Transaction data verification method and device for egg product transaction cabinet

Abstract

The invention provides a transaction data verification method and device for an egg product transaction cabinet. The method comprises the following steps: receiving transaction request data; obtaining physical verification data; performing space-time anchoring on the physical verification data and the transaction request data to generate a verification block; constructing an egg product digital twinborn model; inputting the physical verification data into the egg product digital twinning model, comparing and analyzing the actual physical characteristics of the egg product and the standard characteristic parameters predicted by the egg product digital twinning model, and calculating to obtain the data consistency confidence coefficient; when the data consistency confidence exceeds a preset threshold value, performing encryption processing on the transaction data passing the verification, and synchronously updating the transaction state in the block chain account book; and generating a transaction integrity report containing multiple layers of verification results, and feeding back the verification results to the terminal devices of the two transaction parties. According to the invention, multiple verification mechanisms of transaction data are realized, data counterfeiting and tampering in the transaction process are prevented, the accuracy and real-time performance of data verification are improved, and the reliability of transaction is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and apparatus for verifying transaction data at an egg trading counter. Background Technology

[0002] Rising living standards have driven a continuous increase in demand for poultry and egg products, especially eggs. To provide consumers with convenient purchasing options, more and more vendors are selling eggs through trading counters. As a crucial link between consumers and suppliers, the accuracy and reliability of transaction data at these trading counters are paramount. Existing transaction data verification methods have several problems, such as the ease with which data can be tampered with and inaccurate data, requiring further improvement. Summary of the Invention

[0003] Based on the above-mentioned problems, this invention proposes a method and device for verifying transaction data in egg trading cabinets. Through this invention, a multi-factor verification mechanism for egg transaction data is realized, effectively preventing data fraud and tampering during the transaction process; improving the accuracy and real-time performance of data verification; and providing a reliable, transparent, and secure data verification solution for egg transactions, significantly enhancing the reliability and user trust in smart agricultural product transactions.

[0004] In view of this, one aspect of the present invention proposes a method for verifying transaction data of an egg trading counter, comprising: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values. A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

[0005] Optionally, the step of generating an immutable verification block containing timestamps, geographic location information, and sensor data hash values ​​by spatiotemporally anchoring the physical verification data and transaction request data based on blockchain distributed ledger technology includes: When receiving transaction request data, the system simultaneously obtains the current standard timestamp and the geographical location information of the egg trading cabinet, and packages the timestamp, geographical location information and transaction request data into a spatiotemporal reference package; The physical verification data is hashed to generate a unique sensor data fingerprint, and the sensor data fingerprint is then associated and bound with the spatiotemporal reference package. Construct a verification block data structure by combining the bound spatiotemporal reference packet, sensor data fingerprint, and hash value of the previous block to form a verification block to be written. The verification block is broadcast to each node in the blockchain network using a distributed consensus algorithm. After verification and confirmation by the nodes, the verification block is written into the distributed ledger. Generate a blockchain-based evidence certificate, which includes the hash address of the verification block and network confirmation information, serving as an immutable proof of the transaction data verification.

[0006] Optionally, the step of constructing a digital twin model of eggs based on a pre-trained artificial intelligence algorithm includes: The first pre-trained deep learning model is invoked. The first deep learning model is trained based on multi-dimensional feature data such as weight, spectrum, shape and surface texture of egg samples to establish a mapping relationship between the physical characteristics of eggs and quality grades. Based on the types and specifications of eggs currently in the trading cabinet, the inference function of the first deep learning model is used to generate feature parameter templates for the corresponding egg types, and the basic parameters of the digital twin model of the egg products are initialized with the feature parameter templates.

[0007] Optionally, the step of inputting the physical verification data into the egg product digital twin model in real time, comparing and analyzing the actual physical characteristics of the egg product with the standard characteristic parameters predicted by the egg product digital twin model, and calculating the data consistency confidence level includes: The weight change data, surface spectral feature data, and environmental state data in the physical verification data are standardized according to a preset data format to generate a standardized dataset that meets the input requirements of the digital twin model. The standardized dataset is transmitted to the egg product digital twin model in real time. The egg product digital twin model runs a built-in algorithm based on the input data and outputs the standard characteristic parameters of the corresponding egg product under the current environmental conditions. The standard characteristic parameters include: standard weight range, expected spectral characteristic curve and theoretical quality index. Establish a feature comparison matrix, and match and compare the actual collected physical verification data with the standard feature parameters predicted by the egg product digital twin model item by item to identify the degree of deviation of each feature parameter; Based on the preset feature weight allocation strategy, the degree of deviation of each item is weighted and the overall conformity of the actual egg product with the standard model is comprehensively evaluated. Based on the weighted bias assessment results and combined with the statistical distribution characteristics of historical verification data, a data consistency confidence score between 0 and 1 is calculated and generated as a quantitative indicator for verifying the authenticity of egg products.

