Intelligent asset management statistical method based on big data

Abstract

The invention belongs to the technical field of asset management, and particularly relates to an intelligent asset management statistical method based on big data, which comprises the following steps of: binding an Internet of Things sensor for a physical asset, generating a unique digital fingerprint and writing the unique digital fingerprint into a block chain; adopting distributed approval nodes to approve the transaction request and perform parallel signature verification, and performing risk control on the transaction request; the sensor data is analyzed in real time, the physical asset state is verified, and a response action is executed based on the smart contract; the method comprises the following steps: binding an Internet of Things sensor with physical assets, converting the Internet of Things sensor into digital fingerprints based on a Sha-256 Hash algorithm, writing the digital fingerprints into a block chain, completing registration of the physical assets, and in a subsequent management process, performing transaction request approval and triggering parallel signature verification by adopting distributed approval nodes. On one hand, the problem of transaction examination and approval delay caused by a single examination and approval place can be avoided, and on the other hand, property loss caused when the examination and approval secret key is stolen can be avoided.

Description

Technical Field

[0001] This invention belongs to the field of asset management technology, specifically a big data-based intelligent asset management statistical method. Background Technology

[0002] Asset management is an important basic management task in the normal and stable operation of an enterprise.

[0003] A Chinese patent with publication number CN112150148A discloses a method for protecting enterprise assets based on blockchain technology, including the following steps: acquiring transaction data, wherein the transaction data includes a source address, initial signature data, and secondary signature data; determining whether the transaction data is enterprise transaction data based on the source address; when the transaction data is enterprise transaction data, acquiring secondary signature data and determining whether the secondary signature data is an empty set; when the secondary signature data is not an empty set, determining whether the secondary signature data is consistent with preset verification signature data; when the secondary signature data is consistent with the verification signature data, determining that the transaction data is legitimate approval data, and allowing the transaction data to be packaged and uploaded to the blockchain.

[0004] In existing technologies, asset management requires not only the management and statistics of digital assets, but also the management and statistics of physical assets. The management and statistics of physical assets are relatively more complex because physical assets (such as industrial equipment, medical instruments and other physical items) are disconnected from their digital representations (such as NFTs or smart contracts on the blockchain) during the management process. There is a lack of real-time and reliable binding mechanisms. Furthermore, because physical assets and on-chain assets are disconnected during the management process, they are more easily tampered with and forged under separate management, resulting in the theft and fraud of physical assets.

[0005] Therefore, this invention provides an intelligent asset management statistical method based on big data. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a big data-based intelligent asset management statistical method, comprising the following steps: S1: Bind IoT sensors to physical assets, generate unique digital fingerprints and write them to the blockchain; S2: Transaction requests are approved using distributed approval nodes and parallel signature verification is performed, and risk control is implemented for transaction requests. S3: Analyze sensor data in real time, verify the status of physical assets, and execute response actions based on smart contracts; S4: Record the entire operation log and dynamically optimize risk control strategies.

[0008] Preferably, S1 includes: S11: Embed anti-disassembly sensors in key equipment to collect sensor data in real time, including geographic coordinates and vibration frequency; S12: Preprocess the sensor data, including data anonymization, fingerprint generation, and timestamp signature; S13: Assign a unique NFT identifier to the physical asset and generate a QR code containing the NFT identifier, while anchoring the sensor data hash of the physical asset to the blockchain NFT metadata. Also includes: S14: Scan the QR code to read sensor data and generate a temporary Hasi value. Call the smart contract to compare the hash value on the chain. ,verify If verification fails, a red alert will be triggered and the system will be frozen.

[0009] Preferably, in step S2, the method for performing parallel signature verification on the transaction request is as follows: Deploy a federal approval network consisting of 5 nodes, employing the BFT consensus mechanism: Receive transaction requests and submit them to the smart contract to identify the transaction amount: If the transaction amount is less than the transaction amount threshold, the transaction will be automatically allowed. If the transaction amount exceeds the transaction amount threshold, a 3 / 5 node parallel signature is executed. If the smart contract verifies the signature, the transaction is completed; otherwise, an alarm is triggered and the transaction request is rejected.

[0010] Preferably, the federal approval network also includes a risk control engine, which performs risk modeling based on transaction amount and frequency, counterparty credit rating, and real-time device status. According to the formula: in, As a risk quantification index, The number of transactions on that day. The difference in credit score, Equipment malfunction index; in, As the benchmark credit value, This is the current credit score. Indicates the degree of vibration anomaly. Indicates displacement offset. Indicates an abnormal temperature value. The weighting factor is dynamic and adjusted in real time based on a two-layer attention mechanism; Risk Quantification Index for Output Transaction Requests ; It also includes a risk grading engine for automatically flagging high-risk transactions and triggering multi-node review: Risk Quantification Index for Receiving Transaction Requests ; The risk level of a transaction request is output based on a risk quantification index, and the risk level includes low risk, medium risk and high risk. When a transaction request corresponds to a high-risk level, the transaction is automatically marked as high-risk and a multi-node review is triggered.

[0011] Preferably, the method for outputting the risk level of a transaction request based on the risk quantification index is as follows: Receive risk quantification index And identify risk quantification index Category: when If the risk level is defined as low risk, passage will be automatically granted. when If so, the risk level is defined as medium risk, triggering a single-node review; when If the risk level is high, it will be automatically marked and trigger multi-node review. If the review is successful, the transaction request will be rejected and the related assets will be frozen.

