockchain network; The verification judgment unit is used to determine whether the device fingerprint of the target hardware device passes based on the verification result of the consensus nodes.

[0018] Each unit or module in the system and method disclosed in the application can be deployed on one target server in actual application, or each module can be independently deployed on different target servers, in addition, in order to provide stronger computing processing capability, the modules can also be deployed on a cluster target server according to needs.

[0019] As can be seen, the application stores the reference information of the device fingerprint and the verification logic in the decentralized blockchain network, and automatically executes the verification by using the smart contract, which completely changes the traditional mode of relying on the local fragile verification, effectively prevents algorithm cracking, data tampering, and device identity forgery. And a multi-weight factor algorithm is adopted, which allows the weight of different features to be dynamically adjusted according to the necessity of hardware and the application scenario and to pass through the threshold. Thus, the diversified hardware configuration in the PC open ecology is better adapted. In addition, the determination mechanism based on the response proportion of the consensus nodes ensures that the verification service can still be stably and reliably operated in the case of complex and changeable network environment.

[0020] In order to have a more comprehensive understanding of the application, the specific embodiments of the application are described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0022] Figure 1 The flowchart of the hardware device fingerprint verification method of an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to solve the technical problems existing in the prior art, the present application provides a hardware device fingerprint verification method and system, which combines blockchain network collaborative verification with multi-weight factor algorithm to improve the security, compatibility and robustness of device fingerprint verification.

[0024] The technical solution of the present application is: a hardware device fingerprint verification method, comprising steps S1-S3: specifically: S1: collecting and calculating the device fingerprint information of the target hardware device; S2: broadcasting the processed device fingerprint information to the blockchain network, and verifying the device fingerprint information based on the consensus nodes in the blockchain network; S3: determining whether the device fingerprint of the target hardware device passes based on the verification result of the consensus nodes.

[0025] Please refer to Figure 1 The technical solutions of the present application will be described below in combination with various preferred embodiments and examples.

[0026] S1: collecting and calculating the device fingerprint information of the target hardware device; The present application takes a PC as an example for illustration, and of course the technical solutions and the protection scope of the present application are not limited to the PC, but can be applied to networked network games through other hardware devices such as VR, MR devices or other hardware devices, and the technical solutions of the present application are all within the protection scope of the present application.

[0027] This step is the basic data acquisition and preprocessing link of device fingerprint verification, obtaining the hardware inherent identification information of the target PC device, and calculating the preliminary verification result through the multi-weight factor algorithm.

[0028] First, the inherent identification information of multiple hardware of the target PC device is acquired. These hardware devices are core hardware that can stably identify the device identity after screening, including but not limited to motherboard smBIOS UUID, CPU ID, MAC address, hard disk serial number, BIOS serial number, motherboard serial number, MachineGUID, PCI ID, Windows product ID, etc. The reason for selecting these hardware is that they are highly necessary in PC devices, moderately difficult to obtain, and their inherent identification information is not easy to change, which can effectively guarantee the uniqueness and stability of the device fingerprint.

[0029] After acquiring the inherent identification information of the hardware, MD5 hash processing is performed thereon, and the hash value is obtained and then used for subsequent calculation. MD5 hash processing can convert the original hardware identification information into an irreversible encrypted string, so that even if the data is intercepted in the subsequent transmission and verification process, a third party cannot analyze the original device information, thereby guaranteeing the safety of user privacy.

[0030] Subsequently, the hash-processed information of each hardware is calculated based on a preset weight factor, and a device fingerprint verification result is obtained, and the calculation formula is:

[0031] Among them: Res: is the device fingerprint verification result, and when the value is 1, it indicates that it is passed, and when the value is 0, it indicates that it is not passed, which is the core index for judging the preliminary compliance of hardware identification information.

[0032] n: is the number of hardware devices participating in verification, and in this embodiment, n=9, corresponding to the above-mentioned 9 types of core hardware devices, and the number of hardware participating in verification can be adjusted according to the compatibility requirements of the actual application scene.

