Intelligent analysis system for geological exploration project plan management

Abstract

The invention discloses a geological exploration project plan management intelligent analysis system, and relates to the technical field of geological exploration, and the method comprises the steps: collecting and integrating data, forming a unified data set, and obtaining geological data; performing risk identification and assessment according to the geological data to obtain a risk assessment result; project planning optimization is carried out according to the risk assessment result, and an optimized project planning scheme is obtained; performing resource scheduling and task allocation according to the optimized project planning scheme to obtain a scheduling scheme; performing project execution monitoring according to the scheduling scheme to obtain execution monitoring data; performing cost control and budget analysis according to the execution monitoring data to obtain an analysis result; performing fund flow optimization according to the analysis result to obtain an optimization result; and performing project performance evaluation according to an optimization result in combination with real-time data of project execution. According to the invention, the accuracy of risk prediction and the adaptability of the model are improved, the storage and transmission speed of data is optimized, and the transparency and efficiency of project management are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to an intelligent analysis system for geological exploration project planning management. Background Art

[0002] As geological exploration projects continue to expand in scale and complexity, traditional project management methods have gradually exposed many drawbacks, particularly in resource allocation, task scheduling, risk control, and information transparency. To address these issues, a number of modern technologies have begun to be introduced into geological exploration project management. Among them, deep neural networks (DNNs) and blockchain technology, combined with the application of smart contracts, offer new solutions for project management.

[0003] In geological exploration project management, deep neural networks (DNNs) are used for risk assessment and decision-making optimization. By inputting multi-dimensional data such as geology, climate, and historical disasters, DNNs can learn and predict potential risks during project implementation, helping decision-makers understand project risk status in real time and make more accurate decisions. However, existing applications of DNNs in geological exploration project management still face challenges, primarily in handling complex time series data, effectively integrating prediction results with project execution decision variables, and dynamically adjusting models to address changing environmental factors during project execution.

[0004] At the same time, the combination of blockchain technology and smart contracts has been proposed to ensure transparency, traceability, and automation in project execution. Blockchain can record all critical project data (such as resource allocation, task progress, and risk assessments) in a decentralized manner, ensuring immutability and transparency. Smart contracts can automatically trigger various actions during project execution, such as resource allocation, task scheduling, and risk response, significantly improving project management efficiency. However, existing blockchain and smart contract applications still face technical bottlenecks, particularly in the real-time processing and storage of large amounts of project execution data, as well as the ability to dynamically adjust contract execution conditions based on the project's real-time dynamics.

[0005] 1. In existing technologies, the training effect of deep neural networks is often limited by the quality and quantity of data under the uncertainty and high dimensionality of data collection, and the model output cannot be effectively combined with the actual decision variables in project execution (such as resource allocation, task scheduling, etc.).

[0006] 2. Existing blockchain technologies are limited in performance and speed when processing large-scale, real-time data. This limits the speed and flexibility of the data on the blockchain. Automated execution of smart contracts often lacks the flexibility to respond to unexpected changes, especially when adjusting risk response measures and reallocating resources. Traditional smart contracts struggle to make real-time adjustments, resulting in a lack of flexibility and intelligence in the execution process. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides an intelligent analysis system for geological exploration project planning management to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] In a first aspect, an embodiment of the present invention provides a geological exploration project plan management intelligent analysis system, comprising the following steps:

[0010] S1. Data collection and integration to form a unified data set and obtain geological data;

[0011] S2. Identify and assess risks based on geological data to obtain risk assessment results;

[0012] S3. Optimize the project planning based on the risk assessment results to obtain an optimized project planning scheme;

[0013] S4. Perform resource scheduling and task allocation according to the optimized project planning scheme to obtain a scheduling plan;

[0014] S5. Monitor the project execution according to the scheduling plan and obtain execution monitoring data;

[0015] S6. Conduct cost control and budget analysis based on execution monitoring data to obtain analysis results;

[0016] S7. Optimize the cash flow based on the analysis results to obtain the optimization results;

[0017] S8. Conduct project performance evaluation based on the optimization results and real-time data of project execution.

