A method, storage medium, and equipment for assetizing delivery and maintenance data based on smart city transportation.

Abstract

This invention discloses a method, storage medium, and device for assetizing delivery and maintenance data of smart city transportation. The method includes: collecting delivery and maintenance data of smart city transportation through multi-source heterogeneous sensors and performing data cleaning; preparing quantum keys using entangled states to generate a random density matrix; generating ciphertext from the cleaned delivery and maintenance data using the random density matrix; transmitting the ciphertext from the multi-source heterogeneous sensors to the smart city transportation operation and maintenance service center for decryption; inputting the delivery and maintenance data into a sandbox deployed in the smart city transportation operation and maintenance service center; filtering out suspicious delivery and maintenance data based on risk scoring; and storing the retained delivery and maintenance data as asset data based on a hierarchical and partitioned architecture of a data lake. This invention improves the availability and security of assetized data.

Description

Technical Field

[0001] This invention relates to the field of data assetization technology, specifically to a method, storage medium, and device for assetizing delivery and maintenance data based on smart city transportation. Background Technology

[0002] The delivery and operation data of smart city transportation involves multiple data sources, including roads, vehicles, pedestrians, traffic lights, and public transportation. By constructing dynamic traffic profiles using this data, real-time monitoring and early warning can be achieved, enabling dynamic adjustments to resource allocation. Therefore, assetizing the delivery and operation data of smart city transportation can transform the massive, multi-source, and heterogeneous data generated in the smart city transportation operation and maintenance service system into quantifiable and manageable assets, providing a reliable data foundation for optimizing traffic resource allocation and improving urban governance efficiency.

[0003] However, the assetization process of smart city transportation delivery and operation data still faces the following challenges:

[0004] (1) The delivery and maintenance data of smart city transportation may carry viruses during the collection process, which may damage the integrity of the data, cause data distortion or unusability, and affect the accuracy of traffic decisions. At the same time, virus data may steal or tamper with sensitive information such as traffic flow, vehicle trajectory, and user identity, leading to the leakage of sensitive information. In fact, virus data may even attack servers, causing the smart city transportation system to be paralyzed and causing traffic congestion. (2) During the storage process of smart city transportation delivery and maintenance data, the existing database only supports structured data, while smart city transportation delivery and maintenance data contains a large amount of unstructured and semi-structured data. The data needs to be converted into a unified format through a complex ETL process, which leads to the loss of some data information and processing delays. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method, storage medium, and device for assetizing delivery and maintenance data based on smart city transportation. The delivery and maintenance data of smart city transportation is encrypted and transmitted to the smart city transportation operation and maintenance service center for decryption. After filtering out suspicious data using a sandbox environment, the delivery and maintenance data is managed using data lake technology, thereby improving the availability and security of the assetized data.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution: a method for assetizing delivery and operation and maintenance data based on smart city transportation, comprising the following steps: Step S1: Collect delivery and maintenance data of smart city transportation through multi-source heterogeneous sensors and perform data cleaning; Step S2: Prepare a quantum key through entangled state, generate a random density matrix, generate ciphertext from the cleaned delivery and maintenance data using the random density matrix, and transmit it from the multi-source heterogeneous sensor to the smart city traffic operation and maintenance service center for decryption; Step S3: Input the decrypted delivery and maintenance data into the sandbox deployed in the smart city transportation operation and maintenance service center, and filter out suspicious delivery and maintenance data based on risk scores; Step S4: Store the retained delivery and maintenance data as asset data based on the hierarchical partitioning architecture of the data lake.

[0007] Further, step S2 includes the following sub-steps: Step S2.1: Generate a set of EPR entangled pairs and send them to the multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center respectively. The multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center perform random basis measurement on the received qubits respectively, and retain the qubits with the same measurement basis as the original key. Step S2.2: Prepare a quantum key from the original key using a hash function, and generate a random density matrix based on the quantum key; Step S2.3: Encode the cleaned delivery and maintenance data into a quantum state, generate ciphertext using a random density matrix, and transmit it to the smart city transportation operation and maintenance service center for decryption.

[0008] Furthermore, the set of EPR entangled pairs generated in step S2.1 Represented as:

[0009] in, This indicates that both qubits are in the ground state. This indicates that both qubits are in an excited state.

[0010] Furthermore, the process of generating the ciphertext in step S2.3 is as follows:

[0011] in, This represents the generated ciphertext. This represents the quantum state encoded from the cleaned delivery and maintenance data. The unitary matrix representing the random density matrix satisfies: , express The conjugate transpose of . Represents a random density matrix, , Indicates the first k The first qubit with the same measurement basis i Density operator in pure state, Indicates being in the first i The probability of a pure state.

