Edge-enabled adaptive resource transaction method and device

Abstract

The embodiment of the invention provides an edge-enabled adaptive resource transaction method and device. The method comprises the steps that a resource transaction system model and a reputation model based on the dynamic edge network are established, and the resource transaction system model comprises a buyer model, a seller model and a market model; establishing a transaction strategy optimization model according to the resource transaction system model and the reputation model; performing reputation evaluation on the buyer and the seller to obtain a reputation evaluation result; monitoring and analyzing the market to obtain a monitoring and analyzing result; generating a contract adjustment strategy according to a reputation evaluation result and a monitoring analysis result, and adjusting a contract according to the contract adjustment strategy; performing optimization solution on the transaction strategy optimization model in combination with the adjusted contract to obtain an optimal transaction strategy; and executing the transaction operation according to the optimal transaction strategy. In this way, efficient, flexible and safe resource scheduling can be achieved in the dynamic edge network environment, meanwhile, the transaction risk is reduced, and the resource utilization rate and the market stability are improved.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of computer network communication and artificial intelligence, and particularly to an edge - empowered adaptive resource trading method and device. Background Art

[0002] With the rapid development of artificial intelligence technology, the global AI market scale has been continuously expanding. This trend has promoted the diversification of resource trading models in edge networks, including traditional spot trading models and emerging futures trading models. However, it should be understood that in the current dynamic edge network environment, resource scheduling and trading face multiple challenges. The traditional spot trading model can achieve a fast response to service trading based on real - time market conditions, but it is difficult to cope with the uncertainty brought by market fluctuations. The futures trading model can lock in future resources through long - term contracts, but it lacks flexibility to respond to sudden demand changes. In addition, the existing mechanisms have deficiencies in credit management and cannot accurately evaluate the performance capabilities of both trading parties, thus increasing trading risks. Summary of the Invention

[0003] In a first aspect, an embodiment of the present invention provides an edge - empowered adaptive resource trading method, which includes:

[0004] Establish a resource trading system model and a credit model based on a dynamic edge network, where the resource trading system model includes a buyer model, a seller model, and a market model;

[0005] Establish a trading strategy optimization model according to the resource trading system model and the credit model;

[0006] Conduct credit evaluations on buyers and sellers to obtain credit evaluation results;

[0007] Monitor and analyze the market to obtain monitoring and analysis results;

[0008] Generate a contract adjustment strategy according to the credit evaluation results and the monitoring and analysis results, and adjust the contract accordingly;

[0009] Optimize and solve the trading strategy optimization model in combination with the adjusted contract to obtain an optimal trading strategy;

[0010] Execute trading operations according to the optimal trading strategy.

[0011] In some realizable ways of the first aspect, the buyer model is represented as long - term buyers and occasional buyers;

[0012] For long - term buyers, there are resource demand forecasts and risk preferences;

[0013] For occasional buyers, there are market responses and price sensitivities.

[0014] In some realizable ways of the first aspect, the seller model has a resource supply function, a cost function, and a service quality index.

[0015] In some realizable ways of the first aspect, the market model has a supply-demand balance model, a price mechanism, and uncertainty factors.

[0016] In some realizable ways of the first aspect, the reputation model has reputation evaluation and reputation update; the objective function of the trading strategy optimization model includes maximizing the expected utility of the buyer and maximizing the expected revenue of the seller; the constraint conditions of the trading strategy optimization model include resource constraints, price constraints, reputation constraints, and contract constraints.

[0017] In some realizable ways of the first aspect, reputation evaluation is performed on the buyer and the seller to obtain a reputation evaluation result, including:

[0018] Obtain the transaction history data and market feedback data corresponding to the buyer and the seller;

[0019] Use a machine learning algorithm to process the transaction history data and market feedback data, calculate the reputation evaluation result, and record the reputation evaluation result in the reputation blockchain.

[0020] In some realizable ways of the first aspect, the market is monitored and analyzed to obtain a monitoring and analysis result, including:

[0021] Monitor the market in real time to obtain market dynamic data;

[0022] Use a time series analysis algorithm or a machine learning algorithm to mine and analyze the market dynamic data to obtain a monitoring and analysis result.

[0023] In some realizable ways of the first aspect, a contract adjustment strategy is generated according to the reputation evaluation result and the monitoring and analysis result, including:

[0024] Formulate a contract adjustment strategy according to the reputation evaluation result and the monitoring and analysis result;

[0025] Use a reinforcement learning algorithm to learn and optimize the contract adjustment strategy to obtain an optimal contract adjustment strategy.

