A land information integration system

Abstract

The application relates to a land information integration system, comprising a remote sensing vector module, a space-time GIS module, an artificial intelligence AI module and a historical resource database. The AI module performs data cleaning on the historical resource database through the remote sensing vector module and the space-time GIS module, performs deep analysis and learning on a vector model formed by transformation, optimizes an estimation algorithm of basic information of land resources, makes land information more visualized and integrated, and according to the algorithm optimized through deep learning, development of a preset region is calculated and judged, possible risks can be known in advance, and the risks can be solved in advance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field, and particularly to a land information integration system. BACKGROUND

[0002] Effective and scientific management of land resources is of great significance in a country like China, which has a large population but relatively scarce usable land resources. Land resource informatization and real-time management have become increasingly urgent.

[0003] In today's high-speed development of informatization, it is crucial to establish a large system engineering based on computers, which takes land resource detailed investigation, soil survey, planning, various remote sensing images, topographic maps, control network points, etc. as information sources, and performs acquisition, input, storage, statistical processing, analysis, evaluation, output, transmission and application of land resource information.

[0004] Land information generally includes the following four categories: environmental information, infrastructure information, cadastral information and social and economic information. Among them, environmental information includes climate, soil, geology, topography, riverbed, vegetation, wildlife, etc.; infrastructure information includes public facilities, buildings, transportation systems, etc.; cadastral information includes ownership, measurement, land grading and valuation, land use control, etc.; social and economic information includes economic development level, health, welfare and public order, population distribution, etc.

[0005] Chinese patent publication No. CN112598670B discloses a land supervision platform based on cloud computing. It includes: a remote sensing image acquisition system for acquiring remote sensing images of a target area and transmitting the remote sensing images to a cloud platform; the cloud platform is used for integrating the remote sensing images into a basic map of the target area, obtaining a remote sensing image map of the target area, and further obtaining patch information of the remote sensing image map; selecting a verification patch according to the remote sensing image map, generating a verification task according to the selected verification patch, and sending the verification task to a corresponding on-site evidence terminal; collecting patch on-site image information according to the received verification task, and uploading the collected patch on-site image information to the cloud platform. It realizes remote issuance and review of land verification tasks, and effective integration and recording of land verification tasks, which helps to improve the comprehensive level of land management in the target area. Therefore, the land supervision platform based on cloud computing has the following problems: the images collected by the remote sensing image acquisition system are integrated with the basic map, and the patch area is re-verified and on-site evidenced, and finally the map is completed to the platform, the process has too many traces of patching, the timeliness is not high, the cost performance is low, and time and manpower are wasted without obtaining more effective data. SUMMARY

[0006] To this end, the application provides a land information integration system to overcome the problems of low timeliness and applicability and insufficient comprehensive and intelligent information in the prior art.

[0007] To achieve the above-mentioned purpose, the application provides a land information integration system, comprising a remote sensing vector module, a space-time GIS module, an artificial intelligence AI module, and a historical resource database.

[0008] The remote sensing vector module is used to perform vector processing on the three-dimensional model of aerial surveying and mapping of the preset area to form a vector three-dimensional model.

[0009] The space-time GIS module is used to evaluate the carrying capacity and suitability of various resources in the space of the preset area and mark the space-time dimension.

[0010] The artificial intelligence AI module is used to perform deep learning and analysis on the detailed information of various resources in the space of the preset area, calculate the future development trend, and estimate possible risks.

[0011] The historical resource database is used to store all related land historical geological information of the preset area and add space-time dimensions through the space-time GIS module, so as to serve as a training reference object for the artificial intelligence AI module, update the algorithm and formula, calculate the future development trend of the preset area, and estimate possible risks.

[0012] The remote sensing vector module performs vectorization processing on the land historical geological information data of each period in the historical resource database and forms a vector model, the space-time GIS module adds space-time labels to the vector model according to the historical time of the vector model, the artificial intelligence AI module evaluates all land geological parameters of the marked area of the vector model at that time, performs trend learning and analysis according to various geological information at that time, calculates possible problems in future development, compares the calculation results with real information data after that period, perfects the algorithm, and obtains evaluation parameters of the development of the land ecological space of the preset area after that period, and calculates the development of the preset area according to the evaluation parameters.

[0013] The remote sensing vector module performs vector processing on the three-dimensional data model of aerial surveying and mapping and geological exploration results of the preset area to form a vector three-dimensional model, the space-time GIS module marks each coordinate or area of the vector three-dimensional model, the artificial intelligence AI module evaluates all land geological parameters of the marked area of the vector three-dimensional model at that time, performs trend analysis and planning on all marked areas according to the land geological parameters, calculates the future development trend of the preset area, and estimates possible risks.

[0014] The artificial intelligence (AI) module evaluates all land geological parameters of all marked areas in the vector model at any given time. The AI ​​module defines the time period as T, and all time periods are arranged in ascending order along the time axis as T1, T2, T3…T. n , where n is a positive integer, and the land historical geological data for each period includes soil composition data, geological structure data, mineral distribution data and regional hydrogeological data.

[0015] The artificial intelligence (AI) module sets the evaluation parameters for soil composition data as CS, geological structure data as GS, mineral distribution data as MD, regional hydrogeological data as RH, and comprehensive land geological evaluation as AL.

[0016] AL = W1CS + W2GS + W3MD + W4RH, where W is the weighting coefficient, W1 is the weighting coefficient for soil composition data, W2 is the weighting coefficient for geological structure data, W3 is the weighting coefficient for mineral distribution data, and W4 is the weighting coefficient for regional hydrogeological data. W1 + W2 + W3 + W4 = 100%.

