A regional geological exploration data collection and dynamic updating method and system

Abstract

The embodiment of the present application provides a regional geological exploration data collection and dynamic updating method and system, and belongs to the technical field of geological exploration data processing and dynamic updating. The method introduces a particle swarm optimization algorithm for solving the multi-constraint personalized frequency calculation problem on the basis of regular path selection, and further considers the flexibility improvement caused by the complementary device alternating sampling and the influence control of the complementary difference accumulation on the analysis accuracy of the cloud, so that the data collection efficiency, flexibility and analysis accuracy are synergistically improved. The present application solves the problems that the traditional fixed uploading path and the empirical frequency adjustment cannot adapt to the coupling of the overlapping sub-regional geological dynamic characteristics, the uploading path selection is disordered, the load of the aggregation device is unbalanced, and the network signal is spatio-temporally heterogeneous, and realizes intelligent decision and continuous updating of personalized collection frequency under multi-constraint conditions.

Description

Technical Field

[0001] This invention relates to the field of geological exploration data processing and dynamic updating technology, specifically to a method and system for regional geological exploration data acquisition and dynamic updating. Background Technology

[0002] As geological exploration extends into complex scenarios such as areas affected by active faults, karst collapse-sensitive areas, and high-risk landslide and collapse zones, the spatial configuration and geological dynamics of the exploration area exhibit stronger coupling and heterogeneity. Especially under the superposition of multiple geological processes such as fault creep, karst infiltration, and surface deformation, changes in the state of geological bodies are characterized by suddenness and stages. These scenarios typically require continuous acquisition of monitoring data such as formation pressure over extended periods, relying on cloud-based dynamic analysis and early warning assessments, thus placing higher demands on the timeliness, completeness, and consistency of the collected data.

[0003] Existing geological exploration data acquisition schemes are typically deployed using a single sub-region as the basic organizational unit. These sub-regions are independent of each other, and exploration equipment usually uses a fixed upload path to transmit data back to a specific aggregation device or gateway node, which then uploads the data to the cloud. In this type of scheme, the sampling frequency often uses a fixed or uniform configuration, or is adjusted coarsely by human experience. Furthermore, path selection and frequency adjustment are usually designed separately, lacking coordinated consideration of network signal fluctuations, changes in the load of aggregation devices, and bandwidth resource constraints. This leads to problems such as transmission bottlenecks, data loss, or delayed data updates in complex scenarios.

[0004] In complex scenarios, overlapping sub-regions are common. For example, there may be spatial overlap between fault core areas and karst development areas, or boundary overlap between geologically sensitive areas and cross-topographic areas. This means that monitoring needs at the same location often fall under multiple monitoring targets simultaneously. If a simplified architecture with non-overlapping sub-regions and fixed upload paths is still used, it is difficult to simultaneously address the monitoring needs of multiple areas and the stability of data flow. On the one hand, fixed upload paths are prone to congestion or disconnection when network quality is spatiotemporally heterogeneous or when the load on aggregation devices suddenly increases, preventing high-value data from being transmitted back on time. On the other hand, uniform or empirically based sampling frequencies are difficult to accurately match the geological dynamic differences of different areas, especially overlapping areas. This can easily lead to data redundancy in stable areas and data loss in areas with abrupt changes, thus affecting the accuracy of cloud-based judgments on geological state changes.

[0005] To address these issues, some existing technologies attempt to improve transmission reliability and data update efficiency by increasing the number of aggregation devices, introducing multi-link backup, setting network quality thresholds to filter paths, or adjusting sampling frequencies through rules or experience. However, these approaches often have the following shortcomings: First, multi-path selection mechanisms typically focus only on a single factor in network quality or load, lacking systematic collaborative decision-making under conditions of dynamic changes in both network quality and load. Second, sampling frequency adjustment relies heavily on empirical parameters or a limited number of rules, making it difficult to achieve refined adaptation under multiple constraints such as dynamic geological matching, equipment load capacity, and network bandwidth limitations. Third, within the same sub-region, some exploration equipment may characterize the same, similar, or related geological features. Without considering the complementarity between equipment, redundant acquisition and transmission of similar data can easily occur. Furthermore, even if the equipment is complementary, measurement differences may accumulate over time and affect the accuracy of cloud analysis. Without a mechanism to quantify and suppress the risk of such accumulated differences, the bias in analysis results may also increase.

[0006] Therefore, how to achieve a reasonable selection of upload paths for exploration equipment under a complex exploration architecture with overlapping sub-regions, while taking into account dynamic changes in network quality and dynamic fluctuations in the load of aggregation equipment, and how to form a personalized sampling frequency control mechanism that can adapt to dynamic geological differences while meeting the constraints of equipment load and network bandwidth, and at the same time taking into account the complementary sampling needs of similar equipment and suppressing the cumulative risk of differences between complementary equipment, so as to achieve efficient acquisition and dynamic updating of exploration data, remains an urgent technical problem to be solved in the field of geological exploration data acquisition and cloud analysis. Summary of the Invention

[0007] The purpose of this invention is to provide a method for regional geological exploration data acquisition and dynamic updating, which at least solves the problems of traditional fixed upload paths and empirical frequency adjustment being unable to adapt to the coupling of geological dynamic characteristics of overlapping sub-regions, disordered upload path selection, unbalanced load of aggregation equipment, and spatiotemporal heterogeneity of network signals, and achieves intelligent decision-making and continuous updating of personalized acquisition frequencies under multiple constraints.

[0008] To achieve the above objectives, the first aspect of the present invention provides a method for regional geological exploration data acquisition and dynamic updating. The method includes: acquiring geological background data and topographic data of a target exploration area; dividing a basic sub-region and overlapping region; and establishing a correlation between exploration equipment and aggregation equipment; receiving formation pressure data, equipment acquisition stability coefficient, aggregation equipment load rate, and upload path network quality coefficient collected by the exploration equipment; performing standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization; based on the input dataset and the correlation, performing upload path candidate screening for each exploration equipment, determining the optimal upload path, and calculating the remaining bandwidth of the corresponding optimal aggregation equipment; calculating the complementarity between exploration equipment within the basic sub-region based on the formation pressure data and equipment acquisition characteristics, and generating a complementary equipment group; under the premise of satisfying equipment load and remaining bandwidth constraints, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantization value of complementary equipment differences, and minimizing resource consumption, using a particle swarm optimization algorithm to calculate a personalized acquisition scheme including sampling time sequence rules within the complementary equipment group and the sampling frequency of each equipment; generating frequency adjustment instructions and data update strategies based on the optimal aggregation equipment and the personalized acquisition scheme, and executing geological exploration data acquisition and dynamic updating.

