CN117852324B - Scene construction method based on data twinning - Google Patents
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Abstract
The invention discloses a scene construction method based on data twinning, which relates to the technical field of scene construction, wherein a processing end establishes a digital model of natural resources in a homeland space based on multi-source data, synchronizes the digital model with actual data in real time through a real-time synchronization mechanism, updates the digital model according to the change of the actual data, simulates different resource utilization schemes to perform scene optimization, and provides decision support based on the digital model and the real-time data, wherein the decision support comprises resource management advice, risk assessment and sustainable development planning. According to the scene construction method, the digital model is effectively constructed, the digital model is synchronized with actual data in real time through a real-time synchronization mechanism, timeliness of the digital model is guaranteed, decision support based on the digital model and the real-time data is provided, a user can check the running state of natural resources in a homeland space through the digital model, decision support is provided for the user, and management is facilitated.
Description
Technical Field
The invention relates to the technical field of scene construction, in particular to a scene construction method based on data twinning.
Background
Along with the development of science and technology, digital transformation becomes the trend of various industries, the field of natural resource management is not exceptional, data twinning provides a digital tool for natural resource management, the resource utilization can be better understood, analyzed and optimized by digitally modeling a natural system, and Data twinning (Data Twin) refers to the real-time synchronization of a digital model of an actual physical system or process and Data of the actual system so as to realize the dynamic monitoring, prediction and optimization of the system state, and in the aspects of natural resource management and utilization, the construction of a natural resource scene based on the Data twinning is an advanced method;
The real-time monitoring and prediction of the natural resource system can be realized based on the construction of the natural resource scene of the data twinning, and the digital model can reflect the real-time state by synchronizing with the data of the actual system, thereby helping a manager to better cope with the change and risk of the natural resource.
The prior art has the following defects:
Due to the characteristics of wide distribution region, high space distribution complexity and the like of natural resources in the homeland space, the existing digital model cannot be synchronously updated according to real-time data, so that information lag of the digital model is easy to cause, timeliness is poor, and the digital model cannot provide decision support for users, so that the users cannot manage the natural resources in the homeland space of the corresponding region conveniently.
Disclosure of Invention
The invention aims to provide a scene construction method based on data twinning, which aims to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a data twinning-based scene construction method, the construction method comprising the steps of:
S1: the acquisition end acquires multisource data related to natural resources in a homeland space;
S2: the processing end establishes a digital model of the natural resources of the homeland space based on the multi-source data;
s3: synchronizing the digital model with the actual data in real time through a real-time synchronization mechanism, and updating the digital model according to the change of the actual data;
s4: performing simulation experiments by using a digital model, and simulating different resource utilization schemes to perform scene optimization;
S5: providing decision support based on the digital model and the real-time data, wherein the decision support comprises resource management advice, risk assessment and sustainable development planning;
s6: and sending the generated digital model and decision support to a user interface for display to a user.
In a preferred embodiment, in step S4, the simulation experiment is performed using the digitized model, and simulating different resource utilization schemes for scene optimization includes the following steps:
s401: setting different simulation scenes, wherein the simulation scenes comprise different resource utilization schemes and natural disaster scenes;
S402: running a simulation experiment according to a set scene, simulating different resource utilization and natural disaster conditions, and obtaining a simulation result, wherein the simulation result comprises resource utilization efficiency and environmental influence;
S403: carrying out data analysis on simulation results of the simulation experiment, evaluating various resource utilization and coping with effects of natural disaster schemes in different scenes, and adjusting resource utilization scheme optimization scenes based on the evaluation results;
s404: and sorting the multiple schemes based on the evaluation result, and visually displaying the scheme with the first sorting.
In a preferred embodiment, in step S404, sorting the multiple schemes based on the evaluation result, and visually displaying the scheme of which the first is sorted includes the steps of:
S4041: acquiring historical data and dimension data of a scheme, wherein the historical data comprises a return on investment rate, a carbon emission reduction index and a coefficient of kunity, and the dimension data comprises a resource growth rate;
s4042: normalizing the return on investment, the carbon emission reduction index, the coefficient of kene and the rate of resource increase to obtain the use coefficient of the scheme The computational expression is: /(I) In the above, the ratio of/>For the resource growth rate,/>For return on investment,/>Is carbon emission reduction index,/>Is the coefficient of Kennel,/>、/>、/>Weight coefficients of return on investment, carbon emission reduction index, and coefficient of kene, respectively, and/>；
S4043: all schemes are based on the use coefficientSorting from big to small, visually displaying the first scheme, and selecting other schemes downwards in sequence for displaying if the first scheme does not achieve an ideal effect in the actual process.
In a preferred embodiment, in step S2, the processing end establishes a digitized model of the natural resource of the homeland space based on the multi-source data, which includes the following steps:
S201: preprocessing remote sensing image data after acquiring related remote sensing image data;
s202: determining the types of natural resources existing in the region, and providing labels for classification modeling;
s203: selecting sample points representing various ground objects on the remote sensing image, and marking the types of the sample points;
s204: training the random forest classifier by using sample points to obtain a digital model, and automatically classifying various ground objects by the digital model according to the input remote sensing image data.