[0033] : is the weight score of the i-th hardware device, and the value range is (0, 100]. The weight score is set based on the necessity of the hardware in the PC, the difficulty of obtaining, and the sensitivity, for example, the necessity of CPU ID and motherboard smBIOS UUID is high, and the tampering difficulty is high, so the weight score can be set to be high; the sensitivity of Windows product ID is low, and the weight score can be appropriately reduced.

[0034] At the same time, the weight score supports dynamic adjustment, which can be flexibly adjusted according to the game crowd portrait, device type or application scene demand, to balance the credibility of the device fingerprint verification and the adaptability of diversified devices.

[0035] : the difference between the MD5 hash stored value corresponding to the identification information of the i-th hardware device and the current check value, same is 1, different is 0. The parameter is used to judge whether the hardware information collected this time is consistent with the historical information stored in the blockchain network, and is a key index for preventing information tampering.

[0036] σ: a threshold value for device fingerprint verification, the value range is [0, 1]. σ can be dynamically configured according to security requirements, and the value of σ in a high security scene (such as network game security authentication) is not less than 0.8, and the value of σ in a general security scene (such as general user behavior analysis) is in the range of 0.6-0.8. The threshold value setting can flexibly adapt to the security requirements of different scenes.

[0037] This step combines MD5 hash processing through a multi-weight factor algorithm, which not only guarantees the uniqueness and preliminary compliance of the device fingerprint, but also realizes user privacy protection, and the dynamically adjustable weight distribution and threshold value setting solve the problems of uniform standard and poor compatibility in the prior art.

[0038] S2: broadcast the processed device fingerprint information to the blockchain network, and verify the device fingerprint information based on the consensus nodes in the blockchain network.

[0039] This step relies on the decentralized characteristics of the blockchain to realize a tamper-proof and highly trusted verification process. As a preferred embodiment, the present application also considers the problem of new nodes joining.

[0040] If the target PC device is a device newly added to the blockchain network, it needs to perform a node initialization operation: synchronize and verify the node identity, download the fingerprint verification smart contract application, and complete the initialization of the device node.

[0041] Each terminal node in the blockchain network stores the mapping relationship of all device fingerprints and the fingerprint verification smart contract application, and the initialization process ensures that new devices can access the network and follow uniform verification rules, ensuring the consistency of network collaborative verification.

[0042] After completing the initialization, the target hardware device as a local node broadcasts the device fingerprint information processed by the hash in step S1 to all consensus nodes in the blockchain network. Distributed transmission is adopted for broadcasting, ensuring that all consensus nodes can synchronously receive the data to be verified, avoiding the risk of data loss or tampering caused by single-point transmission.

[0043] After receiving the device fingerprint information, each consensus node calls the locally stored smart contract and verifies based on the multi-weight factor algorithm in step S1.

[0044] The smart contract is an automatic execution protocol pre-written and deployed in the blockchain network, which contains the core rules such as the above-mentioned device fingerprint verification algorithm, weight allocation, threshold standard, etc., and can ensure that the verification logic of all consensus nodes is completely consistent, avoiding the verification result deviation caused by human intervention.

[0045] After the consensus node verification is completed, the verification result (pass or fail) is returned to the local node.

[0046] This step realizes the tamper resistance and consistency of device fingerprint verification through the decentralized architecture of the blockchain network and the automatic execution of the smart contract. Compared with the existing local verification mode, distributed consensus verification makes it necessary to attack the majority of consensus nodes in the network to tamper with the device fingerprint information, greatly improving the attack difficulty and solving the problem of insufficient security of the existing technology.

[0047] S3: Determine whether the device fingerprint of the target hardware device passes based on the verification result of the consensus node.

[0048] This step ensures the robustness of the verification result through the verification result statistics of the consensus node, and the specific technical solution is as follows: First, the local node receives the verification results returned by each consensus node in the blockchain network within a predetermined waiting period. The setting of the waiting period needs to balance the verification efficiency and network stability. In this embodiment, it is set to 3 seconds, which can be flexibly adjusted according to the number of nodes and network delay of the blockchain network, avoiding the influence of the determination result caused by the response timeout of some nodes due to network fluctuations.

