CN114492987A - Asset stock spatialization method, system and storage medium - Google Patents
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Abstract
The disclosure provides an asset stock spatialization method, an asset stock spatialization system and a storage medium, and relates to the technical field of geographic information. The specific scheme is as follows: obtaining an inventory relevance dataset, the relevance dataset comprising: road network data, a remote sensing data set and a low-resolution asset stock distribution data set; training a correlation data set by using an ensemble learning model to obtain index weights corresponding to the correlation data set; obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; and visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map. According to the technology provided by the disclosure, the technical problem that the low resolution ratio of the asset stock distribution is converted into the high resolution ratio is solved, errors are reduced through rich index selection and multi-model combination training, and the accuracy of a prediction result is improved.
Description
Technical Field
The invention relates to the technical field of geographic information, in particular to an asset stock spatialization method, an asset stock spatialization system and a storage medium.
Background
In recent years, experts and scholars have conducted extensive research around natural disaster assessment systems for accuracy and scientificity of disaster assessment. The severity of the disaster is determined by disaster-causing factors, pregnant disaster environment sensitivity and vulnerability of disaster-bearing bodies. The assets stock of the pregnant disaster environment is used as a socioeconomic exposure index for measuring the exposure degree of a disaster bearing body, is used for replacing the total domestic production value to represent the economic risk of suffering from natural disasters, and is widely applied to natural disaster assessment and research.
Based on a disaster evaluation system, the complex disaster-causing factors are matched with the high-resolution asset stock distribution, so that better fusion analysis can be performed. However, the existing socioeconomic data are generally formed by taking administrative divisions as statistical units and summarizing the administrative divisions by means of general survey, sampling and the like. Although reliable data, statistical data can only represent "total values" within a particular administrative region, with no more detailed distribution information for the interior of the region. In disaster risk assessment, disaster-stricken population and economic loss are usually determined according to the proportion of a disaster range to an administrative division area by taking census data (statistical data) as a main data source, and the imbalance of distribution of residential points in an exposure range, the difference of population density and the aggregation of social and economic activities are not considered, so that the traditional disaster risk assessment result is rough and lacks practicability.
Disclosure of Invention
The technical problem that distribution resolution of inventory data obtained by taking administrative divisions as statistical units is low is solved. The present disclosure provides an asset inventory spatialization method, system and storage medium.
According to a first aspect of the present disclosure, there is provided an asset inventory spatialization method, including:
obtaining an inventory relevance dataset, the relevance dataset comprising: road network data, a remote sensing data set and a low-resolution asset stock distribution data set;
training a correlation data set by using an ensemble learning model to obtain index weights corresponding to the correlation data set;
obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; and
and visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
Preferably, the correlation data set further comprises: POI data sets and digital elevation model data sets.
Preferably, the index weight corresponding to the obtained correlation data set is 19% to 23% of the road network data index weight and 28% to 32% of the remote sensing data index weight.
Preferably, the index weight corresponding to the obtained correlation data set is 21% to 25% of the POI data index weight; the digital elevation model data index weight is 23% to 27%.
Preferably, the training of the correlation data set using a ensemble learning model, the ensemble learning model comprising:
a primary model, the primary model comprising: random forests;
a secondary model, the secondary model comprising: a multiple linear regression model.
Preferably, the primary model further comprises: cubist, extreme gradient lifting model.
Preferably, the index weight corresponding to the correlation data set and the low-resolution inventory quantity distribution data set are used, and the low-resolution inventory quantity distribution data set is the inventory quantity distribution data set.
Preferably, the index weight corresponding to the correlation data set and the low-resolution inventory quantity distribution data set are used, and the low-resolution inventory quantity distribution data set is a market-level inventory quantity distribution data set.
According to a second aspect of the present invention, there is provided an inventory spatialization system, comprising:
the data acquisition module is used for acquiring an asset stock correlation data set;
the model training module is used for training the correlation data set by using the ensemble learning model to obtain index weights corresponding to the correlation data set;
the data processing module is used for obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; or for populating missing portions of the inventory dependency data set; and
and the data visualization module is used for visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
Preferably, the method further comprises the following steps:
the verification module is used for verifying the high-resolution asset stock distribution prediction result set;
according to a third aspect of the present invention, there is provided a computer readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method of the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
The invention has the beneficial effects that:
(1) according to the inventory spatialization method provided by the disclosure, the conversion of the inventory distribution from low resolution to high resolution is completed.
