Trusted data space operating system, data value calculation method and related device

Abstract

The application discloses a trusted data space operating system, a data value calculation method and related equipment, and relates to the field of cross-domain data processing.The method is applied to a trusted data space operating system, and the trusted data space operating system adopts a cubic architecture, the cubic architecture comprising a basic component layer, a hardware layer, an application service layer, a connector, a security reinforcement layer and a data service layer, and the method comprises the following steps: the application service layer sends a cross-domain request to the connector; when the connector receives the cross-domain request, the connector sends a target node query request to the basic component layer; the basic component layer performs node screening based on the target node query request, obtains a node list, and sends the node list to the connector; and the connector establishes a connection with the data service layer based on the node list, and performs value calculation on target data in the cross-domain request in the data service layer to obtain data value density. By implementing the technical scheme provided in the application, the evaluation accuracy of data value can be improved.

Description

Technical Field

[0001] The present application relates to the field of cross-domain data processing technology, and specifically to a trusted data space operating system, a data value calculation method, and related equipment. Background Art

[0002] With the advent of the big data era, cross-domain data collaboration has become a key driver of digital transformation across various industries. Enterprises and organizations need to break down data silos and enable data interoperability and value mining across different business systems and departments.

[0003] Currently, common cross-domain data collaboration solutions typically utilize traditional centralized architectures, enabling data aggregation and sharing through a unified data exchange platform. However, in practice, due to the diversity of data sources and the complexity of business scenarios, unstable data transmission quality can impact data integrity and timeliness, directly impacting the actual application of data in business. In cross-domain scenarios, data loss caused by fluctuating transmission quality can impact the actual value of data, resulting in less accurate assessments of data value. Summary of the Invention

[0004] This application provides a trusted data space operating system, a data value calculation method and related equipment, which can improve the accuracy of data value assessment.

[0005] In the first aspect, the present application provides a trusted data space operating system, which adopts a cube architecture. The cube architecture is a hexahedral structure composed of a basic component layer, a hardware layer, an application service layer, a connector, a security reinforcement layer and a data service layer.

[0006] By adopting the above technical solutions, the basic component layer, hardware layer, application service layer, connector, security reinforcement layer and data service layer are organized into a trusted data space operating system with a cube architecture. This spatialized cube architecture design not only optimizes the collaborative efficiency between the various components of the system, but also provides strict security protection through physical space isolation.

[0007] In a second aspect, the present application provides a data value calculation method, which is applied to the above-mentioned trusted data space operating system, and the method includes:

[0008] The application service layer sends a cross-domain request to the connector;

[0009] When the connector receives the cross-domain request, it sends a target node query request to the basic component layer;

[0010] The basic component layer performs node screening based on the target node query request to obtain a node list, and sends the node list to the connector;

[0011] The connector establishes a connection with the data service layer based on the node list, and performs value calculation on the target data in the cross-domain request in the data service layer to obtain data value density.

[0012] By adopting the above technical solution, the application service layer sends a cross-domain request through the connector. The connector establishes a connection with the data service layer based on the node list obtained by filtering the basic component layer, thereby realizing reliable transmission of cross-domain data. The data service layer calculates the value of the target data to obtain the data value density. Since the basic component layer can perform efficient node screening, the data service layer can focus on the evaluation and calculation of data value, avoiding the problem of data loss caused by transmission quality fluctuations in traditional solutions affecting the actual evaluation value of data, and improving the accuracy of data value evaluation in cross-domain scenarios.

[0013] Optionally, the method further includes: the security reinforcement layer calling a corresponding homomorphic encryption algorithm to encrypt the target data, and monitoring the hardware load in the hardware layer in real time;

[0014] When there is an abnormal node whose hardware load is greater than a preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a backup node to replace the abnormal node.

[0015] By adopting the above technical solution, the security reinforcement layer encrypts the target data by calling the homomorphic encryption algorithm and monitors the hardware load in the hardware layer in real time. When it is found that the hardware load of an abnormal node exceeds the preset load threshold, the encryption algorithm corresponding to the abnormal node is switched in time and / or the backup node is started for replacement. This avoids the problem of excessive encryption processing load affecting system stability due to the diversity of data sources and the complexity of business scenarios in traditional solutions, and realizes dynamic adjustment of encryption algorithms and flexible switching of nodes.

[0016] Optionally, the basic component layer performs node screening based on the target node query request to obtain a node list, including:

[0017] Querying the blockchain DHT in the basic component layer for metadata of each node, including node load information and security score;

[0018] The basic component layer monitors the status information of each node in real time through the eBPF probe;

[0019] The basic component layer updates the routing table based on the metadata and the status information, and filters available nodes according to the updated routing table to generate the node list.

[0020] By adopting the above technical solution, the basic component layer obtains node load information and security scores by querying the metadata of each node in the blockchain DHT, and uses eBPF probes to monitor the status information of each node in real time. After dynamically updating the routing table based on the metadata and status information, the available nodes are filtered to generate a node list, achieving accurate perception of node health status and dynamic adjustment of routes. At the same time, the blockchain DHT distributes the node metadata to ensure the credibility and consistency of node information. Combined with the low-overhead monitoring characteristics of the eBPF probe, the accuracy and real-time performance of node screening in cross-domain scenarios are improved, thereby providing reliable node protection for subsequent data transmission.

[0021] Optionally, the connector establishes a connection with the data service layer based on the node list, including:

[0022] The connector establishes an initial connection with the data service layer using the QUIC protocol;

[0023] The connector configures an FEC redundancy packet mechanism in the transmission channel of the initial connection;

[0024] The connector performs redundant encoding on the target data of the cross-domain request based on the FEC redundant packet mechanism to obtain an encoded data packet;

[0025] When the encoded data packet loss is detected, the connector performs data packet recovery based on the FEC redundancy packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

[0026] By adopting the above technical solution, the connector uses the QUIC protocol to establish an initial connection with the data service layer, and configures the FEC redundant packet mechanism in the transmission channel to redundantly encode the target data of the cross-domain request to obtain the encoded data packet. When data packet loss is detected, the data packet can be restored based on the FEC redundant packet mechanism to ensure that the delay of cross-domain transmission does not exceed the preset time threshold, so that the data transmission quality can be stable and controllable even when the data sources are diverse and the business scenarios are complex.