[0018] To further optimize this technical solution, the risk identification and assessment in S2 includes:

[0019] By analyzing geological data, possible abnormal patterns and potential danger areas are extracted, and deep neural network models are used for data training to automatically identify and predict the possibility of geological risks.

[0020] Further optimizing this technical solution, the deep neural network includes:

[0021] Time series data processing:

[0022] ;

[0023] in, : The input feature vector at time t, ,in Input data of different dimensions, including geological data, climate change data, and historical disaster records;

[0024] : The hidden layer output of the LSTM network represents the potential risk status at the current moment;

[0025] : The hidden layer output of the previous moment;

[0026] Risk prediction output:

[0027] ;

[0028] in, : The risk prediction value output by the model indicates the risk level, ranging from 0 to 1;

[0029] : sigmoid activation function; : Weight, set according to actual situation; : Bias item, set according to actual situation;

[0030] Adaptive threshold adjustment:

[0031] ;

[0032] in, : Adjusted risk assessment results; : Risk thresholds adaptively adjusted based on historical data;

[0033] Overall risk assessment:

[0034] ;

[0035] in, : The overall risk assessment results of the project; : The total time of the evaluation.

[0036] To further optimize this technical solution, the project planning optimization in S3 includes:

[0037] Through intelligent optimization algorithms, risk assessment results are combined with existing project plans to optimize project planning schemes.

[0038] To further optimize this technical solution, the resource scheduling and task allocation in S4 include:

[0039] According to the optimized project planning scheme, a linear programming model is used for resource scheduling, various resources are reasonably allocated, and tasks are assigned through intelligent algorithms to maximize the efficiency of task execution.

[0040] To further optimize this technical solution, the project execution monitoring in S5 includes:

[0041] Through the real-time monitoring system, field data is collected and compared with the project plan to identify deviations and problems in project execution.

[0042] To further optimize this technical solution, the cost control and budget analysis in S6 includes:

[0043] Based on execution monitoring data, use regression analysis and predictive models to estimate the real-time cost of the project and perform variance analysis against the budget.

[0044] To further optimize this technical solution, the cash flow optimization in S7 includes:

[0045] Combining blockchain technology and smart contracts, transparent management of project funds is achieved, ensuring clear and traceable fund flows, and using smart contracts to automatically execute fund disbursements.

[0046] Further optimizing this technical solution, the blockchain technology and smart contracts include:

[0047] Smart contract automated execution conditions:

[0048] Set the composite target execution function Indicates the conditions for the automated execution of smart contracts;

[0049] ;

[0050] in, : Decision variables in project planning, including resource allocation, task progress, and risk response;

[0051] : The execution condition of resource allocation, indicating whether resources are allocated according to the optimization results;

[0052] : The execution condition of the task progress, indicating whether the task is completed according to the scheduled progress;

[0053] : The execution conditions of risk response, indicating whether the project takes the predetermined measures when facing risks;

[0054] : Weight coefficient, set according to actual situation;

[0055] Data recording and verification on the blockchain:

[0056] Define a record function , representing data recorded through blockchain;

[0057] ;

[0058] in, Represents the project execution record submitted to the blockchain network, including decision variables , target execution function , risk assessment results ;

[0059] Smart contracts and dynamic risk adjustment:

[0060] ;

[0061] in, :Adjusted execution conditions,

[0062] : Input decision variables Real-time calculation of risk assessment results;

[0063] : Adjustment coefficient, set according to actual situation;

[0064] The trigger mechanism of the contract on the blockchain:

[0065] ;

[0066] in, The contract is triggered when the task progress exceeds the preset threshold.

[0067] To further optimize this technical solution, the functional modules of the geological exploration project planning management intelligent analysis system include:

[0068] Data collection and processing module; risk assessment module; decision optimization and intelligent scheduling module; execution monitoring and feedback module; data storage and verification module; visual reporting and decision support module; smart contract execution and risk response module.

[0069] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a geological exploration project planning management intelligent analysis system as described in the first aspect of the present invention are implemented.