In a preferred embodiment, in step S204, training the random forest classifier using the training samples to obtain a digitized model includes the steps of:
S2041: acquiring remote sensing image data D, wherein the remote sensing image data D represents a data set containing m samples, and each sample has h characteristics;
s2042: selecting a sample set S on the remote sensing image data D, and marking a real category label y for the sample set S to form a training set ；
S2043: single decision treeGenerated by a decision tree algorithm, for each decision tree/>Using training set/>Training, randomly selecting a feature subset at each node, and selecting the best features from the feature subsets to divide until stopping conditions/>, are met
S2044: the random forest comprises a set formed by a plurality of decision trees, the classification result of the random forest is obtained through a voting mechanism, and the expression of the digital model is as follows: in the above, the ratio of/> Representing the classification result,/>Is the number of decision trees in the random forest,/>Representing the classification result of the input feature G by the xth decision tree.
In a preferred embodiment, in step S2043, a decision tree algorithm generates a decision treeThe method comprises the following steps:
decision tree The calculated expression of (2) is: wherein/> Is by the characteristics/>Greater than threshold/>Is/are of the sample set of (C)Is by the characteristics/>Less than or equal to threshold/>Is/are of the sample set of (C)、/>Respectively representing the number of samples of two child nodes,/>、/>The base index,/>, of two child nodes respectivelyFor the sample set of the current node,/>Representing the number of samples in the sample set of the current node,/>Is a feature set,/>For a feature randomly selected from the feature set,/>A threshold value for the node;
Base index The calculated expression of (2) is: /(I)Where Q is the number of classification categories,/>Representing the probability that the sample belongs to the P-th class.
In a preferred embodiment, in step S3, synchronizing the digitized model with the actual data in real time by means of a real-time synchronization mechanism comprises the steps of:
S301: determining the frequency of real-time data transmission, selecting a communication protocol and a transmission mode, wherein the communication protocol comprises HTTP/HTTPS, MQTT, webSocket, selecting an optimal communication protocol by analyzing the performances of different communication protocols in the current environment, and the transmission mode comprises point-to-point communication and a publish-subscribe mode;
S302: unifying the format and coding of real-time data, wherein the data format comprises JSON and XML;
s303: when an abnormality occurs in the data transmission process, the abnormality comprises network interruption and data loss, and the abnormality processing is carried out through an error processing and fault tolerance mechanism, and the abnormality processing comprises a retransmission mechanism and error code processing.
In a preferred embodiment, in step S301, selecting an optimal communication protocol by analyzing the performance of different communication protocols in the current environment comprises the steps of:
S3011: obtaining a maximum response time index, a message confirmation rate and a program crash rate of a communication protocol in the current scene test run;
s3012: the maximum response time index, the message confirmation rate and the program collapse rate are processed in a dimensionless manner, and then the numerical value is taken to comprehensively calculate and obtain the selection coefficient The expression is: /(I)In the above, the ratio of/>In order for the message to be acknowledged at a rate,Is the maximum response time index,/>For program crash rate,/>、/>、/>Proportional coefficients of message acknowledgement rate, maximum response time index, program crash rate, respectively, and/>、/>、/>Are all greater than 0;
S3013: selection coefficients for communication protocols The larger the value is, the better the commissioning performance of the communication protocol in the current scene is, and the coefficient/>, will be selectedThe maximum communication protocol is used as the communication protocol for transmitting data in the current scene.
In a preferred embodiment, the maximum response time index is calculated as: in the above, the ratio of/> Is the execution time of task v,/>Is the execution cycle of task v,/>Representing the maximum response time of task v,/>Is a set of all tasks with high priority,/>Is the execution time of task w.
In a preferred embodiment, the calculated expression for the coefficient of kunity is: in the above, the ratio of/> Representing the income of individual i,/>Is the total population number,/>Is average income,/>Representing the revenue of individual j.
In the technical scheme, the invention has the technical effects and advantages that:
1. The invention collects multisource data related to natural resources in the homeland space through the collecting end, the processing end establishes a digital model of the homeland space natural resources based on the multisource data, the digital model is synchronized with actual data in real time through a real-time synchronization mechanism, the digital model is updated according to the change of the actual data, simulation experiments are carried out by using the digital model, scene optimization is carried out by simulating different resource utilization schemes, decision support based on the digital model and the real-time data is provided, and the decision support comprises resource management advice, risk assessment and sustainable development planning. The scene construction method effectively constructs the digital model to reflect the relation of resource distribution, ecological system state, resource interaction and the like, and enables the digital model to be synchronous with actual data in real time through a real-time synchronous mechanism, so that timeliness of the digital model is guaranteed, finally, decision support based on the digital model and the real-time data is provided, the decision support comprises resource management advice, risk assessment and sustainable development planning, a user can check the running state of natural resources in a homeland space through the digital model, and the decision support is provided for the user, so that the management is facilitated;
2. The invention uses all schemes according to the use coefficient The first scheme is ordered from large to small, visual display is carried out on the first scheme, if the first scheme does not achieve an ideal effect in an actual process, other schemes are selected downwards in sequence to display, and the method is noted that the obtained historical data and dimensional data of the schemes are used for evaluating all the schemes and cannot completely represent that the schemes can have good effects in actual application, but in order to avoid randomness and uncertainty of a display scheme of a building system, the historical data and the dimensional data are comprehensively analyzed to sort and select all the schemes to display, so that the method is beneficial to improving the selection speed and stability of the schemes, further helping users to better make decisions, comprehensively analyzing multiple data and realizing comprehensive analysis.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the method for constructing a scene based on data twinning according to the present embodiment includes the following steps:
the acquisition end acquires multi-source data related to the actual homeland space and natural resources, wherein the multi-source data comprise topography and topography, climate data, land utilization, vegetation coverage, water resources and the like, and the acquired multi-source data are required to be ensured to be accurate, comprehensive and real-time so as to meet the subsequent requirement analysis of the multi-source data;
Determining specific requirements of data acquisition, including required data types, spatial resolution, time resolution and the like, making an acquisition plan, defining an acquisition geographical range and a time period, wherein the definition of the acquisition requirements is helpful to ensure that the data acquired subsequently can meet the requirements of actual analysis, selecting a proper data source to cover various aspects of topography, climate, land utilization, vegetation coverage, water resources and the like, and can comprise satellite remote sensing data, weather station data, land utilization survey data and the like, wherein the selection of the data source needs to comprehensively consider the accuracy, the comprehensiveness and the instantaneity of the data to ensure that all key natural resource information is covered, and performing data acquisition work on the spot or long distance, such as using satellite remote sensing technology, weather stations, hydrograph stations and the like to ensure that the acquired data is accurate and comprehensive;
The method comprises the steps of ensuring the accuracy of equipment and the scientificity of an acquisition method, ensuring the stability and the safety of data transmission, performing quality control in the data acquisition process, including denoising, correcting, rectifying and the like of the data, ensuring the acquired data to be high in quality, ensuring that the control of the data quality is an important step for ensuring the accuracy of the data, timely finding and repairing problems in the acquisition process, integrating and fusing the data acquired from different sources to ensure the comprehensiveness of the data, which possibly relates to the unification of different data formats, spatial resolution and time resolution, wherein the data integration and fusion are key steps for ensuring that the subsequent analysis can comprehensively utilize multi-source data, considering the consistency and the comparability of the data, and if the real-time data is required, setting a real-time data stream processing system, ensuring that the acquired data can meet the requirements on real-time, and ensuring that the real-time data stream processing can ensure the real-time sensing of the system on real-time change for an application scene needing timely response;
Metadata of data is recorded, including information such as data acquisition time, place, acquisition equipment and the like, a metadata management system is established, follow-up tracing and management of the data are facilitated, metadata recording is beneficial to tracing and management of the data, reliability and usability of the data are improved, necessary measures are taken in the whole process of data acquisition, transmission and processing to ensure safety and privacy of the data, and data safety and privacy protection are basic principles of data acquisition and processing and need to be implemented in the whole process.
The method comprises the steps of establishing a digital model of natural resources in a homeland space based on multi-source data, wherein the digital model reflects the relation of resource distribution, ecological system state, resource interaction and the like, ensuring real-time synchronization of the digital model and actual data through a real-time synchronization mechanism, updating the digital model according to the change of the actual data, ensuring that a construction system can timely reflect the change of the actual system, and cooperating with data acquisition and digital model construction steps, wherein the real-time updating through the real-time synchronization mechanism belongs to the prior art, and is not repeated herein.
Performing simulation experiments by using a digital model, simulating different resource utilization schemes, schemes for coping with natural disasters and the like, and performing scene optimization;
Setting various parameters of a digital model, ensuring that the model reflects an actual system, verifying the model, comparing the model with actual data, verifying the accuracy of the model, ensuring that the parameter setting of the digital model is reasonable, verifying the accuracy of the model to lay a foundation for a subsequent simulation experiment, setting different simulation scenes including different resource utilization schemes, natural disaster scenes and the like, considering different decision variables and environmental conditions, setting the scenes to consider the diversity of actual problems, simulating different conditions and decision schemes, operating the simulation experiment, simulating different resource utilization and natural disaster conditions according to the set scenes, and acquiring simulation results including indexes such as resource utilization efficiency, environmental influence and the like, wherein the operation of the simulation experiment needs to ensure the stability and reliability of the model so as to acquire reliable experiment results;
Carrying out data analysis on the results of the simulation experiment, evaluating the effects under different scenes, comparing the advantages and disadvantages of various resource utilization schemes and schemes for dealing with natural disasters, wherein the evaluation of the results needs to consider the set indexes and targets so as to carry out reasonable decision and optimization, adjusting the resource utilization scheme optimization scene based on the evaluation results, formulating an optimization scheme including adjusting the resource utilization scheme, improving natural disaster countermeasures and the like, determining an optimal decision scheme, formulating the optimization scheme, comprehensively considering multiple factors so as to achieve integral optimization, applying the optimization scheme to a digital model, carrying out verification, adjusting the optimization scheme if necessary, ensuring the feasibility and the effect of the actual application of the optimization scheme, and ensuring that the optimization scheme can generate positive influence in practice through verification and adjustment;
Adjusting the resource utilization scheme optimization scenario based on the evaluation result, for example: optimizing emergency response and resource utilization after an earthquake, running different post-disaster emergency response schemes in a digital model, simulating a scene after the earthquake, recording data such as evacuation time, medical service coverage rate, material allocation and the like under each scheme, analyzing simulation experiment results, comparing performance of each emergency response scheme, evaluating changes of indexes such as casualties, property loss degree and the like, optimizing the scene based on the performance evaluation results, and adjusting parameters in the emergency response scheme, such as improving evacuation routes, increasing medical resources and the like.
