Multi-source trusted data access method, system, electronic device and storage medium

Abstract

The present invention provides a multi-source trusted data access method, system, electronic device and storage medium, firstly establishing a system model for processing raw data from multiple data sources; obtaining the weight of each data source; calculating the comprehensive data according to the weight and raw data of each data source; determining the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data; determining the resource scheduling strategy for the comprehensive data according to the computing resource allocation and storage resource allocation; accessing the comprehensive data according to the resource scheduling strategy. The present invention can improve the accuracy and reliability of multi-source trusted access data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a multi-source trusted data access method, system, electronic device and storage medium. Background Art

[0002] Nowadays, in scenarios such as satellite navigation and intelligent transportation, the average of data transmitted from multiple data sources is generally used as accessible data. Data from different data sources are different and their reliability is also different. For example, data source A has low equipment performance and a harsh environment, resulting in low data reliability; while data source B has high equipment performance and a mild environment, resulting in high data reliability. Simply taking the average value will reduce the reliability of the final data due to the influence of the low-reliability data source.

[0003] Therefore, the existing multi-source data access method does not take into account the differences in the reliability of data sources, resulting in reduced accuracy and reliability of accessible data, thereby affecting subsequent business processing. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a multi-source trusted data access method, system, electronic device and non-transitory computer-readable storage medium capable of improving the accuracy and reliability of multi-source accessible data.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a multi-source trusted data access method, the method comprising:

[0007] Build a system model for processing raw data from multiple data sources;

[0008] Obtaining a weight of each of the data sources;

[0009] Calculating the composite data based on the weights of each of the data sources and the raw data;

[0010] Determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data;

[0011] Determining a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation;

[0012] The comprehensive data is accessed according to the resource scheduling strategy.

[0013] The present invention also provides a multi-source trusted data access system, which is used to execute the multi-source trusted data access method as described above, and the system comprises:

[0014] A model building module, used for building a system model for processing raw data from multiple data sources;

[0015] A weight acquisition module, used to acquire the weight of each of the data sources;

[0016] A comprehensive data module, used for calculating comprehensive data according to the weight and original data of each of the data sources;

[0017] A resource allocation module, used to determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data;

[0018] A strategy determination module, used to determine a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation;

[0019] A data access module is used to access the comprehensive data according to the resource scheduling strategy.

[0020] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; a processor for reading and executing the computer software programs, thereby implementing a multi-source trusted data access method as described above.

[0021] In addition, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a multi-source trusted data access method as described above is implemented.

[0022] The beneficial technical effects of the present invention are:

[0023] (1) The present invention uses a weighted average method to integrate data from different data sources, taking into account multiple factors such as the reliability of the data source, transmission power, and data volume, making the final data more accurate and reliable;

[0024] (2) The present invention ensures the reasonable allocation of computing resources among different edge nodes by considering the transmission frequency, reliability and data volume of the data source, which helps to improve the efficiency of edge computing, reduce latency and meet the requirements of applications with high computing demands;

[0025] (3) The present invention ensures the reasonable allocation of storage resources among different edge nodes by considering the data volume, reliability and data size of the data source, which helps to optimize the utilization of storage resources and meet the needs of large data volume applications;

[0026] (4) The present invention optimizes the utilization of system resources, reduces resource waste, and improves the overall performance of the system by reasonably allocating computing and storage resources, ensuring that each edge node maximizes the use of available resources while meeting its needs.

[0027] In summary, the present invention proposes an improved multi-source trusted data access method, system, electronic device and storage medium by comprehensively considering multiple factors such as the reliability of the data source, transmission power, data volume, etc. This method has significant advantages in improving data accuracy and reliability, reasonably allocating computing and storage resources, supporting 6G network requirements, and improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 It is a scenario diagram of a multi-source trusted data access method provided by an embodiment of the present invention;

[0030] Figure 2 is a flow chart of a multi-source trusted data access method provided by an embodiment of the present invention;

[0031] Figure 3 It is a structural diagram of a multi-source trusted data access system provided by an embodiment of the present invention;

[0032] Figure 4 is a schematic diagram of a possible hardware structure of an electronic device provided by an embodiment of the present invention;

[0033] Figure 5 It is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by an embodiment of the present invention.

