Geological data quality inspection method and device and storage medium

Abstract

The application relates to a geological data quality inspection method and device and a storage medium, the method comprising the following steps: establishing a multi-dimensional operator rule library based on the spatial topological relationship of a geological object; the multi-dimensional operator rule library comprises a plurality of operator rule groups of different dimensions; acquiring to-be-inspected geological data and quality inspection scene information of the to-be-inspected geological data; performing semantic analysis processing on the quality inspection scene information to obtain quality inspection key features; searching the multi-dimensional operator rule library based on the quality inspection scene information, determining a plurality of target operator rule groups based on the search result; determining a target operator rule template in the target operator rule group according to the quality inspection key features; instantiating each target operator rule template to generate corresponding target quality inspection operators, and performing modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script; and performing quality inspection processing on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result. Through the application, the problem of low accuracy of geological data quality inspection is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geology, and in particular to a geological data quality inspection method and device and a storage medium. BACKGROUND

[0002] With the wide application of geographic information system (GIS) technology, geological data plays an increasingly important role in resource exploration, disaster warning, urban planning and other fields. However, geological data has the characteristics of multi-source, heterogeneity and dynamic change, resulting in uneven data quality, which seriously affects the application effect based on geological data. Therefore, the quality of geological data needs to be checked.

[0003] Geological data quality inspection refers to a process of systematically verifying and evaluating the accuracy, completeness, logical consistency and other quality characteristics of geological data according to professional standards and technical specifications. The core goal is to ensure the reliability of geological data in production, management and application, and to provide reliable data support for geological survey, resource exploration, environmental assessment and other decision-making. In related technologies, the quality inspection method is mainly manual or simple human-computer interaction. In the face of massive and complex geographic data, manual inspection is time-consuming and labor-intensive, and is greatly affected by subjective factors, making it difficult to ensure accuracy.

[0004] At present, there is no effective solution to the problem of low accuracy of geological data quality inspection in related technologies. SUMMARY

[0005] The embodiments of the present application provide a geological data quality inspection method, device and storage medium to at least solve the problem of low accuracy of geological data quality inspection in related technologies.

[0006] In a first aspect, the embodiments of the present application provide a geological data quality inspection method, which comprises:

[0007] Based on the spatial topological relationship of the geological object, a multi-dimensional operator rule library is established. The multi-dimensional operator rule library includes a plurality of operator rule groups of different dimensions;

[0008] Obtain the geological data to be checked and the quality inspection scene information of the geological data to be checked. Perform semantic analysis processing on the quality inspection scene information to obtain quality inspection key features;

[0009] Based on the quality inspection scene information, search the multi-dimensional operator rule library, and determine a plurality of target operator rule groups based on the search result;

[0010] Determine the target operator rule templates in the target operator rule groups according to the quality inspection key features. Instantiate each target operator rule template to generate a corresponding target quality inspection operator, and perform modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script;

[0011] perform quality inspection processing on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result.

[0012] In some embodiments, the spatial topological relationship of the geological object is used to establish a multi-dimensional operator rule library, including:

[0013] The spatial topological relationship of the geological object is obtained to establish a spatial dimension model including multiple groups of spatial operator rules;

[0014] Based on a preset first knowledge base, a logic rule model including multiple groups of logic operator rules is established; based on preset scene information, a business semantic model including multiple groups of semantic operator rules is established;

[0015] The spatial dimension model, the logic rule model, and the business semantic model are fused to establish the multi-dimensional operator rule library.

[0016] In some embodiments, the dynamic quality inspection script is generated by assembling a spatial quality inspection operator in the spatial dimension model, a semantic quality inspection operator in the business semantic model, and a logic quality inspection operator in the logic rule model; the quality inspection processing on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result includes:

[0017] An shared entity identifier is obtained;

[0018] Based on the shared entity identifier, the spatial quality inspection operator is associated with the semantic quality inspection operator to establish a mapping relationship from spatial features to business semantics, and the logic quality inspection operator is embedded into a spatial semantic relationship chain as a trigger condition to generate a dynamic reasoning path;

[0019] Based on the dynamic reasoning path, the to-be-inspected geological data is subjected to quality inspection processing to obtain the quality inspection result.

[0020] In some embodiments, the instantiation of each target operator rule template to generate a corresponding target quality inspection operator includes:

[0021] Based on the operator rule type of the target operator rule template, a parameter type is obtained;

[0022] In the case where the parameter type is a static type, a static assignment parameter is generated based on the target operator rule template, the static assignment parameter is injected into the target operator rule template for instantiation to generate the target quality inspection operator;

[0023] In a case where the parameter type is a dynamic type, a parameter instruction input is parsed to generate a dynamic parameter, or a preset second knowledge base is called to generate the dynamic parameter; the dynamic parameter is injected into the target operator rule template to be instantiated to generate the target quality inspection operator.

[0024] In some embodiments, the modular assembly processing of each of the target quality inspection operators generates a dynamic quality inspection script, including:

[0025] Based on the operator rule type, a dependency topology relationship of each of the target quality inspection operators is generated; and the modular assembly processing of each of the target quality inspection operators is performed according to the dependency topology relationship to generate the dynamic quality inspection script.

[0026] In some embodiments, the generation of the dependency topology relationship of each of the target quality inspection operators based on the operator type of the target quality inspection operator includes:

[0027] A dependency triple is defined for each target quality inspection operator, and an initial dependency topology graph is constructed based on the dependency triple;

[0028] Based on the operator type, a dynamic operator subset and a no-dependency operator subset in the initial dependency topology graph are determined, and the initial dependency topology graph is adjusted based on the dynamic operator subset and the no-dependency operator subset to generate a target dependency topology graph;

[0029] The dependency topology relationship is generated based on the target dependency topology graph.

[0030] In some embodiments, after the quality inspection result is obtained, the method further includes:

[0031] The quality inspection result is stored as a structured database.

[0032] The structured database is queried to generate geological disaster risk prevention information and a geological element error report, and a visual result is generated according to the geological disaster risk prevention information and the geological element error report.

[0033] In some embodiments, the obtaining of the to-be-inspected geological data includes:

[0034] The original geological data is obtained.

[0035] Redundant information in the original geological data is identified and removed to obtain cleaned geological data; and a property table structure of the cleaned geological data is adjusted to a preset standard result to obtain the to-be-inspected geological data.

[0036] In a second aspect, an embodiment of the present application provides a geological data quality inspection device, including:

[0037] The library building module is configured to build a multi-dimensional operator rule library based on the spatial topological relationship of the geological objects, wherein the multi-dimensional operator rule library comprises a plurality of operator rule groups of different dimensions;

[0038] The acquisition module is configured to acquire to-be-inspected geological data and quality inspection scene information corresponding to the to-be-inspected geological data, and perform semantic analysis processing on the quality inspection scene information to obtain quality inspection key features.

[0039] The retrieval module is configured to retrieve the multi-dimensional operator rule library based on the quality inspection scene information, and determine a plurality of target operator rule groups based on a retrieval result.

