Systems, methods, and computer program products for compiling / updating geologic maps

Abstract

The invention discloses a system, a method and a computer program product for compiling / updating a geological map. The method comprises the following steps: receiving a compiling / updating request of the geological map; a geological basic data items are retrieved from a geological database, the retrieved A geological basic data items are evaluated according to use conditions, and the top N data items are selected as candidate geological basic data items; according to the retrieved B geological basic data items and the retrieved B geological element data items from the geological database, evaluating the retrieved B geological element data items according to the use condition, and selecting the first M data items as candidate geological element data items; the data sets (graphs) corresponding to the N candidate geological basic data items are used as base maps to be combined with the M candidate geological element class data items respectively, and M * N / 2 candidate geological maps are obtained; and receiving at least one geological map selected by a user from the M * N / 2 candidate geological maps, and taking the selected at least one geological map as a compiled / updated geological map.

Description

Technical Field

[0001] The present invention relates to the technical field of geological data processing, and in particular, to a system, method, and computer program product for compiling / updating geological maps. Background Art

[0002] Geological maps are the scientific research results of geological surveys. The survey and analysis techniques used in each geological map formed in different eras are different; and there are a wide variety of survey and analysis techniques used; moreover, the spatial coverage and spatial accuracy of each geological map are also different. There is iteration and repetition of content or partial content among the results expressed on these different geological maps.

[0003] In the information age, big data and artificial intelligence (AI) technologies, as important engines, have promoted the progress of science and technology. In the geological field, big data and AI technologies have made it possible to build geological big data, and one aspect of geological big data includes compiling or updating geological maps using existing geological maps, geological basic data, and geological element data. Summary of the Invention

[0004] The following description includes exemplary methods, systems, and computer program products that embody the technology of the present invention. However, it should be understood that the described invention may be practiced without these specific details in one or more aspects. In other cases, well-known structures and technologies are not shown in detail so as not to obscure the present invention.

[0005] According to one aspect of the present invention, a system for compiling / updating a geological map is provided, including: a user interface module configured to receive a request for compiling / updating a geological map, the request including requirements for geological basic data items and geological feature class data items to be used in the geological map to be compiled / updated; a geological basic data item acquisition module configured to retrieve A geological basic data items from a geological database storing a plurality of geological basic data items and a plurality of geological feature class data items according to the requirements for the geological basic data items to be used in the geological map to be compiled / updated, evaluate the retrieved A geological basic data items according to usage, and select the top N geological basic data items in the evaluation results as candidate geological basic data items, where N is a positive integer and N ≤ A; a geological feature class data item acquisition module configured to retrieve B geological feature class data items from the geological database according to the requirements for the geological feature class data items to be used in the geological map to be compiled / updated, evaluate the retrieved B geological feature class data items according to usage, and select the top M geological feature class data items in the evaluation results as candidate geological feature class data items, where M is a positive integer and M ≤ B; a synthesis module configured to use the data sets (maps) corresponding to the N candidate geological basic data items as base maps and combine them with the M candidate geological feature class data items respectively to obtain M * N / 2 candidate geological maps; wherein, the user interface module is further configured to receive at least one geological map selected by the user from the M * N / 2 candidate geological maps, and an output module configured to output the at least one geological map selected by the user as the compiled / updated geological map.

[0006] According to another aspect of the present invention, a method for compiling / updating a geological map is provided, including: receiving a request for compiling / updating a geological map, the request including requirements for geological basic data items and geological feature class data items to be used in the geological map to be compiled / updated; according to the requirements for geological basic data items to be used in the geological map to be compiled / updated, retrieving A geological basic data items from a geological database storing a plurality of geological basic data items and a plurality of geological feature class data items, and evaluating the retrieved A geological basic data items according to their usage, selecting the top N geological basic data items in the evaluation results as candidate geological basic data items, where N is a positive integer and N≤A; according to the requirements for geological feature class data items to be used in the geological map to be compiled / updated, retrieving B geological feature class data items from the geological database, and evaluating the retrieved B geological feature class data items according to their usage, selecting the top M geological feature class data items in the evaluation results as candidate geological feature class data items, where M is a positive integer and M≤B; using the datasets (maps) corresponding to the N candidate geological basic data items as base maps, and respectively combining them with the M candidate geological feature class data items to obtain M*N / 2 candidate geological maps; receiving at least one geological map selected by a user from the M*N / 2 candidate geological maps; and outputting the at least one geological map selected by the user as the compiled / updated geological map.

[0007] According to yet another aspect of the present invention, a computer program product is provided, the computer program product including program instructions that can be executed by a computing device to cause the computing device to execute the method as described above.

[0008] According to still another aspect of the present invention, a computer system is provided, including: a memory; and at least one processor operably coupled to the memory and configured to execute the method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The invention itself and the usage patterns, objectives, features, and advantages of its preferred embodiments can be better understood by reading the following detailed description of the illustrative embodiments with reference to the accompanying drawings, in which:

[0010] Figure 1A The schematic structure of the metadata included in the geological basic data items in the existing geological database is shown;

[0011] Figure 1B Exemplary geological basic data items stored in the existing geological database are shown;

[0012] Figure 2A The schematic structure of the metadata included in the geological feature class data items in the existing geological database is shown;

[0013] Figure 2B Shows exemplary geological element class data items stored in an existing geological database;

[0014] Figure 3A Shows parameters used to evaluate the usage of geological basic data items according to an embodiment of the present invention;

[0015] Figure 3B Shows partial exemplary records of the usage of geological basic data items according to an embodiment of the present invention;

[0016] Figure 4A Shows parameters used to evaluate the usage of geological element class data items according to an embodiment of the present invention;

[0017] Figure 4B Shows partial exemplary records of the usage of geological element class data items according to an embodiment of the present invention;

[0018] Figure 5 Shows a structural block diagram of a system for compiling / updating a geological map according to an embodiment of the present invention;

[0019] Figure 6 Shows a flowchart of a method for compiling / updating a geological map according to an embodiment of the present invention;

[0020] Figure 7 Shows according to an embodiment of the present invention Figure 6 A flowchart of the method used to evaluate A retrieved geological basic data items according to their usage in the method shown;

[0021] Figure 8 Shows according to an embodiment of the present invention Figure 6 A flowchart of the method used to evaluate B retrieved geological element class data items according to their usage in the method shown; and

[0022] Figure 9 Shows an example of compiling / updating a geological map using the system and method of the present invention. Detailed implementation manners

[0023] Embodiments of the present invention will be described below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the implementation of the present invention may not require some of these specific details. In addition, it should be understood that the present invention is not limited to the specific embodiments described. On the contrary, the present invention can be implemented with any combination of the following features and elements, regardless of whether they relate to different embodiments. And, the specific steps of the methods of different embodiments are not strictly sequential. That is to say, in the case where a method includes a first step and a second step, the first step can be executed first and then the second step, or the second step can be executed first and then the first step.

[0024] As mentioned above, in terms of geology, big data and AI technologies make it possible to construct geological big data. One aspect of geological big data includes compiling or updating geological maps using existing geological maps, geological basic data, and geological element data. Generally speaking, existing geological databases can store existing geological maps, geological basic data, and geological element data. One of the multiple geological basic data items included in the geological database is the existing geological map result data of a certain area, including basic geographical maps, geological maps, remote sensing images, DEM data, etc., which can be used as the base map of the geological map to be compiled or the basis for the geological map to be updated. Each geological basic data item contains a metadata for explaining the relevant information of the geological basic data item for retrieval purposes.

