Regional geological data fusion visual monitoring system and method based on neural network learning

Abstract

The invention belongs to the technical field of geological data monitoring, and discloses a regional geological data fusion visual monitoring system and method based on neural network learning. The method comprises the steps that a regional geological node monitoring neural network learning model and a regional geological surface node monitoring neural network learning model are constructed and fused, and construction of a regional geological three-dimensional model based on neural network learning is completed; node importance identification based on a regional geology three-dimensional space expression vector value adaptive adjustment method is carried out; self-adaptive dynamic node importance arrangement based on a regional geology three-dimensional space expression vector threshold value; and self-adaptive regional geological data fusion visual monitoring based on the regional geological three-dimensional space expression vector value is realized. Compared with the prior art, the method has the advantage that the accuracy of regional geological data fusion visual monitoring and the geological abnormal region recognition precision are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological data monitoring, and in particular relates to a regional geological data fusion visualization monitoring system and method based on neural network learning. Background Art

[0002] Due to the complexity and uncertainty of geological phenomena, geologists consider a variety of geological factors and complex conditions to identify and simulate strata. Within a certain range, they establish a variety of possible implementations to produce the best, most reasonable, and most consistent model design scheme in line with geological laws, revealing the internal structure of geological bodies, the complex spatial variation laws, and the distribution characteristics of attribute parameters, and conducting comprehensive integration and three-dimensional quantitative research and management, directly reflecting the uncertainty and other factors in geological phenomena. Three-dimensional geological identification technology will provide a solid support for the research and development work of geologists, improve the ability to analyze geological bodies, including predicting the location of drilling holes, automatically exploring the occurrence of faults, etc., and by coupling multi-source data in the research area, improve the method of spatial information processing, and provide a unified three-dimensional display channel to support the full-scale and integrated display of exploration data in the same coordinate system. Improving and perfecting the integration or coupling methods of spatial geological data, the expression form of data, and the model description of spatial geological geometric morphology have become urgent needs for geologists. With the rapid development of exploration geophysics, remote sensing image processing and other technologies, it has become possible to couple three-dimensional spatial multi-source data of geological data, photogrammetry data, remote sensing data and geophysical data, which provides the premise and foundation for the rapid and accurate establishment of three-dimensional complex geological models, and broadens the application scope of resource analysis and evaluation of complex geological bodies including thrust faults, inverted folds, etc. The geological model based on multi-source data and its information processing system can also provide important basis for economic evolution, such as investment priority strategy, development plan, future exploration budget, etc.

[0003] In order to solve the above problems, the existing technology carries out stratum identification and analysis technology in VR geological environment, which discloses: geological data preprocessing, three-dimensional stratum model identification and construction, and spatial visualization analysis and correction; each of the above steps needs to be tested according to the "spatial information quality detection model"; with the development of machine learning, active attempts are made to automatically perform the business of identifying or classifying images through computer systems. In particular, attempts are made to use one of the machine learning neural networks (for example, using the deep learning method of convolutional neural networks (CNN)) to automate various classifications or judgments performed by people, and to read regional geological data images by using deep learning in which neural networks are used, so as to judge the state or development trend of geology in a specific area.

[0004] In existing multiple instance learning, since one instance is extracted from one instance package for learning, there is a problem that a large number of instance packages are needed to improve the performance of the neural network. For example, in order to use existing multiple instance learning in a neural network for detecting regional geological evolution, a large number of whole-fault-images marked with flags related to whether evolution exists are required.

[0005] As described above, in multi-instance learning, since a neural network before learning is completed is used in the step of extracting a learning data instance, there is a problem that when extracting multiple data instances from one instance package, there is a high possibility of extracting an erroneous instance.

[0006] Through the above analysis, the problems and defects of the existing technology are as follows: the accuracy of the existing technology in regional geological data fusion visualization monitoring and the accuracy of identifying geological anomaly areas still need to be further improved. Summary of the invention

[0007] In order to overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a regional geological data fusion visualization monitoring system and method based on neural network learning.

[0008] The technical solution is as follows: A regional geological data fusion visualization monitoring method based on neural network learning, comprising:

[0009] S1, constructing a neural network learning model for regional geology and surface node monitoring, and integrating it to complete the construction of a regional geological three-dimensional model based on neural network learning;

[0010] S2, for the constructed regional geological three-dimensional model based on neural network learning, node importance identification is performed based on the adaptive adjustment method of the regional geological three-dimensional spatial expression vector value;

[0011] S3, dynamic node importance arrangement based on adaptive threshold of regional geological three-dimensional spatial expression vector;

[0012] S4, according to the dynamic node importance arrangement, realizes the regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value.

[0013] In step S1, the node monitoring neural network learning model is composed of a regional geological monitoring terminal, a monitoring terminal server and a wireless access network controller;

[0014] The regional geological monitoring terminal is wirelessly connected to Alibaba Cloud;

[0015] The monitoring terminal server has a synthesis function for providing services to the regional geological data fusion visualization monitoring center;

[0016] The wireless access network controller is connected to the cluster to generate drilling data and surface data units for collecting all server information;

[0017] The regional geological data fusion visualization monitoring center randomly traverses any regional geological monitoring terminal node of the regional geological three-dimensional space expression vector to request to query any content of regional geological fusion monitoring. If the transmission time between any nodes in the cluster is less than the transmission time to Alibaba Cloud, the regional geological data fusion visualization monitoring center obtains a response.

[0018] Furthermore, the regional geological data fusion visualization monitoring center received responses, including:

[0019] (1) Traversing the monitoring terminal server of the regional geological three-dimensional space expression vector, synthesizing the request of the regional geological data fusion visualization monitoring center to query the content of the regional geological fusion monitoring, and the server responding to the request of the regional geological data fusion visualization monitoring center to query the regional geological fusion monitoring; checking whether a synthesis of the monitoring terminal server of the traversed regional geological three-dimensional space expression vector is synthesized content;

[0020] (2) if the synthesis regional geological data fusion visualization monitoring center requests to query the regional geological fusion monitoring content, the synthesis sends the content to the regional geological data fusion visualization monitoring center, traverses the regional geological three-dimensional space expression vector and responds to the request of the regional geological data fusion visualization monitoring center to query the local monitoring terminal server of the regional geological fusion monitoring; determines whether other monitoring terminal servers synthesize the content;

[0021] (3) If all regional geological monitoring terminal nodes in the search neural network layer do not have synthetic content, the request to query regional geological fusion monitoring by traversing the regional geological three-dimensional spatial expression vector will be forwarded to Alibaba Cloud;

[0022] (4) The request from the Regional Geological Data Fusion Visualization Monitoring Center to query the regional geological fusion monitoring is responded to in Alibaba Cloud. Alibaba Cloud sends the request to query the regional geological fusion monitoring content to the neural network layer head server, and then sends it to the Regional Geological Data Fusion Visualization Monitoring Center to traverse the regional geological three-dimensional space expression vector server, and finally sends it to the Regional Geological Data Fusion Visualization Monitoring Center.