[0009] Optionally, the geological background data and topographic data of the target exploration area are acquired, the division of basic sub-regions and overlapping regions is performed, and the association between exploration equipment and aggregation equipment is established. This includes: dividing the target exploration area into multiple sub-regions based on geological dynamics and topographic features to obtain several basic sub-regions; calculating the overlap degree between any two basic sub-regions, and determining the corresponding overlapping region when the overlap degree meets a preset threshold; allocating at least one aggregation device to each basic sub-region, and establishing a mapping table between exploration equipment and aggregation equipment for exploration equipment located in the overlapping region.

[0010] Optionally, the system receives formation pressure data, equipment acquisition stability coefficient, aggregated equipment load rate, and upload path network quality coefficient collected by exploration equipment. It then performs standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization. This includes: receiving formation pressure data and equipment acquisition stability coefficient collected by exploration equipment; receiving the aggregated equipment load rate calculated by the aggregation equipment based on the current load and maximum load; calculating the upload path network quality coefficient based on upload path signal strength, transmission delay, and packet loss rate; and generating the input dataset based on formation pressure data, equipment acquisition stability coefficient, aggregated equipment load rate, and upload path network quality coefficient.

[0011] Optionally, based on the input dataset and the association relationship, upload path candidate filtering is performed for each exploration device, including: performing network quality threshold filtering on multiple upload paths associated with the same exploration device based on the upload path network quality coefficient in the input dataset to obtain a path candidate set; when the path candidate set contains at least two paths, sorting them from high to low according to the network quality coefficient and retaining a preset number of core candidate paths; when the path candidate set contains only one path, using that path as the core candidate path; when the path candidate set is empty, selecting the path with the lowest load rate among the associated aggregated devices as the core candidate path.

[0012] Optionally, determining the optimal upload path and calculating the remaining bandwidth of the corresponding optimal aggregation device includes: performing a load threshold judgment based on the load rate of the aggregation device corresponding to the core candidate path; selecting the path with the highest network quality coefficient as the optimal path when the load rate is not higher than the load threshold, and selecting the path with the lowest load rate as the optimal path when all load rates are higher than the load threshold; calculating the remaining bandwidth based on the aggregation device corresponding to the optimal path; performing dynamic verification on the optimal path according to a preset check cycle, and re-performing path candidate screening and optimal path determination when the verification does not meet the preset conditions.

[0013] Optionally, the complementarity between exploration equipment within a sub-region is calculated based on the formation pressure data and equipment acquisition characteristics, and complementary equipment groups are generated. This includes: constructing equipment feature vectors based on the formation pressure data of each exploration equipment within a preset time window in the same basic sub-region, and calculating the feature correlation coefficient between each pair of exploration equipment; determining whether the feature correlation coefficient is lower than a preset correlation threshold, and if so, classifying the corresponding exploration equipment into the same complementary equipment group.

[0014] Optionally, under the premise of satisfying equipment load and remaining bandwidth constraints, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantization value of complementary equipment differences, and minimizing resource consumption, a personalized acquisition scheme including sampling time sequence rules within the complementary equipment group and sampling frequency of each device is calculated using the particle swarm optimization algorithm. This includes: determining the feature difference threshold based on historical acquisition data of devices within the complementary equipment group, and defining the cumulative quantization value of complementary equipment differences as the cumulative result of sampling differences of devices within the group within a preset evaluation period; constructing a fitness function that simultaneously includes geological dynamic matching degree, cumulative quantization of differences, and resource consumption, and setting the fitness function value to zero when the equipment load and remaining bandwidth constraints are not satisfied; and using the particle swarm optimization algorithm to calculate the personalized acquisition scheme including sampling time sequence rules within the complementary equipment group and sampling frequency of each device.

[0015] Optionally, a personalized acquisition scheme is calculated using a particle swarm optimization algorithm, which includes sampling timing rules within the complementary device group and sampling frequencies of each device. This includes: representing particle positions as a joint scheme of sampling frequency and sampling timing, and updating particle velocity and particle position through individual optimal solutions and swarm optimal solutions; performing conflict checks on the sampling timing within the complementary device group in each iteration, ensuring that only at least one device in the complementary device group drives sampling at any given time; calculating the cumulative difference quantization value and performing cumulative difference verification after particle updates; and adjusting the sampling frequency or sampling timing of at least one device in the complementary device group when the cumulative difference quantization value is not less than the maximum allowable cumulative difference quantization value, so that the cumulative difference quantization value falls back to within the threshold.

[0016] Optionally, based on the optimal aggregation device and the personalized acquisition scheme, frequency adjustment instructions and data update strategies are generated to perform geological exploration data acquisition and dynamic updates. This includes: generating frequency adjustment instructions for each exploration device, and issuing the frequency adjustment instructions after grouping and encoding them according to the optimal aggregation device; wherein, the frequency adjustment instructions at least include a device identifier, an optimal aggregation device identifier, and a personalized sampling frequency; formulating a data update strategy based on the personalized sampling frequency and the optimal path network quality, calculating the upload cycle based on the personalized sampling frequency, and performing geological exploration data acquisition and dynamic updates.

[0017] A second aspect of this invention provides a regional geological exploration data acquisition and dynamic update system. The system is used to execute the aforementioned regional geological exploration data acquisition and dynamic update method. The system includes: an acquisition unit, used to acquire geological background data and topographic data of a target exploration area, perform basic sub-region and overlapping region division, and establish an association between exploration equipment and aggregation equipment; a receiving unit, used to receive formation pressure data, equipment acquisition stability coefficient, aggregation equipment load rate, and upload path network quality coefficient collected by the exploration equipment, and perform standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization; and a filtering unit, used to perform upload for each exploration equipment based on the input dataset and the association. The system includes: a path candidate filtering unit to determine the optimal upload path and calculate the remaining bandwidth of the corresponding optimal aggregation device; a calculation unit to calculate the complementarity between exploration devices within the basic sub-region based on the formation pressure data and device acquisition characteristics, and generate complementary device groups; and an update unit to generate frequency adjustment instructions and data update strategies based on the optimal aggregation device and the personalized acquisition scheme, and execute geological exploration data acquisition and dynamic updates.

