Intelligent decision support method and system for power transmission channel carbon sink estimation and recovery potential optimization, storage medium and electronic equipment

Abstract

The invention provides an intelligent decision support method and system for power transmission channel carbon sink estimation and recovery potential optimization, a storage medium and electronic equipment, and the method comprises the steps: carrying out the carbon sink estimation and recovery potential optimization of a power transmission channel, and obtaining a carbon sink estimation result and a recovery potential optimization result; and based on a carbon sink estimation result and a recovery potential optimization result, carrying out intelligent decision support on the user. According to the invention, carbon sink estimation and recovery potential optimization are respectively carried out on the power transmission channel to obtain a carbon sink estimation result and a recovery potential optimization result, and intelligent decision support is carried out on the user based on the results, so that the user does not need to manually carry out carbon sink estimation and recovery potential optimization decision, effective automatic intelligent decision support is provided for the user, and the user experience is improved. The decision-making efficiency is greatly improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer data processing technology, and in particular to an intelligent decision support method, system, storage medium and electronic equipment for carbon sink estimation and recovery potential optimization of transmission channels. Background Art

[0002] As global climate change becomes increasingly serious, carbon emission control has become a key measure for the global response to climate change. As a major source of carbon emissions, the power industry plays a vital role in mitigating climate change.

[0003] Improving the carbon sequestration capacity and resilience of transmission corridors has become a crucial approach to reducing the carbon footprint of power systems. Transmission corridors are not only core infrastructure for power transmission but also contribute to climate change mitigation by improving the surrounding ecological environment and acting as carbon sinks, absorbing atmospheric carbon dioxide. Furthermore, in the face of external shocks such as natural disasters and environmental degradation, optimizing the resilience of transmission corridors to ensure the continued stability of their power transmission and carbon sequestration functions is particularly important.

[0004] To improve the carbon sequestration capacity and restoration potential of transmission corridors, it is necessary to estimate carbon sequestration and make decisions to optimize restoration potential. However, traditional decision-making methods mostly rely on manual operations and lack effective automated intelligent decision-making support, resulting in low decision-making efficiency and high labor costs. Summary of the Invention

[0005] One of the purposes of the present invention is to provide an intelligent decision support method for carbon sink estimation and recovery potential optimization of transmission channels. Carbon sink estimation and recovery potential optimization are performed on transmission channels respectively to obtain carbon sink estimation results and recovery potential optimization results, and based on this, intelligent decision support is provided to users. There is no need for users to manually perform carbon sink estimation and recovery potential optimization decisions, and effective automated intelligent decision support is provided to users, which greatly improves decision-making efficiency and reduces labor costs.

[0006] An embodiment of the present invention provides an intelligent decision support method for estimating carbon sinks and optimizing restoration potential in power transmission channels, comprising:

[0007] Carry out carbon sink estimation and restoration potential optimization for transmission channels respectively, and obtain carbon sink estimation results and restoration potential optimization results;

[0008] Based on the carbon sink estimation results and restoration potential optimization results, users are provided with intelligent decision support.

[0009] Optionally, when estimating carbon sinks for transmission corridors, perform the following steps:

[0010] Obtain the basis for carbon sequestration estimation along the transmission corridor;

[0011] Based on the carbon sink estimation model and according to the carbon sink estimation basis, the carbon sink estimation of the transmission channel is carried out to obtain the carbon sink estimation results.

[0012] Optionally, when optimizing the restoration potential of a transmission channel, perform the following steps:

[0013] Obtaining the basis for optimizing the restoration potential of transmission corridors;

[0014] Based on the restoration potential optimization algorithm, the restoration potential of the transmission channel is optimized according to the restoration potential optimization basis to obtain the restoration potential optimization result.

[0015] Optionally, providing intelligent decision support to users based on the carbon sink estimation results and restoration potential optimization results includes:

[0016] Create a visualization model based on the carbon sink estimation results and restoration potential optimization results;

[0017] When users view the visualization model, a decision-making trigger network is dynamically laid out within the visualization model;

[0018] Trigger the network based on decision-making ideas to assist users in triggering decision-making ideas;

[0019] Plan the decision support timing and decision support strategy for user-triggered decision ideas;

[0020] When the user enters the decision support moment, the user is provided with corresponding decision support based on the decision support strategy.