[0023] In step S2, node importance identification based on the adaptive adjustment method of regional geological three-dimensional spatial expression vector value includes:

[0024] Suppose the neural network is H = (E, F), where E is the set of network nodes, (E = {1, 2 ... E}; F is the edge set in the network, F = {1, 2 ... F}; F ij is the edge between node i and node j, F ij The domain width is u ijIt means that when there is an edge between node i and node j, then u ij >0, otherwise u ij =0;

[0025] S201, node synthesis space, ca i Represents the available resources of the node. The more synthetic resources are available, the more important the node is.

[0026] S202, the sum of the width of the adjacent domain of the node. The expression of the sum of the width of the adjacent domain of the node is:

[0027]

[0028] Where N(i) is the set of neighbor nodes of node i in the network. The larger the sum of the neighbor domain widths of a node, the more important the node is. n is the cumulative number of neighbor domain widths, g is i is the adjacent domain width of node i, j is the jth neighbor node;

[0029] S203, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes depends on the spatial expression parameters of the synthetic content and the degree of attention received, the more spatial expression parameters there are, the more times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes, the more attention received by the content, the more times of traversing the regional geological three-dimensional spatial expression vector nodes; the spatial expression parameters include: rock core, geological boundary, stratum, fault, fold, original section, auxiliary section, DTM / DEM data;

[0030] S204, the nuclearity center of the traversed regional geological three-dimensional space expression vector is constructed by introducing the number of traversed regional geological three-dimensional space expression vectors of the node to construct the nuclearity centrality of the traversed regional geological three-dimensional space expression vector, and this is used as the synthesis evolution standard for determining the network synthesis strategy, and the synthesis position is determined by the position of the node and the traversed regional geological three-dimensional space expression vector situation; the nuclearity centrality of the traversed regional geological three-dimensional space expression vector of the node is the nuclearity of the traversed regional geological three-dimensional space expression vector of all neighboring nodes and the number of traversed regional geological three-dimensional space expression vectors of the node;

[0031] S205, traversing the regional geological three-dimensional space to express the vector stability rate.

[0032] In step S205, the regional geological three-dimensional space is traversed to express the vector stability rate, and the expression is:

[0033]

[0034] In the formula, Vector stability rate The larger the value is, the more stable the traversal regional geological three-dimensional space expression vector of the node is, the easier it is to satisfy the traversal regional geological three-dimensional space expression vector request query regional geological fusion monitoring of the regional geological data fusion visualization monitoring center, and the greater the importance of the node is; l is the Boltzmann constant, which represents the property of the system itself, l = 1 / ln(n); e is the neighbor node, ko j is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​node j, ko e is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​the e-th neighbor node.

[0035] In step S203, the number of times the regional geological three-dimensional space expression vector nodes are traversed includes:

[0036] S2031, regional geological three-dimensional spatial expression vector significance. The attention degree of regional geological three-dimensional spatial expression parameters satisfies the Zipf distribution. The significance expression of the regional geological three-dimensional spatial expression parameter k ranked α is:

[0037]

[0038] In the formula, is the significance value of the regional geological three-dimensional spatial expression parameter k ranked α, α is the ranking value of the regional geological three-dimensional spatial expression parameter, θ is the number of regional geological three-dimensional spatial expression parameters that have received attention, μ is the skewness coefficient of the Zipf distribution, the larger μ is, the easier it is to traverse the regional geological three-dimensional spatial expression parameters with high significance; num is the total number of regional geological three-dimensional spatial expression parameters;

[0039] S2032, the attention degree of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal, by analyzing the long-term traversal regional geological three-dimensional space expression vector records of the regional geological data fusion visualization monitoring center, the stable attention trend of the regional geological data fusion visualization monitoring center is obtained, and the long-term attention of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal i that traverses the regional geological three-dimensional space expression vector is defined as The expression is:

[0040]

[0041] In the formula, is the statistical vector value of the current regional geological monitoring terminal node i; long (ΔL long ) is the current statistical traversal regional geological three-dimensional space expression vector value of all regional geological data fusion visualization monitoring centers in the neural network layer;

[0042] The attention of the regional geological data fusion visualization monitoring center traversing the regional geological three-dimensional space expression vector node i in the recent time is defined as short-term attention The expression is:

[0043]

[0044] In the formula, is the three-dimensional spatial expression vector of regional geology for terminal node i in the recent period of time, o short (ΔL short ) is the statistical flow of the regional geological three-dimensional space expression vector traversed by all regional geological data fusion visualization monitoring centers in the neural network layer in the recent period;

[0045] The current attention trend of the regional geological data fusion visualization monitoring center depends on the long-term and short-term attention of the regional geological data fusion visualization monitoring center. The regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional spatial expression vector and the potential attention of the regional geological monitoring terminal node i is defined as The expression is:

[0046]

[0047] In the formula, l1 is the influence ratio of long-term attention to the current attention of the regional geological data fusion visualization monitoring center, and l2 is the influence ratio of short-term attention to the current attention of the regional geological data fusion visualization monitoring center. Considering that the recent impact is greater, l2 is greater than l1.

[0048] S2033, regional geological data fusion visualization monitoring center, potential traversal of regional geological three-dimensional spatial expression vector trend, the trend of regional geological data fusion visualization monitoring center traversing regional geological three-dimensional spatial expression vector content is not only affected by the attention trend, but also closely related to the attention level of regional geological three-dimensional spatial expression parameters, that is, the regional geological data fusion visualization monitoring center always tends to request query regional geological fusion monitoring of the significant content they pay attention to; the request query regional geological fusion monitoring probability of content k in terminal node i for:

[0049]

[0050] Where v is the number of concerned trends of regional geological three-dimensional spatial expression parameters;

[0051] Assume that the total number of regional geological three-dimensional spatial expression vectors traversed by the regional geological data fusion visualization monitoring center in the previous synthesis cycle is o ave, then the regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional space expression vector of the regional geological monitoring terminal node i, and the potential traversal of the regional geological three-dimensional space expression vector of the content k for:

[0052]

[0053] In step S204, the regional geological three-dimensional space is traversed to express the vector kernel central rate, including:

[0054] (A) Delete all nodes and edges with a connectivity of 1, and record the number of traversal regional geological three-dimensional space expression vectors of the nodes; if there are still nodes with a connectivity of 1, continue the above process and mark the coreness of these deleted nodes as 1. The coreness of the traversal regional geological three-dimensional space expression vector is the number of traversal regional geological three-dimensional space expression vectors of the node plus 1. If the number of traversal regional geological three-dimensional space expression vectors of node j is o j , then the vector kernel degree of the traversal regional geological three-dimensional space expression of node j is ko j , the expression is:

[0055] ko j =o j +1

[0056] (B) For nodes with a connectivity of 2, the coreness of the traversed regional geological three-dimensional space expression vectors of these nodes is marked as the number of traversed regional geological three-dimensional space expression vectors + 2;

[0057] (C) The above process is executed repeatedly until all nodes are deleted and the corresponding traversed regional geological three-dimensional space expression vector kernel is obtained; the number next to the node indicates the number of traversed regional geological three-dimensional space expression vectors.