[0025] Figure 1A The schematic structure of the metadata included in the geological basic data item in the existing geological database is shown. The metadata includes fields such as data number, element (set) name, subject, location area, longitude and latitude range, data type, denominator of scale, accuracy, production year, data format, coordinate system and related parameters, data version, source institution, author, and storage location of the dataset (map). Those skilled in the art will know that using Figure 1A The representation of the metadata structure included in the geological basic data item using a table is just one way of representation, and other ways of representing metadata can also be used, such as text format, XML format, etc. The metadata of the geological basic data item in the geological database can be stored using a relational table in a relational database or using an XML database. The present invention does not limit this.

[0026] Figure 1B An exemplary geological basic data item stored in the existing geological database is shown. Similarly, Figure 1BThe shown table can be stored in a relational database or in an XML database etc. Only five geological basic data items are schematically shown here. Those skilled in the art know that thousands of geological basic data items can be stored in a geological database, and these geological basic data items can adopt Figure 1A the metadata structure shown. When compiling / updating a geological map, the required geological basic data items can first be retrieved from the geological database, and the data set (map) among them can be used as the base map for the geological map to be compiled / updated. When retrieving, requirements can be put forward for the corresponding important fields of the geological basic data items, that is, specific requirements are given for the theme, the area where it is located, the longitude and latitude range, the data type, the scale, and the production year. It should be noted here that Figure 1A and Figure 1B the data types in are divided into two types: vector and raster. The inventor determines that the existing relationship of the corresponding scale is: raster resolution R≈[denominator D of geological map scale / 2000]; denominator D of geological map scale≈[2000*raster resolution R]. Those skilled in the art can perform scale conversion of different data types when retrieving and using information related to the denominator of the scale.

[0027] In addition, one of the geological element class data items among the multiple geological element class data items included in the geological database is the latest research result of a certain area obtained by geological personnel through field surveys or laboratory research, including single or multiple elements (sets) such as the strata, intrusive rocks, metamorphic rocks, faults, etc. in this area, and is the core data for compiling / updating geological maps.

[0028] Figure 2A shows the schematic structure of the metadata included in the geological element class data items in the existing geological database. The metadata includes fields such as data number, element (set) name, theme, area where it is located, longitude and latitude range, data type, denominator of scale, accuracy, data format, coordinate system and related parameters, year, source, author, and storage location of the data set (map). Those skilled in the art can know that using Figure 2A the table to represent the metadata structure included in the geological element class data items is just one representation method, and other ways of representing metadata can also be used, such as text format, XML format, etc. The metadata of the geological element class data items in the geological database can be stored using a relational table in a relational database, and

[0029] can also be stored using an XML database. The present invention does not limit this.

[0030] Figure 2B shows the exemplary geological element class data items stored in the existing geological database. Similarly, Figure 2BThe shown table can be stored in a relational database or an XML database etc. Only seven geological element class data items are schematically shown here. Those skilled in the art know that thousands of geological element class data items can be stored in a geological database, and these geological element class data items can adopt Figure 2A the metadata structure shown. When compiling / updating a geological map, the required geological element class data items can then be retrieved from the geological database as the geological element class data items for the geological map to be compiled or updated. When retrieving, requirements can be put forward for the corresponding entries of the geological element class data items, that is, specific requirements are given for the subject, the area where it is located, the longitude and latitude range, the data type, the scale, the accuracy, the data format, the coordinate system, and the year. It should be noted here that Figure 2A and Figure 2B the data types in also include two types: vector and raster. The inventor determines that the relationship between the corresponding scales is: raster resolution R≈[denominator D of geological map scale / 2000]; denominator D of geological map scale≈[2000 * raster resolution R]. Those skilled in the art can perform scale conversion for different data types when retrieving and using scale-related information.

[0031] The retrieved geological basic data items and geological element class data items need to have the same or similar content in the fields of "area where it is located" and "longitude and latitude range". Then, the data set (map) in the retrieved geological basic data items is used as the base map, and the retrieved geological element class data items are combined with the base map to obtain the compiled / updated geological map.

[0032] Currently, the method for compiling / updating geological map data mainly relies on manpower, that is, geologists, scholars, and professionals retrieve multiple geological basic data items and multiple geological element class data items based on their personal knowledge and experience, compare different types of geographical data, geological data, data, and explanatory materials existing in the area where the geological map to be compiled / updated is located. After comparison, analysis, and discrimination, the geological basic data items that meet the mapping purpose and the geological element class data items corresponding to the same area and longitude and latitude range are selected, and then the two are combined to compile / update the geological map.

[0033] The existing method for compiling / updating geological maps selects data solely based on the subjective experience of geologists and technicians. On the one hand, the efficiency is low, it takes a lot of time and the personal energy of geologists and scholars; on the other hand, there are also problems of low utilization rate of existing achievements and repeated investment; on the other hand, the results of the compiled / updated geological maps are related to the personal qualities of geologists and technicians such as their geological exploration, mapping, and map compilation professional skills, and the quality is very unstable.

[0034] In order to solve the problems existing in the prior art, the present invention considers using the existing experience of users in compiling / updating geological maps to assist geologists and technicians in screening data. Specifically, a system, a method and a computer program product for compiling / updating geological maps are disclosed. The method includes: receiving a request for compiling / updating a geological map, which includes requirements for geological basic data items and geological element data items to be used in the geological map to be compiled / updated; according to the requirements for geological basic data items to be used in the geological map to be compiled / updated, retrieving A geological basic data items from a geological database storing multiple geological basic data items and multiple geological element data items, and evaluating the retrieved A geological basic data items according to the usage situation, selecting the top N geological basic data items in the evaluation results as candidate geological basic data items, where N is a positive integer and N≤A; according to the requirements for geological element data items to be used in the geological map to be compiled / updated, retrieving B geological element data items from the geological database, and evaluating the retrieved B geological element data items according to the usage situation, selecting the top M geological element data items in the evaluation results as candidate geological element data items, where M is a positive integer and M≤B; using the data sets (maps) corresponding to the N candidate geological basic data items as the base map, and combining them with the M candidate geological element data items respectively to obtain M*N / 2 candidate geological maps; receiving at least one geological map selected by the user from the M*N / 2 candidate geological maps, and outputting the at least one geological map selected by the user as the geological map to be compiled / updated. This method can objectively, efficiently and highly accurately utilize the existing achievements in the geological mapping process, thereby solving the problems of low utilization rate of existing achievements and repeated investment in the geological mapping process, and establishing geological maps with high value and high recognition.

[0035] Evaluating the geological basic data items according to the usage situation requires recording the usage situation and the evaluation of the usage situation. Figure 3A Shows the parameters that can be used to evaluate the usage situation of geological basic data items according to an embodiment of the present invention, including the institutional credit score Eo of the geological basic data item, the author credit score E1a, the number of times of use n1, and the average user rating score within the number of times of use And the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items. The evaluation result can be represented by the recommended level score S of the geological basic data item. Here, the first two parameters can use fixed values, and the latter parameters are related to the specific usage situation and need to be calculated in combination with the specific usage situation. In order to obtain Figure 3A Some of the parameters in, such as the number of times of use n1, the average user rating score within the number of times of use And the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items. Figure 3BShows a partial exemplary record of the usage of geological basic data items according to an embodiment of the present invention, Figure 3B The shown record can be gradually added as the user's usage increases. This record can be stored using a database table, or a general EXCEL table, or an XML database, etc. Those skilled in the art will know that the Figure 3A structure can be incorporated into Figure 1A so as to incorporate other fixed information, such as institutional reputation score (Eo), author reputation score (E1a), and information calculated from Figure 3B into the geological basic data items. In other words, the obtained updated Figure 1B will contain information related to usage. It is also possible to store the usage-related information with the Figure 3A structure as a separate data table or other data form, and associate it with the information of Figure 1B using the data number as the primary key.

