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

Abstract

The invention 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, the trusted data space operating system adopts a cube architecture, the cube architecture comprises 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; after the connector receives the cross-domain request, a target node query request is sent to a 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 a connector; and the connector establishes 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 a data value density. By implementing the technical scheme provided by the invention, the evaluation accuracy of the data value can be improved.

Description

Technical Field

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

[0002] With the advent of the big data era, cross - domain data collaboration has become an important driving force for the digital transformation of various industries. Enterprises and organizations need to break data islands and achieve data interconnection and value mining between different business systems and different departments.

[0003] Currently, common cross - domain data collaboration solutions usually adopt traditional centralized architectures and achieve data aggregation and sharing through a unified data exchange platform. However, in actual applications, due to the diversity of data sources and the complexity of business scenarios, the instability of data transmission quality will affect data integrity and timeliness, etc., thus directly affecting the actual application effect of data in business. Especially in cross - domain scenarios, data loss caused by fluctuations in transmission quality will affect the actual evaluation value of data, resulting in a relatively low accuracy of data value evaluation. Summary of the Invention

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

[0005] In a first aspect, this application provides a trusted data space operating system. The trusted data space operating system adopts a cube architecture, and 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.

[0006] By adopting the above - mentioned technical solution, a trusted data space operating system with a cube architecture is formed by combining the basic component layer, the hardware layer, the application service layer, the connector, the security reinforcement layer, and the data service layer. This spatial cube architecture design not only optimizes the coordination efficiency between system components but also provides a strict security guarantee through physical space isolation.

[0007] In a second aspect, this application provides a data value calculation method, which is applied to the above - mentioned trusted data space operating system. 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 filters nodes based on the target node query request to obtain a node list, and sends the node list to the connector; The connector establishes a connection with the data service layer based on the node list, and calculates the value of the target data in the cross-domain request in the data service layer to obtain the data value density.

[0008] 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 screened by the basic component layer, thus realizing the reliable transmission of cross-domain data. And the data value density is obtained by calculating the value of the target data through the data service layer. 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 that the actual evaluation value of data is affected by data loss caused by fluctuations in transmission quality in the traditional solution, and improving the accuracy of data value evaluation in the cross-domain scenario.

[0009] Optionally, the method further includes: 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 with the hardware load greater than the preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a standby node to replace the abnormal node.

[0010] 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 a standby node is started for replacement, avoiding the problem that the system stability is affected due to the heavy encryption processing load caused by the diversity of data sources and the complexity of business scenarios in the traditional solution, and realizing the dynamic adjustment of the encryption algorithm and the flexible switching of nodes.

[0011] Optionally, the basic component layer performs node screening based on the target node query request to obtain a node list, including: Query the metadata of each node in the blockchain DHT in the basic component layer, where the metadata includes node load information and security scores; The basic component layer monitors the status information of each node in real time through eBPF probes; 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.

[0012] By adopting the above technical solution, the basic component layer queries the metadata of each node in the blockchain DHT, obtains the node load information and security score, 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, available nodes are screened to generate a node list, achieving precise perception of the node health status and dynamic adjustment of routing. At the same time, by storing node metadata in a distributed manner in the blockchain DHT, the credibility and consistency of node information are ensured. Combining with the low-overhead monitoring characteristics of eBPF probes, the accuracy and real-time performance of node screening in cross-domain scenarios are improved, thus providing a reliable node guarantee for subsequent data transmission.

[0013] Optionally, the connector establishes a connection with the data service layer based on the node list, including: The connector uses the QUIC protocol to establish an initial connection with the data service layer; The connector configures the FEC redundant 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 encoded data packets; When it is detected that an encoded data packet is lost, the connector performs data packet recovery based on the FEC redundant packet mechanism so that the delay of cross-domain transmission does not exceed a preset time threshold.

[0014] By adopting the above technical solution, the connector uses the QUIC protocol to establish an initial connection with the data service layer, configures the FEC redundant packet mechanism in the transmission channel, performs redundant encoding on the target data of the cross-domain request to obtain encoded data packets, and can perform data packet recovery based on the FEC redundant packet mechanism when it is detected that a data packet is lost, ensuring that the delay of cross-domain transmission does not exceed a preset time threshold, so that the quality of data transmission can still be stably controlled in the case of diverse data sources and complex business scenarios.

[0015] Optionally, in the data service layer, the value calculation is performed on the target data in the cross-domain request to obtain the data value density, including: 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 a weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data; The data service layer performs a weighted calculation on the timeliness, integrity and business conversion rate to obtain the data value density.

[0016] 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 obtains the integrity by weighted calculation of the data coverage and data source diversity. Then, based on the weighted calculation of timeliness, integrity, and business conversion rate, the data value density is obtained, and a comprehensive data value evaluation system is established. Among them, timeliness reflects the real-time value of data, the business conversion rate reflects the application value of data, the weighted calculation of data coverage and data source diversity ensures more accurate integrity evaluation of data, and the comprehensive weighted calculation of the three enables the data value density to objectively reflect the actual value of data in the cross-domain scenario, realizing the accurate quantification of data value.