The method comprises the steps of visually displaying results of an optimization scheme, such as making a chart, a map and the like, writing detailed reports, summarizing results of simulation experiments and the optimization scheme, enabling the results to be visualized and reported to be helpful for better transmitting information to decision makers and stakeholders, enabling decision making and implementation to be carried out, collecting user feedback, monitoring changes of an actual system, updating a digital model and the optimization scheme according to the feedback and the changes, enabling the system to adapt to the changed environment and requirements through continuous feedback and updating, and keeping timeliness and practicability of the model;
The method for analyzing the data of the simulation results of the simulation experiment, evaluating the effects of various resource utilization and coping with natural disaster schemes under different scenes, sequencing the schemes based on the evaluation results, and visually displaying the scheme with the first sequencing comprises the following steps:
acquiring historical data and dimension data of a scheme, wherein the historical data comprises a return on investment rate, a carbon emission reduction index and a coefficient of kunity, and the dimension data comprises a resource growth rate;
Normalizing the return on investment, the carbon emission reduction index, the coefficient of kene and the rate of resource increase to obtain the use coefficient of the scheme The computational expression is: /(I)In the above, the ratio of/>In order for the rate of increase of the resource,For return on investment,/>Is carbon emission reduction index,/>Is the coefficient of Kennel,/>、/>、/>Weight coefficients of return on investment, carbon emission reduction index, and coefficient of kene, respectively, and/>；
From the coefficient of useThe use of coefficients/>, as seen by the computational expression of (a)The larger the value, the better the historical implementation of the description scheme, therefore, all schemes are based on the usage coefficient/>The first scheme is ordered from large to small, visual display is carried out on the first scheme, if the first scheme does not achieve an ideal effect in an actual process, other schemes are selected downwards in sequence to display, and the method is noted that the obtained historical data and dimensional data of the schemes are used for evaluating all the schemes and cannot completely represent that the schemes can have good effects in actual application, but in order to avoid randomness and uncertainty of a display scheme of a building system, the historical data and the dimensional data are comprehensively analyzed to sort and select all the schemes to display, so that the method is beneficial to improving the selection speed and stability of the schemes, further helping users to better make decisions, comprehensively analyzing multiple data and realizing comprehensive analysis. The computational expression of the resource growth rate is: /(I)In the above, the ratio of/>For the final value of resource growth after implementation of the scheme,/>For the resource increment initial value after scheme implementation,/>For the time span, the larger the resource growth rate is, the better the implementation effect of the scheme in the time span is, specifically:
And resources are utilized efficiently: if the resource growth rate is larger, the scheme is more efficient in resource utilization, which may mean that the scheme adopts a more intelligent and more economical method when using the resources, so that the utilization efficiency of the resources is improved;
sustainable development: the increase in the rate of resource growth may reflect the positive contribution of the solution to the goal of sustainable development, and the design and execution of the solution may place more emphasis on long-term sustainability, ensuring that resources are reasonably utilized in the future;
Environmental protection: the high resource growth rate may indicate that the scheme takes environmental protection measures, reduces excessive development or waste of natural resources, and is helpful for reducing environmental burden and protecting health of an ecological system;
Economic benefit: a high rate of resource growth may be related to the economic benefit of the scheme, which may mean that it contributes more to the economy and improves the economic benefit of the resource if the scheme is able to achieve a better increase in resource utilization.
The calculation expression of the return on investment is as follows: in the above, the ratio of/> Representing investment benefits after implementation of the scheme,/>The larger the return on investment is, the better the implementation effect of the scheme is.
The calculated expression of the carbon emission reduction index is: in the above, the ratio of/> For the set reference carbon emission quantity,/>For the actual carbon emission, the larger the carbon emission reduction index is, the better the implementation effect of the scheme is, specifically:
High-efficient emission reduction: if the carbon reduction index is greater, indicating that the scheme is more efficient at reducing carbon emissions, this may mean that the scheme employs innovative, low carbon techniques and methods such that less carbon emissions are produced per unit of economic output;
meets the emission reduction target: the increase in carbon emission reduction index may reflect the strategic objective of carbon emission reduction that the scheme corresponds to, which is particularly important to businesses and government authorities because they are generally responsible for reducing carbon emissions while achieving economic growth;
Sustainable development: the high carbon emission reduction index is generally related to the sustainability and environmental protection of a scheme, and the scheme can adopt a green technology, renewable energy sources and other modes so as to reduce the dependence on non-renewable resources, thereby promoting sustainable development;
environmental responsibility: the greater carbon emission reduction index may reflect the positive performance of the organization on environmental responsibility, with embodiments to mitigate the impact on climate change and environmental burden.
The calculated expression of the coefficient of kunity is: in the above, the ratio of/> Representing the income of individual i,/>Is the total population number,/>Is average income,/>The larger the coefficient of kene, which represents the income of the individual j, the greater the degree of uneven income of the equipment after the implementation of the scheme is, specifically:
unequal possession and use: devices may be concentrated in a small portion of a person or business's hands, resulting in a large share of such person or business in terms of device usage and revenue, while others have and use devices relatively less;
Market share non-uniformity: some brands or models on the device market may dominate, while other brands or models have smaller market shares, resulting in an imbalance in the device revenue distribution;
The technical gap is as follows: devices may have different levels of technology and performance, and high-level devices may be more expensive, resulting in uneven device revenues.
To better illustrate the above scheme, we exemplify the following:
suppose that a simulation experiment is constructed by running a homeland space natural resource scene based on data twinning so as to optimize a water resource utilization scheme of a certain area and a scheme for coping with natural disasters;
Setting different water resource utilization schemes and natural disaster situations, such as drought, flood and the like, running simulation experiments, simulating water resource utilization and natural disaster influence under different scenes, extracting key data, such as water resource supply and demand conditions, flood submerging ranges and the like, arranging the data, preparing for subsequent data analysis, carrying out statistics and space-time analysis on simulation results, knowing the water resource utilization effect and the natural disaster influence under different scenes, comparing data differences and trends under different scenes, evaluating the effects of the water resource utilization and the natural disaster response schemes under each scene according to the data analysis results, comparing the advantages and disadvantages of the different water resource utilization schemes and the natural disaster response schemes, and comprehensively considering economic, environmental and social factors.