[0036] Similarly, when evaluating geological element data items according to usage, it is also necessary to record the usage and the evaluation of the usage. Figure 4A Shows the parameters that can be used to evaluate the usage of geological element data items according to an embodiment of the present invention, including the author reputation score E2a of the geological basic data item, the number of times of use n2, the average user rating score within the number of times of use and the percentage K2 of the number of times of use n2 in the total number of times of use of all B retrieved geological basic data items. The evaluation result can be the recommended level score SS of the geological element data item. Here, the first parameter can use a fixed value, and the subsequent parameters are related to the specific usage situation and need to be calculated in combination with the specific usage situation. In order to obtain Figure 4A some of the parameters in such as the number of times of use n2, the average user rating score within the number of times of use Figure 4B Shows a partial exemplary record of the usage of geological element data items according to an embodiment of the present invention, Figure 4B The shown record can also be gradually added as the user's usage increases. This record can be stored using a database table, or a general EXCEL table, or an XML database. Similarly, those skilled in the art will know that the Figure 4A structure can be incorporated into Figure 2A so as to incorporate other fixed information, such as the author reputation score (E2a) and information calculated from Figure 4B into the geological basic data items. In other words, the obtained updated Figure 2Bwill contain information related to usage. Information related to usage in the form of Figure 4A can also be stored as a separate data table or other data form, and is associated with the information of Figure 2B using the data number as the primary key.

[0037] Those skilled in the art can know that Figure 3A , Figure 3B , Figure 4A and Figure 4B The format shown is a schematic structure. Those skilled in the art can use other forms of structure to describe this information as needed, and the present invention has no limitation on this.

[0038] Figure 5 shows a structural block diagram of a system 500 for compiling / updating a geological map according to an embodiment of the present invention. According to Figure 5 , the system 500 includes a user interface module 510, a geological basic data item acquisition module 520, a geological element class data item acquisition module 530, a synthesis module 540, and an output module 550. As Figure 5As shown, the user interface module 510 is configured to receive a compilation / update request for a geological map from the user 502, and the request contains requirements for geological basic data items and geological feature class data items to be used in the geological map to be compiled / updated. The geological basic data item acquisition module 520 is configured to retrieve A geological basic data items from the geological database 501 storing a plurality of geological basic data items and a plurality of geological feature class data items according to the requirements for the geological basic data items to be used in the geological map to be compiled / updated, and evaluate the retrieved A geological basic data items according to the usage situation, and select the top N geological basic data items in the evaluation results as candidate geological basic data items, where N is a positive integer and N ≤ A. The geological feature class data item acquisition module 530 is configured to retrieve B geological feature class data items from the geological database 501 according to the requirements for the geological feature class data items to be used in the geological map to be compiled / updated, and evaluate the retrieved B geological feature class data items according to the usage situation, and select the top M geological feature class data items in the evaluation results as candidate geological feature class data items, where M is a positive integer and M ≤ B. The synthesis module 540 is configured to use the data sets (maps) corresponding to the N candidate geological basic data items as the base map and combine them with the M candidate geological feature class data items respectively to obtain M * N / 2 candidate geological maps. The user interface module 510 is further configured to receive at least one geological map selected by the user 502 from the M * N / 2 candidate geological maps. The output module 550 is configured to output the at least one geological map selected by the user 502 as the geological map to be compiled / updated. Among the above parameters, A and B are the numbers of the specifically retrieved data items and objectively exist. N and M are parameters set by the user. For example, the user can set M = N = 3 or other values.

[0039] In one implementation, referring to Figure 1A and Figure 2ARequirements for geological basic data items used for geological maps to be compiled / updated include requirements for the following fields of the metadata of geological basic data items: subject, location area, longitude and latitude range, data type, scale denominator, accuracy, and production year; and requirements for geological feature class data items used for geological maps to be compiled / updated include requirements for the following fields of the metadata of geological feature class data items: subject, location area, longitude and latitude range, data type, scale denominator, accuracy, and year, where the location area and longitude and latitude range of the above geological feature class data items are the same as those of the above geological basic data items. In this way, the geological basic data item acquisition module 520 and the geological feature class data item acquisition module 530 can retrieve corresponding data items from the geological database 501 according to the requirements for the respective fields of the metadata of the geological basic data items and the requirements for the respective fields of the metadata of the geological feature class data items. The retrieval by the geological basic data item acquisition module 520 and the geological feature class data item acquisition module 530 is sometimes an iterative process. For example, if the retrieved data item A < N or B < M, it may be necessary to re-enter the retrieval conditions and re-retrieve; or modify the values of M and N, etc.

[0040] In one implementation, the geological basic data item acquisition module 520 needs to utilize the Figure 3A given parameter fields to evaluate the A retrieved geological basic data items according to their usage. Specifically, in the requirements for these parameters, the user can set a scoring system as needed (for example, the user can use a percentile system, a decimal system, or even a normalized number, etc.) to set or obtain specific values. Figure 3A Among the parameters, some parameters can be directly set by the user, such as the institutional reputation score (Eo) and the author reputation score (E1a). These two parameters can also be obtained through methods such as user research. Here, it is assumed that according to the scoring system set by the user, the values of these two parameters have been obtained. For other geological basic data item parameters, namely the number of times of use n1, the average user evaluation score and the percentage K1 of the number of times of use n1 of all the retrieved A geological basic data items in the total number of times of use can be statistically calculated using, for example, Figure 3B the given specific usage situation. Specifically, the number of times of use n1 can be obtained by counting Figure 3B the same data numbers or the same element (set) names. Since A data items are retrieved, it is necessary to count the number of times of use n1 for each of the A geological basic data items j (j = 1, A). For a specific geological basic data item, if the number of times of use is n1, and the user evaluation score E1 for each use i (i = 1 to n1), then the average user rating score within the number of times of use The percentage of the number of times of use n1 in the total number of times of use of all A retrieved geological basic data items Specifically, the geological basic data item acquisition module 520 can be further configured to: first obtain the usage situation of each geological basic data item among the A retrieved geological basic data items, where the usage situation of a geological basic data item includes the number of times of use n1 and the user evaluation score E1 for each use i (i = 1 to n1); then obtain the institutional reputation score Eo, the author reputation score E1a, the number of times of use n1, and the average user rating score within the number of times of use for each geological basic data item among the A retrieved geological basic data items and the percentage K1 of the number of times of use n1 in the total number of times of use of all A retrieved geological basic data items, where Finally, based on the institutional reputation score Eo, the author reputation score E1a, the number of times of use n1, and the average user rating score within the number of times of use for each geological basic data item among the A retrieved geological basic data items and the percentage K1 of the number of times of use n1 in the total number of times of use of all A retrieved geological basic data items, obtain the recommended level score S for each geological basic data item among the multiple geological basic data items as the evaluation result

[0041] There can be various implementation manners on how the geological basic data item acquisition module 520 obtains the recommended level score S of the geological basic data item based on these parameters, because different models can be established to calculate the recommended level score S

[0042] In one embodiment, the recommended level score S can be executed using a pre-trained first neural network model. In this embodiment, it is necessary to pre-train a first neural network model. Neural network models are the most commonly used models in current artificial intelligence (AI). AI refers to the intelligence when a machine can make decisions based on information, which maximizes the chance of success in a given topic. More specifically, AI can learn from a dataset to solve problems and provide relevant recommendations. An artificial neural network (ANN) is a model of the way the nervous system operates. The basic units are called neurons, which are usually organized into layers. ANNs work by simulating a large number of interconnected processing units, which are analogous to an abstract version of neurons. There are usually three parts in an ANN, including an input layer with units representing the input domain, one or more hidden layers, and an output layer with one or more units representing the target domain. These units are connected with different connection strengths or weights. Input data is provided to the first layer, and values are propagated from each neuron to the neurons in the next layer. At a basic level, each layer of the neural network includes one or more operators or functions operably coupled to the output and input. The output of the activation function of each neuron evaluated using the provided input is referred to as activation in this article. Complex neural networks are designed to simulate how the human brain works, so computers can be trained to support ill-defined abstractions and problems where training data is available. Neural network models usually use pre-defined training samples to train the model. During the training process, the connection weights of different layers of the neural network model are iteratively adjusted. When the results for all training samples converge or meet a predetermined condition, the neural network model is trained. At this time, the weights between the layers are determined, and the output results for a new test sample can be inferred.