[0040] The instantiation module is configured to determine target operator rule templates in the target operator rule groups according to the quality inspection key features, instantiate each of the target operator rule templates to generate corresponding target quality inspection operators, and perform modular assembly processing on each of the target quality inspection operators to generate a dynamic quality inspection script.

[0041] The quality inspection module is configured to perform quality inspection processing on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result.

[0042] In a third aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the geological data quality inspection method of the first aspect.

[0043] Compared with the related art, the geological data quality inspection method, device and storage medium provided by the embodiment of the present application have the following advantages. The multi-dimensional operator rule library is built based on the spatial topological relationship of the geological objects, the multi-dimensional operator rule library comprises a plurality of operator rule groups of different dimensions, the to-be-inspected geological data and the quality inspection scene information corresponding to the to-be-inspected geological data are acquired, the quality inspection key features are obtained by performing semantic analysis processing on the quality inspection scene information, the multi-dimensional operator rule library is retrieved based on the quality inspection scene information, a plurality of target operator rule groups are determined based on a retrieval result, target operator rule templates in the target operator rule groups are determined according to the quality inspection key features, each of the target operator rule templates is instantiated to generate corresponding target quality inspection operators, modular assembly processing is performed on each of the target quality inspection operators to generate a dynamic quality inspection script, and quality inspection processing is performed on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result.

[0044] Based on this, in view of the diverse and dynamic quality inspection requirements in geological data quality inspection, a multi-semantics relationship geological feature inspection method based on operator rules is proposed; By constructing an inspection operator rule library supporting complex semantic relationship modeling, and combining with the rule engine to realize the flexible configuration and automatic execution of the inspection process, the accuracy, efficiency and adaptability of geological feature inspection are significantly improved. Moreover, through the instantiation of the pre-defined inspection operator rule library to generate specific inspection tasks, the modular assembly and automatic execution of the quality inspection process are supported; Compared with the traditional fixed script quality inspection method, this method has higher flexibility and maintainability. Users can quickly configure the inspection rule combination according to different application scenarios, and the system automatically schedules and executes and outputs structured inspection results, effectively reducing the errors caused by human intervention, improving the consistency and stability of data quality, and effectively solving the problem of low accuracy in geological data quality inspection.

[0045] The details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more clear and simple. BRIEF DESCRIPTION OF DRAWINGS

[0046] The drawings described herein are used to provide further understanding of the present application, constitute a part of the present application, the schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute improper limitation on the present application. In the drawings:

[0047] Figure 1 It is a hardware structure block diagram of a terminal of a geological data quality inspection method of an embodiment of the present application;

[0048] Figure 2 It is a flow chart of a geological data quality inspection method of an embodiment of the present application;

[0049] Figure 3 It is a schematic diagram of a geological data quality inspection software display interface of an embodiment of the present application;

[0050] Figure 4 It is a structure block diagram of a geological data quality inspection device of an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and explained below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and should not be used to limit the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application. In addition, it should be understood that, although the efforts made in this development process can be complex and lengthy, some design, manufacture or production changes made on the basis of the technical content disclosed in the present application by those of ordinary skill in the art related to the content disclosed in the present application are only routine technical means and should not be understood as insufficient disclosure of the present application.

[0052] In the present application, the term "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0053] Unless otherwise defined, the technical terms or scientific terms involved in the present application should be understood as the usual meaning understood by those of ordinary skill in the art to which the present application belongs. The terms "one", "a", "an", "the", and the like similar words involved in the present application do not represent quantity limitation, but can represent singular or plural. The terms "include", "contain", "have", and any variations thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but can also include steps or units not listed, or can also include other steps or units inherent to these processes, methods, products or devices. The terms "connected", "connected", "coupled" and the like similar words involved in the present application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "multiple" involved in the present application means greater than or equal to two. The term "and / or" describes the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: A exists alone, A and B exist together, and B exists alone. The terms "first", "second", "third" and the like involved in the present application are only to distinguish similar objects, and do not represent a specific order for the objects.

[0054] The method embodiments provided by the present embodiment can be executed in a terminal, a computer or a similar computing device. Taking the case of running on a terminal,Figure 1 is a hardware structure block diagram of a terminal of a geological data quality inspection method according to an embodiment of the present application. As shown in Figure 1 , the terminal can include one or more (only one is shown in Figure 1 ) processor 102 (the processor 102 can include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above terminal can also include a transmission device 106 for communication function and an input and output device 108. Those skilled in the art can understand that Figure 1 the structure shown is only schematic, which does not limit the structure of the above terminal. For example, the terminal can include more or less components than those shown in Figure 1 , or have a different configuration from Figure 1 .

[0055] The memory 104 can be used to store computer programs, such as software programs and modules of application software, and computer programs corresponding to the geological data quality inspection method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, implements the above method. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the terminal through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0056] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network can include a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module which is used to communicate with the Internet in a wireless manner.

[0057] The present embodiment provides a geological data quality inspection method, Figure 2 is a flowchart of a geological data quality inspection method according to an embodiment of the present application, as shown in Figure 2 , the flowchart includes the following steps:

[0058] Step S210, based on the spatial topological relationship of the geological object, a multi-dimensional operator rule library is established; the multi-dimensional operator rule library includes a plurality of different dimension operator rule groups.

[0059] The checking of the multi-semantics relationship helps to find the inconsistency of the geological element content, such as the interaction between the slope unit surface and the related debris flow basin surface, risk prevention area and other elements, and it is necessary to ensure that the dangerous index in the slope unit surface not only meets the logical requirements (such as the dangerous index of the heavy rain working condition > the dangerous index of the heavy rain working condition > the dangerous index of the rain working condition > the dangerous index of the heavy rain working condition), but also keeps consistent with the population risk value and the property risk value. The operator of multi-semantics relationship supports the checking of multi-dimensional semantics relationship between the same type of geological elements and across the geological element categories. For example: intra-class consistency: ensure the logical consistency between the attribute values of each field in the same layer; cross-layer consistency: ensure the consistency of the associated field attribute values between different layers, such as the consistency of the dangerous degree level and the risk value between the slope unit surface and the debris flow basin surface.

[0060] Specifically, based on the spatial topological relationship of the geological object (such as the geometric correlation characteristics of adjacency, inclusion, intersection, etc.), first, the topological structure data between the geological elements is extracted by using the GIS tool or the spatial database function, for example, the spatial position relationship between the fault and the rock layer, the landslide and the residential area is identified; then, combined with the logical rules (such as “the fault should not pass through the complete bedrock”) and the business calculation model (such as “dangerous = stability x rainfall coefficient”) in the geological field, a multi-dimensional operator rule library covering the spatial dimension (topological relationship checking), the logical dimension (business rationality verification) and the business dimension (index quantization calculation) is constructed; the rule library is divided into a plurality of operator rule groups according to the dimension, for example, the spatial topological rule group includes the “distance threshold checking”, the “direction relationship verification” and other templates, the logical constraint rule group includes the “rock matching checking”, the “parameter boundary checking” and other templates, the business calculation rule group includes the “dangerous grading formula”, the “loss estimation model” and other templates, each group of rules is associated across dimensions through a unique identifier, and finally a structured rule set supporting dynamic quality inspection reasoning is formed.