[0012] Furthermore, step S3 includes the following sub-steps: Step S3.1: Input the decrypted delivery and maintenance data into the sandbox of the smart city transportation operation and maintenance service center, and use Suricata rules to capture suspicious delivery and maintenance data; Step S3.2: Construct a risk assessment model that includes static features, dynamic behaviors, and contextual information. Input each piece of suspicious delivery and maintenance data into the risk assessment model and calculate the risk score of the suspicious delivery and maintenance data. Step S3.3: If the risk score of the suspicious delivery and maintenance data is higher than the first risk threshold, delete it directly; otherwise, determine whether the risk score of the suspicious delivery and maintenance data is higher than the second risk threshold. If so, perform manual verification; otherwise, release it directly.

[0013] Furthermore, the construction process of the risk assessment model is as follows:

[0014] in, Risk scores indicating questionable delivery and maintenance data. Static feature scores indicating suspicious delivery and maintenance data. express The weighting coefficients, Dynamic behavioral scores indicating suspicious delivery and maintenance data. express The weighting coefficients, A score indicating contextual information about questionable delivery and maintenance data. express The weighting coefficients.

[0015] Furthermore, the static features include attribute information of the delivered operation and maintenance data, the dynamic behavior includes network communication behavior of the delivered operation and maintenance data, and the context information includes the source IP of the delivered operation and maintenance data.

[0016] Furthermore, the hierarchical and partitioned architecture of the data lake is specifically as follows: a corresponding number of data pools are set up according to the business domains of the delivered operation and maintenance data. Each data pool is equipped with a data storage tank and a miscellaneous storage tank. The miscellaneous storage tank is used to store the original delivered operation and maintenance data, and the data storage tank is used to store the feature data reflecting the delivered operation and maintenance data.

[0017] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program that enables a computer to execute the described method for assetizing delivery and maintenance data based on smart city transportation.

[0018] Furthermore, the present invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the aforementioned method for assetizing delivery and maintenance data based on smart city transportation.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention is based on the data assetization method of smart city transportation delivery and maintenance. The delivery and maintenance data of smart city transportation is transmitted to the smart city transportation operation and maintenance service center through quantum encryption. Quantum keys are prepared by superposition and entanglement characteristics of entangled states. Any eavesdropping will disturb the quantum state and be detected by both parties to the communication, thereby improving the security of delivery and maintenance data transmission. In addition, the smart city transportation operation and maintenance service center uses a sandbox environment to detect and filter out suspicious data, so as to prevent the further spread of suspicious data and damage to the smart city transportation operation and maintenance service center. (2) The present invention utilizes the hierarchical partitioning architecture of data lake to store smart city transportation delivery and maintenance data, and can store smart city transportation delivery and maintenance data including structured, semi-structured and unstructured data in real time, avoid data loss, and realize the full life cycle management of smart city transportation delivery and maintenance data.

[0020] In summary, the method of this invention enables the assetization of smart city transportation delivery and maintenance data, greatly improving the availability and security of the assetized data. Attached Figure Description

[0021] Figure 1 This is a flowchart of the delivery and operation data assetization method based on smart city transportation according to the present invention; Figure 2 This is a schematic diagram of the hierarchical and partitioned architecture of the data lake in this invention. Detailed Implementation

[0022] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings.

[0023] like Figure 1 This is a flowchart of the delivery and operation data assetization method based on smart city transportation according to the present invention. The method includes the following steps: Step S1: Collect smart city transportation delivery and maintenance data through multi-source heterogeneous sensors, and perform data cleaning. The structured delivery and maintenance data includes: traffic equipment status data, traffic maintenance work order data, and traffic flow data, which requires handling missing values, correcting outliers, and standardizing formats. The semi-structured delivery and maintenance data includes: traffic equipment log files, API interface return data, and traffic equipment configuration files, which requires parsing of data nesting structures, format standardization, and field mapping. The unstructured delivery and maintenance data includes: image data, video data, audio data, and text data, which requires image denoising, enhancement processing, speech denoising processing, and text normalization.

[0024] Step S2: Prepare a quantum key through entangled states, generate a random density matrix, and use the random density matrix to generate ciphertext from the cleaned delivery and maintenance data. Transmit the data from the multi-source heterogeneous sensor to the smart city transportation operation and maintenance service center for decryption. This invention prepares a quantum key through the superposition and entanglement characteristics of entangled states. Any eavesdropping will disturb the quantum state and be detected by both parties in the communication, thus improving the security of delivery and maintenance data transmission.