[0012] Preferably, S3 includes: S31: Based on the edge computing gateway, real-time sensor data is de-identified and proof documents are generated; S32: Upload the proof document to the blockchain via a blockchain oracle, verify the data authenticity, and submit the verification result to the smart contract. If the verification is successful, execute the response action, including: Excessive vibration triggers a freeze on NFT asset transfer permissions and simultaneously marks the device as high-risk. If the displacement exceeds the limit, a drone will be activated for on-site inspection, triggering an audible and visual alarm. In the event of a sudden temperature change, the equipment power is remotely reduced, and a maintenance work order is simultaneously generated and sent. S33: In volatile market conditions, based on dynamic weighting factors The adjustment prioritizes executing abnormal device response actions.

[0013] Preferably, S4 includes: The blockchain stores the entire operation log, including operation time, approval nodes, signature time, and decision. When the signing operation is completed, data in four dimensions is automatically captured, and the data is real-time based on edge computing nodes. The collected data is hashed and then written to the blockchain for storage.

[0014] Preferably, step S4 further includes the following step: S41: Each node calculates each feature variable based on local historical data. Contribution : in, As a risk quantification index, For the characteristic variables in the risk quantification formula, including , and , The standard deviation of the characteristic variable; S42: Aggregate the feature variables calculated at each node. Contribution : S43: Based on contribution The formula is adjusted as follows: in, As a regulating factor, The first to participate in federal learning Each approval node ,and .

[0015] Preferably, the distributed approval node employs a threshold signature mechanism to achieve secure key management, the method comprising: Based on the sharing scheme, the administrator key is split into 5 independent fragments, and each fragment is stored separately on the node; When a large transaction triggers a signature requirement, a valid signature is generated by combining three independent fragments based on the BLS aggregate signature algorithm.

[0016] Preferably, the fragment reset process includes: Monitoring node behavior based on the BFT consensus mechanism: When a large transaction occurs, triggering parallel signing by 3 / 5 nodes, if: If any node fails to respond within a timeout period, it is marked as having abnormal behavior. If any node outputs an incorrect signature, an alert is triggered. When abnormal node behavior is detected, switch to a backup node; When a node behavior alarm is detected, the smart contract is invoked to destroy the fragments stored by that node and reset the fragments.

[0017] The beneficial effects of this invention are as follows: 1. The present invention provides an intelligent asset management and statistical method based on big data, which uses IoT sensors to bind physical assets and converts them into digital fingerprints based on the Sha-256 hash algorithm. The digital fingerprints are then written into the blockchain to complete the registration of physical assets. In the subsequent management process, distributed approval nodes are used to approve transaction requests and trigger parallel signature verification. On the one hand, this can avoid the transaction approval delay caused by a single approval location. On the other hand, it can avoid the property loss caused by the theft of approval keys. 2. The intelligent asset management statistical method based on big data described in this invention quantifies the risk of medical equipment through a risk quantification index, and then outputs different decisions based on the risk. Based on local historical data, it calculates the contribution of the transaction frequency term, credit quantification factor, and equipment anomaly factor to the risk quantification index during the calculation process, and then aggregates the contribution calculated by each node. Based on the contribution of the transaction frequency term, credit quantification factor, and equipment anomaly factor, the risk quantification index is optimized, thereby reducing the probability of false negatives and false negatives. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a perspective view of the present invention; Detailed Implementation To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] like Figure 1 As shown in the embodiment of the present invention, an intelligent asset management statistical method based on big data includes the following steps: S1: Bind IoT sensors to physical assets, generate unique digital fingerprints and write them to the blockchain; S2: Transaction requests are approved by distributed approval nodes and verified in parallel signatures, and risk control is performed on transaction requests. S3: Analyze sensor data in real time, verify the status of physical assets, and execute response actions based on smart contracts; S4: Record the entire operation log and dynamically optimize risk control strategies.

[0021] In existing technologies, asset management requires not only the management and statistics of digital assets, but also the management and statistics of physical assets. The management and statistics of physical assets are relatively more complex because physical assets (such as industrial equipment, medical instruments and other physical items) are disconnected from their digital representations (such as NFTs or smart contracts on the blockchain) during the management process. There is a lack of real-time and reliable binding mechanisms. Furthermore, because physical assets and on-chain assets are disconnected during the management process, they are more easily tampered with and forged under separate management, resulting in the theft and fraud of physical assets.

[0022] In one embodiment of the present invention, existing physical assets are first registered, and IoT sensors are used to bind them to the physical assets. The data is then converted into a digital fingerprint using the Sha-256 hash algorithm and written into the blockchain to complete the registration of the physical assets. In subsequent management, distributed approval nodes are used to approve transaction requests. To prevent the theft of transaction keys and the resulting financial loss, in this embodiment, the distributed approval nodes trigger parallel signature verification when approving transaction requests. This avoids transaction approval delays caused by a single approval location and also prevents financial losses due to the theft of approval keys. It is understandable that, assuming transaction approval is based on a key, a hacker could use this key to steal account assets or change the ownership of physical assets, resulting in losses. Therefore, in the actual transaction approval process, risk control of transaction requests is necessary to reduce losses caused by key theft. It is also understandable that, in this embodiment, based on sensor embedding and data anchoring, data such as geographic coordinates and vibration frequency are collected in real time. Through the generation of a unique digital fingerprint, it can serve as a "digital ID card" for physical assets, thereby avoiding data loss or data matching errors during the management process, which could lead to a disconnect between the management of physical assets and on-chain assets. Furthermore, in this embodiment, based on real-time on-chain data from sensors, the real-time status of physical assets is verified through real-time analysis of sensor data. A unique digital fingerprint is generated and strongly bound to the physical asset, preventing tampering and forgery. Additionally, based on a distributed approval network and dynamic risk control, fraud issues can be avoided during subsequent management of physical assets. Furthermore, secure key management is required to prevent the theft of a single key. In summary, based on a multi-layered protection mechanism, from multiple perspectives and levels including on-chain physical asset monitoring, real-time physical asset management, and physical asset management, a closed-loop physical asset management system is achieved, encompassing sensor binding, on-chain anchoring, dynamic verification, and distributed risk control. This solves the problem of tampering and fraud caused by the disconnect between physical assets and on-chain assets, optimizes management efficiency, and strengthens security management.