[0021] Optionally, dynamically deploying a decision-making idea triggering network in the visualization model includes:

[0022] Whenever a first target point is generated in the visualization model, multiple decision-making ideas are matched based on the characteristic distribution of the target range in the visualization model. The first target point is intermittently viewed by the user during the first period of time, and the ratio of the total duration of these intermittent views to the first period of time exceeds a ratio threshold. The target range is the maximum viewing angle range when the user's viewing angle is at the first target point during the first period of time.

[0023] Arrange multiple associated idea node distributions in the visualization model; wherein the same associated idea node distribution includes multiple supporting element nodes of the same decision idea knowledge in the visualization model;

[0024] The nodes of each related idea are distributed and combined together to serve as the decision-making idea trigger network.

[0025] Optionally, triggering the network based on the decision-making idea to assist the user in triggering the decision-making idea includes:

[0026] Continuously obtaining a movement trajectory formed by the movement of the user's viewing center when viewing the visualization model in any second time period in the future;

[0027] Draw a curve of the number of encircling nodes versus time for each associated idea node in the decision-making idea triggering network. The vertical axis of the curve represents the total number of supporting element nodes in the same associated idea node distribution encircled by the minimum encircling sphere of the moving trajectory, and the horizontal axis represents the corresponding encirclement time.

[0028] When the number of encircled nodes-time curves of at least two associated idea node distributions both have peaks and the time difference between the two peaks does not exceed the time difference threshold, stop acquiring the movement trajectory and obtain the minimum encircling sphere of the movement trajectory at the encircling moment of the maximum peak to encircle the encircled supporting element nodes and the unencircled supporting element nodes in the associated idea node distribution corresponding to the maximum peak;

[0029] Matching multiple standard association relationships between unsurrounded support element nodes and surrounded support element nodes and corresponding relationship weights;

[0030] When any unenclosed supporting element node newly appears within the viewing angle of the user viewing the visual model in any third time period in the future, the standard association relationship corresponding to the newly appeared unenclosed supporting element node is used as the target standard association relationship;

[0031] The viewing angle is controlled to be enlarged until all the enclosed supporting element nodes having the target standard association relationship with the newly appeared unenclosed supporting element node appear;

[0032] Map each target standard relationship into the zoomed-in viewing angle in order according to their respective relationship weights from largest to smallest. Each time the user selects a target standard relationship to be mapped, the next target standard relationship is mapped.

[0033] When all target standard association relationships are selected by the user, the decision-making idea of the association idea node distribution corresponding to the maximum peak is triggered.

[0034] Optionally, the planning of the decision support timing and decision support strategy for the decision ideas triggered by the user includes:

[0035] Planning decision support timing, including: the user's latest decision content in the visualization model begins to be inconsistent with the triggered decision thinking, and the user's current thinking reversal ability is lower than the ability threshold;

[0036] Planning a decision support strategy, including: presenting to the user the decision-making support content of the first partially matching idea in the triggered decision-making idea and the second partially matching idea in the idea range after the first partially matching idea;

[0037] The steps for determining the user's current thought reversal capability are as follows:

[0038] When the difference in supporting element nodes between the first partially consistent idea and the second partially consistent idea in the distribution of associated idea nodes corresponding to the triggered decision idea continues to exceed the threshold time and does not enter the user's viewing range of the visualization model within the fourth time period, the user's current idea reversal ability is calculated as a preset target value that is lower than the ability threshold.

[0039] An embodiment of the present invention provides an intelligent decision support system for carbon sink estimation and restoration potential optimization of power transmission channels, comprising:

[0040] The carbon sink estimation and restoration potential optimization module is used to perform carbon sink estimation and restoration potential optimization on the transmission channel respectively, and obtain carbon sink estimation results and restoration potential optimization results;

[0041] The user intelligent decision support module is used to provide intelligent decision support to users based on the carbon sink estimation results and restoration potential optimization results.

[0042] An embodiment of the present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement any of the above methods.

[0043] An embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any one of the methods described above.

[0044] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 This is a flow chart of an intelligent decision support method for carbon sink estimation and restoration potential optimization of power transmission channels in an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of an intelligent decision support system for carbon sink estimation and restoration potential optimization of transmission channels in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] Example 1:

[0051] The embodiment of the present invention provides an intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels, such as Figure 1 Shown, including:

[0052] S1. Carry out carbon sink estimation and restoration potential optimization for the transmission channel respectively to obtain carbon sink estimation results and restoration potential optimization results;

[0053] In S1, carbon sink refers to the ability of vegetation, soil, and other ecosystem components to absorb, store, and fix carbon dioxide through natural processes in areas through which power transmission channels pass (such as the belt-shaped areas of power lines, forests, grasslands, wetlands, etc.); recovery potential refers to the ability of ecosystems in areas through which power transmission channels pass to restore their carbon absorption, carbon storage, and other ecological service functions after being damaged or disturbed. The system performs carbon sink estimation and recovery potential optimization on the transmission channels, respectively, to obtain corresponding carbon sink estimation results and recovery potential optimization results.