[0018] Through the above technical solution, the present invention introduces a particle swarm optimization algorithm on the basis of regular path selection to solve the problem of multi-constraint personalized frequency calculation, and further considers the flexibility improvement brought by the alternating sampling of complementary devices and the impact control of the cumulative complementary differences on the accuracy of cloud analysis, thereby achieving a synergistic improvement in data acquisition efficiency, flexibility and analysis accuracy.

[0019] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart of the steps of a regional geological exploration data acquisition and dynamic updating method provided by one embodiment of the present invention;

[0022] Figure 2 This is a system structure diagram of a regional geological exploration data acquisition and dynamic update system provided in one embodiment of the present invention. Detailed Implementation

[0023] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0024] like Figure 1 As shown, this invention provides a method for regional geological exploration data acquisition and dynamic updating, the method comprising:

[0025] Step S10: Obtain geological background data and topographic data of the target exploration area, perform the division of basic sub-regions and overlapping regions, and establish the association between exploration equipment and aggregation equipment.

[0026] Specifically, the target exploration area is divided into multiple sub-regions based on geological dynamics and topographic features to obtain several basic sub-regions; the overlap degree is calculated for any two basic sub-regions, and the corresponding overlapping area is determined when the overlap degree meets a preset threshold; at least one aggregation device is assigned to each basic sub-region, and a mapping table between exploration devices and aggregation devices is established for exploration devices located in the overlapping area.

[0027] In this embodiment of the invention, by constructing a complex exploration network skeleton, the division of basic sub-regions and overlapping sub-regions is completed, and the association mapping between exploration equipment and aggregation equipment is established, providing a workable structured foundation for subsequent multi-path selection and frequency optimization.

[0028] First, the cloud server reads the geological background data and topographic data of the target exploration area and performs unified coordinate system registration, rasterization or vector overlay processing on them. On this basis, the cloud extracts the element information that can characterize the strength of geological dynamics from the geological background data and the element information that can characterize the differences in surface morphology from the topographic data, and further generates the geological dynamic activity and topographic features used for sub-region division.

[0029] In complex exploration scenarios, geological background data often exists in the form of fault distribution, karst development zones, and historical survey results, which are themselves static backgrounds. However, the geological dynamic activity level used for operational delineation emphasizes the intensity of dynamic changes, requiring structured quantification and classification of the static background. Topographic data often contains information such as elevation, slope, and relief, which can be transformed into stable representations of topographic features for boundary delineation through feature extraction. This transformation chain—from raw data to feature extraction and indicator classification—ensures consistency in data caliber in subsequent delineation steps.

[0030] In one alternative implementation, geological dynamic activity can be obtained through multi-factor weighting:

[0031] ;

[0032] Where A(x,y) represents the geological dynamic activity score at location (x,y); F(x,y) represents the fault influence intensity (e.g., fault density or the transformed value of distance from the fault); K(x,y) represents the karst development intensity index; E(x,y) represents the density index of historical events or anomalies; norm(·) represents the normalization function; and λ1, λ2, and λ3 represent the weighting coefficients.

[0033] Furthermore, A(x,y) can be divided into three levels—high, medium, and low—based on a threshold to represent the geological dynamic activity (high / medium / low). This classification method is merely an example and is not a requirement to use a three-level division.

[0034] Following this, the cloud server divides the target exploration area into multiple sub-regions based on the obtained geological dynamics and terrain features to obtain multiple basic sub-regions; then, based on the spatial overlap relationship between the basic sub-regions, it defines overlapping sub-regions and records the set of basic sub-regions associated with each overlapping sub-region.

[0035] It should be noted that the division of basic sub-regions is used to express the relative consistency of geological dynamics and topographic features within a region; the definition of overlapping sub-regions is used to express the spatial coupling of monitoring needs between two or more basic sub-regions. By setting an overlap threshold, a balance can be struck between excessive overlap leading to management complexity and insufficient overlap leading to missing coupled monitoring, thereby establishing structural boundaries for subsequent multi-path uploading and frequency optimization.

[0036] In one alternative implementation, overlapping sub-regions can be generated by calculating the overlap between two base sub-regions and comparing it with an overlap threshold.

[0037] ;

[0038] in, , These represent any two basic sub-regions; Indicates area; This indicates the degree of overlap; in practical applications, the overlap threshold can preferably be 0.3.

[0039] In this embodiment of the invention, in scenarios where the fault core area and the karst development area overlap, the overlapping sub-region can meet the dual needs of fault activity monitoring and karst permeability monitoring. This allows the equipment sampling scheme within the area to cover both fault activity characteristics and karst-related characteristics, avoiding monitoring blind spots caused by dividing the area into only a single region. For example, the cloud can divide the target exploration area into several basic sub-regions, where the fault core area is divided into a high-activity basic sub-region, the karst development area into a medium-activity basic sub-region, and the stable area into a low-activity basic sub-region. When the overlap between the high-activity basic sub-region and the medium-activity basic sub-region reaches a preset threshold, the cloud defines the overlapping part as an overlapping sub-region and records its associated set of basic sub-regions in the ledger.

[0040] It should be understood that the number of basic sub-regions and the number of basic sub-regions that each overlapping sub-region can be associated with can be adjusted according to actual exploration needs; for example, an overlapping sub-region can be associated with two basic sub-regions or three basic sub-regions, as long as a clear set of attribution can be formed, it can be implemented.

[0041] In this embodiment of the invention, the cloud server configures at least one aggregation device for each basic sub-region and records the maximum load information of each aggregation device; for exploration devices located in overlapping sub-regions, the cloud establishes a mapping table between exploration devices and associated aggregation devices, enabling the exploration device to upload data to any associated aggregation device; and writes the sub-region division results, device association relationships, and aggregation device capability parameters into the cloud device ledger database.

