Geological environment monitoring method and system based on artificial intelligence

Abstract

According to the artificial intelligence-based geological environment monitoring method and system provided by the invention, the structure change element knowledge field set in the ecological balance damage element knowledge field set corresponds to the environment change element knowledge field; characters in the ecological balance destruction element knowledge field set correspond to limiting conditions between adjacent environment change element knowledge fields; calling a knowledge field extraction layer to convert the ecological balance destruction element knowledge field set into regional environment anomaly information of an environment anomaly knowledge field set based on geological environment description information corresponding to the structure change element knowledge field set; and calling a mining layer to convert the regional environment anomaly information into an environment anomaly knowledge field set of a second environment condition description type, and processing the environment anomaly knowledge field set of the second environment condition description type according to a geological environment monitoring model to obtain a geological environment monitoring result. Therefore, the accuracy and reliability of geological environment detection are improved.

Description

Technical Field

[0001] This application relates to the technical field of geological environment monitoring. Specifically, it relates to a geological environment monitoring method and system based on artificial intelligence. Background Art

[0002] The geological environment mainly refers to the hard shell layer beneath the earth's surface, that is, the lithosphere. The geological environment is the product of the earth's evolution. During the weathering process of rocks under the action of solar energy, the consolidated substances are liberated and participate in the geographical environment, and then participate in the geological cycle and even the grand cycle of interstellar matter.

[0003] Currently, with the increasing attention paid to the geological environment, the change of the geological environment is a serious damage to the ecosystem. However, in some sparsely populated areas, monitoring cannot be carried out. Therefore, there is an urgent need for a geological environment monitoring solution, and a monitoring report should be given for the monitored content to solve the above technical problems. Summary of the Invention

[0004] To improve the technical problems existing in the related art, this application provides a geological environment monitoring method and system based on artificial intelligence.

[0005] In a first aspect, a geological environment monitoring method based on artificial intelligence is provided. The method includes: obtaining a set of environmental anomaly knowledge fields of the first environmental situation description type; using different separation layers to divide the set of environmental anomaly knowledge fields into at least two type separation queues; generating a set of ecological balance destruction factor knowledge fields of the set of environmental anomaly knowledge fields according to the at least two type separation queues, where the set of structural change factor knowledge fields in the set of ecological balance destruction factor knowledge fields corresponds to the set of environmental change factor knowledge fields, and the characters in the set of ecological balance destruction factor knowledge fields correspond to the limiting conditions between adjacent environmental change factor knowledge fields; calling a knowledge field extraction layer to convert the set of ecological balance destruction factor knowledge fields into regional environmental anomaly information of the set of environmental anomaly knowledge fields based on the geological environment description information corresponding to the set of structural change factor knowledge fields; calling a mining layer to convert the regional environmental anomaly information into a set of environmental anomaly knowledge fields of the second environmental situation description type, and processing the set of environmental anomaly knowledge fields of the second environmental situation description type according to a geological environment monitoring model to obtain a geological environment monitoring result.

[0006] In this application, the step of using different separation layers to divide the set of environmental anomaly knowledge fields into at least two type separation queues includes: using at least two different separation layers to classify the set of environmental anomaly knowledge fields respectively to obtain at least two type separation queues.

[0007] In this application, the ecological balance destruction factor knowledge field set that generates the environmental anomaly knowledge field set according to the at least two type separation queues includes: respectively performing parsing processing on the at least two type separation queues to obtain at least two parsing processing results; fusing the at least two parsing processing results to obtain the ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set.

[0008] In this application, the knowledge field extraction layer calls the ecological balance destruction factor knowledge field set to be converted into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set, including: calling the knowledge field extraction layer model based on the current geological environment description information to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; wherein, the current geological environment description information includes the description factor set of all characters in the ecological balance destruction factor knowledge field set and the global description content.