[0008] Optionally, the step of encrypting the verified transaction data using a quantum encryption algorithm and synchronously updating the transaction status in the blockchain ledger when the data consistency confidence level exceeds a preset threshold includes: The data consistency confidence level is compared with a preset security threshold. When the confidence level reaches or exceeds the preset security threshold, the verification process is triggered and a verification success identifier is generated. Extract the verified transaction data, including egg product information, identities of both parties, transaction amount, and physical verification results, and combine this data to form a transaction data packet to be encrypted; The quantum key distribution system is activated to generate a quantum encryption key that uniquely corresponds to the current transaction. The transaction data packet is then quantum encrypted using the quantum encryption key to generate encrypted transaction data with quantum security characteristics. Construct a blockchain transaction status update request, encapsulate the encrypted transaction data, verification success identifier and timestamp information, and submit it to the distributed ledger system through the blockchain network interface; After receiving and verifying the status update request, the blockchain network node updates the transaction status from pending verification to verification completed, and records the encrypted transaction data and status change information in the ledger, thus completing the on-chain confirmation of the entire verification process.

[0009] Optionally, the step of generating a transaction integrity report containing multi-layered verification results and feeding back the verification results to the terminal devices of both parties to the transaction via an IoT communication module includes: Based on the verification results of multi-dimensional sensors, the spatiotemporal anchoring information of blockchain, the confidence assessment of digital twin models, and the quantum encryption processing status, a multi-level verification dataset is constructed. Based on the predefined report template, the multi-level verification dataset is classified and organized according to the hierarchical structure of sensor layer, algorithm layer, encryption layer and blockchain layer, and a structured transaction integrity report containing verification time, verification method, verification result and credibility level is generated. Extract the device identifiers and communication address information of both parties to the transaction from the transaction request data, and verify the validity and online status of the terminal devices through the IoT device registry. The IoT communication module is invoked to convert the transaction integrity report into the corresponding data format according to the communication protocol and data format requirements of different terminal devices, and then push it to the terminal devices of the buyer and seller respectively. Monitor the reception and confirmation status of the terminal devices. Once both devices have successfully received the report, record the feedback completion flag in the system and generate a complete archive record of this verification process.

[0010] Optionally, the multi-dimensional sensor array data fusion employs an adaptive weight allocation algorithm, the calculation formula of which is:

[0011] in, The fused sensor validation factor is M; M is the total number of sensors.

[0012] Let be the dynamic weighting coefficient of the m-th sensor at time t; Let be the standardized data value of the m-th sensor at time t; The time decay coefficient of the m-th sensor; denoted as the time delay of the data from the m-th sensor.

[0013] Optionally, the data consistency confidence score is calculated using an evaluation model based on quantified uncertainty, and the calculation formula is as follows:

[0014] in, Confidence level for data consistency; This is the confidence level adjustment parameter; Scoring based on physical feature similarity; Uncertainty weighting factor; The uncertainty of the model prediction is quantified; N is the total number of validation dimensions; This is the reliability metric for the nth dimension; This is the importance index of the nth dimension.

[0015] Optionally, the quantum encryption algorithm employs a dynamic key generation mechanism, the mathematical expression of which is:

[0016] in, A quantum dynamic key associated with the transaction parameter p; A quantum-safe hash function; This represents the XOR operation; Q is the number of quantum entangled pairs. This is the encoded value of the q-th quantum entangled state; It is a quantum phase modulation function; This represents the q-th quantum oscillation frequency; It is the modulus of a large prime number.