[0061] Step S220, obtaining the geological data to be checked and the quality inspection scene information of the geological data to be checked; performing semantic analysis processing on the quality inspection scene information to obtain the quality inspection key features.

[0062] In this step, first, the geological data to be checked (such as geological layers, monitoring sensor data, field investigation records, etc.) is obtained through a data interface or file import method, and its corresponding quality inspection scene information (such as "post-earthquake landslide disaster emergency assessment" or "mineral exploitation area stability routine inspection") is obtained. Then, natural language processing (NLP) technology is used to perform semantic analysis on the scene information, a pre-trained model based on BERT is used to perform word segmentation and entity recognition on the quality inspection scene information, and relationship extraction is performed in combination with a geological domain knowledge graph to extract structured quality inspection key features, such as {disaster type: mudslide, time threshold: 24h, spatial range: mountainous area}. Key features are extracted through word segmentation, entity recognition, and relationship extraction. For example, "heavy rain causes mountainous mudslide risk" can be parsed into time features ("24 hours after heavy rain"), spatial features ("regions with slope > 30° in mountainous areas"), and business objectives ("mudslide danger classification"), and finally a structured set of quality inspection key features is formed, such as {disaster type: mudslide, time threshold: 24h, spatial range: mountainous area, evaluation index: danger}.

[0063] In an optional embodiment, the above-mentioned obtaining geological data to be checked can further include the following steps: obtaining original geological data; identifying and removing redundant information in the original geological data to obtain cleaned geological data; adjusting the attribute table structure of the cleaned geological data to a pre-set standard result to obtain the geological data to be checked.

[0064] Specifically, considering the non-standardization of actual geological data, such as the fact that layer names may carry administrative division information, self-defined identifiers, and other inconsistencies, first, these original data need to be standardized and preprocessed. This process includes renaming each layer name according to the regulations to ensure that it conforms to the standard format, such as removing unnecessary prefixes or suffixes, and using standardized abbreviations and codes to represent specific geographic areas or feature types. In addition, the attribute table structure within each layer needs to be checked and standardized to confirm that the number of fields, names, types, and lengths all meet the standard requirements, and that all mandatory fields have been correctly filled out. Through this series of data cleaning and standardization operations, it can be ensured that the subsequent quality inspection process based on operator matching can be smoothly executed, thereby improving the accuracy and reliability of the entire geological disaster risk assessment work. This step is the basis for efficient and automated quality inspection and provides structured and consistent data support for multi-semantics relationship analysis.

[0065] Step S230, retrieving a multi-dimensional operator rule library based on the quality inspection scene information, and determining a plurality of target operator rule groups based on the retrieval results.

[0066] Step S240, determining a target operator rule template in the target operator rule group according to the quality inspection key feature; instantiating each target operator rule template to generate a corresponding target quality inspection operator, and performing modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script.

[0067] In the above steps S230 to S240, the applicable operator group is determined according to the specific quality inspection requirement item. For example, if the quality inspection requirement involves data integrity checking, the corresponding "integrity checking" operator group will be selected; if the requirement is for attribute value domain validity or consistency checking across layers, the "value domain specification checking" or "cross-layer consistency checking" operator group will be selected. Next, the specific operator entries in the group are further filtered according to the specific content of the quality inspection requirement. For example, when checking the area field of the slope unit surface layer, a specific operator for detecting whether the numerical field is a positive number within a reasonable range will be selected. Subsequently, based on the selected operator and its parameter requirements, the instantiation operation of the operator is performed. This process involves converting the general operator template into a special rule suitable for the current quality inspection task by inputting specific parameter values. For example, for a requirement that needs to verify that the area field in the slope unit surface layer is greater than zero and does not exceed a certain upper limit value, an operator with these specific threshold parameters will be instantiated, and a clear rule name will be assigned, such as "slope unit area validity checking rule_001". Finally, these instantiated operators will be integrated into the entire quality inspection scheme in the form of rules.

[0068] In order to achieve the above-mentioned objectives, based on the rule type of the target quality inspection operator (such as spatial topology verification, logical condition verification, business index calculation, etc.), the input-output dependency relationship is analyzed, the dependency topology relationship is constructed, and the execution order and data transmission path between operators are clarified; then, according to the strong dependency (such as the blocking of the subsequent operator if the preceding operator is not completed) and weak dependency (such as independent operators that can be executed in parallel) in the topology relationship, the modular assembly strategy is adopted to encapsulate the operators as components with clear interfaces (such as spatial verification components, logical decision components), and the component calling order is automatically arranged through the dependency analysis engine, finally generating a dynamic quality inspection script that supports conditional branching (such as "if distance < 50 meters, then execute high-risk process"), parallel computing (such as running "slope checking" and "rock type verification" simultaneously), and dynamic backtracking (such as automatically triggering data reacquisition when verification fails), ensuring that the geological quality inspection task can flexibly combine operator logic according to complex scene requirements, realizing the full-process automation and intelligentization from data quality inspection to risk assessment.

[0069] Specifically, based on the target dependency topology graph, a directed acyclic graph (DAG) scheduling algorithm is adopted to arrange each target quality inspection operator in a dependency order into an execution flow, supporting conditional branching (such as 'if rainfall > threshold, trigger early warning process') and parallel computing (such as'slope verification' and 'lithology verification' operators are executed simultaneously), and finally generating an executable dynamic quality inspection script. Through this dual logging mechanism, intuitive user feedback is provided, and deep technical support capabilities are ensured, thereby realizing an efficient and reliable automated quality inspection process, greatly improving the accuracy and efficiency of geological disaster risk assessment work. This process not only simplifies the operation process, but also enhances the maintainability and scalability of the system.

[0070] Illustratively, the following is a pseudo code of a dynamic quality inspection script, which takes the slope unit surface layer quality inspection in the geological disaster risk assessment results as an example, covering multiple dimensions such as spatial topology verification, attribute value range verification, and business logic verification:

[0071] / / Define the global configuration of the dynamic quality inspection script

[0072] CONFIG {

[0073] DATA_SOURCE: "PA2 " / / Slope unit surface layer data source

[0074] OUTPUT_LOG: "QualityCheckLog.txt" / / Log output path

[0075] ERROR_THRESHOLD: 5 / / Error threshold, exceeding which triggers backtracking

[0076] PARALLEL_EXECUTION: TRUE / / Whether to enable parallel computing

[0077] }

[0078] / / Define the quality inspection operator component interface

[0079] INTERFACE QualityCheckComponent {

[0080] INPUT: DataLayer / / Input data layer

[0081] OUTPUT: CheckResult / / Check result (Pass / Fail + ErrorMessage)

[0082] EXECUTE(): CheckResult / / Execute quality inspection logic

[0083] Dependencies: List <component> / / Pre-requisite components

[0084] }

[0085] / / Define the CheckResult structure

[0086] STRUCT CheckResult {

[0087] STATUS: Boolean / / Inspection status (True: Pass, False: Fail)

[0088] MESSAGE: String / / Detailed information or error description

[0089] DATA: Object / / Processed data or intermediate results

[0090] }

[0091] / / Define a specific QualityCheckComponent component (example: Area Validity Check)

[0092] COMPONENT AreaValidityCheck IMPLEMENTS QualityCheckComponent {

[0093] INPUT: PA2

[0094] OUTPUT: CheckResult

[0095] DEPENDENCIES: [ ] / / No pre-requisites

[0096] EXECUTE() {

[0097] result = new CheckResult()

[0098] FOR each feature in SlopeUnitLayer {

[0099] IF feature.Area <= 0 {

[0100] result.STATUS = FALSE

[0101] result.MESSAGE = "Area value must be greater than 0 for feature ID: " + feature.ID

[0102] BREAK

[0103] }

[0104] }

[0105] IF result.STATUS!= FALSE {

[0106] result.STATUS = TRUE

[0107] result.MESSAGE = "All area values are valid."

