CN-121981823-A - Solution prediction method, device and system based on trade measures

CN121981823ACN 121981823 ACN121981823 ACN 121981823ACN-121981823-A

Abstract

The application provides a solution prediction method, device and system based on trade measures, which comprises the steps of obtaining input parameters of a target intelligent agent from a pre-established dynamic information base according to trade measure information, generating preliminary decision information of the target intelligent agent according to the input parameters and trade measure information, generating a descriptive strategy of the target intelligent agent according to the input parameters, trade measure information, preliminary decision information, associated preliminary decision information of the associated target intelligent agent and iteration optimization parameter values, determining quantization parameters corresponding to key factors in the descriptive strategy in an economics model, solving the economics model according to the quantization parameters and the iteration optimization parameter values to obtain quantization results corresponding to the descriptive strategy, updating the iteration optimization parameter values according to the quantization results, returning to regenerate the descriptive strategy and the quantization results based on the updated iteration optimization parameter values until preset iteration end conditions are met, and generating a solution.

Inventors

HUANG PING
WEI XIAOHUI
JIANG CHENGJIN
WANG CHONGLEI
LI RONGSEN

Assignees

中电科新型智慧城市研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20251219

Claims (10)

1. A method of solution prediction based on trade measures, the method comprising: Acquiring input parameters of a target intelligent agent from a pre-established dynamic information base according to trade measure information, wherein the target intelligent agent is used for making a preliminary decision on a target entity influenced by the trade measure information; generating preliminary decision information of the target agent according to the input parameters and the trade measure information in the target agent; In a decision model, generating a descriptive strategy of a target agent according to the input parameters, the trade measure information, the preliminary decision information, the associated preliminary decision information of the associated target agent and the iterative optimization parameter value, wherein the associated target agent has an interactive relationship with the target agent; determining quantization parameters corresponding to key factors in the descriptive strategy in an economic model, and solving the economic model according to the quantization parameters and the iterative optimization parameter values to obtain a quantization result corresponding to the descriptive strategy; updating the iteration optimization parameter value according to the quantized result, returning to regenerate the descriptive strategy and the quantized result of the target intelligent agent based on the updated iteration optimization parameter value until a preset iteration ending condition is met, and determining the solution of the target intelligent agent according to the descriptive strategy and the quantized result.
2. The method of claim 1, wherein updating the iterative optimization parameter values based on the quantization results comprises: Calculating a decision benefit value of the target agent according to the quantization result; Updating the decision parameter correction value according to the decision benefit value, wherein the iterative optimization parameter value comprises the decision parameter correction value, or updating the decision parameter correction value according to the decision benefit value and updating the quantization parameter correction value according to the quantization result, and the iterative optimization parameter value comprises the decision parameter correction value and the quantization parameter correction value.
3. The method of claim 2, wherein updating the decision parameter modifier value based on the decision benefit value comprises: And if the decision benefit value is smaller than the expected benefit value, determining the decision parameter correction value according to the difference value between the decision benefit value and the expected benefit value.
4. The method of claim 2, wherein updating the quantization parameter correction based on the quantization result comprises: And if the error between the quantized result and the real result is larger than a first preset threshold value, determining the quantized parameter correction value according to the error.
5. The method according to any one of claims 2 to 4, wherein the iteration end condition comprises: The error between the quantized result and the real result is smaller than or equal to a first preset threshold value, and the fluctuation range of the decision gain value is smaller than or equal to a third preset threshold value; or the iteration times reach a set value, and the error between the quantized result and the real result is smaller than or equal to a second preset threshold value.
6. The method according to any one of claims 2 to 4, wherein calculating a decision benefit value of the target agent from the quantified result comprises: and calculating a decision benefit value of the target intelligent agent according to the quantized result, the associated preliminary decision information of the associated target intelligent agent and the resource constraint cost of the target intelligent agent.
7. The method according to claim 1, characterized in that the method further comprises: Acquiring data to be updated from at least one of a pre-constructed knowledge graph library, a predicted event library, an open source information library and an event information library; extracting characteristic information under different modes according to the data to be updated; determining fusion weight of the characteristic information according to the instantaneity, the credibility and the correlation of the characteristic information; According to the fusion weight corresponding to each piece of characteristic information, fusing the characteristic information under different modes to obtain fusion characteristics; The dynamic information base is updated based on the fusion characteristics to update the input parameters.
8. A solution prediction apparatus based on trade measures, comprising: The acquisition unit is used for acquiring input parameters of a target intelligent agent from a pre-established dynamic information base according to trade measure information, wherein the target intelligent agent is used for making a preliminary decision on a target entity influenced by the trade measure information; The first decision unit is used for generating preliminary decision information of the target intelligent agent according to the input parameters and the trade measure information in the target intelligent agent; The second decision unit is used for generating a descriptive strategy of the target intelligent agent according to the input parameters, the trade measure information, the preliminary decision information, the associated preliminary decision information of the associated target intelligent agent and the iterative optimization parameter value in a decision model, wherein the associated target intelligent agent has an interaction relation with the target intelligent agent; the quantization unit is used for determining quantization parameters corresponding to key factors in the descriptive strategy in an economic model, and solving the economic model according to the quantization parameters and the iterative optimization parameter values to obtain a quantization result corresponding to the descriptive strategy; And the iteration unit is used for updating the iteration optimization parameter value according to the quantization result, returning and regenerating the descriptive strategy and the quantization result of the target intelligent agent based on the updated iteration optimization parameter value until a preset iteration ending condition is met, and determining the solution of the target intelligent agent according to the descriptive strategy and the quantization result.
9. A solution prediction system based on trade measures, characterized by comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.