[0025] Step S2 includes the following sub-steps: Step S2.1: Generate a set of EPR entangled pairs and send them to the multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center respectively. The multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center perform random basis measurement on the received qubits respectively, and retain the qubits with the same measurement basis as the original key. A set of EPR entangled pairs generated in this invention Represented as:

[0026] in, This indicates that both qubits are in the ground state. This indicates that both qubits are in an excited state.

[0027] Step S2.2: Prepare a quantum key from the original key using a hash function, and generate a random density matrix based on the quantum key; Step S2.3: Encode the cleaned delivery and maintenance data into a quantum state, generate ciphertext using a random density matrix, and transmit it to the smart city transportation operation and maintenance service center. When generating ciphertext using a random density matrix, its elements have high randomness and are deeply bound to the randomness of the quantum key. Even if an eavesdropper intercepts the ciphertext, they cannot restore the original delivery and maintenance data through reverse engineering, thus ensuring communication security and preventing the delivery and maintenance data from being tampered with.

[0028] The process of generating ciphertext in this invention is as follows:

[0029] in, This represents the generated ciphertext. This represents the quantum state encoded from the cleaned delivery and maintenance data. The unitary matrix representing the random density matrix satisfies: , express The conjugate transpose of . Represents a random density matrix, , Indicates the first k The first qubit with the same measurement basis i Density operator in pure state, Indicates being in the first i The probability of a pure state.

[0030] Step S2.4: The smart city transportation operation and maintenance service center then decrypts the delivered operation and maintenance data based on the random density matrix.

[0031] Step S3: Input the decrypted delivery and maintenance data into the sandbox deployed in the smart city transportation operation and maintenance service center. Filter out suspicious delivery and maintenance data based on risk scoring. The sandbox uses virtualization technology to create an independent space isolated from the actual environment. Input the delivery and maintenance data into the sandbox and run it to capture suspicious data. All operations are restricted to this independent space to prevent the further spread of suspicious data and damage to the smart city transportation operation and maintenance service center. This includes the following sub-steps: Step S3.1: Input the decrypted delivery and maintenance data into the sandbox of the smart city transportation operation and maintenance service center, and use Suricata rules to capture suspicious delivery and maintenance data; Step S3.2: Construct a risk assessment model that includes static features, dynamic behaviors, and contextual information. Input each suspicious delivery and maintenance data into the risk assessment model and calculate the risk score of the suspicious delivery and maintenance data. The static features in the risk assessment model of this invention include: attribute information of delivered operation and maintenance data; dynamic behaviors include: network communication behavior of delivered operation and maintenance data; and context information includes: the source IP of delivered operation and maintenance data. Constructing a risk assessment model using these features can improve the accuracy and comprehensiveness of risk assessment. The construction process of the risk assessment model in this invention is as follows:

[0032] in, Risk scores indicating questionable delivery and maintenance data. Static feature scores indicating suspicious delivery and maintenance data. express The weighting coefficients, Dynamic behavioral scores indicating suspicious delivery and maintenance data. express The weighting coefficients, A score indicating contextual information about questionable delivery and maintenance data. express The weighting coefficients.

[0033] Step S3.3: If the risk score of the suspicious delivery and maintenance data is higher than the first risk threshold, delete it directly; otherwise, determine whether the risk score of the suspicious delivery and maintenance data is higher than the second risk threshold. If so, perform manual verification; otherwise, allow it directly. Wherein, the first risk threshold is greater than the second risk threshold.

[0034] Step S4: The retained delivery and maintenance data is stored as asset data and stored based on the hierarchical and partitioned architecture of the data lake. This enables the real-time storage of smart city transportation delivery and maintenance data, including structured, semi-structured and unstructured data, avoiding data loss and realizing full lifecycle management of smart city transportation delivery and maintenance data.

[0035] like Figure 2 The hierarchical and partitioned architecture of the data lake in this invention is as follows: A corresponding number of data pools are set up according to the business domains of the delivery and maintenance data. Delivery and maintenance data is stored in different data pools according to different business domains to avoid cross-contamination between different types of delivery and maintenance data. Each data pool is equipped with a data storage container and a miscellaneous storage container. The miscellaneous storage container stores the raw delivery and maintenance data, while the data storage container stores characteristic data that reflects the delivery and maintenance data. For structured delivery and maintenance data, columnar storage and Snappy compression are used; for semi-structured delivery and maintenance data, JSON format and GZIP compression are used; and for unstructured delivery and maintenance data, metadata storage is used to reduce the cost of asset-based data storage and improve management efficiency and query performance.