[0017] Optionally, 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 the first calculation formula; The data service layer calculates the business conversion rate of the target data according to the 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, and S is the scarcity factor; The second calculation formula is: ; Wherein, R is the business conversion rate of the target data, E is the effective call times of the target data, A is the total call times of the target data, is the call efficiency coefficient, is the investment return value of the target data.

[0018] By adopting the above technical solution, the data service layer calculates the timeliness by introducing the freshness decay and scarcity characteristics of the target data according to the first calculation formula, so that the timeliness evaluation can reflect the value change of data over time and the market supply situation. At the same time, the second calculation formula is adopted to combine the call efficiency and investment return of the target data to calculate the business conversion rate, realizing the quantitative evaluation of the data application effect and economic value.

[0019] Optionally, 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 the data coverage; The data service layer calculates the data source diversity according to the third calculation formula; The third calculation formula is: ; Wherein, is the data source diversity, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0020] By adopting the above technical solution, the data service layer obtains the data coverage by calculating the ratio of the number of fields of the target data to the theoretical number of fields, and calculates the data source diversity based on the number of data sources and the proportion of each data source by using the third calculation formula. Among them, the data coverage reflects the field completeness of the target data, and the data source diversity reflects the richness of the data sources, so that the integrity level of the data can be comprehensively evaluated in the cross-domain data collaboration scenario.

[0021] In the 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: A cross-domain request sending module, configured to send a cross-domain request from the application service layer to the connector; A target node query module, configured to send a target node query request to the basic component layer when the connector receives the cross-domain request; A node list generation module, configured to screen nodes by the basic component layer based on the target node query request to obtain a node list, and send the node list to the connector; A data value calculation module, configured to establish a connection between the connector and the data service layer based on the node list, and calculate the value of the target data in the cross-domain request in the data service layer to obtain the data value density.

[0022] In the fourth aspect of the present application, a computer storage medium is provided. The computer storage medium stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform the above method steps.

[0023] In the fifth aspect of the present application, an electronic device is provided, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is suitable for being loaded and executed by the processor to perform the above method steps.

[0024] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By establishing a trusted data space operating system with a cubic architecture for the basic component layer, hardware layer, application service layer, connector, security reinforcement layer, and data service layer, this spatial cubic architecture design not only optimizes the collaborative efficiency among system components but also provides strict security through physical space isolation; 2. The application service layer sends cross - domain requests through the connector. The connector establishes a connection with the data service layer based on the node list filtered by the basic component layer, thus realizing the reliable transmission of cross - domain data. And through the data service layer, the value calculation of the target data is performed to obtain the data value density. Since the basic component layer can perform efficient node filtering, the data service layer can focus on the evaluation and calculation of data value, avoiding the problem that the data loss caused by the fluctuation of transmission quality in the traditional scheme affects the actual evaluation value of the data, and improving the accuracy of data value evaluation in the cross - domain scenario. Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the cubic architecture of a trusted data space operating system provided by an embodiment of the present application; Figure 2 It is an example diagram of a three - dimensional service grid provided by an embodiment of the present application; Figure 3 It is a schematic flowchart of a data value calculation method provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the modules of a data value calculation system provided by an embodiment of the present application; Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application.

[0026] Description of the reference numerals: 500, electronic device; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. Detailed Embodiments

[0027] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0028] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0029] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0030] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0031] To facilitate the understanding of the system provided by the embodiments of the present application, before introducing the embodiments of the present application, the background of the embodiments of the present application will be introduced first.

[0032] With the advent of the big data era, enterprises and organizations are urgently in need of breaking data silos and realizing data interconnection and value mining between different business systems and different departments. The trusted data space operating system emerges as a new type of distributed operating system, and its core goal is to provide technical support for the development of the data element market. The trusted data space operating system is a new type of distributed operating system serving the data element market, and its design goal is to achieve the secure circulation and value evaluation of data across organizations and systems. This operating system provides basic support for the secure and trusted transfer of data by constructing a unified data circulation system and value evaluation standard, and combining cryptographic mechanisms and distributed architectures.

[0033] Existing trusted data space operating systems generally adopt traditional centralized architectures and achieve 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 business; secondly, in cross-domain collaboration scenarios, fluctuations in transmission quality often lead to data loss, which not only affects the use value of data, but more seriously damages the accuracy of data value evaluation.

[0034] To break through the above technical limitations, this application proposes a new trusted data space operating system. This trusted data space operating system adopts a cube architecture, reconstructs system functions through a spatial design concept, integrates traditional flat functional modules into a three-dimensional spatial structure, and constructs the fewest multi-party contact surfaces in a cube manner to form an independent operating system for the smallest trusted data space, in the shape of a cube structure.

[0035] Please refer to Figure 1 , which is a schematic diagram of the cube architecture of a trusted data space operating system provided by an embodiment of this application.