[0009] In this application, the knowledge field extraction layer model based on the current geological environment description information is a neural convolution model based on the current geological environment description information; calling the knowledge field extraction layer model based on the current geological environment description information to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set, including: calling the neural convolution model based on the current geological environment description information to perform Z - continuous debugging processing on the current geological environment description information corresponding to the ecological balance destruction factor knowledge field set; determining the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information after the Z - continuous debugging processing.

[0010] In this application, the knowledge field extraction layer model based on the current geological environment description information is called to perform Z consecutive debugging processing on the current geological environment description information corresponding to the ecological balance destruction factor knowledge field set, including: when performing the Z-th consecutive debugging processing by calling the knowledge field extraction layer based on the current geological environment description information, according to the masked description content of the a-th character ba in the ecological balance destruction factor knowledge field set after the previous consecutive debugging processing, the geological environment description information data related to the adjacent characters of the a-th character ba, and the global description content after the previous consecutive debugging processing, debugging to obtain the masked description content of the a-th character ba after this consecutive debugging processing; according to the masked description content of all characters after this consecutive debugging processing, debugging to obtain the global description content after this consecutive debugging processing; when Z is not equal to K, add 1 to Z and repeat the above two steps.

[0011] In this application, the geological environment description information data related to the adjacent characters includes: the splicing information of the previous one, the splicing information of the next one, the splicing information of the previous character, and the splicing information of the next character; according to the first geological environment analysis network, splice the candidate knowledge field vector corresponding to the previous one of the a-th character in this consecutive debugging processing and the regional position description element of the a-th character to obtain the splicing information of the previous one; according to the second geological environment analysis network, splice the candidate knowledge field vector corresponding to the next one of the a-th character in this consecutive debugging processing and the regional position description element of the a-th character to obtain the splicing information of the next one; according to the first geological environment analysis network, splice the masked description content corresponding to the previous character of the a-th character in the previous consecutive debugging processing and the regional position description element of the a-th character to obtain the splicing information of the previous character; according to the second geological environment analysis network, splice the masked description content corresponding to the next character of the a-th character in the previous consecutive debugging processing and the regional position description element of the a-th character to obtain the splicing information of the next character; where the relevant metric values in the first geological environment analysis network and the second geological environment analysis network are the same or different.

[0012] In this application, Z is a preset target value.

[0013] In this application, determining the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information of the Z consecutive debugging processing includes: splicing the Z current geological environment description information of the Z consecutive debugging processing according to the third geological environment analysis network with a set period to obtain the spliced current geological environment description information, which is determined as the regional environmental anomaly information of the environmental anomaly knowledge field set.

[0014] In a second aspect, a geological environment monitoring system based on artificial intelligence is provided, including a processor and a memory that communicate with each other. The processor is configured to read and execute a computer program from the memory to implement the above method.

[0015] The geological environment monitoring method and system based on artificial intelligence provided by the embodiments of the present application obtain a set of environmental anomaly knowledge fields of the first environmental situation description type; divide the set of environmental anomaly knowledge fields into at least two type separation queues using different separation layers; generate an ecological balance destruction factor knowledge field set of the set of environmental anomaly knowledge fields according to the at least two type separation queues. The structural change factor knowledge field set in the ecological balance destruction factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance destruction factor knowledge field set correspond to the limiting conditions between adjacent environmental change factor knowledge fields; call the knowledge field extraction layer to convert the ecological balance destruction factor knowledge field set into regional environmental anomaly information of the set of environmental anomaly knowledge fields based on the geological environment description information corresponding to the structural change factor knowledge field set; call the mining layer to convert the regional environmental anomaly information into a set of environmental anomaly knowledge fields of the second environmental situation description type, and process the set of environmental anomaly knowledge fields of the second environmental situation description type according to the geological environment monitoring model to obtain the geological environment monitoring result, thereby improving the accuracy and reliability of geological environment detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a geological environment monitoring method based on artificial intelligence provided by the embodiments of the present application.

[0018] Figure 2 It is a block diagram of a geological environment monitoring device based on artificial intelligence provided by the embodiments of the present application.