[0043] The first neural network model can adopt common models such as MLP, CNN, RNN, GNN, LSTM, transformer, etc. In a specific implementation of the first neural network model, first, in order to train the first neural network model, training samples need to be obtained, and then the training samples are used to train the first neural network model. Specifically, first, the empirical recommended level scores of each geological basic data item among several geological basic data items by domain experts can be obtained, and then the institutional reputation score Eo, author reputation score E1a, number of times used n1, average user rating score within the number of times used and the percentage K1 of the number of times used n1 in the total number of times used of all A retrieved geological basic data items are used as input vectors, and the empirical recommended level scores of each geological basic data item by the domain experts are used as the output to train the first neural network model. In other words, the vector [institutional reputation score Eo of each geological basic data item, author reputation score E1a, number of times used n1, average user rating score within the number of times used The percentage K1 of the number of times of use n1 in the total number of times of use of all retrieved A geological basic data items, and the empirical recommendation level score S of the domain expert for this geological basic data item form a training sample. During the training process, using normalized data for these data will help the first neural network model converge quickly. If the first neural network model converges or meets the predetermined conditions, at this time, the training of the first neural network model is completed and can be used to calculate the recommendation level score S of the geological basic data item. During the calculation process, the normalized institutional reputation score Eo, author reputation score E1a, number of times of use n1, and the average user rating score within the number of times of use as well as the percentage K1 of the number of times of use n1 in the total number of times of use of all retrieved A geological basic data items can be used as an input vector and input into the first neural network model. The output of the first neural network model obtained is the recommendation level score S of this geological basic data item. The normalization of the institutional reputation score Eo and author reputation score E1a can be directly divided by the score system of the recommendation level score S. For example, it can be normalized by Eo / 100 for percentages. The normalization of the number of times of use n1 can be adjusted as needed. For example, if the maximum number of times of use for each of the retrieved A data items is n, then n1 / n can be used as the normalized number of times of use. Using the above first neural network model for evaluation according to the usage situation not only utilizes the experience of domain experts but also the experience of users, and the obtained evaluation results are more accurate and objective.

[0044] In one implementation, the first empirical model can be set according to the experience of domain experts to calculate the recommendation level score S. For example, in this model, for a geological basic data item, the recommendation level score S includes a recommended score S1 and a reward score S2, and wherein, the recommended score S1 and the reward score S2 are respectively:

[0045]

[0046] wherein, w1 1 、w1 2 、w1 3 are weights defined by the user, F1 is a threshold of the number of times of use n1 defined by the user, G1, H1, and P1 are reward scores for different conditions defined by the user respectively, and K11, K12, and K13 are different thresholds of the percentage K1 of the number of times of use n1 in the total number of times of use of all retrieved A geological basic data items. Using the above first empirical model for evaluation according to the usage situation not only utilizes the experience of domain experts but also the experience of users, and the model has a small amount of calculation, fast calculation speed, and does not require pre-training. The obtained evaluation results are also relatively accurate and objective.

[0047] In one embodiment, in the above first empirical model, S uses a 100-point system. In the evaluation results of Eo and E1a, if the evaluation result is very good, Eo = E1a = 9 points; if the evaluation result is good, Eo = E1a = 6 points; if the evaluation result is average, Eo = E1a = 5 points. The user evaluation score E1 used each time i (i = 1 to n1) uses a 10-point system. The higher the score, the better the data quality of the specific geological basic data item. And among them, w1 1 = 80%, w1 2 = 10%, w1 3 = 10%, F1 = 50, G1 = 20, H1 = 10, P1 = 5, K11 = 50%, K12 = 30%, K13 = 10%. In this embodiment, when the user scores, a score from 0 to 10 needs to be given for the data item. For example Figure 3B is the scoring mechanism adopted

[0048] Similarly, in one embodiment, the geological feature data item acquisition module 530 evaluates the retrieved multiple geological feature data items according to the usage situation, including that the geological feature data item acquisition module 530 is further configured to: obtain the usage situation of each geological feature data item of the retrieved B geological feature data items. Among them, the usage situation of a geological feature data item includes the number of times of use n2 and the user evaluation score E2 of each use i (i = 1 to n2); obtain the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use of each geological feature data item of the retrieved B geological feature data items and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological feature

[0049] data items. Among them, and based on the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use of each geological feature data item of the retrieved B geological feature data items and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological feature data items, obtain the recommended level score SS of each geological feature data item of the multiple geological feature data items as the evaluation result

[0050] Regarding how the geological feature data item acquisition module 530 obtains the recommended level score SS of the geological feature data item based on these parameters, there can be multiple embodiments, because different models can be established to calculate the recommended level score SS of the geological feature data item

[0051] In one implementation, the recommended level score SS of the geological element type data item can be executed using a pre-trained second neural network model. The second neural network model can be trained in the following manner: obtaining the empirical recommended level scores of each geological element type data item among a number of geological element type data items from domain experts; and then using the institutional reputation score E2o, author reputation score E2a, number of times used n2, average user rating score within the number of times used and the percentage K2 of the number of times used n2 in the total number of times used of all B retrieved geological element type data items as input vectors, and using the empirical recommended level scores of the domain experts for each geological element type data item as the output to train the second neural network model; and when the second neural network model converges or meets a predetermined condition, the training of the second neural network model is completed. Then, the trained second neural network model can be applied to calculate the recommended level score SS of the geological element type data item. Specifically, by using the author reputation score E2a, number of times used n2, average user rating score within the number of times used and the percentage K2 of the number of times used n2 in the total number of times used of all B retrieved geological element type data items as input vectors, and inputting them into the trained second neural network model, the output of the trained second neural network model is the recommended level score SS of the geological element type data item. Similarly, the above data is preferably in a normalized form. Using the above second neural network model for evaluation according to usage not only utilizes the experience of domain experts but also the experience of users, and the obtained evaluation results are more accurate and objective.

[0052] Similarly, the recommended level score SS of the geological element type data item can be calculated by setting a second empirical model using the experience of domain experts. In one implementation, based on the author reputation score E2a, number of times used n2, average user rating score within the number of times used of each geological element type data item among the B retrieved geological element type data items the recommended level score of each of the B geological element type data items is calculated using the following second empirical model: in this second model, for a geological element type data item, the recommended level score SS includes a recommended score SS1 and a reward score SS2, and wherein, the recommended score SS1 and the reward score SS2 are respectively:

[0053]

[0054] wherein, w2 1 and w2 2F1 is the weight defined by the user, F2 is the threshold of the usage times n2 defined by the user, G2, H2, and P2 are the reward scores of different conditions defined by the user respectively, and K21, K22, and K23 are different thresholds of the percentage K2 of the usage times n2 in the total usage times of the B geological element type data items retrieved. Evaluating according to the usage situation using the above-mentioned second empirical model not only utilizes the experience of domain experts but also the experience of users. Moreover, the model has a small amount of calculation, a fast calculation speed, and does not require pre-training. The obtained evaluation results are also relatively accurate and objective.