[0036] A trusted data space operating system provided by an embodiment of this application. This trusted data space operating system adopts a cube architecture. Combining Figure 1 it can be seen that this cube architecture includes a basic component layer, a hardware layer, an application service layer, a connector, a security reinforcement layer, and a data service layer. In the embodiment of this application, the trusted data space operating system with this cube architecture can be understood as a microservice network system with six-sided coordination, where: 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 surface, including but not limited to CPU, GPU, MEM, and storage, etc., providing dynamic resources for the basic components through virtualization technology; the application service layer is set on the top surface, 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 side, 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 on 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 limit), etc. The security reinforcement components 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 side, including but not limited to data service capabilities in the financial category (measurement, pricing, data value density algorithm) and governance category (classification and grading), for data processing and data value evaluation. This trusted data space operating system constructs a complete data processing system through the coordinated cooperation of six sides, realizes full-stack support from the underlying hardware to the upper-layer applications, and at the same time forms an overall architecture that cooperates and promotes each other in terms of security, connectivity, and service capabilities.

[0037] It should be noted that the trusted data space operating system with the above cube architecture can be dynamically combined according to the main body, and each main body can be freely combined according to its own position, so as to form a three-dimensional service grid that crosses layers, domains, and main bodies.

[0038] Please refer to Figure 2, which is an example diagram of a three - dimensional service grid provided by an embodiment of this application; combined with Figure 2 As can be seen, in this three - dimensional service grid, different principal parties form a multi - level collaboration relationship based on their role positions in the data circulation link. Specifically, principal parties M and N, as data foundation providers, provide basic data services to principal parties A, B, C, and D; principal parties A, B, C, and D, as data processing parties, can further provide data services to principal party X after receiving the basic data; at the same time, a serial data processing flow is formed among principal parties A, B, C, and D, that is, principal party A passes the processed data to principal party B, principal party B passes the processed data to principal party C, and principal party C passes the processed data to principal party D, thus realizing the step - by - step processing of data. This three - dimensional service grid architecture enables each principal party to flexibly combine according to its own position, forming a multi - dimensional and multi - level data collaboration system.

[0039] In one embodiment, please refer to Figure 3 , a flow schematic diagram of a data value calculation method is specifically proposed. This method can be implemented relying on a computer program, can be implemented relying on a single - chip microcomputer, or can run on a data value calculation system based on the von Neumann architecture. This computer program can be integrated into an application or can run as an independent tool - type application. Specifically, in the embodiment of this application, this method can be applied to the trusted data space operating system with the above - mentioned cube architecture. This method includes steps 10 to 40, and the above steps are as follows: Step 10: The application service layer sends a cross - domain request to the connector.

[0040] Among them, in the embodiment of this application, a cross - domain request refers to a request for data interaction between different principal parties under the three - dimensional service grid architecture. For example, when a principal party needs to access the data of other principal parties, since the data provider and the data requester are located in different trust domains, different network domains, or different business domains, a cross - domain request is required to achieve secure and trusted access to the data.

[0041] Specifically, after the application service layer receives the data request from the requester, it needs to further obtain the complete information of the target data. The application service layer first parses the target data identification information in the data request, and these identification information can include key information such as data type (such as financial, governance, etc.), data name, access permission, etc.; then, the application service layer constructs a cross - domain request containing the requester's identity information, target data identification information, and access credentials based on the parsed identification information; then, the application service layer sends this cross - domain request to the connector through a secure channel, where the secure channel can adopt the TLS encryption transmission mechanism to ensure the security of the cross - domain request during the transmission process.

[0042] Step 20: After the connector receives a cross-domain request, it sends a target node query request to the basic component layer.

[0043] In an embodiment of the present application, the target node refers to a node entity that can provide the required data services in a three-dimensional service mesh architecture. Further, the target node can be understood as a service node with data processing capabilities and meeting security requirements.

[0044] Specifically, after the connector receives a cross-domain request sent by 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 feature information of the target data, including data type identification, data provider information, data access permission requirements, etc.; then, the connector constructs a target node query request based on this feature information. The query request contains query conditions (such as data type, data provider, access permission, etc.), query scope, and query priority and other information. The connector sends the constructed query request to the basic component layer through a secure channel. Among them, the connector will preferentially select the secure container component as the recipient of the query request, and ensure the security of the query process through the isolation environment provided by the secure container. At the same time, the connector also attaches a timestamp and a request identifier to the query request for subsequent request tracking and timeout processing.

[0045] Step 30: The basic component layer filters nodes based on the target node query request, obtains a node list, and sends the node list to the connector.

[0046] In an embodiment of the present application, the node list refers to a set of information of available target nodes that the basic component layer returns to the connector and meets the query conditions.

[0047] Specifically, after the basic component layer receives the target node query request, it filters nodes through the DHT network of the blockchain component. The basic component layer first extracts query parameters such as data type and provider from the query request, and then broadcasts the query request through the DHT network; after each node receives the request, it matches the locally registered node information with the query conditions, and the matching dimensions include but are not limited to data type matching, provider matching, and permission matching, etc.; subsequently, the basic component layer filters the matching nodes, filters out available nodes based on indicators such as node online status and load level, and records the node information in the node list; finally, the basic component layer sends the node list to the connector through a secure channel, realizing efficient node positioning and information return.

[0048] Based on the above embodiment, as an alternative embodiment, the step of the basic component layer filtering nodes based on the target node query request to obtain a node list may further include steps 101-103: 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 scores.