[0034] Explanation of the accompanying drawings: 301 - model building module, 302 - weight acquisition module, 303 - integrated data module, 304 - resource allocation module, 305 - strategy determination module, 306 - data access module, 400 - electronic device, 410 - memory, 411 - computer program, 420 - processor, 500 - computer readable storage medium. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0037] Figure 1 A scenario diagram of a multi-source trusted data access method provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0038] It should be noted that Figure 1 The scenario diagram of a multi-source trusted data access method shown is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0039] Figure 2 A flowchart of a multi-source trusted data access method provided by the present invention includes the following steps:

[0040] Step 201: Establish a system model for processing raw data from multiple data sources.

[0041] In some embodiments, the system model may include data sources, edge computing nodes, and a central controller.

[0042] In some embodiments, the data source may include multiple devices, such as low altitude platform (LAP), high altitude platform (HAP), low earth orbit (LEO) satellite and ground platform. Each data source provides raw data, reliability, transmission power and data volume.

[0043] In some embodiments, edge computing nodes can be deployed at the edge of the network close to the data source for processing and storing data. Each node has computing resources and storage resources.

[0044] The central controller is responsible for collecting data source information, calculating weights and allocating resources. Formulas are used for data processing and resource allocation to ensure efficient operation of the system.

[0045] Step 202: Obtain the weight of each of the data sources.

[0046] In some embodiments, step 202 may include:

[0047] Obtaining the reliability and data transmission power of each of the data sources;

[0048] Obtaining a size adjustment coefficient for each of the original data;

[0049] Obtaining a power adjustment coefficient for each of the data transmission powers;

[0050] The weight of each of the data sources is determined according to the reliability and data transmission power of each of the data sources, as well as the size adjustment coefficient and the power adjustment coefficient.

[0051] In some embodiments, the weight of each of the data sources is expressed as:

[0052]

[0053] in, is the original data size, is the reliability of each data source, is the data transmission power of each data source, is the power regulation factor, is the data size adjustment coefficient, N is the total number of data sources, i and j are the serial numbers of the data sources, indicating the i-th data source and the j-th data source.

[0054] In the specific implementation, is the power adjustment coefficient, which is used to adjust the influence of transmission power on weight. The higher the adjustment coefficient, the greater the influence of transmission power on weight.

[0055] It is the data size adjustment coefficient, which is used to adjust the influence of data size on weight. The higher the adjustment coefficient, the greater the influence of data size on weight.

[0056] In the specific implementation, It is a combination of the credibility of the data source, the data transmission intensity of the data source and the original size of the data. The higher the reliability, the greater the weight. The higher the value, the more stable the data transmission is, and the coefficient is adjusted exponentially. Increase its impact. Data size The larger the value, the exponential adjustment coefficient Enhance its impact.

[0057] It is a weighted summation of the reliability, transmission power and data size of all data sources, which is used to normalize the weights to ensure that the sum of the weights of all data sources is 1.

[0058] The present invention flexibly adjusts the influence of each data source in the comprehensive data by considering the reliability, transmission power and data size of the data source, so that the final weighted average data 𝐷 is more reliable and accurate.

[0059] Step 203: Calculate comprehensive data based on the weight of each data source and the original data.

[0060] In some embodiments, step 203 may include:

[0061] Obtain the reliability, data transmission power, and maximum transmission power of each data source;

[0062] obtaining a noise factor for adjusting the reliability;

[0063] Acquiring a nonlinear adjustment coefficient for adjusting the data transmission power;

[0064] The comprehensive data is determined according to the weight, original data, reliability, data transmission power, maximum transmission power, noise factor, and nonlinear adjustment coefficient of each data source.