[0055] In one implementation, in the above-mentioned second empirical model, SS uses a hundred-mark system. In the evaluation result of E2a, if the evaluation result is very good, E2a = 9 points; if the evaluation result is good, E2a = 6; if the evaluation result is average, E2a = 5 points. The user evaluation score E2 for each use i (i = 1 to n2) uses a ten-mark system. The higher the score, the better the data quality of the specific geological element type data item. And among them, w2 1 = 80%, w2 2 = 20%, F2 = 50, G2 = 20, H2 = 10, P2 = 5, K21 = 50%, K22 = 30%, K23 = 10%.

[0056] When the synthesis module 540 uses the data sets (maps) corresponding to the N candidate geological basic data items selected as the base map and combines them with the M candidate geological element type data items respectively to obtain M*N / 2 candidate geological maps, if the synthesis module 540 determines during synthesis that a specific candidate geological basic data item among the N candidate geological basic data items contains multiple base maps with different resolutions, it receives the resolution used for compiling / updating the geological map input by the user 502 from the user interface module 510. Then the synthesis module 540 converts the multiple base maps with different resolutions into multiple base maps using the resolution used for compiling / updating the geological map and then combines them. In this way, by using the resolution provided by the user to convert maps with different resolutions, the obtained base maps can be better aligned and combined, and the obtained geological map has a better effect. During the combination process, it is necessary to match the vector map and the raster data. The matching relationship between the geological map scale and the raster resolution during matching is:

[0057] The raster resolution R ≈ [denominator D of the geological map scale / 2000]; and

[0058] The denominator D of the geological map scale ≈ [2000 * raster resolution R].

[0059] In one implementation, the present invention also discloses a method for compiling / updating a geological map, Figure 6The flowchart of method 600 for compiling / updating a geological map according to an embodiment of the present invention is shown. As Figure 6 shown, in step S610, a request for compiling / updating a geological map is received, and the request includes requirements for geological basic data items and geological element class data items to be used in the geological map to be compiled / updated. In step S620, according to the requirements for the geological basic data items to be used in the geological map to be compiled / updated, A geological basic data items are retrieved from a geological database storing a plurality of geological basic data items and a plurality of geological element class data items, and the retrieved A geological basic data items are evaluated according to their usage situations, and the top N geological basic data items in the evaluation results are selected as candidate geological basic data items, where N is a positive integer and N ≤ A. In step S630, according to the requirements for the geological element class data items to be used in the geological map to be compiled / updated, B geological element class data items are retrieved from the geological database, and the retrieved B geological element class data items are evaluated according to their usage situations, and the top M geological element class data items in the evaluation results are selected as candidate geological element class data items, where M is a positive integer and M ≤ B. In step S640, the data sets (maps) corresponding to the N candidate geological basic data items are used as base maps and combined with the M candidate geological element class data items respectively to obtain M * N / 2 candidate geological maps. In step S650, at least one geological map selected by the user from the M * N / 2 candidate geological maps is received. In step S660, the at least one geological map selected by the user is output as the geological map to be compiled / updated.

[0060] In an embodiment of method 600, the requirements for the geological basic data items to be used in the geological map to be compiled / updated include requirements for the following items of the metadata of the geological basic data items: subject, location area, longitude and latitude range, data type, scale denominator, accuracy, and production year; and wherein, the requirements for the geological element class data items to be used in the geological map to be compiled / updated include requirements for the following fields of the metadata of the geological element class data items: subject, location area, longitude and latitude range, data type, scale denominator, accuracy, and year, where the geological element class data item and the geological basic data item include the same or approximate location area and longitude and latitude range.

[0061] In an embodiment, Figure 7 The flowchart of method 700 used for evaluating the retrieved A geological basic data items according to their usage situations in the method shown according to an embodiment of the present invention is shown. According to Figure 6 shown, in step S710, the usage situation of each geological basic data item among the retrieved A geological basic data items is obtained, where the usage situation of a geological basic data item includes the number of times of use n1 and the user evaluation score E1 for each use Figure 7 ​i (i = 1 to n1). In step S720, for each of the retrieved A geological basic data items, obtain the institutional credibility score Eo, the author credibility score E1a, the number of times of use n1, the average user rating score within the number of times of use and the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items, where In step S730, based on the institutional credibility score Eo, the author credibility score E1a, the number of times of use n1, the average user rating score within the number of times of use for each of the retrieved A geological basic data items, and the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items, obtain the recommended level score S for each of the multiple geological basic data items as the evaluation result

[0062] In one implementation Figure 7 Step S730 is executed using a pre-trained first neural network model. During the execution, by taking the institutional credibility score Eo, the author credibility score E1a, the number of times of use n1, the average user rating score within the number of times of use for a geological basic data item, and the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items as the input vector, input it into the trained first neural network model, and take the output of the trained first neural network model as the recommended level score S for this geological basic data item.

[0063] In one implementation, the first neural network model is trained as follows: First, obtain the empirical recommended level scores of the domain experts for each of several geological basic data items; then take the institutional credibility score Eo, the author credibility score E1a, the number of times of use n1, the average user rating score within the number of times of use for each of the several geological basic data items, and the percentage K1 of the number of times of use n1 in the total number of times of use of all the retrieved A geological basic data items as the input vector, and take the empirical recommended level scores of the domain experts for each geological basic data item as the output to train the first neural network model; finally, in response to the first neural network model converging or meeting a predetermined condition, the first neural network model training is completed.

[0064] In one implementation Figure 7Step S730 is calculated using the following first empirical model: In this first empirical model, based on the institutional reputation score Eo, author reputation score E1a, number of times used n1, and average user rating score within the number of times used for each of the retrieved A geological basic data items Obtaining the recommendation level score for each of the multiple geological basic data items is calculated using the first empirical model: In this first empirical model, for a geological basic data item, the recommendation level score S includes a recommendation score S1 and a reward score S2, and wherein, the recommendation score S1 and the reward score S2 are respectively:

[0065]

[0066] wherein, w1 1 、w1 2 、w1 3 are weights defined by the user, F1 is the threshold of the number of times used n1 defined by the user, G1, H1, and P1 are respectively the reward scores for different conditions defined by the user, and K11, K12, and K13 are respectively different thresholds of the percentage K1 of the number of times used n1 in the total number of times used of all the retrieved A geological basic data items

[0067] In one implementation manner, Figure 7 The above-mentioned first empirical model parameters used in step S730 are respectively: S uses a percentage system. In the evaluation results of Eo and E1a, if the evaluation result is very good, Eo = E1a = 9 points, if the evaluation result is good, Eo = E1a = 6, if the evaluation result is average, Eo = E1a = 5 points, and the user evaluation score E1 i (i = 1 to n1) uses a 10-point system, and the higher the score, the better the data quality of the specific geological basic data item. And among them, w1 1 = 80%, w1 2 = 10%, w1 3 = 10%, F1 = 50, G1 = 20, H1 = 10, P1 = 5, K11 = 50%, K12 = 30%, K13 = 10%.

[0068] In one implementation manner, Figure 8 shows Figure 6 The flowchart of method 800 used to evaluate the retrieved B geological element type data items according to the usage situation in the method shown. According to Figure 8 , in step S810, obtain the usage situation of each geological element type data item of the retrieved B geological element type data items, wherein, the usage situation of a geological element type data item includes the number of times used n2 and the user evaluation score E2 for each usei (for i = 1 to n2). In step S820, obtain the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use for each geological element class data item among the retrieved B geological element class data items and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological element class data items, where In step S830, based on the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use for each geological element class data item among the retrieved B geological element class data items and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological element class data items, obtain the recommended level score SS for each geological element class data item among the multiple geological element class data items as the evaluation result.