[0049] Specifically, the basic component layer constructs a metadata query request for the blockchain DHT. This request contains parameters such as the query time window and query dimensions. When the query is executed, first, the distributed hash algorithm of the DHT network is used to locate the network locations storing the node metadata; then, the metadata information of each node is obtained in parallel from these locations, including node load information (system resource metrics such as CPU usage rate, memory occupancy rate, storage space, network bandwidth, etc.) and security scores (quantitative scores of security metrics such as encryption algorithm support, access control ability, security vulnerability repair status, etc.). After the query is completed, the basic component layer structures and stores the obtained metadata to provide basic data support for subsequent node screening.

[0050] Step 102: The basic component layer uses eBPF probes to monitor the status information of each node in real time.

[0051] Specifically, the basic component layer uses eBPF probes to monitor the status information of each node in real time to ensure obtaining the real-time operating conditions of the nodes. The eBPF probe programs deployed on each node by the basic component layer monitor, including but not limited to, the following status information: the connection status of the node (the number of TCP connections, connection establishment time, connection holding time), the processing status of the node (request queue length, request processing delay, request success rate), and the running status of the node (process running time, process switching frequency, system call frequency). The eBPF probes collect this status information in real time by attaching to the network protocol stack and process scheduler of the system kernel. For example, when a node receives a new connection request, the probe program will record the timestamp of connection establishment; when a node processes a data request, the probe program will count the processing duration of the request; when a node performs a process switch, the probe program will record the change in the running status of the process.

[0052] 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.

[0053] Specifically, the basic component layer first performs an association analysis on the node metadata in the blockchain DHT and the status information collected by the eBPF probes. When it is found that the connection status of a node is abnormal (such as the number of TCP connections exceeding 1000, the new connection delay exceeding 200 ms), the processing status is abnormal (such as the request queue backlog exceeding 500, the request processing delay exceeding 1 s, the request success rate being lower than 95%), or the running status is abnormal (such as frequent process switching, system call anomalies), the priority weight of this node in the routing table will be reduced. The basic component layer filters out the nodes with normal connection status, stable processing status, and good running status according to the updated routing table, and records the complete information of these nodes in the node list to ensure that the selected nodes can provide reliable data transmission services.

[0054] Based on the above embodiments, as an alternative embodiment, the embodiment of the present application further provides an adaptive regulation mechanism for hardware load, and the specific process includes steps 201 - 202: 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.

[0055] Specifically, the security reinforcement layer needs to encrypt the target data. There is a homomorphic encryption algorithm library preset in the system, and this homomorphic encryption algorithm library contains various encryption algorithms to adapt to different data type processing, such as including fully homomorphic encryption algorithms (such as BGV, CKKS, etc.) and partially homomorphic encryption algorithms (such as Paillier, ElGamal, etc.). The security reinforcement layer selects a suitable homomorphic encryption algorithm according to the type and security requirements of the target data. For example, for numerical data that needs to perform complex calculations, the CKKS algorithm is preferably selected; for statistical data that only requires addition operations, the Paillier algorithm with lower computational overhead can be selected. During the encryption process, the system generates a unique encryption key for each data block and stores the encrypted data and the key in different security domains respectively. At the same time, the security reinforcement layer monitors the hardware load in the hardware layer in real time. The system deploys distributed monitoring agents to collect the performance metrics of hardware nodes every 10 seconds, including CPU usage rate, memory occupancy rate, encryption operation delay, etc. When it is detected that the performance metrics of a certain hardware node exceed the preset threshold (for example, the CPU usage rate continuously exceeds 85%, or the encryption operation delay exceeds 150% of the expected value), this node is marked as an abnormal node.

[0056] Step 202: When there are abnormal nodes with hardware load greater than the preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal nodes and / or starts a standby node to replace the abnormal nodes.

[0057] When there are abnormal nodes with hardware load greater than the preset load threshold, the security reinforcement layer immediately initiates optimization measures. In the embodiments of the present application, encryption algorithm switching and / or standby node replacement operations can be selected according to the load conditions of the abnormal nodes. First, the system evaluates the cause of the abnormality. A three-level load threshold can be set as the judgment criterion. For example, the CPU usage rate in the range of 85%-90% or the memory occupancy in the range of 80%-85% is the first-level threshold range, the CPU usage rate in the range of 90%-95% or the memory occupancy in the range of 85%-90% is the second-level threshold range, and the CPU usage rate greater than 95% or the memory occupancy greater than 90% is the third-level threshold range. By analyzing the performance monitoring data, the system evaluates the specific cause of the load abnormality, including factors such as the complexity of the encryption algorithm, the amount of data processed, and the hardware resource status. For different abnormal situations, the security reinforcement layer can select 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, switching the fully homomorphic encryption algorithm (such as CKKS) to a partially homomorphic encryption algorithm (such as Paillier), or adjusting the encryption parameters to reduce the computational complexity. When the system detects obvious deterioration or failure of the hardware resources, the security reinforcement layer will initiate the standby node replacement process. The system selects a node with good performance status from the pre-configured standby node pool and starts the resource preparation program, including loading necessary encryption libraries, synchronizing configuration information, etc. After ensuring that the standby node is ready, perform the node switching operation: first, migrate the data and encryption context to the standby node; then update the task scheduling table to redirect the subsequent encryption tasks to the new node. The entire replacement process maintains business continuity and ensures that data processing will not be interrupted. In some cases, such as when the system load reaches the third-level threshold range, or there are both algorithm performance problems and hardware resource problems at the same time, the security reinforcement layer will perform both algorithm switching and node replacement operations. In this case, while starting the standby node, the system directly loads a lighter encryption algorithm on the new node to achieve double guarantee of performance optimization. For example, when it is detected that the CPU usage rate of a certain node continuously ranges from 92% to 96% and the memory occupancy ranges from 87% to 92%, the system will start both algorithm switching and standby node replacement to quickly reduce the system load.