[0049] Then, the number of consensus nodes that return a pass is counted b, and the proportion of b to the total number of consensus nodes m (A = b / m) is calculated.

[0050] Finally, the proportion is compared with the preset consensus node threshold β, if A ≥ β, it is determined that the device fingerprint verification of the target PC device passes; otherwise, it is determined to fail.

[0051] The calculation formula of the preset consensus node threshold β is:

[0052] The value of β needs to consider the reliability and verification efficiency of the blockchain network. In this embodiment, β is taken as 0.7, that is, when more than 70% of the consensus nodes return a pass result, it is determined that the device fingerprint verification passes.

[0053] The value of this embodiment is 0.7, and this threshold setting can tolerate a certain degree of network instability and update of consensus nodes in the blockchain network. Even if some nodes cannot respond normally due to network failure or update maintenance, as long as the majority of nodes pass the verification, the verification result can be guaranteed to be effective, and the robustness of the verification process is improved.

[0054] The statistics of the verification results of the consensus nodes and the threshold judgment solve the problem of poor robustness caused by the complexity of the network in the prior art, ensure the reliability and stability of the device fingerprint verification results, and the flexible configuration of the threshold β can adapt to the verification requirements of different scale blockchain networks.

[0055] In addition, as a preferred embodiment, the application also considers that when the PC device hardware is changed (such as replacing the CPU and the mainboard) or the fingerprint verification is abnormal (such as the proportion of multiple verifications being close to β but not reaching β), a device fingerprint resetting step is performed, and a specific resetting method includes the following steps. First, a fingerprint resetting request is initiated by a device user or a system administrator through a master node in the blockchain network. The master node is a node responsible for core configuration management in the blockchain network, has the core functions of permission audit and operation initiation, and ensures the legality of the resetting request.

[0056] Then, the master node broadcasts the resetting request to all consensus nodes in the blockchain network, and after confirmation by a preset consensus mechanism (such as majority node voting confirmation), each consensus node updates the fingerprint mapping relationship of the device stored locally, deletes the old hardware fingerprint information, and stores the new hardware fingerprint information (collected and calculated by the device by performing the S1 step again), thereby completing the fingerprint resetting.

[0057] The device fingerprint resetting step provides an adaptation scheme after the hardware of the device is changed, avoids the situation that the device cannot pass the verification due to hardware update, improves the flexibility and applicability of the system, and prevents security risks caused by illegal resetting requests through the audit of the master node and the confirmation of the consensus mechanism.

[0058] Based on the above embodiment, the application also provides a hardware device fingerprint verification system for implementing the method, and the system includes a fingerprint collection and calculation unit, a blockchain node verification unit, and a verification judgment unit, wherein: The fingerprint collection and calculation unit is used to collect and calculate the device fingerprint information of the target PC device, and is the data input and preprocessing core of the system. Specifically, the fingerprint collection and calculation unit includes the following modules. The hardware information collection module: acquires the nine types of core hardware inherent identification information of the target PC device through a device interface, supports compatible collection of hardware information of PC devices of different brands and models, and solves the problem of compatibility coverage difficulty in the prior art.

[0059] The hash processing module: performs MD5 hash processing on the collected hardware inherent identification information to generate an irreversible encrypted string, thereby ensuring the privacy and security of the original information.

[0060] Algorithm calculation module: built-in multi-weight factor algorithm, store preset hardware weight sum and verification threshold σ, receive hash processed information and perform calculation, output preliminary verification result Res.

[0061] The fingerprint collection calculation unit realizes integrated processing of hardware information collection, encryption and calculation through modular design, which not only guarantees data security, but also adapts to different scene requirements through flexible parameter configuration, improves the compatibility and privacy protection ability of the system.

[0062] The blockchain node verification unit is used to broadcast the processed device fingerprint information to the blockchain network and verify the device fingerprint information based on the consensus nodes in the blockchain network, and is the core verification carrier of the system. Specifically, it includes: Node initialization module: receives the network access request of a new device, coordinates the synchronization and verification of node identity, downloads smart contract application, and completes node registration and configuration synchronization.