[0027] Optionally, performing value calculation on the target data in the cross-domain request in the data service layer to obtain data value density includes:

[0028] The data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively;

[0029] The data service layer calculates the data coverage and data source diversity of the target data respectively, and performs weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data;

[0030] The data service layer performs weighted calculation on the timeliness, integrity and business conversion rate to obtain data value density.

[0031] By adopting the above technical solution, the data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request, and performs weighted calculation on the data coverage and data source diversity to obtain the integrity. Then, based on the weighted calculation of timeliness, integrity and business conversion rate, it obtains the data value density, and establishes a comprehensive data value assessment system. Among them, timeliness reflects the real-time value of data, the business conversion rate reflects the application value of data, and the weighted calculation of data coverage and data source diversity ensures a more accurate data integrity assessment. The comprehensive weighted calculation of the three enables the data value density to objectively reflect the actual value of data in cross-domain scenarios, realizing the accurate quantification of data value.

[0032] Optionally, the data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively, including:

[0033] The data service layer calculates the timeliness of the target data in the cross-domain request according to a first calculation formula;

[0034] The data service layer calculates the business conversion rate of the target data according to a second calculation formula;

[0035] The first calculation formula is: ;

[0036] Wherein, T is the timeliness of the target data, is the freshness weight, is the decay rate, is the generation time interval of the target data, is the scarcity weight, S is the scarcity factor;

[0037] The second calculation formula is: ;

[0038] Wherein, R is the business conversion rate of the target data, E is the number of effective calls of the target data, and A is the total number of calls of the target data. is the call efficiency coefficient, is the return on investment value of the target data.

[0039] By adopting the above technical solution, the data service layer introduces the freshness decay and scarcity characteristics of the target data through the first calculation formula to calculate the timeliness, so that the timeliness evaluation can reflect the value changes and market supply conditions of the data over time; at the same time, the second calculation formula is used to combine the call efficiency of the target data with the return on investment to calculate the business conversion rate, realizing the quantitative evaluation of the data application effect and economic value.

[0040] Optionally, the data service layer calculates the data coverage and data source diversity of the target data, including:

[0041] The data service layer divides the number of fields of the target data by the corresponding theoretical number of fields to obtain data coverage;

[0042] The data service layer calculates the data source diversity according to a third calculation formula;

[0043] The third calculation formula is: ;

[0044] in, is the diversity of data sources, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0045] By adopting the above technical solution, the data service layer obtains data coverage by calculating the ratio of the number of fields of the target data to the theoretical number of fields, and uses a third calculation formula to calculate the data source diversity based on the number of data sources and the proportion of each data source. The data coverage reflects the field completeness of the target data, and the data source diversity reflects the richness of the data source, so that the data integrity level can be comprehensively evaluated in the cross-domain data collaboration scenario.

[0046] In a third aspect of the present application, a data value calculation system is provided, which is applied to the above-mentioned trusted data space operating system. The data value calculation system includes:

[0047] A cross-domain request sending module, configured for the application service layer to send a cross-domain request to the connector;

[0048] A target node query module, configured to send a target node query request to the basic component layer after the connector receives the cross-domain request;

[0049] A node list generating module, configured for the basic component layer to perform node screening based on the target node query request, obtain a node list, and send the node list to the connector;

[0050] A data value calculation module is used for the connector to establish a connection with the data service layer based on the node list, and to perform value calculation on the target data in the cross-domain request in the data service layer to obtain data value density.

[0051] In a fourth aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0052] In a fifth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0053] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0054] 1. By integrating the basic component layer, hardware layer, application service layer, connector, security reinforcement layer, and data service layer into a cube-based trusted data space operating system, this spatialized cube architecture design not only optimizes the collaborative efficiency between system components but also provides strict security protection through physical space isolation.

[0055] 2. The application service layer sends a cross-domain request through the connector. The connector establishes a connection with the data service layer based on the node list obtained by filtering the basic component layer, thereby realizing reliable transmission of cross-domain data. The data service layer calculates the value of the target data to obtain the data value density. Since the basic component layer can perform efficient node screening, the data service layer can focus on the evaluation and calculation of data value, avoiding the problem of data loss caused by transmission quality fluctuations in traditional solutions affecting the actual evaluation value of data, and improving the accuracy of data value evaluation in cross-domain scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a cube architecture of a trusted data space operating system provided by an embodiment of the present application;

[0057] Figure 2 This is an example diagram of a three-dimensional service grid provided in an embodiment of the present application;

[0058] Figure 3 This is a flow chart of a data value calculation method provided in an embodiment of the present application;

[0059] Figure 4 This is a module diagram of a data value calculation system provided by an embodiment of the present application;

[0060] Figure 5This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0061] Description of reference numerals: 500, electronic device; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0063] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0064] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0065] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0066] In order to facilitate understanding of the system provided by the embodiment of the present application, before introducing the embodiment of the present application, the background of the embodiment of the present application is first introduced.

[0067] With the advent of the big data era, businesses and organizations urgently need to break down data silos and enable data interoperability and value mining across diverse business systems and departments. The Trusted Data Space Operating System (TDOS), a new distributed operating system, has emerged to address this need. Its core objective is to provide technical support for the development of the data element market. Serving this market, the TDOS is designed to enable the secure flow and value assessment of data across organizations and systems. By building a unified data flow system and value assessment standards, combined with cryptographic mechanisms and a distributed architecture, this operating system provides the foundation for the secure and trusted flow of data.