Providing decision support based on the digital model and real-time data, wherein the decision support comprises resource management advice, risk assessment, sustainable development planning and the like, and sending the generated digital model and decision support to a user interface for display for a user, so that the user can intuitively check the result of the digital model, input decision parameters and obtain feedback of a construction system, the user interface and the decision support work cooperatively, the user interface is ensured to meet the requirements of the user, and the user can effectively interact with the construction system.
The application collects multisource data related to natural resources in the homeland space through the collecting end, the processing end establishes a digital model of the homeland space natural resources based on the multisource data, the digital model is synchronized with actual data in real time through a real-time synchronization mechanism, the digital model is updated according to the change of the actual data, simulation experiments are carried out by using the digital model, scene optimization is carried out by simulating different resource utilization schemes, decision support based on the digital model and the real-time data is provided, and the decision support comprises resource management advice, risk assessment and sustainable development planning. According to the scene construction method, the digital model is effectively constructed to reflect the relation of resource distribution, ecological system state, resource interaction and the like, the digital model is synchronized with actual data in real time through a real-time synchronization mechanism, timeliness of the digital model is guaranteed, finally, decision support based on the digital model and the real-time data is provided, the decision support comprises resource management advice, risk assessment and sustainable development planning, a user can check the running state of natural resources in a homeland space through the digital model, and the decision support is provided for the user, so that management is facilitated.
Example 2: the processing end establishes a digital model of the natural resources of the homeland space based on the multi-source data;
Acquiring relevant remote sensing image data, which can be data acquired by sensors such as multispectral, hyperspectral, synthetic Aperture Radar (SAR) and the like, so as to ensure that the space-time resolution of the image data is suitable for the scale and the purpose of research; preprocessing the remote sensing image, including atmospheric correction, radiation correction, geometric correction and the like, which are helpful for eliminating noise and deformation in the image and improving the quality of data; determining the type of natural resources existing in a research area, and providing labels for classification modeling, wherein the labels can be obtained by means of field investigation, ground monitoring, existing map data and the like;
Selecting sample points representing various ground objects on a remote sensing image, marking the types of the sample points, establishing a sample set of the sample points of the various ground objects, wherein the sample set is used for training a classification algorithm to ensure that the model can accurately identify different natural resource types, and training a random forest classifier by using training samples to obtain a digital model so that the digital model can automatically classify various ground objects according to input remote sensing image data; classifying the whole remote sensing image by using a trained and verified digital model to generate classified images containing various categories, wherein the images reflect the distribution of different natural resource types in a research area, and performing post-processing steps such as filtering, spectrum fusion and the like to improve the quality of the classified images;
Training the random forest classifier by using training samples to obtain a digital model, so that the digital model can automatically classify various ground objects according to input remote sensing image data, and the method comprises the following steps of:
Acquiring remote sensing image data D, wherein the remote sensing image data D represents a data set containing m samples, each sample has h characteristics, and simultaneously preparing a training set containing sample labels, and y represents a true class label of each sample;
selecting a sample set S on the remote sensing image data D, marking a real category label y for the sample set S, and forming a training set Training set/>The method comprises the steps of including m samples, wherein each sample has h characteristics and corresponding class labels;
The random forest comprises a set formed by a plurality of decision trees, the classification result of the random forest is obtained through a voting mechanism, and the expression of the digital model is as follows: in the above, the ratio of/> Representing the classification result,/>Is the number of decision trees in the random forest,/>Representing the classification result of the x decision tree on the input feature G;
Single decision tree Is generated by a decision tree algorithm, wherein the generation process comprises dividing according to random samples and random features, dividing each node is based on optimal features, minimizing node non-purity, and for each decision tree/>Using training set/>Training, at each node, selecting an optimal feature subset by randomly selecting the optimal feature subset for division until a stopping condition is met (for example, the depth of a tree reaches a preset value), classifying an input sample by each decision tree in a random forest, and determining a final classification result by a majority voting mode;
Single decision tree Is generated by a decision tree algorithm, wherein the generation process comprises the steps of dividing according to random samples and random features, and comprises the following steps: decision tree/>The calculated expression of (2) is: wherein/> Is by the characteristics/>Greater than threshold/>Is/are of the sample set of (C)Is by the characteristics/>Less than or equal to threshold/>Is/are of the sample set of (C)、/>Respectively representing the number of samples of two child nodes,/>、/>The base index,/>, of two child nodes respectivelyFor the sample set of the current node,/>Representing the number of samples in the sample set of the current node,/>Is a feature set,/>For a feature randomly selected from the feature set,/>A threshold value for the node;
Base index The calculated expression of (2) is: /(I)Where Q is the number of classification categories,/>Representing the probability that the sample belongs to the P-th class;
the specific generation logic of the decision tree is as follows: by searching for and making Minimum feature/>And a threshold T, whereby the partitioning of samples is achieved, this process is recursively performed at each node until a stop condition is met (e.g. the number of node samples is less than a certain threshold or the depth of the tree reaches a predetermined value).
Synchronizing the digital model with the actual data in real time through a real-time synchronization mechanism, and updating the digital model according to the change of the actual data;
The method comprises the steps of determining the frequency of real-time data transmission, namely the transmission time interval of data from an acquisition end to a processing end, determining based on the real-time requirements of an application, the speed of data change and other factors, selecting a proper communication protocol and a transmission mode to ensure safe, reliable and efficient data transmission, wherein a common protocol comprises HTTP/HTTPS, MQTT, webSocket and the like, the transmission mode can be point-to-point communication, a publish-subscribe mode and the like, unifying the format and the coding of real-time data to ensure that the data cannot lose information in the transmission process, the common data format comprises JSON, XML and the like, the coding standardization is beneficial to the correct analysis and the processing of the data, an identity verification mechanism is implemented, only legal users or equipment can transmit the data, and meanwhile, taking security measures such as encryption into consideration to protect the confidentiality and integrity of the data.