[0023] In addition, it is necessary to perform real-time status analysis on physical assets to verify whether they are abnormal. When the physical asset status is abnormal, corresponding response actions need to be executed based on smart contracts to prevent the physical asset from being stolen. Finally, in the process of managing and counting physical assets, risk control strategies for physical assets are dynamically optimized based on the full-link operation logs to reduce losses and improve the management and counting of physical assets.

[0024] In one embodiment, S1 includes: S11: Embed anti-disassembly sensors in key equipment to collect sensor data in real time, including geographic coordinates and vibration frequency; S12: Preprocess the sensor data, including data anonymization, fingerprint generation, and timestamp signature; S13: Assign a unique NFT identifier to the physical asset and generate a QR code containing the NFT identifier, while anchoring the sensor data hash of the physical asset to the blockchain NFT metadata. Also includes: S14: Scan the QR code to read sensor data and generate a temporary Hasi value. Call the smart contract to compare the hash value on the chain. ,verify If verification fails, a red alert will be triggered and the system will be frozen.

[0025] When registering physical assets, the first step is to bind the equipment and collect data, mainly including the geographical coordinates and vibration frequency of the physical asset. Taking the asset management of a medical equipment leasing platform as an example, assuming that any medical equipment currently needs to be bound and registered, in one embodiment, an anti-disassembly vibration sensor is installed in the equipment base, and a GPS module is integrated into the control panel for real-time positioning of the medical equipment. In the subsequent asset management process, the location coordinates of the medical equipment are obtained in real time based on the GPS module, the vibration frequency is collected based on the anti-disassembly vibration sensor, and the timestamp of sensor data collection is recorded. Then, the collected sensor data is preprocessed, including data desensitization, that is, removing the last three digits of the data, and then the desensitized sensor data is hashed using Sha-256. The algorithm is transformed into a fingerprint, then combined with a timestamp to generate a timestamp signature. Following this, an NFT asset contract is created and anchored to the blockchain. Notably, during the physical asset registration phase, a unique NFT identifier and a QR code containing the NFT identifier need to be assigned to the physical asset. This QR code is used for on-site verification. For example, after a medical device transaction, the device is received at the transaction location (e.g., the recipient's location). To prevent device tampering, on-site verification is performed at the recipient's location, i.e., the delivery location, by scanning the QR code: First, the QR code is scanned, and the parsed content is id=MRI-SIEMENS-3T-2023-001; real-time data collection obtains the medical device's registration location coordinates and vibration frequency, and then a temporary hash value is generated through hash conversion. With on-chain Hasi value The comparison, in which the on-chain hash value Originating from the blockchain, a red alert is triggered if a shift is detected after comparison. This is understandable; for example, if the QR code on a medical device is copied and used on a counterfeit medical device, and then this device is sold elsewhere, on-site verification will be conducted based on a temporary hash value. With on-chain Hasi value The comparison revealed changes in displacement and the Hasi value on the chain. The location coordinates show it as being in Shanghai, while the counterfeit medical device is located in Suzhou, triggering a red alert to warn of unusual assets.

[0026] The blockchain anchoring phase includes: creating an NFT asset contract with a data structure including a unique asset code, sensor data hash value, current owner, and update time; uploading the de-identified data packet to IPFS via a blockchain oracle to obtain a Content Identifier (CID); data de-identification involves removing the last three digits of the precise coordinates; generating a fingerprint representation to calculate the SHA-256 hash value of the data packet; and timestamp signing by using a gateway key to sign the hash value combined with UTC time.

[0027] In one embodiment, the method for parallel signature verification of transaction requests in step S2 is as follows: Deploy a federal approval network consisting of 5 nodes, employing the BFT consensus mechanism: Receive transaction requests and submit them to the smart contract to identify the transaction amount: If the transaction amount is less than the transaction amount threshold, the transaction will be automatically allowed. If the transaction amount exceeds the transaction amount threshold, a 3 / 5 node parallel signature is executed. If the smart contract verifies the signature, the transaction is completed; otherwise, an alarm is triggered and the transaction request is rejected.