[0058] In step S3, the dynamic node importance arrangement based on the adaptive threshold of the regional geological three-dimensional spatial expression vector includes:

[0059] S301, evolution model, there are n nodes to be ranked in importance, each node has 4 evaluation indicators, and the value of the ath evaluation indicator of terminal node i is c ia , the evolution matrix C composed of all network nodes is expressed as:

[0060]

[0061] S302, nonlinear programming evolution matrix, due to the different dimensions of each index, there are orders of magnitude differences, in order to eliminate the dimension differences between the indicators, the index should be standardized, expressed as:

[0062]

[0063] Where b ia is the standardized value of the ath evaluation index of terminal node i;

[0064] The standard normalized matrix B is expressed as:

[0065]

[0066] S303, calculating a vector threshold based on the regional geological three-dimensional spatial expression according to the exponential entropy;

[0067] Exponential entropy value w a The calculation formula is:

[0068]

[0069] Calculate information entropy redundancy RR a , as shown below:

[0070] RR a =1-w a

[0071] The calculation index is based on the regional geological three-dimensional spatial expression vector threshold u a As shown below:

[0072]

[0073] The index is obtained based on the regional geological three-dimensional spatial expression vector threshold matrix U, as shown in the following formula:

[0074] U=[u1 u2 u3 u4]

[0075] Wherein, u1 is the first regional geological three-dimensional space expression vector threshold, u2 is the second regional geological three-dimensional space expression vector threshold, u3 is the third regional geological three-dimensional space expression vector threshold, and u4 is the fourth regional geological three-dimensional space expression vector threshold;

[0076] The weighted normalized evolution matrix Y is expressed as:

[0077]

[0078] The weighted attribute value K of terminal node i i It is expressed as:

[0079]

[0080] Where Y ia is the weighted normalized evolution value of the ath evaluation index of terminal node i;

[0081] Since the nodes in the neural network achieve collaborative synthesis, the adjacent nodes will also contribute to the synthetic importance of the target node;

[0082] S304, importance calculation, based on the importance evaluation matrix, sum the attribute value of terminal node i and the importance contribution of all adjacent nodes to obtain the importance ξ of terminal node i i , the expression is:

[0083]

[0084] In the formula, K i is the weighted attribute value of terminal node i, θ i Assign a parameter to the contribution, ξ i is the importance of terminal node i after comprehensive evaluation, ξ i Reflects the value of terminal node i in the network;

[0085] The importance evaluation matrix fully considers the node location, the frequency of the vector expressing the traversed regional geological three-dimensional space, the synthetic space, and the available domain width, and reflects the importance of the node more comprehensively around the goal of synthetic value.

[0086] In step S4, regional geological data fusion visualization monitoring based on regional geological three-dimensional spatial expression vector value adaptation is realized, including:

[0087] S401, node synthesis update rate R(i), the expression is:

[0088]

[0089] Where M is the number of regional geological three-dimensional spatial expression parameters updated from terminal node i per unit time, K(Ro m ) is the size of the updated regional geological three-dimensional spatial expression parameter from terminal node i, and I(i) is the synthetic space size of terminal node i;

[0090] S402, node synthesis value ratio J(i), including the stable core rate and synthesis update rate of the traversed regional geological three-dimensional spatial expression vector, expressed as:

[0091]

[0092] If the node synthesis update rate R(i)=0, it means that the node synthesis space is not full, or there is no new regional geological three-dimensional spatial expression parameter input.

[0093] Another object of the present invention is to provide a regional geological data fusion visualization monitoring system based on neural network learning, which is implemented by the regional geological data fusion visualization monitoring method based on neural network learning, and the system includes:

[0094] The regional geological 3D model construction module based on neural network learning is used to construct the regional geological and surface node monitoring neural network learning model, and integrate it to complete the construction of the regional geological 3D model based on neural network learning;

[0095] A node importance recognition module is used to identify the node importance of the regional geological three-dimensional model constructed based on neural network learning, based on the adaptive adjustment method of the regional geological three-dimensional space expression vector value;

[0096] Dynamic node importance ranking module, used for dynamic node importance ranking based on regional geological three-dimensional spatial expression vector threshold adaptive;

[0097] The regional geological data fusion visualization monitoring module is used to realize the regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value according to the dynamic node importance arrangement.

[0098] Combining all the above technical solutions, the beneficial effects of the present invention are as follows: the accuracy of regional geological data fusion visualization monitoring and the accuracy of identifying geological anomaly areas are further improved compared with the prior art. The present invention more accurately selects high-speed synthesis regional geological monitoring terminal nodes by adaptively weighting various features, thereby improving the synthesis hit rate and the response efficiency of the regional geological data fusion visualization monitoring center's request for query of regional geological fusion monitoring. The present invention uses multiple network models and simulates the algorithm from multiple angles. The results show that compared with the existing mechanism, this scheme can obtain better efficiency in traversing the regional geological three-dimensional spatial expression vector synthesis in a more complex network access environment.

[0099] Taking the distributed neural network cluster as the object, the synthesis position of the content is determined by factors such as node synthesis size, adjacent domain width, nuclearity center rate of traversed regional geological three-dimensional space expression vector, and stability of traversed regional geological three-dimensional space expression vector. This method uses the information entropy method to allocate adaptive regional geological three-dimensional space expression vector values, and combines the node update rate to obtain the node with the largest synthesis value. Compared with other related solutions, this method has great advantages in the recognition accuracy and synthesis efficiency of synthesized important nodes.

[0100] The neural network structure of the present invention takes into account the characteristics that the regional geological monitoring terminal nodes can traverse the regional geological three-dimensional space expression vector and forward the traversed regional geological three-dimensional space expression vector request to query the regional geological fusion monitoring, and calculates the characteristics related to node synthesis, including synthesis space, adjacent domain width, traversed regional geological three-dimensional space expression vector core centrality and traversed regional geological three-dimensional space expression vector balance, which provides a basis for considering the importance of node synthesis.

[0101] Aiming at the influence of multiple characteristic values ​​on the node synthesis value. The present invention uses information entropy to adaptively assign a threshold value based on the regional geological three-dimensional space expression vector for each feature. This helps to flexibly and accurately determine the importance of the node; by calculating the update rate, it avoids frequent replacement of the content in the node and improves the monitoring efficiency of the system. The present invention conducts multi-angle experiments under multiple network models. The experimental results show that compared with the existing algorithms, the algorithm can more accurately identify the importance of the node, and can more effectively improve the synthesis efficiency and the response rate of traversing the regional geological three-dimensional space expression vector. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0103] Figure 1 It is a flow chart of a regional geological data fusion visualization monitoring method based on neural network learning provided by an embodiment of the present invention;

[0104] Figure 2 It is a schematic diagram of a regional geological data fusion visualization monitoring system based on neural network learning provided by an embodiment of the present invention;

[0105] In the figure: 1. Regional geological three-dimensional model construction module based on neural network learning; 2. Node importance identification module; 3. Dynamic node importance arrangement module; 4. Regional geological data fusion visualization monitoring module. DETAILED DESCRIPTION

[0106] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0107] Embodiment 1, as Figure 1 As shown, the regional geological data fusion visualization monitoring method based on neural network learning provided by the embodiment of the present invention includes:

[0108] S1, constructing a neural network learning model for regional geology and surface node monitoring, and integrating it to complete the construction of a regional geological three-dimensional model based on neural network learning;

[0109] S2, for the constructed regional geological three-dimensional model based on neural network learning, node importance identification is performed based on the adaptive adjustment method of the regional geological three-dimensional spatial expression vector value;

[0110] S3, dynamic node importance arrangement based on adaptive threshold of regional geological three-dimensional spatial expression vector;

[0111] S4, according to the dynamic node importance arrangement, realizes the regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value.

