Method and system for tracing new pollutants in geology based on big data analysis

Abstract

The invention discloses a big data analysis-based new pollutant traceability method and system in geology, and belongs to the field of new pollutant traceability rendering management, and the method comprises the following steps: analyzing the rendering coefficient of a new pollutant, determining a first rendering optimization strategy of the new pollutant, analyzing the rendering demand index of each first sub-pollutant, and determining a second rendering optimization strategy of the new pollutant; and determining a second rendering optimization strategy of each first sub-pollutant, executing a rendering optimization process of each first sub-pollutant, receiving a rendering optimization completion signal of each first sub-pollutant, analyzing an optimization effect verification category of each first sub-pollutant, and determining a third rendering optimization strategy of each first sub-pollutant. According to the method, the rendering effect is optimized and adjusted, the optimization and adjustment mode is dynamically adjusted according to different rendering effects, powerful technical support is provided for accurate tracing of new pollutants, and the problem that in the prior art, the rendering effect is poor due to rendering overlapping of the new pollutants in the rendering process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new pollutant source tracing and rendering management, and in particular to a method and system for tracing the source of new pollutants in geology based on big data analysis. Background Art

[0002] New pollutant source tracing technology is widely used in important scenarios such as soil pollution tracking, groundwater pollution source analysis, and ecological risk assessment. In actual applications, three-dimensional visualization rendering technology is often used when tracing the source of new pollution. In the actual rendering process, since new pollutants often migrate along channels such as geological faults and cracks, when there are multiple pollution sources, the migration paths of different pollution sources may overlap or intersect in space. In three-dimensional visualization rendering, pollutants are usually drawn in the form of voxels or translucent particle clouds. When processing multiple pollutants, if their spatial ranges overlap, the rendering calculations of each pollution source will be repeated in these overlapping areas, which will affect the accuracy of tracing the source of new pollutants.

[0003] For example, the invention patent announcement with announcement number: CN119648472B discloses a method and system for tracing the source of abnormal water inflow to a sewage treatment plant based on the Fréchet distance. The method divides the water inflow range of the sewage treatment plant into units based on plot information, pipe network information, confluence conditions, monitoring stations, etc., and analyzes the correlation between total phosphorus and conductivity; deploys pipe network water quality monitoring micro-stations, and sorts out the "plant-network-plot-source" pointer relationship library; builds a big data warehouse for sewage treatment plants, cleans and manages relevant data, and connects the data to a pollutant tracing visualization platform; builds a water pollution tracing model for sewage treatment plants, selects incidents of exceeding the total phosphorus standard at the water inlet for tracing the source, and embeds the tracing model into the visualization platform to realize the tracing visualization of the total phosphorus concentration exceeding the standard in sewage treatment plants.

[0004] For example, the invention patent announcement with announcement number: CN112749478B discloses an atmospheric pollution source tracing and diffusion analysis system and method based on the Gaussian diffusion model. The simulation modules adopted include GIS information, pollution diffusion simulation and visual rendering. The regional environment is simulated through information to form a GIS map. According to the monitoring data and the terrain-corrected Gaussian diffusion model, the pollution diffusion concentration changes are superimposed on the GIS map through image processing technology. The wind field distribution after terrain correction is obtained according to the terrain data and meteorological data. The Gaussian diffusion model formula is then selected to obtain the concentration influence of each pollution source on the grid center point of the assessment area, calculate the cumulative concentration value, and obtain the pollutant concentration value of the grid center point according to the calculation. The value is visually rendered on the GIS map, and the visualization result is superimposed on the basic geographic information base map of the assessment area to realize the intuitive display of the prediction results.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the existing technology, the new pollutant source tracing method in geology based on big data analysis focuses on the analysis of the composition, concentration and migration path of new pollutants when visually tracing new pollutants. In the visualization rendering process, static layer overlay, color mapping or fixed priority are often used for graphic rendering. However, in the actual visualization rendering process, when new pollutants overlap in spatial distribution, their corresponding graphic elements may cause the visualization information of some pollutants to be covered by other pollutants due to position superposition, resulting in confusion in pollution source identification, misjudgment of tracing path and deviation in data interpretation, which in turn affects the reliability of new pollutant source tracing. Summary of the Invention

[0007] The first aspect of the present invention provides a method for tracing the source of new pollutants in geology based on big data analysis, comprising the following steps:

[0008] S1, obtaining basic voxel data of the new pollutant, analyzing the rendering coefficient of the new pollutant, and determining the first rendering optimization strategy of the new pollutant.

[0009] S2, obtain the rendering area ratio of each sub-pollutant, combine the rendering coefficient of the new pollutant to obtain the sub-pollutants of each required rendering optimization, record them as each first sub-pollutant, obtain the rendering demand data of each first sub-pollutant, and analyze the rendering demand indicators of each first sub-pollutant.

[0010] S3 , based on the rendering requirement index of each first sub-pollutant, determining a second rendering optimization strategy for each first sub-pollutant, thereby executing a rendering optimization process for each first sub-pollutant.

[0011] S4, receiving a rendering optimization completion signal of each first sub-pollutant, analyzing the optimization effect verification category of each first sub-pollutant, and thereby determining a third rendering optimization strategy for each first sub-pollutant.

[0012] Furthermore, the rendering coefficient of the new pollutant is analyzed as follows: the basic voxel data of the new pollutant includes the number of times the voxel pollution source is covered, the number of times a single voxel is drawn, the proportion of overlapping areas and the average concentration similarity.

[0013] Analyze the rendering coefficients of the new pollutant based on its underlying voxel data.

[0014] The rendering coefficient of the new pollutant is the quantitative data of the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the degree of influence of average concentration similarity. The specific analysis process is: the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the average concentration similarity are compared with the corresponding reference values, and the results of each comparison are coupled with the corresponding importance ratio to obtain the rendering coefficient of the new pollutant.

[0015] Furthermore, the first rendering optimization strategy for the new pollutant is determined, and the specific analysis process is as follows: extracting the rendering coefficient verification factor preset in the database.

[0016] If the rendering coefficient of the new pollutant is greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as pending rendering.

[0017] If the rendering coefficient of the new pollutant is not greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as conventional rendering.