[0028] In existing technologies, when managing assets and handling large volumes of transaction requests, the signature of a single administrator can lag, leading to transaction request failures and potential financial losses. In one embodiment of this invention, to avoid this lag, a federated approval network can be deployed. By arranging distributed approval nodes, the approval process can be accelerated. Furthermore, to address the vulnerability of individual nodes to hacking and key loss, this embodiment uses a BFT consensus mechanism to manage the five nodes. Upon receiving a transaction request, the smart contract can identify the transaction amount. Requests with amounts less than a threshold can be automatically approved, meaning any node can process the request. For requests with amounts greater than the threshold, to prevent key loss due to node hacking, the smart contract automatically executes a 3 / 5 node parallel signature mechanism. For example, assuming a transaction amount threshold of 10,000 yuan, if a request contains a transaction amount of 20,000 yuan, the smart contract will automatically execute a 3 / 5 node parallel signature mechanism. The automated 3 / 5 node parallel signature mechanism requires at least 3 nodes to sign simultaneously, followed by verification by a smart contract. Verification includes the signing time and the hash value of the signature data. If verification passes, the transaction request is processed. If fewer than 3 nodes sign simultaneously, verification fails, an alarm is triggered, and the transaction request is discarded. Under normal circumstances, when the smart contract automatically executes the 3 / 5 node parallel signature mechanism, all 5 nodes should receive the transaction request signature instruction in a very short time. When a node signs and sends the message back to the smart contract, it indicates that the node can respond correctly to the smart contract's instructions. If a node fails to respond within a timeout period, or if the signature received by the node is found to be incorrect after verification, an alarm is triggered, and the transaction request is rejected. Based on this, if a node is hacked, it may fail to respond within a timeout period or fail to verify the signature after responding. Therefore, managing 5 nodes based on the BFT consensus mechanism can, on the one hand, improve the speed of transaction requests and reduce approval delays; on the other hand, it can mitigate financial losses caused by hacking.

[0029] In one embodiment, the federal approval network further includes a risk control engine that performs risk modeling based on transaction amount frequency, counterparty credit rating, and real-time device status. According to the formula: in, As a risk quantification index, The number of transactions on that day. The difference in credit score, Equipment malfunction index; in, As the benchmark credit value, This is the current credit score. Indicates the degree of vibration anomaly. Indicates displacement offset. Indicates an abnormal temperature value. The weighting factor is dynamic and adjusted in real time based on a two-layer attention mechanism; In unforeseen circumstances, such as market volatility caused by a pandemic, the demand for medical equipment leasing surges. At this time, the platform needs to handle a large number of transaction requests, including equipment renewals and transfers. Simultaneously, physical assets face higher security risks, such as malicious misappropriation or abnormal damage. In this situation, if a fixed weighting factor is still used... Using quantitative risk indices can lead to an inability to dynamically respond to market changes, and during periods of volatility, equipment anomaly indices may also be affected. The contribution may be underestimated, leading to underreporting. In this embodiment, the weight... Based on a two-layer attention mechanism, real-time adjustments are made. First, based on market conditions, an LSTM neural network is used to analyze multi-source data, including historical trading frequency, GDP indicators, and platform trading indices, to output market state classifications, including stable periods, volatile periods, and crisis periods. Then, attention weights are assigned to different market states to enhance the identification of volatile / crisis periods. For example, in a volatile market state, the weights... An improvement is needed, triggering a second-layer adjustment; the second-layer attention generates weights under specific market conditions. In this embodiment, the market state output by the first-layer attention mechanism is dynamically generated. The value, according to the rule, is as follows: when the market is identified as being in a stable period, the weight... When the market enters a period of volatility, the weighting To amplify the equipment malfunction index The impact of this, when the market condition shifts to a crisis period, is related to the weighting. Further improvements, such as To comprehensively enhance risk sensitivity; The first layer of attention is used to calculate market state weights, and the second layer of attention generates indicator weights. The weighting adjustment rule is as follows: when the market is in a period of volatility, The risk is automatically increased to more than 1.2 times the benchmark value to enhance the impact of equipment anomaly factors and ensure priority detection of equipment safety during periods of fluctuation. At this point, the risk quantification formula is updated as follows: Exemplary example: In a certain MRI equipment rental transaction, based on the LSTM neural network's recognition that the market is in a period of fluctuation, the attention mechanism outputs... (benchmark) If the equipment malfunction index is at this time Then the contribution of equipment anomalies increases. The value is significantly improved, enabling faster triggering of high-risk freezes and reducing false negatives. Specifically, the weight of the attention mechanism output is increased when market conditions change. This is obtained based on regression analysis of historical data; among which, when the equipment anomaly index... hour, The value increased by 50%, from 0.436 to 0.654, thereby accelerating the identification of high-risk situations and reducing the false negative rate. At the same time, the weights are dynamically adjusted according to market conditions. In conjunction with the federal approval network and smart contracts, it achieves weighting. By adjusting the risk quantification index calculation and ultimately triggering a tiered response for end-to-end risk closed-loop control, it is possible to link equipment anomaly monitoring and transaction approval during periods of volatility, thereby shortening response latency. In summary, the dual-layer attention mechanism, through the process of dynamically generating indicator weights based on market state perception and adaptive risk model, solves the problem of fixed weights. The shortcomings in responding to volatile markets make risk modeling more accurate, the overall solution more efficient, and the full-chain management of physical assets more robust. Risk Quantification Index for Output Transaction Requests ; It also includes a risk grading engine for automatically flagging high-risk transactions and triggering multi-node review: Risk Quantification Index for Receiving Transaction Requests ; The risk level of a transaction request is output based on a risk quantification index, and the risk level includes low risk, medium risk and high risk. When a transaction request corresponds to a high-risk level, the transaction is automatically marked as high-risk and a multi-node review is triggered.