(2) According to the asset stock spatialization method, through rich index selection and multi-model combined training, errors are reduced, and the accuracy of an asset stock distribution prediction result is improved.
(3) According to the asset stock spatialization method, the conversion from the low-resolution asset stock to the high-resolution asset stock is completed by adopting a big data means, and compared with a traditional manual census mode, the cost is reduced, and the efficiency is improved.
Drawings
The drawings are used for better understanding of the technical scheme and do not limit the disclosure. Wherein:
FIG. 1 shows a flow diagram of the disclosed asset inventory spatialization method.
Fig. 2 shows a network architecture schematic of the inventory spatialization system of the present disclosure.
FIG. 3 shows a schematic diagram of the predicted effect of the single model random forest of the present disclosure.
FIG. 4 shows a schematic diagram of the predicted effect of the disclosed single model Cubist.
FIG. 5 shows a schematic diagram of the predicted effect of the single model extreme gradient lifting model of the present disclosure.
Fig. 6 shows a schematic diagram of the predictive effect of the ensemble learning model of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness. It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict.
In addition, in the technical scheme related to the disclosure, the acquisition, storage, application and the like of the socio-economic information (for example, the acquisition of the low-resolution asset stock quantity distribution data set related to the disclosure) all meet the regulations of related laws and regulations, and do not violate the good customs of the public order.
The existing social and economic data are generally formed by taking administrative divisions as statistical units and summarizing in manners of general survey, sampling and the like. Although reliable data, statistical data can only represent "totals" (coarse-grained) within a particular administrative region, with no more detailed distribution information (fine-grained) inside the region. In addition, the manual statistics and summary mode consumes a large amount of manpower and material resources and is low in efficiency.
The asset stock spatialization method provided by the disclosure applies the technology of big data. Selecting factors (remote sensing, poi, road network, elevation model and the like) with strong correlation with social and economic activities as independent variables of the model, and obtaining the weight of each correlation index through combined model training; and (4) obtaining a fine-grained asset stock distribution data set by combining a coarse-grained asset stock distribution data set which is disclosed by artificial statistics and is divided into statistical units, and visualizing the fine-grained asset stock distribution data set.
First embodiment
As shown in fig. 1, the present disclosure provides an asset inventory spatialization method, including:
step 101: obtaining an inventory relevance dataset, the relevance dataset comprising: road network data, a remote sensing data set and a low-resolution asset stock distribution data set.
In this embodiment, the asset spatialization method may be performed on the system of the present disclosure. The system of the present disclosure may be configured in any electronic device to perform the inventory spatialization method of the embodiments of the present disclosure. The electronic device may be a server or a terminal, and is not limited herein.
The manner of acquiring the data set is not limited, for example, acquiring from a network, acquiring from a journal, or acquiring by manual statistics, and is not particularly limited herein. The data set may include various inventory distribution correlation indexes, such as a remote sensing data set, road network data, a POI data set, and an elevation model data set, which are not limited herein.
Optionally, the correlation data set is divided into a training set and a validation set by leave-one-sample method. The training set is used for training the ensemble learning model, and the verification set is used for verifying the assessment and the precision of the ensemble learning model.
In a preferred form, the acquired data set includes: the system comprises a remote sensing data set, a road network data set, a POI data set and an elevation model data set.
Step 102: and training the correlation data set by using an ensemble learning model to obtain index weights corresponding to the correlation data set.
The ensemble learning model may be a single model or a combination of various models, such as a random forest, a Cubist, and a gradient-threshold boosting model, such as a multiple linear regression, or such as a multiple linear regression model, which is not limited herein.
In a preferred mode, the ensemble learning model is divided into a primary model and a secondary model, wherein the primary model comprises a random forest, a Cubist and a limiting gradient lifting model, and the secondary model comprises a multiple linear regression model.
And (3) training a correlation data set by using a primary model (random forest, Cubist and extreme gradient) to obtain index weight values respectively corresponding to the models, and then performing tuning treatment on the index weight values respectively corresponding to the models through a secondary model (multiple linear regression model) to obtain the weight value with a small error with actual asset stock data.