[0018] Furthermore, for each first sub-pollutant, the specific acquisition process is as follows: when the first rendering optimization strategy of the new pollutant is pending rendering, the rendering area ratio threshold is extracted based on the rendering coefficient of the new pollutant.

[0019] If the rendering area ratio of a sub-pollutant exceeds the rendering area ratio threshold, the sub-pollutant is recorded as a sub-pollutant requiring rendering optimization.

[0020] Traverse each sub-pollutant, screen and count to obtain each required rendering optimization sub-pollutant, and record it as each first sub-pollutant.

[0021] Furthermore, the rendering requirement index of each first sub-pollutant is specifically processed as follows: the rendering requirement data of each first sub-pollutant includes the average concentration sub-similarity, the remaining capacity of the buffer area, and the color similarity value.

[0022] The rendering requirement index of each first sub-pollutant is analyzed based on the rendering requirement data of each first sub-pollutant.

[0023] The rendering demand index of each first sub-pollutant is the quantitative data of the influence degree of average concentration sub-similarity, remaining capacity of the cache area and color similarity value. The specific analysis process is: compare the average concentration similarity sub-degree, remaining capacity of the cache area and color similarity value with the corresponding reference value, and couple the results of each comparison with the corresponding importance ratio to obtain the rendering demand index of each first sub-pollutant.

[0024] Furthermore, the second rendering optimization strategy for each first sub-pollutant is determined, and the specific analysis process is as follows: extracting the preset first verification factor of rendering requirement and the second verification factor of rendering requirement from the database.

[0025] If the rendering requirement index of a first sub-pollutant is greater than the first verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as heavyweight rendering.

[0026] If the rendering requirement index of a first sub-pollutant is not greater than the first rendering requirement verification factor and greater than the second rendering requirement verification factor, the second rendering optimization strategy of the first sub-pollutant is recorded as lightweight rendering.

[0027] If the rendering requirement index of a first sub-pollutant is not greater than the second verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as conventional rendering.

[0028] Furthermore, the rendering optimization process of each first sub-pollutant is executed. The specific analysis process is as follows: the rendering requirement first verification factor is subtracted from the rendering requirement index of each first sub-pollutant to obtain the rendering requirement first deviation index of each first sub-pollutant, and recorded as each first deviation index.

[0029] The adjustment parameters corresponding to each first deviation index interval stored in the database are extracted, and the adjustment parameters corresponding to the interval where the first deviation index is located are mapped and extracted, and recorded as the first adjustment parameters of each first sub-pollutant.

[0030] The first adjustment parameters of each first sub-pollutant include a light source intensity supplement value, a sharpness supplement value, a buffer area capacity supplement value, and a saturation supplement value.

[0031] The rendering requirement first verification factor is subtracted from the rendering requirement index of each first sub-pollutant to obtain the rendering requirement second deviation index of each first sub-pollutant, and recorded as each second deviation index.

[0032] The adjustment parameters corresponding to each second deviation index interval stored in the database are extracted, and the adjustment parameters corresponding to the interval where the second deviation index is located are mapped and extracted, and recorded as the second adjustment parameters of each first sub-pollutant.

[0033] The second adjustment parameters of each first sub-pollutant include a transparency supplement value, a buffer remaining capacity reduction value, a resolution supplement value, and a reflection accuracy supplement value of each first sub-pollutant.

[0034] Furthermore, the optimization effect verification category of each first sub-pollutant is analyzed to determine the third rendering optimization strategy of each first sub-pollutant. The specific analysis process is as follows: the rendering requirement index of each first sub-pollutant is re-obtained and recorded as each first rendering requirement index.

[0035] Extract the rendering requirement correction coefficient based on the rendering coefficient of the new pollutant.

[0036] Each first rendering requirement correction index is obtained based on each first rendering requirement index and the rendering requirement correction coefficient.

[0037] If a first rendering requirement correction index is less than or equal to the second rendering requirement verification factor, the optimization effect verification category of the first sub-pollutant is determined as the optimization effect meets the standard, and the third rendering optimization strategy of the first sub-pollutant is recorded as continuous monitoring.

[0038] If a first rendering requirement correction index is greater than the second verification factor of the rendering requirement and less than the rendering requirement index of the first sub-pollutant, the optimization effect verification category of the first sub-pollutant is determined to be optimization valid, and the third rendering optimization strategy of the first sub-pollutant is recorded as performing secondary optimization, and the first sub-pollutant is simultaneously recorded as a sub-pollutant requiring secondary optimization.

[0039] If a first rendering requirement correction index is greater than or equal to the rendering requirement index of the first sub-pollutant, the optimization effect verification category of the first sub-pollutant is determined as optimization failure, and the third rendering optimization strategy of the first sub-pollutant is recorded as executing the backtracking process.

[0040] Traverse each first sub-pollutant, screen and extract to obtain each required secondary optimization sub-pollutant.

[0041] Furthermore, a secondary optimization is performed, and the specific analysis steps are as follows: based on each first rendering requirement correction index, the first rendering requirement correction index of each sub-pollutant requiring secondary optimization is extracted.

[0042] The first rendering requirement correction index of each demand secondary optimization sub-pollutant is subtracted from the second verification factor of the rendering requirement to obtain the first rendering requirement correction deviation index of each demand secondary optimization sub-pollutant, and recorded as each second deviation index.

[0043] The number of optimization cycles for each sub-pollutant requiring secondary optimization is extracted based on each second deviation index.

[0044] The rendering optimization process of each demand secondary optimization sub-pollutant is repeatedly executed based on the number of optimization cycles of each demand secondary optimization sub-pollutant.

[0045] The second aspect of the present invention provides a new pollutant tracing system in geology based on big data analysis, including: a new pollutant optimization judgment module, a new pollutant rendering requirement module, a new pollutant optimization rendering module and a new pollutant rendering verification module.

[0046] The optimization determination module of the new pollutant is used to obtain the basic voxel data of the new pollutant, analyze the rendering coefficient of the new pollutant, and determine the first rendering optimization strategy of the new pollutant.