[0068] Existing trusted data space operating systems generally adopt a traditional centralized architecture, achieving data aggregation and sharing through a unified data exchange platform. However, with the increasing diversification of data sources and the continuous complexity of business scenarios, the instability of data transmission quality directly affects the integrity and timeliness of data, thereby reducing the actual application effect of data in the business; secondly, in cross-domain collaboration scenarios, fluctuations in transmission quality often lead to data loss, which not only affects the use value of the data, but also seriously damages the accuracy of data value assessment.

[0069] In order to break through the above technical limitations, this application proposes a new trusted data space operating system, which adopts a cube architecture, reconstructs the system functions through a spatial design concept, integrates the traditional planar functional modules into a three-dimensional spatial structure, and constructs the minimum multi-party contact surface in a cube manner, forming an independent minimum trusted data space operating system, which is shaped like a cube structure.

[0070] See Figure 1 , which is a schematic diagram of a cube architecture of a trusted data space operating system provided in an embodiment of the present application.

[0071] The embodiment of the present application provides a trusted data space operating system, which adopts a cube architecture and combines Figure 1It can be seen that the cube architecture includes basic component layer, hardware layer, application service layer, connector, security reinforcement layer and data service layer. In the embodiment of the present application, the trusted data space operating system of the cube architecture can be understood as a six-sided collaborative microservice network system, in which: the basic component layer is set on the front, including but not limited to security containers, MPC and blockchain, etc., for providing basic service support; the hardware layer is set on the bottom, including but not limited to CPU, GPU, MEM and storage, etc., providing dynamic resources for basic components through virtualization technology; the application service layer is set on the top, including but not limited to API services, computing services and industry services, etc., for providing various application functions; the connector is set on the right, including but not limited to local multi-source heterogeneous data sources and other trusted data space connectors, etc., to realize the connection of multi-source heterogeneous data; the security reinforcement layer is set at the back, including but not limited to security protection mechanisms in dimensions such as network (iptables / black and white lists), storage (data encryption / authorization) and computing (process scanning / kill restrictions). The security reinforcement component can monitor the status of each layer in real time and drive the dynamic adjustment of the front basic components; the data service layer is set on the left, including but not limited to financial (measurement, pricing, data value density algorithm) and governance (classification and grading) data service capabilities, for data processing and data value assessment. The trusted data space operating system builds a complete data processing system through the coordinated cooperation of six aspects, realizing full-stack support from underlying hardware to upper-level applications, while forming an overall architecture that cooperates and promotes each other in terms of security, connectivity and service capabilities.

[0072] It should be noted that the trusted data space operating system of the above-mentioned cube architecture can be dynamically combined according to the subjects, and each subject can be freely combined according to its own position, thereby forming a three-dimensional service grid that is cross-layer, cross-domain and cross-subject.

[0073] See Figure 2 , is an example diagram of a three-dimensional service grid provided in an embodiment of the present application; combined with Figure 2As can be seen, in this three-dimensional service grid, different parties form multi-level collaborative relationships based on their roles in the data flow chain. Specifically, parties M and N act as data foundation providers, providing basic data services to parties A, B, C, and D. Parties A, B, C, and D act as data processors, and after receiving basic data, they can further provide data services to party X. At the same time, a serial data processing flow is formed between parties A, B, C, and D, where party A transmits processed data to party B, which in turn transmits processed data to party C, which in turn transmits processed data to party D, thus achieving step-by-step data processing. This three-dimensional service grid architecture enables each party to flexibly combine according to its own positioning, forming a multi-dimensional, multi-level data collaboration system.

[0074] In one embodiment, please refer to Figure 3 , a flowchart of a data value calculation method is proposed. This method can be implemented by a computer program, a single-chip microcomputer, or a data value calculation system based on the von Neumann architecture. The computer program can be integrated into an application or run as an independent tool application. Specifically, in the embodiment of the present application, the method can be applied to the trusted data space operating system of the above-mentioned cube architecture. The method includes steps 10 to 40, which are as follows:

[0075] Step 10: The application service layer sends a cross-origin request to the connector.

[0076] In the present application, a cross-domain request refers to a request for data exchange between different parties within a three-dimensional service grid architecture. For example, when one party needs to access the data of another party, a cross-domain request is required to achieve secure and trusted data access because the data provider and the data requester are located in different trust domains, network domains, or business domains.

[0077] Specifically, after receiving the data request from the requester, the application service layer needs to further obtain the complete information of the target data. The application service layer will first parse the target data identification information in the data request. This identification information may include key information such as data type (such as financial, governance, etc.), data name, access rights, etc.; then, based on the parsed identification information, the application service layer will construct a cross-domain request containing the requester's identity information, target data identification information, and access credentials; then, the application service layer will send the cross-domain request to the connector through a secure channel, where the secure channel can use the TLS encrypted transmission mechanism to ensure the security of the cross-domain request during transmission.

[0078] Step 20: After receiving the cross-domain request, the connector sends a target node query request to the basic component layer.

[0079] In the embodiment of the present application, the target node refers to a node entity that can provide the required data service in the three-dimensional service grid architecture. Furthermore, the target node can be understood as a service node that has data processing capabilities and meets security requirements.

[0080] Specifically, after the connector receives a cross-domain request from the application service layer, it needs to determine the specific node location that can provide the target data. The connector first parses the received cross-domain request and extracts the key characteristic information of the target data, including data type identifier, data provider information, data access permission requirements, etc. Then, based on this characteristic information, the connector constructs a target node query request, which contains query conditions (such as data type, data provider, access permission, etc.), query scope, and query priority. The connector sends the constructed query request to the basic component layer through a secure channel. Among them, the connector will give priority to the secure container component as the recipient of the query request, ensuring the security of the query process through the isolated environment provided by the secure container. At the same time, the connector will also attach a timestamp and request identifier to the query request for subsequent request tracking and timeout processing.

[0081] Step 30: The basic component layer performs node screening based on the target node query request, obtains a node list, and sends the node list to the connector.

[0082] In this embodiment of the present application, the node list refers to an information set of available target nodes that meets the query conditions and is returned by the basic component layer to the connector.