[0058] Step 40: The connector establishes a connection with the data service layer based on the node list and calculates the value of the target data in the cross-domain request in the data service layer to obtain the data value density.

[0059] Among them, in the embodiments of the present application, the target data refers to the data object to be processed carried in the cross-domain request.

[0060] The cross-domain request also includes a data description of the target data. The data description refers to the feature description of the target data, which contains the key feature data required to support the value evaluation calculation of the target data, including but not limited to the time feature of the target data (such as the time interval when the data is generated), the quality feature 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 feature of the target data (such as the business type, data revenue, and number of invocations), etc.

[0061] 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 according to the time feature in the data description, calculate the integrity of the target data according to the data quality feature, and calculate the business conversion rate of the target data according to the business feature. Then, the timeliness, integrity, and business conversion rate indicators of the target data are weighted and calculated according to the preset weights to obtain the data value density. This calculation method comprehensively evaluates the actual application value of the data.

[0062] Based on the above embodiments, as an alternative embodiment, the step of the connector establishing a connection with the data service layer based on the node list may further include steps 301 - 304: Step 301: The connector uses the QUIC protocol to establish an initial connection with the data service layer.

[0063] Specifically, the connector needs to establish a reliable transmission channel with the data service layer. In the embodiments 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 information such as the identity identifier of the connector and the supported transmission characteristics. Then, the request message is sent to the data service layer through the UDP protocol, and at the same time, a connection timeout timer is started. When the data service layer receives the request, it returns a response message containing connection confirmation information. After the connector verifies the response message, the handshake process of the QUIC protocol is completed, and an initial connection supporting multiplexing is established.

[0064] Step 302: The connector configures the FEC redundancy packet mechanism in the transmission channel of the initial connection.

[0065] Specifically, the connector configures the FEC redundant packet mechanism in the established QUIC transport channel to improve the reliability of data transmission. The connector first sets the FEC encoding parameters, including the original data packet grouping size (such as every 10 data packets as a group), the number of redundant packets (such as generating 2 redundant packets for each group), and the type of encoding algorithm (such as Reed-Solomon encoding). Then, it registers the FEC processing module in the transport layer of the QUIC protocol, and this module is responsible for performing the encoding and recovery operations of the data packets. At the same time, the connector notifies the data service layer of the FEC configuration information to ensure that both parties use the same redundant packet processing mechanism.

[0066] 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 the encoded data packets.

[0067] Specifically, the connector needs to perform FEC redundant encoding processing on the target data of the cross-domain request. The connector first slices the target data according to the preset grouping size, and each slice corresponds to an original data packet. Then, for each group of original data packets, the connector calls the FEC encoding algorithm to generate redundant packets. For example, when using Reed-Solomon(10,12) encoding, every 10 original data packets will generate 2 redundant packets, and these redundant packets contain the check information for data recovery. After encoding, the connector organizes the original data packets and redundant packets into the encoded data packets according to the encoding rules.

[0068] Step 304: When it is detected that an encoded data packet is lost, the connector performs data packet recovery based on the FEC redundant packet mechanism so that the latency of cross-domain transmission does not exceed the preset time threshold.

[0069] Specifically, the connector continuously monitors the transmission status of the data packets during the transmission process. When it detects that a data packet is lost (such as finding that the data packets are discontinuous through the packet sequence number or receiving a packet loss notification from the receiving end), it immediately starts the FEC-based recovery program. The recovery program first determines the group to which the lost data packet belongs, and then uses the remaining data packets and redundant packets in this group to perform decoding operations to reconstruct the content of the lost data packet. Through this fast local recovery mechanism, the additional latency caused by traditional retransmission is avoided, ensuring that the end-to-end latency of cross-domain transmission does not exceed the preset time threshold (such as 200ms).

[0070] Based on the above embodiments, as an alternative embodiment, the step of calculating the data value density for the target data in the cross-domain request in the data service layer may further include steps 401 - 403: Step 401: The data service layer calculates the timeliness and business conversion rate of the target data in the cross-domain request respectively.

[0071] 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: ; where 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, and S is the scarcity factor.

