CN-122020515-A - Flying dust PM10 list verification method and system integrating navigation and meteorological data

CN122020515ACN 122020515 ACN122020515 ACN 122020515ACN-122020515-A

Abstract

The invention discloses a dust PM10 inventory verification method and system integrating navigation and meteorological data, and relates to the technical field of computer-aided environment management; calculating concentration difference to generate initial contradiction distribution map, combining meteorological parameter field to identify contradiction diagnosis mode, generating competitive correction assumption for list or meteorological field, constructing dynamic weight arbitration function based on meteorological field space heterogeneity to calculate dynamic arbitration weight, weighting and fusing correction assumption to generate comprehensive correction scheme and making iterative update until convergence condition is satisfied. According to the invention, through analyzing the coupling relation between the data, the list estimation error and the meteorological simulation deviation are distinguished, and the collaborative verification and optimization of the multi-source data are realized.

Inventors

SUN YUEYIN
XIE FANGJIAN
TIAN FENG
LIU CHUNLEI
ZHENG XINMEI
Xie Diesong
Dou Daodao
WANG YAN

Assignees

南京市生态环境保护科学研究院

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (10)

1. A dust PM10 list verification method integrating navigation and meteorological data is characterized by comprising the following steps: A1, acquiring the concentration monitoring data of the sailing PM10, the grid gas image field data and the emission list data of the dust PM10 to be checked in a target area, and performing space-time alignment processing on the concentration monitoring data of the sailing PM10, the grid gas image field data and the emission list data of the dust PM10 to be checked to generate a space-time alignment three-dimensional data field comprising an observation concentration field, a meteorological parameter field and a list prediction concentration field; A2, calculating concentration differences based on the observed concentration field and the list predicted concentration field, generating an initial contradiction distribution map representing the spatial differences of the observed concentration field and the list predicted concentration field, and primarily estimating a suspected source region; A3, analyzing the spatial coupling relation between the initial contradiction distribution map and the meteorological parameter field, and identifying a contradiction diagnosis mode representing the corresponding relation between the concentration difference and the specific meteorological condition; a4, generating a plurality of mutually competing correction hypotheses for the emission list data of the dust PM10 to be checked or the grid-type gas image field data according to the contradiction diagnosis mode to form a competitive correction hypothesis set; A5, constructing a dynamic weight arbitration function according to the spatial heterogeneity characteristic of the meteorological parameter field, and calculating a dynamic arbitration weight for arbitrating the competitive correction hypothesis; a6, carrying out weighted fusion on the competitive correction hypothesis set by utilizing the dynamic arbitration weight to generate a spatial distributed comprehensive correction scheme; a7, updating the emission list data of the dust PM10 to be checked or the grid-type gas image field data by applying the comprehensive correction scheme; and A8, feeding the updated data back to A2 to generate an optimized contradiction distribution diagram, and repeatedly executing A3-A7 until the integral difference amplitude of the optimized contradiction distribution diagram meets the convergence condition, and outputting a final check and optimization result.
2. The method for verifying a dust PM10 list fused with navigation and weather data according to claim 1, wherein the performing a space-time alignment process on the navigation PM10 concentration monitoring data, the gridding weather field data, and the dust PM10 emission list data to be verified comprises: Performing spatial interpolation processing on the concentration monitoring data of the sailing PM10, and generating the observation concentration field after local dust contribution separation processing; Resampling wind speed, wind direction and atmospheric stability parameters in the grid meteorological field data to generate the meteorological parameter field which is matched with the observed concentration field in time-space; And simulating by using an atmospheric diffusion model based on the emission list data of the dust PM10 to be verified, and generating the list predicted concentration field which is matched with the observed concentration field in a space-time mode.
3. The method for verifying a dust PM10 inventory fused with navigation and weather data according to claim 1, wherein said analyzing the spatial coupling relationship between the initial contradictory distribution map and the weather parameter field, and identifying a contradictory diagnostic mode characterizing the correspondence between the concentration difference and the specific weather condition, comprises: Extracting wind speed space distribution characteristics from the meteorological parameter field, wherein the wind speed space distribution characteristics comprise the wind speed size, the wind speed direction and the air stability of each grid unit; Carrying out joint space correlation analysis on a negative value region in the initial contradiction distribution diagram, a first wind speed region in the wind speed space distribution characteristic, a first stability region in the atmosphere stability space distribution characteristic and the consistency of a diffusion path of a direction and a pollution source-observation region; When the negative value region simultaneously meets the conditions that the first wind speed region, the first stability region and the direction are consistent with the diffusion path of the pollution source pointing to the observation region in the list, marking the corresponding region as a first contradiction diagnosis mode, and identifying the first contradiction diagnosis mode; carrying out joint space correlation analysis on the positive value region in the initial contradiction distribution diagram, the second wind speed region in the wind speed space distribution characteristic, the second stability region in the atmosphere stability space distribution characteristic and the cumulative path consistency of the direction and the suspected missing report source-observation region; If the positive value region simultaneously meets the conditions that the second wind speed region, the second stability region and the direction are consistent with the accumulated path of the suspected missing report source pointing to the observation region, marking the corresponding region as a second contradiction diagnosis mode, and identifying the second contradiction diagnosis mode; The first contradictory diagnostic mode and the second contradictory diagnostic mode together constitute the contradictory diagnostic mode.