[0042] It should be noted that this mapping table essentially provides exploration equipment within overlapping sub-regions with multiple upload entry points, offering a candidate space for subsequent rule-based selection of upload paths. Simultaneously, the maximum load information explicitly defines the carrying capacity of the aggregation devices, allowing subsequent path selection and frequency optimization to utilize the load rate as a unified constraint metric, avoiding misjudgments caused by relying solely on instantaneous data volume. When an aggregation device experiences upload congestion due to insufficient network coverage or a short-term load surge, exploration equipment within the overlapping sub-regions can switch to other associated aggregation devices based on the rule-based selection results from the cloud, thereby reducing the risk of data loss caused by single-point bottlenecks.

[0043] Step S20: Receive formation pressure data, equipment acquisition stability coefficient, summary equipment load rate, and upload path network quality coefficient collected by the exploration equipment, and perform standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization.

[0044] Specifically, it receives formation pressure data and equipment acquisition stability coefficients collected by exploration equipment, and receives the aggregated equipment load rate calculated by the aggregation equipment based on the current load and maximum load; it calculates the upload path network quality coefficient based on the upload path signal strength, transmission delay, and packet loss rate; and it generates the input dataset based on the formation pressure data, equipment acquisition stability coefficients, aggregated equipment load rate, and upload path network quality coefficients.

[0045] In this embodiment of the invention, a cloud server receives formation pressure data collected and uploaded by exploration equipment. In one optional implementation, the cloud can perform outlier removal, time alignment, and sliding smoothing on the formation pressure data to form time-series data that can be used for correlation analysis, dynamic identification, and frequency matching calculation. Formation pressure data is core data characterizing geological dynamics. Since field sampling may be affected by power fluctuations and transient sensor interference, directly using raw data may lead to misjudgments; preprocessing can improve the robustness of the data.

[0046] Based on this, the cloud server receives operational status data such as voltage and vibration reported by the exploration equipment, or directly receives the equipment acquisition stability coefficient calculated by the equipment itself; and stores the equipment acquisition stability coefficient in association with the formation pressure data according to timestamps. The value range of the equipment acquisition stability coefficient can be set between zero and one, with the closer the value is to one, the more stable the equipment acquisition.

[0047] It should be noted that the equipment acquisition stability coefficient describes the reliability and sustainability of the sampled data under the current state of the equipment. When the equipment is under low voltage or high vibration, even with strong geological dynamics, blindly increasing the sampling frequency may lead to a decrease in data quality or an increased risk of equipment overload. Therefore, introducing this coefficient in subsequent frequency optimization can ensure that the optimization results both match geological dynamics and take into account equipment reliability. The equipment acquisition stability coefficient can be calculated and reported by the equipment itself or calculated by the cloud; its calculation factors are not limited to voltage and vibration, but can also include temperature, sensor self-test results, buffer usage, etc., as long as a quantitative result reflecting the reliability of acquisition can be generated.

[0048] Meanwhile, the cloud server receives the current load information of the aggregation devices and calculates the aggregation device load rate by combining it with the maximum load information. The aggregation device load rate characterizes the busy level of the aggregation devices and serves as a constraint input for upload path selection and frequency optimization. The aggregation devices are responsible for data aggregation, caching, and forwarding; excessively high load rates can lead to increased queuing delays and even a higher risk of packet loss. By normalizing the current load and maximum load to a load rate, the cloud can make comparability judgments across aggregation devices with different hardware capabilities, thereby avoiding diverting large amounts of data to high-load devices during path selection.

[0049] After that, the cloud server receives the monitoring results of the survey equipment on the signal strength, transmission delay, packet loss rate and other data of each upload path, and normalizes and weights them to obtain the network quality coefficient of the upload path. The value of the network quality coefficient can be set between zero and one, and the closer the value is to one, the better the network quality.

[0050] In one alternative implementation, the upload path network quality coefficient is:

[0051] ;

[0052] in, This represents the network quality coefficient from device i to the aggregate device j; Indicates signal strength; Indicates transmission delay; Represents the packet loss rate; norm(·) represents the normalization function; These represent the weighting coefficients.

[0053] It should be noted that the purpose of network quality coefficient fusion calculation is to uniformly map the principles of stronger signal, lower latency, and lower packet loss into the same evaluation value, so that subsequent path selection can be based solely on the network quality coefficient for threshold judgment and sorting, thereby reducing the complexity of operation.

[0054] Finally, the cloud server formats and standardizes the formation pressure data, equipment acquisition stability coefficient, aggregated equipment load rate, and upload path network quality coefficient according to the equipment identifier, sub-region identifier, and timestamp, generating an input dataset for upload path selection and acquisition frequency optimization.

[0055] Step S30: Based on the input dataset and the correlation, perform upload path candidate filtering for each exploration device, determine the optimal upload path, and calculate the remaining bandwidth of the corresponding optimal aggregation device.

[0056] Specifically, based on the network quality coefficients of the upload paths in the input dataset, network quality threshold filtering is performed on multiple upload paths associated with the same exploration equipment to obtain a path candidate set. When the path candidate set contains at least two paths, they are sorted from high to low according to their network quality coefficients, and a preset number of core candidate paths are retained. When the path candidate set contains only one path, that path is used as the core candidate path. When the path candidate set is empty, the path with the lowest load rate among the associated aggregation devices is selected as the core candidate path. Based on the load rate of the aggregation devices corresponding to the core candidate paths, a load threshold judgment is performed. If the load rate is not higher than the load threshold, the path with the highest network quality coefficient is selected as the optimal path. If all load rates are higher than the load threshold, the path with the lowest load rate is selected as the optimal path. Based on the aggregation device corresponding to the optimal path, the remaining bandwidth is calculated. Dynamic verification is performed on the optimal path according to a preset check cycle, and if the verification does not meet the preset conditions, the path candidate filtering and optimal path determination are re-executed.

[0057] In this embodiment of the invention, under the conditions of complex network and dynamic load fluctuations, the optimal upload path for each exploration device is determined through a rule-based strategy, avoiding the computational complexity caused by relying solely on optimization algorithms, while providing residual bandwidth constraint input for subsequent frequency optimization.