[0017] The artificial intelligence (AI) module analyzes all land geological parameters at time T1 and time T2 to determine the trend simulation from time T1 to time T2 and the bottoming out of the ecological red line. The analysis results are then compared with the actual data at time T3 to optimize the AI ​​module's trend simulation algorithm and adjust the weighting coefficients of the land geological comprehensive evaluation parameters. The trend simulation process for the land geological data evaluation parameters is as follows:

[0018] If CS1 < CS2, the AI ​​module determines that the soil composition data evaluation parameter trend from T1 to T2 is upward. During the upward cycle of T2-T1, the increase value is...

[0019] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a slight upward trend. During the upward cycle from T2 to T1, the increase was [value missing].

[0020] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a large upward trend. During the upward cycle from T2 to T1, the increase was [value missing].

[0021] If CS1 = CS2, then the artificial intelligence (AI) module determines that the trend of the soil composition data evaluation parameters from T1 to T2 is a flat trend, with a flat period of T2-T1.

[0022] If CS1 > CS2, the AI ​​module determines that the soil composition data evaluation parameter trend from T1 to T2 is downward. During the downward period of T2-T1, the decrease value is...

[0023] like The AI ​​module determines that the trend from T1 to T2 is a slight downward trend. During the downward period of T2-T1, the decrease in value is...

[0024] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a significant downward trend. During the downward period from T2 to T1, the decrease was [value missing].

[0025] Repeat the above trend process, and sequentially perform trend simulation processes on the geological structure data evaluation parameters GS1 and GS2, mineral distribution data evaluation parameters MD1 and MD2, and regional hydrogeological data evaluation parameters RH1 and RH2 for T1 and T2 respectively, to obtain the trend and trend magnitude of the data evaluation parameters for the period T2-T1.

[0026] The artificial intelligence (AI) module automatically allocates the weight coefficients in AL1 and AL2 based on the geological structure data evaluation parameters GS1 and GS2, mineral distribution data evaluation parameters MD1 and MD2, and regional hydrogeological data evaluation parameters RH1 and RH2 of T1 and T2, resulting in AL1 = W1CS1 + W2GS1 + W3MD1 + W4RH1 and AL2 = W1CS2 + W2GS2 + W3MD2 + W4RH2. By comparing the weight coefficients of the time period in AL1 and AL2, the allocation of each weight coefficient in the comprehensive land geological weight coefficient AL3 of T3 simulation is calculated, resulting in AL3′ = W1CS3′ + W2GS3′ + W3MD3′ + W4RH3′.

[0027] The artificial intelligence (AI) module analyzes the weighting coefficients of the time periods in AL1 and AL2 to derive the weighting adjustment coefficients in the formula of the T3 simulated land geological comprehensive evaluation parameter AL3.

[0028] Based on the data from T1 to T2, the trends and magnitudes of the evaluation parameters are re-analyzed and the weights of each evaluation parameter in the comprehensive land geological evaluation parameter AL3 are reassessed. Let the weight adjustment value of CS be iW1, the weight adjustment value of GS be iW2, the weight adjustment value of MD be iW3, and the weight adjustment value of RH be iW4, where i is a weighting adjustment coefficient and is an integer.

[0029] When the trend of CS is a large increase during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0030] When CS shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0031] When the trend of CS is a large decrease during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0032] When the trend of CS is slightly decreasing during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0033] If the trend of CS remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is iW1′.

[0034] (W2CS+iW1′)+(W2GS+iW2)+(W3MD+iW3)+(W4RH+iW4)=100%.

[0035] When GS shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0036] When GS shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0037] When GS shows a significant downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0038] When GS shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0039] If the trend of GS remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of GS is iW2′.

[0040] (W2CS+iW1)+(W2GS+iW2′)+(W3MD+iW3)+(W4RH+iW4)=100%.

[0041] When MD shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0042] When MD shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0043] When MD shows a large downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0044] When MD shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0045] If the trend of MD remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of MD is iW3′.

[0046] (W2CS+iW1)+(W2GS+iW2)+(W3MD+iW3′)+(W4RH+iW4)=100%.

[0047] When RH shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0048] When RH shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0049] When RH shows a significant downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0050] When RH shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0051] If the trend of RH remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of RH is iW4′.

[0052] (W2CS+iW1)+(W2GS+iW2)+(W3MD+iW3)+(W4RH+iW4′)=100%.

[0053] When CS, GS, MD, and RH show equal trends over the time period from T1 to T2, the AI ​​module determines that the weighting coefficients of CS, GS, MD, and RH will not be adjusted.

[0054] The artificial intelligence (AI) module calculates the time period from T1 to T2, and inputs the data evaluation parameter trend within the T2-T1 period into the T3 time node to obtain the T3 simulated soil composition data evaluation parameter CS3′, T3 simulated geological structure data evaluation parameter GS3′, T3 simulated mineral distribution data evaluation parameter MD3′, and T3 simulated regional hydrogeological data evaluation parameter RH3′. The AI ​​module compares and analyzes the T3 simulated land geological evaluation parameters with the T3 historical land geological evaluation parameters, and adjusts the algorithm to make the T3 simulated land geological evaluation parameters closer to the real data.