[0108] }

[0109] RETURN result

[0110] }

[0111] }

[0112] / / Define a concrete quality inspection operator component (example: slope value range check)

[0113] COMPONENT SlopeAngleCheck IMPLEMENTS QualityCheckComponent {

[0114] INPUT: PA2

[0115] OUTPUT: CheckResult

[0116] DEPENDENCIES: [ ] / / No pre-requisites, can run in parallel with area check

[0117] EXECUTE() {

[0118] result = new CheckResult()

[0119] FOR each feature in PA2 {

[0120] IF feature.SlopeAngle < 0 OR feature.SlopeAngle > 90 {

[0121] result.STATUS = FALSE

[0122] result.MESSAGE = "Slope angle must be between 0 and90 for feature ID: " + feature.ID

[0123] BREAK

[0124] }

[0125] }

[0126] IF result.STATUS != FALSE {

[0127] result.STATUS = TRUE

[0128] result.MESSAGE = "All slope angle values ​​are valid."

[0129] }

[0130] RETURN result

[0131] }

[0132] }

[0133] / / Define specific quality inspection operator components (example: comprehensive risk level inspection)

[0134] COMPONENT RiskLevelCheck IMPLEMENTS QualityCheckComponent {

[0135] INPUT: PA2

[0136] OUTPUT: CheckResult

[0137] DEPENDENCIES: [AreaValidityCheck] / / Dependency area check complete

[0138] EXECUTE() {

[0139] result = new CheckResult()

[0140] FOR each feature in SlopeUnitLayer {

[0141] IF feature.ComprehensiveRiskLevel IN ["Medium-risk area", "High-risk area", "Extremely high-risk area"] {

[0142] IF feature.IsRiskPreventionZone != "Yes" {

[0143] result.STATUS = FALSE

[0144] result.MESSAGE = "Risk prevention zone must be defined for high risk level, feature ID: " + feature.ID

[0145] BREAK

[0146] }

[0147] }

[0148] }

[0149] IF result.STATUS != FALSE {

[0150] result.STATUS = TRUE

[0151] result.MESSAGE = "Risk level and prevention zone settings are consistent."

[0152] }

[0153] RETURN result

[0154] }

[0155] }

[0156] / / Define the execution flow of the dynamic quality inspection script

[0157] SCRIPT DynamicQualityCheck {

[0158] / / Define the list of operator components

[0159] COMPONENTS: [

[0160] areaCheck = new AreaValidityCheck(),

[0161] slopeCheck = new SlopeAngleCheck(),

[0162] riskCheck = new RiskLevelCheck() ]

[0164] / / Build dependency topology graph (DAG)

[0165] TOPOLOGY: {

[0166] areaCheck -> riskCheck / / Area check is a precondition for risk level check

[0167] slopeCheck -> None / / Slope check has no post-dependencies and can be executed in parallel

[0168] }

[0169] / / Execution flow

[0170] EXECUTE() {

[0171] errorCount = 0

[0172] results = [ ]

[0173] / / Execute operators with no or weak dependencies in parallel

[0174] IF PARALLEL_EXECUTION {

[0175] PARALLEL {

[0176] results.append(areaCheck.EXECUTE())

[0177] results.append(slopeCheck.EXECUTE())

[0178] }

[0179] } ELSE {

[0180] results.append(areaCheck.EXECUTE())

[0181] results.append(slopeCheck.EXECUTE())

[0182] }

[0183] / / Execute strongly dependent operators in dependency order

[0184] IF areaCheck.Result.STATUS == TRUE {

[0185] results.append(riskCheck.EXECUTE())

[0186] } ELSE {

[0187] LOG("Skipping RiskLevelCheck due to AreaValidityCheckfailure.")

[0188] }

[0189] / / Summary of inspection results and dynamic backtracking

[0190] FOR each result in results {

[0191] IF result.STATUS == FALSE {

[0192] errorCount++

[0193] LOG(result.MESSAGE, OUTPUT_LOG)

[0194] }

[0195] }

[0196] / / Conditional branch: If the number of errors exceeds the threshold, trigger backtracking.

[0197] IF errorCount > ERROR_THRESHOLD {

[0198] LOG("Error count exceeds threshold, triggering data re-collection.", OUTPUT_LOG)

[0199] TRIGGER_RECOLLECTION()

[0200] } ELSE {

[0201] LOG("Quality check completed with acceptable errorlevel.", OUTPUT_LOG)

[0202] }

[0203] RETURN results

[0204] }

[0205] / / Dynamic backtracking logic

[0206] TRIGGER_RECOLLECTION() {

[0207] / / Trigger data re-collection logic (example)

[0208] SEND_NOTIFICATION("Data quality issue detected, please re-collect data for PA2 .")

[0209] UPDATE_STATUS("Pending Re-collection")

[0210] }

[0211] }

[0212] / / Main program entry

[0213] MAIN {

[0214] script = new DynamicQualityCheck()

[0215] results = script.EXECUTE()

[0216] FOR each result in results {

[0217] PRINT(result.MESSAGE)

[0218] }

[0219] }

[0220] Step S250, based on the dynamic quality inspection script, the geological data to be inspected is processed, and the quality inspection result is obtained.

[0221] Specifically, in the automated quality inspection execution phase, each rule in the quality inspection scheme is configured as an optional state, allowing users to flexibly select the rules that need to be executed according to specific needs. Users start the quality inspection process by clicking the "Start" button, and the system will perform a comprehensive check on the geological data in the order of the selected rules. The entire quality inspection process achieves full-process logging, ensuring that the status and results of each step are tracked in detail. The log content includes but is not limited to the current rule name being executed, the geological elements involved (such as slope unit surface, debris flow basin surface, etc.), quality inspection progress, encountered errors, warnings, and prompt information. To enhance user experience and transparency, some key information will be displayed in a visual manner on the user interface in real-time, such as the current progress bar, a list of successful or failed tasks, and immediate warnings and prompts. These visual outputs help users understand the quality inspection progress in a timely manner and quickly respond to potential problems. At the same time, more detailed background logs will be generated and stored, mainly for developers and technical support teams. These logs contain all technical details, such as specific error stack traces, system call information, etc., facilitating problem troubleshooting and subsequent optimization. The background logs not only record the success and failure of each step, but also capture various warnings and prompt information that may affect the quality inspection results, ensuring the stability and reliability of the system.