Optionally, step 102 specifically includes:
and filling missing parts of the data acquisition data set in a linear fitting mode, for example, filling missing grade city data in the data set according to good correlation between the inventory and the GDP. Then, the acquired data set is divided into a training set and a verification set, the training set is used for model training to obtain index weights, and the verification set is used for verifying the reliability of the model prediction result. Then, training the training set through a primary model to obtain a primary prediction result; then, the prediction result is input into a linear regression model to obtain the weight of each index.
Step 103: and obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set.
The low-resolution asset inventory distribution dataset may be of various granularities, such as an urban asset inventory distribution dataset, a provincial asset inventory distribution dataset, and a coastal asset inventory distribution dataset, which is not specifically limited herein.
In a preferred approach, the low resolution inventory distribution dataset is a market level inventory distribution dataset.
Step 104: and visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
The tools used for visualizing the prediction result set can be various tools, such as Tableau, Canva, Datavisual, Python, ArcGis, and PiktoChart, and are not limited herein.
In a preferred approach, the predictive outcome set visualization tool is ArcGis.
The accuracy of the prediction results obtained by using the ensemble learning model as shown in fig. 2-6 is significantly better than that of the prediction results obtained by using a single model (random forest, Cubist, extreme gradient boosting model).
Second embodiment
As shown in fig. 2, the present disclosure provides an inventory spatialization system, including:
and S201, a data acquisition module for acquiring the inventory relevance data set.
And the S202 model training module is used for training the correlation data set by using the ensemble learning model to obtain index weights corresponding to the correlation data set.
S203, a data processing module for obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; or to fill in missing portions of the inventory dependency data set.
And S204, a data visualization module for visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
In a preferred mode, the asset stock spatialization correlation index dataset acquired by the data acquisition module includes a remote sensing dataset, a road network dataset, a POI dataset and a DEM dataset. And the obtained low-resolution asset stock distribution data set is a market-level asset stock distribution data set.
In some embodiments, the inventory spatialization system provided by the present disclosure further includes:
and the verification module is used for verifying the high-resolution asset stock distribution prediction result set.
In conclusion, the asset stock spatialization method provided by the disclosure realizes the conversion from the low-resolution asset stock to the high-resolution asset stock by using the big data technology; through abundant index selection and multi-model combined training, errors are reduced, and the accuracy of results is improved.
It should be understood that various forms of the flows described above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and the present disclosure is not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. An asset inventory spatialization method, comprising:
obtaining an inventory relevance dataset, the relevance dataset comprising: road network data, a remote sensing data set and a low-resolution asset stock distribution data set;
training a correlation data set by using an ensemble learning model to obtain index weights corresponding to the correlation data set;
obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; and
and visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
2. The inventory spatialization method according to claim 1, wherein said correlation data set further comprises: POI data sets and digital elevation model data sets.
3. The method of claim 1, wherein the index weight corresponding to the correlation data set is 19% to 23% for road network data and 28% to 32% for remote sensing data.
4. The asset inventory spatialization method according to claim 2, wherein the index weight corresponding to the correlation data set is obtained, and the POI data index weight is 21% to 25%; the digital elevation model data index weight is 23% to 27%.
5. The inventory spatialization method according to any one of claims 1 to 4, wherein the correlation data set is trained using an ensemble learning model, said ensemble learning model comprising:
a primary model, the primary model comprising: random forests;
a secondary model, the secondary model comprising: a multiple linear regression model.
6. The inventory spatialization method of claim 5, wherein the primary model further comprises: a limiting gradient lifting model and Cubist.
7. The inventory spatialization method according to any one of claims 1 to 4, wherein index weights corresponding to the correlation data set and a low resolution inventory stock distribution data set are used, the low resolution inventory stock distribution data set being a market-level inventory stock distribution data set.
8. An asset inventory spatialization system, comprising:
the data acquisition module is used for acquiring an asset stock correlation data set;
the model training module is used for training the correlation data set by using the ensemble learning model to obtain index weights corresponding to the correlation data set;
the data processing module is used for obtaining a high-resolution asset stock distribution prediction result set by using the index weight corresponding to the correlation data set and the low-resolution asset stock distribution data set; or for populating missing portions of the inventory dependency data set; and
and the data visualization module is used for visualizing the high-resolution asset stock prediction result set to obtain a high-resolution asset stock distribution map.
9. The inventory spatialization system according to claim 8, further comprising: and the verification module is used for verifying the high-resolution asset stock distribution prediction result set.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program realizes the steps of the method according to any one of claims 2-4 when executed by a processor.
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