[0017] Another aspect of the present invention provides a transaction data verification system for an egg trading cabinet, used to execute a transaction data verification method for an egg trading cabinet, comprising: a server and an egg trading cabinet; The server is configured as follows: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values. A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

[0018] The technical solution of this invention provides a transaction data verification method for egg trading cabinets, comprising: receiving transaction request data; acquiring physical verification data collected in real time by a multi-dimensional sensor array built into the egg trading cabinet, wherein the physical verification data includes egg weight change data, surface spectral characteristic data, and environmental state data; based on blockchain distributed ledger technology, spatiotemporally anchoring the physical verification data and transaction request data to generate an immutable verification block containing timestamps, geographical location information, and sensor data hash values; constructing an egg digital twin model based on a pre-trained artificial intelligence algorithm; inputting the physical verification data into the egg digital twin model in real time, comparing and analyzing the actual physical characteristics of the eggs with the standard characteristic parameters predicted by the egg digital twin model, and calculating the data consistency confidence level; when the data consistency confidence level exceeds a preset threshold, encrypting the verified transaction data using a quantum encryption algorithm and synchronously updating the transaction status in the blockchain ledger; generating a transaction integrity report containing multi-layer verification results, and feeding back the verification results to the terminal devices of both parties to the transaction through an IoT communication module. By combining physical data acquisition from a multi-dimensional sensor array with blockchain technology, a multi-factor verification mechanism for egg transaction data has been implemented, effectively preventing data fraud and tampering during the transaction process. The collaborative work of digital twin models and artificial intelligence algorithms has improved the accuracy and real-time performance of data verification. Quantum encryption technology ensures the secure transmission of verification data, providing a reliable, transparent, and secure data verification solution for egg transactions, significantly enhancing the reliability and user trust in smart agricultural product transactions. Attached Figure Description

[0019] Figure 1 This is a flowchart of a transaction data verification method for an egg trading cabinet provided in one embodiment of the present invention; Figure 2 This is a schematic block diagram of a transaction data verification system for an egg trading cabinet provided in one embodiment of the present invention. Detailed Implementation

[0020] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0022] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] The following reference Figures 1 to 2 This invention describes a method and apparatus for verifying transaction data at an egg trading counter, provided by some embodiments of the present invention.

[0025] like Figure 1 As shown, one embodiment of the present invention provides a method for verifying transaction data of an egg trading counter, comprising: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. It is understood that the multidimensional sensor array includes a weight sensor, a spectral sensor, and a temperature and humidity sensor, which respectively acquire data on the weight change of the eggs, surface spectral characteristics, and environmental conditions.

[0026] Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values. A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; Understandably, pre-trained artificial intelligence algorithms, including machine learning models and deep neural networks, form the technological foundation for building digital twin models of eggs. The digital twin model of eggs is a virtual mapping body built based on these artificial intelligence algorithms to simulate the physical characteristics of real eggs.

[0027] The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

[0028] The technical solution adopted in this embodiment combines physical data acquisition from a multi-dimensional sensor array with blockchain technology to achieve a multi-factor verification mechanism for egg transaction data, effectively preventing data fraud and tampering during the transaction process. The collaborative work of a digital twin model and artificial intelligence algorithms improves the accuracy and real-time performance of data verification. Quantum encryption technology ensures the secure transmission of verification data, providing a reliable, transparent, and secure data verification solution for egg transactions, significantly enhancing the reliability and user trust in smart agricultural product transactions.

[0029] In some possible embodiments of the present invention, the step of spatiotemporally anchoring the physical verification data and transaction request data based on blockchain distributed ledger technology to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values ​​includes: When receiving transaction request data, the system simultaneously obtains the current standard timestamp and the geographical location information of the egg trading cabinet, and packages the timestamp, geographical location information and transaction request data into a spatiotemporal reference package; The physical verification data is hashed to generate a unique sensor data fingerprint, and the sensor data fingerprint is then associated and bound with the spatiotemporal reference package. Construct a verification block data structure by combining the bound spatiotemporal reference packet, sensor data fingerprint, and hash value of the previous block to form a verification block to be written. The verification block is broadcast to each node in the blockchain network using a distributed consensus algorithm. After verification and confirmation by the nodes, the verification block is written into the distributed ledger. Generate a blockchain-based evidence certificate, which includes the hash address of the verification block and network confirmation information, serving as an immutable proof of the transaction data verification.