[0222] For example, refer to Figure 3 , which shows the display interface of the self-developed quality inspection software; where the user imports batch quality inspection data by clicking "New Project" or "Open Project" in the interface; and clicks the "Start Inspection" button; the quality inspection software automatically executes the above series of control processes and displays the quality inspection results in detail in the center of the display interface.

[0223] Through the above steps S210 to S250, a multi-semantics relationship geological element inspection method based on operator rules is proposed for the diverse and dynamically changing quality inspection requirements in geological data quality inspection; by constructing an inspection operator rule library supporting complex semantic relationship modeling, and combining with the rule engine to realize flexible configuration and automated execution of the inspection process, the accuracy, efficiency and adaptability of geological element inspection are significantly improved. Moreover, through the instantiation of the pre-defined inspection operator rule library, specific inspection tasks are generated, supporting modular assembly and automated execution of the quality inspection process; compared with the traditional fixed script quality inspection method, this method has higher flexibility and maintainability. Users can quickly configure inspection rule combinations according to different application scenarios, and the system automatically schedules and executes and outputs structured inspection results, effectively reducing errors caused by human intervention and improving the consistency and stability of data quality, thereby effectively solving the problem of low accuracy in geological data quality inspection.

[0224] In some embodiments, the above-mentioned spatial topological relationship based on geological objects, establishing a multi-dimensional operator rule library, can also include the following steps:

[0225] Obtaining the spatial topological relationship of the geological objects, establishing a spatial dimension model including multiple groups of spatial operator rules; based on a preset first knowledge base, establishing a logic rule model including multiple groups of logic operator rules; based on preset scene information, establishing a business semantic model including multiple groups of semantic operator rules; performing fusion processing on the spatial dimension model, the logic rule model and the business semantic model, and establishing a multi-dimensional operator rule library.

[0226] The traditional geological element checking method usually only focuses on the compliance judgment of a single layer or attribute field, and it is difficult to cope with the complex spatial-semantic dependence relationship between geological elements. In order to improve this problem, in the embodiments of the present application, a spatial-semantic multi-dimensional relationship modeling mechanism is introduced into the field of geological data quality checking, not only considering the attribute characteristics of a single geological object itself, but also deeply analyzing the spatial, logical and business semantic relationship between it and other geological elements. For example, when checking and evaluating the risk index, not only the risk value of a single slope unit, but also the influence of factors such as the surrounding environment (such as population density, building / property distribution) are considered, so as to realize more comprehensive and scientific risk evaluation and quality control.

[0227] Among them, the spatial-semantic multi-dimensional relationship modeling mechanism mainly includes the following key stages: spatial relationship modeling (spatial dimension); semantic relationship modeling (logic and business dimension); multi-dimensional relationship fusion and reasoning; quality evaluation.

[0228] The detailed implementation process is described as follows:

[0229] In the process of constructing the multi-dimensional operator rule library, firstly, in the spatial relationship modeling (spatial dimension) stage, for spatial topological relationship identification, use GIS spatial analysis tools or spatial database functions to judge the topological relationship between geometric objects: adjacent, containing, intersecting, adjacent; for spatial distance and direction modeling, calculate the Euclidean distance, path distance, azimuth, etc. between different geological objects, which can be used to quantify the influence intensity of a certain geological disaster body on the surrounding buildings; for spatial buffer analysis, establish a buffer zone for high-risk areas (such as landslide sources) to identify sensitive targets (such as residential areas, roads) within the influence range; for spatial network analysis, construct a spatial connection network of geological objects, which is used to simulate the propagation path of geological events (such as earthquake waves, debris flow diffusion). Through the geological object ID as the primary key, the spatial operator rule (such as 'the distance between landslide and residential area <100 meters') and the semantic operator rule (such as 'danger level = distance weight × 0.6 + slope weight × 0.4') are mapped one-to-many, and a spatial-semantic joint index table is established, so as to realize efficient retrieval and calling of multi-dimensional rules.

[0230] In the semantic relationship modeling phase, a first knowledge base is constructed based on expert knowledge, logical rule modeling is performed in combination with the first knowledge base, and a reasonable logical relationship between geological elements is defined using an ontology or a rule engine; for example: "a fault should not pass through a complete bedrock", "a landslide should occur on a soft rock layer", and "a collapse should be located in a steep slope area"; formalized languages such as SQL-like rules can be used for expression; at the same time, business semantic modeling is performed, and a business logic model is established in combination with an actual application scenario; wherein, danger = f (slope stability, rainfall, human activity); potential loss = f (population density, building density, economic value). For unstructured text descriptions (such as field records), NLP technology can be used to extract semantic features and perform matching analysis to achieve semantic similarity matching.

[0231] Next, in the multi-dimensional relationship fusion and reasoning phase, a reasoning mechanism based on the Drools rule engine is used to convert spatial relationship rules (such as 'landslide distance from residential area < 100 meters') and semantic rules (such as 'danger level = distance weight x 0.6 + slope weight x 0.4') into an executable rule set, and automatically deduce the quality inspection conclusion through forward reasoning (Forward Chaining). Specifically, by sharing entity identifiers (such as geological object unique IDs), spatial operator rules in the spatial dimension model (such as "landslide distance from residential area < 100 meters" and "fault strike and stratum dip angle > 30°") are deeply associated with semantic operator rules in the business semantic model (such as "danger level = distance weight x 0.6 + slope weight x 0.4" and "repair cost = affected area x unit cost"), a mapping relationship chain from spatial features to business semantics is established; at the same time, logical operator rules in the logical rule model (such as "if a fault passes through a bedrock, then stop stability calculation" and "if rainfall > threshold, then trigger debris flow warning") are embedded as condition trigger nodes in the mapping chain, forming a dynamic reasoning path from spatial feature verification to logical condition triggering, and then to business semantic calculation. For example, when the spatial operator detects that "the landslide distance from the residential area is 80 meters", the logical rule "distance < 100 meters" is triggered, and the semantic operator "danger level = high" is activated. Finally, by integrating these associated rules and trigger chains, a multi-dimensional operator rule library covering spatial constraints, logical verification, and business quantification is generated, realizing an automatic closed-loop reasoning from raw geological data to risk assessment conclusions.

[0232] Through the above embodiments, the spatial-semantic multi-dimensional relationship modeling mechanism is introduced, the complex relationship network between geological elements is constructed, and multi-dimensional fusion analysis is performed, thereby breaking through the limitation of traditional single attribute inspection, and the quality and risk condition of geological data can be more scientifically and comprehensively evaluated, which has a wide application prospect, especially in the field of geological disaster warning. Moreover, by introducing the semantic analysis capability and the rule engine technology, intelligent identification and consistency checking of the complex relationship between geological elements are realized, and the automation level, accuracy and flexibility of the quality inspection process are significantly improved. This method based on the Drools rule engine not only provides strong reasoning capability, but also supports flexible configuration and extension, so that the quality inspection process is more adaptable to the diversified geological scene requirements.