[0055] The simulation process by which the artificial intelligence (AI) module derives the evaluation parameters CS3′ for T3 simulated soil composition data, GS3′ for T3 simulated geological structure data, MD3′ for T3 simulated mineral distribution data, and RH3′ for T3 simulated regional hydrogeological data is as follows:

[0056] The AI ​​module has a periodicity coefficient set to MT. Substituting the period coefficient into the T3 time node yields the trend amplitude of the T3-T2 period.

[0057] When the soil composition data evaluation parameters from T1 to T2 show an upward trend, the AI ​​module determines that the soil composition data evaluation parameters for simulation T3 should be used when the upward cycle is at the simulation time point T3.

[0058] When the trend of soil composition data evaluation parameters from T1 to T2 is a flat trend, the artificial intelligence (AI) module determines that during the flat period T3, the simulated soil composition data evaluation parameters CS3′=CS2=CS1 are CS3′=CS2=CS1.

[0059] When the soil composition data evaluation parameters from T1 to T2 show a downward trend, the AI ​​module determines that at the simulation time point T3, the soil composition data evaluation parameters for simulation T3 should be...

[0060] Repeat the above process to sequentially perform trend simulations on the geological structural data evaluation parameter GS3′, mineral distribution data evaluation parameter MD3′, and regional hydrogeological data evaluation parameter RH3′ at the T3 simulation time node, and obtain the values ​​of the data evaluation parameters with a period of T3 simulation time node.

[0061] The AI ​​module compares CS3′ with CS3, determines the trend from GS2 to GS3, MD3′ with MD3, and RH3′ with RH3 as follows:

[0062] If CS3′ > CS3, then the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is an upward trend, but the trend in the T2 to T3 period is a downward trend.

[0063] If CS3′=CS3, then the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is consistent with the trend in the T2 to T3 period.

[0064] If CS3′ < CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is a downward trend, but the trend in the T2 to T3 period is an upward trend.

[0065] Repeat the above trend process, comparing GS3′ with GS3, MD3′ with MD3, and RH3′ with RH3 in turn, and perform the trend determination process to obtain the periodic trend and specific value of the data evaluation parameters with a period of T3 simulation time node.

[0066] The artificial intelligence (AI) module substitutes the calculated AL3′=W1CS3′+W2GS3′+W3MD3′+W4RH3′ into the T3 historical land geological comprehensive evaluation parameter AL3 for comparative analysis, and adjusts the algorithm to make the T3 simulated land geological comprehensive evaluation parameter close to the T3 historical land geological comprehensive evaluation parameter.

[0067] The process by which the artificial intelligence (AI) module compares the T3 simulated land geological comprehensive evaluation parameters with the T3 historical land geological comprehensive evaluation parameters is as follows:

[0068] When W1CS3′>W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is an upward trend, but the trend in the T2 to T3 period is a downward trend. At this time, the weighted adjustment coefficient i is adjusted downward accordingly.

[0069] When W1CS3′=W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is consistent with the trend in the T2 to T3 period, and the weighted adjustment coefficient i is not adjusted at this time.

[0070] When W1CS3′<W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is a downward trend, but the trend in the T2 to T3 period is an upward trend. At this time, the weighted adjustment coefficient i is adjusted upward accordingly.

[0071] Repeat the above weighting coefficient adjustment process, comparing W2GS3′ with W2GS3, W3MD3′ with W3MD3, and W4RH3′ with W4RH3 in turn, and adjusting the weighting adjustment coefficient i accordingly, to obtain the consistent periodic trend and weighting adjustment coefficient of the land geological comprehensive evaluation parameters with a period of T3 simulation time node.

[0072] The artificial intelligence (AI) module follows the trend analysis process described above.

[0073] In sequence, CS2 and CS3, CS3 and CS4, CS4 and CS5...CS n-2 With CS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters CS4′, CS5′, CS6′, ... CS. n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of CS within a unit period.

[0074] In sequence, GS2 and GS3, GS3 and GS4, GS4 and GS5...GS n-2 With GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters GS4′, GS5′, GS6′, ... GS n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of GS within a unit period.

[0075] In sequence, compare MD2 with MD3, MD3 with MD4, MD4 with MD5...MD n-2 With MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters MD4′, MD5′, MD6′, ... MD n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of MD within a unit period.

[0076] The following pairs of pairs were tested sequentially: RH2 and RH3, RH3 and RH4, RH4 and RH5...RH n-2 With RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters RH4′, RH5′, RH6′, ... RH n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of RH within a unit period.

[0077] The artificial intelligence (AI) module adjusts the analysis process according to all the aforementioned weight coefficients.

[0078] In sequence, W1CS2 and W1CS3, W1CS3 and W1CS4, W1CS4 and W1CS5...W1CS n-2 With W1CS n-1 Weight adjustment analysis was performed to obtain the virtual time node data evaluation parameters W1CS4′, W1CS5′, W1CS6′, ..., W1CS. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W1 needs to be adjusted under the unit trend within a unit period.

[0079] In sequence, W2GS2 and W2GS3, W2GS3 and W2GS4, W2GS4 and W2GS5...W2GS n-2 With W2GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W2GS4′, W2GS5′, W2GS6′, ... W2GS. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W2 needs to be adjusted under the unit trend within a unit period.

[0080] In sequence, W3MD2 and W3MD3, W3MD3 and W3MD4, W3MD4 and W3MD5...W3MD n-2 With W3MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W3MD4′, W3MD5′, W3MD6′, ..., W3MD. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W3 needs to adjust under the unit trend within a unit period.

[0081] In sequence, W4RH2 and W4RH3, W4RH3 and W4RH4, W4RH4 and W4RH5...W4RH n-2 With W4RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W4RH4′, W4RH5′, W4RH6′, ..., W4RH n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W4 needs to adjust under the unit trend within a unit period.