[0054] S2. Provide intelligent decision support to users based on carbon sink estimation results and restoration potential optimization results.

[0055] In S2, the carbon sink estimation results and the restoration potential optimization results can be used as a basis for decision support, providing users with intelligent decision support for improving the carbon sink capacity and restoration potential of the transmission channel.

[0056] This application performs carbon sink estimation and recovery potential optimization on the transmission channel respectively, obtains carbon sink estimation results and recovery potential optimization results, and provides intelligent decision-making support to users based on this. Users do not need to manually perform carbon sink estimation and recovery potential optimization decisions, and provide users with effective automated intelligent decision-making support, which greatly improves decision-making efficiency and reduces labor costs.

[0057] Example 2:

[0058] In one embodiment, in S1, when estimating carbon sinks for a power transmission channel, the following steps are performed:

[0059] S111. Obtain the basis for estimating carbon sequestration in power transmission corridors;

[0060] In S111, the basis for carbon sink estimation includes at least: land type, land cover type, soil characteristics, preparation data, climate data, historical land use, etc. in different areas of the transmission corridor;

[0061] S112. Based on the carbon sink estimation model, perform carbon sink estimation on the transmission channel according to the carbon sink estimation basis to obtain a carbon sink estimation result.

[0062] In S112, the carbon sink estimation model is an artificial intelligence model obtained by machine learning training using a large amount of historical records, manual experience, etc. of carbon sink estimation based on the carbon sink estimation basis as training samples. The model can independently perform carbon sink estimation on the transmission channel based on the carbon sink estimation basis; the obtained carbon sink estimation results include at least: carbon absorption and carbon storage, etc.

[0063] When estimating carbon sinks for transmission channels, the embodiment of the present invention introduces a carbon sink estimation model, and estimates carbon sinks for transmission channels based on the obtained carbon sink estimation basis of the transmission channels, and finally obtains a carbon sink estimation result, thereby improving the accuracy and efficiency of carbon sink estimation for transmission channels.

[0064] Example 3:

[0065] In one embodiment, in S1, when optimizing the restoration potential of the power transmission channel, the following steps are performed:

[0066] S121. Obtaining the basis for optimizing the restoration potential of the transmission channel;

[0067] In S121, the restoration potential optimization basis includes at least: land degradation conditions, vegetation loss degree, vegetation type, soil health status, ecological function, etc. in different areas of the transmission channel; S122, based on the restoration potential optimization algorithm, the restoration potential of the transmission channel is optimized according to the restoration potential optimization basis to obtain a restoration potential optimization result.

[0068] In S122, the restoration potential optimization algorithm includes restoration potential optimization results corresponding to different restoration potential optimization criteria, pre-classified based on historical experience in transmission channel restoration potential optimization. Based on these results, the restoration potential optimization results corresponding to the restoration potential optimization criteria can be determined. The restoration potential optimization results include at least: current restoration potential, optimal restoration measures, and implementation recommendations. Furthermore, the restoration potential optimization algorithm can also be an artificial intelligence algorithm obtained through machine learning training using a large amount of historical experience in transmission channel restoration potential optimization.

[0069] When optimizing the restoration potential of a transmission channel, the embodiment of the present invention introduces a restoration potential optimization algorithm. Based on the obtained restoration potential optimization basis of the transmission channel, the restoration potential of the transmission channel is optimized, and finally a restoration potential optimization result is obtained, thereby improving the accuracy and efficiency of the restoration potential optimization of the transmission channel.

[0070] Example 4:

[0071] When making decisions about enhancing carbon sequestration capacity and restoration potential, users often encounter complex information and scenarios, leading to unclear decision-making. (For example, when selecting a carbon sequestration project, users may consider economic benefits, environmental impacts, and social acceptance simultaneously. However, due to a lack of clear prioritization, they may not be able to determine which factor is more important, ultimately choosing a solution that does not maximize carbon benefits.) This ambiguous decision-making process can lead to inefficient or even erroneous decisions, which in turn affects the carbon management effectiveness of transmission corridors.