[0058] First, for each survey device, the cloud server filters a candidate path set from its associated aggregation devices: paths with a network quality coefficient not lower than a preset threshold are prioritized; when the number of candidate paths reaches a preset number, the candidate paths are sorted by network quality coefficient, and a preset number of core candidate paths are retained; when there are no candidate paths, an emergency mechanism is triggered to select the path with the lowest load rate as the core candidate path. It should be noted that the core of path filtering is to ensure network availability first, and then reduce the scale of subsequent selections. In network fluctuation scenarios, if low-quality paths are not eliminated first, even if a low-load aggregation device is selected later, packet loss and retransmission may still occur, which will amplify the load and bandwidth consumption; therefore, network quality thresholds are used as the primary filtering criterion.

[0059] Based on this, the cloud server performs load threshold judgment on the core candidate paths: when there are paths with a load rate not higher than the load threshold, the path with the highest network quality coefficient is selected as the optimal path; when the aggregation devices corresponding to the core candidate paths are all in a high load state, the path with the lowest load rate is selected as the optimal path; when the emergency mechanism is triggered, the core candidate path is directly determined as the optimal path and marked as an emergency state.

[0060] As is easy to understand, after determining the optimal path, the cloud server calculates the remaining bandwidth of the aggregated device corresponding to the optimal path, and uses the remaining bandwidth as the bandwidth constraint input in the frequency optimization process to avoid bandwidth consumption exceeding the available range due to frequency increases.

[0061] In one alternative implementation, the remaining bandwidth of the aggregation device corresponding to the optimal path is calculated:

[0062] ;

[0063] in, Indicates the remaining bandwidth; This indicates the total bandwidth of the aggregated devices; This indicates the current bandwidth usage of the aggregated devices.

[0064] It should be noted that higher frequencies mean larger amounts of data uploaded, and consequently, increased network bandwidth usage. By explicitly calculating the remaining bandwidth and using it as a constraint input, the cloud can eliminate frequency schemes that theoretically match geological dynamics but cannot be supported during the frequency optimization phase, thus improving the feasibility of the scheme.

[0065] Finally, the cloud server re-collects network quality coefficients and aggregates device load rates according to a preset check cycle to verify the current optimal path. When network quality or load rate fails to meet the verification conditions, the path candidate screening and optimal path determination are re-executed to achieve dynamic path adjustment. It should be noted that both network quality and load are significantly time-varying. If the path remains fixed for a long time, even if the initial optimal path is selected, it may deteriorate due to subsequent environmental changes. Regular verification ensures that the path remains within a relatively optimal range in terms of its current overall condition, guaranteeing data transmission stability.

[0066] Step S40: Based on the formation pressure data and equipment acquisition characteristics, calculate the complementarity between exploration equipment within the basic sub-region and generate complementary equipment groups. Under the premise of satisfying equipment load and remaining bandwidth constraints, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantization value of complementary equipment differences, and minimizing resource consumption, use the particle swarm optimization algorithm to calculate a personalized acquisition scheme that includes sampling time sequence rules within the complementary equipment group and sampling frequency of each equipment.

[0067] Specifically, based on the formation pressure data of each exploration device within a preset time window in the same basic sub-region, device feature vectors are constructed, and the feature correlation coefficients between each pair of exploration devices are calculated. It is determined whether the feature correlation coefficients are lower than a preset correlation threshold. If so, the corresponding exploration devices are divided into the same complementary device group. Based on the historical acquisition data of the devices in the complementary device group, a feature difference threshold is determined, and the cumulative quantification value of the complementary device difference is defined as the cumulative result of the sampling difference of the devices in the group within a preset evaluation period. A fitness function that simultaneously includes geological dynamic matching degree, cumulative difference quantification, and resource consumption is constructed, and the fitness function value is set to zero when the device load and remaining bandwidth constraints are not met. A particle swarm optimization algorithm is used to calculate a personalized acquisition scheme that includes the sampling time sequence rules within the complementary device group and the sampling frequency of each device.

[0068] In this embodiment of the invention, on the one hand, the complementarity between exploration equipment is utilized to improve the flexibility of sampling frequency settings, that is, driving some equipment in the complementary equipment group to sample at the same time can meet the needs of cloud analysis; on the other hand, the cumulative quantification value of the difference between complementary equipment is used as one of the optimization objectives, and the analysis error is suppressed from amplifying over time by minimizing the accumulation of this difference, thereby improving flexibility while ensuring the accuracy of cloud analysis.

[0069] First, the cloud server calculates the feature correlation coefficient between each pair of exploration equipment based on the formation pressure data and equipment acquisition characteristics of each exploration equipment in the same sub-region, and classifies equipment with a correlation coefficient not lower than a preset correlation threshold into the same complementary equipment group.

[0070] In one alternative implementation, the characteristic correlation coefficients between each pair of devices are calculated:

[0071] ;

[0072] in, This represents the correlation coefficient between device a and device b; Represent the k-th dimension feature of device a; represents the mean of feature a; m represents the feature dimension.

[0073] It should be noted that in this embodiment of the invention, complementarity is not equivalent to completeness, but rather refers to having similar characterization capabilities for the same geological phenomenon within a certain time window. When two devices are highly correlated, driving them to sample simultaneously at the same time often results in redundancy. By grouping and introducing intra-group temporal coordination, alternating sampling by devices within a group can be achieved, thereby improving scheduling flexibility without significant information loss. It is easy to understand that the correlation threshold can be preset in the cloud or adaptively updated based on historical data; a complementary device group can contain two devices, or three or more devices, as long as subsequent temporal constraints ensure that at most one device within the group samples within the same time slot.

[0074] Based on this, the cloud server calculates the cumulative quantization value of the differences between complementary devices based on the sampling data of the devices in the group within a preset evaluation period, and uses this cumulative quantization value as one of the optimization targets, so that the optimization process tends to select the sampling frequency and timing scheme with smaller cumulative differences.

[0075] In one alternative implementation, the cumulative quantization value of the complementary device difference is calculated:

[0076] ;

[0077] Where C(t) represents the cumulative quantitative value of the difference within the evaluation period t; n represents the number of devices in the complementary equipment group; Indicates that device a is at time 10:00. Formation pressure data; Indicates the interval between adjacent sampling times; This indicates the number of sampling points within the evaluation period.