[0030] This embodiment ensures the authenticity and non-forgeability of data by strongly binding physical verification data with transaction requests in the time and space dimensions; by utilizing the distributed characteristics and consensus mechanism of blockchain, it realizes the decentralization and tamper-proof of the transaction verification process, effectively solving the single point of failure and data tampering risks of traditional centralized verification systems, and providing a traceable and verifiable trust foundation for egg transactions.

[0031] In some possible embodiments of the present invention, the step of constructing a digital twin model of egg products based on a pre-trained artificial intelligence algorithm includes: The first pre-trained deep learning model is invoked. The first deep learning model is trained based on multi-dimensional feature data such as weight, spectrum, shape and surface texture of egg samples to establish a mapping relationship between the physical characteristics of eggs and quality grades. Based on the types and specifications of eggs currently in the trading cabinet, the inference function of the first deep learning model is used to generate feature parameter templates for the corresponding egg types, and the basic parameters of the digital twin model of the egg products are initialized with the feature parameter templates.

[0032] It is understandable that feature parameter templates refer to the set of standard feature parameters that are pre-learned and stored in the pre-trained model for different egg types, including but not limited to: egg type feature templates (such as the standard weight range of different poultry eggs such as chicken eggs, duck eggs, and goose eggs, typical spectral feature curves of various egg types, shape parameters and size specifications of different egg types, etc.); specification grade parameter templates (such as the quality standard parameters of different grades such as premium, first grade, and second grade, and the weight and size parameters corresponding to large, medium, and small sizes, etc.).

[0033] This embodiment uses a digital twin model built with a pre-trained artificial intelligence algorithm to accurately simulate the physical characteristics and behavioral patterns of real eggs, achieving a high-precision mapping between virtual and reality.

[0034] In some possible embodiments of the present invention, the step of inputting the physical verification data into the egg digital twin model in real time, comparing and analyzing the actual physical characteristics of the egg with the standard characteristic parameters predicted by the egg digital twin model, and calculating the data consistency confidence level includes: The weight change data, surface spectral feature data, and environmental state data in the physical verification data are standardized according to a preset data format to generate a standardized dataset that meets the input requirements of the digital twin model. The standardized dataset is transmitted to the egg product digital twin model in real time. The egg product digital twin model runs a built-in algorithm based on the input data and outputs the standard characteristic parameters of the corresponding egg product under the current environmental conditions. The standard characteristic parameters include: standard weight range, expected spectral characteristic curve and theoretical quality index. Establish a feature comparison matrix, and match and compare the actual collected physical verification data with the standard feature parameters predicted by the egg product digital twin model item by item to identify the degree of deviation of each feature parameter; Based on the preset feature weight allocation strategy, the degree of deviation of each item is weighted and the overall conformity of the actual egg product with the standard model is comprehensively evaluated. Based on the weighted bias assessment results and combined with the statistical distribution characteristics of historical verification data, a data consistency confidence score between 0 and 1 is calculated and generated as a quantitative indicator for verifying the authenticity of egg products.

[0035] This embodiment achieves accurate verification and anomaly detection of egg physical characteristics through a dynamic comparison mechanism of real-time data input and model prediction; it improves the reliability and anti-interference ability of verification results by adopting a multi-feature weighted evaluation method; and it provides an objective basis for transaction decisions through quantitative confidence assessment, effectively preventing fraudulent behaviors such as passing off inferior products as superior ones and adulteration, and significantly improving the security and credibility of egg transactions.

[0036] In some possible embodiments of the present invention, the step of encrypting the verified transaction data using a quantum encryption algorithm and synchronously updating the transaction status in the blockchain ledger when the data consistency confidence level exceeds a preset threshold includes: The data consistency confidence level is compared with a preset security threshold. When the confidence level reaches or exceeds the preset security threshold, the verification process is triggered and a verification success identifier is generated. Extract the verified transaction data, including egg product information, identities of both parties, transaction amount, and physical verification results, and combine this data to form a transaction data packet to be encrypted; The quantum key distribution system is activated to generate a quantum encryption key that uniquely corresponds to the current transaction. The transaction data packet is then quantum encrypted using the quantum encryption key to generate encrypted transaction data with quantum security characteristics. Construct a blockchain transaction status update request, encapsulate the encrypted transaction data, verification success identifier and timestamp information, and submit it to the distributed ledger system through the blockchain network interface; After receiving and verifying the status update request, the blockchain network node updates the transaction status from pending verification to verification completed, and records the encrypted transaction data and status change information in the ledger, thus completing the on-chain confirmation of the entire verification process.