[0019] Figure 3 It is an architecture diagram of a geological environment monitoring system based on artificial intelligence provided by the embodiments of the present application. DETAILED DESCRIPTION

[0020] To better understand the above technical solutions, the technical solutions of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific knowledge fields in the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the embodiments of the present application and the technical knowledge fields in the embodiments can be combined with each other.

[0021] Please refer to Figure 1 , which shows a geological environment monitoring method based on artificial intelligence. This method may include the technical solutions described in the following steps 301 - step 305.

[0022] Step 301, obtain a set of environmental anomaly knowledge fields of the first environmental situation description type.

[0023] Optionally, the set of environmental anomaly knowledge fields of the first environmental situation description type is obtained by mining the knowledge fields of the first environmental situation description type into a set of environmental anomaly knowledge fields of the second environmental situation description type. Among them, the first environmental situation description type includes: geological situation information; the second environmental situation description type includes: different plant growth situations, etc.

[0024] Step 302, use a distinct separation layer to divide the set of environmental anomaly knowledge fields into at least two type separation queues.

[0025] The separation layer is a module that divides the input description content (set of environmental anomaly knowledge fields) into type separation queues of files. Among them, the specific division method is an empirical type set according to empirical facts.

[0026] Among them, the knowledge field is understood as a feature in the present application.

[0027] For example, using a distinct separation layer to process events, at least two type separation queues are obtained. The at least two type separation queues may be different from each other. According to the set classification method, the same or similar data is divided into the same folder.

[0028] Step 303, generate a set of ecological balance destruction factor knowledge fields of the set of environmental anomaly knowledge fields according to at least two type separation queues. The set of structural change factor knowledge fields in the set of ecological balance destruction factor knowledge fields corresponds to the set of environmental change factor knowledge fields, and the characters in the set of ecological balance destruction factor knowledge fields correspond to the limiting conditions between adjacent environmental change factor knowledge fields.

[0029] For example, the ecological balance destruction factor knowledge field can be understood as a feature that destroys the ecological balance.

[0030] Step 304: Invoke the knowledge field extraction layer to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set.

[0031] For example, the structural change factor knowledge field set can be understood as the geological structure feature change factor.

[0032] Optionally, invoke the knowledge field extraction layer model based on the current geological environment description information to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set; wherein, the current geological environment description information includes the description factor set of all characters in the ecological balance destruction factor knowledge field set and the global description content.

[0033] Step 305: Invoke the mining layer to convert the regional environmental anomaly information into the environmental anomaly knowledge field set of the second environmental situation description type, and process the environmental anomaly knowledge field set of the second environmental situation description type according to the geological environment monitoring model to obtain the geological environment monitoring result.

[0034] For example, the regional environmental anomaly information can be understood as the regional environmental anomaly information.

[0035] The environmental anomaly knowledge field set of the second environmental situation description type is the mining result of the environmental anomaly knowledge field set of the first environmental situation description type.

[0036] In summary, the method provided in this embodiment obtains the environmental anomaly knowledge field set of the first environmental situation description type; uses a distinct separation layer to divide the environmental anomaly knowledge field set into at least two type separation queues; generates the ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set according to the at least two type separation queues. The structural change factor knowledge field set in the ecological balance destruction factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance destruction factor knowledge field set correspond to the limiting conditions between adjacent environmental change factor knowledge fields; invokes the knowledge field extraction layer to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; invokes the mining layer to convert the regional environmental anomaly information into the environmental anomaly knowledge field set of the second environmental situation description type, and processes the environmental anomaly knowledge field set of the second environmental situation description type according to the geological environment monitoring model to obtain the geological environment monitoring result, thereby improving the accuracy and reliability of geological environment detection.

[0037] In this embodiment, step 303 in the above embodiment can be alternatively implemented as step 3031 and step 3032, and the method may specifically include the following steps.

[0038] Step 3031, parse at least two type separation queues respectively to obtain at least two parsing results.

[0039] Optionally, divide the environmental anomaly knowledge field set by at least two different separation layers to obtain at least two type separation queues. One type separation queue corresponds to one parsing result.