[0112] Exemplarily, in step S1, completing the construction of a regional geological three-dimensional model based on neural network learning includes:

[0113] Acquire the drilling data of regional geology, and classify the drilling data according to the drilling category number and drilling data capacity; among the drilling data of the same category, select the standard drilling data in this category, calculate the similarity and difference values ​​between other drilling data in this category and the standard drilling data, and generate the standard drilling data and the similarity and difference values ​​in the server according to the preset format;

[0114] Obtain regional geological surface data, which includes depth data and shooting data;

[0115] generating borehole data and surface data, comprising a first synthesis device and a second synthesis device;

[0116] The first synthesis device includes a synthesis processing module, a synthesizer and a generator, specifically:

[0117] A synthesis processing module, used for setting the same type of drilling data as small file packages and writing them into the synthesizer;

[0118] A synthesizer is used to synthesize the small file packages in the memory. The small file packages will be merged into a large drilling data file package according to different queries and then transmitted to the generator;

[0119] Generator, used to generate large drill hole data file packages;

[0120] The second synthesis device also includes a synthesis processing module, a synthesizer and a generator, specifically:

[0121] A synthesis processing module, used for setting the depth data and the shooting data into multiple small file packages according to a preset file capacity and writing them into a synthesizer;

[0122] A synthesizer is used to synthesize multiple small file packages in memory. The multiple small file packages will be merged into a large surface data file package according to different queries and then transmitted to the generator;

[0123] Generator, used to generate large surface data file packages;

[0124] In completing the construction of the regional geological 3D model based on neural network learning, we also built:

[0125] A geological database for transcribing borehole and surface data generated from the server;

[0126] The query module is used to query the data in the process of constructing the regional geological three-dimensional model based on neural network learning.

[0127] An interpolation module is used to interpolate the discrete pre-processed drilling data obtained by sparse sampling in the geological database using a spatial interpolation and fitting algorithm;

[0128] The spatial data field construction module is used to detect and predict the distribution trend of geological information and construct a three-dimensional regular spatial data field.

[0129] In completing the construction of the regional geological three-dimensional model based on neural network learning, a regional geological modeling module is also constructed, which includes:

[0130] The extraction module is used to extract the isosurface of the geological body in the three-dimensional regular data field based on the implicit algorithm, and obtain the isosurface of the geological ore body composed of countless small triangular slices;

[0131] The modeling module is used to obtain multiple modeling points based on the geological ore body isosurface conversion, and to build a regional geological node monitoring neural network learning model based on the modeling points.

[0132] The geological surface modeling module also includes:

[0133] A conversion module, used to obtain the depth data and shooting data of the regional geological surface, convert them into multiple surface modeling points, and simultaneously determine the positions of the regional geological surface and the surface modeling points;

[0134] The surface modeling module is used to construct a regional geological surface node monitoring neural network learning model based on the regional geological surface and the location of the surface modeling points.

[0135] When the surface data acquisition module acquires shooting data, it adopts the first frame rate and the first resolution when shooting video, and shoots images with one tenth of the first frame rate and a second resolution greater than the first resolution, and the image is shot within the same cycle of shooting the video.

[0136] In completing the construction of the regional geological three-dimensional model based on neural network learning, a three-dimensional model construction module is also constructed to fuse the constructed regional geological node monitoring neural network learning model and the constructed regional geological surface node monitoring neural network learning model to complete the construction of the regional geological three-dimensional model based on neural network learning.

[0137] In step S1, the neural network is composed of a regional geological monitoring terminal, a monitoring terminal server and a wireless access network controller; the regional geological monitoring terminal is wirelessly connected, and the regional geological monitoring terminal is connected to Alibaba Cloud through a specific regional geological monitoring terminal; the monitoring terminal server has a synthesis function, which is used to provide services to the regional geological data fusion visualization monitoring center; the wireless access network controller is connected to the generation drilling data and surface data unit in the cluster, which is used to collect all server information;

[0138] The regional geological data fusion visualization monitoring center can randomly traverse any regional geological monitoring terminal node of the regional geological three-dimensional spatial expression vector to request to query any content of regional geological fusion monitoring. Assuming that the transmission time between any nodes in the cluster is less than the transmission time to Alibaba Cloud, the regional geological data fusion visualization monitoring center obtains a response.

[0139] Exemplarily, the method for the regional geological data fusion visualization monitoring center to obtain a response includes:

[0140] (1) If the monitoring terminal server traversing the regional geological three-dimensional space expression vector synthesizes the regional geological data fusion visualization monitoring center to request to query the content of regional geological fusion monitoring, the server directly responds to the request of the regional geological data fusion visualization monitoring center to query the regional geological fusion monitoring; check whether a synthesis of the monitoring terminal server traversing the regional geological three-dimensional space expression vector synthesizes the content;

[0141] (2) if the synthesized regional geological data fusion visualization monitoring center requests to query the regional geological fusion monitoring content, the synthesis sends the content to the regional geological data fusion visualization monitoring center to traverse the regional geological three-dimensional space expression vector and respond to the request of the regional geological data fusion visualization monitoring center to query the local monitoring terminal server of the regional geological fusion monitoring; determine whether other monitoring terminal servers synthesize the content;

[0142] (3) If all regional geological monitoring terminal nodes in the search neural network layer do not have synthetic content, the request to query regional geological fusion monitoring by traversing the regional geological three-dimensional spatial expression vector will be forwarded to Alibaba Cloud;

[0143] (4) The request from the Regional Geological Data Fusion Visualization Monitoring Center to query the regional geological fusion monitoring is responded to in Alibaba Cloud. Alibaba Cloud sends the request to query the regional geological fusion monitoring content to the neural network layer head server, and then sends it to the Regional Geological Data Fusion Visualization Monitoring Center to traverse the regional geological three-dimensional space expression vector server, and finally sends it to the Regional Geological Data Fusion Visualization Monitoring Center.

[0144] Exemplarily, in step S2, the node importance identification based on the regional geological three-dimensional space expression vector value adaptive adjustment method includes:

[0145] Assume that the neural network is H = (E, F), where E is the set of network nodes, E = {1, 2…E}; F is the set of edges in the network, F = {1, 2…F}; F ij is the edge between node i and node j, F ij The domain width is u ij It means that when there is an edge between node i and node j, then u ij >0, otherwise u ij =0;

[0146] S201, node synthesis space, ca i Represents the available resources of the node. The more synthetic resources are available, the more important the node is.