[0069] In one implementation Figure 8 Step S830 of is executed using a pre-trained second neural network model. During the execution, by taking the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use for a geological element class data item and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological element class data items as the input vector, input it into the trained second neural network model, and the output of the trained second neural network model is the recommended level score SS for this geological element class data item.

[0070] In one implementation, the second neural network model is trained as follows: obtain the empirical recommended level score of each geological element class data item among a number of geological element class data items by domain experts; for each geological element class data item among the number of geological element class data items, take the institutional credibility score E2o, the author credibility score E2a, the number of times of use n2, and the average user rating score within the number of times of use and the percentage K2 of the number of times of use n2 in the total number of times of use of all the retrieved B geological element class data items as the input vector, and take the empirical recommended level score of each geological element class data item by the domain expert as the output to train the second neural network model; and in response to the second neural network model converging or meeting a predetermined condition, the training of the second neural network model is completed..

[0071] Those skilled in the art can know that the first neural network model and the second neural network model can adopt the same model, such as models like MLP, CNN, RNN, GNN, LSTM, transformer, etc., or they can respectively adopt different models among the above models.

[0072] In one embodiment, Figure 8 step S830 of Figure 8 calculates using a second empirical model: in this second empirical model, for a geological element class data item, the recommended level score SS includes a recommended score SS1 and a reward score SS2, and wherein, the recommended score SS1 and the reward score SS2 are respectively:

[0073]

[0074] wherein, w2 1 and w2 2 are user-defined weights, F2 is a threshold of the user-defined usage times n2, G2, H2, and P2 are respectively reward scores for different user-defined conditions, and K21, K22, and K23 are respectively different thresholds of the percentage K2 of the usage times n2 in the total usage times of all retrieved B geological element class data items.

[0075] In one embodiment, Figure 8 the parameters of the second empirical model used in step S830 of Figure 8 are respectively: SS uses a 100-point system. In the evaluation result of E2a, if the evaluation result is very good, E2a = 9 points; if the evaluation result is good, E2a = 6; if the evaluation result is average, E2a = 5 points. The user evaluation score E2 i (i = 1 to n2) uses a 10-point system. The higher the score, the better the data quality of the specific geological element class data item. And wherein, w2 1 = 80%, w2 2 = 20%, F2 = 50, G2 = 20, H2 = 10, P2 = 5, K21 = 50%, K22 = 30%, K23 = 10%.

[0076] In the process of respectively combining the data sets (maps) corresponding to N geological basic data items as candidate base maps with M candidate geological element class data items, if it is determined that a specific candidate geological basic data item among the N candidate geological basic data items contains multiple base maps with different resolutions, the resolution used for compiling / updating the geological map input by the user is received, and the multiple base maps with different resolutions are converted into multiple base maps using the resolution for compiling / updating the geological map, and then combined. And wherein, in the process of matching the vector map with the raster data, the matching relationship between the geological map scale and the raster resolution is: raster resolution R ≈ [denominator D of the geological map scale / 2000]; and denominator D of the geological map scale ≈ [2000 * raster resolution R].

[0077] Figure 9 shows an example of compiling / updating a geological map using the system and method of the present invention. In Figure 9Among them, (1) the Asian magmatic rock distribution map comes from the dataset (map) of the geological basic data items selected after evaluation, (2) the ore deposit data, and (3) the plate model data both come from the dataset (map) of the geological element data items selected after evaluation. (4) The deep-time geological and mineral map is the compiled geological map. In this example, if the resolution of the global (1) Asian magmatic rock distribution map is assumed to be x1, and the resolutions of (2) the ore deposit data and (3) the plate model data are assumed to be x2, and if x1 and x2 differ greatly, then the effect of the combined geological map will be very poor. At this time, the user can select a suitable resolution x3, convert both (1) the Asian magmatic rock distribution map and (2) the ore deposit data and (3) the plate model data into maps with the resolution of x3, and when combining the two, a better geological map can be obtained.

[0078] In one implementation, an embodiment of the present invention also discloses a computer system, including: a memory; and at least one processor operably coupled to the memory and configured to execute the method described above.

[0079] The present invention can be a system, a method, and / or a computer program product. The computer program product includes a computer-readable storage medium. Computer-readable program instructions for causing a processor to implement various aspects of the present invention are carried on the computer-readable storage medium. The method of the present invention can be executed on an independent computer system, or on a distributed computing system, or on a cloud platform.

[0080] A computer-readable storage medium can be a tangible device capable of retaining and storing instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, and any suitable combination of the foregoing.

[0081] The computer-readable program instructions described herein can be downloaded to a corresponding computing / processing device from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the corresponding computing / processing device.

[0082] The computer-readable program instructions for carrying out operations of the present invention may be assembly instructions, instruction set architecture instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages and conventional procedural programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network connection, including a local area network or a wide area network, or may be connected to an external computer.

[0083] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable storage media according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved.

[0084] The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The embodiments were chosen and described in order to best explain the principles of the invention, the practical application, and to enable others of ordinary skill in the art to understand the invention with various modifications suited to the particular use contemplated. The terms used herein were chosen to best explain the principles of the embodiments, the practical application, or the technical improvement present in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A system for compiling / updating a geological map, comprising: The user interface module is configured to receive a request for compiling / updating a geological map, wherein the request includes requirements for geological basic data items and geological element class data items used in the geological map to be compiled / updated; The geological basic data item acquisition module is configured to retrieve A geological basic data items from a geological database storing multiple geological basic data items and multiple geological element class data items according to the requirements for the geological basic data items used in the geological map to be compiled / updated, and evaluate the retrieved A geological basic data items according to the usage, and select the top N geological basic data items ranked in the evaluation results as candidate geological basic data items, where N is a positive integer and N≤A; The geological element class data item acquisition module is configured to retrieve B geological element class data items from the geological database according to the requirements of the geological element class data items used in the geological map to be compiled / updated, and evaluate the retrieved B geological element class data items according to the usage, and select the geological element class data items with the top M rankings in the evaluation results as candidate geological element class data items, where M is a positive integer and M≤B; A synthesis module is configured to use the data sets (maps) corresponding to the N candidate geological basic data items as base maps, and respectively combine them with the M candidate geological element class data items to obtain M*N / 2 candidate geological maps; The user interface module is further configured to receive at least one geological map selected by a user from M*N / 2 candidate geological maps, and The output module is configured to output at least one geological map selected by a user as a compiled / updated geological map.

2. The system according to claim 1, wherein: The requirements for geological basic data items used in the geological map to be compiled / updated include requirements for the following fields of metadata of the geological basic data items: subject, region, longitude and latitude range, data type, scale denominator, precision and production year; and wherein, the requirements for geological element class data items used in the geological map to be compiled / updated include requirements for the following fields of metadata of the geological element class data items: subject, region, longitude and latitude range, data type, scale denominator, precision and year, wherein the geological element class data items contain the same or similar region and longitude and latitude range as the geological basic data items.

3. The system according to claim 1 or 2, wherein: The geological basic data item acquisition module evaluates the retrieved A geological basic data items according to the usage conditions, including that the geological basic data item acquisition module is further configured as follows: Obtain the usage of each of the retrieved A geological basic data items, where the usage of a geological basic data item includes the number of times it has been used n1, the user evaluation score E1 for each use i (i=1 to n1); Obtain the institutional reputation score Eo, author reputation score E1a, number of times used n1, and average user rating score within the number of times used for each of the retrieved A geological basic data items And the percentage K1 of the number of times used n1 to the total number of times used of all retrieved A geological basic data items, where as well as Based on the retrieved A geological basic data items, the institutional reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each geological basic data item And the percentage K1 of the number of times n1 is used to the total number of times of use of all the retrieved A geological basic data items, and the recommendation level score S of each geological basic data item of the multiple geological basic data items is obtained as the evaluation result.