[0030] In the above embodiments, the federal approval network, based on the setting of distributed approval nodes, accelerates the approval process on the one hand and avoids hacking and property loss on the other. It primarily targets the transaction process, or the processing of transaction requests. However, regarding the physical assets themselves, such as the medical equipment mentioned in the above embodiments, if the medical equipment is maliciously damaged or resold during the leasing process, it may cause certain losses to the medical equipment leasing platform. In this embodiment, even after the physical assets are transacted, it is necessary to monitor and quantify the risks. Specifically, the physical assets targeted are those under leasing, such as medical equipment under leasing. During the leasing process, based on real-time sensor data from anti-tamper sensors, the risks of the medical equipment can be quantified, thereby outputting different decisions based on the risks. Specifically: Assuming that any medical device is leased, the transaction frequency term... Credit quantification factor Equipment malfunction factors If this is a stable period, then the corresponding weighting factors are all 1, then: Based on the above, it is understandable that, according to the equipment anomaly index, This indicates a low degree of equipment abnormality. In this embodiment, considering that the equipment abnormality index is the core data that dominates the risk quantification index, when the equipment abnormality factor... The larger the output value, the larger the predictable value of the risk quantification index should be. As mentioned above, when the risk quantification index corresponding to the medical device is defined as high risk, the smart contract automatically triggers multi-node review: first, the task is allocated, and the transaction data is broadcast to the three review nodes through the RPC protocol based on the master node. Then, the logic is independently verified by each node, including the signature and rule engine. Based on the BFT consensus mechanism, when 2 / 3 of the nodes pass, a consensus is reached, and the review result is converted into a hash value and written into the blockchain for storage. Among them, vibration anomaly degree Displacement offset and temperature anomalies The calculation method is as follows: Vibration anomaly ; in, Indicates the current vibration frequency. Indicates the reference frequency. Maximum permissible deviation; reference frequency The normal vibration frequency of the equipment; maximum permissible deviation The vibration safety threshold for equipment is 0.3 by default for industrial grade. Displacement offset ; in, Indicates the actual displacement distance. Indicates the safe operating radius; safe operating radius The permissible range of movement for equipment, such as ±0.5 meters for precision equipment; Temperature anomalies ; in, Indicates the current temperature. Indicates standard temperature. This indicates the maximum permissible temperature difference.

[0031] In one embodiment, the method for outputting the risk level of a transaction request based on the risk quantification index is as follows: Receive risk quantification index And identify risk quantification index Category: when If the risk level is defined as low risk, passage will be automatically granted. when If so, the risk level is defined as medium risk, triggering a single-node review; when If the risk level is high, it will be automatically marked and trigger multi-node review. If the review is successful, the transaction request will be rejected and the related assets will be frozen.

[0032] In this embodiment, the risk quantification index range corresponding to low risk is: The range of risk quantification index corresponding to medium risk is: The range of the risk quantification index corresponding to high risk is: It is worth noting that in the risk quantification calculation formula, the equipment anomaly factor... Transaction frequency item Slow growth The credit difference range can be expressed as In this embodiment, the equipment anomaly index is taken into consideration as the core data that dominates the risk quantification index; As mentioned above, equipment malfunction factors In the middle, when When the value is low (e.g.) ), Therefore, when When the value is low, the contribution is small; risk quantification index It tends to be dominated by low-frequency transactions or credit changes; In addition, assuming the baseline parameters This indicates a moderate number of transactions on that day. This indicates a slight decline in creditworthiness; At this time Based on the data performance, when At that time, it was understood that the equipment was working properly. , That is, when When the value is low, The numerical value should be as small as possible to avoid over-responding to normal states and to conform to the automatic release logic. when At that time, it was understood as a minor equipment malfunction. For example, ,but: when At that time, it was understood as a moderate equipment malfunction. For example, ,but: At this point, single-node verification is initiated based on smart contracts, meaning manual verification of the abnormal status of related assets' equipment is performed. If the abnormal status is indeed present, and historical data shows that the equipment abnormality factor... If the risk gradually increases, the transaction request will be upgraded to high risk and rejected; otherwise, the smart contract will execute a parallel signature of 3 / 5 nodes. when At that time, it was understood as a moderate equipment malfunction. For example, , ; When the LSTM neural network outputs a market state during a period of fluctuation, then: when hour: when hour: when hour: when hour: From the data perspective, assuming other data remains constant, based on the market state output by the LSTM neural network, the risk quantification index during a stable period... Compared to risk quantification indices during periods of volatility The risk quantification index is relatively low, and it decreases when the market transitions from a period of stability to one of volatility. The numerical response is more timely, as mentioned above, during periods of fluctuation, when At that time, risk quantification index The value changed from 0.224 to 0.336, thus triggering a medium-risk level. This means that the market-state-based embedding allows the system to respond to risks more quickly. Additionally, the equipment anomaly factor... middle, The value increases with Improvement is achieved through improvement, and The value also changed with It improves along with the improvement, and in terms of the speed of improvement, it improves as... The improvement, with For example, for every increase of 0.1, The numerical difference of the increase doubled, and Therefore, the value is limited to 1 or less, hence the equipment anomaly factor. Overall, with Each increase of 0.1 exhibits a trend of initially increasing slowly and then surging, thus reflecting the use of the equipment anomaly factor as a risk quantification index in this embodiment. The dominant factor in the numerical value can be understood as follows: when quantifying the risk index of a new transaction request, if it is identified that the leased equipment is in an abnormal state, and this abnormal state triggers a high-risk risk rating, then an automatic response based on the smart contract is required to trigger the corresponding response action. For example, suppose a batch of medical equipment is currently being leased, and the other party requests a renewal as the contract is about to expire. This is defined as a new transaction request. However, based on the system's monitoring of the status of the leased equipment, if it is identified that the medical equipment is currently abnormal, and the risk quantification index corresponding to the transaction request is at a high-risk level, then the new transaction request will be rejected, the batch of medical equipment will be frozen, and the rights of the medical equipment transfer owner will be restricted.