[0040] Step 3032, fuse at least two parsing results to obtain an ecological balance disruption factor knowledge field set of the environmental anomaly knowledge field set. The structural change factor knowledge field set in the ecological balance disruption factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance disruption factor knowledge field set correspond to the constraints between adjacent environmental change factor knowledge fields; In this embodiment, the knowledge field extraction layer model based on the current geological environment description information is a neural convolutional model based on the current geological environment description information. In this embodiment, step 304 in the above embodiment can be alternatively implemented as step 701 and step 702, and the method includes the following content.

[0041] Step 701, call the knowledge field extraction layer model based on the ecological balance disruption factor knowledge field set, and perform Z - continuous debugging processing on the current geological environment description information corresponding to the ecological balance disruption factor knowledge field set.

[0042] In an example, Z is a preset target value, and Z is an integer.

[0043] Step 702, determine the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information after Z - continuous debugging processing; The current geological environment description information after Z - continuous debugging processing includes: the description factor set DZ and the global description content LZ of all characters in the ecological balance disruption factor knowledge field set after Z - continuous debugging processing.

[0044] In this embodiment, step 701 in the above embodiment can be alternatively implemented as step 7011, step 7012 and step 7013, and the method may specifically include the following steps.

[0045] Step 7011, when invoking the knowledge field extraction layer based on the current geological environment description information for the Z-th consecutive debugging process, according to the masked description content of the a-th character ba in the ecological balance destruction factor knowledge field set after the previous consecutive debugging process, the geological environment description information data related to the characters adjacent to the a-th character ba, and the global description content after the previous consecutive debugging process, debug to obtain the masked description content of the a-th character ba after the current consecutive debugging process.

[0046] An adjacent character refers to a character that is adjacent to a character.

[0047] For example, when performing the Z-th consecutive debugging process, mark the masked description content of the a-th character ba after the previous consecutive debugging process as, mark the masked description content of the a-th character ba after the current consecutive debugging process as, and mark the global description content after the previous consecutive debugging process as LZ.1.

[0048] For example, it is necessary to obtain the masked description content of the character o3 in the ecological balance destruction factor knowledge field set after the Z-th consecutive debugging process according to the masked description content of the 3rd character o3 after the previous consecutive debugging process, the geological environment description information data related to the adjacent characters o0, o1, o4, and o5 of the character o3, and the global description content LZ.1 after the previous consecutive debugging process.

[0049] Step 7012, according to the masked description content of all characters after the current consecutive debugging process, debug to obtain the global description content after the current consecutive debugging process.

[0050] The knowledge field extraction layer is constructed according to the structural change factor knowledge field set, and performs Z consecutive debugging processes on the current geological environment description information of the ecological balance destruction factor knowledge field set.

[0051] Exemplarily, according to the masked description content of all characters at the Z-th iteration, obtain the global description content LZ after the Z-th consecutive debugging process.

[0052] Step 7013, when Z is not equal to K, increment Z by one and repeat the above two steps.

[0053] For example, perform Z consecutive debugging processes on the current geological environment description information of the ecological balance destruction factor knowledge field set. After obtaining the masked description content of all characters ba after the Z-th consecutive debugging process and the global description content LZ, since Z is not equal to Z, perform the (Z + 1)-th consecutive debugging process on the current geological environment description information of the ecological balance destruction factor knowledge field set until the Z consecutive debugging processes are completed.

[0054] Step 702: Determine the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information processed by Z continuous debugging.

[0055] In one example, determining the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information processed by Z continuous debugging includes: splicing the Z current geological environment description information processed by Z continuous debugging according to the third geological environment analysis network of the set period to obtain the spliced current geological environment description information, and determining it as the regional environmental anomaly information of the environmental anomaly knowledge field set.

[0056] Optionally, after the loop iteration is completed in the knowledge field extraction layer, the third geological environment analysis network is used to process the sample masking description content function of the characters to obtain the final state Da of each character.

[0057] Among them, the function processing methods include weighted processing and other methods.