[0047] The rendering requirement module of the new pollutant is used to obtain the rendering area ratio of each sub-pollutant, combine the rendering coefficient of the new pollutant to obtain the sub-pollutant with each rendering optimization requirement, record it as each first sub-pollutant, obtain the rendering requirement data of each first sub-pollutant, and analyze the rendering requirement indicators of each first sub-pollutant.

[0048] The optimized rendering module of the new pollutant is used to determine the second rendering optimization strategy of each first sub-pollutant based on the rendering requirement index of each first sub-pollutant, thereby executing the rendering optimization process of each first sub-pollutant.

[0049] The rendering verification module of the new pollutant is used to receive the rendering optimization completion signal of each first sub-pollutant, analyze the optimization effect verification category of each first sub-pollutant, and thereby determine the third rendering optimization strategy of each first sub-pollutant.

[0050] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0051] 1. The method for tracing the source of new pollutants in geology based on big data analysis provided by the present invention determines the first rendering optimization strategy for the new pollutants through analysis of the visual rendering of the new pollutants, determines the second rendering optimization strategy for each first sub-pollutant based on the rendering requirement index of each first sub-pollutant, analyzes the optimization effect verification category of each first sub-pollutant, and determines the third rendering optimization strategy for each first sub-pollutant, which effectively improves the intuitiveness and accuracy of the tracing analysis, and effectively solves the problem of low tracing accuracy caused by poor rendering effect due to overlapping rendering in the rendering process in the existing technology.

[0052] 2. The present invention focuses on the analysis of local new pollutants by adjusting the rendering effects of new pollutants, determines the rendering optimization process through specific analysis of the rendering effects of each sub-pollutant, provides different adjustment methods according to the different optimization requirements of each sub-pollutant, and dynamically adjusts the adjustment method, which can effectively improve the effectiveness and accuracy of rendering optimization and improve the efficiency of rendering optimization.

[0053] 3. This invention verifies the rendering optimization effects of new pollutants and categorizes them. Different approaches are implemented based on the optimization effect category. A backtracking process is provided for categories where optimization fails. By backtracking to the state before adjustment, adjustments to rendering effects that failed optimization can be avoided. Dynamic adjustment of the number of cycles in the secondary optimization phase is also provided to prevent waste of computing power caused by over-optimization. Categories where optimization results meet the standards are not optimized, avoiding repeated optimization of pollutants that meet the standards and reducing ineffective computing resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1Flowchart of a method for tracing the source of new pollutants in geology based on big data analysis provided in an embodiment of the present application.

[0055] Figure 2 A schematic diagram of the structure of a new pollutant tracing system in geology based on big data analysis provided in an embodiment of the present application.

[0056] Figure 3 This is a flowchart of the rendering optimization involved in the embodiment of this application.

[0057] Figure 4 This is a flowchart for verifying the rendering optimization effect involved in the embodiment of this application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] Reference Figure 1 As shown, the first aspect of the present invention provides a method for tracing the source of new pollutants in geology based on big data analysis, comprising the following steps:

[0060] S1, obtaining basic voxel data of the new pollutant, analyzing the rendering coefficient of the new pollutant, and determining the first rendering optimization strategy of the new pollutant.

[0061] In this embodiment, the rendering coefficient of the new pollutant is analyzed as follows:

[0062] Collect basic voxel data of new pollutants, including the number of times a voxel pollution source is covered, the number of times a single voxel is drawn, the proportion of overlapping areas, and the average concentration similarity.

[0063] It should be added that the overlapping area ratio refers to the ratio of the number of voxels in the overlapping area of ​​the new pollutant during the three-dimensional visualization rendering process.

[0064] It should be added that the average concentration similarity refers to the average concentration similarity between overlapping pollutants in the whole.

[0065] It should be noted that the number of voxel pollution source coverage is obtained by counting the number of times each voxel is covered by different pollution sources through the spatial database; the number of times a single voxel is drawn is obtained by collecting system program logs, and the proportion of overlapping areas is obtained by using the spatial analysis function of GIS to perform Boolean "union" and "intersection" operations on the influence ranges of all pollution sources; the average concentration similarity is obtained by the weighted cosine similarity algorithm.

[0066] It should be added that when the number of voxel pollution source coverage increases, it usually means that the rendering calculation of the new pollutant will be repeated, resulting in a synchronous increase in the number of times a single voxel is drawn; at the same time, since the overlapping area increases after the new pollutant is rendered, the number of voxel pollution source coverage and the number of times a single voxel is drawn will also increase. If the average concentration similarity increases abnormally, it usually indicates that the rendering of the new pollutant is abnormal, making it difficult to distinguish between each new pollutant, and color distortion may also occur, causing fluctuations in the proportion of overlapping areas and the number of times a single voxel is drawn. There is a positive correlation between the four. When any parameter fluctuates significantly, the other three parameters will also show a synchronous growth trend.

[0067] The number of reference voxel pollution source coverage, the number of reference single voxel drawn, the reference overlap area ratio and the reference average concentration similarity stored in the database were extracted.

[0068] The importance ratio of the number of times voxel pollution sources are covered, the importance ratio of the number of times a single voxel is drawn, the importance ratio of the overlapping area ratio and the importance ratio of the average concentration similarity are extracted from the preset database.

[0069] It should be noted that the importance ratio of voxel pollution source coverage times, the importance ratio of single pixel drawn times, the importance ratio of overlapping area ratio and the importance ratio of average concentration similarity all have value ranges between 0 and 1. The sum of the importance ratio of voxel pollution source coverage times, the importance ratio of single pixel drawn times, the importance ratio of overlapping area ratio and the importance ratio of average concentration similarity is 1. When used, the pre-set value can be directly extracted from the database. The specific extraction method is, for example, to construct a one-to-one mapping set with the voxel pollution source coverage times, the single pixel drawn times, the overlapping area ratio and the average concentration similarity and the corresponding voxel pollution source coverage times importance ratio, the single pixel drawn times importance ratio, the overlapping area ratio importance ratio and the average concentration similarity importance ratio. When used, the obtained voxel pollution source coverage times, the single pixel drawn times, the overlapping area ratio and the average concentration similarity are respectively input into the corresponding mapping set, thereby extracting the importance ratio of voxel pollution source coverage times, the importance ratio of single pixel drawn times, the overlapping area ratio importance ratio and the average concentration similarity importance ratio.