[0072] Second, the appropriate timing of decision support is key to ensuring its effectiveness. Providing decision support at the wrong time can lead to excessive information interference or delayed decision-making, impacting both the quality and efficiency of decision-making. (For example, providing decision support too early can disrupt user judgment, while providing it too late can lead to missing the optimal decision opportunity.)

[0073] Therefore, in order to solve the above problem, in one embodiment, the step S2 of providing intelligent decision support to the user based on the carbon sink estimation results and the restoration potential optimization results includes:

[0074] S21. Create a visualization model based on the carbon sink estimation results and restoration potential optimization results;

[0075] In S21, when creating the visualization model, the carbon sink estimation results and the restoration potential optimization results can be set in the corresponding model areas of the three-dimensional map model of the transmission channel according to their related transmission channel areas, and finally the three-dimensional map model with all settings completed is used as the visualization model;

[0076] S22. When the user views the visualization model, a decision-making idea triggering network is dynamically arranged within the visualization model;

[0077] S23, triggering the network based on the decision-making idea to assist the user in triggering the decision-making idea;

[0078] In S22 to S23, users can use smart terminals (such as mobile phones, tablet computers, etc.) to view the visualization model. When viewing the visualization model, they will have fuzzy ideas about how to improve the carbon sequestration capacity and recovery potential of the transmission channel. The dynamically deployed decision-making idea trigger network helps them trigger clear trigger decision ideas to improve decision-making efficiency.

[0079] S24, planning the decision support timing and decision support strategy for the decision ideas triggered by the user;

[0080] S25. When the user enters the decision support moment, corresponding decision support is provided to the user based on the decision support strategy.

[0081] In S24 to S25, the decision support opportunity refers to the time when the user makes a decision along the triggered decision-making ideas on how to improve the carbon sequestration capacity and recovery potential of the transmission channel and receives decision support. The decision support strategy is the strategy applicable to the user's decision support at this time. When the user enters the decision support opportunity, the user is given corresponding decision support based on the decision support strategy.

[0082] The embodiment of the present invention constructs a visualization model based on the carbon sink estimation results and the restoration potential optimization results, allowing users to clearly display various information related to carbon sink capacity and restoration potential in an intuitive three-dimensional map model. At the same time, by dynamically deploying a decision-making idea trigger network in the visualization model, when users browse the model, it helps users quickly transform from vague ideas to clear decision-making ideas in a triggering manner, thereby effectively clarifying the decision logic, improving decision-making efficiency, avoiding wrong decisions, and enhancing the carbon management effect of the transmission channel. Furthermore, by planning the decision support timing and decision support strategy of the decision ideas triggered by users, the system can accurately grasp the best decision support timing, avoid the impact caused by being too early or too late, and ensure that when users enter the decision support timing, appropriate decision support strategies are provided, thereby significantly improving the quality and efficiency of decisions.

[0083] Example 5:

[0084] In one embodiment, in S22, dynamically deploying a decision-making idea triggering network in the visualization model includes:

[0085] S221. Whenever a first target point is generated in the visualization model, matching multiple decision-making idea knowledge based on the characteristic distribution of the target range in the visualization model; wherein, the center of the user's viewing angle of the visualization model during the most recent first period of time intermittently stays at the first target point, and the ratio of the total duration of the intermittent stays to the first period of time exceeds a ratio threshold; the target range is the maximum viewing angle range when the center of the user's viewing angle of the visualization model during the most recent first period of time stays at the first target point;

[0086] S222: Arrange multiple associated idea node distributions in the visualization model; wherein the same associated idea node distribution includes multiple supporting element nodes of the same decision idea knowledge in the visualization model;

[0087] In S221 to S222, the most recent first time period may be the most recent 5 minutes; when the user views the visualization model, the user views it through the perspective of the terminal operation, which has a perspective center; the perspective center intermittently stays at the first target point means that the perspective center intermittently coincides with the first target point within a period of time, and the corresponding total duration of the intermittent stay refers to the total time the coincidence is maintained; the ratio threshold may be 1 / 2; when the user decides how to improve the carbon sequestration capacity and recovery potential of the transmission channel, the transmission channel will be decided by region. When making a decision for a region, the user will have a global perspective of viewing the region and maintain this perspective for a long time, and will also have a local perspective of looking at different local areas within the region. At this time, a first target point will be generated in the visualization model, and the first target point will be the perspective center of the user's global perspective of viewing the region, and the target range will be the range of the region;