4. A method of calibrating a dust PM10 inventory incorporating navigational and meteorological data according to claim 3, wherein said forming a competitive correction hypothesis set comprises: Generating, for the first contradictory diagnostic mode, a first checklist modification assumption aimed at downregulating checklist emissions and a first meteorological field modification assumption aimed at enhancing meteorological diffusion; generating, for the second contradictory diagnostic mode, a second checklist modification assumption intended to supplement a checklist emissions source and a second meteorological field modification assumption intended to enhance meteorological accumulation; Combining the first list modification assumption, the first meteorological field modification assumption, the second list modification assumption, and the second meteorological field modification assumption to form the competitive modification assumption set.
5. The method for verifying a dust PM10 list with integrated navigation and weather data according to claim 1, wherein the constructing a dynamic weight arbitration function according to the spatial heterogeneity characteristic of the weather parameter field and for arbitrating the dynamic arbitration weight of the competitive correction hypothesis comprises: calculating the spatial gradient of a wind speed field in the meteorological parameter field to generate a wind speed gradient field; dividing a space grid into a weather uniform region and a weather complex region according to gradient values in the wind speed gradient field; And constructing the dynamic weight arbitration function, wherein the dynamic weight arbitration function is used for promoting the weight of the correction assumption aiming at the list data in the weather uniform area and promoting the weight of the correction assumption aiming at the weather field data in the weather complex area, and generating the dynamic arbitration weight by utilizing the dynamic weight arbitration function.
6. The method for verifying a dust PM10 list for merging navigation and weather data according to claim 5, wherein the step of performing weighted merging on the competitive correction hypothesis set by using the dynamic arbitration weight to generate a comprehensive correction scheme comprises: for each spatial grid cell, acquiring the dynamic arbitration weight allocated to the spatial grid cell; Weighting the competitive correction hypothesis applied to the space grid unit by using the dynamic arbitration weight to generate a localization correction quantity of the grid unit; and aggregating the localization correction amounts of all the space grid cells to form the space distributed comprehensive correction scheme.
7. The method for verifying the dust PM10 list fused with the navigation and weather data according to claim 1, wherein the step of determining that the overall difference amplitude of the optimized contradictory profiles satisfies a convergence condition comprises: Calculating the statistical average value of all grid cell concentration residuals in the optimized contradiction distribution diagram to obtain the integral difference amplitude; Obtaining a convergence threshold that characterizes an acceptable error level; And if the total difference amplitude is smaller than the convergence threshold, judging that the convergence condition is met, formally marking the optimized list data obtained by the current iteration as a final discharge list subjected to collaborative verification, and generating a comprehensive diagnosis report containing potential missing report source positioning information.
8. The method for checking a dust PM10 list integrated with navigation and weather data according to claim 1, further comprising, after the outputting the final checking and optimizing result: Extracting the position information of the potential missing report source from the final verification and optimization result; The position information of the potential missing report source is visualized on a geographic information map, and a suspicious emission source distribution thermodynamic diagram is generated; Planning a navigation path for improving monitoring pertinence according to the suspicious emission source distribution thermodynamic diagram, and generating a navigation task optimization suggestion.
9. The method for checking a dust PM10 list integrated with navigation and weather data according to claim 1, further comprising: After the verification method is executed for a plurality of times, the historical meteorological types, the successful contradiction diagnosis modes and the effective dynamic arbitration weights used in each verification are stored in a correlated mode, and a correlated knowledge base is established; before executing a new verification task, acquiring a dominant meteorological type of a current task period; And searching in the associated knowledge base by utilizing the dominant meteorological type, and loading a contradictory diagnosis mode matched with the dominant meteorological type and a dynamic arbitration weight for initializing the generation of the competitive correction hypothesis set and the construction of the dynamic weight arbitration function.
10. A system for performing the dust PM10 inventory verification method of fused navigation and weather data according to any one of claims 1-9, comprising: the multi-source data fusion module is used for acquiring and processing the concentration monitoring data of the sailing PM10, the grid-type gas image field data and the dust-raising PM10 emission list data to be checked so as to generate a space-time alignment three-dimensional data field; The contradiction diagnosis module is used for generating an initial contradiction distribution map based on the space-time alignment three-dimensional data field and a suspected source region which is estimated preliminarily, and analyzing the coupling relation between the suspected source region and the meteorological parameter field to identify a contradiction diagnosis mode; The hypothesis generation and arbitration module is used for generating a competitive correction hypothesis set according to the contradictory diagnosis mode, and calculating dynamic arbitration weight according to the spatial heterogeneity characteristic of the meteorological parameter field so as to generate a comprehensive correction scheme; and the iterative optimization module is used for updating the data by applying the comprehensive correction scheme, and outputting a final verification and optimization result through cyclic iteration until convergence conditions are met.