[0070] Analyze the rendering coefficients of the new pollutant based on its underlying voxel data.

[0071] The rendering coefficient of the new pollutant is the quantitative data of the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the degree of influence of average concentration similarity. The specific analysis process is: the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the average concentration similarity are compared with the corresponding reference values, and the results of each comparison are coupled with the corresponding importance ratio to obtain the rendering coefficient of the new pollutant.

[0072] In a specific embodiment, the rendering coefficient of the new pollutant is specifically expressed as follows:

[0073]

[0074] Among them, VR is the rendering coefficient of the new pollutant, O is the number of times the voxel pollution source is covered, D is the number of times a single voxel is drawn, W is the proportion of overlapping areas, S is the average concentration similarity, O vef is the number of times the reference voxel is covered by the pollution source, D vef is the number of times the reference single pixel is drawn, W vef is the reference overlap ratio, S vef is the reference average concentration similarity, q is the importance ratio of the number of times the voxel pollution source is covered, p is the importance ratio of the number of times a single voxel is drawn, r is the importance ratio of the overlapping area ratio, and k is the importance ratio of the average concentration similarity.

[0075] Reference Figure 3 As shown, this is a rendering optimization flow chart involved in an embodiment of the present application. By analyzing the rendering coefficient of the new pollutant and the rendering requirement index, the rendering optimization process of the new pollutant is determined.

[0076] In this embodiment, the first rendering optimization strategy for new pollutants is specifically analyzed as follows:

[0077] Extract the preset rendering coefficient verification factor from the database.

[0078] If the rendering coefficient of the new pollutant is greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as pending rendering.

[0079] It should be noted that if the rendering coefficient of the new pollutant is greater than the rendering coefficient verification factor, it indicates that the rendering effect of the new pollutant is poor during the rendering process. In this case, further analysis is required to determine whether rendering optimization adjustments are needed. Therefore, the first rendering optimization strategy for the new pollutant is recorded as pending rendering.

[0080] If the rendering coefficient of the new pollutant is not greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as conventional rendering.

[0081] It should be noted that if the rendering coefficient of the new pollutant is not greater than the rendering coefficient verification factor, it means that the new pollutant has low or no overlap during the rendering process, which will not interfere with the visualization of the new pollutant. Therefore, the first rendering optimization strategy for the new pollutant is recorded as conventional rendering.

[0082] It should be explained that regular rendering means continuing to maintain the current rendering state without making any changes.

[0083] S2, obtain the rendering area ratio of each sub-pollutant, combine the rendering coefficient of the new pollutant to obtain the sub-pollutants of each required rendering optimization, record them as each first sub-pollutant, obtain the rendering demand data of each first sub-pollutant, and analyze the rendering demand indicators of each first sub-pollutant.

[0084] In this embodiment, the specific analysis process for each first sub-pollutant is as follows:

[0085] When the first rendering optimization strategy of the new pollutant is pending rendering, the rendering area ratio threshold is extracted based on the rendering coefficient of the new pollutant.

[0086] It should be explained that the rendering area ratio refers to the percentage of the rendering area of ​​a certain sub-pollutant in the overall scene.

[0087] It should be noted that the database stores a mapping set of rendering coefficients of new pollutants and rendering area ratio thresholds. When used, the rendering coefficients of new pollutants obtained in real time are input into the mapping set to extract the rendering area ratio threshold.

[0088] It should also be noted that the larger the rendering coefficient of the new pollutant, the worse the rendering effect. In order to effectively optimize the rendering effect, the corresponding rendering area ratio threshold should be smaller, thereby ensuring the effectiveness of the rendering effect optimization.

[0089] If the rendering area ratio of a sub-pollutant exceeds the rendering area ratio threshold, the sub-pollutant is recorded as a sub-pollutant requiring rendering optimization.

[0090] It should be noted that if the rendering area ratio of a sub-pollutant exceeds the rendering area ratio threshold, it indicates that the degree of overlap of the sub-pollutant is high and the sub-pollutant needs to be treated as a target for rendering optimization adjustment. Therefore, the sub-pollutant is recorded as a sub-pollutant requiring rendering optimization.

[0091] It should be noted that if the rendering area ratio of a certain sub-pollutant does not exceed the rendering area ratio threshold, it means that the degree of overlap of the sub-pollutant is low, and the sub-pollutant does not need to be taken as a target for rendering optimization adjustment.

[0092] Traverse each sub-pollutant, screen and count to obtain each required rendering optimization sub-pollutant, and record it as each first sub-pollutant.

[0093] In this embodiment, the rendering requirement indicator of each first sub-pollutant is specifically processed as follows:

[0094] The rendering requirement data of each first sub-pollutant includes the average concentration sub-similarity of each first sub-pollutant, the remaining capacity of the buffer area of ​​each first sub-pollutant, and the color similarity value of each first sub-pollutant.

[0095] It should be added that the average concentration sub-similarity refers to the average concentration similarity between the corresponding sub-pollutant and other sub-pollutants.

[0096] It should be added that the color similarity value is obtained by subtracting the color difference value from 1.

[0097] It should be noted that the average concentration sub-similarity is obtained by the weighted cosine similarity algorithm; the color difference value is calculated based on the perceptual consistency difference of the CIELAB / LCH color space; and the remaining capacity of the cache area is collected from the system program log.

[0098] It should be noted that as the color similarity value increases, the average concentration sub-similarity also increases, since the average concentration of new pollution is represented by color. This, in turn, leads to a simultaneous increase in the remaining capacity of the cache. Furthermore, as the color similarity value increases, the cache merges and stores similar colors, increasing the remaining capacity and the average concentration sub-similarity. An abnormal increase in the remaining capacity of the cache typically indicates a high degree of consistency in the colors actually stored in the cache, causing fluctuations in the average concentration sub-similarity and the color similarity value. There is a positive correlation between the three parameters; if any one parameter fluctuates significantly, the other three will also show a synchronous growth trend.

[0099] The reference average concentration sub-similarity, reference buffer remaining capacity, and reference color similarity value stored in the database are extracted.