[0033] In one embodiment, S3 includes: S31: Based on the edge computing gateway, real-time sensor data is de-identified and proof documents are generated; S32: Upload the proof document to the blockchain via a blockchain oracle, verify the data authenticity, and submit the verification result to the smart contract. If the verification is successful, execute the response action, including: Excessive vibration triggers a freeze on NFT asset transfer permissions and simultaneously marks the device as high-risk. If the displacement exceeds the limit, a drone will be activated for on-site inspection, triggering an audible and visual alarm. In the event of a sudden temperature change, the equipment power is remotely reduced, and a maintenance work order is simultaneously generated and sent. S33: In volatile market conditions, based on dynamic weighting factors The adjustment prioritizes executing abnormal device response actions.

[0034] In one embodiment, the asset management process based on sensor data includes: first, a real-time monitoring phase, where sensors continuously collect vibration spectra, GPS coordinates, and temperature curves of physical assets; then, anomaly detection is performed at 200ms intervals using an edge computing network; when the edge computing gateway transmits the processed sensor data to the smart contract based on a blockchain verification mechanism, the smart contract executes and verifies the data. If any of the following passes, the smart contract automatically responds with the corresponding action: The verification items include: excessive vibration, displacement exceeding limits, and drastic temperature changes. If any of these items passes the smart contract verification, it indicates that the physical asset has a corresponding problem. The smart contract will then automatically respond to the corresponding action. For example, if excessive vibration of the physical asset is detected, the NFT asset transfer permissions will be frozen, the ownership change permissions of the physical asset will be restricted from the current moment, and the physical asset will be marked as high-risk. Specifically: The criterion for determining if vibration exceeds the standard is the current vibration frequency. This continues for 10 seconds, during which time the response action is to freeze NFT asset transfer permissions and simultaneously mark it as high risk; The criterion for determining if a displacement exceeds the safe operating radius is that it exceeds the limit. At this time, the response action is to start the drone on-site inspection and trigger the audible and visual alarm; The criterion for judging sudden temperature changes is the difference in the rate of temperature change. At this point, the response action is to remotely reduce the device power, simultaneously generate a maintenance work order, and send it.

[0035] In this embodiment, the de-identification processing of sensor data by the edge computing gateway differs from that in the registration phase. From a technical objective perspective, the purpose of the edge computing gateway's de-identification processing of sensor data in this embodiment is to respond to abnormal states in real time, while the purpose of the de-identification processing of sensor data in the registration phase is to establish a physical-digital asset binding relationship. Furthermore, in the registration phase, sensor data de-identification mainly targets device fingerprint data, while in this embodiment, the targeted sensor data includes the real-time status of the device. In addition, the storage location of the de-identified sensor data is also different. It can be understood that the sensor data in the registration phase is stored in NFT metadata, while in this embodiment, the consideration is whether the real-time sensor data represents the abnormal state of the physical asset. Therefore, only temporary caching based on the edge computing gateway is required, which can be retained for a maximum of 72 hours. Smart contracts are automated programs stored on the blockchain that automatically trigger operations when preset conditions are met, without human intervention. Once deployed, their code and logic cannot be modified, and all execution logic is publicly visible to network participants.

[0036] In one embodiment, S4 includes: The blockchain stores the entire operation log, including operation time, approval nodes, signature time, and decision. When the signing operation is completed, data in four dimensions is automatically captured, and the data is real-time based on edge computing nodes. The collected data is hashed and then written to the blockchain for storage.

[0037] In one embodiment, S4 further includes the following step: S41: Each node calculates each feature variable based on local historical data. Contribution : in, As a risk quantification index, For the characteristic variables in the risk quantification formula, including , and , The standard deviation of the characteristic variable; S42: Aggregate the feature variables calculated at each node. Contribution : S43: Based on contribution The formula is adjusted as follows: in, As a regulating factor, The first to participate in federal learning Each approval node ,and .