[0083] Specifically, after receiving a query request for a target node, the basic component layer performs node screening through the blockchain component's DHT network. The basic component layer first extracts query parameters such as the data type and provider from the query request, then broadcasts the query request through the DHT network. After receiving the request, each node matches its locally registered node information with the query criteria, with matching dimensions including but not limited to data type matching, provider matching, and permission matching. The basic component layer then screens the matching nodes, selecting available nodes based on indicators such as node online status and load level, and records the node information in a node list. Finally, the basic component layer sends the node list to the connector via a secure channel, achieving efficient node positioning and information return.

[0084] Based on the above embodiment, as an optional embodiment, the basic component layer performs node screening based on the target node query request to obtain the node list, which may further include steps 101 to 103:

[0085] Step 101: Query the metadata of each node in the blockchain DHT in the basic component layer. The metadata includes node load information and security score.

[0086] Specifically, the foundational component layer constructs a metadata query request for the blockchain's DHT, which includes parameters such as the query time window and query dimensions. When executing a query, the DHT network's distributed hashing algorithm first locates the network locations storing node metadata. It then concurrently retrieves metadata information for each node from these locations, including node load information (system resource metrics such as CPU usage, memory utilization, storage space, and network bandwidth) and security scores (quantitative scores of security metrics such as encryption algorithm support, access control capabilities, and security vulnerability remediation). After the query is complete, the foundational component layer structures and stores the obtained metadata, providing foundational data support for subsequent node screening.

[0087] Step 102: The basic component layer monitors the status information of each node in real time through the eBPF probe.

[0088] Specifically, the basic component layer uses eBPF probes to monitor the status information of each node in real time to ensure that the real-time operating status of the node is obtained. The eBPF probe program deployed by the basic component layer on each node monitors, including but not limited to, the following status information: the node's connection status (number of TCP connections, connection establishment time, connection maintenance time), the node's processing status (request queue length, request processing delay, request success rate), and the node's operating status (process running time, process switching frequency, system call frequency). The eBPF probe collects this status information in real time by being mounted on the network protocol stack and process scheduler of the system kernel. For example, when a node receives a new connection request, the probe program records the timestamp of the connection establishment; when the node processes a data request, the probe program counts the processing time of the request; when the node switches processes, the probe program records the changes in the process's operating status.

[0089] Step 103: The basic component layer updates the routing table based on the metadata and status information, and filters available nodes according to the updated routing table to generate a node list.

[0090] Specifically, the basic component layer first correlates and analyzes node metadata in the blockchain DHT with state information collected by the eBPF probe. If a node's connection status is abnormal (e.g., the number of TCP connections exceeds 1000, new connection latency exceeds 200ms), processing status is abnormal (e.g., a request queue backlog exceeds 500, request processing latency exceeds 1s, request success rate is less than 95%), or operational status is abnormal (e.g., frequent process switching, system call anomalies), the node's priority in the routing table is lowered. Based on the updated routing table, the basic component layer selects nodes with normal connection status, stable processing status, and healthy operational status. The complete information of these nodes is recorded in the node list to ensure that the selected nodes can provide reliable data transmission services.

[0091] Based on the above embodiment, as an optional embodiment, the embodiment of the present application further provides an adaptive control mechanism for hardware load, and the specific process includes steps 201 to 202:

[0092] Step 201: The security reinforcement layer calls the corresponding homomorphic encryption algorithm to encrypt the target data and monitors the hardware load in the hardware layer in real time.

[0093] Specifically, the security reinforcement layer needs to encrypt the target data. The system has a pre-built homomorphic encryption library containing a variety of encryption algorithms to accommodate different data types, including fully homomorphic encryption algorithms (such as BGV and CKKS) and partially homomorphic encryption algorithms (such as Paillier and ElGamal). The security reinforcement layer selects the appropriate homomorphic encryption algorithm based on the target data type and security requirements. For example, the CKKS algorithm is preferred for numerical data requiring complex calculations, while the Paillier algorithm, with its lower computational overhead, is preferred for statistical data requiring only addition operations. During the encryption process, the system generates a unique encryption key for each data block and stores the encrypted data and key in separate security domains. Simultaneously, the security reinforcement layer monitors the hardware load in real time at the hardware layer. A distributed monitoring agent is deployed in the system to collect hardware node performance metrics every 10 seconds, including CPU usage, memory utilization, and encryption operation latency. When the performance indicators of a hardware node are monitored to exceed the preset threshold (for example, the CPU usage continuously exceeds 85%, or the encryption operation delay exceeds 150% of the expected value), the node is marked as an abnormal node.

[0094] Step 202: When there is an abnormal node whose hardware load is greater than a preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a backup node to replace the abnormal node.

[0095] When there is an abnormal node whose hardware load is greater than the preset load threshold, the security reinforcement layer immediately starts the optimization measures. In the embodiment of the present application, the encryption algorithm switching and / or standby node replacement operation can be selected according to the load situation of the abnormal node. First, the system will evaluate the cause of the abnormality and perform an evaluation. Three levels of load thresholds can be set as judgment criteria. For example: the CPU usage rate is 85%-90% or the memory occupancy is 80%-85% for the first-level threshold range, the CPU usage rate is 90%-95% or the memory occupancy is 85%-90% for the second-level threshold range, and the CPU usage rate is greater than 95% or the memory occupancy is greater than 90% for the third-level threshold range. By analyzing the performance monitoring data, the system evaluates the specific causes of the load anomaly, including factors such as the complexity of the encryption algorithm, the amount of data processed, and the status of hardware resources. For different abnormal situations, the security reinforcement layer can choose to perform encryption algorithm switching and / or node replacement operations. When it is found that the performance bottleneck is mainly caused by the computational complexity of the encryption algorithm, the system will automatically switch to an encryption algorithm with lower computational overhead. For example, a fully homomorphic encryption algorithm (such as CKKS) can be switched to a partially homomorphic encryption algorithm (such as Paillier), or encryption parameters can be adjusted to reduce computational complexity. When the system detects significant hardware resource degradation or failure, the security hardening layer initiates a standby node replacement process. The system selects a node with good performance from a pre-configured standby node pool and initiates resource preparation, including loading the necessary encryption libraries and synchronizing configuration information. Once the standby node is ready, the node switchover occurs: first, data and encryption context are migrated to the standby node; then, the task schedule is updated to redirect subsequent encryption tasks to the new node. The entire replacement process maintains business continuity and ensures uninterrupted data processing. In certain situations, such as when system load reaches the third threshold or when both algorithm performance and hardware resource issues exist, the security hardening layer performs both the algorithm switchover and the node replacement simultaneously. In these cases, the system simultaneously launches the standby node and directly loads a lighter-weight encryption algorithm on the new node, achieving dual performance optimization. For example, when it is detected that the CPU usage of a node is continuously in the range of 92%-96% and the memory usage is between 87%-92%, the system will simultaneously initiate algorithm switching and standby node replacement to quickly reduce the system load.