[0070] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a geological exploration project planning management intelligent analysis system as described in the first aspect of the present invention are implemented.

[0071] Compared with the existing technology, the present invention provides an intelligent analysis system for geological exploration project planning management, which has the following beneficial effects:

[0072] This intelligent analysis system for geological exploration project planning and management utilizes a combination of reinforcement learning and transfer learning to adaptively adjust network structures and update model weights in real time, enabling more accurate risk assessment and decision optimization. Furthermore, by integrating IoT technology, the system collects all types of data generated during project implementation in real time and dynamically adjusts input features, thereby improving the accuracy of risk predictions and the adaptability of the model.

[0073] By adopting a high-performance blockchain-based solution, the storage and transmission speed of data are optimized, especially when processing large-scale real-time data, the efficiency of the blockchain network can be guaranteed.

[0074] By introducing an adaptive smart contract mechanism and combining it with real-time risk assessment results, smart contracts can automatically adjust execution conditions and parameters, and flexibly adjust resource allocation, task arrangements, and risk response measures based on actual changes during project execution, thereby improving the transparency and efficiency of project management and enhancing the intelligence level of project execution and risk management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 This is a flow chart of an intelligent analysis system for geological exploration project planning management proposed by the present invention;

[0077] Figure 2 This is a schematic diagram of the deep neural network process of the geological exploration project planning management intelligent analysis system proposed by the present invention;

[0078] Figure 3 This is a flow chart of an intelligent optimization algorithm for a geological exploration project planning management intelligent analysis system proposed by the present invention;

[0079] Figure 4This is a schematic diagram of the blockchain technology and smart contract process of the geological exploration project planning management intelligent analysis system proposed by the present invention;

[0080] Figure 5 This is a module diagram of an intelligent analysis system for geological exploration project planning management proposed by the present invention. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0082] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0083] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0084] Example 1:

[0085] Reference Figures 1 to 4 , which is the first embodiment of the present invention, provides a geological exploration project planning management intelligent analysis system, including the following steps:

[0086] S1. Data collection and integration to form a unified data set and obtain geological data.

[0087] In this embodiment, data from multiple sources (including geological data, climate data, and equipment operation data) is collected to form a unified dataset, ensuring data accuracy and completeness. Collection methods may include ground surveys, sensor data collection, satellite remote sensing data, and field instrument readings. Data integration involves ETL (Extract, Transform, Load) technology, and real-time updates through automated processes ensure data timeliness and accuracy.

[0088] S2. Identify and assess risks based on geological data to obtain risk assessment results.

[0089] In this embodiment, risk identification and assessment include:

[0090] By analyzing geological data, extracting possible abnormal patterns and potential danger zones, and using deep neural network models for data training, the system automatically identifies and predicts the likelihood of geological risks. This system not only considers traditional geological hazard risk factors but also discovers previously unidentified hidden risks within a big data environment.

[0091] Furthermore, the deep neural network includes:

[0092] Time series data processing:

[0093] ;

[0094] in, : The input feature vector at time t, ,in Input data of different dimensions, including geological data, climate change data, and historical disaster records;

[0095] : The hidden layer output of the LSTM network represents the potential risk status at the current moment;

[0096] : The hidden layer output of the previous moment;

[0097] Risk prediction output:

[0098] ;

[0099] in, : The risk prediction value output by the model indicates the risk level, ranging from 0 to 1;

[0100] : sigmoid activation function; : Weight, set according to actual situation; : Bias item, set according to actual situation;

[0101] Adaptive threshold adjustment:

[0102] ;

[0103] in, : Adjusted risk assessment results;

[0104] : Risk thresholds adaptively adjusted based on historical data;

[0105] Overall risk assessment:

[0106] ;

[0107] in, : The overall risk assessment results of the project; : The total time of the evaluation.

[0108] Model usage:

[0109] Data Input: In practice, the system will input real-time data collected during geological exploration (such as soil composition, groundwater levels, and temperature and humidity changes) into the model. In addition, climate data (such as precipitation and temperature) and historical disaster data (such as earthquakes, landslides, and floods) will also be collected.