[0037] This embodiment ensures that only high-credibility transaction data can enter the encryption and on-chain process through a threshold judgment mechanism, effectively filtering out low-quality transactions; quantum encryption technology provides forward security for transaction data, ensuring data security even with the future development of quantum computing technology; and the synchronous update of blockchain state realizes the distributed storage and tamper-proof recording of transaction verification results, providing reliable technical support for transaction dispute resolution and regulatory auditing.

[0038] In some possible embodiments of the present invention, the step of generating a transaction integrity report containing multi-layered verification results and feeding back the verification results to the terminal devices of both parties to the transaction via an IoT communication module includes: Based on the verification results of multi-dimensional sensors, the spatiotemporal anchoring information of blockchain, the confidence assessment of digital twin models, and the quantum encryption processing status, a multi-level verification dataset is constructed. Based on the predefined report template, the multi-level verification dataset is classified and organized according to the hierarchical structure of sensor layer, algorithm layer, encryption layer and blockchain layer, and a structured transaction integrity report containing verification time, verification method, verification result and credibility level is generated. Extract the device identifiers and communication address information of both parties to the transaction from the transaction request data, and verify the validity and online status of the terminal devices through the IoT device registry. The IoT communication module is invoked to convert the transaction integrity report into the corresponding data format according to the communication protocol and data format requirements of different terminal devices, and then push it to the terminal devices of the buyer and seller respectively. Monitor the reception and confirmation status of the terminal devices. Once both devices have successfully received the report, record the feedback completion flag in the system and generate a complete archive record of this verification process.

[0039] This embodiment provides comprehensive and transparent verification information to both parties through the structured organization of multi-level verification results, enhancing the traceability and credibility of the transaction process. The multi-protocol support of the IoT communication module ensures that different types of terminal devices can receive verification results in a timely manner, improving user experience and system compatibility. The real-time feedback mechanism allows both parties to understand the verification status immediately, effectively reducing transaction disputes and information asymmetry, and providing technical support for building a transparent egg trading ecosystem.

[0040] In some possible embodiments of the present invention, the multidimensional sensor array data fusion employs an adaptive weight allocation algorithm, the calculation formula of which is:

[0041] in, The fused sensor validation factor is M; M is the total number of sensors.

[0042] Let be the dynamic weighting coefficient of the m-th sensor at time t; Let be the standardized data value of the m-th sensor at time t; The time decay coefficient of the m-th sensor; denoted as the time delay of the data from the m-th sensor.

[0043] In this embodiment, dynamic weighting coefficient The system adjusts in real time based on the historical accuracy of the sensors and the stability of the current environment to ensure optimal data fusion results under different environmental conditions, thereby improving the reliability and accuracy of multi-source sensor data.

[0044] In some possible embodiments of the present invention, the calculation of the data consistency confidence level adopts an evaluation model based on quantified uncertainty, and the calculation formula is as follows:

[0045] in, Confidence level for data consistency; This is the confidence level adjustment parameter; Scoring based on physical feature similarity; Uncertainty weighting factor; The uncertainty of the model prediction is quantified; N is the total number of validation dimensions; This is the reliability metric for the nth dimension; This is the importance index of the nth dimension.

[0046] This embodiment effectively reduces the false positive rate and improves the robustness of egg product feature recognition in complex environments by introducing an uncertainty quantification mechanism and multi-dimensional reliability assessment.

[0047] In some possible embodiments of the present invention, the quantum encryption algorithm employs a dynamic key generation mechanism, the mathematical expression of which is:

[0048] in, A quantum dynamic key associated with the transaction parameter p; A quantum-safe hash function; This represents the XOR operation; Q is the number of quantum entangled pairs. This is the encoded value of the q-th quantum entangled state; It is a quantum phase modulation function; This represents the q-th quantum oscillation frequency; It is the modulus of a large prime number.

[0049] In this embodiment, the dynamic key generation mechanism utilizes quantum entanglement and phase modulation technology to achieve key generation that is strongly correlated with the transaction content, ensuring the security of transaction data even under quantum computing attacks, while preventing replay attacks and man-in-the-middle attacks.