[0096] Step 40: The connector establishes a connection with the data service layer based on the node list, and performs value calculation on the target data in the cross-domain request in the data service layer to obtain the data value density.

[0097] In this embodiment of the present application, the target data refers to the data object that needs to be processed and is carried in the cross-domain request.

[0098] The cross-domain request also includes a data description of the target data. The data description refers to a feature description of the target data, which includes key feature data required to support the value assessment calculation of the target data, including but not limited to the time characteristics of the target data (such as the time interval for data generation), the quality characteristics of the target data (such as the number of fields, the number of data sources, and the proportion of each data source), and the business characteristics of the target data (such as business type, data revenue, and number of calls).

[0099] Specifically, the node list contains multiple nodes arranged in descending order of priority. The connector selects the node with the highest priority from the node list and establishes a secure connection with the data service layer through the QUIC protocol. After the connection is established, the data service layer calculates the value of the target data based on the data description in the cross-domain request: specifically, it can calculate the timeliness of the target data based on the time characteristics in the data description, calculate the integrity of the target data based on the data quality characteristics, and calculate the business conversion rate of the target data based on the business characteristics. The timeliness, integrity, and business conversion rate indicators of the target data are then weighted according to preset weights to obtain the data value density. This calculation method comprehensively evaluates the actual application value of the data.

[0100] Based on the above embodiment, as an optional embodiment, the step of the connector establishing a connection with the data service layer based on the node list may further include steps 301 to 304:

[0101] Step 301: The connector establishes an initial connection with the data service layer using the QUIC protocol.

[0102] Specifically, the connector needs to establish a reliable transmission channel with the data service layer. In the embodiment of the present application, the QUIC protocol is used to establish the initial connection. The connector first generates a QUIC connection request message, which contains the connector's identity, supported transmission characteristics and other information. The request message is then sent to the data service layer via the UDP protocol, and the connection timeout timer is started at the same time. When the data service layer receives the request, it returns a response message containing connection confirmation information. After the connector verifies the response message, it completes the QUIC protocol handshake process and establishes an initial connection that supports multiplexing.

[0103] Step 302: The connector configures the FEC redundancy packet mechanism in the transmission channel of the initial connection.

[0104] Specifically, the connector configures the FEC redundancy mechanism within the established QUIC transport channel to improve data transmission reliability. The connector first sets the FEC encoding parameters, including the original data packet grouping size (e.g., 10 packets per group), the number of redundancy packets (e.g., 2 redundancy packets per group), and the encoding algorithm type (e.g., Reed-Solomon encoding). The connector then registers the FEC processing module with the QUIC transport layer, which is responsible for encoding and recovering data packets. Simultaneously, the connector notifies the data service layer of the FEC configuration information, ensuring that both parties use the same redundancy processing mechanism.

[0105] Step 303: The connector performs redundant encoding on the target data of the cross-domain request based on the FEC redundant packet mechanism to obtain an encoded data packet.

[0106] Specifically, the connector needs to perform FEC redundancy encoding on the target data of the cross-origin request. The connector first fragments the target data according to the preset packet size, with each fragment corresponding to an original data packet. Then, for each set of original data packets, the connector invokes the FEC encoding algorithm to generate redundant packets. For example, when using Reed-Solomon (10,12) encoding, two redundant packets are generated for every ten original data packets. These redundant packets contain checksum information for data recovery. After encoding, the connector organizes the original and redundant packets into encoded data packets according to the encoding rules.

[0107] Step 304: When the encoded data packet is detected to be lost, the connector performs data packet recovery based on the FEC redundancy packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

[0108] Specifically, the connector continuously monitors the transmission status of data packets during transmission. Upon detecting packet loss (e.g., due to a gap in packet sequence numbers or a packet loss notification from the receiver), it immediately initiates an FEC-based recovery process. This recovery process first identifies the group to which the lost packet belongs, then performs decoding operations using the remaining packets and redundant packets within that group to reconstruct the contents of the lost packet. This rapid local recovery mechanism avoids the additional latency introduced by traditional retransmissions, ensuring that the end-to-end latency of cross-domain transmissions does not exceed a preset time threshold (e.g., 200ms).

[0109] Based on the above embodiment, as an optional embodiment, the step of calculating the value of the target data in the cross-domain request in the data service layer to obtain the data value density may also include steps 401 to 403:

[0110] Step 401: The data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively.

[0111] Specifically, the data service layer calculates the timeliness of the target data in the cross-domain request according to the first calculation formula, and the first calculation formula is: ;

[0112] Among them, T is the timeliness of the target data, is the freshness weight, is the decay rate, is the target data generation time interval, is the scarcity weight, and S is the scarcity factor.