[0110] Time Series Modeling: Input data is modeled using an LSTM network to capture the correlations between different time steps. Because geological and climate data are highly temporal, ignoring this can lead to decreased forecast accuracy.

[0111] Risk prediction: The output of the deep neural network (via the sigmoid activation function) generates a risk prediction value between 0 and 1, which represents the risk level at the current moment. For example, 0.2 indicates low risk and 0.8 indicates high risk.

[0112] Adaptive Thresholds: The system adaptively adjusts risk thresholds based on historical data and geological and climate change trends. For example, if current climate change is more extreme than historical data, the system automatically adjusts the risk assessment criteria to ensure the sensitivity and accuracy of the forecast results.

[0113] Comprehensive Assessment and Decision-Making: Ultimately, the system aggregates the risk prediction results at each time step to produce an overall risk assessment. This result supports decision-making by project managers, helping them understand potential risks in the current and future stages and formulate appropriate response measures.

[0114] S3. Optimize project planning based on risk assessment results to obtain an optimized project planning plan.

[0115] In this embodiment, project planning optimization includes:

[0116] Through intelligent optimization algorithms, risk assessment results are integrated with existing project plans to optimize project plans. This optimization process considers factors such as risk control, resource allocation, and budget control, ensuring efficient project execution while ensuring safety. Dynamic adjustments to project plans can be made based on real-time risk assessment results, thereby improving project success and sustainability.

[0117] Intelligent optimization algorithms include:

[0118] Coupling of risk assessment results and objective function:

[0119] Set up a multi-objective optimization problem for a project, where the objective function includes factors such as cost, time, and resource utilization. Indicates cost, Indicates time, represents the overall risk assessment result, and Represent the weights of cost, time and risk respectively. The objective function is expressed as:

[0120] ;

[0121] in, It is a decision variable in project planning, including resource allocation, task allocation, and schedule scheduling.

[0122] It is a cost function that represents the capital expenditure required to complete the project.

[0123] It is a time function that represents the time required to complete the project.

[0124] is the risk assessment value obtained in step S2, which represents the overall risk of the project;

[0125] Adaptive weight adjustment mechanism:

[0126] As the risk assessment results change, the weights in the objective function Will adjust adaptively. Suppose the risk level at the current moment is , define a weight adjustment function , such that:

[0127] and ;

[0128] in, is a nonlinear function, according to The value is dynamically adjusted For example, when When it is higher, increase The weight of and weight, thereby emphasizing the importance of risk control.

[0129] Iterative update of hybrid optimization algorithm:

[0130] In the optimization process, we use the genetic algorithm GA for global search and the simulated annealing algorithm SA for local optimization. The fitness function of the genetic algorithm is the objective function , and each iteration adjusts the individual fitness according to the objective function value;

[0131] Genetic algorithm update rules:

[0132] ;

[0133] in, and is the parent individual, is the crossover factor, which controls the search direction.

[0134] Simulated annealing algorithm update rules:

[0135] ;

[0136] in, It is a normal distribution, and the simulated annealing algorithm searches for the local optimal solution through random perturbations.

[0137] Constraints:

[0138] During the optimization process, project constraints such as resource constraints and time constraints are expressed through the following linear constraints:

[0139] ;

[0140] in, and Represents the minimum and maximum feasible values ​​of resources and time respectively.

[0141] Model usage:

[0142] Risk assessment input: The overall risk assessment value obtained from step S2 is used as the input of the optimization algorithm to affect the weight distribution in the objective function.

[0143] Objective function calculation: By combining the cost, time, resource, and risk assessment results, the objective function is calculated. This function takes into account cost, time, and risk, thereby balancing multiple objectives during the optimization process.

[0144] Solution optimization: Genetic algorithms are used for global search to find potential optimal solutions in the search space, while simulated annealing algorithms help the algorithm avoid falling into local optimal solutions and further optimize the quality of the solution through local search.

[0145] Adaptive weight adjustment: After each optimization iteration, the weights in the objective function are adjusted based on the real-time risk assessment results. This allows the optimization process to focus more on risk control when the project risk is high, ensuring that the project can proceed smoothly while ensuring safety.