[0147] S202, the sum of the width of the adjacent domain of the node. The expression of the sum of the width of the adjacent domain of the node is:

[0148]

[0149] Where N(i) is the set of neighbor nodes of node i in the network. The larger the sum of the neighbor domain widths of a node, the more important the node is. n is the cumulative number of neighbor domain widths, g is i is the adjacent domain width of node i, j is the jth neighbor node;

[0150] S203, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes depends on the spatial expression parameters of the synthetic content and the degree of attention received, the more spatial expression parameters there are, the more times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes, the more attention received by the content, the more times of traversing the regional geological three-dimensional spatial expression vector nodes; the spatial expression parameters include: rock core, geological boundary, stratum, fault, fold, original section, auxiliary section, DTM / DEM data;

[0151] S204, the nuclearity center of the traversed regional geological three-dimensional space expression vector is constructed by introducing the number of traversed regional geological three-dimensional space expression vectors of the node to construct the nuclearity centrality of the traversed regional geological three-dimensional space expression vector, and this is used as the synthesis evolution standard for determining the network synthesis strategy, and the synthesis position is determined by the position of the node and the traversed regional geological three-dimensional space expression vector situation; the nuclearity centrality of the traversed regional geological three-dimensional space expression vector of the node is the nuclearity of the traversed regional geological three-dimensional space expression vector of all neighboring nodes and the number of traversed regional geological three-dimensional space expression vectors of the node;

[0152] S205, traversing the regional geological three-dimensional space to express the vector stability rate.

[0153] Exemplarily, in step S205, the regional geological three-dimensional space is traversed to express the vector stability rate, and the expression is:

[0154]

[0155] In the formula, Vector stability rate The larger the value is, the more stable the traversal regional geological three-dimensional space expression vector of the node is, the easier it is to satisfy the traversal regional geological three-dimensional space expression vector request query regional geological fusion monitoring of the regional geological data fusion visualization monitoring center, and the greater the importance of the node is; l is the Boltzmann constant, which represents the property of the system itself, l = 1 / ln(n); e is the neighbor node, ko j is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​node j, ko e is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​the e-th neighbor node.

[0156] Exemplarily, in step S203, the number of times of traversing the regional geological three-dimensional space expression vector nodes includes:

[0157] S2031, regional geological three-dimensional spatial expression vector significance. The attention degree of regional geological three-dimensional spatial expression parameters satisfies the Zipf distribution. The significance expression of the regional geological three-dimensional spatial expression parameter k ranked α is:

[0158]

[0159] In the formula, is the significance value of the regional geological three-dimensional spatial expression parameter k ranked α, α is the ranking value of the regional geological three-dimensional spatial expression parameter, θ is the number of regional geological three-dimensional spatial expression parameters that have received attention, μ is the skewness coefficient of the Zipf distribution, the larger μ is, the easier it is to traverse the regional geological three-dimensional spatial expression parameters with high significance; num is the total number of regional geological three-dimensional spatial expression parameters;

[0160] S2032, the attention degree of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal, by analyzing the long-term traversal regional geological three-dimensional space expression vector records of the regional geological data fusion visualization monitoring center, the stable attention trend of the regional geological data fusion visualization monitoring center is obtained, and the long-term attention of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal i that traverses the regional geological three-dimensional space expression vector is defined as The expression is:

[0161]

[0162] In the formula, is the statistical vector value of the current regional geological monitoring terminal node i; log (ΔL long ) is the current statistical traversal regional geological three-dimensional space expression vector value of all regional geological data fusion visualization monitoring centers in the neural network layer;

[0163] The attention of the regional geological data fusion visualization monitoring center traversing the regional geological three-dimensional space expression vector node i in the recent time is defined as short-term attention The expression is:

[0164]

[0165] In the formula, is the three-dimensional spatial expression vector of regional geology for terminal node i in the recent period of time, o short (ΔL short ) is the statistical flow of the regional geological three-dimensional space expression vector traversed by all regional geological data fusion visualization monitoring centers in the neural network layer in the recent period;

[0166] The current attention trend of the regional geological data fusion visualization monitoring center depends on the long-term and short-term attention of the regional geological data fusion visualization monitoring center. The regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional spatial expression vector and the potential attention of the regional geological monitoring terminal node i is defined as The expression is:

[0167]

[0168] In the formula, l1 is the influence ratio of long-term attention to the current attention of the regional geological data fusion visualization monitoring center, and l2 is the influence ratio of short-term attention to the current attention of the regional geological data fusion visualization monitoring center. Considering that the recent impact is greater, l2 is greater than l1.

[0169] S2033, regional geological data fusion visualization monitoring center, potential traversal of regional geological three-dimensional spatial expression vector trend, the trend of regional geological data fusion visualization monitoring center traversing regional geological three-dimensional spatial expression vector content is not only affected by the attention trend, but also closely related to the attention level of regional geological three-dimensional spatial expression parameters, that is, the regional geological data fusion visualization monitoring center always tends to request query regional geological fusion monitoring of the significant content they pay attention to; the request query regional geological fusion monitoring probability of content k in terminal node i for:

[0170]

[0171] Where v is the number of concerned trends of regional geological three-dimensional spatial expression parameters;

[0172] Assume that the total number of regional geological three-dimensional spatial expression vectors traversed by the regional geological data fusion visualization monitoring center in the previous synthesis cycle is o ave , then the regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional space expression vector of the regional geological monitoring terminal node i, and the potential traversal of the regional geological three-dimensional space expression vector of the content k for:

[0173]

[0174] Exemplarily, in step S204, traversing the regional geological three-dimensional space to express the vector kernel degree center rate includes:

[0175] (A) Delete all nodes and edges with a connectivity of 1, and record the number of traversal regional geological three-dimensional space expression vectors of the nodes; if there are still nodes with a connectivity of 1, continue the above process and mark the coreness of these deleted nodes as 1. The coreness of the traversal regional geological three-dimensional space expression vector is the number of traversal regional geological three-dimensional space expression vectors of the node plus 1. If the number of traversal regional geological three-dimensional space expression vectors of node j is o j , then the vector kernel degree of the traversal regional geological three-dimensional space expression of node j is ko j , the expression is:

[0176] ko j =o j +1

[0177] (B) For nodes with a connectivity of 2, the coreness of the traversed regional geological three-dimensional space expression vectors of these nodes is marked as the number of traversed regional geological three-dimensional space expression vectors + 2;

[0178] (C) The above process is executed repeatedly until all nodes are deleted and the corresponding traversed regional geological three-dimensional space expression vector kernel is obtained; the number next to the node indicates the number of traversed regional geological three-dimensional space expression vectors.

[0179] Exemplarily, in step S3, the dynamic node importance ranking method based on regional geological three-dimensional space expression vector threshold adaptively weights each indicator according to the change of network indicator parameters using information entropy theory; according to information entropy theory, the higher the disorder of the indicator set, the greater the amount of information provided by the indicator, and the higher the threshold of the comprehensive evaluation indicator based on the regional geological three-dimensional space expression vector;

[0180] The dynamic node importance arrangement method based on regional geological three-dimensional spatial expression vector threshold adaptive includes:

[0181] S301, evolution model, there are n nodes to be ranked in importance, each node has 4 evaluation indicators, and the value of the ath evaluation indicator of terminal node i is c ia , the evolution matrix C composed of all network nodes is expressed as:

[0182]

[0183] S302, nonlinear programming evolution matrix, due to the different dimensions of each index, there are orders of magnitude differences, in order to eliminate the dimension differences between the indicators, the index should be standardized, expressed as:

[0184]

[0185] Where b ia is the standardized value of the ath evaluation index of terminal node i;

[0186] The standard normalized matrix B is expressed as:

[0187]

[0188] S303, calculating a vector threshold based on the regional geological three-dimensional spatial expression according to the exponential entropy;