Description

Flying dust PM10 list verification method and system integrating navigation and meteorological data Technical Field The invention relates to the technical field of computer-aided environment management, in particular to a dust-raising PM10 list verification method and system integrating navigation and meteorological data. Background The emission list of the dust-raising PM10 is a key data product for quantifying the total amount of PM10 emitted to the atmosphere by various dust-raising sources in a specific area in a certain time, and is an important basis for carrying out the work of analyzing the atmosphere pollution sources, forecasting and early warning the air quality, evaluating the pollution control policy effect and the like. In computer-aided environmental management, the accuracy of the emissions manifest is directly related to the reliability of all subsequent simulations and decisions. Therefore, the effective verification of the initial emission list compiled by the computer model is one of the key links for guaranteeing the scientificity of environment management. The prior art, such as the patent application publication number CN111523717B, CN113514373B, CN114813192B, related to the verification of the emission list of the dust PM10, can be seen that the verification method of the emission list of the dust PM10 in the industry mainly depends on the comparison of environment monitoring data and the predicted concentration of the list. One common way is to use long-term observations of a fixed air quality monitoring site for macroscopic comparison. In order to overcome the defect of insufficient space coverage of a fixed site, in recent years, vehicle-mounted mobile monitoring technology, namely navigation monitoring, is widely used, and a list is more finely checked by acquiring pollutant concentration data with high spatial resolution. Some methods also attempt to introduce weather data, typically by applying a statistical-based weather correction factor to the travel monitoring data prior to comparison with the inventory, in order to subtract the effects of factors such as weather diffusion. The prior art means have obvious technical defects in practical application. The verification method relying on the fixed site is sparse in site layout, the space representativeness is limited, and the emission characteristic of dust emission, which is a pollution source with obvious space heterogeneity, is difficult to effectively capture. When the navigation data are used alone for verification, the instantaneous concentration value is strongly interfered by meteorological conditions such as wind speed, humidity and the like, so that the verification process is difficult to effectively distinguish whether the observed high concentration is caused by high-strength emission or unfavorable meteorological diffusion conditions, and the conclusion often has multiple solutions and uncertainty. Even if the weather correction method is introduced, the simple linear or statistical correction cannot truly reflect the complex nonlinear atmospheric physical process, the accuracy of the verification result is difficult to ensure, and even the error of the weather correction model is possibly wrongly attributed to the emission list, so that the verification conclusion is distorted. Disclosure of Invention The invention aims to provide a dust PM10 list verification method and system integrating navigation and meteorological data, and solves the problems in the background art. In order to solve the technical problems, the invention provides a dust PM10 list verification method integrating navigation and meteorological data, which comprises the following steps that A1, navigation PM10 concentration monitoring data, grid-type meteorological field data and dust PM10 emission list data to be verified in a target area are obtained, space-time alignment processing is carried out on the navigation PM10 concentration monitoring data, the grid-type meteorological field data and the dust PM10 emission list data to be verified, and a space-time alignment three-dimensional data field comprising an observation concentration field, a meteorological parameter field and a list prediction concentration field is generated. A2, calculating concentration differences based on the observed concentration field and the list predicted concentration field, generating an initial contradiction distribution map representing the spatial differences of the observed concentration field and the list predicted concentration field, and primarily estimating a suspected source region. A3, analyzing the spatial coupling relation between the initial contradiction distribution map and the meteorological parameter field, and identifying a contradiction diagnosis mode representing the corresponding relation between the concentration difference and the specific meteorological condition. And A4, generating a plurality of competing correction assumptions for the