[0146] Provide the final optimization plan: After multiple iterations, the optimization algorithm will output an optimal project planning plan, including decisions on resource allocation, time scheduling, etc., to ensure the successful execution of the project under multi-objective constraints.

[0147] S4. Perform resource scheduling and task allocation according to the optimized project planning scheme to obtain a scheduling plan.

[0148] In this embodiment, resource scheduling and task allocation include:

[0149] Based on the optimized project plan, a linear programming model is used for resource scheduling, rationally allocating various resources, such as manpower, equipment, and funds. Tasks are assigned through intelligent algorithms to maximize task execution efficiency. This matching of resources and tasks ensures efficient project implementation. In practice, the system can dynamically adjust resources to address unexpected changes, ensuring timely project completion.

[0150] S5. Monitor the project execution according to the scheduling plan and obtain execution monitoring data.

[0151] In this embodiment, project execution monitoring includes:

[0152] Real-time monitoring systems, such as IoT-based sensor networks, collect on-site data and compare it with project plans to identify deviations and issues in project execution. Leveraging data mining techniques, the system automatically identifies potential bottlenecks in project execution and generates corresponding early warning information, enabling timely adjustments to the implementation plan. With real-time data feedback, project managers can flexibly adjust based on on-site conditions, ensuring that projects proceed smoothly and on schedule.

[0153] S6. Conduct cost control and budget analysis based on execution monitoring data to obtain analysis results.

[0154] In this embodiment, cost control and budget analysis include:

[0155] Based on execution monitoring data, the system utilizes regression analysis and predictive models to assess real-time project costs and analyze variances against budget. By calculating the difference between actual expenditures and budget, the system intelligently predicts future budget needs and provides cost control recommendations. Furthermore, the system issues alerts for potential overspending and automatically adjusts budget allocations based on historical data to ensure projects are completed within the planned budget.

[0156] S7. Optimize the cash flow based on the analysis results to obtain the optimized results.

[0157] In this embodiment, cash flow optimization includes:

[0158] By combining blockchain technology and smart contracts, project funds can be transparently managed, ensuring clear and traceable fund flows and automating fund disbursements using smart contracts. The allocation and use of project funds will be conducted within the framework of smart contracts, ensuring compliance with legal procedures and budget requirements. The system automates the payment approval process and adjusts fund allocation strategies in real time, maximizing fund efficiency and preventing delays or waste.

[0159] Furthermore, the blockchain technology and smart contracts include:

[0160] Smart contract automated execution conditions:

[0161] Set the composite target execution function Indicates the conditions for the automated execution of smart contracts;

[0162] ;

[0163] in, : Decision variables in project planning, including resource allocation, task progress, and risk response;

[0164] : The execution condition of resource allocation, indicating whether resources are allocated according to the optimization results;

[0165] : The execution condition of the task progress, indicating whether the task is completed according to the scheduled progress;

[0166] : The execution conditions of risk response, indicating whether the project takes the predetermined measures when facing risks;

[0167] : Weight coefficient, set according to actual situation;

[0168] Data recording and verification on the blockchain:

[0169] Define a record function , representing data recorded through blockchain;

[0170] ;

[0171] in, Represents the project execution record submitted to the blockchain network, including decision variables , target execution function , risk assessment results ;

[0172] Smart contracts and dynamic risk adjustment:

[0173] During the execution of smart contracts, the execution strategy is adjusted according to the risk assessment results. For example, When it is higher, the smart contract will automatically trigger additional resource allocation and risk response measures;

[0174] ;

[0175] in, :Adjusted execution conditions;

[0176] : Input decision variables Real-time calculation of risk assessment results;

[0177] : Adjustment coefficient, set according to actual situation;

[0178] The trigger mechanism of the contract on the blockchain:

[0179] Smart contracts are executed based on the state changes of the blockchain network. At each key node, the smart contract is triggered to execute through the chain structure on the blockchain. When a certain stage of the project is completed, the blockchain will record the current execution status and trigger subsequent contract execution based on the state change. For example, if the progress of a task is delayed, the blockchain will record this information and trigger the smart contract to adjust the task allocation.