[0058] The knowledge field extraction layer is constructed according to the structural change factor knowledge field set, and Z continuous debugging processing is performed on the current geological environment description information of the ecological balance destruction factor knowledge field set.

[0059] Exemplarily, according to the sample masking description content of character o7 and the global description content LZ after Z continuous debugging processing, the final state D7 of character o7 is obtained.

[0060] According to the final states Da of all characters after debugging, an intermediate vector of the environmental anomaly knowledge field set is obtained.

[0061] In summary, the method provided in this embodiment, by using the knowledge field extraction layer of the neural convolution model based on the current geological environment description information, constructs in the dimension perpendicular to the structural change factor knowledge field set, and performs Z continuous debugging processing on the current geological environment description information of the ecological balance destruction factor knowledge field set, solves the problem that the traditional model can only construct the word sequence of the text and cannot process the parsing result.

[0062] The geological environment description information data related to the character includes: the splicing information of the previous one, the splicing information of the next one, the splicing information of the previous character, and the splicing information of the next character.

[0063] According to the first geological environment analysis network, the candidate knowledge field vector corresponding to the previous one of the a-th character in this continuous debugging process and the regional position description element of the a-th character are spliced to obtain the splicing information of the previous one.

[0064] According to the second geological environment analysis network, the subsequent candidate knowledge field vector corresponding to the a-th character in this continuous debugging process and the regional position description elements of the a-th character are spliced to obtain the subsequent splicing information.

[0065] According to the first geological environment analysis network, the masking description content corresponding to the previous character of the a-th character in the previous continuous debugging process and the regional position description elements of the a-th character are spliced to obtain the splicing information of the previous character.

[0066] According to the second geological environment analysis network, the masking description content corresponding to the subsequent character of the a-th character in the previous continuous debugging process and the regional position description elements of the a-th character are spliced to obtain the splicing information of the subsequent character.

[0067] Optionally, the relevant metric values in the first geological environment analysis network and the second geological environment analysis network are the same or different.

[0068] On the above basis, please refer to Figure 2 , and a geological environment monitoring device 200 based on artificial intelligence is provided. The device includes: A knowledge field acquisition module 210, configured to acquire an environmental anomaly knowledge field set of the first environmental situation description type; A queue differentiation module 220, configured to divide the environmental anomaly knowledge field set into at least two type separation queues by using a separation layer with differences; A knowledge field generation module 230, configured to generate an ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set according to the at least two type separation queues. The structural change factor knowledge field set in the ecological balance destruction factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance destruction factor knowledge field set correspond to the limiting conditions between adjacent environmental change factor knowledge fields; An anomaly information acquisition module 240, configured to call a knowledge field extraction layer to convert the ecological balance destruction factor knowledge field set into regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; A result monitoring module 250, configured to call a mining layer to convert the regional environmental anomaly information into an environmental anomaly knowledge field set of the second environmental situation description type, and process the environmental anomaly knowledge field set of the second environmental situation description type according to a geological environment monitoring model to obtain a geological environment monitoring result.

[0069] On the above basis, please refer to Figure 3, which shows an artificial intelligence-based geological environment monitoring system 300, including a processor 310 and a memory 320 that communicate with each other. The processor is used to read and execute a computer program from the memory to implement the above method.

[0070] On the basis of the above, a computer-readable storage medium is also provided, and the computer program stored thereon implements the above method when running.

[0071] In summary, based on the above solution, an environmental anomaly knowledge field set of the first environmental situation description type is obtained; the environmental anomaly knowledge field set is divided into at least two type separation queues by using a separation layer with differences; an ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set is generated according to at least two type separation queues. The structural change factor knowledge field set in the ecological balance destruction factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance destruction factor knowledge field set correspond to the limiting conditions between adjacent environmental change factor knowledge fields; the knowledge field extraction layer is called to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; the mining layer is called to convert the regional environmental anomaly information into an environmental anomaly knowledge field set of the second environmental situation description type, and the geological environment monitoring result is obtained by processing the environmental anomaly knowledge field set of the second environmental situation description type according to the geological environment monitoring model, so as to improve the accuracy and reliability of geological environment detection.