[0026] In some realizable ways of the first aspect, the algorithm used for optimization solving is a genetic algorithm or a particle swarm optimization algorithm.

[0027] In the second aspect, an embodiment of the present invention provides an edge-empowered adaptive resource trading device, and the device includes:

[0028] A modeling module for establishing a resource trading system model and a reputation model based on a dynamic edge network, where the resource trading system model includes a buyer model, a seller model, and a market model;

[0029] The modeling module is further configured to establish a trading strategy optimization model according to the resource trading system model and the reputation model;

[0030] An evaluation module for evaluating the reputation of buyers and sellers to obtain a reputation evaluation result;

[0031] A monitoring module for monitoring and analyzing the market to obtain a monitoring and analysis result;

[0032] An adjustment module for generating a contract adjustment strategy according to the reputation evaluation result and the monitoring and analysis result, and adjusting the contract accordingly;

[0033] A solving module for optimizing and solving the trading strategy optimization model in combination with the adjusted contract to obtain an optimal trading strategy;

[0034] A trading module for executing trading operations according to the optimal trading strategy.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method as described above.

[0036] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method as described above.

[0037] In the embodiments of the present invention, it is possible to achieve efficient, flexible, and secure scheduling of resources in a dynamic edge network environment, while reducing trading risks and improving resource utilization and market stability.

[0038] It should be understood that the content described in the summary of the invention is not intended to limit the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present invention will become more apparent. The drawings are used to better understand the present invention and do not constitute a limitation to the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0040] Figure 1Flowchart of an edge-empowered adaptive resource trading method provided by an embodiment of the present invention;

[0041] Figure 2 Structural diagram of an edge-empowered adaptive resource trading device provided by an embodiment of the present invention;

[0042] Figure 3 Structural diagram of an exemplary electronic device capable of implementing the embodiments of the present invention. Detailed implementation manners

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] In addition, the term "and / or" in the present invention is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the associated objects before and after.

[0045] To solve the technical problems in the background art, embodiments of the present invention provide an edge-empowered adaptive resource trading method, device, equipment, and storage medium. The following will, with reference to the accompanying drawings, describe in detail an edge-empowered adaptive resource trading method, device, equipment, and storage medium provided by embodiments of the present invention through specific embodiments.

[0046] Figure 1 Flowchart of an edge-empowered adaptive resource trading method provided by an embodiment of the present invention, as Figure 1 shown, the adaptive resource trading method 100 may include:

[0047] S110. Establish a resource trading system model and a reputation model based on a dynamic edge network, where the resource trading system model includes a buyer model, a seller model, and a market model.

[0048] S120. Establish a trading strategy optimization model according to the resource trading system model and the reputation model.

[0049] S130. Conduct reputation assessments on buyers and sellers to obtain reputation assessment results.

[0050] S140. Monitor and analyze the market to obtain monitoring and analysis results.

[0051] S150. Generate a contract adjustment strategy based on the credit assessment result and the monitoring and analysis result, and adjust the contract accordingly.

[0052] S160. Optimize and solve the trading strategy optimization model in combination with the adjusted contract to obtain the optimal trading strategy.

[0053] S170. Execute trading operations according to the optimal trading strategy.

[0054] In the embodiment of the present invention, it is possible to achieve efficient, flexible, and secure scheduling of resources in a dynamic edge network environment, while reducing trading risks and improving resource utilization and market stability.

[0055] For the convenience of further understanding, the above content will be described in detail below in combination with specific embodiments:

[0056] (1) Modeling

[0057] (1.1) Resource trading system model

[0058] (1.1.1) Buyer model

[0059] Buyers are allowed to choose to participate in futures trading or spot trading according to their own needs, so as to flexibly respond to market fluctuations. Therefore, the buyer model is subdivided into:

[0060] Long-term buyers (FBs):

[0061] Resource demand prediction: Use time series analysis algorithms (such as ARIMA, LSTM, etc.) or machine learning algorithms (such as random forest, gradient boosting tree, etc.) to predict the resource demand in the future period.

[0062] Risk preference: Set a risk preference coefficient to measure the tolerance of FBs to risks. The risk preference coefficient affects the choice of FBs between futures trading and spot trading.

[0063] Occasional buyers (OBs):

[0064] Market response: OBs decide whether to participate in the transaction and the transaction quantity according to the current market resource price and supply and demand situation.

[0065] Price sensitivity: Set a price sensitivity coefficient to measure the reaction degree of OBs to price changes.