[0063] Data broadcast module: uses a distributed transmission protocol to broadcast the hash processed information and preliminary verification result Res output by the fingerprint collection calculation unit to all consensus nodes, ensuring the consistency and reliability of data transmission.

[0064] Smart contract execution module: deployed on each consensus node, built-in multi-weight factor algorithm and verification rules consistent with the fingerprint collection calculation unit, automatically executes verification logic after receiving broadcast data, and outputs node verification results.

[0065] Result feedback module: collects the verification results of each consensus node and synchronously feeds back to the local node verification judgment unit, ensuring the real-time transmission of verification results.

[0066] The blockchain node verification unit relies on the decentralized architecture of the blockchain and the automatic execution of the smart contract to realize the tamper resistance and consistency of the device fingerprint verification, greatly improving the security level of the system and solving the problem of insufficient security of the existing local verification mode.

[0067] The verification judgment unit is used to determine whether the device fingerprint of the target PC device passes based on the verification results of the consensus nodes, and is the core of the result output of the system. Specifically, it includes: Result receiving module: receives the verification results of each consensus node from the blockchain node verification unit within a preset waiting period, and invalidates the results of nodes that do not respond within the timeout period.

[0068] Statistical calculation module: counts the number of consensus nodes b that pass the verification, calculates the proportion A = b / m (m is the total number of consensus nodes), and calls the preset consensus node threshold β.

[0069] Result determination module: compare the ratio A with the threshold β, output the final verification result (pass or fail), and feed back the result to the target PC device and the blockchain network for storage.

[0070] Reset trigger module: when detecting multiple verification failures of the device or receiving a reset request from the master node, trigger the fingerprint reset process to coordinate the units to perform new fingerprint collection and verification processes.

[0071] The verification judgment unit ensures the reliability and robustness of the verification result through result statistics, threshold comparison and reset trigger functions, and realizes flexible handling of abnormal situations, improving the stability and applicability of the system.

[0072] Based on the above hardware device fingerprint verification method and hardware device fingerprint verification system, the present application takes the network game security authentication scene as an example to explain a complete process of hardware device fingerprint verification: New device access to network: the player's new PC device accesses the corresponding blockchain network of the network game for the first time, the node initialization module of the blockchain node verification unit coordinates the new device with 10 consensus nodes in the network to reach consensus, downloads the fingerprint verification smart contract application, and completes node initialization.

[0073] Fingerprint collection calculation: the hardware information collection module of the fingerprint collection calculation unit obtains 9 types of hardware inherent identification information such as the motherboard smBIOS UUID, CPU ID, etc. of the PC, the hash processing module performs MD5 hash processing on these information, and the algorithm calculation module performs calculation based on the preset weight distribution (such as CPU ID weight distribution 30, motherboard smBIOS UUID weight distribution 90, MAC address weight distribution 65, hard disk weight distribution 20...) and threshold σ=0.8, and gets Res=1, indicating that the preliminary verification is passed.

[0074] Consensus node verification: the data broadcast module of the blockchain node verification unit broadcasts the hash-processed information and Res=1 to 10 consensus nodes, and each node verifies through the smart contract execution module by calling the same algorithm and parameters, of which 8 nodes return the pass result, and 2 nodes do not respond within the 3-second waiting period due to network delay.

[0075] Result determination: the result receiving module of the verification judgment unit only counts the results of the 8 valid response nodes, the statistical calculation module gets b=8, n=10, A=0.8, calls the preset threshold β=0.7, and because A≥β, the result determination module outputs the verification pass result, which is fed back to the player's PC device, allowing the player to log in to the game.

[0076] Fingerprint reset: if the player replaces the motherboard later, the fingerprint collection and calculation unit calculates Res=0.6<σ=0.8 when logging in again, the verification and judgment unit detects that the verification fails, triggers the reset process, initiates a reset request through the master node, and after being confirmed by more than 6 consensus nodes, the player PC device re-executes the collection and calculation process, updates the fingerprint mapping relationship in the blockchain network, and successfully logs in after resetting.

[0077] The embodiments of the present application also provide an electronic device, comprising a processor, a storage medium and a bus, the storage medium stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine readable instructions to perform the hardware device fingerprint verification method according to any one of the above.