[0233] In some embodiments, the above instantiation of each target operator rule template generates a corresponding target quality inspection operator, and can further include the following steps:

[0234] Based on the operator rule type of the target operator rule template, the parameter type is obtained;

[0235] In the case of static type of the parameter type, a static assignment parameter is generated based on the target operator rule template, the static assignment parameter is injected into the target operator rule template for instantiation, and the target quality inspection operator is generated;

[0236] In the case of dynamic type of the parameter type, a dynamic parameter is generated by parsing the input parameter instruction, or the dynamic parameter is generated by calling a preset second knowledge base; the dynamic parameter is injected into the target operator rule template for instantiation, and the target quality inspection operator is generated.

[0237] Among them, one of the examples of the above target algorithm rule template is shown in Table 1:

[0238] Table 1

[0239]

[0240] In the above Table 1, the parameter type includes static or dynamic type; N / A means not applicable; SlopeAreaValidityTemplate means slope validity template; rainfall_threshold means rainfall threshold; min_area means minimum area threshold; CheckResult means quality inspection result. Among them, the rule code is used to identify the source of the rule, for example, from the general quality inspection rule of the quality inspection technical requirement; the template name is used to identify the purpose of the template, for example, for searching; the static parameter in the parameter field: the fixed value is 0, indicating that the "area" field must be greater than 0, and the parameter type is static, meaning that the parameter value is fixed when the template is defined, and does not depend on external data. The corresponding parameter value is used to provide an example value of the static parameter; if it is dynamic, it will be obtained from the outside; the dynamic parameter in the parameter field: for example, the threshold value (such as the threshold field of the rule) that changes according to real-time data, and the parameter type is dynamic, meaning that the parameter value needs to be parsed at runtime or obtained from a second knowledge base. The corresponding parameter value is used to provide a dynamic parameter; for example, real-time rainfall is obtained from a meteorological data interface; the dependency relationship is used to ensure that the data is cleaned before the template is instantiated (such as the general quality inspection rule: requiring that the submitted data file can be normally opened to ensure the integrity of the data file); the output field, for example, the output state (pass / fail) and the message.

[0241] Specifically, first, the metadata structure of the target operator rule template is parsed, the defined parameter type field (such as param_type: "static" or param_type: "dynamic") is identified, and the dynamic requirement of the parameter is further inferred in combination with the business semantic label of the rule template (such as "spatial distance threshold", "rainfall weighting coefficient"); for example, if the rule template contains "slope threshold" and is marked as static type, the fixed value (such as 30°) is directly read from the template configuration; if the rule template contains "real-time rainfall" and is marked as dynamic type, the real-time value needs to be obtained through an external data interface or knowledge base, and finally an explicit mapping relationship between the parameter type and the value obtaining method is formed.

[0242] When the parameter type is determined to be static, the system directly extracts the fixed value (such as "maximum allowed slope = 35°", "fault buffer distance = 200 meters") from the preset parameter pool of the target operator rule template, or generates a static assignment parameter through the default value configuration in the template; then these parameters are injected into the parameter placeholder of the rule template in the form of key-value pairs (such as {"slope_threshold": 35}), the instantiation of the operator rule is completed, and finally the target quality inspection operator (such as "slope verification operator: if the actual slope > 35°, trigger a warning") that can be independently executed is generated, which does not need external data interaction in the subsequent quality inspection process.

[0243] If the parameter type is dynamic, the system first checks whether there is a user input parameter instruction (such as {"rainfall": "real-time monitoring value"} in the API request), and if so, extracts the dynamic value through the instruction parser; if no external instruction is received, the current rainfall data is obtained through the RESTful API interface call to the preset second knowledge base (such as the meteorological bureau data service), or the geological monitoring data (such as displacement rate, stress change) is obtained through the sensor Internet of Things platform; then the dynamic parameters (such as {"rainfall_threshold": 85}) are injected into the dynamic parameter slots of the target operator rule template, generating target quality inspection operators (such as "rainfall warning operator: if 85 mm > threshold 60 mm, then upgrade risk level") that depend on real-time data, ensuring that the operator can adaptively adjust the quality inspection logic according to environmental changes.

[0244] Through the above embodiments, by distinguishing static / dynamic parameter types, fixed thresholds and real-time data can be automatically adapted, avoiding rule invalidation problems caused by hard-coded parameters, thereby effectively reducing the dependence of traditional quality inspection systems on static data and improving decision-making accuracy under complex geological conditions.

[0245] In some embodiments, the above modular assembly process of each target quality inspection operator to generate a dynamic quality inspection script can also include the following steps:

[0246] Based on the operator rule type, the dependent topology relationship of each target quality inspection operator is generated; and according to the dependent topology relationship, the modular assembly process of each target quality inspection operator is performed to generate a dynamic quality inspection script.

[0247] Among them, based on the rule type of the target quality inspection operator (such as spatial topology verification, logic condition verification, business index calculation, etc.), the input-output dependency relationship is analyzed, the dependent topology relationship is constructed, and the execution order and data transmission path among the operators are clarified; then, according to the strong dependency (such as the blocking of the subsequent operator if the preceding operator is not completed) and the weak dependency (such as independent operators that can be executed in parallel) in the topology relationship, a modular assembly strategy is used to encapsulate the operators into components with clear interfaces (such as spatial verification components, logic decision components), and the component call order is automatically arranged through the dependency analysis engine, finally generating a dynamic quality inspection script supporting conditional branching (such as "if distance < 50 meters, execute high-risk process"), parallel computing (such as simultaneously running "slope verification" and "rock property verification"), and dynamic backtracking (such as automatically triggering data reacquisition when verification fails), ensuring that the geological quality inspection task can flexibly combine operator logic according to complex scene requirements, realizing full-process automation and intelligentization from data quality inspection to risk assessment.

[0248] In some embodiments, the above-mentioned operator type based on the target quality inspection algorithm, the generation of each target quality inspection algorithm dependency topology relationship, can also include the following steps:

[0249] Define a dependency triple for each target quality inspection algorithm, and construct an initial dependency topology graph based on the dependency triple; based on the operator type, determine the dynamic operator subset and the independent operator subset in the initial dependency topology graph, and adjust the initial dependency topology graph based on the dynamic operator subset and the independent operator subset to generate a target dependency topology graph; based on the target dependency topology graph, generate a dependency topology relationship.

[0250] Specifically, first, define a dependency triple (<operator ID, input dependency set, output identifier>) for each target quality inspection algorithm, where the input dependency set specifies the pre-data or intermediate results required for the algorithm to execute, and the output identifier marks the unique data identifier generated by the algorithm; based on the dependency triples of all algorithms, an initial dependency topology graph is constructed through graph theory algorithm, where the nodes represent the operators and the directed edges represent the data flow (such as the edge from "coordinate extraction algorithm" to "distance calculation algorithm" marked "output coordinates").