Determining the frequency of real-time data transmission, selecting a communication protocol and a transmission mode, wherein the communication protocol comprises HTTP/HTTPS, MQTT, webSocket, selecting an optimal communication protocol by analyzing the performances of different communication protocols in the current environment, and the transmission mode comprises point-to-point communication and a publish-subscribe mode, and specifically comprises the following steps:
obtaining a maximum response time index, a message confirmation rate and a program crash rate of a communication protocol in the current scene test run;
after dimensionless processing (removing unit) is carried out on the maximum response time index, the message confirmation rate and the program crash rate, the selection coefficient is obtained by taking the numerical value and comprehensively calculating The expression is: /(I)In the above, the ratio of/>For message acknowledgement rate,/>Is the maximum response time index,/>For program crash rate,/>、/>、/>Proportional coefficients of message acknowledgement rate, maximum response time index, program crash rate, respectively, and/>、/>、/>Are all greater than 0;
From selection coefficients The computational expression of (a) shows that the selection coefficient/>, of the communication protocolThe larger the value is, the better the test running performance of the communication protocol in the current scene is, and the more should be selected as the current communication protocol, so that data is transmitted by the optimal communication protocol;
the maximum response time index is calculated as: In which, in the process, Is the execution time of task v,/>Is the execution cycle of task v,/>Representing the maximum response time of the task v,Is a set of all tasks with high priority,/>The method is the execution time of the task w, the maximum response time index is used for reflecting the instantaneity of the communication protocol, the larger the maximum response time index is, the worse the instantaneity of the communication protocol is, for the instantaneity, webSocket > MQT > HTTP/HTTPS, webSocket and MQTT generally provide better instantaneity, because the two-way communication is supported, the two-way communication is suitable for updating and pushing notification in real time, the HTTP/HTTPS is a request-response model, and the instantaneity is relatively worse;
the message acknowledgement rate is calculated as: in the above, the ratio of/> For the number of successfully received acknowledgement messages,/>For the total number of messages sent, the greater the message acknowledgement rate, the better the reliability of the communication protocol is indicated, for the reliability, the MQTT > WebSocket > HTTP/HTTPs, the MQTT has a message acknowledgement mechanism, provides higher reliability, the WebSocket also supports reliable message transmission, but is relatively simple, the HTTP/HTTPs is also reliable, but may require higher overhead in some cases; the computational expression of the program crash rate is: /(I)In the above, the ratio of/>For the number of crashes of an application,/>The greater the program crash rate, the worse the compatibility of the communication protocol, for which HTTP/HTTPs > WebSocket = MQTT, which is a common protocol, supported by almost all platforms, the WebSocket and MQTT may require specific libraries or support, and thus additional work may be required on some platforms, for the total number of applications running;
In summary, the performance requirements on the communication protocol are different in different scenes, so that the construction system comprehensively analyzes the instantaneity, reliability and compatibility of the communication protocol to select the optimal communication protocol after performing the test operation of the communication protocol in the scenes, and ensures the timeliness and stability of data transmission.
The error processing and fault tolerant mechanism is designed to cope with the problems possibly occurring in the data transmission process, such as network interruption, data loss and the like, and the error processing and fault tolerant mechanism can comprise a retransmission mechanism, error code processing and the like, and the flow control mechanism is considered to prevent excessive data from being transmitted simultaneously to cause network congestion, so that load balancing is realized, different processing ends can be ensured to equally share the burden of data processing, a real-time monitoring system is arranged, the real-time performance and performance of data transmission are tracked, log recording is implemented, and events in the transmission process are recorded, so that the fault detection and performance optimization are facilitated.
Example 3: providing decision support based on the digital model and the real-time data, wherein the decision support comprises resource management advice, risk assessment and sustainable development planning;
Generating advice on resource management based on the digitized model and the real-time data, which may include schemes for rational utilization, development and protection of natural resources, taking into account the impact of the real-time data on the resource status;
Performing risk assessment by using the real-time data and the digital model, analyzing factors which can negatively affect resource management and sustainable development, such as natural disasters, climate change and the like, and providing corresponding risk mitigation strategies;
based on the analysis of the real-time data and the digitized model, a sustainable development plan is formulated, which may include planning in terms of long-term objective formulation, optimizing resource utilization, improving ecosystem stability, etc., to ensure sustainable utilization of resources;
adopting a multi-index decision analysis method, different resource management suggestions, risk assessment and sustainable development planning are incorporated into a comprehensive decision framework, which is helpful for weighing different decision schemes and finding an optimal solution;
the digital model, the real-time data and the decision analysis tool are integrated together, so that the system can provide an intuitive interface, and a decision maker can conveniently check and interact various information;
providing decision support based on a digital model and real-time data, wherein the decision support comprises resource management advice, risk assessment and sustainable development planning, which belong to the prior art means, are not described in detail herein.
A. Based on the digitized model and the real-time data, generating suggestions about resource management specifically includes the steps of:
The method comprises the steps of comprehensively analyzing the state of the current natural resources by using a digital model and real-time data, evaluating the quantity, quality and availability of the resources by considering the change of the real-time data, evaluating the utilization condition of the current resources, including the development degree, the use efficiency and the possible waste or over development condition of the resources, analyzing the space-time distribution of the resources by using the digital model, analyzing the influence of resource management advice on an ecological system by considering the influence of the resource management advice on the ecological system, and analyzing the health condition of the ecological system, wherein the digital model can help evaluate the stability, the species diversity and the ecological balance of the ecological system.