[0189] Exponential entropy value w a The calculation formula is:

[0190]

[0191] Calculate information entropy redundancy RR a , as shown below:

[0192] RR a =1-w a

[0193] The calculation index is based on the regional geological three-dimensional spatial expression vector threshold u a As shown below:

[0194]

[0195] The index is obtained based on the regional geological three-dimensional spatial expression vector threshold matrix U, as shown in the following formula:

[0196] U=[u1 u2 u3 u4]

[0197] Wherein, u1 is the first regional geological three-dimensional space expression vector threshold, u2 is the second regional geological three-dimensional space expression vector threshold, u3 is the third regional geological three-dimensional space expression vector threshold, and u4 is the fourth regional geological three-dimensional space expression vector threshold;

[0198] The weighted normalized evolution matrix Y is expressed as:

[0199]

[0200] The weighted attribute value K of terminal node i i It is expressed as:

[0201]

[0202] Where Y ia is the weighted normalized evolution value of the ath evaluation index of terminal node i;

[0203] Since the nodes in the neural network achieve collaborative synthesis, the adjacent nodes will also contribute to the synthetic importance of the target node;

[0204] S304, importance calculation, based on the importance evaluation matrix, sum the attribute value of terminal node i and the importance contribution of all adjacent nodes to obtain the importance ξ of terminal node i i , the expression is:

[0205]

[0206] In the formula, K i is the weighted attribute value of terminal node i, θ i Assign a parameter to the contribution, ξ i is the importance of terminal node i after comprehensive evaluation, ξ i Reflects the value of terminal node i in the network;

[0207] The importance evaluation matrix fully considers the node location, the frequency of the vector expressing the traversed regional geological three-dimensional space, the synthetic space, and the available domain width, and reflects the importance of the node more comprehensively around the goal of synthetic value.

[0208] Exemplarily, the realization of regional geological data fusion visualization monitoring based on regional geological three-dimensional spatial expression vector value adaptation includes:

[0209] S401, node synthesis update rate R(i), the expression is:

[0210]

[0211] Where M is the number of regional geological three-dimensional spatial expression parameters updated from terminal node i per unit time, K(Ro m ) is the size of the updated regional geological three-dimensional spatial expression parameter from terminal node i, and I(i) is the synthetic space size of terminal node i;

[0212] S402, node synthesis value ratio J(i), including the stable core rate and synthesis update rate of the traversed regional geological three-dimensional spatial expression vector, expressed as:

[0213]

[0214] If the node synthesis update rate R(i)=0, it means that the node synthesis space is not full, or there is no new regional geological three-dimensional spatial expression parameter input.

[0215] The parameters involved in the present invention are shown in Table 1;

[0216] Table 1 Parameters

[0217]

[0218] Embodiment 2, as Figure 2 As shown, an embodiment of the present invention provides a regional geological data fusion visualization monitoring system based on neural network learning, including:

[0219] A regional geological three-dimensional model construction module 1 based on neural network learning is used to construct a regional geological and surface node monitoring neural network learning model, and perform fusion to complete the construction of a regional geological three-dimensional model based on neural network learning;

[0220] The node importance identification module 2 is used to identify the node importance of the regional geological three-dimensional model constructed based on neural network learning, based on the regional geological three-dimensional space expression vector value adaptive adjustment method;

[0221] Dynamic node importance arrangement module 3, used for dynamic node importance arrangement based on regional geological three-dimensional spatial expression vector threshold adaptive;

[0222] The regional geological data fusion visualization monitoring module 4 is used to realize regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value according to the dynamic node importance arrangement.

[0223] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0224] Since the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention, their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0225] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment.

[0226] An embodiment of the present invention also provides a computer device, which includes: at least one processor, a generator, and a computer program generated in the generator and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0227] An embodiment of the present invention further provides a computer-readable generation medium, wherein the computer-readable generation medium generates a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0228] An embodiment of the present invention also provides an information data processing terminal, which, when executed on an electronic device, provides an input interface for a regional geological data fusion visualization monitoring center to implement the steps in the above-mentioned method embodiments. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0229] The embodiment of the present invention further provides a server, which is used to provide a regional geological data fusion visualization monitoring center input interface to implement the steps in the above-mentioned method embodiments when executed on an electronic device.

[0230] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0231] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be generated in a computer-readable generation medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be generated in a computer-readable generation medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, a recording medium, a computer generator, a read-only generator (Read-Only Memory, ROM), a random access generator (radio access network domAccess Memory, RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0232] In order to verify the performance of the regional geological data fusion visualization monitoring of the present invention, the algorithm was compared with other node importance evaluation algorithms, the number of regional geological fusion monitoring responses and the synthesis efficiency of traversing the regional geological three-dimensional space expression vector request query. The experiment evaluated the importance from two perspectives, one was to calculate the importance and level of the node based on the topology (10 nodes), and the other was to construct a new topology (14 nodes) from the perspective of network efficiency to arrange the nodes by importance; this experimental simulation realized a high-speed synthetic network through the NS3 simulator, and realized the regional geological data fusion visualization monitoring proposed by the present invention through coding. The simulation data was imported into Matlab for processing.

[0233] Taking a small sample network as an example, various core node verification methods are used to evaluate the node importance and rank the importance. The results show that many algorithms cannot accurately distinguish the relative importance of nodes, so it is very important to adopt more precise methods and use nodes n1 and n2 more finely.

[0234] Comparison of network access efficiency,Network traversal regional geological three-dimensional space expression vector efficiency (NAE) means that all edges connected to a node are deleted at the same time, which may lead to an increase in the shortest traversal regional geological three-dimensional space expression vector path between nodes, thereby increasing the total length of the path that satisfies all traversal regional geological three-dimensional space expression vectors in the entire network. NAE reflects:

[0235]

[0236] Where V is the shortest path between terminal node i and index node j, and Q is the number of nodes in the network that potentially traverse the regional geological three-dimensional space expression vector for the node.

[0237] Assume that the network has 14 nodes and 28 edges. The number of times each node traverses the regional geological three-dimensional space expression vector is random. All nodes are evaluated for importance. Table 2 shows the node importance arrangement of different importance arrangement methods in the 14-node network. It can be seen that different methods have different node importance arrangements in the same network. Therefore, it is necessary to use the NAE performance index to more accurately analyze the importance of nodes in the network.

[0238] Comparison of the ability to meet the request of traversing regional geological three-dimensional spatial expression vector to query regional geological fusion monitoring. In order to more accurately verify the effectiveness of this method, the experiment used a neural network and used an independent cascade model to compare the effects of different methods on selecting different nodes for traversing regional geological three-dimensional spatial expression vector request to query regional geological fusion monitoring.

[0239] In order to analyze the difference between the proposed method and other methods in evaluating the node response to the request for querying the regional geological fusion monitoring by traversing the regional geological three-dimensional space expression vector, the method is compared with the existing methods respectively. Since the same node meets the same number of requests for querying the regional geological fusion monitoring by traversing the regional geological three-dimensional space expression vector in the same round, three nodes with good evaluability and incomplete consistency are taken as source nodes.