[0251] Next, according to the operator type (static / dynamic) and the independent characteristics (such as the initial data collection algorithm), the topology graph is divided into a dynamic operator subset (operators that depend on real-time data or external interfaces, such as "rainfall monitoring algorithm") and an independent operator subset (root node operators that execute independently, such as "geological layer loading algorithm"), and the initial topology graph is optimized through dynamic edge weight adjustment (such as adding "data ready" trigger conditions for dynamic operators) and redundant edge pruning (such as removing false dependencies between independent operators); the final target dependency topology graph clearly presents the execution order between operators (such as "lithology classification algorithm" must be completed before "stability coefficient algorithm"), parallel relationship (such as "slope verification algorithm" and "fault detection algorithm" can run simultaneously) and dynamic trigger logic (such as "when real-time rainfall > threshold, activate the debris flow warning branch"), thereby providing precise dependency control basis for subsequent modular assembly and execution scheduling of dynamic quality inspection scripts.

[0252] In some embodiments, after obtaining the quality inspection result, the above-mentioned geological data quality inspection method can also include the following steps:

[0253] Store the quality inspection result as a structured database; query the structured database to generate geological disaster risk prevention information and geological element error reports, and generate a visual result based on the geological disaster risk prevention information and the geological element error reports.

[0254] In the final risk assessment report generation stage, the quality inspection results can be output in a structured database format, ensuring the comprehensiveness and traceability of the information. The output content is divided into two main parts:

[0255] First, the geological disaster risk prevention zone information summary covers data statistics and analysis of multiple key dimensions. This includes: total number, number of key prevention zones: the number of key prevention zones and their detailed information; number and distribution of key and general prevention zones; number of easy-to-happen zones: the number of high, medium, low, and not easy-to-happen zones; and the number of danger zones under various rain conditions: the number of different danger levels (such as low, medium, high, and extremely high danger zones) under heavy rain, heavy rain, heavy rain, and extremely heavy rain conditions.

[0256] Secondly, for the output of geological element error content, a detailed error report is also provided, which includes: graphics, specific graphic elements related to errors (such as slope unit surfaces and debris flow basin surfaces); attributes, attribute field information related to errors; corresponding rules, specific rule names and descriptions that cause errors; error content, detailed error descriptions explaining the specific reasons for non-compliance with standards; and improvement suggestions, specific improvement suggestions based on error content to help users quickly locate problems and make corrections. These data will be systematically stored in the database for subsequent query and analysis. More specifically, the error report output includes an error opinion table and a point-line-surface element table with errors; the error opinion table stores all error information found during the quality inspection process, with each record corresponding to a specific error item. The table contains basic error information and related rules; the point-line-surface element table stores geographic elements (points, lines, and surfaces) found with errors during the quality inspection. The attributes of each element include its specific error problems. At the same time, the generated risk assessment report not only provides a scientific basis for geological disaster management decisions, but also improves the quality and reliability of the data through detailed error reports. This process not only simplifies the data analysis process, but also lays a solid foundation for subsequent risk management and disaster prevention work. In addition, this structured output method supports further data mining and visualization, enhancing the transparency and usability of the information.

[0257] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0258] The embodiment also provides a geological data quality inspection device. The device is used for implementing the above-mentioned embodiment and preferred implementation, and details are not repeated. As used below, the terms "module", "unit", "sub-unit", and the like can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0259] Figure 4 FIG. 1 is a structural block diagram of a geological data quality inspection device according to an embodiment of the present application. As shown in FIG. 1, the device comprises a library building module 10, an acquisition module 20, a retrieval module 30, an instantiation module 40, and a quality inspection module 50. Figure 4

[0260] The library building module 10 is configured to build a multi-dimensional operator rule library based on the spatial topological relationship of geological objects. The multi-dimensional operator rule library comprises a plurality of operator rule groups of different dimensions. The acquisition module 20 is configured to acquire geological data to be inspected and quality inspection scene information for the geological data to be inspected. The quality inspection scene information is subjected to semantic analysis processing to obtain quality inspection key features. The retrieval module 30 is configured to retrieve the multi-dimensional operator rule library based on the quality inspection scene information, and determine a plurality of target operator rule groups based on the retrieval result. The instantiation module 40 is configured to determine target operator rule templates in the target operator rule groups according to the quality inspection key features, instantiate each target operator rule template to generate a corresponding target quality inspection operator, and perform modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script. The quality inspection module 50 is configured to perform quality inspection processing on the geological data to be inspected based on the dynamic quality inspection script to obtain a quality inspection result.

[0261] In some embodiments, the library building module 10 is further configured to acquire the spatial topological relationship of geological objects, build a spatial dimension model comprising a plurality of groups of spatial operator rules, build a logic rule model comprising a plurality of groups of logic operator rules based on a preset first knowledge base, build a business semantic model comprising a plurality of groups of semantic operator rules based on preset scene information, and perform fusion processing on the spatial dimension model, the logic rule model, and the business semantic model to build the multi-dimensional operator rule library.

[0262] In some embodiments, the quality inspection module 50 is further configured to acquire a shared entity identifier, associate the spatial quality inspection operator and the semantic quality inspection operator based on the shared entity identifier, build a mapping relationship from spatial features to business semantics, embed the logic quality inspection operator as a trigger condition in a spatial semantic relationship chain to generate a dynamic reasoning path, and perform quality inspection processing on the geological data to be inspected based on the dynamic reasoning path to obtain the quality inspection result.

[0263] ​In some embodiments, the instantiation module 40 is further configured to obtain a parameter type based on the operator rule type of the target operator rule template; in a case where the parameter type is a static type, generate a static assignment parameter based on the target operator rule template, inject the static assignment parameter into the target operator rule template to generate the target quality inspection operator; in a case where the parameter type is a dynamic type, generate a dynamic parameter by parsing an input parameter instruction or calling a preset second knowledge base; and inject the dynamic parameter into the target operator rule template to generate the target quality inspection operator.

[0264] In some embodiments, the instantiation module 40 is further configured to generate a dependency topology relationship of each target quality inspection operator based on the operator rule type; and perform modular assembly processing on each target quality inspection operator according to the dependency topology relationship to generate a dynamic quality inspection script.

[0265] In some embodiments, the instantiation module 40 is further configured to define a dependency triple for each target quality inspection operator, and construct an initial dependency topology graph based on the dependency triple; determine a dynamic operator subset and an independent operator subset in the initial dependency topology graph based on the operator type, and adjust the initial dependency topology graph based on the dynamic operator subset and the independent operator subset to generate a target dependency topology graph; and generate the dependency topology relationship based on the target dependency topology graph.

[0266] In some embodiments, the geological data quality inspection apparatus further comprises a visualization module; the visualization module is configured to store the quality inspection result as a structured database, query the structured database to generate geological disaster risk prevention information and a geological element error report, and generate a visualization result according to the geological disaster risk prevention information and the geological element error report.

[0267] In some embodiments, the obtaining module 20 is further configured to obtain original geological data, identify and remove redundant information in the original geological data to obtain cleaned geological data, and adjust a property table structure of the cleaned geological data to a preset standard result to obtain the to-be-inspected geological data.

[0268] It should be noted that each of the above modules can be a functional module or a program module, and can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor; or each of the above modules can be located in different processors in any combination.

[0269] The embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0270] Optionally, the electronic device described above can further include a transmission device connected with the processor and an input and output device connected with the processor.