[0078] It should be noted that although devices within a complementary equipment group can characterize the same or similar geological features, differences still exist in installation location, sensor sensitivity, noise levels, etc. If only one device is driven for sampling for an extended period, the cloud may miss subtle differences represented by the other device, leading to an accumulation and expansion of analytical bias over time. By quantifying the cumulative difference, this risk can be explicitly incorporated into the optimization process, prompting the system to flexibly alternate sampling while still prioritizing error accumulation in the sampling frequency and timing. It is easy to understand that the calculation of the cumulative difference quantification value is not limited to absolute difference accumulation; it can also use squared difference accumulation, weighted difference accumulation, or accumulation methods incorporating confidence weights.

[0079] Following this, the cloud server constructs a fitness function that simultaneously reflects the geological dynamic matching degree, difference accumulation control, and resource consumption; and introduces equipment load constraints and network bandwidth constraints. When any constraint is not met, the fitness is set to zero to exclude unexecutable solutions.

[0080] In one alternative implementation, the cloud server constructs the fitness function:

[0081] ;

[0082] in, represents the fitness function value; f represents the scheme that includes the sampling timing and sampling frequency within the complementary equipment group; Match(f) represents the geological dynamic matching degree; C(t) represents the cumulative quantitative value of the difference; This represents the maximum permissible cumulative quantitative value of the difference; Resource(f) represents the resource consumption coefficient; Indicates weight; This represents the set of constraints consisting of device load constraints and remaining bandwidth constraints.

[0083] It should be noted that the geological dynamic matching degree is used to ensure that the sampling frequency keeps up with the pace of geological changes; the difference accumulation control is used to ensure that complementary substitution does not introduce unacceptable error accumulation; and the resource consumption is used to avoid oversampling leading to wasted bandwidth and computational resources. Unifying these three into the fitness function allows particle swarm optimization to consider all three types of objectives in a single iteration; and by setting constraints to zero when they are not satisfied, infeasible schemes are quickly eliminated, improving convergence efficiency.

[0084] In a preferred embodiment, the particle position is represented as a joint scheme of sampling frequency and sampling timing, and the particle velocity and particle position are updated through individual optimal solutions and swarm optimal solutions. In each iteration, a conflict check is performed on the sampling timing within the complementary device group, so that only at least one device in the complementary device group is driven to sample at the same time. After the particle is updated, the cumulative difference quantization value is calculated and the cumulative difference verification is performed. When the cumulative difference quantization value is not less than the maximum allowable cumulative difference quantization value, the sampling frequency or sampling timing of at least one device in the complementary device group is adjusted so that the cumulative difference quantization value falls back to within the threshold.

[0085] In this embodiment of the invention, a particle swarm optimization algorithm is introduced to solve a personalized acquisition scheme that includes sampling timing rules within a complementary device group and sampling frequencies of each device.

[0086] First, the cloud server sets the particle swarm size, learning factor, inertia weight, number of iterations, and sampling frequency search range. The particle position is represented as a joint scheme of sampling frequency and sampling timing rules within the complementary device group. An initial particle swarm is randomly generated, and the scheme is guaranteed to meet load and bandwidth constraints during initialization. It should be noted that traditional optimization focusing solely on frequency easily overlooks the time conflict problem of complementary devices. This implementation incorporates timing rules into the particle representation, allowing the algorithm to directly search for the optimal solution in the frequency + timing combination space, thus improving the feasibility of complementary sampling from the source.

[0087] After this, the cloud server calculates the fitness of each particle in each iteration, records the individual optimal solution and the group optimal solution, and updates the particle velocity and particle position accordingly. After the particle is updated, a difference accumulation check is performed. When the difference accumulation quantization value reaches or exceeds the maximum allowable difference accumulation quantization value, the difference is reduced by adjusting the sampling frequency of at least one device in the complementary device group or adjusting the sampling timing. At the same time, a conflict check is performed on the sampling timing within the complementary device group to ensure that at most one device in the complementary device group samples in the same time slot.

[0088] In one alternative implementation, the particle swarm optimization algorithm includes particle velocity and position updates, temporal constraints, and difference accumulation verification.

[0089] ;

[0090] ;

[0091] in, Indicates the position of the particle in the g-th iteration; denoted by , where represents the particle velocity in the g-th iteration; w represents the inertia weight; c1 and c2 represent learning factors; r1 and r2 represent random numbers; pbest represents the individual optimal position; gbest represents the group optimal position; and g represents the iteration round.

[0092] ;

[0093] in, The variable indicates whether device i is sampled in time slot u; n represents the number of devices in the complementary device group; u represents the time slot index.

[0094] ;

[0095] Where C(t) represents the cumulative quantitative value of the difference; This represents the maximum permissible cumulative quantified value of differences.

[0096] It should be noted that the introduction of difference accumulation verification and timing conflict handling during the iteration process is an engineering enhancement of traditional particle swarm optimization: difference accumulation verification is used to control the error risk of complementary substitution within the boundary; timing conflict handling is used to avoid redundancy and bandwidth surges caused by complementary devices sampling at the same time. The combined effect of the two can ensure that the final output scheme meets both the requirements of flexibility and the requirements of accuracy and resource constraints.

[0097] Finally, after the iteration terminates, the cloud server outputs the personalized acquisition scheme corresponding to the group optimal solution. This personalized acquisition scheme includes the sampling frequency of each device within the complementary device group and the sampling timing rules within the group, as well as the sampling frequency of non-complementary devices. It should be noted that the output results are presented in a dual-factor format of frequency and timing to avoid execution bias caused by outputting only the frequency, which could lead to devices within the complementary group still sampling at the same time. Simultaneously, the difference accumulation control objective ensures that the alternating sampling of the complementary group does not consistently favor one device, reducing the risk of accumulated bias in cloud analysis.

[0098] Step S50: Based on the optimal aggregation device and the personalized acquisition scheme, generate frequency adjustment instructions and data update strategies, and execute geological exploration data acquisition and dynamic updates.

[0099] Specifically, a frequency adjustment command is generated for each exploration device, and the frequency adjustment command is grouped and coded according to the optimal aggregation device before being issued; wherein, the frequency adjustment command includes at least the device identifier, the optimal aggregation device identifier, and the personalized sampling frequency; a data update strategy is formulated based on the personalized sampling frequency and the optimal path network quality, and the upload cycle is calculated based on the personalized sampling frequency to perform geological exploration data acquisition and dynamic update.

[0100] In this embodiment of the invention, the outputs of steps S30 and S40 are converted into executable instructions and strategies, forming a loop control of issuance, feedback, verification, and recalculation to ensure that the system adapts to dynamic changes over a long period of time under complex architecture.