[0082] The artificial intelligence (AI) module infers the unit trend and specific value of CS, GS, MD, and RH within a unit period based on the unit trend and specific value of historical geological information data of land. It also combines the total land geological score of the preset area obtained by the land geological data evaluation parameter AL to predict the future development trend of the preset area.

[0083] Compared with the prior art, the beneficial effects of the present invention are that, for historical resource databases, all required land resource information can be stored and analyzed and utilized, and the geological features can also be preset and modified. For land information integration, it is flexible and convenient, the amount of information entered can be large or small, the timeliness and applicability are very high, and it is intelligent enough. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of the land information integration system described in this invention; Detailed Implementation

[0085] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0086] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0087] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0088] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0089] Please see Figure 1As shown, it is a structural schematic diagram of the land information integration system according to an embodiment of the present invention. The system in this embodiment includes a remote sensing vector module, a spatiotemporal GIS module, an artificial intelligence AI module, and a historical resource database.

[0090] Among them, the remote sensing vector module is used to perform vector processing on the three-dimensional model of the preset area by aerial surveying and mapping, so as to form a vector three-dimensional model.

[0091] The spatiotemporal GIS module is used to evaluate the carrying capacity and suitability of various resources in a preset area and to mark them in terms of spatiotemporal dimensions.

[0092] The Artificial Intelligence (AI) module is used to perform deep learning and analysis on detailed information of various resources in a preset area, predict future development trends, and make predictions on potential risks.

[0093] The historical resource database stores all relevant historical geological information of the land in the preset area. The spatiotemporal dimension is added through the spatiotemporal GIS module, which serves as a training reference for the artificial intelligence (AI) module to update algorithms and formulas, enabling it to predict the future development trend of the preset area and make predictions on possible risks.

[0094] In this embodiment, the historical resource database may store preset historical resource data such as land, roads, buildings, plants, spatial relationships, people, or events. It only needs to meet the usage and application requirements of this embodiment, and will not be elaborated further.

[0095] The remote sensing vector module vectorizes the historical geological information data of land for each period in the historical resource database and forms a vector model. The spatiotemporal GIS module adds spatiotemporal markers to the vector model according to its historical time. The artificial intelligence (AI) module evaluates all land geological parameters of all marked areas in the vector model at that time, performs trend learning and analysis based on the various geological information at that time, and predicts the problems that may be encountered in future development. The prediction results are compared with the real information data after that period to improve the algorithm and obtain the evaluation parameters for the subsequent development of the land ecological space of the preset area. The development of the preset area is then predicted based on the evaluation parameters.

[0096] The remote sensing vector module performs vector processing on the 3D data model of the preset area based on aerial surveying and geological exploration results, forming a vector 3D model. The spatiotemporal GIS module marks each coordinate or region of the vector 3D model. The artificial intelligence (AI) module evaluates all land geological parameters of all marked areas in the vector 3D model at the current time, performs trend analysis and planning for all marked areas based on the land geological parameters, predicts the future development trend of the preset area, and makes an assessment of possible risks.

[0097] The artificial intelligence (AI) module evaluates all land geological parameters of all marked areas in the vector model at any given time. The AI ​​module defines the time period as T, and all time periods are arranged in ascending order along the time axis as T1, T2, T3…T. n n is a positive integer. In this embodiment, the value of n is n≥10. The land historical geological data for each period includes soil composition data, geological structure data, mineral distribution data and regional hydrogeological data.

[0098] The artificial intelligence (AI) module sets the evaluation parameters for soil composition data as CS, geological structure data as GS, mineral distribution data as MD, regional hydrogeological data as RH, and comprehensive land geological evaluation as AL.

[0099] AL = W1CS + W2GS + W3MD + W4RH, where W is the weighting coefficient, W1 is the weighting coefficient for soil composition data, W2 is the weighting coefficient for geological structure data, W3 is the weighting coefficient for mineral distribution data, and W4 is the weighting coefficient for regional hydrogeological data. W1 + W2 + W3 + W4 = 100%. In this embodiment, the basic values ​​of W1, W2, W3, and W4 are 25%. When the algorithm needs to be adjusted, the sum of the four items must be equal to 100%, and no item can be 0.

[0100] The artificial intelligence (AI) module analyzes all land geological parameters at time T1 and time T2 to determine the trend simulation from time T1 to time T2 and the bottoming out of the ecological red line. The analysis results are then compared with the actual data at time T3 to optimize the AI ​​module's trend simulation algorithm and adjust the weighting coefficients of the land geological comprehensive evaluation parameters. The trend simulation process for the land geological data evaluation parameters is as follows:

[0101] If CS1 < CS2, the AI ​​module determines that the soil composition data evaluation parameter trend from T1 to T2 is upward. During the upward cycle of T2-T1, the increase value is...

[0102] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a slight upward trend. During the upward cycle from T2 to T1, the increase was [value missing].

[0103] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a large upward trend. During the upward cycle from T2 to T1, the increase was [value missing]. *CS1.

[0104] If CS1 = CS2, then the artificial intelligence (AI) module determines that the trend of the soil composition data evaluation parameters from T1 to T2 is a flat trend, with a flat period of T2-T1.

[0105] If CS1 > CS2, the AI ​​module determines that the soil composition data evaluation parameter trend from T1 to T2 is downward. During the downward period of T2-T1, the decrease value is...