[0271] Optionally, in the embodiment, the processor can be configured to execute the following steps by means of a computer program:

[0272] S1, based on the spatial topological relationship of the geological object, a multi-dimensional operator rule library is established; the multi-dimensional operator rule library includes a plurality of operator rule groups of different dimensions.

[0273] S2, obtain the geological data to be checked and the quality inspection scene information of the geological data to be checked; perform semantic analysis processing on the quality inspection scene information to obtain the quality inspection key features.

[0274] S3, search the multi-dimensional operator rule library based on the quality inspection scene information, and determine a plurality of target operator rule groups based on the search result.

[0275] S4, determine the target operator rule templates in the target operator rule groups according to the quality inspection key features; instantiate each target operator rule template to generate a corresponding target quality inspection operator, and perform modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script.

[0276] S5, perform quality inspection processing on the geological data to be checked based on the dynamic quality inspection script to obtain a quality inspection result.

[0277] It should be noted that the specific examples in the embodiment can refer to the examples described in the above embodiments and optional implementation manners, and the embodiment will not be described here.

[0278] In addition, in combination with the geological data quality inspection method in the above embodiments, the application embodiment can provide a storage medium for implementation. The storage medium has a computer program stored thereon; the computer program is executed by the processor to implement any one of the geological data quality inspection methods in the above embodiments.

[0279] Those skilled in the art should understand that the technical features of the above-described embodiments can be combined in any manner, and in order to make the description concise, all possible combinations of the technical features in the above-described embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0280] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific and detailed manner, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.< / component>

Claims

1. A geological data quality inspection method, characterized in that, The method includes: Based on the spatial topological relationships of geological objects, a multidimensional operator rule base is established, including: Obtain the spatial topological relationships of the geological objects and establish a spatial dimension model including multiple sets of indicator spatial operator rules; Based on a pre-defined first knowledge base, a logical rule model containing multiple sets of instruction logic operator rules is established; based on pre-defined scenario information, a business semantic model containing multiple sets of instruction semantic operator rules is established. The spatial dimension model, the logical rule model, and the business semantic model are fused together to establish the multi-dimensional operator rule library; the multi-dimensional operator rule library includes multiple operator rule groups of different dimensions; Obtain the geological data to be inspected, and the quality inspection scenario information for the geological data to be inspected; perform semantic parsing processing on the quality inspection scenario information to obtain key quality inspection features; Based on the quality inspection scenario information, the multidimensional operator rule base is retrieved, and multiple target operator rule groups are determined based on the retrieval results. Based on the key quality inspection features, the target operator rule templates in the target operator rule group are determined; each target operator rule template is instantiated to generate a corresponding target quality inspection operator, and each target quality inspection operator is modularly assembled to generate a dynamic quality inspection script; The geological data to be inspected is processed based on the dynamic quality inspection script to obtain the quality inspection results.

2. The geological data quality inspection method of claim 1, wherein, The dynamic quality inspection script is generated by assembling the spatial quality inspection operator in the spatial dimension model, the semantic quality inspection operator in the business semantic model, and the logical quality inspection operator in the logical rule model. The quality inspection process based on the dynamic quality inspection script to perform quality inspection on the geological data to be inspected, and to obtain the quality inspection results, includes: Obtain the shared entity identifier; Based on the shared entity identifier, the spatial quality inspection operator is associated with the semantic quality inspection operator to establish a mapping relationship from spatial features to business semantics, and the logical quality inspection operator is embedded as a trigger condition into the spatial semantic relationship chain to generate a dynamic reasoning path; Based on the dynamic reasoning path, the geological data to be inspected is subjected to quality inspection processing to obtain the quality inspection results.

3. The method of claim 1, wherein, The instantiation of each target operator rule template to generate a corresponding target quality inspection operator includes: Based on the operator rule type of the target operator rule template, obtain the parameter type; When the parameter type is static, a static assignment parameter is generated based on the target operator rule template, and the static assignment parameter is injected into the target operator rule template for instantiation to generate the target quality inspection operator. When the parameter type is dynamic, the input parameter instruction is parsed to generate dynamic parameters, or the dynamic parameters are generated by calling a preset second knowledge base; the dynamic parameters are injected into the target operator rule template for instantiation to generate the target quality inspection operator.

4. The method of claim 3, wherein, The modular assembly process of each of the target quality inspection operators to generate a dynamic quality inspection script includes: Based on the operator rule type, the dependency topology of each target quality inspection operator is generated; the target quality inspection operators are modularly assembled according to the dependency topology to generate the dynamic quality inspection script.

5. The method of claim 4, wherein, The generating of the dependency topological relationship of each target quality inspection operator based on the operator rule type comprises: defining a dependency triple for each target quality inspection operator, and constructing an initial dependency topological graph based on the dependency triple; determining a dynamic operator subset and a no-dependency operator subset in the initial dependency topological graph based on the operator type, and adjusting the initial dependency topological graph based on the dynamic operator subset and the no-dependency operator subset to generate a target dependency topological graph; generating the dependency topological relationship based on the target dependency topological graph.

6. The method of claim 1, wherein, After the quality inspection result is obtained, the method further comprises: storing the quality inspection result as a structured database; querying the structured database to generate geological disaster risk prevention information and geological element error reports, and generating a visual result according to the geological disaster risk prevention information and the geological element error reports.

7. The method of claim 1 to 6, wherein, The obtaining of the to-be-inspected geological data comprises: obtaining original geological data; identifying and removing redundant information in the original geological data to obtain cleaned geological data; and adjusting the attribute table structure of the cleaned geological data to a preset standard result to obtain the to-be-inspected geological data.

8. A geological data quality inspection device, characterized by, comprises: a database building module configured to build a multi-dimensional operator rule database based on the spatial topological relationship of a geological object, comprising: obtaining the spatial topological relationship of the geological object, and building a spatial dimension model comprising multiple groups of indicative spatial operator rules; building a logic rule model comprising multiple groups of indicative logic operator rules based on a preset first knowledge base; building a business semantic model comprising multiple groups of indicative semantic operator rules based on preset scene information; and performing fusion processing on the spatial dimension model, the logic rule model, and the business semantic model to build the multi-dimensional operator rule database; the multi-dimensional operator rule database comprises multiple operator rule groups of different dimensions; an acquisition module configured to acquire to-be-inspected geological data and quality inspection scene information of the to-be-inspected geological data, and perform semantic analysis processing on the quality inspection scene information to obtain quality inspection key features; a retrieval module configured to retrieve the multi-dimensional operator rule database based on the quality inspection scene information, and determine multiple target operator rule groups based on a retrieval result; an instantiation module configured to determine target operator rule templates in the target operator rule groups according to the quality inspection key features, instantiate each target operator rule template to generate corresponding target quality inspection operators, and perform modular assembly processing on each target quality inspection operator to generate a dynamic quality inspection script; a quality inspection module configured to perform quality inspection processing on the to-be-inspected geological data based on the dynamic quality inspection script to obtain a quality inspection result.

9. A storage medium, characterized by The storage medium stores a computer program, wherein the computer program is configured to execute the geological data quality inspection method of any one of claims 1 to 7 when running.

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