[0050] Please refer to Figure 2 Another embodiment of the present invention provides a transaction data verification system for an egg trading cabinet, used to execute a transaction data verification method for an egg trading cabinet, comprising: a server and an egg trading cabinet; The server is configured as follows: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values. A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

[0051] It should be known that, Figure 2 The block diagram of the transaction data verification system for the egg trading cabinet shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of this invention. The transaction data verification system for the egg trading cabinet provided in this embodiment can be used to execute various embodiments of the corresponding transaction data verification method for the egg trading cabinet. For specific implementation details, please refer to the descriptions of the respective method embodiments, which will not be repeated here.

[0052] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0053] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0054] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0055] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0056] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0057] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0059] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0060] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for verifying transaction data at an egg trading counter, characterized in that, include: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values; A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

2. The method for verifying transaction data of an egg trading cabinet according to claim 1, characterized in that, The step of generating an immutable verification block containing timestamps, geographic location information, and sensor data hash values ​​by spatiotemporally anchoring the physical verification data and transaction request data based on blockchain distributed ledger technology includes: When receiving transaction request data, the system simultaneously obtains the current standard timestamp and the geographical location information of the egg trading cabinet, and packages the timestamp, geographical location information and transaction request data into a spatiotemporal reference package; The physical verification data is hashed to generate a unique sensor data fingerprint, and the sensor data fingerprint is then associated and bound with the spatiotemporal reference package. Construct a verification block data structure by combining the bound spatiotemporal reference packet, sensor data fingerprint, and hash value of the previous block to form a verification block to be written. The verification block is broadcast to each node in the blockchain network using a distributed consensus algorithm. After verification and confirmation by the nodes, the verification block is written into the distributed ledger. Generate a blockchain-based evidence certificate, which includes the hash address of the verification block and network confirmation information, serving as an immutable proof of the transaction data verification.

3. The transaction data verification method for the egg trading cabinet according to claim 2, characterized in that, The steps for constructing a digital twin model of egg products based on a pre-trained artificial intelligence algorithm include: The first pre-trained deep learning model is invoked. The first deep learning model is trained based on multi-dimensional feature data such as weight, spectrum, shape and surface texture of egg samples to establish a mapping relationship between the physical characteristics of eggs and quality grades. Based on the types and specifications of eggs currently in the trading cabinet, the inference function of the first deep learning model is used to generate feature parameter templates for the corresponding egg types, and the basic parameters of the digital twin model of the egg products are initialized with the feature parameter templates.

4. The transaction data verification method for the egg trading cabinet according to claim 3, characterized in that, The step of inputting the physical verification data into the egg digital twin model in real time, comparing and analyzing the actual physical characteristics of the eggs with the standard characteristic parameters predicted by the egg digital twin model, and calculating the data consistency confidence level includes: The weight change data, surface spectral feature data, and environmental state data in the physical verification data are standardized according to a preset data format to generate a standardized dataset that meets the input requirements of the digital twin model. The standardized dataset is transmitted to the egg product digital twin model in real time. The egg product digital twin model runs a built-in algorithm based on the input data and outputs the standard characteristic parameters of the corresponding egg product under the current environmental conditions. The standard characteristic parameters include: standard weight range, expected spectral characteristic curve and theoretical quality index. Establish a feature comparison matrix, and match and compare the actual collected physical verification data with the standard feature parameters predicted by the egg product digital twin model item by item to identify the degree of deviation of each feature parameter; Based on the preset feature weight allocation strategy, the degree of deviation of each item is weighted and the overall conformity of the actual egg product with the standard model is comprehensively evaluated. Based on the weighted bias assessment results and combined with the statistical distribution characteristics of historical verification data, a data consistency confidence score between 0 and 1 is calculated and generated as a quantitative indicator for verifying the authenticity of egg products.

5. The transaction data verification method for the egg trading cabinet according to claim 4, characterized in that, The step of encrypting the verified transaction data using a quantum encryption algorithm and synchronously updating the transaction status in the blockchain ledger when the data consistency confidence level exceeds a preset threshold includes: The data consistency confidence level is compared with a preset security threshold. When the confidence level reaches or exceeds the preset security threshold, the verification process is triggered and a verification success identifier is generated. Extract the verified transaction data, including egg product information, identities of both parties, transaction amount, and physical verification results, and combine this data to form a transaction data packet to be encrypted; The quantum key distribution system is activated to generate a quantum encryption key that uniquely corresponds to the current transaction. The transaction data packet is then quantum encrypted using the quantum encryption key to generate encrypted transaction data with quantum security characteristics. Construct a blockchain transaction status update request, encapsulate encrypted transaction data, verification success identifier and timestamp information, and submit it to the distributed ledger system through the blockchain network interface; After receiving and verifying the status update request, the blockchain network node updates the transaction status from pending verification to verification completed, and records the encrypted transaction data and status change information in the ledger, thus completing the on-chain confirmation of the entire verification process.