[0101] First, the cloud server generates frequency adjustment instructions for each survey device. These instructions include at least the device identifier, the optimal aggregation device identifier, the personalized sampling frequency, the effective time, and a checksum. After being grouped and encoded according to the optimal aggregation device, these instructions are sent to the corresponding aggregation devices, which then parse and distribute them to the devices for execution. It should be noted that by using paths, instruction binding, and group encoding, execution deviations caused by inconsistencies between the device upload path and the instruction distribution link are avoided. The checksum is used to improve the reliability and security of instruction transmission. The effective time can be determined based on dynamic status results; for example, it can take effect immediately in a sudden change state, and in the next cycle in a non-sudden change state, balancing real-time performance and stability.

[0102] Based on this, the cloud server formulates a data update strategy based on personalized sampling frequency and optimal path network quality, including upload cycle calculation, upload content classification and data priority management, and sets verification rules to support cyclic updates.

[0103] In one optional implementation, the upload period is calculated as follows:

[0104] ;

[0105] in, Indicates the upload cycle (minutes); This indicates the personalized sampling frequency (times / hour).

[0106] It should be noted that linking the upload cycle with the sampling frequency ensures that the data upload pace is consistent with the sampling pace; uploading according to network quality levels avoids congestion when the network is poor; setting data priorities and retention strategies according to dynamic status can improve the efficiency of cloud resource utilization and highlight high-value mutation data.

[0107] Following this, the cloud server sends the grouped encoding instruction set to the aggregation device; after the aggregation device distributes the instruction to the devices for execution, the devices report execution feedback; the cloud retransmits the instruction to devices that fail to execute, and if the retransmission still fails after a preset number of attempts, it marks the device as abnormal and triggers an alarm or manual intervention. Employing multi-level distribution, feedback aggregation, and timeout retransmission can improve instruction coverage and execution reliability in complex architectures, avoiding the inability to implement frequency schemes due to link instability, thus affecting the effectiveness of updates.

[0108] In a preferred embodiment, the cloud server performs multi-dimensional verification of the execution effect, including path consistency verification, frequency matching verification, and transmission stability verification. When any dimension fails to meet a preset threshold, the path selection or frequency optimization is re-executed, and updated instructions and strategies are reissued. It should be noted that multi-dimensional verification can avoid the overall risk being masked by single-dimensional problems such as a path appearing usable but with a mismatched frequency or a frequency matching but unstable transmission. By mapping the cases of unmet conditions to the re-execution of the corresponding steps, a clear and rapid update mechanism can be formed.

[0109] Finally, the cloud server re-collects basic data according to a preset update cycle and re-executes the upload path regularization selection and particle swarm optimization frequency calculation. It compares the differences between the new and current schemes, and when the differences reach a preset trigger condition, it regenerates and issues new instructions and strategies, thus forming a continuous dynamic update. It should be noted that using periodic updates and difference-triggered adjustments can adapt to long-term changes in geological dynamics, network quality, and equipment status; the difference threshold is used to avoid system oscillations caused by frequent adjustments, thereby achieving a balance between adaptability and stability.

[0110] It is easy to understand that the update cycle can be set to hourly, minute-level, or event-triggered according to the needs of the scenario; the difference triggering condition is not limited to a certain criterion, and can be triggered based on any indicator such as decreased path consistency, decreased frequency matching degree, increased risk of accumulated difference, or insufficient bandwidth margin.

[0111] like Figure 2As shown, this invention provides a regional geological exploration data acquisition and dynamic update system. The system is used to execute the aforementioned regional geological exploration data acquisition and dynamic update method. The system includes: an acquisition unit, used to acquire geological background data and topographic data of the target exploration area, perform basic sub-region and overlapping region division, and establish the association between exploration equipment and aggregation equipment; a receiving unit, used to receive formation pressure data, equipment acquisition stability coefficient, aggregation equipment load rate, and upload path network quality coefficient collected by the exploration equipment, and perform standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization; and a filtering unit, used to perform filtering on each exploration equipment based on the input dataset and the association. The system includes: a candidate upload path selection unit to determine the optimal upload path and calculate the remaining bandwidth of the corresponding optimal aggregation device; a calculation unit to calculate the complementarity between exploration devices within the basic sub-region based on the formation pressure data and device acquisition characteristics, and generate complementary device groups; and an update unit to generate frequency adjustment instructions and data update strategies based on the optimal aggregation device and the personalized acquisition scheme, and execute geological exploration data acquisition and dynamic updates. The calculation unit is used to calculate the personalized acquisition scheme that includes sampling timing rules within the complementary device group and the sampling frequency of each device, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantification value of complementary device differences, and minimizing resource consumption, while satisfying device load and remaining bandwidth constraints.

[0112] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0113] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0114] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for regional geological exploration data acquisition and dynamic updating, characterized in that, The method includes: Acquire geological background and topographic data of the target exploration area, perform the division of basic sub-regions and overlapping regions, and establish the relationship between exploration equipment and data aggregation equipment; Receive formation pressure data, equipment acquisition stability coefficient, aggregated equipment load rate, and upload path network quality coefficient collected by exploration equipment, perform standardization processing to generate input datasets for upload path selection and acquisition frequency optimization; Based on the input dataset and the association, upload path candidate filtering is performed for each exploration device to determine the optimal upload path and calculate the remaining bandwidth of the corresponding optimal aggregation device. Based on the formation pressure data and equipment acquisition characteristics, the complementarity between exploration equipment within the basic sub-region is calculated, and complementary equipment groups are generated. Under the premise of satisfying equipment load and remaining bandwidth constraints, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantification value of complementary equipment differences, and minimizing resource consumption, a particle swarm optimization algorithm is used to calculate a personalized acquisition scheme that includes sampling timing rules within the complementary equipment group and the sampling frequency of each device; specifically including: The characteristic difference threshold is determined based on the historical data collected by the devices within the complementary device group, and the cumulative quantitative value of the complementary device difference is defined as the cumulative result of the sampling difference of the devices within the group within the preset evaluation period. A fitness function is constructed that simultaneously includes geological dynamic matching degree, differential accumulation quantification, and resource consumption, and the fitness function value is set to zero when the equipment load and remaining bandwidth constraints are not met. The particle position is represented as a joint scheme of sampling frequency and sampling time sequence, and the particle velocity and particle position are updated by individual optimal solution and group optimal solution; In each iteration, a conflict check is performed on the sampling timing within the complementary device group to ensure that only one device in the complementary device group drives sampling at any given time. After particle update, calculate the cumulative quantization value of the difference and perform the cumulative difference check. When the cumulative quantization value of the difference is not less than the maximum allowable cumulative quantization value of the difference, adjust the sampling frequency of at least one device in the complementary device group or adjust the sampling timing to make the cumulative quantization value of the difference fall back to within the threshold. Based on the optimal aggregation device and the personalized acquisition scheme, frequency adjustment instructions and data update strategies are generated to perform geological exploration data acquisition and dynamic updates.