[0113] In the first calculation formula, it consists of two parts. The first part is , is the time decay term, reflecting the value change of target data over time. is the freshness weight, which indicates the sensitivity of the target data to the timeliness of the data. The value can be 0.6. In different business scenarios, it can also be adjusted according to actual conditions. For example, in high-frequency trading scenarios, The value can be 0.8. e is a natural constant. By using the mathematical property of exponential decay, it can accurately depict the law of nonlinear decrease of data value over time while ensuring computational efficiency. The decay rate is used to control the decay speed of data value. A value of 0.01 indicates that the data value will decay to 37% of its initial value in approximately 100 days. λ can also be adjusted for different types of data: for example, for social media hot data, the λ value can be set to 0.1, causing the data value to decay rapidly within 10 days. The target data generation time interval is uniformly calculated in days. When the data update frequency is minute or hour, the time unit needs to be converted to days to ensure calculation consistency.

[0114] The second part is , is a scarcity item, used to supplement the unique value of the target data. is the scarcity weight, which indicates the unique value of data. The value is 0.4, which is the same as the freshness weight Forming a complementary relationship, ,When the target data has high uniqueness, such as exclusively collected industry data, The value can be raised to 0.6 to highlight the scarcity value of the data. S represents the scarcity factor, a 0-1 score assigned by the data provider based on market supply. The credibility of the score is ensured through technical means such as blockchain evidence storage. For example, proprietary data for a specific industry segment might receive a high scarcity score of 0.9, while publicly available general data might only receive a score of 0.2.

[0115] For example, take the target data of accounts receivable in supply chain finance as an example, where the initial parameters are: , the data features are: Account period The timeliness of the target data is 30 days, and the core enterprise credit data S=0.9. Substituting the above initial parameters and data characteristics into the first calculation formula, we can get the timeliness of the target data: Different data types use different attenuation strategies, that is, different initial parameters can be set according to different data types. , for example, the timeliness of real-time transaction data is: , the timeliness of user portrait data is: .

[0116] As another optional embodiment, in order to more accurately evaluate target data with multi-stage aging characteristics, the embodiment of the present application also proposes a composite attenuation model for calculation. The composite attenuation model is: ,in , For short-term weight, is the long-term weight, is the short-term decay rate, is the long-term decay rate, is the scarcity weight, and S is the scarcity factor. In this compound decay model, the timeliness calculation is decomposed into three dimensions: short-term timeliness, long-term value, and scarcity. The first exponential decay term The second exponential decay term is used to evaluate the short-term timeliness of the data. Used to evaluate the long-term value preservation ability of data, The item continues to evaluate the scarcity value of data. For example, for financial transactions, the short-term sensitivity weight Set to 0.5 to reflect the immediate value of the data; long-term value weight Set to 0.3 to reflect the historical analysis value of the data; scarcity weight Set to 0.2 to balance the scarcity factor. Short-term decay rate Set to 0.1, indicating that the data will decay rapidly to 37% of its initial value within 10 days; the long-term decay rate Setting this to 0.001 causes the data to decay slowly over approximately 1,000 days, preserving its long-term reference value. This composite decay model accurately captures the decay characteristics of data value at different time scales, enabling it to better serve complex business scenarios that require consideration of both immediate and historical value, such as financial quantitative analysis and user behavior research.

[0117] Furthermore, the data service layer calculates the business conversion rate of the target data according to a second calculation formula, which is: .

[0118] Among them, R is the business conversion rate of the target data, E is the number of effective calls of the target data, and A is the total number of calls of the target data. is the call efficiency coefficient, is the return on investment value of the target data.

[0119] In the second calculation formula, it consists of two parts. The first part is , is the call efficiency item, which reflects the use effect of the target data in actual applications. E is the effective call times of the target data, which means the number of times the target data generates actual business value after being called. A is the total call times of the target data. The ratio E / A between the two reflects the effective utilization rate of the data. The call efficiency coefficient For balancing the call features in different application scenarios, in the embodiment of this application is 0.6, Mainly based on business objectives. The second part is , is the return on investment item, which is used to evaluate the return on investment value generated by the target data in the business application. The original ROI value is calculated by tracking the ratio of the income generated after the data application to the input cost, and then the original ROI value is normalized according to the maximum ROI value corresponding to the business, so as to obtain For example, if the data income of a business is: bad debt reduction of 1 million yuan, and the cost is: data procurement 300,000 yuan + processing fee 100,000 yuan, then the original ROI value is The maximum ROI value for this service is 2.0, and the original ROI value is normalized to obtain .

[0120] For example, taking supply chain finance as an example, the target data is used in the risk rating model. The total number of calls in the past 30 days is 50 (the number of effective calls is 40). The score is 0.7 (based on supply chain finance income calculation). , then substitute the above data into the second calculation formula to obtain the business conversion rate of the target data: ,Since the upper limit of the business conversion rate is 1.0, when the business conversion rate exceeds the upper limit, it is taken as 1.0.

[0121] Step 402: The data service layer calculates the data coverage and data source diversity of the target data respectively, and performs weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data.

[0122] Specifically, the data service layer divides the number of target data fields by the corresponding theoretical number of fields to obtain data coverage. The system can set up a standard field template library that defines a set of necessary fields based on different business scenarios. The number of fields in this set is the theoretical number of fields. For example, for user profile data, the necessary field set includes but is not limited to basic attribute fields such as user ID, age, gender, and spending power; for transaction data, the necessary field set includes but is not limited to core business fields such as transaction time, transaction amount, transaction parties, and transaction status. The data service layer maps and compares the number of fields in the target data with the theoretical number of fields in the corresponding scenario to calculate data coverage.

[0123] The data service layer then calculates the data source diversity according to the third calculation formula, which is: ;in, is the diversity of data sources, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0124] The third calculation formula adopts a normalized entropy value calculation method, where n represents the total number of data sources of the target data, reflecting the richness of the data acquisition channels. Indicates the proportion of the i-th data source in the target data, such as data sources such as banks and logistics. It is calculated by dividing the amount of data provided by the data source by the total amount of data. This section calculates the information entropy of the data source distribution and normalizes it by dividing it by lnn to make the diversity scores under different numbers of data sources comparable.

[0125] Finally, a weighted calculation is performed on the data coverage and data source diversity to obtain the integrity of the target data. The integrity calculation can be calculated according to the following formula: ; Where C is the integrity of the target data, are the data coverage weight and data source diversity weight, In the embodiments of the present application, It should be noted that the above data coverage weights and data source diversity weights can be adjusted according to actual conditions in different business scenarios.