[0100] The importance ratio of the average concentration sub-similarity, the importance ratio of the remaining capacity of the buffer area, and the importance ratio of the color similarity value preset in the database are extracted.

[0101] It should be noted that the importance ratio of the average concentration sub-similarity, the importance ratio of the remaining capacity of the cache area and the importance ratio of the color similarity value all range from 0 to 1, and the sum of the importance ratio of the average concentration sub-similarity, the importance ratio of the remaining capacity of the cache area and the importance ratio of the color similarity value is 1. When used, the pre-set value can be directly extracted from the database. The specific extraction method is, for example, to construct a one-to-one mapping set of the average concentration sub-similarity, the remaining capacity of the cache area and the color similarity value with the corresponding average concentration sub-similarity importance ratio, the remaining capacity of the cache area and the importance ratio of the color similarity value respectively. When used, the obtained average concentration sub-similarity, the remaining capacity of the cache area and the color similarity value are respectively input into the corresponding mapping set, so as to extract the importance ratio of the average concentration sub-similarity, the remaining capacity of the cache area and the importance ratio of the color similarity value.

[0102] The rendering requirement index of each first sub-pollutant is analyzed based on the rendering requirement data of each first sub-pollutant.

[0103] The rendering demand index of each first sub-pollutant is the quantitative data of the influence degree of average concentration sub-similarity, remaining capacity of the cache area and color similarity value. The specific analysis process is: compare the average concentration similarity sub-degree, remaining capacity of the cache area and color similarity value with the corresponding reference value, and couple the results of each comparison with the corresponding importance ratio to obtain the rendering demand index of each first sub-pollutant.

[0104] In a specific embodiment, the rendering requirement index of each first sub-pollutant is specifically expressed as follows:

[0105]

[0106] Among them, VQ i is the rendering demand index of the i-th sub-pollutant, TS i is the average concentration similarity of the ith sub-pollutant, R i is the remaining capacity of the buffer area of ​​the i-th sub-pollutant, U i is the color similarity value of the i-th sub-pollutant, TS vef is the reference average concentration similarity, R vef is the remaining capacity of the reference buffer, U vef is the reference color similarity value, a is the importance ratio of the average concentration sub-similarity, b is the importance ratio of the remaining capacity of the buffer area, c is the importance ratio of the color similarity value, i is the sub-pollutant number, i = 1, 2, ..., n, n is the number of sub-pollutants.

[0107] S3 , based on the rendering requirement index of each first sub-pollutant, determining a second rendering optimization strategy for each first sub-pollutant, thereby executing a rendering optimization process for each first sub-pollutant.

[0108] In this embodiment, the second rendering optimization strategy for each first sub-pollutant is specifically analyzed as follows:

[0109] Extract the preset rendering requirement first verification factor and rendering requirement second verification factor from the database.

[0110] It should be noted that the first verification factor of the rendering requirement is greater than the second verification factor of the rendering requirement.

[0111] If the rendering requirement index of a first sub-pollutant is greater than the first verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as heavyweight rendering.

[0112] It should be noted that if the rendering requirement index of a first sub-pollutant is greater than the first validation factor of the rendering requirement, it means that the rendering effect of the sub-pollutant is extremely poor and requires significant adjustment. Therefore, the second rendering optimization strategy for the first sub-pollutant is recorded as heavyweight rendering.

[0113] If the rendering requirement index of a first sub-pollutant is not greater than the first rendering requirement verification factor and greater than the second rendering requirement verification factor, the second rendering optimization strategy of the first sub-pollutant is recorded as lightweight rendering.

[0114] It should be noted that if the rendering requirement index of a first sub-pollutant is not greater than the first rendering requirement verification factor and greater than the second rendering requirement verification factor, it means that the rendering effect of this sub-pollutant is slightly poor and requires minor adjustments. Therefore, the second rendering optimization strategy for this first sub-pollutant is recorded as lightweight rendering.

[0115] If the rendering requirement index of a first sub-pollutant is not greater than the second verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as conventional rendering.

[0116] It should be noted that if the rendering requirement index of a first sub-pollutant is not greater than the second validation factor of the rendering requirement, it indicates that the rendering effect of this sub-pollutant is normal. Even if there is a high overlap area, it still does not affect the visualization of the new pollutant, so no adjustment is required. Therefore, the second rendering optimization strategy for this first sub-pollutant is recorded as normal rendering.

[0117] In this embodiment, the rendering optimization process of each first sub-pollutant is analyzed as follows:

[0118] The rendering requirement first verification factor is subtracted from the rendering requirement index of each first sub-pollutant to obtain the rendering requirement first deviation index of each first sub-pollutant, and recorded as each first deviation index.

[0119] The adjustment parameters corresponding to each first deviation index interval stored in the database are extracted, and the adjustment parameters corresponding to the interval where the first deviation index is located are mapped and extracted, and recorded as the first adjustment parameters of each first sub-pollutant.

[0120] It should be noted that the database stores a mapping set of each first deviation indicator and the first adjustment parameter of each first sub-pollutant. When in use, the first deviation indicators obtained in real time are input into the mapping set to extract the first adjustment parameter of the first sub-pollutant.

[0121] In this embodiment, the lightweight rendering of each sub-pollutant is analyzed in detail as follows:

[0122] The first adjustment parameters of each first sub-pollutant include a light source intensity supplement value, a sharpness supplement value, a buffer area capacity supplement value, and a saturation supplement value.

[0123] It should be noted that the larger the first deviation indicators are, the worse the rendering effect of the sub-pollutant is. In order to better achieve the optimization effect, the corresponding light source intensity supplement value, sharpness supplement value, cache remaining capacity supplement value and saturation supplement value will be larger, so as to better adjust the rendering effect.

[0124] In a specific embodiment, the specific process of lightweight rendering is: if the current light source intensity extracted from the system program log is x0, the current sharpness extracted from the system program log is x1, the current cache remaining capacity extracted from the system program log is x2, and the current saturation extracted from the system program log is x3, the extracted light source intensity supplement value is y0, the extracted sharpness supplement value is y1, the extracted cache remaining capacity supplement value is y2, and the extracted saturation supplement value is y3, then the light source intensity after lightweight rendering is x0+y0, the sharpness after lightweight rendering is x1+y1, the cache remaining capacity after lightweight rendering is x2+y2, and the saturation after lightweight rendering is x3+y3.