6. The transaction data verification method for the egg trading cabinet according to claim 5, characterized in that, The step of generating a transaction integrity report containing multi-layered verification results and feeding back the verification results to the terminal devices of both parties to the transaction via an IoT communication module includes: Based on the verification results of multi-dimensional sensors, the spatiotemporal anchoring information of blockchain, the confidence assessment of digital twin models, and the quantum encryption processing status, a multi-level verification dataset is constructed. Based on the predefined report template, the multi-level verification dataset is classified and organized according to the hierarchical structure of sensor layer, algorithm layer, encryption layer and blockchain layer, and a structured transaction integrity report containing verification time, verification method, verification result and credibility level is generated. Extract the device identifiers and communication address information of both parties to the transaction from the transaction request data, and verify the validity and online status of the terminal devices through the IoT device registry. The IoT communication module is invoked to convert the transaction integrity report into the corresponding data format according to the communication protocol and data format requirements of different terminal devices, and then push it to the terminal devices of the buyer and seller respectively. Monitor the reception and confirmation status of the terminal devices. Once both devices have successfully received the report, record the feedback completion flag in the system and generate a complete archive record of this verification process.

7. The transaction data verification method for the egg trading cabinet according to claim 6, characterized in that, The multi-dimensional sensor array data fusion employs an adaptive weight allocation algorithm, the calculation formula of which is: in, The fused sensor validation factor is M; M is the total number of sensors. Let be the dynamic weighting coefficient of the m-th sensor at time t; Let be the standardized data value of the m-th sensor at time t; The time decay coefficient of the m-th sensor; denoted as the time delay of the data from the m-th sensor.

8. The transaction data verification method for the egg trading cabinet according to claim 7, characterized in that, The data consistency confidence level is calculated using an evaluation model based on quantified uncertainty, and the calculation formula is as follows: in, Confidence level for data consistency; This is the confidence level adjustment parameter; Assign a score based on the similarity of physical features; Uncertainty weighting factor; The uncertainty of the model prediction is quantified; N is the total number of validation dimensions; This is the reliability metric for the nth dimension; This is the importance index for the nth dimension.

9. The transaction data verification method for the egg trading cabinet according to claim 8, characterized in that... The quantum encryption algorithm employs a dynamic key generation mechanism, and its mathematical expression is: in, A quantum dynamic key associated with the transaction parameter p; A quantum-safe hash function; This represents the XOR operation; Q is the number of quantum entangled pairs. This is the encoded value of the q-th quantum entangled state; It is a quantum phase modulation function; This represents the q-th quantum oscillation frequency; It is the modulus of a large prime number.

10. A transaction data verification system for an egg trading counter, used to execute the transaction data verification method for an egg trading counter as described in any one of claims 1 to 9, characterized in that, include: Servers and egg trading counters; The server is configured as follows: Receive transaction request data; The physical verification data is collected in real time by the multi-dimensional sensor array built into the egg trading cabinet. The physical verification data includes the weight change data of the eggs, surface spectral characteristic data, and environmental condition data. Based on blockchain distributed ledger technology, the physical verification data and transaction request data are spatiotemporally anchored to generate an immutable verification block containing timestamps, geographic location information, and sensor data hash values; A digital twin model of egg products is constructed based on pre-trained artificial intelligence algorithms; The physical verification data is input into the egg product digital twin model in real time, and the actual physical characteristics of the egg product are compared and analyzed with the standard characteristic parameters predicted by the egg product digital twin model to calculate the data consistency confidence level. When the data consistency confidence level exceeds a preset threshold, the verified transaction data is encrypted using a quantum encryption algorithm, and the transaction status in the blockchain ledger is updated synchronously. Generate a transaction integrity report containing multi-layered verification results, and feed the verification results back to the terminal devices of both parties to the transaction via an IoT communication module.

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