[0066] (1.1.2) Seller model

[0067] Resource supply function: Set a resource supply function to represent the resource supply capacity of the seller in different time periods.

[0068] Cost function: Construct a cost function that includes fixed costs (such as equipment maintenance, rent, etc.) and variable costs (such as power consumption, network fees, etc.).

[0069] Service quality indicators: Set service quality indicators (such as response time, resource availability, failure rate, etc.) to measure the service quality provided by sellers.

[0070] (1.1.3) Market model

[0071] Supply-demand balance model: Construct a supply-demand balance model to describe the change of resource supply-demand relationship over time.

[0072] Price mechanism: Set a price mechanism (such as market clearing price, price elasticity coefficient, etc.) to simulate the dynamic change of resource price.

[0073] Uncertainty factors: Introduce uncertainty factors such as market fluctuations, technical failures, policy changes, etc., and use methods such as Monte Carlo simulation or stochastic processes for modeling.

[0074] (1.2) Reputation model

[0075] Reputation evaluation: Construct a reputation evaluation model, comprehensively consider factors such as transaction history, user reviews, third-party evaluations, etc., and calculate the reputation values of both parties in the transaction.

[0076] Reputation update: Set reputation update rules, such as time decay, adding points for successful transactions, deducting points for defaults, etc., to update the reputation values of both parties in the transaction in real time.

[0077] (1.3) Transaction strategy optimization model (i.e., optimization problem)

[0078] Refinement of the objective function:

[0079] 1) Maximization of the expected utility of buyers:

[0080] Long-term buyers (FBs): Consider factors such as resource demand satisfaction, cost savings, risk reduction, etc., and construct an expected utility function.

[0081] Occasional buyers (OBs): Mainly consider price advantages and resource availability, and construct an expected utility function.

[0082] 2) Maximization of the expected revenue of sellers:

[0083] Consider factors such as resource supply capacity, cost savings, service quality improvement, etc., and construct an expected revenue function.

[0084] Refinement of the constraint conditions:

[0085] 1) Resource constraint: Ensure the balance of resource supply and demand and avoid resource shortages or surpluses.

[0086] 2) Price constraint: Set a price range to prevent market imbalance caused by excessively high or low prices.

[0087] 3) Reputation constraint: Require that the reputation values of both trading parties reach a certain standard to reduce trading risks.

[0088] 4) Contract constraint: Clearly define contract terms, including resource quantity, price, quality, delivery time, etc., to ensure the rights and interests of both trading parties.

[0089] Optimization algorithms:

[0090] 1) Multi-objective optimization: Adopt multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to simultaneously consider the maximization of the expected utility of buyers and the maximization of the expected revenue of sellers.

[0091] 2) Constraint optimization: Adopt constraint optimization algorithms (such as interior point method, sequential quadratic programming, etc.) to solve the optimal solution that satisfies the constraint conditions.

[0092] 3) Stochastic optimization: Consider uncertain factors and adopt stochastic optimization algorithms (such as stochastic gradient descent, stochastic simulation, etc.) to solve the optimal solution.

[0093] (2) Algorithm design

[0094] Algorithm framework and technical details:

[0095] The algorithm framework of the present invention (hereinafter referred to as Oh-Trust) consists of three core parts: an intelligent reputation update part, a dynamic contract adjustment part, and a trading strategy optimization part. Advanced technical details are incorporated into each part to ensure the efficient operation and security of the system.

[0096] (2.1) Intelligent reputation update part:

[0097] (2.1.1) Reputation calculation engine

[0098] Input: Transaction history data corresponding to both trading parties (including transaction time, resource quantity, price, quality, etc.), market feedback data (such as user evaluations, third-party assessments, etc.).

[0099] Processing: Use machine learning algorithms (such as logistic regression, random forest, etc.) to process the input data and calculate the reputation values of both trading parties. The reputation values comprehensively consider multiple dimensions such as the success rate, default rate, and transaction amount of historical transactions.

[0100] Output: Reputation evaluation results such as the reputation values and reputation levels (such as excellent, good, average, poor, etc.) of both trading parties.

[0101] (2.1.2) Reputation blockchain

[0102] Technology: The blockchain technology is adopted to ensure the immutability and traceability of reputation data.

[0103] Structure: Each transaction block contains information such as the IDs of both trading parties, transaction time, resource details, transaction result (success / failure), and reputation value changes.

[0104] Consensus mechanism: Consensus mechanisms such as Proof of Work (PoW) or Proof of Stake (PoS) are adopted to ensure data consistency among various nodes in the blockchain network.