[0065] In some embodiments, the aggregate data is represented as:

[0066] in, is the original data size, It is comprehensive data. is the weight of the data source, is the reliability of the data source, is the data transmission power of the data source, is the nonlinear adjustment coefficient, is the noise factor, is the maximum transmission power, N is the total number of data sources, and i is the number of the data source.

[0067] It is comprehensive data, which is calculated by weighted average of the data from all data sources. It combines the weight, reliability and transmission power of each data source to make the final result more accurate and reliable.

[0068] is the weight of the data source, which reflects the influence of each data source in the final weighted average result. The weight is calculated based on the reliability, transmission power and data size of the data source;

[0069] is the reliability of the data source, ranging from 0 to 1, with higher Indicates that the data source is more reliable;

[0070] The data transmission power of each data source is used to indicate the strength and stability of data transmission.

[0071] is a nonlinear adjustment coefficient used to enhance the influence of high-power data sources by Perform nonlinear adjustment to increase the weight of high transmission power data source in weighted average;

[0072] is the noise factor, which is used to avoid mathematical errors caused by zero data values ​​and ensure that the formula can be calculated normally in all cases;

[0073] is the maximum transmit power and is used to normalize the transmit power to ensure that the impact of transmit power is appropriate in the calculation.

[0074] In the specific implementation, Represents the weighted sum of data from all data sources, taking into account the impact of their respective weights, reliability, and transmission power. Used for normalization to ensure that the calculated comprehensive data D is a weighted average rather than a simple weighted sum.

[0075] In the above manner, the present invention can effectively integrate data from multiple data sources, ensure that the final comprehensive data D has higher accuracy and reliability, and overcome the accuracy and reliability problems caused by the low reliability data source of the simple averaging method.

[0076] Step 204: Determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data.

[0077] In some embodiments, step 204 may include:

[0078] Obtaining the transmission frequency of each of the data sources;

[0079] Obtaining the frequency at which each of the data sources transmits to the edge node;

[0080] Obtaining total computing resources of the system model;

[0081] The computing resource allocation of the edge computing node to the comprehensive data is determined based on the transmission frequency of each of the data sources, the frequency of transmission to the edge node, and the total computing resources of the system model.

[0082] In some embodiments, the computing resource allocation is represented as:

[0083] in, is the computational resource allocation, is the transmission frequency of the data source, is the frequency of data source transmission to edge nodes, is the total computing resources of the system, N is the total number of data sources, and M is the total number of edge nodes.

[0084] In the specific implementation, is the computing resource allocation, which is the amount of computing resources allocated to edge node k. Computing resources are reasonably allocated according to the contribution and needs of each data source;

[0085] is the transmission frequency of the data source, indicating the communication frequency between the data source and the edge node;

[0086] It is the frequency of data source transmission to edge nodes, indicating the communication frequency between each data source and each edge node;

[0087] is the total computing resource of the system, which is the sum of the computing resources of each device in the model, that is, it needs to be reasonably allocated to each edge node k;

[0088] In the specific implementation, The computing resource requirements of each data source on edge node k are determined by the following factors:

[0089] Transmission frequency : Indicates the communication frequency of the data source. The higher the frequency, the more frequent the data transmission between the data source and the edge node, and the more computing resources are required.

[0090] reliability : Indicates the credibility of the data source. The higher the reliability, the higher the demand for the data source.

[0091] Data volume adjustment: through Adjust the impact of data volume to ensure that data sources with high data volume have a larger share of resource allocation.

[0092] It is a weighted sum of the communication frequency, reliability, and data volume between all data sources and all edge nodes j, which is used to normalize the resource allocation ratio.

[0093] Through the above method, the present invention can effectively allocate the computing resources of the system to each edge node, ensure that the needs of each data source are met, and improve the overall performance and reliability of the system.