B. Using the real-time data and the digitized model, performing risk assessment, analyzing factors that may negatively affect resource management and sustainable development, such as natural disasters, climate change, and the like, and providing a corresponding risk mitigation strategy specifically includes the following steps:
Using real-time data and digitized models, identifying potential risk factors that may negatively impact resource management and sustainable development, which may include natural disasters (e.g., earthquakes, floods, hurricanes, etc.) and effects caused by climate change, vulnerability assessment of the resource management system, knowledge of the vulnerability of the system to different risk factors, which helps determine the coping ability of the system at the face of a particular risk, risk probability and impact assessment of the identified risk factors, assessment of this
The likelihood of some risk occurrence and once that occurs that may have an impact on resource management and sustainable development, a risk mitigation strategy is formulated based on the outcome of the risk assessment, which may include:
The reduction measures are as follows: the probability of risk occurrence is reduced, for example, disaster prevention engineering, ecological restoration and the like are adopted;
the adaptation measures are as follows: improving the adaptability of the system to risks, such as adjusting resource management strategies and adopting more flexible technologies;
and (3) transferring measures: transferring risk to other aspects such as purchasing insurance or establishing emergency reserves;
The method comprises the following steps: under the condition that the risk is controllable and unavoidable, accepting and making a countermeasure;
and (3) aiming at possible risks, a corresponding emergency plan is formulated, so that a clear response flow and a resource allocation scheme are ensured when a risk event occurs.
C. based on the analysis of the real-time data and the digitized model, a sustainable development plan is formulated, which may include planning in terms of long-term objective, optimizing resource utilization, improving ecosystem stability, etc., to ensure sustainable utilization of the resources, including the steps of:
The method comprises the steps of determining long-term targets and willingness of resource management and sustainable development, which may relate to targets in aspects of reasonable utilization of resources, protection of an ecological system, social and economic benefits and the like, evaluating the state of the current resources by using a digital model and real-time data, including quantity, quality, distribution and the like, establishing a resource inventory management system to understand the availability and regeneration capability of the resources, analyzing services of the ecological system to human beings, including water source protection, climate regulation, soil maintenance and the like, wherein the digital model can help quantify the values of the services, provide scientific basis for sustainable development planning, establish reasonable utilization strategies of the resources, determine the maximum sustainable yield of the resources to ensure the utilization of the resources at a sustainable level, identify key ecological systems needing protection and restoration, establish corresponding protection plans, including measures such as establishing protection areas, implementing ecological restoration and the like, so as to enhance the stability of the ecological system, optimize the utilization efficiency of the resources by using the digital model and the real-time data, and the method may relate to adopting more advanced technology, improving production, pushing the recycling economy and the like, improving the utilization efficiency, promoting the utilization of the resources, and promoting the development of the environment protection and the sustainable development, and the development of the novel technology, and the sustainable development, and the method is used for searching for the potential development and the novel technology and the efficient utilization model.
D. By adopting a multi-index decision analysis method, different resource management suggestions, risk assessment and sustainable development plans are incorporated into a comprehensive decision framework, which is helpful for weighing different decision schemes and finding an optimal solution, and comprises the following steps:
Defining decision targets, namely definitely considering key indexes which possibly comprise resource utilization efficiency, economic benefit, ecological system health, social participation and the like, giving appropriate weights to each decision target so as to reflect the relative importance of each target in decision, making weight distribution, namely ranking each decision scheme and planning measure as rows based on expert opinion, stakeholder feedback or output of a mathematical model, taking the decision targets as columns to form decision matrixes, filling the matrix with the scores of each scheme on each target, standardizing the scores in each decision matrix so as to eliminate the influence of different index units and dimensions, comparing all indexes on the same proportion, multiplying the standardized scores with corresponding weights to obtain weighted scores of each scheme on each target, adding the weighted scores to obtain a total score, ranking each scheme based on the total score, and selecting the scheme with the total score as the highest optimal solution.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. A scene construction method based on data twinning is characterized by comprising the following steps: the construction method comprises the following steps:
S1: the acquisition end acquires multisource data related to natural resources in a homeland space;
S2: the processing end establishes a digital model of the natural resources of the homeland space based on the multi-source data;
s3: synchronizing the digital model with the actual data in real time through a real-time synchronization mechanism, and updating the digital model according to the change of the actual data;
s4: performing simulation experiments by using a digital model, and simulating different resource utilization schemes to perform scene optimization;
S5: providing decision support based on the digital model and the real-time data, wherein the decision support comprises resource management advice, risk assessment and sustainable development planning;
S6: sending the generated digital model and decision support to a user interface for user display;
in step S4, performing a simulation experiment using the digitized model, and performing scene optimization by simulating different resource utilization schemes includes the following steps:
s401: setting different simulation scenes, wherein the simulation scenes comprise different resource utilization schemes and natural disaster scenes;
S402: running a simulation experiment according to a set scene, simulating different resource utilization and natural disaster conditions, and obtaining a simulation result, wherein the simulation result comprises resource utilization efficiency and environmental influence;
S403: carrying out data analysis on simulation results of the simulation experiment, evaluating various resource utilization and coping with effects of natural disaster schemes in different scenes, and adjusting resource utilization scheme optimization scenes based on the evaluation results;
S404: sorting the multiple schemes based on the evaluation result, and visually displaying the scheme with the first sorting;
in step S404, sorting the multiple schemes based on the evaluation result, and visually displaying the scheme with the first sorted scheme includes the following steps:
S4041: acquiring historical data and dimension data of a scheme, wherein the historical data comprises a return on investment rate, a carbon emission reduction index and a coefficient of kunity, and the dimension data comprises a resource growth rate;
s4042: normalizing the return on investment, the carbon emission reduction index, the coefficient of kene and the rate of resource increase to obtain the use coefficient of the scheme The computational expression is: /(I)In the above, the ratio of/>In order for the rate of increase of the resource,For return on investment,/>Is carbon emission reduction index,/>Is the coefficient of Kennel,/>、/>、/>Weight coefficients of return on investment, carbon emission reduction index, and coefficient of kene, respectively, and/>；
S4043: all schemes are based on the use coefficientSorting from big to small, visually displaying the first scheme, and selecting other schemes downwards in sequence for displaying if the first scheme does not achieve an ideal effect in the actual process.