[0126] For example, the target data includes 10 fields (theoretically there should be 12 fields), such as accounts receivable amount, payment period, and core enterprise credit rating. The data sources come from 3 banks and 2 logistics companies, such as Bank A (30%), Bank B (30%), Bank C (20%), Logistics A (15%), and Logistics B (5%). , then the data coverage is: , the diversity of data sources is: , then the integrity of the target data is .

[0127] Step 403: The data service layer performs weighted calculation on timeliness, integrity and business conversion rate to obtain data value density.

[0128] Specifically, the data service layer sets the weight coefficients of the three evaluation dimensions according to the characteristics of different business scenarios. In the embodiment of the present application, considering that the business conversion rate directly reflects the actual application value of the data, it is given the highest weight of 0.4; timeliness and integrity are the basic dimensions supporting data quality, each of which is given a weight of 0.3. Then, timeliness, integrity and business conversion rate are weighted and calculated with the corresponding weights to obtain the data value density. It should be noted that the weights corresponding to the above timeliness, integrity and business conversion rate can be adjusted according to actual conditions in different business scenarios.

[0129] For example, the data value density calculation formula is: ;in, is the data value density, is the timeliness weight, with a value of 0.3; is the integrity weight, with a value of 0.4; is the business conversion rate weight, which is 0.3. The data value density is: .

[0130] As another optional embodiment, the data service layer can also adopt differentiated evaluation strategies to calculate the data value density based on different role relationship models in the data supply chain to accurately reflect the contribution of each participant.

[0131] For a one-to-one relationship model, where a single data provider directly connects with a single data user, the system directly uses the provider's data quality parameters and the user's application effectiveness rating. For example, if a company directly purchases user profile data from a third party, the data value density is directly calculated based on the data's timeliness (T), completeness (C), and the user's actual business conversion rate (R). This calculation method can be referred to in step 303 above and will not be further explained here.

[0132] For one-to-many relationship models, the system uses aggregate calculation methods to evaluate data value. Taking the data processing scenario as an example, the original data is processed by multiple processors and then used by multiple users. The timeliness principle is the maximum value: , the average value principle is used for completeness: , the business conversion rate adopts the weighted average principle of the number of users: ,in is the timeliness of the i-th processor, Contribution to the integrity of the i-th processor, is the conversion rate of the jth user, y is the total number of processors, and m is the total number of users.

[0133] For many-to-many relationship patterns, the system uses network value density calculation methods to evaluate the value of complex data fusion scenarios. First, a data flow network diagram can be constructed, with each data node (including providers, processors, and users) as a network node and the data flow relationship as a connecting edge. Then, the importance value of each node is calculated based on the PageRank algorithm or preset business rules. , then calculate the local value density of each node, considering the timeliness, integrity and business conversion rate of the node, and finally calculate the overall data value density of the network according to the following formula: ; Where K represents the total number of nodes in the network graph, Indicates the The local value density of each node, Indicates the The importance value of a node in the network graph, Represents the sum of the importance values ​​of all nodes.

[0134] See Figure 4 , is a module diagram of a data value calculation system provided in an embodiment of the present application. The data value calculation system is applied to a trusted data space operating system, and the data value calculation system includes:

[0135] A cross-domain request sending module, configured for the application service layer to send a cross-domain request to the connector;

[0136] A target node query module, configured to send a target node query request to the basic component layer after the connector receives the cross-domain request;

[0137] A node list generating module, configured for the basic component layer to perform node screening based on the target node query request, obtain a node list, and send the node list to the connector;

[0138] A data value calculation module is used for the connector to establish a connection with the data service layer based on the node list, and to perform value calculation on the target data in the cross-domain request in the data service layer to obtain data value density.

[0139] Optionally, a data value calculation system further includes a node monitoring module, which is used for the security reinforcement layer to call a corresponding homomorphic encryption algorithm to encrypt the target data and monitor the hardware load in the hardware layer in real time;

[0140] When there is an abnormal node whose hardware load is greater than a preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a backup node to replace the abnormal node.

[0141] Optionally, the node list generation module is further configured to query metadata of each node in the blockchain DHT in the basic component layer, wherein the metadata includes node load information and security score;

[0142] The basic component layer monitors the status information of each node in real time through the eBPF probe;

[0143] The basic component layer updates the routing table based on the metadata and the status information, and filters available nodes according to the updated routing table to generate the node list.

[0144] Optionally, the data value calculation module is further configured for the connector to establish an initial connection with the data service layer using the QUIC protocol;

[0145] The connector configures an FEC redundancy packet mechanism in the transmission channel of the initial connection;

[0146] The connector performs redundant encoding on the target data of the cross-domain request based on the FEC redundant packet mechanism to obtain an encoded data packet;

[0147] When the encoded data packet loss is detected, the connector performs data packet recovery based on the FEC redundancy packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

[0148] Optionally, the data value calculation module is further used by the data service layer to respectively calculate the timeliness and business conversion rate of the target data in the cross-domain request;

[0149] The data service layer calculates the data coverage and data source diversity of the target data respectively, and performs weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data;

[0150] The data service layer performs weighted calculation on the timeliness, integrity and business conversion rate to obtain data value density.

[0151] Optionally, the data value calculation module is further configured to calculate, at the data service layer, the timeliness of the target data in the cross-domain request according to the first calculation formula;

[0152] The data service layer calculates the business conversion rate of the target data according to a second calculation formula;

[0153] The first calculation formula is: ;

[0154] Wherein, T is the timeliness of the target data, is the freshness weight, is the decay rate, is the generation time interval of the target data, is the scarcity weight, S is the scarcity factor;

[0155] The second calculation formula is: ;

[0156] Wherein, R is the business conversion rate of the target data, E is the number of effective calls of the target data, and A is the total number of calls of the target data. is the call efficiency coefficient, is the return on investment value of the target data.