[0125] It should be noted that if the light source intensity supplement value, sharpness supplement value, buffer capacity supplement value and saturation supplement value obtained by analysis exceed the maximum allowable values ​​preset in the database, they will be adjusted based on the maximum allowable values.

[0126] The rendering requirement first verification factor is subtracted from the rendering requirement index of each first sub-pollutant to obtain the rendering requirement second deviation index of each first sub-pollutant, and recorded as each second deviation index.

[0127] The adjustment parameters corresponding to each second deviation index interval stored in the database are extracted, and the adjustment parameters corresponding to the interval where the second deviation index is located are mapped and extracted, and recorded as the second adjustment parameters of each first sub-pollutant.

[0128] It should be noted that the database stores a mapping set of each second deviation indicator and the second adjustment parameter of each first sub-pollutant. When used, the second deviation indicators obtained in real time are input into the mapping set to extract the second adjustment parameter of the first sub-pollutant.

[0129] In this embodiment, the heavyweight rendering of each sub-pollutant is analyzed in detail as follows:

[0130] The second adjustment parameters of each first sub-pollutant include a transparency supplement value, a buffer remaining capacity reduction value, a resolution supplement value, and a reflection accuracy supplement value.

[0131] It should be noted that the larger the second deviation indicators are, the worse the rendering effect of the sub-pollutant is. In order to better achieve the optimization effect, the corresponding transparency supplement value, buffer remaining capacity reduction value, resolution supplement value and reflection accuracy supplement value will be larger, thereby better optimizing the rendering.

[0132] In a specific embodiment, the specific process of performing heavyweight rendering is: if the current transparency extracted from the system program log is a0, the current cache remaining capacity extracted from the system program log is a1, the current resolution extracted from the system program log is a2, and the current reflection accuracy extracted from the system program log is a3, the extracted transparency supplement value is b0, the extracted cache remaining capacity reduction value is b1, the extracted resolution supplement value is b2, and the extracted reflection accuracy supplement value is b3, then the transparency after heavyweight rendering is a0+b0, the cache remaining capacity after heavyweight rendering is a1+b1, the resolution after heavyweight rendering is a2+b2, and the reflection accuracy after heavyweight rendering is a3+b3.

[0133] It should be noted that if the transparency, resolution, and reflection accuracy supplement values ​​obtained from the analysis exceed the maximum values ​​preset in the database, the maximum values ​​will be used for adjustment. If the buffer remaining capacity reduction value obtained from the analysis exceeds the minimum values ​​preset in the database, the minimum values ​​will be used for adjustment.

[0134] S4, receiving a rendering optimization completion signal of each first sub-pollutant, analyzing the optimization effect verification category of each first sub-pollutant, and thereby determining a third rendering optimization strategy for each first sub-pollutant.

[0135] Reference Figure 4 As shown, it is a flowchart of the rendering optimization effect verification involved in an embodiment of the present application. By analyzing the first rendering requirement correction index of each sub-pollutant, the optimization effect category is judged, and the rendering optimization effect verification process of each sub-pollutant is determined.

[0136] In this embodiment, the optimization effect verification category of each first sub-pollutant is used to determine the third rendering optimization strategy for each first sub-pollutant. The specific analysis process is as follows:

[0137] The rendering requirement index of each first sub-pollutant is obtained again and recorded as each first rendering requirement index.

[0138] Extract the rendering requirement correction coefficient based on the rendering coefficient of the new pollutant.

[0139] It should be noted that the database stores a mapping set of rendering coefficients of new pollutants and rendering requirement correction coefficients. When used, the rendering coefficients of new pollutants obtained in real time are input into the mapping set to extract the rendering requirement correction coefficients.

[0140] It should be added that the larger the rendering coefficient of the new pollutant, the less expected the current rendering effect of each sub-pollutant is. In order to optimize the rendering effect, the corresponding rendering demand correction coefficient will be larger.

[0141] Each first rendering requirement correction index is obtained based on each first rendering requirement index and the rendering requirement correction coefficient, that is, the product of each first rendering requirement index and the rendering requirement correction coefficient is used as the numerical result of each first rendering requirement correction index.

[0142] If a first rendering requirement correction index is less than or equal to the second rendering requirement verification factor, the optimization effect verification category of the first sub-pollutant is determined as the optimization effect meets the standard, and the third rendering optimization strategy of the first sub-pollutant is recorded as continuous monitoring.

[0143] It should be noted that if the first rendering requirement correction index is less than or equal to the second rendering requirement verification factor, it indicates that after adjustment and optimization, the rendering effect of this sub-pollutant is good. Therefore, the optimization effect verification category for this first sub-pollutant is determined to be satisfactory. Since no further adjustment is required after adjustment and optimization, the third rendering optimization strategy for this first sub-pollutant is recorded as continuous monitoring.

[0144] If a first rendering requirement correction index is greater than the second verification factor of the rendering requirement and less than the rendering requirement index of the first sub-pollutant, the optimization effect verification category of the first sub-pollutant is determined to be optimization valid, and the third rendering optimization strategy of the first sub-pollutant is recorded as performing secondary optimization, and the first sub-pollutant is simultaneously recorded as a sub-pollutant requiring secondary optimization.

[0145] It should be noted that if the first rendering requirement correction index is greater than the second rendering requirement verification factor but less than the rendering requirement index of the first sub-pollutant, it means that the rendering effect of the sub-pollutant has been improved to some extent after rendering optimization adjustment, but the improvement is not very significant. Therefore, the optimization effect verification category of the first sub-pollutant is determined to be effective.

[0146] If a first rendering requirement correction index is greater than or equal to the rendering requirement index of the first sub-pollutant, the optimization effect verification category of the first sub-pollutant is determined as optimization failure, and the third rendering optimization strategy of the first sub-pollutant is recorded as executing the backtracking process.