[0088] The characteristic distribution of the target range includes at least: the type of geographic information within the target range, the type of information related to carbon sequestration capacity and restoration potential, etc. The matching decision-making idea knowledge indicates that the characteristic distribution reflects the clear idea that users should have when making decisions about how to improve carbon sequestration capacity and restoration potential within the target range. For example, if the characteristic distribution shows that the target range involves a forest and wetland boundary area, the wetland has high carbon storage, but the forest area has been degraded due to logging, resulting in low carbon storage, and the wetland restoration potential is assessed to be large, then the clear idea is to prioritize the protection of the wetland area and implement artificial planting and management decisions on the degraded forest to maximize carbon sequestration capacity. The decision-making idea has supporting element nodes in the visualization model. The supporting element nodes are model elements that reflect the clear idea indicated by the decision-making idea knowledge. For example, if the clear idea is to prioritize the protection of the wetland area, the corresponding supporting element nodes are the wetland area location, wetland area geographic information, etc. Multiple supporting element nodes of the same decision-making idea knowledge in the visualization model are combined into an associated idea node distribution. The associated idea node distribution represents the clear idea indicated by the matching decision-making idea knowledge, which can be used to assist users in triggering decision-making ideas.

[0089] S223: All associated idea nodes are distributed and combined to form a decision idea trigger network.

[0090] In S223 , eventually, the associated idea nodes are distributed and combined to form a decision idea triggering network.

[0091] The embodiment of the present invention matches multiple decision-making idea knowledge based on the characteristic distribution of the target range every time the first target point is generated in the visualization model, and arranges multiple related idea node distributions in the visualization model, and finally jointly constructs the related idea node distributions into a decision-making idea trigger network, thereby realizing the dynamic arrangement of the decision-making idea trigger network. This process can continuously and specifically assist users in triggering decision ideas when making decisions, which not only improves the assistance efficiency, but also significantly improves the user experience. At the same time, the first target point and the target range are determined by combining the intermittent stay of the center of the user's perspective when viewing the visualization model in the recent period, so that the two respectively represent the user's global viewing angle of the specific decision area and its decision range, thereby imperceptibly revealing the user's vague ideas, thereby improving the accuracy of the subsequent dynamic arrangement of the decision-making idea trigger network.

[0092] Example 6:

[0093] In one embodiment, the step S23 of triggering the network based on the decision-making idea to assist the user in triggering the decision-making idea includes:

[0094] S231, continuously obtaining a movement trajectory formed by the movement of the user's viewing center when viewing the visualization model in any second time period in the future;

[0095] In S231, any second time period in the future can be any time period of 100 seconds after the network deployment is completed for triggering the decision-making idea; when the perspective center moves, its corresponding position in the visualization model also moves, and the position movement forms a movement trajectory;

[0096] S232. Draw a curve of the number of encircling nodes versus time for each associated idea node distribution in the decision idea triggering network; wherein the vertical axis of the curve of the number of encircling nodes versus time represents the total number of supporting element nodes in the same associated idea node distribution encircled by the minimum encircling sphere of the movement trajectory, and the horizontal axis represents the corresponding encirclement time.

[0097] In S232, as time changes, the movement trajectory also changes, and the total number of supporting element nodes in the same associated idea node distribution surrounded by its minimum bounding sphere also changes. The total number and the corresponding encirclement time of the minimum bounding sphere are mapped into a curve coordinate system with the total number on the vertical axis and the encirclement time on the horizontal axis to obtain multiple coordinate points. These coordinate points are connected to form an encirclement node number-time curve.

[0098] S233: When the number of enclosing nodes-time curves of at least two associated idea node distributions both have peaks and the time difference between the peaks does not exceed the time difference threshold, stop acquiring the movement trajectory and acquire the minimum enclosing sphere of the movement trajectory at the enclosing moment of the maximum peak to enclose the enclosed supporting element nodes and the unenclosed supporting element nodes in the associated idea node distribution corresponding to the maximum peak;