[0094] In some embodiments, step 204 may further include:

[0095] Obtaining the data volume of each of the data sources;

[0096] Obtaining the amount of data transmitted from each data source to the edge computing node;

[0097] Obtaining a maximum amount of data transmitted from each data source to an edge computing node;

[0098] Get the total storage resources of the system model.

[0099] In some embodiments, storage resource allocation is represented as:

[0100]

[0101] in, is the storage resource allocation, is the amount of data from the data source, is the amount of data transmitted from the data source to the edge computing node. is the largest amount of data, It is the total storage resource of the system.

[0102] It is the allocation of storage resources, which reasonably allocates storage resources according to the contribution and needs of each data source;

[0103] is the amount of data from the data source;

[0104] It is the amount of data transmitted from the data source to the edge computing node, indicating the amount of data transmitted between each data source and each edge computing node;

[0105] It is the maximum data volume, used to normalize the data volume to ensure that the impact of data size is appropriate in the calculation;

[0106] It is the total storage resource of the system, which needs to be reasonably allocated to each edge computing node k.

[0107] In the specific implementation, The storage resource requirements for edge computing node k are determined by the following factors:

[0108] Data volume : Indicates the total amount of data provided by the data source. The larger the amount of data, the greater the demand for storage resources by the data source.

[0109] reliability : Indicates the credibility of the data source. The higher the reliability, the higher the demand for the data source.

[0110] Data volume adjustment: through Adjust the impact of data volume to ensure that data sources with high data volume have a larger share of resource allocation.

[0111] It is a weighted sum of the amount of data transmitted, reliability, and data size between all data sources and all edge computing nodes j, which is used to normalize the resource allocation ratio.

[0112] In some embodiments, the maximum amount of data can be determined by factors such as bandwidth limitations, device capabilities, data transmission protocols, transmission time, and network conditions. For example, network bandwidth is an important factor affecting the amount of data transmitted, and the maximum amount of data is usually limited by network bandwidth. The processing power and storage capacity of the data source and edge node will affect the maximum amount of data that can be transmitted. For example, the CPU, memory, and storage capacity of the device will affect its ability to process and transmit data. Different data transmission protocols (such as TCP / IP, UDP, etc.) will have different efficiencies and maximum transmission capabilities, which will also affect the maximum amount of data. In a specific time period, the maximum amount of data that can be transmitted can be estimated by the product of time and bandwidth. Factors such as network delay, packet loss rate, and interference may also affect the actual transmission efficiency, thereby affecting the determination of the maximum amount of data.

[0113] It should be noted that if represents the total storage resource allocation during a specific time period, then Refers to the cumulative amount of all data within this period of time. It refers to the average storage resource allocation during the time period, so it may represent the average amount of data per time unit (such as per second, per minute, etc.) during the time period.

[0114] Through the above method, the present invention can effectively allocate the system's storage resources to each edge computing node, ensure that the needs of each data source are met, and improve the overall performance and reliability of the system.

[0115] Step 205: Determine a resource scheduling strategy for the comprehensive data based on the computing resource allocation and the storage resource allocation.

[0116] In some embodiments, the resource scheduling strategy may include calculating a resource allocation strategy. For each data source i, its transmission frequency may be calculated. and transmit frequency to edge nodes . Calculate the computing resource requirements for each data source i ,in is the reliability of the data source, is the original data size.

[0117] In some embodiments, computing resources can be dynamically allocated according to the demand of each data source, and the allocation of computing resources can be adjusted according to real-time demand to ensure system operation efficiency and performance optimization.

[0118] In some embodiments, the storage resource allocation strategy can calculate the data volume of each data source i. and the amount of data transmitted to edge computing nodes .

[0119] Calculate the storage resource requirements for each data source i ,in is the reliability of the data source, It is the amount of data.

[0120] In some embodiments, storage resources are dynamically allocated based on the demand of each data source, and the normalization factor in the formula is used to ensure that the overall storage resources are reasonably allocated to each edge computing node. The usage of storage resources and the storage demand of data sources can be monitored. The storage resource allocation is adjusted according to real-time demand to improve data access efficiency and the overall performance of the system.