[0072] It should be understood that the above-described system and its modules can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented by using dedicated logic; the software part can be stored in the memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in the processor control code. For example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROK, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can be implemented not only by a hardware circuit of a programmable hardware device such as a very large scale integrated circuit or a gate array, a semiconductor such as a logic chip or a transistor, or a programmable logic device such as a field programmable gate array, but also by software executed by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0073] It should be noted that the beneficial effects that may be produced by different embodiments are different. In different embodiments, the beneficial effects that may be produced can be any one or several combinations of the above, or any other beneficial effects that may be obtained.

[0074] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this application. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are proposed in this application, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0075] Meanwhile, this application uses specific terms to describe the embodiments of this application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain field of knowledge, structure, or feature related to at least one embodiment of this application. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain fields of knowledge, structures, or features in one or more embodiments of this application can be appropriately combined.

[0076] In addition, those skilled in the art can understand that various aspects of this application can be described and illustrated by several patentable types or situations, including any new and useful processes, machines, products, or combinations of substances, or any new and useful improvements to them. Accordingly, various aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components", or "systems". In addition, various aspects of this application may be embodied as a computer product located in one or more computer-readable media, and the product includes computer-readable program codes.

[0077] A computer storage medium may contain a propagated data signal containing computer program codes, such as on a baseband or as part of a carrier wave. This propagated signal may have various forms of manifestation, including electromagnetic form, optical form, etc., or a suitable combination of forms. A computer storage medium can be any computer-readable medium other than a computer-readable storage medium, and this medium can be connected to an instruction execution system, device, or equipment to realize communication, propagation, or transmission for use of the program. The program codes located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0078] The computer program codes required for the operations of various parts of this application can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JDDE, Emerald, C++, C#, VL.ZET, Pbthon, etc., conventional procedural programming languages such as C language, Visual Lasic, Fortran 2003, Perl, COLOL 2002, PHP, DLDP, dynamic programming languages such as Pbthon, Rubb, and Groovb, or other programming languages. This program code can run entirely on the user's computer, or run as an independent software package on the user's computer, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LDZ) or a wide area network (WDZ), or connected to an external computer (e.g., through the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0079] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this application is not used to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on an existing server or mobile device.

[0080] Similarly, it should be noted that, in order to simplify the presentation of the disclosure of this application and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this application, multiple knowledge fields are sometimes merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the knowledge fields required by the object of this application are more than those mentioned in the claims. In fact, the knowledge fields of the embodiments are less than all the knowledge fields of the individual embodiments disclosed above.

[0081] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximate" or "substantially". Unless otherwise specified, "about", "approximate" or "substantially" indicate that the said numbers allow for adaptive variations. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of the present application to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0082] For each patent, patent application, patent application publication and other materials cited in the present application, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into the present application by reference. Except for application history documents that are inconsistent with or conflict with the content of the present application, and also except for documents (currently or subsequently appended to the present application) that limit the broadest scope of the claims of the present application. It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the attached materials of the present application and the content described in the present application, the descriptions, definitions, and / or uses of terms in the present application shall prevail.

[0083] Finally, it should be understood that the embodiments described in the present application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present application may be considered to be in accordance with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0084] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A geological environment monitoring method based on artificial intelligence, characterized in that: The method comprises: Obtain an environmental anomaly knowledge field set of a first environmental situation description type; Using a distinguishable separation layer to divide the set of environmental anomaly knowledge fields into at least two type separation queues; Generate an ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set according to the at least two type separation queues, wherein the structural change factor knowledge field set in the ecological balance destruction factor knowledge field set corresponds to the environmental change factor knowledge field, and the characters in the ecological balance destruction factor knowledge field set correspond to the restriction conditions between adjacent environmental change factor knowledge fields; Calling the knowledge field extraction layer to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; The mining layer is called to convert the regional environmental anomaly information into an environmental anomaly knowledge field set of a second environmental situation description type, and the environmental anomaly knowledge field set of the second environmental situation description type is processed according to the geological environment monitoring model to obtain a geological environment monitoring result.