2. The scene construction method based on data twinning according to claim 1, wherein: in step S2, the processing end establishes a digitized model of natural resources in the homeland space based on the multi-source data, which includes the following steps:
S201: preprocessing remote sensing image data after acquiring related remote sensing image data;
s202: determining the types of natural resources existing in the region, and providing labels for classification modeling;
s203: selecting sample points representing various ground objects on the remote sensing image, and marking the types of the sample points;
s204: training the random forest classifier by using sample points to obtain a digital model, and automatically classifying various ground objects by the digital model according to the input remote sensing image data.
3. The scene construction method based on data twinning according to claim 2, wherein: in step S204, training the random forest classifier using the training sample to obtain a digitized model includes the following steps:
S2041: acquiring remote sensing image data D, wherein the remote sensing image data D represents a data set containing m samples, and each sample has h characteristics;
s2042: selecting a sample set S on the remote sensing image data D, and marking a real category label y for the sample set S to form a training set ；
S2043: single decision treeGenerated by a decision tree algorithm, for each decision tree/>Using training set/>Training, namely randomly selecting a feature subset at each node, and selecting the best features from the feature subsets to divide until a stopping condition is met;
S2044: the random forest comprises a set formed by a plurality of decision trees, the classification result of the random forest is obtained through a voting mechanism, and the expression of the digital model is as follows: in the above, the ratio of/> Representing the classification result,/>Is the number of decision trees in the random forest,/>Representing the classification result of the input feature G by the xth decision tree.
4. A method of constructing a data twinning-based scene in accordance with claim 3, wherein: in step S2043, a decision tree algorithm generates a decision treeThe method comprises the following steps:
decision tree The calculated expression of (2) is: wherein/> Is by the characteristics/>Greater than threshold/>Is/are of the sample set of (C)Is by the characteristics/>Less than or equal to threshold/>Is/are of the sample set of (C)、/>Respectively representing the number of samples of two child nodes,/>、/>The base index,/>, of two child nodes respectivelyFor the sample set of the current node,/>Representing the number of samples in the sample set of the current node,/>Is a feature set,/>For a feature randomly selected from the feature set,/>A threshold value for the node;
Base index The calculated expression of (2) is: /(I)Where Q is the number of classification categories,/>Representing the probability that the sample belongs to the P-th class.
5. The data twinning-based scene construction method of claim 4, wherein: in step S3, synchronizing the digitized model with the actual data in real time by a real-time synchronization mechanism includes the following steps:
S301: determining the frequency of real-time data transmission, selecting a communication protocol and a transmission mode, wherein the communication protocol comprises HTTP/HTTPS, MQTT, webSocket, selecting an optimal communication protocol by analyzing the performances of different communication protocols in the current environment, and the transmission mode comprises point-to-point communication and a publish-subscribe mode;
S302: unifying the format and coding of real-time data, wherein the data format comprises JSON and XML;
s303: when an abnormality occurs in the data transmission process, the abnormality comprises network interruption and data loss, and the abnormality processing is carried out through an error processing and fault tolerance mechanism, and the abnormality processing comprises a retransmission mechanism and error code processing.
6. The scene construction method based on data twinning according to claim 5, wherein: in step S301, selecting an optimal communication protocol by analyzing the performance of different communication protocols in the current environment includes the steps of:
S3011: obtaining a maximum response time index, a message confirmation rate and a program crash rate of a communication protocol in the current scene test run;
s3012: the maximum response time index, the message confirmation rate and the program collapse rate are processed in a dimensionless manner, and then the numerical value is taken to comprehensively calculate and obtain the selection coefficient The expression is: /(I)In the above, the ratio of/>For message acknowledgement rate,/>Is the maximum response time index,/>For program crash rate,/>、/>、/>Proportional coefficients of message acknowledgement rate, maximum response time index, program crash rate, respectively, and/>、/>、/>Are all greater than 0;
S3013: selection coefficients for communication protocols The larger the value is, the better the commissioning performance of the communication protocol in the current scene is, and the coefficient/>, will be selectedThe maximum communication protocol is used as the communication protocol for transmitting data in the current scene.
7. The data twinning-based scene construction method of claim 6, wherein: the calculation expression of the maximum response time index is as follows: in the above, the ratio of/> Is the execution time of task v,/>Is the execution cycle of task v,/>Representing the maximum response time of task v,/>Is a set of all tasks with high priority,/>Is the execution time of task w.
8. The data twinning-based scene construction method as set forth in claim 7, wherein: the calculated expression of the coefficient of the foundation is as follows: in the above, the ratio of/> Representing the income of individual i,/>Is the total population number,/>Is average income,/>Representing the revenue of individual j.
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