[0078] It should be noted that all or part of the steps in the various methods of the above embodiments can be split by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium, which can include but is not limited to a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0079] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fingerprint verification method for a hardware device, characterized in that, Includes the following steps: S1: Collect and calculate the device fingerprint information of the target hardware device; S2: Broadcast the processed device fingerprint information to the blockchain network, and verify the device fingerprint information based on the consensus nodes in the blockchain network; S3: Based on the verification results of the consensus node, determine whether the device fingerprint of the target hardware device passes the verification.

2. The hardware device fingerprint verification method as described in claim 1, characterized in that, S1 specifically includes: Obtain the inherent identification information of multiple hardware components of the target hardware device; The device fingerprint verification result is obtained by calculating the inherent identification information of each hardware device based on a preset weighting factor. The calculation formula is as follows: ； Where Res is the device fingerprint verification result; n is the number of hardware devices participating in the verification; si is the weight score of the i-th hardware device; ri is the difference between the MD5 hash storage value corresponding to the identification information of the i-th hardware device and the current verification value; and σ is the threshold for passing device fingerprint verification.

3. The hardware device fingerprint verification method as described in claim 2, characterized in that, After obtaining the inherent identification information of the hardware, it is subjected to MD5 hash processing to obtain the hash value, which is then used for subsequent calculations to protect the privacy of the original device information.

4. The hardware device fingerprint verification method as described in claim 2, characterized in that, The hardware devices involved in the verification include, but are not limited to: motherboard smBIOS UUID, CPU ID, MAC address, hard drive serial number, BIOS serial number, motherboard serial number, MachineGUID, PCI ID, and Windows product ID.

5. The hardware device fingerprint verification method as described in claim 1, characterized in that, In step S2, the verification of the device fingerprint information based on the consensus nodes in the blockchain network specifically includes: The target hardware device acts as a local node, broadcasting the processed device fingerprint information to all consensus nodes in the blockchain network; After receiving the device fingerprint information, each consensus node calls the locally stored smart contract to perform verification based on the preset device fingerprint verification algorithm, and returns the verification result to the local node.

6. The hardware device fingerprint verification method as described in claim 1, characterized in that, Step S3 specifically involves: The local node receives the verification results returned by each consensus node in the blockchain network within the predetermined waiting period; Count the number of consensus nodes that have passed the verification and calculate their proportion of the total number of consensus nodes; If the proportion is greater than or equal to the preset consensus node threshold β, the device fingerprint verification of the target hardware device is deemed to have passed; otherwise, it is deemed to have failed.

7. The hardware device fingerprint verification method as described in claim 6, characterized in that, The formula for calculating the preset consensus node threshold β is as follows: ； Where b is the number of consensus nodes that successfully verified and responded to the local node, and m is the total number of consensus nodes participating in the verification in the blockchain network.

8. The hardware device fingerprint verification method as described in claim 1, characterized in that, It also includes S4: If a new device joins the blockchain network, it reaches consensus with each consensus node in the blockchain network and downloads the fingerprint verification smart contract application to complete the node initialization.

9. The hardware device fingerprint verification method as described in claim 1, characterized in that, It also includes S5: When the PC device hardware changes or the fingerprint verification is abnormal, a fingerprint reset request is initiated through the master node in the blockchain network. After confirmation by the preset consensus mechanism, the fingerprint mapping relationship of the device stored in each consensus node is updated to complete the fingerprint reset.

10. A hardware device fingerprint verification system for implementing the method as described in any one of claims 1 to 9, characterized in that, The system includes a fingerprint acquisition and calculation unit, a blockchain node verification unit, and a verification and judgment unit, wherein: The fingerprint acquisition and computing unit is used to acquire and calculate the device fingerprint information of the target hardware device; The blockchain node verification unit is used to broadcast the processed device fingerprint information to the blockchain network and verify the device fingerprint information based on the consensus nodes in the blockchain network. The verification and judgment unit is used to determine whether the device fingerprint of the target hardware device passes the verification based on the verification results of the consensus node.

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