2. The method for regional geological exploration data acquisition and dynamic updating according to claim 1, characterized in that, Acquire geological background and topographic data of the target exploration area, perform basic sub-region and overlapping region division, and establish the relationship between exploration equipment and data aggregation equipment, including: The target exploration area is divided into multiple sub-regions based on geological dynamics and topographic features to obtain several basic sub-regions. Calculate the overlap degree for any two basic sub-regions, and determine the corresponding overlapping region when the overlap degree meets a preset threshold. At least one aggregation device is assigned to each basic sub-region, and a mapping table between the exploration devices and the aggregation devices is established for the exploration devices in the overlapping areas.

3. The method for regional geological exploration data acquisition and dynamic updating according to claim 1, characterized in that, The system receives formation pressure data, equipment acquisition stability coefficients, aggregated equipment load rates, and upload path network quality coefficients collected by exploration equipment. It then performs standardization processing to generate an input dataset for upload path selection and acquisition frequency optimization, including: Receive formation pressure data and equipment stability coefficients collected by exploration equipment, and receive the aggregated equipment load rate calculated by the aggregation equipment based on the current load and the maximum load; Calculate the network quality coefficient of the upload path based on the upload path signal strength, transmission delay, and packet loss rate; The input dataset is generated based on formation pressure data, equipment acquisition stability coefficient, aggregated equipment load rate, and upload path network quality coefficient.

4. The method for regional geological exploration data acquisition and dynamic updating according to claim 1, characterized in that, Based on the input dataset and the association, upload path candidate filtering is performed for each exploration device, including: Based on the network quality coefficients of the upload paths in the input dataset, network quality threshold filtering is performed on multiple upload paths associated with the same exploration equipment to obtain a path candidate set; When the path candidate set contains at least two paths, they are sorted from high to low according to the network quality coefficient and a preset number of core candidate paths are retained; when the path candidate set contains only one path, the path is used as the core candidate path; when the path candidate set is empty, the path with the lowest load rate among the associated aggregation devices is selected as the core candidate path.

5. The method for regional geological exploration data acquisition and dynamic updating according to claim 4, characterized in that, Determine the optimal upload path and calculate the remaining bandwidth of the corresponding optimal aggregation device, including: Based on the aggregated device load rate corresponding to the core candidate path, a load threshold judgment is performed. If there is a load rate that is not higher than the load threshold, the path with the highest network quality coefficient is selected as the optimal path. If all load rates are higher than the load threshold, the path with the lowest load rate is selected as the optimal path. Calculate the remaining bandwidth based on the aggregation device corresponding to the optimal path; The optimal path is dynamically verified according to the preset inspection cycle, and the path candidate screening and optimal path determination are re-executed when the verification does not meet the preset conditions.

6. The method for regional geological exploration data acquisition and dynamic updating according to claim 1, characterized in that, Based on the formation pressure data and equipment acquisition characteristics, the complementarity between exploration equipment within a sub-region is calculated, and a complementary equipment group is generated, including: Based on the formation pressure data of each exploration device within a preset time window in the same basic sub-region, construct the device feature vector and calculate the feature correlation coefficient between each pair of exploration devices; Determine whether the correlation coefficient of the feature is lower than a preset correlation threshold. If so, classify the corresponding exploration equipment into the same complementary equipment group.

7. The method for regional geological exploration data acquisition and dynamic updating according to claim 1, characterized in that, Based on the optimal aggregation device and the personalized acquisition scheme, frequency adjustment instructions and data update strategies are generated to perform geological exploration data acquisition and dynamic updates, including: A frequency adjustment command is generated for each exploration device, and the frequency adjustment command is grouped and coded according to the optimal aggregation device before being issued; wherein, the frequency adjustment command includes at least the device identifier, the optimal aggregation device identifier, and the personalized sampling frequency; A data update strategy is formulated based on personalized sampling frequency and optimal path network quality, and the upload cycle is calculated based on personalized sampling frequency to perform geological exploration data acquisition and dynamic updates.

8. A regional geological exploration data acquisition and dynamic updating system, characterized in that, The system is used to execute the regional geological exploration data acquisition and dynamic updating method according to any one of claims 1-7, and the system includes: The acquisition unit is used to acquire geological background data and topographic data of the target exploration area, perform the division of basic sub-regions and overlapping regions, and establish the association between exploration equipment and aggregation equipment. The receiving unit is used to receive formation pressure data, equipment acquisition stability coefficient, summary equipment load rate, and upload path network quality coefficient collected by exploration equipment, and to perform standardization processing to generate input datasets for upload path selection and acquisition frequency optimization. The filtering unit is used to perform upload path candidate filtering for each exploration device based on the input dataset and the correlation relationship, determine the optimal upload path and calculate the remaining bandwidth of the corresponding optimal aggregation device. The calculation unit is used to calculate the complementarity between exploration equipment within the basic sub-region based on the formation pressure data and equipment acquisition characteristics, and generate complementary equipment groups. Under the premise of meeting the constraints of equipment load and remaining bandwidth, with the optimization objectives of maximizing geological dynamic matching degree, minimizing the cumulative quantitative value of complementary equipment differences, and minimizing resource consumption, the particle swarm optimization algorithm is used to calculate a personalized acquisition scheme that includes the sampling time sequence rules within the complementary equipment group and the sampling frequency of each equipment. The update unit is used to generate frequency adjustment instructions and data update strategies based on the optimal aggregation device and the personalized acquisition scheme, and to perform geological exploration data acquisition and dynamic updates.

Images