[0147] It should be noted that if a first rendering requirement correction index is greater than or equal to the rendering requirement index of the first sub-pollutant, the rendering effect of the sub-pollutant has not been optimized, indicating invalid optimization. Therefore, the optimization effect verification category for the first sub-pollutant is determined to be optimization failure. Due to the optimization failure, further optimization adjustments are required, and the third rendering optimization strategy for the first sub-pollutant is recorded as executing the backtracking process.

[0148] It should be noted that the backtracking process refers to tracing the rendering effect of the sub-pollutant back to the time point before adjustment, optimizing and adjusting it again, and recording the failed adjustment data.

[0149] Traverse each first sub-pollutant, screen and extract to obtain each required secondary optimization sub-pollutant.

[0150] In this embodiment, secondary optimization is performed, and the specific analysis steps are as follows:

[0151] The first rendering requirement correction index of each secondary optimization sub-pollutant is extracted based on each first rendering requirement correction index.

[0152] The first rendering requirement correction index of each demand secondary optimization sub-pollutant is subtracted from the rendering requirement second verification factor to obtain the first rendering requirement correction deviation index of each demand secondary optimization sub-pollutant, and recorded as each second correction deviation index.

[0153] The number of optimization cycles for each sub-pollutant requiring secondary optimization is extracted based on each second corrected deviation index.

[0154] It should be noted that the database stores a mapping set of each second corrected deviation indicator and the number of optimization cycles of each required secondary optimization sub-pollutant. When used, the second corrected deviation indicators obtained in real time are input into the mapping set to extract the number of optimization cycles of each required secondary optimization sub-pollutant.

[0155] It should be added that the larger the second correction deviation index is, the less ideal the current rendering effect of each sub-pollutant is. In order to effectively optimize the rendering effect, the corresponding number of cycles will be larger to achieve better optimization effect.

[0156] It should be noted that if the number of optimization cycles obtained by analysis exceeds the maximum number of times allowed preset in the database, the maximum number of times allowed will be used for execution.

[0157] It should be noted that the number of loops refers to the number of jumps between lightweight rendering and heavyweight rendering in the optimization process. Each time a jump from lightweight rendering to heavyweight rendering or from heavyweight rendering to lightweight rendering is performed, the number of loops increases by one until it reaches the maximum value.

[0158] The rendering optimization process of each demand secondary optimization sub-pollutant is repeatedly executed based on the number of optimization cycles of each demand secondary optimization sub-pollutant.

[0159] It should be noted that the rendering optimization process of the secondary optimization sub-pollutant increases the number of loops, and the rest of the content is the same as the rendering optimization process of the first sub-pollutant.

[0160] Reference Figure 2 As shown, the second aspect of the present invention provides a new pollutant tracing system in geology based on big data analysis, including:

[0161] The optimization determination module of the new pollutant is used to obtain the basic voxel data of the new pollutant, analyze the rendering coefficient of the new pollutant, and determine the first rendering optimization strategy of the new pollutant.

[0162] The rendering requirement module of the new pollutant is used to obtain the rendering area ratio of each sub-pollutant, combine the rendering coefficient of the new pollutant to obtain the sub-pollutant with each rendering optimization requirement, record it as each first sub-pollutant, obtain the rendering requirement data of each first sub-pollutant, and analyze the rendering requirement indicators of each first sub-pollutant.

[0163] The optimized rendering module of the new pollutant is used to determine the second rendering optimization strategy of each first sub-pollutant based on the rendering requirement index of each first sub-pollutant, thereby executing the rendering optimization process of each first sub-pollutant.

[0164] The rendering verification module of the new pollutant is used to receive the rendering optimization completion signal of each first sub-pollutant, analyze the optimization effect verification category of each first sub-pollutant, and thereby determine the third rendering optimization strategy of each first sub-pollutant.

[0165] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0170] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for tracing the source of new pollutants in geology based on big data analysis, characterized by: The following steps are involved: S1, obtaining basic voxel data of the new pollutant, analyzing the rendering coefficient of the new pollutant, and determining the first rendering optimization strategy of the new pollutant; S2: Obtain the rendering area ratio of each sub-pollutant, combine it with the rendering coefficient of the new pollutant to obtain the sub-pollutant with optimized rendering requirements, record it as each first sub-pollutant, obtain the rendering demand data of each first sub-pollutant, and analyze the rendering demand index of each first sub-pollutant; S3, determining a second rendering optimization strategy for each first sub-pollutant based on the rendering requirement index of each first sub-pollutant, thereby executing a rendering optimization process for each first sub-pollutant; S4, receiving a rendering optimization completion signal of each first sub-pollutant, analyzing the optimization effect verification category of each first sub-pollutant, and thereby determining a third rendering optimization strategy for each first sub-pollutant.

2. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The rendering coefficient of the new pollutant is analyzed in the following specific way: Basic voxel data of the new pollutant, including the number of voxel pollution source coverage, the number of times a single voxel is drawn, the proportion of overlapping areas, and the average concentration similarity; Analyze the rendering coefficient of the new pollutant based on the basic voxel data of the new pollutant; The rendering coefficient of the new pollutant is quantitative data of the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the degree of influence of average concentration similarity. The specific analysis process is: the number of voxel pollution source coverage, the number of single voxels drawn, the proportion of overlapping areas and the average concentration similarity are compared with the corresponding reference values, and the results of each comparison are coupled with the corresponding importance ratio to obtain the rendering coefficient of the new pollutant.

3. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The specific analysis process of the first rendering optimization strategy for determining new pollutants is as follows: Extract the preset rendering coefficient verification factor from the database; If the rendering coefficient of the new pollutant is greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as pending rendering; If the rendering coefficient of the new pollutant is not greater than the rendering coefficient verification factor, the first rendering optimization strategy of the new pollutant is recorded as conventional rendering.

4. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The specific process of obtaining the first sub-pollutants is as follows: When the first rendering optimization strategy of the new pollutant is pending rendering, extracting a rendering area ratio threshold based on the rendering coefficient of the new pollutant; If the rendering area ratio of a sub-pollutant exceeds the rendering area ratio threshold, the sub-pollutant will be recorded as a sub-pollutant requiring rendering optimization; Traverse each sub-pollutant, screen and count to obtain each required rendering optimization sub-pollutant, and record it as each first sub-pollutant.

5. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The specific process of the rendering requirement index of each first sub-pollutant is as follows: Rendering requirement data for each first sub-pollutant, including average concentration sub-similarity, buffer remaining capacity, and color similarity value; Analyzing rendering requirement indicators of each first sub-pollutant based on the rendering requirement data of each first sub-pollutant; The rendering requirement index of each first sub-pollutant is quantitative data of the influence degree of average concentration sub-similarity, remaining capacity of the buffer area and color similarity value. The specific analysis process is: comparing the average concentration similarity sub-degree, remaining capacity of the buffer area and color similarity value with the corresponding reference value, and coupling the results of each comparison processing with the corresponding importance ratio to obtain the rendering requirement index of each first sub-pollutant.

6. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 5, characterized in that: The specific analysis process of determining the second rendering optimization strategy for each first sub-pollutant is as follows: Extracting the preset rendering requirement first verification factor and rendering requirement second verification factor from the database; If the rendering requirement index of a first sub-pollutant is greater than the first verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as heavyweight rendering; If the rendering requirement index of a first sub-pollutant is not greater than the first rendering requirement verification factor and greater than the second rendering requirement verification factor, then the second rendering optimization strategy of the first sub-pollutant is recorded as lightweight rendering; If the rendering requirement index of a first sub-pollutant is not greater than the second verification factor of the rendering requirement, the second rendering optimization strategy of the first sub-pollutant is recorded as conventional rendering.

7. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The specific analysis process of executing the rendering optimization process of each first sub-pollutant is as follows: Subtracting the rendering requirement index of each first sub-pollutant from the first rendering requirement verification factor to obtain the first rendering requirement deviation index of each first sub-pollutant, and recording it as each first deviation index; Extracting the adjustment parameters corresponding to each first deviation index interval stored in the database, and mapping and extracting the adjustment parameters corresponding to the interval where the first deviation index is located, and recording them as the first adjustment parameters of each first sub-pollutant; The first adjustment parameters of each first sub-pollutant include a light source intensity supplement value, a sharpness supplement value, a buffer remaining capacity supplement value, and a saturation supplement value of each first sub-pollutant; Subtracting the first verification factor of the rendering requirement from the rendering requirement index of each first sub-pollutant to obtain the second deviation index of the rendering requirement of each first sub-pollutant, and recording it as each second deviation index; Extracting the adjustment parameters corresponding to each second deviation index interval stored in the database, and mapping and extracting the adjustment parameters corresponding to the interval in which the second deviation index is located, and recording them as the second adjustment parameters of each first sub-pollutant; The second adjustment parameters of each first sub-pollutant include a transparency supplement value, a buffer remaining capacity reduction value, a resolution supplement value, and a reflection accuracy supplement value of each first sub-pollutant.

8. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 1, characterized in that: The optimization effect verification category of each first sub-pollutant is analyzed to determine the third rendering optimization strategy for each first sub-pollutant. The specific analysis process is as follows: Re-obtaining the rendering requirement index of each first sub-pollutant, and recording it as each first rendering requirement index; Extract rendering demand correction coefficient based on the rendering coefficient of the new pollutant; Obtaining each first rendering requirement correction index based on each first rendering requirement index and a rendering requirement correction coefficient; If a first rendering requirement correction index is less than or equal to the second rendering requirement verification factor, the optimization effect verification category of the first sub-pollutant is determined to be the optimization effect meets the standard, and the third rendering optimization strategy of the first sub-pollutant is recorded as continuous monitoring; If a first rendering requirement correction index is greater than the second rendering requirement verification factor and less than the rendering requirement index of the first sub-pollutant, then the optimization effect verification category of the first sub-pollutant is determined to be optimization effective, and the third rendering optimization strategy of the first sub-pollutant is recorded as performing secondary optimization, and the first sub-pollutant is simultaneously recorded as a sub-pollutant requiring secondary optimization; If a first rendering requirement correction index is greater than or equal to the rendering requirement index of the first sub-pollutant, the optimization effect verification category of the first sub-pollutant is determined to be optimization failure, and the third rendering optimization strategy of the first sub-pollutant is recorded as executing the backtracking process; Traverse each first sub-pollutant, screen and extract to obtain each required secondary optimization sub-pollutant.

9. The method for tracing the source of new pollutants in geology based on big data analysis as claimed in claim 8, characterized in that: The secondary optimization is performed, and the specific analysis steps are as follows: Extracting the first rendering requirement correction index of each secondary optimization sub-pollutant based on each first rendering requirement correction index; Subtract the second verification factor of the rendering requirement from the first rendering requirement correction index of each demand secondary optimization sub-pollutant to obtain the first rendering requirement correction deviation index of each demand secondary optimization sub-pollutant, and record it as each second deviation index; Extracting the number of optimization cycles for each required secondary optimization sub-pollutant based on each second deviation index; The rendering optimization process of each demand secondary optimization sub-pollutant is repeatedly executed based on the number of optimization cycles of each demand secondary optimization sub-pollutant.

10. A system for applying the method for tracing the source of new pollutants in geology based on big data analysis as described in any one of claims 1 to 9, characterized in that: include: New pollutant optimization determination module, new pollutant rendering requirement module, new pollutant optimization rendering module and new pollutant rendering verification module; The optimization determination module of the new pollutant is used to obtain basic voxel data of the new pollutant, analyze the rendering coefficient of the new pollutant, and determine the first rendering optimization strategy of the new pollutant; The rendering requirement module of the new pollutant is used to obtain the rendering area ratio of each sub-pollutant, combine the rendering coefficient of the new pollutant to obtain each sub-pollutant with rendering optimization requirements, record them as each first sub-pollutant, obtain the rendering requirement data of each first sub-pollutant, and analyze the rendering requirement index of each first sub-pollutant; The optimized rendering module of the new pollutant is used to determine the second rendering optimization strategy of each first sub-pollutant based on the rendering requirement index of each first sub-pollutant, thereby executing the rendering optimization process of each first sub-pollutant; The rendering verification module of the new pollutant is used to receive the rendering optimization completion signal of each first sub-pollutant, analyze the optimization effect verification category of each first sub-pollutant, and thereby determine the third rendering optimization strategy of each first sub-pollutant.

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