[0106] like The AI ​​module determines that the trend from T1 to T2 is a slight downward trend. During the downward period of T2-T1, the decrease in value is...

[0107] like The AI ​​module then determined that the soil composition data evaluation parameters from T1 to T2 showed a significant downward trend. During the downward period from T2 to T1, the decrease was [value missing].

[0108] Repeat the above trend process, and sequentially perform trend simulation processes on the geological structure data evaluation parameters GS1 and GS2, mineral distribution data evaluation parameters MD1 and MD2, and regional hydrogeological data evaluation parameters RH1 and RH2 for T1 and T2 respectively, to obtain the trend and trend magnitude of the data evaluation parameters for the period T2-T1.

[0109] The artificial intelligence (AI) module automatically allocates the weight coefficients in AL1 and AL2 based on the geological structure data evaluation parameters GS1 and GS2, mineral distribution data evaluation parameters MD1 and MD2, and regional hydrogeological data evaluation parameters RH1 and RH2 of T1 and T2, resulting in AL1 = W1CS1 + W2GS1 + W3MD1 + W4RH1 and AL2 = W1CS2 + W2GS2 + W3MD2 + W4RH2. By comparing the weight coefficients of the time period in AL1 and AL2, the allocation of each weight coefficient in the comprehensive land geological weight coefficient AL3 of T3 simulation is calculated, resulting in AL3′ = W1CS3′ + W2GS3′ + W3MD3′ + W4RH3′.

[0110] The artificial intelligence (AI) module analyzes the weighting coefficients of the time periods in AL1 and AL2 to derive the weighting adjustment coefficients in the formula of the T3 simulated land geological comprehensive evaluation parameter AL3.

[0111] Based on the data from T1 to T2, the trends and magnitudes of the evaluation parameters are re-analyzed and the weights of each evaluation parameter in the comprehensive land geological evaluation parameter AL3 are reassessed. Let the weight adjustment value of CS be iW1, the weight adjustment value of GS be iW2, the weight adjustment value of MD be iW3, and the weight adjustment value of RH be iW4, where i is a weighting adjustment coefficient and is an integer.

[0112] When the trend of CS is a large increase during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0113] When CS shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0114] When the trend of CS is a large decrease during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0115] When the trend of CS is slightly decreasing during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0116] If the trend of CS remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is iW1′.

[0117] (W2CS+iW1′)+(W2GS+iW2)+(W3MD+iW3)+(W4RH+iW4)=100%.

[0118] When GS shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0119] When GS shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0120] When GS shows a significant downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0121] When GS shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0122] If the trend of GS remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of GS is iW2′.

[0123] (W2CS+iW1)+(W2GS+iW2′)+(W3MD+iW3)+(W4RH+iW4)=100%.

[0124] When MD shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0125] When MD shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0126] When MD shows a large downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0127] When MD shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0128] If the trend of MD remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of MD is iW3′.

[0129] (W2CS+iW1)+(W2GS+iW2)+(W3MD+iW3′)+(W4RH+iW4)=100%.

[0130] When RH shows a large upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 1.5.

[0131] When RH shows a slight upward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 1.2.

[0132] When RH shows a significant downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment coefficient of CS is 0.8.

[0133] When RH shows a slight downward trend during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of CS is 0.5.

[0134] If the trend of RH remains flat during the time period from T1 to T2, the AI ​​module determines that the weighted adjustment value of RH is iW4′.

[0135] (W2CS+iW1)+(W2GS+iW2)+(W3MD+iW3)+(W4RH+iW4′)=100%.

[0136] When CS, GS, MD, and RH show equal trends over the time period from T1 to T2, the AI ​​module determines that the weighting coefficients of CS, GS, MD, and RH will not be adjusted.

[0137] The artificial intelligence (AI) module calculates the time period from T1 to T2, and inputs the data evaluation parameter trend within the T2-T1 period into the T3 time node to obtain the T3 simulated soil composition data evaluation parameter CS3′, T3 simulated geological structure data evaluation parameter GS3′, T3 simulated mineral distribution data evaluation parameter MD3′, and T3 simulated regional hydrogeological data evaluation parameter RH3′. The AI ​​module compares and analyzes the T3 simulated land geological evaluation parameters with the T3 historical land geological evaluation parameters, and adjusts the algorithm to make the T3 simulated land geological evaluation parameters closer to the real data.

[0138] The simulation process by which the artificial intelligence (AI) module derives the evaluation parameters CS3′ for T3 simulated soil composition data, GS3′ for T3 simulated geological structure data, MD3′ for T3 simulated mineral distribution data, and RH3′ for T3 simulated regional hydrogeological data is as follows:

[0139] The AI ​​module has a periodicity coefficient set to MT. Substituting the period coefficient into the T3 time node yields the trend amplitude of the T3-T2 period.

[0140] When the soil composition data evaluation parameters from T1 to T2 show an upward trend, the AI ​​module determines that the soil composition data evaluation parameters for simulation T3 should be used when the upward cycle is at the simulation time point T3.

[0141] When the trend of soil composition data evaluation parameters from T1 to T2 is a flat trend, the artificial intelligence (AI) module determines that during the flat period T3, the simulated soil composition data evaluation parameters CS3′=CS2=CS1 are CS3′=CS2=CS1.

[0142] When the soil composition data evaluation parameters from T1 to T2 show a downward trend, the AI ​​module determines that at the simulation time point T3, the soil composition data evaluation parameters for simulation T3 should be...