[0038] In practical applications, since the risk quantification index corresponding to a transaction request involves transaction frequency, credit quantification factor, and device anomaly factor, the calculation of the risk quantification index is easily affected by the contribution of these factors, leading to missed and false alarms. In one embodiment of this invention, based on local historical data, the contribution of the transaction frequency, credit quantification factor, and device anomaly factor to the risk quantification index is calculated. The contribution calculated by each node is then aggregated, and the risk quantification index is optimized based on the contribution of these factors, thereby reducing the probability of missed and false alarms. (Example:) Assuming the market is in a stable period, the calculated equipment anomaly index of the corresponding physical asset in a given transaction request is... ; ,and ; ; but: ; ; Based on the above, when the equipment malfunction index... When the dominant factor influencing the risk quantification index is the optimized risk quantification index... Will be due to equipment malfunction index And by amplifying, high-risk freezes can be triggered more quickly; If a transaction request contains an abnormal equipment index for the corresponding physical asset... ; ,and ; ; ; ; In summary, in this scenario, because credit quantification factors play a dominant role, therefore, even However, in calculating the risk quantification index At that time, due to the large credit quantification factor, credit deterioration pushes up the risk quantification index. This could trigger a high-risk freeze; Among them, the regulating factor In the formula, such as ,when hour, This ensures that equipment malfunction factors remain at their original adjustment factors; furthermore... , For every 0.1 change in the contribution level, the adjustment factor is adjusted by only 1%, avoiding drastic fluctuations in the risk quantification index calculation that could lead to distortion. Furthermore, when the contribution level is poor... exist When the interval is reached, the adjustment factor Risk Quantification Index fluctuation It meets industrial-grade stability requirements; In step S43, based on contribution... And market state optimization weight factors, such as the contribution of equipment anomaly factors when the output of the LSTM neural network is in a period of fluctuation. When it is high, it will automatically increase. It also uses federated learning to aggregate data from each node to update the parameters of the LSTM neural network, thereby achieving weight distribution. Real-time adjustments.

[0039] In one embodiment, the distributed approval node employs a threshold signature mechanism to achieve secure key management, the method comprising: Based on the sharing scheme, the administrator key is split into 5 independent fragments, and each fragment is stored separately on the node; When a large transaction triggers a signature requirement, a valid signature is generated by combining three independent fragments based on the BLS aggregate signature algorithm.

[0040] In traditional technologies, administrator keys are centrally managed. When the system is hacked, the keys are easily lost, leading to financial losses. This can also be understood as a single point of leakage caused by the easy loss of centrally stored keys or a single key. In one embodiment of this invention, a threshold signature mechanism is used to achieve secure key management. This includes splitting the administrator key into 5 independent fragments, or 5 independent private keys, based on the Shamir secret sharing scheme. Based on the above, when a large transaction triggers a signature requirement, the requirement of 3 / 5 nodes signing in parallel is needed to generate a valid transaction signature. In this embodiment, when a large transaction triggers a signature requirement, at least 3 independent private keys are needed to sign in parallel to generate a valid transaction signature based on the BLS aggregate signature algorithm. Furthermore, if any node in the system is hacked, since the node only holds 1 independent private key and cannot independently generate a valid transaction signature, the system will automatically isolate the node and initiate a fragment reset process. As an example, a medical equipment rental platform has 5 nodes, and each of the 5 nodes holds an independent fragment, specifically: The system includes storage nodes s1 in Beijing, s2 in Shanghai, s3 in Guangzhou, s4 in Chengdu, and s5 in Urumqi, with three backup nodes configured in Nanjing, Wuhan, and Xi'an.

[0041] When the medical equipment rental platform generates a large transaction, the smart contract triggers the requirement for parallel signatures from 3 / 5 nodes corresponding to the large transaction. This involves using the independent fragments s1, s2, and s3 held by Beijing, Shanghai, and Guangzhou respectively for parallel signature generation, and using the BLS aggregation signature algorithm to generate a valid transaction signature. If, during the aforementioned large transaction, the Shanghai node is hacked, the system automatically isolates the Shanghai node and generates a new independent fragment s2, which is distributed to a backup node, such as Nanjing. At this time, the node holding the independent private key temporarily changes to: Beijing node storage s1, Nanjing node storage s2, Guangzhou node storage s3, Chengdu node storage s4, Urumqi node storage s5.

[0042] In one embodiment, the fragment reset process includes: Monitoring node behavior based on the BFT consensus mechanism: When a large transaction occurs, triggering parallel signing by 3 / 5 nodes, if: If any node fails to respond within a timeout period, it is marked as having abnormal behavior. If any node outputs an incorrect signature, an alert is triggered. When abnormal node behavior is detected, switch to a backup node; When a node behavior alarm is detected, the smart contract is invoked to destroy the fragments stored by that node and reset the fragments.