[0121] It can be understood that the above two resource allocation strategies can be implemented in combination to ensure efficient processing and management of integrated data: dynamically allocate computing and storage resources according to the real-time needs of data sources and the actual use of system resources to maximize system performance and resource utilization. Use advanced algorithms and real-time data analysis to make reasonable resource allocation decisions to cope with the changing needs and priorities of different data sources. Through reasonable resource allocation strategies, the processing of integrated data can not only reduce the negative impact of low-reliability data sources, but also improve the overall accuracy and reliability of the data.

[0122] In summary, the above strategies are not only applicable to current data processing needs, but can also effectively cope with more complex and diverse data processing scenarios in the future, such as the efficient processing and resource management required in 6G networks and SAGIN.

[0123] Step 206: Access the comprehensive data according to the resource scheduling strategy.

[0124] In some embodiments, by dynamically allocating computing resources, the system can allocate appropriate computing resources to edge computing nodes that process comprehensive data based on the demand, reliability, and transmission frequency of each data source. This ensures timely processing and response of data and improves the system's ability to respond to real-time data processing needs.

[0125] It can be understood that the resource scheduling strategy ensures the efficient use of computing resources when processing comprehensive data. Through reasonable allocation, it ensures that each computing node can balance processing tasks under different load conditions, avoiding resource waste and overload.

[0126] In some embodiments, the system can effectively manage the storage requirements of data according to the storage resource allocation policy, which includes storing the comprehensive data on the appropriate storage nodes and dynamically adjusting the storage space according to the requirements to ensure the security and accessibility of the data.

[0127] By optimizing the use of storage resources, the system can handle the archiving and management needs of large amounts of data. This includes archiving data to appropriate storage media or nodes based on the storage requirements of the data source for long-term preservation and subsequent access.

[0128] The resource scheduling strategy ensures real-time access and processing capabilities for comprehensive data. By optimizing the allocation of computing and storage resources, the system can respond to data source input in a timely manner and ensure the immediate processing and analysis of data.

[0129] By properly allocating computing and storage resources, resource scheduling strategies can not only optimize data processing speed, but also improve the accuracy and reliability of data processing. This is done by avoiding the negative impact of low-reliability data sources in the simple averaging method, thereby ensuring the quality and validity of the comprehensive data.

[0130] The resource scheduling strategy can adapt to diverse data processing scenarios, including processing large-scale data, heterogeneous requirements of different data sources, and the development of future network and computing technologies. This enables the system to flexibly respond to changes in emerging technologies and data processing requirements, such as the application scenarios of 6G networks and SAGIN.

[0131] In summary, the resource scheduling strategy based on the present invention not only improves the processing efficiency and quality of comprehensive data, but also enhances the adaptability and reliability of the system to complex data processing environments, providing a solid foundation for various data applications and services.

[0132] Figure 3 A schematic diagram of the structure of a multi-source trusted data access system provided by the present invention.

[0133] like Figure 3As shown, a multi-source trusted data access system proposed in an embodiment of the present invention includes:

[0134] A model building module 301 is used to build a system model for processing raw data from multiple data sources;

[0135] A weight acquisition module 302, used to acquire the weight of each of the data sources;

[0136] A comprehensive data module 303, used to calculate comprehensive data according to the weight and original data of each of the data sources;

[0137] A resource allocation module 304, used to determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data;

[0138] A strategy determination module 305, configured to determine a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation;

[0139] The data access module 306 is used to access the comprehensive data according to the resource scheduling strategy.

[0140] See also Figure 4 , Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0141] Build a system model for processing raw data from multiple data sources;

[0142] Obtaining a weight of each of the data sources;

[0143] Calculating the composite data based on the weights of each of the data sources and the raw data;

[0144] Determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data;

[0145] Determining a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation;

[0146] The comprehensive data is accessed according to the resource scheduling strategy.