[0143] Repeat the above process to sequentially perform trend simulations on the geological structural data evaluation parameter GS3′, mineral distribution data evaluation parameter MD3′, and regional hydrogeological data evaluation parameter RH3′ at the T3 simulation time node, and obtain the values ​​of the data evaluation parameters with a period of T3 simulation time node.

[0144] The AI ​​module compares CS3′ with CS3, determines the trend from GS2 to GS3, MD3′ with MD3, and RH3′ with RH3 as follows:

[0145] If CS3′ > CS3, then the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is an upward trend, but the trend in the T2 to T3 period is a downward trend.

[0146] If CS3′=CS3, then the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is consistent with the trend in the T2 to T3 period.

[0147] If CS3′ < CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is a downward trend, but the trend in the T2 to T3 period is an upward trend.

[0148] Repeat the above trend process, comparing GS3′ with GS3, MD3′ with MD3, and RH3′ with RH3 in turn, and perform the trend determination process to obtain the periodic trend and specific value of the data evaluation parameters with a period of T3 simulation time node.

[0149] The artificial intelligence (AI) module substitutes the calculated AL3′=W1CS3′+W2GS3′+W3MD3′+W4RH3′ into the T3 historical land geological comprehensive evaluation parameter AL3 for comparative analysis, and adjusts the algorithm to make the T3 simulated land geological comprehensive evaluation parameter close to the T3 historical land geological comprehensive evaluation parameter.

[0150] The process by which the artificial intelligence (AI) module compares the T3 simulated land geological comprehensive evaluation parameters with the T3 historical land geological comprehensive evaluation parameters is as follows:

[0151] When W1CS3′>W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is an upward trend, but the trend in the T2 to T3 period is a downward trend. At this time, the weighted adjustment coefficient i is adjusted downward accordingly.

[0152] When W1CS3′=W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is consistent with the trend in the T2 to T3 period, and the weighted adjustment coefficient i is not adjusted at this time.

[0153] When W1CS3′<W1CS3, the AI ​​module determines that the trend of CS3′ in the T1 to T2 period is a downward trend, but the trend in the T2 to T3 period is an upward trend. At this time, the weighted adjustment coefficient i is adjusted upward accordingly.

[0154] Repeat the above weighting coefficient adjustment process, comparing W2GS3′ with W2GS3, W3MD3′ with W3MD3, and W4RH3′ with W4RH3 in turn, and adjusting the weighting adjustment coefficient i accordingly, to obtain the consistent periodic trend and weighting adjustment coefficient of the land geological comprehensive evaluation parameters with a period of T3 simulation time node.

[0155] The artificial intelligence (AI) module follows the trend analysis process described above.

[0156] In sequence, CS2 and CS3, CS3 and CS4, CS4 and CS5...CS n-2 With CS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters CS4′, CS5′, CS6′, ... CS. n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of CS within a unit period.

[0157] In sequence, GS2 and GS3, GS3 and GS4, GS4 and GS5...GS n-2 With GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters GS4′, GS5′, GS6′, ... GS n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of GS within a unit period.

[0158] In sequence, compare MD2 with MD3, MD3 with MD4, MD4 with MD5...MD n-2 With MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters MD4′, MD5′, MD6′, ... MD n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of MD within a unit period.

[0159] The following pairs of pairs were tested sequentially: RH2 and RH3, RH3 and RH4, RH4 and RH5...RH n-2 With RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters RH4′, RH5′, RH6′, ... RH n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters to analyze and obtain the unit trend and specific value calculation formula of RH within a unit period.

[0160] The artificial intelligence (AI) module adjusts the analysis process according to all the aforementioned weight coefficients.

[0161] In sequence, W1CS2 and W1CS3, W1CS3 and W1CS4, W1CS4 and W1CS5...W1CS n-2 With W1CS n-1 Weight adjustment analysis was performed to obtain the virtual time node data evaluation parameters W1CS4′, W1CS5′, W1CS6′, ..., W1CS. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W1 needs to be adjusted under the unit trend within a unit period.

[0162] In sequence, W2GS2 and W2GS3, W2GS3 and W2GS4, W2GS4 and W2GS5...W2GS n-2 With W2GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W2GS4′, W2GS5′, W2GS6′, ... W2GS. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W2 needs to be adjusted under the unit trend within a unit period.

[0163] In sequence, W3MD2 and W3MD3, W3MD3 and W3MD4, W3MD4 and W3MD5...W3MD n-2 With W3MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W3MD4′, W3MD5′, W3MD6′, ..., W3MD. n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W3 needs to adjust under the unit trend within a unit period.

[0164] In sequence, W4RH2 and W4RH3, W4RH3 and W4RH4, W4RH4 and W4RH5...W4RH n-2 With W4RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W4RH4′, W4RH5′, W4RH6′, ..., W4RH n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical world node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that W4 needs to adjust under the unit trend within a unit period.

[0165] The artificial intelligence (AI) module infers the unit trend and specific value of CS, GS, MD, and RH within a unit period based on the unit trend and specific value of historical geological information data of land. It also combines the total land geological score of the preset area obtained by the land geological data evaluation parameter AL to predict the future development trend of the preset area.