[0099] In S233, when the number of surrounding nodes on the time curve of the same associated idea node distribution increases, it means that the user's ideas will be closer and closer to the ideas of the associated idea node distribution when continuing to make decisions in the future. When they are closest, a peak will appear; the time difference threshold can be 10 seconds; when the number of surrounding nodes - time curves of at least two associated idea node distributions have peaks and the time difference between the two peaks does not exceed the time difference threshold, it means that the user's ideas when making decisions in a short period of time are continuously closest to the ideas of at least two associated idea node distributions. At this time, it is urgent to help the user sort out his closer ideas in order to determine a clear decision-making idea as soon as possible. Therefore, the acquisition of the movement trajectory is stopped. The situation in which the minimum surrounding sphere of the movement trajectory at the moment of the maximum peak surrounds the supporting element node in the associated idea node distribution corresponding to the maximum peak is the key basis for helping the user sort out his closer ideas. The surrounded supporting element node is the supporting element node of the minimum surrounding sphere at this moment, and correspondingly, the unsurrounded supporting element node is the unsurrounded supporting element node;

[0100] S234, matching multiple standard association relationships between unsurrounded supporting element nodes and surrounded supporting element nodes and corresponding relationship weights;

[0101] In S234, the standard association relationship refers to the association relationship between two supporting element nodes for users to consider from the surrounded supporting element nodes to the unenclosed supporting element nodes. For example, the surrounded supporting element nodes are high-carbon emission equipment that have attracted the attention of users, while the unenclosed supporting element nodes are those that have not yet attracted the attention of users but may have an impact on carbon emissions. In this case, the standard association relationship is the carbon emission source association; the relationship weight represents the ability of the standard association relationship for users to consider from the surrounded supporting element nodes to the unenclosed supporting element nodes, and can be preset in advance by technical personnel based on the actual ability situation; S235, when any unenclosed supporting element node newly appears within the user's viewing angle of the visualization model in any third time period in the future, the standard association relationship corresponding to the newly appeared unenclosed supporting element node is used as the target standard association relationship;

[0102] S236, controlling the viewing angle range to be enlarged until all the enclosed supporting element nodes having the target standard association relationship with the newly appeared unenclosed supporting element node appear;

[0103] S237, mapping each target standard association relationship into the magnified viewing angle range in order from largest to smallest according to their respective relationship weights. Each time the mapping is performed, when the user confirms the mapped target standard association relationship, the next target standard association relationship is mapped;

[0104] S238. When all target standard association relationships are selected by the user, the decision-making idea of the association idea node distribution corresponding to the maximum peak is triggered.

[0105] In S235 to S238, any third time period in the future can be any time period of 80 seconds in the future after the standard association relationships and corresponding relationship weights are matched. When any unenclosed supporting element node newly appears within the user's viewing angle of the visualization model in any third time period in the future, it indicates that the user needs to consider the unenclosed supporting element node from the enclosed supporting element node. Then, the relevant target standard association relationship is determined, and the viewing angle is controlled to be enlarged until all the enclosed supporting element nodes with the newly appeared unenclosed supporting element node having the target standard association relationship appear. Each target standard association relationship is mapped into the enlarged viewing angle in descending order according to its relationship weight. Mapping means setting it into the range for the user to view. The user will try to consider the unenclosed supporting element node by viewing the mapped target standard association relationship. If it is confirmed that it needs to be considered in subsequent decision-making, the mapped target standard association relationship is selected. Finally, when all target standard association relationships are selected by the user, the decision idea of the association idea node distribution corresponding to the maximum peak is triggered as the idea that helps the user clarify.

[0106] The embodiment of the present invention draws a surrounding node number-time curve of each associated idea node distribution in the decision-making idea triggering network based on the movement trajectory formed by the movement of the user's perspective center when viewing the visualization model in any second time period in the future, and quickly determines the time to assist the user's rational thinking based on the peak situation of the curve, and then prepares to provide assistance to the user in triggering the decision-making idea at the most appropriate time, which greatly improves the effect of assisting the user in triggering the decision-making idea; secondly, combined with the situation of the minimum surrounding ball of the moving trajectory at the moment of surrounding the maximum peak surrounding the supporting element node in the associated idea node distribution corresponding to the maximum peak, multiple standard association relationships and corresponding relationship weights are matched, thereby helping the user to consider the surrounding supporting element nodes from the surrounding supporting element nodes to the unsurrounded supporting element nodes, which greatly improves the accuracy, efficiency and comprehensiveness of assisting the user in triggering the decision-making idea.