[0147] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by the processor 420, the following steps are implemented:

[0148] Build a system model for processing raw data from multiple data sources;

[0149] Obtaining a weight of each of the data sources;

[0150] Calculating the composite data based on the weights of each of the data sources and the raw data;

[0151] Determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data;

[0152] Determining a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation;

[0153] The comprehensive data is accessed according to the resource scheduling strategy.

[0154] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0157] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-source trusted data access method, characterized in that: The method comprises: Establishing a system model for processing raw data from multiple data sources, the model including data sources, edge computing nodes, and a central controller; Obtaining the weight of each of the data sources includes: obtaining the reliability and data transmission power of each of the data sources; obtaining the size adjustment coefficient of each of the original data; obtaining the power adjustment coefficient of each of the data transmission powers; determining the weight of each of the data sources according to the reliability and data transmission power of each of the data sources, as well as the size adjustment coefficient and the power adjustment coefficient; the weight of each of the data sources is expressed as: Among them, d i is the original data size, R i is the reliability of each data source, P i is the data transmission power of each data source, α is the power adjustment coefficient, γ is the data size adjustment coefficient, N is the total number of data sources, i and j are the serial numbers of the data sources, indicating the i-th data source and the j-th data source; Calculating the composite data based on the weights of each of the data sources and the raw data; Determine the computing resource allocation and storage resource allocation of the edge computing node to the comprehensive data; Determining a resource scheduling strategy for the comprehensive data according to the computing resource allocation and the storage resource allocation; The comprehensive data is accessed according to the resource scheduling strategy.

2. The method according to claim 1, characterized in that: The step of calculating the comprehensive data according to the weight and the original data of each of the data sources comprises: Obtain the reliability, data transmission power, and maximum transmission power of each data source; obtaining a noise factor for adjusting the reliability; Acquiring a nonlinear adjustment coefficient for adjusting the data transmission power; The comprehensive data is determined according to the weight, original data, reliability, data transmission power, maximum transmission power, noise factor, and nonlinear adjustment coefficient of each data source.

3. The method according to claim 2, characterized in that The comprehensive data is expressed as: Among them, d i is the original data size, D is the comprehensive data, ω i is the weight of the data source, R i is the reliability of the data source, P i is the data transmission power of the data source, β is the nonlinear adjustment coefficient, σ is the noise factor, P max is the maximum transmission power, N is the total number of data sources, and i is the number of the data source.

4. The method according to claim 3, characterized in that The determining the allocation of computing resources of the edge computing node to the comprehensive data includes: Obtaining the transmission frequency of each of the data sources; Obtaining the frequency at which each of the data sources transmits to the edge node; Obtaining total computing resources of the system model; The computing resource allocation of the edge computing node to the comprehensive data is determined according to the transmission frequency of each of the data sources, the frequency of transmission to the edge node, and the total computing resources of the system model.

5. The method according to claim 4, characterized in that The computing resource allocation is expressed as: Among them, C k is the computing resource allocation, f i is the transmission frequency of the data source, f ij is the frequency of data source transmission to edge nodes, C total is the total computing resources of the system, N is the total number of data sources, and M is the total number of edge nodes.

6. The method according to claim 5, characterized in that The determining of the edge computing node's allocation of storage resources to the comprehensive data includes: Obtaining the data volume of each of the data sources; Obtaining the amount of data transmitted from each data source to the edge computing node; Obtaining a maximum amount of data transmitted from each data source to an edge computing node; Get the total storage resources of the system model.

7. The method according to claim 6, characterized in that The storage resource allocation is expressed as: Among them, S k is the storage resource allocation, v i is the amount of data from the data source, v ij is the amount of data transmitted from the data source to the edge computing node, d max is the maximum data volume, S total It is the total storage resource of the system.

8. A multi-source trusted data access system, characterized in that: The system comprises a model building module, a weight acquisition module, a comprehensive data module, a resource allocation module, a strategy determination module and a data access module, and is used to execute the multi-source trusted data access method as claimed in claim 1.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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