[0166] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0167] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A land information integration system, characterized in that, It includes a remote sensing vector module, a spatiotemporal GIS module, an artificial intelligence (AI) module, and a historical resource database, among which... The remote sensing vector module is used to perform vector processing on the three-dimensional model of the preset area obtained by aerial surveying and mapping to form the current vector three-dimensional model, and to perform data vectorization processing on the land historical geological information of a unit period in the historical resource database to form a historical vector three-dimensional model. The spatiotemporal GIS module is used to add spatiotemporal dimension markers to the geological feature values ​​of the preset area to form the current spatiotemporal vector model, and to add spatiotemporal dimension markers to the historical vector 3D model to form the historical spatiotemporal vector model; The historical resource database is used to store historical geological information of the land in the preset area and serves as the information training library for the artificial intelligence (AI) module. The AI ​​module learns and updates the algorithm based on the information training library, so that the algorithm can be applied to the preset area in the next stage. The artificial intelligence (AI) module is used to perform deep learning and analysis on the geological feature values ​​of the historical spatiotemporal vector model, evaluate the land geological feature values ​​of the historical spatiotemporal vector model, perform trend learning and analysis on the historical spatiotemporal vector model based on historical evaluation parameters, calculate the simulated information data for a certain preset unit period, compare it with the real information data for the corresponding unit period, and predict the land information development trend of the preset area. The artificial intelligence (AI) module analyzes the trends of the land geological information data evaluation parameters at time T1 and time T2, determines the simulated trend from time T1 to time T2 and the specific values ​​for adjusting the comprehensive land geological evaluation parameters, obtains the simulated land geological information data evaluation parameters at time T3, compares and analyzes the simulated land geological information data evaluation parameters at time T3 with the historical land geological information data evaluation parameters at time T3, and adjusts the weight coefficients in the comprehensive land geological evaluation parameters based on the comparison and analysis results. The artificial intelligence (AI) module evaluates the land geological information data of the spatiotemporal vector model. The AI ​​module sets a certain unit period node as T, and all period nodes are arranged in ascending order of time as T1, T2, T3...T. n , where n is a positive integer, and the land geological information data includes soil composition data, geological structure data, mineral distribution data and regional hydrological data; The artificial intelligence (AI) module sets the evaluation parameter for soil composition data as CS, the evaluation parameter for geological structure data as GS, the evaluation parameter for mineral distribution data as MD, the evaluation parameter for regional hydrological data as RH, and the comprehensive land geological evaluation parameter as AL. AL = W1CS + W2GS + W3MD + W4RH, where W is the weighting coefficient, W1 is the weighting coefficient for soil composition data, W2 is the weighting coefficient for geological structure data, W3 is the weighting coefficient for mineral distribution data, and W4 is the weighting coefficient for regional hydrological data. W1 + W2 + W3 + W4 = 100%.

2. The land information integration system according to claim 1, characterized in that, The trend analysis process of the artificial intelligence (AI) module for land geological information data evaluation parameters is as follows: If CS1 < CS2, then the AI ​​module determines that the soil composition data evaluation parameter trend from time T1 to T2 is an upward trend, and the increase value is [value missing] during the upward period of T2-T1. like The AI ​​module determines that the soil composition data evaluation parameters from time T1 to T2 show a small upward trend, with the increase value being [value missing] during the upward period from T2 to T1. like The AI ​​module determines that the soil composition data evaluation parameters from time T1 to T2 show a large upward trend. During the upward period from T2 to T1, the increase in value is... If CS1 = CS2, then the artificial intelligence (AI) module determines that the trend of the soil composition data evaluation parameters from time T1 to T2 is a flat trend, and the flat period is T2-T1; If CS1 > CS2, then the AI ​​module determines that the soil composition data evaluation parameter trend from time T1 to T2 is a downward trend, and the decrease value is [value missing] during the downward period of T2-T1. like The AI ​​module determines that the trend from time T1 to T2 is a small downward trend, and the decrease value is [value missing] during the downward period of T2-T1. like The AI ​​module then determines that the soil composition data evaluation parameters from time T1 to T2 show a large downward trend. During the downward period of T2-T1, the decrease in value is... Repeat the above trend process, and sequentially perform trend analysis on the geological structure data evaluation parameters GS1 and GS2, mineral distribution data evaluation parameters MD1 and MD2, and regional hydrogeological data evaluation parameters RH1 and RH2 at times T1 and T2, respectively, to obtain the trends and trend values ​​of the land geological information data evaluation parameters with a period of T2-T1.

3. The land information integration system according to claim 2, characterized in that, The artificial intelligence (AI) module calculates the time period from T1 to T2, and inputs the trend of the land geological information data evaluation parameters within the T2-T1 period into the time node of T3 to obtain the simulated soil composition data evaluation parameters CS3′, simulated geological structure data evaluation parameters GS3′, simulated mineral distribution data evaluation parameters MD3′, and simulated regional hydrological data evaluation parameters RH3′ at T3. The AI ​​module then compares and analyzes the simulated land geological information data evaluation parameters at T3 with the historical land geological information data evaluation parameters at T3.

4. The land information integration system according to claim 3, characterized in that, The process by which the artificial intelligence (AI) module derives the evaluation parameters CS3′, GS3′, MD3′, and RH3′ from the simulated land geological information data at time T3 is as follows: When the soil composition data evaluation parameters at times T1 to T2 show an upward trend, the artificial intelligence (AI) module determines that the simulated soil composition data evaluation parameters at time T3 are at the simulation time node with the upward period being T3. When the trend of the soil composition data evaluation parameters from time T1 to T2 is a flat trend, the artificial intelligence (AI) module determines that the flat period is time T3, and the simulated soil composition data evaluation parameters at time T3 are CS3′=CS2=CS1. When the soil composition data evaluation parameters at times T1 to T2 show a downward trend, the artificial intelligence (AI) module determines that at the simulation time node T3, the simulated soil composition data evaluation parameters at time T3 are... Repeat the above process of obtaining the evaluation parameters of the simulated land geological information data at time T3, and sequentially perform trend simulations on GS3′, MD3′, and RH3′ at the simulated time node at time T3 to obtain the values ​​of the evaluation parameters of the land geological information data with a period of time T3.