[0036] This invention relates to a method for assetizing delivery and maintenance data in smart city transportation. The method involves encrypting and transmitting the delivery and maintenance data of smart city transportation to the smart city transportation operation and maintenance service center, decrypting it, filtering out suspicious data using a sandbox environment, and then using data lake technology to manage the delivery and maintenance data, thereby improving the availability and security of the assetized data.

[0037] In one technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program that enables a computer to execute the present invention's method for assetizing delivery and maintenance data based on smart city transportation.

[0038] In one technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the delivery and maintenance data assetization method of the present invention based on smart city transportation.

[0039] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disc read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0040] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0041] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for assetizing delivery and operation data based on smart city transportation, characterized in that, Includes the following steps: Step S1: Collect delivery and maintenance data of smart city transportation through multi-source heterogeneous sensors and perform data cleaning; Step S2: Prepare a quantum key through entangled states, generate a random density matrix, and use the random density matrix to generate ciphertext from the cleaned delivery and maintenance data. Transmit the ciphertext from the multi-source heterogeneous sensors to the smart city transportation operation and maintenance service center for decryption; including the following sub-steps: Step S2.1: Generate a set of EPR entangled pairs and send them to the multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center respectively. The multi-source heterogeneous sensor and the smart city traffic operation and maintenance service center perform random basis measurement on the received qubits respectively, and retain the qubits with the same measurement basis as the original key. A set of EPR entangled pairs generated Represented as: in, This indicates that both qubits are in the ground state. This indicates that both qubits are in an excited state; Step S2.2: Prepare a quantum key from the original key using a hash function, and generate a random density matrix based on the quantum key; Step S2.3: Encode the cleaned delivery and maintenance data into a quantum state, generate ciphertext using a random density matrix, and transmit it to the smart city transportation operation and maintenance service center for decryption; The process of generating ciphertext is as follows: in, This represents the generated ciphertext. This represents the quantum state encoded from the cleaned delivery and maintenance data. The unitary matrix representing the random density matrix satisfies: , express The conjugate transpose of . Represents a random density matrix, , Indicates the first k The first qubit with the same measurement basis i Density operator in pure state, Indicates being in the first i The probability of a pure state; Step S3: Input the decrypted delivery and maintenance data into the sandbox deployed in the smart city transportation operation and maintenance service center, and filter out suspicious delivery and maintenance data based on risk scores; Step S4: Store the retained delivery and maintenance data as asset data based on the hierarchical partitioning architecture of the data lake.

2. The method for assetizing delivery and operation data based on smart city transportation according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S3.1: Input the decrypted delivery and maintenance data into the sandbox of the smart city transportation operation and maintenance service center, and use Suricata rules to capture suspicious delivery and maintenance data; Step S3.2: Construct a risk assessment model that includes static features, dynamic behaviors, and contextual information. Input each piece of suspicious delivery and maintenance data into the risk assessment model and calculate the risk score of the suspicious delivery and maintenance data. Step S3.3: If the risk score of the suspicious delivery and maintenance data is higher than the first risk threshold, delete it directly; otherwise, determine whether the risk score of the suspicious delivery and maintenance data is higher than the second risk threshold. If so, perform manual verification; otherwise, release it directly.

3. The method for assetizing delivery and operation data based on smart city transportation according to claim 2, characterized in that, The process of constructing the risk assessment model is as follows: in, Risk scores indicating questionable delivery and maintenance data. Static feature scores indicating suspicious delivery and maintenance data. express The weighting coefficients, Dynamic behavioral scores indicating suspicious delivery and maintenance data. express The weighting coefficients, A score indicating contextual information about questionable delivery and maintenance data. express The weighting coefficients.

4. The method for assetizing delivery and operation data based on smart city transportation according to claim 3, characterized in that, The static features include: attribute information of the delivered operation and maintenance data; the dynamic behavior includes: network communication behavior of the delivered operation and maintenance data; and the context information includes: the source IP of the delivered operation and maintenance data.

5. A method for assetizing delivery and operation data based on smart city transportation according to claim 1, characterized in that, The hierarchical and partitioned architecture of the data lake is as follows: a corresponding number of data pools are set up according to the business domains of the delivered operation and maintenance data. Each data pool is equipped with a data storage tank and a miscellaneous storage tank. The miscellaneous storage tank is used to store the original delivered operation and maintenance data, and the data storage tank is used to store the feature data reflecting the delivered operation and maintenance data.

6. A computer-readable storage medium storing a computer program, characterized in that, The computer program causes the computer to execute the delivery and maintenance data assetization method based on smart city transportation as described in any one of claims 1-5.

7. An electronic device, characterized in that, include: The method for assetizing delivery and maintenance data based on smart city transportation as described in any one of claims 1-5 includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

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