[0240] The first three nodes of each evaluation method have differences in the efficiency of satisfying the traversal of the regional geological three-dimensional space expression vector request query regional geological fusion monitoring. It can be seen that compared with other existing methods, the method of the present invention is superior to other methods in satisfying the traversal of the regional geological three-dimensional space expression vector request query regional geological fusion monitoring, and can satisfy more traversal of the regional geological three-dimensional space expression vector request query regional geological fusion monitoring in the same round. Analysis shows that due to the introduction of the concept of access core, the connectivity, access frequency and access diversity of the nodes are fully considered, which can better meet the access request query regional monitoring.

[0241] The present invention considers the frequency of the traversed regional geological three-dimensional space expression vector of the request query regional geological fusion monitoring node, and the "important" node found is the node with the least comprehensive hop count. It can find the "important" node more accurately than Betw.

[0242] The total number of contents replaced by the node by the synthesis replacement. This parameter mainly includes the number of synthesis in the simulation time and the total number of synthesis. This parameter can be used to analyze the load of the synthesis system and determine the synthesis position by considering the importance of the node and the replacement frequency, so as to make the load of each node more balanced and effective.

[0243] This method is guided by the research object of meeting the needs of the regional geological data fusion visualization monitoring center, and adaptively allocates thresholds based on the regional geological three-dimensional spatial expression vector to various factors affecting the node synthesis importance. It is more effective than other synthetic evolution methods in meeting the regional geological data fusion visualization monitoring center's request to query regional geological fusion monitoring.

[0244] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A regional geological data fusion visualization monitoring method based on neural network learning, characterized in that: The method includes: S1, constructing a neural network learning model for regional geology and surface node monitoring, and integrating it to complete the construction of a regional geological three-dimensional model based on neural network learning; S2, for the constructed regional geological three-dimensional model based on neural network learning, node importance identification is performed based on the adaptive adjustment method of the regional geological three-dimensional spatial expression vector value; S3, dynamic node importance arrangement based on adaptive threshold of regional geological three-dimensional spatial expression vector; S4, according to the dynamic node importance arrangement, realizes the regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value.

2. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 1 is characterized in that: In step S1, the node monitoring neural network learning model is composed of a regional geological monitoring terminal, a monitoring terminal server and a wireless access network controller; The regional geological monitoring terminal is wirelessly connected to Alibaba Cloud; The monitoring terminal server is used to provide services to the regional geological data fusion visualization monitoring center; The wireless access network controller is connected to the cluster to generate drilling data and surface data units for collecting all server information; The regional geological data fusion visualization monitoring center randomly traverses any regional geological monitoring terminal node of the regional geological three-dimensional space expression vector to request to query any content of regional geological fusion monitoring. If the transmission time between any nodes in the cluster is less than the transmission time to Alibaba Cloud, the regional geological data fusion visualization monitoring center obtains a response.

3. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 2 is characterized in that: The regional geological data fusion visualization monitoring center received responses, including: (1) Traversing the monitoring terminal server of the regional geological three-dimensional space expression vector, synthesizing the request of the regional geological data fusion visualization monitoring center to query the content of the regional geological fusion monitoring, and the server responding to the request of the regional geological data fusion visualization monitoring center to query the regional geological fusion monitoring; checking whether a synthesis of the monitoring terminal server of the traversed regional geological three-dimensional space expression vector is synthesized content; (2) if the synthesis regional geological data fusion visualization monitoring center requests to query the regional geological fusion monitoring content, the synthesis sends the content to the regional geological data fusion visualization monitoring center, traverses the regional geological three-dimensional space expression vector and responds to the request of the regional geological data fusion visualization monitoring center to query the local monitoring terminal server of the regional geological fusion monitoring; determines whether other monitoring terminal servers synthesize the content; (3) If all regional geological monitoring terminal nodes in the search neural network layer do not have synthetic content, the request to query regional geological fusion monitoring by traversing the regional geological three-dimensional spatial expression vector will be forwarded to Alibaba Cloud; (4) The request from the Regional Geological Data Fusion Visualization Monitoring Center to query the regional geological fusion monitoring is responded to in Alibaba Cloud. Alibaba Cloud sends the request to query the regional geological fusion monitoring content to the neural network layer head server, and then sends it to the Regional Geological Data Fusion Visualization Monitoring Center to traverse the regional geological three-dimensional space expression vector server, and finally sends it to the Regional Geological Data Fusion Visualization Monitoring Center.

4. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 1 is characterized in that: In step S2, node importance identification based on the adaptive adjustment method of regional geological three-dimensional spatial expression vector value includes: Suppose the neural network is H = (E, F), where E is the set of network nodes, E = {1, 2…E}; F is the edge set in the network, F = {1, 2…F}; F ij is the edge between node i and node j, F ij The domain width is u ij It means that when there is an edge between node i and node j, then u ij >0, otherwise uij=0; S201, node synthesis space, ca i Represents the available resources of the node; S202, the sum of the width of the adjacent domain of the node. The expression of the sum of the width of the adjacent domain of the node is: In the formula, N(i) is the set of neighbor nodes of node i in the network, n is the cumulative number of adjacent domain widths, and g i is the adjacent domain width of node i, j is the jth neighbor node; S203, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes, the number of times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes depends on the spatial expression parameters of the synthetic content and the degree of attention received, the more spatial expression parameters there are, the more times of traversing the regional geological three-dimensional spatial expression vector nodes for the nodes, the more attention received by the content, the more times of traversing the regional geological three-dimensional spatial expression vector nodes; the spatial expression parameters include: rock core, geological boundary, stratum, fault, fold, original section, auxiliary section, DTM / DEM data; S204, the nuclearity center of the traversed regional geological three-dimensional space expression vector is constructed by introducing the number of traversed regional geological three-dimensional space expression vectors of the node to construct the nuclearity centrality of the traversed regional geological three-dimensional space expression vector, and this is used as the synthesis evolution standard for determining the network synthesis strategy, and the synthesis position is determined by the position of the node and the traversed regional geological three-dimensional space expression vector situation; the nuclearity centrality of the traversed regional geological three-dimensional space expression vector of the node is the nuclearity of the traversed regional geological three-dimensional space expression vector of all neighboring nodes and the number of traversed regional geological three-dimensional space expression vectors of the node; S205, traversing the regional geological three-dimensional space to express the vector stability rate.

5. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 4 is characterized in that: In step S205, the regional geological three-dimensional space is traversed to express the vector stability rate, and the expression is: In the formula, Vector stability rate The larger the value is, the more stable the traversal regional geological three-dimensional space expression vector of the node is, the easier it is to satisfy the traversal regional geological three-dimensional space expression vector request query regional geological fusion monitoring of the regional geological data fusion visualization monitoring center, and the greater the importance of the node is; l is the Boltzmann constant, which represents the property of the system itself, l = 1 / ln(n); e is the neighbor node, ko j is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​node j, ko e is the vector kernel of the geological three-dimensional space expression of the traversed area of ​​the e-th neighbor node.

6. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 4 is characterized in that: In step S203, the number of times the regional geological three-dimensional space expression vector nodes are traversed includes: S2031, regional geological three-dimensional spatial expression vector significance, the regional geological three-dimensional spatial expression parameter attention degree satisfies the Zipf distribution, and the significance expression of the regional geological three-dimensional spatial expression parameter k ranked α is: In the formula, is the significance value of the regional geological three-dimensional spatial expression parameter k ranked α, α is the ranking value of the regional geological three-dimensional spatial expression parameter, θ is the number of regional geological three-dimensional spatial expression parameters that have received attention, μ is the skewness coefficient of the Zipf distribution, the larger μ is, the easier it is to traverse the regional geological three-dimensional spatial expression parameters with high significance; num is the total number of regional geological three-dimensional spatial expression parameters; S2032, the attention degree of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal, by analyzing the long-term traversal regional geological three-dimensional space expression vector records of the regional geological data fusion visualization monitoring center, the stable attention trend of the regional geological data fusion visualization monitoring center is obtained, and the long-term attention of the regional geological data fusion visualization monitoring center to the regional geological monitoring terminal i that traverses the regional geological three-dimensional space expression vector is defined as The expression is: In the formula, is the statistical vector value of the current regional geological monitoring terminal node i; long (ΔL long ) is the current statistical traversal regional geological three-dimensional space expression vector value of all regional geological data fusion visualization monitoring centers in the neural network layer; The attention of the regional geological data fusion visualization monitoring center traversing the regional geological three-dimensional space expression vector node i in the recent time is defined as short-term attention The expression is: In the formula, is the three-dimensional spatial expression vector of regional geology for terminal node i in the recent period of time, o short (ΔL short ) is the statistical flow of the regional geological three-dimensional space expression vector traversed by all regional geological data fusion visualization monitoring centers in the neural network layer in the recent period; The current attention trend of the regional geological data fusion visualization monitoring center depends on the long-term and short-term attention of the regional geological data fusion visualization monitoring center. The regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional spatial expression vector and the potential attention of the regional geological monitoring terminal node i is defined as The expression is: In the formula, l1 is the influence ratio of long-term attention to the current attention of the regional geological data fusion visualization monitoring center, and l2 is the influence ratio of short-term attention to the current attention of the regional geological data fusion visualization monitoring center. Considering that the recent impact is greater, l2 is greater than l1. S2033, regional geological data fusion visualization monitoring center, potential traversal of regional geological three-dimensional space expression vector trend, regional geological data fusion visualization monitoring center tends to request query regional geological fusion monitoring of the significant content they are concerned about, the request query regional geological fusion monitoring probability of content k in terminal node i for: Where v is the number of concerned trends of regional geological three-dimensional spatial expression parameters; Assume that the total number of regional geological three-dimensional spatial expression vectors traversed by the regional geological data fusion visualization monitoring center in the previous synthesis cycle is o ave , then the regional geological data fusion visualization monitoring center traverses the regional geological three-dimensional space expression vector of the regional geological monitoring terminal node i, and the potential traversal of the regional geological three-dimensional space expression vector of the content k for:

7. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 4 is characterized in that: In step S204, the regional geological three-dimensional space is traversed to express the vector kernel central rate, including: (A) Delete all nodes and edges with a connectivity of 1, and record the number of traversal regional geological three-dimensional space expression vectors of the nodes; if there are still nodes with a connectivity of 1, continue the above process and mark the coreness of these deleted nodes as 1. The coreness of the traversal regional geological three-dimensional space expression vector is the number of traversal regional geological three-dimensional space expression vectors of the node plus 1. If the number of traversal regional geological three-dimensional space expression vectors of node j is o j , then the vector kernel degree of the traversal regional geological three-dimensional space expression of node j is ko j , the expression is: write j =you j +1 (B) For nodes with a connectivity of 2, the coreness of the traversed regional geological three-dimensional space expression vectors of these nodes is marked as the number of traversed regional geological three-dimensional space expression vectors + 2; (C) The above process is executed repeatedly until all nodes are deleted and the corresponding traversed regional geological three-dimensional space expression vector kernel is obtained; the number next to the node indicates the number of traversed regional geological three-dimensional space expression vectors.

8. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 1 is characterized in that: In step S3, the dynamic node importance arrangement based on the adaptive threshold of the regional geological three-dimensional spatial expression vector includes: S301, evolution model, there are n nodes to be ranked in importance, each node has 4 evaluation indicators, and the value of the ath evaluation indicator of terminal node i is c ia , the evolution matrix C composed of all network nodes is expressed as: S302, nonlinear programming evolution matrix, standardizes the index and eliminates the dimension difference between the indicators, expressed as: Where b ia is the standardized value of the ath evaluation index of terminal node i; The standard normalized matrix B is expressed as: S303, calculating a vector threshold based on the regional geological three-dimensional spatial expression according to the exponential entropy; Exponential entropy value w a The calculation formula is: Calculate information entropy redundancy RR a , the expression is: The calculation index is based on the regional geological three-dimensional spatial expression vector threshold u a , the expression is: The index is obtained based on the regional geological three-dimensional spatial expression vector threshold matrix U, expressed as: U=[u1 u2 u3 u4] Where, u1, u2, u3, and u4 are the vector thresholds of the geological three-dimensional space expression of the first, second, third, and fourth regions respectively; The weighted normalized evolution matrix Y is expressed as: The weighted attribute value K of terminal node i i It is expressed as: Where Y ia is the weighted normalized evolution value of the ath evaluation index of terminal node i; S304, importance calculation, based on the importance evaluation matrix, sum the attribute value of terminal node i and the importance contribution of all adjacent nodes to obtain the importance ξ of terminal node i i , the expression is: In the formula, K i is the weighted attribute value of terminal node i, θ i Assign a parameter to the contribution, ξ i is the importance of terminal node i after comprehensive evaluation, ξ i Reflects the value of terminal node i in the network.

9. The regional geological data fusion visualization monitoring method based on neural network learning according to claim 8 is characterized in that: In step S4, regional geological data fusion visualization monitoring based on regional geological three-dimensional spatial expression vector value adaptation is realized, including: S401, node synthesis update rate R(i), the expression is: Where M is the number of regional geological three-dimensional spatial expression parameters updated from terminal node i per unit time, K(Ro m ) is the size of the updated regional geological three-dimensional spatial expression parameter from terminal node i, and I(i) is the synthetic space size of terminal node i; S402, node synthesis value proportion J(i), traversing the regional geological three-dimensional space expression vector stable core degree center rate and synthesis update rate, the expression is: If the node synthesis update rate R(i) = 0, it means that the node synthesis space is not full, or there is no new regional geological three-dimensional space expression parameter input.

10. A regional geological data fusion visualization monitoring system based on neural network learning, characterized in that: The system is implemented by the regional geological data fusion visualization monitoring method based on neural network learning according to any one of claims 1 to 9, and the system includes: A regional geological three-dimensional model construction module (1) based on neural network learning is used to construct a regional geological and surface node monitoring neural network learning model, and to integrate and complete the construction of a regional geological three-dimensional model based on neural network learning; A node importance identification module (2) is used to identify the node importance of the regional geological three-dimensional model constructed based on neural network learning, based on the regional geological three-dimensional space expression vector value adaptive adjustment method; A dynamic node importance ranking module (3), used for dynamic node importance ranking based on adaptive threshold of regional geological three-dimensional spatial expression vector; The regional geological data fusion visualization monitoring module (4) is used to realize regional geological data fusion visualization monitoring based on the adaptive regional geological three-dimensional spatial expression vector value according to the dynamic node importance arrangement.

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