[0072] In the first calculation formula, it consists of two parts. The first part is , which is the time decay term and reflects the value change of the target data over time. Among them, is the freshness weight, indicating the sensitivity of the target data to data timeliness. In the embodiments of the present application can take the value of 0.6. In different business scenarios, it can also be adjusted according to the actual situation. For example, in the high-frequency trading scenario, can take the value of 0.8. e is the natural constant. Using the mathematical characteristics of exponential decay, while ensuring the calculation efficiency, it can also accurately depict the law of non-linear decrease of data value over time. is the decay rate, which is used to control the decay speed of the data value. In the embodiments of the present application takes the value of 0.01, indicating that the data value will decay to 37% of the initial value within about 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 to make the data value decay rapidly within 10 days. is the generation time interval of the target data, which is uniformly calculated in days. When the data update frequency is in minutes or hours, the time unit needs to be converted to days to ensure the consistency of the calculation.

[0073] The second part is , which is the scarcity term and is used to supplement the evaluation of the unique value of the target data. Among them, is the scarcity weight, indicating the data uniqueness value. In the embodiments of the present application takes the value of 0.4, forming a complementary relationship with the freshness weight . When the target data has high uniqueness, such as industry data collected exclusively, the value can be increased to 0.6 to highlight the scarce value of the data. S is the scarcity factor, which is a 0-1 score evaluated by the data provider based on the market supply situation, and it is necessary to ensure the credibility of the score through technical means such as blockchain evidence storage. For example, the proprietary data of a certain sub-industry may obtain a high scarcity score of 0.9, while the general data that can be obtained through public channels may only have a score of 0.2.

[0074] Exemplarily, taking the target data of accounts receivable in supply chain finance as an example, the initial parameters are as follows: , and the data feature is: payment period is 30 days, and the core enterprise credit data S = 0.9. Then, substituting the above initial parameters and data features into the first calculation formula, the timeliness of the target data is obtained: . Different differential decay strategies are adopted for different data types, that is, corresponding different initial parameters can be set according to different data types , for example, the timeliness of real-time transaction data is: , and the timeliness of user portrait data is: .

[0075] As another alternative embodiment, in order to more accurately evaluate the target data with multi-stage timeliness characteristics, the embodiment of the present application also proposes a composite decay model for calculation. The composite decay model is: , where , is the 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 composite decay model, the timeliness calculation is decomposed into three dimensions: short-term timeliness, long-term value, and scarcity. The first exponential decay term is used to evaluate the short-term timeliness of the data, and the second exponential decay term is used to evaluate the long-term preservation ability of the data, term then continues to evaluate the scarcity value of the data. For example, for financial transactions, the short-term sensitivity weight is set to 0.5 to reflect the immediate value of the data; the long-term value weight is set to 0.3 to reflect the historical analysis value of the data; the scarcity weight is set to 0.2 to balance the scarcity factor. The short-term decay rate is set to 0.1, indicating that the data will quickly decay to 37% of the initial value within 10 days; the long-term decay rate is set to 0.001, enabling the data to slowly decay within about 1000 days and maintain its long-term reference value. This composite decay model can accurately depict the decay characteristics of data value on different time scales, such as being able to better serve complex business scenarios that need to consider both immediate value and historical value, such as financial quantitative analysis, user behavior research, etc.

[0076] Further, the data service layer calculates the business conversion rate of the target data according to the second calculation formula, and the second calculation formula is: .

[0077] Wherein, R is the business conversion rate of the target data, E is the number of effective calls of the target data, 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.

[0078] In the second calculation formula, it consists of two parts. The first part is , which is the call efficiency term and reflects the usage effect of the target data in actual applications. E is the number of effective calls of the target data, indicating the number of times the target data generates actual business value after being called. A is the total number of calls of the target data, and the ratio E / A of the two reflects the effective utilization rate of the data. The call efficiency coefficient is used to balance the call characteristics in different application scenarios. In the embodiments of the present application is 0.6, which is mainly set according to business objectives. The second part is , which is the return on investment term and is used to evaluate the return on investment value generated by the target data in business applications. The ROI raw value is calculated by tracking the ratio of the revenue generated after data application to the input cost, and then the ROI raw value is normalized according to the maximum ROI value corresponding to the business, so as to obtain . For example, the data revenue of a certain business is: the bad debt is reduced by 1 million yuan, and the cost is: data procurement of 300,000 yuan + processing fee of 100,000 yuan. Then the ROI raw value is . The maximum ROI value corresponding to this business is 2.0, and the ROI raw value is normalized to obtain .

[0079] Exemplarily, still 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 times (the number of effective calls is 40 times), the score is 0.7 (based on the revenue measurement of supply chain finance), , then substituting the above data into the second calculation formula, the business conversion rate of the target data is obtained: , since the upper limit of the business conversion rate is 1.0, when the business conversion rate exceeds the upper limit, 1.0 is taken.

[0080] Step 402: The data service layer calculates the data coverage and data source diversity of the target data respectively, and performs a weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data.

[0081] Specifically, the data service layer divides the number of fields of the target data by the corresponding theoretical number of fields to obtain the data coverage , and the system can set a standard field template library, which defines a set of necessary fields according to different business scenarios, and the number of fields in this set of necessary fields is the theoretical number of fields. For example, for user portrait data, the necessary fields include but are not limited to basic attribute fields such as user ID, age, gender, and consumption ability; for transaction data, the necessary fields include but are not limited to core business fields such as transaction time, transaction amount, both parties to the transaction, and transaction status. The data service layer maps and compares the number of fields of the target data with the theoretical number of fields in the corresponding scenario to calculate the data coverage.