[0157] Optionally, the data value calculation module is further configured to divide the number of fields of the target data by the corresponding theoretical number of fields at the data service layer to obtain data coverage;

[0158] The data service layer calculates the data source diversity according to a third calculation formula;

[0159] The third calculation formula is: ;

[0160] in, is the diversity of data sources, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0161] It should be noted that the above embodiments provide systems that implement their functions using only the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0162] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executed by a data value calculation method such as the above embodiment. The specific execution process can be found in the specific description of the above embodiment and will not be repeated here.

[0163] Please refer to Figure 5 The present application also discloses an electronic device. Figure 5 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0164] The communication bus 502 is used to implement the connection and communication between these components.

[0165] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0166] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0167] The processor 501 may include one or more processing cores. Using various interfaces and circuits, the processor 501 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 505, as well as accesses data stored in the memory 505, to perform various server functions and process data. Optionally, the processor 501 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 501 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented as a separate chip, rather than integrated into the processor 501.

[0168] Among them, the memory 505 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Reference Figure 5 , as a computer storage medium, the memory 505 may include an operating system, a network communication module, a user interface module, and an application program for a data value calculation method.

[0169] exist Figure 5 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application program storing a data value calculation method in the memory 505. When executed by one or more processors 501, the electronic device 500 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0170] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0172] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0175] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0176] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The description and examples are to be considered as illustrative only.

Claims

1. A data value calculation method, characterized in that: Applied to a trusted data space operating system, the trusted data space operating system adopts a cube architecture, the cube architecture is a hexahedron structure composed of a basic component layer, a hardware layer, an application service layer, a connector, a security reinforcement layer, and a data service layer, the method includes: The application service layer sends a cross-domain request to the connector; When the connector receives the cross-domain request, it sends a target node query request to the basic component layer; The basic component layer performs node screening based on the target node query request to obtain a node list, and sends the node list to the connector; The basic component layer performs node screening based on the target node query request to obtain a node list, including: Querying the blockchain DHT in the basic component layer for metadata of each node, including node load information and security score; The basic component layer monitors the status information of each node in real time through the eBPF probe; The basic component layer updates the routing table based on the metadata and the status information, and filters available nodes according to the updated routing table to generate the node list; The connector establishes a connection with the data service layer based on the node list, and performs value calculation on the target data in the cross-domain request in the data service layer to obtain data value density; The connector establishes a connection with the data service layer based on the node list, including: The connector establishes an initial connection with the data service layer using the QUIC protocol; The connector configures an FEC redundancy packet mechanism in the transmission channel of the initial connection; The connector performs redundant encoding on the target data of the cross-domain request based on the FEC redundant packet mechanism to obtain an encoded data packet; When the encoded data packet loss is detected, the connector performs data packet recovery based on the FEC redundancy packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

2. The data value calculation method according to claim 1, characterized in that: The method further comprises: The security reinforcement layer calls the corresponding homomorphic encryption algorithm to encrypt the target data and monitors the hardware load in the hardware layer in real time; When there is an abnormal node whose hardware load is greater than a preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a backup node to replace the abnormal node.

3. The data value calculation method according to claim 1, characterized in that: Performing value calculation on the target data in the cross-domain request in the data service layer to obtain data value density includes: The data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively; The data service layer calculates the data coverage and data source diversity of the target data respectively, and performs weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data; The data service layer performs weighted calculation on the timeliness, integrity and business conversion rate to obtain data value density.

4. The data value calculation method according to claim 3, characterized in that: The data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively, including: The data service layer calculates the timeliness of the target data in the cross-domain request according to a first calculation formula; The data service layer calculates the business conversion rate of the target data according to a second calculation formula; The first calculation formula is: ; Wherein, T is the timeliness of the target data, is the freshness weight, is the decay rate, is the generation time interval of the target data, is the scarcity weight, S is the scarcity factor; The second calculation formula is: ; Wherein, R is the business conversion rate of the target data, E is the number of effective calls of the target data, and A is the total number of calls of the target data. is the call efficiency coefficient, is the return on investment value of the target data.

5. The data value calculation method according to claim 3, characterized in that: The data service layer calculates the data coverage and data source diversity of the target data, including: The data service layer divides the number of fields of the target data by the corresponding theoretical number of fields to obtain data coverage; The data service layer calculates the data source diversity according to a third calculation formula; The third calculation formula is: ; in, is the diversity of data sources, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

6. A data value calculation system, characterized in that: Applied to a trusted data space operating system, the trusted data space operating system adopts a cube architecture, which is a hexahedron structure consisting of a basic component layer, a hardware layer, an application service layer, a connector, a security reinforcement layer, and a data service layer. The data value calculation system includes: A cross-domain request sending module, configured for the application service layer to send a cross-domain request to the connector; A target node query module, configured to send a target node query request to the basic component layer after the connector receives the cross-domain request; A node list generation module is configured to cause the basic component layer to perform node screening based on the target node query request to obtain a node list, and send the node list to the connector; wherein the basic component layer performs node screening based on the target node query request to obtain a node list, including: querying the blockchain DHT in the basic component layer for metadata of each node, wherein the metadata includes node load information and security score; the basic component layer monitors the status information of each node in real time through an eBPF probe; the basic component layer updates the routing table based on the metadata and the status information, and filters available nodes according to the updated routing table to generate the node list; A data value calculation module is configured for the connector to establish a connection with the data service layer based on the node list, and to perform value calculation on the target data in the cross-domain request in the data service layer to obtain a data value density; wherein, the connector establishes a connection with the data service layer based on the node list, including: the connector establishes an initial connection with the data service layer using the QUIC protocol; the connector configures an FEC redundancy packet mechanism in the transmission channel of the initial connection; The connector redundantly encodes the target data of the cross-domain request based on the FEC redundant packet mechanism to obtain an encoded data packet; when the encoded data packet is detected to be lost, the connector recovers the data packet based on the FEC redundant packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

7. An electronic device, characterized in that: The electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 5.

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