[0107] Example 7:

[0108] In one embodiment, the step S24 of planning the decision support timing and decision support strategy for the decision ideas triggered by the user includes:

[0109] S241, planning decision support timing, including: the latest decision content of the user in the visualization model begins to be inconsistent with the triggered decision thinking, and the user's current thinking reversal ability is lower than the ability threshold;

[0110] In S241, when the user makes a decision according to the triggered decision-making idea, decision content will be continuously generated. If the latest decision content begins to be inconsistent with the triggered decision-making idea, and the user's current thinking reversal ability is lower than the ability threshold, it means that the user is most in need of decision support, and this is used as the decision support opportunity. Thinking reversal ability refers to the ability of the user to return from the current thinking or decision direction to the previous thinking point that was inconsistent with the triggered decision-making idea during the decision-making process;

[0111] S242, planning a decision support strategy, including: presenting to the user the decision-making support content of the first partially matching idea in the triggered decision-making idea for the decision content before the latest decision content made by the user, and the second partially matching idea in the idea range after the first partially matching idea;

[0112] In S242, before the user makes the latest decision, a decision content is generated. The triggered decision idea corresponds to a first partially matching idea. The first partially matching idea and the second partially matching idea within the subsequent idea range (e.g., within two idea steps) each have decision-making support content. The decision-making support content is for the user to refer to in order to return to the corresponding partially matching idea for subsequent decision-making, such as relevant idea guidance information. When the user views the decision-making support content, he or she will automatically return to the corresponding partially matching idea.

[0113] In S241, the steps for determining the user's current thought reversal capability are as follows:

[0114] S2411. When the difference supporting element nodes in the distribution of associated idea nodes corresponding to the triggered decision idea of the first partially consistent idea and the second partially consistent idea continue to exceed the threshold time and do not enter the user's viewing angle of the visualization model within the fourth time period, the user's current idea reversal ability is calculated as a preset target value that is lower than the ability threshold.

[0115] In S2411, the first partially consistent idea and the second partially consistent idea both have corresponding supporting element nodes in the associated idea node distribution, and the different supporting element nodes among the supporting element nodes possessed by the two are difference supporting element nodes; the threshold duration can be 120 seconds; the fourth period is within 300 seconds after the user enters the decision support opportunity; the preset target value is a value lower than the ability threshold; when the difference supporting element node continues to exceed the threshold duration without entering the user's viewing angle of the visualization model, it means that the user has failed to retrace his ideas for a long time, and the user's current idea retracement ability is calculated as the preset target value lower than the ability threshold.

[0116] The embodiment of the present invention plans the decision support timing based on whether the user's latest decision content is consistent with the triggered decision idea and the user's idea return ability, and plans the decision support strategy based on the decision auxiliary content of the first partially consistent idea and the second partially consistent idea within the idea range after the first partially consistent idea, which greatly improves the decision support timing of the decision idea triggered by the user and the planning accuracy, comprehensiveness and efficiency of the decision support strategy, and improves the applicability of the system; secondly, the idea return ability is determined based on the time length that the difference support element node has not continuously entered the user's viewing angle of the visualization model, thereby improving the accuracy of determining the idea return ability.

[0117] Example 8:

[0118] The embodiment of the present invention provides an intelligent decision support system for carbon sink estimation and restoration potential optimization of transmission channels, such as Figure 2 Shown, including:

[0119] Carbon sink estimation and restoration potential optimization module 1, used to perform carbon sink estimation and restoration potential optimization on the transmission channel respectively, and obtain carbon sink estimation results and restoration potential optimization results;

[0120] The user intelligent decision support module 2 is used to provide intelligent decision support to users based on the carbon sink estimation results and restoration potential optimization results.

[0121] Example 9:

[0122] An embodiment of the present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement any of the above methods.

[0123] Example 10:

[0124] An embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any one of the methods described above.

[0125] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels, characterized by: include: Carry out carbon sink estimation and restoration potential optimization for transmission channels respectively, and obtain carbon sink estimation results and restoration potential optimization results; Based on the carbon sink estimation results and restoration potential optimization results, users are provided with intelligent decision support.

2. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 1, characterized in that: When estimating carbon sequestration for a transmission corridor, the following steps are performed: Obtain the basis for carbon sequestration estimation along the transmission corridor; Based on the carbon sink estimation model and according to the carbon sink estimation basis, the carbon sink estimation of the transmission channel is carried out to obtain the carbon sink estimation results.

3. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 1, characterized in that: When optimizing the restoration potential of a transmission channel, the following steps are performed: Obtaining the basis for optimizing the restoration potential of transmission corridors; Based on the restoration potential optimization algorithm, the restoration potential of the transmission channel is optimized according to the restoration potential optimization basis to obtain the restoration potential optimization result.

4. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 1, characterized in that: The intelligent decision support for users based on the carbon sink estimation results and restoration potential optimization results includes: Create a visualization model based on the carbon sink estimation results and restoration potential optimization results; When users view the visualization model, a decision-making trigger network is dynamically laid out within the visualization model; Trigger the network based on decision-making ideas to assist users in triggering decision-making ideas; Plan the decision support timing and decision support strategy for user-triggered decision ideas; When the user enters the decision support moment, the user is provided with corresponding decision support based on the decision support strategy.

5. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 4, characterized in that: The dynamically laying out the decision-making idea triggering network in the visualization model includes: Whenever a first target point is generated in the visualization model, multiple decision-making ideas are matched based on the characteristic distribution of the target range in the visualization model. The first target point is intermittently viewed by the user during the first period of time, and the ratio of the total duration of these intermittent views to the first period of time exceeds a ratio threshold. The target range is the maximum viewing angle range when the user's viewing angle is at the first target point during the first period of time. Arrange multiple associated idea node distributions in the visualization model; wherein the same associated idea node distribution includes multiple supporting element nodes of the same decision idea knowledge in the visualization model; The nodes of each related idea are distributed and combined together to serve as the decision-making idea trigger network.

6. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 5, characterized in that: The triggering of the network based on the decision-making idea to assist the user in triggering the decision-making idea includes: Continuously obtaining a movement trajectory formed by the movement of the user's viewing center when viewing the visualization model in any second time period in the future; Draw a curve of the number of encircling nodes versus time for each associated idea node in the decision-making idea triggering network. The vertical axis of the curve represents the total number of supporting element nodes in the same associated idea node distribution encircled by the minimum encircling sphere of the moving trajectory, and the horizontal axis represents the corresponding encirclement time. When the number of encircled nodes-time curves of at least two associated idea node distributions both have peaks and the time difference between the two peaks does not exceed the time difference threshold, stop acquiring the movement trajectory and obtain the minimum encircling sphere of the movement trajectory at the encircling moment of the maximum peak to encircle the encircled supporting element nodes and the unencircled supporting element nodes in the associated idea node distribution corresponding to the maximum peak; Matching multiple standard association relationships between unsurrounded support element nodes and surrounded support element nodes and corresponding relationship weights; When any unenclosed supporting element node newly appears within the viewing angle of the user viewing the visual model in any third time period in the future, the standard association relationship corresponding to the newly appeared unenclosed supporting element node is used as the target standard association relationship; The viewing angle is controlled to be enlarged until all the enclosed supporting element nodes having the target standard association relationship with the newly appeared unenclosed supporting element node appear; Map each target standard relationship into the zoomed-in viewing angle in order according to their respective relationship weights from largest to smallest. Each time the user selects a target standard relationship to be mapped, the next target standard relationship is mapped. When all target standard association relationships are selected by the user, the decision-making idea of the association idea node distribution corresponding to the maximum peak is triggered.

7. The intelligent decision support method for carbon sink estimation and restoration potential optimization of transmission channels according to claim 5, characterized in that: The decision support timing and decision support strategy of the decision ideas triggered by the planning user include: Planning decision support timing, including: the user's latest decision content in the visualization model begins to be inconsistent with the triggered decision thinking, and the user's current thinking reversal ability is lower than the ability threshold; Planning a decision support strategy, including: presenting to the user the decision-making support content of the first partially matching idea in the triggered decision-making idea and the second partially matching idea in the idea range after the first partially matching idea; The steps for determining the user's current thought reversal capability are as follows: When the difference in supporting element nodes between the first partially consistent idea and the second partially consistent idea in the distribution of associated idea nodes corresponding to the triggered decision idea continues to exceed the threshold time and does not enter the user's viewing range of the visualization model within the fourth time period, the user's current idea reversal ability is calculated as a preset target value that is lower than the ability threshold.

8. An intelligent decision support system for carbon sink estimation and restoration potential optimization of transmission channels, characterized by: include: The carbon sink estimation and restoration potential optimization module is used to perform carbon sink estimation and restoration potential optimization on the transmission channel respectively, and obtain carbon sink estimation results and restoration potential optimization results; The user intelligent decision support module is used to provide intelligent decision support to users based on the carbon sink estimation results and restoration potential optimization results.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

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