[0180] ;

[0181] in, The contract is triggered when the task progress exceeds the preset threshold.

[0182] Model usage:

[0183] Automated Smart Contract Execution: During project execution, smart contracts automatically execute based on the execution conditions (such as resource allocation, task progress, and risk control) calculated from optimization results. Smart contracts dynamically adjust project tasks and resource allocation based on these execution conditions.

[0184] Real-time blockchain monitoring: All project execution data, including task status, resource usage, and risk assessment results, will be recorded on the blockchain, ensuring immutability and transparency. This mechanism ensures fairness for all parties and streamlines project management.

[0185] Dynamic risk adjustment: Whenever the risk assessment results change, the smart contract will automatically adjust the execution conditions according to the new risk assessment value to ensure that the project progresses smoothly within the risk control range.

[0186] S8. Conduct project performance evaluation based on the optimization results and real-time data of project execution.

[0187] In this embodiment, the system combines real-time project execution data and cash flow optimization results to evaluate project performance. By building a comprehensive evaluation model and incorporating multi-dimensional performance evaluation techniques (such as KPIs and Business Scale), it provides a comprehensive analysis of the overall project execution effectiveness. The system outputs evaluation results for various indicators and provides quantitative scores for project success rates, resource utilization, cost control capabilities, and other indicators. These evaluation results not only provide experience references for subsequent similar projects but also support management decision-making, thereby enhancing overall project management capabilities.

[0188] Example 2:

[0189] Reference Figure 5 , which is the second embodiment of the present invention, provides a geological exploration project planning management intelligent analysis system, including the following functional modules:

[0190] Data Collection and Processing Module: This module is responsible for collecting various data required for geological exploration projects, including geology, climate, historical disaster records, resource usage, and equipment operation data, ensuring data accuracy and completeness. It also pre-processes the collected data, including cleaning, denoising, and standardization, to facilitate input into subsequent models.

[0191] Risk Assessment Module: This module uses deep neural networks (DNNs) to predict and assess the risks of geological exploration projects, processing multi-dimensional, time-series data inputs. By training the DNN model, the system can predict potential risks during project execution based on historical and environmental data, providing quantitative support for subsequent optimization and decision-making.

[0192] Decision Optimization and Intelligent Scheduling Module: Through optimization algorithms, this module automatically adjusts resource allocation, task priorities, and schedules based on risk assessment and project requirements, ensuring that projects can be executed under optimal conditions while minimizing risks.

[0193] Execution Monitoring and Feedback: This module ensures that projects are executed as planned and provides project managers with visual progress reports through real-time data feedback. By monitoring the real-time status of projects, managers can quickly identify implementation issues and take necessary adjustments to ensure that project progress is not disrupted.

[0194] Data Storage and Verification Module: This module ensures transparency and data security during project execution. Using blockchain, it provides real-time recording and verification, preventing data tampering or loss that can occur in traditional centralized databases. Furthermore, thanks to the decentralized nature of blockchain, all project participants can see the true progress of the project, further enhancing transparency in project management.

[0195] Visual Reporting and Decision Support: This module uses a graphical interface and data analysis tools to visualize project execution data, optimization decision results, and risk assessment information for project managers. Information such as project progress, resource utilization, and risk trends can be displayed in real time through charts and dashboards, helping managers make informed decisions.

[0196] Smart Contract Execution and Risk Response Module: This module, based on blockchain's smart contract mechanism, automates various project management operations, such as resource allocation, task assignment, and risk response. When new risks arise or specific conditions are met, smart contracts automatically trigger corresponding actions, such as adjusting resource allocation or initiating risk response measures, enhancing the intelligence of project management.

[0197] Example 3:

[0198] This embodiment also provides a computer device, which is suitable for a geological exploration project planning management intelligent analysis system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a geological exploration project planning management intelligent analysis system as proposed in the above embodiment.

[0199] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent analysis system for geological exploration project planning management is implemented as proposed in the above embodiment.