5. The land information integration system according to claim 4, characterized in that, The artificial intelligence (AI) module compares and analyzes the evaluation parameters of the simulated land geological information data at time T3 and the evaluation parameters of the historical land geological information data at time T3. The trend comparison process of CS3′ with CS3, GS3′ with GS3, MD3′ with MD3, and RH3′ with RH3 by the AI ​​module is as follows: If CS3′ > CS3, then the AI ​​module determines that the trend of CS3′ during the period from T1 to T2 is an upward trend, but the trend during the period from T2 to T3 is a downward trend. If CS3′=CS3, then the artificial intelligence (AI) module determines that the trend of CS3′ during the period from T1 to T2 is consistent with the trend during the period from T2 to T3. If CS3′ < CS3, then the AI ​​module determines that the trend of CS3′ during the period from T1 to T2 is a downward trend, but the trend during the period from T2 to T3 is an upward trend. Repeat the above trend comparison process, comparing the trends of GS3′ with GS3, MD3′ with MD3, and RH3′ with RH3 in turn, to obtain the periodic trends and specific values ​​of the land geological information data evaluation parameters at the simulated time node T3.

6. The land information integration system according to claim 5, characterized in that, The artificial intelligence (AI) module substitutes the calculated AL3′=W1CS3′+W2GS3′+W3MD3′+W4RH3′ into the historical land geological comprehensive evaluation parameter AL3 at time T3 for comparison and adjusts the weighting coefficients. The process is as follows: When W1CS3′>W1CS3, the AI ​​module determines that the trend of CS3′ in the period from T1 to T2 is an upward trend, but the trend in the period from T2 to T3 is a downward trend. At this time, the weighted adjustment coefficient i is adjusted downward accordingly. When W1CS3′=W1CS3, the AI ​​module determines that the trend of CS3′ during the period from T1 to T2 is consistent with the trend during the period from T2 to T3. At this time, the weighted adjustment coefficient i is not adjusted. When W1CS3′<W1CS3, the AI ​​module determines that the trend of CS3′ in the period from T1 to T2 is a downward trend, but the trend in the period from T2 to T3 is an upward trend. At this time, the weighted adjustment coefficient i is adjusted upward accordingly. Repeat the above weighting coefficient adjustment process, comparing W2GS3′ with W2GS3, W3MD3′ with W3MD3, and W4RH3′ with W4RH3 in turn, and adjusting the weighting adjustment coefficient i accordingly, to obtain the consistent periodic trend and weighting adjustment coefficient of the land geological comprehensive evaluation parameters at the simulated time node of time T3.

7. The land information integration system according to claim 6, characterized in that, The artificial intelligence (AI) module follows the above trend analysis process and weight coefficient adjustment analysis process as follows: In sequence, CS2 and CS3, CS3 and CS4, CS4 and CS5...CS n-2 With CS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters CS4′, CS5′, CS6′, ... CS. n ′, respectively, GS2 and GS3, GS3 and GS4, GS4 and GS5...GS n-2 With GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters GS4′, GS5′, GS6′, ... GS n ′, respectively, for MD2 and MD3, MD3 and MD4, MD4 and MD5...MD n-2 With MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters MD4′, MD5′, MD6′, ... MD n ′, respectively, for RH2 and RH3, RH3 and RH4, RH4 and RH5...RH n-2 With RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters RH4′, RH5′, RH6′, ... RH n The virtual time node data evaluation parameters were compared with the historical world node data evaluation parameters, and the unit trends and specific value calculation formulas of CS, GS, MD and RH within a unit period were analyzed. In sequence, W1CS2 and W1CS3, W1CS3 and W1CS4, W1CS4 and W1CS5...W1CS n-2 With W1CS n-1 Weight adjustment analysis was performed to obtain the virtual time node data evaluation parameters W1CS4′, W1CS5′, W1CS6′, ..., W1CS. n ′, respectively, for W2GS2 and W2GS3, W2GS3 and W2GS4, W2GS4 and W2GS5...W2GS n-2 With W2GS n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W2GS4′, W2GS5′, W2GS6′, ..., W2GS. n ′, respectively, for W3MD2 and W3MD3, W3MD3 and W3MD4, W3MD4 and W3MD5...W3MD n-2 With W3MD n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W3MD4′, W3MD5′, W3MD6′, ..., W3MD. n ′, respectively, for W4RH2 and W4RH3, W4RH3 and W4RH4, W4RH4 and W4RH5...W4RH n-2 With W4RH n-1 Trend analysis was performed to obtain the virtual time node data evaluation parameters W4RH4′, W4RH5′, W4RH6′, ..., W4RH n The obtained virtual time node land geological comprehensive evaluation parameters are compared with the historical time node land geological comprehensive evaluation parameters to analyze the specific weight values ​​that need to be adjusted for W1, W2, W3, and W4 under the unit trend within a unit period.

8. The land information integration system according to claim 7, characterized in that, The artificial intelligence (AI) module infers the unit trend and specific value of the preset area within a unit period based on the unit trends and specific values ​​of CS, GS, MD, and RH obtained from historical land geological information data. It also combines the total land geological score of the preset area obtained from the land geological data evaluation parameter AL to predict the future development trend of the preset area.

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