2. The method according to claim 1, characterized in that The method of using different separation layers to divide the environmental anomaly knowledge field set into at least two type separation queues includes: using at least two different separation layers to classify the environmental anomaly knowledge field set respectively to obtain at least two type separation queues.

3. The method according to claim 1, characterized in that The ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set generated according to the at least two type separation queues includes: Parsing the at least two type separation queues respectively to obtain at least two parsing results; The at least two analytical processing results are merged to obtain an ecological balance destruction factor knowledge field set of the environmental anomaly knowledge field set.

4. The method according to claim 3, characterized in that The calling knowledge field extraction layer converts the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set, including: Call the knowledge field extraction layer model based on the current geological environment description information to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set; wherein the current geological environment description information includes the description factor set and global description content of all characters in the ecological balance destruction factor knowledge field set.

5. The method according to claim 4, characterized in that The knowledge field extraction layer model based on the current geological environment description information is a neural convolution model based on the current geological environment description information; calling the knowledge field extraction layer model based on the current geological environment description information to convert the ecological balance destruction factor knowledge field set into the regional environmental anomaly information of the environmental anomaly knowledge field set based on the geological environment description information corresponding to the structural change factor knowledge field set, including: The neural convolution model based on the current geological environment description information is called to perform Z-continuous debugging processing on the current geological environment description information corresponding to the ecological balance destruction factor knowledge field set; and the regional environmental anomaly information of the environmental anomaly knowledge field set is determined according to the current geological environment description information processed by the Z-continuous debugging.

6. The method according to claim 5, characterized in that The knowledge field extraction layer model based on the current geological environment description information is called to perform Z-continuous debugging processing on the current geological environment description information corresponding to the ecological balance destruction factor knowledge field set, including: When the knowledge field extraction layer based on the current geological environment description information is called to perform the Zth continuous debugging process, according to the cover description content of the ath character ba in the ecological balance destruction factor knowledge field set after the previous continuous debugging process, the geological environment description information data related to the characters close to the ath character ba and the global description content after the previous continuous debugging process, the cover description content of the ath character ba after the current continuous debugging process is debugged; According to the masked description contents of all characters after the continuous debugging process, debugging obtains the global description contents after the continuous debugging process; When Z is not equal to K, Z is increased by one and the above two steps are repeated.

7. The method according to claim 6, characterized in that The geological environment description information data related to the characters include: The concatenation information of the previous one, the concatenation information of the next one, the concatenation information of the previous character and the concatenation information of the next character; splicing the candidate knowledge field vector corresponding to the previous one of the a-th character in the continuous debugging process and the regional position description element of the a-th character according to the first geological environment analysis network to obtain the previous one splicing information; splicing the candidate knowledge field vector corresponding to the next character of the a-th character in the continuous debugging process and the regional position description element of the a-th character according to the second geological environment analysis network to obtain the next splicing information; splicing the mask description content corresponding to the previous character of the a-th character in the previous continuous debugging process and the regional position description element of the a-th character according to the first geological environment analysis network to obtain the splicing information of the previous character; splicing the mask description content corresponding to the next character of the a-th character in the previous continuous debugging process and the regional position description element of the a-th character according to the second geological environment analysis network to obtain splicing information of the next character; The relevant metric values ​​in the first geological environment analysis network and the second geological environment analysis network are the same or different.

8. The method according to claim 6, characterized in that The Z is a preset target value.

9. The method according to claim 5, characterized in that The method of determining the regional environmental anomaly information of the environmental anomaly knowledge field set according to the current geological environment description information processed by the Z continuous debugging includes: splicing the Z current geological environment description information processed by the Z continuous debugging according to a third geological environment analysis network of a set period to obtain the spliced ​​current geological environment description information, and determining it as the regional environmental anomaly information of the environmental anomaly knowledge field set.

10. A geological environment monitoring system based on artificial intelligence, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 9.

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