[0105] (2.2) Dynamic contract adjustment part

[0106] (2.2.1) Market monitoring and analysis

[0107] Data source: The market is monitored in real time to obtain market dynamic data, including resource prices, supply and demand changes, market trends, etc.

[0108] Analysis: Methods such as time series analysis algorithms or machine learning algorithms are adopted to deeply mine and analyze the market dynamic data to obtain the monitoring and analysis results.

[0109] Early warning: Thresholds are set. When the market dynamic data exceeds the thresholds, the early warning mechanism is triggered to prompt the need for contract adjustment.

[0110] (2.2.2) Intelligent contract adjustment

[0111] Strategy: According to the reputation evaluation results and monitoring and analysis results, contract adjustment strategies are formulated. The strategies include price adjustment, resource quantity adjustment, contract clause modification, etc.

[0112] Algorithm: Reinforcement learning algorithms (such as Q-learning, Deep Q-Network, etc.) are adopted to train an agent to learn and optimize the contract adjustment strategy.

[0113] Execution: The agent automatically executes the contract adjustment operation according to the optimal contract adjustment strategy obtained through learning and optimization to ensure that the contract terms are synchronized with market changes.

[0114] (2.3) Transaction strategy optimization part

[0115] (2.3.1) Strategy generation and optimization:

[0116] Model: A transaction strategy optimization model is constructed, including an expected utility function, constraint conditions, etc.

[0117] Algorithm: Multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are adopted to solve the optimal transaction strategy.

[0118] Iteration: By continuously iterating and optimizing, gradually approaching the optimal solution to improve the efficiency and effectiveness of the trading strategy.

[0119] (2.3.2) Strategy Execution and Monitoring:

[0120] Execution: According to the generated trading strategy, execute trading operations, including contract signing, resource allocation, etc.

[0121] Monitoring: Real-time monitor the trading process to ensure the smooth progress of the transaction. Once abnormal situations (such as trading failures, resource shortages, etc.) are detected, immediately trigger the emergency handling mechanism.

[0122] In summary, the Oh-Trust proposed by the present invention combines the advantages of futures trading and spot trading. By introducing an intelligent reputation update mechanism, it realizes the efficient, flexible, and secure scheduling of resources. At the same time, it can reduce trading risks and enhance the expected utility of both trading parties and market stability.

[0123] It should be noted that the application scenarios of Oh-Trust are as follows:

[0124] Oh-Trust is applicable to various scenarios that require efficient resource scheduling and trading optimization, such as cloud computing platforms, Internet of Things networks, big data analysis, etc. Especially in edge networks, Oh-Trust can significantly improve resource utilization efficiency, reduce trading costs, and enhance market competitiveness.

[0125] The implementation methods of Oh-Trust are as follows:

[0126] The implementation methods of Oh-Trust can include but are not limited to: software development, system integration, cloud computing services, etc. By combining these implementation methods with the existing network infrastructure, Oh-Trust can be quickly deployed and applied to achieve innovation in resource scheduling and trading models.

[0127] The maintenance and management of Oh-Trust are as follows:

[0128] In order to maintain the efficient operation and continuous optimization of Oh-Trust, corresponding maintenance and management mechanisms need to be established. This includes regularly updating algorithms, monitoring market dynamics, evaluating the reputation values of both trading parties, etc. Through these measures, it can be ensured that Oh-Trust always remains in the best state and provides stable and efficient services for both trading parties.

[0129] 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 invention is not limited by the described action sequence, because according to the present invention, 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 optional embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0130] The above is the introduction to the method embodiments. The following further illustrates the solution of the present invention through device embodiments.

[0131] Figure 2 The following is a structural diagram of an edge-empowered adaptive resource trading device provided by an embodiment of the present invention. As Figure 2 shown, the adaptive resource trading device 200 may include:

[0132] A modeling module 210, configured to establish a resource trading system model and a reputation model based on a dynamic edge network, where the resource trading system model includes a buyer model, a seller model, and a market model.

[0133] The modeling module 210 is further configured to establish a trading strategy optimization model according to the resource trading system model and the reputation model.

[0134] An evaluation module 220, configured to perform reputation evaluation on buyers and sellers to obtain a reputation evaluation result.

[0135] A monitoring module 230, configured to monitor and analyze the market to obtain a monitoring and analysis result.

[0136] An adjustment module 240, configured to generate a contract adjustment strategy according to the reputation evaluation result and the monitoring and analysis result, and adjust the contract accordingly.