[0082] Then the data service layer calculates the data source diversity according to the third calculation formula, and the third calculation formula is: ; where is the data source diversity, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0083] This 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 data acquisition channels. represents the proportion of the i-th data source in the target data. For example, for data sources such as banks and logistics, it is calculated by dividing the data volume provided by this data source by the total data volume. This part calculates the information entropy of the data source distribution, and is normalized by dividing by lnn, so that the diversity scores under different numbers of data sources are comparable.

[0084] Finally, a weighted calculation is performed on the data coverage and the data source diversity to obtain the integrity of the target data, and this 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 the data source diversity weight respectively, , in the embodiment of the present application, , it should be noted that the above data coverage weight and data source diversity weight can be adjusted according to the actual situation in different business scenarios.

[0085] Exemplarily, the target data contains 10 fields such as accounts receivable amount, account period, and core enterprise credit rating (theoretically there should be 12 fields), and 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%), Logistics B (5%), , then the data coverage is: , and the data source diversity is: , the integrity of the target data is .

[0086] Step 403: The data service layer performs weighted calculation on timeliness, integrity, and business conversion rate to obtain the data value density.

[0087] 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, the highest weight of 0.4 is given to it; timeliness and integrity, as the basic dimensions to support data quality, are each given a weight of 0.3. Then, weighted calculation is performed on timeliness, integrity, and business conversion rate respectively 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 the actual situation in different business scenarios.

[0088] Exemplarily, the data value density calculation formula is: ; where 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, with a value of 0.3, then the data value density is: .

[0089] As another alternative embodiment, the data service layer can also adopt a differentiated evaluation strategy to calculate the data value density for different role relationship models in the data supply chain to accurately reflect the contributions of all parties involved.

[0090] For the one-to-one relationship model, that is, the scenario where a single data provider directly connects to a single data user, the system directly adopts the data quality parameters of the provider and the application effect score of the user. For example, if an enterprise directly purchases the user portrait data of a third party, the data value density is directly calculated from the timeliness T, integrity C of the data and the business conversion rate R actually obtained by the user, and the calculation method of step 303 can be referred to above, which will not be elaborated here.

[0091] For the one-to-many relationship model, the system uses an aggregation calculation method to evaluate the data value. Taking the data processing scenario as an example, the original data is processed by multiple processors and then used by multiple users. Then, the timeliness adopts the maximum value principle: , the integrity adopts the average value principle: , the business conversion rate adopts the weighted average principle of the number of users: , where is the timeliness of the i-th processor, The integrity contribution of the $i$-th processor is the conversion rate of the $j$-th user, $y$ is the total number of processors, and $m$ is the total number of users.

[0092] For the many-to-many relationship model, the system uses the network value density calculation method 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 network nodes and data flow relationships as connection edges. Then, the importance value of each node is calculated based on the PageRank algorithm or preset business rules. Then, the local value density of each node is calculated, considering the timeliness, integrity, and business conversion rate of the node. Finally, the overall data value density of the network is calculated according to the following formula: ; where $K$ represents the total number of nodes in the network diagram, represents the local value density of the -th node, represents the importance value of the -th node in the network diagram, represents the sum of the importance values of all nodes.

[0093] Please refer to Figure 4 , which is a schematic diagram of the modules of a data value calculation system provided by an embodiment of this application. The data value calculation system is applied to a trusted data space operating system. The data value calculation system includes: A cross-domain request sending module, used for the application service layer to send a cross-domain request to the connector; A target node query module, used for the connector to send a target node query request to the basic component layer after receiving the cross-domain request; A node list generation module, used 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; A data value calculation module, used for the connector to establish a connection with the data service layer based on the node list and calculate the value of the target data in the cross-domain request in the data service layer to obtain the data value density.

[0094] Optionally, a data value calculation system further includes a node monitoring module, used for the security reinforcement layer to call the corresponding homomorphic encryption algorithm to encrypt the target data and monitor the hardware load in the hardware layer in real time; When there is an abnormal node with a hardware load greater than a preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a standby node to replace the abnormal node.

[0095] Optionally, the node list generation module is further configured to query the metadata of each node in the blockchain DHT in the basic component layer, where the metadata includes node load information and security scores; The basic component layer monitors the status information of each node in real time through eBPF probes; 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.

[0096] Optionally, the data value calculation module is further configured to use the QUIC protocol for the connector to establish an initial connection with the data service layer; 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 redundancy packet mechanism to obtain an encoded data packet; When it is detected that the encoded data packet is 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.

[0097] Optionally, the data value calculation module is further configured to calculate the timeliness and business conversion rate of the target data in the cross-domain request by the data service layer 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 the data value density.

[0098] Optionally, the data value calculation module is further configured to calculate the timeliness of the target data in the cross-domain request by the data service layer according to the first calculation formula; The data service layer calculates the business conversion rate of the target data according to the second calculation formula; The first calculation formula is: ; where T is the timeliness of the target data, is the freshness weight, is the attenuation rate, is the generation time interval of the target data, is the scarcity weight, and 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, A is the total number of calls of the target data, is the call efficiency coefficient, and is the return on investment value of the target data.