[0200] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0201] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0202] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0203] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0204] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0205] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent analysis system for geological exploration project planning management, characterized by: The following steps are involved: S1. Data collection and integration to form a unified data set and obtain geological data; S2. Identify and assess risks based on geological data to obtain risk assessment results; S3. Optimize the project planning based on the risk assessment results to obtain an optimized project planning scheme; S4. Perform resource scheduling and task allocation according to the optimized project planning scheme to obtain a scheduling plan; S5. Monitor the project execution according to the scheduling plan and obtain execution monitoring data; S6. Conduct cost control and budget analysis based on execution monitoring data to obtain analysis results; S7. Optimize the cash flow based on the analysis results to obtain the optimization results; S8. Conduct project performance evaluation based on the optimization results and real-time data of project execution.

2. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The risk identification and assessment in S2 includes: By analyzing geological data, possible abnormal patterns and potential danger areas are extracted, and deep neural network models are used for data training to automatically identify and predict the possibility of geological risks.

3. The intelligent analysis system for geological exploration project planning management according to claim 2, characterized in that: The deep neural network includes: Time series data processing: ； in, : The input feature vector at time t, ,in Input data of different dimensions, including geological data, climate change data, and historical disaster records; : The hidden layer output of the LSTM network represents the potential risk status at the current moment; : The hidden layer output of the previous moment; Risk prediction output: ； in, : The risk prediction value output by the model indicates the risk level, ranging from 0 to 1; : sigmoid activation function; : Weight, set according to actual situation; : Bias item, set according to actual situation; Adaptive threshold adjustment: ； in, : Adjusted risk assessment results; : Risk thresholds adaptively adjusted based on historical data; Overall risk assessment: ； in, : The overall risk assessment results of the project; : The total time of the evaluation.

4. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The project planning optimization in S3 includes: Through intelligent optimization algorithms, risk assessment results are combined with existing project plans to optimize project planning schemes.

5. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The resource scheduling and task allocation in S4 include: According to the optimized project planning scheme, a linear programming model is used for resource scheduling, various resources are reasonably allocated, and tasks are assigned through intelligent algorithms to maximize the efficiency of task execution.

6. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The project execution monitoring in S5 includes: Through the real-time monitoring system, field data is collected and compared with the project plan to identify deviations and problems in project execution.

7. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The cost control and budget analysis in S6 includes: Based on execution monitoring data, use regression analysis and predictive models to estimate the real-time cost of the project and perform variance analysis against the budget.

8. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The cash flow optimization in S7 includes: Combining blockchain technology and smart contracts, transparent management of project funds is achieved, ensuring clear and traceable fund flows, and using smart contracts to automatically execute fund disbursements.

9. The intelligent analysis system for geological exploration project planning management according to claim 8, characterized in that: The blockchain technology and smart contracts include: Smart contract automated execution conditions: Set the composite target execution function Indicates the conditions for the automated execution of smart contracts; ； in, : Decision variables in project planning, including resource allocation, task progress, and risk response; : The execution condition of resource allocation, indicating whether resources are allocated according to the optimization results; : The execution condition of the task progress, indicating whether the task is completed according to the scheduled progress; : The execution conditions of risk response, indicating whether the project takes the predetermined measures when facing risks; : Weight coefficient, set according to actual situation; Data recording and verification on the blockchain: Define a record function , representing data recorded through blockchain; ； in, Represents the project execution record submitted to the blockchain network, including decision variables , target execution function , risk assessment results ; Smart contracts and dynamic risk adjustment: ； in, :Adjusted execution conditions; : Input decision variables Real-time calculation of risk assessment results; : Adjustment coefficient, set according to actual situation; The trigger mechanism of the contract on the blockchain: ； in, The contract is triggered when the task progress exceeds the preset threshold.

10. The intelligent analysis system for geological exploration project planning management according to claim 1, characterized in that: The functional modules of the geological exploration project planning management intelligent analysis system include: Data collection and processing module; risk assessment module; decision optimization and intelligent scheduling module; execution monitoring and feedback module; data storage and verification module; visual reporting and decision support module; smart contract execution and risk response module.

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