4. The system according to claim 3, wherein: Based on the retrieved A geological basic data items, the institutional reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each geological basic data item and the percentage K1 of the number of times n1 used in the total number of times of all retrieved A geological basic data items used, and obtaining the recommendation level score S of each geological basic data item of the multiple geological basic data items as the evaluation result using the trained first neural network model to execute, during the execution, the institution reputation score Eo, the author reputation score E1a, the number of times n1 used, and the average user rating score within the number of times a geological basic data item is used are combined. And the percentage K1 of the number of times n1 is used to the total number of times A geological basic data items are retrieved is used as an input vector and input into the trained first neural network model. The output of the trained first neural network model is obtained as the recommendation level score S of the geological basic data item.

5. The system according to claim 4, wherein: The first neural network model is trained in the following manner: Obtaining the experience-recommended level score of each geological basic data item from domain experts; The institution reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each of the several geological basic data items are and the percentage K1 of the number of times n1 is used to the total number of times A geological basic data items are retrieved, as an input vector, and the experience recommendation level score of each geological basic data item by the domain experts is used as an output to train the first neural network model; as well as In response to the first neural network model converging or satisfying a predetermined condition, the training of the first neural network model is completed.

6. The system according to claim 3, wherein: Based on the institutional reputation score Eo, author reputation score E1a, number of times used n1, and average user rating score within the number of times used of each geological basic data item retrieved A geological basic data items The recommended level score of each geological basic data item of the plurality of geological basic data items is calculated using a first empirical model: in the first empirical model, for one geological basic data item, the recommended level score S includes a recommended score S1 and a reward score S2, and Among them, the recommendation score S1 and the reward score S2 are: Among them, w11, w12, and w13 are user-defined weights, F1 is the threshold of the number of uses n1 defined by the user, G1, H1, and P1 are the reward scores for different conditions defined by the user, and K11, K12, and K13 are different thresholds of the percentage K1 of the number of uses n1 to the total number of uses of all A retrieved geological basic data items.

7. The system according to claim 6, wherein: S uses a percentage system. In the evaluation results of Eo and E1a, if the evaluation result is very good, Eo = E1a = 9 points, if the evaluation result is good, Eo = E1a = 6 points, if the evaluation result is average, Eo = E1a = 5 points, and the user evaluation score E1 for each use is i (i=1 to n1) adopts a 10-point system, the higher the score, the better the data quality of the specific geological basic data item, and among them, w11=80%, w12=10%, w13=10%, F1=50, G1=20, H1=10, P1=5, K11=50%, K12=30%, K13=10%.

8. The system according to claim 1 or 2, wherein: The geological element data item acquisition module evaluates the retrieved B geological element data items according to the usage conditions, including that the geological element data item acquisition module is further configured as follows: Obtain the usage of each of the retrieved B geological element data items, where the usage of a geological element data item includes the number of times it has been used n2, the user evaluation score E2 for each use i (i=1 to n2); Obtain the author's reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each of the retrieved B geological element data items And the percentage of the number of times used n2 to the total number of times used of all retrieved B geological element data items K2, where, as well as Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items And the percentage K2 of the number of times n2 used to the total number of times of use of all the retrieved B geological element class data items, and the recommendation level score SS of each geological element class data item of the multiple geological element class data items is obtained as the evaluation result.

9. The system according to claim 8, wherein: Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items and the percentage K2 of the number of times used n2 in the total number of times used of all retrieved B geological element data items, and obtaining the recommendation level score of each geological element data item of the plurality of geological element data items is performed using the trained second neural network model, and during the execution, the author reputation score E2a, the number of times used n2, and the average user rating score within the number of times used of a geological element data item are combined. And the percentage K2 of the number of times n2 is used to the total number of times all the retrieved B geological element class data items are used as an input vector and input into the trained second neural network model. The output of the trained second neural network model obtained is the recommendation level score SS of the geological element class data item.

10. The system according to claim 9, wherein: The second neural network model is trained in the following manner: Obtaining the experience-recommended level score of each geological element data item in a number of geological element data items by domain experts; The institution reputation score E2o, the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item in the plurality of geological element data items are and the percentage K2 of the number of times n2 used in the total number of times all the retrieved B geological element class data items are used as input vectors, and the experience recommendation level score of each geological element class data item by the domain experts is used as output to train the second neural network model; as well as In response to the second neural network model converging or satisfying a predetermined condition, the training of the second neural network model is completed.

11. The system according to claim 8, wherein: Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items The recommended level score of each geological element class data item of the B geological element class data items is calculated using the following second empirical model: In the second model, for a geological element class data item, the recommended level score SS includes a recommended score SS1 and a reward score SS2, and Among them, the recommended score SS1 and the reward score SS2 are: Among them, w21 and w22 are user-defined weights, F2 is the threshold of the number of uses n2 defined by the user, G2, H2, and P2 are the reward scores for different conditions defined by the user, and K21, K22, and K23 are different thresholds of the percentage K2 of the number of uses n2 to the total number of uses of all retrieved B geological feature class data items.

12. The system according to claim 11, wherein: SS uses a percentage system. In the evaluation results of E2a, if the evaluation result is very good, E2a = 9 points, if the evaluation result is good, E2a = 6 points, if the evaluation result is average, E2a = 5 points. The user evaluation score E2a for each use is i (i=1 to n2) adopts a 10-point system, the higher the score, the better the data quality of the specific geological element class data item, and among them, w21=80%, w22=20%, F2=50, G2=20, H2=10, P2=5, K21=50%, K22=30%, K23=10%.

13. The system according to any one of claims 1 to 12, wherein: In response to the synthesis module determining that a specific candidate geological basic data item among the N candidate geological basic data items contains multiple base maps with different resolutions, the resolution used for compiling / updating the geological map is received from the user interface module, and the synthesis module converts the multiple base maps with different resolutions into multiple base maps using the resolution used for compiling / updating the geological map, and then combines them, and wherein, in the process of matching the vector map with the raster data, the matching relationship between the scale of the geological map and the raster resolution is: Grid resolution R ≈ [geological map scale denominator D / 2000]; and The denominator of the geological map scale D≈[2000*grid resolution R].

14. A method for compiling / updating a geological map, comprising: Receiving a request for compiling / updating a geological map, wherein the request includes requirements for geological basic data items and geological element class data items used in the geological map to be compiled / updated; According to the requirements of geological basic data items used in the geological map to be compiled / updated, A geological basic data items are retrieved from a geological database storing multiple geological basic data items and multiple geological element class data items, and the retrieved A geological basic data items are evaluated according to the usage, and the geological basic data items ranked top N in the evaluation results are selected as candidate geological basic data items, where N is a positive integer and N≤A; According to the requirements of the geological element class data items used in the geological map to be compiled / updated, B geological element class data items are retrieved from the geological database, and the retrieved B geological element class data items are evaluated according to the usage, and the geological element class data items ranked top M in the evaluation results are selected as candidate geological element class data items, where M is a positive integer and M≤B; The data sets (maps) corresponding to the N candidate geological basic data items are used as base maps, and are combined with the M candidate geological feature class data items to obtain M*N / 2 candidate geological maps; receiving at least one geological map selected by a user from among M*N / 2 candidate geological maps; and At least one geological map selected by the user is output as a compiled / updated geological map.

15. The method according to claim 14, wherein: The requirements for geological basic data items used in the geological map to be compiled / updated include requirements for the following items of metadata of the geological basic data items: subject, region, longitude and latitude range, data type, scale denominator, precision and production year; and wherein, the requirements for geological element class data items used in the geological map to be compiled / updated include requirements for the following fields of metadata of the geological element class data items: subject, region, longitude and latitude range, data type, scale denominator, precision and year, wherein the geological element class data items contain the same or similar region and longitude and latitude range as the geological basic data items.