[0099] 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 by the data service layer to obtain the data coverage; The data service layer calculates the data source diversity according to the third calculation formula; The third calculation formula is: ; Wherein, is the data source diversity, n is the number of data sources in the target data, is the proportion of the i-th data source in the target data.

[0100] It should be noted that: when the system provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is 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 belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0101] The embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded and executed by a processor to perform a data value calculation method such as the above embodiment. The specific execution process can refer to the specific description of the above embodiment and will not be repeated here.

[0102] Please refer to Figure 5 The present application also discloses an electronic device. Figure 5 It is a schematic structural diagram of an electronic device disclosed in the 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.

[0103] Among them, the communication bus 502 is used to realize the connection and communication between these components.

[0104] Among them, the user interface 503 may include a display screen (Display), a camera (Camera), and optionally the user interface 503 may further include a standard wired interface and a wireless interface.

[0105] Among them, the network interface 504 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0106] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts within the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and by calling data stored in the memory 505, it executes various functions of the server and processes data. Optionally, the processor 501 may be implemented in at least one of the hardware forms of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 501 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 501 and may be implemented separately by a single chip.

[0107] Among them, the memory 505 may include random access memory (RAM) and may also include 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, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. The memory 505 may optionally also be at least one storage device located far from the aforementioned processor 501. Refer to Figure 5 , in the memory 505 as a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program of a data value calculation method.

[0108] InFigure 5 In the electronic device 500 shown, the user interface 503 is mainly used to provide an interface for the user to input and obtain the data input by the user; while the processor 501 can be used to call an application program stored in the memory 505 that calculates a data value. When executed by one or more processors 501, the electronic device 500 is caused to execute one or more of the methods described in the foregoing embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences 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 essential to the present application.

[0109] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0111] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0113] When the integrated unit is implemented in the form of 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, in essence, or the part that contributes to the prior art, or all or part of this 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, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0114] The foregoing are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other implementation manners of the present disclosure after considering the specification and the practice of the present disclosure.

[0115] This application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include well-known common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary.

Claims

1. A trusted data space operating system, characterized in that, The trusted data space operating system adopts a cube architecture, and 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.

2. A data value calculation method, characterized in that, Applied to the trusted data space operating system as described in claim 1, 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 filters nodes based on the target node query request to obtain a node list, and sends the node list to the connector; The connector establishes a connection with the data service layer based on the node list, and calculates the value density of the target data in the cross-domain request in the data service layer.

3. The data value calculation method according to claim 2, wherein The method further includes: 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 with the hardware load greater than the preset load threshold, the security reinforcement layer switches the encryption algorithm corresponding to the abnormal node and / or starts a standby node to replace the abnormal node.

4. The data value calculation method according to claim 2, wherein The basic component layer filters nodes based on the target node query request to obtain a node list, including: Querying the metadata of each node in the blockchain DHT in the basic component layer, where the metadata includes node load information and security scores; The basic component layer real-time monitors the status information of each node through eBPF probes; 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.

5. The data value calculation method according to claim 2, wherein The connector establishes a connection with the data service layer based on the node list, including: The connector uses the QUIC protocol to establish an initial connection with the data service layer; The connector configures an FEC redundant 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 it is detected that the encoded data packet is lost, the connector performs data packet recovery based on the FEC redundant packet mechanism so that the latency of cross-domain transmission does not exceed the preset time threshold.

6. The data value calculation method according to claim 2, wherein Calculating the value density of the target data in the cross-domain request in the data service layer, including: 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 a weighted calculation on the data coverage and data source diversity to obtain the integrity of the target data; The data service layer performs a weighted calculation on the timeliness, integrity, and business conversion rate to obtain the data value density.

7. The data value calculation method according to claim 6, wherein 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 the first calculation formula; The data service layer calculates the business conversion rate of the target data according to the second calculation formula; The first calculation formula is as follows: ; where 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, and S is the scarcity factor; The second calculation formula is as follows: ; Among them, R is the business conversion rate of the target data, E is the effective call times of the target data, and A is the total call times of the target data. is the call efficiency coefficient. is the return on investment value of the target data.

8. The data value calculation method according to claim 6, 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 the data coverage; The data service layer calculates the data source diversity according to the third calculation formula; The third calculation formula is as follows: ; Among them, 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.

9. A data value calculation system, characterized in that, Applied to the trusted data space operating system as described in claim 1, the data value calculation system includes: A cross-domain request sending module, configured to send a cross-domain request from the application service layer to the connector; A target node query module, configured to send a target node query request to the basic component layer when the connector receives the cross-domain request; A node list generation module, configured to screen nodes based on the target node query request by the basic component layer to obtain a node list, and send the node list to the connector; A data value calculation module, configured to establish a connection between the connector and the data service layer based on the node list, and calculate the value of the target data in the cross-domain request in the data service layer to obtain the data value density.

10. An electronic device, characterized in that, It includes a processor, a memory, a user interface, and a network interface. 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 2-8.

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