Description

Solution prediction method, device and system based on trade measures Technical Field The application belongs to the technical field of computers, and particularly relates to a solution prediction method, device and system based on trade measures. Background With the development of globalization of trade, trade measures directly affect import and export costs, and have great influence on economic development of enterprises, individuals and regions. When trade measures change, enterprises or individuals often make some adjustments to reduce losses caused by the trade measure changes, and the earlier the time the enterprise or individual should deal with, the less the impact caused by the trade measure changes. With the development of artificial intelligence technology, there are emerging related technologies for predicting the influence of trade measures based on artificial intelligence analysis and generating solutions for the influence of trade measures. However, in the related art, verification of the prediction result is lacking, and thus, the generated solution is low in accuracy. Disclosure of Invention The embodiment of the application provides a solution prediction method, device and system based on trade measures, which can improve the prediction accuracy of the solution under the influence of the trade measures. In a first aspect, an embodiment of the present application provides a solution prediction method based on trade measures, including: Acquiring input parameters of a target intelligent agent from a pre-established dynamic information base according to trade measure information, wherein the target intelligent agent is used for making a preliminary decision on a target entity influenced by the trade measure information; generating preliminary decision information of the target agent according to the input parameters and the trade measure information in the target agent; In a decision model, generating a descriptive strategy of a target agent according to the input parameters, the trade measure information, the preliminary decision information, the associated preliminary decision information of the associated target agent and the iterative optimization parameter value, wherein the associated target agent has an interactive relationship with the target agent; determining quantization parameters corresponding to key factors in the descriptive strategy in an economic model, and solving the economic model according to the quantization parameters and the iterative optimization parameter values to obtain a quantization result corresponding to the descriptive strategy; updating the iteration optimization parameter value according to the quantized result, returning to regenerate the descriptive strategy and the quantized result of the target intelligent agent based on the updated iteration optimization parameter value until a preset iteration ending condition is met, and determining the solution of the target intelligent agent according to the descriptive strategy and the quantized result. In a second aspect, an embodiment of the present application provides a solution prediction apparatus based on trade measures, including: The acquisition unit is used for acquiring input parameters of a target intelligent agent from a pre-established dynamic information base according to trade measure information, wherein the target intelligent agent is used for making a preliminary decision on a target entity influenced by the trade measure information; The first decision unit is used for generating preliminary decision information of the target intelligent agent according to the input parameters and the trade measure information in the target intelligent agent; The second decision unit is used for generating a descriptive strategy of the target intelligent agent according to the input parameters, the trade measure information, the preliminary decision information, the associated preliminary decision information of the associated target intelligent agent and the iterative optimization parameter value in a decision model, wherein the associated target intelligent agent has an interaction relation with the target intelligent agent; the quantization unit is used for determining quantization parameters corresponding to key factors in the descriptive strategy in an economic model, and solving the economic model according to the quantization parameters and the iterative optimization parameter values to obtain a quantization result corresponding to the descriptive strategy; And the iteration unit is used for updating the iteration optimization parameter value according to the quantization result, returning and regenerating the descriptive strategy and the quantization result of the target intelligent agent based on the updated iteration optimization parameter value until a preset iteration ending condition is met, and determining the solution of the target intelligent agent according to the descriptive strategy and the qu