[0137] A solving module 250, configured to optimize and solve the trading strategy optimization model in combination with the adjusted contract to obtain an optimal trading strategy.

[0138] A trading module 260, configured to execute trading operations according to the optimal trading strategy.

[0139] It can be understood that Figure 2 each module / unit in the adaptive resource trading device 200 shown has the function of implementing Figure 1 each step in the adaptive resource trading method 100 shown, and can achieve its corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0140] Figure 3It is a structural diagram of an exemplary electronic device capable of implementing the embodiments of the present invention. The electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 300 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed in the present invention.

[0141] As Figure 3 shown, the electronic device 300 may include a computing unit 301, which may perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0142] Multiple components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disc, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0143] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly embodied in a computer-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be executed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0144] The various embodiments described above in the present invention can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] The program code for implementing the methods of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of the present invention, a computer-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the method of the embodiments of the present invention. For the sake of brevity of description, it will not be elaborated herein.

[0148] In addition, the present invention also provides a computer program product, which includes a computer program that implements method 100 when executed by a processor.

[0149] It should be understood that various forms of the processes shown above may be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. The present invention is not limited herein.

[0150] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An edge-enabled adaptive resource trading method, characterized in that: The method comprises: Establish a resource transaction system model and a reputation model based on a dynamic edge network, where the resource transaction system model includes a buyer model, a seller model, and a market model; Establish a trading strategy optimization model based on the resource trading system model and reputation model; Conduct credit evaluation on buyers and sellers and obtain credit evaluation results; Monitor and analyze the market and obtain monitoring and analysis results; Generate contract adjustment strategies based on credit assessment results and monitoring and analysis results, and adjust contracts accordingly; Combine the adjusted contract to optimize the trading strategy optimization model and obtain the optimal trading strategy; Execute trading operations according to the optimal trading strategy.

2. The method according to claim 1, characterized in that The buyer model described is represented as long-term buyers, occasional buyers; For long-term buyers, there are resource demand forecasts and risk appetite; For occasional buyers, there is market responsiveness and price sensitivity.

3. The method according to claim 1, characterized in that The seller model includes resource supply function, cost function and service quality index.

4. The method according to claim 1, characterized in that: The market model includes a supply and demand balance model, a price mechanism, and uncertainty factors.

5. The method according to claim 1, characterized in that The reputation model includes reputation evaluation and reputation update; the objective function of the transaction strategy optimization model includes maximizing the buyer's expected utility and maximizing the seller's expected profit; the constraints of the transaction strategy optimization model include resource constraints, price constraints, reputation constraints, and contract constraints.

6. The method according to claim 1, characterized in that The credit evaluation of the buyer and the seller is performed to obtain the credit evaluation results, including: Obtain transaction history data and market feedback data corresponding to buyers and sellers; Machine learning algorithms are used to process transaction history data and market feedback data, calculate reputation evaluation results, and record the reputation evaluation results in the reputation blockchain.

7. The method according to claim 1, characterized in that The monitoring and analysis of the market to obtain the monitoring and analysis results includes: Monitor the market in real time and obtain market dynamic data; Use time series analysis algorithms or machine learning algorithms to mine and analyze market dynamic data to obtain monitoring and analysis results.

8. The method according to claim 1, characterized in that The generating of the contract adjustment strategy according to the credit evaluation results and the monitoring and analysis results includes: Formulate contract adjustment strategies based on credit assessment results and monitoring and analysis results; The reinforcement learning algorithm is used to learn and optimize the contract adjustment strategy to obtain the optimal contract adjustment strategy.

9. The method according to claim 1, characterized in that: The algorithm used for the optimization solution is a genetic algorithm or a particle swarm optimization algorithm.

10. An edge-enabled adaptive resource trading device, characterized in that: The device comprises: A modeling module is used to establish a resource transaction system model and a reputation model based on a dynamic edge network, wherein the resource transaction system model includes a buyer model, a seller model, and a market model; The modeling module is also used to establish a transaction strategy optimization model based on the resource transaction system model and the reputation model; The evaluation module is used to evaluate the reputation of buyers and sellers and obtain the reputation evaluation results; Monitoring module, used to monitor and analyze the market and obtain monitoring and analysis results; The adjustment module is used to generate a contract adjustment strategy based on the credit evaluation results and monitoring and analysis results, and adjust the contract accordingly; The solution module is used to optimize and solve the trading strategy optimization model in combination with the adjusted contract to obtain the optimal trading strategy; The trading module is used to perform trading operations according to the optimal trading strategy.

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