16. The method according to claim 14 or 15, wherein: The retrieved A geological basic data items are evaluated according to their usage, including: Obtain the usage of each of the retrieved A geological basic data items, where the usage of a geological basic data item includes the number of times it has been used n1, the user evaluation score E1 for each use i (i=1 to n1); Obtain the institutional reputation score Eo, author reputation score E1a, number of times used n1, and average user rating score within the number of times used for each of the retrieved A geological basic data items And the percentage K1 of the number of times used n1 to the total number of times used of all retrieved A geological basic data items, where as well as Based on the retrieved A geological basic data items, the institutional reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each geological basic data item And the percentage K1 of the number of times n1 is used to the total number of times of use of all the retrieved A geological basic data items, and the recommendation level score S of each geological basic data item of the multiple geological basic data items is obtained as the evaluation result.

17. The method according to claim 16, wherein: Based on the retrieved A geological basic data items, the institutional reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each geological basic data item and the percentage K1 of the number of times n1 used in the total number of times of all retrieved A geological basic data items used, and obtaining the recommendation level score S of each geological basic data item of the multiple geological basic data items as the evaluation result using the trained first neural network model to execute, during the execution, the institution reputation score Eo, the author reputation score E1a, the number of times n1 used, and the average user rating score within the number of times a geological basic data item is used are combined. And the percentage K1 of the number of times n1 is used to the total number of times A geological basic data items are retrieved is used as an input vector and input into the trained first neural network model. The output of the trained first neural network model is obtained as the recommendation level score S of the geological basic data item.

18. The method according to claim 17, wherein: The first neural network model is trained in the following manner: Obtaining the experience-recommended level score of each geological basic data item from domain experts; The institution reputation score Eo, the author reputation score E1a, the number of times it has been used n1, and the average user rating score within the number of times it has been used for each of the several geological basic data items are and the percentage K1 of the number of times n1 is used to the total number of times A geological basic data items are retrieved, as an input vector, and the experience recommendation level score of each geological basic data item by the domain experts is used as an output to train the first neural network model; as well as In response to the first neural network model converging or satisfying a predetermined condition, the training of the first neural network model is completed.

19. The method according to claim 16, wherein: Based on the institutional reputation score Eo, author reputation score E1a, number of times used n1, and average user rating score within the number of times used of each geological basic data item retrieved A geological basic data items The recommended level score of each geological basic data item of the plurality of geological basic data items is calculated using a first empirical model: in the first empirical model, for one geological basic data item, the recommended level score S includes a recommended score S1 and a reward score S2, and Among them, the recommendation score S1 and the reward score S2 are: Among them, w11, w12, and w13 are user-defined weights, F1 is the threshold of the number of uses n1 defined by the user, G1, H1, and P1 are the reward scores for different conditions defined by the user, and K11, K12, and K13 are different thresholds of the percentage K1 of the number of uses n1 to the total number of uses of all A retrieved geological basic data items.

20. The method according to claim 19, wherein: S uses a percentage system. In the evaluation results of Eo and E1a, if the evaluation result is very good, Eo = E1a = 9 points, if the evaluation result is good, Eo = E1a = 6 points, if the evaluation result is average, Eo = E1a = 5 points, and the user evaluation score E1 for each use is i (i=1 to n1) adopts a 10-point system, the higher the score, the better the data quality of the specific geological basic data item, and among them, w11=80%, w12=10%, w13=10%, F1=50, G1=20, H1=10, P1=5, K11=50%, K12=30%, K13=10%.

21. The method according to claim 14 or 15, wherein evaluating the retrieved B geological element class data items according to usage comprises: Obtain the usage of each of the retrieved B geological element data items, where the usage of a geological element data item includes the number of times it has been used n2, the user evaluation score E2 for each use i (i=1 to n2); Obtain the author's reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each of the retrieved B geological element data items And the percentage of the number of times used n2 to the total number of times used of all retrieved B geological element data items K2, where, as well as Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items And the percentage K2 of the number of times n2 used to the total number of times of use of all the retrieved B geological element class data items, and the recommendation level score SS of each geological element class data item of the multiple geological element class data items is obtained as the evaluation result.

22. The method according to claim 21, wherein: Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items and the percentage K2 of the number of times used n2 in the total number of times used of all retrieved B geological element data items, and obtaining the recommendation level score of each geological element data item of the plurality of geological element data items is performed using the trained second neural network model, and during the execution, the author reputation score E2a, the number of times used n2, and the average user rating score within the number of times used of a geological element data item are combined. And the percentage K2 of the number of times n2 is used to the total number of times all the retrieved B geological element class data items are used as an input vector and input into the trained second neural network model. The output of the trained second neural network model obtained is the recommendation level score SS of the geological element class data item.

23. The method according to claim 22, wherein: The second neural network model is trained in the following manner: Obtaining the experience-recommended level score of each geological element data item in a number of geological element data items by domain experts; The institution reputation score E2o, the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item in the plurality of geological element data items are and the percentage K2 of the number of times n2 used in the total number of times all the retrieved B geological element class data items are used as input vectors, and the experience recommendation level score of each geological element class data item by the domain experts is used as output to train the second neural network model; as well as In response to the second neural network model converging or satisfying a predetermined condition, the training of the second neural network model is completed.

24. The method according to claim 21, wherein: Based on the author reputation score E2a, the number of times it has been used n2, and the average user rating score within the number of times it has been used for each geological element data item retrieved from the B geological element data items The recommended level score of each geological element class data item of the B geological element class data items is calculated using the following second empirical model: In the second model, for a geological element class data item, the recommended level score SS includes a recommended score SS1 and a reward score SS2, and Among them, the recommended score SS1 and the reward score SS2 are: Among them, w21 and w22 are user-defined weights, F2 is the threshold of the number of uses n2 defined by the user, G2, H2, and P2 are the reward scores for different conditions defined by the user, and K21, K22, and K23 are different thresholds of the percentage K2 of the number of uses n2 to the total number of uses of all retrieved B geological feature class data items.

25. The method according to claim 24, wherein: SS uses a percentage system. In the evaluation results of E2a, if the evaluation result is very good, E2a = 9 points, if the evaluation result is good, E2a = 6 points, if the evaluation result is average, E2a = 5 points. The user evaluation score E2a for each use is i (i=1 to n2) adopts a 10-point system, the higher the score, the better the data quality of the specific geological element class data item, and among them, w21=80%, w22=20%, F2=50, G2=20, H2=10, P2=5, K21=50%, K22=30%, K23=10%.

26. The method according to any one of claims 14 to 25, wherein in the process of combining the data sets (maps) corresponding to N geological basic data items as candidate base maps with M candidate geological element class data items respectively, in response to determining that a specific candidate geological basic data item among the N candidate geological basic data items contains multiple base maps with different resolutions, receiving the resolution used for compiling / updating the geological map input by the user, converting the multiple base maps with different resolutions into multiple base maps using the resolution used for compiling / updating the geological map, and then combining them, and wherein, In the process of matching vector map and raster data, the matching relationship between geological map scale and raster resolution is: Grid resolution R ≈ [geological map scale denominator D / 2000]; and The denominator of the geological map scale D≈[2000*grid resolution R].

27. A computer program product, comprising a computer-readable storage medium having program instructions stored therein, wherein the program instructions can be executed by a computing device to cause the computing device to perform the method according to any one of claims 14 to 26.

28. A computer system comprising: Memory; as well as At least one processor is operably coupled to the memory and configured to execute the method as claimed in any one of claims 14-26.

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