[0043] It is understandable that, continuing with the above embodiments, in the process of large transactions, if any node fails to respond within a timeout period when fulfilling the requirement of 3 / 5 nodes to sign in parallel using the BFT consensus mechanism, it indicates that the node has a communication problem, possibly due to a network outage. Conversely, if any node outputs an incorrect signature, it indicates that the node may have been compromised. It is understood that, for the above, a node failing to respond within a timeout period is a passive failure, such as a network or hardware problem, while a node outputting an incorrect signature necessarily indicates an intrusion, which is an active attack. To prevent security issues, when any node outputs an incorrect signature when responding to the BFT consensus mechanism, the independent private key is immediately frozen, destroyed, and the fragment is reset. For example, in the above embodiment, if Shanghai is compromised, the independent private key stored in Shanghai is frozen and destroyed, and the independent private key s2 is reset and distributed to other backup nodes, such as Nanjing. Furthermore, if any node fails to respond within a timeout period when responding to the BFT consensus mechanism, the node is switched, such as changing from Beijing, Shanghai, and Guangzhou to Beijing, Guangzhou, and Chengdu. The rule for identifying incorrect signatures based on the BFT consensus mechanism is to compare the hash values ​​of the signature data of each node using a smart contract.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1.A big data-based intelligent asset management statistical method, characterized in that: The method comprises the following steps: S1: binding the Internet of Things sensor to the physical asset, generating a unique digital fingerprint and writing it into the blockchain; S2: adopting distributed approval nodes to approve transaction requests and perform parallel signature verification, and performing risk control on the transaction requests; S3: real-time analysis of sensor data, verification of the state of the physical asset, and execution of response actions based on the smart contract; S4: recording the full-link operation log and dynamically optimizing the risk control strategy. 2.The big data-based intelligent asset management statistical method according to claim 1, characterized in that: The S1 comprises: S11: embedding an anti-disassembly sensor in the key equipment to collect real-time sensor data, including geographic coordinates and vibration frequency; S12: preprocessing the sensor data, including data desensitization, fingerprint generation, and timestamp signature; S13: assigning a unique NFT identifier to the physical asset and generating a two-dimensional code containing the NFT identifier, while anchoring the sensor data hash value of the physical asset to the blockchain NFT metadata; Further comprising: S14: Scan the two-dimensional code to read the sensor data and generate a temporary hash value , Call the smart contract to compare the on-chain hash value , Verify , If the verification fails, trigger a red alert and freeze. 3.The big data-based intelligent asset management statistical method according to claim 2, characterized in that: In the S2, the method for parallel signature verification of the transaction request is: Deploy a federal approval network, including 5 nodes, using a BFT consensus mechanism: Receive the transaction request and submit it to the smart contract to identify the transaction amount: When the transaction amount is less than the transaction amount threshold, it is automatically released; When the transaction amount is greater than the transaction amount threshold, perform 3 / 5 node parallel signature, if the smart contract verification is passed, the transaction is completed, otherwise trigger an alarm and reject the transaction request. 4.The big data-based intelligent asset management statistical method according to claim 3, characterized in that: The federal approval network also includes a risk control engine that models risks based on transaction amount frequency, counterparty credit rating, and real-time device state: According to the formula: wherein, is a risk quantification index, is the number of transactions for the day, is the difference in credit values, is an equipment anomaly index; wherein, is a reference credit value, is a current credit value, represents a vibration abnormality degree, represents a displacement offset degree, represents a temperature abnormal value, is a dynamic weight factor, which is adjusted in real time based on a double-layer attention mechanism; Risk quantification index for output transaction requests ; It also includes a risk grading engine for automatically marking high-risk transactions and triggering multi-node review: Receiving a risk quantification index corresponding to a transaction request ; Based on the risk quantification index, the risk level of the transaction request is output, including low risk, medium risk, and high risk; When the risk level corresponding to the transaction request is high risk, the high-risk transaction is automatically marked and multi-node review is triggered. 5.The big data-based intelligent asset management statistical method according to claim 4, characterized in that: The method for outputting the risk level of the transaction request based on the risk quantification index is: Receiving a risk quantification index and identifying a risk quantification index Classification: When then define the risk level as low risk, automatically release; When then define the risk level as medium risk, triggering single-node review; When then define the risk level as high risk, automatically label and trigger multi-node review, when the review is passed, reject the transaction request and freeze the associated assets. 6.The big data-based intelligent asset management statistical method according to claim 5, characterized in that: The S3 comprises: S31: desensitizing real-time sensor data based on edge computing gateway to generate a proof file; S32: uploading the proof file to the blockchain through the blockchain oracle to verify the data authenticity, and submitting the verification result to the smart contract, if the verification is passed, the response action is executed, including: Vibration exceeds the standard, freeze NFT asset transfer permission, and synchronously mark high risk; Displacement out of bounds, start unmanned aerial vehicle on-site inspection, trigger sound and light alarm; Temperature changes dramatically, remotely reduce device power, synchronously generate maintenance work order and send; S33: In the volatile market state, based on the adjustment of the dynamic weight factor , the device abnormal response action is preferentially performed. 7.The big data-based intelligent asset management statistical method according to claim 6, characterized in that: The S4 comprises: Store the full-link operation log in the blockchain, including operation time, approval node, signature time consumption, and decision; Capture four-dimensional data automatically when the signature operation is completed, and ensure data real-time based on edge computing nodes, hash convert the collected data, and write it into the blockchain for storage. 8.The big data-based intelligent asset management statistical method according to claim 7, characterized in that: The S4 further comprises the following steps: S41: Each node calculates the contribution degree of each feature variable based on local historical data :​ wherein, is a risk quantification index, is a characteristic variable in the risk quantification formula, including , and , denotes the standard deviation of the characteristic variable; S42: aggregate the contribution of each feature variable calculated by each node :​ S43: According to the contribution degree The adjustment formula is: wherein, is a modulating factor, represents the i-th approval node participating in federated learning, , and . 9.The big data-based intelligent asset management statistical method according to claim 8, characterized in that: The distributed approval nodes use a threshold signature mechanism to achieve key security management, including: Based on the sharing scheme, the administrator key is split into 5 independent fragments, and each fragment is stored in a node. When a large transaction triggers the signature requirement, the BLS-based aggregation signature algorithm is used to collect three independent fragments to generate a valid signature. 10.The big data-based intelligent asset management statistical method according to claim 9, characterized in that: The fragment reset process includes: The BFT consensus mechanism is used to supervise the behavior of the nodes: When a large transaction occurs, triggering the parallel signature of 3 / 5 nodes, if: Any node does not respond within the timeout period, it is marked as abnormal behavior; Any node outputs an incorrect signature, it is marked as a behavior warning; When an abnormal node behavior is detected, switch to a backup node; When a node behavior warning is detected, call the smart contract to destroy the fragments stored by the node and reset the fragments.

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